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Record: 1- Title:
- A behavioral economic reward index predicts drinking resolutions: Moderation revisited and compared with other outcomes.
- Authors:
- Tucker, Jalie A.. Department of Health Behavior, University of Alabama at Birmingham, Birmingham, AL, US, jtucker@uab.edu
Roth, David L.. Department of Biostatistics, University of Alabama at Birmingham, Birmingham, AL, US
Vignolo, Mary J.. Department of Health Behavior, University of Alabama at Birmingham, Birmingham, AL, US
Westfall, Andrew O., ORCID 0000-0002-0468-4695. Department of Biostatistics, University of Alabama at Birmingham, Birmingham, AL, US - Address:
- Tucker, Jalie A., Department of Health Behavior, School of Public Health, University of Alabama at Birmingham, 1665 University Boulevard, 227 Ryals, Birmingham, AL, US, 35294-0022, jtucker@uab.edu
- Source:
- Journal of Consulting and Clinical Psychology, Vol 77(2), Apr, 2009. pp. 219-228.
- NLM Title Abbreviation:
- J Consult Clin Psychol
- Page Count:
- 10
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- Journal of Consulting Psychology
- Other Publishers:
- US : American Association for Applied Psychology
US : Dentan Printing Company
US : Science Press Printing Company - ISSN:
- 0022-006X (Print)
1939-2117 (Electronic) - Language:
- English
- Keywords:
- problem drinking, natural resolution, moderation, behavioral economics, rewards, abstinence, relapse prediction
- Abstract:
- Data were pooled from 3 studies of recently resolved community-dwelling problem drinkers to determine whether a behavioral economic index of the value of rewards available over different time horizons distinguished among moderation (n = 30), abstinent (n = 95), and unresolved (n = 77) outcomes. Moderation over 1- to 2-year prospective follow-up intervals was hypothesized to involve longer term behavior regulation processes than abstinence or relapse and to be predicted by more balanced preresolution monetary allocations between short-term and longer term objectives (i.e., drinking and saving for the future). Standardized odds ratios (ORs) based on changes in standard deviation units from a multinomial logistic regression indicated that increases on this 'Alcohol-Savings Discretionary Expenditure' index predicted higher rates of abstinence (OR = 1.93, p = .004) and relapse (OR = 2.89, p < .0001) compared with moderation outcomes. The index had incremental utility in predicting moderation in complex models that included other established predictors. The study adds to evidence supporting a behavioral economic analysis of drinking resolutions and shows that a systematic analysis of preresolution spending patterns aids in predicting moderation. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Alcohol Abuse; *Alcohol Rehabilitation; *Prediction; *Rewards; *Behavioral Economics; Relapse (Disorders); Sobriety
- Medical Subject Headings (MeSH):
- Alcohol Drinking; Choice Behavior; Economics; Female; Follow-Up Studies; Humans; Male; Middle Aged; Patient Acceptance of Health Care; Prospective Studies; Reward; Token Economy
- PsycINFO Classification:
- Drug & Alcohol Rehabilitation (3383)
- Population:
- Human
Male
Female - Location:
- US
- Age Group:
- Adulthood (18 yrs & older)
- Tests & Measures:
- Alcohol-Savings Discretionary Expenditure (ASDE) index
Drinking Problems Scale
Health and Daily Living Form-health portion
Alcohol Dependence Scale DOI: 10.1037/t00030-000
Michigan Alcoholism Screening Test DOI: 10.1037/t02357-000
Situational Confidence Questionnaire - Grant Sponsorship:
- Sponsor: National Institute on Alcohol Abuse and Alcoholism
Grant Number: R01 AA008972; K02 AA000209
Recipients: No recipient indicated - Conference:
- Annual meeting of the Research Society on Alcoholism, Annual Workshop: Mechanisms of Behavior Change in Behavioral Treatment, Third, Jul, 2007, Chicago, IL, US
- Conference Notes:
- Portions of this research were presented at the aforementioned meeting.
- Methodology:
- Empirical Study; Quantitative Study
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- Accepted: Dec 16, 2008; Revised: Dec 5, 2008; First Submitted: Dec 27, 2007
- Release Date:
- 20090323
- Correction Date:
- 20130114
- Copyright:
- American Psychological Association. 2009
- Digital Object Identifier:
- http://dx.doi.org/10.1037/a0014968
- PMID:
- 19309182
- Accession Number:
- 2009-03774-003
- Number of Citations in Source:
- 42
- Persistent link to this record (Permalink):
- http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2009-03774-003&site=ehost-live
- Cut and Paste:
- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2009-03774-003&site=ehost-live">A behavioral economic reward index predicts drinking resolutions: Moderation revisited and compared with other outcomes.</A>
- Database:
- PsycINFO
A Behavioral Economic Reward Index Predicts Drinking Resolutions: Moderation Revisited and Compared With Other Outcomes
By: Jalie A. Tucker
Department of Health Behavior, University of Alabama at Birmingham;
David L. Roth
Department of Biostatistics, University of Alabama at Birmingham
Mary J. Vignolo
Department of Health Behavior, University of Alabama at Birmingham
Andrew O. Westfall
Department of Biostatistics, University of Alabama at Birmingham
Acknowledgement: Mary J. Vignolo is now affiliated with the American College of Rheumatology Research and Education Foundation, Atlanta, GA. Andrew O. Westfall is now affiliated with the Department of Obstetrics and Gynecology, University of Alabama at Birmingham School of Medicine, and the Centre for Infectious Disease Research, Lusaka, Zambia.
This research was supported in part by Grants R01 AA008972 and K02 AA000209 from the National Institute on Alcohol Abuse and Alcoholism. The authors thank Paula D. Rippens, Bethany C. Black, and H. Russell Foushee for their contributions to the data collection phase of the research, and G. Alan Marlatt for his comments on an earlier version of this article. Portions of this research were presented at the Third Annual Workshop Mechanisms of Behavior Change in Behavioral Treatment held at the annual meeting of the Research Society on Alcoholism, Chicago, IL, July 2007.
Behavioral economic models of choice behavior have been widely applied to an analysis of substance misuse and other addictive behaviors in humans (e.g., Bickel & Marsch, 2001; Green & Kagel, 1996; Vuchinich & Tucker, 1996, 1998). Behavioral economics involves a merger of operant approaches to understanding choice behavior, particularly impulsive choice (Ainslie, 1975), with microeconomic models of consumer behavior (Rachlin, Battalio, Kagel, & Green, 1981). Both focus on how individuals allocate limited resources such as time, money, and behavior to obtain commodities available at different costs and over different delays, and strength of preference for a given commodity (e.g., drug use) is inferred from the relative resources or behavior allocated to obtain it (Premack, 1965; Rachlin, 1971). For example, the well-established matching law quantifies how humans and animals alike distribute or “match” relative response rates in proportion to the relative rates of reinforcement available from different activities (Herrnstein, 1970).
This approach is well suited to studying demand for drugs in relation to other commodities available in the natural environment (Vuchinich & Tucker, 1996, 1998). Behavioral economic models view substance misuse as a persistent preference for short-term rewards and a devaluation of larger, delayed rewards that support adaptive functioning. Research has consistently shown that preference for substance use decreases as constraints on access to the substance increase and as constraints on access to valued non-drug-related alternatives decrease (Vuchinich & Tucker, 1998). Moreover, persons with addictive behavior problems tend to devalue, or discount, delayed rewards more than normal controls (Bickel & Marsch, 2001). Control of their current behavior is less sensitive to delayed consequences, such as the adverse long-term effects of substance use.
As applied to attempts to resolve alcohol problems, these findings suggest that problem drinkers with greater sensitivity to longer term contingencies, even when drinking heavily, should have a better prognosis and that shifting control of behavior from shorter to longer term contingencies should promote resolution stability. Our earlier prospective studies of resolution attempts by community-dwelling treated and untreated problem drinkers supported this hypothesis (Tucker, Foushee, & Black, 2008; Tucker, Vuchinich, Black, & Rippens, 2006; Tucker, Vuchinich, & Rippens, 2002). Shortly after initiation of abstinence or problem-free moderation drinking, participants reported their monetary expenditures on alcoholic beverages and other commodities during the year before resolution onset using an expanded Timeline Followback (TLFB) interview (Sobell & Sobell, 1992). Establishing relative preference for alcohol through allocation of monetary resources is based on experimental work showing that the relative values of different, concurrently available commodities can be quantified by measuring choice among the commodities under varying constraints (Herrnstein, 1970; Premack, 1965). Many different activities are available in the natural environment, and monetary allocation offers a common metric to assess their relative reinforcement value (Vuchinich, Tucker, & Harllee, 1988).
Our main research focus was on studying successful natural recoveries achieved without treatment. Natural recovery samples typically include middle-income to upper income individuals (Sobell, Sobell, & Toneatto, 1992) who have complex, fixed, and recurring expenditures (e.g., mortgages, automatic payroll deductions) as well as considerable discretionary expenditures (e.g., for recreation, alcohol, voluntary savings). Because their preferences should be more readily expressed within discretionary, as opposed to fixed, spending patterns, the proportion of discretionary spending on alcoholic beverages was compared with money put into savings for future use, which was conceptualized as representing the value of rewards available over shorter and longer time horizons, respectively. Greater relative allocation to savings than to drinking, reflected in lower values on this Alcohol-Savings Discretionary Expenditure (ASDE) index, was viewed as indicating higher relative preferences for delayed rewards made possible by savings compared with more immediate rewards from drinking.
As hypothesized, problem drinkers who maintained stable resolutions had lower preresolution ASDE values than those who had unstable resolutions and relapsed at any point during the 1- to 2-year follow-ups (Tucker et al., 2002, 2006, 2008). The ASDE index had unique incremental utility in predicting stable versus unstable resolutions after controlling for established outcome predictors (e.g., problem severity, drinking practices), and it had predictive utility across intervention-naive and intervention-exposed resolution groups (Tucker et al., 2006). In addition to predicting long-term resolution stability, the ASDE index predicted drinking patterns during the early months of the postresolution period in a study that implemented Interactive Voice Response (IVR) self-monitoring with recently resolved, untreated problem drinkers (Tucker et al., 2008).
This research showed that contextually sensitive measures of the reward value of drinking in relation to other activities added unique information in an account of resolution outcomes. However, in these studies many more participants achieved stable abstinent than nonabstinent resolutions, so predictors of moderation apart from abstinence could not be investigated in the studies individually. A more extensive analysis with a larger sample that includes more participants who drank in a sustained nonproblem manner is needed to examine specific predictors of moderation. This issue has gained renewed importance as interventions continue to expand beyond abstinence-oriented treatments for alcohol-dependent persons, to include population-based public health interventions for the untreated majority with less severe problems for whom moderation is a more common and acceptable outcome (Tucker, 2003). Moderation outcomes are more common among untreated problem drinkers who quit on their own compared with the minority who seek treatment, partly because treatment seekers have more serious problems. Although early treatment research found moderation to be associated with lower problem severity, younger age, and stable life circumstances (reviewed by Miller & Munoz, 2005; Rosenberg, 2004), there have been few recent advances, with the exception that higher self-efficacy to resist drinking in high risk situations has been associated with moderation outcomes (Saladin & Santa Ana, 2004).
To obtain a sufficient sample to investigate stable moderation apart from other outcomes, we pooled the data from our three prior studies and conducted new analyses to evaluate the utility of the ASDE index in distinguishing stable nonabstinent resolutions from stable abstinent resolutions and unstable resolutions that involved problem drinking at some point over the 1- to 2-year follow-ups. Our interest in examining this issue in a re-analysis comes from early theorizing about the processes involved in moderation (Marlatt, 1985) and from preliminary findings in our IVR study (Tucker et al., 2008) that supported the theorizing. Over two decades ago, Marlatt (1985, pp. 329–344) raised the interesting but still unstudied hypothesis that abstinence and relapse are opposite ends of the same dynamic behavioral regulation process, reflecting over- and undercontrol of the daily act of drinking, respectively. Moderation was thought to involve a different regulation process that depends on “lifestyle balance” and repetitive choices to drink well within the boundaries of extreme restraint or loss-of-control drinking. To the extent that the ASDE index is a functional measure of preference for alcohol in relation to delayed rewards made possible by savings, one would expect successful moderate drinkers to organize their behavioral allocation (tracked via financial expenditures) over longer intervals compared with those who relapse or abstain. Framing Marlatt's (1985) “differential regulation” hypothesis within behavioral economic theory, lower ASDE values, reflecting more balanced monetary allocations between short-term and longer term objectives (i.e., drinking and saving for the future), should predict moderation compared with other outcomes.
The pooled data set included 30 drinkers with moderation outcomes, which was sufficient to evaluate the hypothesis that stable resolutions involving some moderation drinking over 1 to 2 years would be predicted by lower preresolution ASDE values compared with other outcomes. After this primary behavioral economic hypothesis was evaluated, established moderation predictors assessed at baseline, including problem severity, alcohol dependence, and self-efficacy, were included with the ASDE index in multinomial logistic regression models to determine if the index had unique incremental predictive utility in distinguishing outcomes among participants who resumed drinking (relapse or moderation) and among those who remained resolved (abstinence or moderation). A series of multivariable models were used to subject the ASDE index to a rigorous systematic evaluation after controlling for multiple covariates and to maintain favorable events-to-predictor ratios in each model (Peduzzi, Concato, Kemper, Holford, & Feinstein, 1996) within the limits set by the number of moderation cases in the pooled data set. We hypothesized that the ASDE index would provide significant incremental predictive utility over predictors suggested by prior research and would be particularly sensitive for distinguishing moderated and relapsed outcomes—that is, the differential regulation processes theorized by Marlatt (1985) and assessed by our behavioral economic index should be most apparent among participants who engaged in some postresolution drinking.
Method Sample Selection and Characteristics
Participants were recruited from the community via media advertisements in metropolitan areas in Alabama, Florida, Georgia, Mississippi, and Tennessee. The advertisements asked for research volunteers who had recently overcome a drinking problem with or without treatment. Respondents to the advertisements called a toll-free number, received a description of the research, and were screened with the Michigan Alcoholism Screening Test (MAST; Selzer, 1971), Alcohol Dependence Scale (ADS; Skinner & Horn, 1984), and Drinking Problems Scale (DPS; Cahalan, 1970). Eligible participants were scheduled for interviews in a place convenient for them. All studies were conducted in compliance with university institutional review board and American Psychological Association ethical standards for research with humans. Participants were informed that the research was covered by a confidentiality shield issued by the U.S. Department of Health and Human Services.
Eligibility criteria included a minimum 5-year drinking problem history (M = 16.70 years, SD = 9.31), no current other drug misuse (except nicotine), and recent cessation of problem drinking (M = 3.93 months resolved, SD = 1.78). Resolution onset was defined as the most recent date that participants began abstaining or drinking in a nonproblem manner without further heavy drinking. At all assessments, moderation was determined using criteria associated with low health risks related to drinking (Sobell et al., 1992): (a) <55 g (70 ml) of 190-proof ethanol consumed per drinking day; (b) no dependence symptoms (as assessed by the ADS); and (c) no alcohol-related negative health, psychosocial, vocational, financial, or legal consequences (as assessed by the DPS). These criteria are consistent with other drinking guidelines (e.g., National Institute on Alcohol Abuse and Alcoholism [NIAAA], 2005; World Health Organization, 2000) that generally set upper limits at ≤4 drinks/day for men and ≤3 drinks/day for women.
All of the studies included untreated problem drinkers who had initiated natural resolutions, and Tucker et al. (2006) also included a group that had received alcohol treatment from a qualified provider or attended more than two Alcoholics Anonymous meetings within about 12 months of resolution onset. Two studies (Tucker et al., 2002, 2006) required an initial resolution of 2 to 6 months and had a 2-year follow-up; the third study that involved IVR self-monitoring (Tucker et al., 2008) required a shorter initial resolution of 1 to 3 months and had a 1-year follow-up. Otherwise, the studies used identical selection criteria and follow-up procedures. Summed across studies, 205 (81.03%) of the 253 initially enrolled participants completed the 1-year follow-up required for inclusion in the pooled sample; 202 provided useable income and expenditure data and were included in the data analyses. Attrition was due to participant withdrawal or lost contact (42), significant discrepancies between participant and collateral reports of drinking (5), or death (1).
Although not an inclusion criterion, all participants met third- (Tucker et al., 2002) or fourth- (Tucker et al., 2006, 2008) edition Diagnostic and Statistical Manual of Mental Disorders criteria for alcohol dependence (American Psychiatric Association, 1987, 1994). As shown in Table 1, ADS scores fell in the moderate to low substantial dependence range (Skinner & Horn, 1984). Consistent with research showing that seeking help is associated with more severe problems, ADS, MAST, and DPS scores were relatively higher in the one study that included participants with a help-seeking history (Tucker et al., 2006). Otherwise, no differences were found across studies in preresolution drinking practices, postresolution outcomes, demographic characteristics, and preresolution income and expenditures on alcoholic beverages and money put into savings.
Sample Characteristics at Initial Assessment as a Function of Resolution Status at Follow-Up
Because our goal was to predict stable moderation apart from other outcomes, we classified participants conservatively into mutually exclusive groups based on drinking practices and problems over the entire follow-up. Those who abstained or drank moderately without problems at all follow-up points were considered to have stable resolutions, either resolved abstinent (RA) or resolved nonabstinent (RNA). Those who engaged in any problem drinking were considered to have unstable resolutions (UR), even if they later abstained or moderated.
Table 1 summarizes the sample characteristics as a function of participants' postresolution drinking status based on all follow-up data. During the initial resolution period required for inclusion, 89.1% of participants had abstained continuously and 10.9% had engaged in moderate drinking. Most participants' current drinking goal choice was informed by personal experience; 85% had made one or more serious resolution attempts in the past, with moderation attempts outnumbering abstinence attempts by about 4:1. During the present 1- to 2-year follow-up, 47.0% of participants maintained continuous or nearly continuous abstinence, 14.9% engaged in some moderate drinking with no problem drinking, and 38.1% engaged in problem drinking at some point. Of those who drank moderately, women consumed a mean of 27.5 ml of 190-proof ethanol per drinking day (SD = 8.56), and men consumed a mean of 38.9 ml (SD = 19.0), which fall within NIAAA (2005) gender-adjusted guidelines for low-risk drinking. Of those who drank heavily, over half relapsed during the first postresolution year. Mean quantities consumed per postresolution drinking day were 93.8 ml (SD = 51.1) for women and 123.8 ml (SD = 75.0) for men. During the first postresolution year, the mean and median number of drinking days for RNA participants was 73.93 (SD = 111.51) and 5.50 days, respectively, and the mean and median for UR participants was 44.23 (SD = 67.23) and 20.0 days, respectively.
Procedure
A trained interviewer conducted 1.5- to 3.0-hour individual interviews at baseline and at the annual follow-up points. After giving written informed consent, participants were administered a noninvasive breath test (Alco-Sensor III; Intoximeters, Inc., St Louis, MO) to verify sobriety. All predictors were derived from the initial interview, which covered drinking practices, life contexts, and monetary allocation during the year before participants' recent resolution up to the time of the interview. The follow-up assessments covered the time since the last interview. Brief phone interviews conducted midway between the annual follow-ups assessed drinking and help-seeking status and maintained contact. Participants received $40 for each annual interview and completion of questionnaires that they returned by mail, $10 for each phone interview, and a $50 bonus if they completed all assessments. The procedures that provided the data for the analyses are summarized in the next section and in Tucker et al. (2002, 2006, 2008).
Drinking practices and money spent on alcohol
Established TLFB procedures (Sobell & Sobell, 1992) were used to assess daily drinking practices during the preresolution year and again at each annual follow-up point. Participant reports of ounces of beer, wine, and liquor intake were converted to milliliters of 190-proof ethanol for analysis. Participants also reported how much money they spent each day on alcoholic beverages, regardless of whether the beverages were consumed. This was not excessively difficult because alcoholic beverages are sold in standard quantities, and problem drinkers typically buy and consume large quantities of a limited range of preferred beverages. As needed, TLFB interviewing techniques were used to facilitate reports of money spent on alcohol (e.g., use of anchor events, identification of sustained behavior patterns).
Monetary allocation
Participants reported their monetary income and expenditures during the same periods using an expanded set of commodity classes derived from U.S. federal consumer expenditure surveys (Vuchinich & Tucker, 1996). They were instructed to bring in financial records (e.g., bank records, paycheck stubs), and documented information was recorded first; 59.4% of participants provided some financial records. Then TLFB interviewing techniques were used to complete the financial assessment. Income in dollars was reported by source (e.g., work income, unemployment benefits, pensions, loans). Expenditures were reported in three general categories, each with subcategories, including housing (e.g., mortgage, rent, utilities), consumable goods (e.g., food, tobacco, alcohol), and other (e.g., entertainment, transportation, loan payments, money saved). Reports in each category typically involved many transactions during the assessment interval, which were summed to obtain category totals for analysis. In addition to direct verification using financial records, internal consistency and reliability checks on participants' reports of monetary allocation patterns supported their accuracy (reported in Tucker et al., 2006).
As described in Tucker et al. (2002, 2006, 2008), expenditures during the preresolution year were separated into obligatory and discretionary categories. Obligatory expenditures were for essential, ongoing, and largely fixed costs of living, including housing, food, transportation, medical, loan, and automatic payroll deductions (e.g., taxes, retirement, health insurance). Discretionary expenditures were for less essential commodities that could be purchased intermittently, including recreation, entertainment, alcohol, tobacco, other consumable goods, gifts, and elective savings. The ASDE index was computed as the proportion of discretionary expenditures summed over the preresolution year spent on alcohol minus the proportion of preresolution discretionary expenditures put into savings. ASDE values could range from 1.0 to –1.0, with lower scores representing proportionally less spending on alcohol and more on savings.
Questionnaires
After each interview, participants completed questionnaires that assessed moderation predictors in addition to those assessed during screening. Self-efficacy expectations to resist urges to drink heavily in high-risk situations were assessed using the Situational Confidence Questionnaire (SCQ; Annis & Graham, 1988), and health status was assessed using the health portion of the Health and Daily Living Form (HDL; Moos, 1985). Questionnaires were scored using established methods. Table 1 presents the total scores from the initial assessment.
Checks on data quality
In every study, in addition to checks on participants' financial reports, their reports relevant to the inclusion criteria and follow-up drinking status were assessed through collateral interviews or participant reliability checks when collaterals were unavailable. These data, summarized here, were reported previously (Tucker et al., 2002, 2006; Tucker, Foushee, Black, & Roth, 2007). Summed across studies, collaterals were interviewed at least once for 75.61% of the enrolled sample. Participant data were excluded when collaterals failed to verify participant reports relevant to the inclusion criteria or their drinking status during the follow-up. This rarely occurred (less than 2% of the initial sample of 253). For participants' retained for analysis, good to excellent agreement levels were found for drinking dimensions that could be directly observed by collaterals (e.g., alcohol-related problems, types of beverages consumed, date of initial resolution). The reliability of participant reports of drinking practices and money spent on alcohol also was examined for participants in the IVR study and was found to be excellent (Tucker et al., 2007). These findings strongly suggest that participants retained in the sample were reporting accurately.
Statistical Analysis
The mutually exclusive outcome groups were based on participants' drinking practices and problems over the entire follow-up interval: RA (n = 95)—continuous abstinence; RNA (n = 30)—some low-risk drinking with no problem drinking; or UR (n = 77)—one or more drinking episodes that exceeded the moderation criteria at any point. These conservative operational definitions deliberately separated recovering problem drinkers who resumed alcohol consumption into outcome groups on the basis of whether they engaged in any high-risk drinking, regardless of their terminal outcome status.
To evaluate the main behavioral economic hypotheses, the preresolution year monetary allocations to alcohol and savings, computed as a proportion of discretionary expenditures, were first examined as a function of drinking outcome status in a 3 (outcome status) × 2 (allocation type) analysis of variance (ANOVA) with repeated measures on the second factor. The Outcome × Allocation interaction effect from this analysis was used to determine whether the difference in these allocations, which constituted the ASDE index, was significantly related to outcome group membership. Once confirmed, we used a series of three-group multinomial logistic regression analyses to examine the utility of the ASDE index in predicting outcome group membership in relation to established predictors, including measures of problem severity (MAST, ADS, HDL physical health subscale, problem duration, help-seeking history), TLFB reports of preresolution drinking (days well functioning [abstinent and light drinking days combined], mean milliliters of ethanol consumed per drinking day), self-efficacy to resist heavy drinking (SCQ), and demographic characteristics.
Because these analyses focused on identifying predictors of moderated outcomes, the RNA group was the referent group so that the results yielded RA versus RNA and UR versus RNA contrasts and associated odds ratios (ORs) that indicated effect sizes. Although the limited RNA participants (30) prohibited comprehensive multivariable models that included all predictors simultaneously (Peduzzi et al., 1996), the RNA sample was sufficient to maintain a favorable event-to-variable ratio in a series of multinomial logistic regressions that included, first, the ASDE index alone, and then, in subsequent models, the ASDE index plus one other predictor. The latter analyses determined if the predictive utility of the ASDE index was independent of the predictive effects of each established predictor. Significant effects from the two-predictor models were then used to construct three-predictor models that evaluated whether the ASDE index continued to predict RNA outcomes beyond significant problem severity and drinking quantity measures. A final four-variable model examined whether the ASDE continued to have unique predictive utility when three other significant predictors from the three-variable models were included simultaneously. Continuous predictor variables in all logistic regression models were standardized to have a mean of 0 and a standard deviation of 1. The ORs and associated 95% confidence intervals (CIs) were based on a 1-standard deviation change in the predictors and allowed direct comparisons across predictors.
Results Tests of the Behavioral Economic Hypotheses
Table 1 summarizes univariate differences between the three outcome groups for established moderation predictors, and Table 2 summarizes group differences for the expenditure data from the preresolution year, including the ASDE index and expenditure components from which it was derived. The 3 × 2 ANOVA on the proportions of discretionary expenditures indicated an expected allocation main effect, F(1, 199) = 78.56, p < .0001, which reflected greater overall proportional allocation to drinking than savings prior to resolution. More important, as shown in Figure 1, a significant interaction effect was obtained that supported the hypotheses, F(2, 199) = 11.12, p = .0001. Comparisons using Tukey's honestly significant difference test showed that, as predicted, RNA participants had significantly lower discrepancies between alcohol and savings allocation proportions than both UR participants (p < .05) and RA participants (p < .05). RNA participants allocated proportionally less discretionary spending to alcohol and more to savings compared with UR participants (ps < .05) and less to alcohol than RA participants (p < .05).
Behavioral Economic Variables Based on Preresolution Monetary Allocation Patterns as a Function of Resolution Status at Follow-Up
Figure 1. Resolution Status × Allocation Type interaction among components of the Alcohol-Savings Discretionary Expenditure (ASDE) index based on the proportion of discretionary expenditures for alcohol and savings during the year prior to resolution onset (y axis). The error bars represent the standard errors of the drinking outcome group means.
The multinomial logistic regression analysis that included the ASDE index as the sole predictor revealed significant effects for both the UR versus RNA contrast (OR = 2.89, 95% CI = 1.77, 4.73, p < .0001) and the RA versus RNA contrast (OR = 1.93, 95% CI = 1.23, 3.02, p = .004). The OR from the first contrast indicated that a 1-standard-deviation increase in the ASDE index was associated with a 2.89-fold increase in the odds of resuming problem drinking compared with stable moderation. The OR from the second contrast indicated that a 1-standard-deviation increase in the ASDE index was associated with a 1.93-fold increase in the odds of stable abstinence compared with moderation.
The preceding logistic regression used drinking outcome assignments based on all available data from the 202 participants who had an ASDE score and at least 1 year of follow-up data. When this analysis was restricted to participants who were followed for 2 years (n = 152) and thus provided the longest continuous behavioral records, the ASDE index remained a significant predictor of both the UR versus RNA contrast (OR = 2.79, CI = 1.56, 5.00, p = .0006) and the RA versus RNA contrast (OR = 1.95, CI = 1.14, 3.32, p = .014). The same pattern of results was observed in an additional sensitivity analysis (n = 193) that excluded 4 RNA and 5 UR participants who were mostly abstinent but occasionally drank either moderately or heavily (UR vs. RNA: OR = 2.55, CI = 1.54, 4.21, p = .0003; RA vs. RNA: OR = 1.74, CI = 1.10, 2.75, p = .017). Consistent with the main behavioral economic hypothesis, frequent moderate drinkers had the lowest and frequent heavy drinkers had the highest mean ASDE scores.
Overall, these results supported the hypotheses concerning the ASDE index. The composite index separated the RNA group from the RA and UR groups, which were more similar.
Predictive Utility of the ASDE Index Relative to Established Moderation Predictors
Table 3 summarizes the results of logistic regressions that included the ASDE index and one other moderation predictor and also presents the correlations between the ASDE index and the other predictors. Four findings are noteworthy. First, the ASDE index showed low to modest correlations with all other predictors, ranging from .00 to .36 (rs > .14 or < −.14 were significant at p < .05), indicating that the ASDE was largely unrelated to the other predictors and capable of contributing new information to the prediction of outcomes in the multivariable models. Second, the analyses replicated many established moderation predictors, including lower dependence, fewer psychosocial and health problems, shorter problem durations, lower quantities consumed on drinking days, absence of help seeking, stable sociodemographic characteristics, and higher self-efficacy. Third, when the two-predictor models were run, the ASDE index remained a robust predictor of outcomes among the subset of participants who drank during the follow-up. The UR versus RNA contrast was significant in all models that included another predictor, indicating that the ASDE index explained unique variance after accounting for the other predictor. Fourth, the ASDE index distinguished outcomes among the subset of participants who maintained resolution. The RA versus RNA contrast was significant for the index when it was included with another predictor in all but two models that included either the MAST or mean milliliters per drinking day.
Multinomial Logistic Regressions Using the ASDE Index and One Established Moderation Predictor
On the basis of these results, additional three-variable logistic regressions were conducted that included the ASDE index and two other variables of conceptual interest or empirical utility. The MAST and ADS were not included in the same model because they were highly correlated (r = .68, p < .001) and would, therefore, be largely redundant in the same model. Table 4 presents the results of the three-variable models that we examined. In all eight complex models, the ASDE index continued to separate the UR and RNA groups. The index was not highly effective in separating the RA and RNA groups, although trends that approached significance for the ASDE were observed in Models 1, 3, and 8. A positive help-seeking experience and mean milliliters ethanol/drinking day consistently separated all three outcome groups, with absence of help seeking and lower quantities consumed being associated with an RNA status. The MAST, ADS, and SCQ contributed significantly to the separation of the UR and RNA groups. The MAST and ADS also separated the RA and RNA groups in models that did not include drinks per drinking day, but inclusion of that variable attenuated their predictive utility for the RA–RNA contrast.
Multinomial Logistic Regression Models Using the ASDE Index With Two Moderation Predictors
A final, comprehensive four-variable model was constructed from the strongest predictors identified in Table 3, namely the MAST, SCQ, mean milliliters ethanol/drinking day, and ASDE index. For the UR–RNA contrast, the SCQ (OR = 0.30, CI = 0.12, 0.74, p = .009), mean milliliters ethanol/drinking day (OR = 7.26, CI = 1.77, 29.74, p = .006), and ASDE (OR = 2.46, CI = 1.27, 4.77, p = .008) were statistically significant unique predictors, whereas for the RA–RNA contrast, the MAST (OR = 1.96, CI = 1.03, 3.72, p = .04) and mean milliliters ethanol/drinking day (OR = 9.54, CI = 2.37, 38.33, p = .002) were the statistically significant unique predictors.
DiscussionThe findings add to evidence supporting a behavioral economic analysis of drinking resolutions and extend the utility of a measure of preference for alcohol derived from preresolution spending patterns to predict moderation. Stable resolutions involving moderate alcohol use over 1- to 2-year follow-ups were associated with proportionally more preresolution discretionary monetary allocation to savings and less to alcohol compared with continuously abstinent resolutions and unstable resolutions that involved some problem drinking. Lower ASDE values presumably reflect more balanced monetary allocations between short-term and longer term objectives, suggesting that the temporal intervals over which problem drinkers organize and allocate their behavior, even while drinking heavily, may help identify those most able to make a transition to stable moderate use.
The support for the ASDE index in this re-analysis of pooled data from prior studies was obtained in conjunction with results that replicated established moderation predictors. As found during the controlled drinking debate (cf. Marlatt, 1983), moderation was associated with greater social stability and with lower problem severity, including shorter drinking histories, lower alcohol dependence and quantities consumed per drinking day, and fewer alcohol-related psychosocial problems (Miller & Munoz, 2005; Rosenberg, 2004). Higher self-efficacy to resist heavy drinking in high-risk situations also predicted moderation, which replicated recent findings that added this variable to those identified during the controlled drinking debate (Saladin & Santa Ana, 2004).
When included in models with other significant predictors, the ASDE index added unique information to the prediction of moderation and was especially effective in distinguishing outcomes among participants who engaged in postresolution drinking. The ASDE index was significant for the RNA–UR contrast in all models evaluated. Drinkers who maintained moderation had lower ASDE, MAST, and ADS scores and higher self-efficacy scores than those who relapsed.
The ASDE index also predicted outcomes among participants who remained resolved, separating the abstinent and moderation groups in models that included the ASDE alone or with one other predictor. The index was less effective in separating these groups in complex models that included quantities consumed, an attenuation that may be due in part to heterogeneity in the RA group. Some abstainers may be able to drink moderately but have not exposed themselves to postresolution alcohol use, whereas others might resume problem drinking. This unobserved moderation versus relapsed outcome among abstainers can, therefore, limit the full predictive significance of individual predictors of moderation outcomes. Given this attenuated separation, for purposes of choosing an initial abstinence or moderation drinking goal, it seems prudent clinically to require multiple favorable indicators of likely success at moderation until further research can establish decision-making algorithms that satisfactorily separate all three outcome groups. The present findings suggest that supplementing the MAST, ADS, and SCQ with questions about consumption quantities on drinking days and money spent on alcohol and put into savings provides a sound basis for making clinical judgments about initial drinking goal choice.
The ASDE findings also have implications for behavioral economic research and addictive behavior change applications. Evidence is accumulating that addictive behaviors are characterized by a foreshortened view of the future and that successful behavior change will likely involve a shift from a shorter to a longer view of the future and organizing behavior accordingly. In addition to the present support based on money allocation patterns in the natural environment, behavioral economic research on temporal discounting of hypothetical money and health outcomes has consistently found steeper discount functions in smokers, problem drinkers, opiate addicts, and gamblers (Bickel & Marsch, 2001). Social psychological studies have similarly found that the time perspectives of substance abusers are more present oriented and less future oriented than for normal controls (Henson, Carey, Carey, & Maisto, 2006; Keough, Zimbardo, & Boyd, 1999). Addiction treatment outcomes also are predicted by behavioral impulsivity measures that span laboratory and naturalistic assessments, including delay discounting of hypothetical rewards (e.g., Yoon et al., 2007), demand curve analysis based on hypothetical alcohol purchase tasks (MacKillop & Murphy, 2007), questionnaire measures of relative reinforcement value (e.g., Murphy, Correia, Colby, & Vuchinich, 2005; Schmitz, Sayre, Hokanson, & Spiga, 2003), and experiential discounting tasks that assess delay discounting using real, rather than hypothetical, monetary rewards (Krishnan-Sarin et al., 2007).
Such measures of behavioral impulsivity developed within a behavioral economic framework guided by the matching law (Herrnstein, 1970) show inconsistent relationships with personality questionnaires that assess traitlike impulsive tendencies, and the latter measures have no utility in predicting treatment outcomes (e.g., Krishnan-Sarin et al., 2007). Although it remains to be determined the extent to which the behavioral economic measures assess common or different dimensions of behavioral impulsivity and the relative reinforcing efficacy of substances (MacKillop & Murphy, 2007; Reynolds, Richards, Horn, & Karraker, 2004), they appear to measure functional changes in preferences for substance use and nondrug alternatives that are at the core of the dynamic addictive process. The ASDE index as currently assessed is decidedly among the more molar behavioral economic measures and appears to measure the relative reinforcement value of alcohol in the context of resource allocation to commodities available over different temporal intervals. Although the ASDE may prove to be less sensitive to short-term preference shifts than are brief measures of the current demand for drugs, it provides a comprehensive benchmark based on behavior patterns in the natural environment against which to evaluate the utility of briefer measures of relative preferences, including laboratory preparations.
Regardless of whether foreshortened views of the future are a cause or consequence of the addictive process, or both, it seems likely that interventions may facilitate positive change by promoting contact with the set of delayed positive consequences, or the sober “consumption bundle,” that typically flows from a sober lifestyle. Behavior patterns with delayed positive consequences often are more valuable as a whole compared with discrete acts chosen day-to-day (Rachlin, 1995), and the likelihood of maintaining longer term, higher yield patterns is presumably greater once contact is made with the delayed positive consequences. Drawing attention to delayed consequences or signaling their future availability (e.g., via self-monitoring, motivational interviewing, or decisional balance exercises) is one way to shift behavioral organization toward the future. Such approaches may reduce the appeal of short-term discrete rewards, like substance use, by helping people frame choices as involving an extended series of linked behaviors, events, and outcomes with higher overall value (Chapman, 1996; Rachlin, 1995).
The present research has limitations that merit attention in future studies. First, despite pooling across studies, the number of participants with stable moderation resolutions was still relatively small, which limited the number of predictors that could be evaluated simultaneously in multivariate models. Although quite coherent across models, the results merit replication with a larger sample of moderate drinkers. Second, because two of the earlier studies (Tucker et al., 2002, 2006) included too few moderate drinkers to analyze them separately from abstainers, the positive ASDE findings in those studies may have been amplified by including RNA participants in the stable resolution group along with RA participants. However, RNA participants constituted less than 12% of the stable resolution groups in these studies, suggesting that the overall ASDE results and interpretation were appropriate. Third, future studies of resolution should expand the window of selection to include problem drinkers who have resolved quite recently or are contemplating doing so. Participants with drinking problems that fall short of clinical diagnostic criteria also should be studied, and drinking-related inclusion criteria should be relaxed to allow research on outcomes that fall short of stable moderation but entail substantial reductions in drinking and related harm.
Pursuing the public health implications of understanding pathways to and predictors of moderation will require expanding the scope of research to include new concepts and methods, such as those provided by behavioral economics. Better characterizing the moderate use of alcohol by individuals with a history of problem drinking may provide new insights about the behavior regulation processes involved in resolution and relapse and help guide innovative behavior change strategies that increase contact with the population with problems.
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Submitted: December 27, 2007 Revised: December 5, 2008 Accepted: December 16, 2008
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Source: Journal of Consulting and Clinical Psychology. Vol. 77. (2), Apr, 2009 pp. 219-228)
Accession Number: 2009-03774-003
Digital Object Identifier: 10.1037/a0014968
Record: 2- Title:
- A communication-based intervention for nonverbal children with autism: What changes? Who benefits?
- Authors:
- Gordon, Kate. Institute of Psychiatry, King’s College London, London, England, kate.gordon@kcl.ac.uk
Pasco, Greg. Centre for Research in Autism and Education, Department of Psychology and Human Development, Institute of Education, London, England
McElduff, Fiona. Department of Paediatric Epidemiology and Statistics, Institute of Child Health, London, England
Wade, Angie. Department of Paediatric Epidemiology and Statistics, Institute of Child Health, London, England
Howlin, Pat. Institute of Psychiatry, King’s College London, London, England
Charman, Tony. Centre for Research in Autism and Education, Department of Psychology and Human Development, Institute of Education, London, England - Address:
- Gordon, Kate, Institute of Psychiatry, King’s College London, Addiction Sciences Building, 4 Windsor Walk, SE5 8AF, London, England, kate.gordon@kcl.ac.uk
- Source:
- Journal of Consulting and Clinical Psychology, Vol 79(4), Aug, 2011. pp. 447-457.
- NLM Title Abbreviation:
- J Consult Clin Psychol
- Page Count:
- 11
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- Journal of Consulting Psychology
- Other Publishers:
- US : American Association for Applied Psychology
US : Dentan Printing Company
US : Science Press Printing Company - ISSN:
- 0022-006X (Print)
1939-2117 (Electronic) - Language:
- English
- Keywords:
- autism, communicative form, communicative function, intervention response predictors, psychosocial intervention, communication-based intervention
- Abstract:
- Objective: This article examines the form and function of spontaneous communication and outcome predictors in nonverbal children with autism following classroom-based intervention (Picture Exchange Communication System [PECS] training). Method: 84 children from 15 schools participated in a randomized controlled trial (RCT) of PECS (P. Howlin, R. K. Gordon, G. Pasco, A. Wade, & T. Charman, 2007). They were aged 4–10 years (73 boys). Primary outcome measure was naturalistic observation of communication in the classroom. Multilevel Poisson regression was used to test for intervention effects and outcome predictors. Results: Spontaneous communication using picture cards, speech, or both increased significantly following training (rate ratio [RR] =1.90, 95% CI [1.46, 2.48], p < .001; RR = 1.77, 95% CI [1.35, 2.32], p < .001; RR = 3.74, 95% CI [2.19, 6.37], p < .001, respectively). Spontaneous communication to request objects significantly increased (RR = 2.17, 95% CI [1.75, 2.68], p < .001), but spontaneous requesting for social purposes did not (RR = 1.34, 95% CI [0.83, 2.18], p = .237). Only the effect on spontaneous speech persisted by follow-up (9 months later). Less severe baseline autism symptomatology (lower Autism Diagnosis Observation Schedule [ADOS] score; C. Lord et al., 2000) was associated with greater increase in spontaneous speech (RR = 0.90, 95% CI [0.83, 0.98], p = .011) and less severe baseline expressive language impairment (lower ADOS item A1 score), with larger increases in spontaneous use of speech and pictures together (RR = 0.62, 95% CI [0.44, 0.88], p = .008). Conclusion: Overall, PECS appeared to enhance children's spontaneous communication for instrumental requesting using pictures, speech, or a combination of both. Some effects of training were moderated by baseline factors. For example, PECS appears to have increased spontaneous speech in children who could talk a little at baseline. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Autism Spectrum Disorders; *Communication; *Intervention; Communication Systems
- Medical Subject Headings (MeSH):
- Autistic Disorder; Child; Child, Preschool; Communication Disorders; Female; Follow-Up Studies; Humans; Male; Nonverbal Communication; Treatment Outcome
- PsycINFO Classification:
- Developmental Disorders & Autism (3250)
Health & Mental Health Treatment & Prevention (3300) - Population:
- Human
Male
Female - Location:
- England
- Age Group:
- Childhood (birth-12 yrs)
Preschool Age (2-5 yrs)
School Age (6-12 yrs) - Tests & Measures:
- Autism Diagnosis Observation Schedule
Expressive One Word Picture Vocabulary Test
Mullen Scales of Early Learning - Methodology:
- Empirical Study; Quantitative Study
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- Accepted: Apr 20, 2011; Revised: Dec 28, 2010; First Submitted: Mar 25, 2010
- Release Date:
- 20110725
- Correction Date:
- 20151207
- Copyright:
- American Psychological Association. 2011
- Digital Object Identifier:
- http://dx.doi.org/10.1037/a0024379
- PMID:
- 21787048
- Accession Number:
- 2011-15510-002
- Number of Citations in Source:
- 45
- Persistent link to this record (Permalink):
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- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2011-15510-002&site=ehost-live">A communication-based intervention for nonverbal children with autism: What changes? Who benefits?</A>
- Database:
- PsycINFO
A Communication-Based Intervention for Nonverbal Children With Autism: What Changes? Who Benefits?
By: Kate Gordon
Institute of Psychiatry, King's College London, England;
Greg Pasco
Centre for Research in Autism and Education, Department of Psychology and Human Development, Institute of Education, London, England
Fiona McElduff
Department of Paediatric Epidemiology and Statistics, Institute of Child Health, London, England
Angie Wade
Department of Paediatric Epidemiology and Statistics, Institute of Child Health, London, England
Pat Howlin
Institute of Psychiatry, King's College London, England
Tony Charman
Centre for Research in Autism and Education, Department of Psychology and Human Development, Institute of Education, London, England
Acknowledgement: We thank all the participating schools, children, and their parents; PECS Consultants Sue Baker and Teresa Webb from Pyramid UK; and The Three Guineas Trust for their generous support of this project.
Over the past two decades, there has been accumulating evidence for the effectiveness of psychosocial programs for young children with autism. These tend to incorporate a mix of behavioral, developmental, and educational approaches, and although methods may vary, their general goals are to enhance cognitive, communication, and social skills while minimizing rigid and repetitive and other problem behaviors (see Lord & McGee, 2001; Rogers & Vismara, 2008, for reviews). In part due to the design of studies, however, there have been few opportunities to examine the detail of exactly what changes and who benefits as a result of these interventions.
In terms of measuring what changes, many early autism intervention studies, in particular within the applied behavior analysis field, used global measures of outcome such as IQ scores and school placement (Dawson & Osterling, 1997; Howlin, Magiati, & Charman, 2009). More recent studies have used a wider range of measures, including standardized tests of adaptive behavior, expressive and receptive language, and measures of autism severity (Howard, Sparkman, Cohen, Green, & Stanislaw, 2005; Remington et al., 2007). Some studies have attempted to include measures of change in skills or behaviors specifically targeted by the intervention, for example, parents' or carers' knowledge about autism (Jocelyn, Casiro, Beattie, Bow, & Kneisz, 1998). Furthermore, a small number of studies include naturalistic or quasinaturalistic measures of communication, for example, observing spontaneous communication and language in parent–child interactions (Aldred, Green, & Adams, 2004); observing parents' use of facilitative strategies during social interaction with their child (McConachie, Randle, Hammal, & Le Couteur, 2005); recording the frequency and rate of children's turn-taking, joint attention, and requesting behaviors (Yoder & Stone, 2006a, 2006b); and observing structured play and joint attention acts within parent– and experimenter–child interactions (Kasari, Freeman, & Paparella, 2006; Kasari, Paparella, Freeman, & Jahromi, 2008). In addition to increasing the face validity of the research, demonstration of change in specific behaviors is likely to be helpful in elucidating exactly how an intervention is working (Kazdin & Nock, 2003).
To examine who benefits from intervention, attempts have been made to study the effects of preintervention child characteristics on outcome, most notably IQ and age. For IQ, this usually involves examining correlations between preintervention IQ and postintervention outcome (Eldevik, Eikeseth, Jahr, & Smith, 2006; Harris & Handleman, 2000; Remington et al., 2007) or the comparison of outcomes for high-IQ versus low-IQ subgroups (Aldred et al., 2004; Ben Itzchak, Lahat, Burgin, & Zachor, 2008; Ben-Itzchak & Zachor, 2007). Significant positive associations have been found between preintervention IQ and outcome. However, Yoder and Compton (2004) highlighted the flaws of testing for moderators by exploring correlations or comparison of subgroups' effect sizes. They emphasized the importance of using statistical methods that enable differentiation of predictors of growth or progress from predictors of intervention response. Where participants have been randomly assigned to intervention or control conditions, the appropriate method for identifying predictors of intervention response is to test for statistical interactions between child characteristics and group assignment in relation to the outcome variables. Although such statistical tests of moderator effects are well established in medical trial literature and used increasingly in the psychiatry field (Kraemer, Wilson, Fairburn, & Agras, 2002) to date, they have only been used in two studies of autism intervention (Kasari et al., 2006, 2008; Yoder & Stone, 2006a, 2006b).
Kasari et al. (2006) randomized 58 preschool children with autism (aged 3–4) to either joint attention training, symbolic play training, or a control condition, demonstrating that both active interventions were effective at enhancing social communication skills. In a later study, they presented further analysis revealing that growth in expressive language was positively predicted by a number of joint attention and symbolic play variables (Kasari et al., 2008). Yoder and Stone (2006a, 2006b) used multiple regression to demonstrate the moderating effects of baseline joint attention abilities. Their randomized trial (N = 36) compared Picture Exchange Communication Training (PECS; Bondy & Frost, 1998) with Responsive Education and Prelinguistic Milieu Training (RPMT; Yoder & Warren, 1999). Children who initiated joint attention relatively more frequently at baseline benefited more from RPMT in terms of their postintervention frequency of joint attention initiations, whereas children who initiated joint attention less frequently at baseline benefited more from PECS (Yoder & Stone, 2006a). In a later analysis, using mixed-level modeling, they found object exploration also moderated intervention response (Yoder & Stone, 2006b). Thus, children who displayed object exploration behaviors more frequently at baseline benefited most from PECS, showing greater increases in production of nonimitative words at outcome. The children who showed lower object exploration at baseline benefited more with respect to word production if they had received RPMT.
These studies notwithstanding, to date relatively few studies of psychosocial intervention for children with autism have effectively investigated what changes in response to treatment and who benefits. Common use of standardized IQ and language assessments to measure outcome has meant there has been relatively little naturalistic analysis of change in the form and function of children's communication as a result of intervention. Insufficiently sized samples and lack of randomization have impeded investigation into treatment moderators.
The Present StudyEnhancing spontaneity in everyday communication has been highlighted as one of the most important goals of intervention for children with autism (Lord & McGee, 2001). The aim of PECS is to teach spontaneous and functional communication to children with autism in a social context (Bondy & Frost, 1998). PECS is widely used in home and educational settings (Preston & Carter, 2009; Sulzer-Azaroff, Hoffman, Horton, Bondy, & Frost, 2009), and there is evidence from a number of single-case and case-series studies (Charlop-Christy, Carpenter, Le, LeBlanc, & Kellet, 2002; Ganz & Simpson, 2004; Kravits, Kamps, Kemmerer, & Potucek, 2002; Magiati & Howlin, 2003; Schwartz, Garfinkle, & Bauer, 1998), a school-based controlled study (Carr & Felce, 2007), and two randomized controlled trials (RCTs) (Howlin, Gordon, Pasco, Wade, & Charman, 2007; Yoder & Stone, 2006a, 2006b) that PECS can lead to improved communication skills in this group.
Howlin et al. (2007) conducted a pragmatic RCT of PECS. The study was designed to test the “real world” effectiveness of PECS. Initial training was delivered to teachers and classroom assistants by PECS consultants at workshops. PECS was subsequently applied by school staff in the classroom under regular supervision by PECS consultants. Naturalistic observations of rates of children's communication were used as the primary outcomes to measure intervention effects. Immediately after training had ended (approximately 5 months), the rate at which children spontaneously initiated communication (IC) had significantly increased. Overall rates of children's use of picture cards (P) to communicate (i.e., spontaneous or prompted) had also significantly increased. By the 9-month follow-up, however, these effects had disappeared. Overall rates of children's speech/vocalization (S) (including spontaneous and prompted) did not increase.
In the present article, using the sample from Howlin et al. (2007), we explore exactly which communication forms were used by children more spontaneously as a result of PECS training, which communicative functions increased and which children benefited most from the intervention. We aimed to build on previous studies by applying appropriate analysis to this relatively large sample to address the following questions:
1. Did PECS training act specifically to increase children's spontaneous communication using the picture cards or did its effect generalize to support greater spontaneity using speech as well? Previous studies have demonstrated that PECS supports children to communicate spontaneously using picture cards (e.g., Bondy & Frost, 1994; Charlop-Christy et al., 2002; Ganz & Simpson, 2004; Kravits et al., 2002) and sometimes speech (e.g., Bondy & Frost, 1994; Charlop-Christy et al., 2002; Ganz & Simpson, 2004). Two studies using more naturalistic measures of outcome also suggest that PECS can increase spontaneous communication using picture cards and speech together (Carr & Felce, 2007; Yoder & Stone, 2006a).
2. Did PECS increase children's spontaneous communication purely for instrumental requesting or did the training also lead to increased spontaneous communication for more social purposes? PECS training initially focuses on teaching children to make requests for objects. Later training phases aim to broaden the range of communicative functions, such as sharing attention through commenting (Bondy, Tincani, & Frost, 2004). Studies have shown that PECS training can be used successfully to teach children spontaneous requesting for objects (Carr & Felce, 2007; Ganz & Simpson, 2004; Kravits et al., 2002), and some have demonstrated effects on other forms of noninstrumental, more social communication (Schwartz et al., 1998; Yoder & Stone, 2006b).
3. Which children benefited most from PECS training? PECS was specifically developed for children with autism to obviate the need for prerequisite communication skills. It might be hypothesized therefore that response to PECS training would not be predicted by such factors as language comprehension skills or autistic symptomatology. Within the autism intervention literature more generally, however, higher IQ has been associated with better outcome (e.g., Harris & Handleman, 2000; Schwartz et al., 1998). Yoder and Stone's (2006a, 2006b) studies have been the only systematic investigation of PECS response predictors to date, finding that children who were most impaired in terms of baseline language and joint attention skills were those who gained most from PECS training. Given the lack of research in this area, we took an exploratory approach in the present study, investigating the potential moderating effect on PECS training of four factors measured at baseline: chronological age, expressive language, autistic symptomatology, and cognitive ability.
Method
Participants of the RCT
Eighty-four children (73 boys, 11 girls) from 17 classes in special needs elementary schools participated in the study. Classes were broadly similar, with a child–adult ratio of approximately 2:1. Class teaching programs varied, but most classes adopted an eclectic approach incorporating a range of visual systems and structured teaching, often based on the TEACCH methodology (Mesibov, Shea, & Schopler, 2004). Picture cards were present in most classrooms in the treatment and nontreatment groups even at baseline, though these were not necessarily used according to PECS principles. All schools were situated in Greater London or the south east of England. Children were aged between 4 and 10 years (mean age at baseline = 6.8 years, SD = 1.26), and all had an intellectual disability. Ethnicity, socioeconomic status, and comorbidity data were not formally collected. This was a community-based study that included all suitable children whose parents consented. To be eligible, children had to (a) have a formal clinical diagnosis of autism, (b) use little or no functional language (i.e., no more than single words), (c) have no sensory impairment, and (d) be aged between 4 and 11 years and (e) not using PECS beyond Phase 1.
Informed consent and ethical approval
Written informed consent to participate in the study was obtained from the parent or guardian of each child and from a senior member of staff from each school. The original trial protocol was prospectively reviewed and approved by the Wandsworth Local Research Ethics Committee (Ref. IAS/der/02.42.6).
Describing the group at baseline
To obtain baseline data on autism severity, all children were assessed using Module 1 of the Autism Diagnosis Observation Schedule (ADOS; Lord et al., 2000). The ADOS is an interactive semistructured assessment of communication, social interaction, imagination, and repetitive and stereotyped interests. Assessment consists of a range of activities and social presses providing a standardized context in which to observe specific behaviors. There are four modules. Module 1 was used in this study, designed for use with preverbal individuals or for those whose expressive language is still at single word or simple phrase level. Seventy-five children met the ADOS criteria for a diagnosis of autism; nine children met criteria for autism spectrum disorder. Score from Item A1 of the ADOS was used as an index of expressive language ability (0 = regular use of utterance of two or more words; 1 = occasional phrases only, mostly single words; 2 = recognizable single words only; 3 = at least one word or word approximation but fewer than five words; 4 = no words or word approximations). Thirty-eight children (45%) used no words or word approximations during the ADOS, 31 (37%) used single words, and 15 (18%) used at least one phrase. Most children (64%) scored 0 on the Expressive One Word Picture Vocabulary Test (Brownell, 2000). Nonverbal developmental quotient (NVDQ = nonverbal mental age equivalent/chronological age ×100) was ascertained using the Mullen Scales of Early Learning (Mullen, 1995). Group median NVDQ was 29.90 (interquartile range was 21.20–40.52). In summary, the sample comprised children with clear autism and who were very impaired with regard to verbal and nonverbal skills.
Design of the RCT
As a group-randomized control trial, class groups (each including approximately six children and two to three staff) were assigned into one of three intervention groups. The immediate treatment group (ITG; five class groups, 26 children) received training immediately after the baseline assessment; the delayed treatment group (DTG; six class groups, 30 children) received training about 9 months later, immediately after Time 2 assessment; the no-treatment group (NTG; six class groups, 28 children) received no training. Staggering of treatment across two time periods maximized the number of children involved in the study and allowed investigation of the continued effectiveness of any immediate treatment effects noted. The data analyses incorporated each child contributing all measurements within all control, treatment, and posttreatment periods; thus, statistical power was not compromised by the three-arm approach to data collection. Differences between the three groups at baseline were analyzed and reported in Howlin et al. (2007). The analysis was designed to adjust for these differences. Figure 1 shows recruitment, the points at which intervention was delivered, and when each of the three groups was observed.
Figure 1. Flow chart illustrating sample selection, recruitment, training, and outcome assessment. Adapted from “The Effectiveness of Picture Exchange Communication System (PECS) Training for Teachers of Children With Autism: A Pragmatic, Group Randomised Controlled Trial,” by P. Howlin, R. Kate Gordon, G. Pasco, A. Wade, and T Charman, 2007, Journal of Child Psychology and Psychiatry, 48, pp. 473–481. Copyright 2007 by John Wiley and Sons.
Outcome measurement
The outcome measure was a 15-min videotaped observation, intended to be an ecologically valid measure of communication skills. Children were filmed in their class snack sessions at Time 1 (baseline) and twice further over a period of 20 months (2 academic years). Snack sessions were selected as these were likely to create the most opportunities for children to make spontaneous requests. Furthermore, daily snack sessions occurred in all the classes and were broadly similar. These sessions usually lasted approximately 15 min and involved all children and class staff sitting at tables in the classroom or school kitchen. Drinks and food snacks such as fruit or cookies were given out or were on offer for children to request. Where classes used picture cards, these were usually made available for children (e.g., by placing a large board at the front of the classroom or by handing out books with the cards inside).
Children's communication was coded from the videotape using an observation schedule designed specifically for this study (Classroom Observation Schedule for Measuring Intentional Communication; Pasco, Gordon, Howlin, & Charman, 2008). The primary outcome variable was frequency of child-initiated communication (IC). Frequencies of different communication modalities used (such as the number of times a child used a picture card [P] and/or speech/vocalization [S] to communicate) were also recorded; communication functions were recorded by counting each time a child communicated for the purpose of requesting objects (R) and for the purpose of requesting a social interaction or commenting (D). In this way, a single communication act might produce three or more codes, for example, as a spontaneous initiation (IC), of the use of a picture card (P), and for the purpose of requesting (R).
Data analysis
Where outcomes are numerical counts of relatively rare events, Poisson regression is a useful method for analysis (Dobson, 2002). The Poisson regression model expresses the log outcome rate as a linear function of a set of predictors. In this study, Poisson regression models were produced for each of the five outcome variables of interest, concerned with form or function of children's spontaneously initiated communication: spontaneous communicative initiation using picture cards (IC-P); spontaneous communication using speech (IC-S); spontaneous communication using both simultaneously (IC-PS); spontaneous communication to request for objects (IC-R), and spontaneous communication to request for social routine or commenting (IC-D). The regression models were created within the Stata IC Version 10 (StataCorp., 2003).
As can be seen in Figure 1, the data set comprised data from three time points in the three different experimental groups. Multilevel models were used that took account of the longitudinal nature of the measurements, time trends, differing treatment regimes within the same individuals over time, and within child correlations between repeated measurements (Goldstein, 2003). The standard errors of the model parameters were thus adjusted for any within-child (across time) or within-class (between children) correlations. Models also allowed adjustment for any group differences at baseline in terms of age, developmental level, expressive language, and autistic symptom severity. Each model included an independent binary intervention variable (i.e., intervention or no intervention), a further binary variable to denote follow-up (this occurred at Time 3 for the ITG group only), a time variable (continuous in order to adjust for differences in the actual lengths of time between observations, i.e., Time 1 = 0 days), and an offset to adjust for the difference in the lengths of snack times for individual children.
In Poisson regression, effect size is represented by the rate ratio (RR) that estimates the relative rate of change in the mean number of events attributable to each explanatory variable. For example, for a binary intervention variable, the RR represents the relative difference in mean frequency of spontaneous initiations for children in the intervention group compared with those not in the intervention group. For continuous variables (e.g., baseline age [months]), the RR represents the relative difference in the mean frequency of initiations for every increase in one unit of the explanatory variable—in this case, for every month older the child was at baseline. An RR of 1 indicates no change, and so, for example, an RR of 1.2 represents an increase of 20% for each unit increase; an RR of 0.7 represents a decrease of 30% for each unit increase. RRs for estimates from the five models (for each of the five outcome variables) are reported along with 95% confidence intervals and p values. These models were not independent and were interpreted jointly taking into account the relationship between the various outcomes.
Testing for intervention moderators
Where PECS had a significant effect, a second round of analyses was conducted in order to identify potential intervention response moderators. If baseline factors (i.e., chronological age, autistic symptomatology, expressive language, or developmental quotient) independently predicted progress at postintervention (shown in Table 2), tests for Intervention × Baseline Factor interactions were conducted to explore whether they also predicted a specific intervention response. The RR for the interaction term represents the impact of the baseline variable on the outcome over and above any existing variance due to a main intervention effect or a main effect of the baseline factor.
Rate Ratio Estimates (and 95% Confidence Intervals) for Each of the Five Outcome Variables
Results
The results are presented in two parts. First, we examined the impact of PECS training on children's spontaneous communication using three different communication modalities and for two different functions. Second, controlling for differences, we tested baseline variables for their potential moderating effect on the intervention.
Change in spontaneous communication following PECS training
Table 1 shows the median rate of initiations per minute for the five variables in each of the three treatment arms at each of the three time points. The rates in bold are immediately following PECS training in the ITG and DTG. The italicized rates are at 9-month follow-up (ITG only). These figures indicate some changes following PECS training. For example, in the DTG, the median rate of spontaneous initiation of communication using picture cards went from 0 to 0.44 per minute, that is, more than 6 times per 15-min snack session. In the ITG, the median rate of spontaneous communication using speech or vocalization went from 0.03 to 0.13 per minute, and in the DTG, the median rate of spontaneous requesting rose from 0.03 to 0.46 times per minute. Despite these group effects, for each of the form and function variables, some children remained at zero, even after PECS training. For example, of the 56 children in the ITG and DTG, 12 were still not using picture cards to spontaneously communicate at all after the training, and nine were still not making spontaneous requests.
Median Initiations Made Per Minute at Each Time Point for Each of the Three Treatment Groups
Table 2 shows the results of the Poisson analysis for each variable. RRs are shown for change attributable to the intervention immediately post-PECS training, at 9-month follow-up (for the ITG group only), and for each of the nonintervention variables measured at baseline. Initiations using picture cards (IC-P), using speech (IC-S), and using both simultaneously (IC-PS) all increased significantly following training (RR = 1.90, 95% CI [1.46, 2.48], p < .001; RR = 1.77, 95% CI [1.35, 2.32], p < .001; RR = 3.74, 95% CI [2.19, 6.37], p < .001, respectively). The average increase observed was similar in size for IC-P and IC-S and about twice as large for IC-PS. However, it should be noted that the confidence intervals are wide, and in all instances the data are compatible with a twofold increase in the RR, so we cannot necessarily infer that the effect is any greater for IC-PS. Spontaneous requesting for objects (IC-R) significantly increased following training (RR = 2.17, 95% CI [1.75, 2.68], p < .001), but requesting for social routine or commenting (IC-D) did not (RR = 1.34, 95% CI [0.83, 2.18], p = .237). Children in the ITG (n = 26) were observed again at follow-up (approximately 9 months after the end of the training period). Although the effect on spontaneous initiation using speech/vocalization (IC-S) had persisted (RR = 1.70, 95% CI [1.12, 2.58], p = .012), none of the other effects were significant (IC-P, RR = 0.69, 95% CI [0.41, 1.15], p = .15; IC-PS, RR = 1.90, 95% CI [0.76, 4.76], p = .17; IC-R, RR = 1.11, 95% CI [0.76, 1.62], p = .60).
Variables moderating the effect of PECS training
We analyzed baseline variables for their power to predict progress in general and to predict specific response to treatment. Seven baseline variables (shown in bold in Table 2) were independently and significantly related to general progress at postintervention and so were testable as potential moderators of the intervention effects. Of these seven Intervention × Baseline Variable interactions tested, two were found to be significant. The impact of the intervention on children's spontaneous initiation of communication using speech/vocalization (IC-S) was moderated by baseline autistic symptomatology (RR = 0.90, 95% CI [0.83, 0.98], p = .011). As can be seen in Figure 2, children whose autistic symptomatology score was lowest at baseline (i.e., least severe symptoms) showed the largest increases in spontaneous use of speech/vocalization following intervention. Each unit increase in ADOS score was associated with a 10% decrease in average rate of initiation using speech/vocalization (IC-S). Baseline expressive language did not moderate intervention effects for this outcome (RR = 1.05, 95% CI [0.90, 1.24], p = .524).
Figure 2. Graph showing the moderating effect of autistic symptomatology on the effect of PECS on children's spontaneous communication using speech/vocalization (Autism Diagnosis Observation Schedule [ADOS] scores are on a severity scale; higher score means more severe symptomatology).
Baseline expressive language moderated the effect of PECS training on children's spontaneous initiation using picture cards and speech/vocalization together (RR = 0.62, 95% CI [0.44, 0.88], p = .008). Expressive language was rated on a severity scale. Figure 3 shows that those children with the most expressive language at baseline (lower score represents better expressive language) showed the biggest increase in their use of picture cards and speech/vocalization together to spontaneously initiate communication. Each unit increase in expressive language deficit score was associated with a 38% decrease in the average rate of initiations using picture cards and speech together (IC-PS). Neither baseline developmental quotient nor autistic symptomatology moderated the effects of the intervention for this outcome (RR = 1.01, 95% CI [0.98, 1.05], p = .539 and RR = 0.94, 95% CI [0.79, 1.12], p = .477, respectively). As can be seen in Table 2, baseline developmental quotient (DQ) predicted of rate of initiation using picture card (IC-P) and rate of initiation for the purpose of instrumental requesting (IC-R) immediately postintervention, but interaction tests demonstrated that this did not moderate the effects of the training on these behaviors (for IC-P, DQ × Intervention, RR = 0.99, 95% CI [0.98, 1.01], p = .214; for IC-R, DQ × Intervention, RR = 0.99, 95% CI [0.98, 1.00], p = .212).
Figure 3. Graph showing the moderating effect of baseline expressive language on children's spontaneous communication using picture cards and speech/vocalization. ADOS = Autism Diagnosis Observation Schedule.
Discussion
PECS is recognized as an effective intervention for increasing communication in children with autism (Preston & Carter, 2009; Sulzer-Azaroff et al., 2009), and our RCT demonstrated specifically that PECS training can significantly enhance the spontaneity of children's communication (Howlin et al., 2007). In this article, we asked exactly how PECS training increased this communicative spontaneity and for which children. That is, we wanted to examine, first, whether the increased spontaneity was confined to communication using the picture symbols or whether PECS also impacted on the spontaneity of children's use of speech/vocalization. Second, we wished to examine whether the increased spontaneous communication was being used only for instrumental purposes (e.g., getting a snack) or whether children were also spontaneously initiating communication for more social purposes as a result of PECS training. Third, we wanted to identify factors that might be moderating the effect of PECS training and therefore predictive of which children might benefit most from the training. We used Poisson regression analysis to examine the children's spontaneous communication using different communication modalities and for different functions and to test for interactions between the intervention and baseline child variables.
The naturalistic and relatively fine-grained outcome measurement meant that it was possible to analyze exactly how PECS was enhancing children's spontaneous communication in an everyday situation. A small number of previous intervention studies have examined the form of children's communication but have not focused purely on spontaneous unprompted communication. The present analyses revealed that although PECS training did lead to children spontaneously communicating more using the picture cards, it also led to increased spontaneity in children's use of speech and their use of picture cards and speech in combination. The training appears to have increased spontaneous requesting for objects or help but not spontaneous requesting for social routine or commenting.
In contrast to some other reports (Bondy & Frost, 1994; Charlop-Christy et al., 2002), in our primary analysis of PECS RCT, we did not observe an effect of the intervention on overall use of speech (Howlin et al., 2007). The present analysis revealed, however, that PECS did enhance the use of speech as a modality to spontaneously initiate communication, as well as enhancing spontaneity using picture cards. So, although it would appear that PECS training did not enhance speech development per se, for those children who were already using some speech or vocalization, PECS appears to have provided a structure for them to use this mode to communicate without prompting. It would seem that PECS fostered spontaneity more generally across modalities rather than just acting to increase children's use of picture cards. Furthermore, the effect of PECS training on children's spontaneous speech/vocalization appears to have been particularly robust as it was also observed 9 months after the end of the training period in the group who received PECS training early on. There was no long-term effect on spontaneous use of picture cards.
Detailed analysis of the functions of spontaneous communication in autism intervention is also rare. In this study, analysis revealed a clear effect on children's spontaneous communication for the purposes of requesting for objects, such as a drink or a toy, which is the first communicative function taught through PECS teaching phases (Frost & Bondy, 2002). This replicates findings from earlier research (Schwartz et al., 1998; Yoder & Stone, 2006a, 2006b). There was no effect of training on children's spontaneous communication for social purposes. This might be due to the fact that the children in this sample had severe autism symptoms and, as a group, were very delayed with regard to verbal and nonverbal skills. Furthermore, the discrepancy between instrumental and social communication is perhaps to be expected given that the children were observed in class snack sessions. It is possible that observation of children in other nonsnack sessions might have revealed effects of training on communication for other noninstrumental purposes. Also, it is possible that if the training had persisted for longer or had been more intense, changes in spontaneous social, noninstrumental communication might have been seen. Some case study reports have described children successfully learning to communicate for social interaction purposes such as commenting (e.g., Schwartz et al., 1998; Webb, 2000), although, to date, no experimental trials have demonstrated this effect of PECS.
Two baseline variables appeared to moderate the effect of PECS training. First, less severe autistic symptomatology at baseline predicted the greatest increases in spontaneous speech. Second, higher level of expressive language at baseline predicted greater increases in spontaneous use of speech and picture cards together. This is to be expected, as more severe autism and greater language disability are not independent. Thus, the fact that the least severely autistic children and those with the most expressive language showed the greatest improvements in these areas is consistent with the autism intervention literature more generally (e.g., Harris & Handleman, 2000; Kasari et al., 2008). We observed no interactions between PECS training and any of the abilities measured at baseline on children's spontaneous use of the picture cards or spontaneous requesting. Yoder and Stone's (2006a,2006b) studies have been the only other systematic examination of moderators of PECS intervention. They compared PECS with RPMT, and although there was no overall difference between the interventions, children who were most impaired in baseline language and joint attention skills gained most in terms of their joint attention skills from PECS training, whereas the more able children made better progress with RPMT. The present study did not replicate the finding that the less able children benefited more from PECS. A potential explanation is that, as a group, the children in the present sample were less able than Yoder and Stone's sample. In Yoder and Stone's sample, the mean nonverbal mental age was 18.8 months (standard deviation 4.5 months) at 3 years of age (Table 1 in Yoder & Stone, 2006a, p. 429), meaning that the mean NVDQ was approximately 50, whereas the mean NVDQ in our sample was around 30.
Despite the fact that all children in the present study were very impaired in terms of their verbal and nonverbal skills, spontaneous use of pictures to communicate and spontaneous requesting did increase, and this was not predicted by better baseline language or less severe autism symptoms. This suggests that PECS training was equally accessible to these children in terms of teaching these skills specifically. This seems to support the idea that, beyond the need for some very basic cognitive skills required in order to exchange the cards (e.g., object permanence), few preexisting verbal or nonverbal skills are required to learn to use PECS (Bondy & Frost, 1998).
Strengths and limitations of the present study
The unique quality of the data presented here is that they are derived from an examination of “real world” effectiveness. The study took an inclusive approach to recruitment, aiming to include all suitable schools within a defined but large geographical area in the south east of England and included all suitable children whose parents consented. As the trial was community based, intervention was delivered to teachers and classroom staff via a workshop and follow-up visits to the schools. Teachers had to implement the program amidst all the other pressures and distractions of running a classroom for children with special educational needs and from a wide variety of backgrounds. Children were required to access the intervention in spite of their severe autistic symptoms, language impairments, and perhaps other comorbid problems. In other words, PECS training that was delivered and evaluated seemed to be a realistic representation of PECS training most children are likely to receive.
The design of the study and analysis ensured that the use of three treatment groups did not detract from the numbers effectively used for the intervention and no-intervention groups. The study was, in fact, strengthened by having within-individual comparisons (i.e., the delayed intervention group) as well as between-group comparisons over the same time frame. The use of the multilevel model allowed for efficient use of data in the three-treatment arm format adopted in this study, taking into account correlations between repeats from the same individuals and allowing for the serial nature of measurements under different treatment conditions. The design also enhanced the power to investigate the immediate effects of the intervention and enabled investigation of the longer term effects of the training. The incorporation of baseline data further strengthened the results.
The interaction analysis applied in this study is relatively novel to this field and demonstrates the possibility of using relatively sophisticated statistical models to test for moderator effects on interventions for children with autism. As has been discussed above, this has been rarely done in the autism field in the past, and thus there is very little reliable information about who benefits most from various interventions for children with autism.
As this trial was conducted primarily in schools, we had little direct contact with parents, aside from their consenting for their child to take part in the study. As a consequence, we did not collect systematic information on family variables such as ethnicity and other background factors (socioeconomic status, parental income) that might also be related to differential outcome, nor did we collect detailed information on potential school moderating factors. Instead, we focused on child characteristics as moderating factors. Generalization of these findings will require replication in samples with well-described demographic information as well as well-characterized schools/classrooms.
For logistical reasons (i.e., limited resources), we were limited to observing children in their classrooms. We opted to observe them during snack sessions as this was a session that created more opportunities for children to make spontaneous requests, relative to less structured sessions, and this was a common feature on the timetables of all classes involved in the study. However, the snack sessions are relatively brief periods, when children are usually highly motivated to make approaches for food, and so the data may not represent changes in children's communication in other less structured or less motivating contexts. Observations of children communicating in other class sessions or at home would have revealed the extent to which the observed effects generalized out of the relatively structured setting of class snack time.
With regard to the analysis of intervention response predictors, we were limited to testing four child factors measured at baseline. It is possible that other factors, not measured, were moderating the intervention effects, including those that were external to the children (i.e., environmental factors). It is likely that differences between the classes and the ways in which PECS was implemented also influenced children's progress. Treatment fidelity measures will be important for future studies of psychosocial interventions.
A limitation of the analysis of response predictors was that, although relatively sophisticated, essentially it was based on comparing subgroups, and the study was not primarily powered for this. The chance of Type II errors is thus increased. The results do, however, provide a good basis for further discussion. In the future, as more intervention studies are conducted and there is greater consistency in approach across research, pooling of samples may be possible, thus increasing statistical power for identifying invention response moderators.
Implications
In summary, the findings show that classroom-based PECS training enhances children's ability to make spontaneous instrumental requests not only using pictures but also using speech, or a combination of both. It also shows that, similar to other interventions, less impaired children appear to show the most improvement in these areas. Where these improvements were seen, they represented noticeable change in children's communication. For example, in one treatment group, the median rate of spontaneously initiated communication using PECS went from 0 times per 15-min snack session up to more than 6 times, and the median rate of spontaneous requesting rose from ~0.5 times to ~7 times per snack session. It is important to remember, however, that these figures are based on group effects. Such impressive gains were not seen in all children who received PECS training, and for some children, no gains were made at all. Nevertheless, for a child who has not been communicating at all to request even twice in a 15-min snack session represents a meaningful change.
The study also has important methodological implications. This article builds on the findings of one of the larger RCTs conducted in the autism field to date (Howlin et al., 2007; though see Aman et al., 2009; Green et al., 2010, for larger studies). The adequately sized sample, well described in terms of verbal and nonverbal abilities, provides the opportunity for analysis of the specific effects of psychosocial intervention for children with autism. The study demonstrates the feasibility of applying robust statistical techniques to pragmatic, “real world” trials in this field to elucidate exactly what changes and for whom.
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Submitted: March 25, 2010 Revised: December 28, 2010 Accepted: April 20, 2011
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Source: Journal of Consulting and Clinical Psychology. Vol. 79. (4), Aug, 2011 pp. 447-457)
Accession Number: 2011-15510-002
Digital Object Identifier: 10.1037/a0024379
Record: 3- Title:
- A measure of perceived family stigma: Validity in a military sample.
- Authors:
- Zhou, Anne Q.. Department of Psychology, University of Minnesota, Minneapolis, MN, US, zhou0395@umn.edu
Whealin, Julia M., ORCID 0000-0002-2876-6783. Clinical Informatics Service, VA Pacific Islands Health Care System, Honolulu, HI, US
Wang, Chun. Department of Psychology, University of Minnesota, Minneapolis, MN, US
Lee, Richard M.. Department of Psychology, University of Minnesota, Minneapolis, MN, US - Address:
- Zhou, Anne Q., Department of Psychology, University of Minnesota, 75 East River Parkway, Minneapolis, MN, US, 55455, zhou0395@umn.edu
- Source:
- Psychological Assessment, Vol 29(9), Sep, 2017. pp. 1167-1181.
- NLM Title Abbreviation:
- Psychol Assess
- Page Count:
- 15
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- Psychological Assessment: A Journal of Consulting and Clinical Psychology
- ISSN:
- 1040-3590 (Print)
1939-134X (Electronic) - Language:
- English
- Keywords:
- family stigma, military veterans, measurement invariance, scale development, help seeking
- Abstract (English):
- The primary aim of the present study was to evaluate the reliability and validity of the newly developed Perceived Family Stigma Scale (PFSS) in a diverse sample of 623 military veterans. The PFSS is a 4-item scale that has acceptable internal consistency (α = .86) and strong interitem correlations (r = .51 to .76). Confirmatory factor analysis (CFA) indicated the single factor model was a good fit statistically (χ2[df = 2, N = 620] = .34, p = .84) and descriptively (CFI = 1.00, RMSEA < .001). Multigroup CFA was performed to test the measurement invariance of the PFSS across demographic indicators. The PFSS achieved full scalar invariance across deployment history, education level, urban/rural location, marital status, and military rank, and partial scalar invariance across gender, ethnicity/race, and income level. Results of a logistic regression analysis indicated significant relationships of mean PFSS scores and gender with likelihood of needing help for an emotional problem, above and beyond a measure of self- and public stigma. Specifically, each point increase in mean PFSS scores predicted an almost 4 times higher probability of reporting a need for help, and men were also 6 times more likely than women to report a need for help. However, there was a significant relationship between the PFSS and gender such that, for women, each 1 point increase in mean PFSS scores predicted a likelihood of reporting a need/desire for help for an emotional problem 3 times that of men. (PsycINFO Database Record (c) 2018 APA, all rights reserved)
- Impact Statement:
- Public Significance Statement—The present study provides a valid, short scale for researchers and clinicians to use to measure the construct of family stigma. Additionally, the present study highlights the importance of studying family stigma as a barrier to care. (PsycINFO Database Record (c) 2018 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Military Veterans; *Rating Scales; *Stigma; *Military Families; Help Seeking Behavior; Test Construction; Test Validity
- PsycINFO Classification:
- Tests & Testing (2220)
Military Psychology (3800) - Population:
- Human
Male
Female - Location:
- US
- Age Group:
- Adulthood (18 yrs & older)
- Tests & Measures:
- Perceived Family Stigma Scale
Self-Help and Treatment Services Utilization Survey
Perceived Stigma and Barriers to Care Scale DOI: 10.1037/t40690-000
Devaluation of Consumers Scale DOI: 10.1037/t64986-000 - Grant Sponsorship:
- Sponsor: Department of Veterans Affairs, US
Grant Number: PPO 09-314-1
Other Details: Human Services Research and Development Award
Recipients: Whealin, Julia M. - Methodology:
- Empirical Study; Quantitative Study
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- First Posted: Nov 28, 2016; Accepted: Oct 4, 2016; Revised: Oct 3, 2016; First Submitted: Apr 17, 2016
- Release Date:
- 20161128
- Correction Date:
- 20180315
- Copyright:
- American Psychological Association. 2016
- Digital Object Identifier:
- http://dx.doi.org/10.1037/pas0000416
- PMID:
- 27893227
- Accession Number:
- 2016-56897-001
- Persistent link to this record (Permalink):
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- Cut and Paste:
- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2016-56897-001&site=ehost-live">A measure of perceived family stigma: Validity in a military sample.</A>
- Database:
- PsycINFO
A Measure of Perceived Family Stigma: Validity in a Military Sample
By: Anne Q. Zhou
Department of Psychology, University of Minnesota;
Julia M. Whealin
Clinical Informatics Service, VA Pacific Islands Health Care System, Honolulu, Hawaii, and Department of Psychiatry, University of Hawaii School of Medicine
Chun Wang
Department of Psychology, University of Minnesota
Richard M. Lee
Department of Psychology, University of Minnesota
Acknowledgement: This material is based upon work supported in part by a Department of Veterans Affairs Human Services Research and Development Award (PPO 09-314-1 to Julia M. Whealin). The contents do not represent the views of the VA or the United States Government. Thank you to Sandra Bozik-Lyman, MA and Dawna Nelson, MSW for their assistance collecting these data.
Mental health stigma refers to negative attitudes and beliefs about people with mental illnesses, or people who seek mental health services, and is one of the most persistent personal barriers to mental health service utilization. Mental health stigma specifically contributes to prejudice and discrimination against people with mental illness that prevents people from seeking or receiving services (Corrigan & Penn, 1999; Corrigan & Wassel, 2008; Van Brakel, 2006). The present study examined perceived family stigma—an individual’s perceptions or awareness of their family’s negative attitudes and beliefs about the attributes of people with mental illness (see Table 1)—in a predominantly White and Asian American/Pacific Islander (AAPI) sample of U.S. military veterans, as AAPI veterans are at particularly high risk for mental health problems (Tsai & Radhakrishnan, 2012).
Types of Stigma
Various subcategories of mental health stigma exist (see Table 1), the most basic of which are self-stigma and public stigma (Corrigan, 2004). Self-stigma reflects an individual’s personal attitude about stigma, whereas public stigma reflects an individual’s perception about stigma in their external environment (e.g., belief that others will have negative views of an individual with mental illness). Both forms of mental health stigma can have a negative impact on mental health care utilization, given that fear of potential discrimination may discourage treatment seeking (Corrigan & Wassel, 2008), as well as intensify psychosocial difficulties (Livingston & Boyd, 2010). Research measures have been developed to assess various forms of self-stigma and public stigma (Corrigan, Watson, & Barr, 2006; Mak & Cheung, 2010; Ritsher, Otilingam, & Grajales, 2003; Skopp et al., 2012; Struening et al., 2001; Vogel, Wade, & Haake, 2006; Vogel, Wade, & Ascheman, 2009; Vogt, Fox, & Di Leone, 2014). However, current measures of family stigma often conflate it with other sources of perceived public stigma (e.g., Endorsed and Anticipated Stigma Inventory; Vogt et al., 2014), or do not specifically measure perceived family stigma (e.g., Perceived Stigma and Barriers to Care Scale; Britt et al., 2008). As such, it is important that we look at perceived family stigma as its own unique construct both due to how perceived family stigma may be experienced differently than other forms of perceived stigma (Moses, 2010) and the saliency of family in veteran and military cultures.
As family can be an important source of support to those who suffer from mental health issues (Scarpa, Haden, & Hurley, 2006), perceived family stigma may dissuade individuals from seeking help from family and nonfamily members, including professional mental health service providers (Acosta et al., 2014), and act as a significant barrier to mental health service utilization (Vogt et al., 2014). For example, Leaf, Bruce, and Tischler (1986) found that women were less likely to seek treatment if they believed their families would “get upset” at them for doing so. Spoont and colleagues (2014) also found a relationship between stigma from “people in your life” and service use. Although perceived family stigma was not directly measured, Spoont et al. found that the odds of initiating mental health treatment were greater for veterans who were encouraged to seek help by friends and/or family than for those who were not. Thus, the empirical support generally supports a negative relationship between perceived family stigma and likelihood of seeking mental health services.
Over time, individuals may internalize family stigma beliefs as a part of their own value system (Fung, Tsang, & Cheung, 2011). Internalizing requires that individuals adopt others’ (in this case, family members’) attitudes as their own (Corrigan & Rao, 2012). In a study of 300 family members of mentally ill patients (Shrivastava, Johnston, De Sousa, Sonavane, & Shah, 2014), 84.4% of participants reported that observation of family attitudes and/or hearing offensive comments contributed to the patients’ internalizing these negative stereotypes (e.g., internalized stigma, also known as self-stigma). Once internalized, perceived family stigma can lead to negative outcomes including low self-esteem, low empowerment (Livingston & Boyd, 2010) and increased risk for suicide (Griffiths, Crisp, Jorm, & Christensen, 2011). These negative outcomes can be a very real concern given the occurrence of “affiliative stigma” (Chang et al., 2015; Mak & Cheung, 2008), which describes family members’ internalization of public stigma as a result of their affiliation with the individual with mental illness. In other words, affiliative stigma occurs when the family member internalizes their perceived courtesy stigma (Corrigan & Miller, 2004; see Table 1). Alternatively, “reflective stigma” involves situations where individuals believe that their mental illness could cause their family members to experience pubic stigma, although the individual does not necessarily endorse or internalize the stigma themselves. For example, Gilbert, Gilbert, and Sanghera (2004) assessed Asian women via focus groups and found that the primary concern participants had about seeking mental health treatment was the shame that seeking such treatment would bring to loved ones, whereas their personal attitudes (i.e., self-stigma) was much less of a barrier to seeking help.
Perceived family stigma may be particularly salient for some groups over others. AAPIs, for instance, may be more vulnerable to perceived family stigma given the cultural values endorsed by individuals who identify as AAPI. Research has found that, in general, AAPIs endorse values such as maintaining face and have a tendency for a collectivistic orientation, which make it likely for AAPIs to consider their reputation not only a reflection of themselves but also that of their family (Kim, Atkinson, & Umemoto, 2001; Lin & Tsai, 2016). Given the importance of honoring the family in AAPI cultures (Sandhu, 1997), this fear of bringing shame to the family may be a particularly salient barrier to seeking mental health services in this population, even over and above other barriers. Previous research by Yang, Phelan, and Link (2008), which looked at community attitudes of efficacy and shame in Chinese Americans, demonstrated that familial and community stigma may play a role in why they hesitate to seek counseling. More broadly, Golberstein, Eisenberg, and Gollust (2008) found that AAPIs perceived higher levels of stigma and were significantly less likely to use services than Whites. Thus, it is important to identify how perceived family stigma in and of itself (separate from public stigma) differentially impacts utilization in AAPIs. Outside of ethnic/racial differences, there may also be gender differences in perceived family stigma. Research by Ojeda and Bergstresser (2008) suggested that men were more likely than women to endorse items suggesting that fear of stigma from others (e.g., neighbors) was a barrier impacting mental health service seeking decisions. As this finding suggests men may be more likely than women to be concerned about opinions from outside sources, it is likely that this may also apply to the opinions of family members. As such, men may be more likely to report higher perceived family stigma levels, which could potentially impact their mental health service seeking.
In addition to its potential impact on specific sociodemographic groups, perceived family stigma is also expected to be a salient factor on mental health service seeking for Active Duty and veteran populations. A fundamental characteristic across military populations is the focus on self-reliance, as well as the emotional control underlying the warrior way of life, referred to as the “warrior ethos” (Hall, 2008). Values such as stoicism are often prized within Active Duty and veteran cultures, resulting in a decreased likelihood that mental health problems will be discussed or acknowledged. Importantly, the warrior ethos value can extend to family members as well, who are expected to represent their service member positively (Kuehner, 2013). Unlike civilian families, military families are acculturated to military values as a function of many of the characteristics of military life—not having a choice about where you live, often being required to move to new locations every few years, often living in military-only bases, and involvement in social activities (Hall, 2008). When this is the case, family members are likely to adopt the warrior ethos, and, as a result, may covertly and overtly discourage each other from talking about mental health problems (DeCarvalho & Whealin, 2012). This can lead to perceptions that mental health problems would be viewed negatively by family members and fears that mental health problems would reflect poorly on the family, all aspects of perceived family stigma.
Concerns related to perceived family stigma can be highly problematic given the prevalence of mental health sequalae, such as posttraumatic stress disorder (PTSD), that results from combat and other operational stressors (Barrett, Gray, Doebbeling, Clauw, & Reeves, 2002; Hoge et al., 2004; Iversen et al., 2008; O’Toole, Marshall, Schureck, & Dobson, 1999). Of the 289,328 Iraq and Afghanistan veterans who entered the VA health care system between 2002 and 2008, 21.8% were diagnosed with PTSD (Seal et al., 2009). However, despite the high rates of military-related sequelae, most veterans who are diagnosed with a mental health disorder underutilize mental health services. Among U.S. veterans who served in Iraq and Afghanistan, for instance, almost half of those who screened positive for PTSD and/or depression reported that they had not received any treatment in the previous year (Schell & Marshall, 2008). In order to address this gap in mental health care, it is important that we look at the barriers to seeking help. Perceived family stigma is one such important barrier that needs to be studied, both due to its potential salience within the Active Duty and veteran population, as well as previous research that has indicated that stigma-related concerns are one such barrier that influence veterans’ intentions to seek mental health treatment (Acosta et al., 2014; Leaf et al., 1986; Ouimette et al., 2011; Pietrzak, Johnson, Goldstein, Malley, & Southwick, 2009; Vogt et al., 2014). Thus, the influence of the military cultural values (for example, the warrior ethos, which may promote mental health stigma) that permeate the family in combination with the amount of military-related mental health sequelae veterans face, makes perceived family stigma potentially highly relevant to veterans and their mental health service seeking. However, very little of the research has looked at perceived family stigma, so much of our understanding of perceived family stigma is limited. Additionally, previous research on public stigma has often focused on White military populations, with little attention to AAPI military populations, despite the salience of family as a factor impacting treatment seeking within this population. As such, the present study provides much needed information on perceived family stigma in the APPI veteran population.
Given the lack of published measures to assess perceived family stigma, the goal of the present study was (a) develop a short, survey-based measure of perceived family stigma, (b) evaluate the psychometric properties of the Perceived Family Stigma Scale (PFSS), and (c) explore correlates and outcomes (i.e., need or desire for help for emotional problems) related to perceived family stigma. As such, the present study specifically sought to demonstrate the reliability and convergent validity of a self-report scale that exclusively measures perceived family stigma. In addition, this study examined the factor invariance of the items in the PFSS across demographic factors that have been shown to correlate with mental health service use, such as gender, ethnicity/race, marital status, income level, rural versus urban residence, deployment status, and rank in the military (Cohen & Hesselbart, 1993; Fikretoglu, Guay, Pedlar, & Brunet, 2008; Hoge, Auchterlonie, & Milliken, 2006; Lin, Goering, Offord, Campbell, & Boyle, 1996; Mackenzie, Gekoski, & Knox, 2006; Stewart, Jameson, & Curtin, 2015; Sue, Fujino, Hu, Takeuchi, & Zane, 1991). Furthermore, we explored PFSS by examining correlates of PFSS as well as the relationship between PFSS and need or desire for help for emotional problems, as well as whether the relationship was moderated by gender or ethnicity/race. We had no specific hypotheses regarding the differential associations for other demographic groups (i.e., based upon income level, deployment history, education level, rural status, marital status, or rank in the military). As research has found that individuals who feel unable to disclose their emotional problems to significant others have higher levels of mental illness (Belsher, Ruzek, Bongar, & Cordova, 2012; Cordova, Cunningham, Carlson, & Andrykowski, 2001; Lepore, Silver, Wortman, & Wayment, 1996), we hypothesized that veterans with low PFSS scores would be more likely to express a desire for help for an emotional problem compared to those with high PFSS scores, but did not have specific hypotheses whether this relationship would be moderated by gender or ethnicity/race.
Method Procedure
A survey was conducted among veterans and members of the U.S. Air and Army Guard using a multistage mailing procedure (Mangione, 1998) in 2011. A cover letter and fact sheet explaining the study and consent process were mailed, followed by three reminder letters. An incentive of $20 was included with the survey to provide remuneration for veterans’ time. This research was approved by the VA Pacific Islands Health Care System Institutional Review Board.
Participants
Participants were a Hawaii sample of 623 veterans and national guard members that served in the post September 2001 conflicts formally known as Operation Enduring Freedom (OEF), Operation Iraqi Freedom (OIF), and Operation New Dawn (OND). Because over 40% of veterans serving after September 11, 2001 are from rural areas, samples were stratified by rural/urban residence using VA guidelines (West et al., 2010), in which urban residents are defined as those living in a U.S. Census-defined urbanized area. Rural residents included anyone not defined as urban.
Of a total of 450 veterans randomly selected from Hawaii’s OEF/OIF/OND program registry (with a roster of 2,565 OEF/OIF/OND veterans) who were mailed surveys, 233 (58% response rate) returned completed surveys. Of a total of 800 veterans randomly selected from a list of all enrolled National Guard members in the state of Hawaii (n = 5,300) who were mailed surveys, 390 (49% response rate) returned completed surveys. The total sample from both Hawaii samples was 623 veterans between November 2011 and May, 2012. Following recommended practices (U.S. Department of Health & Human Services, 2011) and methods used in previous studies (Kaneshiro, Geling, Gellert, & Millar, 2011), participants were grouped based on the specific ethnic-racial categories they provided. The sample consisted of 139 East Asians (Chinese/Japanese/Korean), 113 170 Native Hawaiians/Pacific Islanders (NHPI), and 124 Whites. Given the limited number of participants from other ethno-racial groups (e.g., Latinos/Latinas, Blacks), participants who did not fall into one of the four ethnic-racial categories above were all grouped under an Other ethnicity/race category. The two samples did not differ by gender, age, education, rural–urban region, or marital status. Compared to the general population of veteran and National Guard members, the current samples did not differ by age, gender, or marital status, but were more likely to identify as AAPI.
Measures
Perceived Family Stigma Scale (PFSS)
Prior to psychometrically testing the items as a part of the present study, a multistep process was used to develop the PFSS. With this preliminary work, researchers systematically reviewed the literature, created a draft instrument, and refined the scale items.
A scale development team was brought together that consisted of the second author’s research team and diverse mental health scholars (e.g., experts from relevant cultural groups—including military populations, rural populations, and ethnically diverse populations—who had clinical expertise working with those groups). This team reviewed the literature on mental health stigma theory (Corrigan, 2004; Corrigan & Wassel, 2008; Link & Phelan, 2001; Livingston & Boyd, 2010; Van Brakel, 2006), the literature on mental health stigma in diverse groups (Abdullah & Brown, 2011; Atkinson & Gim, 1989; P.W. Corrigan & Miller, 2004; Davis, Ressler, Schwartz, Stephens, & Bradley, 2008; Judd et al., 2006; Yang et al., 2008) including military populations (Hoge et al., 2004; Kim, Thomas, Wilk, Castro, & Hoge, 2010; Pietrzak et al., 2009; Stecker, Fortney, Hamilton, & Ajzen, 2007; Vogt, 2011), and work that assessed stigma experienced by family members of those with mental health problems (Corrigan, 2004; Wahl & Harman, 1989; Struening et al., 2001; Alvidrez, 1999; Murray-Swank et al., 2007; Fung et al., 2011; Shrivastava et al., 2014; Livingston & Boyd, 2010; Stuart, 2008). The team additionally reviewed literature on attitudinal and cultural factors related to seeking help for mental health problems (Alvidrez, 1999; Finley et al., 2010; Gorman, Blow, Ames, & Reed, 2011; Murray-Swank et al., 2007; Ortega & Rosenheck, 2000; Yoshioka, Gilbert, El-Bassel, & Baig-Amin, 2003). The literature review found that although other scales had been developed that assessed stigma experienced by family members of those with mental health problems, no scale had yet been developed that systematically targeted perceived family stigma among individuals with mental health problems.
After verifying that a perceived family stigma construct had not yet been developed, the team collectively identified the concepts that comprised the perceived family stigma construct using a rational approach (Haynes, Richard, & Kubany, 1995). The concepts included perceptions of family secrecy, shame, blame, and doubt in one’s ability because of a mental health disorder. Five items were then developed that reflected the perceived family stigma construct: (a) “People in my family do not talk about mental health problems with people outside of my family”; (b) “It would reflect badly on people in my family if people knew I had a mental health problem”; (c) “People in my family would blame me for the problem”; (d) “People in my family might have less confidence in me”; and (e) “People in my family might treat me differently.” Participants were instructed to rate how much each concern would affect their likelihood of seeking services if they were suffering from a mental/emotional problem. Items and question stems were formatted to be consistent with the wording of existing measures of stigma in military populations (e.g., Britt et al., 2008).
From this work, the second author evaluated the items via telephone interviews with 40 veterans who had been diagnosed with PTSD as a part of a larger project (Whealin et al., 2012). Veterans were read the items and asked to describe what each item was assessing, thus judging whether items were understandable, culturally appropriate, and reflective of the intended construct. The assessor then determined whether the veteran easily comprehended the construct intended by each item (yes/no). Based on interview feedback, Item (e), “People in my family might treat me differently,” was determined to be unclear and redundant to the other items and was removed from the scale. Additionally, Item (b), “It would reflect badly on people in my family if people knew I had a mental health problem,” was clarified to read, “My family would look bad or be ashamed if people knew I had a mental health problem.”
The aim of the focus groups was to identify individual, family, and community factors that facilitate or deter veterans’ mental health service seeking rather than to exclusively assess perceived family stigma. After piloting the procedure at three sites, veterans and family members were recruited from five targeted locations in the rural Pacific Islands. Purposeful sampling was used to ensure that veterans and family members who were representative of different war eras were included at each location. Six veteran (N = 37) and six family member (N = 38) focus groups were conducted using a semistructured interview guide (Whealin, Nelson, Mahoney, & Kawasaki, 2016). Focus groups were transcribed verbatim using a Grounded Theory approach to guide constructs and verify resultant themes (Glaser & Strauss, 1967). Perceived family stigma emerged as one construct within a “Family Facilitators and Barriers” theme and consisted of the need to keep mental health problems within the family and the perception that family members thought less of them because of their mental health problem(s).
Drawing from the telephone interviews and focus groups, the second author then met with cultural experts who further refined questions so that items were consistent with the terminology of diverse veteran populations. For example, interview and focus group members often described PTSD simply as stress and used the term problem to refer to a disorder. As a result, we purposefully replaced disorder and PTSD with terms that reflected participants’ usage and comprehension.
Perceived Stigma and Barriers to Care Scale (PSBCS)
The PSBCS (Britt et al., 2008) consists of 13 items that query respondents to: “Rate each of the possible concerns that might affect your decision to receive mental health counseling or services if you ever had a problem” (Hoge et al., 2004). Items measure obstacles that prevent or dissuade individuals from seeking mental health treatment (e.g., “I don’t have good transportation”), as well as self-stigma (“It would be too embarrassing”) and public stigma (“I would be seen as weak”). Factor analyses in veteran and active duty samples have revealed a two-factor solution: self and public stigma-based barriers to care (8 items) and logistical barriers to care (5 items). Responses are measured on a 5-point Likert-type scale (1 = Strongly Disagree to 5 = Strongly Agree). We calculated the mean response to items for a total score and for the two subscales, with higher scores reflecting greater perceived barriers to care. Comparison data were available for N = 6153 combat veterans, including stratified samples of soldiers who met screening criteria for psychopathology (n = 731) and those who did not (n = 5422) following deployments to Iraq and Afghanistan (Hoge et al., 2004). The internal consistency for this scale was excellent (α = .92).
Devaluation of Consumers Scale (DCS)
The DCS (Struening et al., 2001) is an eight-item scale based on Link, Cullen, Struening, Shrout, and Dohrenwend’s (1989) original scale that examined perceptions of community attitudes toward people with mental illness (i.e., public stigma). Participants rate their level of agreement with eight statements, such as “Most people would accept a person who once had a serious mental illness as a close friend.” Responses are measured on a 4-point Likert-type scale (1 = Strongly Disagree to 4 = Strongly Agree). We summed responses to all items for a total score ranging from 8 to 32, with higher scores reflecting greater perceptions of community stigma. Internal consistency of DCS score was adequate in this sample (α = .66) but slightly worse than in a previous sample (α = .82; Struening et al., 2001).
Need or want for help for emotional problems
One item was used from The Self-Help and Treatment Services Utilization Survey (Fontana et al., 2006)—a psychometrically sound 20-item scale that assesses perceived need for health care services—to assess for need or want for help for emotional problems: “In the last 3 months, have you needed or wanted help for an emotional problem (such as stress, anger, or depression).” Participants answered either “yes” or “no”.
Data Analysis
First, the concurrent and incremental validity of the PFSS was tested. To test the concurrent validity of the PFSS, the PFSS was correlated with the PSBCS and the DCS, both of which are established measures of stigma and barriers to care. To demonstrate its use as a measure of a barrier to help seeking, the incremental validity of the PFSS over the PSBCS was tested using hierarchical logistic regression. Using hierarchical logistic regression, two models were compared, one with just the PSBCS regressed on the binary item from the Self-Help and Treatment Services Utilization Survey assessing perceived need for mental health care services (“In the last 3 months, have you needed or wanted help for an emotional problem [such as stress, anger, or depression]?”), and the second model that also included the PFSS. The PFSS would be determined to have incremental validity if PFSS significantly predicted likelihood of needing or wanting help for emotional problems over and above the PSBCS.
An analysis of variance (ANOVA) was also used to explore the correlates of the PFSS using SPSS, where the PFSS was set as the dependent variable and eight categorical independent variables—gender, ethnicity/race, income level, deployment history, education level, urban/rural location, marital status, and military rank—were identified. Due the limited number of research conducted on perceived family stigma thus far, a more exploratory approach was implemented in order to better understand how perceived family stigma might differ across various demographic groups. As such, in addition to the main effects, the model tested included all two-way interactions between the independent variables; more complex interactions (i.e., three-way) were not included in the model. Effect sizes were reported using ηp2 using cut-off scores following those established by Cohen (1988) as well as Olejnik and Algina (2000), where values of .01 indicate a small effect size, .06 a medium effect size, and .14 a large effect size. The relationship between the PFSS and the likelihood of participants needing or wanting help for an emotional problem was also tested using SPSS. A logistic regression was performed with desire for help for emotional problems as the dependent variable, and PFSS as the independent variable. To test whether gender or race moderated the relationship between PFSS and need or want for help, the model also included a two-way interaction between PFSS and gender as well as a two way-interaction between PFSS and ethnicity/race. All missing data were handled using the SPSS default of list wise deletion.
Multigroup categorical confirmatory factor analysis (CFA) was used to test the measurement invariance of four items that comprised the PFSS across gender, ethnicity/race, income level, deployment history, education level, urban/rural location, marital status, and military rank, following the guidelines set by Muthén and Asparouhov (2002). A single factor four-item model was specified for all steps of the measurement invariance test utilizing weighted least squares means and variance adjusted (WLSMV) estimation with THETA parameterization in Mplus (Muthén & Muthén, 2012). WLSMV estimation was utilized as previous research has suggested that treating ordered categorical data as continuous data would lead to distorted results, particularly when doing measurement invariance analyses (Lubke & Muthén, 2004). Thus, to make meaningful group comparisons, categorical data should be analyzed with models meant for categorical outcomes. Additionally, in order to avoid response categories with low endorsement rates (e.g., less than 5% response rate), categories with low response rates were collapsed with either the preceding or following category. As the strongly disagree and strongly agree categories of the indicator variables had a less than 5% response rate, the strongly disagree and disagree categories were combined to form one category, and the strongly agree and agree categories were combined as well to form their own category. Thus, the PFSS items were rescaled as 5-point response items.
All missing data were handled using pairwise deletion, as the WLSMV estimation does not allow the use of full information maximum likelihood (FIML) to handle missing data. For the configural invariance model, factor loadings and thresholds were both freely estimated, while the factor mean and variance were constrained to 0 and 1 in all groups for identification. For the measurement invariance model, factor loadings were constrained to equivalency and thresholds remained free; the factor mean was constrained to 0 in the target group, but freely estimated in the alternative group(s) while the factor variance was constrained to 1 in all groups for identification. For the scalar invariance model, both factor loadings and thresholds were constrained to equivalency, while the factor mean and variance were constrained to 0 and 1, respectively in the target group, but freely estimated in the alternative group(s) for identification purposes.
The overall fit at each step was determined by the model’s statistical and descriptive fit. A model is determined to have acceptable fit if it meets the cut-off scores indicating acceptable model fit for the descriptive fit indices: the comparative fit index (CFI), Tucker-Lewis Index (TLI), and the root mean square error of approximation (RMSEA). Utilizing the cut-off scores proposed by Bentler (1990); Hu and Bentler (1999), and Steiger (1990), (a) CFI values greater than .95 indicated good model fit and values greater than .90 indicated acceptable model fit; (b) TLI values greater than .95 indicated good model fit; and (c) RMSEA values less than .05 indicated good model fit and values less than .08 indicated acceptable model fit. For configural invariance only, just acceptable model fit was necessary to establish configural invariance. If configural invariance could not be established, the fit of the model across the baseline groups were examined separately instead. However, for metric and scalar invariance to be established there must be acceptable model fit and each model also has to demonstrate that it fits the data as well as the preceding model. This was done with a chi-square difference test using nested model comparisons through the DIFFTEST procedure in Mplus, as it is not possible to do a traditional chi-square difference test when using WLSMV estimation. The idea is to find a more parsimonious model (i.e., more restrained invariance model) without significant fit loss. If invariance could not be established, the modification indices will be examined, and revised invariance models will be tested to see if partial invariance could be established following procedures set by Steenkamp and Baumgartner (1998).
In the case that noninvariance is found, the effect size of the noninvariance on the perceived family stigma scores will be examined using the latent trait estimation approach suggested in Tay, Meade, and Cao (2015). It is a simple heuristic approach to determine if the presence of invariance affects the interpretation of whether reference and focal groups differ in latent trait levels substantially. To do so, the latent mean differences between the configural and scalar models will be calculated, with the differences interpreted similar to Cohen’s d (.2 = small, .5 = medium, .8 = large). As latent means are constrained in the model specifications used in the tests of measurement invariance, a different set of parameters will be used to obtain the latent means. DELTA parameterization and WLSMV estimation will be used for all analyses. For the configural model, factor means and variances will be constrained to 0 and 1, respectively, in the target group but freed in the alternative group(s). Additionally, the factor loading of the reference variable will be constrained to 1 across both the target and alternative group(s), the factor loadings of other variables are kept free. All thresholds of the reference variable are held to equivalency, while only the first thresholds of all other items will be constrained to equivalency. For the scalar model, the factor loading of the reference variable will be constrained to one, while the factor loadings of other variables will be constrained to equivalency across groups. All thresholds will be held to equivalency, and the factor means and variances will be constrained to 0 and 1, respectively, in the target group but freed in the alternative group(s).
ResultsThe PFSS had acceptable internal consistency (α = .86) as well as strong interitem correlations (values ranged from r = .51 to .76; see Table 2). The PFSS also correlated with the Perceived Stigma and Barriers to Care Scale (PSBCS) (r[588] = .68, p < .001) and the Devaluation of Consumers Scale (DCS) (r[588] = .44, p < .001) to demonstrate concurrent validity. Moreover, PFSS significantly predicted likelihood of needing or wanting help for emotional problems (p < .001) over and above the PSBCS (p = .562), which demonstrated the incremental validity of the PFSS over the PSBCS. The CFA results indicated that the single factor model was a good fit statistically, χ2(2, N = 620) = .34, p = .84 and descriptively (CFI = 1.00, TLI = 1.00, RMSEA < .001) to the PFSS, indicating a good overall fit to the data irrespective of group membership. The standardized factors loadings were large and statistically significant (.759 to .971).
Family Stigma Scale Interitem Correlation
Measurement Invariance
The measurement invariance results indicated that the PFSS had full measurement invariance across deployment history, education level, urban/rural location, marital status, and military rank. However, the PFSS demonstrated only partial measurement invariance across gender, ethnicity/race, and income level. Across gender, configural invariance was achieved, but full metric invariance was not. An examination of the modification indices revealed that the factor loading for the “people in my family do not talk about mental health problems with people outside our family” (fam_talk) item contributed the most to the worse fit of the metric invariance model in comparison to the configural invariance model. Across ethnicity/race, the PFSS demonstrated configural and metric invariance, but not scalar invariance. An examination of the modification indices revealed that the second threshold for the “people in my family would blame me for the problem” (fam_blame) item for the White ethnic group was the most problematic. Across income, the PFSS demonstrated configural invariance but not metric invariance. An examination of the modification indices revealed that the factor loading for the fam_talk item for participants who made more than $50,000 was the most problematic. See Table 3 for the measurement invariance results and Table 4 for standardized factor loadings.
Invariance Model Results by Group
Standardized Item Factor Loadings (Standard Errors) for All Groups
Finally, the effect size of the noninvariance across gender, ethnicity, and income was calculated. For gender, the female group was set as the target group. The latent mean for males in the configural model was .20 and was .27 in the scalar model (Δ = .07). Across ethnicity, the East Asian ethnic group was set as the target group. The latent means in the configural model were −.07, −.23, −.30, and were −.10, −.19, −.49 in the scalar model for the Filipino (Δ = −.03), NHPI (Δ = .04), and White (Δ = −.19) groups respectively. Across the income groups, the less than $35,000 income group was set at the target group. The latent means for the income group between $35,000 and $50,000 was −.08 in the configural model and −.11 in the scalar model, (Δ = −.03). For the greater than $50,000 income group, the latent mean was −.11 in the configural model and .01 in the scalar group (Δ = .12).
Demographic Correlates
There was a significant interaction between urban/rural location and deployment history, F(1, 419) = 5.97, p = .015, ηp2 = .014. For participants who had never been deployed, the estimated marginal mean PFSS scores were higher among those who currently live in a rural area compared to an urban area (M = 1.88 vs. M = 1.69). For participants who had previously been deployed to a combat area, the estimated marginal mean PFSS scores were lower for those who live in rural areas compared to those who live in urban areas (M = 1.73 vs. M = 1.90). Similarly, there was a significant interaction between income level and education level, F(4, 419) = 4.06, p = .003, ηp2 = .037. For participants who were high school graduates or did not complete high school, estimated marginal mean PFSS scores were higher when participants earned between $35,001 and $50,000 than when participants earned less than $35,000 (M = 1.67 vs. M = 1.91), although estimated marginal mean PFSS scores were lower when participants earned more than $50,000 (M = 1.84). For participants who had at least some college-level education or were a college graduate, estimated marginal mean PFSS scores increased as income increased (less than $35,000 [M = 1.50], $35,001–$50,000 [M = 1.66], more than $50,000 [M = 1.78]). Participants who had more than a college graduate education level (i.e., graduate school) had the highest estimated marginal mean PFSS scores when they made less than $35,000 (M = 2.59) compared to those who made between $35,001 and $50,000 (M = 1.25) and those who made more than $50,000 (M = 1.99). There were no other significant interactions.
In addition to the significant interaction, there was one statistically significant main effect by rank, F(2, 419) = 3.87, p = .022, ηp2 = .018. An examination of the estimated marginal means revealed that the mean PFSS scores of those who were enlisted were significantly higher than the mean PFSS scores of those who were senior enlisted, all else being equal, M = 1.87 versus M = 1.44, p = .026. See Table 5 for the estimated marginal means of the main effects. Furthermore, while PFSS scores of different ethnic/racial groups were not found to differ significantly, pairwise comparisons indicated that those who identified as Filipino did report significantly higher PFSS levels than Asians (Korean, Chinese, or Japanese; p = .043) and Native Hawaiian/Pacific Islanders (p = .029).
Estimated Marginal Means for Main Effects
Need for Help for Emotional Problems
There was a significant interaction between gender and PFSS, controlling for ethnicity/race, p = .018. This suggests that while an increase in mean PFSS scores predicted a higher probability of report of a need or want for help for an emotional problem for both men and women, for women each 1-point increase in mean PFSS scores predicted a likelihood of reporting a need/desire for help for an emotional problem 3.077 times that of men (see Figure 1). There was no significant interaction between ethnicity/race and mean PFSS scores. Furthermore, results indicated significant main effects by PFSS and gender, although these findings are tempered by the aforementioned significant interaction. Specifically, the odds that the participant would report a need or want for help for an emotional problem increased by 3.80 times, all else being equal, for every 1-point increase in PFSS, p < .001. The odds of men reporting a need or want for help for an emotional problem also were 6.34 times that of women’s, p = .020. There was no significant main effect of ethnicity/race.
Figure 1. Predicted log odds of reporting a need or want for help for an emotional problem.
DiscussionUntil now, there has been a lack of a valid and reliable measure of perceived family stigma, an individual’s perceptions of familial attitudes and beliefs about mental illness and/or people with mental illness. Instead, previous measures of perceived family stigma have often been conflated with measures of other sources of public stigma. Given that stigma from families may be experienced differently than stigma from other sources (Moses, 2010), a measure of perceived family stigma is a needed. The present study provides preliminary support for the reliability and psychometric validity of the PFSS. The PFSS demonstrated concurrent validity with a multidimensional measure of mental health care barriers widely used for military populations (the PSBCS; Britt et al., 2008) and a measure of public stigma (the DCS; Struening et al., 2001). The PFSS was also significantly correlated with reported need/desire for help for an emotional problem, providing additional evidence that the scale measures the construct it was intended to measure. Importantly, the PFSS demonstrated incremental validity over the PSBCS, suggesting that the PFSS may be a useful measure, particularly when examining the relationship between perceived family stigma and service use in veteran populations.
As hypothesized, higher scores on the PFSS were associated with greater likelihood of wanting/needing help for an emotional problem. Men were 6 times more likely than women to report a need for help with an emotional problem. However, as PFSS scores increased, this disparity decreased until women responded as more likely than men to report a need for help (see Figure 1). This finding is in line with previous research that has found that individuals who feel unable to disclose their emotional problems to significant others (in this case family members) are more likely to report having mental health concerns (Belsher et al., 2012; Cordova et al., 2001; Lepore et al., 1996). Previous research by Leaf and colleagues (1986), where 3,058 community members were interviewed found that concerns about family members’ reactions to their seeking mental health treatment help dissuaded women, with mental health problems from initiating treatment. This suggests that, despite indicating greater need or desire for help with emotional problem as suggested by the present study, individuals who experience high perceived family stigma may not be seeking the help that they need. Given that the present study did not actually look at subsequent treatment seeking, research that evaluates the role of perceived family stigma in predicting an unmet need for mental health treatment may prove to be a useful next step in identifying barriers to care.
The demographic variations in perceived family stigma are also of interest. Perceived family stigma levels were not found to differ significantly across broad ethnic/racial groups, which is consistent with previous work that examined veterans (e.g., Pietrzak et al., 2009). However, pairwise comparisons did indicate that veterans that identified as Filipino reported significantly higher perceived family stigma than those who identified as East Asian (Chinese, Japanese, Korean) or Native Hawaiian/other Pacific Islander. The present study highlights the importance of examining nonaggregated APPI populations. Although little research has been done looking specifically at mental health utilization and barriers in Filipino veterans, Sanchez and Gaw (2007) argued that, in traditional Filipino culture, an individual’s mental illness is reflected upon the entire family and is “associated with shame and stigma.” This suggests that perceived family stigma may be a particularly influential factor for Filipinos when making decisions about mental health care compared to other AAPI veterans groups, and warrants further investigation.
Perceived family stigma levels were also shown to differ significantly across military rankings. Senior enlisted veterans had the lowest perceived family stigma levels, followed by enlisted veterans, with officers reporting the highest perceived family stigma levels among the three groups. Although enlisted veterans had significantly higher PFSS scores than senior enlisted veterans, perceived family stigma was not significantly different between senior enlisted and officers or between enlisted and officers. Previous research has demonstrated that higher rank is associated with lower interest in receiving help for a mental health problem in veterans with mental health disorders (Brown, Creel, Engel, Herrell, & Hoge, 2011), suggesting that higher ranked individuals may perceive greater stigma and would be less willing seek help for their issues. However, most previous research has not separated enlisted from senior enlisted staff when examining stigma, the latter being a distinct subgroup with higher military rank (e.g., E5–E9 vs. E1–E4) than other enlisted service members and, often, greater longevity compared to both regular enlisted and officers. In addition to military rank, there were significant differences in perceived family stigma when taking into consideration the interactions between rural/urban residency and deployment history and between income and education levels. Whereas more research is needed to understand why there were significant differences in PFSS scores across these groups, these results indicate that some groups may be at risk of presenting with higher marginal PFSS mean scores, which could impact their likelihood of initiating mental health treatment (Spoont et al., 2014). It may be of interest for future researchers to also look at the impact of other factors that can influence education level, such as disability.
In the present study, there were no significant differences in mean PFSS scores across gender and socioeconomic status. The nonsignificant findings regarding gender are consistent with other research that has examined military populations (Elnitsky et al., 2013), but contrasts with Ojeda and Bergstresser’s (2008) study that found civilian men were more likely than civilian women to report perceived public stigma as a barrier to mental health service seeking. It is possible that the nonsignificant finding regarding gender in military populations could be due to the unique characteristics of the population. This explanation is supported by Hourani, Williams, Bray, Wilk, and Hoge (2016), who found that male and female active duty personnel have comparable rates of service utilization, which the authors suggested could be due to similar levels of stigma and access to care between men and women. Although research has not examined between-group SES differences in military samples, the present findings are consistent with research that has found no correlation between income and perceived stigma in nonmilitary populations (Ojeda & Bergstresser, 2008).
As the PFSS was a new scale, one of the purposes of the present study was also to test its invariance across gender, ethnicity/race, marital status, income level, rural versus urban residence, deployment status, and rank in the military. The scale was invariant across marital status, rural versus urban residence, deployment status, and rank in the military, which suggests that the latent variable of perceived family stigma is conceptualized similarly across those who are of different marital status, those who live in rural or urban residences, those who have previously been deployed and those who have not, as well as across those who are of different military ranks. As such, latent factor means and variances, as well as standardized measures of association can be compared across these groups. Additionally, findings suggests that any differences in latent means across these groups is a reflection of true latent mean differences rather than differences in observed item means.
The PFSS met partial but not full invariance across gender, ethnicity/race, and income. Thus, comparisons of latent factor variances or means across these groups need to take into consideration this lack of invariance. For example, the lack of full metric invariance indicates that the units of measurement are different across groups, and that difference score comparisons may not be valid as the items were using nonequivalent units of measurement. Similarly the lack of full scalar invariance suggests that differences in latent variable means may not truly reflect differences in the latent variable but the observed variable instead. However, given the small effect sizes of the lack of invariance, it is likely that valid comparisons of perceived family stigma latent means and variances could be made across gender, ethnicity/race, and income. Additionally, as previous literature has indicated that meaningful comparisons can be made if there is partial invariance present (Byrne, Shavelson, & Muthén, 1989), the authors would argue that PFSS scores could be compared across the subgroups tested in the present study, while taking the following into consideration.
Partial metric invariance was achieved across both gender and income. Notably, there was only one item—“people in my family do not talk about mental health problems with people outside our family”—that was noninvariant across both gender and income groups. All other items demonstrated full factor invariance. Across gender, this item loaded more strongly onto the latent variable of perceived family stigma for men than for women, while across income the item loaded more strongly onto the latent variable for those who made more than $50,000. This set of findings suggest that the “people in my family do not talk about mental health problems with people outside our family” item was more representative of the perceived family stigma latent variable for men than for women, and for those who make more than $50,000 when compared to those who make less than $35,000 or between $35,000 and $50,000. As only partial metric invariance could be established, only partial scalar invariance could be established across gender and income as well.
One possibility for the lack of invariance is due to gender differences in willingness to disclose mental issues. For example, boys are less likely to talk with friends and nonparent family members about their emotional problems than girls and are more likely to report higher levels of perceived stigma (Chandra & Minkovitz, 2006), as well as having less willingness to disclose mental issues or emotional problems (Jorm & Wright, 2008; Su, Wang, & Lin, 2013). Given their greater unwillingness to disclose mental illnesses—particularly to nonfamily members—men may be more likely to anticipate stigma as a result of disclosure than are women. Taking into consideration the limited number of studies that have explored the relationship between income and perceived family stigma, it is difficult to conclude whether there are differences between different income brackets in willingness to disclose mental issues with nonfamily members. One possible explanation for this finding may come from parenting research, which showed that parents of low SES families were less likely to talk with their children than parents of high SES families (Gottfried & Gottfried, 1984). Since there is more communication in high SES families, the lack of communication may then be interpreted as disapproval or stigma against mental illnesses.
While full metric invariance was established across the ethno-racial groups, full scalar invariance could not be established. When latent means were equivalent, White participants had a slightly higher probability of agreeing that perceived blame from their family would affect whether they attended mental health counseling than participants of other ethnic groups. This finding is of particular interest as, considering the importance Asian American and NHPI groups place on family (Julian, McKenry, & McKelvey, 1994), we would expect that Whites would either show an equivalent or lower probability of agreeing that perceived blame from their family would affect whether they attended mental health counseling. However, to the best of the authors’ knowledge, there has yet to be any research that has examined how the relationship between ethnicity/race and perceived blame from family members impacts mental health utilization. As such, it is uncertain what factors are contributing to the lack of scalar noninvariance, although one potential reason could be due to underlying ethnic group differences in mental health attitudes between Whites and Asian American/NHPI groups (Fogel & Ford, 2005; Lam, Tsang, Chan, & Corrigan, 2006; Leong & Lau, 2001).
There were a number of limitations that should be noted about the current study. First, the study sampled veterans residing in Hawaii and, as a result, may not generalize to veterans residing in other geographic regions or to nonveteran populations. Military culture can have a strong impact on military families and how mental health problems are handled (Hall, 2008; DeCarvalho & Whealin, 2012). It is important to keep in mind that in military and other cultural subgroups (Yang et al., 2008), the reputation of individuals is seen as a reflection of their families. Thus, it is probable that perceived family stigma may play a more salient or unique role in veteran populations than in nonveteran populations, where community and peers may play a more or as salient role as family. Furthermore, measurement invariance was tested only across White, Native Hawaiian/Pacific Islander, Filipino, and East Asian subgroups due to the limited number of participants of other ethnicities/races. Moreover, while full invariance is not required to make meaningful interpretations, the lack of full invariance could have influenced the results, although the influence is likely to be minimal given the small effect sizes of the noninvariance. Also, while the present study has shown that there are differences in levels of perceived family stigma across different demographic groups (i.e., rank), the mechanisms contributing to these differences are still unknown. The relationship between perceived family stigma, reported mental health service need, and mental health service utilization is a key point that future research needs to address.
Future research should continue development of the PFSS as well as testing its psychometric properties. Although the purpose of the present study was to develop a short survey given the ease of implementation of brief scales in both clinical and research settings, the limited number of items generated is also a limitation of the present study. While perceived family stigma was theorized as consisting of both public and self-stigma and the PFSS was comprised of items that addressed both dimensions, the limited number of items only allowed for the analysis of PFSS as an unidimensional construct. As such, it is probable that the scale would benefit from further expansion, such as the addition of more items that would address unique aspects of family stigma, which would allow for more complex exploration of perceived family stigma as a multidimensional construct. Similarly, though the present study collected information on logistic barriers to care, self-stigma, and public stigma through the PSBCS, we were only able to test the incremental validity of the PFSS over the PSBCS as we did not collect data on a specific form of self or public stigma, such as peer stigma. Thus, one limitation of the present study was the lack of exploration of the discriminate validity of the PFSS from these other specific forms of self and public stigma. However, as the main purpose of the present study was to develop an initial scale of family stigma, the study was mainly exploratory in nature. As such, the scale developed was not meant to be a finalized scale, but rather serve as the foundation upon which further research on family stigma can be built. Despite these limitations, the PFSS proved to be a valid instrument that could be used to further aid research on perceived family stigma.
In conclusion, future research should (a) examine whether the present findings can be replicated in civilian and other veteran populations, (b) continue to test the factor invariance of the PFSS, preferably across demographic groups that the present study was not able to test, (c) test whether perceived family stigma may predict service seeking, and (d) continue to develop the PFSS and test its psychometric properties. Given the importance of understanding the impact of family stigma on the need for help as well as subsequent seeking of such help, measurements and instruments for understanding stigma are needed. The present study helps to validate the PFSS for use with military veterans. Moreover, the present study is the first to examine how perceived family stigma correlates with veterans’ reported need/desire for help with an emotional problem. Despite its limitations, the findings of the study highlight the importance of more fully understanding family stigma. Given the dearth of research on the impact of family stigma, follow-up research is needed to replicate current findings and to evaluate whether family stigma influences disclosure of mental health problems, internalized stigma, and mental health treatment initiation. With this study, it is our hope that future research will look further at perceived family stigma and its potential impact on decreasing the gap between those who need help and those who successfully obtain help for mental health disorders.
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Submitted: April 17, 2016 Revised: October 3, 2016 Accepted: October 4, 2016
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Source: Psychological Assessment. Vol. 29. (9), Sep, 2017 pp. 1167-1181)
Accession Number: 2016-56897-001
Digital Object Identifier: 10.1037/pas0000416
Record: 4- Title:
- A meta-intervention to increase completion of an HIV-prevention intervention: Results from a randomized controlled trial in the state of Florida.
- Authors:
- Albarracín, Dolores. Department of Psychology and Marketing, University of Illinois at Urbana Champaign, Champaign, IL, US, dalbarra@illinois.edu
Wilson, Kristina. Florida Department of Health in Duval County, Jacksonville, FL, US
Durantini, Marta R.. Department of Psychology, University of Illinois at Urbana Champaign, Champaign, IL, US
Sunderrajan, Aashna. Department of Psychology, University of Illinois at Urbana Champaign, Champaign, FL, US
Livingood, William. Department of Office of the Dean, College of Medicine, University of Florida, Jacksonville, Jacksonville, FL, US - Address:
- Albarracín, Dolores, Department of Psychology and Marketing, University of Illinois at Urbana Champaign, 603 East Daniel Street, Champaign, IL, US, 61820, dalbarra@illinois.edu
- Source:
- Journal of Consulting and Clinical Psychology, Vol 84(12), Dec, 2016. pp. 1052-1065.
- NLM Title Abbreviation:
- J Consult Clin Psychol
- Page Count:
- 14
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- Journal of Consulting Psychology
- Other Publishers:
- US : American Association for Applied Psychology
US : Dentan Printing Company
US : Science Press Printing Company - ISSN:
- 0022-006X (Print)
1939-2117 (Electronic) - Language:
- English
- Keywords:
- HIV, intervention, persuasion, retention, health promotion, randomized controlled trial
- Abstract (English):
- Objective: A randomized control trial with 722 eligible clients from a health department in the State of Florida was conducted to identify a simple, effective meta-intervention to increase completion of an HIV-prevention counseling program. Method: The overall design involved 2 factors representing an empowering and instrumental message, as well as an additional factor indicating presence or absence of expectations about the counseling. Completion of the 3-session counseling was determined by recording attendance. Results: A logistic regression analysis with the 3 factors of empowering message, instrumental message, and presence of mediator measures, as well as all interactions, revealed significant interactions between instrumental and empowering messages and between instrumental messages and presence of mediator measures. Results indicated that (a) the instrumental message alone produced most completion than any other message, and (b) when mediators were not measured, including the instrumental message led to greater completion. Conclusions: The overall gains in completion as a result of the instrumental message were 16%, implying success in the intended facilitation of counseling completion. The measures of mediators did not detect any experimental effects, probably because the effects were happening without much conscious awareness. (PsycINFO Database Record (c) 2017 APA, all rights reserved)
- Impact Statement:
- What is the public health significance of this article?—This study shows that presenting a video that connects HIV-prevention counseling with outcomes and services that are important to clients (e.g., access to information about jobs, access to unrelated health services, opportunities to discuss emotional concerns) at the end of the first session increases completion of a 3-session counseling program. Treatment completion enhances outcomes in many domains, including HIV prevention. (PsycINFO Database Record (c) 2017 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Counseling; *Health Promotion; *HIV; *Intervention; *Retention; Persuasive Communication; Prevention
- PsycINFO Classification:
- Health & Mental Health Treatment & Prevention (3300)
- Population:
- Human
Male
Female - Location:
- US
- Age Group:
- Adulthood (18 yrs & older)
- Tests & Measures:
- Number of Sexual Partners Measure
Alcohol Use Measure
Drug Use Measure
Injection Drug Use Measure
Intentions to Use Condoms Measure
Condom Use and Unprotected Sex Measure DOI: 10.1037/t58419-000 - Clinical Trial Number:
- NCT01152281
- Methodology:
- Clinical Trial; Empirical Study; Quantitative Study
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- First Posted: Oct 27, 2016; Accepted: Jul 4, 2016; Revised: May 9, 2016; First Submitted: Nov 3, 2015
- Release Date:
- 20161027
- Correction Date:
- 20170417
- Copyright:
- American Psychological Association. 2016
- Digital Object Identifier:
- http://dx.doi.org/10.1037/ccp0000139
- PMID:
- 27786499
- Accession Number:
- 2016-51676-001
- Persistent link to this record (Permalink):
- http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2016-51676-001&site=ehost-live
- Cut and Paste:
- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2016-51676-001&site=ehost-live">A meta-intervention to increase completion of an HIV-prevention intervention: Results from a randomized controlled trial in the state of Florida.</A>
- Database:
- PsycINFO
A Meta-Intervention to Increase Completion of an HIV-Prevention Intervention: Results From a Randomized Controlled Trial in the State of Florida
By: Dolores Albarracín
Department of Psychology and Marketing, University of Illinois at Urbana Champaign;
Kristina Wilson
Florida Department of Health in Duval County, Jacksonville, Florida
Marta R. Durantini
Department of Psychology, University of Illinois at Urbana Champaign
Aashna Sunderrajan
Department of Psychology, University of Illinois at Urbana Champaign
William Livingood
Department of Office of the Dean, College of Medicine, University of Florida, Jacksonville
Acknowledgement:
Retention and completion are critical components of the effectiveness of HIV-prevention interventions in real-world conditions and have established psychological determinants, such as attitudes and intentions (Albarracín, Durantini, Earl, Gunnoe, & Leeper, 2008; Noguchi, Albarracín, Durantini, & Glasman, 2007). Increasing retention is vital for public health because multisession behavioral interventions to reduce HIV risk are often more efficacious than single-session ones (Albarracín et al., 2005; Crepaz et al., 2014; Johnson et al., 2009; Meader et al., 2013). For example, the positive behavior change elicited by HIV-prevention interventions for clients of STI clinics is d = 0.33 for multisession programs, but only d = 0.06 for single-session programs (analyses of the data from Albarracín et al., 2005). However, when tested under conditions similar to the ones that are likely during actual implementation (e.g., lack of payments or other incentives), these multisession interventions show relatively low retention (Noguchi et al., 2007). Specifically, with the exception of interventions with captive audiences (e.g., prisons, inpatients), which show 100% completion rates, experimental interventions show a rate of completion of approximately 50% for participants initially enrolled (Branson, Peterman, Cannon, Ransom, & Zaidi, 1998; McMahon, Malow, Jennings, & Gómez, 2001). Without high retention, HIV-prevention interventions have less of an impact on behavior and clinical outcomes. Estimated associations for behavior change show that interventions with less than 50% retention rates produce a long-term decrease in HIV-safe behavior (d = −0.29), compared with an increase in HIV-safe behavior (d = 0.41) for those with 100% retention rates (Johnson et al., 2009). The present research examined the efficacy of two simple, postsession, messages to increase retention in a three-session risk-reduction counseling program. These messages were designed to either empower clients as agents responsible for their own change or highlight the instrumental outcomes of the intervention in terms of participants’ lives (e.g., addressing health concerns other than HIV, offering employment related information). The experimental design included five conditions, namely each of these messages, a combination of both, as well as two control conditions. The outcome variable was completion of a three-session counseling program.
Ensuring Retention in HIV-Prevention Programs Variability in Exposure to Behavioral Intentions
A number of interventions have been produced to change behaviors that place people at risk for HIV (Albarracín et al., 2005; Centers for Diseases Control and Prevention [CDC], 2007; Lorimer et al., 2013). These interventions are typically tested under conditions that ensure the validity of the outcome assessments (Cook & Campbell, 1979). Thus, researchers try to involve community members to see if a particular intervention works for them. Social networks are called upon to recruit these participants and numerous incentives and facilitators are used to ensure access to the desired sample of exposed participants, as well as low attrition (De Walque et al., 2012; Exner, Hoffman, Parikh, Leu, & Ehrhardt, 2002; Lauby et al., 1996; Linnan et al., 2002; Packel et al., 2012; Rabinowitz, 2002; Raj et al., 2001; Roffman, Picciano, Bolan, & Kalichman, 1997; Schilling & Sachs, 1993; Schweitzer, 1997; Tobias, Wood, & Drainoni, 2006). Although these procedures are necessary to determine whether a program works for an exposed population (efficacy trial), they remove the reluctance to participate when the intervention is implemented (Catania, Gibson, Chitwood, & Coates, 1990; Lauby et al., 1996; Packel et al., 2012). Contemporary research must thus address the fundamental scientific problem of variability in exposure to behavioral interventions, including completion of a program designed to elicit behavioral or medical change.
Despite the above-mentioned method of removing selection and attrition during tests of intervention efficacy, in real-world conditions, people choose to take part in preventive interventions (Albarracín et al., 2008; Condelli, Koch, & Fletcher, 2000; DiFranceisco et al., 1998; Hennessy, Mercier, Williams, & Arno, 2002; Noguchi et al., 2007; Rutledge, Roffman, Picciano, Kalichman, & Berghuis, 2002; Veach, Remley, Kippers, & Sorg, 2000; Wagenaar et al., 2012). Given limited time and interest, clients of health facilities can accept or refuse to take part in an HIV-prevention counseling session (Albarracín et al., 2008; Grady, Kegeles, Lund, Wolk, & Farber, 1983; Katz et al., 2015; Noguchi et al., 2007; Minder, Müller, Gillmann, Beck, & Stuck, 2002; Wilson & Albarracín, 2015). Moreover, some of the audiences most vulnerable to HIV are the least likely to complete HIV-prevention interventions (Earl et al., 2009; Liu et al., 2014; Noguchi et al., 2007; Wilson & Albarracín, 2015; Yancey, Ortega, & Kumanyika, 2006). In particular, frequent condom users are more likely to complete pro–condom-use interventions than infrequent ones (Earl et al., 2009; Noguchi et al., 2007). Thus, efficacious interventions may not reach the vulnerable audiences in need of interventions.
Given that interventions need to fully reach vulnerable audiences, not just willing ones, it is imperative to develop and test procedures that increase participation by these populations (Albarracín et al., 2008; Wilson & Albarracín, 2015). Procedures can be designed to change an audience’s behavior with respect to the preventive interventions themselves, including enrollment and retention. These procedures, termed meta-interventions, entail a standardized introduction or context change (e.g., delivery setting) intended to increase exposure to a behavioral intervention (Albarracín et al., 2008; Albarracín, Leeper, Earl, & Durantini, 2008; Wilson, Durantini, Albarracín, Crause, & Albarracín, 2013). In past research, participants with prior infrequent condom use were offered an HIV-counseling session using one of four scripted introductions to the program (Albarracín et al., 2008). A randomly assigned meta-intervention conveying that counseling participants are free not to change (empowering video) was more effective than other introductions (one promising change and another providing basic information about the counseling) or no introduction (just an offer to take part). Unobtrusive observers recorded the extent to which participants agreed to the counseling when asked, and also collected supplementary data on participants’ reading of brochures and viewing of videos. As hypothesized, the empowering meta-intervention produced high levels of enrollment in the counseling (Albarracín et al., 2008). In addition, viewing the video had an independent effect on enrollment, such that viewers of the video were more likely to enroll in counseling than nonviewers (Albarracín et al., 2008).
Selective Exposure to Interventions
Retention in HIV-prevention interventions can be understood as a form of selective exposure to information (Albarracín & Mitchell, 2004; Earl & Nisson, 2015; Noguchi et al., 2007). Selective exposure comprises biased information seeking behavior and was first studied by Festinger (1964; for reviews see Eagly & Chaiken, 1993; Freedman & Sears, 1965; Frey, 1986). Exposure to an intervention (in this case, staying in and completing a program) depends on two sets of motivations (i.e., goals or desired endstates; Lewin, 1926; see also Atkinson, 1964; McClelland, 1951; Nuttin, 1980; for other classifications of human motives, see Chaiken, Wood & Eagly, 1996; Eagly, 2007; Johnson & Eagly, 1989; Noguchi et al., 2007; Prislin & Wood, 2005; Wyer & Albarracín, 2005). On the one hand, individuals are motivated to achieve subjective self-validation, which comprises the defense of prior beliefs and practices in the domain of HIV prevention (Albarracín & Mitchell, 2004; Albarracín et al., 2008; Noguchi et al., 2007; see also Kunda, 1990; Molden & Higgins, 2005). On the other hand, individuals are motivated to maximize objective outcomes, such as reducing their risk for HIV and achieving other personal and emotional outcomes (Hart et al., 2009; Noguchi et al., 2007; Vanable et al., 2012).
A primary human motive is to achieve self-validation (Chaiken, Wood & Eagly, 1996; Eagly, 2007; Johnson & Eagly, 1989; Prislin & Wood, 2005; Wyer & Albarracín, 2005), and interventions may or may not fulfill it (Albarracín et al., 2008; Noguchi et al., 2007). Presumably due to the self-validation motive, individuals who engage in high-risk behavior are reluctant to enroll and stay in HIV-prevention interventions (Albarracín et al., 2008; Earl et al., 2009; Noguchi et al., 2007; Wilson & Albarracín, 2015).
Considering this, it is possible to design empowering messages to decrease defensiveness when recipients encounter a potential intervention that urges novel or even rejected practices (e.g., using condoms for nonusers; Albarracín et al., 2008). For example, past research has found an advantage in telling participants that change is up to them, that an intervention will simply open doors, and that they may or may not change if they participate. This type of meta-intervention puts recipients in a more active role by placing the burden of change upon them, while indirectly encouraging them to actively seek change (Amaro, 1995; Amaro & Raj, 2000; Albarracín et al., 2008; Freire, 1972; Higa, Marks, Crepaz, Liau, & Lyles, 2012; Putnam, 1911). Further, people are more likely to expose themselves to persuasive communications if they believe that they can resist their influence (Albarracín & Mitchell, 2004; Brehm, 1972; Brehm & Cohen, 1962; Watzlawick, 1978). For example, as infrequent condom users often do not want to use condoms (Albarracín, Johnson, Fishbein, & Muellerleile, 2001), highlighting the option of resistance increases their exposure to condom-use interventions (Albarracín et al., 2008). These processes have been investigated to improve enrollment in HIV programs (Albarracín et al., 2008), but not to achieve completion. As the dynamic of enrollment is similar to retention (Noguchi et al., 2007), similar messages may also increase retention in an HIV-prevention counseling program.
Besides self-validation, an important human motive is to maximize objective outcomes (Hart et al., 2009; Noguchi et al., 2007). Retention in an intervention is therefore likely to depend on the degree to which the intervention fulfills this motive (Albarracín et al., 2008; Earl et al., 2009; Noguchi et al., 2007; Vanable et al., 2012). For HIV-risk reduction interventions, objective outcomes include HIV-risk reduction (Floyd, Prentice-Dunn, & Rogers, 2000; Rosenstock, Strecher, & Becker, 1994), but also emotional and instrumental support (Durantini & Albarracín, 2009; Vanable et al., 2012). For people who engage in high-risk behavior, the risk-reduction outcome can conflict with the self-validation motive (Albarracín et al., 2008; Earl et al., 2009; Noguchi et al., 2007). Thus, emphasizing that the objective of an intervention is to change participants’ risk behavior can lead participants to reject the intervention and feel manipulated (Albarracín et al., 2008). In contrast, emphasizing the emotional, social and instrumental value of an intervention beyond HIV prevention can entice participation (Durantini & Albarracín, 2007, 2009; Liu et al., 2014). Past research supports this assertion, showing that, women seek out programs that provide social and emotional support (i.e., company, encouragement, and affection), whereas men seek out programs that provide instrumental support (i.e., health care or payments; Durantini & Albarracín, 2007, 2009). In this light, we tested the effect of messages that emphasize the emotional, instrumental, and (non-HIV related) physical health outcomes of returning to the sessions of an HIV-prevention and counseling program.
HIV Prevention in the State of FloridaAt the end of 2012, an estimated 1,218,400 people in the United States were living with HIV/AIDS (Hall et al., 2015). HIV incidence has remained relatively stable since the mid-1990s, with an estimated 50,000 persons becoming infected with HIV on any given year (Hall et al., 2008). Based on confidential-name–based HIV reports, 47,352 cases of HIV/AIDS were diagnosed in 35 U.S. areas (33 states, Guam, and the U.S. Virgin Islands) in 2013. Up to 2012, the cumulative number of individuals dead by HIV was 658,507, with Florida having one of the highest HIV disease death rates in the U.S. (Florida Department of Health [FDH], 2012). Also, in 2013, Florida ranked first in new infections per year (5,377 new infections) and second in number of cumulative reported HIV cases (49,058; CDC, 2013). In 2014, the largest estimated proportion of HIV/AIDS diagnoses in Florida was for men who have sex with men (MSM), and ethnic minority adults and adolescents infected through heterosexual contact (FDH, 2015). Clearly, protecting Floridians is a national health priority, particularly those from African American backgrounds, who are highly represented in our population.
In Florida, prevention is the most important tool to avoid an even more accelerated epidemic (FDH, 2007). Duval County (Electoral district 4) is an important area that has received relatively little research attention (compared with Dade County, e.g.). Considering sheer number of cases in 2014, Duval County, which includes Jacksonville, ranks 1st for Gonorrhea and 4th for Chlamydia (FDH, 2014a), and 4th for HIV (FDH, 2014b). Of 67 counties in the state of Florida, these rankings place the region at very high risk. Given these findings, ensuring intervention effectiveness for this population is key.
The Present ResearchA randomized control trial was used to test the impact of video meta-interventions designed to either empower clients or remind them of the various objective goals fulfilled by the HIV-counseling program, and to compare these videos with control videos. The empowering meta-intervention entailed presenting the recipient as the motor of the behavior change (Albarracín et al., 2008). This strategy emphasized that the program could not change behavior unless the individual wanted it to. The instrumental video included descriptions of the sort of information and referrals the counselor could provide, in addition to giving information and guidance about HIV prevention. There was also a condition that combined the empowering and instrumental messages, as well as two control conditions. One control included stories about people living with HIV that were used in all experimental videos. The other control was more minimal, and simply presented educational information on reducing STIs. Thus, the design comprised five conditions to analyze their impact on completion of a CDC-recommended, three-session counseling program.
Our design also included a factor signaling whether perceptions of the video were measured. Although it was important to attempt to measure whether the videos induced expectations of empowerment and instrumental outcomes, including such blunt measures often alters the outcomes of experimental designs (Dholakia & Morwitz, 2002; Morwitz, Johnson, & Schmittlein, 1993). As a compromise, we randomized whether these measures appeared, and so only half of the sample completed these measures immediately after watching the meta-intervention video.
Method Enrollment
Clients from the STI clinics from the Florida Department of Health in Duval County were recruited (via flyers, referrals) for a study testing a three-session counseling program. To be eligible, individuals had to be between 18 and 35 years of age, report engaging in sexual activity in the past three months, and report using condoms never or occasionally. Participants were excluded if they were HIV-positive, or were trying to get pregnant or had a partner who was trying to get pregnant. Eligible participants were scheduled for their first study appointment. To ensure initial enrollment, participants were paid $35 for attending the first session, and $15 for subsequent sessions. The study was approved by the Institutional Review Boards (IRBs) of the University of Illinois, University of Pennsylvania, and Florida Department of Health, and each participant provided informed consent. Figure 1 describes all exclusions and Ns resulting from assignment procedures. The maximal control was by design smaller than the other conditions, including the minimal control. The trial was preregistered in clinicaltrials.gov (NCT01152281).
Figure 1. Recruitment and assignment.
Participants
Seven hundred twenty-two eligible participants (58% female) attended the initial counseling session, with a retention rate of 76% for the second session and a completion rate of 63% at the third session. The mean age of the sample was 26.54 (SD = 4.78). The majority of participants were African American (79%), and generally had an income under $9,999 per year (58%). Eighty-five percent reported having a main partner with whom they had a relationship on average of 2.37 years (SD = 2.10). Condom use in this sample was low, with only 1.1% reporting always using a condom when they had sex with their main partner. A full description of the same appears in Table 1.
Sample Description
The Counseling Intervention
The model of counseling that was used entailed a client-centered, cost-effective HIV-prevention program (CDC, 1993, 2007; Holtgrave, Valdiserri, Gerber, & Hinman, 1993; Kamb et al., 1998) facilitated by a counselor. This model’s efficacy has been demonstrated to significantly reduce STIs in a large multisite study (Kamb et al., 1998) and continues to be recommended as a standard for one-on-one counseling (CDC, 2007). The counseling seeks to reduce HIV risk behaviors by giving information, identifying risk behaviors, as well as steps to change them, and developing behavioral skills enabling safer behavior. This counseling can involve one or more sessions lasting at least 20 min, all of them following the same format. In our proposed study, a three-session model was used.
During the first session, participants received information regarding HIV transmission and prevention tailored to their culture, language, sex, gender, age, and educational level. The counselor ensured that the participant understood the information and that all of their misconceptions were corrected. The participant was encouraged to ask questions and clear their doubts. Following the informative part of the session, the counselor performed a personalized risk assessment, encouraging the participant to identify, understand, and acknowledge the behaviors and circumstances that put them at risk for being infected by HIV. Addressed topics included factors associated with risk behavior, such as using drugs or alcohol before sex, underestimating personal risk, having low self-efficacy, having distorted or fatalistic beliefs, and misperceiving peer norms. In addition, the counselor examined previous attempts made by the participant to reduce their risk and identified the reasons for their success or failure in these situations. This in-depth exploration allowed the counselor to help the participant consider ways to reduce personal risk and commit to a single, reachable step toward change. Once this risk assessment was complete, the counselor asked the participant to describe the risk-reduction step to be attempted (while acknowledging positive steps made), and helped the participant identify and commit to additional behavioral steps. Testing was also discussed, with referrals provided as needed.
During the following sessions, the counselor and the participant explored the success or failure of the steps proposed, and adjusted goals to the participant’s achievements. Furthermore, the second and third sessions also included a module for providing emotional support and addressing instrumental and/or medical concerns, in addition to HIV. This inclusion fulfilled the goal of providing supporting objective outcomes highlighted in some of the meta-intervention conditions. Among other things, after the HIV-risk reduction portion of the second and third counseling session was complete, the counselor discussed the physical and psychological symptoms, made referrals and provided information. This modification allowed us to test the effectiveness of messages that emphasized emotional and physical outcomes, with the counseling providing some venue for relief.
The counselors had good fidelity ratings using standard observation lists, and great high on cultural competency as measured with a valid and reliable questionnaire (Ponterotto, Alexander, & Grieger, 1995; Ponterotto, Potere, & Johansen, 2002). The counselors used written guides and records to ensure the use of a standardized procedure, and were closely supervised and retrained periodically. Therefore, after initial intensive training before the trial began, a check of videotaped sessions was performed to ensure proper application of the protocol. A random sample of 38 sessions showed 100% adherence to seven key dimensions of the protocol, which included appropriate introduction to the session, adequate performance of the risk assessment, proper evaluation of personal resources, proper evaluation of barriers, adequate integration of personal resources into newly set goals, and a clear closure. The average duration of the sessions was 25 min.
Meta-Intervention Messages
The messages were 24- to 34-min videos presented at the end of the first counseling session, to infer effects on retention at the second and third sessions. There were five videos, one for each condition, one resulting from crossing two meta-interventions, and two control videos.
The first experimental video, lasting 28 min, presented a meta-intervention conveying the message of being empowered (empowering condition). The video presented community members who talked about their experiences with HIV and counseling. This content was interspersed with messages delivered by these characters and professionals, conveying that subsequent counseling sessions were not intended to force change upon individuals. The stories were set at local places in North Florida (e.g., a fishing environment, a bar) with local music used in the background. The videos contained material in both Spanish and English, subtitled to the other language.
The second experimental video, lasting 26 min, presented a meta-intervention emphasizing the objective outcomes associated with HIV-prevention counseling (instrumental condition). This video presented the same stories as the first message. However, this experimental video also emphasized the emotional, social and objective (i.e., non-HIV/STI health) outcomes of returning to the counseling sessions. In this message, characters and professionals described how HIV-prevention counseling was also a venue to discuss personal problems, such as violence in the home or depression, and the extent to which many clients find emotional relief and social support from participating in the counseling. This message thus conveyed how HIV-prevention counseling often facilitates the treatment of other health problems, while also providing a venue for obtaining information about, and referral to, social services.
The third experimental video, lasting approximately 34 min, combined the first and the second meta-intervention messages. The final two videos were both control videos. The first, which lasted approximately 26 min, included the same stories and locations as those presented in the other three videos, but did not contain any of the meta-intervention messages (minimal control condition). The second, lasting 24 min, contained neither stories presented by local characters, nor any meta-intervention messages, but simply presented short vignettes aimed at increasing behavioral skills, perceived risk and knowledge about reducing STIs (maximal control condition; selected from video developed by Warner et al., 2008).
The use of two control conditions let us disentangle the effects of the meta-intervention messages from the community stories about HIV and counseling. Specifically, the difference between the minimal control video and those used in the three experimental conditions was the absence of a meta-intervention message; the rest of the content (e.g., the community stories) remained the same. The maximal control did not have either, and only presented risk and facts about STIs. Thus, it became possible to see whether differences in the three experimental conditions were attributable to the combination of the meta-intervention message and stories that were included, or were exclusive to the meta-intervention message. This condition was added after the project was funded and therefore had to be smaller because of funding constraints.
Design
This study crossed two meta-interventions: (a) a video message empowering or validating the client to return to the sessions, and (b) a video message highlighting opportunities for emotional and instrumental support (e.g., information about cardiovascular health, referrals etc.) facilitated by HIV-prevention counseling. The design had another factor, which concerned the inclusion of measures of expectation induced by the video, which were to appear immediately following the video. Only half of the sample completed these measures with the objective of avoiding measurement sensitivity. Thus, our design was a 2 (empowering meta-intervention: present vs. absent) × 2 (instrumental meta-intervention: present vs. absent) × 2 (measurement of mediators: present or absent) between-subjects factorial with the addition of a minimal control condition.
Baseline Measures
Data was collected using audio computer-assisted self-interview (ACASI) procedures. With this technique, participants listened to the question, while simultaneously reading them on the screen. ACASI procedures have been reported to increase accuracy with respect to non-normative behaviors and responses, thus decreasing the effects of social desirability and experimental demand (see, e.g., Des Jarlais et al., 1999; Mensch, Hewett, & Erulkar, 2003; Williams et al., 2000). Questionnaires were available in Spanish for participants who preferred it.
Baseline questionnaires measuring past behavior, intentions, and demographics were collected from participants before the start of their first counseling session. Questionnaire items were first transformed to a z-score, and then averaged, to produce a composite measure of condom, drug, alcohol and injection drug use, as well as a composite measure for number of sexual partners and intentions to use condoms.
Condom use and unprotected sex
Participants were asked about their condom use during intercourse in (a) the past month, (b) the past three months and (c) the past six months, as well as (d) how often they use condoms in general, (e) how many times they engaged in unprotected sex in the past six months, and (f) whether they used a condom the last time they had intercourse. These questions were asked in reference to participants’ main and other partner(s), and had acceptable internal consistency (α = .69 for main partner, and α = .63 for other partner).
Number of sexual partners
Participants were asked about the number of sexual partners they had in (a) the past 48 hours, (b) the past month, and (c) the past six months. This measure (National Institute of Drug Abuse [NIDA], 1991, 1993) had good internal consistency in our sample (α = .77; see also Edwards, Fisher, Johnson, Reynolds, & Redpath, 2007; Needle et al., 1995).
Alcohol use
Participants were asked to report their behaviors related to prior alcohol use. For those participants who reported that they drink alcohol, alcohol-use consisted of a single-item measure including reports of the number of times participants used alcohol during the past week.
Drug use
Participants were also asked to report their behaviors related to prior drug use. Drug-use measures included reports of the number of times participants used drugs (in general, as well as heroin, crack, and cocaine) during (a) the past 48 hours and (b) the past month. This measure had poor internal consistency in our sample (α = .52).
Injection drug use
Injection drug use was differentiated from the broader measure of drug use, as the level of HIV risk conferred by intravenous drug users is higher. Participants were asked (a) the number of times they injected drugs, (b) the frequency of sharing syringes, (c) the number of sharing partners, and (d) the number of times the equipment was sterilized between uses over a period of the past 48 hours, past month, and past six months. These questions were validated against HIV infection rates by Anthony et al. (1991), and had good internal consistency in our study (α = .85).
Intentions to use condoms
Participants were also asked about their intentions to use condoms, using previously validated measures (Albarracín et al., 2000; Earl et al., 2009; Kamb et al., 1998). Specifically, participants were asked how likely it was for them to use a condom with their partner (a) the next time they had intercourse, (b) every time for the next three months they had intercourse, and (c) every time for the next six months they had intercourse. Participants were also asked about (d) the strength of their intentions and (e) their motivation to use condoms with their partner in the next six months. These questions were asked in reference to participants’ main and other partner(s), and had excellent internal consistency (α = .94 for main partner, and α = .96 for other partner).
In addition to the above measures, participants were also asked standard items from the General Social Survey (http://gss.norc.org/) to assess structural variables, namely household income, level of education, race/ethnicity, and employment.
Measures of Video Acceptability, Counseling Expectations, and Return Intentions
The design included measures of the acceptability of the video, expectations of the following counseling sessions, and intentions to return. Measures were completed after the presentation of the video, by only half of the participants. Items for each measure were first transformed to a z-score, and then averaged, to create a composite measure for video acceptability, counseling expectations and return intentions.
To gauge video acceptability, participants were asked whether the video presented was (a) interesting, (b) useful, (c) enjoyable, (d) clear, and (e) relevant, as well as whether the video (f) made participants think, (g) taught them about condom use, and (h) presented new information. Participants were also asked whether the video made them (i) nervous, (j) worry, (k) feel compelled to do something they did not want to do, and (l) feel forced to change their beliefs or behaviors. Items (i) to (l) were first reverse scored, and then averaged with items (a) to (h). These measures had high internal consistency (α = .80).
Participants were also given measures of expectations about the counseling. Specifically, we asked participants whether they thought that the counseling would (a) force or (b) compel them to do things they did not like, (c) make them do things to please the counselor, (d) increase HIV safe behavior, (e) help them discuss health problems besides HIV and STIs, and (f) help them with their emotional concerns. Items (a) through (c) addressed empowerment expectations (α = .65), and items (d) through (f) addressed instrumental outcomes (α = .76).
Finally, participants were asked about the (a) strength of their return intentions and (b) how much they would enjoy returning (α = .67). All these measures were included as potential process data.
Completion Measure
Retention was observed during the last two sessions. When participants started the first session, the counselor indicated that the complete counseling program included two additional follow-up sessions. We measured retention by taking into account whether the participant completed all three sessions.
ResultsAcross the board, there was a high completion rate of 63%, which is probably attributable to the use of payments for attendance at the return sessions. Before analyzing the outcome of the meta-intervention, we compared the demographic and behavioral profile of our sample. Any incidental difference was then controlled for in the main analysis.
Comparability Across Conditions
Although random assignment was intended to ensure comparability across conditions, we performed periodic checks to make sure there were no gender, age or race biases in the participant distribution. Table 1 provides a summary of relevant sample characteristics, by overall sample, as well as broken down by condition. One-way ANOVAs and chi-square tests revealed no significant difference in these variables across our five conditions (ps > .077), with the exception of age and race. Variability in race across conditions approached significance, χ(18) = 28.71, p = .052. There was a significant difference in the age of participants across conditions, F(9, 712) = 2.15, p = .024. A Tukey post hoc test revealed that participants’ age was significantly lower in the combined instrumental and empowering meta-intervention condition, when no measures of return expectations and intentions were included (M = 25.08, SD = 4.46), compared with the same condition presented when those variables were measured (M = 27.52, SD = 5.06). There were no significant differences in age across the other conditions (ps > .089).
Effects on Video Acceptability, Counseling Expectations, and Return Intentions
A multivariate analysis of variance, with our five meta-intervention message conditions as a factor, was conducted to analyze the impact of our experimental factors on video acceptability, counseling expectations (either empowering or instrumental) and the intention to return to counseling. Results revealed no significant effect of meta-intervention message (p = .14), indicating that our experimental factor did not affect participants’ reported acceptability of the video, empowering or instrumental expectations of counseling, or their intentions to return to the next counseling session. These findings suggest that any effect of the video either occurred outside of awareness, or could not be clearly reported by our participants on the scales that we developed. Means and standard deviations for video acceptability, counseling expectations and return intentions are presented in Table 2. The means in all cases were above the midpoints of the scales and suggest favorable perceptions of the video and a program perceived to be acceptable.
Means and Standard Deviations for Video Acceptability, Counseling Expectations, and Return Intentions Presented Across Meta-Intervention Message Conditions
Main Experimental Results
A logistic regression analysis with our three factors of empowering message, instrumental message, and mediator measurement presence, as well as all interactions, was conducted to analyze the impact of our experimental factors on counseling completion. In this analysis, the two control conditions were combined but a separate consideration of these two conditions does not alter our results. The analyses entailed a forward removal of predictors. The results from this analysis appear in Table 3. Results revealed a significant two-way interaction between the presentation of instrumental and empowering messages, as well as a significant interaction between mediator measure presence and presentation of instrumental messages (see Figure 2). Results indicated that the instrumental message alone was better than any of the other messages. Furthermore, the instrumental message was more effective than the empowering message in the absence of measures of mediating expectations. The overall gains in completion as a result of the instrumental message were 16%, suggesting success in the intended facilitation of counseling completion.
Final Results From Logistic Regression Analysis
Figure 2. Effects of meta-interventions.
DiscussionThis paper reported a large and complex randomized controlled trial testing meta-interventions to increase completion of a CDC-recommended counseling for HIV prevention. Our results identified a successful program—one that incorporates the counseling within a broader spectrum of goals that are likely salient to the clients of most programs to prevent, test for, and treat HIV. This finding is particularly impressive given that the completion rates in the sample were fairly high, probably attributable to a combination of excellent counseling technique and highly effective counselors, in addition to the use of payments for follow-up sessions. In other words, the room for improvement may have been limited to begin with, or at least, limited relative to the usually lower completion rates in the average comparable HIV-prevention trial (see Albarracín et al., 2005). Also, although the use of payments seemed desirable given pilot data showing lower completion than that ultimately obtained, hindsight suggests that the payments might have reduced the sensitivity of our completion measure.
The instrumental meta-intervention seemed important to test as it involves a patient centered approach to interventions (Lauver et al., 2002; Morgan & Yoder, 2012; Robinson, Callister, Berry, & Dearing, 2008) that is entirely consistent with psychological theories of persuasion and motivation. For a message to be well received, it is necessary for its content to be relevant to the audience and sufficiently consistent to ensure high level of message-consistent thinking and low levels of counterarguing (Albarracín, Johnson, & Zanna, 2005; Albarracín & Vargas, 2010). In the case of the instrumental message, highlighting the various personal goals that can be met through contact with the health system and associated services clearly retained participants who otherwise may have dropped out from the program. Future research should be conducted to replicate this finding in other areas, particularly HIV testing, HIV treatment, and introduction of pharmacological agents, such as PrEP.
Three aspects of our findings are noteworthy. First, the empowering meta-intervention, which had impressive results in a trial to increase acceptance of HIV-prevention counseling (Albarracín et al., 2008), did not yield improved completion. This result highlights that the determinants of enrollment and retention are different, with defensiveness playing a key role in initiation, but lack of relevance or perceived purpose probably underlying drop out. Second, the average completion rate was rather high and so our meta-intervention may have stronger effects when completion is low to begin. In our case, the high quality of the counseling and intensive training and supervision of the counselors, along with the payments, decreased the need for an intervention to ensure completion. Replications in conditions that are more conducive to higher drop out will therefore be highly informative. Third, as is common in testing behavioral interventions, the mediation analysis shed no light on the variables that led to the treatment outcome. It is of course possible that expectations did change, but participants did not have full introspective access to these contents due to the operation of relatively nonconscious processes. More likely, however, the questions were too involved and required a level of metacognition that is unfortunately not frequent for a sample with a low level of education. Perhaps a less directive assessment, such as a qualitative interview, may in the future increase understanding of the reasons underlying the success of the instrumental message.
Effects of the Mediators
The introduction of the mediators was expected to affect completion by sensitizing clients to the importance of completion. Often, calling attention to what the goals of a study are can introduce demands effects (Barabasz & Barabasz, 1992). An interesting study on measurement effects, however, was conducted by Glasman and colleagues (2015), who found that introducing measurements of risky behavior decreased the effect of a prevention intervention, suggesting a potential underestimation of the effect of behavioral programs. Both the demand and efficacy reduction patterns are entirely consistent with what we found. The inclusion of mediator measures increased completion, while also decreasing sensitivity to the meta-intervention. It seems likely that in-depth questions elicit cognitive and motivational processes, such as self-talk, that distract recipients from fully processing messages received immediately before (Glasman et al., 2015) or after (in our study) receiving a persuasive communication.
Remaining Questions and Limitations
There are several important questions to address, including possible differences in the intervention as a function of the delivered meta-intervention. For all clients, during the second and third counseling sessions, counselors discussed the physical and psychological symptoms associated with HIV, addressed instrumental and medical concerns, and provided emotional support for the participants regardless of the condition they were randomized to. Thus, concern over the influence of a meta-intervention condition on counselors’ interactions with participants is mitigated by the fact that the delivery of the counseling and the delivery of the meta-intervention messages were done by different team members. The counselor was blind to the meta-intervention condition, and so, all subsequent interactions with participants could not have been biased by knowledge of experimental condition.
With respect to generalizability to the population, we restricted the sample of participants to 18- to 35-year-olds because the estimated number of diagnoses of HIV infections in the U.S. is highest for this age range (Center for Disease Control, 2014). Additionally, prior work has shown that the mean age of participants enrolling in HIV-prevention intervention programs falls within this range (e.g., Liu et al., 2014; Wilson, Durantini, Albarracín, Crause, & Albarracín, 2013). Despite this age range restriction, we do not believe the generalizability of the studies should be affected, as the meta-interventions used target broad psychological themes, such as seeking self-validation or maximizing objective outcomes, which are not limited to specific age groups.
With respect to generalizability to the intervention format, a three-session counseling program was selected both because previous work we have done showed three-session interventions were a good length (Liu et al., 2014), and because of cost considerations. In principle, it is possible that the meta-intervention messages used in this study might have different efficacy with a longer program. However, given that completion was very high to begin, a program with lower rates of completion may show a stronger effect of the meta-interventions. Furthermore, we limited the presentation of the meta-intervention messages to participants who attended the first session as we were interested in the effect of these messages on completion of a program after it starts. Prior work we have conducted has already addressed the benefits of certain meta-intervention messages on increasing enrollment in HIV-prevention intervention programs (Albarracín et al., 2008), where it made more sense to present these messages to the entire sample at baseline.
One important consideration is the potential effect of the financial incentive to participation used in this study. As is well known, paying individuals for performing a task can reduce the perception of freedom of choice, and in turn decrease intrinsic motivation for the task (see, e.g., Festinger, 1964). In this context, payments could have led to lesser motivation to complete the program than lack of payments. This possibility seems unlikely because of the very high completion rates we obtained in our study. A likely possibility, however, is that the payment might have decreased motivation for the empowering condition, which emphasized freedom of choice. For example, emphasizing that clients are active participants and the motor of change may have reminded them of the payment and thus reduce their motivation to complete the program. Thus, future work should test the meta-intervention in the absence of payments.
It is important to further consider the effect of the meta-interventions, particularly the fact that the empowering one seemed to offer no benefits. In this regard, although empowering messages have been effective at eliciting enrollment before the intervention is delivered, a retention meta-intervention of this type may be directly in conflict with the obvious behavior-change intent of the program. The preventive nature of the program is likely to be apparent from a first session in which participants are encouraged to identify, understand and acknowledge the behaviors and circumstances that put them at risk for being infected with HIV. Also, it seems possible that the video might become impractical in some contexts, particularly if it were excessively long. However, the counseling session lasted 25 min in average, which along with a 26-min instrumental video would result in a first session of 51 min of length. Thus, it seems possible to integrate this meta-intervention into the regular operation of a clinic.
Although efficacious interventions are key tools in the prevention, detection, and treatment of HIV, the public health impact of these interventions are likely reduced when vulnerable populations do not complete the program. Our findings provide evidence that a meta-intervention simply describing HIV-prevention counseling as a venue where one can discuss personal problems or medical needs, and receive appropriate referrals to community resources, appears to be a promising strategy for increasing retention. The development of cost-effective tools to retain clients in multisession HIV-prevention programs could have a significant impact on the lives of those at greatest risk for HIV infection and may play a pivotal role in decreasing the number of new HIV infections in Florida, and in the Nation.
Footnotes 1 Only one participant asked for the Spanish version of the measures.
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Submitted: November 3, 2015 Revised: May 9, 2016 Accepted: July 4, 2016
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Source: Journal of Consulting and Clinical Psychology. Vol. 84. (12), Dec, 2016 pp. 1052-1065)
Accession Number: 2016-51676-001
Digital Object Identifier: 10.1037/ccp0000139
Record: 5- Title:
- A method for making inferences in network analysis: Comment on Forbes, Wright, Markon, and Krueger (2017).
- Authors:
- Steinley, Douglas. Department of Psychological Sciences, University of Missouri, Columbia, MO, US, steinleyd@missouri.edu
Hoffman, Michaela. Department of Psychological Sciences, University of Missouri, Columbia, MO, US
Brusco, Michael J.. Department of Business Analytics, Information Systems & Supply Chain, Florida State University, Tallahassee, FL, US
Sher, Kenneth J.. Department of Psychological Sciences, University of Missouri, Columbia, MO, US - Address:
- Steinley, Douglas, Department of Psychological Sciences, University of Missouri, 210 McAlester Hall, Columbia, MO, US, 65211, steinleyd@missouri.edu
- Source:
- Journal of Abnormal Psychology, Vol 126(7), Oct, 2017. pp. 1000-1010.
- NLM Title Abbreviation:
- J Abnorm Psychol
- Page Count:
- 11
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- The Journal of Abnormal and Social Psychology; The Journal of Abnormal Psychology; The Journal of Abnormal Psychology and Social Psychology
- ISSN:
- 0021-843X (Print)
1939-1846 (Electronic) - Language:
- English
- Keywords:
- network analysis, Monte Carlo tests, multivariate binary data
- Abstract (English):
- Forbes, Wright, Markon, and Krueger (2017) make a compelling case for proceeding cautiously with respect to the overinterpretation and dissemination of results using the increasingly popular approach of creating 'networks' from co-occurrences of psychopathology symptoms. We commend the authors on their initial investigation and their utilization of cross-validation techniques in an effort to capture the stability of a variety of network estimation methods. Such techniques get at the heart of establishing 'reproducibility,' an increasing focus of concern in both psychology (e.g., Pashler & Wagenmakers, 2012) and science more generally (e.g., Baker, 2016). However, as we will show, the problem is likely worse (or at least more complicated) than they initially indicated. Specifically, for multivariate binary data, the marginal distributions enforce a large degree of structure on the data. We show that some expected measurements—such as commonly used centrality statistics—can have substantially higher values than what would usually be expected. As such, we propose a nonparametric approach to generate confidence intervals through Monte Carlo simulation. We apply the proposed methodology to the National Comorbidity Survey – Replication, provided by Forbes et al., finding that the many of the results are indistinguishable from what would be expected by chance. Further, we discuss the problem of multiple testing and potential issues of applying methods developed for 1-mode networks (e.g., ties within a single set of observations) to 2-mode networks (e.g., ties between 2 distinct sets of entities). When taken together, these issues indicate that the psychometric network models should be employed with extreme caution and interpreted guardedly. (PsycINFO Database Record (c) 2017 APA, all rights reserved)
- Impact Statement:
- General Scientific Summary—We show that accounting for the base rates of criteria and controlling for the distribution of severity rates within a population can result in network models that are no different than random chance. As such, it is imperative that we validate these models with additional approaches and perspectives and not rely solely on psychometric network analytic approaches. (PsycINFO Database Record (c) 2017 APA, all rights reserved)
- Document Type:
- Comment/Reply
- Subjects:
- *Experimental Replication; *Psychopathology; *Symptoms; Causality; Inference
- PsycINFO Classification:
- Research Methods & Experimental Design (2260)
Psychological & Physical Disorders (3200) - Grant Sponsorship:
- Sponsor: National Institutes of Health, US
Grant Number: R01AA023248-01
Recipients: Steinley, Douglas; Sher, Kenneth J. - Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- Accepted: Jul 20, 2017; Revised: Jul 19, 2017; First Submitted: May 17, 2017
- Release Date:
- 20171106
- Correction Date:
- 20171127
- Copyright:
- American Psychological Association. 2017
- Digital Object Identifier:
- http://dx.doi.org/10.1037/abn0000308
- PMID:
- 29106283
- Accession Number:
- 2017-49368-014
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- Database:
- PsycINFO
By: Douglas Steinley
Department of Psychological Sciences, University of Missouri;
Michaela Hoffman
Department of Psychological Sciences, University of Missouri
Michael J. Brusco
Department of Business Analytics, Information Systems & Supply Chain, Florida State University
Kenneth J. Sher
Department of Psychological Sciences, University of Missouri
Acknowledgement: Douglas Steinley and Kenneth J. Sher were partially supported by National Institutes of Health Grant R01AA023248-01.
We commend the authors—Forbes, Wright, Markon, and Krueger (2017)—for bringing to light one of the issues that has arisen recently when attempting to make inferences about the structure of so-called psychopathology networks. Specifically, Forbes et al. note that there are emerging regarding our ability to determine the extent that network structures obtained from two (or more) samples are similar or different in key respects. That is, do they replicate? Judging the similarity of various symptom network solutions can be challenging for a number of reasons, not the least of which is the number of parameters modeled including the number of edges (paths) connecting nodes (symptoms) and various measures that are derived from these, perhaps most critically, centrality measures that speak to the relative importance of individual symptoms vis a vis the total network. Depending upon the specific measures of centrality under consideration, such centrality measures could represent a powerful way to identify symptoms that are more fundamental (as opposed to accessory) to a given syndrome, bridge distinct symptom networks, or strongly related to other symptoms.
However, to have confidence in the results of any single network estimation procedure, it is important to be able to demonstrate its replicability, that is how similar are the estimated edge weights and centrality parameters across two different samples. Problems in establishing “reproducibility” is an increasing focus of concern in both psychology (e.g., Pashler & Wagenmakers, 2012) and science more generally (e.g., Baker, 2016). As noted above, the sheer number of parameters estimated make establishing the similarity of two more networks challenging and traditional approaches such as intuitive “eye balling” of the magnitude of various measures of agreement, concordance, or replicability (for instance, the Pearson correlation coefficient, the intraclass correlation)—many of which will not be appropriate—fails to provide a framework for making strong inferential statements regarding replicability of network parameterizations. Moreover, for technical reasons, measures of association based on binary data provide special challenges owing to their sensitivity to base rates (i.e., marginal frequencies) which may differ across samples either by design or sampling effects. Having a methodology for assessing network similarity that adequately addresses this challenge would make it possible for researchers to validly judge reproducibility and replicability.
In this commentary, we describe a simple yet effective method for generating empirical confidence intervals for whatever statistic is desired for inspection and demonstrate its use on a sample data set under the conditions described by Forbes et al. (2017). We then go on to provide some general comments regarding its application.
More specifically, we first review the notion of empirically creating appropriate null distributions for quantities of interest. We then discuss the application of such approaches to traditional one-mode networks where one set of nodes that are similar to each other or the same type (e.g., individuals). Following this, we provide an overview of the structure of two-mode matrices as commonly thought of in terms of network structure or graph theory, where there are two different types of nodes (e.g., symptoms and people). After that, we introduce the proposed hypothesis testing for two-mode binary networks and explicate the method by reanalyzing one of the data sets from Forbes et al. (2017) to obtain confidence intervals under an appropriate null distribution for edges and centrality statistics. This allows us to conduct subsequent statistical tests by determining whether the statistic of interest lies within the null confidence interval. If it does, then we conclude that the observed network statistic is performing the same as it would under a random two-mode network; if it does not, then a process of potential interest is likely driving the observed value for the tested statistic.
Background for Proposed TestIn multivariate statistics, it is often difficult to derive the exact sampling distribution for many quantities of interest. A standard practice is to generate random data with appropriate constraints, fit the hypothesized model to these generated data, and then compare the quantity of interest when computed under random data with that in the observed network. If they are very similar, then we cannot conclude that the observed data are any different than random data. Such an approach is often employed in other areas of multivariate statistics (e.g., use of parallel analysis in factor analysis, Horn, 1965).
In parallel analysis, the eigenvalues obtained from the covariance (or correlation) matrix derived from the sample data are compared with the eigenvalues obtained from covariance (or correlation) matrices arising from completely random data. The central idea behind of parallel analysis is that if a set of measured variables arises from a common factor model with f factors, then the f largest eigenvalues as computed from the data set should exceed that of the expected (e.g., average) eigenvalues of random data. One critical factor in making this comparison is how the random data are generated. Specifically, the constraints that are placed on each of the data sets in the comparison population are critical. If the eigen-decomposition is on the correlation matrix, a common constraint is the generated data should have the same sample size (e.g., the same number of observations) and the same number of variables. If the eigen-decomposition is on the covariance matrix, an additional constraint for consideration would be to ensure that the randomly generated variables had the same variances.
Similarly, corresponding procedures for comparing observed fit statistics to fit statistics derived from random data has been implemented in the domain of cluster analysis. Specifically, Steinley (2006, 2007, 2008) and Steinley and Brusco (2011) generated distributions based on random data to test the quality of a cluster solution and to determine whether the correct number of clusters had been chosen, respectively. Using this general approach, Steinley (2004) developed a method for sampling cluster agreement-matrices to approximate the statistic’s sampling distribution. Although used extensively in multivariate statistics, such approaches have also seen use in traditional network analyses—as explained in the following section.
One-Mode Networks
For one-mode networks (e.g., the rows and the columns of the adjacency matrix describe the same entities, such as a friendship network) statistical inference has long been an active area of research and methodological development because the standard inferential techniques relying on independence of observations does not apply. As a solution, it has been argued that the testing network statistics of interest can be obtained by generating a null distribution from a population of networks that have an appropriate structure that the researcher wishes to control. Generally, these networks are generated uniformly (e.g., each network has an equal chance of being selected and included in the population. Anderson, Butts, and Carley (1999) described an approach to conduct hypothesis testing on network level statistics when controlling for the number of observations (e.g., the number of actors or nodes) and the overall density (e.g., the number of links). Additional constraints can be added to the generation process, such as controlling for the number of mutual, asymmetric, and null ties. Furthermore, for one-mode networks, the notion of generating networks with specific properties has flourished with recent developments in exponential random graph modeling (see Hunter & Handcock, 2006).
Two-Mode Matrices
Two-mode data binary data has long been of interest in the psychological sciences (Arabie & Hubert, 1990; Arabie, Hubert, & Schleutermann, 1990; Brusco & Steinley, 2006, 2007a, 2007b, 2009, 2011; Brusco, Shireman, & Steinley, in press; Rosenberg, Van Mechelen, & De Boeck, 1996). In terms of networks and graph theory, two-mode binary data can be thought of as a bipartite graph and represented in a standard data matrix with n rows and p columns (where, often, rows represent the subjects and columns represent the variables). In the network literature, these are often deemed affiliation matrices. Until recently, there has been a dearth of methods for generating binary affiliation matrices with fixed margins, with the ones being used somewhat cumbersome and inefficient and too slow for application to large networks (Admiraal & Handcock, 2008; Chen, Diaconis, Holmes, & Liu, 2005). However, within the last few years, a pair of papers have appeared (Harrison & Miller, 2013; Miller & Harrison, 2013) that provide a computationally efficient method for generating affiliation matrices with fixed margins.
Interestingly, the motivating example for this work arises out of ecology to test cohabitation of species in biogeographical data, where the data are often of the form Species × Habitat. As a bit of background, original work by Diamond (1975) made extensive claims about “species assembly rules” that aimed to explain the interaction between specific species and their chosen habitats—indeed, some of the relationships between species and habitats were quite strong. However, Connor and Simberloff (1979) argued that, if one controlled for the base rates of the different species and the different habitats, then many of these so-called “strong” associations could have arisen by random chance. According to Miller and Harrison (2013), this set off years of argument about the nature of null hypothesis testing and whether to control for the marginal distributions or not. Owing to the fact that the effect disappears when the marginal distributions are controlled for, the current guidelines recommend that the marginal distributions are fixed such that observed effects are not due to the base rates of the rows and columns alone.
If the two-mode matrix is viewed as a contingency table, then fixing both marginal distributions corresponds to Fisher’s exact test, with the following assumptions:
- Each observation is classified into one and only one category of the row variable and into one and only one category of the column variable.
- The N observations come from a random sample such that each observation has the same probability of being classified into the ith row and the jth column as any other observation.
- The null hypothesis is: The event of an observation being in a particular row is independent of that same observation being in a particular column.
This framework will serve to generate the random matrices for our test, as described below.
Proposed TestThe goal of this commentary is to present such a test based on generating a set(s) of matrices from prespecified marginal distributions. The desire is to provide a statistical context for evaluating the magnitude some measures of correspondence with respect to the expected parameter estimates generated from random processes with known base rates. Further, much like the ecology literature, we argue that in the psychopathology literature it is imperative to compare observed results to null distributions that have the same prevalences of the symptoms themselves and the same distributions of severity across the individuals. This is a necessary condition to begin making precise, generalizable statements about specific symptoms by guaranteeing that any observed/interpreted relationships between the variables is attributable to their co-occurrence within the same individuals are not an artifact imposed by the marginal distributions.
Holding the within-person marginal distributions constant is necessary because the goal of network analysis in some ways is much more lofty that that of the common cause models, such as factor analysis. In the latter, it is often assumed that all items loading onto the same factor should be (theoretically) exchangeable; however, in network analysis the goal is to discover the explicit connections between individual items—as such, the items themselves take on a much more pronounced role in the modeling process. Specifically, for factor models, knowing that a subject endorsed 3 of 5 items on a disorder is often enough; in fact, that is what much of the diagnostic literature is predicated on. Conversely, for network models, the specific 3 items are much more important because the goal is to make causal (or pseudocausal) connections between specific items. The bar is much higher. Likewise, holding the within-item prevalences constant is extremely important as well. Specifically, it can easily be shown that items that have a higher prevalence of occurring are more likely to have observed edges in the network model. Consequently, it is important to model random items with the same rates of occurrence to ensure that observed network edges are not merely a byproduct of items that have been endorsed more than other items. Without such a reference, researchers will remain in the dark about what their findings indicate with these newer network models. The following two subsections describe how the procedure works, with a more mathematical explanation provided in the Appendix.
Algorithm for Within Network Importance
The ingredients for testing so-called “within” network importance are fairly straightforward. Namely, all that is required is the original data matrix Xn×p with n rows and p columns as well as the estimated network statistic of interest, say θ̂. The goal is then to obtain the sampling distribution for θ under the null distribution that the location of the ones (i.e., the presence of the symptom, say) in the binary matrix X - marginal distributions for both the rows and columns.
Procedurally, the process is fairly simple. First, the user must choose the number of random data matrices to generate. After this is chosen, each random matrix is generated such that it has identical column totals (e.g., each item prevalence is the same in the random matrices as in the observed matrix) and row totals (e.g., the severity profiles of the observations in the random matrices are the same as in the observed matrix). However, in the random generation process, the elements in the random matrices are generated such that the rows and columns are independent of each other conditional on the row totals and column totals (e.g., the marginal distributions) being the same. After all of the random matrices are generated, the statistic of interest (e.g., edge weights, centrality parameters, etc.) is computed on each of the random matrices. Finally, the statistic for the observed data is compared with the reference distribution derived from the random matrices, allowing for the calculation of a percentile score. If this score is between the 2.5 percentile and the 97.5 percentile, we can say that the observed data are providing results that are consistent with what we would expect to see in random data. That is, the p values associated with the estimates do not exceed a nominal level of.05.
Algorithm for Between Importance
The first algorithm discusses determining whether the observed data deviates from the what would be observed via random chance and concerns network elements that are internal to one data set (hence, the moniker of “within”). However, it is also possible to compute the correspondence or stability of network statistics across different data sets, which we will term as “between.” To compute the correspondence between two networks, in additional data set, Yn2×p, is required (note that while the number of variables must be the same, the two data sets may have different numbers of observations). Network structures are then derived from both X and Y, and a measure of correspondence is computed. Then, pairs of random matrices are generated, with one corresponding to X and one corresponding to Y, and the respective marginal distributions are fixed to those of the observed matrices. The same measure of correspondence is computed for each pair for random matrices, allowing for the generation of sampling distribution under random chance to be obtained. The computation of percentile scores than proceeds in the same fashion as described for the within network importance.
Relationship to Bootstrapping
Recently, Epskamp, Borsboom, and Fried (in press) have introduced methods for applying bootstrap techniques to psychological networks. These methods are designed to (a) assess the accuracy of estimated network connections, (b) investigate the stability of centrality indices, and (c) test whether network connections and centrality estimates for different variables differ from each other. The primary difference between the methodology proposed above and bootstrapping approach is that the former is assessing whether the observed effects are different than what we would expect from random chance while the latter assess stability and accuracy of coefficient estimation.
As such, it is possible, and as we see in the examples below, perhaps expected (because of the constraints placed on the network space due to the marginal distributions) that estimated network effects can be both simultaneously stable and not different than random chance. Additionally, a confidence interval around the either the connections between variables or the centrality estimates, as derived from the bootstrap approach, cannot include zero and still overlap with (or even fall within) the confidence interval indicating what we would expect by chance alone. In that instance, the estimate would be stable, but not particularly interesting/informative—and, in such a scenario we would caution against interpreting the estimate because, while the effect is shown to exist, it is not different than what would be expected by chance alone. We summarize the four possible outcomes derived from the outcomes of the two testing procedures in Table 1.
Four Potential Outcomes Between Bootstrap Procedure and Random Chance
In Table 1, we provide the more general possibility of testing hypotheses (or constructing confidence interval) concerned with comparing the effects with a prespecified null value, v0. Likewise, we can construct, from the procedure outlined above, the expected value of the effect under random chance, vrc. Then, the combined information of whether the estimate (v̂) is different than the hypothesized value (v0) and/or the value under random chance (vrc) is useful for determining the interestingness/importance of an effect. Obviously, the most interesting effect will be situations where it is shown that the estimate is both different from the hypothesized value and what is expected under random chance. Conversely, if effects are not different from what is expected under random chance, the information derived from the bootstrap method is uninteresting regardless of the degree of stability, accuracy, or difference from the hypothesized value. Lastly, when the effects are different from chance but not different from the hypothesized value, the result could be potentially interesting. In terms of the network diagram, this would correspond to unobserved links between pairs of variables. As such, each observed link can be interesting (different from zero, different from random chance) or uninteresting (not different from random chance); likewise, each unobserved link could be uninteresting (not different from zero, not different from random chance) or potentially interesting (not different from zero, different from random chance). We use “potentially” interesting because relationships (e.g., presence of links) and conditional independence (e.g., absence of links) are both inferred when the network diagram is visually inspected.
ExampleThe goal of these analyses is to infer the true underlying causal relationships between psychological symptoms, thus a significant result would be one that informs us of relations above and beyond what their prevalence rates tell us. Given the length constraints of the commentary, we focus on demonstrating the within-network evaluation algorithm for the NCS-R data as fit with the Ising model. Specifically, we will demonstrate the construction of confidence intervals around the estimated edge weights and centrality statistics as estimated using the qgraph package in the R statistical computing environment. To begin with, we need the marginal distributions of the NCS-R data. The frequency of each symptom is provided in Table 2, while the severity frequency (e.g., the number of symptoms endorsed by each subject, ranging from 0–18) is provided in Figure 1. From these data, we can immediately see the issue with only fixing one of the distributions. For instance, if we only fixed the prevalence of each symptom, then they would be distributed equally across the subjects, resulting in the average subject being assigned 3.75 symptoms. This would completely ignore the role of the severity continuum and its distribution in forming the network structure. Likewise, if we fixed only the severity marginals, we would see equally distributed counts over all of the symptoms, resulting in each having the same expected prevalence; consequently, differing prevalence rates alone could potentially define the “network” structure.
Frequency of Symptoms in the NCS-R Data
Figure 1. Frequency distribution of number of symptoms in NCS-R data showing the number of individuals with different numbers of symptoms.
Centrality Statistics
We implemented the algorithm as described above. For this small example, we generated 1000 matrices with marginals fixed to the values indicated in Table 2 and Figure 1. For each parameter of interest, we sorted the 1000 values from highest to lowest and created an empirical 99% confidence interval by taking the 5th and 995th ordered value. While we provide 99% confidence intervals here, more discussion is provided at the end of this section regarding choosing appropriate confidence levels. The confidence intervals for the centrality statistics are displayed in Table 3. From Table 3, we see that there are some variables that have significant centrality statistics when compared with what would be expected from chance. Significant values of centrality statistics are noted with an asterisk. For betweenness, we see that not all nonzero centrality statistics are significant, the most egregious being the betweenness score for “Anxiety about > 1 Event” is 38; however, we find that is not significantly different than would be expected by chance. This indicates that the magnitude of the centrality statistics does not necessarily indicate its relevance. We see similar results for closeness centrality, where 33% of the statistics are significant; unfortunately, without a formal test, it is impossible to determine which are different than chance as all of the estimates are within.017 of each other and magnitude alone does not confer significance. For strength, about two thirds of the centrality statistics are significant.
Confidence Intervals for Centrality Statistics
Finally, it is important to note that sometimes the centrality statistics are significantly less than would be expected by chance. This is true for “Chronic Anxiety” for betweenness and closeness, as well as “Muscle Tensions” for strength. This complicates the evaluation of this type of output because it is unknown whether observed results are significantly greater than chance, significantly less than chance, or no different than chance. Once again, the necessity for a formal test is highlighted.
Edge Weights
The difficulty is exacerbated for evaluating the edgeweights as displayed in Table 4. In Table 4, a “+” in the lower-triangle indicates the estimated edgeweight is significantly greater than chance, a “−” indicates the estimated edgeweight is less than chance, and a blank indicates that the value fell within the 99% confidence interval. One of the first things to notice is the prevalence of minus signs in Table 4. This indicates the absence of a link is just as important as the presence of a link. Of the 153 possible links, only 99 were significantly different than chance (65%), indicating that approximately one third of the estimated network is functioning equivalently to what we would expect by chance. Furthermore, with the three possible classifications of any individual edgeweight being (a) significantly greater than expected by chance, (b) significantly less than expected by chance, and (c) no different than expected by chance, the edgeweights are classified into those categories at the almost equal rates of 30%, 35%, and 35%, respectively. This uniform distribution of edgeweights to the three possible outcomes indicates that evaluating the results from these models by mere inspection of the edgeweights themselves is noninformative.
Estimates and Significance for Edge Weights
Once again, however, the specific edgeweights that are significant cannot be deduced by any pattern of edgeweights or their raw magnitude. For instance, the probability of an edgeweight being no different than chance if the observed edgeweight is greater than zero is 44%—so an observed edgeweight greater than zero is almost as equally as likely to be due to chance as not. To make it more salient, that would mean that nearly half of the edges depicted on the left side of Figure 3 in the Forbes et al. article are due to chance alone. Additionally, some of the zeros are included in the confidence intervals and some are not. Specifically, the probability of an edgeweight being significantly worse than chance given the observed edgeweight is less than or equal to zero is 28%. In terms of the graphical representation, that would mean that nearly 3 of 10 of the lines that are absent on the left hand side of Figure 3 are absent because of chance. Such variability in the confidence of the fidelity of observed edges in the figure renders these types of depictions of networks almost useless. This calls for an implementation of a formal testing procedure to assess the relevance of reported edgeweights or their absence.
Figure 2 illustrates the four possible outcomes as described in Table 1. The upper-left panel indicates an “interesting” finding between depressed mood and loss of interest. The edgeweight (1.93) falls above the distribution for random chance, and the bootstrap confidence interval around the edgeweight does not include zero. The lower-left panel indicates a “potentially interesting” connection between sleep problems (depression) and anxiety about more than one event. Specifically, the estimated value of zero is nonsignificant by both the eLasso and the bootstrap confidence interval around the estimate (0.00); however, the distribution based on random chance alone would expect this connection to be greater than what was observed. The final two panels on the right indicate two different “uninteresting” scenarios. The lower right panel shows the distribution for the edgeweight between chronic anxiety and sleep problems from anxiety—both distributions encompass the observed estimate of zero. The upper-right panel indicates a situation, in this case the relationship between loss of interest and psychomotor disturbances. The bootstrap test indicates that the edgeweight is significantly different than zero; however, the distribution derived from random matrices indicates that the observed value of 0.52 is not unexpected.
Figure 2. Examples of bootstrap compared with random distribution.
Confidence Levels
Above, we used a 99% confidence level to help control for multiple tests and illustrate the general notion of the differences between generating confidence intervals that reflect accuracy and stability through a bootstrap procedure versus the types of confidence intervals that aid in determining whether effects are different than would be expected by random chance alone. However, it is imperative to realize that even a more stringent confidence interval level will not aid in overcoming the threat of Type I error rates in the context of so many tests. Generally, there are
different tests for edgeweights and 3p tests for centrality measures (e.g., one for each variable on strength, closeness, and betweenness). In Table 4, there are
unique effects—for an α = .05, there would be (.05)153 = 7.65 significant effects based on chance alone, with the more stringent α = .01, (.01)(153) = 1.53 significant effects are expected to be attributable to chance alone.
In situations that rely on multiple testing, it is common to correct each individual test’s alpha to obtain an overall familywise α that corresponds to the prespecified value. The most conservative adjustment is the Bonferroni correction, which divides the familywise α by the total number of tests to obtain an appropriate level of α for each individual test—in this case, the adjusted α is (.05)/153 = .0003. As Epskamp, Borsboom, and Fried (in press) indicate, this type of correction can be overly burdensome and lead to the possibility of not finding any of the true effects (e.g., Type II errors). Beyond the inherent inverse relationship between Type I and Type II error, the computational burden to generate the requisite number of bootstrap samples for such small values of α can result in the procedure not being used in practice. For instance, generating enough samples to be able to obtain a 99.97% confidence interval would require around 10,000 samples; however, somewhere in the neighborhood of 50,000–10,000 would be preferable. As such, in their recommendations, Epskamp et al. (in press) indicate that corrections for chance will be relegated to future research, and in the interim, it is appropriate to proceed with uncorrected tests for the edgeweights.
While the sentiment to proceed with testing each test at α = .05 (the default setting in the Espkamp et al. bootstrapping software) is understandable, cautions, nonetheless, are called for. First, the probability of committing at least one Type I error when conducting multiple tests is 1 - (1 - α)T, where T is the number of tests being conducted. For instance, if you conduct two tests and do not correct for α the probability of committing at least one Type I error is 1 − (.95)2 = .0975; however, for the example above, the probability of making at least one Type I error is 1 − (.95)153 = .9997—an almost certainty. Furthermore, it is well known that the magnitude of p values cannot be compared to determine which effects are more likely to be significant and which are more likely to be either insignificant or a potentially a Type I error. The inability to do such a ranking is readily seen if one considers alternative correction methods beyond the Bonferroni correction. Specifically, the Holm-Bonferroni method (Holm, 1979) is uniformly more powerful than the Bonferroni correction; however, it proceeds by comparing the lowest p value to the strictest criterion, and then, as p values increase, the comparison criteria becomes less stringent. A similar test that is more powerful, but require assumptions regarding the joint distribution of the test statistics is Hochberg’s step-up procedure (Hochberg, 1988); likewise, one could consider Hommel’s (1988) stagewise rejective multiple test procedure. Unfortunately, each of these procedures would require in increase in the number of bootstrap samples—which would lead to the potential relaxation of the requirement to control for the familywise error rate.
Beyond the potential to incorrectly assume that effects are significant when they are not, the inattention to appropriately control for multiple testing has the potential to call into question all prior research in an area. This can be seen best with the application of multiple testing in fMRI research, another area that relies on a large number of tests and constructs substantive theory from patterns of significance. Recently, Eklund, Nichols, and Knutsson (2016) demonstrated that somewhere in the neighborhood of 40,000 fMRI studies may be invalid due to the inability to control for Type I error rates appropriately. Furthermore, in their conclusion the authors make a particularly relevant statement: “Although we note that metaanalysis can play an important role in teasing apart false-positive findings from consistent results, that does not mitigate the need for accurate inferential tools that give valid results for each and every study.” Because we are in the early stages of developing psychological networks, the most prudent course of action is also establishing appropriate inferential tools immediately and prevent the need to potentially correct hundreds or even thousands of published studies 5, 10, or 20 years from now. This view of tightening the reins for publication is further supported by the recommendation of a group of statisticians who recently weighed in on the reproducibility of psychological science (Johnson et al., 2017): “The results of this reanalysis provide a compelling argument for both increasing the threshold required for declaring scientific discoveries and for adopting statistical summaries of evidence that account for the high proportion of tested hypotheses that are false.” In fact, they suspect that potentially 90% (!!!) of tests performed in psychology experiments are testing negligible effects.
Fidelity of Data
Lastly, we note that there are several decisions made at the data analytic level that can affect and alter the results of any given network model. Some examples would include how the data are collected or how the responses are coded. For instance, it is possible that specific criteria could be coded differently depending on the interview employed to collect the data. Another example would be the so-called “skip out” issue that can occur in epidemiological questionnaires. For “skip out” items, there are gateway items that are assessed first—if the response to the gateway item is negative, then the remainder of the items are not asked, whereas if the response to the gateway item is affirmative, then all subsequent related items are asked. A question arises on how to handle the subsequent items if they were not assessed. A common approach is to assume that if the gateway item is not endorsed, then all subsequent subitems are not endorsed either—this is akin to a logical data imputation. Whether this is appropriate or not will depend on the specific set of questions and criteria being assessed. If it is inappropriate, the estimated effects will be biased; however, that being said, these biases will not effect the validity of the testing approach described above, nor does it mitigate the issues with not controlling for multiple testing.
Discussion Replicability
From these findings, we see that the results of many network analyses, as conducted on binary data, might be overstating their findings. Although space limitations only allowed for a modest analysis of the performance the Isingfit model on one data set, current work is expanding the investigation to a broader class of binary matrices as well as extending to the three other types of network models mentioned by Forbes et al. (2017). Preliminary results indicate that the findings will be similar to what was observed for the Isingfit model. In short, we second the conclusion of Forbes et al. and offer the addendum that discovering believable results, at the specific symptom level (whether that is the relationship between pairs of symptoms) or variable level statistics (e.g., centrality statistics), will likely be much more difficult than previously envisioned. As shown above, this is because empirical findings are difficult to distinguish from random chance, and we do not believe that it would be too strong of a suggestion that previously published findings using this methodology should be reevaluated using the above testing procedure. Without this additional testing, future research based on existing findings will likely lead to a significant degree of nonreplicability as the findings are potentially no difference than chance. While the network approach represents an important alternative view of diagnostic systems that could provide new insights into both the basic structure of psychopathology and identify promising targets for intervention by virtue of their centrality, existing methods must be considered unproven. Research practitioners must appreciate the limitations of the existing state-of-the-art and develop and refine approaches likely to provide more robust and interpretable solutions.
Are Psychopathology “Networks” Actually Networks?
In the first footnote, we note that much of the terminology in recent psychopathology network analysis has been borrowed from the traditional network analysis literature—much of which is rooted in psychology and sociology (see Wasserman & Faust, 1994). To determine whether the transferability of methods in traditional network analysis to psychopathology networks is warranted (or should be taken at face value), it is worth highlighting the differences between the two. Generally, there are two types of networks that can be considered: (1) networks that directly assess the relationships between the same set of observations (e.g., one-mode matrices as described above), and (2) affiliation networks where the connections are assessed between two sets of observations (two-mode matrices as described above). Clearly, psychopathology networks fall into the class of affiliation matrices where the connections are measured between observation and diagnostic criteria. The relationships between the criteria are then then derived by transforming the two-mode affiliation matrix to a one-mode so-called “overlap/similarity” matrix between the criteria, where traditional network methods are applied to this overlap/similarity matrix. Faust (1997) indicated that potential pitfalls arise when applying standard centrality measures to networks derived from affiliation matrices: “In going from the affiliation relation to either the actor co-membership relation or the event overlap relation, one loses information about the patterns of affiliation between actors and events. Thus, one needs to be cautious when interpreting centralities for these one-mode relations” (p. 189). The concerns of Faust (1997) are related directly to the concerns raised in the introduction where the methods developed for one-mode networks are applied to the two-mode networks (e.g., affiliation network or bipartite graph).
ConclusionAs Johnson et al. (2017) argue, the editorial policies (and funding priorities) must be compelled to adapt to higher standards prior to putting their stamp of approval on results. Given the nature of problems that confront psychometric network modeling, including (but not limited to): (a) the possibility of observing “significant” effects that are not different than random chance, (b) the difficulties induced by conducting numerous significant tests, which is not controlled for via bootstrapping, and (c) the uncertainty regarding applying traditional methodology developed for one-mode networks to two-mode networks, we wholeheartedly agree with the recommendation by Forbes and company to turn a skeptical eye toward these models. Additionally, given the results from the example above and the theoretical issues that surround multiple testing and appropriate reference distributions, we do not agree with Epskamp, Borsboom, and Fried (in press) that it is reasonable to continue conducting tests at the α = .05 while we wait for methodologists to develop procedures that address the shortcomings. Rather, it is the other way around: wait until methodologists develop the appropriate fixes then proceed with fitting these models. Not following such an approach runs the very real risk of creating a series of publications that contain results that are not reproducible and likely no different than what is expected under one of the most basic models of chance in all of categorical data analysis.
Although we have provided numerous caveats and cautions to employing psychometric network models, we do believe that they are opening a potentially important area of research for us to consider. Specifically, the moving more to a causal structure model, as opposed to a common cause (such as latent variable) model holds great appeal as the parallelism to traditional medical models of diseases is enhanced. In our view, it is likely that the true model (or at least the best fitting models) will be a hybrid of network models and latent variable models. Further, although we provide extreme caution in using these methods to motivate theory, we do believe that such techniques can be a useful tool in the arsenal of the researcher when used to supplement a rich, substantive knowledge regarding the psychopathology being studied. Additionally, we note that by using correlations (or functions thereof) the network models have wed themselves closely to traditional latent variable models in terms of how the relationships between variables are conceptualized. However, there is a rich history in assessing the similarity between binary items in general and elements within a bipartite graph specifically. Broadening approaches beyond the logistic regression framework encapsulated by the Isingfit approach (and perhaps even abandoning the notion of correlation as the foundational measure of association) could open the entire field of psychometric networks to entirely new horizons. Finally, many of the concerns raised in this commentary could be alleviated if we move from the current state of exploratory psychometric networks to confirmatory psychometric networks, allowing a priori specified effects to be tested rather being hamstrung out of the gate by the need to correct for multiple testing.
Footnotes 1 We note that there is a good deal of appropriation of terminology from the social network analysis literature used in these analyses; we would caution too much transference as the psychopathology networks are induced from bipartite graphs versus being observed directly.
2 The eigenvalues from the completely random data are obtained by averaging over several—usually one thousand or more—sets of eigenvalues that have been obtained from independently generated random data.
3 Such an approach has been superceded by the derivation of a closed-form solution for comparing the equivalence of two partitions (Steinley, Brusco, & Hubert, 2016), but the basic logic still applies.
4 We note that most of the time v0 = 0.
5 Note that under many situations, the expected value of the effect is often zero (e.g., no effect). For instance, in the simple case of a t test or a linear regression model, generating data from random distributions—accomplished by randomly assigning group labels for the t test or randomly generating predictors uncorrelated with the dependent variable for regression—we would expect mean differences of zero and slopes of zero. The deviation from no effect for these psychological networks is due to the restriction of the range of the sample space of potential networks that is induced from the observed marginal distributions.
6 The between network importance algorithm would be implemented in a similar fashion, but because of space constraints we have not included an example here.
7 The data set was provided to us by the first author, Miriam Forbes, of the target article.
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APPENDIX APPENDIX A: Algorithms for Testing Replicability when Marginal Distributions are Fixe
The ingredients for testing so-called “within” replicability are fairly straightforward. Namely, all that is required is the original data matrix Xn×p with n rows and p columns as well as the network statistic of interest, say θ̂. The goal is then to obtain the sampling distribution for θ under the null distribution that the location of the ones in the binary matrix X are random subject to the observed marginal distributions for both the rows and columns.
The algorithm proceeds as follows:
- Compute θ̂ from X; compute rx and cx (the marginal distributions for the rows and columns, respectively, which in this case are just sums across the columns and rows).
- Choose the number of random data matrices, R, to generate.
- Generate the ith random matrix, Mi, such that rmi = rx and cMi = cx (e.g., the marginal distributions of the random matrices will be equivalent to those of the observed matrix, X).
- Compute θi for i = 1, . . ., R. Order the θ’s from smallest to largest, letting the ordered vector of θ be denoted as θ(o).
- Create a confidence interval under the null distribution of random association as
. - If the observed test statistic, θ̂, falls within the interval in Step 5 then we cannot reject the null. Consequently, we conclude the observed value could have arisen by random chance alone.
Algorithm for Between Replicability
The algorithm for testing so-called “between” replicability is almost identical. All that is required is a second data matrix, Yn2×p, with which we want to compare the original data matrix xn1×p. The process proceeds as follows:
- Compute θ̂XY from f(X, Y), where f(·) is a function of correspondence between the two structures uncovered from X and Y (for instance, the function could be the correlation of the edge weights between the two network structures). Additionally, compute rx and cx from X; compute from Y, rY and cY from Y.
- Choose the number of pairs of random data matrices, R, to generate.
- Generate the ith pair of random matrices (Mi, Ni), such that rMi = rx and cMi = cx and rNi = rY and cNi = cY.
- Compute θi for i = 1, . . ., R. Order the θ’s from smallest to largest, letting the ordered vector of θ be denoted as θ(o).
- Create a confidence interval under the null distribution of random association as
. - If the observed test statistic, θ̂, falls within the interval in Step 5 then we cannot reject the null. Consequently, we conclude the observed value could have arisen by random chance alone.
Submitted: May 17, 2017 Revised: July 19, 2017 Accepted: July 20, 2017
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Source: Journal of Abnormal Psychology. Vol. 126. (7), Oct, 2017 pp. 1000-1010)
Accession Number: 2017-49368-014
Digital Object Identifier: 10.1037/abn0000308
Record: 6- Title:
- A new decisional balance measure of motivation to change among at-risk college drinkers.
- Authors:
- Collins, Susan E.. Addictive Behaviors Research Center, University of Washington, Seattle, WA, US, susan.collins@gmx.net
Carey, Kate B.. Center for Health and Behavior, Syracuse University, Syracuse, NY, US
Otto, Jacqueline M.. Addictive Behaviors Research Center, University of Washington, Seattle, WA, US - Address:
- Collins, Susan E., Addictive Behaviors Research Center, University of Washington, Box 351629, Seattle, WA, US, 98195, susan.collins@gmx.net
- Source:
- Psychology of Addictive Behaviors, Vol 23(3), Sep, 2009. pp. 464-471.
- NLM Title Abbreviation:
- Psychol Addict Behav
- Page Count:
- 8
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- Bulletin of the Society of Psychologists in Addictive Behaviors; Bulletin of the Society of Psychologists in Substance Abuse
- Other Publishers:
- US : Educational Publishing Foundation
Society of Psychologists in Addictive Behaviors - ISSN:
- 0893-164X (Print)
1939-1501 (Electronic) - Language:
- English
- Keywords:
- decisional balance, motivation to change, college drinking, alcohol use, measure development, at risk students
- Abstract:
- In this study, an open-ended decisional balance worksheet was used to elicit self-generated pros and cons of current drinking and reducing drinking, which were then quantified to create a decisional balance proportion (DBP) reflecting movement toward change (i.e., counts of pros of reducing drinking and cons of current drinking to all decisional balance fields). This study’s goal was to examine the convergent, discriminant, and predictive validity of the DBP as a measure of motivation to change. Participants were college students (N = 143) who reported having engaged in weekly heavy, episodic drinking and who had participated in a larger randomized clinical trial of brief motivational interventions (K. B. Carey, M. P. Carey, S. A. Maisto, & J. M. Henson, 2006). Findings indicated partial support for convergent and discriminant validity of the DBP. Compared with Likert scale measures of decisional balance and readiness to change, DBP scores reflecting greater movement toward change best predicted reductions in heavy drinking quantity and frequency and experience of alcohol-related consequences, although some of these effects decayed by the 12-month follow-up. Findings suggest that the DBP is a valid measure of motivation to change among at-risk college drinkers. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Alcohol Drinking Attitudes; *At Risk Populations; *Decision Making; *Motivation; *Readiness to Change; College Students; Measurement
- Medical Subject Headings (MeSH):
- Adolescent; Adult; Alcoholic Intoxication; Alcoholism; Counseling; Decision Making; Female; Humans; Male; Motivation; Psychometrics; Reproducibility of Results; Risk Factors; Students; Surveys and Questionnaires; Temperance; Young Adult
- PsycINFO Classification:
- Substance Abuse & Addiction (3233)
Personality Traits & Processes (3120) - Population:
- Human
Male
Female - Location:
- US
- Age Group:
- Adulthood (18 yrs & older)
Young Adulthood (18-29 yrs) - Tests & Measures:
- Daily Drinking Questionnaire
Readiness to Change Questionnaire DOI: 10.1037/t00434-000
Marlowe-Crowne Social Desirability Scale DOI: 10.1037/t05257-000
Rutgers Alcohol Problem Index DOI: 10.1037/t00517-000 - Grant Sponsorship:
- Sponsor: National Institute on Alcohol Abuse and Alcoholism
Grant Number: T32AA007455
Other Details: Institutional Training Grant, Mary E. Larimer
Recipients: No recipient indicated
Sponsor: National Institute on Alcohol Abuse and Alcoholism
Grant Number: AA12518
Recipients: Carey, Kate B. - Methodology:
- Empirical Study; Quantitative Study
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- Accepted: Feb 20, 2009; Revised: Jan 1, 2009; First Submitted: Oct 1, 2008
- Release Date:
- 20090921
- Correction Date:
- 20130114
- Copyright:
- American Psychological Association. 2009
- Digital Object Identifier:
- http://dx.doi.org/10.1037/a0015841
- PMID:
- 19769430
- Accession Number:
- 2009-14441-007
- Number of Citations in Source:
- 31
- Persistent link to this record (Permalink):
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- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2009-14441-007&site=ehost-live">A new decisional balance measure of motivation to change among at-risk college drinkers.</A>
- Database:
- PsycINFO
A New Decisional Balance Measure of Motivation to Change Among At-Risk College Drinkers
By: Susan E. Collins
Addictive Behaviors Research Center, University of Washington;
Kate B. Carey
Center for Health and Behavior, Syracuse University
Jacqueline M. Otto
Addictive Behaviors Research Center, University of Washington
Acknowledgement: Susan E. Collins’s time was supported by National Institute on Alcohol Abuse and Alcoholism (NIAAA) Institutional Training Grant T32AA007455 awarded to Mary E. Larimer at the University of Washington. This study was supported in part by NIAAA Grant AA12518 awarded to Kate B. Carey.
A big thanks goes to Dan J. Neal for his statistical insights and advice, to Carrie Luteran for assembling the data sets, and Sonia Kaur for her help with data entry. Thanks also to Sandra Eck for the helpful ongoing discussions about decisional balance measurement in new participant populations.
Decision making often involves consideration of a set of possible behavioral options and an evaluation of the consequences of each option. These consequences may range from desirable to undesirable effects, also referred to as pros and cons (Janis & Mann, 1977). A decisional balance has thus been operationalized as a representation of the pros and cons of a certain behavior and its potential alternatives.
The explicit consideration of the decisional balance—either as a written or counselor-facilitated exercise—was originally proposed to reduce decision-making errors by making people more cognizant of the decision-making process and the factors contributing to their decisions (Janis, 1968). In this context, decisional balance was designed as a therapeutic exercise to facilitate the complete and realistic assessment of the net value of a potential behavior. As this technique has evolved over time, clinicians have started to employ guided decisional balance exercises as a means of enhancing motivation to change risky health behaviors (Dimeff, Baer, Kivlahan, & Marlatt, 1999; Miller, 1999). Specifically, clients articulate and examine ambivalence about their current behavior to determine whether the weight of the evidence is accumulating toward the need for behavior change (Miller, 1999). In the college drinking literature, a limited number of studies have used a guided, open-ended decisional balance as an intervention tool, but the results of guided interventions have been mixed with regard to alcohol use outcomes (Carey, Carey, Maisto, & Henson, 2006; S. E. Collins & Carey, 2005; LaBrie, Pedersen, Earlywine, & Olsen, 2006).
Alternatively, the data generated in a decisional balance could reflect resolve to enter into a course of action (Janis & Mann, 1977), thus serving as a proxy for motivation to change. This idea was echoed in the work of DiClemente and colleagues (e.g., DiClemente et al., 1991), who have since recognized decisional balance as a marker for the initiation of different stages of change (Pollak, Carbonari, DiClemente, Niemann, & Mullen, 1998). In fact, the stages of change have been operationalized algorithmically as a function of change in the decisional balance (Hall & Rossi, 2008; Prochaska, 1994). Thus, the historical development of the decisional balance construct shows its potential to both enhance and reflect people’s motivational states. In this study, we focused on the role of decisional balance as an assessment tool rather than as an intervention procedure.
The existence of a relationship between decisional balance and motivation to change is evident; however, the nature of this relationship has been described differently in different theoretical contexts. Furthermore, how decisional balance is measured has included the consideration of the pros and cons of a behavior or the pros and cons of an alternative behavior. What exactly decisional balance measures, however, has been less clearly defined. For example, some researchers have asserted that it represents the decision-making process itself (Fischoff & Quadrel, 1991; Janis & Mann, 1977), others have posited that it represents one dimension of motivation to change (Miller, 1999), and yet others have suggested that it is a covariate or perhaps a mechanism involved in transitioning through various stages of behavior change (Prochaska et al., 1994). Considering the ambiguity in the relationship between motivation and decisional balance, more research is needed to establish the validity and clinical utility of decisional balance as a measure of motivation to change.
Decisional Balance MeasuresReflecting the ambiguity surrounding the decisional balance construct, several decisional balance measures for drinking have been designed over the past 2 decades. The Alcohol Decisional Balance Scale assesses the pros and cons of maintaining one’s current alcohol use using a 42-item Likert scale questionnaire (King & DiClemente, 1993). The Alcohol and Drug Consequences Questionnaire is a 28-item, 6-point Likert scale questionnaire designed to assess the pros and cons of changing alcohol or drug behavior (Cunningham, Gavin, Sobell, Sobell, & Breslin, 1997). Finally, the Decisional Balance for Immoderate Drinking (DBID) was developed for college students (Migneault, Velicer, Prochaska, & Stevenson, 1999). This measure consists of 25 Likert scale questions assessing on a 5-point scale the importance of selected pros and cons of “immoderate” drinking. Different sets of these original 25 items may be summed to form either two 20-item scales (pros vs. cons of immoderate drinking) or three 18-item scales (pros, potential cons, and actual cons).
Despite initially promising psychometric evaluations of these questionnaires, conceptual weaknesses can be identified. First, all of these measures have focused exclusively on the pros and cons of current drinking or the pros and cons of reducing or changing drinking, which precludes the evaluation of the decisional balance as a whole. An incomplete decisional balance has been viewed as problematic in decision-making theory because of the potential for overlooked consequences to create new ambivalence after a decision has been reached (Janis & Mann, 1977). Similarly, an incomplete measure of decisional balance may fail to take into account all aspects of a person’s current motivation to change. One empirical study found that consideration of both the target behavior and an alternative behavior nearly doubled the number of pros and cons spontaneously produced across multiple risky behaviors (Beyth-Marom, Austin, Fischhoff, Palmgren, & Jacobs-Quadrel, 1992). Particularly among adolescents in that study, negative social consequences were reported more often in assessing the cons of not drinking. Thus, a focus on only one half of the decision-making process (i.e., either the pros and cons of current or alternative behavior) might lead to an incomplete and potentially less predictive measure of decisional balance.
Another problem with decisional balance measures to date is that the pros and cons are generated by researchers instead of by participants themselves. This approach may be inadequate in capturing motivation to change authentically and accurately. First, if researchers approach the topic from an academic perspective, they may identify different pros and cons of current drinking versus drinking behavior change than participants (Fischoff & Quadrel, 1991). Also, the language used by researchers to describe the pros and cons can, in and of itself, influences participants’ interpretation of the item and thus the participants’ answers (Beyth-Marom et al., 1992; Fischoff & Quadrel, 1991). On a related point, in providing participants with the “correct” pros and cons of drinking, researchers may be artificially constructing the decision-making process to which participants passively respond. This approach may have the unwanted side effect of making respondents aware of pros and cons they may not have otherwise considered and that may not represent their own unique decision-making process. In contrast, use of an open-ended response format allows participants to express their actual motivational state rather than respond to researchers’ perspectives and values (Fischoff & Quadrel, 1991).
In light of these concerns, it is plausible that an open-ended, participant-generated decisional balance could provide a more accurate measure of motivation to change and better predict drinking outcomes among college drinkers. In an exploratory analysis, Collins and Carey (2005) presented some evidence that, when used as an assessment of motivation, the pattern of responses generated during a decisional balance exercise predicted drinking outcomes with modest success. Specifically, among participants receiving an in-person decisional balance exercise, a greater proportion of pros to cons of changing one’s drinking predicted short-term drinking outcomes.
Current StudyThe current study was designed to extend these preliminary findings and examine an expanded, open-ended, four-field decisional balance worksheet as a measure of motivation to change drinking among at-risk college drinkers. The decisional balance worksheet prompted respondents to report the pros and cons of their current drinking versus reduced drinking. Next, the number of pros and cons reported in each field was converted into a proportion representing the decisional balance toward change or the decisional balance proportion (DBP).
The goal of this study was to test the validity of the DBP as a new measure of motivation to change drinking. We hypothesized that the DBP would evince convergent validity by positively correlating with an alternative, Likert scale measure of readiness to change, and positively correlating with the cons and negatively correlating with the pros of drinking as measured by continuous decisional balance scales. Furthermore, we hypothesized that the DBP would evince discriminant validity by showing nonsignificant correlations with measures assessing dissimilar constructs (i.e., social desirability and demographic variables). Finally, we hypothesized that DBP change scores would evince predictive validity. Initial increases in the decisional balance reflecting movement toward drinking behavior change would predict greater decreases in heavy drinking indices over the follow-up period compared with a DBP reflecting no movement or movement away from change.
Method Participants
Participants consisted of 143 undergraduate volunteers who had participated in a randomized clinical trial of two types of brief motivational interventions (see Carey et al., 2006). Inclusion criteria for this trial were (a) reporting at least one heavy drinking episode in an average week or at least four heavy drinking episodes in the past month, (b) being 18–25 years of age, (c) being a freshman, sophomore, or junior in college, and (d) consenting to participate. Only students who had participated in the first year of the larger study were included in the current secondary analyses because the format of the original measure was changed after the first year. The new format allowed for electronic data scanning but, as a consequence, limited the number of potential entries. Because the number of entries in each field of the decisional balance was the primary focus of the current study, the data set includes only the participants who had the opportunity to respond to the unrestricted format.
The subsample of 143 students in the current study was predominately female (68%, n = 97), and the average age was 19.20 years (SD = 0.87). The sample consisted of 65% freshmen, 27% sophomores, and 7% juniors. The majority self-identified as White (87%), whereas 2% self-identified as Black/African American, 5% as Asian/Pacific Islander, 3% as Hispanic/Latino/a, and 4% as other or multiracial. Membership in a fraternity or sorority was reported by 24% of the sample, and most participants reported living on campus (89%) or in a fraternity or sorority house (2%), as opposed to off campus (8%).
Measures
A set of demographic questions assessed participants’ age, gender, year in college, ethnicity, on- or off-campus residence, and membership in an on-campus Greek organization. Social desirability was measured using a 13-item short form of the Marlowe Crowne Social Desirability Scale (Reynolds, 1982). The alpha in the current sample was adequate (α = .63).
The decisional balance worksheet was modeled after a scale used to assess the accessibility of alcohol expectancies (see Stacy, Leigh, & Weingardt, 1994). Because it is a free-recall task, it was administered prior to other Likert scale measures of decisional balance. Participants recorded each “advantage” and “disadvantage” of “continuing to drink as you are now” and “drinking less than you do now” on prenumbered lines on the open-ended decisional balance worksheet. The counts of the pros and cons were obtained by summing the prenumbered lines filled in by participants and formed the main explanatory variable for this study, DBP, which may be written as
where subscripts red = reducing drinking and cur = current drinking. DBP scores at 0.5 represent an even balance between pros and cons of reducing drinking and current drinking. Scores between 0.5 and 1.0 indicate a balance tipped toward reducing drinking, and DBP scores between 0.0 and 0.5 indicate a balance tipped toward maintenance of current drinking. In the current study, baseline DBP was used in the convergent and discriminant validity tests, whereas change in the DBP from baseline to 1-month follow-up was used in predictive validity tests.
The 12-item Readiness to Change Questionnaire (RTCQ; Rollnick, Heather, Gold, & Hall, 1992) was scored as a continuous measure of readiness to change (Budd & Rollnick, 1996). The RTCQ was used in convergent and predictive validity analyses in the current study. The alpha reached an adequate level of item consistency (α = .80).
The DBID (Migneault et al., 1999) is a 25-item Likert scale measure developed for college students. Different sets of these original 25 items may be summed to form either two scales (10-item Pros vs. 10-item Cons of immoderate drinking) or three scales (10-item Pros, 3-item Potential Cons, and 5-item Actual Cons). The 18-item, three-factor solution was used in tests of convergent and predictive validity in the current study. In the overall sample, reliability was calculated for Pros (α = .81), Potential Cons (α = .71), and Actual Cons (α = .52).
All drinking assessments used the previous 30 days as a uniform time frame and defined a drink as a 10- to 12-oz can or bottle of 4%–5% beer, a 4-oz glass of 12% table wine, a 12-oz bottle or can of wine cooler, or a 1.25-oz shot of 80-proof liquor either straight or in a mixed drink. A modified version of the Daily Drinking Questionnaire (R. L. Collins, Parks, & Marlatt, 1985) allowed for calculation of drinking frequency and quantity per heaviest drinking week. Using this measure, participants also estimated the frequency of heavy episodic drinking (HED), defined as five or more drinks for men and four or more drinks for women on one occasion (Wechsler et al., 2002). This measure yielded three of the alcohol outcome variables.
The Rutgers Alcohol Problem Index (RAPI; White & Labouvie, 1989) consists of 23 items assessing alcohol-related problems and was specifically developed for use with adolescents and young adults. Participants used a Likert scale to indicate how many times in the past 30 days they experienced each problem listed (i.e., 0 = 0 times, 1 = 1–2 times, 2 = 3–5 times, 3 = 6–10 times, 4 = more than 10 times), and a summary score represented severity of problems. Adequate internal consistency was obtained in the overall sample (α = .82).
Procedure
Participants attending group baseline sessions provided informed consent and completed the measures mentioned above; all received course credit for their participation. Those who reported engaging in HED at least 4 times in the past month were invited via telephone to participate in a randomized clinical trial that included various brief intervention and assessment conditions (for details, see Carey et al., 2006). Following the intervention period, participants were invited to attend 1-, 6-, and 12-month in-person follow-up assessments for which they filled out the same questionnaires and received course credit, $20, and $25, respectively.
Results Preliminary Data Analysis
The distributions of the explanatory and outcome variables were examined for univariate outliers and deviation from the expected distributions. All drinking outcome measures were positively skewed count variables that approximated the negative binomial distribution. There were no extreme univariate outliers (see Table 1 for all means and standard deviations).
Descriptive Statistics for Explanatory and Response Variables (N = 143)
Convergent Validity
To determine the convergent validity of the DBP, we conducted bivariate Spearman correlations between the baseline DBP and baseline summary scores for the RTCQ, DBID–Pros, DBID–Potential Cons, and DBID–Actual Cons. Although the correlations with the DBP were all in the expected directions, only the correlations between the DBP and RTCQ and DBID–Actual cons were statistically significant (see Table 2 for correlations).
Bivariate Correlations Between the DBP, RTCQ, and DBID Scales
Discriminant Validity
Bivariate Spearman correlations and Mann–Whitney U tests were conducted to test the hypothesized lack of association between the baseline DBP and gender, U(N = 143) = –0.26, p = .80; race/ethnicity, U(N = 143) = –1.32, p = .19; housing situation, U(N = 142) = 0.78, p = .44; social desirability, ρ = –0.09, p = .28; age, ρ = –0.12, p = .14; and Greek membership, U(N = 143) = –1.03, p = .30. As all tests were nonsignificant, discriminant validity of this measure was supported.
Predictive Validity of Change in Decisional Balance
Analysis plan
Population-averaged generalized estimating equation (Zeger & Liang, 1986) models were conducted using STATA 10 (StataCorp, 2007) and tested the change in DBP from baseline to 1-month follow-up as a predictor of heavy drinking outcomes over the 12-month follow-up period. For those unfamiliar with population-averaged generalized estimating equation models, they may be conceptualized as marginal regression models that can be applied to data conforming to different types of distributions (e.g., normal, Poisson, negative binomial, binomial) and can take into account nonindependence resulting from data clustering (e.g., longitudinal data collected on one participant).
The outcome variables were based on a time frame of the previous 30 days and included drinking quantity and frequency during the heaviest drinking week, HED frequency, and RAPI score. Because the distributions of the drinking outcome variables were positively skewed, and the variables were typically overdispersed count/integer responses (e.g., number of drinks consumed during the heaviest drinking week), negative binomial distributions were specified (cf. Neal & Simons, 2007). To enhance interpretability of the regression coefficients, we used the log link for these models. For all variables, repeated measures on one case served as the clustering variable. Because the drinking outcome variables were longitudinal, unevenly spaced, and variably correlated, we used an unspecified correlation structure (Hardin & Hilbe, 2003). The unspecified correlation structure allowed for the correlation in the drinking outcome data at each time point to be taken into account in the overall model estimation.
Three separate models for each of the drinking outcome variables were used to test the relative predictive abilities of DBP, RTCQ, and DBID change scores. The DBP models included five predictors: a linear time variable that compared drinking outcomes at baseline, 1-, 6-, and 12-month follow-ups (coded as 0, 1, 6, and 12, respectively); a quadratic time variable, which took into account the fact that alcohol use over time often follows a curvilinear versus a straight linear path; the DBP change score, which reflected movement in the balance toward or away from change (1-month follow-up minus baseline DBP); and both Linear and Quadratic Time × DBP interactions. Similar but nonnested models involving change on the RTCQ scale and on the three DBID scales were also run and were subsequently compared on goodness of fit using the quasi-likelihood under the independence model information criterion (QICu; Hardin & Hilbe, 2003). Similar to the Akaike’s information criterion tests, statistically superior models have the lowest QICu scores (Hardin & Hilbe, 2003).
Quantity: Heaviest drinking week
The DBP model (QICu = 5124.78) for quantity during the heaviest drinking week was significant, Wald χ2(5, N = 143) = 14.16, p = .01, and statistically superior to both the RTCQ (QICu = 14458.37) and DBID (QICu = 17711.27) models. After controlling for time and baseline DBP, there were significant Linear Time × DPB (IRR = 0.78, SE = 0.07, p = .006) and Quadratic Time × DBP (IRR = 1.02, SE = 0.007, p = .007) interactions. As shown in Figure 1, all participants seemed to decrease their heavy drinking quantity between baseline and 1-month follow-up, possibly as a result of assessment reactivity. However, increases in DBP scores over the initial 1-month period predicted subsequent decreases in drinking quantity at the 6-month follow-up, an effect that decayed by the 12-month follow-up. Decreasing DBP scores tended to predict an increase in drinking quantity, which was followed by a reduction at the 12-month follow-up. On the other hand, relatively stable DBPs were associated with little change in drinking quantity.
Figure 1. Graph of mean quantity per heaviest drinking week by time point and level of change in decisional balance proportion (DBP). The DBP change scores represent change in the DBP between baseline and the 1-month follow-up. For clarity of presentation, groups were formed to represent different levels of change in the DBP. The stable DBP group in the figure is centered on the mean DBP change score (M = –0.004, SD = 0.20) in this sample and includes difference scores ranging from –0.20 to 0.20. These scores correspond to 1 SD below and 1 SD above the mean, respectively. The decreasing DBP group represents participants whose DBP change scores were at least 1 SD below the mean (DBP < –0.20), and the increasing DBP group represents participants whose DBP change scores were at least 1 SD above the mean (DBP > 0.20).
Frequency: Heaviest drinking week
The DBP model (QICu = 343.23) for frequency during the heaviest drinking week was statistically superior to both the RTCQ (QICu = 1100.58) and DBID (QICu = 1280.73) models, and was significant, Wald χ2(5, N = 143) = 15.36, p = .009. After controlling for time and baseline DBP, there were significant Linear Time × DPB (IRR = 0.84, SE = 0.05, p = .003) and Quadratic Time × DBP (IRR = 1.01, SE = 0.005, p = .005) interactions. After all groups initially decreased on heavy drinking frequency, increasing DBP over the initial 1-month period predicted greater decreases in drinking frequency during the heaviest drinking week—until the 12-month follow-up, when this decreasing effect decayed (see Figure 2). Decreasing DBP scores tended to predict increases in drinking frequency up to the 6-month time point, followed by a reduction at the 12-month follow-up. On the other hand, relatively stable DBPs were associated with little change in drinking frequency.
Figure 2. Graph of mean frequency per heaviest drinking week by time point and level of change in decisional balance proportion (DBP).
HED
The best model according to the QICu was the DPB model (QICu = 1481.03) compared with the RTCQ (QICu = 4069.71) and DBID (QICu = 4891.80) models; however, none of the omnibus model tests for HED outcomes were significant (all ps > .18).
RAPI
Compared with the RTCQ (QICu = 5375.68) and DBID (QICu = 6493.62) models, the DBP (QICu = 1975.94) model provided the best fit for self-reported alcohol-related problems, Wald χ2(5, N = 143) = 13.12, p = .02. After controlling for time and baseline DBP, there was a significant Linear Time × DPB interaction (IRR = 0.85, SE= 0.07, p = .046). As shown in Figure 3, all participants reported initial decreases in RAPI. Increases in DBP over the initial 1-month period, however, predicted a stable linear decrease in alcohol-related problems over the follow-up period. On the other hand, decreasing DBP scores tended to predict increases in alcohol-related problems up to the 6-month time point, followed by a downward trend at the 12-month follow-up. On the other hand, relatively stable DBPs are associated with relatively stable experience of alcohol-related problems.
Figure 3. Graph of mean alcohol-related problems (Rutgers Alcohol Problem Index [RAPI] score) by time point and level of change in decisional balance proportion (DBP).
DiscussionThis study provided an examination of a DBP as a measure of motivation to change among at-risk college drinkers. The DBP was generated from responses to an open-ended decisional balance worksheet assessing pros and cons of current drinking versus reducing drinking; it was constructed to reflect the extent to which the decisional balance was tipped toward change.
Convergent validity of the DBP was partially supported in this study. As predicted, initial DBP positively and significantly correlated with readiness to change as measured by the RTCQ. This finding provided convergent validity for the DBP as a measure of motivation to change. Furthermore, the weighted importance of current negative outcomes (DBID–Actual Cons) in participants’ decisions to drink was significantly, albeit weakly, associated with DBP scores. The somewhat weak effect may indicate that the DBID and the DBP measure overlapping yet distinct constructs, or it may reflect the relatively low reliability of the DBID–Actual Cons scale. The latter point is a psychometric issue that may have limited the power to optimally assess convergent validity with this scale.
The DBP was not significantly correlated with the pros scale of the DBID, which taps into the importance of positive aspects of “immoderate” drinking (e.g., “I feel happier when I drink”). However, this nonsignificant correlation is understandable: The DBP was constructed to reflect the tilt of the decisional balance toward change, not the status quo. In fact, the DBP represents the weight of the cons of current drinking plus the pros of changing relative to all fields in the balance. Because this proportion does not explicitly highlight the pros of current drinking, they may be “passively” outweighed.
The fact that the DBP was not correlated with the DBID–Potential Cons scale reflects the mixed findings regarding convergent validity of the DBP. However, this lack of association may also be interpreted in the context of the cognitive and memory literature, which asserts that individual drinking experience influences the accessibility of certain thoughts about alcohol use. Specifically, frequently encountered outcomes, such as those represented by the DBID–Actual Cons scale (e.g., “Drinking makes me feel out of control”), may be more accessible than hypothetical, potential outcomes, such as those measured by the DBID–Potential Cons scale (e.g., “Drinking could kill me”; Stacy et al., 1994). On the other hand, the DBP, which reflects an open-ended assessment of the decisional balance, may be even more accurate in assessing the most personally salient pros and cons of a behavior and may be more reflective of an individual’s current motivational state than the DBID scales.
Evidence for the discriminant validity of the DBP was obtained. Our findings confirmed that the DBP did not significantly correlate with demographic measures (i.e., gender, race/ethnicity, year in college, Greek membership, age) or social desirability. Perusal of the recent literature reveals no studies involving college students that have demonstrated associations between motivation and basic demographic variables or social desirability. Thus, consistent with empirical precedent and theory, the DBP appears to be an independent construct that may be used with a range of college students.
Findings in this study revealed that changes in the DBP predicted heavy drinking outcomes among at-risk college drinkers. In fact, the DBP models fit the data better than did models including RTCQ and DBID change scores as predictors of alcohol use over time. Furthermore, the DBP models were the only ones that yielded consistently significant predictors of drinking outcomes. Across three of the four drinking outcome variables, movement toward change (i.e., an increase in proportion of pros of reduced drinking and cons of current drinking to total item count from baseline to 1-month follow-up) predicted reductions in drinking over the initial follow-up period.
However, this finding was tempered by a curvilinear effect on two of the four drinking outcome variables, drinking quantity and frequency, which complicated the initial linear findings. After participants with increasing motivation to change initially decreased their heavy drinking quantity and frequency (and vice versa), there was an apparent decay in this effect at the 12-month follow-up. This regression to the mean, however, does not necessarily indicate that the DBP does not predict drinking as hypothesized. It is highly possible that the relatively short-term changes in the DBP that occurred initially from baseline to 1-month posttest may be most helpful in predicting more proximal changes in drinking. Thus, the more distal drinking measured 1 year after the initial motivation ratings may not be as reliably predicted by the DBP. This explanation fits with the literature on motivation, which suggests that motivation to change drinking is a fluid state rather than a stable trait (Miller, 1999). It also corresponds to the developmental literature that documents temporal variation in college student drinking patterns over weeks and years (Del Boca, Darkes, Greenbaum, & Goldman, 2004; Schulenberg, O’Malley, Bachman, Wadsworth, & Johnston, 1996). Further studies are needed to establish the temporal robustness of changes in motivation as measured by the DBP. Perhaps models assessing the time-varying and parallel change in DBP and drinking would be a helpful next step.
Conceptually, the DBP corresponds to decisional balance and motivation to change theory better than the other measures (i.e., RTCQ and DBID) tested as predictors in this study. Unlike previous studies involving decisional balance measures (e.g., Cunningham et al., 1997; King & DiClemente, 1993; Migneault et al., 1999; Velicer, DiClemente, Prochaska, & Brandenburg, 1985), the DBP integrates all four fields of the decisional balance: the pros and cons of both current drinking and drinking reduction. Because both theory and empirical findings have indicated that the consideration of the pros and cons of both current behavior as well as behavior change are key to accurate assessment of a person’s current motivation to change (Beyth-Marom et al., 1992; Janis & Mann, 1977), the use of the decisional balance worksheet may represent a step forward in decisional balance measurement. Furthermore, the fact that the input for the DBP is participant- instead of researcher-generated may make this measure a more accurate and personally relevant representation of one’s motivation to change than the RTCQ and DBID (Fischoff & Quadrel, 1991). Finally, the open-ended format of the decisional balance worksheet lends itself to potential qualitative as well as quantitative representations of motivation to change.
Limitations
This study comprised a nonrandom sample of at-risk college drinkers who had participated in a larger intervention trial. Considering the potential confounding effects of the nonrandom selection and exposure to brief interventions, it is necessary to replicate these results on a larger, randomly selected, nontreatment sample. Furthermore, the relatively homogeneous racial and ethnic composition of the current sample raises questions as to the external validity of the current findings. This sample consisted of predominantly White, non-Hispanic students; thus, further study of the DBP and its ability to predict drinking outcomes in more diverse samples is necessary to ensure its generalizability to other populations. We also recognize that the DBP focuses on numbers of items generated rather than their content. Although potential information may be gained by considering item content as well, the DBP has the advantage of rapid and reliable scoring. Despite these limitations, the current results provide additional support for and expansion on a quantification of a drinking decisional balance originally introduced by Collins and Carey (2005).
Conclusions
This study has provided evidence for the convergent, discriminant, and predictive validity of a new decisional balance measure of motivation to change drinking behavior among at-risk college drinkers. This study adds to the literature because previous conceptualizations of the decisional balance measure were researcher- instead of participant-generated and were limited in their scope (i.e., assessed either pros and cons of drinking or of changing behavior but not both). Furthermore, this measure appears to predict longitudinal drinking better than established, Likert scale measures of readiness to change and decisional balance. Larger scale studies should be conducted to provide additional support for the psychometric integrity, clinical utility, and generalizability of this decisional balance measure.
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Submitted: October 1, 2008 Revised: January 1, 2009 Accepted: February 20, 2009
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Source: Psychology of Addictive Behaviors. Vol. 23. (3), Sep, 2009 pp. 464-471)
Accession Number: 2009-14441-007
Digital Object Identifier: 10.1037/a0015841
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- A prospective study of pediatric loss of control eating and psychological outcomes.
- Authors:
- Tanofsky-Kraff, Marian. Section on Growth and Obesity, Program on Developmental Endocrinology and Genetics, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, U.S. Department of Health and Human Services, MD, US, mtanofsky@usuhs.edu
Shomaker, Lauren B.. Section on Growth and Obesity, Program on Developmental Endocrinology and Genetics, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, U.S. Department of Health and Human Services, MD, US
Olsen, Cara. Department of Preventive Medicine and Biometrics, Uniformed Services University of the Health Sciences, MD, US
Roza, Caroline A.. Section on Growth and Obesity, Program on Developmental Endocrinology and Genetics, Eunice Kennedy Shriver National Institute of Child Health and Human Development, MD, US
Wolkoff, Laura E.. Section on Growth and Obesity, Program on Developmental Endocrinology and Genetics, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, U.S. Department of Health and Human Services, MD, US
Columbo, Kelli M.. Section on Growth and Obesity, Program on Developmental Endocrinology and Genetics, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, U.S. Department of Health and Human Services, MD, US
Raciti, Gina. Section on Growth and Obesity, Program on Developmental Endocrinology and Genetics, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, U.S. Department of Health and Human Services, MD, US
Zocca, Jaclyn M.. Section on Growth and Obesity, Program on Developmental Endocrinology and Genetics, Eunice Kennedy Shriver National Institute of Child Health and Human Development, MD, US
Wilfley, Denise E.. Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, US
Yanovski, Susan Z.. Section on Growth and Obesity, Program on Developmental Endocrinology and Genetics, Eunice Kennedy Shriver National Institute of Child Health and Human Development, MD, US
Yanovski, Jack A., ORCID 0000-0001-8542-1637. Section on Growth and Obesity, Program on Developmental Endocrinology and Genetics, Eunice Kennedy Shriver National Institute of Child Health and Human Development, MD, US - Address:
- Tanofsky-Kraff, Marian, Department of Medical and Clinical Psychology, Uniformed Services University of the Health Sciences, 4301 Jones Bridge Road, Bethesda, MD, US, 20814-4712, mtanofsky@usuhs.edu
- Source:
- Journal of Abnormal Psychology, Vol 120(1), Feb, 2011. pp. 108-118.
- NLM Title Abbreviation:
- J Abnorm Psychol
- Page Count:
- 11
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- The Journal of Abnormal and Social Psychology; The Journal of Abnormal Psychology; The Journal of Abnormal Psychology and Social Psychology
- ISSN:
- 0021-843X (Print)
1939-1846 (Electronic) - Language:
- English
- Keywords:
- adolescence, binge eating disorder, childhood, depression, loss of control eating, psychosocial distress, emotional distress, anxiety, weight gain
- Abstract:
- Loss of control (LOC) eating in youth is associated cross-sectionally with eating-related and psychosocial distress and is predictive of excessive weight gain. However, few longitudinal studies have examined the psychological impact and persistence of pediatric LOC eating. We administered the Eating Disorder Examination and self-reported measures of depressive and anxiety symptoms to 195 boys and girls (mean age = 10.4 years, SD = 1.5) at baseline and again 4.7 years (SD = 1.2) later to 118 of these youth. Missing data were imputed. Baseline report of LOC was associated with the development of partial- or full-syndrome binge eating disorder (p = .03), even after accounting for the contribution of sex, race, baseline characteristics (age, disordered eating attitudes, and mood symptoms), body mass index growth between baseline and follow-up, and years in study. Half (52.2%; 95% CI [1.15, 6.22]) of children who endorsed experiencing LOC at baseline reported persistence of LOC at follow-up (p = .02). Compared with children who never reported LOC eating or reported LOC only at baseline, those with persistent LOC experienced significantly greater increases in disordered eating attitudes (ps < .001) and depressive symptoms (p = .027) over time. These data suggest that LOC eating in children is a problematic behavior that frequently persists into adolescence and that persistent LOC eating is associated with worsening of emotional distress. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Adolescent Psychopathology; *Anxiety; *Binge Eating; *Child Psychopathology; *Major Depression; Distress; Eating Behavior; Emotional States; Psychosocial Factors; Weight Gain
- Medical Subject Headings (MeSH):
- Adolescent; Affect; Body Composition; Body Mass Index; Child; Depression; Disease Progression; Feeding and Eating Disorders; Female; Humans; Logistic Models; Male; Obesity; Prospective Studies
- PsycINFO Classification:
- Eating Disorders (3260)
- Population:
- Human
Male
Female - Location:
- US
- Age Group:
- Childhood (birth-12 yrs)
School Age (6-12 yrs)
Adolescence (13-17 yrs) - Tests & Measures:
- Eating Disorder Examination-Version 12OD/C.2-Adapted for children
Standard Pediatric Eating Episode Interview
State–Trait Anxiety Inventory for Children A–Trait Scale
Children's Depression Inventory - Grant Sponsorship:
- Sponsor: National Institutes of Health, Eunice Kennedy Shriver National Institute of Child Health and Human Development, Intramural Research Program, US
Grant Number: Z01-HD-00641
Recipients: Yanovski, Jack A.
Sponsor: National Center on Minority Health and Health Disparities
Other Details: Supplemental funding
Recipients: Yanovski, Jack A.
Sponsor: National Institute of Diabetes and Digestive and Kidney Diseases
Grant Number: 1R01DK080906-01A1
Recipients: Tanofsky-Kraff, Marian
Sponsor: USUHS, US
Grant Number: R072IC
Recipients: Tanofsky-Kraff, Marian
Sponsor: National Institute of Mental Health, US
Grant Number: K24MH070446
Recipients: Wilfley, Denise E. - Methodology:
- Empirical Study; Quantitative Study
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- First Posted: Nov 29, 2010; Accepted: Aug 8, 2010; Revised: Aug 6, 2010; First Submitted: Jan 22, 2010
- Release Date:
- 20101129
- Correction Date:
- 20120827
- Digital Object Identifier:
- http://dx.doi.org/10.1037/a0021406
- PMID:
- 21114355
- Accession Number:
- 2010-24303-001
- Number of Citations in Source:
- 66
- Persistent link to this record (Permalink):
- http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2010-24303-001&site=ehost-live
- Cut and Paste:
- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2010-24303-001&site=ehost-live">A prospective study of pediatric loss of control eating and psychological outcomes.</A>
- Database:
- PsycINFO
A Prospective Study of Pediatric Loss of Control Eating and Psychological Outcomes
By: Marian Tanofsky-Kraff
Section on Growth and Obesity, Program on Developmental Endocrinology and Genetics, Eunice Kennedy Shriver National Institute of Child Health and Human Development;
National Institutes of Health, U.S. Department of Health and Human Services;
Department of Medical and Clinical Psychology, Uniformed Services University of the Health Sciences;
Lauren B. Shomaker
Section on Growth and Obesity, Program on Developmental Endocrinology and Genetics, Eunice Kennedy Shriver National Institute of Child Health and Human Development;
National Institutes of Health, U.S. Department of Health and Human Services;
Department of Medical and Clinical Psychology, Uniformed Services University of the Health Sciences
Cara Olsen
Department of Preventive Medicine and Biometrics, Uniformed Services University of the Health Sciences
Caroline A. Roza
Section on Growth and Obesity, Program on Developmental Endocrinology and Genetics, Eunice Kennedy Shriver National Institute of Child Health and Human Development
Laura E. Wolkoff
Section on Growth and Obesity, Program on Developmental Endocrinology and Genetics, Eunice Kennedy Shriver National Institute of Child Health and Human Development;
National Institutes of Health, U.S. Department of Health and Human Services;
Department of Medical and Clinical Psychology, Uniformed Services University of the Health Sciences
Kelli M. Columbo
Section on Growth and Obesity, Program on Developmental Endocrinology and Genetics, Eunice Kennedy Shriver National Institute of Child Health and Human Development;
National Institutes of Health, U.S. Department of Health and Human Services;
Department of Medical and Clinical Psychology, Uniformed Services University of the Health Sciences
Gina Raciti
Section on Growth and Obesity, Program on Developmental Endocrinology and Genetics, Eunice Kennedy Shriver National Institute of Child Health and Human Development;
National Institutes of Health, U.S. Department of Health and Human Services;
Department of Medical and Clinical Psychology, Uniformed Services University of the Health Sciences
Jaclyn M. Zocca
Section on Growth and Obesity, Program on Developmental Endocrinology and Genetics, Eunice Kennedy Shriver National Institute of Child Health and Human Development
Denise E. Wilfley
Department of Psychiatry, Washington University School of Medicine
Susan Z. Yanovski
Section on Growth and Obesity, Program on Developmental Endocrinology and Genetics, Eunice Kennedy Shriver National Institute of Child Health and Human Development;
Division of Digestive Diseases and Nutrition, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, U.S. Department of Health and Human Services
Jack A. Yanovski
Section on Growth and Obesity, Program on Developmental Endocrinology and Genetics, Eunice Kennedy Shriver National Institute of Child Health and Human Development
Acknowledgement: Jack A. Yanovski is a commissioned officer in the U.S. Public Health Service (PHS). The opinions and assertions expressed herein are those of the authors and are not to be construed as reflecting the views of the PHS, the Uniformed Services University of the Health Sciences (USUHS), or the U.S. Department of Defense. The authors report no competing interests. Research support was provided by National Institutes of Health Intramural Research Program Grant Z01-HD-00641 from the Eunice Kennedy Shriver National Institute of Child Health and Human Development to Jack A. Yanovski, supplemental funding from the National Center on Minority Health and Health Disparities to Jack A. Yanovski, National Institute of Diabetes and Digestive and Kidney Diseases Grant 1R01DK080906-01A1 to Marian Tanofsky-Kraff, USUHS Grant R072IC to Marian Tanofsky-Kraff, and National Institute of Mental Health Grant K24MH070446 to Denise E. Wilfley.
Binge eating disorder (BED), presently considered a form of “eating disorder not otherwise specified” (American Psychiatric Association [APA], 2000), is characterized by recurrent episodes of binge eating without regular compensatory behaviors. Binge episodes are defined as the consumption of a large amount of food during which a sense of loss of control (LOC) over eating is experienced (APA, 2000). BED is common among obese adults and is associated with dysfunctional eating attitudes, marked psychiatric distress (Wilfley, Wilson, & Agras, 2003), and impairments in physical health (Johnson, Spitzer, & Williams, 2001). With the inclusion of BED under consideration for the next edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM), establishing the clinical relevance of early eating patterns that may precede the development of the disorder is warranted.
Very limited longitudinal research has examined the precursors of BED. However, prospective studies in adolescent samples have identified some predictors of eating disorders that include binge eating in their symptomatology (Killen et al., 1996, 1994; McKnight Investigators, 2003). Three prospective studies of adolescent girls who did not carry a diagnosis of a subclinical or clinical eating disorder but who scored highly on measures of “weight concern” (Killen et al., 1996, 1994) and thin body preoccupation (McKnight Investigators, 2003) were found to be at high risk for developing partial- or full-syndrome eating disorders. A fourth study that examined overweight adolescent boys and girls replicated the finding that weight importance predicted disordered eating (Neumark-Sztainer, Wall, Story, & Sherwood, 2009). In this study, binge eating at baseline was not predictive of increases in disordered eating, but the study did not specifically examine the development of partial- or full-syndrome eating disorders. To our knowledge, no study has prospectively examined the development of disordered eating, including the development of BED, in younger children. Studying children prior to adolescence is especially warranted because the emergence of disordered eating behavior may begin as early as middle childhood (Tanofsky-Kraff, 2008).
Although youth often report binge eating, it is typically with less frequency than required to meet criteria for BED (Tanofsky-Kraff, 2008). Traditional interview assessments of binge eating behavior require that a binge episode be diagnosed only if the amount of food consumed is deemed “unambiguously large” (Bryant-Waugh, Cooper, Taylor, & Lask, 1996; Fairburn & Cooper, 1993). Given the varying energy needs of physically developing boys and girls, it is often difficult to make this determination for children of different ages. For example, the consumption of an entire large pizza by a child or adolescent of any age would likely be considered unambiguously large. By contrast, an amount of five slices of pizza eaten by a 16-year-old boy might be less clear and thus deemed an ambiguously large amount of food that, even if accompanied by a sense of LOC over eating, might not be classified as an objective binge eating episode. However, the experience of LOC over eating, regardless of whether the reported amount of food consumed is considered unambiguously or ambiguously large, is common in youth (Tanofsky-Kraff, 2008). Thus, the term LOC eating, as opposed to binge eating, is often adopted when working with children and adolescents in order to be inclusive of all episodes involving LOC (Shomaker et al., 2009).
Most of the existing pediatric literature describes children who report only one episode of LOC eating in the month prior to assessment or even less frequent episodes (Tanofsky-Kraff, 2008). In spite of its low frequency, LOC eating in children is associated with greater body mass index (BMI; kg/m2) and body fat mass as well as greater psychological distress compared with youth without such behaviors (Tanofsky-Kraff, 2008). Both reported binge (Field et al., 2003; Stice, Cameron, Killen, Hayward, & Taylor, 1999; Tanofsky-Kraff et al., 2006) and LOC (Tanofsky-Kraff, Yanovski, et al., 2009) eating in youth predict excessive body weight gain in longitudinal studies of children and adolescents. Although infrequent LOC episodes in young children might be anticipated to be precursors to exacerbated disordered eating, this supposition has not been well tested. None of the aforementioned prospective studies of eating disorders has examined LOC eating as a predictor (Killen et al., 1996, 1994; McKnight Investigators, 2003; Neumark-Sztainer et al., 2009).
Few theoretical models exist to describe the development of exacerbated disordered eating in young children. Adult theories suggest that the development and maintenance of binge eating episodes are tied to negative affect (e.g., Heatherton & Baumeister, 1991; Leon, Fulkerson, Perry, & Early-Zald, 1995). Binge eating may temporarily reduce momentary negative affective states by serving as a maladaptive coping strategy (Arnow, Kenardy, & Agras, 1992) or as an “escape” from self-awareness (Heatherton & Baumeister, 1991). Yet, a pernicious cycle is proposed to develop in which binge eating ultimately promotes worsening of mood (Barker, Williams, & Galambios, 2006). Longitudinal data examining the development of depression among adolescent girls indicate that symptoms of bulimia nervosa, including binge eating, predict the onset of major depression (Stice, Hayward, Cameron, Killen, & Taylor, 2000). Additionally, adolescent girls' depressive symptoms and binge eating may interact cyclically to maintain binge eating behaviors (Presnell, Stice, Seidel, & Madeley, 2009).
Although the temporal relationship between LOC eating and negative affect has not been well studied in youth, it might be expected that preadolescent children's LOC eating would precede and predict the development of exacerbated disordered eating and negative affect. Whereas the onset of LOC eating episodes are often reported during childhood (Tanofsky-Kraff, 2008), clinically relevant shape, weight, and eating concerns, as well as dietary restraint and depressive symptoms, typically do not emerge until adolescence (Lewinsohn, Rohde, Seeley, Klein, & Gotlib, 2000; Stice, Killen, Hayward, & Taylor, 1998). Although self-reports of negative moods states among non-treatment-seeking children with LOC eating patterns are consistently higher than among youth without LOC, scores are typically well below clinically significant levels (Tanofsky-Kraff, 2008). Children with LOC often report an experience of “numbing”—a feeling that they are unaware of what is going on in the moment—during LOC episodes (Tanofsky-Kraff et al., 2007), suggesting that children with LOC may have difficulty describing emotional states and that LOC behavior may evolve into serving a similar affective coping function as has been described among adults (Arnow et al., 1992; Heatherton & Baumeister, 1991). Likewise, the state negative affect that often ensues from children's LOC eating episodes (Tanofsky-Kraff et al., 2007; Tanofsky-Kraff, Marcus, Yanovski, & Yanovski, 2008) could be expected to promote affective distress and worsening of disordered eating attitudes and behaviors as youth enter adolescence.
We therefore tested a number of hypotheses about the prospective relationships among childhood LOC, disordered eating (restraint and eating, shape, and weight concerns), and negative affect (measured as depressive and anxiety symptoms). Our primary hypothesis was that childhood LOC eating would predict increased disordered eating and negative affect in adolescence. Furthermore, we expected that adolescents whose childhood LOC persisted (i.e., who reported LOC eating at both baseline and follow-up) would experience the poorest psychosocial functioning at follow-up, compared with those who never had LOC or whose childhood LOC resolved (i.e., who reported LOC episodes only at baseline). Finally, although the prevalence of eating disorders is relatively low during early adolescence (Lewinsohn, Striegel-Moore, & Seeley, 2000; Stice, Presnell, & Bearman, 2001), we investigated LOC eating among middle childhood youth as a precursor of partial- and full-syndrome BED during adolescence.
Method Participants
A non-treatment-seeking community sample of overweight and nonoverweight children (age 6–13 years) was studied between July 1999 and August 2009. By design, the sample was enriched for overweight children. Participants were recruited through two waves of notices mailed to first- through fifth-grade children in the Montgomery County and Prince George's County, Maryland, school districts; advertisements in local newspapers; and two mailings to local family physicians and pediatricians. Mailings to families and physicians requested participation of children willing to undergo phlebotomy (multiple blood draws) and imaging assessments (i.e., magnetic resonance imaging, pelvic ultrasound for girls, wrist x-ray for determination of bone age) for studies investigating hormones and metabolic functioning in children. Mailings also specified that no treatment would be offered. In addition to the questionnaire and interview assessments, for those families that agreed to participate, children also underwent air displacement plethysmography (Life Measurement Inc., Concord, CA), bioelectrical impedance, skinfold thickness to determine body composition, various measures of insulin sensitivity, repeated urine collections, and a medical history and a physical examination that involved determination of pubertal stage by a pediatric endocrinologist or nurse practitioner. Children's compensation ranged from $70 to $170 per visit depending on their level of participation, with the higher amount for the full assessment panel. All understood that they would not receive treatment as part of the study but would be financially compensated for their participation. Subjects were healthy and medication-free for at least 2 weeks prior to baseline evaluation. Children provided written assent and parents gave written consent for participation in the protocol. This study was approved by the Eunice Kennedy Shriver National Institute of Child Health and Human Development institutional review board.
Procedures
At baseline, the Eating Disorder Examination (Version 12OD/C.2; EDE; Fairburn & Cooper, 1993) adapted for children (Bryant-Waugh et al., 1996) was administered to each participant to determine the presence or absence of LOC eating, as described previously (Tanofsky-Kraff et al., 2007, 2004). On the basis of their responses to the child EDE, participants were categorized as engaging in objective binge episodes (unambiguous overeating with LOC), subjective binge episodes (LOC with ambiguous overeating or without overeating), objective overeating (overeating without LOC), or no episodes (normal meals involving neither LOC nor overeating) over the 28 days prior to assessment. As described previously (Tanofsky-Kraff, Faden, Yanovski, Wilfley, & Yanovski, 2005), all children were queried as to whether they had ever experienced LOC over eating (reporting at least one instance of an objective or a subjective binge episode ever, that is, LOC ever). The EDE generates four subscales—restraint (cognitive and behavioral dietary restraint), eating concern, shape concern, and weight concern—as well as a global score. These continuous variables were used as measures of children's disordered eating attitudes. Variables generating the subscales are independent of those identifying eating episodes (Tanofsky-Kraff et al., 2004). Tests of the EDE adapted for children have demonstrated good interrater reliability (Spearman rank correlations from .91 to 1.00) and discriminant validity in eating disordered samples and matched controls age 8–14 years (Christie, Watkins, & Lask, 2000). Among nonoverweight and overweight 6- to 13-year-olds, the child version of the EDE revealed excellent interrater reliability, with a Cohen's kappa for presence of the different eating episode categories of 1.00 (p < .001; Tanofsky-Kraff et al., 2004). The child version differs from the adult EDE only in that its script has been edited to make it more accessible to children age 8–14 years. Both versions generate the same eating episodes and subscales. In a sample including a broad age spectrum (8–18 years), the child and adult EDEs have been successfully combined (Tanofsky-Kraff et al., 2007). The EDE has good interrater reliability for all episode types (Spearman correlation coefficients: ≥ .70; Rizvi, Peterson, Crow, & Agras, 2000).
Data collection at follow-up was identical to the baseline assessment other than the adult EDE (Fairburn & Cooper, 1993), as opposed to the child version, was administered to all returning participants. Although children were seen annually for physical assessments, the EDE was administered only at the baseline and follow-up visits.
In addition, the Standard Pediatric Eating Episode Interview (Tanofsky-Kraff et al., 2007) was administered following the overeating section of the EDE to assess the contextual, behavioral, physical, and emotional aspects of aberrant eating episodes, including the associated features of binge episodes as defined by the DSM (4th ed., text rev.; DSM–IV–TR; APA, 2000). The Standard Pediatric Eating Episode Interview is designed to distinguish eating episodes with LOC from those without LOC (Tanofsky-Kraff et al., 2007).
Partial-syndrome BED was defined as reports of fewer than eight episodes per month for 6 months of LOC eating involving unambiguously large amounts of food (full-syndrome BED) but at least four episodes of LOC eating involving equivocally large and/or unambiguously large amounts of food on average per month, for at least 3 months. For partial- or full-syndrome BED, LOC episodes were characterized by at least three DSM–IV–TR-associated features of binge eating episodes (e.g., eating more rapidly than normal, eating when not physically hungry; APA, 2000). Although consistent with one study of children and adolescents (Tanofsky-Kraff et al., 2007), this definition is more conservative than that of most adult studies that have typically used a frequency criterion of at least one binge or LOC episode per month (Crow, Agras, Halmi, Mitchell, & Kraemer, 2002; Striegel-Moore et al., 2000).
At baseline and follow-up, participants completed the Children's Depression Inventory, a reliable and well-validated 27-item self-rated measure of depressive symptoms (Kovacs, 1982). Internal consistency reliability in this widely used measure is good, with coefficients ranging from .80 to .87 across samples of non-treatment-seeking community youth and children in school settings, age 7–17 years (Kovacs, 1982; Ollendick & Yule, 1990; Saylor, Finch, Spirito, & Bennett, 1984; Smucker, Craighead, Craighead, & Green, 1986). The total score was used. In the present sample, the Children's Depression Inventory demonstrated very good temporal stability (intraclass correlation = .41, p = .001). Children also completed the State–Trait Anxiety Inventory for Children A–Trait Scale, a 20-item self-report measure of trait anxiety that is widely used and psychometrically sound (Spielberger, Gorsuch, Lushene, Vagg, & Jacobs, 1983). Internal consistency for this well-validated questionnaire is very good, with correlations ranging from .78 to .91 in samples of elementary school and community children, none of whom were seeking psychiatric or medical treatment (Muris, Merckelbach, Ollendick, King, & Bogie, 2002; Papay & Spielberger, 1986; Spielberger et al., 1983). In the present sample, the measure demonstrated good temporal stability (intraclass correlation = .39, p = .002).
Height and weight were measured, and BMI was calculated, as previously described (Tanofsky-Kraff et al., 2004).
Statistical Analysis
Analyses were conducted with SPSS 16.0 or SAS 8.0. Skew and kurtosis were satisfactory on all variables, and outliers were adjusted to fall 1.5 times the interquartile range below or above the 25th or 75th percentile (Behrens, 1997). This strategy was used because it minimizes outliers' influence on the characteristics of the distribution, minimally changes the distribution overall, and avoids potential bias associated with eliminating outliers altogether. Demographic characteristics for those with missing data were examined with independent-samples t tests or chi-square analyses to test differences between children who did and did not complete a follow-up assessment. Missing data were imputed with the multiple imputation procedure in SAS. The missing data model included demographic variables of age (years), sex (male vs. female), race/ethnicity (non-Hispanic Caucasian vs. other), socioeconomic status (Hollingshead, 1975), and puberty according to the stages of Tanner (Marshall & Tanner, 1969, 1970); years in study; both baseline and follow-up values of BMI; disordered eating attitudes (restraint, eating concern, shape concern, and weight concern subscales and global score); LOC eating (presence vs. absence); and symptoms of depression and anxiety. Because of the large missing data fraction, 10 imputed data sets were produced (Allison, 2009; Schafer & Graham, 2002). Following standard procedures for multiple imputation, each data set was analyzed separately, and the results for the 10 data sets were combined via the MIANALYZE procedure in SAS.
Independent-samples t tests were used to compare youth with and without baseline LOC ever (presence vs. absence) on demographic characteristics, baseline and follow-up disordered eating, and depressive and anxiety symptoms. A series of analyses of covariance were conducted to examine whether the independent variable of baseline LOC ever (presence vs. absence) predicted the dependent variables of follow-up disordered eating attitudes (global, restraint, eating concern, shape concern, and weight concern), symptoms of depression, and symptoms of anxiety. We examined both “simple” adjusted models that accounted for years between baseline and follow-up and the respective baseline symptom (disordered eating, depressive symptoms, or anxiety symptoms), and models that adjusted for these covariates as well as baseline age (years), sex (male vs. female), race/ethnicity (non-Hispanic Caucasian vs. other), and BMI change between baseline and follow-up.
Logistic regression was used to describe the unadjusted odds of the dependent variable of LOC at follow-up (presence vs. absence) based on the independent variable of baseline LOC ever status (presence vs. absence). Odds of LOC at follow-up based on baseline LOC ever were also examined after adjusting for years between baseline and follow-up. Change in number of LOC eating episodes in the past month (defined continuously) between baseline and follow-up were described with paired-samples t tests among the whole sample, as well as just among children who reported persistent LOC eating (present at both baseline and follow-up). Repeated measures analysis of variance was used to adjust these comparisons for time between baseline and follow-up. Another series of analyses of covariance were conducted to investigate whether the independent variable of LOC persistence predicted the dependent variables of follow-up disordered eating attitudes (global, restraint, eating concern, shape concern, and weight concern), depressive symptoms, and anxiety symptoms. LOC persistence was defined categorically as (a) no LOC at baseline or follow-up (“never LOC”); (b) LOC ever at baseline, no LOC at follow-up (“resolved”); (c) no LOC ever at baseline, LOC at follow-up (“emergent”); and (d) LOC at both baseline and follow-up (“persistent”). Simple models were examined adjusting only for years between baseline and follow-up and the respective baseline symptom (disordered eating, depressive symptoms, or anxiety symptoms), and we also examined models that adjusted for these covariates as well as baseline age (years), sex (male vs. female), race/ethnicity (non-Hispanic Caucasian vs. other), and BMI change between baseline and follow-up. As recommended (Cohen, 1990; Saville, 1990), two-tailed, least squares difference tests were used to follow up on all pairwise comparisons between groups.
A logistic regression was conducted to assess predictors of the dependent variable of follow-up partial- or full-syndrome BED onset. The independent variables included LOC ever (presence vs. absence), disordered eating attitudes, and depressive and anxiety symptoms as predictors. The sets of covariates considered were years between baseline and follow-up alone, plus years between baseline and follow-up, baseline age (years), sex (male vs. female), race/ethnicity (non-Hispanic Caucasian vs. other), and BMI change between baseline and follow-up.
ResultsOne hundred ninety-five children (age 6–13 years) were seen for a baseline visit. Baseline data for 162 participants have been reported elsewhere (Tanofsky-Kraff et al., 2005, 2004). Of the 195 study participants, 118 (60.5%) completed a follow-up assessment an average of 4.7 years (SD = 1.2; range: 2.6–7.1) later. Compared with those who did not return for a follow-up assessment, youth who completed a follow-up visit were older at baseline (M = 10.38, SD = 1.49 vs. M = 9.63, SD = 1.63, p = .01) but did not significantly differ in sex, race, or baseline BMI; prevalence of LOC eating; disordered eating; or depressive or anxiety symptoms. As described above, data were imputed for the 77 children who did not return for a follow-up assessment, and combined results from the imputed data are presented.
At baseline, no child met full- or partial-syndrome criteria for a DSM–IV–TR eating disorder. Forty-six children (23.6%) reported having experienced LOC eating at least once in their lifetime. Of these participants, approximately half (n = 22, 47.8%) experienced at least one recent LOC episode in the month prior to assessment. In terms of the types and number of episodes in the past month, nine children reported only objective binge episodes (eight reported one episode, and one reported four episodes), 11 reported only subjective binge episodes (four children reported one episode, five reported two episodes, one reported three episodes, and one reported four episodes), and two reported one objective binge episode and one subjective binge episode in the prior month. In line with previously published data (Tanofsky-Kraff et al., 2005), youth with baseline LOC ever had significantly higher baseline BMI, BMI z scores (Kuczmarski et al., 2002), disordered eating attitudes, and depressive symptoms (ps < .01; see Table 1) compared with those who never experienced LOC. Compared with those without baseline LOC, youth with baseline LOC ever were also significantly younger at follow-up (p = .04) and continued to have significantly higher follow-up BMI, BMI z scores (Kuczmarski et al., 2002), and EDE global scores as well as higher restraint and shape and weight concern subscale scores (ps < .01; see Table 2).
Participant Characteristics at Baseline
Participant Characteristics at Follow-Up
Baseline LOC Eating as a Predictor of Follow-Up Disordered Eating and Negative Affect
In simple models adjusting only for years between baseline and follow-up and the respective baseline subscale score, baseline LOC ever was associated with greater follow-up EDE global, restraint, and shape and weight concern (ps < .02). Even after adjusting for additional covariates (baseline age, sex, race/ethnicity, and BMI change), baseline LOC ever predicted greater follow-up EDE global, restraint, and shape and weight concern scores (ps ≤ .04; see Figure 1). Baseline LOC ever significantly predicted follow-up anxiety in both the simple model adjusting for only baseline anxiety and years between baseline and follow-up (p = .03) and a model adjusting for all covariates (p = .05). Baseline LOC ever did not predict follow-up eating concern or depressive symptoms in either model (ps > .48).
Figure 1. Baseline loss of control eating and follow-up disordered eating attitudes. Experiencing loss of control over eating ever at baseline predicted increases on the Eating Disorder Examination restraint, shape concern, and weight concern subscales at follow-up. Analysis adjusted for baseline age, sex, race, years in study, body mass index growth, and respective baseline disordered eating subscale. Error bars indicate one standard error above and below the mean. N = 195, ps ≤ .04.
Descriptive Information on Persistence of LOC Eating
The presence of LOC ever at baseline was associated with more than a twofold greater likelihood of reported LOC at follow-up (OR = 2.35, 95% CI [1.10, 5.01], p = .03). Among those reporting LOC ever at baseline (n = 46), 52.2% (n = 24) reported persistent LOC eating in the month prior to follow-up assessment. In contrast, among those who did not report LOC ever at baseline (n = 149), only 30.9% (n = 46) reported emergent LOC at follow-up; the majority of this group (69.1%; n = 103) never reported LOC at either time. Of those endorsing LOC ever at baseline, 47.8% (n = 22) were resolved at follow-up. After adjusting for years between baseline and follow-up, baseline LOC ever remained a significant predictor of follow-up LOC (OR = 2.67, 95% CI [1.15, 6.22], p = .02). When persistence was characterized in terms of number of LOC episodes in the past month, LOC episodes per month for the whole sample increased from baseline (M = 0.19, SE = 0.04) to follow-up (M = 0.53, SE = 0.16, p = .04). Similarly, among the 24 youth who endorsed baseline LOC ever and follow-up LOC, the average number of LOC episodes significantly increased by 1.33 (SE = 0.53) episodes per month between baseline (M = 0.79, SE = 0.24) and follow-up (M = 2.12, SE = 0.40, p = .01). Significant increases in number of LOC episodes continued to be observed after accounting for years between baseline and follow-up, both in the whole sample and just among those with persistent LOC (ps < .04).
Persistence of LOC Eating as a Predictor of Disordered Eating and Negative Affect
In simple models (only covariates of years between baseline and follow-up and respective baseline subscale) and in models accounting for all covariates, there were significant overall effects for LOC persistence (never LOC, resolved, emergent, persistent) for all four follow-up EDE subscales (all main effects ps < .004): global score in model adjusting for all covariates, F(3, 143) = 12.12, p < .001 (see Figure 2A). In simple adjusted models, pairwise comparisons indicated that youth with persistent LOC had significantly higher scores than the never LOC group for EDE global score and all four EDE subscales (ps < .006); higher scores than the resolved group on EDE global score, eating concern, and shape concern (ps < .02); and higher scores than the emergent LOC group on EDE global score (p = .04). Youth with emergent LOC had higher scores on EDE global and all four subscales compared with the never LOC group (ps < .02), and emergent LOC youth also had higher EDE eating concern than youth with resolved LOC (p = .02). These significant differences remained after accounting for all covariates (ps < .05).
Figure 2. (A) Persistent loss of control (LOC) eating and follow-up disordered eating. Eating Disorder Examination global score at follow-up is shown. Children who reported ever experiencing LOC at baseline and LOC at follow-up (persistent) had significantly greater disordered eating at follow-up compared with youth who never reported LOC (never LOC), those who reported LOC at baseline only (resolved), and those who reported LOC at follow-up (emergent). Analysis adjusted for baseline age, sex, race, years in study, body mass index growth, and baseline global disordered eating. N = 195. Main effect p < .001. (B) Persistent LOC eating and follow-up depressive symptoms. Children who reported ever experiencing LOC at baseline and LOC at follow-up (persistent) had significantly greater total scores on the Children's Depression Inventory at follow-up compared with youth who never reported LOC (never LOC) and those who reported LOC at baseline only (resolved). Analysis adjusted for baseline age, sex, race, years in study, body mass index growth, and baseline depressive symptoms. Error bars indicate one standard error above and below the mean. N = 195. Main effect p = .03.
In a simple adjusted model and a model accounting for all covariates, there was an overall main effect of LOC persistence on symptoms of depression: in model adjusting for all covariates, F(3, 262) = 3.11, p = .03 (see Figure 2B). In a simple adjusted model, pairwise comparisons indicated that persistent LOC youth had higher follow-up depressive symptoms than the never LOC group (p = .03) and the resolved LOC group (p = .01). The same significant differences were observed when accounting for all covariates (ps < .05). LOC persistence showed nonsignificant trends for association with follow-up symptoms of anxiety both in the simple adjusted model, F(3, 311) = 2.44, p = .06, and after adjusting for all covariates, F(3, 300) = 2.28, p = .08.
Development of Partial- or Full-Syndrome BED
At follow-up, nine (4.5%) participants met partial- or full-syndrome criteria for BED (LOC episodes in the month prior to assessment: M = 5.7, SE = 1.3; range: 4–15). A subset (60%) of EDEs and Standard Pediatric Eating Episode Interview for these participants were taped and corated to examine interrater reliability for presence of the partial- and full-syndrome BED diagnosis. Cohen's kappa for the identification of partial- or full-syndrome BED was 1.00 (p < .001). In the model examining the development of partial- or full-syndrome BED at follow-up, only baseline reports of LOC ever served as a significant contributor (OR = 10.8, 95% CI [1.3, 88.1], p = .03), after accounting for all other variables in the model including BMI change, baseline symptoms of depression and anxiety, and all four EDE subscales at baseline. Because the sample of participants who developed BED was small, yielding a very wide confidence interval for the predictive value of LOC ever, we reanalyzed the model removing all nonsignificant variables except years in the study (sex, race, baseline age, depressive and anxiety symptoms, and disordered eating, BMI growth). Baseline reports of LOC ever continued to serve as a significant predictor of BED (OR = 5.07, 95% CI [1.1, 24.5], p = .04). This analysis suggested that youth who reported ever having experienced LOC at baseline were greater than 5 times more likely to develop partial- or full-syndrome BED at follow-up.
Follow-Up Exploratory Analyses
Secondary analyses were conducted to examine predictors of LOC onset at follow-up. To examine predictors of the dependent variable of follow-up LOC eating onset (presence vs. absence), we conducted binary logistic regression analyses with only the subset of children who did not endorse baseline LOC ever (n = 149). The independent variables were baseline disordered eating attitudes (global, restraint, shape concern, and weight concern), depressive symptoms, and anxiety symptoms. The sets of covariates considered were years between baseline and follow-up alone, and then years between visits and baseline age (years), sex (male vs. female), race/ethnicity (non-Hispanic Caucasian vs. other), and BMI change between baseline and follow-up. In the subset of children who reported no LOC ever at baseline, neither disordered eating attitudes nor symptoms of depression or anxiety predicted follow-up emergent LOC eating in any model (ps > .28).
To further explore the nature of the significant relationships observed between persistence of LOC and increases in disordered eating and negative affect, we conducted a series of follow-up linear multiple regressions regressing the dependent variables of change scores (follow-up minus baseline) in disordered eating (EDE global score, restraint, eating concern, shape concern, and weight concern subscales), depressive symptoms, and anxiety symptoms on the independent variables of change score in LOC eating episodes in the past month. In both simple adjusted models accounting only for years between baseline and follow-up and models accounting for all covariates, there was no significant association between change in number of LOC episodes and any of the dependent variables (all ps > .50).
DiscussionUsing the Eating Disorder Examination, a well-accepted interview assessment method for identification of eating disorders, we found, among a sample of non-treatment-seeking children age 6–13 years who were reassessed approximately 5 years later, that those who reported having ever experienced loss of control over their eating at baseline were significantly more likely to develop partial- or full-syndrome binge eating disorder than children who had never experienced LOC. The presence of reported LOC eating at baseline also predicted increases in disordered eating attitudes and symptoms of anxiety at follow-up but not in depressive symptoms. However, children with reported LOC eating at both baseline and follow-up experienced the greatest disordered eating within all domains and the greatest increases in symptoms of depression.
The finding that childhood LOC is a significant indicator for the development of persistent or worsened disordered eating attitudes and anxiety symptoms almost 5 years later is novel and may have important clinical implications. LOC eating has been consistently associated in cross-sectional studies with increased psychological distress (Tanofsky-Kraff, 2008) and found to be predictive of excess body weight gain in young children (Tanofsky-Kraff, Yanovski, et al., 2009). Our data suggest that infrequent reports of LOC may also be a precursor for increased disordered eating and the development of partial- or full-syndrome BED. Some preliminary studies indicate that reducing binge and LOC eating may be effective for both weight loss (Jones et al., 2008) and obesity prevention (Tanofsky-Kraff, Wilfley, et al., 2009) in youth. It remains to be determined whether such interventions may simultaneously prevent the development of eating disorders.
Almost 5% of the sample developed partial- or full-syndrome BED at follow-up. Although there are limited data on the prevalence of BED in early adolescence, rates for LOC eating, typically defined as one episode in the month prior to assessment or even less frequent LOC episodes, range from approximately 2% to 40%, with higher estimates among weight-loss-treatment-seeking (vs. community) samples and higher prevalence among adolescents (vs. children) and in studies using questionnaires rather than diagnostic interviews (Tanofsky-Kraff, 2008). In adult samples, the rate of DSM–IV–TR BED is approximately 3% in the general population (Hudson, Hiripi, Pope, & Kessler, 2007), with higher estimates among individuals seeking weight-loss treatment (de Zwaan, 2001). Although our sample was not seeking weight loss treatment, it differed from other pediatric community samples because it was enriched for overweight youth, which likely contributed to the relatively higher rate of partial- and full-syndrome BED found at follow-up.
It is notable that only LOC, as opposed to weight, shape, and eating concerns, or negative affect, was a clinically relevant behavior for the development of BED. This finding is in contrast to studies that examined the development of any eating disorder (i.e., not exclusively BED) during adolescence, where body shape and weight concerns were important predictors for later eating disorders (Killen et al., 1996, 1994; McKnight Investigators, 2003). Although the aforementioned studies included different covariates than those included in the present analyses, our findings generally did not differ whether we conducted simple analyses adjusting only for years to follow-up and the respective baseline variable or analyses including all covariates. It should be noted that these investigations did not examine baseline LOC eating as a predictor. Our results may be related to the developmental differences in emotional and cognitive constructs that are found during middle childhood versus adolescence. With regard to symptoms of depression and anxiety, it is possible that at baseline the children in our sample had yet to develop symptoms of mood problems. Given that our sample consisted of non-treatment-seeking children without a current eating disorder, depressive symptoms and trait anxiety may not have been a common component of their baseline psychological status. Indeed, the baseline scores reported by the sample were well below clinically significant cutoffs (Kazdin, Colbus, & Rodgers, 1986; Lobovits & Handal, 1985; Spielberger et al., 1983). LOC eating may conceivably be an early behavioral marker preceding, or even possibly masking, disordered eating attitudes and negative affect. In a multisite study of children and adolescents, the experience of numbing was highly correlated with LOC eating episodes (Tanofsky-Kraff et al., 2007). These data point to another possibility; namely, that children reporting LOC may be less aware of their emotional experiences than of their actual behaviors. Both suppositions may potentially support “escape theory,” which views binge eating as a motivated attempt to escape aversive self-awareness or emotional distress (Heatherton & Baumeister, 1991). Regardless, during middle childhood, reported eating patterns may thus be more salient in predicting later exacerbated disordered eating in adolescence than reports of either body weight or mood-related distress.
Children who reported having engaged in LOC eating even one time at baseline were at greater risk for the development of increased shape and weight concerns at follow-up than those who did not report such episodes. Notably, overconcern with body shape and weight is a key feature across the eating disorder diagnoses (Wilfley, Schwartz, Spurrell, & Fairburn, 2000) and is associated with greater impairment when present in individuals with BED (Grilo, Masheb, & White, 2010). However, those youth who reported LOC at both baseline and follow-up were most at risk for exacerbated disordered eating and increases in depressive symptoms, with the average unadjusted Children's Depression Inventory total score nearing the clinical concern cutoff of 12 (Kazdin et al., 1986; Lobovits & Handal, 1985). These findings persisted even after accounting for the contribution of BMI growth, suggesting that the increased distress predicted by LOC cannot be attributed solely to excess weight gain. Coupled with data indicating that binge and LOC eating have been shown to predict excess weight and fat gain in youth (Field et al., 2003; Stice et al., 1999; Tanofsky-Kraff et al., 2006; Tanofsky-Kraff, Yanovski, et al., 2009), our findings support proposals that obesity and eating disorder interventions should be coordinated (Neumark-Sztainer et al., 2009; Yanovski, 2003).
Our findings from the present and prior (Tanofsky-Kraff, Yanovski, et al., 2009) studies suggest that in young children, the report of infrequent objective or subjective binge eating episodes that together constitute LOC eating is predictive of untoward results. Even quite rare LOC eating episodes appear to be associated with later adverse psychological outcomes. Although this may be the result of an overly conservative estimate of what constitutes a “large” amount of food in growing children, the experience of LOC eating may identify youth who are consuming more than they want to eat. It is possible that individuals with reported LOC have some disturbance in satiety signaling or reward activation pathways (Adam & Epel, 2007; Davis et al., 2008). The relationship between LOC and eating in the absence of hunger has been documented (Tanofsky-Kraff, Ranzenhofer, et al., 2008). Further, data indicate that youth with LOC tend to consume highly palatable dessert and snack-type foods (Hilbert, Tuschen-Caffier, & Czaja, 2010; Tanofsky-Kraff, McDuffie, et al., 2009; Theim et al., 2007); thus, LOC behaviors may be related to variations in opioid (reward) pathways, stemming from genetic polymorphisms in opioid–dopamine receptor genes, as suggested in studies of adults with and without BED (Davis et al., 2008). Though little is known about the neural circuitry of LOC eating in children, there are neuroimaging data in emotional eaters (Bohon, Stice, & Spoor, 2009) and individuals with BED (Schienle, Schäfer, Hermann, & Vaitl, 2009) to suggest that the processing of food-related stimuli may be fundamentally different in persons with and without aberrant eating patterns. Longitudinal data are needed to explore these potential neural pathways underlying LOC eating and the development of BED.
Strengths of this investigation include the prospective design, the use of a structured interview, and measured heights and weights. Limitations include the fact that children who reported LOC eating ever (i.e., prior to the EDE time frame) did not supply information on size of the meal consumed, the use of questionnaires to assess depressive and anxiety symptoms, and the relatively small sample at follow-up. Moreover, there was only one EDE follow-up assessment, which precluded an examination of the average duration of LOC eating during the course of the follow-up period. Because of this limitation, we are also unable to explore why approximately 48% of those with baseline LOC eating no longer reported such behaviors at follow-up. In addition, despite the fact that children were not seeking treatment, they were not recruited in a population-based fashion, were enriched for overweight, and had to travel to the National Institutes of Health Clinical Research Center to participate. These factors likely limit the generalizability of our findings to young children willing to participate in research that involves both physical and psychological measurements. Indeed, studying young children with various physical assessments, especially those that are often perceived as invasive (i.e., phlebotomy), likely renders a sample that is not reflective of the general population. Nevertheless, families were recruited for metabolic studies and understood that they would not receive treatment, and neither parents nor children had prior knowledge that they would be asked about disordered eating behaviors and attitudes.
In conclusion, among a nontreatment sample of overweight and nonoverweight middle-childhood youth, those who reported having engaged in LOC eating are more likely to develop worsening disordered eating. Moreover, those whose LOC eating persists over time appear to be at higher risk for increases in symptoms of depression. Future investigation is necessary to determine whether interventions aimed at reducing LOC eating during middle childhood are efficacious in the prevention of both eating disorders and excessive weight gain.
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Submitted: January 22, 2010 Revised: August 6, 2010 Accepted: August 8, 2010
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Source: Journal of Abnormal Psychology. Vol. 120. (1), Feb, 2011 pp. 108-118)
Accession Number: 2010-24303-001
Digital Object Identifier: 10.1037/a0021406
Record: 8- Title:
- A randomized clinical trial of Motivational Interviewing to reduce alcohol and drug use among patients with depression.
- Authors:
- Satre, Derek D.. Department of Psychiatry, University of California, CA, US, dereks@lppi.ucsf.edu
Leibowitz, Amy. Division of Research, Kaiser Permanente Northern California Region, CA, US
Sterling, Stacy A.. Division of Research, Kaiser Permanente Northern California Region, CA, US
Lu, Yun. Division of Research, Kaiser Permanente Northern California Region, CA, US
Travis, Adam. Department of Psychiatry, Kaiser Permanente Southern Alameda, CA, US
Weisner, Constance. Department of Psychiatry, University of California, CA, US - Address:
- Satre, Derek D., University of California, San Francisco, 401 Parnassus Avenue, Box 0984, San Francisco, CA, US, 94143, dereks@lppi.ucsf.edu
- Source:
- Journal of Consulting and Clinical Psychology, Vol 84(7), Jul, 2016. pp. 571-579.
- NLM Title Abbreviation:
- J Consult Clin Psychol
- Page Count:
- 9
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- Journal of Consulting Psychology
- Other Publishers:
- US : American Association for Applied Psychology
US : Dentan Printing Company
US : Science Press Printing Company - ISSN:
- 0022-006X (Print)
1939-2117 (Electronic) - Language:
- English
- Keywords:
- depression, alcohol, cannabis, hazardous drinking, motivational interviewing
- Abstract (English):
- Objective: This study examined the efficacy of Motivational Interviewing (MI) to reduce hazardous drinking and drug use among adults in treatment for depression. Method: Randomized controlled trial based in a large outpatient psychiatry program in an integrated health care system in Northern California. The sample consisted of 307 participants ages 18 and over who reported hazardous drinking, drug use (primarily cannabis) or misuse of prescription drugs in the prior 30 days, and who scored ≥5 on the Patient Health Questionnaire (PHQ-9). Participants were randomized to receive either 3 sessions of MI (1 in person and 2 by phone) or printed literature about alcohol and drug use risks (control), as an adjunct to usual outpatient depression care. Measures included alcohol and drug use in the prior 30 days and PHQ-9 depression symptoms. Participants completed baseline in-person interviews and telephone follow-up interviews at 3 and 6 months (96 and 98% of the baseline sample, respectively). Electronic health records were used to measure usual care. Results: At 6 months, MI was more effective than control in reducing rate of cannabis use (p = .037); and hazardous drinking (≥4 drinks in a day for women, ≥5 drinks in a day for men; p = .060). In logistic regression, assignment to MI predicted lower cannabis use at 6 months (p = .016) after controlling for covariates. Depression improved in both conditions. Conclusions: MI can be an effective intervention for cannabis use and hazardous drinking among patients with depression. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
- Impact Statement:
- What is the public health significance of this article?—Hazardous drinking and drug use are common among patients with depression. Results of this study indicate that motivational interviewing is a promising treatment approach to assist these patients. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Alcohol Abuse; *Drug Abuse; *Drug Rehabilitation; *Major Depression; *Motivational Interviewing; Cannabis; Prescription Drugs
- PsycINFO Classification:
- Drug & Alcohol Rehabilitation (3383)
- Population:
- Human
Male
Female
Outpatient - Location:
- US
- Age Group:
- Adulthood (18 yrs & older)
Young Adulthood (18-29 yrs)
Thirties (30-39 yrs)
Middle Age (40-64 yrs)
Aged (65 yrs & older) - Tests & Measures:
- Alcohol Readiness Ruler
Cannabis Readiness Ruler
Addiction Severity Index DOI: 10.1037/t00025-000
Patient Health Questionnaire-9 DOI: 10.1037/t06165-000 - Grant Sponsorship:
- Sponsor: National Institutes of Health
Grant Number: R01AA020463; P50DA009253
Recipients: No recipient indicated - Methodology:
- Clinical Trial; Empirical Study; Quantitative Study
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- First Posted: Mar 17, 2016; Accepted: Feb 8, 2016; Revised: Jan 20, 2016; First Submitted: Jun 17, 2015
- Release Date:
- 20160317
- Correction Date:
- 20160623
- Copyright:
- American Psychological Association. 2016
- Digital Object Identifier:
- http://dx.doi.org/10.1037/ccp0000096
- PMID:
- 26985728
- Accession Number:
- 2016-13461-001
- Number of Citations in Source:
- 45
- Persistent link to this record (Permalink):
- http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2016-13461-001&site=ehost-live
- Cut and Paste:
- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2016-13461-001&site=ehost-live">A randomized clinical trial of Motivational Interviewing to reduce alcohol and drug use among patients with depression.</A>
- Database:
- PsycINFO
Record: 9- Title:
- A tutorial on count regression and zero-altered count models for longitudinal substance use data.
- Authors:
- Atkins, David C.. Department of Psychiatry and Behavioral Sciences, University of Washington, Seattle, WA, US, datkins@uw.edu
Baldwin, Scott A.. Department of Psychology, Brigham Young University, Provo, UT, US
Zheng, Cheng. Department of Biostatistics, University of Washington, Seattle, WA, US
Gallop, Robert J.. Applied Statistics Program, West Chester University, West Chester, PA, US
Neighbors, Clayton. Department of Psychology, University of Houston, TX, US - Address:
- Atkins, David C., Department of Psychiatry and Behavioral Sciences, 1100 Northeast 45th Street, Suite 300, Seattle, WA, US, 98105, datkins@uw.edu
- Source:
- Psychology of Addictive Behaviors, Vol 27(1), Mar, 2013. pp. 166-177.
- NLM Title Abbreviation:
- Psychol Addict Behav
- Page Count:
- 12
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- Bulletin of the Society of Psychologists in Addictive Behaviors; Bulletin of the Society of Psychologists in Substance Abuse
- Other Publishers:
- US : Educational Publishing Foundation
Society of Psychologists in Addictive Behaviors - ISSN:
- 0893-164X (Print)
1939-1501 (Electronic) - Language:
- English
- Keywords:
- count regression, longitudinal data, multilevel models, addictive behaviors, substance use
- Abstract:
- [Correction Notice: An Erratum for this article was reported in Vol 27(2) of Psychology of Addictive Behaviors (see record 2013-21666-002). The URL for the supplemental material was incorrect throughout the text due to a production error. Supplemental material for this article is available at: http://dx.doi.org/10.1037/a0029508.supp. The online version of this article has been corrected.] Critical research questions in the study of addictive behaviors concern how these behaviors change over time: either as the result of intervention or in naturalistic settings. The combination of count outcomes that are often strongly skewed with many zeroes (e.g., days using, number of total drinks, number of drinking consequences) with repeated assessments (e.g., longitudinal follow-up after intervention or daily diary data) present challenges for data analyses. The current article provides a tutorial on methods for analyzing longitudinal substance use data, focusing on Poisson, zero-inflated, and hurdle mixed models, which are types of hierarchical or multilevel models. Two example datasets are used throughout, focusing on drinking-related consequences following an intervention and daily drinking over the past 30 days, respectively. Both datasets as well as R, SAS, Mplus, Stata, and SPSS code showing how to fit the models are available on a supplemental website. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Drug Addiction; *Drug Usage; *Models; *Statistical Regression; Longitudinal Studies; Statistical Data
- Medical Subject Headings (MeSH):
- Humans; Models, Statistical; Regression Analysis; Statistics as Topic; Substance-Related Disorders
- PsycINFO Classification:
- Statistics & Mathematics (2240)
Substance Abuse & Addiction (3233) - Grant Sponsorship:
- Sponsor: National Institute on Alcohol Abuse and Alcoholism
Grant Number: AA016099 and AA014576
Recipients: Neighbors, Clayton (Prin Inv)
Sponsor: National Institute on Alcohol Abuse and Alcoholism
Grant Number: AA019511
Other Details: David C. Atkins’ time was supported by the aforementioned grant.
Recipients: Mun, Eun-Young (Prin Inv) - Supplemental Data:
- Appendixes Internet
Tables and Figures Internet - Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- First Posted: Aug 20, 2012; Accepted: Jun 18, 2012; Revised: Jun 15, 2012; First Submitted: Nov 22, 2011
- Release Date:
- 20120820
- Correction Date:
- 20130701
- Copyright:
- American Psychological Association. 2012
- Digital Object Identifier:
- http://dx.doi.org/10.1037/a0029508; http://dx.doi.org/10.1037/a0029508.supp(Supplemental)
- PMID:
- 22905895
- Accession Number:
- 2012-22398-001
- Number of Citations in Source:
- 32
- Persistent link to this record (Permalink):
- http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2012-22398-001&site=ehost-live
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- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2012-22398-001&site=ehost-live">A tutorial on count regression and zero-altered count models for longitudinal substance use data.</A>
- Database:
- PsycINFO
A Tutorial on Count Regression and Zero-Altered Count Models for Longitudinal Substance Use Data
By: David C. Atkins
Department of Psychiatry and Behavioral Sciences, University of Washington;
Scott A. Baldwin
Department of Psychology, Brigham Young University
Cheng Zheng
Department of Biostatistics, University of Washington
Robert J. Gallop
Applied Statistics Program, West Chester University
Clayton Neighbors
Department of Psychology, University of Houston
Acknowledgement: We thank Eun-Young Mun, Zac Imel, Isaac Rhew, Jennifer Kirk, and an anonymous reviewer for helpful feedback that improved the manuscript in numerous ways. The example data included in the article were collected via the National Institute on Alcohol Abuse and Alcoholism (NIAAA) Grants AA016099 and AA014576 (Clayton Neighbors, PI), and David C. Atkins' time was supported in part by NIAAA Grant AA019511 (Eun-Young Mun, PI).
What is the impact of personalized normative feedback on drinking related problems in college students over time? How does weekend versus weekday drinking vary by gender and fraternity/sorority status when assessed on a daily basis? Many questions about alcohol and substance use focus on change across time, and the methods used to analyze these questions need to account for the longitudinal nature of the data. Generalized linear mixed models (GLMMs; Gelman & Hill, 2007; Hedeker & Gibbons, 2006; also called hierarchical [or multilevel] generalized linear modeling, Raudenbush & Bryk, 2002; Snijders & Bosker, 1999) are increasingly common analytic approaches for longitudinal data, given their flexible handling of unbalanced repeated measures (i.e., individual participants may have unique numbers and timings of assessments) and the widespread availability of software for estimating such models. Moreover, GLMMs are appropriate for continuous as well as discrete outcomes.
However, the distributions of alcohol and substance abuse outcomes have characteristic shapes: They are often positively skewed and bounded by zero. Moreover, there can be a large stack of data points at zero, indicating individuals and/or occasions without drinking, use, or related problems. These distributions reflect that alcohol and substance use outcomes are often count data, representing a total number of something, be it drinks, days using, or number of problems. Except in special circumstances (e.g., specially selected samples with high drinking or drug use), statistical models that assume normally distributed residuals will provide poor fit to such data and will lead to incorrect confidence intervals and p values. Instead, count regression approaches such as Poisson or negative binomial regression or zero-altered count models (e.g., zero-inflated or hurdle models) are much more appropriate for these types of data (Atkins & Gallop, 2007; Coxe, West, & Aiken, 2009; Hilbe, 2011; Neal & Simons, 2007; Simons, Neal, & Gaher, 2006).
In the past, addictions researchers have often ignored (or not been aware of) violations of distributional assumptions or have attempted to deal with them in nonoptimal ways. Count regression models are beginning to be applied to addictions data (e.g., Gaher & Simons, 2007; Lewis et al., 2010), but accessible resources on how to apply these models to longitudinal data are scarce. The present article provides a tutorial in analytic methods for count data from longitudinal studies, focusing on extensions to GLMMs for count outcomes. We use two examples from our research to illustrate the need for, and application of, longitudinal count models. Data and computer code to run the analyses in R, Mplus, SAS, Stata, and SPSS are available on a supplementary website (http://depts.washington.edu/cshrb/newweb/statstutorials.html), though note that not all software can run all models that are covered here at present time. The outline of the article is as follows: Introduction to example data and research questions, brief overview of count regression models, GLMMs for count regression models, analyses and interpretation of example data, and discussion of software and practical issues in using these methods. In addition, there is a technical appendix containing important, but more advanced, material (see online supplemental material; http://dx.doi.org/10.1037/a0029508.supp). We assume that readers have a basic familiarity with linear mixed models (i.e., hierarchical linear or multilevel models assuming normally distributed errors) and count regression models, though both are introduced briefly here and introductory resources are highlighted throughout.
Motivating ExamplesThe first example dataset is drawn from an intervention study aimed at reducing problematic drinking in college students (Neighbors et al., 2010). The current article focuses on gender differences across 2 years in alcohol-related problems, as measured by the Rutgers Alcohol Problem Index (RAPI; White & Labouvie, 1989). The dataset includes 3,616 repeated measures across five time points from 818 individuals. The second dataset involves intensive, daily assessment of drinking. The data come from a larger intervention study of event-specific prevention (i.e., drinking related interventions for 21st birthdays and spring break), but the current data are observational. Specifically, these data record the number of drinks for each day over approximately the last 30 days for 980 individuals (23,992 total person-days), as measured by the timeline follow-back interview (TLFB). These data came from a survey study of 21st birthday drinking and consequently include some extreme drinking events relative to a random sample of student's drinking (Neighbors et al., 2011). Analyses focus on drinking differences by gender, Greek status (i.e., fraternity/sorority membership), and weekend (Thurs–Sat) versus weekday (Sun–Wed). (Note that Thursday was included as part of the “weekend” given that drinking on Thursday was more similar to Friday and Saturday drinking than other days of the week.)
Count Regression ModelsCount variables are often positively skewed and often include many observations at zero. The top row of Figure 1 displays (unconditional) distributions of drinking (TLFB) and alcohol problems (RAPI), which are strongly skewed with a mode of zero. The bottom row of Figure 1 shows histograms of residuals from regressing the RAPI on gender and time assuming normally distributed residuals (i.e., ordinary least squares [OLS] regression): on the left without any transformation and on the right with a log transformation of the RAPI. These plots show that skewed count regression outcomes will rarely meet the distributional assumptions of OLS regression or linear mixed models. Moreover, count outcomes will also typically violate the equal variances (i.e., homoskedasticity) assumption of linear models as count outcomes have a direct relationship between their mean and variance, where higher levels of the outcome have greater variance. Although transforming the outcome is a commonly suggested strategy for skewed data, a stack of zeroes will not be smoothed out by a transformation. Moreover, focusing on the TLFB data, the distribution shown in Figure 1 is suggestive of two different types of associations or research questions: (a) what is related to no drinking versus any drinking (i.e., zero vs. nonzero), and (b) what is related to the amount of drinking when there is drinking? Although the zero/nonzero aspects of the RAPI data are not quite as notable, a similar set of questions could be asked of the RAPI data. As will be described later, zero-inflated and hurdle models have submodels that focus on these two questions. Prior to introducing count regression models, we consider the qualities of the example data that are related to the questions just posed.
Figure 1. Plots of frequency counts of daily drinks from timeline follow-back (TLFB; upper left) and Rutgers Alcohol Problems Index (RAPI; upper right). Residuals from fitting an ordinary least squares regression to the RAPI or log-transformed RAPI are in lower left and lower right, respectively.
Consider how the proportion of individuals drinking versus not drinking and number of drinks on drinking days are related to covariates in the TLFB data. Figure 2 presents means and 95% confidence intervals (CIs) for number of drinks on drinking days (top half of graph) and proportion of days drinking (bottom half of graph) by weekday versus weekend, Greek status, and gender.
Figure 2. Means and 95% confidence intervals for drinking on drinking days (top row) and proportion of individuals drinking (bottom row). Means are stratified by weekend versus weekday, male versus female, and fraternity/sorority member or not.
There are strong differences in the proportion of people drinking for weekend versus weekday, with less prominent differences for number of drinks when drinking. Moreover, from this descriptive view, the number of drinks is highly related to fraternity/sorority status, whereas proportion of people drinking does not appear as strongly related to Greek status.
Figure 3 shows a similar set of plots for the RAPI by gender across time. As with the TLFB drinking data, patterns of association appear different across the two aspects of the outcome. Over the five assessments (and following intervention for most participants), the proportion of individuals with any alcohol problems is dropping, with a greater proportion of men consistently reporting more alcohol problems (right panel). When examining the number of alcohol problems (given some problems), we see evidence for divergence between the sexes, with women showing slight decreases in number of alcohol problems, whereas men appear to show slight increases, though with notable variability as seen in the confidence intervals (left panel). Thus, these graphs serve to highlight that different associations can occur with outcomes with notable zeroes (i.e., whether there is any drinking [zero vs. not zero] and amount of drinking when any drinking).
Figure 3. Means and 95% confidence intervals for number of problems when there are any problems (left) and proportion of individuals reporting any drinking-related problems (right). Means are stratified by assessment period and gender.
Before moving on to discuss GLMMs for count outcomes, we briefly introduce count regression models (more thorough introductions can be found in articles by Atkins & Gallop, 2007, and Coxe et al., 2009, and the book by Hilbe, 2011). The basic count regression model is Poisson regression, which is one of the generalized linear models (McCullagh & Nelder, 1989). There are two critical differences between OLS regression and Poisson regression. First, the outcome (conditional on covariates) is assumed to be distributed as a Poisson random variable as opposed to a Normal random variable. The top row of Figure 4 depicts three different Poisson distributions, with varying means (denoted by the Greek letter mu). This figure underscores that the Poisson distribution is a discrete distribution for non-negative integers, the exact qualities of count variables (i.e., a count variable cannot be negative or fractional). Second, in Poisson regression the linear predictor of the regression model (i.e., the right-hand side of the regression equation) is connected to the outcome via a natural logarithm link function. Although this is not identical to transforming the outcome, it does mean that the regression coefficients from a Poisson model are on a log scale. Similar to logistic regression, raw coefficients are typically raised to the base e (i.e., exponentiated, the antilog function) and interpreted as rate ratios. Like odds ratios (ORs), rate ratios are inversely proportional around one (i.e., a rate ratio of 3 is equal in strength but opposite in direction to a rate ratio of 1/3). Rate ratio interpretation will be described in greater detail later, in the applications section.
Figure 4. Plots of frequency counts of data simulated from Poisson distributions with three different means (top row). The bottom row contains frequency counts of data simulated from negative binomial distributions with the same mean but varying dispersion.
The Poisson distribution (and regression) has an Achilles' heel of sorts in that it has the property that the mean equals the variance. In real data the variance often far exceeds the mean, and we would say that the data are overdispersed relative to the Poisson distribution. Using descriptive statistics, dispersion is typically defined by the ratio of the variance to the mean. Thus, a Poisson distribution assumes a dispersion parameter of 1 (also called equidispersion). A dispersion parameter of 3 would indicate a variance value of three times the mean, which would (descriptively) indicate overdispersion. Both of our example datasets show evidence of overdispersion simply based on descriptive statistics (TLFB: M = 1.2, Var = 9.3; RAPI: M = 6.3, Var = 82.9), though overdispersion can also be influenced by the longitudinal nature of these data and may be accounted for by covariates included in the model. Note that this description is meant to convey the intuitive ideas underlying overdispersion, but formal tests can (and should) be used in regression modeling of count data (see, e.g., Hilbe, 2011).
The negative binomial model extends the Poisson model by allowing the mean and variance to be different. The lower row of Figure 4 presents three different negative binomial distributions. Note that each of these distributions has the same mean, but the dispersion varies, highlighting the primary difference between the Poisson and negative binomial distributions. The Poisson regression model is a special case of the negative binomial model, and when the mean equals the variance, the two will yield identical results. However, when the variance exceeds the mean, the negative binomial is more appropriate, and its standard errors will be reliably larger than those from the Poisson, reflecting the additional variance in the outcome. In practice, the Poisson model is rarely a good fitting model for exactly this reason, and selecting a Poisson model when the data are overdispersed will yield overly liberal statistical tests (i.e., p values will appear significant when they are not in reality, using a more appropriate model).
As seen in Figure 4, the negative binomial regression model can fit highly skewed data, including data with a relatively large number of zeroes. However, when there is a clear stack of zeroes in the data and especially when the nonzero distribution is not a smooth extension from the zeroes, alternative models may be appropriate. Two closely related models explicitly handle count data with zeroes above and beyond what would be predicted by a negative binomial model: zero-inflated models and hurdle models (see, e.g., Hilbe, 2011; Zeileis, Kleiber, & Jackman, 2008). Both models include a logistic regression for the zeroes in the data and a count regression (either Poisson or negative binomial) for the counts. However, zero-inflated and hurdle models take different approaches to dividing the data around zero. The predicted zeroes in a zero-inflated model come from both the count distribution and an extra mass of zeroes and are a type of mixture model in which the distribution at zero arises from two sources (i.e., count and logistic submodels). Thus, the logistic regression model in a zero-inflated model is for “excess zeroes,” over and above what would be predicted by the count distribution. Hurdle models, on the other hand, model all the zeroes in the logistic regression, and nonzero counts are modeled by a truncated count regression (i.e., truncated because it does not include zero). Hurdle models are related to the more general class of models, often called “two-part models,” in which a logistic model for zero versus nonzero is combined with a model for nonzero values. In the current article we consider only Poisson and negative binomial for nonzero models, but this does not have to be the case (see, e.g., health care costs as in Buntin & Zaslavsky, 2004). In many instances zero-inflated models and hurdle models will yield similar results; however, hurdle models are more straightforward to interpret as all zeroes are handled in one portion of the model, and computationally, hurdle models are somewhat easier to fit as the two parts of the model can be fit independently of one another.
All of the count regression models introduced thus far assume independent observations. This assumption is violated in longitudinal data because repeated observations on the same individual will be correlated. To accommodate this nonindependence of observations, mixed model extensions to count regressions can be used.
Generalized Linear Mixed Models for Count OutcomesThe top half of Figure 5 displays change across time in the RAPI for eight randomly selected individuals. As seen in that plot individuals started the study with a wide range of alcohol problems, and some individuals made notable changes in alcohol problems, whereas others did not. To illustrate the variability in intercepts (i.e., initial RAPI) and slopes (i.e., change over time in RAPI), Poisson regressions were fit to each individual's data (i.e., RAPI was regressed on time for each individual separately, using a Poisson regression; see Singer & Willett, 2003, for general discussion of this approach). The distributions of intercepts and slopes are plotted in the bottom of Figure 5. The means of these distributions are descriptive estimates of average intercepts (M = 8.2) and slopes (M = 0.99), and the distributions themselves highlight the variability across individuals in intercepts and slopes.
Figure 5. RAPI scores plotted over time for eight randomly selected individuals (top row). Distribution of intercepts (bottom left) and slopes (bottom right) from fitting separate Poisson regressions to each individual's data.
GLMMs for longitudinal data extend this logic of individual growth curves by including subject-specific variability in one or more components of the model (e.g., intercepts, slopes) via additional variance terms that describe a distribution of regression coefficients across individuals within the study. GLMMs have been referred to as random coefficient models for precisely this reason. Using the systems of equations or hierarchical format with explicit Level 1 and Level 2 models, a Poisson GLMM for the RAPI data might be
which is identical to the composite form of the model by substituting the Level 2 equations into their Level 1 counterparts:
where t indexes time, i indexes individuals, and the linear predictor (i.e., right-hand side of equation) is connected to the mean of the outcome via a natural logarithm link function. Male is a dummy-variable for gender (female = 0, male = 1), and Time measures time in months since the start of the study. The two variance terms (u0i, u1i) describe the deviations of each participant's intercept and slope around the overall trajectory for the sample, defined by the fixed effects (i.e., b0+ b1Male + b2Time). As is common with error terms, these variances are assumed (multivariate) normally distributed. Finally, the model assumes that the conditional distribution of the outcome given fixed and random effects is Poisson distributed. It is important to note that both the fixed and random effects are connected to the outcome via the link function, which has implications for interpretation of the model (see discussion of marginal vs. conditional effects, below).
The results for the model in Equation 1 are presented in Table 1. Focusing first on the fixed effects, the subject-specific rate ratios (RRs) are simply the exponentiated coefficients (e.g., for the intercept, e1.45 = 4.28). The intercept RR provides the estimated alcohol problems at baseline for women (i.e., when all covariates are equal to zero, as in all regression models), conditional on the random effects. The RR of 1.29 for men indicates that their alcohol problems are 29% higher than women on average. Generally, the distance above or below 1 is interpreted as the percentage increase or decrease in the outcome for a one-unit increase in the predictor. Similarly, the 0.97 RR for time implies a 3% reduction in RAPI for each 1-month increase. Let's consider the interpretation of RRs a bit more closely. The predicted regression lines based on the fixed effects are shown in Figure 6, and the specific values are presented in the figure as well. If this were a linear mixed model or OLS regression, the predicted regression lines would be perfectly straight and parallel (i.e., because there is no interaction between Male and Time), but it is clear from the figure that the predicted regression lines for men and women from the Poisson GLMM are curved and are getting closer to one another over time. Poisson models with their log link functions are sometimes called multiplicative models, whereas OLS regression (and linear mixed models) are considered additive. For the latter, the regression coefficients tell us how much to “add” to our prediction for each one-unit increase in a covariate.
GLMM Poisson Results for RAPI Data
Figure 6. Predicted (unit-specific) RAPI scores from Poisson GLMM for men and women over time, with specific values noted in text.
The interpretation of multiplicative models is more complicated. First, we exponentiate the fixed effects in Equation 1b:
where exp is the exponentiating function, raising to the base e. We then reexpress Equation 1c using a property of exponetiated sums:
Equation 1d shows why Poisson models are considered multiplicative: The exponentiated coefficients—the RRs—provide a multiplying factor for each unit change in the covariate. Thus, the RR of 1.29 for men indicates that men's predicted alcohol problems are 1.29 times women's predicted alcohol problems at each level of time, which is identical to saying that it is 29% higher (e.g., at time = 0, 4.28 × 1.29 = 5.52, and at time = 24 months, 2.03 × 1.29 = 2.62). For every 6 months, the model predicts alcohol problems are falling by e−0.03∗6 = 0.83 (i.e., an RR for each 6-month change in time). We can similarly confirm that each 6-month change in predicted alcohol problems is 17% less (i.e., multiply by 0.83) than the preceding predicted value. Thus, for OLS regression and linear mixed models, coefficients describe the amount added to predictions with each one-unit covariate change, whereas Poisson and Poisson GLMM coefficients describe the amount multiplied.
One critical feature of these coefficients that has not been widely noted in the applied literature is that these coefficients (and their interpretation) are conditional on specific values of the random-effects distribution. In linear mixed models, fixed-effect coefficients can be interpreted as “averaging over” the random effects (sometimes called marginal coefficients), but this is not true in GLMM with nonidentity link functions (see, e.g., Heagerty & Zeger, 2000 and Raudenbush & Bryk, 2002, Chapter 10). This issue of conditional versus marginal coefficients (also called unit-specific vs. population-average) is discussed in the online supplemental material (see http://dx.doi.org/10.1037/a0029508.supp), along with additional resources.
There are several considerations in judging how well the model fits the data. First, similar to all regression models, we should consider whether we have the correct predictors included in the model and whether any terms have nonlinear associations with the outcome. These decisions should be informed by past research, theory, and thorough descriptive statistics and graphs. In fact, it is hard to overemphasize the importance of thorough descriptive analyses to help guide decisions in building complicated models, such as those considered here. In addition, similar to many regression models, GLMMs assume that variance terms are normally distributed and homoskedastic, and plots of these assumptions can be found in the accompanying online materials (see eFigure 1 in online supplementary material at http://dx.doi.org/10.1037/a0029508.supp). Second, similar to linear mixed models we should consider which terms in our model should have corresponding random effects; with GLMMs decisions about which random effects to include are usually determined by testing nested models via deviance tests (also called likelihood ratio chi-square tests; Molenberghs & Verbeke, 2005). Based on descriptive statistics and graphs as well as deviance tests for random effects, the initial model for the RAPI was extended to include an interaction between gender and time and an additional per-observation random effect (discussed below). These results are shown under Model 2 in Table 1.
Most (but not all) GLMMs are fit via a procedure called maximum likelihood, and when the iterative fitting procedure stops, a deviance statistic is reported (technically, −2 × log-likelihood, and sometimes reported as −2LL). The difference between deviances from two nested models is distributed as a chi-square random variate and hence can be tested using a chi-square distribution with degrees of freedom equal to the difference in parameters between the models (see Singer & Willett, 2003, for further details). Deviance tests can also be used with count regression GLMMs, with one major caveat. Fitting GLMMs with non-normal outcomes (e.g., binary or count outcomes) is considerably more challenging, and there are a variety of optimization strategies and algorithms. Only certain algorithms have been shown to be accurate enough to allow deviance tests of nested models. A brief overview of algorithms and approaches is presented in the online supplemental material (see http://dx.doi.org/10.1037/a0029508.supp).
There are two final, critical aspects of model evaluation for GLMMs for count outcomes: overdispersion and zeroes. As noted earlier, the Poisson GLMM assumes that, conditional on fixed and random effects, the distribution of the outcome is distributed as a Poisson variable, with its property of equidispersion (i.e., mean equal to the variance). The most straightforward way to examine whether data are overdispersed relative to the Poisson is to fit a model that allows for overdispersed data. Logically, we might consider the negative binomial. For technical details that go beyond our focus here, the negative binomial model is challenging (though not impossible) to extend to random effects (see, e.g., Hilbe, 2011, pp. 488–501), though some software packages are beginning to incorporate the negative binomial with random effects (e.g., Mplus, R, SAS). With GLMMs we can extend the Poisson model to include a per-observation error term, which captures overdispersion. This type of model is often called an overdispersed Poisson model, which is functionally similar to a negative binomial model (see Rabe-Hesketh & Skrondal, 2008, pp. 389–390, or Ver Hoef & Boveng, 2007, for further discussion). Moreover, adding the extra error term is very similar to a residual error term, as in models for normally distributed data, and can be assessed via a deviance test. Model 2 in Table 1 fits an overdispersed Poisson GLMM, incorporating this extra error term. A deviance test for assessing the improvement in fit due to the addition of the per-observation error term is highly significant, χ2(1) = 2,032.8, p < .01.
Earlier, we commented on the number of zeroes descriptively in the RAPI data, and a final component with count regression models is to examine how well the model replicates the number of zeroes, and more generally, the distribution of counts in the raw data; that is, what does the model predict the histogram of counts looks like, and specifically, what the zeroes look like? A figure included with the supplementary online material presents the raw distribution of the RAPI along with estimates from model two in Table 1 (see Figure 2 at http://dx.doi.org/10.1037/a0029508.supp). Although Model 2 appears quite accurate for counts greater than 2, there is noticeable lack of fit for counts of 0, 1, and 2. Essentially, the Poisson distribution is not flexible enough to fit the steep drop of counts from 0 to 1, with much more moderate change for counts greater than 1. As a result the raw RAPI data has 756 zeroes and 395 ones, whereas the final overdispersed Poisson GLMM predicts 616 zeroes and 546 ones. This is a primary statistical motivation for considering a zero-inflated or hurdle mixed model.
Following our example with the RAPI, a hurdle mixed model would be
where t indexes time, i indexes individuals, l and c index logistic and count portions of the model, and p is the proportion of RAPI scores greater than 0. As noted earlier, zero-inflated and hurdle models have two submodels, one related to the zeroes and a second related to the counts. The key difference between hurdle and zero-inflated models is how they handle zeroes: Hurdle models cleanly divide the models, with all zeroes accounted for in the logistic regression, whereas zero-inflated models treat the zeroes as a mixture (i.e., both submodels in zero-inflated models contribute zeroes). As we saw with the overdispersed Poisson GLMM, the hurdle GLMM presented above adds random effects to the linear predictors, with the major difference now being that there are two linear predictors. The random intercept in the logistic model implies that there is variability across individuals in the likelihood of reporting any problems, whereas the random intercept and slope in the count regression models variability in the intercept and change across time in number of problems when there are problems reported. A random slope for time in the logistic portion would model individual variability over time in proportion of zeroes.
Prior to fitting the hurdle mixed model, let us say a word about when to use zero-inflated or hurdle models. Similar to many model selection decisions, the choice between models should include statistical considerations, theoretical considerations, and parsimony. As noted earlier the overdispersed Poisson GLMM of the RAPI data underpredicted zeroes (756 in the raw data vs. 616 predicted by the model), and with the TLFB data, the same type of model led to a highly non-normal distribution for the per-observation error term, based on a histogram and quantile-quantile plot of this error term. Thus, in both of these cases a zero-inflated or hurdle mixed model might be preferred on statistical grounds. However, the statistical motivations just noted do not automatically mean that covariates have important and distinct relationships across the logistic and count portions of a hurdle (or zero-inflated) model. That is, sometimes a hurdle or zero-inflated mixed model will fit the data better, but the conclusions are largely the same compared to a Poisson GLMM (typically because the covariates are primarily related to the nonzero counts). In this scenario, parsimony might suggest the simpler Poisson GLMM is adequate as it yields similar conclusions, though we strongly suggest fitting the more complex model to test whether the simpler model is adequate. Finally, theory may make clear predictions about the proportion of days with any drinking or the amount drunk on days with drinking. These would clearly prefer hurdle or zero-inflated models.
Results from the hurdle mixed model shown in Equations 2a and 2b are presented in Table 2. In examining Table 2, we find that these results map on to the earlier descriptive graphs quite closely. In particular, the count portion of the model shows that men tend to have more alcohol problems when there are problems at the start of the study (RR = 1.21). Because of the coding of the gender dummy-variable, the main effect of time describes the change in women's alcohol problems across time. The RR of 0.98 implies that, conditional on the random effects, women's RAPI (i.e., alcohol problems when there are problems) is dropping 2% with each successive assessment, whereas the interaction between gender and time implies that men's alcohol problems (when there are problems) are barely changing at all, which is confirmed by estimating the men's simple slope for time, RR = 0.99, 95% CI [0.98, 1.00].
Over-Dispersed Poisson Hurdle Mixed Model Results for RAPI Data
The logistic portion of the model describes the proportion of the sample reporting some alcohol problems. (Note that software for zero-inflated and hurdle models are not always consistent as to whether the logistic portion predicts zeroes as opposed to nonzeroes. Here it is predicting nonzeroes.) The ORs in Table 2 show that there are no differences between the sexes in the proportion of individuals reporting alcohol problems at the start of the study, OR = 0.83, 95% CI [0.59, 1.18]. For women, the odds of reporting any alcohol problems decreases by 3% per month, OR = 0.97, 95% CI [0.95, 0.99], conditional on the random effects. For men, their rate of change is somewhat slower, OR = 1.02, 95% CI [0.99, 1.04].
TLFB ApplicationIn turning to the TLFB data, the earlier plots showed that there was a very large stack of zeroes and that covariate effects (i.e., gender, weekend, and fraternity/sorority status) might vary across submodels defined by likelihood of any drinking (i.e., zero vs. not zero) and amount of drinking when there is drinking. These considerations would point toward a zero-inflated or hurdle model; moreover, preliminary models using an overdispersed Poisson GLMM showed that there was a very poor fit between predicted and observed count distribution, and the per-observation random-effect distribution was not close to being normally distributed. Given these considerations, a hurdle overdispersed Poisson GLMM was fit to the TLFB data, fitting main and two-way interaction effects in both submodels via three dummy variables: Male (0 = Women, 1 = Men), Weekend (0 = Sun/Wed, 1 = Thurs/Sat), and FratSor (0 = Not in fraternity/sorority, 1 = Fraternity/Sorority). A random intercept and random slope for weekend were included in both the logistic and truncated count submodels, as well as the per-observation random intercept in the truncated count submodel. Conditional (or unit-specific) fixed-effects are shown in Table 3. In the logistic portion of the model, there is a single, significant effect for weekend: Many more students drink, regardless the quantity, on the weekends as compared to the weekdays, OR = 3.06, 95% CI [2.60, 3.54]. In the count submodel, all three two-way interactions are significant. One approach to interpreting interactions is to use “simple slopes” (see, e.g., Jaccard, 2001). However, in the present case, the model is estimating mean drinking on drinking days for eight specific groups—for each combination of gender, weekend, and fraternity/sorority status. To help interpret the findings of the count submodel, we estimated marginal (or population-average), predicted means for each of these eight groups, which are shown in Figure 7. The figure reveals several interesting findings. Not surprisingly, fraternity and sorority members drink more on drinking days than students who are not in fraternities and sororities, and fraternity and sorority members' drinking is similar on weekdays versus weekend—when they are drinking (i.e., the count submodel in a hurdle is only for nonzeroes). However, contrasts of weekend versus weekday drinking for men and women who are not in fraternities and sororities shows that they do drink more on weekends, women: RR = 1.07, 95% CI [0.99, 1.14], p = .06; men: RR = 1.20, 95% CI [1.12, 1.28], p < .01. Thus, the hurdle mixed model provides a more nuanced view of drinking differences across these groups, parsing out drinking versus not drinking and mean drinking on drinking days.
Over-Dispersed Poisson Hurdle Mixed Model Results for TLFB Data
Figure 7. Predicted (marginal) drinks for each unique combination of gender, weekend (WE) versus. weekday (WD), and fraternity/sorority status from count submodel of overdispersed Poisson hurdle mixed model.
Software for GLMMsStatistical software packages are steadily expanding their coverage of GLMMs and the models covered in the current paper. Table 4 presents an overview of SPSS, Stata, R, SAS, and Mplus, describing which of the models covered in the present article are possible to fit. As seen there, the basic Poisson GLMM is implemented in all software, whereas negative binomial GLMM and zero-altered models are less widely available. The online materials provide computer code for fitting some example models in each of the packages mentioned, and the online supplementary material covers additional details related to fitting GLMM, which is both more technical but still practically important (see http://dx.doi.org/10.1037/a0029508.supp). Not all software packages were able to fit all models described in the present article (e.g., SAS NLMIXED can fit zero-inflated mixed models but could not fit all the variance components describe in the TLFB example). Over the coming years, there will likely be greater software availability for fitting the types of models covered here as well as more robust fitting algorithms.
Comparison of Software for Fitting Generalized Linear Mixed Models (GLMM)
Summary
The present article discussed extensions to count regression and zero-altered count regression models to longitudinal data based on GLMM. We hope that this presentation, along with the appendix and available data and code, helps addiction researchers to learn and appropriately apply these models. Given the research designs and data that addiction researchers often collect, the models covered here are often the most appropriate analytic tools. At the same time, like other statistical models that are finding their way to the applied research literature (e.g., growth mixture models; Muthén & Muthén, 2000), multilevel count models are complex. The types of assumptions and general considerations in model fitting grow exponentially from linear models to generalized linear models to linear mixed models to generalized linear mixed models. We encourage researchers to explore and use these tools as their hypotheses and data merit, though also note that they require a serious commitment of time and study to be used appropriately.
Footnotes 1 The six graphs in Figure 4 were created by simulating 1,000 random draws from the specified Poisson or negative binomial distributions. Thus, these are similar to frequency histograms, except there is no binning of multiple values.
2 There are alternatives to the negative binomial distribution in terms of dealing with overdispersed count data, including quasi-Poisson models, Poisson-normal models, and robust standard errors. Details on these models can be found in Hilbe (2011).
3 Note that the distribution of slopes appears somewhat odd, with spikes at extremely high or low values. This reflects that some individuals have incomplete data, and hence, slopes fit to two or three points of data can yield very extreme values. The subject-specific predictions from GLMMs pool information about the sample and the individual's data, leading to more sensible fits for individuals (see, e.g., discussion of empirical Bayes estimates in Raudenbush & Bryk, 2002).
4 The notation in Equation 1 is slightly nonstandard. In the statistical literature it is common to see GLMMs described in terms such as: η = XB + Zb, with g(μ) = η, and g( ) = log. Translating into words this means that η is the linear predictor, or right-hand side of the model, including both fixed and random effects (i.e., XB and Zb, respectively). The linear predictor is connected to the mean of the outcome (i.e., μ) via some function. With the Poisson model, the link function is the log.
5 Estimating the predicted number of zeroes based on a GLMM takes us into somewhat more technical material. The Poisson GLMM can be represented by Pois(XB + Zb), where X is the design matrix of the fixed-effects; B is a vector of fixed-effects coefficients; Z is the design matrix of the random effects; and b is a vector of random-effects variances. The key thing to realize is that the formula within the parentheses defines the fitted values of the model. Thus, we can read this equation as follows: The data are distributed as a Poisson random variable with a mean structure defined by the fixed and random effects of the GLMM. Similar to the discussion of conditional effects, the random effects create additional challenges in estimating the model-based distribution of counts. The accompanying R code shows one method for simulating from the fitted model to estimate the predicted distribution of counts.
6 We note in passing that the hurdle mixed models reported in this article were fit using the MCMCglmm package (Hadfield, 2010) in R, which uses a Bayesian approach to model estimation. For many practical problems maximum likelihood estimation and Bayesian MCMC estimation will yield similar if not identical results, though there are basic differences in both inferential philosophy as well as estimation. A brief discussion is provided in the technical appendix of the online material (see http://dx.doi.org/10.1037/a0029508.supp). In this particular instance, the choice is quite pragmatic as MCMCglmm provides the most flexible option for fitting these types of models in R.
7 To estimate marginal (or population-average) estimates from a GLMM, it is necessary to include both fixed and random effects. The online supplementary material provides details and the example of how the present estimates were created (see http://dx.doi.org/10.1037/a0029508.supp).
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Submitted: November 22, 2011 Revised: June 15, 2012 Accepted: June 18, 2012
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Source: Psychology of Addictive Behaviors. Vol. 27. (1), Mar, 2013 pp. 166-177)
Accession Number: 2012-22398-001
Digital Object Identifier: 10.1037/a0029508
Record: 10- Title:
- 'A tutorial on count regression and zero-altered count models for longitudinal substance use data': Correction to Atkins et al. (2012).
- Authors:
- Atkins, David C.. Department of Psychiatry and Behavioral Sciences, University of Washington, Seattle, WA, US, datkins@uw.edu
Baldwin, Scott A.. Department of Psychology, Brigham Young University, Provo, UT, US
Zheng, Cheng. Department of Biostatistics, University of Washington, Seattle, WA, US
Gallop, Robert J.. Applied Statistics Program, West Chester University, West Chester, PA, US
Neighbors, Clayton. Department of Psychology, University of Houston, TX, US - Address:
- Atkins, David C., Department of Psychiatry and Behavioral Sciences, 1100 Northeast 45th Street, Suite 300, Seattle, WA, US, 98105, datkins@uw.edu
- Source:
- Psychology of Addictive Behaviors, Vol 27(2), Jun, 2013. Special Issue: Neuroimaging Mechanisms of Change in Psychotherapy for Addictive Behaviors. pp. 379.
- NLM Title Abbreviation:
- Psychol Addict Behav
- Page Count:
- 1
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- Bulletin of the Society of Psychologists in Addictive Behaviors; Bulletin of the Society of Psychologists in Substance Abuse
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- US : Educational Publishing Foundation
Society of Psychologists in Addictive Behaviors - ISSN:
- 0893-164X (Print)
1939-1501 (Electronic) - ISBN:
- 1-4338-1635-0
- Language:
- English
- Keywords:
- count regression, longitudinal data, multilevel models, addictive behaviors, substance use
- Abstract:
- Reports an error in 'A tutorial on count regression and zero-altered count models for longitudinal substance use data' by David C. Atkins, Scott A. Baldwin, Cheng Zheng, Robert J. Gallop and Clayton Neighbors (Psychology of Addictive Behaviors, 2013[Mar], Vol 27[1], 166-177). The URL for the supplemental material was incorrect throughout the text due to a production error. Supplemental material for this article is available at: http://dx.doi.org/10.1037/a0029508.supp. The online version of this article has been corrected. (The following abstract of the original article appeared in record 2012-22398-001.) Critical research questions in the study of addictive behaviors concern how these behaviors change over time: either as the result of intervention or in naturalistic settings. The combination of count outcomes that are often strongly skewed with many zeroes (e.g., days using, number of total drinks, number of drinking consequences) with repeated assessments (e.g., longitudinal follow-up after intervention or daily diary data) present challenges for data analyses. The current article provides a tutorial on methods for analyzing longitudinal substance use data, focusing on Poisson, zero-inflated, and hurdle mixed models, which are types of hierarchical or multilevel models. Two example datasets are used throughout, focusing on drinking-related consequences following an intervention and daily drinking over the past 30 days, respectively. Both datasets as well as R, SAS, Mplus, Stata, and SPSS code showing how to fit the models are available on a supplemental website. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
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Substance Abuse & Addiction (3233) - Format Covered:
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- Release Date:
- 20130701
- Correction Date:
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- 2013-21666-002
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Correction to Atkins et al. (2012)
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Source: Psychology of Addictive Behaviors. Vol. 27. (2), Jun, 2013 pp. 379)
Accession Number: 2013-21666-002
Digital Object Identifier: 10.1037/a0033147
Record: 11- Title:
- Adolescent change language within a brief motivational intervention and substance use outcomes.
- Authors:
- Baer, John S.. Alcohol and Drug Abuse Institute, University of Washington, Seattle, WA, US, jsbaer@u.washington.edu
Beadnell, Blair. Alcohol and Drug Abuse Institute, University of Washington, Seattle, WA, US
Garrett, Sharon B.. Alcohol and Drug Abuse Institute, University of Washington, Seattle, WA, US
Hartzler, Bryan. Alcohol and Drug Abuse Institute, University of Washington, Seattle, WA, US
Wells, Elizabeth A.. Alcohol and Drug Abuse Institute, University of Washington, Seattle, WA, US
Peterson, Peggy L.. Alcohol and Drug Abuse Institute, University of Washington, Seattle, WA, US - Address:
- Baer, John S., Alcohol and Drug Abuse Institute, University of Washington, 1107 NE 45th Street, Suite 120, Seattle, WA, US, 98105, jsbaer@u.washington.edu
- Source:
- Psychology of Addictive Behaviors, Vol 22(4), Dec, 2008. pp. 570-575.
- NLM Title Abbreviation:
- Psychol Addict Behav
- Page Count:
- 6
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- Bulletin of the Society of Psychologists in Addictive Behaviors; Bulletin of the Society of Psychologists in Substance Abuse
- Other Publishers:
- US : Educational Publishing Foundation
Society of Psychologists in Addictive Behaviors - ISSN:
- 0893-164X (Print)
1939-1501 (Electronic) - Language:
- English
- Keywords:
- adolescents, motivational interviewing, substance use, change language
- Abstract:
- Homeless adolescents who used alcohol or illicit substances but were not seeking treatment (n = 54) were recorded during brief motivational interventions. Adolescent language during sessions was coded on the basis of motivational interviewing concepts (global ratings of engagement and affect, counts of commitment to change, statements about reasons for change, and statements about desire or ability to change), and ratings were tested as predictors of rates of substance use over time. Results indicate that statements about desire or ability against change, although infrequent (M = 0.61 per 5 min), were strongly and negatively predictive of changes in substance use rates (days of abstinence over the prior month) at both 1- and 3-month postbaseline assessment (ps < .001). Statements about reasons for change were associated with greater reductions in days of substance use at 1-month assessment (p < .05). Commitment language was not associated with outcomes. Results suggest that specific aspects of adolescent speech in brief interventions may be important in the prediction of change in substance use. These relationships should be examined within larger samples and other clinical contexts. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Drug Abuse; *Interviewing; *Language; *Motivation
- Medical Subject Headings (MeSH):
- Adolescent; Alcoholism; Female; Follow-Up Studies; Health Education; Homeless Youth; Humans; Intention; Interview, Psychological; Male; Motivation; Psychotherapy, Brief; Semantics; Street Drugs; Substance-Related Disorders; Treatment Outcome; Verbal Behavior
- PsycINFO Classification:
- Substance Abuse & Addiction (3233)
- Population:
- Human
Male
Female - Location:
- US
- Age Group:
- Adolescence (13-17 yrs)
Adulthood (18 yrs & older)
Young Adulthood (18-29 yrs) - Tests & Measures:
- Time Line Follow-Back interview
Motivational Interviewing Skill Code - Grant Sponsorship:
- Sponsor: National Institute on Drug Abuse
Grant Number: R01 DA15751
Recipients: No recipient indicated - Methodology:
- Empirical Study; Quantitative Study
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- Accepted: Apr 23, 2008; Revised: Apr 4, 2008; First Submitted: Oct 5, 2007
- Release Date:
- 20081208
- Copyright:
- American Psychological Association. 2008
- Digital Object Identifier:
- http://dx.doi.org/10.1037/a0013022
- PMID:
- 19071983
- Accession Number:
- 2008-17215-013
- Number of Citations in Source:
- 23
- Persistent link to this record (Permalink):
- http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2008-17215-013&site=ehost-live
- Cut and Paste:
- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2008-17215-013&site=ehost-live">Adolescent change language within a brief motivational intervention and substance use outcomes.</A>
- Database:
- PsycINFO
Adolescent Change Language Within a Brief Motivational Intervention and Substance Use Outcomes
By: John S. Baer
Alcohol and Drug Abuse Institute, University of Washington;
Department of Psychology, University of Washington;
Blair Beadnell
Alcohol and Drug Abuse Institute, University of Washington;
School of Social Work, University of Washington
Sharon B. Garrett
Alcohol and Drug Abuse Institute, University of Washington
Bryan Hartzler
Alcohol and Drug Abuse Institute, University of Washington
Elizabeth A. Wells
Alcohol and Drug Abuse Institute, University of Washington;
School of Social Work, University of Washington
Peggy L. Peterson
Alcohol and Drug Abuse Institute, University of Washington;
Department of Psychology, University of Washington
Acknowledgement: The research reported herein was supported by National Institute on Drug Abuse Grant R01 DA15751. We gratefully acknowledge the contributions of research assistants Sarah Bowen and Dana Rhule; counselors Jennifer Mullane, Melissa Phares, and Maija Ryan; data coders Avry Todd and Kate Hallman; the staff at New Horizons Ministries; and Dan Kivlahan for comments on a draft of this article.
Motivational interviewing (MI; Miller & Rollnick, 2002) is a popular, empirically based counseling method for a range of health-related problems. Defined as a “directive, client-centered counseling style for eliciting behaviour change by helping clients explore and resolve ambivalence” (Rollnick & Miller, 1995, p. 326), MI emphasizes formation of collaborative therapeutic relationships with clients through which client language about change may be strategically elicited and reinforced. Change language is defined as client expressions of problems with the current state, benefits of change, and hope and optimism about future change (Miller & Rollnick, 2002). In articulating a theory for MI, Hettema, Steele, and Miller (2005) stated that (a) the practice of MI should elicit increased levels of change and decreased levels of resistance from clients, (b) the extent to which clients verbalize arguments against change (resistance) during MI will be inversely related to the degree of subsequent behavior change, and (c) the extent to which clients verbalize change talk (arguments for change) during MI will be directly related to the degree of subsequent behavior change.
MI should be considered among therapeutic approaches based in the “ordinary language” of clients, in that therapeutic discourse is understood on the basis of common interpretations of what clients say about thoughts, feelings, and behavior (Moyers et al., 2007). The empirical relationship between client language and outcomes thus assumes a central role in the suggested impact of MI; yet evidence for this hypothesized relationship is limited. With respect to verbalizations contrary to change, Miller, Benefield, and Tonigan (1993) reported that independent ratings of client interrupting, arguing, off-task responses, and other negative responses within a brief intervention about alcohol use were strongly predictive of poorer 1-year drinking outcomes. Recently, Moyers et al. (2007) coded client language of participants randomly selected from each of three Project MATCH therapy conditions. Rates of both positive and counter–change language in the first clinical session across treatments predicted follow-up drinking outcomes.
Further evidence of a link between positive client change language and clinical outcomes in MI sessions was reported by Amrhein, Miller, Yahne, Palmer, and Fulcher (2003) in sessions with illicit drug users as they presented for treatment. Client language was subjected to a linguistic analysis that subdivided change language into elements thought to vary with respect to the nature and degree of commitment to change (commitment, desire, ability, need, readiness, and reasons). Clients who reduced their substance use during and after treatment made more frequent commitment to change statements during the evaluation of a change plan at the end of the interview compared with those who intermittently used substances over time or who never improved. Other aspects of change language were predictive of commitment language.
This small, emerging evidence base provides some support for the notion that client change language can prospectively predict clinical outcomes. These studies, however, have been completed with adults who were seeking treatment (or a free checkup; Miller et al., 1993). Yet MI has been adopted in numerous clinical and nonclinical contexts (Miller & Rollnick, 2002). One common focus for brief interventions in opportunistic contexts is with young people, who seldom seek services on the basis of their own concerns for health and safety (Baer & Peterson, 2002; Monti, Colby, & O'Leary, 2001). Despite this interest, we are unaware of studies that describe the target of MI sessions, describe youth verbalizations about behavior change, or examine whether these relate to current or future behavior. Such information would seem important given that aspects of adult language are still developing in adolescents (Nippold, 2000; O'Kearney & Dadds, 2005). Hence, adolescents receiving feedback about drug use risks may talk about change very differently than adults seeking treatment.
The current analyses sought to extend prior studies of client language within MI sessions by examining the language of homeless youths who use alcohol and illicit substances and were recruited for a risk reduction program (Baer, Garrett, Beadnell, Wells, & Peterson, 2007) that utilized a brief motivational intervention (BMI). We tested whether global ratings of client behavior in MI sessions, and behavioral ratings of positive and counter–change language and commitment talk, would predict changes in substance use from baseline to 1- and 3-month follow-ups.
Method Design and Procedures
All procedures were approved by the University of Washington Institutional Review Board. A sample of youths (ages 13–19 years) was recruited from a nonprofit, faith-based drop-in center (Baer et al., 2007) by study counselors. Inclusion criteria included lack of stable housing and report of substance use in the prior 30 days. The 127 youths were randomly assigned to receive four sessions of a BMI (n = 75) or drop-in center treatment-as-usual (n = 52), which included no specific clinical intervention, although case management and other services were available. Only youths receiving BMI are included in current analyses. As MI was not found to be superior to treatment-as-usual in this study (see Baer et al., 2007), and treatment-as-usual received no specific treatment sessions, mediating tests of the function of client change for MI were not possible. Follow-up assessments were conducted via appointments and intercept at the drop-in center and on the streets both 1 month and 3 months postbaseline. Following baseline interviews, youths assigned to BMI stayed for the first of four BMI sessions, with later BMI sessions scheduled in the following 4 weeks. Baseline interviews and the BMI sessions were conducted by a master's-level clinician, with follow-up interviews conducted by alternative project staff. As described elsewhere (Baer et al., 2007), participants received cash for completing study assessments and vouchers redeemable at local retail stores for completed BMI sessions. With consent of participants, baseline interviews and BMI sessions were audiotaped.
Sample description and retention
In the original study attrition was not associated with experimental condition, baseline demographic, or substance use rates. Participants identifying themselves as minority racial group members were retained at slightly higher rates compared with those self-identified as nonminority. Of 75 youths assigned to BMI, 21 were removed from analyses due to extensive missing data or concerns about validity of reported substance use. These included youths who (a) were incarcerated in the 30 days prior to baseline assessment (n = 3) or a follow-up assessment (n = 4), (b) received strongly negative interviewer ratings regarding consistency of their responses (n = 2), (c) refused to be audiotaped (n = 6) or were eliminated because of taping malfunction for at least half of the session (n = 4), (d) attended only highly abbreviated sessions (n = 1), and (e) missed both follow-up assessments (n = 1). No differences were seen between those included versus those excluded in relation to gender, age, or racial or ethnic minority (all ns). At baseline the 21 excluded by these criteria reported less substance use in the previous 30 days than the 54 included (days abstinent M = 14.0, SD = 8.8 vs. M = 7.5, SD = 8.9), t(73) = 2.84, p < .01.
The resulting youth sample for current analysis showed a fairly even gender distribution (54% male vs. 46% female) and mean age of 17.9 years (SD = 1.3). Reported ethnicity was 59% Caucasian, 17% multiracial, 7% Native American, 7% African American, 6% Hispanic or Latino, and 4% Asian or Pacific Islander. The sample reported high rates of substance use, with a mean of 7.5 (SD = 8.9) days of abstinence from substances in the month prior to baseline assessment. Alcohol use in the past month was reported by 89.1% of the sample. Marijuana was the most commonly used drug in the past 30 days by youth report (94.4%), followed by “club drugs” (57.1%), methamphetamine (53.5%), hallucinogens (35.9%), cocaine (33.3%), and opiates other than heroin (33.3%).
Intervention description
The BMI followed the model of an extended substance use “checkup” in which information or exercises about patterns and risks of substance use are provided as personal feedback (Miller, Sovereign, & Krege, 1988). Intervention content is described elsewhere (Baer et al., 2007). Interventionists were trained in MI techniques, then supervised via regular-session audiotape review by John S. Baer (a licensed psychologist, member of the Motivational Interviewing Network of Trainers, and an experienced MI trainer). Interventionists were trained to be nonconfrontational in tone and to provide advice about risk reduction only with permission. Initial sessions averaged 17 min (SD = 7), with subsequent sessions averaging 33 min (SD = 11). Of the 54 youths assigned to BMI, 27 attended all four sessions, 9 attended three, 9 attended two, and 9 attended one. Mean intervention time across sessions was 79.8 min (SD = 42.9). Youth ratings of the intervention were overwhelmingly positive (Baer et al., 2007).
Measures
Substance use frequency and severity
Participants were asked to recall substance use in the prior 30 days through a modified Time Line Follow-Back interview (Sobell & Sobell, 1993). Given that individuals used many combinations of substances over the 30-day period, an omnibus measure of “days of abstinence” from alcohol, marijuana, and other drug use (excluding tobacco) was calculated. We used abstinence days for conceptual clarity because days of use may represent days of use of any number of substances, whereas abstinence is clearly defined by no substance use.
Validation of drug use self-report
Urinalysis was used to calculate sensitivity and specificity of self-reported cocaine, amphetamine, opiate, and marijuana use at the 3-month follow-up. With 41 of 54 youths (76%) providing urinalysis, no evidence of systematic underreporting was observed (Baer et al., 2007).
Indices of intervention discourse
Audiotaped BMI sessions were coded by three trained raters. The scoring system utilized components of the Motivational Interviewing Skill Code (Version 1.0; Miller, 2000; Moyers, Martin, Catley, Harris, & Ahluwalia, 2003) and psycholinguistic framework of Amrhein et al. (2003) to characterize adolescent language in BMI sessions. Based on the Motivational Interviewing Skill Code, the scoring system included three global ratings of client qualities on 7-point Likert scales (1 = not at all, 7 = strong): (a) affect, how openly and directly emotions were expressed; (b) cooperation, how much responsibility for completing in-session tasks was shared; and (c) disclosure, how much personal information was revealed. An additional global score, task orientation, was added to reflect how much the adolescent remained focused and engaged during therapeutic tasks.
In line with Amrhein et al. (2003), frequency of youth statements about change, termed change talk (Miller & Rollnick, 2002), was tallied relative to substance-related risk reduction, promotion of health and safety, or service utilization. Three categories were coded to reflect differing types of language and degree of interest in change noted in prior research (Amrhein et al., 2003) and based on qualities of adolescent language observed in this and previous studies. These included (a) commitment, or statements of explicit intention to change (or maintain) behavior (e.g., “I'm not going to use meth again” or “I'm never going to stop smoking pot”); (b) reasons, or statements providing a rationale for (or against) behavior change (e.g., “When I'm high I fight with my girlfriend” or “Using drugs helps me cope with being on the street”); and (c) desire/ability, or statements that indicate desire, willingness, or ability or self-efficacy to change or not to change (e.g., “I'd like to cut back my drinking” or “I can't deal with not using because I know it'll be too painful”). Irrespective of categorization, each instance of change talk was also assigned a positive or negative valence reflecting if the statement was in favor of or opposed to reduced substance use, reduced health risks, or increased service utilization. Coders recorded tallies of each type of change talk across 5-min intervals within each session. To control for varying degrees of talkativeness, session lengths, and attendance, we converted tallies to average rates of change talk per 5 min. Overall rates of each type of change talk were calculated across individual sessions, or across all sessions attended, as the total tally across 5-min intervals divided by number of 5-min intervals.
Training and Reliability of Raters
Three raters received extensive training in recognizing and rating MI elements in therapeutic dialogue and attended weekly supervision meetings where scoring dilemmas were discussed. Interrater reliability was assessed from a subset of sessions that all three rated (n = 15, 8.3%). Intraclass correlations, computed as a two-way random model, were acceptable on the basis of Cicchetti (1994) criteria (above .40), although lowest for commitment language (see Table 1).
Coding Reliability, Means, and Standard Deviation for Youth and Counselor Language, Averaged Across Sessions
ResultsDescriptives for session codes are shown in Table 1. For the 7-point scale, mean global scores for cooperation, affect, and task orientation were between 4 and 5, whereas youth disclosure was just above 5. Youths more often expressed reasons for, reasons against, and desire/ability for change, and less often expressed desire/ability against, commitment for, and commitment against change.
Intercorrelations between global scores, change language, and substance use rates at baseline and follow-up assessments are presented in Table 2. As can be seen, desire/ability comments against change were significantly correlated with substance use rates at follow-up assessments. There also are several modest correlations among global ratings and among rates of use of different forms of change language. Among desire/ability and commitment, respectively, statements for and against change were correlated. This may reflect greater talkativeness or suggest that youths may tend to use one form of change language to express both positive and negative motivation. Partial correlations (controlling for the sum of other talk types) suggest that the former may be true for desire/ability for and against change (r = .35, p < .05 vs. partial r = .23, ns) and the latter true for commitment for and against change (r = .40, p < .01 vs. partial r = .39, p < .01).
Intercorrelations Among Language Codes, Global Ratings, and Substance Use Rates (n = 50–54)
Multiple regression assessed whether session language predicted changes in substance use rates. For each follow-up point, multiple regression predicting substance use abstinence in the prior 30 days was performed in Mplus 5.0 through robust maximum likelihood estimation, for sets of predictors. Three participants were missing assessment at 1 month and 4 at 3 months (all 54 had data for at least one follow-up). To assess prediction of change in substance use rates and control for treatment exposure, we used regressions that included baseline prior 30-day abstinence and the number of feedback sessions attended. Demographic characteristics (gender, race, age) were unrelated to either predictors or outcomes in preliminary analyses and were excluded in regressions to preserve statistical power in this small sample. Because counselors were not associated with substance use outcomes in the original study, they also were excluded in regression analyses. As noted in our prior study (Baer et al., 2007), days of abstinence increased over time for those receiving BMI (not differing from those in the control group). For youths included in current analyses, average days of abstinence over the prior 30 days was 7.5 (SD = 8.9) at baseline, then increased to 11.5 (SD = 10.8) at 1-month follow-up and 11.0 (SD = 10.6) at 3-month follow-up.
Tests of the four youth global scores on change in days of abstinence showed that higher task orientation predicted more days of abstinence at the 3-month but not the 1-month follow-up (standardized β = .44, p < .01). No other effects were statistically significant.
In regressions predicting 1-month abstinence rates from youth verbalizations about change, two of the six youth talk rates had statistically significant effects. Reasons in favor of change predicted greater abstinence, and desire/ability against change predicted less abstinence (standardized β = .23 and −.57, respectively, p < .05 and .001). Only desire/ability against change predicted less abstinence at the 3-month time point (standardized β = −.41, p < .05). Figure 1 illustrates the magnitude of effect based on desire/ability against change. Those who expressed more than the median desire/ability against change reported mean abstinence days at the 1- and 3-month follow-ups of 6.9 and 8.7 (SD = 9.1 and 10.0, respectively), whereas those making less than the median amount reported mean abstinence days of 16.2 and 13.4 (SD = 10.5 and 11.0, respectively).
Figure 1. Days of abstinence depending on desire/ability against change talk (individuals above vs. below the median).
Supplemental analyses addressed possible alternative interpretations of results from regression analyses. Outlier analyses (Cohen, Cohen, West, & Aiken, 2003) in which Cook's distance and leverage scores were computed and outliers were removed showed no substantive changes in these findings. Regressions for 1- and 3-month outcomes including significant predictors from the two original regression analyses (global ratings and rates of change language) suggested that the effect of task orientation on 3-month abstinence did not persist once change language was included. Additional analyses revealed no predictive effects of youth talk in the first BMI session, of changes in rates of language across sessions, or differential effects of change talk rated in the first half versus second half of sessions.
Finally, we explored why reasons for change had a nonsignificant zero-order correlation with 1-month abstinence (r = −.03; see Table 2) but was a significant predictor in multivariate regressions (including those controlling for outlier effects). Review of partial correlations with other variables in the regression model suggested statistical suppression only in relation to desire/ability against change (partial r = .27, p = .065). A 2 × 2 analysis of variance of the relation between these two variables in prediction of change in substance use rates, on the basis of median split of each, suggested an interaction, F(1, 3) = 3.10, p = .09. When desire/ability against change was high, there was a detrimental effect on change in abstinence regardless of whether reasons for change was low or high (mean abstinence change = 1.0 and 0.5, for those with low and high reasons for change, respectively, SD = 5.5 and 7.9); however, when desire/ability against change was low, reasons for change appears to affect abstinence (mean abstinence change = 2.9 and 11.7, for those with low and high reasons for change, respectively, SD = 11.2 and 10.3).
DiscussionOur examination of youth verbalizations during BMI sessions provides support for one of the basic tenets of MI: that client change language is related to subsequent behavior change. Despite myriad psychological and social problems among homeless youths and their general disengagement from broader social systems, and despite that this sample was not seeking treatment, change language in BMI sessions regarding substance use or service utilization significantly and prospectively predicted changes in substance use. Analyses suggest that two aspects of youth language about change are differentially indicative of actual behavioral change: statements about reasons in favor of change and statements counter to the desire or ability to change.
The strongest effect was noted for desire/ability language against change. Such comments were strongly and prospectively associated with substance use reductions at both follow-ups, despite being fairly uncommon (mean frequency of .61 per 5 min). Thus, data suggest that within BMI sessions with adolescents, a few comments about not wanting, needing, or being able to change bode poorly for subsequent reduction in substance use. Such utterances may be a marker of a specific type of resistance in MI sessions; resistance has been associated with poor clinical outcomes among adolescents (Karver, Handelsman, Fields, & Bickman, 2006; Shirk & Karver, 2003). This finding is also consistent with literature on behavioral intentions (Fishbein & Ajzen, 1975; Fishbein, Hennessy, Yzer, & Douglas, 2003; Webb, Baer, Getz, & McKelvey, 1996), despite these observational codes differing from typical measures of attitudes and behavioral intention.
Stated reasons for change also reveal a significant prospective relation with outcomes, albeit smaller than that noted for comments about desire/ability against change and possibly relevant only when statements of desire/ability against change are absent. Prior research (Baer, Peterson, & Wells, 2004) suggests that most homeless youths describe themselves as precontemplative or contemplative about change. We expected that, at best, homeless youths would express ambivalence about change in substance use. Given the brief intervention process where feedback is provided about risks and counselors strategically work to elicit positive reasons for change, we expected that problem recognition expressed through reasons for change might emerge. Compared with other forms of change language, positive reasons for change were the most common form of verbalization (1.32 per 5-min interval). Still, this rate does not suggest that these BMI sessions were filled with discussions about negative aspects of substance use and benefits of reduction and cessation. A relatively few stated reasons for change may be important for future behavior nonetheless, especially if youths are not otherwise negative with respect to their desire or ability to change. Our data do not suggest that the rate of such language changed across or within sessions.
Commitment language, which Amrhein et al. (2003) have argued as most important in the prediction of substance use outcomes, was not found to be associated with change in this study. Amrhein et al.'s study differed from this one in that it took place among adults who were seeking treatment for drug problems, and the brief MI intervention included a discussion of a change plan at the end. In contrast, our protocol with homeless youths varied from one to four sessions and followed a booklet from which adolescents could choose topics for discussion. In this context, commitment language was rare (0.27 per 5 min in favor of change, 0.18 per 5 min against change), perhaps accurately reflecting the fact that youths were not seeking assistance. Yet an intervention that includes exercises that elicit commitment language (such as developing a change plan) might produce positive findings about commitment language. Additionally, commitment language among these youths proved difficult to code; intraclass correlations were lowest for this category. A different rating method for this language category might lead to different results.
It is important to acknowledge that this study tests the relationship between youth verbalizations and subsequent behavior only and does not test a complete causal chain for MI efficacy. Several additional features of the study should cause readers to draw conclusions cautiously. We would be more confident in these relationships if we had a larger sample and more variability in youth substance use. Despite a careful approach to data analysis (including evaluating the influence of few cases or outliers), the inherent overfitting of regression models requires that the relationships we have reported be replicated. The study is also limited by the use of self-reported substance use (although urine testing suggests that reports were not systematically biased). Results are based on those youths who were not recently incarcerated and who allowed audiotaping. Generalization to other adolescent populations, both housed and homeless, requires study. Results also relied on a specific coding scheme that was informed by prior studies yet altered by investigators for this study. Alternative coding schemes, which either combine or split constructs differently, may suggest different patterns and predictive relationships. Although we analyzed only those codes that reached a minimum level of confidence based on Cicchetti's (1994) standards, higher and more consistent interrater reliability would enhance confidence in the observed relationships.
Although not providing a direct mediating test, the relationships reported here should provide support for those who design brief interventions in which counselors are taught to pay careful attention to and elicit change language from adolescent clients. Youth verbalizations about change may provide one method of assessment for likelihood of change, which could lead to tailored interventions. For the clinician, taping, transcribing, and reliably coding verbalizations are potentially unfeasible. If these results are replicated, attention to more parsimonious means of assessing change talk will allow for clinical application.
Our data suggest that negative comments about desire or ability against change, even when observed infrequently, are relatively strongly predictive of poor outcomes. Additionally, even among youths with multiple social and psychological difficulties, positive statements about reasons for change may indicate that risk reduction is likely. Whether and how the process of language development itself moderates this relationship would be a fruitful area for exploration. In addition, it is left for future research to establish that the specific eliciting and reinforcing of such change language (e.g., through MI) can in fact alter the course of substance use and other risk behavior among high-risk youths.
Footnotes 1 To assess beliefs about risks and harms associated with substance use, we used codes for reasons for and reasons against change that included statements referring to other people in addition to self. Analyses were completed with and without such codes without appreciable differences in results.
2 Despite lower coding reliability, scores for commitment language were included in initial predictive analyses. Study results did not differ with subsequent exclusion of commitment language from analyses.
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Submitted: October 5, 2007 Revised: April 4, 2008 Accepted: April 23, 2008
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Source: Psychology of Addictive Behaviors. Vol. 22. (4), Dec, 2008 pp. 570-575)
Accession Number: 2008-17215-013
Digital Object Identifier: 10.1037/a0013022
Record: 12- Title:
- Adolescents with suicidal and nonsuicidal self-harm: Clinical characteristics and response to therapeutic assessment.
- Authors:
- Ougrin, Dennis, ORCID 0000-0003-1995-5408. Department of Child and Adolescent Psychiatry, King’s College London, London, United Kingdom, dennis.ougrin@kcl.ac.uk
Zundel, Tobias. Adolescent Department, Tavistock Centre, London, United Kingdom
Kyriakopoulos, Marinos. Department of Psychosis Studies, King’s College London, London, United Kingdom
Banarsee, Reetoo. Department of Public Health and Primary Care, Imperial College London, London, United Kingdom
Stahl, Daniel. Department of Biostatistics, King’s College London, London, United Kingdom
Taylor, Eric. Department of Child and Adolescent Psychiatry, King’s College London, London, United Kingdom - Address:
- Ougrin, Dennis, Department of Child and Adolescent Psychiatry, King’s College London, Institute of Psychiatry PO85c, De Crespigny Park, London, United Kingdom, SE5 8AF, dennis.ougrin@kcl.ac.uk
- Source:
- Psychological Assessment, Vol 24(1), Mar, 2012. pp. 11-20.
- NLM Title Abbreviation:
- Psychol Assess
- Page Count:
- 10
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- Psychological Assessment: A Journal of Consulting and Clinical Psychology
- ISSN:
- 1040-3590 (Print)
1939-134X (Electronic) - Language:
- English
- Keywords:
- adolescents, nonsuicidal self-injury, self-harm, suicide attempt, therapeutic assessment
- Abstract:
- Self-harm is one of the best predictors of death by suicide, but few studies directly compare adolescents with suicidal versus nonsuicidal self-harm. Seventy adolescents presenting with self-harm (71% young women, ages 12–18 years) who participated in a randomized controlled trial were divided into suicidal and nonsuicidal self-harm categories using the Columbia Classification Algorithm of Suicide Assessment. Adolescents with suicidal self-harm were more likely than those with nonsuicidal self-harm to be young women, 22/23 (96%) versus 34/47 (72%), odds ratio (OR) = 8.33, 95% confidence interval (CI) [1.03, 50.0]; had a later age of onset of self-harm, 15.4 years vs. 13.8 years, mean difference = 1.6, 95% CI [.8, 2.43]; and used self-poisoning more often, 18/23 (78%) versus 11/47 (23%), OR = 3.43, 95% CI [2.00, 5.89]. Only those with nonsuicidal self-harm had an improvement on Children's Global Assessment Scale score following a brief therapeutic intervention, mean difference = 8.20, 95% CI [.97, 15.42]. However, there was no interaction between treatment and suicidality. There are important differences between adolescents presenting with suicidal and nonsuicidal self-harm. Suicidal self-harm in adolescence may be associated with a less favorable response to therapeutic assessment. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Attempted Suicide; *Psychological Assessment; *Self-Injurious Behavior; *Suicide; Psychotherapeutic Techniques; Suicidal Ideation
- Medical Subject Headings (MeSH):
- Adolescent; Age of Onset; Child; Conduct Disorder; Demography; Depression; Female; Humans; Interview, Psychological; Logistic Models; Prevalence; Psychiatric Status Rating Scales; Randomized Controlled Trials as Topic; Self-Injurious Behavior; Sex Distribution; Suicidal Ideation; Treatment Outcome; United States
- PsycINFO Classification:
- Clinical Psychological Testing (2224)
Behavior Disorders & Antisocial Behavior (3230) - Population:
- Human
- Location:
- United Kingdom
- Age Group:
- Childhood (birth-12 yrs)
School Age (6-12 yrs)
Adolescence (13-17 yrs)
Adulthood (18 yrs & older)
Young Adulthood (18-29 yrs) - Tests & Measures:
- Trial of Therapeutic Assessment in London
Therapeutic Assessment Quality Assurance Tool
Columbia Classification Algorithm of Suicide Assessment
Children's Global Assessment Scale
Strengths and Difficulties Questionnaire DOI: 10.1037/t00540-000 - Grant Sponsorship:
- Sponsor: Psychiatry Research Trust
Recipients: No recipient indicated
Sponsor: West London Primary Care Research Consortium
Recipients: No recipient indicated - Methodology:
- Empirical Study; Quantitative Study
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- First Posted: Aug 22, 2011; Accepted: Jun 1, 2011; Revised: May 13, 2011; First Submitted: Aug 14, 2010
- Release Date:
- 20110822
- Correction Date:
- 20120827
- Copyright:
- American Psychological Association. 2011
- Digital Object Identifier:
- http://dx.doi.org/10.1037/a0025043
- PMID:
- 21859219
- Accession Number:
- 2011-18186-001
- Number of Citations in Source:
- 59
- Persistent link to this record (Permalink):
- http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2011-18186-001&site=ehost-live
- Cut and Paste:
- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2011-18186-001&site=ehost-live">Adolescents with suicidal and nonsuicidal self-harm: Clinical characteristics and response to therapeutic assessment.</A>
- Database:
- PsycINFO
Adolescents With Suicidal and Nonsuicidal Self-Harm: Clinical Characteristics and Response to Therapeutic Assessment
By: Dennis Ougrin
Department of Child and Adolescent Psychiatry, King's College London, London, United Kingdom;
Tobias Zundel
Adolescent Department, Tavistock Centre, London, United Kingdom
Marinos Kyriakopoulos
Department of Psychosis Studies, King's College London
Reetoo Banarsee
Department of Public Health and Primary Care, Imperial College London, London, United Kingdom
Daniel Stahl
Department of Biostatistics, King's College London
Eric Taylor
Department of Child and Adolescent Psychiatry, King's College London, London, United Kingdom
Acknowledgement: This research was supported by grants from Psychiatry Research Trust and West London Primary Care Research Consortium. We thank Gordana Milavic, Jo Fletcher, Paul Calaminus, Azeem Majeed, Robert Goodman, and Derek Bolton for their clinical, managerial, and research support.
Self-harm in adolescence is a common problem (Evans, Hawton, Rodham, & Deeks, 2005) with lifetime prevalence of attempted suicide of 9.7%, whereas an additional 13.2% of adolescents engage in self-harm at some point during that period. Self-harm is one of the strongest predictors of eventual death by suicide in adolescence, increasing the risk up to 10-fold (Hawton & Harriss, 2007).
Despite its high prevalence, there is no agreement about the definition of self-harm. Hawton et al. (2003) defined (deliberate) self-harm as intentional self-injury or self-poisoning, irrespective of type of motivation or degree of suicidal intent. Many European investigators use this definition (Hawton et al., 2003; Schmidtke et al., 1996), which is also used in Australia (De Leo & Heller, 2004) and New Zealand (Carter, Reith, Whyte, & McPherson, 2005). Many American researchers subdivide self-harm into two main groups, suicidal acts with intent to die and instrumental suicide-related behavior with no intent to die (Silverman, Berman, Sanddal, O'Carroll, & Joiner, 2007), and have proposed revisions to the Diagnostic and Statistical Manual of Mental Disorders disorders and criteria to include the new category Non-Suicidal Self Injury (American Psychiatric Association, 2010). Although there is much debate on this issue, few studies have investigated differences between patients with suicidal and nonsuicidal self-harm.
If differences between the two groups exist, they could be related to one of these three domains: demographic characteristics, clinical characteristics, and self-harm-related variables (Walsh, 2006). Most of the proposed differences have not been demonstrated in large population-based studies directly comparing suicidal versus nonsuicidal groups.
Differences in Sociodemographic Characteristics, Prevalence, and OnsetVery few studies directly compare sociodemographic characteristics of suicide attempters versus nonsuicidal self-harmers (Muehlenkamp, 2005; Nock, Joiner, Gordon, Lloyd-Richardson, & Prinstein, 2006; Sarkar, Sattar, Gode, & Basannar, 2006; Walsh, 2006). In the largest study investigating these differences, those with suicidal self-harm were more likely to be young women with less education from the Southern and Western United States (Nock & Kessler, 2006). A significant fact with respect to the design of this study was that the sample was drawn from young people who all initially classified their behavior as suicidal. However, nearly half of those were subsequently reclassified as having carried out a “suicidal gesture,” that is, self-harm without true suicidal intent.
There are suggestions that nonsuicidal self-harm is equally prevalent among young men and young women (Gratz, 2001; Muehlenkamp & Gutierrez, 2007), although other studies found a female preponderance (Ross & Heath, 2002). Other possible sociodemographic differences may include a higher prevalence of nonsuicidal self-harm in Caucasians as compared with other racial groups (Muehlenkamp & Gutierrez, 2004, 2007).
Differences in Self-Harm BehaviorSeveral studies propose that those who attempt suicide tend to favor high-lethality methods such as self-poisoning, whereas those who self-harm without suicidal intent are more likely to use low-lethality methods such as self-cutting (Csorba, Dinya, Plener, Nagy, & Páli, 2009; Favazza & Conterio, 1989; Walsh & Rosen, 1988). However, no method of self-harm is exclusively related to suicidal intent, and adolescents may use different methods of self-harm at different times (Nock et al., 2006). Up to 70% of adolescents who self-harm without suicidal intent also attempt suicide (Nock et al., 2006). Guertin, Lloyd-Richardson, Spirito, Donaldson, and Boergers (2001) viewed nonsuicidal self-harm as a complicating factor in suicidal self-harm.
Regarding chronicity, some studies indicate that adolescents who self-harm with no suicidal intent, compared with adolescents with suicidal intent, are more likely to engage in repetitive self-harming behavior (Csorba et al., 2009; Pattison & Kahan, 1983). However, an important minority of adolescents who repeatedly attempt suicide has also been described (Hawton & Harriss, 2008). Up to 55% of adolescents with nonsuicidal self-injury also repeatedly attempt suicide (Nock et al., 2006).
There are no consistent findings differentiating suicidal and nonsuicidal self-harm in terms of time of onset. The frequency of both behaviors increases in adolescence and young adulthood and then diminishes over time. However, the peak onset for nonsuicidal self-harm could be between 12 and 14 years (Muehlenkamp & Gutierrez, 2004; Ross & Heath, 2002), whereas for suicidal self-harm, the peak onset is around the age of 16 (Nock et al., 2008).
Differences in Clinical CharacteristicsAdolescents with both suicidal and nonsuicidal self-harm overwhelmingly (in about 90% of cases) satisfy diagnostic criteria for one or more psychiatric disorders (Jacobs, 1999; Nock et al., 2006), depression being the most common diagnosis in both groups. These results, however, only apply to the clinical samples, and it may be that in the nonreferred adolescents, the rate of psychiatric disorders is much lower. Nock and Kessler (2006) also found that adolescents with suicidal versus nonsuicidal self-harm have a higher prevalence of depression, drug abuse and drug dependence, conduct disorder and antisocial personality disorder, phobias, and multiple diagnoses. Limitations of the methodology of this study were discussed above; however, the results are important in the light of other studies that found differences between the two groups in depression scores, suicidal thinking, and attitude to life (Csorba et al., 2009; Jacobson, Muehlenkamp, Miller, & Turner, 2008; Muehlenkamp & Gutierrez, 2004), as well as the prevalence of anxiety disorders (Brausch & Gutierrez, 2010; Csorba et al., 2009; Jacobson et al., 2008; Muehlenkamp & Gutierrez, 2004). Regarding conduct problems, there have also been reports of a higher prevalence of externalizing disorders in nonsuicidal adolescents with self-harm (Grøholg, Ekeberg, & Haldorsen, 2000).
Therapeutic ResponseAt present, most random allocation studies do not distinguish between adolescents with suicidal and nonsuicidal self-harm (Chanen et al., 2008; Cotgrove, Zirinsky, Black, & Weston, 1995; Harrington et al., 1998; Hazell et al., 2009; Wood, Trainor, Rothwell, Moore, & Harrington, 2001). In addition, despite psychosocial assessment having distinct therapeutic value (Poston & Hanson, 2010), no studies demonstrate a differential response of adolescents with different self-harm subtypes to a brief therapeutic intervention. There is good evidence of therapeutic response to a combination of medication and psychotherapy in young people with depression, including those with self-harm (Brent et al., 2008, 2009; TADS Team, 2007).
The literature on subtypes of adolescents with self-harm is limited and developing. To investigate the possible differences between the adolescents with suicidal and nonsuicidal self-harm, we studied the participants of the Trial of Therapeutic Assessment in London (TOTAL; Ougrin et al., 2011). In the TOTAL, 26 clinicians were randomized to deliver either therapeutic assessment (TA), a 30-min manualized intervention in addition to assessment as usual (AAU), or AAU alone. There were 73 adolescents assessed; 70 (96%) of those agreed to participate in the study. Twenty (57%) adolescents in the TA and 18 (51%) in the AAU arm were assessed jointly with their parent or guardian. An average of five adolescents were assessed by each clinician in the TA arm (range = 1–12) and an average of 4.4 (range = 1–14) were assessed in the AAU group. The mean age of the participants was 15.6 years (SD = 1.4). The majority of the participants in the sample were White (n = 37, 53%). Fifty-six (80%) participants were young women, 28 (40%) self-harmed by self-poisoning alone, 37 (53%) self-harmed by self-injury alone, and 5 (7%) self-harmed by both self-poisoning and self-injury. Forty-one (59%) had self-harmed previously. There was no statistically significant difference between the TA and AAU arms in the proportion of the adolescents presenting with suicidal self-harm (31% vs. 34%, χ2 = .065, p < .8). Overall, 53 (76%) participants met diagnostic criteria for at least one diagnosis, 42 (60%) had a primary diagnosis of an emotional disorder, nine (13%) had a primary diagnosis of a disruptive disorder, and two (3%) had other diagnoses. The adolescents in the TA arm were significantly more likely than those in the AAU arm to attend the first treatment appointment following the assessment, 29 (83%) versus 17 (49%), odds ratio (OR) = 5.12, 95% confidence interval (CI) [1.49, 17.55], and more likely to attend four or more treatment sessions, 14 (40%) versus 4 (11%), OR = 5.19, 95% CI [2.22, 12.10]. Three months after the initial assessment, there were no statistically significant differences between the intervention and the control arms on the Strengths and Difficulties Questionnaire (SDQ) scores, 15.6 versus 16.0, mean difference = −0.37, 95% CI [−3.28, 2.53], or the Children's Global Assessment Scale (CGAS) scores, 64.6 versus 60.1, mean difference = 4.49, 95% CI [−0.98, 9.96]. Only 34 (49%) of the parent-rated SDQs were available at follow-up; hence, only patient-rated versions were analyzed (available for 63 [90%] of the adolescents). The intervention and the control arms did not differ on repetition of self-harm: 9/35 (26%) in the TA group and 9/35 (26%) in the AAU group, χ2 = .0, p = 1.
Using the TOTAL sample, on the basis of the available literature, we hypothesized that adolescents with suicidal self-harm, compared with those in the nonsuicidal self-harm group, will be more likely to be young women and depressed. They also will be more likely to use self-poisoning as a method of self-harm and more likely to benefit from TA as measured by the CGAS and the SDQ scores. We also hypothesized that adolescents in the nonsuicidal self-harm group, compared with those in the suicidal self-harm group, will be more likely to have a diagnosis of conduct disorder, will have an earlier age of onset of self-harm, and will be more likely to have a history of previous self-harm.
In summary, there are significant gaps in the knowledge of the possible differences between adolescents with suicidal and nonsuicidal self-harm, yet these differences may be important in the classification, phenomenology, and eventually differential interventions for the two groups. In this article, we investigate the clinical and sociodemographic differences between the adolescents presenting with suicidal and nonsuicidal self-harm.
Method Participants
Adolescents 12 to 18 years old not currently engaged with psychiatric services who had self-harmed and been referred for a psychosocial assessment met the inclusion criteria and were eligible for participation in the trial. Exclusion criteria were gross reality distortion (e.g., owing to psychotic illness or intoxication), known history of moderate or severe learning disability, lack of fluent English, immediate risk of violence or suicide, and the need for inpatient psychiatric admission. Self-harm was defined as self-injury or self-poisoning irrespective of the underlying intent (National Institute for Health and Clinical Excellence [NICE], 2004), in line with British national guidelines.
The referral for psychosocial assessment was made either following a screening at the emergency departments of four inner London hospitals or following an urgent family doctor's referral to the local outpatient child and adolescent mental health services. Both the referring practitioner and the emergency department staff were blind to the allocation of the adolescents to either TA or AAU.
Eligible participants were approached with respect to participating in the trial after they had received medical clearance after an episode of self-harm. Parents (and 16- or 17-year-old participants) signed an informed consent document and adolescents younger than 16 years of age assented to participate. All participants and their guardians, if present, completed the SDQ and were assigned a CGAS score.
Interventions and Procedure
Control arm: Assessment as usual
AAU included a standard psychosocial history and risk assessment and followed the recommendations set out in the NICE (2004) guidelines. The assessment letter was sent to the relevant community team, and a copy was sent to the family in accordance with the trusts' policies.
A random sample of 10 (29%) of the clinical evaluations was selected to evaluate fidelity. Two independent psychiatrists rated adherence to 17 points that should be covered in a standard clinical self-harm evaluation (NICE, 2004). Adherence to these 17 points averaged 81.2%, with a minimum of 71% and a maximum of 100%. Interrater agreement was acceptable (overall Cohen's κ = .73, p < .001, range: .43–1).
Intervention arm: TA
The major components of TA are as follows.
- Assessing risk and taking a standard psychosocial history (approximately 1 hr).
- Taking a 10-min break to review the information gathered and to prepare for the rest of the session, followed by a 30-min intervention covering the next four steps.
- Jointly constructing a diagram (based on the cognitive analytic therapy paradigm) that consists of three elements: reciprocal roles, core pain, and maladaptive procedures (Ryle, 2010).
- Identifying a target problem.
- Considering and enhancing motivation for change.
- Exploring potential exits (i.e., ways of breaking the vicious cycles identified).
- Describing the diagram and the exits in an “understanding letter.” In addition to the understanding letter, the family also receives the usual assessment letter.
The assessment process was manualized, although assessing clinicians used clinical judgment when deciding on the best approach to exits. Family members were involved in all stages of TA whenever possible. Clinicians received training in TA over five half days accompanied by weekly homework and a video assessment before and after training with independent fidelity assessment.
To ensure fidelity to TA, we selected a random sample of 10 (29%) of the clinicians' audiotaped evaluations. Two independent clinicians rated adherence to the seven components of TA. Adherence to these seven points averaged 90.7%, with a minimum of 71.4% and a maximum of 100%. Interrater agreement was moderate (overall Cohen's κ = .64, p < .001, range: .59–1). All 10 tapes showed that the clinicians had achieved the required level of competence in TA (33 points or more on the objective subscale of the Therapeutic Assessment Quality Assurance Tool, a 0–50 scale completed by an independent clinician rating the extent to which the five objectives of TA had been achieved).
Three months after the initial assessment, three higher specialist trainees in psychiatry unaware of the patients' allocation, conducted face-to-face interviews with the participants and their guardians, if available. If a face-to-face interview was not possible, a telephone follow-up interview was conducted. Participants and their guardians, whenever available, also completed the follow-up version of the SDQ.
Randomization occurred at the level of clinician, and 26 clinicians were randomized. Twenty-two clinicians were from Center 1, and two each were from Centers 2 and 3. Randomization was conducted by a senior psychiatrist independent of the study clinicians. The randomization was stratified by center, and two blocks (block lengths 22 and 4) were created using a permuted block design to ensure that equal numbers of clinicians from each center were allocated to either intervention or control groups. The randomization scheme was generated using Web-based randomization software (http://www.randomization.com). The clinicians were informed of their allocation by e-mail. Once randomized, the clinicians administered either TA or AAU to all eligible adolescents presenting with self-harm for assessment as part of their routine work until a total of 70 adolescents were recruited. Randomization occurred irrespective of the type of self-harm in the adolescents.
Power calculation for the TOTAL study (Ougrin et al., 2011) was based on the results of a pilot study (Ougrin, Ng, & Low, 2008). It was assumed that 75% of the participants in the intervention (TA) group and 40% of the participants in the control (AAU) group would attend the first community treatment session. The software nQuery Advisor 4.0 (Elashoff, 2000) was used to establish that 35 participants in each group (70 in total) were required. With the sample size of 70 and 80% power, differences between the two groups equal to or greater than an effect size of .48 for continuous variables and equal to or greater than an odds ratio of 2.79 for dichotomous variables at the two-sided 5% level were detectable.
It was not possible to keep the clinicians unaware of the intervention they were delivering. Participants were unaware as to what type of assessment they were receiving. The study statistician and the researchers conducting follow-up assessments were unaware of the participants' allocation.
Measures
Clinical diagnosis
Clinical diagnosis was recorded using the International Classification of Diseases 10th edition (ICD–10) criteria (World Health Organization, 1992). To aid clinicians with the diagnostic process, the electronic Patient Journey System was used in 66 of the 70 assessments. The system provides examples of questions based on ICD–10 criteria for a range of psychiatric disorders and requires that clinicians arrive at a primary diagnosis. Primary clinical diagnoses were collapsed to form the following four groups: (a) no diagnosis, (b) emotional disorders (including depressive and anxiety disorders), (c) disruptive disorders (including conduct and hyperkinetic disorders), and (d) other disorders.
Sociodemographic data
Basic demographic data were collected using the standard self-report forms used for the assessment of all new patients at the participating National Health Service hospital trusts. Ethnicity was recorded on the basis of a self-report question. The patients had to initially choose from 16 different categories. These were subsequently collapsed into two: White or non-White. The parental marital status question initially had eight response categories. These were collapsed into either intact (living with two biological parents) on nonintact.
A rating of socioeconomic status was assigned by the assessing clinician on the basis of the occupation of the main breadwinner in the family (Office of Population Censuses and Surveys, 1991): Social Class I = higher professional and managerial occupations; Social Class II = intermediate managerial, administrative, or professional; Social Class III A = supervisory or clerical and junior managerial, administrative, or professional; Social Class III B = skilled manual workers; Social Class IV = semiskilled and unskilled manual workers; and Social Class V = casual or lowest grade workers, pensioners, and others who depend on the state for their income. This scale was also collapsed into two categories: manual and nonmanual workers.
Suicidality
The Columbia Classification Algorithm of Suicide Assessment (Posner, Oquendo, Gould, Stanley, & Davies, 2007) was used to classify the young people on the basis of the index episode of self-harm. This is a classification algorithm in which the criteria for a suicide attempt include both self-injurious behavior and suicidal intent (at least some intention to commit suicide). The Columbia Classification Algorithm of Suicide Assessment has been shown to have good validity and reliability (interclass correlation coefficient = .89). Because an episode of self-harm was an inclusion criterion for TOTAL, we used the following three categories to classify each index episode: (a) suicide attempt; (b) self-injurious behavior, no suicidal intent; and (c) self-injurious behavior, suicidal intent unknown. The clinical description of each index episode was independently rated by two senior psychiatrists. Disagreements were resolved by consensus. The three categories were then dichotomized into suicidal self-harm, which included Categories a and c, and nonsuicidal self-harm. Clinical guidelines suggest that the assessing clinicians should err on the side of caution when assessing adolescents with self-harm. The presence of suicidal intent in the adolescents with unknown intent should therefore be assumed (American Academy of Child & Adolescent Psychology, 2001; NICE, 2004).
Measures of general psychopathology and function
Self-rated and parent-rated versions of the SDQ were used. The SDQ (Goodman, 2001) consists of 25 items that make up five 5-item subscales assessing conduct problems, hyperactivity–inattention, emotional symptoms, peer problems, and prosocial behavior. Each item is rated on a 3-point scale: not true (a score of 0), somewhat true (a score of 1), or certainly true (a score of 2). The total difficulty score ranges from 0 to 40, and the Prosocial Behavior subscale score is not included in the total. A score of 20 or more indicates clinical abnormality. Only adolescent-reported SDQs were used, as only 39 (56%) of the adolescents had parental baseline scores.
The CGAS was also used. The CGAS (Shaffer et al., 1983) is a clinician-rated scale assessing the functional status of a young person on a scale of 1–100, with a score of at least 70 being indicative of adequate functioning.
Statistical Analysis
Comparisons between the adolescents randomized to TA versus AAU are described in Ougrin et al., 2011. Here, all comparisons were conducted between the adolescents with suicidal and nonsuicidal self-harm.
To evaluate the impact of TA, we used a random effects model with the intervention (TA vs. AAU), suicidality (suicidal vs. nonsuicidal self-harm), and the interaction between the intervention and suicidality as independent factors and CGAS and SDQ change scores as dependent variables. Patients were not sampled individually but as a group treated by a clinician. To adjust for possible clinicians' effects, we included clinician as a random factor in the model that explicitly models the possible correlation between patients of the same clinician. We chose the CGAS and SDQ change score, from baseline to follow-up, as a dependent variable. We selected this design because the comparison groups used in this study (adolescents with and without suicidal intent) were different from the intervention and control arms created by the original randomization (adolescents allocated to TA vs. AAU irrespective of the intent; Ougrin et al., 2011), and we could not therefore use the analysis of covariance approach to analyzing clustered randomized controlled trials.
ResultsThere was a good level of agreement between the two raters in classifying suicidal and nonsuicidal cases (κ = .69, p < .001). The disagreements were resolved by consensus.
There were no significant differences between the two groups on most demographic characteristics studied. Suicidal adolescents were more likely to be young women (see Table 1).
Demographic Characteristics of Adolescents With Suicidal and Nonsuicidal Self-Harm
Of the clinical characteristics, the following two variables were found to be significantly different between the two groups. Young people with suicidal self-harm were more likely to use self-poisoning as the method of self-harm, and they were more likely to start self-harming at an older age (see Table 2).
Clinical Characteristics of Adolescents With Suicidal and Nonsuicidal Self-Harm
The following two factors were entered into logistic regression: age of onset and method of self-harm, adjusted for clustering around clinicians, age, and sex. Both method of self-harm and age of onset were significantly associated with type of self-harm. Adolescents with self-poisoning were more likely to belong to the suicidal self-harm group. Adolescents with suicidal self-harm were more likely to have a later age of onset of self-harm (see Table 3).
Summary of Hierarchical Regression Analysis for Variables Predicting Differences Between Adolescents With Suicidal and Nonsuicidal Self-Harm
Impact of TA
In addition, we studied the impact of having TA versus AAU on two main clinically relevant findings: the SDQ and the CGAS scores. The SDQ and the CGAS score change from baseline to the 3-month follow-up assessment were used.
A random effects model with the intervention as an independent factor and the clinician as a random factor showed that there was a significant difference in the intervention effect on the CGAS score change in the nonsuicidal group but not in the suicidal group. For the mean difference between TA and AAU in the suicidal group, M = 0.22, 95% CI [−10.66, 11.11], t(18) = 0.043, p = .966; for the mean difference between TA and AAU in the nonsuicidal group, M = 6.39, 95% CI [0.67, 12.11], t(43) = 2.25, p = .03. However, this difference in change score between the suicidal and nonsuicidal groups was accompanied by a nonsignificant interaction between intervention and suicidality: F(1, 61) = 1.27, p = .26 (two factorial random effects model with intervention [TA vs. AAU] and type of self-harm [suicidal vs. nonsuicidal] as fixed factors).
Suicidal and nonsuicidal adolescents did not differ on the SDQ score change at the 3-month follow up. Random effects models with the intervention as an independent factor and the clinician as a random factor showed no significant difference in the SDQ score change between TA and AAU in either the nonsuicidal group or the suicidal group. For the mean difference between AAU and TA in the suicidal group, M = 3.12, 95% CI [−1.49, 7.73], t(18) = 1.42, p = .172; for the mean difference between AAU and TA in the nonsuicidal group, M = −0.42, 95% CI [−3.56, 2.72], t(41) = 0.27, p = .79.
A two-factorial random effects model did not reveal a significant interaction between type of intervention and suicidality, F(1, 59) = 1.68, p = .20, with the change of the SDQ score as the dependent variable and intervention (TA vs. AAU) and type of self-harm (suicidal vs. nonsuicidal) as independent variables. Furthermore, there was no significant main effect of type of intervention, F(1, 59) = 0.98, p = .33, and suicidality, F(1, 59) = 0.46, p = .50.
Nonsuicidal Self-Injury
We repeated all of the analyses with the nonsuicidal self-injury group (n = 33) compared with other types of self-harm group (n = 30) and separately compared with the suicide attempt group (n = 23). The outcome of these analyses closely resembled the above results and no additional associations were found.
DiscussionWe found three main differences between the adolescents presenting with suicidal and nonsuicidal self-harm in this study: (a) Adolescents with suicidal self-harm tend to have a later age of onset of this behavior, (b) they tend to use self-poisoning more often as the self-harm method, and (c) they are more likely to be young women. In addition, they may respond to TA less favorably than adolescents with nonsuicidal self-harm do. We did not find any differences in other demographic or clinical characteristics. To our knowledge, this is the first study in which the differential response of these two groups to a brief therapeutic intervention at the point of initial assessment was investigated.
Many descriptive studies sought to compare adolescents with nonsuicidal self-harm and those with a combination of suicidal and nonsuicidal self-harm (Brausch & Gutierrez, 2010; Claes et al., 2010). Whereas it is certainly true that significant overlap may exist between these two behaviors, we chose to classify the adolescents in our study on the basis of the index episode of self-harm, in line with other researchers (Grøholg et al., 2000). It is possible that some of the adolescents had a history of both suicidal and nonsuicidal self-harm, yet retrospective assessments of suicidality are unreliable and the index episode approach is the closest to clinical practice.
Differences in Sociodemographic Characteristics
Many authors report self-harm in general to be more common in young women than in young men (Gratz, 2001; Muehlenkamp & Gutierrez, 2007), unlike completed suicide (Ougrin, Banarsee, Dunn-Toroosian, & Majeed, 2010), although this finding is not universal. Several studies also found a female preponderance in those with suicidal versus nonsuicidal self-harm (Muehlenkamp & Gutierrez, 2004; Nock & Kessler, 2006). This study supports the previous findings of a female preponderance in the risk of self-harm overall and in suicidal versus nonsuicidal self-harm in particular. It is possible that community samples with a lower severity of self-harm have a different sex ratio to clinical samples (Madge et al., 2008).
Differences in Self-Harm Characteristics
Our findings are in line with other studies and suggest a strong link between cutting and nonsuicidal self-harm on one hand and self-poisoning and suicidal self-harm on the other (Csorba et al., 2009). However, we did not find an increased likelihood of repeat self-harm in the nonsuicidal group, which was contrary to the findings of other studies (Brausch & Gutierrez, 2010; Csorba et al., 2009).
There is increasing evidence of self-harm being one part of a continuum of suicidal process ranging from thoughts of self-harm to completed suicide (Stanley, Winchel, Molcho, & Simeon, 1992). One longitudinal study demonstrated that nonsuicidal self-harm predicts future nonsuicidal self-harm rather than future suicidal attempts (Wichstrøm, 2009), whereas another demonstrated nonsuicidal self-harm to be a strong predictor of suicide attempts (Wong, 2007). Our findings of nonsuicidal self-harm starting earlier in life than suicidal self-harm are of interest in the light of the theory proposing that suicide is often a result of the learned ability to hurt oneself (Joiner, 2005). Contrary to our expectations, we did not find a greater likelihood of having had a previous episode of self-harm in the nonsuicidal group as compared with the suicidal group.
Differences in Clinical Characteristics
We did not find any differences in the likelihood of having a psychiatric diagnosis between the two groups, although well over 70% of the adolescents satisfied criteria for a psychiatric diagnosis in both groups. As other studies have reported a greater prevalence of psychiatric disorders (up to 90%), it may be that these were underestimated in this study, perhaps because we did not use a full semistructured interview to establish the diagnosis. We did not find an increased likelihood of the diagnosis of depression in the suicidal group, unlike previous studies (Brausch & Gutierrez, 2010; Csorba et al., 2009). However, depression was the most common diagnosis, and the absolute difference between the two groups was large, with 49% of nonsuicidal and 69% of suicidal adolescents satisfying criteria for a depressive condition.
Previous studies have associated more externalizing problems with nonsuicidal self-harm (Grøholg et al., 2000). All adolescents with conduct disorder were in the nonsuicidal group in this study sample. The difference between the two subgroups, however, did not reach a statistically significant level, possibly because of low statistical power. The absolute difference in the diagnosis of conduct disorder was substantial and possibly clinically significant.
Differences in Response to TA
We found no difference in response as measured by the SDQ, but we found that adolescents with nonsuicidal self-harm were more likely to have a higher CGAS score 3 months after the initial TA. However, the differences found were no longer significant when the Intervention × Suicidality interaction was considered, which may be explained by insufficient power. This finding was contrary to our hypothesis. We reasoned that the suicidal group would have a greater risk of having a diagnosis of depression and would therefore have a better response to community treatment on the basis of the combination of psychological treatment and pharmacological intervention (TADS Team, 2007). However, neither of these hypotheses was confirmed. It may be that the absence of clinical improvement in the suicidal group was partly mediated by nonsignificant differences in the prevalence of psychiatric disorders and partly reflected a more refractory nature of psychiatric disorders in this group. Other authors also noted that those with suicidal self-harm are likely to have worse posttreatment outcomes than nonsuicidal comparison groups (Dougherty et al., 2009) and that there is poor treatment engagement in the adolescents with suicidal and nonsuicidal self-harm (Ougrin & Latif, 2011). Both measures of clinical outcome used in this study are rather broad and may have therefore obscured important differences in specific clinical subdomains. Moreover, the 3-months follow-up period may have been too short to allow for the full benefit of the interventions to be realized.
Nonsuicidal Self-Injury
It would appear from the analysis that the nonsuicidal self-injury group closely resembles the nonsuicidal self-harm (which includes adolescents with both nonsuicidal self-poisoning and nonsuicidal self-injury) group. Using the term nonsuicidal self-harm may be preferable to nonsuicidal self-injury, as it encompasses those with nonsuicidal self-poisoning who would have otherwise fallen through the dichotomized classification of nonsuicidal self-injury versus suicide attempts.
Limitations
We did not investigate other important differences that may exist between the adolescents with suicidal and nonsuicidal self-harm. In particular, we did not investigate possible differences in hopelessness, impulsiveness, disordered eating, problem-solving skills, and parental support, all of which have been shown to be potentially different between the two groups (Boergers, Spirito, & Donaldson, 1998; Grøholg et al., 2000; Hargus, Hawton, & Rodham, 2009) and also potentially important for treatment choices. Hopelessness is especially important, as it appears to predict future suicide in those with self-harm, yet the reports from previous studies have not been consistent in establishing the differences between the two subgroups (Brausch & Gutierrez, 2010; Muehlenkamp & Gutierrez, 2007). In addition, our sample size was small, potentially contributing to a low power to detect differences between the two groups.
We did not use a semistructured diagnostic schedule to establish the diagnosis, and this may have contributed to the underestimation of the prevalence of clinical diagnoses in this sample. However computerized prompting integrated with the electronic patients' record system contributed to the standardization of the diagnoses. Although it is an important limitation of the study, using clinical diagnosis has the benefit of generalizing to clinicians' routine practice.
We considered adolescents with clear suicidal intent and those with unclear intent to belong to one group as opposed to combining the latter with the adolescents having clear nonsuicidal intent. This division is clearly arbitrary; however, we reasoned that the young people with mixed or unclear intent should probably be treated as suicidal in clinical practice.
Suicidality rating was done by two independent raters. Neither was blind to the study hypotheses; however, the description of the index episode of self-harm was anonymized and the raters had no access to any other information about the adolescents at the time of the rating. Finally, as mentioned above, the short follow-up period may have prevented the discovery of further differences in treatment response between the two groups.
Clinical Implications
Adolescents with nonsuicidal and suicidal self-harm differ on several important clinical and sociodemographic characteristics and may respond to treatment differently. TA may be especially important in the nonsuicidal group. The nonsuicidal self-injury group seems to be similar to a broadly defined nonsuicidal self-harm group, which includes both nonsuicidal self-poisoning and nonsuicidal self-injury. Clinicians should consider suicidal intent carefully during their initial assessment.
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Submitted: August 14, 2010 Revised: May 13, 2011 Accepted: June 1, 2011
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Source: Psychological Assessment. Vol. 24. (1), Mar, 2012 pp. 11-20)
Accession Number: 2011-18186-001
Digital Object Identifier: 10.1037/a0025043
Record: 13- Title:
- Age of onset, symptom threshold, and expansion of the nosology of conduct disorder for girls.
- Authors:
- Keenan, Kate. Departments of Psychiatry and Behavioral Neuroscience, University of Chicago, Chicago, IL, US, kkeenan@yoda.bsd.uchicago.edu
Wroblewski, Kristen. Departments of Psychiatry and Behavioral Neuroscience, University of Chicago, Chicago, IL, US
Hipwell, Alison. Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, US
Loeber, Rolf. Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, US
Stouthamer-Loeber, Magda. Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, US - Address:
- Keenan, Kate, Department of Psychiatry, University of Chicago, MC 3077, 5841 South Maryland Avenue, Chicago, IL, US, 60637, kkeenan@yoda.bsd.uchicago.edu
- Source:
- Journal of Abnormal Psychology, Vol 119(4), Nov, 2010. Oppositional Defiant Disorder and Conduct Disorder: Building an Evidence Base for DSM-5. pp. 689-698.
- NLM Title Abbreviation:
- J Abnorm Psychol
- Page Count:
- 10
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- The Journal of Abnormal and Social Psychology; The Journal of Abnormal Psychology; The Journal of Abnormal Psychology and Social Psychology
- ISSN:
- 0021-843X (Print)
1939-1846 (Electronic) - Language:
- English
- Keywords:
- conduct disorder, diagnosis, girls, phenotype, validity, nosology, antisocial behavior
- Abstract:
- The study of conduct disorder (CD) in girls is characterized by several nosologic controversies that center on the most common age of onset, the most valid symptom threshold, and the possible inclusion of other manifestations of antisocial behavior and dimensions of personality as part of the definition of CD. Data from a prospective, longitudinal study of a community sample of 2,451 racially diverse girls were used to empirically inform these issues. Results revealed that adolescent-onset CD is rare in girls. There was mixed support for the threshold at which symptoms are associated with impairment: Parent-reported impairment provided the clearest evidence of maintaining the current Diagnostic and Statistical Manual of Mental Disorders (4th ed.; American Psychiatric Association, 1994) threshold of 3 symptoms. The impact of callousness and relational aggression on impairment varied by informant, with small effects for parent- and youth-reported impairment and larger effects for teacher-rated impairment relative to the effects for CD. These results support arguments for revising the typical age of onset of CD for girls but for maintaining the current symptom threshold. The results also suggest the need to consider subtyping according to the presence or absence of callousness. Given its content validity, relational aggression requires further study in the context of oppositional defiant disorder and CD. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Conduct Disorder; *Onset (Disorders); *Symptoms; Antisocial Behavior; Human Females; Phenotypes
- Medical Subject Headings (MeSH):
- Adolescent; Age Factors; Age of Onset; Aggression; Child; Conduct Disorder; Diagnostic and Statistical Manual of Mental Disorders; Female; Humans; Logistic Models; Prevalence; Prospective Studies; Psychometrics
- PsycINFO Classification:
- Behavior Disorders & Antisocial Behavior (3230)
- Population:
- Human
Female - Location:
- US
- Age Group:
- Childhood (birth-12 yrs)
Preschool Age (2-5 yrs)
School Age (6-12 yrs) - Tests & Measures:
- Children’s Peer Relationship Scale
Psychopathy Screening Device
Child Global Assessment Scale
Loneliness and Social Dissatisfaction Questionnaire DOI: 10.1037/t04784-000 - Grant Sponsorship:
- Sponsor: National Institute of Mental Health
Grant Number: R01 MH56630
Recipients: Loeber, Rolf
Sponsor: National Institute of Mental Health
Grant Number: R01 MH66167
Recipients: Keenan, Kate
Sponsor: National Institute of Mental Health
Grant Number: K01 MH71790
Recipients: Hipwell, Alison - Methodology:
- Empirical Study; Quantitative Study
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- First Posted: Sep 20, 2010; Accepted: Jan 28, 2010; Revised: Jan 27, 2010; First Submitted: Jun 4, 2009
- Release Date:
- 20100920
- Correction Date:
- 20120827
- Copyright:
- American Psychological Association. 2010
- Digital Object Identifier:
- http://dx.doi.org/10.1037/a0019346
- PMID:
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Age of Onset, Symptom Threshold, and Expansion of the Nosology of Conduct Disorder for Girls
By: Kate Keenan
Departments of Psychiatry and Behavioral Neuroscience, University of Chicago;
Kristen Wroblewski
Department of Health Studies, University of Chicago
Alison Hipwell
Department of Psychiatry, University of Pittsburgh
Rolf Loeber
Department of Psychiatry, University of Pittsburgh
Magda Stouthamer-Loeber
Department of Psychiatry, University of Pittsburgh
Acknowledgement: This work was supported by National Institute of Mental Health Grants R01 MH56630 to Rolf Loeber, R01 MH66167 to Kate Keenan, and K01 MH71790 to Alison Hipwell.
Conduct disorder (CD) is a disorder described as occurring more commonly among boys than girls, regardless of age at assessment (American Psychiatric Association, 1994). In a large, community-based sample, the rate of CD in girls was below 1% in childhood and ranged from 1.4–3.3% at ages 13–15 years, whereas for boys the rate ranged from 0.5–2.8% in childhood and from 3.2–5.4% at ages 13–15 years (Maughan, Rowe, Messer, Goodman, & Meltzer, 2004). Similar patterns have been reported in other large epidemiologic samples, in which boys are reported to have higher rates of CD than are girls across age, with a narrowing of the sex difference in adolescence because of an increase in new cases of CD among girls during adolescence (Moffitt, 2003). Such findings, however, are largely based on assessing the rate of CD at a single time point in children at different ages, without determining the age at which the first symptom appeared (e.g., Maughan et al., 2004). The age at which girls meet criteria for CD (i.e., endorse three symptoms in a 12-month period) is often interpreted as the age of onset of CD, although that is not consistent with the Diagnostic and Statistical Manual of Mental Disorders (4th ed.; DSM–IV; American Psychiatric Association, 1994) definition of age of onset of CD. Moreover, some studies of the developmental course of antisocial behavior in girls have incorporated measures of delinquency and/or used different informants at different ages (e.g., Moffitt, Caspi, Rutter, & Silva, 2001), complicating the assessment of developmental patterns of CD. Thus, the developmental pattern of CD in girls is still not well documented, despite the fact that current etiological theories identify age of onset as a critical dimension in determining causal risk factors and prognosis (e.g., Moffitt, 2003).
Although the empirical basis for many childhood disorders in DSM–IV represented an improvement over previous versions of the DSM, the operationalization of DSM–IV CD was based on a very small number of girls. Only 25% of the 440 participants in the DSM–IV field trials for oppositional defiant disorder (ODD) and CD were female, and only 5% (n = 24) of these girls met criteria for CD (Lahey et al., 1994, 1998). Similarly, in the National Institute of Mental Health's Methods for Epidemiology of Child and Adolescent Mental Disorders Study (MECA), which was also used to test the validity of DSM–IV CD, only 19 girls (1.5% of the total sample) were reported to meet criteria for CD (Lahey et al., 1998). Thus, the validity of DSM–IV CD for girls has been questioned (e.g., Hartung & Widiger, 1998; Zoccolillo, Tremblay, & Vitaro, 1996). One debate focuses on whether the symptom threshold used to determine presence or absence of CD reflects a gender bias in the definition of CD (Hartung & Widiger, 1998; Zoccolillo et al., 1996). Zoccolillo et al. (1996) argued that a threshold of two symptoms of CD is a more sensitive and specific threshold than three symptoms for girls. Using a sample of girls who were well characterized in terms of disruptive behavior in early childhood, changing the threshold from three to two symptoms and adding a DSM–III symptom of rule violation resulted in a significant increase in the rate of DSM–III–R CD among girls who were reported to have high and persistent antisocial behavior in childhood (Zoccolillo et al., 1996). The definition of early antisocial behavior, however, was quite broad including symptoms of attention-deficit/hyperactivity disorder and ODD. Additional tests of optimal symptom threshold using alternative criteria are still needed.
Another debate centers on whether additional, female-sensitive symptoms should be included in the definition of CD, such as relational forms of aggression (e.g., overtly excluding someone from play; Crick & Grotpeter, 1995; Crick, Ostrov, & Werner, 2006; Xie, Cairns, & Cairns, 2002) and indirect aggression (e.g., spreading rumors and gossip; Björkqvist, Lagerspetz, & Kaukiainen, 1992). Crick and Grotpeter (1995) and Björkqvist et al. (1992) argued that girls are more likely to engage in indirect than direct aggression. Whether or not the addition of indirect aggression items to the pool of CD symptoms will increase the validity of the disorder for girls, by accounting for additional variance in impairment for example, remains to be tested. In the only study conducted to date, relational aggression did not appear to explain additional variance in impairment for girls (or for boys) after controlling for DSM–IV CD (Keenan, Coyne, & Lahey, 2008). The rate of CD in that sample, however, was relatively low. In addition, there was no evidence that relational aggression occurred at a higher rate in girls than in boys (Keenan et al., 2008). These results notwithstanding, the research agenda for DSM–V CD includes determining the potential of relational aggression to increase the validity of CD for girls (Moffitt et al., 2008).
A third debate is whether dimensions of psychopathy, such as callousness and lack of emotion need to be incorporated in the defining constructs of childhood CD for both sexes (Frick, Bodin, & Barry, 2000; Lynam, 1997; Pardini, Obradovic, & Loeber, 2006; Schrum & Salekin, 2006), but perhaps particularly for girls. Ratings of callousness have demonstrated unique moderate associations with disruptive behaviors in both community and clinic-referred samples of school-age children and early adolescents (Frick et al., 2000). Frick et al. (2000) reported that 60% of children scoring high on narcissism but low on the other personality dimensions were girls, and 25% of that group met criteria for ODD or CD, compared with less than 1% of children scoring low on all the dimensions. Thus, the inclusion of personality dimensions that are theoretically linked to antisocial behavior across development may be useful in defining a subtype of CD in girls.
The goal of the present study is to examine the validity of the current DSM–IV nosology of CD in girls by addressing the following questions:
- Is adolescent onset (i.e., the first CD symptom occurring at or later than age 10, as per DSM–IV) the most common age of onset among girls meeting criteria for DSM–IV CD?
- Is there evidence to support a change in the symptom threshold for CD in girls, as demonstrated by similar levels of impairment at lower symptom thresholds?
- Is there significant overlap between CD symptoms and callousness and relational aggression?
- Do relational aggression and callousness explain unique variance in impairment after controlling for CD?
Providing data to answer the above questions will further shape, and hopefully narrow, the debate on identifying the most valid operational definition of CD for girls. In addition, we explore whether race moderates the findings on onset, symptom threshold, and utility of relational aggression and callousness to the diagnosis of CD.
Method Participants
In the Pittsburgh Girls Study (PGS), a stratified, random household sampling with oversampling of households in low-income neighborhoods, was used to identify girls who were between the ages of 5 and 8 years. Neighborhoods in which at least 25% of the families were living at or below the poverty level were fully enumerated (i.e., all homes were contacted to determine if the household contained an eligible girl), and a random selection of 50% of the households in nonrisk neighborhoods were enumerated during 1998 and 1999. The enumeration identified 3,118 separate households in which an eligible girl resided. From these households, families who moved out of state and families in which the girl would be age ineligible by the start of the study were excluded. When two age-eligible girls were enumerated in a single household, one girl was randomly selected for participation. Of the 2,992 eligible families, 2,875 (96%) were successfully re-contacted to determine their willingness to participate in the longitudinal study. Of those families, 85% agreed to participate, resulting in a total sample size of 2,451. The 2,451 girls were relatively evenly distributed across the four age groups (5–8 years). Approximately half of the girls were African American (52%), 41% were European American, and the remaining girls were described as multiracial or representing another race. Nearly all the primary caregivers were biological mothers (92%). More than half of the caregivers were cohabiting with a husband or partner, about 47% of parents had completed 12 years or fewer of education, and 25% of the families had yearly incomes of less than $15,000.
Retention of the sample has been very high. In Table 1, we present retention data by age, which ranges from a high of 97.5% for age 7 to 87.8% for age 15 data. We note that only two cohorts have been assessed at age 14 and only one at age 15, to date. Some of the variability in retention from year to year is due to difficulty tracking participants; a minority of families has refused to participate over the years (<3% at age 15). Comparisons of those assessed and those not assessed at each age were conducted using chi-square tests. Girls who were not assessed at ages 10–15 years were more likely to be from families not receiving public assistance; European American girls were less likely to have been assessed at ages 11, 12, and 14 years. In addition, there was no difference in number of CD symptoms or CD diagnosis at the initial assessment between girls who were and were not retained.
Number of Participants Contributing Data to Each Age of Assessment and Unweighted and Weighted Prevalence of CD
The University of Pittsburgh Institutional Review Board approved all study procedures. Written informed consent was obtained from the primary caregiver, and verbal assent was obtained from the child.
Measures
Information on girls' age, race, and whether the household was in receipt of public assistance (e.g., Women, Infants, and Children; food stamps; welfare) was collected by parental report. Asian American girls and girls with unknown race (n = 26) were excluded from analyses on race effects, resulting in comparisons between African American girls and European American girls.
The Child Symptom Inventory–4 (CSI; Gadow & Sprafkin, 1994) was used to assess DSM–IV symptoms of CD over the past year via parent and child report. Symptoms were scored on a 4-point scale: never, sometimes, often, or very often. CD symptoms that require the frequency to be often (i.e., bullies, fights, lies to con, stays out late, truancy) were scored as present if either informant endorsed the behavior at the level of often or very often, and all other CD symptoms were scored as present if endorsed at any level of frequency by either informant. As per DSM–IV, age of onset of CD was defined as age at which the first symptom was reported. Internal consistency for the CD scale was high for parent and youth across age (.69–.79).
DSM–IV ODD was assessed using parent report on the Child Symptom Inventory. A threshold of four or more symptoms of ODD endorsed at the level of often or very often was used to define presence or absence of DSM–IV ODD (alpha ranging from .83–.90 across ages). Adequate concurrent validity and sensitivity and specificity of ODD and CD symptom scores to clinicians' diagnoses are reported for the Child Symptom Inventory (Gadow & Sprafkin, 1994).
Parents and teachers were administered the relational aggression subscale of the Children's Peer Relationship Scale (Crick & Grotpeter, 1995). Concurrent and predictive validity has been established via negative associations with peer acceptance and liking and psychological adjustment (Crick & Grotpeter, 1995; Crick et al., 2006). Internal consistency for this scale was high for parent and teacher reports across age (range = .84–.95). Consistent with the approach to defining a positive endorsement for CD symptoms, the highest level of endorsement by either informant was used to generate a total score for relational aggression (range = 5–25). Girls scoring one standard deviation above the mean (at or above approximately the 85th percentile) for the sample (>15) were defined as relationally aggressive.
The callous/unemotional subscale from the Psychopathy Screening Device (Frick et al., 2000) was administered to the parent and teacher. This subscale has shown good predictive validity by predicting level of severity and stability of antisocial behavior among children with conduct problems (Frick, Stickle, Dandreaux, Farrell, & Kimonis, 2005). The highest level of endorsement by parent or teacher was used to generate a callousness score (range = 0–8). Internal consistency was moderate (αs = .51–.67 across age and informant). Girls scoring one standard deviation above the mean for the sample (>4) were defined as callous.
Parent-rated impairment was assessed via the Child Global Assessment Scale (C-GAS; Setterberg, Bird, & Gould, 1992), which is a measure of impairment developed for children 4–18 years of age that has been validated for use by parents (Bird et al., 1996). Scores on the C-GAS range from 1 to 100, with each decile containing a description of the degree of impairment in school and with family and peer relations. Parent-reported impairment was operationally defined as C-GAS scores of 60 or below (Bird et al., 1996).
Teachers provided global ratings of the child's functioning in school by responding to two questions each scored on a 4-point Likert scale: “During the last two months, how often have you gotten annoyed or upset with this student?” and “During the last two months, how happy has this student seemed?” (scored in the reverse direction). These two scores were combined (range = 2–8), and girls whose scores fell one standard deviation above the mean (>5) were defined as significantly impaired by teacher report. The impairment questions were designed to assess global impairment in psychosocial functioning in the school environment. Data on teacher-rated impairment were available through age 13 years of age.
Children reported on functioning in their peer relations using the total score on the Loneliness and Social Dissatisfaction Questionnaire (Asher & Wheeler, 1985) from ages 7 to 10 (αs = .86–.91 across age), and the self-competence score on the Perception of Peers and Self (Rudolph, Hammen, & Burge, 1995) at ages 10–14 (αs = .45–.52 across age). Both scales are used to assess the child's perception of her functioning with peers. Changes in the administration of these scales were based on the developmental appropriateness of the items. Administration of the two scales overlapped at age 10, which yielded a moderate level of correlation (Spearman r = .46, p < .001). Children who score high on the Loneliness and Social Dissatisfaction Questionnaire have been reported by teachers to be more aggressive and disruptive, but not more shy, than children who score low on the scale (Cassidy & Asher, 1992).
Data Reduction and Analyses
The PGS uses an accelerated longitudinal design, with relatively equal numbers of girls at ages 5, 6, 7, and 8 years being enrolled in the study at Wave 1, followed by annual assessments. For rate and weighted prevalence of DSM–IV CD, we present data from ages 7 through 15, the oldest age for which we have data available on CD symptoms. For all other analyses we included a developmental span from ages 7 to 14 years, with the exception of analyses using teacher-rated impairment as the dependent measure, which included ages 7–13 years.
All analyses were conducted with weighted data to correct for the oversampling of the low-income neighborhoods to generate prevalence rates that are representative of the population in the City of Pittsburgh. We compared the ratio of girls living in low-income versus non-low-income neighborhoods in the PGS (40.92/59.08 = 0.6926) with the ratio from the Census data (27.59/72.41 = 0.3810) and divided the two to determine how much more weight should be assigned to a girl from a non-low-income neighborhood (1:1.8178). Using this weighting factor and the n for the two groups, a weight of .6742 was assigned to girls from the low-income neighborhoods and 1.2257 to the girls from the non-low-income neighborhoods.
Given the large sample size, significant differences are reported for p values that are less than .01.
The rate of CD was generated across age and race. Comparisons of the prevalence between groups were conducted using logistic regression analysis, and the difference in the distribution of age of onset by race was tested via Wilcoxon rank-sum test. Generalized estimating equation (GEE; Zeger & Liang, 1986) regression models using STATA software (Version 11; StataCorp, College Station, TX) were used to test the symptom threshold and the relative contribution of callousness and relational aggression to impairment, after controlling for a diagnosis of CD, and to test the interactive effects of CD and callousness and CD and relational aggression on impairment. GEE is appropriate for nested, longitudinal designs in part because it can specify a working correlation matrix that accounts for within-subject correlations of repeated observations over multiple data waves. Accounting for the correlation structure of the data avoids the assumption that measurements taken at successive points in time are not correlated. This results in a more efficient analysis, unbiased regression parameters and improved power to detect significant changes over time. GEE models can also be used with unbalanced designs (Diggle, Liang, & Zeger, 1994) in which some children provide more data points than others, as is the case in the current design.
Results Prevalence of DSM–IV CD by Race and Poverty and Co-Occurrence of CD and ODD
Of the 2,393 girls (i.e., the analytic sample, excluding the 26 girls on the basis of race and 32 girls missing CD data in all waves), 560 (21.2% weighted) met criteria for DSM–IV CD in at least 1 assessment year. The weighted prevalence of CD ranged from 4.9% at age 11 years to 8.9% at age 15 years (see Table 1). One hundred twenty-four European American girls (12.4% weighted) and 436 African American girls (30.1% weighted) met criteria for CD in at least 1 assessment year, yielding a significant effect of race on prevalence (OR = 3.0, 95% CI = 2.5–3.8, p < .001). Among families never receiving public assistance, 67 of 340 African American girls (19.2% weighted) and 53 of 674 European American girls (7.9% weighted) met criteria for DSM–IV CD (OR = 2.8, 95% CI = 1.9–4.0, p < .001). Among girls whose families had received public assistance in at least one year, 369 of 1,062 African American girls (34.1% weighted) and 71 of 317 European American girls (22.1% weighted) met criteria for DSM–IV CD at least once (OR = 1.8, 95% CI = 1.4–2.4, p < .001). Thus, poverty is associated with an approximate doubling of the rate of CD in both racial groups, but the effect of race on the rate of CD remains after controlling for poverty. This is further supported by the lack of a statistically significant Race × Public Assistance interaction (p = .08) and by the OR for race, after adjusting for receipt of public assistance, remaining statistically significant (OR = 2.1, 95% CI = 1.7–2.7, p < .001). These results are consistent with those reported by Bird et al. (2001), who found that race/ethnicity was associated with the rate of CD in the MECA study in a way that appeared to be independent of poverty.
Of the 560 girls who met criteria for CD in at least 1 assessment year, 255 (47.3% weighted) also met criteria at least once for DSM–IV ODD. Of the 1,833 girls who never met criteria for CD, 177 (10.0% weighted) met criteria at least once for DSM–IV ODD.
1. Is adolescent onset (i.e., the first CD symptom occurring at or later than age 10) the most common age of onset among girls meeting criteria for DSM–IV CD?
Age of onset of CD was defined as the age at first reported symptom among the 560 girls who met criteria for DSM–IV CD. The majority of girls had childhood onsets, and this did not vary significantly by race (p = .24 by Wilcoxon rank-sum test). Of the girls who met criteria for DSM–IV CD, 504 (89.8% weighted) had an age of onset between 7 and 9 years of age, and 56 (10.2% weighted) had an age of onset between 10 and 15 years of age (see Figure 1). An age of onset after 9 years of age, therefore, was rare in this sample. In fact, 62% of the 560 girls who met criteria for CD had three symptoms within a 12-month period before the age of 10 years.
Figure 1. Cumulative age of onset of Diagnostic and Statistical Manual of Mental Disorders (4th ed.; American Psychiatric Association) conduct disorder among girls (n = 560).
Among girls meeting criteria for CD, the most common symptoms reported at 7, 8, and 9 years of age were destruction of property (41.3–44.0%), stealing without confrontation (37.9–40.8%), cruelty to others (22.6–24.0%), and lying to con (17.6–24.6%).
2. Is there evidence to support a change in the symptom threshold for DSM–IV CD in girls, as demonstrated by similar levels of impairment at lower symptom thresholds?
The proportion of girls with impairment at each symptom level of CD (none, one, two, and three or more) was examined for each of the three measures of impairment (parent-, teacher-, and youth-rated). Results are depicted in Figure 2. Using parent-reported impairment the threshold of three symptoms is generally supported. The rate of impaired girls increased by 100% from zero to one symptom (4% to 9%), 50% from one to two symptoms (9% to 14%), and 100% from two to three or more symptoms (14% to 28%). For teacher- and youth-reported impairment, the pattern was more linear.
Figure 2. Impairment by level of conduct disorder symptoms.
The likelihood of impairment for girls with three or more symptoms was compared with the likelihood for girls with none, one, and two symptoms of CD. A lack of a significant difference between symptom levels would suggest that functional impairment is equivalent at each level. Results are presented in Table 2. The likelihood of parent-rated impairment was significantly higher at three or more symptoms than at zero, one, or two symptoms. For example, the likelihood of impairment was twice as high among girls manifesting three symptoms compared with those with two symptoms (OR = 2.1, 95% CI = 1.6–2.8, p < .001). This effect did not vary by race.
Likelihood of Impairment at Different Symptom Thresholds
The same was true for teacher-rated impairment, although the magnitude of effect was not quite as strong: The likelihood of impairment was increased by 70% among girls manifesting three symptoms compared with those with two symptoms (OR = 1.7, 95% CI = 1.3–2.2, p = .002). Race did not interact with CD symptoms to predict teacher-rated impairment. According to youth report of impairment, there was no significant difference in the likelihood of being impaired between those with two and those with three or more symptoms (OR = 1.3, 95% CI = 1.0–1.6, p = .018); this was true across racial groups.
3. Is there significant overlap between CD symptoms and callousness and relational aggression?
A high level of callousness (i.e., greater than or equal to 1 SD above the mean for the sample) was reported at least once for approximately 44% of girls (40.5% weighted). High scores on callousness were more common among girls who met criteria for CD (68.2%; 65.5% weighted) than among girls who did not meet criteria for CD (36.9%; 33.8% weighted). This almost twofold increase in the rate of high level of callousness was statistically significant (OR = 3.7, 95% CI = 3.0–4.6, p < .001) and was not affected by race. Most girls who scored high on callousness (63.9%; 65.7% weighted), however, did not meet criteria for CD.
Nearly half of the sample (46.1%; 43.4% weighted) scored a standard deviation above the mean on relational aggression in at least 1 year. High scores on relational aggression were more common among girls who met criteria for CD (73.8%; 72.5% weighted) than among girls who did not meet criteria for CD (37.6%; 35.5% weighted). This level of overlap was statistically significant (OR = 4.8, 95% CI =3.8–5.9, p < .001) and was not affected by race. Most girls who scored high on relational aggression (62.5%; 64.6% weighted) did not meet criteria for CD.
4. Do relational aggression and/or callousness explain unique variance in impairment after controlling for DSM–IV CD and impairment at the previous assessment?
Because there were no a priori hypotheses about the nature of the association between callousness and relational aggression, and because the research to date has been conducted separately, their respective contribution to concurrent and later impairment controlling for CD was tested in separate GEE models. In each model, prior impairment (at time T − 1), diagnosis of CD, and either callousness or relational aggression were entered as main effects. The interaction between CD diagnosis and callousness/relational aggression also was tested. Interaction terms were dropped from the model if they were not statistically significant. For the predictive models, diagnosis of CD at time T − 1 and either callousness or relational aggression at time T − 1 were included to examine their contribution to impairment at time T. For the concurrent models, diagnosis of CD at time T and either callousness or relational aggression at time T were included with impairment at time T as the dependent variable. Results reported in Tables 3 and 4 are from weighted analyses.
Incremental Utility of Callousness to Impairment Ratings by Parent, Teacher, and Youth
Incremental Utility of Relational Aggression to Impairment Ratings by Parent, Teacher, and Youth
Because of the accelerated longitudinal design of the PGS, sample sizes varied by age. Although unbalanced designs are permitted with GEE regression modeling, to verify that girls who were included at the older ages were not unrepresentative of the sample as a whole, the interaction terms involving the cohort to which the participant belonged (Cohorts 5, 6, 7, or 8) and prior impairment at time T − 1 were tested in models predicting impairment at time T. None were found to be statistically significant (data not shown).
Across the three indices of impairment, callousness was significantly associated with a greater likelihood of impairment both concurrently and prospectively, with one exception (see Table 3). Relational aggression accounted for increased risk of parent- and teacher-rated impairment, both concurrently and prospectively, but not for youth-reported impairment (see Table 4).
The pattern of association between relational aggression and callousness and impairment, however, differed by informant. For parents, there was a threefold to fourfold increase in the odds of concurrent and later impairment as a function of meeting criteria for CD, with relational aggression and callousness generating odds ratios of about 2.0 or less. The reverse was true according to teacher-rated impairment; callousness and relational aggression were associated with a fourfold to fivefold increase in the odds of concurrent impairment, whereas the association between impairment and CD diagnosis was weaker. Prospectively, however, the effects of relational aggression and callousness on teacher-rated impairment were diminished (OR = 1.9). For youth report, CD increased the risk of impairment modestly, and only callousness accounted for unique variance in the risk for impairment, and only concurrently.
Only one significant interaction effect was detected: the interaction of relational aggression and CD on concurrent teacher-rated impairment (OR = 0.4, 95% CI = 0.3–0.7, p = .001; see Table 4). The effect, however, was in the opposite direction that one would have expected. Among those not relationally aggressive, the odds of teacher-reported impairment given the presence of CD were 3.1 times greater than the odds given the absence of CD (p < .001). In contrast, among girls who were high on relational aggression, the odds of teacher-reported impairment given the presence of CD were only 1.3 times greater than the odds given the absence of CD (p = .076).
DiscussionResults from the present study support and extend the existing research on DSM CD in girls. The use of a prospective, longitudinal dataset, beginning in early childhood, provided an opportunity to generate information about the developmental course, symptom threshold, overlap with and incremental validity in risk for impairment resulting from relational aggression and callousness that extends the results generated via cross-sectional studies and studies incorporating measures of delinquency.
In the present sample, in which DSM–IV CD symptoms were assessed in girls from ages 7–15 years by parents and youth, the lifetime, weighted prevalence of CD was 21.2%, the prevalence across age ranged from 4.9–8.9%, and the most common age of onset was prior to the age of 10 years. Approximately half of the more than 500 girls who met criteria for CD in at least 1 year during the period of assessment were reported to have manifest the first symptom at 7 years of age, and close to 90% who met DSM–IV criteria for CD had an onset before age 10.
At first glance, our results on prevalence and age of onset appear to be in contrast to the findings from large epidemiologic studies such as the B-CAMHS99 described by Maughan et al. (2004) and the Dunedin Longitudinal Study (Moffitt et al., 2001). There are a few explanations for this. The first is that the period of assessment in the present study did not extend far enough into adolescence, and therefore, a significant increase in prevalence during adolescence was not observed. Maughan et al. (2004) reported that the rate of CD in girls in the B-CAMHS99 increased from 0.3% at age 12 to 1.3% at age 13, 2.0% at age 14, and 3.3% at age 15. Thus, because we only assessed girls through age 15 and had the fewest observations at that age, our estimates of onset are biased toward the younger ages.
However, B-CAMHS99 is a cross-sectional study, not longitudinal. Thus, the rate of CD at each age is generated by separate groups of girls. Moreover, because the base rate of CD in the B-CAMHS99 was low (n = 42), age comparisons are based on small Ns, potentially yielding somewhat unreliable comparisons. Two possibilities may explain the very low rate of CD in the B-CAMHS99. Symptoms of CD were assessed via a screening measure, the response to which determined whether the remaining CD symptoms should be assessed. Following this, clinician-based diagnoses were generated. It is possible that the screening measure was not very sensitive for girls. In addition, the demographics of the CAMHS99 sample are much different than that in the PGS, even after weighting back to the population of the City of Pittsburgh. In the B-CAMHS99, 91% of the participants were White, and about 20% were living in single-parent households.
Another large epidemiologic and prospective study with which our data should be compared is the Dunedin Longitudinal Study, in which the rate of CD and development of new cases in girls was reported from ages 11–18 (Moffitt et al., 2001). In that study, the estimated prevalence of DSM–IV CD in girls was 18%, 16%, 22%, and 26% at ages 11, 13, 15, and 18, respectively, yielding a lifetime prevalence of 46%. These rates are higher than what were reported in the PGS and demonstrate only modest variability across ages 11–15 years. Because the rate of DSM–IV CD was so high, the authors used a cutoff of five or more CD symptoms for all further analyses on sex differences in CD. This new cutoff resulted in prevalence rates of 3%, 5%, 8%, and 3%, respectively. Although the “peak” for girls in both cumulative and new cases (i.e., five or more CD symptoms in the past 12 months) was age 15 (Moffitt et al., 2001), age of onset of the first symptom was not ascertained. One could reasonably posit, however, that accumulating five or more CD symptoms in a single year, would be relatively rare, and thus, the most common age of onset was probably less than 15 years of age.
We note that impairment was not used to define CD in the present study. In the DSM–IV, an impairment criterion was added for many mental disorders, including CD. There was little empirical support for adding the criterion. Recent evidence for depression, from data from the National Comorbidity Survey Replication, suggests that an impairment criterion is redundant with the distress and impairment inherent in the symptoms (Wakefield, Schmitz, & Baer, 2010). For CD, in which the symptoms are meant to reflect violating the basic rights of others and violations of social norms, the same case could be made vis-à-vis “impaired social functioning.” We suggest that future studies of CD provide prevalence data with and without the DSM–IV impairment criteria, so that comparisons can be made based on diagnoses derived from symptom criteria.
In only a few studies has age of onset of CD, as defined by the age at which the first symptom is manifest, in girls been assessed, but the results are consistent with our findings. Lahey et al. (1998) reported age of onset of CD using retrospective report in two separate samples: a clinical sample of 4–17-year-olds and a household sample of 9–17-year-olds, both of which were used for the DSM–IV field trials. Among the 24 girls meeting criteria for CD in the clinic sample, 15 (62.5%) reported a childhood onset. Of the 19 girls with CD in the household sample, 14 (73.7%) had a childhood onset (Lahey et al., 1998). In a study designed to test the theory that girls with CD followed a delayed onset pathway, McCabe, Rodgers, Yeh, and Hough (2004) found that among girls with a history of social service use, close to half had an onset of CD before the age of 10. Prospective studies are the best method for determining age of onset of CD, but none have been conducted in which CD symptoms are assessed from early in life. Côté, Zoccolillo, Tremblay, Nagin, and Vitaro (2001) reported that of 28 girls diagnosed with DSM–IV CD at age 15, 64.3% were characterized as following medium to high disruptive behavior problem trajectories from ages 6–12.
Together, data on age of onset assessed via retrospective recall or estimated from prospective studies of broad measure of disruptive behavior problems are generally consistent with those of the present study, in which a prospective assessment of age of onset was conducted, and suggest that the most common pathway to CD for girls is via an exacerbation or intensification of symptoms from childhood to adolescence, rather than initiation or acute onset of CD during adolescence. Clearly, such findings are highly significant for prevention and intervention research, with the present data suggesting that indicated preventions for the majority of girls should target the period of early to middle childhood. Moreover, these results call into question approaches to conceptualizing CD in girls that have at their foundation the assumption that most girls begin to manifest CD in adolescence, such as the “delayed-onset pathway” (Silverthorn & Frick, 1999).
Regarding the test of symptom threshold, the results vary by informant. For parent-reported impairment using the C-GAS, which has substantial data supporting its validity as a measure of global impairment, there is a significant difference in the likelihood of having a C-GAS score that falls at or below 60 at the level of three or more symptoms compared with two symptoms. The same result was found for teachers, but the comparison was not significant when the informants were the girls themselves. Because adults are the most commonly used informants regarding a child's level of functional impairment, one could make an argument that the results using parent and teacher report should be weighed more heavily than youth report in determining symptom threshold for a disorder. This could be refuted, however, by the possibility that when youths report specifically on the domain within which they are expert, their reports of impairment demonstrate greater validity, thus generating more reliable results.
Overall, it is difficult to fully defend the threshold of three symptoms on the basis of the results of the present study, but there is even less support for changing the symptom threshold. The pattern of association between impairment and number of symptoms was essentially linear for all three informants, although less so for parent informants. The linearity of the results, however, does not necessarily support lowering the threshold from three symptoms to two symptoms. Rather, the results renew the debate regarding the artificiality of categorical diagnoses and the loss of information on severity when ignoring the dose response nature of the association between symptoms of CD and outcomes (e.g., Fergusson & Horwood, 1995).
Finally, we addressed the extent to which callousness and relational aggression add to the concurrent and predictive association with impairment among girls with CD. These two constructs evolve from different theoretical traditions. Assessing individual differences in callousness in children has emerged as a means by which to link personality and disorder in children and identify a possible subtype of CD that is likely to persist into adulthood (Frick et al., 2000). Relational aggression has been proposed as the primary means by which girls engage in aggressive acts and thus serves as a female-specific form of aggressive behavior that could be included in the diagnostic nosology.
With regard to expanding the nosology to include either relational aggression or callousness, it is important to note that the current approach to identifying atypical rates of relational aggression and callousness (i.e., using a standard deviation above the mean to define high scores; Crick & Grotpeter, 1995; Frick et al., 2000) generates a lifetime rate that is approximately twice as high as the rate of CD. Although the overlap is significant, the majority of high scorers on relational aggression and callousness do not meet criteria for CD. This means that simply adding symptoms of relational aggression and callousness would likely result in a significant increase in the rate of CD.
One approach to determining whether these two constructs would be useful expansions of the current CD nosology is whether they explain unique variance in impairment and/or interact with CD to predict impairment. In general, the two constructs do provide additional information regarding current and later impairment after controlling for CD and previous level of impairment. In fact, teachers appear to experience callousness and relational aggression as more impairing than CD, although the magnitude of effect is not maintained from one year to the next. The criterion of explaining unique variance, however, was essentially met.
Given the fact that the rate of callousness and relational aggression is relatively high, an alternative approach to simply adding symptoms to the current nosology is to consider including subtypes. For callousness, this appears to be a viable option. The items are not better measured by another childhood disorder, and conceptually, it would provide a link to personality dimensions relevant to the study of antisocial behavior. The lack of significant interaction effects between callousness and CD, however, calls for caution in deciding how to move forward in incorporating psychopathy into the DSM nosology for CD in girls.
The data from the present study on relational aggression provide little guidance on how to proceed. Relational aggression explained unique variance in current and later parent- and teacher-rated impairment but not youth-rated impairment. The relationship between CD and teacher-reported impairment appeared to be moderated by relational aggression status, but in a way that was unexpected: The odds ratio for CD (1.3) among girls with high relational aggression was lower than that for girls with low relational aggression (OR = 3.1). Although this may be a spurious finding, the results suggest less stability in the nature of the association between relational aggression, CD, and teacher-reported impairment. From a measurement perspective, it is still not clear whether relational aggressive behaviors overlap too highly with ODD symptoms. Most items measuring relational aggression (e.g., spreading rumors about someone to make other children not like that person) appear to be more consistent with the ODD symptom “spiteful and vindictive.” Further tests of the utility of relational aggression in the context of both CD and ODD are needed. It may be that once a diagnosis of ODD is included in the model, relational aggression no longer provides unique information on impairment.
Despite the effect of race on the rate of CD, even after controlling for poverty, there were no significant effects of race on age of onset, symptom threshold, overlap with relational aggression and callousness, or utility of relational aggression and callousness to the diagnosis of CD in this sample of girls. Explaining the disproportionately higher rate of CD among African American girls will require further investigation that incorporates measures of perceived discrimination (Coker et al., 2009) and neighborhood context (Zalot, Jones, Forehand, & Brody, 2007), which have demonstrated empirical links to girls' conduct problems and which are associated but do not completely overlap with receipt of public assistance. The evidence for the validity of the DSM–IV CD for African American girls, however, is equally strong as the evidence for the validity of DSM–IV CD for European American girls.
Although the lack of boys in the present sample precludes the testing of sex differences in the present study, the results do have implications for the descriptions of purported sex differences in CD in DSM–V and the specificity of the language used to describe those differences. For example, in the text accompanying the description of DSM–IV CD, the statement that, “The ratio of males to females with Conduct Disorder is lowest for the Adolescent-Onset Type than for the Childhood-Onset Type” (American Psychiatric Association, 1994, p. 87), is unlikely to be true given the rarity of adolescent-onset CD in the present sample and is not consistent with data from the MECA study, in which the proportion of girls among youth with the adolescent-onset and childhood-onset types were generally equivalent (Lahey et al., 1998). The statement that, “Whereas confrontational aggression is more often displayed by males, females tend to use more nonconfrontational behaviors” (American Psychiatric Association, 1994, p. 88) may be somewhat misleading. “Nonconfrontational aggression” behaviors are not listed in DSM–IV CD, so it is unclear how this construct would be operationally defined. If relational aggression was being considered “nonconfrontational,” then it will be important to first confirm that such behaviors provide evidence of construct validity before listing them as alternative manifestations of aggression within the diagnosis of CD.
In summary, the most definitive finding of the present study was that the onset of CD for most girls is in childhood. The lack of a definitive finding on symptom threshold speaks to the continued struggle of forcing a set of behaviors with a continuous distribution into categories, but this is not specific to girls or to CD for that matter. Thus, there is no evidence supporting the need to lower the threshold of CD from three to two symptoms and minimal support for maintaining the current symptom threshold of three symptoms. There does appear to be some support for continued exploration of callousness as a possible subtype of an additional set of symptoms within the diagnosis of CD, but the recommendation for relational aggression is to conduct further tests of its utility in the context of both CD and ODD.
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Submitted: June 4, 2009 Revised: January 27, 2010 Accepted: January 28, 2010
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Source: Journal of Abnormal Psychology. Vol. 119. (4), Nov, 2010 pp. 689-698)
Accession Number: 2010-19529-001
Digital Object Identifier: 10.1037/a0019346
Record: 14- Title:
- An empirical examination of distributional assumptions underlying the relationship between personality disorder symptoms and personality traits.
- Authors:
- Wright, Aidan G. C.. Western Psychiatric Institute and Clinic, University of Pittsburgh School of Medicine, PA, US, wright.aidan@gmail.com
Pincus, Aaron L.. Department of Psychology, Pennsylvania State University, PA, US
Lenzenweger, Mark F., mlenzen@binghamton.edu - Address:
- Lenzenweger, Mark F., Department of Psychology, State University of New York at Binghamton, Science IV, Binghamton, NY, US, 13902-6000, mlenzen@binghamton.edu
- Source:
- Journal of Abnormal Psychology, Vol 121(3), Aug, 2012. pp. 699-706.
- NLM Title Abbreviation:
- J Abnorm Psychol
- Page Count:
- 8
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- The Journal of Abnormal and Social Psychology; The Journal of Abnormal Psychology; The Journal of Abnormal Psychology and Social Psychology
- ISSN:
- 0021-843X (Print)
1939-1846 (Electronic) - Language:
- English
- Keywords:
- count regression, hurdle models, personality disorders, personality traits, zero-inflated distributions, symptoms
- Abstract:
- This article examines the relationship between personality disorder (PD) symptoms and personality traits using a variety of distributional assumptions. Prior work in this area relies almost exclusively on linear models that treat PD symptoms as normally distributed and continuous. However, these assumptions rarely hold, and thus the results of prior studies are potentially biased. Here we explore the effect of varying the distributions underlying regression models relating PD symptomatology to personality traits using the initial wave of the Longitudinal Study of Personality Disorders (N = 250; Lenzenweger, 1999), a university-based sample selected to include PD rates resembling epidemiological samples. PD symptoms were regressed on personality traits. The distributions underlying the dependent variable (i.e., PD symptoms) were variously modeled as normally distributed, as counts (Poisson, Negative-Binomial), and with two-part mixture distributions (zero-inflated, hurdle). We found that treating symptoms as normally distributed resulted in violations of model assumptions, that the negative-binomial and hurdle models were empirically equivalent, but that the coefficients achieving significance often differ depending on which part of the mixture distributions are being predicted (i.e., presence vs. severity of PD). Results have implications for how the relationship between normal and abnormal personality is understood. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Personality Disorders; *Personality Traits; *Statistical Regression; *Symptoms; Models
- Medical Subject Headings (MeSH):
- Adolescent; Female; Humans; Longitudinal Studies; Male; Models, Psychological; Personality; Personality Disorders; Personality Inventory; Self Report; Young Adult
- PsycINFO Classification:
- Personality Disorders (3217)
- Population:
- Human
Male
Female - Age Group:
- Adulthood (18 yrs & older)
- Tests & Measures:
- International Personality Disorder Examination
Revised Interpersonal Adjective Scales–-Big Five DOI: 10.1037/t10655-000 - Grant Sponsorship:
- Sponsor: National Institute of Mental Health, US
Grant Number: MH45448; F31MH087053
Recipients: Wright, Aidan G. C.; Lenzenweger, Mark F. - Methodology:
- Empirical Study; Quantitative Study
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- First Posted: Jun 25, 2012; Accepted: May 22, 2012; Revised: Apr 27, 2012; First Submitted: Oct 2, 2011
- Release Date:
- 20120625
- Correction Date:
- 20130114
- Copyright:
- American Psychological Association. 2012
- Digital Object Identifier:
- http://dx.doi.org/10.1037/a0029042
- PMID:
- 22732004
- Accession Number:
- 2012-16776-001
- Number of Citations in Source:
- 30
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- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2012-16776-001&site=ehost-live">An empirical examination of distributional assumptions underlying the relationship between personality disorder symptoms and personality traits.</A>
- Database:
- PsycINFO
An Empirical Examination of Distributional Assumptions Underlying the Relationship Between Personality Disorder Symptoms and Personality Traits
By: Aidan G. C. Wright
Western Psychiatric Institute and Clinic, University of Pittsburgh School of Medicine;
Department of Psychology, The Pennsylvania State University;
Aaron L. Pincus
Department of Psychology, The Pennsylvania State University
Mark F. Lenzenweger
Department of Psychology, State University of New York at Binghamton;
Personality Disorders Institute, Weill Cornell Medical College;
Acknowledgement: This research was supported by grants (MH45448, Lenzenweger; F31MH087053, Wright) from the National Institute of Mental Health, Washington, DC. The views and opinions contained within are solely those of the authors and do not reflect the official position of the funding source.
This manuscript reports on work that represents a portion of the first author's dissertation. We thank Jerry S. Wiggins for providing consultation on the initial use of the Revised Interpersonal Adjectives Scales–Big Five, and Lauren Korfine for project coordination in the early phase of the study. We are indebted to Michael N. Hallquist for generously sharing his time to instruct the first author on the intricacies of the R statistical package. Emily B. Ansell, Nicholas R. Eaton, Christopher J. Hopwood, and Kristian E. Markon each provided helpful comments on an early draft of this work.
Personality disorder (PD) researchers have called for an integration of normal and pathological personality functioning within comprehensive dimensional models of personality (e.g., Depue & Lenzenweger, 2005; Widiger, Livesley, & Clark, 2009). It has been argued that normal personality exists on a continuum of functioning with PDs (Widiger & Trull, 2007). A large empirical literature examining the relationship between traits and PDs contributed to the seminal decision to use a dimensional trait system to conceptualize phenotypic variation in PD in DSM-5 (Skodol et al., 2011). This research relies primarily on cross-sectional correlations and linear regression to model the relationship between personality traits and PD symptoms. However, these analytic tools suffer from limitations when the underlying distribution of the variables is severely non-normal, as is the case with PD symptoms in the population. Alternative approaches that more accurately model the observed PD symptom distribution may provide better estimates of the relationship between personality and its disorder, and may offer new insights into the nature of that relationship.
The extant nosology of PDs represents personality pathology as a collection of “distinct clinical syndromes” (p. 689; American Psychiatric Association, 2000) that differ categorically from normative functioning and each other. These distinctions have been criticized as arbitrary and lacking in robust scientific support (Widiger & Trull, 2007), and diagnostic criteria treated as dimensional markers for disorders perform better by empirical standards (Morey et al., 2007). However, measuring disorders dimensionally cannot confirm their continuity with normal functioning (Lenzenweger & Clarkin, 2005), and the accumulated research presents a mixed picture related to this issue. Meta-analyses (Samuel & Widiger, 2008; Saulsman & Page, 2004) demonstrate that basic personality traits exhibit significant and replicable relationships to PD, but the association between traits and PDs are generally only modest in size (Mdn |r| = .15; range = .02–.54; Samuel & Widiger, 2008). Furthermore, in regression models, the five-factor domains and facets generally only explain a minority of the variance in PDs (e.g., Bagby, Costa, Widiger, Ryder, & Marshall, 2005). Thus, normal personality traits and PD are not interchangeable representations of functioning, despite clearly recognizable shared content (Krueger et al., 2011). Other researchers and theorists have highlighted the general impairment associated with PDs (Kernberg, 1984; Livesley & Jang, 2005; Pincus, 2005). Relatedly, a number of investigators have found that PD is primarily characterized by higher neuroticism, lower conscientiousness, and lower agreeableness, with less in the way of distinction beyond this core trait profile (Hopwood et al., 2011; Morey et al., 2002; Saulsman & Page, 2004). Thus, it may be that a particular combination of traits reflects personality pathology generally, with further differentiation occurring in the presence of this profile.
The question of continuity in personality and its pathology has taken center stage in the development of the DSM-5, which will shift to a dimensional model based on the robust empirical findings suggesting that PD is fundamentally dimensional in nature (Skodol et al., 2011). However, in part because of issues raised here, DSM-5 will distinguish between personality dysfunction and the description of that dysfunction using pathological traits (Hopwood et al., 2011; Krueger et al., 2011). Indeed, the sum of the empirical literature leaves an unclear picture of how PD and personality traits are related to each other. It may be that PD is dimensionally continuous with basic personality traits (e.g., Depue & Lenzenweger, 2005). Alternatively, continuities and discontinuities may exist in these relationships, with some driven by the presence of PD and others, perhaps more subtle, driven by the severity of PD beyond its presence (see Lenzenweger & Clarkin, 2005). What is clear is that the theoretical goal of integrating normative personality traits and PD remains elusive.
The key theoretical questions of how personality and PD relate to one another are also inherently questions of methodology. Dimensional approaches can make varied distributional assumptions that may have relevance for advancing the understanding of the relationship between normal and abnormal personality. Research examining this question relies almost exclusively on standard correlation and linear regression. These approaches make several important assumptions (i.e., normality of residuals, homoscedasticity, linearity of relationship, independence), that, when violated, can bias estimation (Cohen, Cohen, West, & Aiken, 2003). Less serious are biased standard errors, which can produce incorrect significance tests for parameters. More serious violations occur when the actual effect of a relationship is misestimated. A major contributing source to the violation of these assumptions is the distribution of the variables being modeled.
In the population, the actual distribution of PD symptoms is highly positively skewed with a majority of individuals suffering from no symptoms. Figure 1 provides an example of such a distribution using the narcissistic personality disorder (NPD) features in the first wave of the Longitudinal Study of Personality Disorders (LSPD; Lenzenweger, 1999), the dataset used here. This histogram is characteristic of a count distribution. Modeling techniques for counts are primarily based on the Poisson and Negative-Binomial (NB) distributions and can be used for regression when appropriate (Cameron & Trivedi, 1998; Long, 1997). Of note is the large number of zeros in the distribution, which has important implications for modeling the relationship between the symptoms and other variables of interest. These zeros carry important information about who does and does not possess symptoms of PD. With large numbers of zeros in the data, two potential modifications to basic count models are recommended: zero-inflated and hurdle models (Atkins & Gallop, 2007; Cameron & Trivedi, 1998; Long, 1997). Zero-inflated models estimate a group of individuals based on the excess of zeros beyond a standard Poisson or NB model, which are treated as individuals who can only take on a zero value. Hurdle models make a binary distinction between those who have a zero value versus those who have a nonzero value. Despite this distinction in the treatment of zero-values, both models estimate separate regression coefficients for the zero versus nonzero (e.g., absence vs. presence of PD) and the count (e.g., severity of PD) portion of the models. These models are ideal for evaluating whether the traits that give rise to any symptoms of PDs are the same as those that predict the number of symptoms once present.
Figure 1. Observed Longitudinal Study of Personality Disorders (LSPD) narcissistic personality disorder features.
The current study explores the relationship of personality traits to PD symptoms using regression models capable of appropriately modeling the distribution of symptoms in the population. We use the LSPD sample, which is made up of participants recruited both with and without significant pathology, unlike samples selected based on shared diagnostic status or for high levels of pathology. As a result, the distributions of PD symptoms closely matches those found in epidemiological samples (Lenzenweger, 2008). The LSPD dataset is ideal for the types of investigations pursued here because it captures the boundary between those individuals whose personalities function well and those who evidence dysfunction.
Our first aim was to evaluate whether the assumptions of linear regression are violated when predicting PD symptoms and to compare Poisson, zero-inflated Poisson (ZIP), Poisson hurdle (PH), negative-binomial (NB), zero-inflated negative-binomial (ZINB), and negative-binomial hurdle (NBH) regression models that predict PD symptoms from personality trait scores. Our second and more substantive aim involves comparing the patterns of significant regression coefficients to determine the effect of varying distributional assumptions on the relationship between traits and PDs. Attention is also given to differences in patterns in the prediction of presence versus severity of PD features in the zero-inflated/hurdle models.
Method Participants
Detail concerning the participant selection procedure in the LSPD is given elsewhere (Lenzenweger, 1999). The 250 participants were drawn from a nonclinical university population, are balanced on gender (53% women), and the mean age at entry into the study was 18.88 years (SD = 0.51). Approximately half of the participants were selected based on putative positive PD status as assessed by a self-report PD screener. This ensured an adequate sampling of PD pathology in a nonclinical population. Based on diagnostic assessments conducted by experienced clinicians, 11% of the participants qualified for a PD diagnosis of some sort, and 45.2% met the criteria for an Axis I diagnosis (Lenzenweger, 1999). Rates of diagnosed PDs in the sample were as follows: paranoid = 1.2%, schizoid = 1.2%, schizotypal = 1.6%, antisocial = 0.8%, borderline = 1.6%, histrionic = 3.5%, narcissistic = 3.1%, obsessive–compulsive = 1.6%, passive-aggressive = 0.8%, avoidant = 1.2%, dependent = 0.8%, and not otherwise specified = 4.3%. It is important to note that these rates closely mirror the rates of PD found in large epidemiological samples (Lenzenweger, 2008).
Measures
At baseline, participants completed a diagnostic clinical interview and self-report personality measures. Only data from these initial assessments are used here.
The International Personality Disorder Examination (IPDE; Loranger, 1999) was used to assess PD symptomatology. The DSM–III–R criteria were assessed in this study because these were in effect at the time the LSPD was undertaken. The interrater reliability for IPDE assessments (based on intraclass correlation coefficients) was excellent, ranging between .84 and .92 for all PD dimensions used for this study. For each symptom, an individual may receive a score of 0 (absent or normal), 1 (exaggerated/accentuated), or 2 (criterion/pathological). These values are summed within each disorder to create a count of disorder related features.
The Revised Interpersonal Adjective Scales–Big Five (IASR-B5; Trapnell & Wiggins, 1990) contains 124 trait descriptive adjectives rated on a 0 to 8 scale that provides scores for the personality trait dimensions of Dominance, Affiliation, Conscientiousness, Neuroticism, and Openness. Coefficient alphas ranged from .82 to .96.
ResultsA series of regression models were estimated in R Package PSCL (Zeileis, Kleiber, & Jackman, 2008). Each PD's count and the Total PD count were regressed on each personality trait score separately. A model was estimated for each personality trait separately in keeping with past literature, and because the traits are orthogonal in theory, but in practice often exhibit relationships that attenuate regression coefficients when entered simultaneously in a model. Trait predictors were standardized. For each trait-PD pairing, a set of models was estimated with Normal, Poisson, ZIP, PH, NB, ZINB, and NBH distributions specified for the PD counts.
The linear regression models were evaluated by testing linearity, normality of the residuals, and homoscedasticity. A minority (22%) of the models violated the assumption of linearity. However, all model residuals exhibited significant skewness (M = 2.75; range = 1.83–4.41) and excess kurtosis (M = 10.50; range = 3.59–25.89). Visual analyses suggested that the assumption of homoscedasticity was untenable for all models. In addition, 36% of the models predicted negative symptom counts. Thus, the assumptions of linear regression models do not hold and a considerable proportion result in the prediction of impossible values, all of which emphasizes the need for count based models.
Poisson regression models make strict assumptions about the variance of the observations that are frequently violated in applied research (Coxe, West, & Aiken, 2009). The NB differs from the Poisson in that it estimates an additional parameter for the variance of counts, and therefore these two models are nested and can be compared using likelihood ratio tests (LTRs; Long, 1997). In each case, the NB model was a significant improvement over its Poisson counterpart. Models which account for the large stack of zeros in the data using either zero-inflation or a hurdle are non-nested relative to the basic count models and each other, and therefore require an alternative test. To compare non-nested models, we employed a Vuong (1989) non-nested LRT. The Vuong LRT compares two models under the null hypothesis “that the competing models are equally close to the true data generating process” (p. 307) such that a significant statistic favors one model over the other, and a nonsignificant statistic suggests that the models are equivalent. When models are quantitatively equivalent additional criteria are required to select between models. One common approach is to select the model with the fewest estimated parameters, placing a premium on parsimony. Yet there may be a conceptual/theoretical preference for considering more complex models under these conditions. As Atkins and Gallop (2007) argue, a histogram like that depicted in Figure 1 would appear to suggest two separate processes: There are those individuals without any personality pathology, and among those that have it, a range of severity. They go on to note that the two-step count models are well-suited for investigating “psychological models in which there are two processes and where the determinants of those processes differ” (p. 733). Our results of a comprehensive comparison of models suggest that with few exceptions, the NB, ZINB, and NBH models are equivalent. When a model was favored, it was a two-step model, and between those the hurdle models. In addition, a number of ZINB models were nonidentified. Therefore, we retain for consideration the NB and NBH models.
Table 1 reports the regression coefficients for the Normal, NB, and NBH and their significance. No formula exists for transforming the different results to the same effect size for direct comparison across models. Therefore, what is most informative is the valence and significance level of each coefficient. Odds/rate ratios of 1.0 indicate no effect, and those below 1.0 indicate a negative association between predictor and outcome. Given the number of coefficients we highlight notable findings, and refer readers to Table 1 for a more detail. Importantly, similar patterns emerge across all models. Specifically, radical differences, such as a valence change, do not occur. However, pattern differences in significant coefficients across steps of the NBH models suggest different processes associated with the presence versus severity of PD symptoms of different types. We briefly summarize these findings organized around the trait dimensions.
Summary of Coefficients From Models Regressing Personality Disorder Symptoms on Personality Traits
Summary of Coefficients From Models Regressing Personality Disorder Symptoms on Personality Traits
Dominance most often predicted PD presence (schizoid, histrionic, dependent, obsessive-compulsive) and less commonly PD severity (narcissistic) or both (schizotypal, avoidant). Affiliation, in contrast, tended to predict both presence and severity (schizoid, schizotypal, antisocial, borderline, narcissistic, and obsessive-compulsive) and otherwise just presence (paranoid, avoidant) when considering individual PDs. Conscientiousness only predicted PD presence (paranoid, borderline, narcissistic, dependent). Neuroticism was a strong predictor, most commonly of both presence and severity (paranoid, schizotypal, borderline, histrionic, dependent, obsessive-compulsive). Interesting deviations from this include the fact that neuroticism only predicted severity in antisocial, but not presence, and presence but not severity in narcissistic features. Openness only predicted presence of Narcissistic PD, a result that is not easily interpreted. Finally, the Total PD symptom model is considered separately as the hurdle step predicting presence reflects a more stringent step between those with any PD and those with none at all. Here only neuroticism is predictive of both PD presence and severity, although conscientiousness and affiliation also predict severity.
DiscussionThe current study addresses an implicit assumption and likely limitation in much of the prior work linking personality traits and PD—specifically, although PD is not a normally distributed phenomenon in the population, it has consistently been modeled as such. First, we demonstrated that the basic assumptions of linear regression are violated and frequently result in the prediction of impossible values (i.e., negative counts). Second, we found that NB and NBH models do a comparable job of fitting the count distributions of PD symptoms and they cannot be distinguished quantitatively. Nevertheless, despite the flexibility of the NB distribution to account for a large proportion of individuals with zero values, this feature of the data is suggestive of distinct processes that are worth examining via two-step count models (Atkins & Gallop, 2007; Long, 1997). With these models interesting differences emerge across the two steps. When predicting individual diagnostic constructs, results suggest that neuroticism and affiliation are predictive of both PD presence and severity, whereas dominance is more often, and conscientiousness is exclusively, predictive of the presence of PD symptoms.
These results have implications for understanding the relationship between normal-range personality traits and PD. Issues related to both dimensionality and continuity in personality and PD have emerged from the proposed revisions for DSM-5. The proposed two-step diagnostic process for DSM-5 defines PD using a continuum of self/interpersonal impairment, with separate maladaptive personality traits provided to characterize phenotypic variation in the expression of an individual's core personality pathology. The models employed here are consistent with this approach, distinguishing between aspects of personality related to the presence of PD, and those related to its expression once present. Contrasting the results of the models predicting the count of all PD symptoms with the other model is also informative. The Total PD hurdle models are special in that they serve to represent general personality pathology, and the first step differentiates between those who have absolutely no pathology from those with any degree of pathology. The Total PD models suggest that only neuroticism differentiates those with any pathology from those without, which is consistent with research that shows it is an important predictor of myriad public health outcomes, psychological and otherwise (Lahey, 2009). Neuroticism also predicts severity along with low affiliation and low conscientiousness. This pattern of associations with severity was the same as that found by Hopwood and colleagues (2011) in a clinical sample. Findings here point to the fact that other variables besides normative traits (with the exception for a propensity to experience negative emotions) are responsible for the presence any PD, although other traits can characterize the variability in phenotypic expression of distinct PDs and overall PD severity. The implication is that normative traits alone are not ideal to differentiate normal from abnormal. The DSM-5 proposal to draw on self and interpersonal processes to define general personality pathology with traits used to clarify specific forms of PD is consistent with these results, although future research should incorporate the distinct predictors in these models to formally test the proposal.
A complementary way of understanding these results is that basic traits exert themselves at different levels of pathology. Limited prior research suggests that the level of traits across the spectrum of PD is nonlinear (O'Connor, 2005), indicating that normative traits may be more or less informative for distinguishing individuals at different levels of PD dimensions. Figure 2 illustrates this, continuing with NPD as the exemplar. Trait scores for those individuals without symptoms occur across the range of values, indicating that knowing someone's trait level without knowing their pathology is often diagnostically uninformative. For example, there are individuals at all levels of Dominance, including high levels, without any NPD symptoms. Yet, once there is any narcissistic pathology, rising severity is associated with increases in dominance. The opposite is true with neuroticism. Those without narcissistic pathology are lower on average, but once pathology is present, neuroticism is unrelated to severity. These fine-grained relationships suggest that PDs are not reducible to sums of basic traits, but are more complex in their structure of associations.
Figure 2. Scatter plots of personality trait scores and narcissistic personality disorder (NPD) features.
Several caveats must be mentioned. First, our present sample was more homogenous in age, education, and social class than the U.S. population. Second, given that the participants were selected from a population of first-year university students, the sample may have been somewhat censored for individuals affected by very severe PDs. However, the results from the linear regression models are highly consistent with prior work (e.g., Samuel & Widiger, 2008), suggesting generalizability. Additionally, the distributions of all psychiatric disorders in the LSPD sample are consistent with the U.S. population distribution (Kessler, Chiu, Demler, & Walters, 2005; Lenzenweger, 2008). Third, although some may feel that running individual models for each trait does not provide the most accurate picture of these relationships, the overwhelming majority of prior research adopts this approach, and therefore we employed it to provide a clear comparison for readers. Fourth, we should highlight that the linear regression models are still quite robust, and exhibit patterns of associations consistent with the NB models. Thus, we are confident that prior studies have accurately identified general relationships between traits and PD. However, a necessary next step in the empirical study of PD is to move beyond this level of analysis to elucidate the exact structure of these relationships in addition to identifying etiologic and mechanistic processes (e.g., underlying neurobehavioral systems, see Depue & Lenzenweger, 2005).
In summary, we examined the effect of varying the distribution of PD symptoms in regression models with personality traits. To appropriately model them requires the use of count distributions, and two-step count models provide opportunities to examine discontinuities in these relationships. In the past, modeling the distributions implemented here might have been more challenging, but a number of user-friendly statistical packages now include these as standard features. We used R, but Mplus, Stata, SAS, and SPSS can employ some or all of these distributions. When these approaches are adopted, a more refined picture emerges suggesting that PD is not merely the tail end of a continuous distribution of normal traits, and the traits associated with the presence of PD are not always those associated with increasing severity. Although we do not argue that these results are definitive and recognize that they should be replicated, we suggest that these analytic approaches are more appropriate, have the potential to elucidate some of the issues associated with continuity and discontinuity in personality and its pathology, and inform ongoing efforts to refine the diagnosis of PD.
Footnotes 1 The distribution in Figure 1 is highly representative of each of the PDs in the LSPD.
2 Full results available upon request.
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Submitted: October 2, 2011 Revised: April 27, 2012 Accepted: May 22, 2012
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Source: Journal of Abnormal Psychology. Vol. 121. (3), Aug, 2012 pp. 699-706)
Accession Number: 2012-16776-001
Digital Object Identifier: 10.1037/a0029042
Record: 15- Title:
- Analysis of Minnesota Multiphasic Personality Inventory-2-Restructured Form response bias indicators as suppressors or moderators in a medical setting.
- Authors:
- Wershba, Rebecca E.. Department of Psychology, Arizona State University, AZ, US, werre5@gmail.com
Locke, Dona E. C.. Department of Psychiatry and Psychology, Mayo Clinic, Scottsdale, AZ, US
Lanyon, Richard I.. Department of Psychology, Arizona State University, AZ, US - Address:
- Wershba, Rebecca E., Department of Psychiatry, Cambridge Health Alliance, 1493 Cambridge Street, Cambridge, MA, US, 02139-9991, werre5@gmail.com
- Source:
- Psychological Assessment, Vol 27(2), Jun, 2015. pp. 733-737.
- NLM Title Abbreviation:
- Psychol Assess
- Page Count:
- 5
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- Psychological Assessment: A Journal of Consulting and Clinical Psychology
- ISSN:
- 1040-3590 (Print)
1939-134X (Electronic) - Language:
- English
- Keywords:
- MMPI-2-RF, epilepsy, response bias, nonepileptic seizures
- Abstract:
- The use of response bias indicators in psychological measurement has been contentious, with debate as to whether they actually suppress or moderate the ability of substantive psychological indicators to identify the construct of interest. Suppression would indicate that predictor variables contain invalid variance that the bias indicators can suppress, while moderation would indicate differential levels of predictive validity at different levels of bias. Response bias indicators on the Minnesota Multiphasic Personality Inventory (MMPI)-2-Restructured Form (MMPI-2-RF) [infrequent responses (F-r), infrequent somatic responses (Fs), infrequent psychopathology responses (Fp-r), adjustment validity (K-r), uncommon virtues (L-r), symptom validity (FBS-r), and Response Bias Scale (RBS)] were tested to determine whether they suppressed or moderated the ability of the Restructured Clinical Scale 1 (RC1) and Neurologic Complaints (NUC) scale to discriminate between epileptic seizures (ES) and nonepileptic seizures (NES, a conversion disorder that is often misdiagnosed as ES). The MMPI-2-RF was completed by 399 patients with a confirmed diagnosis of ES or NES via Epilepsy Monitoring Unit evaluation. Moderated logistic regression was used to test for moderation, and logistic regression was used to test for suppression. Most of the response bias variables showed a suppressor effect, but moderator effects were not found. These findings extend the use of bias indicators to a psychomedical context. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Epilepsy; *Inventories; *Minnesota Multiphasic Personality Inventory; *Response Bias; *Seizures
- PsycINFO Classification:
- Clinical Psychological Testing (2224)
Psychological & Physical Disorders (3200) - Population:
- Human
Male
Female - Tests & Measures:
- Minnesota Multiphasic Personality Inventory-2-Restructured Form
Response Bias Scale
Restructured Clinical Scale 1
Neurologic Complaints Scale
True Response Inconsistency Scale
Variable Response Inconsistency Scale
Minnesota Multiphasic Personality Inventory - Grant Sponsorship:
- Sponsor: University of Minnesota Press
Other Details: MMPI-2/MMPI-2-RF rescoring grant
Recipients: No recipient indicated - Methodology:
- Empirical Study; Quantitative Study
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- First Posted: Mar 2, 2015; Accepted: Dec 15, 2014; Revised: Nov 21, 2014; First Submitted: Jun 18, 2013
- Release Date:
- 20150302
- Correction Date:
- 20150608
- Copyright:
- American Psychological Association. 2015
- Digital Object Identifier:
- http://dx.doi.org/10.1037/a0038802
- PMID:
- 25730164
- Accession Number:
- 2015-08363-001
- Number of Citations in Source:
- 12
- Persistent link to this record (Permalink):
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- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2015-08363-001&site=ehost-live">Analysis of Minnesota Multiphasic Personality Inventory-2-Restructured Form response bias indicators as suppressors or moderators in a medical setting.</A>
- Database:
- PsycINFO
Analysis of Minnesota Multiphasic Personality Inventory-2-Restructured Form Response Bias Indicators as Suppressors or Moderators in a Medical Setting / BRIEF REPORT
By: Rebecca E. Wershba
Department of Psychology, Arizona State University
Dona E. C. Locke
Department of Psychiatry and Psychology, Mayo Clinic, Scottsdale, Arizona
Richard I. Lanyon
Department of Psychology, Arizona State University
Acknowledgement: Rebecca E. Wershba is now at Department of Psychiatry, Cambridge Health Alliance.
This research was supported in part by an MMPI-2/MMPI-2-RF rescoring grant from the University of Minnesota Press. We thank Roger Millsap, PhD, and Joseph G. Hentz, MS, for their statistical input.
Response bias, as assessed by what have traditionally been termed validity scales, has long been a source of concern for psychologists and others who create and use psychological assessment instruments. Response bias has been described as a “consistent tendency to respond inaccurately to a substantive indicator, resulting in a systematic error in prediction” (McGrath, Mitchell, Kim, & Hough, 2010). Response bias might create an artificially good impression of a person’s psychological functioning; this is known as positive impression management (PIM). Conversely, negative impression management (NIM) refers to a response bias that indicates functioning that is worse than the actual condition.
The Minnesota Multiphasic Personality Inventory (MMPI)-2-Restructured Form (MMPI-2-RF; Ben-Porath & Tellegen, 2008) includes a number of response bias indicators, both for PIM, uncommon virtues (L-r) and adjustment validity (K-r), and for NIM, infrequent responses (F-r); infrequent somatic responses (Fs); and infrequent psychopathology responses (Fp-r). Additional bias indicators include symptom validity (FBS-r) and Response Bias Scale (RBS), both of which include aspects of PIM and NIM. The MMPI-2-RF and MMPI-2 validity scales have been shown to be effective at detecting NIM (Gervais, Lees-Haley, & Ben-Porath, 2007; Wygant et al., 2009) and PIM (Baer, Wetter, Nichols, Greene, & Berry, 1995; Sellbom & Bagby, 2008) and distinguishing between malingering and genuine medical conditions (Sellbom, Wygant, & Bagby, 2012). The wealth of current research plus a variety of bias indicators makes the MMPI-2-RF an excellent tool for further research in response bias.
There are two possible ways in which response bias scales can improve prediction of a criterion by a substantive indicator. It could act as a suppressor, which means that it suppresses invalid variance in a companion predictor variable. For example, the K correction was designed for the original MMPI to suppress invalid variance due to the expected effect of “defensiveness” in assessing psychopathology, on the assumption that highly defensive persons are less forthcoming about impaired psychological functioning. A response bias scale can also act as a moderator by changing the predictive ability of the substantive indicators at lower or higher levels. For example, given a low response bias scale score, the substantive indicator may predict a criterion with a high degree of accuracy, but as response bias increases, the substantive indicator may lose predictive validity.
Although it is generally accepted that response bias scores reflect a respondent’s general approach to responding to a test, there is debate as to whether high scores affect the substantive scales in a clinically significant way. Indeed, McGrath et al.’s (2010) review article concluded that there is insufficient evidence that response bias indicators affect the relationship between a substantive indicator and a criterion to a practically meaningful extent. The studies reviewed involved personality assessment, workplace variables, emotional disorders, eligibility for disability, and forensic assessment. There was insufficient evidence for drawing conclusions regarding the latter three populations, but for the first two, evidence indicated only mild support for the utility of bias indicators. However, a major concern regarding this review by prominent researchers in the bias indicator field (e.g., Rohling et al., 2011) involved the overly wide-ranging nature of conclusions reached based on the articles reviewed. The issues for the main populations studied in that review (those in the workplace) primarily involved PIM, as opposed to the NIM concerns that are prominent in psychopathological, forensic, or litigating populations. An additional concern with the review is that each study addressed either moderation or suppression but not both, raising the possibility that response bias might have affected the substantive indicators’ accuracy in ways that were not measured.
One promising area in which to study the clinical effect of response bias is the psychomedical field. An appropriate population for such a study is patients with a differential diagnosis of epileptic seizures (ES) or nonepileptic seizures (NES), a conversion disorder that is often misdiagnosed as ES (Cragar, Berry, Fakhoury, Cibula, & Schmitt, 2002). Clinician observation of seizure activity may raise suspicion for NES, as the seizure-like activity in NES is typically physiologically inconsistent with epileptic seizure semiology. A patient with a possible diagnosis of NES may be sent to an Epilepsy Monitoring Unit (EMU), in which video-electroencephalogram (vEEG) is used as the “gold standard” to differentiate between ES and NES. Here, vEEG detects the presence or absence of epileptiform discharge during observed seizure-like activity, and can be used to accurately confirm a diagnosis that might be suggested by an office exam or psychological assessment.
The MMPI-2-RF is one such test that has been utilized to discriminate between ES and NES. Recent research findings have shown that a cut score of 65 on the Restructured Clinical Scale 1 (RC1; somatic complaints) scale discriminated between these disorders at an overall hit rate of 68% (Locke et al., 2010). The Neurological Complaints (NUC) scale was also a useful discriminator with an overall hit rate of 67%. It is possible that the utility of the substantive scales could be improved by incorporating the potential suppressor or moderator effect of MMPI-2-RF validity scale scores on the RC1 or NUC scales.
Aim of the Present StudyThe goal of the present study was to evaluate whether response bias indicators (K-r, L-r, F-r, Fs, Fp-r, FBS-r, RBS) impact substantive scales in this unique population and to determine whether any such impact occurs through moderation or suppression.
Method Participants
Participants were patients who had been evaluated in the Epilepsy Monitoring Unit (EMU) at the Mayo Clinic Hospital in Phoenix, Arizona between April 2001 and April 2009. This sample has been previously utilized in three studies with different goals and analyses (Locke et al., 2010; Locke & Thomas, 2011; Thomas & Locke, 2010). A total of 664 patients were admitted during this time period. All were given the MMPI-2 as part of a standard neuropsychological evaluation, which was then rescored to the MMPI-2-RF. Diagnosis was determined by a board certified neurologist and fellowship-trained epilepsy specialist on the basis of the vEEG findings during admission.
Of the 664 patients, 221 were diagnosed with epilepsy, 219 were diagnosed with NES, 24 with both ES and NES, 166 were indeterminate, and 34 patients were diagnosed with other physiological disorders such as sleep, autonomic nervous system, or vascular disorders. Details of the specific criteria for each diagnostic category can be found in Locke et al. (2010). Patients other than pure ES or NES were excluded from the study. We also excluded the readmissions among the NES and ES patients (n = 11) and protocols invalid due to missing items or random responding (True Response Inconsistency Scale (TRIN) or Variable Response Inconsistency Scale (VRIN) >80, n = 24; cannot say ≥15, n = 6). After exclusions, the sample included 196 ES and 203 NES patients. Demographic and medical history information was collected via record review.
Statistical Analyses
To determine whether moderation or suppression existed for these patients, binary logistic regression was utilized. Moderation was tested through moderated logistic regression. Suppression was tested through stepwise logistical regression, and comparing the standardized regression coefficient of the substantive indicator when the bias indicator was included as a covariate to when it was not included. If addition of the bias indicator significantly affected the outcome, it remained in the equation; otherwise, it was removed. If the coefficient of the substantive scale was higher when the bias indicator was included, this was evidence of suppression.
ResultsIn a previous study examining MMPI-2-RF scale differences between the ES and NES groups in this population, Locke et al. (2010) presented detailed data on group differences for all the MMPI-2-RF scales as well as effect sizes related to those differences. It was shown that the largest effect sizes occurred with the RC1 (ηp2 = 0.108) and NUC (ηp2 = 0.113) scales. These two scales were therefore selected as the focus of the present analyses. Demographic and other comparisons (see Table 1) showed that the NES group had a higher percentage of females than the ES group, used more psychotropic medications, and had a greater likelihood of a history of psychiatric treatment.
Demographic Data and Psychiatric History for Each Diagnostic Group
Table 2 contains mean differences between the ES and NES groups on MMPI-2-RF scales of interest, using gender and psychiatric medications as covariates. NES patients scored significantly higher than ES patients on substantive scales RC1 and NUC, and on validity scales Fs, FBS-r, RBS, and K-r. Table 2 also provides the percentage of persons in each group scoring at or above the cut-score for each scale that indicates the greatest degree of validity problems and/or over- or underreporting; as suggested by the MMPI-2-RF manual, this occurs at T = 100, with the exception of scales L-r (T = 80) and K-r (T = 70).
Differences Between Diagnostic Groups on Relevant Mean Scores on MMPI-2-RF Scales, and Percentage of Group Members Over- or Under-Responding
Correlations were calculated between each of the substantive indicators, the bias indicators, and the criterion variables; these are shown in Table 3. Bias indicators were found to be minimally related to the diagnosis of NES and ES (all correlations ≤0.15), with the exception of Fs (r = 0.16), FBS-r (r = .32), and RBS (r = .16).
Correlations Between Substantive Indicators RC1 and NUC, Response Bias Indicators F-r, Fp-r, Fs, FBS-r, RBS, L-r, and K-r, and ES/NES Criterion
Moderation Analyses
Moderated logistic regression yielded no significant interactions, indicating no moderation of bias indicators for predictions from either the RC1 or NUC scales.
Suppression Analyses
These results are shown in Table 4. RC1 was first entered alone in the binary logistic regression model. There was an increase in the regression coefficient for RC1 with the addition of F-r, Fs, L-r, K-r, and RBS. FBS-r and Fp-r were not significant.
Regression Coefficients With the Addition of Bias Indicator for Each Pair of Substantive and Bias Indicators
Similarly, NUC was first entered alone in the binary logistic regression model. As shown in Table 4, there was an increase in the coefficient for NUC with the addition of F-r, L-r, and K-r. Fs, Fp-r, and RBS were not significant. FBS decreased the coefficient of NUC, indicating that FBS-r did not act as a suppressor but did have independent additive value.
Additional analyses were run to assess the change in the odds ratio (OR) for a 5- or 10-point change in the substantive variable (e.g., roughly [1/2] to 1 standard deviation) after adding the suppressor variable. This was accomplished by utilizing the formula “exp(5 [or 10]*β after adding the suppressor) − exp(5 [or 10]*β before adding the suppressor).” Changes in odds ratios are reported in Table 5. For every 10-point increase in RC1, the odds of NES increased by 94% (OR 1.94). Adjusting for F-r had the greatest impact on the association between RC1 and NES. When adjusting for F-r, a 10-point increase in RC1 was associated with a 144% increase in the odds of NES (adjusted OR 2.44).
Odds Ratios (OR) of Substantive Indicators to Predict NES/ES for 5- and 10-Point Changes With the Addition of Bias Indicators for Each Pair of Substantive and Bias Indicators
DiscussionThe goal of the present study was to psychometrically assess whether bias indicators act in a moderating or suppressing manner in an Epilepsy Monitoring Unit population. Results showed that, in this sample, moderation was not found. For RC1, suppression was found for bias indicators F-r, Fs, L-r, K-r, and RBS. For NUC, suppression was found for F-r, L-r, and K-r. FBS-r had additive but not suppressive values. It is possible that FBS-r and RBS did not act as suppressors because both are independent predictors of diagnosis. Fp-r was not found to be a suppressor for either RC1 or NUC.
These results contribute to the bias indicator literature and further dispute the conclusions of McGrath et al. (2010) that bias indicators neither moderate nor suppress substantive indicators. Their conclusions were drawn in regard to several settings in which bias indicators are used, including personality assessment and workplace variables, but not in a medical setting. They acknowledged the dearth of data in other contexts, in which the utility of bias indicators is currently untested. The data from the present study indicate that in the context of psychomedical testing, there are indeed suppression effects of MMPI-2-RF bias indicators on substantive indicators for ES/NES discrimination. These data therefore support the position of Rohling et al. (2011), who state that there is evidence in clinical situations (especially in neuropsychology) to support the utility of bias indicators in enhancing prediction. At the least, these findings support the need for further studies on how bias scales can affect substantive scales in similar settings. Although suppression was disaplayed in this sample, the changes in odds ratios with the addition of bias variables were not large. However, depending upon the application, care providers may benefit from even small increases in predictive power.
A limitation of the study involved the demographic characteristics of the present sample. More than 90% of the patients were Caucasian. Although this was a true sample of EMU patients at this particular location, it may not reflect psychosocial differences among ethnic groups. It would also be important to include secondary gain, for example, disability or litigation, as an additional covariate, as this may affect a patient’s manner of response on the MMPI-2-RF. This variable is now explicitly collected in the EMU’s evaluation process, but was not systematically collected and documented in this retrospective sample. Although one of the great strengths of the present study is the large sample size, it would nonetheless be important to cross-validate these regression coefficients in a separate study. Although our sample had 80% power (alpha 0.05) to detect a main effect of 0.3 SD, the logistic regression may have been underpowered to detect subtle moderator effects. It is also worth noting that, while vEEG is the gold standard in diagnosis of epilepsy and/or nonepileptic seizures, misdiagnosis can still occur.
The present study lends support to the practice of using bias indicators as suppressors and raises many interesting questions for future studies. It is hoped that future studies will investigate the use of suppressors in other psychomedical settings, where bias scales may not traditionally be utilized beyond excluding persons that have bias scores above a particular cut score.
References Baer, R. A., Wetter, M. W., Nichols, D. S., Greene, R., & Berry, D. T. R. (1995). Sensitivity of MMPI-2 validity scales to underreporting of symptoms. Psychological Assessment, 7, 419–423. 10.1037/1040-3590.7.4.419
Ben-Porath, Y. S., & Tellegen, A. (2008). MMPI-2-RF: Manual for administration, scoring and interpretation. Minneapolis, MN: University of Minnesota Press.
Cragar, D. E., Berry, D. T. R., Fakhoury, T. A., Cibula, J. E., & Schmitt, F. A. (2002). A review of diagnostic techniques in the differential diagnosis of epileptic and nonepileptic seizures. Neuropsychology Review, 12, 31–64. 10.1023/A:1015491123070
Gervais, R., Lees-Haley, P., & Ben-Porath, Y. (2007). Predicting SVT performance with the MMPI-2-RF, FBS-r, RBS, and Fs scales. Poster Presented at the 27th Annual Meeting of the National Academy of Neuropsychology, Scottsdale, AZ.
Locke, D. E. C., Kirlin, K. A., Thomas, M. L., Osborne, D., Hurst, D. F., Drazkowski, J. F., . . .Noe, K. H. (2010). The Minnesota Multiphasic Personality Inventory-2-Restructured Form in the epilepsy monitoring unit. Epilepsy & Behavior, 17, 252–258. 10.1016/j.yebeh.2009.12.004
Locke, D. E., & Thomas, M. L. (2011). Initial development of Minnesota Multiphasic Personality Inventory-2-Restructured Form (MMPI-2-RF) scales to identify patients with psychogenic nonepileptic seizures. Journal of Clinical and Experimental Neuropsychology, 33, 335–343. 10.1080/13803395.2010.518141
McGrath, R. E., Mitchell, M., Kim, B. H., & Hough, L. (2010). Evidence for response bias as a source of error variance in applied assessment. Psychological Bulletin, 136, 450–470. 10.1037/a0019216
Rohling, M. L., Larrabee, G. J., Greiffenstein, M. F., Ben-Porath, Y. S., Lees-Haley, P., Green, P., & Greve, K. W. (2011). A misleading review of response bias: Comment on McGrath, Mitchell, Kim, and Hough (2010). Psychological Bulletin, 137, 708–712. 10.1037/a0023327
Sellbom, M., & Bagby, R. M. (2008). Validity of the MMPI-2-RF (restructured form) L-r and K-r scales in detecting underreporting in clinical and nonclinical samples. Psychological Assessment, 20, 370–376. 10.1037/a0012952
Sellbom, M., Wygant, D., & Bagby, M. (2012). Utility of the MMPI-2-RF in detecting non-credible somatic complaints. Psychiatry Research, 197, 295–301. 10.1016/j.psychres.2011.12.043
Thomas, M. L., & Locke, D. E. (2010). Psychometric properties of the MMPI-2-RF Somatic Complaints (RC1) scale. Psychological Assessment, 22, 492–503. 10.1037/a0019229
Wygant, D. B., Ben-Porath, Y. S., Arbisi, P. A., Berry, D. T. R., Freeman, D. B., & Heilbronner, R. L. (2009). Examination of the MMPI-2 restructured form (MMPI-2-RF) validity scales in civil forensic settings: Findings from simulation and known group samples. Archives of Clinical Neuropsychology, 24, 671–680. 10.1093/arclin/acp073
Submitted: June 18, 2013 Revised: November 21, 2014 Accepted: December 15, 2014
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Source: Psychological Assessment. Vol. 27. (2), Jun, 2015 pp. 733-737)
Accession Number: 2015-08363-001
Digital Object Identifier: 10.1037/a0038802
Record: 16- Title:
- Application of item response theory to tests of substance-related associative memory.
- Authors:
- Shono, Yusuke. School of Community and Global Health, Claremont Graduate University, Claremont, CA, US, yusuke.shono@cgu.edu
Grenard, Jerry L.. School of Community and Global Health, Claremont Graduate University, Claremont, CA, US
Ames, Susan L.. School of Community and Global Health, Claremont Graduate University, Claremont, CA, US
Stacy, Alan W.. School of Community and Global Health, Claremont Graduate University, Claremont, CA, US - Address:
- Shono, Yusuke, School of Community and Global Health, Claremont Graduate University, 675 West Foothill Boulevard, Suite 310, Claremont, CA, US, 91711, yusuke.shono@cgu.edu
- Source:
- Psychology of Addictive Behaviors, Vol 28(3), Sep, 2014. pp. 852-862.
- NLM Title Abbreviation:
- Psychol Addict Behav
- Page Count:
- 11
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- Bulletin of the Society of Psychologists in Addictive Behaviors; Bulletin of the Society of Psychologists in Substance Abuse
- Other Publishers:
- US : Educational Publishing Foundation
Society of Psychologists in Addictive Behaviors - ISSN:
- 0893-164X (Print)
1939-1501 (Electronic) - Language:
- English
- Keywords:
- adolescents, item response theory, construct validity, word-association test, substance use, alcohol use, marijuana use, at risk youth
- Abstract:
- A substance-related word-association test (WAT) is one of the commonly used indirect tests of substance-related implicit associative memory and has been shown to predict substance use. This study applied an item response theory (IRT) modeling approach to evaluate psychometric properties of the alcohol- and marijuana-related WATs and their items among 775 ethnically diverse at-risk adolescents. After examining the IRT assumptions, item fit, and differential item functioning (DIF) across gender and age groups, the original 18 WAT items were reduced to 14 and 15 items in the alcohol- and marijuana-related WAT, respectively. Thereafter, unidimensional one- and two-parameter logistic models (1PL and 2PL models) were fitted to the revised WAT items. The results demonstrated that both alcohol- and marijuana-related WATs have good psychometric properties. These results were discussed in light of the framework of a unified concept of construct validity (Messick, 1975, 1989, 1995). (PsycINFO Database Record (c) 2016 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *At Risk Populations; *Drug Usage; *Item Response Theory; *Test Validity; *Word Associations; Alcohol Drinking Patterns; Marijuana Usage; Memory
- Medical Subject Headings (MeSH):
- Adolescent; Alcohol Drinking; Association; Female; Humans; Logistic Models; Male; Marijuana Smoking; Memory; Psychological Theory; Psychometrics; Substance-Related Disorders; Word Association Tests; Young Adult
- PsycINFO Classification:
- Tests & Testing (2220)
Learning & Memory (2343) - Population:
- Human
Male
Female - Location:
- US
- Age Group:
- Adolescence (13-17 yrs)
Adulthood (18 yrs & older)
Young Adulthood (18-29 yrs) - Tests & Measures:
- Drug Use Questionnaire DOI: 10.1037/t31524-000
Word Association Test DOI: 10.1037/t01414-000
Rutgers Alcohol Problem Index DOI: 10.1037/t00517-000 - Grant Sponsorship:
- Sponsor: United States Department of Health & Human Services, National Institutes of Health, National Institute on Drug Abuse, US
Grant Number: DA024659-04 and DA023368-06
Recipients: No recipient indicated - Methodology:
- Empirical Study; Quantitative Study
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- First Posted: Aug 18, 2014; Accepted: Dec 27, 2013; Revised: Dec 20, 2013; First Submitted: May 9, 2013
- Release Date:
- 20140818
- Correction Date:
- 20140915
- Copyright:
- American Psychological Association. 2014
- Digital Object Identifier:
- http://dx.doi.org/10.1037/a0035877
- PMID:
- 25134051
- Accession Number:
- 2014-33492-001
- Number of Citations in Source:
- 70
- Persistent link to this record (Permalink):
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- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2014-33492-001&site=ehost-live">Application of item response theory to tests of substance-related associative memory.</A>
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Application of Item Response Theory to Tests of Substance-Related Associative Memory
By: Yusuke Shono
School of Community and Global Health, Claremont Graduate University;
Jerry L. Grenard
School of Community and Global Health, Claremont Graduate University
Susan L. Ames
School of Community and Global Health, Claremont Graduate University
Alan W. Stacy
School of Community and Global Health, Claremont Graduate University
Acknowledgement: This research was supported by two grants from the United States Department of Health & Human Services, National Institutes of Health, National Institute on Drug Abuse (DA024659-04 and DA023368-06). We thank Amy Custer for her work on this project.
In the past two decades, implicit memory and cognition approaches have gained substantial popularity in addiction and health behavior research. Focusing on the role of spontaneously activated cognitions on behavior (Stacy & Wiers, 2010; Wiers & Stacy, 2006), researchers have examined automatic/implicit cognitive processes at different levels of analysis, ranging from attention (e.g., Bradley, Field, Mogg, & De Houwer, 2004; Mogg & Bradley, 2002) to memory (e.g., Krank & Goldstein, 2006; Stacy, 1995, 1997) and attitude (e.g., Chassin, Presson, Sherman, Seo, & Macy, 2010; Huijding, de Jong, Wiers, & Verkooijen, 2005; Houben, Havermans, & Wiers, 2010) by using various indirect tests of implicit cognitive processes related to addictive and health behaviors (see Ames et al., 2007; Stacy, Ames, & Grenard, 2006; Stacy & Wiers, 2010, for review). A recent meta-analysis examining the relationship between substance-related implicit cognition and substance use revealed that the substance-related implicit word-association test (WAT) was the best predictor of substance use with the largest effect size (mean r = .38) among other implicit measures (Rooke, Hine, & Thorsteinsson, 2008). Although studies have reported good reliability of WAT (Ames et al., 2007; Preece, 1978), to the best of our knowledge, no comprehensive psychometric evaluations of WAT have been conducted in research on addiction, cognition, or memory. The current study extends previous research by applying a comprehensive item response theory (IRT) framework to understand and improve the psychometric properties of WAT items and estimation of underlying latent traits of alcohol- and marijuana-related associative memory.
WATs in Addiction and Health Behavior ResearchThe WAT is one of the most commonly used indirect memory tests for assessing the retrieval of preexisting substance-related associations in memory (Stacy, 1995, 1997). In substance-related WAT, a series of substance-related cue words or phrases are presented one by one visually or auditorily, and participants are asked to generate the first word or short phrase that comes to mind when they think of the cue. It is assumed that an association of a cue–target pair gets strengthened with repetitive encounters with a substance-related cue (e.g., “feeling good”) and target behavior (e.g., marijuana use). Therefore, those who frequently engage in substance use are more likely than those who do not to spontaneously think of substance-use behavior in response to substance-related cues in WAT.
Accumulated evidence has shown that substance- or risky behavior-related implicit associative memory, measured by WAT, has strong predictive power for substance use, including alcohol (Ames & Stacy, 1998; Kelly, Masterman, & Marlatt, 2005; Stacy, 1997), marijuana (Ames et al., 2007; Ames & Stacy, 1998; Stacy, 1997), and cigarette use (Grenard et al., 2008; Kelly, Haynes, & Marlatt, 2008), as well as risky sexual behavior (Ames, Grenard, & Stacy, 2013; Grenard, Ames, & Stacy, 2013; Stacy, Ames, Ullman, Zogg, & Leigh, 2006). Given the successful application of WAT to a wide range of issues in health and cognition, it is important to fully understand the psychometric characteristics and construct validity of the measure. The IRT modeling framework provides one of the most comprehensive strategies available to accomplish these goals and has a number of advantages over the traditional classical test theory (CTT) approach (Reise, Ainsworth, & Haviland, 2005).
IRT Applied to Substance-Related WATIRT consists of a series of statistical models specified to describe the probability of endorsing an item as a function of an underlying latent trait (θ). In the context of the alcohol-related WAT, IRT describes the association between the probability that a participant generates an alcohol-related response to a given WAT item and his or her level of the latent alcohol-related implicit associative memory. The use of IRT in psychometric evaluation has several advantages over classical test theory (CTT). First, IRT allows for detailed investigation of WAT items in relation to the latent alcohol-related associative memory. It provides parameter estimates of item difficulty (b) and item discrimination (a). The b parameter indicates (1) how difficult a given WAT item is and (2) what level of latent memory association is needed so that 50% of participants would endorse an alcohol-related response to a given WAT item. Participants whose trait levels (i.e., alcohol-related associative memory) are higher are likely to generate an alcohol-related response to a WAT item with a higher b parameter value. The a parameter tells how effectively a WAT item differentiates among individuals with different levels of latent implicit alcohol-related associative memory. The item with a higher value of a is a good item because such an item discriminates effectively between individuals of slightly different levels of latent alcohol-related implicit associations in memory.
A critical advantage of IRT over CTT is that IRT is sample-invariant whereas CTT is sample-dependent (Hambleton & Jones, 1993). Under the situation in which an IRT model fits the data, the item parameter estimates (i.e., the a and b parameters) can be interpreted independent of the study sample (item-parameter invariance; Lord, 1980). Similarly, a latent trait can be estimated independent of a set of test items used in a study (person-parameter invariance; Lord, 1980). These sample invariant characteristics are not true in CTT. In CTT, item discrimination (i.e., item–total correlation), item difficulty (i.e., proportion of correct) and scale scores (i.e., the summed score) are completely dependent on a sample. Thus, an estimate of a latent trait score in CTT is largely affected by the characteristics of a study sample (Hambleton & Jones, 1993).
Another advantage of IRT is that reliability can be estimated with great flexibility. In CTT, a reliability estimate (e.g., Kuder–Richardson Formula 20, Cronbach’s coefficient alpha) is a fixed constant for all items. In contrast, reliability in IRT can be estimated at any point in the range of an underlying latent trait. Moreover, reliability estimates can be computed at both the item and test levels, using the item information and test information functions (IIF and TIF), respectively. In our substance-related WAT, we determined the extent to which each WAT item and WAT as a test accurately estimated a specific level of implicit substance-related associative memory.
Last, the IRT framework allows for the investigation of differential item functioning (DIF). The DIF analysis assesses whether or not a test item functions equivalently across subgroups of a study sample while controlling for the overall difference in the latent trait levels. For example, if the a or b parameter of a given WAT item is different between male and female participants with the same level of the latent implicit alcohol-related associative memory, the item is considered to exhibit DIF and could be a threat to the construct validity of the alcohol-related WAT (Kristjansson, Aylesworth, McDowell, & Zumbo, 2005).
Current StudyThe current study evaluated psychometric properties of two forms of a substance-related WAT, marijuana- and alcohol-related, using a unidimensional IRT modeling approach. The data were collected as part of a large-scale longitudinal study of dual-process theory and drug use in adolescents and consisted of 775 ethnically diverse, at-risk high school students in Southern California. The adolescent sample was chosen because of sufficient variability in alcohol, marijuana, and other drug use as well as the importance of this age group for the study of drug use progression.
The aims of the study were to (a) evaluate parameters of substance-related WAT items including item difficulty and item discrimination, (b) examine the precision of WAT at the item and test levels, (c) estimate the latent trait scores (i.e., the level of substance-related implicit associative memory) for each participant, and (d) evaluate criterion validity through the association between WAT scores and substance-use measures. Results of comprehensive psychometric validation of substance-related WAT will be discussed in light of the framework of a unified concept of construct validity (Messick, 1975, 1989, 1995). The comprehensive IRT approach illustrated in this article is applicable to a wide variety of measurement issues in associative memory and other areas of addiction and health behavior research.
Method Participants
The participants were 775 continuation high school (CHS) students (340 female) in the greater Los Angeles area. Their participation in this study did not require current or past history of substance use. The participants’ ages ranged from 14 to 20, of which 94% were between the ages of 15 and 18. The study sample comprised Hispanic (62.5%), Non-Hispanic White (12.5%), mixed race/ethnicity (18.7%), Black (3.2%), and other race/ethnicity that included Asian, Native American, and “other” (3.1%). They were recruited from classes from 42 CHSs, which were selected from over 100 CHSs in the region. The schools sampled did not provide any drug education programs to their students.
Measures
Word-association test
As described in the introduction, the substance-related WAT is an indirect memory test designed to assess the spontaneous retrieval of preexisting substance-related associations in memory (e.g., Stacy, 1997). The current study used two formats of WAT, an outcome-behavior association task (OBAT) and a compound-cue version of WAT. In OBAT, all cues are phrases that are related to affective outcomes of drug use (e.g., “feeling good”). In the compound-cue WAT, cues consist of either a combination of location and affective outcome phrases (two compound cues; e.g., “my bedroom, feeling good”) or a combination of situation, location, and affective outcome phrases (three compound cues; e.g., “weekend, friend’s house, having fun”). Fillers are cues that are unrelated to substance use (e.g., “doing homework”). Each of the three cue types had six target cues and two filler cues, totaling 18 target and six filler cues. Each trial started by visually presenting a cue phrase in the center of a computer display, and participants were instructed to respond with the first behavior or action that came to mind as quickly as possible. Responses were typed in a text box that appeared right below where the cue was presented. The next trial was generated by participants’ clicking a text button that reads “click here to continue” or after 21 seconds elapsed since the presentation of a cue, whichever came first.
The self-coding procedure (Frigon & Krank, 2009; Krank, Schoenfeld, & Frigon, 2010) was employed to code the WAT responses upon completion of the WAT session. In this procedure, participants were presented with a WAT cue and their typed response on the computer display, along with a list of 12 behavior categories (e.g., alcohol, marijuana, tobacco, exercise, etc.). They were asked to check one or more categories that were related to their responses. A checked response was coded 1 and an unchecked response was coded 0, and these scores were summed to yield a total WAT score for each category. In the current study, the scores for alcohol- and marijuana-related responses were examined separately.
Drug use: Marijuana and alcohol
Frequency of drug use was measured by a self-report drug-use questionnaire (Stacy et al., 1990; Stacy, 1997) that asked participants to indicate how many times they had used each drug in the past year and the past 30 days. The questionnaire was an 11-point rating scale, with frequency response options ranging from 1 (None) to 11 (91 + times). The reliability and validity of these self-reported drug-use measures were demonstrated elsewhere (e.g., Stacy et al., 1990).
Other variables
Participants’ demographics (age, gender, and language use), scores on the Rutgers Alcohol Problem Index (RAPI; White & Labouvie, 1989), and frequencies of simultaneous polydrug use (Collins, Ellickson, & Bell, 1998) were also assessed. These measures were used as predictors in missing data analyses reported below (see Data Analysis Plan for more details).
Procedure
We contacted each continuation high school (CHS) to arrange recruitment and obtained both written assent from eligible students and consent from their parents. Assent and consent forms explained that the purpose of the study was to investigate teenagers’ health behaviors, requiring their participation in three assessments over the course of two years to complete the study. Computer-based assessments were administered during regular school hours in groups of up to 20 (M = 11.67 participants per session) in a classroom that was provided by each CHS. Data collectors set up a mobile computer laboratory in each classroom that included 20 laptop computers supplied by the research project. Upon arrival to the laboratory, participants were randomly assigned to a computer. After the instructions were given, the assessments began by participants’ pressing any number key on the keyboard. The rest of the assessments were self-directed by the computer program. A session lasted an average of 60 minutes. Participants received a $10 movie ticket in exchange for their participation during Wave 1; the data reported in this article.
Data analysis plan
Evaluation of the psychometric properties of marijuana- and alcohol-related WAT items consisted of three steps. The analyses were conducted separately for each type of WAT.
IRT assumption checking
We tested two assumptions of the IRT model, unidimensionality and local independence, using both categorical confirmatory factor analysis (CCFA) and IRT methods. In assessing unidimensionality, we fit a one-factor CCFA model with weighted least-squares with mean and variance adjustment (WLSMV; Muthén, du Toit, & Spisic, 1997), by constraining all WAT items to load onto a single latent factor of implicit alcohol-related (or marijuana-related) associative memory. The model fit was evaluated according to the guidelines of Hu and Bentler (1999). A unidimensional two-parameter logistic (2PL) model was also fitted to the data to evaluate unidimensionality. We evaluated overall model fit by examining Maydeu-Olivares-Joe’s M2 (Maydeu-Olivares & Joe, 2006), a limited-information overall fit statistic, as well as the item-level model fit by assessing the S-χ2, an item misfit index (Orlando & Thissen, 2000).
The local independence (LI) assumption was evaluated by checking modification indices (MI) of residual covariances in the one-factor CCFA model and the local dependence (LD) statistics (Chen & Thissen, 1997) in the 2PL model. Potential local dependence (LD) is suspected when an excess correlation between a pair of items is observed after controlling for a single latent construct (Thissen & Steinberg, 2009). This suggests a violation of LI, implying to some investigators that the two items ask the exact same questions twice (Varni et al., 2010).
Differential item functioning
DIF tests were conducted to test for item invariance across gender and age groups. Two types of DIF were examined: Uniform DIF implies that the item exhibits a difference in the b parameter between two groups. Nonuniform DIF reflects a difference in the a parameter between two groups. Note that a group difference in DIF is examined while controlling for the overall group difference in the levels of the latent trait. The current study used a one-step Wald test (Cai, Thissen, & du Toit, 2011; Woods, Cai, & Wang, 2013), in which designated anchor items were used to link the latent trait metric for two groups. Anchor items were those items designated not to vary across groups. We identified the anchored items from a two-step Wald test (see Langer, 2008, for more detail) before conducting the one-step Wald test. In the one-step Wald test, a model fit was conducted in the following one-step manner: The mean and standard deviation (SD) of the reference group were fixed to 0 and 1, respectively, and the mean and SD of the focal group and the item parameters (the a and b parameters) were estimated at the same time. The item parameters for the designated anchor items were constrained to be equal between the two groups, whereas those for the candidate items were free to vary between the two groups. The software we used, flexMIRT (Cai, 2012), produces results of the Wald χ2 test for the comparisons of the candidate item(s) between the two groups. In comparisons between male and female participants, we used male participants as the reference group. In comparisons between younger (14–16 years old) and older participants (17 years old and above), the reference group was the young group.
IRT: Item-parameter estimation
We evaluated item parameter estimates of any alternative sets of WAT items suggested by the preceding analysis. Both 1PL and 2PL models were fitted to examine whether the a parameter should be fixed or varied across the WAT items. Further investigated was the amount of information each WAT item and the total WAT scale provided with respect to the latent trait. The item containing more information at a given level of the latent trait is considered more reliable. Latent trait scores for alcohol- and marijuana-related implicit associative memory were estimated separately as a function of various WAT-item scores, using expected a posteriori (EAP) estimation.
Criterion-related validity
Criterion-related validity coefficients of substance-related WATs were calculated by separately correlating marijuana- and alcohol-related WAT scores with respective drug-use frequencies from the past 30 days and 1 year. A nonparametric bootstrap method (Efron, 1979, 1987; Efron & Tibshirani, 1985) was used to estimate the Pearson correlation coefficients (termed r*) and their confidence intervals, as the assumption of bivariate normality was violated. We used a bias-corrected and accelerated (BCa) procedure (Efron, 1987) to construct confidence intervals for r* between the following pairs of variables: alcohol-WAT scores and alcohol use from the past 30 days; alcohol-WAT scores and alcohol use from the past year; marijuana-WAT scores and marijuana use from the past 30 days; and marijuana-WAT scores and marijuana use from the past year.
Missing Data
The missing data rates on the WAT ranged from 2% to 15% across 18 items, which was not unexpected with open-ended item formats. In the IRT analyses, list-wise deletion (LWD) of missing data was implemented. The use of LWD in IRT analyses is supported by several IRT simulation studies that have demonstrated acceptable-to-good parameter estimates of item discrimination and difficulty (Finch, 2008), no bias of uniform DIF detection with missing at random (MAR) data (Robitzsch & Rupp, 2009), and very close results to a complete data set (i.e., a data set with no missing data) in terms of power, Type I error rate, and effect sizes in the detection of nonuniform DIF (Finch, 2011).
In the criterion-related validity analysis that was conducted with psychometrically validated WAT items, multiple imputation (MI; Rubin, 1987) was used for missing data to obtain unbiased estimates of parameters. Multivariate imputation by chained equations (van Buuren, Boshuizen, & Knook, 1999; van Buuren & Oudshoorn, 2000) was used as the specific form of multiple imputation, applying the mice package (van Buuren & Groothuis-Oudshoorn, 2011) in the R statistical environment (R Development Core Team, 2012). This technique has recently gained popularity (Azur, Stuart, Frangakis, & Leaf, 2011) due to its ability to model each variable with missing data, regardless of its distribution (see van Buuren & Groothuis-Oudshoorn, 2011, for detailed procedures).
ResultsParticipants’ demographic variables and their alcohol and marijuana use are summarized in Table 1. To determine whether or not the school-cluster variables should be taken into account in subsequent analyses, we computed the design effect and intraclass correlation for alcohol and marijuana use among the average of 42 CHSs. A design effect of 2.0 was used as a cut-off (see Muthén & Satorra, 1995). The design effect (intraclass correlation in parentheses) for alcohol and marijuana use was 1.8 (.016) and 1.5 (.012), respectively. Thus, the school-cluster variable was not included in our analyses.
Demographic Variables and Substance Use
Alcohol-Related Word Association
IRT assumption checking
Both CCFA and 2PL models showed a good fit to the data, indicating unidimensionality of the 18 alcohol-related WAT items. Results from CCFA, conducted using Mplus, Version 6.11 (Muthén & Muthén, 2011), revealed fit indices as follows: comparative fit index (CFI) = .944, Tucker-Lewis Index (TLI) = .937, and root mean square error of approximation (RMSEA) = .045, with a 90% confidence interval (CI) of .036 to .054. All of the 18 factor loadings were significant (p < .01), ranging from .46 to .75. A 2PL model was fitted using flexMIRT, Version 1.0.4.3 (Cai, 2012) and indicated a good model fit (RMSEA = .04). Regarding the LI assumption, there were three potential item pairs with LD, implied by relatively large values of modification indices (MI) for residual covariances: (1) “friend’s house, feeling a rush” and “weekend, friend’s house, feeling a rush,” (2) “friend’s house, feeling a rush” and “feeling a rush,” and (3) “my bedroom, feeling good” and “my bedroom, feeling relaxed.” In the IRT analysis, no indication of LD item pairs (LD χ2 > 10) was obtained. Only one item (“weekend, party, feeling high”) showed a poor item fit (p < .0001). After examining the item contents, we set aside “friend’s house, feeling a rush” and “weekend, party, feeling high,” from a subsequent analysis. The model fit was slightly improved after removing these two items, CFI = .961, TLI = .955, and RMSEA = .037 (90% CI = .027 − .047).
DIF
The DIF test detected only one item exhibiting DIF across gender. The item “weekend, friend’s house, feeling a rush” discriminated more effectively for male (a = 2.51) than female participants (a = 1.27; p < .02). Thus, the item was excluded from the subsequent analysis. We also dropped “feeling high” because the discrimination parameter for the male group was substantially low (a = .78). With regard to age groups, the item “feeling more relaxed” was the only item with a significant uniform DIF (p < .02), indicating that this item was easier for older (b = 1.62) than younger participants (b = 2.31). However, we expected that some items might be more difficult at younger ages while still being potentially applicable to later changes with increasing age. Thus, the item remained in the analysis.
IRT: Item parameter estimation
Our revised alcohol-related WAT, reduced to 14 items (α = .80), was fitted with both 1PL and 2PL models. Both models indicated a good fit (RMSEA = .04 and .03 for 1PL and 2PL, respectively), with no evidence of a violation of LI. A likelihood ratio test revealed a significant improvement in fit by the 2PL, relative to the 1PL, G2 (17) = 42.27, p < .001. These results indicated that alcohol-WAT data were reproduced by the model better when the a parameters were estimated freely (2PL), rather than being constrained to be equal (1PL). Table 2 presents the estimated parameters for both models. The common a parameter in 1PL was 2.02. In 2PL, the a parameters ranged from 1.62 to 2.46, indicating that all 14 alcohol WAT items effectively differentiated the participants across different levels of the latent trait. The b parameters in both models were very similar for each item. For most of the WAT items, moderate to strong levels of implicit alcohol-related memory associations were needed to endorse alcohol-related responses. All of these parameter estimates are graphically represented in the item-characteristic curves (ICC; Figure 1).
Item Parameter Estimates for 1PL and 2PL in the Alcohol- and Marijuana-Related WAT
Figure 1. Item characteristic curves for the revised alcohol (solid) and marijuana (dashed) WAT items. The x-axes show the level of theta (latent implicit alcohol- or marijuana-related associative memory), with 0 representing the average level of theta. The y-axes show the proportion of alcohol- or marijuana-related responses generated to each WAT cue.
All ICCs show that the probability of endorsing alcohol-related responses was low for those participants whose latent trait levels were below 1.0. The slopes of most WAT items were steepest throughout the range of the latent levels from about 1.0 to 2.0. These items also provided most information about the latent trait (i.e., most reliable) in this range of the latent level (see Figure 2). The amount of information provided by each WAT item was summed to create the test information curve (TIC; see Figure 3), which demonstrates that the alcohol-related WAT is most reliable at moderate-to-high levels of the latent trait. Estimated latent trait scores (see Table 3) revealed that those who endorsed one alcohol-related response were estimated to possess an average level of latent alcohol-related associative memory. As participants endorsed more alcohol-related responses, their latent score increased.
Figure 2. Item information functions for the revised alcohol (solid) and marijuana (dashed) WAT items. The x-axes show the level of theta (latent implicit alcohol- or marijuana-related associative memory), with 0 representing the average level of theta. The y-axes show the amount of information (I), an index of how accurately each item contributes to estimate the latent trait at a given level of theta.
Figure 3. Test information functions (TIFs) for the revised alcohol (solid) and marijuana (dashed) WATs. The x-axis shows the level of theta (latent implicit alcohol- or marijuana-related associative memory), with 0 representing the average level of theta. The y-axis shows the amount of information (I), an index of how accurately each form of WAT estimates the latent trait at a given level of theta. I = 5, 10, and 20 is equivalent to a reliability estimate of .80, 90, and .95, respectively.
Alcohol- and Marijuana-Related WAT Scores, Latent Trait Scores (EAP) and Their Standard Deviations
Marijuana-Related Word Association
IRT assumption checking
CCFA showed a good fit of the marijuana-model, CFI = .978, TFI = .975, RMSEA = .043 (90% CI = .034–.052). IRT analysis revealed an adequate model fit (RMSEA = .05) with no indication of a poor item fit. Thus, the marijuana-related WAT was determined unidimensional. With regard to LI, both CCFA and 2PL detected only one potential LD item pair, “feeling high” and “my bedroom, feeling high.” After reviewing the item contents, we removed the latter item from the analysis.
DIF
For gender, two items were detected as having uniform DIF: “feeling a rush” (p = .02) and “forgetting problems” (p = .03). The endorsement of the marijuana-related response “Feeling a rush” was easier for females (b = 1.59) than for males (b = 2.03) after matching the two groups on the latent trait. Conversely, “forgetting problems” was easier for males to endorse (b = 1.18) than for females (b = 1.44). These two items were removed from the subsequent analyses. As for age groups, no items exhibited a significant DIF.
IRT: Item parameter estimation
The revised marijuana-related WAT had a total of 15 items (α = .87). Both 1PL and 2PL models fit the data adequately (RMSEA = .05) with no sign of LD item pairs. A likelihood ratio test showed that 2PL had a significantly better fit than 1PL, G2 (17) = 77.88, p < .001. Estimated item parameters by both models and ICCs (2PL only) are presented in Table 2 and Figure 1, respectively. In 2PL, the a parameter varied from 1.37 to 3.62 and the b parameters ranged from −.48 to 2.05. As shown in Figure 2, IIFs show that “feeling high” was the only item most reliable at the below-average level of the latent trait. Still, TIF illustrates that the marijuana WAT was most reliable around the moderate-to-high levels of the latent trait continuum (see Figure 3). Estimated latent trait scores showed that a total marijuana WAT score of 3 corresponded to the average level of the latent marijuana-related associative memory. A monotonically increasing relationship was observed between the total WAT scores and the latent trait scores (see Table 3).
Criterion-Related Validity
Table 4 shows the correlations between substance-related WAT scores and drug-use frequencies. In both alcohol- and marijuana-related WATs, the participants who endorsed more substance-related responses tended to report higher frequencies of substance uses both in past year use (r* = .44 [BCa CI = .37–.51] and .56 [BCa CI = .50–.61], for alcohol and marijuana, respectively) and past 30-days use (r* = .38 [BCa CI = .30–.47] and .48 [BCa CI = .42–.54], for alcohol and marijuana, respectively).
Bivariate Correlations (BCa 95% CI) Between the Substance-Related WAT Scores and Frequencies of Past Drug Use
DiscussionThe present study was the first to apply a comprehensive psychometric framework using IRT approaches to evaluate psychometric properties of alcohol- and marijuana-related WATs in a sample of ethnically diverse, at-risk adolescents. Our results have demonstrated that both forms of WAT have good psychometric properties when subjected to a comprehensive latent variable and IRT analyses. The discussion below focuses on key findings regarding item and scale properties as well as evidence of construct validity (Messick, 1989, 1995).
Alcohol- and Marijuana-Related WAT: Scale Properties
The original 18 WAT items were reduced to 14 and 15 items in alcohol- and marijuana-related WAT, respectively. Items were removed because they exhibited poor item fit (two items each in both WATs), LD issues (one item each in both WATs), or gender bias (one item in the alcohol WAT and two items in the marijuana WAT). Excluding these items improved the revised versions of the substance-related WAT. As expected, both forms of WAT were shown to be unidimensional and most reliable with individuals with moderate-to-high levels of latent alcohol- or marijuana-related associative memory (see Figure 3). A monotonically increasing relationship between the total WAT scores and estimated latent trait scores was observed in both WATs (see Table 3). These results confirmed that the substance-related WATs measure a single construct of substance-related associative memory, as it purports to do. Furthermore, the total alcohol- and marijuana-WAT scores were positively correlated with frequencies of respective past substance-use behaviors, providing strong evidence of criterion-related validity. This finding is in agreement with that of Krank et al. (2010), who reported that self-coded WAT scores were positively associated with past 30-days alcohol use among college students and added to evidence supporting the use of self-coded scoring procedures (Frigon & Krank, 2009; Krank et al., 2010).
Alcohol- and Marijuana-Related WAT: Item Properties
Item discrimination for all items in both WATs showed high discrimination parameters (a > 1.35). Among the items, some of the compound cues exhibited very high discrimination parameter values, especially in the marijuana-related WAT. Those compound cues included “friend’s house, having fun” (a = 3.62), “friend’s house, hanging out, feeling good” (a = 3.14), and “Friday night, friend’s house, having fun” (a = 3.20). A possible explanation for this is that when a positive affective outcome cue was combined with a peer cue to create a compound cue, its item discrimination was further improved. This explanation is consistent with some theories of adolescent substance use that focus on peer influence as a pivotal risk factor (e.g., Hawkins, Catalano, & Miller, 1992; Petraitis, Flay, & Miller, 1995).
With respect to item difficulty, most of the WAT items were most reliable at the moderate-to-high levels of the latent construct. However, a slightly different pattern of results was observed across the two WATs. In the alcohol WAT, all but one item (“Friday night, friend’s house, having fun”) had item-difficulty parameter estimates greater than 1.0. This indicates that the probability of endorsing alcohol-related responses to the items was lower than .5 for the participants with below moderate levels of latent alcohol-related associative memory. In contrast, the marijuana-related WAT contained a mix of items with moderate-to-high difficulty parameters and items with lower difficulty parameters. This led the marijuana WAT to cover a wider range of the latent-trait continuum than the alcohol WAT. For example, even among those participants with a lower level of latent marijuana-associative memory, half of them endorsed marijuana-related responses to the cues, “feeling high” (b = −.48) and “weekend, party, feeling high” (b = .20). On the other hand, these two cues were not good ones for alcohol. Both items showed a poor model fit and hence were excluded from the revised alcohol-WAT. Further, the only item with the phrase “feeling high” in the revised alcohol-WAT had a high difficulty parameter estimate (“my bedroom, feeling high,” b = 1.97). Hence, we consider “feeling high” as a cue strongly associated with marijuana, particularly at a lower range of the latent trait. This suggests that inclusion of behavior-specific cues may further improve the psychometric properties of substance-related WAT.
Alcohol and Marijuana-Related WAT: Unified Concept of Validity
Traditionally, construct validity has been examined by use of multitrait-multimethod matrix (MTMM matrix, Campbell & Fiske, 1959) or confirmatory factor analysis (CFA, Jöreskog, 1969; Kenny & Kashy, 1992; Stacy, Widaman, Hays, & DiMatteo, 1985) procedures to gather evidence of convergent and discriminant validity. In contrast, Messick (1989, 1995) suggested six aspects of construct validity, arguing that construct validity of a measurement instrument should be justified by use of the available evidence for a wide variety of aspects of construct validity, including content, substantive, structural, generalizability, external, and consequential aspects. The current study showed that the substance-related WATs exhibited evidence of each of these aspects of construct validity. For example, the content aspect is evidenced by the fact that all substance-related WAT items were selected from, or created based on, past studies that reported the utility of WAT as a measure of substance-related implicit associative memory (e.g., Ames et al., 2007). A unidimensional structure of both forms of WATs supports the structural aspect of construct validity, indicating that a single construct of alcohol- or marijuana-related implicit associative memory is evaluated in the WAT. Regarding the substantive aspect, which requires empirical evidence of response consistencies from data, both forms of WAT revealed good internal consistency across a range of the latent trait. For example, the amount of information (I) exceeded 5.0, which is equivalent to a reliability estimate of .80, at the underlying latent trait levels between 0 and 2.0 (see Figure 3). In terms of the generalizability aspect of construct validity, the DIF tests demonstrated that all items in the revised version of the alcohol- and marijuana-related WATs were invariant across gender and age groups. Finally, although we were not able to investigate any evidence of convergent and discriminant validity in the current study, the obtained evidence of criterion-related validity for both forms of WAT justifies the external aspect of construct validity. As reported above, a significant correlation was found between substance-related WAT scores and frequencies of substance use, both in the past 30 days and the past year. Overall, the current study revealed multiple lines of evidence for the construct validity of the alcohol- and marijuana-related WAT, in accord with the unified concept of construct validity (Messick, 1989, 1995).
Limitations
Several caveats in the present study need to be addressed. First, item invariance was examined only across gender and age groups due to the limited number of samples representing different subgroups (e.g., ethnicity). Thus, the current WAT items might have shown DIF across other subgroups. Future investigations that explore DIF of substance-related WAT items could be conducted across ethnicity and other defining characteristics. Second, because the data were cross-sectional, the direction of the possible causal relationship between WAT scores and past drug use was not inferred. Last, drug-use behavior was measured via a self-report questionnaire, thus responses are sensitive to demand characteristics and/or social desirability bias. However, under circumstances in which adolescents were assured that responses would be confidential, adolescent self-reports have been shown to be accurate (Dent, Sussman, & Stacy, 1997; Donohue, Hill, Azrin, Cross, & Strada, 2007).
ConclusionDespite these limitations, the present study revealed sound psychometric properties of the alcohol- and marijuana-related WAT. Both forms of WATs were most reliable at moderate-to-high levels of the underlying implicit alcohol- or marijuana-related associative memory. Knowledge of the level of reliability at different levels of the latent trait is one of the several fundamental advantages of IRT over traditional psychometric evaluation (e.g., CTT), in addition to advantages of sample invariance, flexibility, and rigor in evaluating differential item functioning. The IRT and construct validation procedures shown here are useful for a wide range of research topics in addiction as well as basic cognitive research on WAT. Although the procedures can be applied to any presumed measures of an underlying trait, it may be surprising that these highly quantitative procedures can be effectively applied to responses that are self-generated and open-ended—the responses are essentially qualitative in origin. When such responses are amenable to numeric coding, they can be usefully integrated into formal and comprehensive tests of psychometrics and construct validity as revealed here.
Footnotes 1 In several recent studies by other investigators on continuation high schools (CHSs) in the greater Los Angeles area (e.g., Barnett et al., 2013; Sussman, Sun, Rohrbach, & Spruijt-Metz, 2012), sample characteristics (including the male-to-female ratio, the mean age, racial/ethnic profile, and past alcohol and marijuana use) were very similar to those in the current study. Although demographic information was not available on all CHSs in the region, the general consistency across diverse studies in the region suggests that the present sample is at least similar to other samples previously drawn from the population.
2 Although it may appear that the alcohol- and marijuana-related WATs should be analyzed by a multidimensional IRT approach, we used a unidimensional approach, as we used the same set of items for both substances. The use of overlapping items was inevitable to take into account individual differences in substance-related associative memory, as has been evidenced in previous studies (see Stacy, Galaif, Sussman, & Dent, 2006; Sussman, Stacy, Ames, & Freedman, 1998). A compensatory multidimensional model was also not relevant because a WAT response related to one substance should not be compensated by one’s level on the construct of the second substance. Thus, the two forms of WATs were analyzed separately.
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Submitted: May 9, 2013 Revised: December 20, 2013 Accepted: December 27, 2013
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Source: Psychology of Addictive Behaviors. Vol. 28. (3), Sep, 2014 pp. 852-862)
Accession Number: 2014-33492-001
Digital Object Identifier: 10.1037/a0035877
Record: 17- Title:
- Application of the social action theory to understand factors associated with risky sexual behavior among individuals in residential substance abuse treatment.
- Authors:
- Reynolds, Elizabeth K.. Center for Addictions, Personality, and Emotion Research, University of Maryland-College Park, College Park, MD, US, ereynolds@psyc.umd.edu
Magidson, Jessica F.. Center for Addictions, Personality, and Emotion Research, University of Maryland-College Park, College Park, MD, US
Bornovalova, Marina A.. Department of Psychology, University of Minnesota, MN, US
Gwadz, Marya. Center for Drug Use and HIV Research, National Development and Research Institutes, NY, US
Ewart, Craig K.. Department of Psychology, Syracuse University, NY, US
Daughters, Stacey B.. School of Public Health, University of Maryland-College Park, College Park, MD, US
Lejuez, C. W.. Center for Addictions, Personality, and Emotion Research, University of Maryland-College Park, College Park, MD, US - Address:
- Reynolds, Elizabeth K., Department of Psychology, University of Maryland, Biology Psychology Building, College Park, MD, US, 20742, ereynolds@psyc.umd.edu
- Source:
- Psychology of Addictive Behaviors, Vol 24(2), Jun, 2010. pp. 311-321.
- NLM Title Abbreviation:
- Psychol Addict Behav
- Page Count:
- 11
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- Bulletin of the Society of Psychologists in Addictive Behaviors; Bulletin of the Society of Psychologists in Substance Abuse
- Other Publishers:
- US : Educational Publishing Foundation
Society of Psychologists in Addictive Behaviors - ISSN:
- 0893-164X (Print)
1939-1501 (Electronic) - Language:
- English
- Keywords:
- HIV, event level, risky sexual behavior, substance users, social action theory, residential substance abuse treatment
- Abstract:
- Risky sexual behavior (RSB) is a leading cause of HIV/AIDS, particularly among urban substance users. Using the social action theory, an integrative systems model of sociocognitive, motivational, and environmental influences, as a guiding framework, the current study examined (1) environmental influences, (2) psychopathology and affect, (3) HIV-related attitudes and knowledge, and (4) self-regulatory skills/deficits as factors associated with event-level condom use (CU) among a sample of 156 substance users residing at a residential substance abuse treatment center (M age = 41.85; SD = 8.59; 75% male). RSB was assessed using event-level measurement of CU given its advantages for improved accuracy of recall and ability for an examination of situational variables. A logistic regression predicting event-level CU indicated the significant contribution of partner type (environmental influences), less favorable attitudes towards condoms (HIV-related attitudes and knowledge), and higher levels of risk-taking propensity (self-regulatory skills/deficits) in predicting greater likelihood of not having used a condom at one's most recent sexual encounter. This study contributes to the literature examining HIV risk behaviors among substance users within a theory-driven model of risk. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Drug Rehabilitation; *Psychological Theories; *Residential Care Institutions; *Sexual Risk Taking; *Social Behavior; Drug Usage; HIV
- Medical Subject Headings (MeSH):
- Adolescent; Adult; Condoms; Female; HIV Infections; Health Knowledge, Attitudes, Practice; Humans; Logistic Models; Male; Middle Aged; Psychological Theory; Residential Treatment; Risk-Taking; Sexual Behavior; Social Control, Informal; Substance Abuse Treatment Centers; Substance-Related Disorders
- PsycINFO Classification:
- Substance Abuse & Addiction (3233)
Drug & Alcohol Rehabilitation (3383) - Population:
- Human
Male
Female - Location:
- US
- Age Group:
- Adulthood (18 yrs & older)
Young Adulthood (18-29 yrs)
Thirties (30-39 yrs)
Middle Age (40-64 yrs) - Tests & Measures:
- Structured Clinical Interview for Diagnostic and Statistical Manual of Mental Disorders—Nonpatient version
Attitudes Toward Condom Scale
Barratt Impulsiveness Scale—Version 11
Multidimensional Personality Questionnaire—Brief Form DOI: 10.1037/t03689-000
Childhood Trauma Questionnaire--Short Form DOI: 10.1037/t09716-000 - Grant Sponsorship:
- Sponsor: National Institute on Drug Abuse
Grant Number: R01 DA19405
Recipients: No recipient indicated
Sponsor: National Institute on Drug Abuse
Grant Number: 1 F31 DA023302–01A1
Recipients: No recipient indicated - Methodology:
- Empirical Study; Quantitative Study
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- Accepted: Dec 11, 2009; Revised: Oct 26, 2009; First Submitted: Apr 14, 2009
- Release Date:
- 20100621
- Correction Date:
- 20120827
- Copyright:
- American Psychological Association. 2010
- Digital Object Identifier:
- http://dx.doi.org/10.1037/a0018929
- PMID:
- 20565157
- Accession Number:
- 2010-12599-014
- Number of Citations in Source:
- 77
- Persistent link to this record (Permalink):
- http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2010-12599-014&site=ehost-live
- Cut and Paste:
- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2010-12599-014&site=ehost-live">Application of the social action theory to understand factors associated with risky sexual behavior among individuals in residential substance abuse treatment.</A>
- Database:
- PsycINFO
Application of the Social Action Theory to Understand Factors Associated With Risky Sexual Behavior Among Individuals in Residential Substance Abuse Treatment
By: Elizabeth K. Reynolds
Center for Addictions, Personality, and Emotion Research and Department of Psychology, University of Maryland, College Park;
Jessica F. Magidson
Center for Addictions, Personality, and Emotion Research and Department of Psychology, University of Maryland, College Park
Marina A. Bornovalova
Department of Psychology, University of Minnesota
Marya Gwadz
The Center for Drug Use and HIV Research, National Development and Research Institutes, New York
Craig K. Ewart
Department of Psychology, Syracuse University
Stacey B. Daughters
School of Public Health, University of Maryland, College Park
C. W. Lejuez
Center for Addictions, Personality, and Emotion Research and Department of Psychology, University of Maryland, College Park
Acknowledgement: This work was supported by NIDA Grant R01 DA19405 and NIDA Grant 1 F31 DA023302–01A1. NIDA had no further role in study design; in the collection, analysis, and interpretation of data; in the writing of the report; or in the decision to submit the paper for publication.
Despite advances in HIV prevention efforts, an estimated 1.1 million individuals are living with HIV/AIDS in the United States (Centers for Disease Control and Prevention, 2008), and approximately 56,300 new infections occurred in 2006 (Hall et al., 2008). Risky sexual behavior (RSB) is currently the most common means of acquiring HIV; approximately 83% of new infections are acquired through sexual transmission (Centers for Disease Control and Prevention, 2008). Individuals who engage in illicit substance use are especially vulnerable to the contraction of HIV through RSBs (e.g., Hoffman, Klein, Eber, & Crosby, 2000). Risk is increased further when considering individuals living in low-income urban areas where HIV risk factors including injection drug use and prostitution occur at higher rates than most other settings (Adimora, & Schoenbach, 2005; Rhodes, Singer, Bourgois, Friedman, & Strathdee, 2005). Given the elevated HIV risk via sexual transmission in urban, substance using populations, there is a clear need to understand the processes underlying RSB in this specific population.
In previous research, a variety of variables have been found to be related to RSB including demographic/background variables such as age, gender, ethnicity, and education (e.g., Avants, Marcotte, Arnold, & Margolin, 2003; Ensminger, Anthony, & McCord, 1997; Johnson, Cunningham-Williams, & Cottler, 2003; Miller & Neaigus, 2002), HIV status (Kalichman, Rompa, & Webster, 2002; Semple, Patterson, & Grant, 2000), past experience of sexual or emotional abuse (Arriola, Louden, Doldren, & Fortenberry, 2005; Senn, Carey, Vanable, Coury-Doniger, & Urban, 2006), disinhibition (Hayaki, Anderson, & Stein, 2006; Lejuez, Simmons, Aklin, Daughters, & Dvir, 2004), negative emotionality and affect (Lehrer, Shrier, Gortmaker, & Buka, 2006; Mustanski, 2007), psychopathology (Johnson et al., 2003; McMahon, Malow, Devieux, Rosenberg, & Jennings, 2008), general self-esteem (Semple, Grant, & Patterson, 2005), partner type (Macaluso, Demand, Artz, & Hook, 2000), and attitudes towards condoms (Somlai et al., 2000). This body of work provides important insight into understanding RSB among urban substance users; yet, there remain open directions for future inquiry. Specifically, there is a need to utilize a theoretical framework to understand the interplay of key variables underlying RSB among urban substance users.
In considering frameworks to understand and intervene on RSB, social–cognitive based models most often have been applied and used to guide interventions. Social–cognitive theory highlights ways in which peoples' beliefs about their personal capabilities, or self-efficacy, influence their attempts to alter ingrained habits (Bandura, 1986). Social–cognitive informed interventions most frequently include the provision of basic HIV information, risk personalization, modeling, skills building (including problem solving and relapse prevention) and to a lesser extent social support enhancement (van Empelen et al., 2003). Although these social–cognitive interventions have been shown to be relatively successful in reducing RSB among drug users (see van Empelen et al., 2003, for a review), there is a clear need for further improvement. Specifically, researchers have identified that the social–cognitive models have failed to account for self-regulatory processes and contextual factors (Bagozzi, 1992; Gollwitzer, 1990; Schwarzer, 1992). Yet, these internal and environmental context factors need to be altered or mobilized to support health risk behavior self-change (Ewart, 2009) and may be of particular importance for urban substance users considering noted difficulties in self-regulation (Palfai, 2006) and high environmental risk (Adimora & Schoenbach, 2005).
Moving in this direction, the social action theory (SAT), an integrative systems model of social-motivational, cognitive, and environmental processes, may provide a novel and potentially productive framework for understanding RSB among urban substance users (Ewart, 1991, in press). SAT was developed as an extension of individual-level psychological theories to address the broad complexities of public health problems. The overarching goal is the detection and manipulation of environmental and self-regulatory skills/deficits that can promote health and/or hinder health behaviors and habits (Johnson, Carrico, Chesney, & Morin, 2008). Initial conceptualizations of SAT focused on behavioral health more broadly defined. From these original roots, SAT has been applied to HIV risk reduction and the prevention of HIV risk behavior (Gore-Felton et al., 2005; Lightfoot, Rotheram-Borus, Milburn, & Swendeman, 2005). For example, previous work has identified the domains of SAT, including contextual and self-regulatory skills/deficits, to be predictive of youth sexual behavior (Mellins, Dolezal, Brackis-Cott, Nicholson, & Meyer-Bahlburg, 2007).
SAT elaborates on existing HIV-related social–cognitive models that emphasize cognitive appraisals and beliefs (e.g., Bandura, 1994; Fisher, Fisher, Williams, & Malloy, 1994) by focusing on social motivational and contextual influences that energize and shape behavior, specifically highlighting the ways in which important self-goals, frequently practiced routines, social-emotional competence, and social power affect substance use and other risky behaviors (Ewart, in press; Lightfoot et al., 2005). Applied to RSB, SAT views the choice of engaging in RSB as influenced by the personal regulatory resources and social power afforded by environmental context (variables that have historical impact on the individual as well as those relevant in the immediate context of the RSB) in combination with the individual's psychopathology and affect, HIV-related attitudes and knowledge, and self-regulatory skills/deficits.
Using SAT as a guiding framework, the current study sought to examine variables associated with event-level RSB among residents of an urban, substance-use treatment facility. In addition to demographic variables and HIV status, predictor variables used for this study were derived from four key domains of SAT: (1) environmental influences (i.e., childhood trauma and characteristics of last sexual intercourse), (2) forms of psychopathology and affect (substance use, depression, anxiety, borderline and antisocial personality disorders, and negative emotionality), (3) HIV-related attitudes and knowledge (condom attitudes and HIV knowledge), and (4) self-regulatory skills/deficits (trait nonplanning impulsivity, delay discounting, and risk-taking propensity). From these more general domains it is important to consider specific variables that capture the relevance of SAT to RSB.
To assess environmental influences on RSB, we utilized a measure of historical experience that would capture physical, emotional, and sexual childhood abuse. We also assessed the characteristics of the last sexual intercourse (using a measure adapted from Tortu, McMahon, Hamid, & Neaigus, 2000) that included variables such as most recent partner type, whether this partner is an injection drug user, HIV positive, and/or having sexual intercourse with other people, as well as whether the participant was drunk or high at the last sexual encounter. These two measures capture historical as well as current environmental influences that may dispose one to engage in RSB by shaping social motives, self-regulatory skills/deficits, and social power in risky situations.
The components of the second domain of SAT, forms of psychopathology and affect, are conceptualized as factors that may interfere with one's ability to self-regulate (i.e., engage in safe sexual practices). To assess psychopathology and affect, we used the Structured Clinical Interview for Diagnostic and Statistical Manual of Mental Disorders (4th ed., DSM–IV; American Psychiatric Association), nonpatient version (SCID–IV–NP; First, Spitzer, Gibbon, & Williams, 2001) to assess for the presence of Axis I and Axis II disorders that may be related to RSB engagement. Specifically, the diagnoses most related to internal-affective states included mood and anxiety disorders, borderline personality disorder (BPD), and antisocial personality disorder (ASPD). In addition to diagnostic status, we also included continuous measures of substance use frequency as well as negative emotionality. Coupling diagnostic information with continuous information related to substance use frequency and affect captures a full picture of the psychopathology/affect domain conceptualized by SAT as being related to RSB.
To assess forms of knowledge and attitudes related to HIV, we utilized the Attitudes Toward Condom Scale (Somlai et al., 2000), which assesses not only negative attitudes related to condom use, but also perceived barriers related to condom nonuse. We also assessed HIV knowledge using an adapted survey from the Center for AIDS Intervention Research (CAIR; see Somlai et al., 2000), which assessed participants' understanding of HIV/AIDS risk, risk reduction steps, and condom usage. The combination of assessing condom attitudes and HIV knowledge encompasses the third domain of SAT related to RSB.
In the assessment of the final domain of SAT, self-regulatory skills/deficits, we used a battery of disinhibition measures. We included trait nonplanning impulsivity (which assesses not planning for the future, lack of self-control, and intolerance of cognitive complexity) given its link to self-regulatory skills/deficits defined by the SAT. We assessed delay discounting using the Delay Discounting Task (DDT; Kirby, Petry, & Bickel, 1999), which captures the degree to which an individual shows preference for either small, immediate rewards or larger, delayed rewards, and fits with the social-emotional competence component of SAT given its relation to factors associated with condom use decision making. To supplement self-report assessment with behavioral assessment of disinhibition, we also utilized the Balloon Analogue Risk Task (BART; Lejuez et al., 2002) to assess risk-taking propensity, a variable previously demonstrated to be associated with RSB in this type of sample (Lejuez et al., 2004). SAT considers RSB to result from self-regulatory skills/deficits as they relate to motivation and problem solving (Ewart, 1991, in press); this battery of behavioral and self-report measures of disinhibition address the self-regulatory skills/deficits delineated by SAT to be related to RSB.
Regarding our main outcome, RSB is often assessed using global association measures that include questioning one's general frequency of RSB during a given period of time (e.g., 6 months; Ross, Hwang, Zack, Bull, & Williams, 2002; Sanchez, Comerford, Chitwood, Fernandez, & McCoy, 2002; Somlai, Kelly, McAuliffe, Ksobiech, & Hackl, 2003). Although useful in many respects, global assessment of past frequency of RSB is subject to inaccuracy of recall and gross frequency count biases (Chawarski, Pakes, & Schottenfeld, 1998). Further, this method of assessment provides little information in terms of situational or context-specific conditions, resulting in condom use being treated a context-independent outcome (Kiene, Barta, Tennen, & Armeli, 2009; Weinhardt & Carey, 2000).
To address the limitations of global assessment of RSB, the utilization of event-level measurement (Leonard, & Ross, 1997; Temple, & Leigh, 1992) has been introduced into research concerning HIV risk behavior (e.g. Gillmore et al., 2002; Tortu et al., 2000). Event-level measurement typically assesses particular aspects of a recent sexual event, such as type of partner and whether one was under the influence of alcohol. In event-level measurement, participants describe behavior during a particular event rather than report general trends or averages of behavior (LaBrie, Earleywine, Schiffman, Pederson, & Marriot, 2005). Despite the potential limitations of underreporting due to forgetting of events (Schroder, Carey, & Vanable, 2003) and the lack of estimate of overall likelihood of RSB, event-level measurement is thought to avoid overreporting and rounding and allows for an examination of situational variables (Kiene et al., 2009).
Few studies have used this event-level methodology with at-risk samples of substance users and within a more comprehensive theory-driven model of risk. Indeed, event-level studies largely are limited to samples including college students (e.g., Brown, & Vanable, 2007; LaBrie et al., 2005) and nonsubstance dependent adult samples (e.g., Ibañez, Van Oss Marin, Villarreal, & Gomez, 2005; Leonard, & Ross, 1997). Of the studies that have utilized event-level data among substance users (e.g., Leigh, Ames, & Stacy, 2008; Scheidt, & Windle, 1996; Tortu et al., 2000; Watkins, Metzger, Woody, & McLellan, 1993), the predictors have largely focused on the effect of intoxication, sexual history (Scheidt, & Windle, 1996), and drug use severity (Scheidt & Windle, 1996; Watkins et al., 1993) and have rarely been incorporated into a broader model of risk. In sum, the current study examined four components of SAT in relation to event-level CU among a sample of substance users in residential substance abuse treatment.
Method Participants
Participants included 161 men and women receiving treatment at the Salvation Army Harbor Light residential substance abuse treatment facility located in Northeast Washington, DC. Five individuals were found to meet DSM–IV criteria for psychosis and thus were excluded from further analyses. The final sample (n = 156) ranged in age from 18 to 60 years, with a mean age of 42.85 years (SD = 8.59). Of the sample, 54.6% were crack/cocaine dependent, 15.1% heroin dependent, 10.6% marijuana dependent, and 34.3% alcohol dependent (of note adds up to more than 100% due to the fact that participants could be dependent on more than one drug). There were 25.0% who did not meet any dependence criteria, 44.3% were dependent on one drug, 23.6% were dependent on two drugs, 5.0% were dependent on three drugs, and 2.1% were dependent on four drugs. With regard to racial/ethnic background, 86.5% of the participants were African American, 7.1% were White, 1.9% were Hispanic/Latino, 1.3% were Native American, and 3.2% reported “other.” In terms of highest education level, 20.0% had not completed high school, 40.0% completed high school (or received a GED), and 40.0% completed at least some college, technical, or trade school. The majority of the sample reported current unemployment, and 53.9% reported a household income of less than $10,000 a year. Patients entered the treatment center either voluntary or under a pretrial-release-to-treatment program through the District of Columbia Pretrial Services Agency (53.3%). This program offers drug offenders who are awaiting trial the option to receive substance abuse treatment as a way to ensure appearance in court, provide community safety, and address an underlying cause of recidivism. Patients were contracted to a specific length of stay on entry into the treatment center. For the current sample, contract lengths included 30 days (41.4%), 60 days (29.7%), 90 days (6.3%), or 180 days (22.6%).
Procedures
Assessment sessions were held on Friday afternoons in a private room at the Salvation Army Harbor Light facility. Residents at the treatment center were approached and asked if they would be willing to participate in a study examining sexual behavior among substance users. They were told that the session would last up to 3 hr and that they would be paid $25 in the form of a grocery store gift card on discharge from treatment. Interested participants were given a more detailed explanation of the procedures and asked to provide written informed consent approved by the University of Maryland Institutional Review Board. Given issues of reading comprehension, efforts were made to ensure that participants understood all facets of the consent form and the study itself. Next, a diagnostic interview, the SCID–IV–NP (First et al., 2001), was administered in a separate private room by a trained senior research staff member. If not eligible following the SCID–IV–NP due to the presence of psychotic symptoms, participants were debriefed and paid $10 in grocery gift cards for their time. Following completion of the diagnostic interview, the participants completed a battery of questionnaires (described in detail below).
Although the participants were completing the questionnaires, individuals trained in administering the behavioral task took participants one by one into an adjacent room where they completed the computer task (i.e., BART). The order of completion of the questionnaires was counterbalanced across participants. For entry into the treatment center (independent of the current study), individuals were required to evidence abstinence from all substance use and to have completed a detoxification program if needed. As a result, acute drug effects likely did not affect the participants' scores on the testing battery. Once the participants completed each aspect of the assessment session (i.e., clinical interview, questionnaire packet, and computer task), they were told how much money they earned and signed a receipt. This payment ($25 in grocery store gift card) was deposited into their account at Harbor Light, which they received on discharge from the residential treatment center.
Measures
Event-level assessment of CU (event-level CU)
Participants reported whether a condom was used during their last sexual intercourse. This question, based on work by Tortu et al. (2000), served as the dichotomous dependent variable (coded: 0 = condom used, 1 = condom not used).
Demographic information and HIV status
A short self-report questionnaire was administered to obtain age, gender, race, education level, marital status, and total yearly income. In addition, participants reported their HIV status.
SAT derived independent variables
Domain 1: Environmental influences
Childhood trauma
As a measure of childhood trauma, we used the three abuse subscales (physical, emotional, and sexual) of the short form of the Childhood Trauma Questionnaire (Bernstein, Stein, & Newcomb, 2003). The CTQ–SF assesses childhood maltreatment experiences (i.e., “while you were growing up”) using a 5-point scale ranging from 1 (never true) to 5 (very often true). The CTQ–SF shows convergent and discriminant validity with other trauma measures (Bernstein et al., 1994; Fink, Bernstein, Handelsman, Foote, & Lovejoy, 1995). Internal consistency for the scale was adequate with Cronbach's α = .94.
Characteristics of last sexual intercourse
Based on Tortu et al. (2000), we administered a 10-item questionnaire on one's most recent sexual intercourse. Contextual predictors of CU from this measure included: type of partner that was involved in the most recent sexual intercourse encounter (commercial: defined as money or drugs being exchanged for sex; casual: defined as no committed relationship; or regular: defined as a committed relationship including a boyfriend/girlfriend or spouse), as well as whether this most recent partner was an injection drug user, HIV positive, and/or having sexual intercourse with other people. Further, the respondent was asked to indicate whether at their last sexual encounter they were drunk and/or high, and if they also engaged in oral or anal sex. Finally, the questionnaire asked whether the sexual encounter occurred in the last year.
Domain 2: Psychopathology and affect
Psychopathology
The SCID–IV–NP (First et al., 2001) was used to determine the presence of current Axis I disorders (i.e., clinical disorders) including depression, anxiety (presence of any anxiety disorder including panic, social phobia, generalized anxiety disorder, specific phobia, posttraumatic stress disorder), psychosis (for exclusion criteria), and substance dependence as well as Axis II disorders (i.e., personality disorders) including borderline personality disorder (BPD) and antisocial personality disorder (ASPD). This measure has demonstrated reliability (First et al., 2001). All eligible participants were administered the interviews in a private area by trained research staff.
Substance use frequencies
Frequency of drug (i.e., marijuana, heroin, and crack/cocaine) and alcohol use was assessed with a standard substance use questionnaire modeled after the Alcohol Use Disorders Identification Test (AUDIT; Saunders, Aasland, Babor, de la Fuente, & Grant, 1993). Specifically, participants were asked how often they used each substance in the past year prior to treatment. Response options were: 0 (never), 1 (one time), 2 (monthly or less), 3 (2 to 4 times a month), 4 (2 to 3 times a week), and 5 (4 or more times a week). This measure was used to supplement dependence diagnoses from the SCID–IV–NP with more detailed information on substance use frequency.
Negative emotionality
The Multidimensional Personality Questionnaire Brief Form (MPQ–BF; Patrick, Curtin, Tellegen, 2002) is a 155-item version of the original 300-item MPQ (Tellegen, 1982) developed to assess a variety of personality traits and temperamental dispositions. The higher order factor of Negative Emotionality (NEM) is comprised of the traits of Stress Reactivity, Alienation, and Aggression, and we utilized only this subscale given its previously demonstrated link to RSB (Lehrer et al., 2006). Internal consistency for the scale was good (α = .89).
Domain 3: HIV-related attitudes and knowledge
Condom attitudes
The Attitudes Toward Condoms Scale (Somlai et al., 2000) consists of eight items focusing on negative attitudes regarding CU and beliefs concerning barriers to CU, such as perceptions and connotations of CU, perceived unreliability of condoms in preventing sexually transmitted diseases, and embarrassment when purchasing condoms. The scale allows for level of agreement with each item using a 6-point Likert scale ranging from 1 (strongly disagree) to 6 (strongly agree). A participant's negative attitudes toward condoms scale score reflects the mean of the eight items, with higher values reflecting more negative attitudes toward CU. Internal consistency for the scale was acceptable (α = .66).
HIV knowledge
Adapted from a survey developed by CAIR (see Somlai et al., 2000), participants' understanding of HIV/AIDS risk, risk reduction steps, condom usage, and safer sex practices was assessed using a 22-item true–false scale. Scores on this scale range from 0 to 22, reflecting the number of questions correctly answered. Internal consistency for the scale was acceptable (α = .67).
Domain 4: Self-regulatory skills/deficits
Trait nonplanning impulsivity
Trait impulsivity was assessed using the Barratt Impulsiveness Scale (Version 11, BIS–11; Patton, Stanford, & Barratt, 1995). The BIS–11 is a 30-item, self-report questionnaire that contains three subscales, which have been termed Motor Impulsiveness, Cognitive Impulsiveness, and Nonplanning. We utilized only the Nonplanning subscale, which we conceptualized as being most relevant to condom nonuse given its focus on a lack of planning for the future, specifically targeting lack of self-control and intolerance of cognitive complexity. The questions require participants to rate how often a statement applies to them based on the following scale: 1 (rarely/never), 2 (occasionally), 3 (often), to 4 (always/almost always). The BIS–11 has been normed on a variety of sample populations, including college students, inpatient substance abusers, and prison inmates. Internal consistency for the scale was acceptable with Cronbach's α = .68.
Delay discounting
Developed by Kirby et al. (1999), the DDT provides a measure of the degree to which an individual shows preference for either small, immediate rewards or larger, delayed rewards, which may be stated as the rate at which the subjective value of deferred rewards decreases as a function of the delay until they are received. The DDT was provided in a paper-and-pencil format, consisting of a fixed set of 27 choices between smaller, immediate rewards and larger delayed rewards. For example, on the first trial, participants were asked “Would you prefer $54 today, or $55 in 117 days?” Delays included in this questionnaire ranged from 7 to 186 days. The presentation order of the delays was contrived so as not to correlate choice amounts, ratios, differences, delays, or discount rate implied by indifference to the two rewards. Previous research has shown that individual's discount curves are well described by the hyperbolic discount function (Mazur, 1987) V = A/(1 + kD), where V is the present value of the delayed reward A at delay D, and k is a free parameter that determines the discount rate. As k increases, the person discounts the future more steeply. Therefore, k can be thought of as an impulsiveness parameter, with higher values corresponding to higher levels of impulsiveness.
Risk-taking propensity
The BART (Lejuez et al., 2002) was used as a behavioral measure of risk-taking propensity. In this task, the computer screen displays a small simulated balloon accompanied by a balloon pump. Participants are directed to pump the simulated balloon to earn as much money as possible, taking into consideration that the balloon can pop at any time. When a balloon explodes, all money in the temporary bank is lost and the next uninflated balloon appears on the screen. At any point during each balloon trial, the participant can stop pumping the balloon by clicking a “Collect $$$” button, which transfers all money from a temporary bank to a permanent bank. After each balloon explosion or money collection, the participant's exposure to that balloon will end, and a new balloon will appear until a total of 20 balloons (i.e., trials) are completed. These 20 trials are comprised of different balloon types, all with the same probability of exploding. Participants are not given any detailed information about the probability of an explosion, but are told that at some point each balloon will explode and this explosion can occur as early as the first pump all the way up to the point at which the balloon expands as large as the computer screen. The primary dependent measure on the BART was the adjusted number of pumps across trials. An independent review by Harrison, Young, Butow, Salkeld, and Soloman (2005) of state of the art risk measurement strategies identified the BART has having excellent reliability (test–retest) and validity (including convergent and divergent).
Data Analytic Strategy
Analyses were conducted with the dichotomous variable of CU versus nonuse as the dependent variable. Primary analyses began with descriptive statistics for the entire sample across the dependent variable of event-level CU as well as each of the variables in the four domains of SAT: (1) environmental influences, (2) psychopathology and affect, (3) HIV-related attitudes and knowledge, and (4) self-regulatory skills/deficits. Next, we examined differences between those who used a condom versus those who did not at the last sexual encounter on all study variables using paired samples t tests and chi-square. Each variable found to significantly differ for the CU versus nonuse groups were included in a multivariate logistic regression analysis. All analyses used a two-tailed alpha of .05.
ResultsAs a preliminary analysis, we examined the descriptives of the last sexual encounter for the whole sample as shown in Table 1. For the dependent variable, condom nonuse, 66.0% reported not using a condom during this last sexual encounter. In terms of timing, 92.9% reported that the sexual intercourse occurred in the last year and 7.1% reported that the sexual encounter did not occur in the last year. Next, differences between those who used a condom versus not at last sexual encounter were examined; results are shown in Table 1. Of note, substance use frequencies were reported rather than dependence diagnoses given the more descriptive nature of continuous data, but it is notable that the same findings (nonsignificant relationships) were obtained when dependence diagnoses were used instead. Those who did and did not use a condom did not differ significantly on the following environmental influences: history of childhood trauma, whether the partner injected drugs with a needle, was HIV positive, or was having intercourse with other people, or whether the participant was drunk or high during the last sexual intercourse. Those who did and did not use a condom did not differ significantly on the following psychopathology and affect variables: presence of a diagnosis of major depression, any anxiety disorder, BPD, or ASPD, negative emotionality, frequency of crack, heroin, alcohol, and marijuana use. Last, they also did not differ on HIV-related knowledge, or on the following self-regulatory skills/deficits: trait nonplanning impulsivity, delay discounting (all ps > .06).
Differences in Condom Use at Last Sexual Intercourse
Those who used a condom were significantly older than those who did not use a condom, t(154) = –1.99, p < .05. The CU versus nonuse groups significantly differed on the type of relationship with the partner, χ2(2, N = 156) = 11.03, p < .01. A larger percentage of those who did not use a condom described their partner as regular. Those who did not use a condom scored higher on risk-taking propensity as indexed by the BART, t(154) = –1.98, p < .05. And finally, those who did not use a condom reported more negative attitudes towards condoms then those who did use a condom, t(154) = –3.02, p < .01.
Finally, we conducted a multivariate logistic regression analysis to determine the independent contribution of each variable related to condom nonuse at a univariate level. Each independent variable found to have a significant univariate relationship to condom nonuse was included in a multivariate logistic regression analysis. Results are reported in Table 2. Overall, the model correctly classified 71.8% of the sample. Age did not remain significant (p > .05). Partner type, risk-taking propensity, and condom attitudes each were significant. Specifically, respondents were significantly less likely to report unprotected sex with casual partners, odds ratio (OR) = 0.25, 95% confidence interval (CI) [0.10, 0.60], p = .002, and commercial partners, OR = 0.26, 95% CI [0.09, 0.75], p = .01, compared to regular partners. For risk-taking propensity, a one unit increase in level of BART was associated with an approximately one and half greater likelihood of not having used a condom at last sexual intercourse, OR = 1.61, 95% CI [1.09, 2.34], p = .02. Finally, a one unit increase in negative condom attitudes was associated with an approximately one and three quarter greater likelihood of not having used a condom at last sexual intercourse, OR = 1.78, 95% CI [1.11, 2.85], p = .02.
Multivariate Logistic Regression Predicting Condom Nonuse at Last Sexual Intercourse
DiscussionThis study sought to examine variables associated with CU measured via event-level assessment among a sample of urban substance users. Utilizing the SAT as a guiding framework, a variety of SAT derived predictors were examined including: (1) environmental influences (i.e., childhood trauma and characteristics of last sexual intercourse), (2) forms of psychopathology and affect (diagnoses of depression, anxiety, BPD, and ASPD, substance use frequencies, and negative emotionality), (3) HIV-related attitudes and knowledge (condom attitudes and HIV knowledge), and (4) self-regulatory skills/deficits (trait nonplanning impulsivity, delay discounting, and risk-taking propensity). Findings from the logistic regression indicated that regular partner type, higher levels of risk taking propensity, and negative condom attitudes all contributed uniquely to an increased likelihood of condom nonuse.
These results represent one of the first attempts to apply the SAT to understanding the factors influencing RSB in an urban, largely minority, substance abusing sample utilizing event-level methodology. The selected independent variables represent a range of theoretically relevant factors related to RSB according to SAT, and the final model supports several key aspects of SAT theory, specifically how it posits the various dimensions contribute to health risk behavior and in this case RSB. Specifically, the significance of contextual, cognitive, and self-regulatory factors suggests the utility of SAT. Although the findings leave questions open for future exploration, the resulting model for RSB is conceptually meaningful, as it suggests that having sex with a regular partner, having negative attitudes towards condoms, and being prone to take risks combine to confer a relationship with engagement in RSB (i.e., condom nonuse). In the following sections we discuss the findings in the context of each SAT domain.
Within the domain of environmental influences, the finding that having sex with a regular partner was related to condom nonuse is consistent with prior work with nondrug using samples (e.g., Macaluso et al., 2000), but deserves additional attention in considering its meaning in the context of this study sample (urban substance users). In many samples, the choice to have intercourse without a condom when one's partner is regular may be thought of as a fairly low-risk behavior. However, condom nonuse even with a regular partner may pose elevated risk in this particular population given the prevalence of injection drug use, risky sexual behavior, and/or commercial sex in urban, substance using environments. Future studies would benefit from collecting more objective information on the regular partner, as this description may vary greatly among respondents, as well as determining the specific routines and situations in which sex with regular partners does or does not increase risk for condom nonuse. This finding supports the proposal by van Empelen and colleagues (2003) to develop programs that specifically target safe sexual behaviors of drug users in the steady relationship context.
Also from the domain of environmental influences, an additional notable finding was that being drunk and/or high during the last incident of sexual intercourse was not significantly related to CU. Although a common perception exists that alcohol and other substances negatively interfere with CU, a variety of studies have questioned this notion and posited that one's likelihood to use a condom may remain unchanged regardless of intoxication state (e.g., Leigh, Vanslyke, Hoppe, Rainey, Morrison, & Gillmore, 2008; Weinhardt & Carey, 2000). In a chronic substance using sample, disentangling the effect of substance use on CU is challenging, given that the pharmacological effects of different types of drugs have been shown to have different impacts on RSB (Leigh, Ames, & Stacy, 2008). In a recent study, only amphetamines (smoked or injected) were consistently related to decreased CU; alcohol use was not related to decreased CU, and cocaine and marijuana were not significantly related to CU in either direction (Leigh et al., 2008a). Thus, our finding that being drunk or high at the last sexual encounter was not related to CU may not be surprising when considered in the context of the existing literature, particularly given that the two most prevalent drug dependencies in our sample were cocaine (54.6%) and alcohol (34.3%). Another potential factor that may have limited a significant relationship between intoxication and CU is the chronic nature of the sample's substance use. For the individuals in our sample, intoxication or activities aimed at future substance use often are pervasive aspects of daily routines, and therefore isolating acute pharmacological effects on CU may be complicated.
Considering psychopathology and affect, no significant relationships with CU emerged, which is consistent with a meta-analysis reporting no significant correlation between depressive symptomatology, anxiety, anger, and sexual risk taking (Crepaz & Marks, 2001). However, the absence of findings also may have been overly influenced by the timing of assessment. That is, diagnoses certainly vary over time and may have in fact had been relevant at the time of the last sexual encounter. As with more global measures of RSB, this type of timing issue is a weakness of event-level measurement across a variety of variables. Nevertheless, it is likely that this limitation is most impactful for variables assessing psychopathology as compared to more stable variables such as attitudes and self-regulatory skills/deficits, suggesting the importance of future work considering other strategies to adequately assess the impact of these variables. It will be necessary to move beyond cross-sectional data and to examine within-person associations (Kalichman & Weinhardt, 2001). For example, using an online diary, Mustanski (2007) found that lower levels of positive affect are related to increased sexual risk behaviors. The diary method offers the advantage of a short time period between behavior and recording allowing for detailed information about the co-occurrence of events and moods that fits well with the internal and environmental aspects of SAT.
Within the domain of HIV-related attitudes and knowledge, condom attitudes predicted event-level RSB. This finding supports previous studies that have demonstrated the relevance of condom attitudes in predicting RSB (e.g., Robertson & Levin, 1999; Zamboni, Crawford, & Williams, 2000), and as such, intervention efforts that target condom attitudes as a means to increase safe sex practices (e.g., Jemmott, Jemmott, Braverman, & Fong, 2005; Wingood et al., 2006) may be useful to extend to urban substance using populations that face heightened HIV risk, with a particular focus on those with a propensity to take risks. The lack of significant relationship with HIV knowledge is consistent with prior research demonstrating that HIV/AIDS knowledge and perceived risk have little predictive value for safe sexual behavior (c.f. van Empelen et al., 2003). Findings for condom attitudes despite the absence of findings for HIV knowledge suggests that it is not necessarily what is known about the risks and consequences associated with HIV, but one's internal representation (attitudes in this case) resulting from knowledge and other external variables.
Finally, risk-taking propensity, as indexed by performance on the BART (a tendency that may limit self-regulatory resources) was related to condom nonuse during one's last sexual encounter. This is consistent with the Lejuez et al. (2004) study that found risk-taking propensity, as indexed by the BART, to be significant predictor of RSB, measured globally, above and beyond a host of variables. These findings serve as an extension of previously published reports and suggest the value of adding behavioral assessments such as the BART in understanding RSB, given that they are not compromised by limitations in individuals' ability to recognize and report on their own behavioral tendencies (Leigh & Stall, 1993; Tortu et al., 2000). Interpreting the factors underlying this relationship is difficult as the current study fails to rule out the possibility that both RSB as well as risk-taking propensity are the consequences of immersion in a substance using lifestyle as well as the direct consequence of neurotoxic illicit drugs, such as crack/cocaine (Booth, Kwiatkowski, & Chitwood, 2000; Hoffman et al., 2000; Ross et al., 2002). Further, this study fails to explain why trait nonplanning and delay discounting, both variables within the disinhibition umbrella, were not related to condom use as previous work among substance using samples has found impulsivity, measured with the Eysenck I7 Questionnaire (Eysenck, Pearson, Easting, & Allsopp, 1985), to be a statistically significant predictor of sexual risk after adjusting for the effects of demographic variables and substance use frequency (Hayaki et al., 2006). Future studies should be designed to systematically tease apart the potential contributions of dispositional factors from long-term negative effects of drug use in trying to better understand the origins of RSB. In addition, future work should also carefully consider the measurement of disinhibition as it is considered to be a multidimensional construct. It may be that certain aspects of disinhibition (e.g., risk-taking propensity) may be related to RSB whereas others are not. It is necessary to parse apart the differential findings with various measures of disinhibition, particularly the lack of consistency with self-report and behavioral tasks and the lack of significant correlation, as was found in this study, between measures that tap disinhibition.
The current study has a number of strengths, including the use of an at-risk, underserved sample of urban, primarily minority substance users, the application of the SAT framework, and the application of event-level measurement and behavioral assessment of risk-taking propensity to this population. However, several limitations should also be considered in interpreting the results. As noted above, there were multiple confounds associated with timing. It is unclear the extent to which measurement of study variables at our assessment accurately represent the manifestation of that variable at the most recent sexual experience, with only the contextual variables affecting condom use being variables in our battery directly tied to the most recent sexual encounter (i.e., to what extent are current condom attitudes at the assessment similar to those at the most recent sexual encounter; could not using a condom impact one's condom attitudes). Also presenting interpretative challenges, the current study did not assess the precise timing of the last sexual encounter (just whether the sexual encounter occurred in the previous year), thereby preventing statistical control for important factors (e.g., how long ago the last sexual encounter took place) to assess the temporal relationship among the predictors and our outcome measure.
Although the event-level measurement may help with overreporting and rounding and allows for an examination of situational variables, it is also important to acknowledge its limitations. There is the potential that the event reported may not be representative of the participant's overall behavior, and therefore cannot provide information on cumulative risk. The single event does not allow within person comparisons on behavior with regard to the environmental variables assessed, such as partner type or substance use, leaving open questions about whether the individual participant's behavior varies with regard to these factors. Further, the event-level report is still retrospective. Future work examining person-level predictors (e.g., risk taking propensity, condom attitudes) in addition to event characteristics (e.g., intoxication state, partner type) would benefit from repeated sampling with techniques as a daily diary to determine which variables are consistently associated with discrete behavior. Further, there is some evidence that recalls up to 90 days are reliable (e.g., Ajzen & Fishbein, 2004; Jaccard, McDonald, Wan, Dittus, & Quinlan, 2002); thus, in future work it may be best to limit event-level measurement to sexual encounters within the last 90 days or to conduct a prospective study that attempts to link current variables with sexual behavior.
Another limitation is the failure to assess sexual orientation and/or gender of sexual partner, a necessary target for future work. A final limitation is the size of the sample. Although sample size is acceptable for main findings, a number of interactions were examined (see footnote) that were not found to be significant. Although power was indeed limited for these analyses, it is notable that the odds ratios and confidence ratios indicate that these were very modest. Nevertheless, future studies would benefit form larger samples to further evaluate the utility of the SAT model, including examination of potential interaction effects.
The current data support a model framed in SAT in which having sex with a regular partner, having negative attitudes towards condoms, and being prone to take risks combine to confer a relationship with engagement in RSB (i.e., condom nonuse). Despite limitations and the questions raised by our findings, the current results add to the growing body of literature evaluating and predicting RSB within urban substance using populations and begins to lay the groundwork for future investigations that examine event-level RSB in the context of more elaborate environmental and self-regulatory predictors. By identifying personal and environmental self-regulatory resources that directly affect social interactions, problem solving, and self-efficacy, SAT applied to RSB has the potential to identity interventions that can be used to enhance health by altering self-goals, strategies, and environments. From a public health standpoint, identifying factors that influence HIV risk behaviors will continue to be critical, particularly as evidence grows suggesting that urban, minority substance users are at high risk for HIV infection.
Footnotes 1 We also conducted several follow-up analyses. First, we examined if the relationship of condom use with risk taking propensity as well as condom attitudes varied by partner type. The interactions of partner type and risk taking propensity, OR = 1.00, 95% CI [0.97, 1.04], p = .94, and partner type and condom attitudes, OR = 1.36, 95% CI [0.71, 2.60], p = .36 were not significant. Next, although there was not a significant association between being drunk or high and condom use, an interaction between partner type and being drink or high was examined based on prior work demonstrating a significant interaction. The interaction of being drunk or high with partner type was not significant, OR = 2.05, 95% CI [0.66, 6.33], p = .21. Finally we examined gender interactions with the variables significantly related to condom use based on the univariate findings as shown in Table 1. There was not a significant interaction of gender with partner type, OR = 1.05, 95% CI [0.28, 3.93], p = .94, gender with condom attitudes, OR = 1.08, 95% CI [0.42, 2.75], p = .87, or gender with risk taking propensity, OR = 1.02, 95% CI [0.97, 1.07], p = .43.
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Submitted: April 14, 2009 Revised: October 26, 2009 Accepted: December 11, 2009
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Source: Psychology of Addictive Behaviors. Vol. 24. (2), Jun, 2010 pp. 311-321)
Accession Number: 2010-12599-014
Digital Object Identifier: 10.1037/a0018929
Record: 18- Title:
- Are body dissatisfaction, eating disturbance, and body mass index predictors of suicidal behavior in adolescents? A longitudinal study.
- Authors:
- Crow, Scott. Department of Psychiatry, University of Minnesota Medical School, Minneapolis, MN, US, crowx002@umn.edu
Eisenberg, Marla E.. Division of General Pediatrics and Adolescent Health, University of Minnesota, Minneapolis, MN, US
Story, Mary. Division of General Pediatrics and Adolescent Health, University of Minnesota, Minneapolis, MN, US
Neumark-Sztainer, Dianne. Division of General Pediatrics and Adolescent Health, University of Minnesota, Minneapolis, MN, US - Address:
- Crow, Scott, Department of Psychiatry, University of Minnesota Medical School, F290 2450 Riverside Avenue, Minneapolis, MN, US, 55454-1495, crowx002@umn.edu
- Source:
- Journal of Consulting and Clinical Psychology, Vol 76(5), Oct, 2008. pp. 887-892.
- NLM Title Abbreviation:
- J Consult Clin Psychol
- Page Count:
- 6
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- Journal of Consulting Psychology
- Other Publishers:
- US : American Association for Applied Psychology
US : Dentan Printing Company
US : Science Press Printing Company - ISSN:
- 0022-006X (Print)
1939-2117 (Electronic) - Language:
- English
- Keywords:
- suicide, obesity, body dissatisfaction, eating disorders, suicidal ideation, body mass index
- Abstract:
- Disordered eating, body dissatisfaction, and obesity have been associated cross sectionally with suicidal behavior in adolescents. To determine the extent to which these variables predicted suicidal ideation and attempts, the authors examined these relationships in a longitudinal design. The study population included 2,516 older adolescents and young adults who completed surveys for Project EAT-II (Time 2), a 5-year follow-up study of adolescents who had taken part in Project EAT (Time 1). Odds ratios for suicidal behaviors at Time 2 were estimated with multiple logistic regression. Predictor variables included Time 1 extreme and unhealthy weight control behaviors (EWCB and UWCB), body dissatisfaction, and body mass index percentile. Suicidal ideation was reported by 15.2% of young men and 21.6% of young women, and suicide attempts were reported by 3.5% of young men and 8.7% of young women. For young women, suicidal ideation at Time 2 was predicted by Time 1 EWCB. The odds ratio for suicide attempts was similarly elevated in young women who had reported EWCB at Time 1. These odds ratios for both suicidal ideation and suicide attempts remained elevated even after controlling for Time 2 depressive symptoms. In young men, EWCB was not associated with suicidal ideation or suicide attempts 5 years later. Body mass index and body dissatisfaction did not predict suicidal ideation or suicide attempts in young men or young women. These results emphasize the importance of EWCB. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Body Image; *Body Mass Index; *Eating Disorders; *Obesity; *Suicide; Dissatisfaction; Suicidal Ideation
- Medical Subject Headings (MeSH):
- Adolescent; Body Image; Body Mass Index; Cohort Studies; Depression; Feeding and Eating Disorders; Female; Humans; Longitudinal Studies; Male; Minnesota; Obesity; Personality Inventory; Psychometrics; Risk Factors; Sex Factors; Suicide, Attempted; Young Adult
- PsycINFO Classification:
- Psychological & Physical Disorders (3200)
- Population:
- Human
Male
Female - Location:
- US
- Age Group:
- Adolescence (13-17 yrs)
Adulthood (18 yrs & older)
Young Adulthood (18-29 yrs) - Grant Sponsorship:
- Sponsor: US Department of Health and Human Services, Health Resources and Services Administration, Maternal and Child Health Bureau, US
Grant Number: R40 MC 00319
Recipients: No recipient indicated
Sponsor: Minnesota Obesity Center, US
Grant Number: P30DK 60456
Recipients: No recipient indicated
Sponsor: National Institute of Mental Health
Grant Number: K02 MH 65919
Recipients: No recipient indicated - Methodology:
- Empirical Study; Longitudinal Study; Quantitative Study
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- Accepted: Apr 25, 2008; Revised: Apr 14, 2008; First Submitted: Aug 8, 2007
- Release Date:
- 20081006
- Copyright:
- American Psychological Association. 2008
- Digital Object Identifier:
- http://dx.doi.org/10.1037/a0012783
- PMID:
- 18837605
- Accession Number:
- 2008-13625-017
- Number of Citations in Source:
- 38
- Persistent link to this record (Permalink):
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- Cut and Paste:
- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2008-13625-017&site=ehost-live">Are body dissatisfaction, eating disturbance, and body mass index predictors of suicidal behavior in adolescents? A longitudinal study.</A>
- Database:
- PsycINFO
Are Body Dissatisfaction, Eating Disturbance, and Body Mass Index Predictors of Suicidal Behavior in Adolescents? A Longitudinal Study
By: Scott Crow
Department of Psychiatry, University of Minnesota Medical School, Minneapolis;
Marla E. Eisenberg
Division of General Pediatrics and Adolescent Health, University of Minnesota, Minneapolis
Mary Story
Division of General Pediatrics and Adolescent Health, University of Minnesota, Minneapolis;
Division of Epidemiology and Community Health, University of Minnesota, Minneapolis
Dianne Neumark-Sztainer
Division of General Pediatrics and Adolescent Health, University of Minnesota, Minneapolis;
Division of Epidemiology and Community Health, University of Minnesota, Minneapolis
Acknowledgement: This study was supported by Maternal and Child Health Bureau Grant R40 MC 00319 from the Health Resources and Services Administration, Department of Health and Human Services; by Minnesota Obesity Center Grant P30DK 60456; and by National Institute of Mental Health Grant K02 MH 65919.
Suicide is a leading cause of death in adolescents (Goldsmith, 1986), and suicidal ideation and suicide attempts are common. In reviewing the published literature on suicidal behaviors during adolescence, Evans, Hawton, Rodham, and Deeks (2005) found mean lifetime rates of suicidal ideation of 29.9%; of respondents, 9.7% had reported suicide attempts. It appears that the frequency of suicidal behavior is typically underestimated by families (Breton, Tousignant, Bergeron, & Berthiaume, 2002; Klimes-Dougan, 1998) and might be underestimated by clinicians. Suicidal behavior results in substantial utilization of emergency care and hospitalization (Olfson, Gameroff, Marcus, Greenberg, & Shaffer, 2005).
Numerous correlates of suicidal behavior during adolescence have been identified. These correlates include family history of suicidal behavior (Brent & Mann, 2005), substance use disorders, early childhood maltreatment (Bridge, Goldstein, & Brent, 2006), and psychiatric disorders (Andrews & Lewinsohn, 1992; Brent et al., 1988; Fergusson, Beautrais, & Horwood, 2003; Kessler, Borges, & Walters, 1999). The psychiatric disorders implicated include, most prominently, mood disorders and eating disorders but also anxiety disorders and psychotic disorders. It appears that eating disorders may carry the highest suicide risk of any psychiatric disorder (Franko & Keel, 2006; Harris & Barraclough, 1994).
Although the link between diagnosable eating disorders and suicidal behavior has been recognized for some time, recent evidence suggests a correlation between suicidal behavior and body dissatisfaction, as well as disordered eating behaviors that fall short of the threshold for eating disorder, not otherwise specified, diagnosis (Ackard, Neumark-Sztainer, Story, & Perry, 2003; Crow, Eisenberg, Story, & Neumark-Sztainer, 2008; Rafiroiu, Sargent, Parra-Medina, Drane, & Valois, 2003). For example, in a school-based study of 13-year-olds, higher levels of body dissatisfaction predicted suicidal attempts over 2-year follow-up (Rodriguez-Cano, Beato-Fernandez, & Llario, 2006). Similarly, Crow et al. (2008) reported that unhealthy weight control behaviors (UWCB) and body dissatisfaction both correlated cross-sectionally with suicidal behavior in adolescents. In a sample of high-school-age adolescents, body image and, in particular, body attitudes and feelings predicted suicidal ideation (Brausch & Muehlenkamp, 2007).
Whether overweight and obesity are correlated with suicidal behavior has been unclear; some studies have found such a correlation (Carpenter, Hasin, Allison, & Faith, 2000; Falkner et al., 2001; Moore, Stunkard, & Srole, 1962), but others have not (Crow et al., 2008; Hallstrom & Noppa, 1981; Kittel, Rustin, Dramaix, deBacker, & Kornitzer, 1978; Neumark-Sztainer, Story, French, et al., 1997). Association of obesity and suicidal behavior in adolescents would represent a significant public health concern, given the marked increases in prevalence of obesity among adolescents.
Cross-sectional relationships have been described repeatedly, but the extent to which body dissatisfaction, disordered eating, or obesity is predictive of the occurrence of suicidal behaviors over time is not known. Identifying factors that are predictive of suicide risk is essential for treatment and prevention. In this study, we examined the longitudinal relationships between body dissatisfaction, weight control behaviors, weight status, and self-reported suicidal behavior using a nonclinical sample of adolescents and young adults. To expand on previous work, we examined a larger group of both male and female adolescents over a longer period of time at multiple ages. We hypothesized that body dissatisfaction, weight control behaviors, and obesity would be positively associated with self-reported suicidal ideation and suicide attempts at 5-year follow-up.
Method Participants
Data for this analysis came from Project EAT-II (Neumark-Sztainer, Wall, Eisenberg, Story, & Hannan, 2006; Neumark-Sztainer, Wall, Guo, et al., 2006). Project EAT-II was a follow-up study of adolescents who had participated in Project EAT (Neumark-Sztainer, Story, Hannan, & Croll, 2002; Neumark-Sztainer, Story, Hannan, Perry, & Irving, 2002), which had examined dietary intake, weight status, eating behaviors, and socioenvironmental and demographic correlates in a large, ethnically diverse study population. Project EAT involved 4,746 Minnesota middle and high school students who were initially surveyed during 1998–1999. In Project EAT-II, we aimed to recontact all of the original participants 5 years after the initial study. A total of 1,074 (22.6%) of the original cohort was lost to follow-up, mainly because no address was found at follow-up (n = 591) or contact information was missing at Time 1 (n = 411). Of the original participants, 3,672 were contacted by mail in 2003–2004 and 2,516 (68.4% of those contacted; 1,386 female and 1,130 male) completed the Project EAT-II survey. The follow-up sample was more likely to be White and at higher socioeconomic status (SES); likely, this distribution was due to higher mobility in the lower SES and non-White groups, which were heavily represented in the original EAT sample. We used population weights that reflected the original sample in all analyses to address this shortcoming. About one third of the sample was in the high-school-age cohort (mean age = 17.2 years), and two thirds of the sample was in the young adult group (mean age = 20.4 years). The sample was ethnically diverse and comprised Asian Americans (19.2%), African Americans (18.7%), Hispanics/Latinos (5.8%), Native Americans (3.6%), and individuals of mixed race (4.3%); the remainder (48.4%) were Whites. A wide socioeconomic distribution was seen: Most participants fell in the lower middle, middle, or upper middle SES categories; 17.4% were of low SES, and 13.8% were of high SES. Further details of Project EAT-II are available elsewhere (Neumark-Sztainer, Wall, Eisenberg, et al., 2006; Neumark-Sztainer, Wall, Guo, et al., 2006). All study procedures for both Project EAT and Project EAT-II were approved by the University of Minnesota's Human Subject Committee.
Measures
Weight status
At Time 1, weight and height were measured by trained Project EAT staff in school settings. Weight was measured in light clothing, and height was measured without shoes. Body mass index (BMI; weight [kg]/height squared [m2]) was calculated, and participants were classified as being underweight (<15th BMI percentile), average weight (15th–85th BMI percentile), moderately overweight (85th–95th BMI percentile), or very overweight (≥95th BMI percentile), according to gender- and age-based cutpoints recommended by the Centers for Disease Control and Prevention (Kuczmarski et al., 2000).
Body satisfaction
Participants completed a 10-item scale (Pingitore, Spring, & Garfield, 1997) on which satisfaction with separate body parts and characteristics was rated from 1 (very dissatisfied) to 5 (very satisfied). For example, an item on the scale asks “How satisfied are you with your stomach?” A score ranging from 10 to 50 was created; greater scores indicated greater levels of body satisfaction (Pingitore et al., 1997). Cronbach's alpha at Time 2 was .92 for young women and .93 for young men for the composite score. In this study, test–retest reliability was acceptable, with Pearson correlations of .68–.77 in a racially diverse subgroup of 252 7th- and 10th-grade participants. The median score was 35; the top quartile cutoff was 41, and the lowest quartile cutoff was 28. In the current analyses, the lowest quartile was considered to have “body dissatisfaction.”
Suicidal thoughts and behaviors
Students reported on both suicide attempts and suicidal ideation. The survey questions used included “Have you ever thought about killing yourself?” and “Have you ever tried to kill yourself?” Response options included “no,” “yes, in the past year,” and “yes, more than a year ago.”
Those who reported past-year suicidal ideation or attempts at Time 2 were categorized as cases, regardless of their Time 1 suicidal status. Those who reported suicidal ideation or attempts “more than a year ago” at Time 2 but no suicidal reports at Time 1 were also categorized as cases. All others were considered to have no suicidal thoughts or behaviors at Time 2. In this way, we were able to identify suicidal ideation and attempts that had occurred after the assessment of Time 1 predictor variables. Two-week test–retest Spearman correlations at Time 1 were .78 (ideations) and .80 (attempts).
UWCB
The UWCB assessed included (1) taking diet pills, (2) “making myself vomit,” (3) using laxatives, (4) using diuretics, (5) fasting, (6) eating very little food, (7) using a food substitute (powder/special drink), (8) skipping meals, and (9) smoking more cigarettes. For each item, participants were asked to answer “yes” or “no” to “Have you done any of the following things in order to lose weight or keep from gaining weight during the past year?” Participants who endorsed any of Items 1–4 were classified as using extreme weight control behaviors (EWCB); participants who endorsed any of Items 5–9 were considered to have UWCB.
Depressive symptoms
We used a six-item scale to assess depressive symptoms (Kandel & Davies, 1982). At Time 2, the scale had a Cronbach's alpha of .80 for young men and .81 for young women. For the current analyses, those who scored in the highest quartile were considered to have high levels of depressive symptoms.
Demographic variables
SES was categorized into five levels with an algorithm based on highest educational level completed by either parent plus eligibility for free or reduced-price lunch, eligibility for public assistance, and parental employment status (Neumark-Sztainer, Story, Hannan, & Croll, 2002). Race/ethnicity was assessed by asking “Do you think of yourself as (a) White, (b) Black or African American, (c) Hispanic or Latino, (d) Asian American, (e) Hawaiian or Pacific Islander, (f) American Indian or Native American, or (g) other race?” Participants were asked to check all that applied. For purposes of this analysis, all non-Whites were grouped together. Analyses were adjusted for race/ethnicity on the basis of this grouping.
Data Analysis
Data were weighted to adjust for differential response rates with the response propensity method (Little, 1986), in which the inverse of the estimated probability that an individual would respond at Time 2 was used as the weight. Estimates were therefore generalizable to the population represented by the original Time 1 Project EAT sample.
We used separate multiple logistic regression analyses to estimate odds ratios for Time 2 suicidal ideation and attempts. In unadjusted analyses, four Time 1 weight-related variables (weight status, body dissatisfaction, EWCB, and UWCB) were entered simultaneously. A second model adjusted for race, SES, and age group. This model was further adjusted for high depressive symptoms at Time 2, given that depression has been associated with suicidal behavior.
ResultsWeight distribution, frequencies of EWCB and UWCB at Time 1, suicidal behavior at Time 2, and participant characteristics are shown in Table 1. UWCB were endorsed by the majority of young women (57.0%) and a large percentage of the young men (31.1%). EWCB were less common (12.9% of young women and 3.9% of young men). Suicidal ideation was reported by 21.6% of young women (12.6% in the past year; 8.9% more than 1 year earlier) and by 15.2% of young men (8.3% in the past year; 7.0% more than 1 year earlier) at Time 2, whereas suicide attempts were reported by 8.7% of young women and 3.5% of young men at Time 2.
Demographics and Key Variables by Gender
The unadjusted and adjusted relationships between Time 1 weight-related variables (weight status, body dissatisfaction, EWCB, and UWCB) and Time 2 suicidal ideation are shown in Table 2. For young women, EWCB were predictive of later suicidal ideation (odds ratio [OR] = 1.98, 95% confidence interval [CI] = 1.34–2.93). These ORs remained elevated even after we had adjusted for demographic variables and Time 2 depressive symptoms (OR = 1.79, 95% CI = 1.19–2.71). In contrast, among young men, the relationship between EWCB and suicidal ideation was not statistically significant. Furthermore, UWCB, body dissatisfaction, and weight status at baseline each failed to predict suicidal ideation at follow-up for male or female participants.
Weight Status, Body Dissatisfaction, and Weight Control Behaviors at Time 1 and Suicidal Ideation at Time 2
Table 3 shows the relationship between Time 1 weight status, body dissatisfaction, weight control behaviors, and Time 2 reported suicide attempts. Similarly, EWCB in young women was associated with a significantly elevated OR for suicide attempts (OR = 2.53, 95% CI = 1.53–4.18). These ORs remained elevated after we had adjusted for demographic variables and Time 2 depressive symptoms (OR = 2.41, 95% CI = 1.43–4.07). No association between suicide attempts and EWCB was found among young men. As with suicidal ideation, UWCB, body dissatisfaction, and weight status were not significantly associated with suicide attempts among either male or female participants over 5-year follow-up.
Weight Status, Body Dissatisfaction, and Weight Control Behaviors at Time 1 and Suicide Attempts at Time 2
DiscussionThe results of this study show that, in young women but not in young men, EWCB at baseline were predictive of suicidal ideation and suicide attempts at 5-year follow-up independent of depressive symptoms. Contrary to our hypotheses, body dissatisfaction, UWCB, and weight status were not predictive of suicidal behavior 5 years later in male or female participants.
These findings are consistent with the results of several previous studies that have shown an association between both syndromal eating disorders (Harris & Barraclough, 1994) and limited eating disorder symptoms and suicidal behaviors (Crow et al., 2008; Miotto, De Coppi, Frezze, & Preti, 2003). Previous studies were cross-sectional, however, and the current study indicates that EWCB are predictive of suicidal ideation and suicide attempts over time. Our results suggest that EWCB might be a risk factor or risk marker for later suicidality. Although the rates of suicidal ideation and attempts endorsed by participants were high, they were in the range of those reported in other community-based studies (Centers for Disease Control, 2000; Kessler et al., 1999).
Differential associations between EWCB and suicidal thoughts and behaviors were found between young men and young women. The smaller sample size of young men endorsing EWCB (as well as suicidal behaviors) and the resulting limited power to detect such efforts may explain this finding, given that the ORs observed were similar in male and female participants (but confidence intervals were larger in the former). An alternative explanation may be that differing societal attitudes regarding the importance of weight and shape make EWCB more emotionally salient for women. If this were the case, EWCB might have had more psychopathological consequences for young women than for young men.
Results from the current study differ from previous cross-sectional work, which has found an association between body dissatisfaction and suicidal behavior in adolescents (Crow et al., 2008; Miotto et al., 2003). The reasons for these divergent findings are unclear. Perhaps body dissatisfaction is associated with psychosocial distress that might have short-term but not long-term links to suicidal behavior. This study, with reassessment after 5 years, did not examine short-term relationships between these variables. Alternatively, body dissatisfaction and UWCB may be so common among young women at present as to be considered normative; perhaps only more severe psychopathology (e.g., weight control behaviors defined as “extreme”: purging, laxative use, and diuretic use) is predictive of suicidal behavior. This relationship might operate at the personality trait level. For example, underlying impulsivity has been associated with both disordered eating and suicidal behavior. Recent work has suggested that altered serotonin function may be correlated with the co-occurrence of self-harm, impulsivity, and bulimic symptoms (Steiger et al., 2001).
The results of this study further our understanding of the relationship between body weight and suicidal behavior. The finding that weight was unrelated to suicidal behavior is consistent with some prior work in this area (Crow et al., 2008; Hallstrom & Noppa, 1981; Kittel et al., 1978; Mukamal, Kawachi, Miller, & Rimm, 2007; Neumark-Sztainer, Story, Resnick, & Blum, 1997) but contradicts other studies (Carpenter et al., 2000; Falkner et al., 2001; Neumark-Sztainer, Story, Resnick, & Blum, 1997). Although the cross-sectional prior studies have been mixed, it is noteworthy that the two prospective studies examining this question (Mukamal et al., 2007, and the current study) have not found higher BMI to be predictive of suicide.
There are a number of strengths and some limitations to the current project. The sample size was large and diverse, in terms of gender, ethnicity, and SES. In addition, the use of a prospective design with 5-year follow-up broadened our understanding of the relationship between EWCB and later suicidal behavior. Limitations include the lack of extensive detail regarding suicidal ideation and suicide attempts. It would be helpful to have more information in this regard, particularly on the timing, frequency, and severity of suicidal behaviors over the 5-year period. There is evidence that varying the method of suicide assessment leads to variation in the reported rate of suicidal behavior (Prinstein, Nock, Spirito, & Grapentine, 2001). Similarly, more detailed measures of body dissatisfaction and disordered eating behaviors/attitudes would have been helpful and might have yielded different findings. Attrition at 5-year reassessment is another limitation. Finally, although our study was prospective, it nonetheless entailed some degree of retrospective recall at the 5-year reassessment point.
These findings have important implications for future research and practice. They help clarify the link between disordered eating and suicidal behavior. These findings further emphasize the importance of screening for EWCB. Levels of disordered eating that may fall short of diagnostic thresholds have been viewed as less serious, but such disordered eating is tied to psychosocial and physical morbidity and, this study suggests, is associated with an increased risk of suicidal behavior. It is unclear from the current study whether EWCB lead to suicidal thoughts/behaviors through some underlying mechanism or rather are a sign of more global distress and a risk marker for suicide risk. In either case, the use of EWCB by an adolescent suggests a need for careful monitoring of the adolescent's mental health. Future research should examine this issue over even longer periods of observation. In addition, it would be of interest to know whether the risk for adolescent suicidal behavior conferred by EWCB carries over into adulthood. If the link between EWCB and suicidal behavior is confirmed, efforts directed at preventing the onset of disordered eating might diminish the risk of subsequent suicidal behavior.
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Submitted: August 8, 2007 Revised: April 14, 2008 Accepted: April 25, 2008
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Source: Journal of Consulting and Clinical Psychology. Vol. 76. (5), Oct, 2008 pp. 887-892)
Accession Number: 2008-13625-017
Digital Object Identifier: 10.1037/a0012783
Record: 19- Title:
- Are clinical diagnoses of Alzheimer's disease and other dementias affected by education and self-reported race?
- Authors:
- Teresi, Jeanne A.. Columbia University Stroud Center, New York, NY, US
Grober, Ellen. Department of Neurology, Montefiore Medical Center, Bronx, NY, US
Eimicke, Joseph P., jpeimicke@aol.com
Ehrlich, Amy R.. Division of Geriatrics of the Department of Medicine, Albert Einstein College of Medicine, Bronx, NY, US - Address:
- Eimicke, Joseph P., Research Division, Hebrew Home at Riverdale, 5901 Palisade Avenue, Bronx, NY, US, 10471, jpeimicke@aol.com
- Source:
- Psychological Assessment, Vol 24(3), Sep, 2012. pp. 531-544.
- NLM Title Abbreviation:
- Psychol Assess
- Page Count:
- 14
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- Psychological Assessment: A Journal of Consulting and Clinical Psychology
- ISSN:
- 1040-3590 (Print)
1939-134X (Electronic) - Language:
- English
- Keywords:
- Alzheimer's disease, bias, clinical diagnosis, education, self-reported race
- Abstract:
- A randomized controlled trial examined whether the diagnostic process for Alzheimer's disease and other dementias may be influenced by knowledge of the patient's education and/or self-reported race. Four conditions were implemented: diagnostic team knows (a) race and education, (b) education only, (c) race only, or (d) neither. Diagnosis and clinical staging was established at baseline, at Wave 2, and for a random sample of Wave 3 respondents by a consensus panel. At study end, a longitudinal, 'gold standard' diagnosis was made for patients with follow-up data (71%). Group differences in Diagnostic and Statistical Manual of Mental Disorders (4th ed.; American Psychiatric Association, 1994) diagnosis were estimated using logistic regression and generalized estimating equations. Sensitivity and specificity were examined for baseline diagnosis in relation to the gold standard, longitudinal diagnosis. Despite equivalent status on all measured variables across waves, members of the 'knows race only' group were less likely than those of other groups to receive a diagnosis of dementia. At final diagnosis, 19% of the 'knows race only' group was diagnosed with dementia versus 38% to 40% in the other 3 conditions (p = .038). Examination of sensitivities and specificities of baseline diagnosis in relation to the gold standard diagnosis showed a nonsignificant trend for lower sensitivities in the knowing race conditions (0.3846), as contrasted with knowing education only (0.480) or neither (0.600). The finding that knowledge of race may influence the diagnostic process in some unknown way is timely, given the recent State-of-the-Science conference on Alzheimer's disease prevention, the authors of which called for information about and standardization of the diagnostic process. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Alzheimer's Disease; *Medical Diagnosis; *Racial and Ethnic Groups; *Response Bias; *Self-Report; Education
- Medical Subject Headings (MeSH):
- Aged; Aged, 80 and over; Alzheimer Disease; Consensus; Continental Population Groups; Dementia; Diagnostic and Statistical Manual of Mental Disorders; Educational Status; Female; Geriatrics; Humans; Longitudinal Studies; Male; Neuropsychology; Psychiatry; Self Report; Sensitivity and Specificity; Severity of Illness Index
- PsycINFO Classification:
- Neuropsychological Assessment (2225)
Neurological Disorders & Brain Damage (3297) - Population:
- Human
Male
Female - Location:
- US
- Age Group:
- Adulthood (18 yrs & older)
Aged (65 yrs & older) - Tests & Measures:
- Free and Cued Selective Reminding Test
Standardized Mini Mental Status
Consortium to Establish a Registry for Alzheimer’s Disease Memory tests
Clinical Dementia Rating DOI: 10.1037/t28287-000
Memory Impairment Screen DOI: 10.1037/t28561-000
Geriatric Depression Scale DOI: 10.1037/t00930-000
Informant Questionnaire on Cognitive Decline in the Elderly DOI: 10.1037/t03166-000 - Grant Sponsorship:
- Sponsor: National Institute on Aging
Grant Number: Grant AG017854
Recipients: Grober, Ellen
Sponsor: National Institute on Aging, Resource Centers for Minority Aging Research
Grant Number: Grant P30-AG15272-12S2
Recipients: Teresi, Jeanne A.
Sponsor: National Institute for Minority Health and Health Disparities, Columbia University Center for Excellence on Minority Health and Health Disparities
Grant Number: Grant P60, MD000206
Recipients: Teresi, Jeanne A.
Sponsor: National Institute on Aging, Claude Pepper Older Americans Independence Center
Grant Number: Grant P30-AG028741
Recipients: Teresi, Jeanne A. - Methodology:
- Empirical Study; Quantitative Study
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- First Posted: Feb 6, 2012; Accepted: Nov 22, 2011; Revised: Oct 7, 2011; First Submitted: Dec 21, 2010
- Release Date:
- 20120206
- Correction Date:
- 20140616
- Copyright:
- American Psychological Association. 2012
- Digital Object Identifier:
- http://dx.doi.org/10.1037/a0027008
- PMID:
- 22309001
- Accession Number:
- 2012-02772-001
- Number of Citations in Source:
- 67
- Persistent link to this record (Permalink):
- http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2012-02772-001&site=ehost-live
- Cut and Paste:
- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2012-02772-001&site=ehost-live">Are clinical diagnoses of Alzheimer's disease and other dementias affected by education and self-reported race?</A>
- Database:
- PsycINFO
Are Clinical Diagnoses of Alzheimer's Disease and Other Dementias Affected by Education and Self-Reported Race?
By: Jeanne A. Teresi
Columbia University Stroud Center, Faculty of Medicine;
New York State Psychiatric Institute;
Research Division, Hebrew Home at Riverdale;
Division of Geriatrics and Gerontology, Faculty of Medicine, Weill Medical College of Cornell University
Ellen Grober
Department of Neurology, Montefiore Medical Center and Albert Einstein College of Medicine
Joseph P. Eimicke
Research Division, Hebrew Home at Riverdale;
Division of Geriatrics and Gerontology, Faculty of Medicine, Weill Medical College of Cornell University;
Amy R. Ehrlich
Division of Geriatrics of the Department of Medicine, Albert Einstein College of Medicine and Montefiore Medical Center
Acknowledgement: This work is supported in part by National Institute on Aging (NIA) Grant AG017854 to Ellen Grober and NIA Resource Centers for Minority Aging Research Grant P30-AG15272-12S2 (subcontract to Jeanne A. Teresi); National Institute for Minority Health and Health Disparities, Columbia University Center for Excellence on Minority Health and Health Disparities Grant P60, MD000206 (subcontract to Jeanne A. Teresi); and NIA Claude Pepper Older Americans Independence Center Grant P30-AG028741 (subcontract to Jeanne A. Teresi).
A state-of-the-science conference on prevention of Alzheimer's disease and cognitive decline concluded that there is an “absence of highly reliable consensus-based diagnostic criteria for cognitive decline, mild cognitive impairment and Alzheimer's disease,” and the “available criteria have not been uniformly applied” (Daviglus et al., 2010, p. 22). Diagnostic criteria have been operationalized in different ways across studies (Seshadri et al., 2011; Wilson et al., 2011). Additionally, little is known about the possible influence of education and race on the diagnostic process, despite the substantial literature on their effects on cognitive test scores (Mayeux et al., 2011). Research among older persons has demonstrated that differences in educational and cultural experiences and language of administration can affect cognitive test performance (Crane, Gibbons, Jolley, & van Belle, 2006; Dorans & Kulick, 2006; Edelen-Orlando, Thissen, Teresi, Kleinman, & Ocepek-Welikson, 2006; Gurland, Wilder, Cross, Teresi, & Barrett, 1992; Jones, 2006; Morales, Flowers, Gutierrez, Kleinman, & Teresi, 2006; Mungas, Marshall, Weldon, Haan, & Reed, 1996; Ramírez et al., 2001; Ramírez, Teresi, Holmes, Gurland, & Lantigua, 2006; Teresi et al., 1995; Teresi, Holmes, Ramírez, Gurland, & Lantigua, 2001; Teresi, Kleinman, & Ocepek-Welikson, 2000). Such findings have resulted in the use of scale adjustments for factors such as age, education, and race. However, age-adjusted scores may not improve predictive value (e.g., Hayden et al., 2003; Lindeboom, Launer, Schmand, Hooyer, & Jonker, 1996), and the use of race and education adjustments for cognitive screening measures and neuropsychological tests used to detect dementia has been debated (Berkman, 1986; Kittner et al., 1986; Stern et al., 1992). The use of adjustments has been questioned on the grounds that education cannot be studied as a risk factor if it is already adjusted in diagnostic tests (Heun, Papassotiropoulos, & Jennssen, 1998; Kittner et al.). Moreover, the results of several studies have demonstrated that application of education adjustments to neuropsychological tests are not supported consistently (Jones & Gallo, 2001) and can produce deleterious effects, performing worse in relation to diagnosis than the unadjusted scores (e.g., Kraemer, Moritz, & Yesavage, 1998). Finally, such adjustments cannot be uniformly applied across different race, cultural, educational, and language groups (e.g., Mayeux et al., 2011). However, despite (or perhaps because of) such adjustments, differences in dementia risk and prevalence between Black people and White people have been observed in some studies (Demirovic et al., 2003; Shadlen et al., 2006; Tang et al., 2001).
Although bio-markers are increasingly proposed as proxies for the neuropathologic markers of Alzheimer's disease (Querbes et al., 2009), longitudinal diagnosis using neuropsychological tests of memory and cognition and informant assessment of function remain the “gold standard” (Morris et al., 2006). To our knowledge, there is nothing in the literature, however, about the influence of race and education on clinical decision making; this begs the question as to whether the diagnosis itself may be biased. The aim of these analyses was to examine whether the diagnostic process may itself be influenced by knowledge of the education and or self-reported race of the patient.
How might race and education influence the clinical judgment of dementia in elderly patients? If level of education is thought to be a summary statement about associated prior experience and ability, then poor performance of a well-educated patient can more readily be taken to reflect a decline in functioning. Conversely, the meaning of the same poor performance in a patient with little education is ambiguous and cannot readily be attributed to dementia. The educational bias of mental status test items may introduce additional unreliability into the clinical diagnostic process, because some clinicians may correct for this bias and some may not. Moreover, these “corrections” may not be documented as part of a formal adjustment. Even with an algorithmically driven diagnostic process, it is possible that the final consensus diagnosis could be affected by education and race in some unknown and undocumented way.
Aims and HypothesesTo estimate race and education effects on the diagnostic process, patients from an urban geriatric primary care practice were randomly assigned to one of four conditions defined by whether the diagnostic team was given information about the patient's self-reported race and/or education. Rates of dementia diagnosis were compared across groups. It was hypothesized that, given the expected equivalence on background and cognitive test performance across randomized groups, there would be no significant differences in rates of diagnosis at baseline, follow-up, or study end. To our knowledge, this is the first and only extant study using a rigorously designed, randomized trial of the influence of self-reported race and education on the diagnostic process.
Method Design
The study was a longitudinal randomized controlled trial with four conditions: diagnostic team knew (a) both the patient's race and education, (b) education only, (c) race only, and (d) neither. Cross-sectional diagnosis and clinical staging were established at baseline, at Wave 2, and for a random sample of patients at Wave 3 (the end of the funded study period) by a consensus panel independent of screening test results and diagnostic outcomes from other waves. A gold standard, longitudinal diagnosis was also established at study end by consideration of the patient's longitudinal record.
Clinical Setting
The study took place in the Geriatric Ambulatory Practice (GAP), an urban academic primary care practice staffed by geriatricians at Montefiore Medical Center in the Bronx, New York. The study was part of a project to develop and validate strategies for identification of mild dementia in primary care (Grober, Hall, Lipton, & Teresi, 2008; Grober, Hall, McGinn, et al., 2008).
Procedures
All study procedures were approved by the local Institutional Review Board. Recruitment began in January, 2003, and final follow-up was completed in July, 2007. Master's-level psychology assistants evaluated patients with (a) candidate dementia screening tests, (b) an independent diagnostic battery of neuropsychological tests, and (c) informant responses to a structured clinical interview covering six domains of cognitive and daily functioning (Morris, 1993). The 2-hr neuropsychological evaluation was usually completed in two sessions approximately 3–4 months apart (Mdn = 91 days, M = 121 days), in accordance with scheduling practices at the GAP. During routine clinical visits, GAP geriatricians, blinded to the results of all neuropsychological testing, used the Clinical Dementia Rating (CDR; Hughes, Berg, Danziger, Coben, & Martin, 1982) to rate the cognitive and functional status of their patients.
Measures
Shown in Table 1 are the tests used for screening, cross-sectional consensus diagnosis and longitudinal, gold standard diagnosis. The major domains are demographics, global function, psychiatric status, social support, memory, cognitive performance, instrumental activities of daily living, informant ratings (memory, judgment and problem solving, orientation, personality and behavior), activities of daily living, medical history, and medications. These measures are described in detail elsewhere (Grober, Hall, McGinn, et al., 2008). In the reports that were reviewed for diagnosis for the first 100 patients, published norms (50th, sixth, second percentiles) for the Consortium to Establish a Registry for Alzheimer's Disease (CERAD) List Learning and Praxis items (Welsh et al., 1994) were printed alongside the patients' scores. (Because of the high prevalence of visual impairment, list recall was relied on more than figure recall.) Subsequently, using data from the first 100 GAP patients minus the data from patients diagnosed with dementia, the fifth, 10th, and 50th percentiles for the GAP sample were computed for all the neuropsychological tests. These were the reference scores used for the rest of the project. These scores fall below the norms that were published in 2009 (Welsh-Bohmer et al., 2009) for the CERAD battery for persons with more than 12 years of education and above the norms for persons with less than 12 years of education. Because of the age groupings, direct comparison cannot be made. At the time of the study, there were no education-based norms for the CERAD tests, and they were not viewed as required (see Welsh et al., 1994).
Measures Collected at Each Wave of Data
Diagnostic procedures
Specialized summary reports were created that included the information shown in Table 1. These data were used to determine presence or absence of dementia according to Diagnostic Statistical Manual of Mental Disorders (4th ed.; DSM–IV; American Psychiatric Association, 1994) criteria for dementia. To avoid diagnostic circularity, members of the consensus panel were not granted access to the screening test results or to the CDR (Hughes et al., 1982) ratings assigned by the patient's provider.
The diagnostic consensus panel consisted of a neuropsychologist, a geriatrician, and a geriatric psychiatrist. Before meeting at the consensus conference, raters made an independent determination of the patient's diagnostic status and then rated the patient's cognitive performance and activities of daily living using the CDR scale (Hughes et al., 1982; Morris, 1993). At consensus conferences, a patient's status was discussed when there was any disagreement on diagnostic criteria or CDR box score. Disagreements on diagnostic criteria were resolved first, followed by disagreements on CDR box scores. The final CDR rating was based on the pattern of box scores (Morris, 1993). At each wave of data, consensus diagnoses were made with the available neuropsychological scores and informant interview responses for that wave. If informant data were not available, a diagnosis was not made for that time point. For participants without an informant interview to document functional decline, the primary care physician rating of the CDR (Hughes et al., 1982; Morris, 1993) was used if there were at least two ratings so that change could be assessed.
At the gold standard final follow-up evaluation, the scores from each preceding cross-sectional evaluation, including the diagnostic battery, informant interviews, and the provider's CDR ratings, were reviewed by the neuropsychologist (EG) without knowledge of the diagnostic outcome assigned at cross-section. Using all available longitudinal information except the Free and Cued Selective Reminding Test (FCSRT; Grober & Buschke, 1987), the Standardized Mini Mental State (SMMS; Molloy, Alernayehu, & Roberts, 1991), the Memory Impairment Screen (MIS; Buschke et al., 1999), oral trailmaking (Ricker & Axelrod, 1994), animal fluency (Rosen, 1980), freehand clock drawing (Freeman et al., 1994; Sunderland et al., 1989), Informant Questionnaire of Cognitive Decline in the Elderly (IQCODE; Jorm & Korten, 1988), race, and education for those patients randomized to the arms where this information was withheld, EG assigned the final diagnosis using DSM–IV criteria and a final CDR score. A final, gold standard clinical diagnosis was made if a subject had at least one follow-up neuropsychological assessment. In the case of missing informant data, the availability of provider CDR box scores, indicating cognitive and functional abilities at baseline and follow-up, were used in determining the gold standard.
Patients were classified as having no dementia, a dementia present at baseline (i.e., prevalent dementia), or dementia present only at follow-up (i.e., incident dementia). Interrater reliability was assessed for 50 patients randomly selected from the 252 followed prospectively by having the geriatrician assign a final diagnosis and CDR score; this provided a measure of agreement for the longitudinal, gold standard diagnosis.
Patients with dementia were subtyped by the neurologist (AS) through chart review using established criteria for probable or possible Alzheimer's disease, probable or possible vascular dementia, probable or possible Lewy Body dementia, and frontotemporal dementia (Chui et al., 1992; Knopman et al., 2005; McKeith, Perry, & Perry, 1999; McKhann et al., 1984). Subtyping decisions were based on detailed review of the patients' paper and computerized medical records, including social and family history, vascular and other risk factors, medications, and laboratory results; neuroimaging reports were available for the majority of patients (82%; 68% had computed tomography, 9% had magnetic resonance imaging, 23% had both). Particular attention was paid to the onset, nature, and development of neurological and cognitive complaints.
Study Participants
Participants met the following criteria: aged 65 years or older; had adequate vision and hearing to complete the neuropsychological tests; described themselves as either White (not of Hispanic origin), Black (not of Hispanic origin), or Hispanic; provided the name of a family member or friend who had known them for at least 5 years and had spoken English since age 30. Because the project was focused on identifying patients with mild dementia, patients with an SMMS of less than 18 were excluded, except for two illiterate patients with scores of 13. Of the 1,041 potential participants from the GAP contacted by phone, 685 were ineligible because of factors such as ethnicity or language, advanced dementia, lack of interest, incomplete recruitment assessment, illness, enrollment in another study, severe visual impairment, lack of informant, and admission to a nursing home (see Figure 1). Three hundred fifty-six patients completed the baseline assessment and were randomized to one of the four study arms. Among the 356 randomized, eight were excluded from these analyses because they were Hispanic and were recruited later in the study. The remaining 348 were included in the analyses; all were case-conferenced at baseline. Among these, 229 were assessed at Wave 2; however, 37 did not have an informant interview, and others were not assessed for other reasons given in Figure 1, leaving 190 with cross-sectional Wave 2 diagnoses (see Figure 1). Ninety-nine patients who were due for assessment prior to the end of funding were reassessed for a third neuropsychological examination (Wave 3), and a random sample of 33 was case-conferenced. Because agreement between Wave 3 consensus diagnosis and the gold standard was perfect, no additional cases were selected for comparison. In total, 252 (71%) were followed prospectively and assigned a gold standard diagnosis. The average follow-up time was about 3 years (baseline to longitudinal diagnosis, M = 3.19 years, SD = 1.04). Of these, the gold standard longitudinal diagnosis was based on Wave 1 and 2 data for 133 patients; Wave 1, 2, and 3 data for 96 patients; and Wave 1 and additional provider CDR ratings, including that at the final visit, for 23 patients.
Figure 1. Study flow diagram. DSM–IV = Diagnostic and Statistical Manual of Mental Disorders (4th ed.; American Psychiatric Association, 1994); CDR = Clinical Dementia Rating (Morris, 1993); GAP = Geriatric Ambulatory Practice; w1 = Wave 1; w3 = Wave 3.
Statistical Methods
Randomization was conducted, and data were analyzed independently by biostatistical staff at the Data Coordinating Center.
Randomization procedures
Following completion of the baseline assessment, respondents were randomized to one of four groups. Because of rolling enrollment, randomization occurred on an ongoing basis.
Interrater reliability
Interrater reliability was assessed using the kappa statistic to examine agreement among all raters, as well as to compare two raters at a time for the cross sectional diagnoses. Kappa and the intraclass correlation coefficient were used to assess agreement for the 50 longitudinal reliability study diagnoses (see Table 2).
Agreement Among Raters for the Longitudinal Reliability Study Diagnoses
Study arm comparisons
Differences in baseline characteristics among groups were analyzed using Pearson chi-square tests for categorical variables and analysis of variance (ANOVA) for continuous variables (see Table 3). Group differences in observed rates of DSM–IV diagnosis at each cross-section and for the longitudinal diagnoses were presented (see Table 4). Prediction of longitudinal diagnosis was conducted with logistic regression analyses using both a complete-cases and an intent-to-treat approach (see Table 5). In sensitivity analyses, missing data were handled with maximum-likelihood-based imputation, using the expectation maximization (EM) algorithm approach. A sensitivity analysis was also performed including the baseline diagnosis in the logistic regression analyses of Wave 2 (not shown) and the longitudinal diagnosis (see Table 5). In multivariate analyses, the reference group was the standard method: “knows both education and race.” Dummy variables were coded using methods recommended by Kraemer and Blasey (2004). Models with and without cognitive test predictors were examined. As shown in Table 1, also included were baseline covariates for the Geriatric Depression Scale (Yesavage et al., 1982–1983), IQCODE, and the SMMS. Finally, the sensitivity and specificity was examined for each group's baseline diagnosis in relation to the longitudinal, gold standard. Longitudinal analyses predicting change in DSM–IV diagnosis was performed using generalized estimating equation repeated measures assuming a binomial distribution with logit link (presented in Figure 2). Up to three diagnoses, corresponding to three time periods for each participant (baseline cross-sectional consensus, Wave 2 cross-sectional consensus, and final longitudinal gold-standard diagnosis, corresponding to the study end) were included in the analyses. (Wave 3 was not included because of the small number of respondents and potential tautology with the longitudinal diagnosis). The model had the following terms: education (reference group is 12 years and above), race (reference group is White), group status (reference group is “knows both education and race”), wave, wave squared, and interactions (Group × Wave, and Group × Wave Squared). The terms used in interactions were centered to reduce colinearity (Kraemer & Blasey, 2004). Quadratic terms were used to model the nonlinearity of the outcome over time.
Baseline Demographic Information and Selected Neuropsychological Variables by Study Arm
Baseline Demographic Information and Selected Neuropsychological Variables by Study Arm
Observed Rates of DSM–IV Criteria Diagnoses of Dementia by Group for Baseline, Wave 2, and Longitudinal “Gold Standard” Diagnosis
Expanded Logistic Regression Models Predicting DSM–IV Criteria Diagnosis for the Longitudinal “Gold Standard”
Figure 2. Adjusted means for meeting Diagnostic and Statistical Manual of Mental Disorders (4th ed.; American Psychiatric Association, 1994) criteria for “gold standard,” longitudinal diagnosis of dementia. Note that longitudinal analyses predicting change in DSM–IV diagnosis was performed using SPSS generalized estimating equation repeated measures assuming a binomial distribution with logit link. Up to three diagnoses, corresponding to three time points over an average of 4 years of data collection and 3 years of diagnostic data for each participant were included in the analyses. The diagnostic time points were baseline (0), Wave 2 (average time from baseline diagnosis of 1.96 years), and longitudinal diagnosis (average time since baseline diagnosis of 3.19 years). Terms used in interactions were centered to reduce colinearity. Quadratic terms were used to model the nonlinearity of the outcome over time. The analysis of variance comparing endpoint (longitudinal) diagnosis across groups was significant (F = 265.513, p < .0001). The difference between “knows education and race” (reference group) and “knows race only” was estimated as 0.197 (p < .0001).
Results Interrater Reliability
The kappa statistics (see the top half of Table 2) when comparing all raters ranged from 0.34 for “personal care impairment” to 0.73 for “meets DSM–IV criteria for dementia” for Wave 1 and from 0.46 for “personal care impairment” to 0.84 for “memory impairment” for Wave 2 (0.72 for DSM–IV criteria).
When comparing individual raters (see the bottom half of Table 2), the estimated kappas for Rater 1 versus Rater 2 for DSM–IV criteria were 0.84 for Wave 1 and 0.93 for Wave 2. The kappas for Rater 1 versus Rater 3 were 0.62 for Wave 1 and 0.59 for Wave 2. For Rater 2 versus Rater 3, the kappas were 0.71 for Wave 1 and 0.59 for Wave 2.
For the 50 longitudinal, gold standard reliability diagnoses, the kappa and intraclass correlation coefficients were 1.00 (not shown), indicating 100% agreement. There were no differences in race, gender, education, and age between those randomly selected for the reliability study and those not selected.
Study Arm Comparisons
Loss to follow-up
The flow diagram in Figure 1 shows the number of patients randomized to each arm of the study and the outcome at each cross-sectional wave and at final follow-up. Of the 104 patients without follow-up, eight were excluded because they were Hispanic, 12 were deceased, nine declined further evaluation, 13 left the practice, three were dropped by the request of their provider, nine had incomplete protocols, and seven were enrolled near the end of the study period, precluding follow-up. The remaining 43 patients either did not return for a second follow-up or had no informant data available. Compared with the 104 patients lost to follow-up, the prospective sample had a significantly larger proportion of women (85.3% vs. 76.5%, p = .046), less education (M = 12.25 vs. 13.04 years, p = .005), and higher baseline free recall on the FCSRT (M = 27.54 vs. 25.13, p = .02). SMMS scores were similar (26.6 vs. 26.4).
Examination of the rates of loss-to-follow-up showed no significant or substantial differential attrition: 25.8% in the “knows education and race” group, 28.4% in the “knows education only” group, 24.7% in the “knows race only” group, and 27.4% in the “don't know education and race” group. There were also no significant differences across groups in loss-to-follow-up of cases diagnosed with dementia. Examination of the 252 given a final longitudinal, gold standard diagnosis showed that the groups remained balanced on all variables, with no significant difference among groups.
Table 3 shows the demographic information and selected neuropsychological variables for patients as a function of study arm. The patients in the study arms did not differ in age (p = .565), education (p = .194), or race (p = .746). Also shown in Table 3 are the results of various neuropsychological tests across study arms. As shown, the groups were equivalent on every measure, indicating balance in terms of background characteristics and baseline measures of cognitive function.
Irrespective of group status, Black participants tended to have higher baseline rates of dementia than White participants (20.5% vs. 15.1%); however, the difference was not significant (p = .123; results are not shown.) At final longitudinal, gold standard diagnosis, 36% of White participants and 32% of Black participants were diagnosed as having dementia. Those with less than 12 years of education (27.3%), as contrasted with those with 12 or more years of education (14%), were more likely to receive a baseline diagnosis of dementia (p = .015). However, these differences were not significant in subsequent waves. At final gold standard diagnosis, rates of dementia for those with 12 years or less of education was about 38% as contrasted with 27% for those with 13 or more years of education, a nonsignificant difference.
Table 4 shows the DSM–IV criteria diagnosis by wave and group. At all waves, the condition “knows race only” had fewer diagnosed cases of dementia; at baseline, the conditions in which race was known were associated with lower diagnosed rates of dementia (13.5% and 15.3%), in contrast to groups in which race was not known (19.3% and 24.7%). There is a trend for “knowing race,” as contrasted with not knowing race and education to be significantly related to underdiagnosis (unadjusted p = .07). Combining the two race-known categories revealed that knowing race resulted in a dementia diagnosis for only 14.4% of patients, versus 21.7% of patients for those conditions where race was not known at baseline (p = .076). At the final gold standard diagnosis, the difference was most pronounced for the “knows race only” category. As contrasted with the condition, “knows race only,” with 19% diagnosed as having dementia, almost twice as many (38% to 40%) in the other three conditions received a final, longitudinal, gold standard diagnosis of dementia (p = .038). These trends were consistent with the earlier waves of data but were more pronounced for Wave 2 and the final diagnosis.
Multivariate and longitudinal analyses
Shown in Table 5 are the results of the logistic regression predicting longitudinal (gold standard) diagnosis with complete cases (with and without inclusion of baseline diagnosis) and the results of intent-to-treat estimates, using the EM algorithm. The original multivariate complete case analyses predicting longitudinal (gold standard) diagnosis (results not shown), entering group status, actual racial group membership, education, and their interactions showed that the only significant variable was “knows race only” group status (p = .020). As expected, entry of cognitive test data in the model predicting gold standard diagnosis resulted in significant cognitive test predictors (p < .001); controlling for all of these variables, the “knows race only” variable remained significant (p = .013; see Table 5). Racial status was also significant (p = .015); because of the younger average age of Black participants, controlling for age eliminates this effect, but the effects of the “knows race only” category remained significant. Sensitivity analyses using an intent-to-treat approach and a method for treating missing data (the EM algorithm) lessened the effect of the “knows race only” group membership, changing the odds of diagnosis from 0.302 to 0.375 (p = .026). Adjusted rates (proportions, means) of diagnosis from the longitudinal (gold standard) analyses predicting change in DSM–IV diagnosis are plotted in Figure 2. These means are consistent with the observed rates of diagnosis over time shown in Table 4. Consistent with the other analyses the endpoint means are significantly different (see note in Figure 2).
Sensitivity analyses
Because there were slight, nonsignificant differences between the “knows race only” and the other groups for two neuropsychological test variables (p < .15), in sensitivity analyses (not shown), additional variables (oral trailmaking and CERAD figure recall) were added to the variables in the analyses presented in Table 5. The results did not change, and estimates were very similar to the original.
Examination of sensitivities and specificities of baseline ratings against a final gold standard diagnosis showed that there was a nonsignificant trend for lower sensitivities (0.3846 in both groups) in the conditions in which race was known (knowing education and race, or knowing race only, as contrasted with the conditions in which only education was known (0.480) or neither was known (0.600; p = .14). At the second wave, the estimated sensitivities were 0.500 (knowing race only), 0.529 (knowing education only), 0.571 (knowing education and race), and 0.714 (knowing neither).
DiscussionDiagnostic criteria are operationalized in different ways across settings and are often the result of a single assessment with no follow-up; moreover, the effects of race, culture, and education on the diagnostic measures are not well studied (Mayeux et al., 2011). The main aim of this study was to examine differences in rates of diagnosis across randomly assigned groups that varied in the type of demographic group status presented to diagnosticians. Because of randomization, if no bias were operating in the diagnostic process, it was hypothesized that the “treatment” groups would be equivalent in terms of proportion of cases diagnosed with dementia. To our knowledge this is the first study to use a rigorous design to examine the effects of race and education on the actual diagnosis of dementia.
Both the primary and the sensitivity analyses using ITT resulted in a similar finding. Although the sensitivity analyses reduced the effects slightly, the trend remained for those in the “knows race only” group to be less likely to receive a diagnosis of dementia at baseline and longitudinally, compared with the other conditions (ps = .013, .026). Using the more conservative estimates from the sensitivity analyses, the odds that an average person in the “knows race only” group would receive a final, gold standard diagnosis of dementia was about one third of that of an average person in the reference group, the condition in which both race and education were known. The other groups' members were as likely as those in the reference group to receive a dementia diagnosis. The final, longitudinal, gold standard diagnosis yielded twice as many diagnoses of dementia in the three other conditions as in the “knows race only” condition.
Given the perfect interrater reliability for the final consensus diagnosis and the balance among all variables, including demographic and individual neuropsychological tests, it appears as if knowing race had an impact on diagnoses of dementia at baseline and across subsequent waves. The fact that knowing neither race nor education at baseline was the condition with the highest sensitivity in relation to the longitudinal, gold standard diagnosis and that the conditions with knowledge of race yielded the lowest sensitivities seems to indicate that knowledge of these demographic factors, particularly race, may have resulted in overadjustment.
How might race and education operate to influence clinical decision making when physicians must decide whether an elderly patient is suffering from a dementia? Two widely established set of findings may influence the physician's decision making. On the one hand, educational level has been identified as a possible risk factor for dementia (e.g., Callahan et al., 1996; Roe, Xiong, Miller, & Morris, 2007; Stern et al., 1994) and, thus, could bias the physician to diagnose dementia in a patient with low education compared with a patient of high educational background. On the other hand, educational level affects mental status scores (e.g., Crum, Anthony, Bassett, & Folstein, 1993; Murden, McRae, Kaner, & Bucknam, 1991) and neuropsychological test scores (Lezak, 1995; Stern et al., 1992) in the elderly. Poor performance of a well-educated person can more readily be taken to reflect dementia than the same poor performance in a patient with little education. The ambiguity that arises when educational level is not known (so that low scores cannot readily be attributed to dementia) may lead the physician to be reluctant to diagnosis dementia. Adjusting for educational differences did not eliminate differences in dementia risk and prevalence between Black and White participants in some studies (Demirovic et al., 2003; Shadlen et al., 2006; Tang et al., 2001). In the present study, irrespective of randomization group status, those with lower education were significantly more likely to be diagnosed with dementia at baseline, and this trend continued at Wave 2; however, the longitudinal result was not significant for the gold standard diagnosis. This finding may be relevant to the group comparisons. It is possible that knowledge of the literature relating educational level to dementia may have resulted in overadjustment and lower rates of diagnosis when education was not known in the “knows race only group.”
If not knowing education when race is known results in the reluctance to diagnose dementia, then how are we to understand that the highest sensitivity in relation to the longitudinal gold standard diagnosis occurred in the condition when both education and race were unknown? One hypothesis is that in this condition, raters viewed the patient's scores relative to the norms provided, which were the 50th, 10th, and fifth percentiles, based on the whole cohort; “unconscious” adjustments for the patient's scores were not possible because education and race were unknown. This hypothesis is supported by the group comparisons at final follow-up. This condition (education and race unknown) yielded the same proportion of dementia diagnoses as the two conditions in which education was known.
By refraining from diagnosing dementia at final follow-up when race was known but education was not, the raters may have valued specificity over sensitivity. Specificity of dementia diagnoses is particularly important in primary care settings because of the resources needed to confirm the diagnosis and determine dementia subtype. In fact, poor specificity of popular dementia screening tools has led to the view that cost-effective and accurate dementia screening is not possible in primary care (Boustani et al., 2005). New methods that may aid in detection of very mild dementia have been described (Grober, Hall, Lipton, & Teresi, 2008; Grober, Hall, McGinn, et al., 2008).
Although improvement in sensitivity with the gold standard was expected as incident cases were diagnosed, the differential sensitivities between the study arms were unexpected. Although only a trend, the finding that there were differential sensitivities between the groups in which race and education were known, as contrasted with the group in which neither was known, supports the conclusion that knowledge of these sociodemographic variables may have lessened the agreement with the final gold standard.
The finding that the “knows race only” group resulted in lower rates of diagnosis and lower sensitivity with the longitudinal gold standard criterion variable may reflect the bias to err on the conservative side in the absence of knowledge about education; however, at baseline the group “knowing race and education” also received lower rates of diagnosis, which may support the notion that knowledge of race influenced the diagnostic process in some unknown way. Some studies show higher rates of dementia in Black than White patients (Tang et al., 2001; Shadlen et al., 2006; Perkins et al., 1997), and there was a nonsignificant trend for higher rates among Black participants at baseline across the entire combined sample for this study. It is possible that knowledge of these published findings about differential rates of dementia between White and Black people may have resulted in an overadjustment and lower baseline rates of diagnosis in the “knows race” groups, which was sustained at follow-up in the condition in which race was known but education was not.
This study has some limitations. First, the rolling randomization procedure resulted in differences in the number of subjects in each group, although the groups were balanced on all baseline variables so that the randomization procedure produced equivalent groups. A second limitation is the loss to follow-up of about one fourth of the sample, thus resulting in possible attrition bias. On the other hand, intent-to-treat analyses included all subjects as randomized, and different missing data algorithms and approaches were used in sensitivity analyses, all of which yielded very similar estimates and model-based mean estimates that were almost identical to the unadjusted means. Moreover, groups remained balanced on most variables at the time of longitudinal, gold standard follow-up, and no significant differences across groups were observed in rates of loss-to-follow-up among baseline cases with dementia; in fact, there was slightly less loss to follow-up among individuals with dementia in the “knows race” group.
Another limitation relates to treating “race” as a monolithic entity. It has been argued that race, as a construct, lacks biologic basis because there is more genetic diversity among groups defined by race than between them; thus, it has been recommended that race be deconstructed using variables such as educational quality and reading level (see Manly, 2006). On the other hand, mental and other healthcare providers view the collection of information about race, ethnicity and language as important to the selection of culturally sensitive treatments (see Baker et al., 2007); and the National Research Council reports on measuring racial discrimination (Blank, Dabady, & Citro, 2004), while pointing out that race is a complex social construct, recommended the collection of such data for inclusion in policy research. The implication for bias analyses is that race and ethnicity should still be examined; however, the limitation of such groupings is acknowledged.
A final limitation relates to the generalizability of the findings to other settings that use different diagnostic processes. In Alzheimer's disease research centers, patients usually undergo an evaluation by a neurologist, whose ratings are used with test scores and informant interviews in determining the diagnostic outcomes. With the addition of a neurologist's input, the potential for the diagnostic process to be influenced by factors such as race or education may be lessened. In primary care settings, physicians are familiar with the cultural and educational background of their patients but often lack access to dementia screening tools appropriate for identifying dementia in multiethnic, multicultural cohorts. It is noted that the best way to study potential bias in diagnostic ratings is to use a randomized design that includes conditions in which education and race were unknown. Thus, the present study design introduced an element of unavoidable artificiality that is absent in actual clinical encounters or research settings in which race and education are usually known; nonetheless, the findings do identify the potential for the diagnostic process to be influenced by factors such as race, particularly in the absence of knowledge about education. This finding is timely in light of the recent State-of-the-Science conference on prevention of Alzheimer's disease and cognitive decline (Daviglus et al., 2010), the authors of which called for more information about and standardization of the diagnostic process.
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Submitted: December 21, 2010 Revised: October 7, 2011 Accepted: November 22, 2011
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Source: Psychological Assessment. Vol. 24. (3), Sep, 2012 pp. 531-544)
Accession Number: 2012-02772-001
Digital Object Identifier: 10.1037/a0027008
Record: 20- Title:
- Assessing motivational interviewing integrity for group interventions with adolescents.
- Authors:
- D'Amico, Elizabeth J.. RAND Corporation, Santa Monica, CA, US
Osilla, Karen C.. RAND Corporation, Santa Monica, CA, US
Miles, Jeremy N. V.. RAND Corporation, Santa Monica, CA, US
Ewing, Brett. RAND Corporation, Santa Monica, CA, US
Sullivan, Kristen. Department of Clinical, Counseling, and School Psychology, University of California-Santa Barbara, CA, US
Katz, Kristin. Department of Clinical, Counseling, and School Psychology, University of California-Santa Barbara, CA, US
Hunter, Sarah B.. RAND Corporation, Santa Monica, CA, US - Address:
- D'Amico, Elizabeth J., RAND Corporation, 1776 Main Street, PO Box 2138, Santa Monica, US, 90407-2138
- Source:
- Psychology of Addictive Behaviors, Vol 26(4), Dec, 2012. pp. 994-1000.
- NLM Title Abbreviation:
- Psychol Addict Behav
- Page Count:
- 7
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- Bulletin of the Society of Psychologists in Addictive Behaviors; Bulletin of the Society of Psychologists in Substance Abuse
- Other Publishers:
- US : Educational Publishing Foundation
Society of Psychologists in Addictive Behaviors - ISSN:
- 0893-164X (Print)
1939-1501 (Electronic) - Language:
- English
- Keywords:
- adolescents, alcohol and drug use, group intervention, motivational interviewing, at risk youth
- Abstract:
- The group format is commonly used in alcohol and other drug (AOD) adolescent treatment settings, but little research exists on the use of motivational interviewing (MI) in groups. Further, little work has assessed the integrity of MI delivered in group settings. This study describes an approach to evaluate MI integrity using data from a group MI intervention for at-risk youth. Using the Motivational Interviewing Treatment Integrity (MITI) scale, version 3.1, we coded 140 group sessions led by 3 different facilitators. Four trained coders assessed the group sessions. Agreement between raters was evaluated using a method based on limits of agreement, and key decisions used to monitor and calculate group MI integrity are discussed. Results indicated that there was adequate agreement between raters; we also found differences on use of MI between the MI-intervention group and a usual-care group on MI global ratings and behavioral counts. This study demonstrates that it is possible to determine whether group MI is implemented with integrity in the group setting and that MI in this setting is different from what takes place in usual care. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Alcohol Rehabilitation; *Drug Rehabilitation; *Motivational Interviewing; *Group Intervention; Adolescent Psychopathology; At Risk Populations
- Medical Subject Headings (MeSH):
- Adolescent; Communication; Female; Humans; Male; Motivational Interviewing; Psychotherapy, Group; Substance-Related Disorders
- PsycINFO Classification:
- Drug & Alcohol Rehabilitation (3383)
- Population:
- Human
Male
Female - Location:
- US
- Age Group:
- Adolescence (13-17 yrs)
- Tests & Measures:
- Motivational Interviewing Treatment Integrity scale
- Grant Sponsorship:
- Sponsor: National Institute of Drug Abuse, US
Grant Number: R01DA019938
Recipients: D'Amico, Elizabeth J. - Methodology:
- Empirical Study; Quantitative Study; Treatment Outcome
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- First Posted: May 28, 2012; Accepted: Jan 9, 2012; Revised: Nov 8, 2011; First Submitted: Aug 1, 2011
- Release Date:
- 20120528
- Correction Date:
- 20130107
- Copyright:
- American Psychological Association. 2012
- Digital Object Identifier:
- http://dx.doi.org/10.1037/a0027987
- PMID:
- 22642853
- Accession Number:
- 2012-13792-001
- Number of Citations in Source:
- 40
- Persistent link to this record (Permalink):
- http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2012-13792-001&site=ehost-live
- Cut and Paste:
- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2012-13792-001&site=ehost-live">Assessing motivational interviewing integrity for group interventions with adolescents.</A>
- Database:
- PsycINFO
Assessing Motivational Interviewing Integrity for Group Interventions With Adolescents
By: Elizabeth J. D'Amico
RAND Corporation, Santa Monica, California;
Karen C. Osilla
RAND Corporation, Santa Monica, California
Jeremy N. V. Miles
RAND Corporation, Santa Monica, California
Brett Ewing
RAND Corporation, Santa Monica, California
Kristen Sullivan
Department of Clinical, Counseling, and School Psychology, University of California, Santa Barbara
Kristin Katz
Department of Clinical, Counseling, and School Psychology, University of California, Santa Barbara
Sarah B. Hunter
RAND Corporation, Santa Monica, California
Acknowledgement: We thank the Council on Alcoholism and Drug Abuse for their support of this project. We would also like to thank Cally Sprague and Susana Lopez for their help on the study. The current study was funded by a grant from the National Institute of Drug Abuse (R01DA019938) to Elizabeth D'Amico.
The group format is commonly used to treat adolescents who use alcohol and other drugs (AOD; Kaminer, 2005; Vaughn & Howard, 2004), even though little is known about the distinguishing factors of effective and ineffective group interventions (Engle, Macgowan, Wagner, & Amrhein, 2010). Recent work with at-risk adolescents indicates that motivational interviewing (MI) interventions (Miller & Rollnick, 2002; Rollnick, Miller, & Butler, 2008) can be successful, as MI offers a collaborative, nonjudgmental, and nonconfrontational approach (Naar-King & Suarez, 2010). Although studies of youth receiving individual MI have shown effectiveness (Baer, Garrett, Beadnell, Wells, & Peterson, 2007; D'Amico, Miles, Stern, & Meredith, 2008; Monti et al., 2007; Spirito et al., 2004), there are only four published group MI studies (Bailey, Baker, Webster, & Lewin, 2004; D'Amico, Osilla, & Hunter, 2010; Engle et al., 2010; Schmiege, Broaddus, Levin, & Bryan, 2009). Furthermore, MI treatment integrity has been mainly conducted with individual interventions (Baer et al., 2004; Martino, Ball, Nich, Frankforter, & Carroll, 2008; Moyers, Martin, Manuel, Hendrickson, & Miller, 2005; Vader, Walters, Prabhu, Houck, & Field, 2010). Only one study, to date, has evaluated MI integrity with adolescent group sessions (Engle et al., 2010) using measures of MI competence and adherence, such as the Motivational Interviewing Treatment Integrity scale (MITI; Moyers, Martin, Manuel, Miller, & Ernst, 2010).
Treatment integrity is important for ensuring that a therapy is delivered as intended by the treatment developers (Beidas & Kendall, 2010; Godley, Garner, Smith, Meyers, & Godley, 2011; Rakovshik & McManus, 2010). The MITI scale (Moyers et al., 2010) is a widely used measure of MI competence and adherence. Engle et al. (2010) used the MITI scale to examine the group process and found that group facilitator empathy was associated with more positive commitment language (e.g., “I'm quitting for the summer”), which was then associated with reduced AOD use (Engle et al., 2010). Although this study was pioneering, additional studies are needed, as only 19 sessions were coded, only one of the MITI global ratings (empathy) was assessed, and there was no comparison group. Thus, to move the field forward, more detailed treatment-integrity analyses (i.e., more group sessions and a comparison group) are needed so that we can assess how MI groups differ from other adolescent groups.
Finally, in order for treatment integrity to be successfully measured, alternative measurements to intraclass correlation (ICC) are needed to assess interrater agreement (e.g., Shrout & Fleiss, 1979). Calculating the ICC is not always feasible, as there may be different sets of raters for each session or there may be missing data (e.g., one session might be coded by all raters but another session may not). Thus, other approaches that compensate for these limitations must be utilized in order for studies to quantify interrater agreement.
The current study addresses these gaps by using the global and behavioral counts on the MITI to (a) assess agreement between raters, (b) calculate interrater agreement using two alternative methods to the ICC, and (c) evaluate the use of MI between a group led by facilitators trained and supervised in MI compared with a group with a self-help and Alcoholics Anonymous focus. This is an important first step in understanding whether the MITI is feasible for use in group settings and for determining MI integrity in this setting. We hypothesized that coders would reliably assess MI behaviors and that trained and supervised MI facilitators would deliver more MI-consistent behavior than the facilitator in the usual care (non-MI) group sessions.
Method Study Overview
This study was conducted as part of a randomized clinical trial (D'Amico, Hunter, et al., 2010). Procedures were approved by the research institution's internal review board. We collaborated with the Council on Alcoholism and Drug Abuse, a nonprofit organization in Santa Barbara, California, that operates a Teen Court (TC) for first-time offending youth. Based on earlier assessments, adolescents who do not need intensive treatment can participate in TC in lieu of formal juvenile justice processing. As part of TC, youth with a first time AOD offense are required to attend six AOD awareness groups, along with other sanctions (e.g., community service). Adolescents who successfully complete their sentence have the offense expunged from their record.
The AOD awareness groups occur weekly. Entrance to the groups is based on rolling admission, as each session can stand alone without a teen having to complete a previous session—that is, attending Session 2 does not require information from Session 1. Teens enter the sessions based on their time of referral to the program. Teens were randomized to either a usual care (UC) control condition or the experimental MI group intervention called Free Talk (FT). Teens were automatically assigned to attend the UC groups if they or their parent refused participation. Refusals (10%) were mostly due to lacking time or transportation to complete a baseline survey before their first group session. There were no demographic or offense differences between refusers and participants. See Table 1 for FT participant characteristics.
Characteristics of the Adolescents in the Free Talk and Usual Care Groups (N = 102)
The Current Study
This study focuses on the facilitator's behavior and whether this behavior can be reliably coded during adolescent group sessions. We provide detailed information on monitoring of a group MI intervention with at-risk adolescents (n = 102) across a large number of group sessions (n = 140) and compare these MI adolescent groups with usual care groups using the MITI.
Intervention Condition: Free Talk Groups
Free Talk was developed as part of a Stage I study (Rounsaville, Carroll, & Onken, 2001) where each group session was iteratively tested to determine feasibility and acceptability of intervention content (D'Amico, Osilla, et al., 2010). The facilitator manual for FT included a protocol for each session and utilized a MI approach. At the beginning of each session, the facilitator discussed the group guidelines (e.g., confidentiality, respect for others in the group). These were provided in a MI-consistent way (e.g., ensuring members were respectful of one another). FT is a manualized intervention that provides some pyschoeducation and focuses on encouraging change talk (see D'Amico, Osilla, et al., 2010, for content). For example, throughout all sessions, the focus is on providing reflections, asking open -ended questions to increase collaboration, affirming and supporting the adolescents to increase support and autonomy, and increasing change talk in the group by being aware of the DARN-C (Desire, Ability, Reason, Need, and Commitment; Amrhein, 2009). We also used tools to promote behavioral change, such as the decisional-balance activity and willingness and confidence rulers (Ingersoll, Wagner, & Gharib, 2006; Miller & Rollnick, 2002). Sessions lasted 55 min and mean group size was 4.5 adolescents (SD = 1.98).
Free Talk Training and Integrity Monitoring
The FT sessions were led by one of three facilitators (all female and White) who were psychology doctoral students with prior at-risk teen work experience. They received 40 hours of MI and FT training delivered by E.D. and K.O., clinical psychologists affiliated with the Motivational Interviewing Network of Trainers (MINT).
Facilitators were instructed to use MI to best fit a group format while still attending to individuals (D'Amico, Feldstein Ewing, et al., 2010; Velasquez, Stephens, & Ingersoll, 2006). For example, reflections were often stated to address the group process (e.g., “Many of you have been making positive changes in your lives”), and yet facilitators also responded to the varying individual experiences and needs (e.g., rolling with the resistance of one youth while trying to actively maintain the commitment language of another). In addition, facilitators monitored participant feedback and redirected negative or unhelpful comments to create a safe place for participation and mutual respect.
All FT groups were digitally audio recorded. MINT trainers reviewed all recordings and provided 1-hr weekly supervision to facilitators. The MITI was used to monitor performance (i.e., provide feedback during supervision).
Control Condition: Usual Care Groups
The UC condition was led by one facilitator who was male and Hispanic. The curriculum followed an abstinence-based Alcoholics Anonymous approach. Topics included group check-in, discussion of personal triggers, consequences of AOD use, educational videos, discussion of personal experiences with AOD use, and myths about AOD use. Like the FT groups, each session lasted about 55 min.
Coding
Overview
One hundred and 40 sessions (70 FT and 70 UC sessions) were coded by four psychology doctoral students. Per the MITI protocol, a randomly selected 20-minute segment was coded for both FT and UC sessions (Moyers et al., 2010). FT sessions were coded using digital audio recordings and UC sessions were coded from live observation, as not all teens attending these groups were study participants, so it was not possible to record these sessions. Raters were trained to not disrupt the UC groups by arriving early, sitting off to the side, not interacting with teens, and being discreet with coding materials.
MITI
The MITI is a widely used instrument for coding competency and adherence to MI and has been used in numerous studies to assess MI integrity (Jensen et al., 2011; Moyers et al., 2005; Tollison et al., 2008; Turrisi et al., 2009). It was specifically designed for clinical trials and is a reliable and valid tool (Madson & Campbell, 2006; Moyers et al., 2005). Integrity is measured through the use of global scores (e.g., amount of collaboration) and behavioral counts (e.g., number of reflections; Moyers et al., 2010). The MITI is used to assess the therapist's behavior during a session and focuses solely on the therapist's behavior; thus, the MITI can easily be utilized to assess a group facilitator's behavior (e.g., Engle et al., 2010). MITI 3.1 has five global scales (collaboration, empathy, evocation, autonomy/support, and direction) that are scored on a scale from 1 (low) to 5 (high). MITI competency is defined as an average of 4 on the global ratings (Moyers et al., 2010). As noted in the MITI manual, collaboration occurs when there is little power differential, there is agreement on goals, and clients are encouraged to share the talking. Empathy occurs when the facilitator expresses client understanding and attempts to understand client point of view. Evocation occurs when the facilitator encourages clients to brainstorm reasons and ideas for how to change. Autonomy/support occurs when the facilitator emphasizes and supports client's personal choice. Direction occurs when the facilitator exerts influence on the session and generally does not miss opportunities to direct client toward the target behavior or referral question (Moyers et al., 2010). In the group setting, the facilitator responds to group members, thus, group members influence the facilitator's behavior. For example, if a facilitator is not collaborative and does not ask open-ended questions, then there will be less participation and less sharing of the talking. Facilitator responsiveness to group member behavior is captured by the MITI global scores.
The rater also counts specific behaviors that occur during each coded segment, including open-ended questions, closed-ended questions, MI-adherent (e.g., “If it's ok with you, I'd like to share some information with you”) and nonadherent statements (e.g., “You need to stop drinking”), and simple (e.g., “some of you are ready to make changes”) and complex reflections (e.g., “some of you are hoping that by making changes, things will improve in your lives”). Whereas global scores have a range limit (1 to 5), behavioral counts have no upper end on the scale; thus, these scores can vary by session to a greater degree.
Coding Training
Four raters received about 40 hours of training, which included a half-day MITI training and practice coding assignments (http://casaa.unm.edu/codinginst.html). Similar to other studies (Moyers et al., 2005; Tollison et al., 2008), raters met weekly to discuss discrepancies. Interrater agreement was stable over time. Raters coded both UC and FT group sessions.
Interrater Agreement for MITI
All 140 sessions were coded by at least one rater, with 48 (34%) sessions coded by two raters (19 UC and 29 FT) and 25 (18%) FT sessions coded by three raters. We did not have three raters code UC sessions, given that these groups were coded “live” and this would have been disruptive. Raters were not the same two people each time, as they were assigned to code sessions randomly. Thus, the ICC could not be calculated, as different raters coded different sessions and ICC is not defined in the presence of missing data. In addition, using the ICC in this case would not be an accurate comparison, as it is like reporting correlations among items rather than a coefficient alpha. Interpretation of these coefficients would also be difficult because many of them have small sample sizes and hence we do not have a sense of the sampling distribution of the statistic.
We therefore used two different methods to quantify agreement between raters. Our first method was distribution-free: We calculated the difference between each rater's rating of a session and the mean rating for that session. To see how close the majority of ratings were to the mean, we ordered these differences from the smallest to the largest. We then took the 95th percentile (e.g., if there were 100 differences, we took the 95th rating, referred to as D95 in Table 2). Thus, the D95 value indicates that 95% of differences between each rater and the mean rating are smaller than this value. The D95 is more appropriate for dichotomous variables, as it makes no distributional assumptions.
MITI Measures of Agreement Between Raters and Mean Scores by Rater Across Both the Free Talk and Usual Care Groups
In our second method, we used a normal distribution approach of calculating the within-session standard deviation (WSSD) to provide an estimate of the difference between raters (Altman & Bland, 1983; Bland & Altman, 1986, 2007). The WSSD is closely related to the average of the difference between raters, ∑(x−
/N, where
is the mean for the session and x represents the set of ratings. The WSSD is given by the formula for the standard deviation (∑(x−
)2/(N−1))0.5. Because the WSSD squares the differences between each rating and the mean, larger deviations between raters lead to proportionally larger estimates. If we assume differences are normally distributed, we expect 95% of ratings to lie within about two standard deviations of that mean. For interpretation purposes, we therefore report 2*WSSD, which allows us to compare the two methods.
These two methods make different assumptions about the distribution of ratings. The D95 method does not make distributional assumptions, but it does not use all of the information that is available regarding the values. In contrast, the WSSD method uses all the information and assumes that data are normally distributed and measured on a continuous scale. Using two methods allowed us to examine interrater agreement sensitivity and compare the results of the two methods.
These agreement measures have two advantages over the ICC. First, the ICC is a ratio of between-group variance to total variance. The ICC depends not only on the extent of the agreement between raters but also on the variance of the measures. Thus, the ICC could be lower or higher, depending on the level of variance, even if the rater agreement level was the same in both cases. Second, the ICC presents agreement on a scale from 0.0 (no agreement) to 1.0 (perfect agreement). Although the scale allows for a comparison across studies, the ICC has little interpretive value of how actual scores differ between raters, as the units of the ICC are not the same as the units on the scale. This variation is very important for scales like the MITI that have items ranging from 1 to 5 points because it provides data on how to recalibrate coding to ensure that they match.
Results Interrater Agreement
We calculated the limits of agreement between the raters for each of the MITI dimensions using the D95 and 2*WSSD methods. Table 2 shows ratings for both groups combined across all four raters. For MITI global ratings using the D95 method, raters were within 0.5 points of the mean for the session 95% of the time. Using the 2*WSSD method, the level of agreement was similar, with expected 95% limits ranging from 0.50 (autonomy/support and empathy) to 0.62 (evocation).
For the behavioral counts, there was no upper limit; thus, we expected to see larger differences. Both approaches yielded comparable limits of agreement. The D95 limits ranged from 3.56 (complex reflections) to 5.56 (MI-adherent and closed questions). The 2*WSSD limits ranged from 3.26 (complex reflections) to 5.54 (closed questions). As one would expect, MI-nonadherent counts had lower D95 (0.50) and 2*WSSD (0.36). There are no published MITI benchmarks for what consists of good agreement. For this study, we consider within 1 point on the MITI global scores and within 6 points on the behavioral counts as good levels of agreement.
MI Differences by Group
Table 3 shows differences between the means of all raters across groups. Overall, both global and behavioral count scores were higher for the FT group compared with the UC group, indicating greater MI integrity in the FT group sessions.
MITI Means and Standard Deviations Across Raters by Group
DiscussionThis study is one of the first to assess MI integrity using data from an adolescent group intervention. First, this study found that group MI can be efficiently and reliably assessed using alternatives to ICC. We used two innovative methods to address agreement and both found comparable results. These methods are more useful than the ICC because they can be calculated when using multiple coders and are better able to quantify the difference between multiple raters, providing us with a more direct interpretation of the level of agreement. For example, a D95 or 2*WSSD score of 2 tells us that 95% of raters are expected to be within 2 points of the mean of all raters. A clinical judgment must then be made to determine whether the raters are sufficiently close to agreement, which depends upon on the units of the original measure. A difference of 2 points on global ratings is very large compared with behavioral counts. Thus, our findings of less than .62 difference between a rater and the mean score on the global ratings and 5.56 on the behavioral counts suggest that the raters were in fairly close agreement. Using these indices of interrater agreement could be useful in clinical practice, as it bridges the gap between research and practice by allowing for real-world situations such as multiple raters or supervisors.
We found that it is feasible to train facilitators to conduct MI in an adolescent group setting and that MI can be measured and implemented with integrity. Overall, global scores for the FT group were in the competent range and behavioral counts for MI behaviors were high. We expected this, given the intensive training and supervision; MI fidelity might not be as high with less supervision. In addition, future research could measure integrity across more facilitators.
We found large differences between the FT and UC groups on collaboration, empathy, evocation, and autonomy/support, suggesting that FT groups were more likely to encourage power sharing during the session, had repeated efforts to gain understanding of teens' viewpoints, had more acceptance of teens' reasons for change, and were more likely to emphasize support of client autonomy. The behavioral count data showed a similar pattern with more simple and complex reflections and open-ended questions in FT groups. More MI-nonadherent behavior was observed in the UC groups, such as confronting and advising without permission compared with the FT groups. These findings are not surprising, given that the UC group facilitator did not receive MI training or supervision but rather followed an Alcoholics Anonymous group treatment approach. Our study findings suggest that, similar to MI research with individuals (Moyers et al., 2005), group MI could make the overall group process more collaborative and therefore more effective, and this may reduce the likelihood of iatrogenic effects that are sometimes seen in groups of at-risk youth (e.g., Dishion, McCord, & Poulin, 1999; Dodge, Dishion, & Lansford, 2006). For example, as shown with individual work (Magill, Apodaca, Barnett, & Monti, 2010; Moyers, Martin, Houck, Christopher, & Tonigan, 2009), the increased evocation and autonomy/support may provide adolescents with a safe place to explore reasons for change without fear of being forced to change; the guiding and empathic style of MI may then help move participants toward positive behavior change.
This study had limitations. First, raters were not blind to study condition. Similar to other MI brief interventions (e.g., Hettema, Steele, & Miller, 2005), FT sessions followed a protocol and could be easily distinguished from other types of sessions. Second, our UC control condition had to be coded by live observation; thus, it is possible that some information was missed. However, raters coded both UC and FT with one pass, so we feel confident that raters captured most of the behaviors during the UC sessions. Moreover, our results indicated that raters were consistent in their ratings across both groups and had high agreement, suggesting that they were not biased. Third, no prior research has employed these statistical methods to address interrater agreement for the MITI; thus, no consensus exists regarding how much difference among raters might be “acceptable.” We do not consider this a problem, as cutoff values may vary across studies and may not be quantifiable, and the use of such values could limit researchers from interpreting results objectively. Thus, it is important to use clinical judgment when evaluating the level of agreement with these methods. Finally, we did not assess group cohesion or engagement; more work is needed in this area.
In sum, this study takes an important first step in documenting group MI with at-risk youth. This study is among the first to provide within-session standard deviations for all global and behavioral counts that comprise the MITI scoring system. Thus, this work can provide a potential resource to practitioners and researchers who do group work and are interested in using MI and measuring MI integrity in this setting. The next step in this work is to examine intervention efficacy; we are currently conducting a randomized trial to assess the short-term effects of this intervention on AOD use among at-risk teens.
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Submitted: August 1, 2011 Revised: November 8, 2011 Accepted: January 9, 2012
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Source: Psychology of Addictive Behaviors. Vol. 26. (4), Dec, 2012 pp. 994-1000)
Accession Number: 2012-13792-001
Digital Object Identifier: 10.1037/a0027987
Record: 21- Title:
- Assessing posttraumatic stress in military service members: Improving efficiency and accuracy.
- Authors:
- Fissette, Caitlin L.. Texas A&M University, TX, US
Snyder, Douglas K.. Texas A&M University, TX, US, d-snyder@tamu.edu
Balderrama-Durbin, Christina. Texas A&M University, TX, US
Balsis, Steve. Texas A&M University, TX, US
Cigrang, Jeffrey. Wright-Patterson Air Force Base, Dayton, OH, US
Talcott, G. Wayne. Department of Preventative Medicine, University of Tennessee Health Science Center, TN, US
Tatum, JoLyn. Wright-Patterson Air Force Base, Dayton, OH, US
Baker, Monty. Wilford Hall Ambulatory Surgical Center, Lackland Air Force Base, San Antonio, TX, US
Cassidy, Daniel. Wilford Hall Ambulatory Surgical Center, Lackland Air Force Base, San Antonio, TX, US
Sonnek, Scott. Wilford Hall Ambulatory Surgical Center, Lackland Air Force Base, San Antonio, TX, US
Heyman, Richard E.. Department of Cariology and Comprehensive Care, New York University, NY, US
Smith Slep, Amy M.. Department of Cariology and Comprehensive Care, New York University, NY, US - Address:
- Snyder, Douglas K., Texas A&M University, Department of Psychology, Mailstop 4235, College Station, TX, US, 77843-4235, d-snyder@tamu.edu
- Source:
- Psychological Assessment, Vol 26(1), Mar, 2014. pp. 1-7.
- NLM Title Abbreviation:
- Psychol Assess
- Page Count:
- 7
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- Psychological Assessment: A Journal of Consulting and Clinical Psychology
- ISSN:
- 1040-3590 (Print)
1939-134X (Electronic) - Language:
- English
- Keywords:
- PTSD Checklist, item response theory (IRT), military, posttraumatic stress disorder, veterans
- Abstract:
- Posttraumatic stress disorder (PTSD) is assessed across many different populations and assessment contexts. However, measures of PTSD symptomatology often are not tailored to meet the needs and demands of these different populations and settings. In order to develop population- and context-specific measures of PTSD it is useful first to examine the item-level functioning of existing assessment methods. One such assessment measure is the 17-item PTSD Checklist–Military version (PCL-M; Weathers, Litz, Herman, Huska, & Keane, 1993). Although the PCL-M is widely used in both military and veteran health-care settings, it is limited by interpretations based on aggregate scores that ignore variability in item endorsement rates and relatedness to PTSD. Based on item response theory, this study conducted 2-parameter logistic analyses of the PCL-M in a sample of 196 service members returning from a yearlong, high-risk deployment to Iraq. Results confirmed substantial variability across items both in terms of their relatedness to PTSD and their likelihood of endorsement at any given level of PTSD. The test information curve for the full 17-item PCL-M peaked sharply at a value of θ = 0.71, reflecting greatest information at approximately the 76th percentile level of underlying PTSD symptom levels in this sample. Implications of findings are discussed as they relate to identifying more efficient, accurate subsets of items tailored to military service members as well as other specific populations and evaluation contexts. (PsycINFO Database Record (c) 2017 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Item Response Theory; *Military Personnel; *Military Veterans; *Posttraumatic Stress Disorder; Checklist (Testing)
- Medical Subject Headings (MeSH):
- Adult; Checklist; Female; Humans; Iraq War, 2003-2011; Logistic Models; Male; Middle Aged; Military Personnel; Psychological Theory; Psychometrics; Reproducibility of Results; Stress Disorders, Post-Traumatic; Veterans; Young Adult
- PsycINFO Classification:
- Neuroses & Anxiety Disorders (3215)
Military Psychology (3800) - Population:
- Human
Male
Female - Location:
- US
- Age Group:
- Adulthood (18 yrs & older)
Young Adulthood (18-29 yrs)
Thirties (30-39 yrs)
Middle Age (40-64 yrs) - Tests & Measures:
- PTSD Checklist--Military Version DOI: 10.1037/t05198-000
PTSD Checklist - Methodology:
- Empirical Study; Longitudinal Study; Quantitative Study
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- First Posted: Sep 9, 2013; Accepted: Jul 12, 2013; Revised: Jul 11, 2013; First Submitted: Sep 13, 2012
- Release Date:
- 20130909
- Correction Date:
- 20170615
- Digital Object Identifier:
- http://dx.doi.org/10.1037/a0034315
- PMID:
- 24015857
- Accession Number:
- 2013-32038-001
- Number of Citations in Source:
- 32
- Persistent link to this record (Permalink):
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- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2013-32038-001&site=ehost-live">Assessing posttraumatic stress in military service members: Improving efficiency and accuracy.</A>
- Database:
- PsycINFO
Assessing Posttraumatic Stress in Military Service Members: Improving Efficiency and Accuracy
By: Caitlin L. Fissette
Department of Psychology, Texas A&M University
Douglas K. Snyder
Department of Psychology, Texas A&M University;
Christina Balderrama-Durbin
Department of Psychology, Texas A&M University
Steve Balsis
Department of Psychology, Texas A&M University
Jeffrey Cigrang
Wright-Patterson Air Force Base, Dayton, Ohio
G. Wayne Talcott
Department of Preventative Medicine, University of Tennessee Health Science Center
JoLyn Tatum
Wright-Patterson Air Force Base
Monty Baker
Wilford Hall Ambulatory Surgical Center, Lackland Air Force Base, San Antonio, Texas
Daniel Cassidy
Wilford Hall Ambulatory Surgical Center, Lackland Air Force Base, San Antonio, Texas
Scott Sonnek
Wilford Hall Ambulatory Surgical Center, Lackland Air Force Base, San Antonio, Texas
Richard E. Heyman
Department of Cariology and Comprehensive Care, New York University
Amy M. Smith Slep
Department of Cariology and Comprehensive Care, New York University
Acknowledgement: The views expressed in this article are those of the authors and do not necessarily reflect the official policy or position of the Department of the Air Force, the Department of Defense, or the U.S. government. This work was supported in part by an award from the Military Operational Medicine Research Program, U.S. Army Medical Research and Materiel Command, to Jeffrey Cigrang.
Over 2 million American men and women have served in Operations Enduring Freedom or Iraqi Freedom since September 11, 2001. This sustained mobilization has exacted an enormous toll on the service members deployed to these theaters of combat—of whom more than 800,000 have deployed multiple times (Sheppard, Malatras, & Israel, 2010). In a large study of help-seeking veterans returning from Iraq and Afghanistan, 37% received a mental health diagnosis—with the most prevalent diagnosis (22%) being posttraumatic stress disorder (PTSD; Seal et al., 2009). Many more returning service members have exhibited subthreshold symptoms of PTSD such that they endorse the exposure Criterion A and Criterion B symptom cluster and also meet diagnostic criteria for either the Criterion C or Criterion D symptom clusters but not both (Blanchard, Hickling, Taylor, Loos, & Gerardi, 1994). Reliably and efficiently assessing posttraumatic stress is critical to identifying those service members or veterans most in need of treatment.
According to the National Defense Authorization Act (2009), all service members of the Armed Forces deployed in connection with a contingency operation are required to receive mental health assessments at four time points across the deployment cycle. At each time point, the service member is assessed for the presence of PTSD symptoms as well as other indicators of mental health functioning. PTSD assessment begins with the four-item Primary Care PTSD Screen (PC-PTSD; Prins et al., 2003), with two or more endorsed questions resulting in a follow-up assessment using the 17-item PTSD Checklist (PCL; Weathers, Litz, Herman, Huska, & Keane, 1993). Several alternative versions of the PCL have been developed that address “a stressful military experience” for military personnel and veterans (PCL-M; Weathers et al., 1993), “a stressful experience from the past” for civilians (PCL-C; Weathers et al., 1993), or “the stressful event” for respondents identifying a specific traumatic event (PCL-S; Weathers et al., 1993); item content for the three versions is otherwise identical.
Although standardized assessment of PTSD can differ across settings, the PTSD Checklist–Military version (PCL-M; Weathers et al., 1993) continues to be one of the most widely used measures of posttraumatic stress in both military and Veterans Affairs (VA) health-care settings upon service members’ return home. However, despite its widespread use and overall reliability and validity, the PCL has some notable shortcomings. Primary among these is the reliance upon an aggregate score in which all items receive the same weight (Bliese et al., 2008), neglecting important differences in both the prevalence and diagnostic efficiency among individual items. Indeed, prior research with the PCL using both item-total correlations and cluster score correlations (Blanchard, Jones-Alexander, Buckley, & Forneris, 1996; Lang & Stein, 2005) as well as examining item parameters (Bliese et al., 2008) has shown that items vary considerably in their discriminative information.
When conducting clinical assessment or rendering selection or intervention decisions based on the PCL’s aggregate score, any combination of positively endorsed items is assumed to have equal predictive utility for evaluating PTSD regardless of whether these are high-prevalence symptoms relatively nonspecific to PTSD (e.g., trouble falling or staying asleep, feeling irritable) versus symptoms with lower prevalence but that are highly specific to PTSD (e.g., either trouble remembering or involuntarily reexperiencing a stressful military experience). However, it is likely that certain items would provide more information within a particular setting and that optimal item subsets would vary as a function of changes in context. Moreover, a cutoff score of 30 (see Lang, Laffaye, Satz, Dresselhaus, & Stein, 2003, and Yeager, Magruder, Knapp, Nicholas, & Frueh, 2007) could be obtained by ratings of mild or moderate impact on a large number of items endorsed at low levels of the PTSD latent construct or alternatively by ratings of more severe impact on a smaller number of items endorsed at high levels of the PTSD construct—presumably with quite different implications for intervention.
In addition, in some assessment contexts (e.g., when screening for PTSD during deployment to a theater of combat, or in comprehensive batteries assessing for a broad spectrum of mental and physical health concerns), inclusion of the full 17-item PCL may not be realistic. Instead, such settings would benefit from a subset of three or four items specifically selected for the prevalence and severity of PTSD symptoms in that population, and for various prevention or intervention purposes of the assessment targeting different PTSD thresholds. Consequently, assessment of posttraumatic stress in service members and veterans may be enhanced by studying characteristics of individual items with respect to both their likelihood of endorsement at a given level or threshold of PTSD symptomatology and their degree of relatedness (or discriminative validity) to PTSD. Results of such an analysis could be used to adapt the PCL or tailor item selection for more efficient and accurate assessment of posttraumatic stress—targeting specific thresholds of this syndrome at varying levels depending on the setting and purpose (e.g., identifying subthreshold PTSD among active military for secondary prevention strategies prior to redeployment versus triaging impaired military personnel in-theater for crisis intervention).
Item response theory (IRT; Embretson & Reise, 2000) is ideally suited for examining item characteristics for purposes of adapting a measure to specific populations and settings. Specifically, using two-parameter logistic (2PL) analyses for each item, two defining characteristics can be identified, including a discrimination or slope parameter (a) and a difficulty parameter (b). The a parameter describes how closely an item relates to some latent construct (in this case, posttraumatic stress). The b parameter indicates the point on the latent construct at which the probability of endorsing the item is equal to 0.50. In combination, these two parameters enable one to select a subset of items having the highest discriminative potential at any given level of the targeted construct.
The current investigation examined item characteristics of the PCL in a sample of U.S. service members returning from a yearlong, high-risk deployment to Iraq. It was anticipated that, consistent with previous research, items would exhibit considerable variability with respect to both their rates of endorsement and their relatedness to PTSD. The goal of the study was to identify distinguishing item characteristics to facilitate tailoring more efficient assessment strategies for service members and veterans in future clinical and research applications.
Method Participants
Participants were a cohort of 196 active-duty service members from a larger longitudinal investigation assessing a variety of risk and protective factors impacting U.S. Air Force (USAF) Security Forces across a yearlong deployment to Iraq. Two detachments of Airmen (N = 318) were tasked to train Iraqi police, a high-risk mission that required patrolling in communities with insurgent fighters; they deployed in two consecutive, 1-year deployment cycles during 2009 and 2010. Based on responses to a measure of deployment experiences described by Hoge and colleagues (2004), participants described high levels of exposure to combat-related stressful experiences during their time in Iraq. Nearly all (97%) knew someone who had been seriously injured or killed; over 70% had seen dead or seriously injured Americans, had witnessed extensive physical devastation and its impact on vulnerable citizens, or had experienced hostile reactions from civilians they were trying to help; and more than half had patrolled in areas with land mines, had aided in the removal of unexploded ordinances, or had been fired upon.
Following their deployment and return from Iraq, these Security Forces Airmen returned to their original bases scattered across the United States and other countries. At 6 to 9 months postdeployment, the Airmen were invited to return to Lackland Air Force Base (AFB) in San Antonio, Texas, as part of this study to participate in focus-group discussions and complete follow-up measures—and 169 elected to do so. Reasons for the reduced sample at follow-up were that, at 6 or more months postdeployment, some of the Airmen had already separated from the military, a few chose not to participate, and some were not able to travel to the location of the follow-up assessment (Lackland AFB) despite provision of travel funds. To facilitate their participation, Airmen who could not attend the follow-up conference were invited to respond via a web-based survey, and an additional 35 participated via this method. Of the 204 Airmen participating in the follow-up assessment (85 at 6 months and 119 at 9 months postdeployment), eight failed to complete the PCL-M, resulting in a final study sample of 196 participants. These 196 Airmen did not differ from the larger sample of 318 Airmen assessed prior to deployment on any measure of demographic characteristics or predeployment measure of individual emotional or behavioral functioning, or intimate relationship functioning (all ps > .50).
Prior to completing measures at either pre- or postdeployment, the research team informed participants about the purpose of the study, the anonymity of their survey responses, and the volunteer nature of their participation. Study procedures were reviewed by, and conducted in full compliance with, the USAF Institutional Review Board. Airmen were provided with the study questionnaire inside a blank envelope and created a personal identifier known only to them, based on their mother’s first and last initials, the calendar day on which they were born, and their hair color. No compensation was provided for completing study measures. However, approximately 98% of Airmen present at pre- and postdeployment assessment settings chose to participate.
Participants ranged in age from 19 to 46 years (M = 25.4, SD = 5.7); 181 (93%) were male, and 15 (7%) were female. The majority (67%) self-identified as Caucasian, 12% as Latino/a, 11% as African American, 7% as Asian, and 3% as “other.” Officers constituted 4% of the sample, with the other pay grades distributed as follows: E1–E3 (junior enlisted): 24%, E4–E6 (midlevel enlisted or noncommissioned officers): 65%, and E7–E9 (senior noncommissioned officers): 7%. Most of the sample (77%) had deployed at least once previously, with 38% having had two or more prior deployments. Rates of PTSD as assessed by the PCL-M increased from 7% predeployment (6% mild to moderate, 1% severe) to 47% at postdeployment (26% mild to moderate, 21% severe) with a mean PCL-M score of 21.8 (SD = 6.1) prior to deployment and 35.4 (SD = 16.1) at postdeployment follow-up.
Measure
The PTSD Checklist (PCL; Weathers et al., 1993) was developed at the National Center for PTSD as a brief, self-report inventory for assessing the 17 symptoms of PTSD outlined in the Diagnostic and Statistical Manual of Mental Disorders (DSM–IV;American Psychiatric Association, 1994); items also correspond to the three clusters of PTSD: reexperiencing (Criterion B), avoidance/numbing (Criterion C), and hyperarousal (Criterion D). In the current study, the military version of this measure (PCL-M; Weathers et al., 1993) was used, asking respondents to consider the impact of their exposure to “stressful military experiences” and to rate each item regarding how much they had been “bothered by the problem in the past month” on a 5-point Likert scale ranging from 1 (not at all) to 5 (extremely), with scores ranging from 17 to 85. The PCL-M demonstrates excellent internal consistency (α = .96) and test–retest reliability (r = .96; Weathers et al., 1993) and correlates highly with other standardized measures of PTSD (Forbes, Creamer, & Biddle, 2001). In the present study, for purposes of analysis using a two-parameter logistic model, participants’ responses to each item were dichotomized in a manner consistent with that in previous literature (e.g., Weathers et al., 1993) such that omitted items or ratings of 1 or 2 (not at all or a little bit) were treated as nonendorsement, whereas ratings of 3, 4, or 5 (moderate, quite a bit, or extremely) were treated as endorsement of that item.
ResultsAs a preliminary analysis, confirmatory factor analysis (CFA) was used to determine whether the PCL-M demonstrated sufficient unidimensionality in this sample for further analysis using IRT. Evidence for unidimensionality is essential for meeting the two basic assumptions of IRT—that items assess a single underlying construct (e.g., posttraumatic stress) and that items are locally independent. When conducting IRT analysis, local independence can be assumed once unidimensionality has been established (Hambleton, Swaminathan, & Rogers, 1991). Evidence for unidimensionality was evaluated using Mplus Version 5.2 (Muthén & Muthén, 2007) with two goodness-of-fit indices: the Tucker–Lewis Index (TLI; Tucker & Lewis, 1973) and the comparative fit index (CFI; Bentler, 1990). Strong evidence for unidimensionality is obtained if the TLI and the CFI are both greater than .95. In the present sample, goodness-of-fit indices for the one-factor model suggested good unidimensionality, with TLI and CFI values of .98 and .97, respectively.
For comparison purposes, additional CFAs examined a three-factor model based upon DSM–IV criteria (American Psychiatric Association, 1994) as well as four-factor models proposed by King, Leskin, King, and Weathers (1998) and Simms, Watson, and Doebbeling (2002). Results provided support for these alternative models as well, with four-factor models obtaining modestly stronger support than alternative one- or three-factor models—TLI and CFI values of .98 and .98 for the three-factor DSM–IV model, and .99 and .99 for both the King et al. (1998) and Simms et al. (2002) four-factor models, respectively.
However, further inspection of a scree plot derived from principal components analysis of the PCL-M data provided additional support for a one-factor solution in this sample of USAF Security Forces following deployment. Specifically, the first factor obtained an eigenvalue of 8.42 and accounted for 50% of the explained variance, compared to eigenvalues of less than 1.46 and percentages of explained variance of less than 9% for subsequent factors. These results, combined with findings from the CFA, demonstrated sufficient unidimensionality of PCL-M data in this sample to proceed with IRT analysis.
Given preliminary evidence supporting the underlying assumptions of IRT in this sample, item parameters for the 17 items of the PCL-M were estimated using two-parameter logistic (2PL) analyses in Multilog Version 7 (Thissen, Chen, & Bock, 2003). Results of these analyses are presented in Table 1, listing for each item the discrimination or slope (a) parameter and the difficulty (b) parameter, along with item content. As anticipated, findings indicated substantial variability across items both in terms of their relatedness to PTSD and their likelihood of endorsement at any given level of PTSD. These differences and their implications are highlighted in Figures 1 and 2, in which item characteristic curves (ICCs) are plotted for each item with the latent construct PTSD (θ) represented on the x-axis and the probability of endorsing the item represented on the y-axis.
Item Content and Parameters for 17 Items of the PCL-M
Figure 1. 17-item characteristic curves with bookend difficulty-level (b) curves highlighted. PTSD = posttraumatic stress disorder.
Figure 2. 17-item characteristic curves with bookend discrimination-level (a) curves highlighted. PTSD = posttraumatic stress disorder.
Figure 1 contrasts two items that, although having similar levels of relatedness to PTSD, have quite different likelihoods of being endorsed, depending on the underlying level of PTSD (θ) being experienced by the service member. Specifically, Item 13 (“trouble falling or staying asleep”) has a 0.50 probability of being endorsed at low levels of PTSD (when θ = −0.07, representing the 48th percentile level), whereas Item 8 (“trouble remembering important parts of a stressful military experience”) does not obtain a 0.50 probability of being endorsed until the level of underlying PTSD reaches a much higher level (i.e., when θ = 1.21, representing the 89th percentile level). Hence, if one wanted to select a subset of items that would distinguish service members experiencing the most acute levels of PTSD for crisis intervention while deployed to a combat environment, the three best items (with highest b levels) would include Items 8, 12, and 3 (“trouble remembering important parts of a stressful military experience,” “feeling as if your future will somehow be cut short,” and “suddenly acting or feeling as if a stressful military experience were happening again,” respectively). By contrast, for screening at the lowest levels of PTSD for further assessment or prevention purposes—for example, prior to or following deployment—the three best items would include Items 13, 14, and 16 (“trouble falling or staying asleep,” “feeling irritable or having angry outbursts,” and “being watchful or on guard,” respectively).
By comparison, Figure 2 contrasts two items that have similar likelihood of being endorsed at a given level of PTSD (in this case, a 0.50 probability at approximately the 73rd percentile level) but have sharply different levels of relatedness (or predictive validity) to PTSD. Specifically, Item 17 (“feeling jumpy or easily startled”) has only modest relation to the underlying construct of PTSD (a = 1.77), compared with the strong relatedness to PTSD (a = 5.47) for Item 4 (“feeling very upset when something reminded you of a stressful military experience”). That is, if one desired specifically to target individuals in the top quartile of PTSD symptom severity (θ values approaching 0.675), the three most discriminating items at that level would include Items 4, 2, and 5 (“feeling upset when something reminded you of a stressful military experience,” “repeated, disturbing dreams of a stressful military experience,” and “having physical reactions … when something reminded you of a stressful military experience,” respectively), whereas the three least discriminating items would be Items 17, 11, and 15 (“feeling jumpy or easily startled,” “feeling emotionally numb or being unable to have loving feelings for those close to you,” and “having difficulty concentrating,” respectively).
Figure 3 presents the test information curve for the full 17-item PCL-M, reflecting both the overall discriminative capacity of this measure and that point along the PTSD latent construct at which maximum discrimination is provided. In this case, the curve peaks sharply at a value of θ = 0.71, reflecting greatest information at approximately the 76th percentile level of underlying PTSD in this sample—with most of the discriminative information being provided between θ = −0.5 (31st percentile level) and θ = 1.5 (93rd percentile level).
Figure 3. Test information curve for the full 17-item scale. PTSD = posttraumatic stress disorder.
Finally, to test for potential significant differences in item functioning between pre- and postdeployment data, we conducted IRT-based likelihood ratio differential item functioning (DIF) testing (Thissen, Steinberg, & Gerrard, 1986). This type of DIF testing involves statistically comparing IRT models with chi-square difference tests. These tests require first identifying anchor items to establish a common scale (to define the same latent variable) between the groups. We used a procedure adapted from Kim and Cohen (1995). Initial anchoring analyses and subsequent primary DIF analyses were conducted using a program developed by Thissen (2001), adopting a conservative studywide Bonferroni correction to reduce the chances of a false positive. For the analyses presented here, only one item was eligible for subsequent DIF testing, Item 12 (“feeling as if your future will somehow be cut short”).
For the primary DIF analyses, we fitted the 2PL model with a and b parameters constrained equal for both groups, and with a and b parameters permitted to vary by group. The constraints significantly decreased model fit, and thus we identified evidence of DIF for this item, χ2(2) = 26.0, p < .001. We then conducted subsequent analyses to determine more specifically if the DIF was driven by the a parameters, the b parameters, or both. Specifically, we compared a model with a parameters constrained to be equal between groups but b parameters free to vary to a model that permitted both item parameters to vary between groups. We found no significance here, indicating no difference between the two groups’ a parameters, χ2(1) = 0.7, p = .40. In other words, this item did not differ in its strength of relation to the latent PTSD continuum across groups. Then, we conducted a b DIF test conditional on equal slope parameters between the two groups. We found significance here, indicating a difference between the two groups’ b parameters of 1.00 standard deviation, χ2(1) = 25.3, p < .001.
This finding indicates that Item 12 (“feeling as if your future will somehow be cut short”) functioned differently when administered at pre- versus postdeployment. Specifically, the item was more readily endorsed by individuals predeployment, even when controlling for group mean differences. Conceptually, this differential functioning reflects that this item would likely have very different meaning to someone deploying for combat versus returning home to the relative safety of his or her community. Indeed, as noted earlier, only those service members experiencing the highest levels of PTSD symptomatology would be likely to endorse this item after returning stateside, as such an endorsement would indicate impaired functioning and distress. Conversely, such an endorsement prior to deployment may simply reflect an understandable concern elicited by the anticipation of entering a dangerous, unpredictable environment. In other words, when this item is endorsed at predeployment it may simply reflect that the service member is acknowledging that he or she is about to enter a war zone and thus has valid concerns about his or her future. Upon return, when facing a relatively safe future, only a few service members see their future bleakly or anticipate it being “cut short.” These individuals very well may be experiencing significant PTSD. Stated yet another way, an endorsement of this item at predeployment can be viewed with relatively less alarm. Such an endorsement prior to deployment may reflect an understandable concern elicited by the anticipation of entering a dangerous, unpredictable environment.
DiscussionHigh rates of posttraumatic stress at both diagnostic and subthreshold levels among service members during and following deployment to a theater of combat operations compel both efficient and accurate assessment strategies for evaluating PTSD symptom severity throughout the deployment cycle. Identifying service members at all levels of PTSD intensity is particularly important given that veterans with subthreshold PTSD tend to be overlooked by clinicians and researchers despite their experiencing substantial trauma-related symptoms (Grubaugh et al., 2005). Differences in prevalence rates as well as differing objectives related to specific populations or evaluation contexts require the ability to tailor assessment strategies based on individual item characteristics. Although the PCL-M constitutes the most widely used measure of PTSD symptoms in both military and veteran health-care settings, aggregate scores based on the 17-item measure are not optimally matched to specific populations or evaluation contexts. As suggested by previous research and confirmed in the present study, items differ considerably with respect to their relatedness to PTSD and their likelihood of endorsement at a given level of an underlying PTSD construct. Additionally, any given aggregate score on the 17-item PCL remains ambiguous with respect to interpretation, because of multiple ways in which any given score may be obtained.
Moreover, in some evaluation contexts the full 17-item PCL may not be realistic to administer—for example, in comprehensive screenings examining a broad range of potential physical and mental disorders, or during interviews in primary care settings or by supervising officers in theaters of combat (in which only three to five items related to PTSD may be possible). In such situations, which subset of items comprises the optimal composite depends entirely on the objectives of the evaluation context. As apparent from Table 1 and explicated in the Results section, items best suited for identifying individuals at the highest levels of posttraumatic stress (with high levels of the b parameter) differ from those optimally suited to screen for individuals at the lowest levels. In theaters of combat where retaining the maximum number of deployed personnel is paramount, only those individuals experiencing acute levels of PTSD symptoms may be pulled from operations or reassigned to less critical functions. By contrast, when screening for potential PTSD-related difficulties following deployment, or screening for subthreshold PTSD among redeploying personnel, items more sensitive but less specific to PTSD (with low levels of the b parameter) may be desired. Using item parameters provided in Table 1, both clinicians and researchers could select a subset of items having high discrimination within a narrow range of PTSD severity (high levels of the a parameter within a narrow range of the b parameter), or the same number of items having lower discrimination (a) at any specific point but exhibiting moderate discrimination across a broader range of PTSD severity.
It is worth noting, in this context, that items on the PCL that correspond to the four items comprising the Primary Care PTSD Screen (PC-PTSD) developed by Prins et al. (2003) were not consistently those obtaining the highest discrimination (a parameter) values in the present study. For example, the item used by Prins et al. to capture hyperarousal (“being constantly on guard, watchful, or easily startled”) had lower discrimination values on analogous PCL items among Airmen in this study than did items reflecting “feeling irritable or having angry outbursts” or “having difficulty concentrating.” Similarly, the PC-PTSD item intended to capture reexperiencing (“having nightmares … or thoughts about it when you did not want to”) had lower discrimination values on analogous PCL items in this study compared to PCL items in this sample reflecting “feeling very upset when something reminded you of a stressful military experience” or “suddenly acting or feeling as if a stressful military experience were happening again.” The ideal subset of items for any specific assessment context will necessarily reflect both the item characteristics for representative samples in that setting and the specific purposes of the evaluation (e.g., the threshold level of PTSD being targeted by the assessment).
Item parameters for the PCL-M identified in this study were derived from a predominantly (93%) male sample of 196 service members following a yearlong deployment on a high-risk mission to Iraq. The composition of the sample did not permit comparison of parameters for women versus those for men, and hence the item characteristics obtained here may not generalize to women active-duty service members or veterans. Moreover, both the difficulty level of individual items and their relatedness to PTSD may differ when assessed at times other than postdeployment (e.g., predeployment or in theater). The rates of endorsement for specific items and their relation to PTSD might also be hypothesized to vary depending on the specialized training or experiences of specific personnel units contributing to varying levels of risk or resiliency to posttraumatic stress or other sequelae of exposure to combat-related stressors.
Similarly, item characteristics of the PCL may vary as a function of the type of stressor experienced—for example, when using civilian versions of the PCL to assess traumatic response to cancer (e.g., Andrykowski, Cordova, Studts, & Miller, 1998; DuHamel et al., 2004; Shelby, Golden-Kreutz, & Andersen, 2005; Smith, Redd, DuHamel, Vickberg, & Ricketts, 1999), motor vehicle accident (Blanchard et al., 1996), sexual harassment or assault (e.g., Blanchard et al., 1996; Palmieri & Fitzgerald, 2005), or intimate partner violence (e.g., Krause, Kaltman, Goodman, & Dutton, 2007). In these and similar applications, adopting IRT as a basis for examining item characteristics of the PCL may facilitate more efficient and accurate assessment by tailoring specific subsets of items optimally matched to the population, setting, and objectives of the evaluation context.
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Submitted: September 13, 2012 Revised: July 11, 2013 Accepted: July 12, 2013
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Source: Psychological Assessment. Vol. 26. (1), Mar, 2014 pp. 1-7)
Accession Number: 2013-32038-001
Digital Object Identifier: 10.1037/a0034315
Record: 22- Title:
- Assessment of generalized anxiety disorder diagnostic criteria in the National Comorbidity Survey and Virginia Adult Twin Study of Psychiatric and Substance Use Disorders.
- Authors:
- Kubarych, Thomas S.. Department of Psychiatry, Virginia Commonwealth University, Richmond, VA, US, tkubarych@vcu.edu
Aggen, Steven H.. Department of Psychiatry, Virginia Commonwealth University, Richmond, VA, US
Hettema, John M.. Department of Psychiatry, Virginia Commonwealth University, Richmond, VA, US
Kendler, Kenneth S.. Department of Psychiatry, Virginia Commonwealth University, Richmond, VA, US
Neale, Michael C.. Department of Psychiatry, Virginia Commonwealth University, Richmond, VA, US - Address:
- Kubarych, Thomas S., Department of Psychiatry, Virginia Commonwealth University, P.O. Box 980126, Richmond, VA, US, 23298-1026, tkubarych@vcu.edu
- Source:
- Psychological Assessment, Vol 20(3), Sep, 2008. pp. 206-216.
- NLM Title Abbreviation:
- Psychol Assess
- Page Count:
- 11
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- Psychological Assessment: A Journal of Consulting and Clinical Psychology
- ISSN:
- 1040-3590 (Print)
1939-134X (Electronic) - Language:
- English
- Keywords:
- psychiatric epidemiology, generalized anxiety disorder, measurement invariance, structured interviews, validity, DSM-IV diagnostic criteria
- Abstract:
- The authors investigated measurement properties of the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition, generalized anxiety disorder (GAD) criteria in the National Comorbidity Survey and the Virginia Adult Twin Study of Psychiatric and Substance Use Disorders (VATSPSUD). The two studies used different widely used instruments. There were significant (p < .001) differences in measurement of GAD due to age, study, and age-study interaction on item thresholds and factor loadings of GAD, especially when different stem-probe structures of interviews were taken into account. Item thresholds were estimated to differ by as much as -.74 as a function of age and .40 as a function of study. Despite these differences, factor scores derived from symptom criteria strongly predicted categorical diagnostic outcomes based on symptom count. It is concluded that interview structure, especially the stem-probe format of structured interviews, and wording had significant effects on study findings; that future studies in psychiatric epidemiology should use common structured interviews as much as possible; and that factor scores can be used in conjunction with sum scores as cut points to retain the advantages of both dimensional and categorical classification. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Diagnosis; *Epidemiology; *Generalized Anxiety Disorder; *Interviews; *Psychological Assessment; Diagnostic and Statistical Manual; Substance Use Disorder; Diagnostic Criteria
- Medical Subject Headings (MeSH):
- Adolescent; Adult; Anxiety Disorders; Comorbidity; Female; Humans; Male; Mental Disorders; Middle Aged; Substance-Related Disorders; Surveys and Questionnaires; Twins; United States; Virginia
- PsycINFO Classification:
- Clinical Psychological Testing (2224)
Neuroses & Anxiety Disorders (3215) - Population:
- Human
Female - Location:
- US
- Age Group:
- Adolescence (13-17 yrs)
Adulthood (18 yrs & older)
Young Adulthood (18-29 yrs)
Thirties (30-39 yrs)
Middle Age (40-64 yrs) - Tests & Measures:
- Structured Clinical Interview for DSM-III-R
Composite International Diagnostic Interview DOI: 10.1037/t02121-000 - Grant Sponsorship:
- Sponsor: National Institutes of Health
Grant Number: MH-65322; MH-40828; MH/AA/DA-49492
Recipients: No recipient indicated - Methodology:
- Empirical Study; Quantitative Study; Twin Study
- Supplemental Data:
- Tables and Figures Internet
Other Internet - Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- Accepted: Apr 23, 2008; Revised: Apr 2, 2008; First Submitted: Jul 31, 2006
- Release Date:
- 20080908
- Correction Date:
- 20160616
- Copyright:
- American Psychological Association. 2008
- Digital Object Identifier:
- http://dx.doi.org/10.1037/a0012764; http://dx.doi.org/10.1037/a0012764.supp(Supplemental)
- PMID:
- 18778157
- Accession Number:
- 2008-12234-002
- Number of Citations in Source:
- 26
- Persistent link to this record (Permalink):
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Assessment of Generalized Anxiety Disorder Diagnostic Criteria in the National Comorbidity Survey and Virginia Adult Twin Study of Psychiatric and Substance Use Disorders
By: Thomas S. Kubarych
Department of Psychiatry, Virginia Commonwealth University;
Steven H. Aggen
Department of Psychiatry, Virginia Commonwealth University
John M. Hettema
Department of Psychiatry, Virginia Commonwealth University
Kenneth S. Kendler
Department of Psychiatry, Virginia Commonwealth University
Michael C. Neale
Department of Psychiatry, Virginia Commonwealth University
Acknowledgement: This work was supported by National Institutes of Health Grants MH-65322, MH-40828, and MH/AA/DA-49492.
We acknowledge the contribution of the Virginia Twin Registry, now part of the Mid-Atlantic Twin Registry (MATR), to ascertainment of participants for this study. The MATR, directed by J. Silberg and L. Eaves, has received support from the National Institutes of Health, the Carman Trust, and the W. M. Keck, John Templeton, and Robert Wood Johnson Foundations.
Psychiatric epidemiology is the study of the distribution of psychopathology in the general population, along with the risk factors that influence that distribution. In modern psychiatric research, assessments are made with specific criteria, such as those proposed in the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition, Text Revision (DSM–IV–TR, American Psychiatric Association, 2000). A common scientific activity is to compare rates of disorder across different groups, such as men versus women, or across different populations. Although seemingly simple, such comparisons rest on a number of assumptions, and conclusions may be invalid if these assumptions are not met.
First, we must recognize that although diagnostic classification systems produce a binary affected classification versus unaffected classification, this distinction is somewhat arbitrary. Variation in the number of symptoms reported still exists, among both those who are classified as having the disorder and those who are not. Therefore, a latent trait perspective may prove most useful for research inquiries into the prevalence of psychiatric disorders (Krueger, Markon, Patrick, & Iacono, 2005; Pickles & Angold, 2003). Rather than counting the number of symptoms endorsed on a questionnaire or interview and then assigning a yes-or-no diagnosis based on a predetermined cutoff, users of the latent trait approach regard symptoms as imperfect measures of an unobserved dimension, called the latent trait or factor, which is hypothesized to cause the observed symptoms.
Second, to conclude that the mean of a latent trait differs between groups, we must first know that the same latent trait is measured equivalently in both groups. If a diagnosis is based on symptom count, failure of this assumption can lead to incorrect conclusions, reducing the extent to which findings can be replicated across both populations and interview instruments. This important issue has received insufficient attention in psychiatric research. For example, discrepancies in prevalence rates of common psychiatric disorders between the Epidemiological Catchment Area (ECA) study and the National Comorbidity Survey (NCS) have been reported (Regier et al., 1998; Frances, 1998). To determine whether these differences in prevalence are genuine, we must establish that the measurement instruments used assessed the same constructs in an equivalent manner. Otherwise, we may conclude that prevalence rates differ when, in fact, only the measurement of the disorder differs. Similarly, we may be at risk of concluding that rates do not differ when, in fact, they do, although measurement differences have obscured them.
Two possible sources of failure of the measurement equivalence assumption exist. One is that the measuring instruments are genuinely different in content. Even subtle alterations in the wording or the order of the questions within the two interviews may cause participants to interpret the question differently and may thus violate the assumption of measurement equivalence. Another is that differences between the samples may also invalidate the interpretation of observed differences in diagnostic rates. For example, suppose that just one symptom of a putative disorder is rarely endorsed by men. This difference at the symptom level may well lead to different rates of diagnosis in men and women, but it does not necessarily mean that the liability to the disorder as a whole actually differs between them. Should either or both of these violations of measurement equivalence be present, observed differences in prevalence rates would be biased.
Such problems are not restricted to simple comparisons of the prevalence of disorders in different samples and populations. If measurement differs between instruments or samples, we may mislead ourselves into searching for (and perhaps finding) different causes of comorbidity when, in fact, no difference in comorbidity exists. Different patterns of familial resemblance, such as a higher heritability in men than in women, may also be caused by failures of measurement invariance (Lubke, Dolan, & Neale 2004). In principle, the same mechanism may also cause different degrees of association both between disorders and their putative risk factors and between two different disorders.
Currently, although DSM criteria are standardized, their assessment is not. That is, there exist a variety of interview instruments that may be used to derive a DSM diagnosis. Therefore, both interview structure and sample differences may contribute to measurement noninvariance of DSM disorders. It is possible to test for instrument structure differences when we have largely overlapping instruments. Interview instruments differ somewhat in wording and structure (Spitzer & Williams, 1985; World Health Organization, 1993). Most of these interviews include a stem–probe structure, such that only participants who have responded positively to the stem item (which is normally required for most DSM diagnoses) are asked the remaining probe items. Some structured interviews place all such stem items at the beginning of the interview, to prevent participants from learning to answer “no” to stems and to hence avoid multiple probes, whereas others, to improve the flow of the interview, group the stem and probe items together for each disorder. To assess the structure and prevalence of symptom criteria in the general population, it is necessary for one to take into account the stem–probe structure of these interviews (Kubarych, Aggen, Hettema, Kendler, & Neale, 2005).
Ideally, to compare the performance of two interview formats, we would assess the same participants with both interviews. There are problems with this approach, however. First, simply re-asking the same questions may change the participant's understanding of the item and may therefore yield inconsistent responses. Second, due to the stem–probe construction, large sample sizes would be needed in order to obtain a sufficient sample of participants who have responded to the probe items on both occasions.
In this article, we assess measurement equivalence between the NCS (Kessler, 2002) and the Virginia Adult Twin Study of Psychiatric and Substance Use Disorders (VATSPSUD, Kendler and Prescott, 2006). These studies used very similar DSM structured interviews, although the NCS interview was based on the Composite International Diagnostic Interview (World Health Organization, 1993) and the VATSPSUD was based on the Structured Clinical Interview for Diagnostic and Statistical Manual of Mental Disorders, Third Edition, Revised (SCID DSM–III–R; Spitzer and Williams, 1985). Our approach allows us to address the following questions: (a) Are the rates of generalized anxiety disorders (GAD) from these two samples directly comparable? If not, what are the main sources of difference between them? (b) Does age account for any differences in symptom reporting? (c) If there are measurement differences between the two studies, how much practical, clinical significance do these differences make?
We use item–factor modeling (described below) to assess the effects of age, study (VATSPSUD vs. NCS), and Age × Study interaction on (a) the latent trait, (b) the factor loadings, and (c) the item thresholds of the DSM criteria for GAD criteria. We hope that the information provided will assist in the future development of optimized and standardized instruments that would facilitate more accurate assessments of liability to GAD, which in turn would improve the quality of both research studies and clinical practice.
Method Participants and Measures
Sample 1 consisted of 2,163 White women and girl twins from the first wave of the population-based VATSPSUD. The twins were identified through birth certificates maintained by the Virginia Department of Health Statistics. The sample was restricted to White persons because it was estimated that it would be possible to interview less than 100 twin pairs from non-White persons, too few to obtain reliable estimates of heritabilities. The response rate was 92%. The age range was 17 years to 54 years (M = 30.1, SD = 7.6). The assessment of GAD symptoms was based on the SCID interview (Spitzer & Williams, 1985). In this interview, stem items are placed adjacent to probe items for a disorder. The lifetime prevalence of DSM–III–R GAD (American Psychiatric Association, 1987) with a 6-month duration criteria, without hierarchy (excluding participants who met the criteria for major depression), was 6.5%.
Sample 2 consisted of a subset (women and girls) of the 8,098 participants of the NCS (Kessler, 2002). Participants were noninstitutionalized civilians in the 48 contiguous United States, aged from 15 years to 54 years (for women, M = 33.3, SD = 10.6). The response rate was 82.6%. Lifetime prevalence of DSM–III–R GAD without hierarchy in this sample was 6.6%. The assessment of GAD symptoms was based on the Composite International Diagnostic Interview. The version of this interview used in the NCS places all probes at the beginning of the interview. To minimize differences in participants between the two samples, only women and girl participants were used. NCS data were collected between September 1990 and February 1992. The major demographic characteristics of the VATSPSUD and NCS are summarized in Table 1.
Demographic Characteristics of VATSPSUD and NCS Samples
Procedure
Interviews for the VATSPSUD sample were conducted from January 1987 through July 1989. Approximately 10% of the interviews were conducted by telephone, primarily when participants resided outside of Virginia. Interviewers had an undergraduate degree in a behavioral science, as well as a master's degree in a clinical area or 2 years of clinical experience, and 2 weeks of training for in-person interviews. A psychiatrist reviewed each interview and made clinical diagnoses (Kendler & Prescott, 2006).
Data collected from twins are not independent. Previous studies have often dealt with this dependency by randomly selecting one twin from each twin pair. Recent studies have shown that the resulting loss of information in this approach is much more severe than any effect of even moderate twin pair resemblance (Rebollo, de Moor, Dolan, & Boomsma, 2006). We chose instead to model fully the dependency in the data (Neale, Aggen, Maes, Kubarych, & Schmitt, 2006). The twin pair correlation on the factor was .481 for monozygotic (MZ) twins and .237 for dizygotic (DZ) twins.
For the NCS, a multistage, area-probability sampling was used. NCS interviewers had an average of 5 years of interviewing experience and attended a 7-day, study-specific training program on the use of the version of the Composite International Diagnostic Interview used in the NCS (Kessler et al., 1994). Due to the closeness of the periods in which the two studies were carried out, January 1987 to July 1989 versus September 1990 to February 1992, we do not expect cohort effects to be as substantial as those of age and study.
In addition to their placement at different places in the interview discussed above, variations in wording of the stems may also have introduced differences in the samples selected to receive the probes. The VATSPSUD stem asks whether the participant has had a period of at least 1 month when he or she has felt anxious, nervous, or worried more days than not. In the NCS interview, after being asked whether they have had a period of at least 1 month of worry or anxiety in their lifetime, participants were asked whether they were worried about more than one thing at a time, about what other people might do or what might happen to others, and whether they were worried about their mental or physical health. It is thus impossible to construct an equivalent stem between the two studies.
This inconsistency leaves us with a dilemma. We cannot obtain parameter estimates for the general population without taking the skip pattern produced by the stem–probe interview format into account (Kubarych et al., 2005), yet the skip pattern differs between the two studies. Therefore, we will present the analyses for Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition, GAD (DSM–IV; American Psychiatric Association, 1994) criteria both with and without taking the skip pattern into account. The DSM–IV criteria are a subset of the DSM–III–R criteria (Kubarych et al., 2005).
Statistical Model
The data for the DSM GAD criteria are binary items. Just as logistic and ordinal regression offer appropriate alternatives to linear regression when modeling binary or ordinal data, item factor analysis models offer appropriate alternatives to linear factor analysis when modeling binary or ordinal item responses. Structural equation modeling and item response theory (IRT) models are, in fact, variants of the more general item factor analysis framework. Both structural equation modeling and IRT approaches have been used to study measurement noninvariance. Transforming parameter estimates from structural equation modeling to IRT is straightforward; in fact, for the binary case, factor loadings are directly interpretable as IRT discrimination parameters, and item thresholds are directly interpretable as IRT difficulty (severity) parameters (Wirth and Edwards, 2007; Takane & DeLeeuw, 1987).
In the present report, we used a single-group item factor analysis model with covariates. In this model, the covariates may affect (a) the mean of the factor (latent trait; e.g., whether the mean of the latent variable differs between the NCS and the VATSPSUD samples or whether the trait mean differs between younger and older participants), (b) the variance of the factor, (c) the factor loadings (i.e., the regressions of the items on the factor), and (d) the means of the individual items, which, for binary data, are proportional to the item thresholds. Thus, we can distinguish between changes in the factor mean and variance, which may be considered genuine effects, and changes in the factor loadings or in the item means, which are changes in the functioning of the measurement instrument. The combined data from both NCS and VATSPSUD were treated as a single sample, with study (NCS or VATSPSUD) as one of the covariates. Age and Age × Study interaction (computed by multiplying age and study) were also studied as covariates.
For binary item data, it is not possible to estimate both the factor mean and variance effects simultaneously with all the factor loading and item threshold effects, due to underidentification of the model. There are, however, two key comparisons that can be made to test for measurement noninvariance. First, we can compare the fit of a model that specifies the effects of a covariate (age or study in this case) on the factor loadings against a model that specifies the same covariate effects on the latent variance. Second, we can compare the fit of a model that specifies covariate effects on the item thresholds with one that specifies covariate effects on the latent factor mean. We also compare the combined effects of age, study, and Age × Study interaction on item thresholds with their combined effects on the latent mean to assess whether there are additional differences in thresholds due to Age × Study interaction. We can compare the combined effects of age, study, and Age × Study interaction on factor loadings with their effects on the latent variance to test for additional differences in factor loadings due to Age × Study interaction.
First, we fitted a baseline model in which none of the covariates was allowed to affect the factor means, factor variances, item thresholds, or factor loadings. Second, we fitted models in which age or study, separately; age and study, together; or age, study, and Age × Study interaction, together, were allowed to affect the mean and variance of the latent trait (liability to GAD) and compared these with the baseline model. These comparisons assess whether the latent trait mean or variance differs across age or study and do so without accounting for possible measurement noninvariance. For evidence of whether the same construct is being measured across studies or age, we fitted models in which the covariates were allowed to affect the thresholds and compared these models with their factor mean counterparts. Similarly, we fitted models in which the covariates were allowed to affect the factor loadings and compared these with covariate effects on the factor variance. Lastly, to determine which items had the largest effects on measurement differences, we compared the effect of the covariates on each item separately. A flowchart of the model fitting sequence is available as supplemental material.
Statistical modeling makes use of various criteria for choosing between different models. We present two commonly used criteria in Table 2 through 11. The first is −2 times the logarithm of the likelihood function (−2lnL). This statistic is based on the likelihood or joint probability of the data for particular parameter values; taking the logarithm and multiplying by −2 yields a statistic that is useful for model comparison. The difference between the −2lnL statistics of two nested models is, under certain regularity conditions, asymptotically distributed as chi-square, with degrees of freedom equal to the difference between the number of parameters in the two models (MacCallum, 1995). The second, Akaike information criteria (AIC), is called an information-theoretic criterion because it emphasizes minimizing the amount of information required to express the data in the model, therefore favoring parsimonious representations of the data. Lower (more negative) values of information theoretic criteria such as AIC reflect more parsimonious models of the data (Akaike, 1987).
Model Comparison of Effect of Age, Study, and Age × Study Interaction on Liability DSM–IV Generalized Anxiety Disorder (Probes Only)
ResultsTo test for dimensionality, accounting for the systematically missing data (participants who do not endorse the stems are missing on all probes; see Kubarych et al., 2005), we conducted full information maximum likelihood (FIML) factor analysis on the combined samples for the six DSM–IV probes plus a stem item (seven items). The analyses were conducted in Mx (Neale, Boker, Xie, & Maes, 2002). For one- and two-factor models, Δχ2(8, N = 6,426) = 10.47, p > .05. We concluded that it was reasonable to treat the seven items as unidimensional, though this is not a necessary assumption.
Analysis of Probes Only
We then followed the model fitting sequence described above. A model with no effects on the factor means or variances, item thresholds, or factor loadings for any of the covariates was fit first as the baseline for comparison. We then tested for effects on the latent mean and variance of liability to GAD due to age, alone; study, alone; age and study, together; and age, study, and Age × Study interaction, together. The results for the probes only are displayed in Table 2. The effects on the latent mean (Models 2 through 5) are significant for age (p = .001), study, age and study, and Age × Study interaction (p < .001). Compared with the baseline model, which specifies no differences whatsoever between the two studies, there are significant effects on the latent variance (Models 10 through 13) for age (p = .002), age and study together (p = .001), and age, study, and Age × Study interaction (p = .003). These comparisons do not account for possible failures of measurement invariance.
As previously stated, the fairest comparison with which to test for invariance of item thresholds is to compare the model with the effects of the covariate(s) on the item thresholds against the model with the effects of the same covariate(s) on the latent mean. The results for the six DSM–IV probes, only, are given in Table 2; all comparisons for thresholds (Models 6 through 9) are highly significant (p < .001). These tests do indicate failures of measurement invariance. They imply that the level of the latent trait at which participants are responding positively to the items differs across the covariates. We can use information-theoretic criteria AIC, described earlier, to compare these models for parsimony. The same model with age and study, but not interaction, is most parsimonious for the DSM-IV GAD thresholds (AIC = −8027.26).
To test for failures of measurement invariance with respect to factor loadings, we compared the models including the effects of the covariate(s) on the factor loadings with the models including the effect of the same covariate(s) on the variance of the latent trait liability to GAD. These are Models 14 through 17 in Table 2. Study (p = .021) has a significant effect on the factor loadings.
The NCS sample was recruited from the 48 contiguous U.S. states, whereas the VATSPSUD sample was recruited from individuals born in Virginia. The possibility remains that the measurement noninvariance we detected was due to regional effects. We therefore performed the same comparisons restricting the NCS sample to the southeast region (which reduces the NCS sample size and, hence, the power of the test). There was still significant measurement noninvariance between the NCS and the VATSPSUD. Due to the significant differences obtained when restricting the NCS sample to the south region, we tested whether there was measurement noninvariance within the NCS by comparing the southeast region NCS sample with the rest of the NCS. Even within the NCS study, there were significant measurement noninvariance effects on both thresholds and factor loadings. These results are available in the supplemental material for this article.
Analysis Accounting for Stem–Probe Structure
Table 3 displays the results with the full sample, taking into account the stem–probe structure of the interviews. The most striking difference is that the effects of all covariates on the factor loadings are now highly significant (p < .001). The effects on the latent mean are no longer significant except for the model with age, study, and Age × Study interaction. The effects on the thresholds, however, remain highly significant for all covariates. The effect of age on the latent variance is no longer significant, whereas the effect of study on the latent variance becomes significant. Overall, measurement noninvariance increases when the stems are included in the analysis. In the general population, both thresholds and loadings contribute to measurement differences between the NCS and the VATSPSUD.
Model Comparison of Effect of Age, Study, and Age × Study Interaction on Liability to DSM–IV Generalized Anxiety Disorder (Including Stems)
Item-Level Effects
Having identified measurement noninvariance effects for study on factor loadings, age, study, and Age × Study on thresholds at the global level, we sought to determine which particular items were noninvariant. We ran the same series of model comparisons as we did in the global case, but testing the threshold and factor loading effects one item at a time, instead of all together. The results are displayed in Table 4 through 11. In these models, a positive effect of study indicates a higher threshold or factor loading in NCS than in VATSPSUD. Similarly, positive effects of age indicate increasing thresholds or factor loadings with age.
DSM–IV Generalized Anxiety Disorder Item-by-Item Tests for Effects of Age on Thresholds
Table 4 shows the effect sizes of age on each of the item thresholds. There is a large (−.74) effect for “Did you often tire easily?” A lower threshold corresponds to a higher endorsement frequency, and we expect older participants to tire more easily than do younger participants. The threshold for this item changes more than would be expected given how much the GAD factor changes with age. Failure to take into account the general tendency of older people to tire more easily would yield artificially increased diagnoses of GAD in this population. That is, older participants' tiring gives systematically different information about GAD. This diagnostic criterion does not provide the same information about GAD in older participants as it does in younger participants. The effect is statistically significant and of substantial size. The other significant effect is also large (.73 for “Were you often irritable or especially impatient”). Here, the higher threshold indicates the reverse: Older participants systematically report being less irritable or impatient than younger participants.
The two largest effects for study on thresholds (Table 5) also show the pattern of being in opposite directions: the threshold for irritability and impatience increases (higher in the NCS, .40), whereas the threshold for difficulty concentrating is lower in the NCS (−.31). All items show significant effects for study on their thresholds, some positive, some negative. Table 6 shows the effects on the items when both age and study are in the model, and Table 7 shows the results adding the interaction term.
DSM–IV Generalized Anxiety Disorder Item-by-Item Tests for Effects of Study on Thresholds
DSM–IV Generalized Anxiety Disorder Item-by-Item Tests for Effects of Age and Study Together on Thresholds
DSM–IV Generalized Anxiety Disorder Item-by-Item Tests for Effects of Age, Study, and Age × Study on Item Thresholds
Effect sizes for age on factor loadings are shown in Table 8. As with the thresholds, it is “Did you often tire easily?” and “Were you often irritable or especially impatient” that show significant effects of age on factor loadings. Tiring easily becomes more discriminating with age, whereas irritability and impatience become less discriminating. As can be seen in Table 9, four items show significant effects of study on factor loadings. Table 10 (age and study) and 11 (age, study, and Age × Study) give the results for combinations of age and study.
DSM-IV Generalized Anxiety Disorder Item-by-Item Tests for Effects of Age on Factor Loadings
DSM–IV Generalized Anxiety Disorder Item-by-Item Tests for Effects of Study on Factor Loadings
DSM–IV Generalized Anxiety Disorder Item-by-Item Tests for Effects of Age and Study Together on Factor Loadings
DSM–IV Generalized Anxiety Disorder Item-by-Item Tests for Effects of Age, Study, and Age × Study on Factor Loadings
Clinical Significance
The difficulty of determining the clinical significance of discrepancies in prevalence in epidemiological studies has been widely discussed in the literature (Regier et al., 1998; Frances, 1998; Muthén, 1996; Rodebaugh, Woods, Heimberg, Liebowitz & Schneier, 2006). Full DSM–IV diagnostic criteria are available as supplemental material. We do not have data in the VATSPSUD interview for DSM–IV Criterion B (the person finds it difficult to control the worry) and E (the symptoms cause impairment in functioning) and do not deal with hierarchy—participants were not excluded if they met the criteria for major depression. A DSM–IV diagnosis for GAD cannot be obtained without at least three of the six criteria in this study, which are listed under Section C in DSM–IV. We assessed the clinical significance of measurement noninvariance by cross-tabulating sum scores of three or more against factor scores uncorrected for measurement noninvariance and factor scores corrected for measurement noninvariance.
To best examine agreement between factor scores and sum scores, we chose the factor score threshold that yielded the same proportion of factor scores as the symptom criteria (e.g., because 18% of participants had sum scores of 3 or more, we chose the 82nd percentile of the factor scores as the cutoff). The agreement between factor scores and sum scores was examined before and after correcting for measurement noninvariance. In the full sample, without correcting for measurement noninvariance, 157 participants are potentially misclassified; only 103 participants, or 34% less, are potentially misclassified when factor scores are corrected for measurement noninvariance. We then performed the same analyses separately for the NCS and VATSPSUD. For the VATSPSUD, without correcting for measurement effects, 135 of 2,163 participants (about 6%) were potentially misclassified; when corrected factor scores were used, only 89 out of 2,163 (about 4%) were potentially misclassified. For the NCS, 14 out of 4,263 participants (less than 1%) were potentially misclassified with uncorrected factor scores, versus 14 with corrected factor scores. The greater disagreement between the factor scores and the sum scores in the VATSPSUD than in the NCS is highly significant, χ2(1, N = 6,426) = 130.36. This difference may be due to the different locations of the stems, which were all at the beginning of the interview in the NCS, but not in the VATSPSUD. The results with factor scores corrected for measurement noninvariance are shown in Table 12.
Cross Tabulation of Diagnoses Based on Factor Scores Corrected for Measurement Noninvariance Versus Sum Scores: Full Sample
The measurement noninvariance effects of age in this study tended to be counterbalancing: the strong effect of age on the threshold “Do you tire easily?” (−.73) is counterbalanced by the strong positive effect of age on irritability (.74). It is still worth inquiring, however, whether misclassification is correlated with age. To determine whether participants in the tails of the age distribution are preferentially misclassified, we computed biserial correlations between factor scores (corrected and uncorrected) and the absolute value of age deviation from the mean. There was very little correlation between age and misclassification (biserial correlation = −.048 for corrected factor scores; biserial correlation = −.064 for uncorrected factor scores).
Even though the samples differ in their agreement between diagnoses and factor scores, in both, the agreement is very good, especially when correcting for measurement noninvariance. Therefore, use of corrected factor scores for research provides maximum clinical validity. Conversely, if a clinician were to use factor scores for diagnosis, a cutoff could be used that would provide diagnoses close to DSM. A major advantage of the factor score approach is that it provides quantitative information about differences among cases and noncases. This alternate metric may prove especially useful in evaluating treatment or prevention effects.
DiscussionIn this study, we detected differences in the measurement of a common psychiatric disorder between two widely published population-based samples. Although we cannot establish the clinical significance of these findings, our analyses indicated that factor scores derived from symptom criteria strongly predicted diagnostic outcome based on sum score—that is, the participants who would be considered candidates for a diagnosis based on sum scores would be almost all the same participants who would have been selected on the basis of factor scores, especially in the NCS. This is an important result from both research and clinical perspectives because factor scores are measured on a continuous scale. From a research perspective, statistical power is much greater when a continuous variable is used instead of a dichotomized case–noncase variable. From a clinical perspective, a continuous variable can provide information about the effects of intervention on a participant's percentile standing on the latent variable. Thus, one might hypothetically find that a given intervention has changed a participant's standing on the latent trait from the 99th percentile to the 95th percentile, 80th percentile, or 50th percentile.
This result, indicating that comparison of rates of DSM-IV GAD diagnoses across the two samples is not substantially impacted by measurement noninvariance, may or may not generalize to other disorders; to comparisons across different groups, such as men and women; or even to different definitions of GAD. Therefore, caution is warranted for any comparison across studies or groups until the effects of measurement noninvariance have been investigated. Similarly, any comparison of rates of GAD across ages should be interpreted with caution, in either longitudinal or cross-sectional studies. In practice, the clinician may consider tiredness to be a more significant symptom in a younger patient and irritability or impatience to be more significant in an older patient.
Older individuals frequently report that they tire more easily, for reasons (e.g., physical fitness) that are likely not due to changes in liability to GAD. All other things being equal, an older individual who has responded positively to tiring easily is likely to have a lower liability to GAD than a younger individual. However, this item remains salient for diagnostic purposes at the older ages; in fact, it discriminates slightly better among older than among younger participants. The factor loading increases with age by a different degree, according to which covariates are included as moderators: by .38 with age alone (Table 8), by .28 with both age and study included as covariates (Table 10), and by .57 with age, study, and Age × Study interaction all used as covariates (Table 11). We expect older patients, for example, to tire more easily. It seems less clear why older participants should be less irritable and impatient and, at the same time, more keyed up and on edge. The results with respect to study are more troubling and less easily understood. Why, for example, should there be such a big difference in thresholds for the difficulty concentrating item?
There are many possible explanations for the measurement differences in these studies. First, their samples differ demographically, as summarized in Table 1. A limitation of the VATSPSUD is its lack of ethic minorities in the sample. As stated above, this exclusion occurred because VATSPSUD researchers estimated that the minority sample would be too small to obtain reliable estimates of genetic and environmental variance components. Second, the studies used different sampling techniques. VATSPSUD researchers interviewed as many twins as possible from a database maintained by the Virginia Department of Health Statistics. The NCS used a stratified, multistage, area probability sample.
A third possible source of measurement differences is that the placement of the stems differs between the NCS interview, in which all stems are grouped at the beginning of the interview, and the VATSPSUD, in which they are not. Possibly, some participants in the VATSPSUD have learned that by saying “no” to the stems, they can avoid having to answer a long list of additional questions. The interviews also differ slightly in their wording of the items, as documented in the supplemental material. These are, however, only hypotheses, and further research is needed to resolve between these alternatives. Because, for their cost-effectiveness and statistical power, studies depend in part on accurate measurement (Rao & Gu, 2002), we believe that these issues deserve a high priority. To avoid the possibilities of differences in wording of items or placement of stems causing measurement noninvariance, we recommend that future studies in psychiatric epidemiology use a commonly agreed on interview as much as possible. The alternative would seem to be developing ever more complex statistical methods for adjusting for differences between studies, which may, in turn, require larger sample sizes and make comparisons between studies or samples impossible for all but the most statistically sophisticated researchers.
Moving on to the probes, the NCS interview prefaces the probes with the sentence “When you were worried or anxious, were you also…” followed by short, bullet-type questions, whereas the VATSPSUD has no prefacing sentence and uses full sentences rather than bullets for the probes. Also, the qualifier often appears in five of the six VATSPSUD probes but in none of the NCS probes. It is quite possible that these minor differences in wording caused differences in the endorsement frequencies. This possibility, however, may be masked by differences in selection due to the different wording of the stem items.
We found greater measurement noninvariance when the skip pattern produced by the stem–probe format of the structured interview was taken into account than when analyses were performed only on the probes. This increase highlights the importance of taking the stem–probe structure into account when the objective of a study is to obtain parameter estimates that are valid for the general population.
Our findings are especially important with respect to current research priorities (Cuthbert, 2005). The National Institute of Mental Health (NIMH) is currently giving priority to research aimed at understanding the pathophysiology of mental disorders. Measurement issues such as those described in this article are crucial for these goals. Advances in fields such as neuroscience and genetics, as in any science, depend on accurate, valid measurement. Identifying reliable genetic associations, biomarkers, and neural circuits are facilitated by detailed and accurately measured phenotypes. Some genes involved in psychiatric disorders are likely to be relevant to a spectrum of disorders rather than a single DSM diagnosis. Krueger et al. (2002), for example, found that heritability of an externalizing factor was 81%. Identifying genes related to the externalizing spectrum will be relevant to all the disorders in the spectrum. Improved measurements may greatly assist our understanding of how different genes, environments, and neural circuits interact to produce psychopathology. We are currently investigating whether other disorders, such as major depression, have measurement noninvariance across studies, sex, and age. These issues are particularly salient in light of the notoriously high comorbidity among psychiatric disorders. As Cuthbert (2005) points out, the common strategy of selecting participants with one and only one disorder may be biased because such participants may be less severely affected than are those who are diagnosed with more than one disorder.
As stated above, the fact that we did not find many participants misclassified due to measurement noninvariance could be due to the lack of a DSM–IV diagnosis in these data and does not preclude participants being misclassified in other disorders or due to other covariates. At the same time, the close relationship between the quasi-diagnostic criteria and the estimated factor scores implies that factor scores provide valuable supplemental information. These data should, by providing greater statistical power, assist in the identification of high risk populations and of risk factors that increase liability to GAD. The clinical significance of measurement noninvariance deserves further study. It is possible, for example, that measurement noninvariance across sex may be responsible for reported sex differences in certain disorders. It is also possible that some cut points for a given disorder generate higher rates of measurement noninvariance misclassification than do others.
LimitationsOur analyses were limited to women and girls and might not generalize to men and boys. These findings also pertain to the associated symptom criteria, not the binary diagnosis of GAD. The participants in the VATSPSUD are 100% twins, whereas only 2% of the NCS participants would be expected to be twins. Twins do not differ significantly from nontwins, however. It was impossible to construct an equivalent stem between the two studies, and the results might differ if the interviews had used the same stems. The NCS used a multistage, area probability sampling method, whereas the VATSPSUD participants were identified through birth certificates maintained by the Virginia Department of Health Statistics.
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Submitted: July 31, 2006 Revised: April 2, 2008 Accepted: April 23, 2008
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Source: Psychological Assessment. Vol. 20. (3), Sep, 2008 pp. 206-216)
Accession Number: 2008-12234-002
Digital Object Identifier: 10.1037/a0012764
Record: 23- Title:
- Association of solitary binge drinking and suicidal behavior among emerging adult college students.
- Authors:
- Gonzalez, Vivian M.. University of Alaska Anchorage, Department of Psychology, Anchorage, AK, US, viviangonzalez@uaa.alaska.edu
- Address:
- Gonzalez, Vivian M., University of Alaska Anchorage, Department of Psychology, 3211 Providence Drive, Anchorage, AK, US, 99508, viviangonzalez@uaa.alaska.edu
- Source:
- Psychology of Addictive Behaviors, Vol 26(3), Sep, 2012. pp. 609-614.
- NLM Title Abbreviation:
- Psychol Addict Behav
- Page Count:
- 6
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- Bulletin of the Society of Psychologists in Addictive Behaviors; Bulletin of the Society of Psychologists in Substance Abuse
- Other Publishers:
- US : Educational Publishing Foundation
Society of Psychologists in Addictive Behaviors - ISSN:
- 0893-164X (Print)
1939-1501 (Electronic) - Language:
- English
- Keywords:
- binge drinking, drinking alone, heavy episodic drinking, suicidal ideation, suicide attempt, college students, emerging adults
- Abstract:
- [Correction Notice: An Erratum for this article was reported in Vol 26(3) of Psychology of Addictive Behaviors (see record 2012-13892-001). In the article, there is an error in the introductory paragraph. The number of students who had seriously considered attempting suicide in the Barrios, Everett, Simon, & Brener (2000) study should have been reported as 11.4%, not 1.4%. Additionally, in the Participants section, data for the study were collected from March 2009 to September 2010, not March 2009 to January 2010 as reported.] Emerging adult college students who binge drink in solitary contexts (i.e., while alone) experience greater depression and suicidal ideation than do students who only binge drink in social contexts, suggesting that they may be at greater risk for suicidal behavior. This study examined the association of a previous suicide attempt, one of the best predictors of future suicide attempts and suicide, and severity of recent suicidal ideation with drinking in solitary and social contexts. Participants were binge drinking, emerging adult (18- to 25-year-old) college students (N = 182) drawn from two studies of college drinkers. A logistic regression analysis revealed that both suicide attempt history and severity of suicidal ideation were significantly associated with a greater likelihood of being a solitary binge drinker as opposed to only a social binge drinker. Students with a previous suicide attempt were nearly four times more likely to be solitary binge drinkers. Multiple regression analyses revealed that suicide attempt history was significantly associated with greater frequency and quantity of drinking in solitary, but not social contexts. Suicidal ideation was significantly associated with drinks per solitary drinking day, but not frequency of solitary drinking once suicide attempt history was accounted for. Given the associations found between solitary binge drinking and a history of suicide attempts, as well as greater severity of recent suicidal ideation, it appears that these students are in need of suicide prevention efforts, including treatment efforts aimed at reducing binge drinking. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Alcohol Drinking Patterns; *Attempted Suicide; *Binge Drinking; *Suicidal Ideation; *Suicide; Adult Development; College Students
- Medical Subject Headings (MeSH):
- Adolescent; Adult; Binge Drinking; Cross-Sectional Studies; Depressive Disorder, Major; Female; Humans; Male; Northwestern United States; Risk Factors; Social Isolation; Statistics as Topic; Students; Suicidal Ideation; Suicide, Attempted; Surveys and Questionnaires; Young Adult
- PsycINFO Classification:
- Behavior Disorders & Antisocial Behavior (3230)
- Population:
- Human
Male
Female - Location:
- US
- Age Group:
- Adulthood (18 yrs & older)
Young Adulthood (18-29 yrs) - Tests & Measures:
- National Institute on Alcohol Abuse and Alcoholism’s Alcohol Consumption Question Set
Adult Suicidal Ideation Questionnaire DOI: 10.1037/t03904-000
Suicidal Behaviors Questionnaire—Revised DOI: 10.1037/t14542-000 - Grant Sponsorship:
- Sponsor: University of Alaska Anchorage, US
Other Details: Chancellor’s Fund
Recipients: Gonzalez, Vivian M.
Sponsor: National Institute on Alcohol Abuse and Alcoholism
Grant Number: R21AA018135
Recipients: Gonzalez, Vivian M. - Methodology:
- Empirical Study; Followup Study; Quantitative Study
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- First Posted: Jan 30, 2012; Accepted: Dec 8, 2011; Revised: Dec 7, 2011; First Submitted: Apr 7, 2011
- Release Date:
- 20120130
- Correction Date:
- 20130114
- Copyright:
- American Psychological Association. 2012
- Digital Object Identifier:
- http://dx.doi.org/10.1037/a0026916
- PMID:
- 22288976
- Accession Number:
- 2012-02608-001
- Number of Citations in Source:
- 48
- Persistent link to this record (Permalink):
- http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2012-02608-001&site=ehost-live
- Cut and Paste:
- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2012-02608-001&site=ehost-live">Association of solitary binge drinking and suicidal behavior among emerging adult college students.</A>
- Database:
- PsycINFO
Association of Solitary Binge Drinking and Suicidal Behavior Among Emerging Adult College Students
By: Vivian M. Gonzalez
University of Alaska Anchorage;
Acknowledgement: See page 620 for a correction to this article.
This research was supported by funds provided through the University of Alaska Anchorage Chancellor's Fund and a National Institute on Alcohol Abuse and Alcoholism grant (R21AA018135) to Vivian M. Gonzalez.
College students have high rates of “binge” drinking, defined as four or more drinks for women or five or more drinks for men in one sitting or on one occasion (Wechsler, Davenport, Dowdall, & Moeykens, 1994), and alcohol problems (Knight et al., 2002; Slutske, 2005; Timberlake et al., 2007). American college students also engage in high rates of suicidal ideation and behavior (Brener, Hassan, & Barrios, 1999; Furr, Westefeld, McConnell, & Jenkins, 2001), with 1.7% reporting a suicide attempt and 1.4% reporting seriously having considered attempting suicide during the preceding year (Barrios, Everett, Simon, & Brener, 2000).
There is a well-established link between alcohol use and disorders and suicide attempts and completions (Borges, Walters, & Kessler, 2000; Cherpitel, Borges, & Wilcox, 2004; Hufford, 2001; Wilcox, Conner, & Caine, 2004). Among college students, individuals with suicidal ideation are more likely to binge drink, and alcohol problems in this population are associated with increased rates of suicidal ideation and attempts (Brener et al., 1999; Gonzalez, Bradizza, & Collins, 2009; Levy & Deykin, 1989; Stephenson, Pena-Shaff, & Quirk, 2006).
Research suggests that the drinking context plays an important role in the association between alcohol use and negative affect, including suicidality. In college students, drinking tends to occur for social reasons (Kuntsche, Knibbe, Gmel, & Engels, 2005; LaBrie, Hummer, & Pedersen, 2007; Stewart, Zeitlin, & Samoluk, 1996) and in social contexts (Christiansen, Vik, & Jarchow, 2002; Mohr et al., 2001). However, drinking in response to negative experiences and affect is associated with drinking in solitary, as opposed to social, contexts (Mohr et al., 2001). Students who engage in solitary binge drinking (i.e., while alone or when no one else is drinking) have more severe depression (Christiansen et al., 2002) and suicidal ideation (Gonzalez, Collins, & Bradizza, 2009) than students who only binge drink in social contexts.
Depression and suicidal ideation are associated with suicide and suicide attempts (Brown, Beck, Steer, & Grisham, 2000; Kuo, Gallo, & Tien, 2001; Kuo, Gallo, & Eaton, 2004). Given the association of solitary binge drinking with these variables, solitary binge drinkers may be at higher risk for suicidal behavior than are students who binge drink only in social contexts. However, no study to date has examined the association between solitary drinking and previous suicide attempts, one of the best predictors of risk for suicide and suicide attempts (Borges, Angst, Nock, Ruscio, & Kessler, 2008; Harris & Barraclough, 1997; Oquendo et al., 2004).
This study examined whether individuals with a suicide attempt history were more likely to be solitary as opposed to social binge drinkers. The associations of suicide attempt history and suicidal ideation with frequency and quantity of drinking in social and solitary contexts also were explored to further examine the association of drinking context and suicidality.
Method Participants
Participants for the current study were drawn from two studies of emerging adult college drinkers (Gonzalez & Skewes, 2011; Gonzalez, Reynolds, & Skewes, 2011). For both studies, eligibility criteria included (a) being a full- or part-time university student and (b) being between the ages of 18 and 25 years old. Each study had additional selection criteria. In Study 1, participants had to have at least one solitary or social binge drinking episode per month during a typical month in the past year. In Study 2, participants had to have consumed at least four standard drinks in the past month and report either current sadness or loss of pleasure. The sadness and loss of pleasure items were adapted from the Beck Depression Inventory-II (Beck, Steer, & Brown, 1996), with items needing to be endorsed as at least a 1 (i.e., not enjoying things as much or feeling sad much of the time) to meet the inclusionary criterion. Although Study 2 included the sadness or loss of pleasure criterion, combining these samples was advantageous for the current study, as depression has been found to be associated with the key variables of interest in this study: solitary binge drinking and suicidality. Data in these studies were collected from March 2009 to January 2010. For the current study, individuals were not included if they did not binge drink during a typical month in the past year (n = 31), gave out-of-range and/or highly inconsistent and illogical responses across alcohol use items (n = 25), or were missing drinking data (n = 2).
Participants were 182 binge drinking, emerging adult (18- to 25-year-old) female (72.5%, n = 132) and male (27.5%, n = 50) college students attending a large, open-enrollment university in the Northwest. The average age was 21.1 years old (SD = 1.9). The sample was 74.7% White, 10.4% Alaska Native or American Indian (including mixed heritage), 4.4% Latino, 4.4% multiethnic, 2.2% Asian American, 1.6% Pacific Islander, and 1.1% African American. The majority of participants were full-time students (88.5%) and single (95.6%). With regard to living arrangements, 23.8% lived in their parents' home, 53.5% lived off campus (not in a parent's home), and 22.7% lived on campus. The sample was 19.8% freshman, 24.2% sophomore, 24.2% junior, 23.6% senior, 6.0% graduate students, and 2.2% nondegree-seeking.
Procedures
In both Studies 1 and 2, participants were recruited via flyers posted on campus and e-mails directed at 18- to 25-year-old students via their student e-mail accounts. Flyer and e-mail solicitations directed potential participants to a webpage that described the respective study in general terms (e.g., “a study of college student lifestyle and mood”) and included a questionnaire with items that screened for study eligibility embedded among distractor items. Those who met eligibility criteria for each respective study were scheduled for an in-person data collection session where study materials were presented in random order using MediaLab version 2006 software (Jarvis, 2006) on laptop computers. Participants were compensated for their time with a gift card ($20 for Study 1 and $30 for Study 2, given the longer protocol) to a supermarket/gas station or coffee-shop chain. After data collection, all participants were individually debriefed and given referral information for counseling services, as well as suicide/crisis-hotline phone numbers. The study protocols were approved by the institutional review board of the university.
Measures
Alcohol consumption
Solitary and social alcohol consumption were measured using items modified from the National Institute on Alcohol Abuse and Alcoholism's (NIAAA) alcohol consumption question set (NIAAA, 2003). Three separate items for social (defined as “with other people who were drinking”) and solitary (defined as “while alone or no one else was drinking”) contexts asked the following for a typical month in the past year: drinking days per month, number of standard drinks consumed on a typical drinking day, and the number of days on which binge drinking (i.e., four or more for women, or five or more for men, standard drinks on one occasion or sitting) occurred. Participants were provided with a handout that defined a standard drink (e.g., 12 oz. of beer, 5 oz. of wine, 8 to 9 oz. of malt liquor, or 1.5 oz. of 80-proof liquor).
Suicide attempt history
The Suicidal Behaviors Questionnaire—Revised (SBQ-R; Osman et al., 2001) is a 4-item self-report measure of suicidal behavior and ideation. The first item assesses lifetime suicidal ideation and behavior (“Have you ever thought about or attempted to kill yourself?”), with mutually exclusive response options ranging from no history of suicidal ideation or behavior (never) to history of a suicide attempt (I have attempted to kill myself). This item was used in the current study to categorize participants as having or not having a suicide attempt history. In young adult samples, the SBQ-R demonstrates high 2-week test–retest reliability (r = .95) and good convergent validity (Cotton, Peters, & Range, 1995).
Suicidal ideation
The Adult Suicidal Ideation Questionnaire (ASIQ; Reynolds, 1991a) is a 25-item self-report measure of suicidal thoughts and behavior experienced during the past month. Items range from general wishes that one were dead to thoughts of planning a suicide attempt and are rated on a 7-point scale (0 = never had the thought to 6 = almost every day). The ASIQ demonstrates high 1-week test–retest reliability (r = .86) and good convergent validity (Gutierrez, Osman, Kopper, Barrios, & Bagge, 2000; Reynolds, 1991b). It also evidences predictive validity, with total score predicting suicide attempts over a 3-month follow-up period (Osman et al., 1999). In the current sample, coefficient alpha for the ASIQ was .97.
Analyses
A hierarchal logistic regression was used to examine the influence of a previous suicide attempt and severity of suicidal ideation on binge drinking group (social only = 0, solitary = 1). For these analyses, if a participant reported (a) no episodes of solitary binge drinking during a typical month in the past year, and (b) had at least one episode of social binge drinking, then they were classified as a social binge drinker (n = 129). If a participant reported a binge drinking episode while alone or when no one else was drinking at least once during a typical month in the past year, then they were classified as a solitary binge drinker (n = 53). Solitary binge drinkers also could have episodes of social binge drinking, and previous research has found that nearly all do engage in social binge drinking (Gonzalez, Collins et al., 2009).
Four separate hierarchical multiple regression analyses for frequency of drinking and drinks per drinking day in social and solitary contexts were conducted to examine the association of a previous suicide attempt and suicidal ideation with each drinking variable. The same independent variables were entered in the multiple regression analyses and logistic regression analysis described above. In the first step of the analyses, age, gender, and ethnicity were entered to control for their possible effects on drinking and suicidality variables. In the second step, suicide attempt history (no attempts = 0, previous attempt = 1) was entered. In the final step, severity of suicidal ideation was entered to examine whether suicidal ideation was significantly associated with the given drinking dependent variable after accounting for suicide attempt history.
Given that the data for the current research came from two studies, analyses were repeated, including data source (Study 1 vs. 2) in the models in Step 1 and mean-centered interaction terms of Data Source × Suicide Attempt History and Data Source × Suicidal Ideation included in a final step. The interaction terms allowed an examination of whether the degree of relationship between suicide attempt history and severity of suicidal ideation and the dependent variables differed between the samples in the two studies.
In order to improve normality and reduce the influence of outliers, suicidal ideation, frequency of social drinking, and drinks per social drinking day were square-root transformed. Frequency of solitary drinking and drinks per solitary drinking day, which were more substantially skewed, were log transformed.
ResultsIn the study sample, 22.6% (n = 12) of solitary binge drinkers and 8.5% (n = 11) of social binge drinkers reported a previous suicide attempt. Means, standard deviations, and correlations among the study variables are presented in Table 1. The majority of solitary binge drinkers also engaged in social binge drinking at least one day per month (94.3%). An analysis of covariance, controlling for age, gender, and ethnicity revealed that solitary binge drinkers engage in social binge drinking more days per month (M = 5.03, SD = 3.98) than individuals who only binge drink socially (M = 3.38, SD = 2.81; F(1, 177) = 10.01, p = .002, η2 = .05). Solitary binge drinkers reported a mean of 3.04 (SD = 3.97) solitary binge days per month. Among social binge drinkers, 50.4% engaged in solitary drinking at least one day a month.
Means, Standard Deviations, and Intercorrelations of Study Variables
The sequential logistic regression analysis revealed that having a previous suicide attempt was significantly associated with a greater likelihood of being a solitary binge drinker as opposed to only a social binge drinker (OR = 3.76, p = .006; see Table 2). Severity of suicidal ideation also was significantly associated with being a solitary binge drinker. In the third step of the analysis, suicide attempt history remained significantly associated with a greater likelihood of being a solitary binge drinker even when suicidal ideation was controlled for (OR = 2.76, p = .046), suggesting that both suicidality (suicidal ideation and attempts) variables were independently associated with a greater likelihood of being a solitary binge drinker.
Logistic Regression Predicting Being a Solitary Binge Drinker
Separate hierarchical multiple regression analyses were conducted to examine the association of suicidality with frequency of social drinking and with drinks per social drinking day. Frequency of drinking in social contexts was not significantly associated with a previous suicide attempt (ΔR2 < .001, p = .85) or with severity of suicidal ideation (ΔR2 = .005, p = .33). Similarly, drinks per social drinking day was not significantly associated with a previous suicide attempt (ΔR2 = .001, p = .61) or with severity of suicidal ideation (ΔR2 = .003, p = .47).
Given the higher suicidality among solitary binge drinkers, it was possible that social and solitary binge drinkers differed in the strength of association between social drinking context and suicidality. Therefore, a hierarchical regression analysis was conducted to examine potential differences between binge drinking groups in these associations. In the first step, age, gender, ethnicity, binge drinking group, previous suicide attempt, and suicidal ideation (mean centered) were entered into the model. In the second step, two cross-product interaction terms were entered: Binge Drinking Group × Suicide Attempt and Binge Drinking Group × Suicidal Ideation. The addition of these interaction terms did not add significantly to the regression models for frequency of social drinking (ΔR2 = .014, p = .28) or drinks per social drinking day (ΔR2 = .012, p = .30), indicating that there was not a significant difference between social and solitary binge drinkers. These analyses suggest that for both binge drinking groups, neither frequency nor amount of drinking in social contexts was significantly associated with suicidality.
The hierarchical multiple regression analyses examining the association of a previous suicide attempt and severity of suicidal ideation with solitary drinking frequency and drinks per solitary drinking day revealed that a previous suicide attempt was associated with more frequent solitary drinking (ΔR2 = .041, p = .004; see Table 3) and with more drinks per solitary drinking day (ΔR2 = .037, p = .008). Suicidal ideation was not significantly associated with frequency of solitary drinking once suicide attempt history was accounted for (ΔR2 = .012, p = .12). However, suicidal ideation was associated with more drinks per solitary drinking day (ΔR2 = .033, p = .010). Finally, repeating these analyses while controlling for data source (Study 1 vs. 2) revealed the same patterns of significance for the suicidality variables as reported above. The final step in the models containing the interaction terms of data source by suicide attempt history and suicidal ideation were nonsignificant in all analyses (ΔR2 between .002 and .015), suggesting that results found do not differ significantly by data source despite differences in the selection criteria between studies.
Multiple Regression Analyses Predicting Solitary Drinking
DiscussionThe findings of this study suggest that binge drinking students with a suicide attempt history are significantly more likely to engage in solitary binge drinking. Students with a previous suicide attempt were nearly four times more likely to be solitary as opposed to only social binge drinkers. Those experiencing greater severity of recent suicidal ideation also were more likely to be solitary binge drinkers. Both a previous suicide attempt and recent suicidal ideation were independently associated with a greater likelihood of solitary binge drinking.
A history of a suicide attempt was associated with more frequent solitary drinking and having more drinks per solitary drinking day. In contrast, suicide attempt history and severity of suicidal ideation were not associated with frequency of social drinking or the amount of alcohol consumed on social drinking days for either social or solitary binge drinkers. The findings are consistent with that of a previous study that found that severity of suicidal ideation was significantly associated with solitary, but not social, binge drinking among underage students with a history of suicidal ideation (Gonzalez, Collins et al., 2009), and extends the findings to emerging adult students who were not selected for their suicidal ideation history.
Future studies are needed to examine how or why suicidal ideation and a history of a suicide attempt are associated with solitary binge drinking. One potential way that a previous suicide attempt and suicidal ideation may be related to solitary binge drinking is through drinking to cope with negative affect. According to motivational models of alcohol use, drinking to cope is motivated by efforts to escape, avoid, or lessen negative affect (Cooper, Frone, Russell, & Mudar, 1995). Drinking to cope appears to motivate binge drinking in the absence of the social influences commonly associated with drinking among emerging adult students (Christiansen et al., 2002). Consistent with this notion, frequency of solitary binge drinking in a previous study was found to be associated with severity of suicidal ideation and motivated by drinking to cope (Gonzalez, Collins et al., 2009).
Motivational models of alcohol use also suggest that coping-skill deficits contribute to a reliance on alcohol to cope with negative affect (Cooper et al., 1995; Cooper, Agocha, & Sheldon, 2000; Cox & Klinger, 1988). Similarly, cognitive–behavioral models of suicidality note the important role of coping-skill deficits in suicidal ideation and behavior (Reinecke, 2006; Rudd, 2006). Consistent with these models, suicidal ideation and behavior and drinking to cope are associated with greater use of avoidant coping strategies (Britton, 2004; Edwards & Holden, 2001; Reinecke, DuBois, & Schultz, 2001; Williams & Kleinfelter, 1989). Individuals with a history of a suicide attempt may be more likely to engage in solitary binge drinking in order to cope with distress, as well as be more likely to suffer from distress compared with social binge drinkers, in part owing to poorer coping skills. Future studies are needed to examine this possibility, as well as other potential links between solitary drinking and suicidality.
An important limitation of this study was the cross-sectional design. Because of this it is not known whether suicidal ideation motivates solitary binge drinking, solitary binge drinking plays a causal role in suicide attempts, or if these relationships are reciprocal or indirect. Another limitation was the overrepresentation of women in the sample. This study also was limited to emerging adult college binge drinkers and therefore may not generalize to noncollege student emerging adults or nonbinge drinking students. Studies are needed to examine solitary binge drinking in relation to negative affect and suicidality with noncollege population samples.
In conclusion, the current findings suggest that solitary binge drinkers are in particular need of suicide prevention efforts. Binge drinking alone among individuals with a suicide attempt history and/or greater severity of suicidal ideation is alarming in regard to risk for suicidal behavior, as intoxication can impede adaptive coping, increase aggression, and worsen mood (Hufford, 2001). Solitary binge drinkers are in need of treatment efforts aimed at reducing their alcohol misuse, given their greater frequency of social binge drinking as well as additional episodes of solitary binge drinking. This may serve to reduce the likelihood for alcohol dependence, as well as suicide risk within this population.
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Submitted: April 7, 2011 Revised: December 7, 2011 Accepted: December 8, 2011
This publication is protected by US and international copyright laws and its content may not be copied without the copyright holders express written permission except for the print or download capabilities of the retrieval software used for access. This content is intended solely for the use of the individual user.
Source: Psychology of Addictive Behaviors. Vol. 26. (3), Sep, 2012 pp. 609-614)
Accession Number: 2012-02608-001
Digital Object Identifier: 10.1037/a0026916
Record: 24- Title:
- 'Association of solitary binge drinking and suicidal behavior among emerging adult college students': Correction to Gonzalez (2012).
- Authors:
- Gonzalez, Vivian M.. University of Alaska Anchorage, Department of Psychology, Anchorage, AK, US, viviangonzalez@uaa.alaska.edu
- Address:
- Gonzalez, Vivian M., University of Alaska Anchorage, Department of Psychology, 3211 Providence Drive, Anchorage, AK, US, 99508, viviangonzalez@uaa.alaska.edu
- Source:
- Psychology of Addictive Behaviors, Vol 26(3), Sep, 2012. pp. 620.
- NLM Title Abbreviation:
- Psychol Addict Behav
- Page Count:
- 1
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- Bulletin of the Society of Psychologists in Addictive Behaviors; Bulletin of the Society of Psychologists in Substance Abuse
- Other Publishers:
- US : Educational Publishing Foundation
Society of Psychologists in Addictive Behaviors - ISSN:
- 0893-164X (Print)
1939-1501 (Electronic) - Language:
- English
- Keywords:
- binge drinking, drinking alone, heavy episodic drinking, suicidal ideation, suicide attempt, college students, emerging adults
- Abstract:
- Reports an error in 'Association of solitary binge drinking and suicidal behavior among emerging adult college students' by Vivian M. Gonzalez (Psychology of Addictive Behaviors, Advanced Online Publication, Jan 30, 2012, np). In the article, there is an error in the introductory paragraph. The number of students who had seriously considered attempting suicide in the Barrios, Everett, Simon, & Brener (2000) study should have been reported as 11.4%, not 1.4%. Additionally, in the Participants section, data for the study were collected from March 2009 to September 2010, not March 2009 to January 2010 as reported. (The following abstract of the original article appeared in record 2012-02608-001.) Emerging adult college students who binge drink in solitary contexts (i.e., while alone) experience greater depression and suicidal ideation than do students who only binge drink in social contexts, suggesting that they may be at greater risk for suicidal behavior. This study examined the association of a previous suicide attempt, one of the best predictors of future suicide attempts and suicide, and severity of recent suicidal ideation with drinking in solitary and social contexts. Participants were binge drinking, emerging adult (18- to 25-year-old) college students (N = 182) drawn from two studies of college drinkers. A logistic regression analysis revealed that both suicide attempt history and severity of suicidal ideation were significantly associated with a greater likelihood of being a solitary binge drinker as opposed to only a social binge drinker. Students with a previous suicide attempt were nearly four times more likely to be solitary binge drinkers. Multiple regression analyses revealed that suicide attempt history was significantly associated with greater frequency and quantity of drinking in solitary, but not social contexts. Suicidal ideation was significantly associated with drinks per solitary drinking day, but not frequency of solitary drinking once suicide attempt history was accounted for. Given the associations found between solitary binge drinking and a history of suicide attempts, as well as greater severity of recent suicidal ideation, it appears that these students are in need of suicide prevention efforts, including treatment efforts aimed at reducing binge drinking. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
- Document Type:
- Erratum/Correction
- Subjects:
- *Alcohol Drinking Patterns; *Attempted Suicide; *Binge Drinking; *Suicidal Ideation; *Suicide; Adult Development; College Students
- PsycINFO Classification:
- Behavior Disorders & Antisocial Behavior (3230)
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- First Posted: May 28, 2012
- Release Date:
- 20120528
- Correction Date:
- 20151123
- Digital Object Identifier:
- http://dx.doi.org/10.1037/a0028972
- PMID:
- 22642852
- Accession Number:
- 2012-13892-001
- Persistent link to this record (Permalink):
- http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2012-13892-001&site=ehost-live
- Cut and Paste:
- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2012-13892-001&site=ehost-live">'Association of solitary binge drinking and suicidal behavior among emerging adult college students': Correction to Gonzalez (2012).</A>
- Database:
- PsycINFO
Correction to Gonzalez (2012)
In the article “Association of Solitary Binge Drinking and Suicidal Behavior Among Emerging Adult College Students,” by Vivian M. Gonzalez (Psychology of Addictive Behaviors, Advance online publication, January 30, 2012. doi: 10.1037/a0026916), there is an error in the introductory paragraph. The number of students who had seriously considered attempting suicide in the Barrios, Everett, Simon, & Brener (2000) study should have been reported as 11.4%, not 1.4%. Additionally, in the Participants section, data for the study were collected from March 2009 to September 2010, not March 2009 to January 2010 as reported.
This publication is protected by US and international copyright laws and its content may not be copied without the copyright holders express written permission except for the print or download capabilities of the retrieval software used for access. This content is intended solely for the use of the individual user.
Source: Psychology of Addictive Behaviors. Vol. 26. (3), Sep, 2012 pp. 620)
Accession Number: 2012-13892-001
Digital Object Identifier: 10.1037/a0028972
Record: 25- Title:
- Associations of descriptive and reflective injunctive norms with risky college drinking.
- Authors:
- Collins, Susan E.. Department of Psychiatry and Behavioral Sciences, University of Washington–Harborview Medical Center, Seattle, WA, US, collinss@uw.edu
Spelman, Philip J.. Department of Psychiatry and Behavioral Sciences, University of Washington–Harborview Medical Center, Seattle, WA, US - Address:
- Collins, Susan E., University of Washington–Harborview Medical Center, 325 Ninth Avenue, Box 359911, Seattle, WA, US, 98104, collinss@uw.edu
- Source:
- Psychology of Addictive Behaviors, Vol 27(4), Dec, 2013. pp. 1175-1181.
- NLM Title Abbreviation:
- Psychol Addict Behav
- Page Count:
- 7
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- Bulletin of the Society of Psychologists in Addictive Behaviors; Bulletin of the Society of Psychologists in Substance Abuse
- Other Publishers:
- US : Educational Publishing Foundation
Society of Psychologists in Addictive Behaviors - ISSN:
- 0893-164X (Print)
1939-1501 (Electronic) - Language:
- English
- Keywords:
- alcohol use, college drinking, descriptive norms, reflective injunctive norms, risky drinking
- Abstract:
- The current study describes the relative predictive power of descriptive norms (i.e., how much the target student believes referents 'drink until they get drunk') and reflective injunctive norms (i.e., target student’s perception of referents’ approval of the target student drinking until drunk) across various reference groups. The aim of this study was to gain further insight into which types of norms and reference groups are most highly concurrently correlated with risky drinking. It was hypothesized that both reflective injunctive and descriptive norms would be significantly positively correlated with risky drinking outcomes, and that more proximal reference group norms would be more highly predictive than more distal reference group norms. Participants (N = 837) were college students on the U.S. west coast who completed questionnaires in the context of a longitudinal parent study. Cross-sectional, zero-inflated negative binomial regressions were used to test the relative strengths of correlations between descriptive and reflective injunctive norms (i.e., for typical college students, closest friend, person whose opinion they value most, and closest family member) and risky drinking (i.e., peak alcohol quantity, frequency of heavy drinking episodes, and alcohol-related problems). Findings showed that descriptive and reflective injunctive norms were most consistently, strongly and positively correlated with risky drinking when they involved referents who were closer to the target college drinkers (i.e., closest friend and person whose opinion you value the most). Norms for typical college students were less consistent correlates of risky drinking. These findings may contribute to the knowledge base for enhancing normative reeducation and personalized normative feedback interventions to include more personally salient and powerful normative information. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Alcohol Abuse; *Risk Taking; *Social Norms; College Students
- Medical Subject Headings (MeSH):
- Adult; Alcohol Drinking; Female; Humans; Male; Peer Group; Reference Values; Risk-Taking; Students; Universities; Young Adult
- PsycINFO Classification:
- Substance Abuse & Addiction (3233)
- Population:
- Human
Male
Female - Location:
- US
- Age Group:
- Adulthood (18 yrs & older)
- Tests & Measures:
- Personal Information Questionnaire
Frequency Quantity Questionnaire
Timeline Followback Questionnaire
Rutgers Alcohol Problems Index
Reflective Injunctive Norms Questionnaire DOI: 10.1037/t28864-000 - Grant Sponsorship:
- Sponsor: Alcohol and Drug Abuse Institute
Grant Number: 65-1951
Recipients: No recipient indicated
Sponsor: National Institute on Alcohol Abuse and Alcoholism
Grant Number: K22 AA018384
Other Details: Career Transition Award
Recipients: No recipient indicated
Sponsor: National Institute on Alcohol Abuse and Alcoholism
Grant Number: R01 AA012547
Recipients: No recipient indicated - Methodology:
- Empirical Study; Quantitative Study
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- Accepted: Mar 20, 2013; Revised: Mar 11, 2013; First Submitted: Sep 14, 2012
- Release Date:
- 20131223
- Correction Date:
- 20140317
- Copyright:
- American Psychological Association. 2013
- Digital Object Identifier:
- http://dx.doi.org/10.1037/a0032828
- PMID:
- 24364691
- Accession Number:
- 2013-44431-004
- Number of Citations in Source:
- 38
- Persistent link to this record (Permalink):
- http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2013-44431-004&site=ehost-live
- Cut and Paste:
- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2013-44431-004&site=ehost-live">Associations of descriptive and reflective injunctive norms with risky college drinking.</A>
- Database:
- PsycINFO
Associations of Descriptive and Reflective Injunctive Norms With Risky College Drinking / BRIEF REPORT
By: Susan E. Collins
Department of Psychiatry and Behavioral Sciences, University of Washington – Harborview Medical Center;
Philip J. Spelman
Department of Psychiatry and Behavioral Sciences, University of Washington – Harborview Medical Center
Acknowledgement: Supported by a Small Grant from the Alcohol and Drug Abuse Institute (Grant No. 65-1951), a National Institute on Alcohol Abuse and Alcoholism (NIAAA) Career Transition Award (Grant No. K22 AA018384), and the NIAAA (Grant No. R01 AA012547).
The negative consequences associated with the consistently high rates of heavy episodic drinking (i.e., ≥ 4 drinks for women and ≥ 5 drinks for men; Dawson, Grant, Stinson, & Chou, 2004; Nelson, Xuan, Lee, Weitzman, & Wechsler, 2009) among college students are well-documented. Heavy episodic drinking affects both college drinkers (e.g., accidents and falls resulting in injury; risky sex; unwanted sexual advances, rape, and sexual assault; driving while intoxicated; problems with authorities; Abbey, 2002; Hingson, Heeren, Zakocs, Kopstein, & Wechsler, 2002) and their communities (e.g., sleep disruption; property damage; verbal, physical, or sexual violence; Langley, Kypri, & Stephenson, 2003; Wechsler & Nelson, 2008). The pressing severity of the problem has led researchers to explore correlates and predictors of heavy-episodic drinking among college students—including family history, demographics, personality factors, drinking motives, expectancies, attitudes, peer influences, drinking onset, and, perhaps most prolifically, perceived norms—to better understand how to effectively intervene (Baer, 2002; Borsari, Murphy, & Barnett, 2007).
Dozens of studies have shown strong associations between norms and college drinking (see Borsari & Carey, 2003; Borsari & Carey, 2001, for reviews). Most of these studies have evaluated the importance of descriptive norms, or the target student’s beliefs about how much other reference groups drink, and have collectively shown that college students consistently overestimate referents’ drinking (Borsari & Carey, 2003). Descriptive norms have been shown to be positively associated with alcohol outcomes (e.g., Baer, Stacy, & Larimer, 1991; Larimer et al., 2009; Larimer et al., 2011; Larimer, Turner, Mallett, & Geisner, 2004; Perkins, 2007; Perkins, Haines, & Rice, 2005).
Injunctive norms, or how much the target student believes referents approve or disapprove of drinking in general, have been researched to a lesser extent, but studies have shown similar outcomes. Specifically, college students perceive reference groups to approve of drinking more than is actually the case (Borsari & Carey, 2003; DeMartini, Carey, Lao, & Luciano, 2011; LaBrie, Napper, & Ghaidarov, 2012). On the other hand, the association between injunctive norms and drinking outcomes is more complex. Studies have shown that self-other discrepancies (i.e., the perceived difference between the target’s and the referents’ behaviors and attitudes) created by injunctive norms are larger than for descriptive norms (Borsari & Carey, 2003). The association of injunctive norms with drinking outcomes varies by reference group: the closer the referents, the greater the positive association between perceived referent approval of drinking and the target student’s drinking and alcohol-related problems (LaBrie, Hummer, Neighbors, & Larimer, 2010; Neighbors et al., 2008).
Reflective injunctive norms are a more personally referenced version of injunctive norms, and they were originally introduced as “subjective norms” in the theory of reasoned action (Ajzen & Fishbein, 1980) and, later, in the theory of planned behavior (Ajzen, 1991).
Reflective injunctive norms represent the extent to which the target believes referents approve of the target’s own drinking (Ajzen, 2002). As applied in the theory of planned behavior, reflective injunctive norms have been shown to evince a positive association with intent to engage in risky drinking, and, in some studies, an indirect effect on risky drinking via intent (Collins & Carey, 2007; Collins, Witkiewitz, & Larimer, 2011; Huchting, Lac, & LaBrie, 2008; Johnston & White, 2003).
Current Study Aim and HypothesesThe aim of this study was to assess the relative predictive power of parallel descriptive and reflective injunctive norms across various peer groups and thereby to gain further insight into which types of norms and reference groups are most highly concurrently correlated with risky drinking. It was hypothesized that both reflective injunctive and descriptive norms would be significantly positively correlated with risky drinking outcomes (i.e., peak drinking quantity, heavy drinking episode [HDE] frequency, and alcohol-related problems), and that more proximal reference group norms would be more highly predictive than more distal reference group norms.
Method Participants
Participants were 837 (63.9% female, 0.1% transgender) college students at two, 4-year universities on the U.S. west coast who participated in a longitudinal parent study (response rate = 70%; Collins et al., 2011). Please see Table 1 for sample description.
Sociodemographic Sample Description (N = 837)
Measures
The Personal Information Questionnaire was used to assess participants’ age, gender, year in college, race and ethnicity, and membership in an on-campus Greek organization. This measure was used to describe the baseline sample.
The Reflective Injunctive Norms Questionnaire was made up of indicators from the Subjective Norms Questionnaire, a measure based on suggestions by Ajzen (2002), and modified from a previous study (Collins & Carey, 2007). Participants rated how much various referents would approve or disapprove of their “drinking until you get drunk” in the next 30 days, on a scale ranging from 1 (highly disapprove) to 5 (highly approve). Target groups included “an average college student at your university,” “your closest friend,” “your closest family member,” and “the person whose opinion you value most.” The question stem was rephrased to assess descriptive norms for each of the reference groups (e.g., “Will an average college student at your university drink until they get drunk at least once in the next 30 days?”). Participants could agree or disagree with these statements (1 = strongly disagree to 5 = strongly agree). Items for descriptive (α = .96) and reflective injunctive (α = .96) norms showed good internal consistency.
The Frequency−Quantity Questionnaire (adapted from Borsari & Carey, 2000; Collins, Carey, & Sliwinski, 2002; Dimeff, Baer, Kivlahan, & Marlatt, 1999) includes open-ended items assessing participants’ self-aggregated alcohol frequency and quantity of alcohol consumption in the past 30 days.
The Timeline Followback Questionnaire (TLFB; Sobell & Sobell, 1992) was used to aggregate HDE frequency in the past 30 days. The TLFB is a set of monthly calendars that allows for a retrospective evaluation of drinking for each day of the previous month.
Alcohol-related problems were assessed using the Rutgers Alcohol Problems Index (White & Labouvie, 1989), which asks participants how often (0 = never to 4 = more than 10 times) they experienced 23 drinking-related consequences over the previous 30 days. The overall score showed acceptable internal consistency (α = .94).
Procedure
Potential participants for the current study were emailed invitations. Invitations included the study URL and a randomly generated personal identification number with which participants logged into the secure study website. Participants provided informed consent and completed the baseline assessment, which included the measures noted above and took approximately 45 min. They were paid $20 for their time.
Data Analysis Plan
Three regression models were conducted in Stata, Version 11.2 to determine the relative contributions of reflective injunctive and descriptive norms in the prediction of risky drinking outcomes (i.e., alcohol quantity during the heaviest drinking occasion in the past 30 days [peak alcohol quantity], HDE, and alcohol-related problems). Because drinking outcome variables were overdispersed, positively skewed count/integer responses and evinced a preponderance of zeros, zero-inflated negative binomial (ZINB) models were used (Cameron & Trivedi, 1998). ZINB is a subset of generalized linear models for count outcomes that are positively skewed and have more zero responses than would be expected given the distribution. ZINB models two processes: the zero-inflated portion of the model, which is a Bernoulli trial to determine the probability that an observation is consistently zero, and a negative binomial portion of the model, which determines the association if the observation is a feasible count response predicted by the negative binomial distribution (Hardin & Hilbe, 2007).
ResultsDescriptive statistics for the predictors and outcomes are presented in Table 2, and zero-order correlations are listed in Table 3.
Descriptive Statistics for Predictors and Outcomes
Zero-Order Correlations (Bivariate Spearman’s ρ) Between Norms and Risky Drinking
ZINB Model Outcomes
Peak alcohol quantity
The omnibus model was significant, χ2(8, n = 831) = 87.51, p < .001, Vuong z = 7.68, p < .001, Nagelkerke pseudo R2 = .30. The negative binomial portion of the model indicated that descriptive norms for a typical college student inversely predicted peak alcohol quantity, whereas descriptive norms for a respondent’s closest friend positively predicted peak alcohol quantity. Reflective injunctive norms for one’s closest friend were positively associated with peak alcohol quantity (see Table 4 for model parameters). The zero-inflated portion of the model indicated that both descriptive and reflective injunctive norms for one’s closest friend inversely predicted zero inflation. Therefore, the greater the agreement that one’s closest friend would drink until drunk and approve of the respondent drinking until drunk, the lower the likelihood the student belonged to the “consistent zero,” or abstinent part of the distribution. Additionally, the greater the perceived approval by the person whose opinion they valued the most, the less likely respondents were to belong to the consistent zero group (see Table 4).
Zero-Inflated Negative Binomial Model Parameters
Zero-Inflated Negative Binomial Model Parameters
Heavy drinking episodes
The omnibus model was significant, χ2(8, n = 831) = 53.57, p < .001, Vuong z = 4.42, p < .001, Nagelkerke pseudo R2 = .30. The negative binomial portion of the model indicated that descriptive norms for a typical college student inversely predicted HDE, whereas descriptive norms for a respondent’s closest friend positively predicted HDE. Regarding reflective injunctive norms, similar relationships existed for norms regarding typical students and one’s closest friend (see Table 4). The zero-inflated portion of the model indicated that both descriptive and reflective injunctive norms for one’s closest friend inversely predicted zero inflation (see Table 4).
Alcohol-related problems
The omnibus model was significant, χ2(8, n = 797) = 16.45, p = .04, Vuong z = 4.01, p < .001, Nagelkerke pseudo R2 = .20. None of the negative binomial results was significant for predicting alcohol-related problems (see Table 4). The zero-inflated portion of the model indicated that descriptive norms for one’s closest friend and the person whose opinion the respondent valued most inversely predicted zero inflation (see Table 4).
DiscussionThe aims of this study were to test the relative predictive power of parallel descriptive and reflective injunctive norms across various peer groups. It was hypothesized that both reflective injunctive and descriptive norms would be associated with risky drinking outcomes (i.e., peak drinking quantity, HDE frequency, and alcohol-related problems), and that more proximal referents would be more highly predictive than more distal referents.
Summary of Current Findings
Findings largely corresponded to hypotheses. Zero-order correlations showed that both descriptive and reflective injunctive norms regarding closer referents (in descending level of association: closest friend, person whose opinion you value most, and closest family member) showed stronger positive correlations with risky drinking and related problems than norms regarding typical college students.
Next, our multivariate analyses afforded the unique opportunity to differentiate between two aspects of risky drinking: the odds of typically engaging in nonrisky versus risky drinking as well as the extent of risky drinking. Regarding the former, we were able to note which types of norms predicted drinking outcomes for people who were likely never to drink, experience HDE, or alcohol-related problems. Specifically, the norm for one’s closest friend was the primary predictor among descriptive norms, such that greater belief one’s closest friend would drink until they got drunk was associated with lower likelihood of being either abstinent or a lighter drinker. Greater conviction that both one’s closest friend and person whose opinion one values most would drink until drunk was also associated with a lower likelihood of nonproblem drinking. Regarding reflective injunctive norms, we found that greater agreement that one’s closest friend or person whose opinion one values most would approve of one drinking until drunk was associated with lower likelihood of being either abstinent or a light drinker.
Second, we examined the prediction of the extent of risky drinking by descriptive and injunctive norms. We found that descriptive and reflective injunctive norms for one’s closest friend served as the most consistent, positive predictors of risky drinking outcomes, with reflective injunctive norms for the person whose opinion respondents value most being a similarly consistent positive predictor. Unlike in the zero-order correlations, however, closest family member was never significantly associated with risky drinking outcomes, and neither descriptive nor reflective injunctive norm was associated with the extent of one’s experience of alcohol-related problems. Finally, descriptive norms regarding a typical college student were inversely—not positively—associated with risky drinking outcomes in the multivariate models.
Findings in the Context of the Norms Literature
Our findings replicated and extended those of other recent studies of norms and their relative weight across various reference groups (Larimer et al., 2009; Larimer et al., 2011) and norm types (LaBrie et al., 2010; Neighbors et al., 2007; Neighbors et al., 2008). Regarding the similarities between other studies and the current study, we noted that both descriptive and reflective injunctive norms using typical college students as the reference group were positively associated with risky drinking in zero-order correlations—although not as strongly positively correlated as closer reference groups. When examined in the context of other reflective injunctive and descriptive norms, however, norms for typical students were inversely associated with experience of HDE and peak alcohol quantity. A similar pattern of findings has been found across a few studies whose authors have attributed it to a potential negative suppressor effect (cf. Neighbors et al., 2007; Neighbors et al., 2008). Although more studies are needed to further parse and confirm this finding, it may be concluded that norms regarding closer referents and not typical college students are more powerful and consistent predictors of risky drinking outcomes.
Although it replicated findings from previous studies, the current study also expanded on these findings to include new reference groups (e.g., “person whose opinion you value most,” “closest family member”), a target behavior that is more specific to HDE experienced by college students (i.e., “drinking until you got drunk”), and a different type of injunctive norm (i.e., reflective injunctive norms) also known as “subjective norms” in the theory of planned behavior (Ajzen, 1991). Although reflective norms have been explored in a couple of prior studies in the college drinking norms literature, this has only been done in the context of opposite-sex perceptions and has not been personally referenced (Hummer, LaBrie, Lac, Sessoms, & Cail, 2012; LaBrie, Cail, Hummer, Lac, & Neighbors, 2009). Because they capture people’s perceptions of others’ approval of their own behavior, the current findings suggest reflective injunctive norms may be personally relevant and affectively salient.
Study Limitations
Because we did not additionally assess injunctive norms, it is impossible to compare the relative contributions of the reference groups’ approval of drinking until drunk in general (injunctive norms) versus approval of the target student’s drinking until drunk (reflective injunctive norms). Future studies may include both types of injunctive norms to understand their relative predictive abilities.
The norms questionnaires did not ask participants to indicate exactly who certain referents were (e.g., whom participants had in mind when considering their “closest family member” or “person whose opinion they valued most”). Nonetheless, we were able to address the research question: what types of norms are most predictive of risky drinking outcomes. Future studies may incorporate qualitative methods to describe these most salient referents in greater detail.
Finally, this study is a cross-sectional representation of the associations between college drinking and norms. To better understand how norms may affect college drinking trajectories, it will be important to replicate such findings in longitudinal studies. Despite these limitations, however, the current study was able to introduce new reference groups, a new target behavior, and a different type of injunctive norm. In doing so, we were able to replicate and expand on the existing college norms literature.
Conclusion and Future Directions
This study indicates that descriptive and reflective injunctive norms are most highly, positively correlated with college drinking when they involve referents who are closer to the target college drinkers (i.e., closest friend and person whose opinion you value the most). These findings may contribute to the knowledge base for enhancing normative reeducation and personalized normative feedback interventions to include more personally salient and powerful normative information.
Future studies should include assessment of both injunctive and reflective injunctive norms to better characterize the difference between these two aspects of social approval, and to understand whether one may be more affectively salient than the other. In turn, future studies of personalized normative feedback may include feedback on closer referents to create more powerful interventions that inspire greater and potentially more lasting drinking behavior change for college students. This suggested direction is challenging, because it is much more difficult to gather information on specific referents to assess the normative discrepancy. On the other hand, future studies could explore feedback on reflective injunctive norms without relying on normative discrepancy. It is possible that simply making college drinkers aware of their normative beliefs could build discrepancy (e.g., self-other or ideal current drinking) that could, in turn, influence drinking behavior.
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Submitted: September 14, 2012 Revised: March 11, 2013 Accepted: March 20, 2013
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Source: Psychology of Addictive Behaviors. Vol. 27. (4), Dec, 2013 pp. 1175-1181)
Accession Number: 2013-44431-004
Digital Object Identifier: 10.1037/a0032828
Record: 26- Title:
- Attentional bias toward suicide-related stimuli predicts suicidal behavior.
- Authors:
- Cha, Christine B.. Department of Psychology, Harvard University, Cambridge, MD, US
Najmi, Sadia. Department of Psychology, Harvard University, Cambridge, MD, US
Park, Jennifer M.. Harvard Medical School, Boston, MD, US
Finn, Christine T.. Harvard Medical School, Boston, MD, US
Nock, Matthew K.. Department of Psychology, Harvard University, Cambridge, MD, US, nock@wjh.harvard.edu - Address:
- Nock, Matthew K., Department of Psychology, Harvard University, 33 Kirkland Street, 1280, Cambridge, MD, US, 02138, nock@wjh.harvard.edu
- Source:
- Journal of Abnormal Psychology, Vol 119(3), Aug, 2010. pp. 616-622.
- NLM Title Abbreviation:
- J Abnorm Psychol
- Page Count:
- 7
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- The Journal of Abnormal and Social Psychology; The Journal of Abnormal Psychology; The Journal of Abnormal Psychology and Social Psychology
- ISSN:
- 0021-843X (Print)
1939-1846 (Electronic) - Language:
- English
- Keywords:
- Stroop task, attentional bias, prediction, suicide, suicidal thoughts
- Abstract:
- [Correction Notice: An erratum for this article was reported in Vol 119(4) of Journal of Abnormal Psychology (see record 2010-23724-010). The description of the Stroop Task is corrected.] A long-standing challenge for scientific and clinical work on suicidal behavior is that people often are motivated to deny or conceal suicidal thoughts. The authors proposed that people considering suicide would possess an objectively measurable attentional bias toward suicide-related stimuli and that this bias would predict future suicidal behavior. Participants were 124 adults presenting to a psychiatric emergency department who were administered a modified emotional Stroop task and followed for 6 months. Suicide attempters showed an attentional bias toward suicide-related words relative to neutral words, and this bias was strongest among those who had made a more recent attempt. Importantly, this suicide-specific attentional bias predicted which people made a suicide attempt over the next 6 months, above and beyond other clinical predictors. Attentional bias toward more general negatively valenced words did not predict any suicide-related outcomes, supporting the specificity of the observed effect. These results suggest that suicide-specific attentional bias can serve as a behavioral marker for suicidal risk, and ultimately improve scientific and clinical work on suicide-related outcomes. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Suicidal Ideation; *Suicide; *Attentional Bias; Attention; Stroop Effect
- Medical Subject Headings (MeSH):
- Adult; Attention; Female; Humans; Logistic Models; Male; Predictive Value of Tests; Stroop Test; Suicide, Attempted; Surveys and Questionnaires
- PsycINFO Classification:
- Behavior Disorders & Antisocial Behavior (3230)
- Population:
- Human
- Age Group:
- Adulthood (18 yrs & older)
- Tests & Measures:
- Self-Injurious Thoughts and Behavior Interview
Beck Scale for Suicidal Ideation - Grant Sponsorship:
- Sponsor: National Institute of Mental Health
Grant Number: R03MH076047
Recipients: No recipient indicated
Sponsor: Norlien Foundation
Recipients: Nock, Matthew K. - Methodology:
- Empirical Study; Quantitative Study
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- Accepted: Mar 18, 2010; Revised: Mar 15, 2010; First Submitted: Dec 7, 2009
- Release Date:
- 20100802
- Correction Date:
- 20101206
- Copyright:
- American Psychological Association. 2010
- Digital Object Identifier:
- http://dx.doi.org/10.1037/a0019710
- PMID:
- 20677851
- Accession Number:
- 2010-15289-018
- Number of Citations in Source:
- 32
- Persistent link to this record (Permalink):
- http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2010-15289-018&site=ehost-live
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- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2010-15289-018&site=ehost-live">Attentional bias toward suicide-related stimuli predicts suicidal behavior.</A>
- Database:
- PsycINFO
Attentional Bias Toward Suicide-Related Stimuli Predicts Suicidal Behavior
By: Christine B. Cha
Department of Psychology, Harvard University
Sadia Najmi
Department of Psychology, Harvard University
Jennifer M. Park
Harvard Medical School
Christine T. Finn
Harvard Medical School
Matthew K. Nock
Department of Psychology, Harvard University;
Acknowledgement: Christine T. Finn is now at the Dartmouth Medical School.
This research was supported by National Institute of Mental Health Grant R03MH076047 and by a grant from the Norlien Foundation awarded to Matthew K. Nock.
Suicide is a leading cause of death in the United States and worldwide (Nock et al., 2008). Mortality data indicate that one person dies by suicide somewhere around the world every 40 s (Krug, Dahlberg, Mercy, Zwi, & Lozano, 2002). The high rate of suicide results in part from the fact that assessment primarily depends on people's explicit self-report, which is unreliable because people often are motivated to deny their suicidal thoughts (Busch, Fawcett, & Jacobs, 2003). Developing more objective and scientifically rigorous ways of determining who is at risk for suicidal behavior is essential for both scientific and clinical work addressing this devastating behavior problem.
The National Institute of Mental Health (NIMH) Strategic Plan lists as one of its primary objectives the identification of biological and behavioral markers associated with mental disorders and clinical behavior problems (NIMH, 2009). Behavioral markers are objectively observable, behavior-based factors that indicate some underlying disease process and can aid in case identification, assessment, and treatment (Biomarkers Definitions Working Group, 2001; NIMH, 2009). Impressive progress has been made toward identifying biological markers associated with mental disorders (e.g., Kemp et al., 2009; Sawa & Cascella, 2009; Su et al., 2009); however, parallel research on behavioral markers has been lacking. Although biomarkers such as genetic mutations or neurotransmitter dysfunction undoubtedly influence the development of different psychological traits (e.g., impulsiveness), they are unlikely to accurately predict specific behavioral outcomes such as suicide attempt.
Recent advances in psychological science have made it possible to objectively measure psychological characteristics that may be associated with specific thoughts, feelings, and behaviors. For instance, Nock and colleagues recently showed that people who engage in nonsuicidal self-injury and suicidal behaviors show an implicit identification with self-injurious behavior on an objective, performance-based test (i.e., faster response when pairing “Me” with “Cutting” vs. “Me” with “Not Cutting”; Nock & Banaji, 2007a, 2007b). Moreover, performance on a death-specific version (i.e., faster response pairing “Me” with “Death”) of this test predicts subsequent suicide attempts beyond common clinical predictors (Nock et al., 2010), suggesting that implicit death- or suicide-specific cognition can serve as a behavioral marker for suicide risk. Additional research aimed at identifying behavioral markers for suicide attempt is needed to improve the ability to better detect and predict suicidal behavior.
Attentional bias, which involves selective allocation of attentional resources toward specific aspects of stimuli, is a cognitive process that may further help to explain and predict suicidal behaviors. Cognitive theories of emotional disorders propose that distinct attentional biases—along with broader cognitive structures influencing all aspects of information processing (i.e., schemas)—increase vulnerability toward particular disorders (Beck, 1976; Beck, Emery, & Greenberg, 2005). Empirical findings can elucidate pathways through which this may occur, and earlier research has suggested that attentional bias toward particular disorder-related stimuli indicates accessibility of the respective disorder-relevant thoughts. For example, studies in which the emotional Stroop task is used (Williams, Mathews, & MacLeod, 1996) have demonstrated that depression is associated with attentional bias toward depressed-content words. Anxiety- (Foa, Feske, Murdock, Kozak, & McCarthy, 1991; McNally, Kaspi, Riemann, & Zeitlin, 1990; Teachman, Smith-Janik, & Saporito, 2007) and substance use-specific (Cox, Fadardi, & Pothos, 2006) Stroop effects have also received empirical support.
More recently, attentional bias has been theorized to play a role in the pathway to suicide. Wenzel and Beck (2008) proposed that that suicide-specific attentional bias—in combination with state hopelessness—leads to a fixation on suicide as the sole escape solution and ultimately to a suicide attempt. Measuring attentional bias would be an important initial step toward testing this theory and indirectly assessing the likelihood of future suicide attempt.
To date, only two studies have examined attentional bias toward suicide-related words. Williams and Broadbent (1986) found that recent suicide attempters took longer to name the color of suicide-related words relative to neutral words compared with control groups. Building on this work, Becker, Strohbach, and Rinck (1999) showed that past-year suicide attempters took significantly longer to name the color of suicide-related words than both neutral and generally negative words, whereas there were no differences in latencies among control participants. This latter finding suggests that suicide attempters attend specifically toward information relevant to suicidal thoughts and behaviors.
Despite initial support for the presence of suicide-specific attentional bias, several key issues remain unaddressed. First, it is not known whether suicide-specific attentional bias is associated with likelihood of future suicide attempt. Cognitive theories of suicide propose this relation (Wenzel & Beck, 2008); however, earlier work has been entirely cross-sectional in nature. Second, prior studies have assessed only bivariate relations between attentional bias and suicide attempt, as well as partial correlations within groups of suicide attempters. As a result, it is not known whether attempters and nonattempters demonstrate different degrees of attentional bias in the presence of other risk factors for suicide (e.g., mood disorder).
The present study was designed to address these limitations and to advance the understanding and prediction of suicidal behavior in two ways. First, we hypothesized that people who had made a suicide attempt would show an attentional bias toward suicide-related words. If present, we expected that this bias would be strongest among those who made the most recent suicide attempts. Second, we hypothesized that this suicide-specific attentional bias would prospectively predict which patients will make a suicide attempt during the next 6 months, above and beyond clinician prediction and known risk factors. In order to determine the specificity of these effects, we examined attentional bias toward both suicide-related and negatively valenced (i.e., unrelated to suicide) stimuli relative to neutral stimuli.
Method Participants
Participants were 124 adults presenting to a psychiatric emergency department (ED). All participants were drawn from a larger sample of 198 adults seeking acute psychiatric care. Of the 198 adults, 143 were administered the modified Stroop task. Fifty-five people did not complete the Stroop task due to various reasons (e.g., initial presence of cognitive impairment, discharge from hospital). Of the 143 who completed the Stroop, 12 were excluded as outliers (described below), and seven were excluded from analyses due to unreliable reports of suicidal behavior at each time point (e.g., repeatedly changing responses as to whether or not he or she has a history of suicide). There were no significant differences between those included versus excluded from the study on sex, race/ethnicity, or types of Axis I diagnoses, χ2s(1, N = 195) = 0.00–3.69, ps = .06–.99, Φs = .00–.14, or degree of Axis I disorder comorbidity, t(193) = 0.8, p = .45, d = 0.11. Those included were slightly younger (M = 34.5, SD = 11.8) than those who were excluded (M = 38.7, SD = 12.5), t(193) = 2.3, p = .02, d = 0.33. Sample size for the present study provides sufficient statistical power (.78–.99, with α = .05, two-tailed tests) to detect medium-large effects, respectively.
Measures
Attentional bias
Attentional bias toward suicide-related and negatively valenced stimuli was measured using a modified Stroop task (Stroop, 1935). This performance-based measure records response latencies of how quickly participants identify the color of different words presented on a computer screen. Larger response latencies were interpreted as representing greater interference due to the semantic content of presented words. In the present study, stimuli for the task were presented and response latencies recorded using Empirisoft DirectRT v2004 software (Jarvis, 2004). Directions presented on the screen at the beginning of the task instructed participants to indicate the color of each presented word as quickly and as accurately as possible. Each trial started with a blank white screen for 3 s followed by the presentation of a centered “+” for 2 s. The “+” was then replaced by the word printed in red or blue, which remained on the screen until a response was recorded. Participants were instructed to indicate the color of the words as quickly and as accurately as possible by pressing the red or blue key on the computer keyboard. They first completed eight practice trials, followed by 48 critical trials. In the critical trials, participants were presented with suicide-related words (suicide, dead, funeral), negatively valenced words (alone, rejected, stupid), and neutral words (museum, paper, engine). Suicide-related and negatively valenced words were selected on the basis of prior studies using behavioral measures assessing suicide-related constructs (e.g., Nock et al., 2010), as well as on the basis of general relevance to these clinical topics. They did not significantly differ in length, concreteness, emotionality, or frequency of use in the English language, ts(4) = 0.10–0.74, ps = .50–.93, ds = 0.10–0.74. Trials were presented in a new random order to each participant. Interference for suicide-related stimuli (i.e., suicide-specific attentional bias) was calculated by subtracting latencies for neutral words from latencies for suicide-related words. Similarly, interference for negatively valenced stimuli (i.e., attentional bias toward negative content) was calculated by subtracting latencies for neutral words from latencies for negatively valenced words.
Trials with correct responses were included in the analysis. Trials with response latencies ± 2 standard deviations from each participant's mean response latency were eliminated. Additionally, participants (n = 6) for whom the mean response latency was ± 2 standard deviations from the mean response latency for all participants were excluded from analysis, as were participants (n = 6) for whom the error rate was 2 standard deviations above the error rate for all participants. When compared across all participants, the response latencies for suicide-related (M = 788.16 ms), negatively valenced (M = 775.02 ms), and neutral (M = 775.96 ms) words did not significantly differ from one another (ps = .14–.93, ds = 0.03–0.40).
Demographic and psychiatric factors
Information on demographic and psychiatric risk factors was collected from participants' medical records in the ED. Psychiatric risk factors were assessed by categorizing Axis I diagnoses according to overall class of disorders and by calculating the total number of current Axis I diagnoses.
History of suicidal behavior
History of suicide attempt was measured using the Self-Injurious Thoughts and Behavior Interview (SITBI; Nock, Holmberg, Photos, & Michel, 2007), which assesses presence, frequency, and other characteristics of a broad range of self-injurious thoughts and behaviors. These characteristics were assessed over time frames of lifetime, past year, past month, and past week. Baseline history of suicide attempt was defined as the presence of at least one suicide attempt in the participant's life. Recency of suicide attempt was coded using the following values: 0 (never), 1 (lifetime but not in the past year), 2 (past year but not in the past month), 3 (past month but not in the past week), and 4 (past week). History of multiple suicide attempts was also coded (0,1) on the basis of lifetime frequency values of suicide attempt. Nock et al. (2007) reported fair to excellent interrater reliability (κ = 1.0), test–retest reliability over a 6-month period (κ = .80), and construct validity (κ = .65) of the SITBI Suicide Attempt subscale. The SITBI was conducted in person at baseline and over the phone at follow-up. To improve detection of follow-up suicide attempts, medical records were reviewed for documentation of whether a participant had returned to the same hospital due to a suicide attempt within 6 months of the baseline assessment. Reports of follow-up suicide attempt from the SITBI and from medical records demonstrated a high level of agreement (κ = .75). Finally, severity of suicidal ideation at baseline was assessed using the Beck Scale for Suicidal Ideation, a commonly used self-report measure that has shown to have excellent validity and reliability (Beck & Steer, 1991).
Clinician and patient prediction of future suicide attempt
A brief questionnaire was completed by each participant's primary clinician in the ED (e.g., attending psychiatrist, resident, psychiatry intern, psychology intern). Questionnaire items assessed knowledge of the participant's history of suicide attempt as well as the clinician's prediction of a future suicide attempt within the next 6 months. The latter was measured using the following question: “Based on your clinical judgment and all that you know of this patient, if untreated, what is the likelihood that this patient will make a suicide attempt in the next 6 months? (0–10, with 0 being no likelihood and 10 being very high likelihood).” Patient prediction of future suicide attempt was assessed in the SITBI using the following question: “On this scale of 0 to 4, what is the likelihood that you will make a suicide attempt in the future?”
Procedure
Consistent with standard clinical care at the study site, after initial evaluation by an ED clinical staff member, patients typically remained in the ED for 1–4+ hr while awaiting further evaluation, transfer to another unit, or discharge from the hospital. During this time, a research team member approached patients and explained the study with permission from the attending psychiatrist. All study participants met inclusion criteria: adult status (≥18 years old) and presentation to the ED. Individuals were not recruited for the study if there was presence of any factor impairing their ability to effectively participate (e.g., inability to speak or write English fluently, presence of a gross cognitive impairment, presence of extremely agitated or violent behavior). Eligible participants were asked to provide informed consent and were administered baseline measures and the modified Stroop task in the ED. Participants were then interviewed via phone approximately 6 months following the date of their baseline interview. All procedures were approved by the university and hospital institutional review boards.
Results Participant Characteristics
Lifetime suicide attempters and nonattempters did not differ significantly on age, sex, race/ethnicity, or presence of most current Axis I disorders (see Table 1). There were significantly more cases of mood disorder among suicide attempters than nonattempters. As a result, we statistically controlled for the presence of mood disorder in all subsequent analyses.
Characteristics of the Sample
Attentional Bias and Suicide Attempts
Our first hypothesis was that patients with a history of suicide attempt would show an attentional bias toward suicide-related stimuli but not toward negatively valenced stimuli relative to psychiatrically distressed control participants. Consistent with this prediction, independent sample t tests revealed that interference for suicide-related words was significantly greater among suicide attempters than nonattempters, t(122) = 2.37, p = .02, d = 0.43, but no group differences in interference for negatively valenced words, t(122) = 0.57, p = .57, d = 0.10 (see Figure 1). Results were unchanged after statistically controlling for the presence of a mood disorder: Interference for suicide-related words was significantly related to suicide attempt (OR = 1.01, CI = 1.00, 1.01, p = .02), whereas interference for negatively valenced words was not (OR = 1.00, CI = 0.99, 1.01, p = .46). These results indicate that for each 1-ms increase in Stroop performance, there is a 1% increase in the odds of a suicide attempt. Notably, Stroop response latencies for neutral words did not significantly differ between suicide attempters and nonattempters at baseline, t(122) = 0.19, p = .85, d = 0.03, or follow-up, t(58) = 0.34, p = .73, d = 0.09.
Figure 1. Error bars represent standard error of the mean. * p < .05.
We also hypothesized that attentional bias toward suicide-related stimuli would be significantly associated with recency of suicide attempt, even after controlling for relevant clinical predictors (i.e., mood disorder). Multinomial regression analyses revealed that interference for suicide-related words was related to recency of suicide attempt (R2 = .14), Model χ2(8, N = 124) = 16.68, p = .03. Specifically, interference for suicide-related words was associated only with suicide attempt occurring within the past week (vs. no history of suicide attempt; OR = 1.01, CI = 1.00, 1.01, p = .03), but not in the past month, past year, or in one's lifetime beyond the most recent year (ps = .06–.58). Attentional bias toward negatively valenced words was unrelated to recency of suicide attempt (R2 = .09), Model χ2(8, N = 124) = 11.28, p = .19.
Attentional Bias as a Behavioral Marker for Future Suicide Attempt
Our final hypothesis was that attentional bias toward suicide-related stimuli would prospectively predict suicide attempt above and beyond common clinical predictors. These results are based on the 60 participants who completed the 6-month follow-up assessment, 10 of whom reported attempting suicide during the 6-month period. The 60 follow-up participants were demographically and clinically similar to baseline-only participants, except that there were significantly fewer cases of alcohol use disorder in the follow-up sample, χ2(1, N = 124) = 7.38, p = .01, Φ = −0.24. Baseline (55%) and follow-up (60%) suicide attempters did not significantly differ in history of multiple attempts as measured at baseline, χ2(1, N = 124) = 0.09, p = .77, Φ = −0.04.
Most importantly, attentional bias toward suicide-related stimuli measured at baseline added incrementally to the prediction of suicide attempts during the follow-up period, even after controlling for commonly used clinical predictors, including history of mood disorder, history of multiple suicide attempt, severity of suicidal thoughts, and both patient and clinician prediction of a future suicide attempt (see Table 2). Attentional bias toward negatively valenced stimuli did not predict follow-up suicide attempt status.
Hierarchical Logistic Regression Predicting Suicide Attempt During the 6-Month Follow-Up Period (N = 60)
DiscussionOne of the greatest barriers to studying suicidal thoughts and behaviors has been the reliance on self-report to assess these constructs. We attempted to overcome this challenge by examining whether suicide attempters show a specific attentional bias toward suicide-related stimuli, and whether this bias can predict subsequent suicidal behavior. Results of this study support our primary hypotheses by showing that suicide-specific attentional bias was related to history and recency of past attempts and, most importantly, that it predicted future suicide attempt above and beyond common clinical predictors. Suicide-specific attentional bias was indeed more strongly associated with suicide attempt than negatively valenced attentional bias, given that the latter was not related to any suicide-related outcome. Consistent with prior studies (e.g., Becker et al., 1999), even the bivariate relation between suicide attempt history and attentional bias to negatively valenced words was not significant. This is likely due to the fact that nonattempters in the present study were patients presenting to the psychiatric emergency center who also experienced a substantial degree of distress, albeit not directly from suicide attempt, and therefore demonstrated similar levels of attentional bias to negatively valenced stimuli as did suicide attempters.
These results provide the first evidence that a suicide-specific attentional bias can serve as a behavioral marker for subsequent suicide attempt. Past studies assessing suicide-specific (Becker et al., 1999; Williams & Broadbent, 1986) and general (e.g., Harkavy-Friedman et al., 2006; Keilp, Gorlyn, Oquendo, Burke, & Mann, 2008; Malloy-Diniz, Neves, Abrantes, Fuentes, & Corrêa, 2009) attentional bias among suicide attempters have been cross-sectional in nature. In contrast, the present prospective design showed that this attentional bias is not only associated with but also precedes suicide attempt. This finding is unlikely due to the baseline association between suicide-specific attentional bias and lifetime history of suicide attempts, because (a) the strength of this attentional bias seemed to vary as a function of how recently a patient had attempted suicide (i.e., likely not stable over time), and (b) it predicted future attempt controlling for baseline history, as discussed below. These findings support Wenzel and Beck's (2008) cognitive theory of suicide and suggest that suicide-specific attentional bias possibly accelerates the likelihood of suicide attempt, and that it indeed precedes this outcome. Future work is encouraged to test other aspects of this theory by prospectively examining the effects of suicide-specific attentional bias in the context of hopelessness and by assessing the potential mediating role of attentional fixation.
The present study also revealed that suicide-specific attentional bias is a behavioral marker of suicide attempt adding predictive value in two ways. First, suicide-specific attentional bias predicts future suicide attempt above and beyond known risk factors, namely, history of mood disorder and suicide attempt, severity of prior suicidal thoughts, and patient and clinician prediction of future suicide attempts. This finding builds on prior work showing bivariate relations between Stroop performance and suicide attempt (e.g., Becker et al., 1999; Keilp et al., 2008; Malloy-Diniz et al., 2009). Statistically controlling for mood disorder rather than sampling for mood disorder (e.g., Malloy-Diniz et al., 2009) allowed us to assess attentional bias in relation to suicide attempt in a more representative clinical sample. These findings thereby provide a more complete understanding of risk factors for suicide attempt.
Second, the finding that suicide-specific attentional bias predicts future suicide attempt above and beyond clinicians' predictions is especially noteworthy and underscores the value of using objective behavioral measures to predict future behavior. Previous research suggests that an actuarial (i.e., statistical) approach toward predicting human behavior may be just as good, if not more accurate, than clinicians' predictions (Dawes, 1996). In the case of predicting suicide attempt, an additional challenge is that the information based on which the clinician must predict a patient's outcome may be deliberately misleading because suicidal patients may be motivated to conceal such intentions to avoid unwanted treatment (Beck & Steer, 1989; Pierce, 1977). Although we did not directly test the clinical utility of the “Suicide Stroop task” in the present study, it is an important first step toward developing objective tools that can aid in clinical decision making regarding suicide risk assessment.
These findings should be interpreted in light of several limitations. First, many participants were excluded due to the Stroop scoring criteria, and those included in this study were younger than those excluded. Results based on Stroop performance may therefore be best generalized to younger adult samples. Second, the sample size was relatively small, and a number of cases were lost at follow-up. Future studies replicating these effects should include large and clinically diverse samples at both baseline and follow-up. Third, our assessment was somewhat narrow in the present study and did not test whether the identified behavioral marker provides better prediction of suicide attempts than other bio- or behavioral markers. The development of methods for collecting and combining such data represent one of the most important directions for future research in this area. Ultimately, the most accurate understanding and prediction of suicidal behavior will emerge from a synthesis of data from behavioral, biological, and other sources. Despite these limitations, this study represents an important step toward improving the understanding and prediction of suicide attempts. With further empirical support, behavioral markers have the potential to aid scientists and clinicians in assessing suicidal patients and ultimately intervening to prevent future suicide attempts.
Footnotes 1 We acknowledge that the term cognitive marker also is appropriate here, but we use behavioral marker because we operationalized cognitive factors using objective, behavioral measures.
2 Stroop interference is referred to as attentional bias to maintain consistency with previous suicide Stroop studies and relevant theories. Some suggest the Stroop task may capture other cognitive processes (e.g., response bias; MacLeod, Mathews, & Tata, 1986). We acknowledge this possibility and encourage future research to tease apart the distinction between attentional and response biases in relation to suicide attempt (e.g., via visual dot probe task).
3 Results were identical when controlling for overall history of suicide attempt (i.e., replacing history of multiple attempts), such that interference for suicide-related (R2 = 0.37, b = 0.02, SE = 0.01, Wald = 5.21, OR = 1.02, CI = 1.00, 1.03, p = .02) but not for negatively valenced words (R2 = 0.19, b = 0.00, SE = 0.01, Wald = 0.03, OR = 1.00, CI = 0.99, 1.01, p = .86). However, we report multiple attempts in the final model given that this is a more rigorous test.
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Submitted: December 7, 2009 Revised: March 15, 2010 Accepted: March 18, 2010
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Source: Journal of Abnormal Psychology. Vol. 119. (3), Aug, 2010 pp. 616-622)
Accession Number: 2010-15289-018
Digital Object Identifier: 10.1037/a0019710
Record: 27- Title:
- Baseline reaction time predicts 12-month smoking cessation outcome in formerly depressed smokers.
- Authors:
- Kassel, Jon D.. Psychology Department, University of Illinois-Chicago, Chicago, IL, US, jkassel@uic.edu
Yates, Marisa. Psychology Department, University of Illinois-Chicago, Chicago, IL, US
Brown, Richard A.. Butler Hospital, Providence, RI, US - Address:
- Kassel, Jon D., Psychology Department, University of Illinois at Chicago, 1007 West Harrison Street, Behavioral Sciences Building, Room 1009 (MC 285), Chicago, IL, US, 40506-0044, jkassel@uic.edu
- Source:
- Psychology of Addictive Behaviors, Vol 21(3), Sep, 2007. pp. 415-419.
- NLM Title Abbreviation:
- Psychol Addict Behav
- Page Count:
- 5
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- Bulletin of the Society of Psychologists in Addictive Behaviors; Bulletin of the Society of Psychologists in Substance Abuse
- Other Publishers:
- US : Educational Publishing Foundation
Society of Psychologists in Addictive Behaviors - ISSN:
- 0893-164X (Print)
1939-1501 (Electronic) - Language:
- English
- Keywords:
- smoking, depression, reaction time, neurocognitive deficits, nicotine dependence, smoking cessation
- Abstract:
- Burgeoning evidence points to a positive association between cigarette smoking and depression. Moreover, depressive symptomatology, whether historical, current, or subsyndromal, appears to negatively influence smoking cessation efforts. Whereas depression is typically assessed via clinical interview or self-report, rarely are the known neurocognitive deficits linked to depression (e.g., global slowing) assessed in the context of smoking cessation research. Hence, this study examined whether simple reaction time--color naming of affectively neutral words--is predictive of 12-month smoking cessation outcome among a sample of formerly depressed smokers (N = 28). Results revealed a significant, positive correlation between reaction time and depressive symptoms such that those who exhibited slower reaction times were at heightened risk to relapse. Baseline depressive symptoms, as assessed via self-report, neither correlated with nor predicted smoking cessation outcome. Results from logistic regression analyses further showed that reaction time added incremental variance to the prediction of smoking cessation outcome. Therefore, simple reaction time may capture aspects of depression not typically assessed in self-report questionnaires. These results are discussed in terms of their theoretical and clinical implications for smoking cessation research. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Major Depression; *Neurocognition; *Reaction Time; *Smoking Cessation; *Tobacco Smoking; Nicotine
- Medical Subject Headings (MeSH):
- Adult; Affect; Cognitive Therapy; Color Perception; Decision Making; Depressive Disorder; Female; Humans; Male; Neuropsychological Tests; Outcome Assessment (Health Care); Prognosis; Reaction Time; Semantics; Smoking Cessation; Tobacco Use Disorder; Verbal Behavior
- PsycINFO Classification:
- Drug & Alcohol Rehabilitation (3383)
- Population:
- Human
Male
Female - Age Group:
- Adulthood (18 yrs & older)
- Tests & Measures:
- Beck Depression Inventory DOI: 10.1037/t00741-000
Fagerstrom Test for Nicotine Dependence - Grant Sponsorship:
- Sponsor: National Institute on Drug Abuse
Grant Number: DA08511
Recipients: Brown, Richard A. - Methodology:
- Empirical Study; Quantitative Study
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- Accepted: Nov 8, 2006; Revised: Nov 7, 2006; First Submitted: May 31, 2006
- Release Date:
- 20070917
- Copyright:
- American Psychological Association. 2007
- Digital Object Identifier:
- http://dx.doi.org/10.1037/0893-164X.21.3.415
- PMID:
- 17874893
- Accession Number:
- 2007-13102-016
- Number of Citations in Source:
- 37
- Persistent link to this record (Permalink):
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- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2007-13102-016&site=ehost-live">Baseline reaction time predicts 12-month smoking cessation outcome in formerly depressed smokers.</A>
- Database:
- PsycINFO
Baseline Reaction Time Predicts 12-Month Smoking Cessation Outcome in Formerly Depressed Smokers
By: Jon D. Kassel
Department of Psychology, University of Illinois at Chicago;
Marisa Yates
Department of Psychology, University of Illinois at Chicago
Richard A. Brown
Butler Hospital, Providence, Rhode Island;
Department of Psychiatry and Human Behavior, Brown Medical School
Acknowledgement: This study was partially supported by National Institute on Drug Abuse Grant DA08511 to Richard A. Brown. We gratefully acknowledge Michelle Ricci for her able assistance in software programming. Portions of this article were presented at the fifth annual meeting of the Society for Research on Nicotine and Tobacco, San Diego, California, March, 1999.
The pathways to becoming a smoker are, no doubt, complex (e.g., Jamner et al., 2003; Kassel, Weinstein, Skitch, Veilleux, & Mermelstein, 2005). However, of the numerous factors believed to heighten vulnerability to smoking initiation, development of nicotine dependence, and smoking relapse, the role played by various forms of psychopathology and emotional distress appears particularly critical (Kassel, Stroud, & Paronis, 2003). That is, numerous studies have reliably found high smoking rates among selected populations of individuals with mental illness. For example, drawing upon a large, nationally representative sample in the United States, Lasser et al. (2000) found that individuals with a lifetime history of any psychiatric disorder had higher rates of lifetime and current smoking compared to individuals who had never suffered from mental illness. Indeed, other investigations have reported similar findings, demonstrating strong and reliable associations between psychiatric disorders—particularly depression (Kassel & Hankin, 2006)—and cigarette smoking among adults (Breslau, Kilbey, & Andreski, 1991; Covey, Glassman, & Stetner, 1998; Degenhardt & Hall, 2001).
Unfortunately, relapse is the modal outcome among those attempting to quit smoking (Piasecki, 2006). Whereas the best available treatments yield 1-year abstinence rates approaching 30–35%, even among smokers who successfully quit for a full year, as many as 40% eventually return to regular smoking (U.S. Department of Health and Human Services, 1990). Smokers who attempt to quit on their own fare even less well, with relapse rates ranging from 90% to 97% (Cohen et al., 1989; Hughes et al., 1992). Negative affect and symptoms of depression appear to be strongly implicated in these high relapse rates.
Identification of the extent to which depression predisposes to smoking relapse has emerged as a critical area of research inquiry in recent years. Indeed, a burgeoning research literature points to both strong between- and within-person associations between depressive symptoms and smoking relapse (see Kassel & Hankin, 2006; Kassel et al., 2003). The presence of clinically significant levels of negative affect and depressive symptoms is frequently predictive of relapse (Glassman et al., 1990; Hall, Munoz, Reus, & Sees, 1993). For instance, one study reported that the likelihood of quitting smoking was 40% lower among depressed smokers compared with nondepressed smokers (Anda et al., 1990). Glassman et al. (1990) found a quit rate of 14% for those meeting criteria for major depression, whereas 31% of participants with no psychiatric diagnosis successfully quit. Moreover, even in the absence of current symptomatology, history of depression may serve to heighten risk for both relapse (Covey, 1999; Glassman et al., 1993; though see Hitsman, Borrelli, McChargue, Spring, & Niaura, 2003) and recurrence of depressive symptomatology subsequent to cessation (Covey, Glassman, & Stetner, 1997). Indeed, history of recurrent major depression (i.e., having two or more past episodes) appears to be a particularly robust predictor of poor smoking cessation outcome (Brown et al., 2001; (Haas, Munoz, Humfleet, Reus, & Hall, 2004). Niaura et al. (2001) demonstrated that even low (subclinical) levels of depressive symptoms assessed at baseline among smokers enrolled in a cessation program were predictive of time to first cigarette smoked after attempted quitting.
One potential limitation of the rather extensive database examining the link between depression and smoking relapse is that depression (be it at the clinical or subclinical level) has been primarily assessed via clinical interview or self-report. There are clearly problems inherent in sole reliance on these types of measurement strategies. Although self-report measures are simple, inexpensive, and easy to use, they are susceptible to biases, social expectations, and attributions, and can ultimately measure only one's conscious experience of depressive symptomology. Correspondingly, a large literature demonstrates that depression—even depressive symptoms that fall short of clinical diagnosis—is reliably associated with a host of neurocognitive deficits (e.g., Porter, 2003; White, Myerson, & Hale, 1997). For example, Pardo, Pardo, Humes, and Posner (2006) recently conducted a study assessing depressed and nondepressed individuals' performance on tasks measuring phasic alerting and covert orientation of visuospatial attention. Their findings suggest that global slowing is a major cognitive deficit in depression. In fact, such findings support the results of a meta-analysis demonstrating that task-independent, generalized global slowing accounts for the apparent specific cognitive deficits found in unipolar depression (White et al., 1997).
Global slowing may be best viewed as a manifest difference in speeded trials between two comparison groups (Pardo et al., 2006). That is, global slowing represents a very real deficit in processing speed among individuals, revealed by comparison with an appropriate reference group. However, one should not conceive of global slowing as a phenomenon necessarily related to specific disorders (e.g., depression) or groups of people (e.g., older adults). Rather, global slowing has been observed across diverse samples, including sleep-deprived individuals, those who abuse central nervous system depressants (e.g., barbiturates and alcohol), and individuals with other psychiatric disorders, such as schizophrenia (Ryan, Russo, & Greeley, 1996).
Whereas psychomotor and cognitive slowing of the kind reported above are widely recognized as critical components of the depression syndrome, rarely are such cognitive measures utilized in the context of smoking cessation research. Because there is also reason to believe that, in recent years, the remaining population of smokers is hardening (i.e., becoming more nicotine dependent or exhibiting more comorbid psychopathology, including depression; Warner & Burns, 2003), it is incumbent upon tobacco researchers and clinicians alike to further elucidate the admittedly complex association between depressive symptomatology and smoking cessation. Toward this end, we prospectively examined the relationship between reaction time—an index of processing speed—and smoking cessation outcome in a group of formerly depressed smokers. Specifically, we were interested in ascertaining whether a simple measure of reaction time would correlate with a self-report measure of depression and add to the predictive utility of a self-report measure of depression in predicting long-term smoking cessation outcome.
Method Participants
Participants in the present study were recruited from the community and were already participating in a large treatment outcome study (N = 179) comparing standard smoking cessation treatment with treatment incorporating cognitive–behavioral therapy for depression (see Brown et al., 2001 for details). For the present study, 28 randomly selected participants agreed to take part in an investigation examining “cognitive factors related to smoking cessation success.” These smokers (mean age = 48.8 years, 58% female) had smoked an average of 29 years (SD = 10.4), smoked a mean of 28 cigarettes a day (SD = 14.3), had a mean Fagerstrom Test for Nicotine Dependence (FTND; Heatherton, Kozlowski, Frecker, & Fagerstrom, 1991) score of 7.4 (SD = 1.6), reported an average of 7.7 previous smoking cessation attempts (SD = 11.1), and presented with a mean Beck Depression Inventory (BDI; Beck, Ward, Mendelson, Mock, & Erbaugh, 1961) score of 8.7 (SD = 5.7). As assessed with the Structured Clinical Interview for DSM-III-R-Non-Patient Edition (SCID-NP; Spitzer, Williams, Gibbon, & First, 1990), all participants met diagnostic criteria for past, but not present, history of major depression.
Procedures
Participants were administered an emotional Stroop task (see Williams, Mathews, & MacLeod, 1996) prior to quitting smoking (T1) and immediately after their target quit date (T2). The emotional Stroop task assesses verbal response latency to colored words presented on a computer screen. Words either were of neutral semantic content (e.g., pencil, table, and carpet) or were emotionally laden target words (e.g., depression-related words, such as sad, dejected, and worried; anxiety-related words, such as nervous, worried, and anxious; anger-related words, such as angry, mad, and hostile; and smoking-related words, such as cigarette, smoke, and ashtray). For the purposes of the present analyses, we considered only subjects' response times (RTs; time to name out loud the color of presented words) to neutral, nonemotionally laden words at T1. As such, the assessed RTs served as a rather pure index of processing speed, unencumbered by potential emotional interference effects or by nicotine withdrawal (evident at T2).
After brief instruction and a practice trial conducted during the T1 visit, participants were presented a total of 120 (20 neutral) interspersed words, displayed one at a time on a video graphics array monitor in red, green, yellow, or blue. Their task was to indicate the color in which respective words appeared by saying the color of the word (thereby ignoring the semantic content of the stimulus) out loud as quickly as they could. Response latency was recorded via a microphone linked to a Macintosh software program designed to detect and record verbal latency to the nearest millisecond.
Thus, our primary dependent variable was 7-day point-prevalence abstinence rate (Hughes et al., 2003) assessed at 12 months posttreatment. The intent-to-treat principle was used, as was biochemical verification (expired air carbon monoxide and salivary cotinine), in order to confirm 12-month smoking status. Hence, participants were categorized as either abstinent or nonabstinent at 12 months posttreatment. Predictor variables included RT to name the color of only the affectively neutral words, nicotine dependence (FTND), and baseline (T1) depressive symptoms (BDI).
ResultsEven though we are interested in only the reaction time to the emotionally neutral words for the purposes of the present article, to place the RT measure in broader context, Figure 1 displays reaction times to all word categories at both T1 and T2. Initial correlational analyses are in Table 1. As anticipated, RT was significantly and positively correlated with depressive symptomatology at Visit 1 (p < .01); slower response times were associated with higher BDI scores. Moreover, RT was significantly correlated with 12-month point-prevalence abstinence rates (p < .01), such that slower response times to the naming of the color of neutral words were associated with a greater likelihood of nonabstinent smoking status. To explore the possibility that the observed relationship between RT and smoking outcome was simply attributable to the significant correlation between depressive symptoms and RT, we conducted a partial correlational analysis, in which we assessed the relationship between RT and 12-month outcome, while controlling for T1 BDI scores. The correlation remained significantly negative (pr = −.41, p < .04).
Figure 1. Reaction time displayed by visit (Visit 1 = prequit, Visit 2 = postquit) and word category. Error bars represent standard error of the mean. Dep = depression; Anx = anxiety; Smok = smoking.
Correlation Matrix of Variables
Depressive symptoms at both T1 and the 12-month follow-up were modestly, though nonsignificantly, associated with smoking outcome in the anticipated direction (both ps < .14); higher depressive symptoms were associated with smoking outcomes of nonabstinence. Of note, nicotine dependence (FTND) was not significantly correlated with 12-month smoking status (p = .11).
Outcome data revealed that of the 28 participants, 18 were nonabstinent at 12 months posttreatment, yielding a success rate of 36%. Next, we conducted an independent sample t test, in order to see whether 12-month smoking status was influenced by RTs assessed at Visit 1. Consistent with the correlational analysis, results revealed that those who were smoking (nonabstinent) at 12 months evidenced significantly higher RTs (M = 818.83 ms, SD = 120.91) relative to those who were abstinent (M = 692.54 ms, SD = 92.96), t(26) = 2.86, p = .008 (seeFigure 2).
Figure 2. Reaction time displayed by 12-month smoking cessation outcome (abstainers vs. nonabstainers). Error bars represent standard error of the mean.
Finally, to derive a better sense of the predictive utility of the RT measure (and based on the conceptual and statistical associations between RT and depressive symptoms), we conducted a stepwise binary logistic regression analysis, in which baseline depression (BDI–T1) served as the predictor variable in the first block, followed by RT entered in the next block; 12-month smoking cessation outcome was the dependent variable. Whereas analyses of the model with BDI alone revealed reasonable goodness-of-fit, Hosmer and Lemeshow test, χ2(6, N = 28) = 4.51, p = .61, the omnibus test of model coefficients was only marginally significant, χ2(1, N = 28) = 2.90, p = .09 (Nagelkerke R2 = .14). Further, the odds ratio yielded by the BDI was 0.86, with an overall classification rate of 64% of participants correctly classified as either abstinent (4/10, 40%) or nonabstinent (14/18, 78%). Inclusion of RT in the model also yielded an adequate goodness-of-fit index, Hosmer and Lemeshow test, χ2(7, N = 28) = 10.90, p = .14. However, with the addition of RT in the model, the omnibus test reached significance, χ2(2, N = 28) = 8.51, p < .02 (Nagelkerke R2 = .36). The overall classification rate reached 75%, with 83% of nonabstinent (15/18) and 60% of abstinent (6/10) individuals correctly classified (odds ratios of .96 and .97 for the BDI and RT measures, respectively). Moreover, univariate tests showed that whereas RT was a significant predictor of smoking cessation outcome (Wald = 3.89, p < .05), the BDI was not (Wald = 0.12, p > .70).
DiscussionIt is well established that there are strong and reliable associations between depression and smoking (Kassel & Hankin, 2006). Moreover, this relationship appears particularly strong in the context of smoking relapse; those smokers who are currently depressed, or even symptom-free but present with a history of depression, may be at greater risk for relapse subsequent to cessation (Wilhelm, Wedgwood, Niven, & Kay-Lambkin, 2006). Acknowledging that these data are based on measures of depressive symptomatology almost entirely derived from clinical interview and self-report, we hoped to improve on this situation by incorporating a measure of simple reaction time in the current study. The rationale here was that neurocognitive slowing has emerged as a potent marker of depressive symptomatology in and of itself (e.g., Pardo et al., 2006; White et al., 1997) and hence could potentially add discriminant validity to the prediction of smoking cessation outcomes.
As anticipated, we found that RT was positively correlated with depressive symptomatology, as measured by the BDI. Thus, both measures appear to be tapping shared aspects of depression. More important, however, RT was found to independently predict 12-month smoking cessation outcomes. Furthermore, this finding was derived from a sample of smokers who were not currently depressed but rather shared a history of at least one major depressive episode. Also of note was the observation that, whereas RT did predict cessation outcome, the BDI did not. Partial correlational analysis revealed that even when statistically controlling for the potential influence of the BDI, the association between RT and smoking status 12 months later held up. Hence, RT appears to be tapping an aspect of depression critical to its influence on smoking cessation. As such, future research needs to explore the mechanisms underlying these links.
Several limitations of the present need to be acknowledged. First, the sample size was relatively small. Thus, the extent to which our findings can be replicated with larger samples needs to be determined. Second, the sample was restricted to smokers with a history of major depressive disorder, none of whom met criteria for current major depression. Therefore, it remains to be seen whether slowed RT serves as a vulnerability factor for relapse among smokers without a history of depression or for smokers who enter treatment while in the throes of a major depressive episode. Correspondingly, it is conceivable that RT may actually have served as a proxy for some broader or related constructs, distinct from depression, for example, distress tolerance (Brown, Lejuez, Kahler, Strong, & Zvolensky, 2005) or task persistence (Brandon et al., 2003). Hence, in the absence of a control group comprising smokers without a history of depression, it becomes difficult to definitively attribute the observed RT differences to neurovegetative aspects of depression. Third, we used only one measure to assess neurocognitive functioning. Whereas it has been argued that depressive symptoms are associated with task-independent cognitive slowing (Pardo et al., 2006), future research should nonetheless further explore the association observed in this study by utilizing different types of cognitive tasks to see if, indeed, more specific cognitive deficits are predictive of cessation outcomes.
Finally, it is important to note that whereas the finding that RT predicts smoking status 12 months later is clearly important and, as far as we are aware, the first of its kind, more work needs to be done with respect to better understanding the underlying mechanisms that govern depression–smoking associations (see Kassel and Hankin, 2006, and Kassel et al., 2003, for a discussion of these issues). The present study demonstrated that a distal predictor influenced outcome many months later, but precisely how this relationship operates at the level of the individual smoker is as yet unknown. Indeed, delineation of within-subject, dynamic processes that contribute to smoking relapse (and relapse to other drugs and addictive behaviors as well) awaits future scrutiny (Shiffman, 2005).
In sum, this study suggests that one particular manifestation of depression—neurocognitive retardation as measured by reaction time on a simple decision task—accurately predicted treatment outcome 1 year later for 75% of our sample. Though these results need to be replicated and extended in future studies, one implication of our findings is that an RT measure could serve as a quick and cost-efficient method by which to assess aspects of depressive symptomatology not tapped by self-report measures. Indeed, the importance of fully understanding the link between smoking relapse and depression cannot be overstated. In recent years, virtually all state-of-the-art smoking cessation programs have come to incorporate some aspect of cognitive–behavioral therapy specifically aimed at reducing depressive symptoms (Wilhelm et al., 2006). Moreover, pharmacotherapy in the form of antidepressant medication has emerged as a first-line, effective approach to treating the smoker who desires to quit (Fiore et al., 2000). Because today's population of smokers is hardening, according to some researchers, and presenting with comorbid psychopathology, such as depression, it is critical that tobacco researchers continue to clarify the relationship between smoking and depression, as future lives are clearly at stake.
Footnotes 1 Thirty consecutive study participants were asked to partake in this supplemental study, of whom 2 declined.
2 Whereas the overall treatment outcome study from which this sample was derived comprised two treatment conditions (see Brown et al., 2001), no main effects for treatment were found in the full sample. Nonetheless, we assessed whether treatment condition influenced any of the findings reported in the present study; in all instances, inclusion of treatment condition did not change the reported findings.
3 During the RT task, errors to the neutral words were exceedingly rare, occurring, on average, less than once per participant. As such, there were no response speed–accuracy trade-off effects for RTs to the neutral words (r = −.05, p > .80).
4 The overall classification rate of 75% should be understood in context such that, even in the absence of any independent variables, the correct classification rate approaches 64%. Hence, RT ultimately allows for the correct prediction of 3 more individuals than would be seen using no predictors at all.
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Submitted: May 31, 2006 Revised: November 7, 2006 Accepted: November 8, 2006
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Source: Psychology of Addictive Behaviors. Vol. 21. (3), Sep, 2007 pp. 415-419)
Accession Number: 2007-13102-016
Digital Object Identifier: 10.1037/0893-164X.21.3.415
Record: 28- Title:
- Behavioral activation for depressed breast cancer patients: The impact of therapeutic compliance and quantity of activities completed on symptom reduction.
- Authors:
- Ryba, Marlena M., ORCID 0000-0001-6963-4034. Department of Psychology, The University of Tennessee– Knoxville, Knoxville, TN, US
Lejuez, C. W.. Department of Psychology, The University of Maryland, College Park, MD, US
Hopko, Derek R.. Department of Psychology, The University of Tennessee–Knoxville, Knoxville, TN, US, dhopko@utk.edu - Address:
- Hopko, Derek R., Department of Psychology, The University of Tennessee– Knoxville, 307 Austin Peay Building, Knoxville, TN, US, 37996-0900, dhopko@utk.edu
- Source:
- Journal of Consulting and Clinical Psychology, Vol 82(2), Apr, 2014. pp. 325-335.
- NLM Title Abbreviation:
- J Consult Clin Psychol
- Page Count:
- 11
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- Journal of Consulting Psychology
- Other Publishers:
- US : American Association for Applied Psychology
US : Dentan Printing Company
US : Science Press Printing Company - ISSN:
- 0022-006X (Print)
1939-2117 (Electronic) - Language:
- English
- Keywords:
- behavioral activation, breast cancer, depression, mediation, process of change, therapeutic compliance, environmental reward
- Abstract:
- Objective: Behavioral activation (BA) is an empirically validated treatment that reduces depression by increasing overt behaviors and exposure to reinforcing environmental contingencies. Although research has identified an inverse correlation between pleasant or rewarding activities and depression, the causal relation between increased structured activities and reduced depression has not directly been studied. Method: In the context of a recent randomized trial (Hopko, Armento, et al., 2011), this study used longitudinal data and growth curve modeling to examine relationships among the quantity of activities completed, proportion of activities completed (i.e., therapeutic compliance), environmental reward, and depression in breast cancer patients treated with BA treatment for depression (n = 23). Results: Therapeutic compliance with assigned activities was causally related to depression reduction, whereas the specific quantity of completed activities was not systematically related. Logistic regression indicated that for patients completing all assigned activities, treatment response and remission were achieved for all patients. Neither therapeutic compliance nor the quantity of completed activities was directly associated with self-reported environmental reward during the BA interval (Session 3 to posttreatment), and environmental reward did not mediate the relation between activation and depression. Conclusions: Patient compliance with BA assignments is causally associated with depression reduction, whereas the quantity of completed activities is less relevant toward conceptualizing positive treatment outcome. Study findings are discussed in the context of behavioral models of depression and BA therapy. (PsycINFO Database Record (c) 2017 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Behavior Therapy; *Breast Neoplasms; *Major Depression; *Treatment Compliance; *Treatment Outcomes; Environment; Rewards
- Medical Subject Headings (MeSH):
- Adult; Aged; Behavior Therapy; Breast Neoplasms; Depressive Disorder; Female; Humans; Middle Aged; Patient Compliance; Reinforcement (Psychology); Treatment Outcome
- PsycINFO Classification:
- Behavior Therapy & Behavior Modification (3312)
- Population:
- Human
- Location:
- US
- Age Group:
- Adulthood (18 yrs & older)
- Tests & Measures:
- Anxiety Disorders Interview Schedule–IV
Beck Depression Inventory–II DOI: 10.1037/t00742-000
Environmental Reward Observation Scale DOI: 10.1037/t51595-000
Hamilton Rating Scale for Depression DOI: 10.1037/t04100-000 - Grant Sponsorship:
- Sponsor: Susan G. Komen for the Cure Research
Grant Number: BCTR0706709
Recipients: Hopko, Derek R. - Methodology:
- Empirical Study; Longitudinal Study; Quantitative Study; Treatment Outcome
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- First Posted: Dec 23, 2013; Accepted: Nov 12, 2013; Revised: Oct 31, 2013; First Submitted: Jun 6, 2013
- Release Date:
- 20131223
- Correction Date:
- 20170323
- Copyright:
- American Psychological Association. 2013
- Digital Object Identifier:
- http://dx.doi.org/10.1037/a0035363
- PMID:
- 24364801
- Accession Number:
- 2013-44755-001
- Number of Citations in Source:
- 50
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- Database:
- PsycINFO
Behavioral Activation for Depressed Breast Cancer Patients: The Impact of Therapeutic Compliance and Quantity of Activities Completed on Symptom Reduction
By: Marlena M. Ryba
Department of Psychology, The University of Tennessee–Knoxville
C. W. Lejuez
Department of Psychology, The University of Maryland, College Park
Derek R. Hopko
Department of Psychology, The University of Tennessee–Knoxville;
Acknowledgement: This research was supported by Susan G. Komen for the Cure Research Grant BCTR0706709 awarded to Derek R. Hopko.
Behavioral activation (BA) is the therapeutic process of increasing overt behaviors to facilitate exposure to reinforcing environmental contingencies and subsequent reductions in depression (Hopko, Lejuez, Ruggiero, & Eifert, 2003). BA has evolved significantly in recent decades (Dimidjian, Barrera, Martell, Munoz, & Lewinsohn, 2011; Hopko, Ryba, McIndoo, & File, in press), and with growing empirical support, BA is now considered an empirically validated intervention and is an appealing treatment option for depression across a range of settings (Cuijpers, van Straten, & Warmerdam, 2007; Ekers, Richards, & Gilbody, 2008; Sturmey, 2009). There is still much to be learned about the process of change in BA, however, and no systematic longitudinal research has explored whether increased activation and environmental reinforcement are in fact central mediators of change. Accordingly, in the context of a recently completed randomized trial of BA and problem-solving therapy for depressed breast cancer patients (Hopko, Armento, et al., 2011), the current study was designed to more clearly explicate the relations among structured BA, environmental reward, and depression.
BA is rooted in behavioral models of depression that implicate decreased response-contingent positive reinforcement (RCPR) for nondepressive behavior as the causal factor in eliciting depression (Ferster, 1973; Lewinsohn, 1974). This reduction in RCPR is attributable to a decrease in the number and range of reinforcing stimuli available to an individual for such behavior, a lack of skill in obtaining reinforcement, and/or an increased frequency of punishment (Lewinsohn, 1974). This view suggests that depressed behavior results from a combination of reinforcement for depressed behavior and a lack of reinforcement or even punishment for more healthy alternative behaviors (Ferster, 1973; Hopko, Lejuez, et al., 2003; Lewinsohn, 1974). As a result, depressed individuals often experience significant behavioral inhibition and avoidance behaviors, the central target of contemporary behavioral treatments for depression: behavioral activation (BA; Martell, Addis, & Jacobson, 2001) and the brief behavioral activation treatment for depression (BATD; Lejuez, Hopko, & Hopko, 2001; revised version, BATD–R; Lejuez, Hopko, Acierno, Daughters, & Pagoto, 2011). Although BA approaches commonly are based on behavioral principles of reinforcement and functional assessment, specific treatment strategies differ across interventions (Kanter et al., 2010), with activity monitoring and activity scheduling being the two constant features of both BA protocols (Addis & Martell, 2004; Lejuez et al., 2001). The BA method (Martell et al., 2001) incorporates strategies of change including identification of avoidance patterns, teaching functional assessment of behavior, guided activity, mental rehearsal, periodic distraction, mindfulness training, rumination-cued activation, and skills training. Alternatively, BATD focuses on functional assessment of depressed behavior, identification of activities based on individualized goals and a life values assessment, and structured systematic activation using a hierarchy of activities. These contemporary versions of BA are considered more idiographic than conventional behavior therapy (Hopko, Lejuez, et al., 2003). Perhaps most important, BA and BATD moved away from the assumption that pleasant activities have reinforcing properties and instead focus on environmental contingencies maintaining depressed behavior, unique value systems, and the targeting of avoidance through an emotional acceptance and behavior change paradigm.
Compelling treatment outcome research suggests BA strategies have broad applicability across a wide range of settings and clinical populations. In one of the more compelling studies, BA was comparable to antidepressant medication and superior to cognitive therapy in treating severe depression (Dimidjian et al., 2006), results that were maintained at 2-year follow-up (Dobson et al., 2008). BA also has been effectively used with depressed patients in various settings and among individuals with coexistent medical problems such as HIV, cancer, brain trauma, and obesity, as well as coexistent psychiatric problems such as anxiety disorders, schizophrenia, and borderline personality disorder (see Hopko et al., in press, for a comprehensive review). Three independent meta-analyses support the efficacy of BA in treating depression (Cuijpers et al., 2007; Ekers et al., 2008; Mazzucchelli, Kane, & Rees, 2009).
BA models of depression attribute affective change to increases in RCPR for healthy behaviors. Several studies support this model, demonstrating a relationship between depressed mood and the frequency of pleasant activities and increased reinforcement (Grosscup & Lewinsohn, 1980; Hopko, Armento, et al., 2003; Lewinsohn & Libet, 1972; Lewinsohn & Shaffer, 1971; Lewinsohn & Shaw, 1969; MacPhillamy & Lewinsohn, 1974). In a study examining the relation of activation and depression using daily diary methods, self-reported depression was inversely related to general activity level as well as the level of self-reported reward or pleasure obtained through engaging in overt behaviors (Hopko, Armento, et al., 2003). Another recent study showed depressed college students engaged less frequently in social, physical, and educational behaviors (Hopko & Mullane, 2008). Although these cross-sectional data are significant, there continues to be a paucity of systematic longitudinal research that adequately demonstrates the process by which therapeutic effects are obtained in BA. The lack of research documenting a causal relationship between exposure to reinforcers and depression reduction was partially due to the lack of available statistical meditational analyses during early stages of BA research. Although three recent studies render support for a relationship between activation and reduced depression via meditational effects of environmental reinforcement (Carvalho & Hopko, 2011; Carvalho, Trent, & Hopko, 2011; Ryba & Hopko, 2012), none of these studies incorporated a sophisticated longitudinal design that allowed examination of a definitive temporal relationship between BA and reduced depressive affect.
A number of theoretical and empirical questions pertaining to BA remain unanswered and warrant continued scientific investigation to allow researchers to better conceptualize and refine behavioral treatments for depression. For example, although BA researchers and theorists implicate activity scheduling (and subsequent exposure to environmental reward) as the primary active component in BA, no systematic research has supported this hypothesis. Indeed, some versions of BA include many treatment components (e.g., skills training, rumination-cued activation, cognitive rehearsal) that raise speculation of whether alternate change mechanisms account for favorable treatment outcomes (Addis & Martell, 2004; Martell et al., 2001). Second, at this stage of BA research, it is largely unclear whether the specific quantity of behaviors or the proportion completed (i.e., treatment compliance) is more essential toward conceptualizing positive treatment outcome. Clarification of the process of change in BA would assist mental health providers and facilitate further BA treatment refinement by identifying components of BA that account for significant outcome variance. To address these issues, this study examined relationships among the quantity of activities completed, proportion of activities completed (i.e., therapeutic compliance), environmental reward, and depression reduction in breast cancer patient treated with eight sessions of BATD. Study hypotheses were as follows:
Hypothesis 1: As BATD progressed, the overall quantity of activities assigned was predicted to increase.
Hypothesis 2: As BATD progressed, treatment compliance (i.e., proportion of activities completed) was predicted to increase.
Hypothesis 3: The proportion of activities completed and reduced depression would be mediated by increased environmental reward.
Hypothesis 4: The quantity of activities completed and reduced depression would be mediated by increased environmental reward.
Hypothesis 5: Individuals completing a greater quantity of activities and greater proportion of assigned activities were predicted to achieve treatment response and remission at posttreatment.
Method Participants
Participants were 23 breast cancer patients with a diagnosis of major depression who were treated at the University of Tennessee Medical Center’s Cancer Institute as part of a randomized clinical trial (Hopko, Armento, et al., 2011). All participants provided informed consent prior to study enrollment. Patients were recruited through clinic screening, clinic brochures, and oncologist referral. Patients interested in study participation completed a pretreatment diagnostic assessment that included the Anxiety Disorders Interview Schedule–IV (ADIS–IV; Brown, Di Nardo, & Barlow, 1994) and self-report instruments outlined in the following section. Advanced doctoral students conducted psychological assessments and were supervised by the principal investigator (DH) in the context of audiotape review and discussion, resulting in a consensus diagnosis. Individuals were eligible to participate if they were older than age 18, had been diagnosed with breast cancer, had a principle (and primary) diagnosis of major depression, and were not psychotic or cognitively impaired. The clinical trial included 80 depressed breast cancer patients, of which 42 were assigned to BATD. For the purposes of this study, only BATD patients who completed and returned all behavioral activation monitoring logs were included (n = 23).
The majority of these patients were White (91.3%); 8.7% were African American. The mean age was 57 years (SD = 11.3), and the average length of education was 15.2 years (SD = 2.2). Marital status was as follows: married (56.5%), single (21.7%), divorced (17.4%), and separated (4.3%). As reported in the randomized trial (Hopko, Armento, et al., 2011), for purposes of generalizability, antidepressant and antianxiety medication usage was not exclusionary. In this sample, 11 patients (48%) were prescribed antidepressant or antianxiety medication, and all had been stabilized at a consistent dosage for 8 weeks prior to study assessment, with no variations in medications or dosages throughout the trial (as reported by patients prior to each BATD session). The average time since breast cancer diagnosis was 2.7 years (SD = 1.9), and average tumor size was 2.57 cm (SD = 0.5). Patients of all cancer stages were included: Stage 0 (lobular carcinoma in situ or ductal carcinoma in situ: n = 7, 30%); Stage 1 (n = 7, 30%); Stage 2 (n = 5, 22%); Stage 3 (n = 3, 14%); and Stage 4 (n = 1, 4%). In terms of cancer treatment, 87% of patients had surgery (i.e., lumpectomy, mastectomy), 64% had chemotherapy, and 70% had radiation treatment. All patients in this sample had successfully completed their respective cancer treatment regimen prior to commencing BATD. Important insofar as assessing representation of the entire BATD sample (n = 42), other than failing to maintain monitoring logs, a series of analyses of variance for continuous variables and chi-square analyses for categorical variables indicated that the study sample (n = 23) and holdout sample (n = 19) did not statistically differ on any demographic, cancer-related, or psychological variables, including treatment response, χ2(1) = 2.29, p = .20, and remission, χ2(1) = 0.02, p = .99, rates following BATD (see Hopko, Armento, et al., 2011), as well as pretreatment depression severity, anxiety severity, social support, environmental reward, or self-reported bodily pain. For pretreatment depression severity, the study sample and holdout group did not differ on either the Beck Depression Inventory–II (BDI–II; Beck, Steer, & Brown, 1996), t(78) = 0.38, p = .71, or the Hamilton Rating Scale for Depression (Hamilton, 1960), t(78) = 0.87, p = .39. The two samples also did not differ as a function of the number of coexistent anxiety disorders, t(78) = 0.75, p = .46.
Assessment Measures
Behavioral monitoring
In BATD, the master activity log is used by the clinician to track weekly patient progress. All activities are listed on the master activity log, including (a) the number of times the patient eventually would like to complete the activity in a 1-week period (i.e., ideal frequency) and (b) the duration of the activity. In the initial session of BATD, fewer activities are monitored, with the number of activities progressively increasing in subsequent weeks as a function of patient success. On the behavioral checkout that is maintained by the patient, the frequency and duration of goals for each week also are listed, and the patient records which behaviors are completed on a daily basis. The patient returns the behavioral checkout to the clinician each week, and the information is transferred to the master activity log. If the patient has achieved (or exceeded) goals, the clinician likely will increase the frequency and/or duration for the following week (assuming the patient has not met the ideal goal). If a behavioral assignment was not completed, the clinician and patient decide if the assignment was reasonable or whether it was excessive or improbable given the patient’s abilities. In the former case, the goal might be the same for the next week or its importance (i.e., consistency with life values) re-evaluated. In the latter case, the clinician and patient might strongly consider reducing the weekly goal, and potentially the ideal goal. The rate at which new activities are added can occur slowly or rapidly across patients and generally is determined based on individual circumstances.
Beck Depression Inventory–II
The BDI–II consists of 21 items rated on a 4-point Likert scale. The BDI–II has excellent reliability and validity in depressed adults (Beck et al., 1996). The psychometric properties of the BDI–II also have been studied in cancer patients and a medical care sample, with strong predictive validity as it pertains to a diagnosis of clinical depression, strong internal consistency (α = .94), and adequate item-total correlations (range = .54–.74; Arnau, Meagher, Norris, & Bramson, 2001; Katz, Kopek, Waldron, Devins, & Thomlinson, 2004; α = .84; range = 14–60; M = 27.0, SD = 8.5, for the present study).
Environmental Reward Observation Scale
The EROS (Armento & Hopko, 2007) is a 10-item measure assessing exposure to environmental rewards deemed essential for increasing response-contingent positive reinforcement (RCPR; Lewinsohn, 1974). RCPR is defined as positive or pleasurable outcomes that follow behaviors; the outcomes may be extrinsic (e.g., social, monetary) or intrinsic (e.g., physiological, feeling of achievement) and increase the likelihood the behaviors will occur in the future. Decreased RCPR is a central predictor of increased depression (Lewinsohn, 1974). Higher scores on the EROS suggest increased environmental reward. Sample items include “The activities I engage in have positive consequences,” and “Lots of activities in my life are pleasurable.” Based on psychometric research with three independent college samples, the EROS has strong internal consistency (α = .85–.86) and excellent test–retest reliability (r = .85) and correlates strongly with other psychometrically sound measures of depression (r = from –.63 to –.69) and anxiety (Armento & Hopko, 2007). In this study, internal consistency was adequate (α = .78; M = 22.7, SD = 4.6).
Behavioral Activation Therapy for Depression (BATD)
BATD focuses on increasing overt behaviors to bring patients into contact with reinforcing environmental contingencies and corresponding improvements in thoughts, mood, and quality of life (Hopko, Lejuez, et al., 2003). Within BATD (Hopko & Lejuez, 2007; Lejuez et al., 2001, 2011), the process of increasing RCPR follows the basic principles of extinction, shaping, fading, and in vivo exposure (Hopko, Lejuez, et al., 2003). Initial sessions involved assessing the function of depressed behavior, establishing patient rapport, motivational exercises focused on the pros and cons of behavioral change, depression and breast cancer psychoeducation, and introduction of the treatment rationale. Within BATD, systematically increased activity is a necessary precursor toward the reduction of overt and covert depressed behavior. Patients began with a self-monitoring (daily diary) exercise to examine already occurring daily activities to provide a baseline measurement and ideas of activities to target during treatment. Patients were asked to keep a daily diary during 4 days of the week and to monitor their primary overt behaviors at half-hour intervals (from 8:00 a.m. to 2:00 a.m.). For each behavior, they also were asked to indicate their level of reward or pleasure on a 4-point Likert scale. Following monitoring, emphasis shifted to identifying values and goals within life areas that included family, social, and intimate relationships; education; employment and career; hobbies/recreation; volunteer work/charity; physical/health issues; spirituality; and anxiety-eliciting situations (Hayes, Strosahl, & Wilson, 1999). An activity hierarchy was then constructed in which 15 activities were rated from “easiest” to “most difficult” to accomplish. Using the master activity log and behavioral checkout to monitor progress, patients progressively moved through the hierarchy, from easier behaviors to the more difficult. The process of assigning behavioral activation goals began in Session 3. Weekly goals were recorded on a behavioral checkout form that the patient returned to therapy each week. At the start of each session, the behavioral checkout was examined and discussed, with the following weekly goals established as a function of patient success or difficulty. Although not outside the scope of BATD, attention to ongoing cancer treatment or cancer survivor issues were not directly addressed, in the former case because all breast cancer patients in this sample were provided psychotherapy following cancer treatment. In total, BATD involved eight sessions of approximately 1 hr in duration.
Therapists and Treatment Integrity
Advanced clinical psychology (doctoral) students served as therapists in this study. All therapists were skilled in the administration of BATD, had been trained by the principal investigator (DH), and had been regularly practicing BATD with patients for a minimum of 2 years prior to the study. To ensure competent provision of BATD, we audiotaped all sessions, and all therapists met for weekly individual supervision meetings with the principal investigator (DH). A total of 15% of tapes were selected randomly for ratings of therapist competence and adherence by an independent evaluator with expertise in behavioral therapy. Ratings were made on Likert scales ranging from 0 (no adherence/competence) to 8 (complete adherence/competence) on a session-by-session basis, with ratings based on adherence and ability in completing session objectives highlighted in the BATD treatment manual. Consistent with the very high ratings previously reported for the entire BATD sample (Hopko, Armento, et al., 2011), ratings of sessions conducted with this patient sample indicated high therapist adherence (M = 7.2; SD = 0.7) and competence (M = 6.9; SD = 1.0) to the BATD protocol.
Procedure
Following recruitment and screening procedures, eligible participants were administered the ADIS–IV and all self-report measures. All psychological assessments and treatment sessions were conducted at the Cancer Institute. Advanced doctoral students in clinical psychology conducted the comprehensive assessments. Patients subsequently engaged in their 8-week (one-on-one) treatment. At the beginning of each session, the BDI–II and EROS were completed to assess depression and environmental reward. For the purposes of this study, the master activity logs and behavioral checkouts were reviewed to assess the number of activities assigned and the proportion of activities completed by each patient.
Statistical Analyses
BA assignments commenced in the third session, meaning that the impact of the quantity and proportion of activities completed as they related to depression and environmental reward began to be assessed at Week 4 of treatment through the posttreatment assessment. Accordingly, the longitudinal data consisted of six observations: Sessions 4 (Data Point 1) through 8 and posttreatment (Data Point 6)]. Growth curve modeling was used to test levels of change in a dependent variable over time and incorporated within-subject and between-subjects predictors. The general model was as follows:
The value of the dependent variable for patient i at time t was equal to a subject-specific intercept plus a subject-specific time slope. Setting t1 = 0, t2 = 1, t3 = 2, and so on; the intercept γ0i is the expected value for patient i at the first observation. If the expectation is that all patients have a similar baseline score that is not dependent on other variables, it can be written as a function of the overall average baseline across all individuals plus a random noise component representing person-specific differences from the mean.
The value of γ1i reflects how rapidly each patient’s score on the dependent variable changes over time. Because each patient has her own growth trajectory, differences in the intercept and slopes are also modeled by individual-specific covariates. For example, it is predicted that depression severity declines more quickly as patients engage in more activities. Thus, we can model γ0i as a function of total activities completed (or proportion completed).
Substituting:
where the coefficient
on the interaction tests the significance of total activities completed on time. Because there is an interaction, a main effect for total activities completed also is included. This is done by adding total activities completed to the model for the intercept.
This yields the final model:
The beta parameters represent fixed effects, or the average slopes and intercept across all patients in the sample. The r parameters represent random effects. They are not estimated as traditional regression coefficients. Rather, they are summarized by their variances as variance components. The larger the variance component, the greater the variability in growth trajectories between patients.
The previous model represents a between-subjects analysis, since it uses the total number of activities completed across time as the primary predictor. The data also contain week-specific values for the number of activities completed and proportion completed. Growth models also allow for the inclusion of time-varying predictors. In this case, the model simplifies to:
Due to perfect multicollinearity between the time-varying activities measure and the total activities completed variable, the within-subject model was tested separately from the between-subjects model. Both were estimated to determine whether there were differences between week-specific activities completed and the total number of activities completed across the assessment period. The models described were all estimated using the MIXED command in SPSS Version 20.
Finally, it is also possible to test for mediation using a growth curve model, although it is more complicated for longitudinal relative to cross-sectional data (Selig & Preacher, 2009). Testing mediation is simplified in within-subject analyses, since the between-subjects model involves an interaction. The process for within-subject data amounts to fitting a simultaneous equations model in which the activities variable is both a predictor of depression severity and an outcome determined by environmental reward. Due to the fact that both equations involve random effects (as they are both growth models with time-varying variables), this part of the estimation was done using Mplus (Version 6.1; Muthén & Muthén, 2007). Standard errors for the indirect effects were estimated using bootstrapping.
The first hypothesis predicted that the number of activities assigned would increase as therapy progressed. For this hypothesis, the number of activities at time point t was the dependent variable in a growth model, and time was the sole predictor. The second hypothesis predicted that general compliance (proportion of activities completed) would increase as therapy progressed. This assertion was tested in the same manner as the first hypothesis, except that proportion of activities completed was the dependent variable. The third hypothesis predicted that depression severity would systematically decrease as the proportion of activities completed increased. In this case, depression was the dependent variable in the growth model. In the between-subjects model, the total proportion of activities completed was the predictor, and its interaction with time was tested to determine if a higher level of compliance resulted in quicker reductions in depression. A within-subject analysis determined if variations in weekly compliance had short-term effects on depression. A mediation analysis assessed whether any observed relationship between compliance and depression was partially or fully accounted for by the relationship between compliance and environmental reward. The third hypothesis also predicted that compliance (proportion of activities completed) would lead to increased environmental reward. Due to the simultaneous equations used to test mediation in Hypothesis 3, this test was integrated into the same model. Specifically, the model included an equation in which environmental reward was a direct consequence of compliance. If the coefficient for the effect of compliance on reward was significant, Hypothesis four would be supported. The fourth hypothesis was similar to the third, except total activities completed, rather than proportion completed, was the dependent variable. The same tests for direct effects and mediation were conducted. The fifth hypothesis predicted that the proportion of activities and quantity of completed activities would significantly impact treatment response and remission. Because each of these dependent variables was measured on a dichotomous scale, logistic regression was used. The independent variables were total proportion of activities completed and total quantity of activities completed. Results are reported as odds ratios, where a one-unit increase in the proportion of activities completed (or quantity of activities completed) is associated with a β increase in the odds of response or remission.
Response and Remission Criteria
Consistent with methods highlighted in previous trials of BA (Dimidjian et al., 2006; Hopko, Armento, et al., 2011), response represented significant symptomatic improvement, whereas remission represented improvement to the point of being asymptomatic within normal range. On the BDI–II, response was defined as at least a 50% reduction from baseline. Remission was defined as scores ≤ 10 on the BDI–II.
ResultsSupporting the first hypothesis and consistent with the progressive framework of BATD, results indicated that the number of assigned activities and number of completed activities significantly increased over time. As illustrated in Figure 1, during the first week of BA assignments (i.e., following Session 3), the average number of assigned activities was 12 (95% confidence interval, or CI, [9.29, 14.62]), nearly doubling to 23.39 (95% CI [17.84, 28.94]) at the conclusion of treatment. Additionally, there was greater variability in the number of assigned activities at the end of treatment. The number of activities assigned was expected to increase weekly by 2.16 (SE = 0.49, p < .01). However, the rate of increase was higher for some individuals and lower for others (σ2 = 4.35, SE = 1.66, p = .01). There was also significant variability in the initial number of assigned activities (σ2 = 29.36, SE = 14.21, p = .04). Patients who initially had fewer assigned activities had more substantial increases throughout treatment. The trend of completed activities over time is highlighted in Figure 2. During the first week of treatment, the average number of completed activities was 12.91 (95% CI [9.90, 15.93]) and 22.56 (95% CI [17.03, 28.10]) in the final week. Patients on average completed an additional 1.9 (SE = 0.51, p < .01) activities each week.
Figure 1. Average number of assigned behaviors. CI = confidence interval.
Figure 2. Average number of completed behaviors. CI = confidence interval.
When examining Hypothesis 2, we found that results were nonsignificant and indicated that the proportion of activities completed did not systematically increase during the course of BATD (B = −0.01, SE = 0.01, p = .36). As illustrated in Figure 3, following the first assignment, the average proportion of activities completed was 1.12 (95% CI [0.95, 1.30]), compared with 1.00 (95% CI [0.85, 1.16]) following the final activation session. The proportion of activities completed was essentially unchanged over time, primarily due to a ceiling effect whereby all patients were largely compliant with behavioral activation assignments.
Figure 3. Proportion of completed behaviors. CI = confidence interval.
Results partially supported Hypotheses 3 and 4. Figure 4 displays depression severity as a function of treatment session. Significant patient improvement was evident across behavioral activation sessions, whereby during the first week, the average BDI–II score was 14.83 (95% CI [11.53, 18.13]), decreasing to 10.04 (95% CI [6.87, 13.21]) by the final week of BATD. Figure 5 also shows a trend for increased environmental reward over time as a function of behavioral activation, with initial environmental reward of 26.13 (95% CI [24.45, 27.81]) increasing to 29.22 (95% CI [27.34, 31.06]) by the final week of treatment. Between-subjects analysis demonstrated that the average proportion of activities completed was significantly associated with decreased depression (B = −13.53, SE = 6.97, p < .05). The interaction between average proportion of completed activities and time was tested to determine if higher levels of compliance led to faster reductions in depression. The interaction was not significant (B = −0.12, SE = 1.11, p = .91). When a similar model was applied with environmental reward as the dependent variable, the average proportion of activities completed did not have a significant main effect (B = 2.19, SE = 3.69, p = .56) or interaction with time (B = 0.33, SE = 0.68, p = .63).
Figure 4. Average depression (Beck Depression Inventory–II; BDI–II) scores. CI = confidence interval.
Figure 5. Average Environmental Reward Observation Scale scores. CI = confidence interval.
Within-subject analysis incorporated week-specific values for proportion of completed activities. The results reinforce findings from the between-subjects model. As the proportion of activities completed in a given week increased, depression severity also decreased for that week (B = −4.04, SE = 1.40, p < .01). This result was observed beyond the general trend of diminishing depression severity, which was also significant (B = −1.24, SE = 0.27, p < .01). Thus, higher levels of compliance with behavioral activation assignments were effective in attenuating depression. As in the between-subjects model, the within-subject model showed that compliance did not have an effect on environmental reward. That is, the proportion of activities completed in a given week was not significantly related to that week’s score on the environmental reward scale (B = 0.94, SE = 0.81, p = .25), although the time trend was significant (B = 0.65, SE = 0.12, p < .01). With no evidence that proportion of completed activities had a significant effect on environmental reward, it was unlikely that the relationship between proportion of completed activities and depression was mediated by environmental reward. A multilevel mediation model confirmed there was no indirect effect (B = .026, SE = 0.037, p = .48).
When the total number of completed activities rather than proportion of completed activities was the primary predictor, quantity of completed activities did not affect depression severity either directly (B = −0.03, SE = 0.05, p = .53) or through its interaction with time trend (B = 0.00, SE = 0.01, p = .70). Only the time trend was significant in the model, with average depression severity decreasing each week (B = −1.48, SE = 0.78, p < .05). Similarly, findings showed a null effect of total completed activities on environmental reward. The main effect of total activities completed was not significant (B = 0.01, SE = 0.02, p = .71), and neither was the interaction with time (B = 0.00, SE = 0.00, p = .89). This was also demonstrated in the within-subject models, with total activities completed in a given week having no effect on that week’s depression score (B = 0.00, SE = 0.06, p = .94) and no effect on the respective week’s environmental reward score (B = −0.02, SE = 0.03, p = .59). With no effect of quantity of completed activities on depression severity or environmental reward, the key components of mediation were absent. Estimating a mediation model in Mplus yielded a nonsignificant within-subject indirect effect of −0.966 (SE = 1.128, p = .39), confirming the lack of mediation.
Logistic regression was used to examine Hypothesis 5, whether treatment response and remission were more likely to occur with a greater proportion of activities completed and greater quantity of activities completed. For treatment response, the average proportion of activities completed was significant (−21.19, SE = 9.33, p = .02). When patients completed all assigned activities, BDI–II treatment response was evident across all patients, exp (B) < .01, (95% CI [.00, .06]). The high pseudo R2 values (Cox & Snell = .44, Nagelkerke = .64) also suggested very strong effect sizes. For depression remission, results were again significant (B = −9.97, SE = 4.95, p = .04). The odds ratio suggested that for those patients who completed all assigned activities, the odds of not achieving remission were essentially zero, exp (B) < .01 (95% CI [0.00, 0.77]). Model fit statistics indicated that the proportion of activities completed was a good predictor of remission outcomes (Cox & Snell = .27, Nagelkerke = .38). No effect was found when considering the predictive power of quantity of completed activities on BDI–II treatment response, B = −0.01, SE = 0.01, p = .55; exp (B) = .99 (95% CI [0.97, 1.02]), or remission, B = .00, SE = 0.01, p = .79; exp (B) = 1, (95% CI [0.98, 1.02]). Thus, relative to quantity of activities completed, treatment compliance was a better predictor of treatment outcome.
DiscussionContemporary BA treatments aim to attenuate depression via increased RCPR. Although specific intervention strategies differ across BA protocols, structured activation assignments are common to all approaches. This study examined longitudinal data to better explicate the process of change in BATD via growth curve modeling and examining relationships among the quantity of activities completed, proportion of activities completed (i.e., therapeutic compliance), environmental reward, and depression reduction. Findings demonstrate that while the average number of assigned and completed activities systematically increased over time, there was no progressive change in therapeutic compliance, with overall compliance being exceptionally high throughout psychotherapy. Extraordinary patient compliance with BATD may be reflective of a number of patient-centered, therapy-related, and social and economic factors, and potentially high therapist competence in assigning and reviewing homework (Jin, Sklar, Oh, & Li, 2008; Weck, Richtberg, Esch, Hofling, & Stangier, 2013). This finding is significant in that it supports the feasibility and tolerability of BATD for patients presenting with complex clinical presentations including a coexistent psychological disorder and medical illness.
When examining the effect of therapeutic compliance and quantity of completed activities on depression, study hypotheses were partially supported. Results indicated that from pre- to posttreatment, depression decreased as patients completed a higher proportion of activities. On a more microanalytical level, results revealed systematic reductions in depression during weeks where therapeutic compliance was highest. Somewhat unexpectedly, there was no significant effect on depression as a function of increased quantity of completed activities. Therefore, results suggest patient adherence to behavioral assignments is more critical in reducing depression relative to simply completing a greater number of activities. In fact, when examining the impact of therapeutic compliance on treatment response and remission, results were highly compelling and emphasize the importance of patient compliance toward positive BATD treatment outcome. Indeed, patient compliance with activation assignments resulted in favorable BATD treatment outcome. In terms of understanding BATD process of change, completing a greater proportion of assigned activities not only presumably increases RCPR but also likely facilitates greater self-efficacy, accomplishment, and mastery within valued life areas, and subsequent reductions in depression.
The finding of a limited relationship between quantity of activities completed and depression reduction is somewhat inconsistent with previous research showing a positive correlation between activity level and elevated mood. Accounting for this finding, it is conceivable that as the quantity of behavioral assignments increases, their significance pertaining to identified life values decreases. Indeed, initial behavioral assignments are not only prescribed according to their level of difficulty but also their relevance toward addressing the most important life values and their significance toward achieving the most principal life goals. Accordingly, a greater breadth of assigned behaviors may involve activities becoming more generic, less salient in terms of being value based and directly related to immediate life goals, and consequently less apt to result in environmental reinforcement and decreased depression. Another possible explanation may be related to the process of activity scheduling that involves assigning activities of progressively increasing difficulty (e.g., they require more time or effort, require underdeveloped skills, are associated with more anxiety, and avoidance motivation is stronger). With increased behavioral assignments, this process may involve inclusion of activities with less potential reinforcement value and possibly greater likelihood of aversive or unpleasant experiences and consequences that might prevent depression attenuation (e.g., lack of success, heightened anxiety). Highly important to acknowledge in this sample of breast cancer survivors, a committed engagement in fewer but highly valued (and possibly less difficult) activities might be the preferred mode of behavioral activation as patients seek to extract meaning in life, often following a very demanding breast cancer treatment regimen.
Analyses of the role of environmental reward revealed unexpected results given previous studies demonstrating the meditating effects of reinforcement on the relationship between behavioral activation and depression (Carvalho, Trent, et al., 2011; Carvalho & Hopko, 2011; Ryba & Hopko, 2012). Current findings yielded no support for either the proportion or quantity of completed activities as significantly related to self-reported environmental reward. However, study limitations should be taken into account when interpreting this finding, particularly the self-report method of assessing reinforcement. Because direct measurement of reinforcement would require direct observations of environmental contingencies across time and is not overly pragmatic, the practice of using self-report strategies to assess environmental reward, pleasure, and reinforcement is the common alternative. As such, environmental reward as measured in the current study may be an inadequate proxy for actual response-contingent positive reinforcement experienced in the natural environment.
Important to highlight, BA and increased environmental reinforcement likely is not the only critical mediator of change in BATD. For example, the sudden gain literature suggests the beneficial effects of BATD are at least partially independent of the activation process itself, with 50% of sudden gains, or large symptom improvements between one treatment interval, occurring prior to the activation process that commences in Session 3 (Hopko, Robertson, & Carvalho, 2009; Hunnicutt-Ferguson, Hoxha, & Gollan, 2012; Kelly, Cyranowski, & Frank, 2007; Tang & DeRubeis, 1999). This means that sudden gains may be partially reflective of developing therapeutic alliance, psychoeducation, environmental modification (i.e., reducing reinforcement for depressed behavior), and structured value assessment that occur in the first two BA sessions. Additionally, it is conceivable that sudden gains partially reflect self-activation in the absence of therapist guidance. In a recent study of sudden gains in depressed patients receiving BA (Hunnicutt-Ferguson et al., 2012) and consistent with previous data (Hopko et al., 2009), 67% of sudden gains occurred in the first few sessions of behavioral activation. In the present study, although increased proportion of activation assignments completed directly mediated depression reduction, it also is true that BDI–II depression severity had already decreased 45% from baseline (i.e., from moderate to mild depression) prior to beginning structured activation in Session 3. This early and significant reduction in depression symptoms prior to activation emphasizes the need to further examine nonspecific therapy factors such as patient motivation for treatment, perceived support, previous therapy experiences, or patient-specific risk/protective factors such as chronicity of depression and comorbid diagnoses (Hunnicutt-Ferguson et al., 2012). The important point here is that in the context of methodological problems with directly measuring environmental reinforcement and frequently large reductions in depression that occur prior to activation, environmental reinforcement may remain highly operative as a mediator in BATD—although other important mediators of change most definitely must be considered.
Although findings are provocative, several important study limitations are noteworthy. First, a larger sample size would have increased power and confidence in study findings. For example, although mediation models have been examined in smaller samples, a larger sample size would have allowed for better assessment of the potential mediating effects of environmental reward (Fritz & MacKinnon, 2007). Second, as this study followed from a randomized clinical trial and was not specifically designed to account for all possible mediators of change, future work should assess the impact of common factors on treatment outcome (e.g., therapeutic alliance, level of therapist reinforcement, patient self-efficacy). Third, it is conceivable that study recruitment strategies yielded a unique cohort of breast cancer patients that on some unmeasured variable may have been distinct from the population of breast cancer patients. Fourth, as discussed, the ideal method of assessing reinforcement via direct observations of environmental contingencies was not feasible, resulting in the use of a self-report measure of environmental reward (EROS). As “environmental reward” is not synonymous with “environmental reinforcement,” no definitive conclusion can be drawn about the relationship of reinforcement with treatment response to BATD. In addition, as the EROS was designed to assess environmental reward within “the past several months” but was administered weekly in this study, this measure characteristic could have complicated data interpretation and possibly contributed to Type II error in examining environmental reward as a mediating variable. Although still not optimal, the Reward Probability Index (RPI; Carvalho, Gawrysiak, et al., 2011) may have been a preferred proxy measure of environmental reinforcement as unlike the EROS, the RPI better assesses the construct of response-contingent positive reinforcement (i.e., number of reinforcers, availability of reinforcers, ability to obtain reinforcement, exposure to aversive events; Lewinsohn, 1974). Fifth, although BATD practitioners in this study had been trained by the senior author and had been educated in the principles and practice of BATD for a minimum of 2 years, they certainly would not be qualified as experts given their status as unlicensed clinical practitioners.
Sixth, daily diary logs were used to track activity assignments and completion. Although patients received careful instruction and reported procedural adherence, it cannot be definitively stated that activities were logged reliably or accurately. Seventh, because only 55% of the original sample of patients treated with BA (Hopko, Armento, et al., 2011) returned all behavioral monitoring logs, this analyzable subsample may not be entirely representative of the entire BA cohort or breast cancer patients in general. Although this subsample and holdout sample (i.e., those not returning monitoring logs) did not statistically differ on all primary psychological, demographic, and cancer-related study variables, had comparable levels of pretreatment depression severity, and had similar response and remission rates to BATD, it is conceivable that between-group differences existed on some unmeasured variable(s). As many anecdotal therapist reports and audiotaped session transcripts indicated, however, a majority of (holdout) BATD patients who did not successfully return behavioral monitoring logs to therapy sessions nevertheless communicated strong adherence to weekly activation assignments. Nonetheless, significant findings cannot be generalized to the holdout sample or population of breast cancer patients with absolute confidence. Eighth, the study examined the quantity and proportion of completed activities but did not differentiate among types of activities (e.g., social, physical, educational). Categorization of activities may have provided a more detailed understanding of whether engagement in certain types of activities was relevant in the process and outcome of BA. Finally, since the study examined process of change among a highly educated and largely White sample of depressed breast cancer patients, further inquiry into the generalizability of study findings to other patient samples is warranted.
In closing, study findings provide novel insight into the process and outcome of BA and have important clinical and research implications. Results highlight the efficacy of BA and suggest therapeutic compliance is a vital component toward increasing the probability of positive treatment outcome. Highly provocative, findings also suggested that “more” behavioral activation as defined by an increased quantity of completed behaviors does not necessarily correspond with improved treatment response. Further research should more systematically examine dose-response relationships associated with BA while also being mindful of whether certain categories or types of activities are more relevant toward conceptualizing treatment outcome. Given the empirical support and practicality of BA, continued investigation of process factors is warranted. Dismantling studies of treatment components of differing BA approaches also may be beneficial toward better isolating strategies most essential to engendering healthy behaviors. Although in the present study medication use was not examined in primary analyses (but used as a covariate) due to findings that medication use was unassociated with treatment response and remission in this sample (Hopko, Armento, et al., 2011), because combined therapy may be associated with improved treatment response in some depressed patients (Hollon et al., 2005), this issue also should be further examined in understanding the process and outcome of BA. In addition, although factors associated with treatment failure in behavior activation have been discussed that include noncompliance with behavioral assignments (Hopko, Magidson, & Lejuez, 2011), strategies most effective in promoting compliance with activation are largely unknown and require further investigation. The uncomplicated and easily disseminated approach of BA has many potential applications in a broad range of clinical settings and by a diverse network of providers (Ekers, Richards, McMillan, Bland, & Gilbody, 2011). The more precisely the process of change is understood, the better equipped researchers and clinicians will be to further refine and deliver the most parsimonious and efficacious form of BA.
Footnotes 1 As reported, the study and holdout samples did not significantly differ on any primary psychological, demographic, and cancer-related variables. Although sophisticated statistical procedures such as multiple imputation for missing data were considered, because of excessive missing data (64%) in the holdout sample on the behavioral monitoring logs on which patients were to record compliance with assigned activities, multiple imputation procedures were not used as doing so would have violated statistical assumptions (Little & Rubin, 2002; Rubin, 1987) and potentially rendered data invalid and non-interpretable.
2 All growth curve modeling and mediation analyses also were conducted using medication status (i.e., not medicated, stabilized on medication) and pretreatment depression severity as covariates. Results remained consistent with those reported.
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Submitted: June 6, 2013 Revised: October 31, 2013 Accepted: November 12, 2013
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Source: Journal of Consulting and Clinical Psychology. Vol. 82. (2), Apr, 2014 pp. 325-335)
Accession Number: 2013-44755-001
Digital Object Identifier: 10.1037/a0035363
Record: 29- Title:
- Between- and within-person associations between negative life events and alcohol outcomes in adolescents with ADHD.
- Authors:
- King, Kevin M.. Department of Psychology, University of Washington, Seattle, WA, US, kingkm@uw.edu
Pedersen, Sarah L.. Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, US
Louie, Kristine T.. Department of Psychology, University of Washington, Seattle, WA, US
Pelham, William E. Jr.. Department of Psychology, Florida International University, FL, US
Molina, Brooke S. G.. Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, US - Address:
- King, Kevin M., Department of Psychology, University of Washington, Box 351525, Seattle, WA, US, 98195, kingkm@uw.edu
- Source:
- Psychology of Addictive Behaviors, Vol 31(6), Sep, 2017. pp. 699-711.
- NLM Title Abbreviation:
- Psychol Addict Behav
- Page Count:
- 13
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- Bulletin of the Society of Psychologists in Addictive Behaviors; Bulletin of the Society of Psychologists in Substance Abuse
- Other Publishers:
- US : Educational Publishing Foundation
Society of Psychologists in Addictive Behaviors - ISSN:
- 0893-164X (Print)
1939-1501 (Electronic) - Language:
- English
- Keywords:
- between-within individual differences, adolescent alcohol involvement, attention-deficit hyperactivity disorder, generalized estimating equations, zero-inflated count models
- Abstract:
- Escalations in alcohol use during adolescence may be linked with exposure to negative life events, but most of this research has focused on between-person associations. Moreover, adolescents with attention-deficit hyperactivity disorder (ADHD) may be an especially vulnerable population, reporting more life events and alcohol involvement and may even be more sensitive to the effects of life events on alcohol outcomes compared with those without ADHD. We tested the between- and within-person effects of the number and perceptions of negative life events on the development of alcohol use outcomes from age 14 to 17 years in 259 adolescents with and without ADHD using generalized estimating equations. Between-person differences in exposure to negative life events across adolescence, but not the perception of those events, were associated with a higher likelihood of alcohol use and drunkenness at age 17 years. Within-person differences in life events were associated with alcohol use above and beyond that predicted by an adolescents’ typical trajectory over time. Parent- and teacher-reported ADHD symptoms were associated with more negative perceptions of life events and with greater alcohol use and drunkenness at age 17 years, but symptoms did not moderate the life event–alcohol association. Interventions should consider the variables that produce vulnerability to life events as well as the immediate impact of life events. That the accumulation of life events, rather than their perceived negativity, was associated with alcohol outcomes indicates that interventions targeting the reduction of negative events, rather than emotional response, may be more protective against alcohol use in adolescence. (PsycINFO Database Record (c) 2017 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Alcohol Drinking Patterns; *Attention Deficit Disorder with Hyperactivity; *Individual Differences; *Life Experiences; *Adolescent Characteristics; Alcohol Intoxication
- PsycINFO Classification:
- Psychological Disorders (3210)
- Population:
- Human
Male
Female - Location:
- US
- Age Group:
- Adolescence (13-17 yrs)
- Tests & Measures:
- Structured Clinical Interview for DSM for Nonpatients
Michigan Alcoholism Screening Test-Short
Health Behavior Questionnaire
Adolescent Perceived Events Scale DOI: 10.1037/t04204-000 - Grant Sponsorship:
- Sponsor: National Institute on Alcohol Abuse and Alcoholism, US
Grant Number: AA011873, AA007453, and AA00202
Recipients: No recipient indicated
Sponsor: Sponsor name not included
Grant Number: DA12414, MH50467, MH12010, ESO5015, AA12342, DA016631, MH065899, KAI-118-S1, DA85553, MH077676, MH069614, MH62946, MH53554, MH069434, IES LO3000665A, IESR324B060045, and NS39087
Recipients: No recipient indicated
Sponsor: National Institute on Alcohol Abuse and Alcoholism, US
Grant Number: K01AA021135
Recipients: Pedersen, Sarah L.
Sponsor: ABMRF/The Foundation for Alcohol Research, US
Recipients: Pedersen, Sarah L. - Methodology:
- Empirical Study; Longitudinal Study; Interview; Quantitative Study
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- First Posted: Jul 13, 2017; Accepted: May 12, 2017; Revised: May 11, 2017; First Submitted: Jan 28, 2017
- Release Date:
- 20170713
- Correction Date:
- 20170911
- Copyright:
- American Psychological Association. 2017
- Digital Object Identifier:
- http://dx.doi.org/10.1037/adb0000295
- PMID:
- 28703610
- Accession Number:
- 2017-30121-001
- Persistent link to this record (Permalink):
- http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2017-30121-001&site=ehost-live
- Cut and Paste:
- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2017-30121-001&site=ehost-live">Between- and within-person associations between negative life events and alcohol outcomes in adolescents with ADHD.</A>
- Database:
- PsycINFO
Between- and Within-Person Associations Between Negative Life Events and Alcohol Outcomes in Adolescents With ADHD
By: Kevin M. King
Department of Psychology, University of Washington;
Sarah L. Pedersen
Department of Psychiatry, University of Pittsburgh
Kristine T. Louie
Department of Psychology, University of Washington
William E. Pelham Jr.
Departments of Psychology and Psychiatry, Florida International University
Brooke S. G. Molina
Departments of Psychiatry, Psychology and Pediatrics, University of Pittsburgh
Acknowledgement: This research was principally supported by grants from the National Institute on Alcohol Abuse and Alcoholism: AA011873, AA007453, and AA00202. Additional support was provided by DA12414, MH50467, MH12010, ESO5015, AA12342, DA016631, MH065899, KAI-118-S1, DA85553, MH077676, MH069614, MH62946, MH53554, MH069434, IES LO3000665A, IESR324B060045, and NS39087. Sarah L. Pedersen was supported on grants from the National Institute on Alcohol Abuse and Alcoholism (K01AA021135) and ABMRF/The Foundation for Alcohol Research.
These hypotheses and analyses have not been presented in any form prior to this article.
Alcohol use disorder is most prevalent between ages 18 and 29 years (Grant et al., 2015), but the development (e.g., the initiation of use and the first appearance of problems) of these disorders begins earlier in adolescence (Meich, Johnston, O’Malley, Bachman, & Schulenberg, 2015). As the average quantity and frequency of alcohol use increases in the general population from adolescence into young adulthood, variability in alcohol use also steadily increases with age, with some adolescents escalating rapidly into heavy drinking and eventually problems (e.g., Hussong, Bauer, & Chassin, 2008), whereas others remain light or moderate drinkers. Only a fraction of those who begin drinking in adolescence eventually develop an alcohol use disorder, with prevalence estimates of the past year of any alcohol use disorder between ages 18 and 29 years at approximately 26% (Grant et al., 2015). As such, understanding what influences the developmental trajectories of alcohol involvement across adolescent development remains a priority of research.
Although a large literature has outlined the pathways by which externalizing behaviors may influence the development of adolescent alcohol involvement (see review by Chassin, Colder, Hussong, & Sher, 2016), emerging evidence suggests that exposure to negative life events may independently shape alcohol use trajectories and presage worsening outcomes (Keyes, Hatzenbuehler, Grant, & Hasin, 2012). Negative life events predict higher levels of alcohol use, alcohol-related problems, and alcohol use disorders among adolescents and young adults (Cerbone & Larison, 2000; King & Chassin, 2008; Wills, Sandy, & Yaeger, 2002). Prospective studies that have focused on change over time in alcohol use suggested that exposure to negative life events was associated with escalating trajectories of alcohol use during adolescence (King, Molina, & Chassin, 2009; Wills, Sandy, Yaeger, Cleary, & Shinar, 2001).
This prior research on the negative life event–alcohol use association focused on between-person associations, in which those who report more negative life events at an earlier time point exhibited, on average, higher levels or greater increases in alcohol use and problems over time. However, most hypotheses about the role of negative life events in alcohol use focus on within-person processes, hypothesizing that alcohol use occurs when an individual is both exposed to negative life events and utilizes maladaptive coping strategies in the face of those negative life events (Chassin et al., 2016; Sher, Grekin, & Williams, 2005). Some individuals are more likely to experience negative life events, either because of contextual or individual factors such as parenting, temperament, or socioeconomic status (King, Molina, & Chassin, 2008). Thus, it is important to disaggregate the between-individual effects of exposure, which may reflect more stable individual differences in the propensity to experience negative life events, from the within-individual effects of the life events themselves, which may better represent the process of stress adaptation as well as other time-varying factors that influence both stress and drinking (Curran & Bauer, 2011).
To date, one study showed that time-varying differences in negative life events were related to time specific increases (i.e., those not accounted for by an adolescent’s average trajectory of use) in alcohol use and binge drinking during adolescence (Aseltine & Gore, 2000), but that study did not explicitly separate the within- and between-person associations of life events with alcohol use (Enders & Tofighi, 2007). Using a latent growth curve modeling framework, a second study showed that family life events were related to between- and within-person differences in alcohol use during adolescence (King et al., 2009). However, more recent methodological studies have suggested that some of the methods of that study, such as binning ordinal measures of alcohol use frequency (McGinley & Curran, 2014) or failing to disaggregate between- from within-person variance as predictors in growth models (Curran, Howard, Bainter, Lane, & McGinley, 2014), may have inflated the time-varying associations in those models. Finally, neither prior study accounted for the heavily skewed and zero-inflated nature of adolescent alcohol use (Atkins, Baldwin, Zheng, Gallop, & Neighbors, 2013).
Attention-Deficit Hyperactivity Disorder (ADHD), Negative Life Events, and Alcohol Use
Substantial evidence suggests that adolescents with ADHD are at a heightened risk of experiencing negative life events: ADHD is associated with academic and social difficulties that may directly increase the likelihood of experiencing negative life events (e.g., getting bad grades, fighting with peers [Barkley, 2006]). Moreover, many children with ADHD continue to meet diagnostic criteria in adolescence (e.g., Barkley, Murphy, & Fischer, 2008), and even more experience impairment despite subclinical levels of ADHD symptoms (Sibley et al., 2012). Continued ADHD-related impairments may also indirectly increase the experience of other negative life events (e.g., parental decisions to restrict activities or resources). Finally, children and adolescents with ADHD come from families in which they are exposed to higher levels of negative life events, such as exposure to marital conflict, divorce, general family adversity, and parental alcoholism (Counts, Nigg, Stawicki, Rappley, & von Eye, 2005; Knopik et al., 2006; Wymbs et al., 2008). It may be that adolescents with a diagnosis of ADHD report higher levels of negative life events because of the downstream effects of early ADHD (such as diminished peer relations), the background variables associated with a diagnosis of ADHD (such as family problems), or the effects of continued impairment from ADHD.
Adolescents with ADHD not only may report greater exposure to negative life events, but they may also be especially sensitive to their effects. Their diminished skills for coping with distress (Hampel, Manhal, Roos, & Desman, 2008; Molina, Marshal, Pelham, & Wirth, 2005), among other vulnerability characteristics such as weak executive function and generally increased impulsivity, support this hypothesis. One study from our group found a stronger cross-sectional association between academic life events (e.g., doing poorly on an exam) and problem alcohol use for adolescents with, versus without, ADHD histories (Marshal, Molina, Pelham, & Cheong, 2007). Relatedly, other widely studied environmental factors, such as peer alcohol use (Marshal, Molina, & Pelham, 2003) and parental monitoring (Walther et al., 2012), have been shown to be more strongly related to alcohol use for those with ADHD compared with those without ADHD. Understanding the potential for stress vulnerability in ADHD is particularly important, given the increased risk for alcohol use disorder that characterizes this population in adulthood (e.g., Lee, Humphreys, Flory, Liu, & Glass, 2011).
Whereas much prior research on negative life event exposure and alcohol use has utilized simple count measures of life events (which require an adolescent to report whether an event occurred), research on negative life events highlights the importance of considering individuals’ perception of events (Grant et al., 2003). For example, Davis and Compas (Davis & Compas, 1986) found that the desirability of a life event (whether rated as negative or positive) was positively related to students’ perception that they could cope with events (r = .84). Moreover, the mere number of stressors and how stressors are perceived may explain different variation in psychopathology (Duggal et al., 2000). Classic models of stress and psychopathology argue that stressors should be perceived as a challenge or threat to the individual (Lazarus & Folkman, 1984), although reviews of the stress literature show that both counts of stressors and an individuals’ perception of those stressors can be useful (Grant et al., 2004). To date, most research has relied on counts of stressors and has not considered whether an adolescent’s perception of stressors explains variance in alcohol use. Adolescents with ADHD are known to overestimate their competence and underestimate their impairment across a number of life domains (Evangelista, Owens, Golden, & Pelham, 2008; Hoza et al., 2004). It may be that adolescents with ADHD report a higher number of life events but (due to cognitive biases) report that they are less impactful or upsetting and that their perception of events differentially alters the relation between negative life events and alcohol outcomes.
Current Study
The current study extends prior research by examining the between- and within-person associations of negative life events and alcohol outcomes in a sample of adolescents with and without a well-established diagnosis of ADHD during childhood, using statistical methods that better account for the skewed and zero-inflated nature of those outcomes. The main goal of the current study was to replicate prior work while attempting to explicitly address the methodological challenges raised in recent studies as well as extending these models to a new high-risk sample, adolescents with ADHD. We hypothesized that both between- and within-individual differences in exposure to negative life events would be associated with alcohol use. The second goal of the current study was to test whether children diagnosed with ADHD reported greater numbers of negative life events during adolescence and to examine the contribution of concurrent ADHD symptoms to negative life events. Third, we aimed to test whether adolescents with ADHD showed stronger associations between life events and alcohol behaviors during adolescence. We hypothesized that ADHD history would predict the experience of negative life events and, most importantly, strengthen the association between life events and alcohol outcomes. Finally, we compared simple counts of negative life events with the adolescent’s perception of the negativity of those events.
Method Participants
More detailed information on the recruitment of the Pittsburgh ADHD Longitudinal Study (PALS) may be found in another report (Molina et al., 2012).
ADHD group
Participants with childhood ADHD were diagnosed with Diagnostic and Statistical Manual of Mental Disorders III, revised (DSM–III–R) or DSM–IV ADHD in childhood, at an average age of 9.40 years (SD = 2.27). Participants with ADHD were selected for longitudinal follow-up with annual interviews because of their diagnosis of ADHD and participation in a summer treatment program for children with ADHD, an 8-week intervention that included behavioral modification, parent training, and psychoactive medication trials where indicated (Pelham & Hoza, 1996).
Participants with ADHD were assessed in childhood using standardized parent and teacher DSM–III–R and DSM–IV disruptive behavior disorder symptom rating scales (Pelham, Gnagy, Greenslade, & Milich, 1992) and a standardized semistructured diagnostic interview administered to parents by a PhD-level clinician. Two PhD-level clinicians independently reviewed all ratings and interviews to confirm DSM diagnoses, and when disagreement occurred, a third clinician reviewed the file and the majority decision was used. Exclusion criteria for follow-up was assessed in childhood and included a full-scale intelligence quotient <80, a history of seizures or other neurological problems, and/or a history of pervasive developmental disorder, schizophrenia, or other psychotic or organic mental disorders. At the first PALS follow-up interview, which occurred on a rolling basis between 1999 and 2003, the mean age was 17.75 years (SD = 3.39 years, range = 11–25).
Non-ADHD group
Adolescents without ADHD were recruited into the PALS when those with ADHD were recruited for follow-up. Non-ADHD comparison participants were recruited on a rolling basis to ensure demographic similarity to the ADHD group (age within 1 year, sex, race, highest parental education) and were recruited from the same regional area as the participants with ADHD. Individuals who met DSM–III–R criteria for ADHD (presence of eight or more symptoms reported by either the parent or young adult participant), currently or historically, were excluded. Non-ADHD comparison participants with subthreshold ADHD symptomatology, or with other psychiatric disorders, were retained.
Procedure
Interviews for the PALS were conducted annually in adolescence. Interviews were conducted in the ADD Program offices by postbaccalaureate research staff. Informed consent was obtained and all participants were assured confidentiality of all disclosed material except in cases of impending danger or harm to self or others (reinforced with a Department of Health and Human Services Certificate of Confidentiality). In cases in which distance prevented participant travel to the research offices, information was collected through a combination of mailed and telephone correspondence; home visits were offered as need dictated. Self-report questionnaires were completed either with paper and pencil or Web-based versions on a closed circuit Internet page. All procedures were approved by the Institutional Review Board of Western Psychiatric Institute and Clinic.
Selection of the Current Sample
Data were selected from the first four annual interviews of adolescents based on procedures used elsewhere to test longitudinal hypotheses about adolescent functioning (Molina et al., 2012). Participants were selected if they were interviewed one or more times between the ages of 14 and 17 years. Because multilevel modeling and generalized estimating equations make use of all available data at Level 1 (Raudenbush & Bryk, 2002), participants were excluded only if they were not interviewed between ages 14 and 17 years. For the resulting subsample (n = 259), there were no statistically significant differences between the ADHD (n = 146) and non-ADHD (n = 113) groups on sex or ethnic/racial minority but a statistically significant difference for highest parental education and household income (lower in the ADHD group). For analysis, data were organized by age at interview to allow modeling of life events and alcohol use longitudinally by age (Bollen & Curran, 2006). This provided data for life events and alcohol use at one (n = 43), two (n = 79), three (n = 86), or four (n = 51) occasions. Participants provided data at ages 14 (n = 114), 15 (n = 158), 16 (n = 166), and 17 years (n = 167). To estimate between-person associations, we had data from 259 participants with 756 observations. To estimate within-person associations, we had data from 216 participants (with 689 repeated observations), 129 of whom (with 388 observations) reported any alcohol use. Table 1 provides descriptive statistics for the current sample.
Demographics of Current Sample (n = 259)
Measures
Background variables: Parental characteristics, sex, and race
Because they have been shown to influence the occurrence of life events, alcohol use, or both, for all analyses we initially controlled for the baseline presence of a parental alcohol use disorder, parental antisociality, maternal depressive symptoms, parental divorce, sex, and race. Parental alcohol use disorder was coded as present if either parent met criteria on the Structured Clinical Interview for DSM for Nonpatients (Spitzer, Williams, Gibbon, & First, 1990) by their own report or was reported by the other parent on the Michigan Alcoholism Screening Test–Short (Selzer, Vinokur, & van Rooijen, 1975). The parent on the Michigan Alcoholism Screening Test–Short focuses on consequences from problematic drinking, and a score of 3 or higher was coded as having an alcohol use disorder. These two assessments were combined and coded as 1, either parent met criteria based on self/other-report, vs. 0, neither parent met criteria based on self/other-report. Parental antisociality was coded as present if either parent met criteria on the Structured Clinical Interview for DSM for Nonpatients (Spitzer et al., 1990). Maternal depressive symptoms were assessed by maternal self-report on the 21-item Beck Depression Inventory (Beck, Steer, & Carbin, 1988). Sex was self-reported (0, female, 1, male), as was race (0, White, 1, non-White).
Negative life events
Negative life events in the past year were assessed annually with 120 items from the Adolescent Perceived Events Scale (Compas, Davis, Forsythe, & Wagner, 1987). The Adolescent Perceived Events Scale has been used extensively in prior research and has been shown to predict both internalizing and externalizing symptoms in adolescents (Grant et al., 2003; McMahon, Grant, Compas, Thurm, & Ey, 2003). For each item, adolescents indicated whether an event occurred and, using a 9-point scale, the degree to which it was experienced as negative or positive (1, extremely bad, to 9, extremely good). Example life events were parents getting divorced, parent loses a job, having few or no friends, not getting along with parents of friends, doing poorly on an exam or paper, problems or arguments with teachers or principal, getting in trouble or being suspended from school, death of a family member, change in the health of a friend, and hospitalization of a family member or relative. We excluded 31 items from the original scale that assessed minor life events (such as going to church/synagogue or helping other people), psychological symptoms, bereavement/illness (which also occurred very rarely), substance use, or ADHD diagnosis or treatment, leaving 89 total negative life events. Following prior work with this scale (Wagner & Compas, 1990), we computed both a count of negative life events and a score reflecting the subjective evaluation of those negative events. Prior research has indicated that both the accumulation of life events as well as the adolescent’s perception of them are independent predictors of psychopathology (Grant, Compas, Thurm, McMahon, & Gipson, 2004). The count was comprised of all items rated by the adolescent as at least slightly bad (4) to extremely bad (1). The subjective evaluation score was computed as the mean of the adolescent’s ratings of all events that were rated as at least slightly bad after reverse scoring the ratings (e.g., extremely bad [4]). Across age, these two scores were correlated very weakly, r = .18, p < .001).
Table 1 provides descriptive statistics for negative life events. On average, adolescents reported approximately 11 different negative life events in the past year (range = 0–58) and reported an average perception of 2.36, which represents a response of somewhat bad.
ADHD symptoms
Childhood diagnosis of ADHD is described above. At each wave, ADHD symptoms were measured using parent and teacher report of 18 DSM–IV ADHD symptoms (Pelham, Gnagy, Greenslade, & Milich, 1992), scored on a scale of 0 (not at all) to 3 (very much). We then took the maximum score across the two raters for each symptom and computed a mean across all symptoms at each age.
Alcohol Use
Alcohol use was assessed at each annual interview with a structured paper-and-pencil substance use questionnaire (Molina & Pelham, 2003; Molina, Pelham, Gnagy, Thompson, & Marshal, 2007). The substance use questionnaire is an adaptation of existing measures, including the Health Behavior Questionnaire (Jessor, Donovan, & Costa, 1989) and the National Household Survey on Drug Abuse interview (Substance Abuse and Mental Health Services Administration, 1992) and includes both lifetime exposure questions (e.g., have you ever had a drink, age of first drink) and quantity/frequency questions for alcohol and other substances. The current study utilized two items that assessed frequency of use and drunkenness over the past 12 months. Items used a 12-point scale (from never to several times a day). We tested alcohol outcomes separately because there are concerns in the literature about combining across alcohol outcomes and/or converting them to pseudocount variables (as we did previously; King et al., 2009), particularly when they are measured with an ordinal scale (McGinley & Curran, 2014).
Analytic Strategy
We were interested in predicting between-person differences in negative life events during adolescence from childhood and adolescent ADHD symptoms and in predicting within- and between-person differences in alcohol outcomes during adolescence from life events and ADHD. However, our alcohol outcomes were heavily skewed and zero-inflated, in that many adolescents did not report drinking, and when they did, most reported fairly low levels. This produces nonnormality in the residuals, violating the assumptions of multilevel models (MLMs) and can produce bad parameter estimates and misleading inferences (Atkins, Baldwin, Zheng, Gallop, & Neighbors, 2013). Thus, to predict alcohol outcomes, we used generalized estimation equations (GEEs; Zeger, Liang, & Albert, 1988), GEE readily allows the estimation of zero-inflated and hurdle count models (such as zero-inflated Poisson or hurdle negative binomial models), which may better estimate the response generation process for these variables while also accounting for the effects of clustering within the individual. MLMs and GEEs were well suited as analytic approaches because both estimate parameters using the available Level 1 data (i.e., repeated observations of individuals across age), do not require all Level 2 observations (i.e., participants) to have identical or balanced observations at Level 1 and readily allow the separation of between- and within-components of variance in predictors and outcomes (Raudenbush & Bryk, 2002).
We tested our hypotheses in R 3.2.3 using the nlme and pscl packages with the maximum likelihood estimator. We used MLMs to predict negative life events across adolescence and GEEs to predict alcohol outcomes. To estimate GEEs, we estimated generalized linear models with the appropriate link function (such as negative binomial or hurdle negative binomial) using the r package pscl (Zeileis, Kleiber, & Jackman, 2008) and used a custom sandwich estimator to correct model standard errors for clustering (D. Hu, personal communication). This corrects the standard errors of the fixed effects for the effects of clustering while avoiding the problems that can arise from trying to model the random covariance structure (i.e., slope and intercept variability) of complex distributions such as zero-inflated count distributions. Because our hypotheses were related to fixed effects only (such as estimating the associations between ADHD and the slope of alcohol use), rather than on obtaining a population level estimate of individual differences in slopes, GEEs were an appropriate choice to test the current hypotheses.
Zero-inflated hurdle models
Although alcohol outcomes were measured on an ordinal scale, we utilized models for count data because they best fit the distributions of the alcohol variables. Across all alcohol outcomes, model fit indices (Bayesian information criterion and Akaike information criteria) suggested that zero-inflated or hurdle-negative binomial models best fit the data; we chose hurdle negative binomial models because they best fit our interpretation of adolescents’ actual behavior. Hurdle negative binomial models separately model the presence or absence of the outcome (i.e., the hurdle, or likelihood) and, among those with any level of the outcome, the count (or level) of the outcome as a negative binomial distribution, which has a variance that is greater than its mean (Hilbe, 2011). Thus, for each outcome, coefficients predicting the likelihood of the outcome occurring (i.e., whether or not an adolescent reported drinking in the past year) may be transformed into an odds ratio (OR), which predicts the relative odds of an event occurring. Coefficients predicting the level of an outcome are converted to a rate ratio (RR), which predicts the number of events (such as the number of drinks in the past year that an adolescent may report).
Centering of life events
To disaggregate the between- and within-person associations of negative life events with alcohol outcomes, we used a combination of centering within cluster at Level 1 and grand-mean centering at Level 2 (Enders & Tofighi, 2007), which perfectly separates variation in a given predictor into within- and between-person variability. Centering within cluster is achieved by subtracting a participant-level mean across observations from each participant’s score at each time point. This provides a time-specific score that reflects only within-person variance, and observations at each time point essentially become a deviation score, representing that person’s deviation from their own average at that time point. The participant’s mean score across observations may be grand-mean centered by subtracting each participant’s mean from the sample average of all participant means, which can then be entered as their between-person variable. This score represents a participant’s average deviation from the sample mean and reflects their average level of life event exposure across adolescence. These resulting centering within cluster and grand-mean centered scores are perfectly uncorrelated (r = .00) because they partial within- and between-person variance in life event exposure over time. In this way, a multilevel model may be utilized to address state-trait questions by a simple centering scheme.
Model-fitting approach
We followed a standardized approach to model fitting. For all model comparisons, we relied on Akaike information criteria and Bayesian information criterion as tests of relative model fit (Raftery, 1995) prior to applying the GEE correction to account for clustering within subjects. We first tested for the general shape of change for both alcohol outcomes, comparing linear and quadratic models. Because there was little variability in alcohol outcomes at age 14 years, we used age 17 years as the intercept and estimated growth from age 14 to 17 years, as in our previous research (Molina et al., 2012). To ensure that the main hypothesis tests were not biased by unmodeled dependencies in the data, we tested all covariate by predictor interactions as well as the between- by within-person effects of both count and perceptions of life events. This is recommended as best practice for model building in regression models (Allison, 1977), and simulations have shown that not including or estimating interactions that exist in models can induce substantial bias in the main effects coefficients (Vatcheva, Lee, McCormick, & Rahbar, 2015).
To balance the risk of alpha inflation against model misspecification, we used an a priori threshold of p < .01 to retain significant covariate by predictor interactions and refrained from interpreting any interactions we did retain to avoid speculation about nonhypothesized interactions. Then we examined the main effects of between- and within-person negative life events on each outcome. Next, we tested whether childhood ADHD was associated with life events during adolescence and compared those models with ones measuring the effects of concurrent ADHD symptoms. Finally, we tested whether childhood diagnosis and concurrent symptoms of ADHD moderated the between- and within-person associations of life events with each alcohol outcome. Again, because we had only general hypotheses about this effect (i.e., that life events would have a stronger relation for those with ADHD or more ADHD symptoms), we used the Benjamini-Hochberg correction (Benjamini & Hochberg, 1995) to control for the false discovery rate. We initially controlled for parental alcoholism, antisociality, and depression in all analyses, but controlling for them did not change the magnitude of the coefficients or inferences from the final models, so we dropped them for the sake of parsimony. Thus, the final models controlled for sex, race, and parental divorce to control for baseline differences.
Results Descriptive Statistics
Adolescents with and without childhood diagnoses of ADHD reported similar counts and perceptions of life events (all t(277) < 1.28, p > .20), averaging approximately 11 negative life events and reporting them to be somewhat bad on average. Among those with a childhood ADHD diagnosis, the average ADHD symptom score from age 14 to 17 years was 1.51 (SD = .63), whereas the average ADHD symptom score for those without a childhood diagnosis was lower (M = .48, SD = .37), t(277) = −15.42, p < .001.
Only two covariate-by-predictor interactions were significant at our a priori threshold of p < .01. A significant interaction of race (White vs. non-White) by divorce (nondivorced vs. divorced parents) (p = .0022) suggested that the effects of parental divorce on the likelihood of any alcohol use were smaller for non-White adolescents (OR = .05) than for White adolescents (OR = .60). Because these interactions were not hypothesized, we do not interpret them further, but we did include them in all further models to reduce model misfit and to improve coefficient estimation.
Unconditional Models of Alcohol Involvement
We illustrate the unconditional growth models in Figure 1 to aid interpretation. For all alcohol outcomes, a linear effect of time best fit the data, with all fit indices for the quadratic model greater than those for the linear models, suggesting the linear model of time fit better than the quadratic. Unconditional model results, with estimates of intercepts and slopes, are presented in Table 2. Generally, the likelihood of alcohol involvement across each outcome increased over time, with the odds of reporting any alcohol use or drunkenness increasing by 1.7–1.9 per year from age 14 to 17 years. In other words, for every year that passed, the likelihood of any alcohol use or drunkenness nearly doubled. For example, the probability of reporting any alcohol use rose from less than 20% at age 14 years to more than 50% by age 17 years, whereas the probability of reporting any drunkenness rose from less than 10% to around 35%.
Figure 1. Unconditional growth models of past year alcohol involvement. a, frequency of use; b, frequency of drunkenness. Model predicted probability and level of alcohol outcomes by age. Confidence intervals were simulated using the simcf package (C. Adolph, personal communication).
Unconditional Growth Model
There were also linear increases in the frequency of alcohol use over time, with drinking increasing by 18–19% per year (controlling for the influence of the covariates) among those who reported any alcohol use. On the other hand, those who reported any drunkenness exhibited no increases in the level over time, even as other adolescents became more likely to drink in that manner.
Negative Life Events and Alcohol Involvement
We then examined the between- and within-person associations of negative life events with alcohol use during adolescence, predicting the likelihood and level of alcohol outcomes from between- and within-person variability in the number and perception of life events.
The most consistent effect we observed was that between-person differences in the average count of negative life events over time were associated with higher age 17 year likelihoods of alcohol use (OR = 1.10) and drunkenness (OR = 1.069). Figure 2 illustrates the effects of life events on trajectories of the likelihood of alcohol use. Additionally, within-person variance in the number of negative life events was related to an increased likelihood of drunkenness (OR = 1.045) and a higher level (RR = 1.035) of alcohol use, meaning that in any given year, reporting more negative life events than expected was related to higher likelihoods of reporting drunkenness and more frequent alcohol use than what would be predicted by that adolescent’s own trajectory of use. Moreover, after correction for the false discovery rate, we observed an association of within-person variance in the number of negative life events and the likelihood of any alcohol use (OR = 1.033, p = .06).
Figure 2. Between-person differences in life event exposure predicts heightened probability of alcohol use over time. Model predicted probability of alcohol use by age at −1, mean and +1 SD of between-person count life event exposure. Confidence intervals were simulated using the simcf package (C. Adolph, personal communication).
On the other hand, the adolescent’s perception of those life events was not related to either drinking outcome at either the between- or within-person level.
Predicting Trait-Negative Life Events From ADHD
We next tested whether childhood ADHD predicted a larger number or worse perception of negative life events during adolescence using MLM. There was substantial between (54%) and within-person (46%) variability in adolescents’ report of both the number and perception of negative life events. None of the covariates were related to the perception or number of negative life events across adolescence. Moreover, only the effect of ADHD on the perception of negative life events approached significance (b = .152, SE = 0.082, p = .063), which suggested that adolescents with ADHD reported marginally more negative perceptions of negative life events from age 14 to 17 years but did not report any more or fewer negative life events relative to adolescents without ADHD.
This effect seemed to be confirmed in separate models using adolescent ADHD symptoms as a predictor of life events: The average level of ADHD symptoms across adolescence was associated with reporting more negative perceptions (but not higher numbers) of negative life events (b = .12, SE = .05, p = .035). In other words, higher levels of average ADHD symptoms across adolescence were related to more negative perceptions of life events on average.
Does ADHD Moderate the Effects of Life Events on Alcohol Involvement Across Adolescence?
Finally, we tested whether the relation between counts or perceptions of negative life events on alcohol use differed, depending on childhood ADHD diagnosis or adolescent ADHD symptoms. Table 3 presents these final results. We tested this hypothesis by including ADHD diagnosis or adolescent ADHD symptoms (in separate models) as predictors of level and change in alcohol involvement and as moderator(s) of the between- and within-person effects of life events described above. There were no main effects of childhood ADHD or moderation of life events by childhood ADHD that survived correction for the false discovery rate. This suggested that there was little support for the notion that childhood ADHD moderated the effects of life events on alcohol use.
Effects of Negative Life Events and ADHD on Alcohol Involvement
Between-person differences in ADHD symptoms during adolescence were associated with higher levels of alcohol use frequency (RR = 1.31) and drunkenness (RR = 1.53) at age 17 years. Among those who reported average or high levels of average ADHD symptoms across adolescence, the level of alcohol outcomes rose accordingly. There were no other main effects of ADHD symptoms during adolescence, nor did ADHD symptoms moderate the effects of life events on alcohol outcomes. Moreover, the associations between life events and alcohol use were largely unchanged with the inclusion of ADHD symptoms.
DiscussionThe goals of the current study were to extend prior research on the between- and within-person associations between negative life events and alcohol involvement during adolescence in a high-risk sample using a broad measure of life events and statistical models that better accounted for between- and within-person variability in life events as well as the nonnormal distributions of alcohol outcomes in adolescence. We tested whether ADHD was associated with heightened vulnerability to negative life event exposure and whether ADHD predicted a stronger association between negative life event exposure and alcohol involvement. Overall, our results largely suggested that the number, but not the perception, of negative life events was associated with both between- and within-person changes in the level or likelihood of alcohol involvement during adolescence. Only ADHD symptoms that persisted across adolescence were associated with more negative perceptions of life events (but not the number of life events) as well as with higher levels of alcohol use and drunkenness. Neither childhood diagnosis of ADHD nor persistence of ADHD symptoms strengthened the association between life event exposure and alcohol involvement.
Previous work (King et al., 2009) suggested that both between- and within-person exposure to uncontrollable stressors (familial life events) were related to trajectories of alcohol use. We partially replicated and extended this finding, showing that between-person differences in the number of negative life events were associated with an increased likelihood of alcohol involvement. Specifically, adolescents who reported an average number of negative life events that was 1 SD above the mean across adolescence also reported a likelihood of any drinking that was nearly twice as high (OR = 1.96; obtained by multiplying the model coefficient by the SD of between-person count of life events and then exponentiating) and a likelihood of getting drunk that was greater than 1.5 times as high (OR = 1.61) as an adolescent at the mean number of life events. Conversely, adolescents whose perceptions of life events were 1 SD more negative than average were no more likely to drink (OR = 1.04) or report getting drunk (OR = 1.05) than those whose perceptions were at the sample average. One interpretation of this finding is a third-variable explanation: Adolescents who are prone to experience negative life events, such as those with high levels of personality risk, or those in environmental contexts that expose them to high levels of adversity over time, are also more likely to drink and get drunk. Alternately, these findings may suggest that negative life events may be impactful because of their occurrence, rather than by their perception by the adolescent (Duggal et al., 2000). Altering appraisals of negative life events may be less effective than interventions that might seek to either reduce the number of negative life events themselves (by reducing controllable negative life events, such as by improving social skills) or by providing environmental supports that may counter the effects of uncontrollable negative life events whether or not an adolescent perceives them to be negative (e.g., increasing involvement in prosocial activities). Interestingly, most research and theory on interventions to address stress among youth emphasize improved individual’s coping or emotion regulation skills (Izard, 2002), but our findings lead us to speculate that supplemental approaches that counter the loss of resources that accompany accumulated negative life events (e.g., transportation to extramural activities needed after parental job loss; tutoring to raise poor grades) might be especially helpful. A growing literature on interventions that directly addresses ADHD-related impairments in adolescence is also relevant (Sibley et al., 2016). Future studies contrasting these approaches, and their associations with alcohol and other health risk behaviors, are warranted.
We also observed a relatively consistent within-person association: above and beyond the variance explained by age, when adolescents reported more negative life events than what was typical for them, in that same year they had higher likelihoods of drunkenness and higher levels of alcohol use that were not explained by their developmental trajectories of alcohol involvement (the association with the likelihood of alcohol use approached significance). Adolescents who reported life events in a year that were 1 SD above the average number of life events they reported across the study also reported 15% higher levels (RR = 1.15) of alcohol use, and 1.20 times the odds (OR = 1.20) of reporting drunkenness in that year, relative to the expected level and likelihood at their average number of life events. On the other hand, the relative associations of within-person fluctuations in perceptions with the level of alcohol use and the likelihood of drunkenness were much smaller and not significant (RR = .91, OR = .89, respectively). This finding extends our previous work (King et al., 2009) by showing that the associations of a broader range of negative life events beyond the relatively narrow range of family related life events captured by that earlier study are related to increased risk. Future interventions/preventions may directly benefit from these results. For example, alcohol prevention/intervention efforts could focus on adolescents who report a recent life event (e.g., such as parental divorce or school transitions) because this may be a time when alcohol use will subsequently increase. Moreover, our findings suggest that it is the events themselves, not the adolescents’ perception of them, that explain the within-person associations of negative life events with alcohol outcomes. Future research should explore the degree to which these state associations generalize to other forms of externalizing and internalizing psychopathology and whether other time varying factors (such as social support or coping skills) may moderate the associations of life events with psychopathology to guide the target of intervention. However, we should also caution that these within-person associations, which represent retrospective associations at the yearly level, cannot determine the true direction of effect; studies with a more time-sensitive design can bring us closer to an understanding of the connections between stress and alcohol use in the moment.
In general, adolescents with a childhood diagnosis of ADHD were no different in terms of their experience of the number or perception of negative life events between ages 14 and 17 years. However, between-person differences in current ADHD symptoms (i.e., parent- and teacher-reported symptoms during adolescence) were associated with a more negative perception of negative life events. Although a proliferation of studies have shown that adolescents and young adults with ADHD histories perceive less symptomatology and impairment than reported by their peers or parents (e.g., Mrug, Hoza, & Bukowski, 2004), this positive self-perception bias may not fully extend to perception of negative life events as stressful. This finding does not rule out the possibility that adolescents with ADHD actually underreport the occurrence of negative life events but perceive those that do occur as more negative. This differential finding may suggest that it is inattention to event occurrence that explains positive biases. Both symptoms of and impairment from ADHD for many adolescents continue to persist into adolescence (Barkley et al., 2008; Sibley et al., 2012), and these impairments may contribute to the perception of negative life events as more negative or stressful, particularly in the familial, school, and social domains as adolescents navigate the challenges of developing autonomy and individuation from parents, increasingly challenging school and social demands (Bagwell, Molina, Pelham, & Hoza, 2001; Kent et al., 2011). A number of studies have shown that symptom persistence in adolescence is associated with other externalizing problems such as oppositional defiant disorder and conduct disorder (i.e., Costello & Maughan, 2015), emotion problems, suicidality, and academic failure and dropout (Costello & Maughan, 2015; Kessler et al., 2014) as well as early adult substance use (Howard et al., 2015) including, in this sample, an association between ADHD symptom persistence, delinquency, and frequency of alcohol use (Molina et al., 2012, and replicated in the current study).
On the other hand, neither these life event perceptions, nor the experience of the negative life events themselves, were more strongly associated with alcohol outcomes for adolescents with a history of ADHD or with ongoing ADHD symptoms. These results conflict with prior work suggesting that adolescents with ADHD may be more susceptible to environmental conditions in regard to alcohol use (e.g., peer alcohol use, parenting factors, Belendiuk, Pedersen, King, Pelham, & Molina, 2016; Marshal et al., 2007; Walther et al., 2012). The differences between prior studies and the current one may reflect the longitudinal nature of the current study, the emphasis on the between- and within-individual differences in negative life events, or the treatment of alcohol use as a zero-inflated count outcome. It may also be important to consider additional dimensions of life event perception beyond positivity and negativity, such as the impact a life event has on an adolescent, or how important an adolescent views the event itself. Further research utilizing multiple informants of life events (e.g., school records, parental report) would also be useful as a check on the role of positive self-perception bias on our findings. In addition, a direct comparison of the effects of parent-reported impairments from ADHD, with the most typical being academic, behavioral, and social (Barkley et al., 2008), with those of self-reported negative life events associated with the experience of these impairments would further specify sources of alcohol use vulnerability for adolescents with ADHD. Mechanistic studies of negative affect, versus impairment-driven, pathways could follow (Molina & Pelham, 2014).
It should also be considered that both our null and significant findings could have been influenced by low power to detect effects in the current study, particularly at the within-person level, and that lack of support for certain effects (especially interactions) may at best suggest that effects that do exist may be smaller than the significant effects we were able to detect. Those effects were only estimable using data from individuals with more than one time point (n = 216 participants with 689 repeated observations, and only 129 of those participants reported any alcohol use). It is not well understood what study or design factors influence statistical power in GEEs, especially for hurdle count GEEs, and there are no guidelines for standard measures of effect size for count models in terms of what constitutes a small, medium, or large effect size. The relatively low variability in alcohol outcomes in the current study may have influenced our ability to detect associations, and thus, the null effects we report may not be reliably ruled out unless they are replicated in other samples. Moreover, some studies have raised the concern that effect size estimates from smaller samples, even if statistically significant, may be unreliable (Kraemer, Mintz, Noda, Tinklenberg, & Yesavage, 2006). Although it is not clear whether 689 repeated observations should be considered small for a GEE with a hurdle-negative binomial outcome, given the low variability in alcohol outcomes, this possibility should be considered, and it would be important to replicate the current findings to determine the degree to which the effect size estimates are reliable.
There are several strengths to the current study. First, we modeled our alcohol outcomes in a way that accounted for the heavily zero-inflated and skewed nature of the data and avoided combining across outcomes when doing so has been shown to produce misestimation (McGinley & Curran, 2014). Our application of multiple methods (such as testing between- and within-individual effects of negative life events, count and perceived life events, and childhood vs. concurrent ADHD) allowed a more nuanced examination of the current hypotheses. This is particularly important in light of the increasing awareness of p-hacking (Simmons, Nelson, & Simonsohn, 2011), practices that bias research studies toward presenting positive findings. As such, we intentionally presented all of our findings across all operationalizations of predictors and outcomes and used a relatively conservative approach to alpha correction with the Benjamini-Hochberg correction to provide a clear and hopefully reliable picture of how and when ADHD and negative life events are associated with alcohol use.
At the same time, several limitations warrant acknowledgment. Moreover, whereas we utilized zero-inflated hurdle models to account for the nonnormal distributions in alcohol outcomes, the alcohol items were ordinal in nature and not true counts. It may be that ignoring this may have inflated the estimates of alcohol use in the current study. Second, our reliance on self-report, particularly of life events, may have resulted in an underreporting in these events, particularly by the ADHD group. Moreover, we collapsed multiple categories of negative life events (both major and minor and life events of different sources). Although the goal here was to measure a general sense of the negative life event load, it could be that more precise findings could be obtained by a more fine-grained analysis of the effects of subcategories of negative life events. Unfortunately, there are few theoretically driven approaches to categorizing life events (but see Pillow, Barrera, & Chassin, 1998), and doing so for the current manuscript would have dramatically increased the risk of alpha inflation.
Despite these limitations, the current study adds significantly to the literature by examining negative life events with multiple different approaches over time. Specifically, these findings highlight the complexity of the negative life events–alcohol association and indicate the importance of examining both the number of life events as well as the perception of how negative these events are to the adolescent. Furthermore, future efforts focused on decreasing alcohol use the year following a negative life event may help reduce the escalation of adolescent alcohol use. Lastly, understanding how negative life events relate to alcohol use for adolescents with ADHD underscores the possibility that targeting negative life events, acute as well as chronic, may ultimately decrease risk for alcohol use disorder in this at risk population.
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Submitted: January 28, 2017 Revised: May 11, 2017 Accepted: May 12, 2017
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Source: Psychology of Addictive Behaviors. Vol. 31. (6), Sep, 2017 pp. 699-711)
Accession Number: 2017-30121-001
Digital Object Identifier: 10.1037/adb0000295
Record: 30- Title:
- Case complexity as a guide for psychological treatment selection.
- Authors:
- Delgadillo, Jaime. Clinical Psychology Unit, Department of Psychology, University of Sheffield, Sheffield, United Kingdom, jaime.delgadillo@nhs.net
Huey, Dale. Primary Care Psychological Therapies Service, Greater Manchester West Mental Health NHS Foundation Trust, Salford, United Kingdom
Bennett, Hazel. Primary Care Psychological Therapies Service, Greater Manchester West Mental Health NHS Foundation Trust, Salford, United Kingdom
McMillan, Dean. Hull York Medical School, University of York, United Kingdom - Address:
- Delgadillo, Jaime, Clinical Psychology Unit, University of Sheffield, Cathedral Court, Floor F, 1 Vicar Lane, Sheffield, United Kingdom, S1 1HD, jaime.delgadillo@nhs.net
- Source:
- Journal of Consulting and Clinical Psychology, Vol 85(9), Sep, 2017. pp. 835-853.
- NLM Title Abbreviation:
- J Consult Clin Psychol
- Page Count:
- 19
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- Journal of Consulting Psychology
- Other Publishers:
- US : American Association for Applied Psychology
US : Dentan Printing Company
US : Science Press Printing Company - ISSN:
- 0022-006X (Print)
1939-2117 (Electronic) - Language:
- English
- Keywords:
- psychotherapy, stratified medicine, mental health, case complexity
- Abstract (English):
- Objective: Some cases are thought to be more complex and difficult to treat, although there is little consensus on how to define complexity in psychological care. This study proposes an actuarial, data-driven method of identifying complex cases based on their individual characteristics. Method: Clinical records for 1,512 patients accessing low- and high-intensity psychological treatments were partitioned in 2 random subsamples. Prognostic indices predicting post-treatment reliable and clinically significant improvement (RCSI) in depression (Patient Health Questionnaire-9; Kroenke, Spitzer, & Williams, 2001) and anxiety (Generalized Anxiety Disorder-7; Spitzer, Kroenke, Williams, & Löwe, 2006) symptoms were estimated in 1 subsample using penalized (Lasso) regressions with optimal scaling. A PI-based algorithm was used to classify patients as standard (St) or complex (Cx) cases in the second (cross-validation) subsample. RCSI rates were compared between Cx cases that accessed treatments of different intensities using logistic regression. Results: St cases had significantly higher RCSI rates compared to Cx cases (OR = 1.81 to 2.81). Cx cases tended to attain better depression outcomes if they were initially assigned to high-intensity (vs. low intensity) interventions (OR = 2.23); a similar pattern was observed for anxiety but the odds ratio (1.74) was not statistically significant. Conclusions: Complex cases could be detected early and matched to high-intensity interventions to improve outcomes. (PsycINFO Database Record (c) 2017 APA, all rights reserved)
- Impact Statement:
- What is the public health significance of this article?—Complex cases tend to have a poor prognosis after psychological treatment for depression and anxiety problems. An evidence-based model of defining complexity is proposed to guide therapists in matching patients to treatments of differing intensity. The findings indicate that this personalized method of treatment selection could lead to better outcomes for complex cases and could improve upon decisions that are informed by clinical judgment alone. (PsycINFO Database Record (c) 2017 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Mental Health Services; Psychotherapy; Treatment
- PsycINFO Classification:
- Health & Mental Health Services (3370)
- Population:
- Human
Male
Female - Location:
- United Kingdom
- Age Group:
- Adolescence (13-17 yrs)
Adulthood (18 yrs & older)
Young Adulthood (18-29 yrs)
Thirties (30-39 yrs)
Middle Age (40-64 yrs)
Aged (65 yrs & older)
Very Old (85 yrs & older) - Tests & Measures:
- Standardized Assessment of Personality–Abbreviated Scale
Work and Social Adjustment Scale
Generalized Anxiety Disorder 7 DOI: 10.1037/t02591-000
Patient Health Questionnaire-9 DOI: 10.1037/t06165-000 - Methodology:
- Empirical Study; Quantitative Study
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- Accepted: May 29, 2017; Revised: May 16, 2017; First Submitted: Nov 14, 2016
- Release Date:
- 20170831
- Copyright:
- American Psychological Association. 2017
- Digital Object Identifier:
- http://dx.doi.org/10.1037/ccp0000231
- Accession Number:
- 2017-36111-001
- Persistent link to this record (Permalink):
- http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2017-36111-001&site=ehost-live
- Cut and Paste:
- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2017-36111-001&site=ehost-live">Case complexity as a guide for psychological treatment selection.</A>
- Database:
- PsycINFO
Case Complexity as a Guide for Psychological Treatment Selection
By: Jaime Delgadillo
Clinical Psychology Unit, Department of Psychology, University of Sheffield;
Dale Huey
Primary Care Psychological Therapies Service, Greater Manchester West Mental Health NHS Foundation Trust, Salford, United Kingdom
Hazel Bennett
Primary Care Psychological Therapies Service, Greater Manchester West Mental Health NHS Foundation Trust, Salford, United Kingdom
Dean McMillan
Hull York Medical School and Department of Health Sciences, University of York
Acknowledgement: We thank Jan R. Böhnke for helpful comments on an earlier version of this article.
A commonly held view in clinical psychology is that complex cases require suitably intensive interventions guided by formulations that account for obstacles to improvement (Tarrier, 2006). Clinical wisdom reflected in treatment textbooks suggests that a variety of factors can complicate treatment, such as chronic symptoms, comorbidity, personality disorders, physical illnesses, and so forth (Beck, 1998; Hawton, Salkovskis, Kirk, & Clark, 1989; Tarrier, Wells, & Haddock, 1998). Along these lines, Ruscio and Holohan (2006) proposed a list of more than 40 factors that characterize complex cases, clustered around several themes including symptoms, safety, physical, intellectual, personality, and other features. Evidently, case complexity is a heterogeneous concept and there is little consensus about the features that define such cases.
Moreover, the empirical literature casts doubt over the predictive value of many variables presumed to hinder the effectiveness of therapy (Garfield, 1994). A case in point is found in the study by Myhr et al. (2007), in which only five out of 10 variables thought to be indicative of poor suitability for cognitive therapy were (weakly) correlated with post-treatment outcomes. It is also well documented that clinicians’ prognostic assessment of patients tends to be inaccurate (Ægisdóttir et al., 2006; Grove & Meehl, 1996), often failing to identify cases at risk of poor treatment outcomes (Hannan et al., 2005). In another study, patients randomly assigned to brief manualized interventions offered in a stepped care model had comparable outcomes to patients whose treatments were selected and informed by clinical judgment (Van Straten, Tiemens, Hakkaart, Nolen, & Donker, 2006). Such evidence calls into question clinicians’ ability to match patients to treatments and supports current guidelines to apply a stepped care approach (National Institute for Health and Care Excellence [NICE], 2011). Overall, three key problems are apparent: a lack of conceptual clarity about complex cases, a gap between clinical judgment and research evidence, and limitations in clinicians’ ability to identify and select optimal treatments for complex cases.
Concerns regarding complexity are not exclusive to the practice of psychotherapy. The simultaneous growth and ageing of the general population have confronted many other areas of health care with the challenges of treating patients who present with multiple chronic conditions (Smith & O’Dowd, 2007), leading some to question the usefulness of evidence-based guidelines that are formulated for “prototypical” patients (Boyd et al., 2005; Tinetti, Bogardus, & Agostini, 2004). Consequently, theoretical models to account for case complexity in medicine have been proposed in the last decade. Some of these models conceptualize complexity as arising from a combination of clinical (e.g., diagnostic), biological, socioeconomic, cultural, environmental, and behavioral factors that are statistically associated with clinical prognosis (Safford, Allison, & Kiefe, 2007; Schaink et al., 2012). Individual patients may have protective or risk factors across these domains, and their overall complexity level results from the sum of risks. In an attempt to move beyond mere description, Shippee, Shah, May, Mair, and Montori (2012) proposed a cumulative complexity model that attempts to explain how risk factors accumulate and interact to influence health care outcomes. They proposed that (clinical, socioeconomic, cultural) risk factors complicate health care outcomes by disrupting the balance between patient workload (i.e., number and difficulty of daily life demands including self-care) and patient capacity (i.e., resources and limitations affecting ability to meet demands). From this perspective, effective health care for complex cases would not only require intensive treatment of acute symptoms and specific disease mechanisms, but also attending to wider biopsychosocial aspects that may redress the balance between demands and capacity. Common to these models are the focus on empirically supported prognostic factors, the consideration of factors across multiple domains, and the conceptual understanding of case complexity as resulting from the accumulation of risks and challenges to self-management.
Informed by these theoretical models emerging from the biomedical sciences, this study investigated the impact of case complexity in routine psychological care. Considering the problems outlined above, we sought to assess the merits of an actuarial, data-driven, cumulative model of defining case complexity. Specific objectives were (a) to identify prognostic variables associated with psychological treatment outcomes, (b) to develop an algorithm that could aid clinicians in identifying complex cases at risk of poor outcomes, (c) to determine whether or not complex cases respond differentially to treatments of differing levels of intensity, (d) to ascertain the extent to which patients are adequately matched to available stepped care interventions.
Method Setting and Interventions
This study was based on the analysis of clinical data routinely collected by a primary care psychological therapy service in Northern England. The study was approved as a service evaluation by the local National Health Service (NHS) Trust, which did not require formal ethical approval. The service offered low- and high-intensity interventions for depression and anxiety problems, as part of the Improving Access to Psychological Therapies (IAPT) program (Clark et al., 2009). Low-intensity treatments (LIT) consisted of brief (<8 sessions lasting 30 min) psychoeducational interventions based on principles of cognitive behavioral therapy (CBT). These were highly structured interventions, supported by didactic materials and delivered by a workforce of psychological wellbeing practitioners trained to a standard national curriculum (Bennett-Levy et al., 2010). High-intensity treatments (HIT) were lengthier (up to 20 sessions lasting around 60 min) interventions including CBT and counseling for depression. These interventions were also protocol-driven, delivered by postgraduate level counselors and psychotherapists, following national treatment guidelines (NICE, 2010) and competency frameworks (e.g., Roth & Pilling, 2008). All therapists practiced under regular clinical supervision (weekly or fortnightly) to ensure ethical practice and treatment fidelity. These interventions were organized in a stepped care model (NICE, 2011), where most patients initially accessed LIT and those with persistent and/or severe symptoms accessed HIT. Initial treatment assignment was determined by therapists who carried out standardized intake assessments.
Measures and Data Sources
Primary outcome measures
Patients accessing IAPT services self-complete standardized outcome measures on a session-to-session basis to monitor response to treatment. The Patient Health Questionnaire-9 (PHQ-9; Kroenke et al., 2001) is a nine-item screening tool for major depression, where each item is rated on a 0 to 3 Likert scale, yielding a total depression severity score between 0 and 27. A cut-off ≥10 has been recommended to detect clinically significant depression symptoms (Kroenke et al., 2001), and a difference of ≥6 points between assessments is indicative of reliable change (Richards & Borglin, 2011).
The Generalized Anxiety Disorder-7 (GAD-7; Spitzer, Kroenke, Williams, & Löwe, 2006) is a seven-item measure developed to screen for anxiety disorders. It is also rated using Likert scales, yielding a total anxiety severity score between 0 and 21. A cut-off score ≥8 is recommended to identify the likely presence of a diagnosable anxiety disorder (Kroenke, Spitzer, Williams, Monahan, & Löwe, 2007), and a difference of ≥5 points is indicative of reliable change (Richards & Borglin, 2011). Pre-treatment and last observed PHQ-9 and GAD-7 scores were available for analysis.
Other measures
The Work and Social Adjustment Scale (WSAS; Mundt, Marks, Shear, & Greist, 2002) is a measure of functioning across five domains: work, home management, social leisure activities, private leisure activities, family and close relationships. Each item is rated on a scale of 0 (no impairment) to 8 (very severe impairment), rendering a total functional impairment score between 0 and 40.
The Standardized Assessment of Personality–Abbreviated Scale (SAPAS) is an eight-item questionnaire developed to screen for the likely presence of a personality disorder (Moran et al., 2003). Each question prompts respondents to endorse specific personality traits (yes/no), yielding a total score between 0 and 8 where a cut-off >3 is indicative of cases with a high probability of diagnosable personality disorders. The WSAS and SAPAS were gathered at the time of initial assessments.
De-identified treatment and demographic data were also available, including information on referral sources, the intensity and sequence of treatments received (LIT and/or HIT along the stepped care pathway), age, gender, ethnicity and employment status. Formal diagnostic assessments were not carried out in routine care, but primary presenting problems noted in clinical records were available in summary form as group-level percentages.
Sample Characteristics
The study included case records for a total of 2,202 patients who had been discharged from the service at the time of data collection. Complete data (described earlier) were available for 1,512 (68.7%) cases. More than half were females (63.9%), with a mean age of 41.99 (SD = 14.54; range = 16 – 87) and of white British ethnic background (88.2%). A quarter (24.9%) of all cases were unemployed and/or in receipt of incapacity benefits. Approximately 59.9% were referred to treatment by general medical practitioners; the remainder self-referred (24.3%) or were referred by other social and health care providers (15.8%). The presenting problems noted in clinical records were depression (21.0%), recurrent depression (6.6%), obsessive–compulsive disorder (4.4%), adjustment disorders (5.7%), somatoform disorders (0.4%), eating disorders (0.4%), phobic disorders (5.7%), other anxiety disorders (42.4%), and unspecified mental health problems (13.4%). Mean baseline severity scores for the whole cohort were PHQ-9 = 14.86 (SD = 6.33), GAD-7 = 13.27 (SD = 5.07), WSAS = 18.39 (SD = 9.46), SAPAS = 3.82 (SD = 1.89; cases with SAPAS >3 = 54.2%). Many patients had comorbid presentations, where 71.4% of cases had case-level symptoms in both PHQ-9 and GAD-7. Approximately 76.6% of patients were initially assigned to LIT and 23.4% were initially assigned to HIT. Overall, 40.6% only accessed LIT, 36.0% accessed LIT + HIT, and 23.4% only accessed HIT. Overall, 31.3% dropped out of treatment (32.2% of those initially assigned to LIT; 28.5% of those initially assigned to HIT).
Statistical Analysis
Consistent with the objectives of the study, data analyses were performed in four stages aiming to develop, validate and assess the clinical utility of a cumulative complexity model. The primary analyses were carried out in the dataset of cases with complete data (N = 1,512). Following a cross-validation approach, we partitioned this dataset into two random halves which were treated as estimation (N = 755) and validation (N = 757) samples. In order to assess the potential influence of missing data, a single imputed estimation sample (N = 1,108) was derived using an expectation-maximization method (Schafer & Olsen, 1998) and was used for sensitivity analyses described below.
Stage I involved the development of a prognostic index and classification method to identify complex cases in routine care. The dependent variable in all models was a binary indicator of post-treatment reliable and clinically significant improvement (RCSI), with separate models for depression (PHQ-9) and anxiety (GAD-7) measures. RCSI was determined using the criteria proposed by Jacobson and Truax (1991), based on combining reliable change indices for PHQ-9 (≥6) and GAD-7 (≥5) described by Richards and Borglin (2011) and diagnostic cut-offs for each measure (PHQ-9 < 10; GAD-7 < 8). The dependent variable was coded as follows: 0 = RCSI; 1 = no RCSI, such that the prognostic models would be constructed to identify (more complex) cases with increased probability of poor outcomes.
As an initial variable screening step, we used univariate logistic regressions to examine the goodness-of-fit (based on −2 log likelihood test and magnitude of AIC and BIC statistics) of linear and nonlinear trends for continuous variables, as well as alternative ways to model the SAPAS Questionnaire (as a total score, dichotomized based on a cut-off >3, or entered as a series of eight binary items). Entering all eight SAPAS binary items yielded the best fitting models in preliminary tests (i.e., lowest AIC and BIC, significant −2 log likelihood tests) and confirmed that only five items were significant (p < .05) predictors of outcome. Furthermore, baseline severity (PHQ-9, GAD-7), impairment (WSAS) and age variables were optimally modeled using nonlinear trends. Age was rescaled to ordinal decade groups (e.g., teens, twenties, thirties, etc.) and reverse scored (oldest group coded 0, youngest group coded 6) on the basis of the observed trend of correlations between age and RCSI.
Informed by these preliminary tests, we applied penalized categorical regressions with optimal scaling (CATREG-Lasso) in the main analysis. CATREG applies classical linear regression to predictor variables that are transformed to categorical quantifications which are optimally suited to explore nonlinear relations in the data (Gifi, 1990). Continuous variables were thus transformed using a monotonic spline scaling level to examine nonlinear associations with the dependent variable. Variable selection and regularization were performed combining the Lasso procedure (Tibshirani, 1996) and the .632 bootstrap resampling method (Efron & Tibshirani, 1997). The Lasso imposes a penalty term that shrinks coefficients toward zero, penalizing the sum of the squared regression coefficients. This yields more generalizable prediction equations compared with conventional regression models which are prone to overfitting and are less reliable in the presence of multicollinearity. Because using different penalty terms results in different shrunken coefficients, resampling techniques are often used to determine an optimal penalty. The .632 bootstrap resampling method is a smoothed version of the leave-one-out cross-validation strategy, which permits the estimation of a model’s expected prediction error. This resampling method was applied 1,000 times to each Lasso model, iteratively increasing the penalty term in 0.01 units, until all coefficients were shrunk to zero. The one-standard-error rule was applied to select the most parsimonious Lasso model within one standard error of the model with minimum expected prediction error. The predictors entered into CATREG-Lasso models included clinical (baseline PHQ-9, GAD-7, WSAS), personality (SAPAS items 1, 2, 3, 5, 7) and demographic variables (age groups, gender, ethnicity, employment status). Shrunken coefficients from the optimal models were used to calculate a prognostic index (PI) for each patient, where a higher PI denotes poorer prognosis. PIs were retained in the CATREG quantifications scale, with signed and continuous scores centered at zero.
The above procedure was conducted in the estimation samples with complete and imputed data, allowing us to compare the area under the curve (AUC) for the PIs derived from each dataset as an indicator of predictive accuracy. PIs derived using complete and imputed samples had comparable AUC statistics albeit with some shrinkage observed in the imputed dataset (PHQ-9: 0.67 ± 0.04 vs. 0.63 ± 0.05; GAD-7: 0.74 ± 0.04 vs. 0.66 ± 0.04). Therefore, subsequent analyses were applied in the dataset with complete data.
In Stage II, we applied receiver operating characteristic (ROC) curve analysis (Altman & Bland, 1994) in the estimation sample to determine empirical cut-offs that optimally balanced sensitivity and specificity on each PI. Consistent with our assumptions about clinical complexity, cases where both (PHQ-9 and GAD-7) PIs were above empirical cut-offs were classed as complex (Cx), and others (including all those with subclinical symptoms) were classed as standard (St) cases. The agreement of both PIs was taken as a conservative means of minimizing “false positive” classifications, and limiting the Cx classification to cases with the poorest prognoses across both outcome domains. We then tested our assumptions about prognosis and cumulative complexity in the validation subsample, with cases whose symptoms were above diagnostic cut-offs for each outcome measure (PHQ-9: N = 675; GAD-7: N = 755). ROC curve analyses were used to assess how well the PIs (using Lasso-based shrunken coefficients from the estimation sample) performed out-of-sample (in a statistically independent validation sample). In addition, separate logistic regression models were applied for each outcome (PHQ-9, GAD-7), where the dependent variable was post-treatment RCSI status (0 = no RCSI; 1 = RCSI) and the predictors included case complexity (0 = Cx, 1 = St) controlling for baseline severity of symptoms (PHQ-9 or GAD-7, respectively).
Stage III analyses were also conducted in the validation sample. A logistic regression model predicting (HIT vs. LIT) group membership based on clinical and demographic characteristics was performed to estimate propensity scores, denoting the predicted probability of completing a treatment episode at HIT. Propensity scores were entered as a covariate in subsequent analyses to control for confounding by indication. Next, logistic regression models were applied with RCSI status as a dependent variable, entering baseline severity (PHQ-9 or GAD-7, respectively), propensity scores, and treatment pathway (LIT or HIT only vs. LIT + HIT) as predictors. The models were performed separately in the subgroups of Cx (N = 269) and St (N = 425) cases (with available data to estimate propensity scores), to minimize multicollinearity between propensity scores and case complexity dummy variables in the same model.
In Stage IV, we assessed the extent to which initial treatment assignment (LIT or HIT) determined by clinical judgment was consistent with the assignment that would be indicated by the prognostic method described previously. A prognostic treatment assignment was coded for all patients, where starting at HIT was recommended for Cx cases and starting at LIT was recommended for all other cases. Next, agreement codes were noted for each case in the full sample, where “1” indicated agreement between clinical judgment and prognosis, and “0” indicated disagreement. Agreement codes were aggregated across the entire sample to estimate a “hit rate,” denoting the percentage of cases where clinicians’ decisions converged with a prognostic strategy for treatment assignment. Next, we applied Cohen’s kappa across agreement codes to derive a Treatment Matching precision (TMaP) score, which takes into account the probability that “hit rates” may be due to chance. The TMap score is therefore a robust measure of convergence between clinical and empirical decision-making strategies, ranging between 1 (perfect agreement) and −1 (complete disagreement), where 0 is indicative of agreement by chance. TMaP scores were estimated for the full sample and for individual clinicians that undertook initial assessments and made decisions about treatment assignment for at least 20 patients (to eschew extreme scores in caseloads with small base rates).
Results Estimation of Prognostic Equations
Using the CATREG-Lasso procedure in the estimation sample, we arrived at prognostic models that explained between 9% (PHQ-9: optimal scaling adjusted R2 = 0.09) and 15% (GAD-7: adjusted R2 = 0.15) of variance in posttreatment RCSI. Regression and ROC curve model estimates for each outcome measure are presented in Table 1 (with detailed outputs in Appendix A). Several predictors were selected into optimal Lasso models, including demographic (age, ethnicity, employment), personality (SAPAS items: 2 = interpersonally avoidant, 3 = suspicious, 5 = impulsive, 7 = dependent), and clinical features (baseline PHQ-9, GAD-7, WSAS).
Estimation of Prognostic Indices Using Penalized Categorical Regression With Optimal Scaling
The R2 share statistic reflects the relative contribution of each predictor to the model’s overall adjusted R2, after partialing out the specific and combined effects of the other variables. In the depression model, demographics had relatively greater explanatory influence (22.5%) relative to personality (14.7%) and clinical features (15%), although the remaining R2 variance was large (47.9%) and reflected the combined influence of all variables in the model. In the anxiety model, clinical features (55.9%) had two to three times greater explanatory power relative to personality (23.9%) and demographic features (15.2%), leaving only 5% of the remaining R2 variance to combined effects. The F tests for specific variables in both models suggested that the removal of clinical factors (particularly PHQ-9) significantly deteriorated the predictive power of regression models. AUC statistics for the depression (0.67, SE = 0.02) and anxiety (0.74, SE = 0.02) prognostic indices applied to predict RCSI in the estimation sample were both statistically significant (p < .001); ROC curves are shown in Appendix B.
Validation of Case Complexity Model
PIs using the shrunken coefficients derived from the estimation sample were applied in the validation sample, yielding stable and statistically significant (p < .001) AUC estimates for depression (0.64, SE = 0.02) and anxiety (0.70, SE = 0.02) measures (see ROC curves in Appendix B). Overall, 28.6% of all patients were classified as Cx by the prognostic classification rule derived using ROC curve analyses. The proportion of Cx cases was lower in the subsample of patients who only accessed LIT (15.9%) by comparison to those who accessed LIT + HIT (37.3%) and those who only accessed HIT (36.7%); χ2(2) = 97.05, p < .001.
As illustrated in Figure 1, logistic regression models (see Table 2) confirmed that St cases were significantly more likely to attain RCSI in depression (OR = 1.81) and anxiety (OR = 2.81) symptoms compared with Cx cases, after controlling for baseline severity.
Figure 1. Reliable and clinically significant improvement (RCSI) in cases classified as standard (St) and complex (Cx).
Validation of Prognostic Indices Applied Out-of-Sample-Using Logistic Regression
Case Complexity and Treatment Selection
Logistic regression models presented in Table 3 indicated that Cx cases had a significantly greater probability of RCSI in depression symptoms if they directly accessed HIT, by comparison to a standard stepped care pathway LIT + HIT (OR = 2.23, p = .01). There was also a trend indicating the same advantage of HIT for Cx cases in the anxiety model, although this did not reach statistical significance (OR = 1.74, p = .08). No significant differences were found between treatment pathways in the regression models applied to St cases. These analyses controlled for baseline symptom severity and propensity scores (derived from logistic regression model in Appendix C). The results for the depression outcomes are illustrated in Figure 2; where Cx cases that were initially assigned to HIT (optimal prognostic treatment assignment) had a 16.3% increased probability of RCSI by comparison to Cx who were assigned to a conventional stepped care pathway (LIT + HIT).
Logistic Regression Models Assessing Case Complexity and Treatment Selection
Figure 2. Reliable and clinically significant improvement (RCSI) in cases classified as standard (St) and complex (Cx) according to treatment pathway.
Clinical Judgment Versus Prognostic Models
The aggregated hit rate in the full sample indicated that clinicians’ treatment assignment decisions agreed with the prognostic strategy in 65.6% of cases. The TMaP score for the full sample, however, was low (κ = 0.09, SE = 0.02, p < .001). A closer examination of individual therapists’ treatment assignment decisions (N = 1,247 nested within 26 therapists) revealed considerable variability in their hit rates (range = 36.5% to 84.7%; M = 62.9, SD = 14.3) and TMaP scores (range = −0.27 to 0.44; M = 0.05, SD = 0.20). As shown in Figure 3, hit rates and TMaP scores were moderately correlated (r = .67, p < .001), and approximately 48% of therapists had TMaP scores <0.
Figure 3. Distribution of hit rates and treatment matching precision (TMaP) scores across 26 therapists.
Discussion Main Findings
This study set out to contribute to the understanding of case complexity in psychological care, in view of the limited conceptual clarity and evidence base surrounding this topic. Our findings demonstrate that (a) several patient characteristics have a cumulative effect on treatment outcomes, (b) it is possible to make reasonably accurate prognoses using this information, and (c) prognostic models can help us to operationalize case complexity in a way that is clinically useful. Cases classed as Cx (28.6%) on the basis of prognostic data tended to have significantly poorer outcomes after psychological treatment. Furthermore, Cx cases were two times (OR = 2.23) more likely to attain RCSI in depression symptoms if they were initially assigned to a high-intensity intervention instead of usual stepped care. A similar trend was observed for anxiety symptoms, although this did not reach statistical significance.
A Conceptual Bridge Between Prognosis and Case Complexity
These results lend support to the clinical notion that some cases are more difficult to treat due to various complicating factors (Ruscio & Holohan, 2006), although clinicians’ intuitions and treatment planning are often inconsistent with the evidence base (Garb, 2005). We found that treatment assignment decisions guided by clinical judgment were consistent with prognostic models in 65.6% of cases. This rate of agreement could be achieved by chance, or simply by mechanically following stepped care guidelines and assigning all cases initially to LIT, because the base rate of Cx cases is relatively low (under 30%). This was evidenced more clearly by examining the aggregated TMaP score (0.09) which was close to zero. Overall, the findings indicate that depression improvement (RCSI) rates for Cx cases could be significantly increased (by approximately 16.3%) if clinical judgment was supported by prognostic treatment selection models.
This gap between practice and science is perhaps accentuated by an unwieldy literature on the topic of prognosis in psychological care. Previous authors have attempted to synthesize findings across multiple studies to elucidate predictors of depression and anxiety outcomes (e.g., Driessen & Hollon, 2010; Haby, Donnelly, Corry, & Vos, 2006; Hamilton & Dobson, 2002; Keeley et al., 2008; Kessler et al., 2017; Licht-Strunk et al., 2007; Nilsen, Eisemann, & Kvernmo, 2013). Although some convergent findings are evident, meta-analytic reviews that privilege data from clinical trials are limited by typically small samples with sparse and heterogeneous prognostic variables, often gathered in highly selected participants (i.e., those with specific disorders) that may not be representative of complex cases seen in routine care (Chambless & Ollendick, 2001). Naturalistic cohort studies can offer informative evidence to complement findings from controlled trials, especially where multiple variables are measured systematically across large health care populations, as exemplified in this study. Several such studies are yielding replicated findings (e.g., Beard et al., 2016; Delgadillo, Moreea, & Lutz, 2016; Delgadillo, Dawson, Gilbody, & Böhnke, 2017; Firth, Barkham, Kellett, & Saxon, 2015; Goddard, Wingrove, & Moran, 2015; Licht-Strunk et al., 2009).
Overall, the emerging literature on outcome prediction points to factors clustered around clinical (i.e., baseline symptom severity, diagnosis, comorbidity, functioning and disability, physical illnesses), demographic (i.e., age, ethnicity, employment, socioeconomic deprivation, marital status), characterological (i.e., personality disorder diagnoses or traits, interpersonal problems and style, trait anxiety and neuroticism), and dispositional domains (i.e., readiness to change, expectancy). Informed by advances in the biomedical literature (Safford et al., 2007; Shippee et al., 2012), we propose that complex cases in psychological care are characterized by the presence of measurable factors that map onto multiple domains (clinical, demographic, characterological and dispositional), which are statistically associated with clinical prognosis and have a cumulative—detrimental—effect on treatment outcomes. The concept of case complexity is, therefore, dimensional (i.e., degrees of complexity on a continuum), and complex cases can be distinguished from others using empirically derived population norms and classification rules.
Case complexity may challenge psychological improvement through several mechanisms. One possibility is that an accumulation of disadvantages (e.g., poverty, interpersonal difficulties, functional impairment, outgroup derogation due to minority ethnic status) could disrupt the balance between life stressors and coping resources (Shippee et al., 2012). Complexity could also interfere with adequate engagement with therapy, for example, by undermining expectancy, which is a well-established predictor of treatment outcomes (Constantino et al., 2011). Baseline severity is an important contributor to complexity, so another possibility is that high baseline severity does not completely block improvement but may dampen the effect of treatment (i.e., cases with high severity can attain reliable improvement even if their symptoms do not reach subclinical levels). Furthermore, our findings suggest that specific features (i.e., demographic, clinical, characterological) influence specific clinical outcomes (remission of depression, anxiety) differentially. For example, demographic factors (e.g., young age, unemployment) had a considerably larger influence over depression outcomes relative to clinical and characterological factors. Future research could focus on exploring the relative contribution of different prognostic domains to multiple outcome domains (symptoms, quality of life, functioning) and the mechanisms through which these cumulative disadvantages may complicate or undermine treatment.
Limitations
Some limitations should be considered when interpreting the results of this study. As is common in naturalistic data sets, we encountered several cases with missing data (>30%). To deal with this, we applied multiple imputation and sensitivity analyses that yielded similar prognostic models, albeit with some shrinkage observed in the imputed dataset. On this basis, it was appropriate to perform further validation analyses using cases with complete data, to simulate how prognostic assessments would be applied in routine care, where data imputation of missing values is unfeasible.
Another limitation concerning the data used in this study was that we only had access to pre-post outcome measures for the entire treatment pathway, and it was not possible to disaggregate the outcomes for LIT and HIT for cases that accessed both steps. However, we were able to determine that Cx that only accessed HIT tended to have better outcomes compared to those who accessed LIT + HIT (a lengthier and costly treatment pathway). This suggests that there are no benefits of having LIT sessions preceding HIT, and hence the advantage of being initially assigned to HIT may not be solely due to having a lengthier treatment episode. Previous research using more granular outcomes data for each treatment step suggested that cases with poor prognostic features had a higher probability of dropout and lower probability of improvement at the LIT step by comparison to HIT (Delgadillo et al., 2016). These emerging findings suggest that assigning complex cases directly to HIT seems justified, although future randomized controlled trials of this strategy are necessary to determine if it is indeed more cost-effective.
Other limitations include the lack of formal diagnostic assessments and the analysis of a limited number of prognostic variables. It is known, for example, that specific diagnoses such as post-traumatic stress disorder, eating disorders and obsessive–compulsive disorder are associated with poorer outcomes in stepped care services (Delgadillo et al., 2017), and it is plausible that such diagnoses could interact with other prognostic features. Notwithstanding these limitations, it is remarkable that this narrow range of variables yielded an accurate and clinically useful prognostic model. Other studies using routine practice data have shown that similar variables can be used to identify subgroups of cases with depression and anxiety problems that attain similar outcomes (Delgadillo et al., 2016; Lutz, Lowry, Kopta, Einstein, & Howard, 2001; Lutz et al., 2005; Saunders, Cape, Fearon, & Pilling, 2016).
Clinical Implications
In line with recent findings in stepped-care psychological treatment settings (Delgadillo et al., 2016; Lorenzo-Luaces, DeRubeis, van Straten, & Tiemens, 2017), the present study provides further evidence that applying prognostic indices to guide personalized treatment recommendations is likely to improve treatment outcomes. Low-intensity guided self-help interventions are recommended as first-line treatments for several common mental disorders (NICE, 2011) and are becoming widely available in routine stepped care services (Clark, 2011). The application of evidence-based treatment selection algorithms like the one demonstrated in this study could help to maximize the cost-effectiveness of LIT by selectively offering it to those who are most likely to derive benefits. Equally, prognostic models could be used to fast-track complex cases to HIT in a timely way.
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APPENDICES APPENDIX A: Penalized Categorical Regression Models With Optimal Scaling
ANOVA for PHQ-9 Model (Estimation Sample)
Figure A1. Lasso paths for Patient Health Questionnaire–9 (PHQ-9) model. See the online article for the color version of this figure.
Lasso-Based Model Coefficients
Figure A2. Categorical quantifications and transformation plots for Patient Health Questionnaire–9 (PHQ-9) model (significant predictors only).
ANOVA for GAD-7 Model (Estimation Sample)
Figure A3. Lasso paths for Generalized Anxiety Disorder–7 Scale (GAD-7) model. See the online article for the color version of this figure.
Lasso-Based Model Coefficients
Figure A4. Categorical quantifications and transformation plots for Generalized Anxiety Disorder–7 Scale (GAD-7) model (significant predictors only).
ANOVA for PHQ-9 Model (Estimation Sample)
Figure A1. Lasso paths for Patient Health Questionnaire–9 (PHQ-9) model. See the online article for the color version of this figure.
Lasso-Based Model Coefficients
Figure A2. Categorical quantifications and transformation plots for Patient Health Questionnaire–9 (PHQ-9) model (significant predictors only).
ANOVA for GAD-7 Model (Estimation Sample)
Figure A3. Lasso paths for Generalized Anxiety Disorder–7 Scale (GAD-7) model. See the online article for the color version of this figure.
Lasso-Based Model Coefficients
Figure A4. Categorical quantifications and transformation plots for Generalized Anxiety Disorder–7 Scale (GAD-7) model (significant predictors only). APPENDIX B: Receiver Operating Characteristic (ROC) Curves
Figure B1. Patient Health Questionnaire–9 (PHQ-9) prognostic index (PI) as a predictor of post-treatment reliable and clinically significant improvement: Estimation sample.
Figure B2. Patient Health Questionnaire–9 (PHQ-9) prognostic index (PI) as a predictor of post-treatment reliable and clinically significant improvement: Validation sample.
Figure B3. Generalized Anxiety Disorder–7 scale (GAD-7) prognostic index (PI) as a predictor of post-treatment reliable and clinically significant improvement: Estimation sample.
Figure B4. Generalized Anxiety Disorder–7 scale (GAD-7) prognostic index (PI) as a predictor of post-treatment reliable and clinically significant improvement: Validation sample.
Figure B1. Patient Health Questionnaire–9 (PHQ-9) prognostic index (PI) as a predictor of post-treatment reliable and clinically significant improvement: Estimation sample.
Figure B2. Patient Health Questionnaire–9 (PHQ-9) prognostic index (PI) as a predictor of post-treatment reliable and clinically significant improvement: Validation sample.
Figure B3. Generalized Anxiety Disorder–7 scale (GAD-7) prognostic index (PI) as a predictor of post-treatment reliable and clinically significant improvement: Estimation sample.
Figure B4. Generalized Anxiety Disorder–7 scale (GAD-7) prognostic index (PI) as a predictor of post-treatment reliable and clinically significant improvement: Validation sample. APPENDIX C: Logistic Regression Model Predicting (High-Intensity Treatment vs. Low-Intensity Treatment) Group Membership
Variables in the Equation
Variables in the EquationSubmitted: November 14, 2016 Revised: May 16, 2017 Accepted: May 29, 2017
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Source: Journal of Consulting and Clinical Psychology. Vol. 85. (9), Sep, 2017 pp. 835-853)
Accession Number: 2017-36111-001
Digital Object Identifier: 10.1037/ccp0000231
Record: 31- Title:
- Childhood cognitive measures as predictors of alcohol use and problems by mid-adulthood in a non-Western cohort.
- Authors:
- Luczak, Susan E.. Department of Psychology, University of Southern California, Los Angeles, CA, US, luczak@usc.edu
Yarnell, Lisa M.. Department of Psychology, University of Southern California, Los Angeles, CA, US
Prescott, Carol A.. Department of Psychology, University of Southern California, Los Angeles, CA, US
Raine, Adrian. Departments of Criminology, Psychology, and Psychiatry, University of Pennsylvania, PA, US
Venables, Peter H.. Department of Psychology, University of York, York, United Kingdom
Mednick, Sarnoff A.. Department of Psychology, University of Southern California, Los Angeles, CA, US - Address:
- Luczak, Susan E., Department of Psychology, SGM 501, University of Southern California, Los Angeles, CA, US, 90089-1061, luczak@usc.edu
- Source:
- Psychology of Addictive Behaviors, Vol 29(2), Jun, 2015. pp. 365-370.
- NLM Title Abbreviation:
- Psychol Addict Behav
- Page Count:
- 6
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- Bulletin of the Society of Psychologists in Addictive Behaviors; Bulletin of the Society of Psychologists in Substance Abuse
- Other Publishers:
- US : Educational Publishing Foundation
Society of Psychologists in Addictive Behaviors - ISSN:
- 0893-164X (Print)
1939-1501 (Electronic) - Language:
- English
- Keywords:
- Indian, African, intelligence, Joint Child Health Project, prospective research design
- Abstract:
- This study examined the relationship between childhood cognitive functioning and academic achievement and subsequent alcohol use and problems in a non-Western setting. We examined longitudinal data from a birth cohort sample (N = 1,795) who were assessed at age 11 years on cognitive measures and then approximately 25 years later on lifetime alcohol use and alcohol use disorder symptom count. The sample was from Mauritius (eastern Africa), which allowed us to examine these relationships in a non-Western society with a different social structure than is typical of prior cognitive studies on primarily White samples in Western societies. Poorer performance on the Trail Making Test B-A in childhood predicted being a lifetime drinker, even after covarying for gender, childhood psychosocial adversity, and Muslim religion. Lower academic achievement and verbal IQ, but not performance IQ, were predictive of subsequent alcohol problems after including demographic covariates; the relationship between verbal IQ and alcohol problems was stronger in females than males. A nonlinear relationship emerged for Trails, suggesting that only more extreme impairment on this measure was indicative of later alcohol problems. Results of this study provide evidence that verbal deficits and poor academic performance exist in a general cohort sample by age 11 years (when 99% were nondrinkers) for those who go on to develop alcohol problems. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Academic Achievement; *Alcohol Drinking Patterns; *Alcoholism; *Cognitive Ability; Intelligence Quotient
- Medical Subject Headings (MeSH):
- Adult; Alcohol Drinking; Alcoholism; Alcohols; Child; Cognition; Educational Status; Female; Humans; Male; Neuropsychological Tests
- PsycINFO Classification:
- Substance Abuse & Addiction (3233)
- Population:
- Human
Male
Female - Location:
- Mauritius
- Age Group:
- Childhood (birth-12 yrs)
School Age (6-12 yrs)
Adulthood (18 yrs & older) - Tests & Measures:
- WISC (Wechsler Intelligence Scale for Children)
Trail Making Test DOI: 10.1037/t00757-000 - Grant Sponsorship:
- Sponsor: National Institutes of Health, US
Grant Number: K08 AA14265; R01 AA10207; R01 AA18179
Recipients: No recipient indicated
Sponsor: Mauritian Ministry of Health, Mauritania
Recipients: No recipient indicated
Sponsor: Medical Research Council
Recipients: No recipient indicated
Sponsor: Wellcome Trust
Recipients: No recipient indicated - Methodology:
- Empirical Study; Longitudinal Study; Interview; Quantitative Study
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- First Posted: Jan 26, 2015; Accepted: Oct 6, 2014; Revised: Sep 30, 2014; First Submitted: Jul 10, 2014
- Release Date:
- 20150126
- Correction Date:
- 20150615
- Copyright:
- The Author(s). 2015
- Digital Object Identifier:
- http://dx.doi.org/10.1037/adb0000043
- PMID:
- 25621419
- Accession Number:
- 2015-03022-001
- Number of Citations in Source:
- 39
- Persistent link to this record (Permalink):
- http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2015-03022-001&site=ehost-live
- Cut and Paste:
- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2015-03022-001&site=ehost-live">Childhood cognitive measures as predictors of alcohol use and problems by mid-adulthood in a non-Western cohort.</A>
- Database:
- PsycINFO
Childhood Cognitive Measures as Predictors of Alcohol Use and Problems by Mid-Adulthood in a Non-Western Cohort / BRIEF REPORT
By: Susan E. Luczak
Department of Psychology, University of Southern California, and Department of Psychiatry, University of California, San Diego;
Lisa M. Yarnell
Department of Psychology, University of Southern California
Carol A. Prescott
Department of Psychology and Davis School of Gerontology, University of Southern California
Adrian Raine
Departments of Criminology, Psychology, and Psychiatry, University of Pennsylvania
Peter H. Venables
Department of Psychology, University of York
Sarnoff A. Mednick
Department of Psychology, University of Southern California
Acknowledgement: Supported by the National Institutes of Health (Grants K08 AA14265, R01 AA10207, and R01 AA18179), the Mauritian Ministry of Health, the Medical Research Council, and the Wellcome Trust. We thank the staff of the Joint Child Health Project for their assistance with data collection and management, the Joint Child Health Project participants for their lifelong participation in this project, and John L. Horn, PhD, for his mentorship on this study.
Developmental models of the etiology of alcohol problems have proposed several pathways for how childhood cognitive deficits relate to later alcohol problems. Deviance proneness models purport that underlying behavioral dysregulation manifests in part as cognitive deficits and poor academic performance in childhood and later as alcohol problems in adulthood (see Gorenstein & Newman, 1980; Sher, 1991; Zucker, Chermack, & Curran, 2000). Alternative models have proposed more direct paths—that high intelligence and academic success can lead to both increased likelihood of being a lifetime drinker by placing individuals in heavier drinking environments (e.g., college) as well as decreased likelihood of developing alcohol problems via better opportunities in adulthood that may buffer against problems (see Johnson, Hicks, McGue, & Iacono, 2009). Conversely, pressure to maintain academic achievement may result in distress (e.g., Stoeber & Rambow, 2007), which in turn can lead to alcohol use and problems (Crum et al., 2006; Schulenberg, Bachman, O’Malley, & Johnston, 1994). It is also possible that multiple processes are involved, with both behavioral undercontrol and contextual factors contributing to the link between early cognitive deficits and later alcohol involvement.
General population sample studies have demonstrated that relatively higher IQs are typically found among low-to-moderate drinkers compared with abstainers and heavy drinkers (see Anstey, Windsor, Rodgers, Jorm, & Christensen, 2005; Müller et al., 2013, for reviews). In longitudinal studies, higher childhood IQ has been positively associated with alcohol use and higher consumption levels in early and later adulthood (Johnson, Hicks, McGue, & Iacono, 2009; Kanazawa & Hellberg, 2010). For example, in a U.S. national sample of young adults assessed over a 5-year interval, higher verbal IQ predicted increased risk for subsequent drinking and decreased risk for problems among drinkers even after covarying for socioeconomic status (Windle & Blane, 1989). This is consistent with a prospective study of a general Scottish sample that found higher verbal IQ at 11 years was associated with alcohol problems 40 years later after covarying for socioeconomic position (Batty, Deary, & Macintyre, 2006). Taken together, these studies suggest that higher IQ, and in particular verbal IQ, is predictive of increased likelihood of being a lifetime drinker and decreased likelihood of alcohol-related problems later in life, and that these associations are not accounted for by sociodemographic correlates of higher verbal abilities.
Academic achievement is also a potential childhood cognitive predictor of subsequent alcohol involvement. Support for poor academic achievement associated with subsequent alcohol use and problems also has been found in longitudinal studies and general samples with varying ranges of follow-up (see, e.g., Duncan, Duncan, Biglan, & Ary, 1998; Hawkins, Catalano, & Miller, 1992; Schulenberg et al., 1994). For example, Hayatbakhsh, Najman, Bor, Clavarino, and Alati (2011) demonstrated that poorer school performance at age 14 years predicted alcohol problems 21 years later in a general sample of Australian students, although already at age 14 over 60% of the sample indicated drinking in the past week. Studies that examine academic achievement in younger samples are needed to help clarify if poor academic achievement is a predictor of subsequent alcohol involvement or just a consequence of early alcohol use.
In this study, we present longitudinal data from a birth cohort sample that was assessed at age 11 years on measures of cognitive and academic ability (prior to the typical age of onset of alcohol use) and then approximately 25 years later for lifetime alcohol use and alcohol use disorder (AUD) symptoms. The sample was from the island of Mauritius (a middle-income eastern African nation), allowing for the examination of these relationships in a non-Western society that values education and academic performance (the population has an 89% literacy rate and public primary and secondary education are free; Central Intelligence Agency, 2013; Southern and Eastern Africa Consortium for Monitoring Educational Quality [SACMEQ], 2012), but where childhood cognitive performance is not linked to heavy drinking environments as it often is in Western societies (e.g., college; Slutske et al., 2004). However, as in Western societies, both intelligence and school success in Mauritius may enable individuals to obtain financial and personal resources that increase the likelihood and opportunities for social drinking, even if buffering against risk for alcohol problems (see Johnson et al., 2009; Müller et al., 2013). Such contextual factors may affect relationships between childhood cognitive performance and subsequent alcohol involvement; thus, examining these associations in novel societies such as Mauritius will help determine the generalizability of the developmental models that have been generated using data primarily from Western societies (see Luczak et al., 2014). Given our prior findings with this sample that found gender and an index of psychosocial adversity (based on familial, housing, and environmental variables) were associated with IQ (Liu, Raine, Venables, & Mednick, 2004; Lynn, Raine, Venables, Mednick, & Irwing, 2005), and that being Muslim was protective for lifetime drinking but not for alcohol problems among drinkers (Luczak et al., 2014) we recognized the importance of including gender, psychosocial adversity, and Muslim religion when examining the link between cognition and alcohol use in this sample.
MethodData were from the Joint Child Health Project (JCHP), a longitudinal study in Mauritius that has followed a birth cohort of 1,795 children since 1972 when they were 3 years old (see Raine, Liu, Venables, Mednick, & Dalais, 2010). The original sample was 51% male and, similar to the population of the island, 69% were of Indian heritage, 26% Creole (admixture of primarily African descent), and 6% other (primarily of Chinese and French heritage); 17% were Muslim.
Childhood Data Collection Phase
At age 11 years, participants were assessed on cognitive ability and home environment (see each scale for n values, which differ across scales because a cyclone brought this testing phase to an early end). The 11-year-old sample did not differ significantly from the 3-year-old sample on gender, ethnicity, or psychosocial adversity (see Raine, Reynolds, Venables, Mednick, & Farrington, 1998).
All instruments were administered to children individually by trained research staff. The official language of Mauritius is English, and schooling is conducted primarily in English; but because the common spoken language on the island is Kreol, instructions were given in Kreol. Seven subtests of the Wechsler Intelligence Scale for Children (WISC; Wechsler, 1949) were administered to 1,258 children. These subtests were used to create estimates of Performance IQ (PIQ; Picture Completion, Block Design, Object Assembly, Coding, and Mazes) and Verbal IQ (VIQ; Similarities and Digit Span). Scores were standardized and normalized within the sample (see Lynn et al., 2005 and Raine, Yaralian, Reynolds, Venables, & Mednick, 2002, for details).
The Trail Making Test (TMT; U.S. Army, 1944) was administered to 1,157 of the children. The two components of this task (Parts A [TMT-A] and B [TMT-B]) assess visuomotor tracking, motor speed, and attention, and TMT-B also contains a working memory component requiring mental flexibility (Lezak, 1995; Reitan, 1958). The difference score for TMT-B versus TMT-A (TMT B-A), an indicator of complex divided attention and sequencing (Strauss, Sherman, & Spreen, 2006), was calculated and corrected for age (M = 11.1 years, SD = 0.70) by residualization (see Raine, Reynolds, Venables, & Mednick, 2002).
At the end of primary school (sixth year of school), students take the Certificate of Primary Education (CPE) achievement examination that covers a range of academic topics (English, French, Mathematics, Environmental Studies; see SACMEQ, 2012). Unweighted scores on the CPE range from 0−20 and represent an overall index of academic achievement. CPE scores were obtained from official records for 1,415 of the sample.
Midadulthood Data Collection Phase
In mid–adulthood (M = 36.9 years, SD = 1.39), all available participants (n = 1,209 [67%]) were assessed for lifetime alcohol use and problems (other 9% abroad, 4% refused, 2% deceased, and 18% unable to contact). Written informed consent was obtained, and the research was approved by the University of Southern California Institutional Review Board. The sample assessed in adulthood did not differ significantly from the 3-year-old sample on ethnicity or psychosocial adversity, but was more likely to be male (55% vs. 51%; see Luczak et al., 2014).
Trained research staff interviewed participants about their drinking histories in Kreol. Lifetime drinkers were defined as those who had ever consumed at least one standard drink (about 14 g of alcohol), and the age when the first standard drink was consumed was obtained. Lifetime drinkers were assessed for lifetime Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM–IV) AUD symptoms (American Psychiatric Association, 1994) using the Structured Clinical Interview for DSM–IV Diagnosis (Spitzer, Gibbon, & Williams, 1997. Only 15 (1.2%) participants reported they had consumed a standard drink and none (0%) endorsed having an AUD symptom as of age 11 years.
Final Analytic Sample
We removed four participants who were developmentally delayed, resulting in a final analytic sample of 1,107 with childhood cognitive and adult alcohol data (see Table 1). This sample was 72% Indian, 21% Creole, and 7% other; 55% were male and 22% Muslim. Scores on the cognitive measures and psychosocial adversity did not differ significantly from the full sample assessed at age 11 years. Lifetime drinker (66%) and AUD (16%) prevalence were similar to those previously reported for the full sample assessed in midadulthood (67% and 16%, respectively; Luczak et al., 2014). In those who endorsed an AUD symptom (n = 205), mean symptom count was 3.7 (SD = 2.46; range: 1–11).
Intercorrelations Among Cognitive Measures and Demographic Covariates
Data Analyses
We ran logistic regression models to examine cognitive predictors and covariates of being a lifetime drinker. We used zero-inflated negative binomial (ZINB) regression models to examine cognitive predictors and demographic covariates of AUD symptom count. ZINB is a two-part parametric mixture model for count data that have a large proportion of zero values and a highly skewed distribution of nonzero values, as is typically found for AUD symptoms in general populations samples (see Pardini, White, & Stouthamer-Loeber, 2007). ZINB models are also appropriate when there is heteroscedasticity in the count, which may occur if one covariate group (e.g., males, Muslims) produces different counts than another (Neelon & O’Malley, 2014).
We first modeled each cognitive predictor alone, then with demographic covariates (i.e., gender, childhood psychosocial adversity, and Muslim religion). All predictor variables were normally distributed (skew < |.90|, all kurtosis < |1.76|; note that we divided IQ variables by 10 to put all predictors on similar scales to yield more interpretable betas). We ran a final set of models that simultaneously entered the cognitive measures that were significant individual predictors of AUD symptoms to examine these cognitive variables in concert. Models with significant quadratic or interaction terms (created through cross-multiplication based on centered predictors) are presented only when one of these terms was significant.
ResultsTable 1 shows basic associations among the predictors in our models. Consistent with our prior publications (Liu et al., 2004; Lynn et al., 2005; Yarnell et al., 2013), being male correlated with higher PIQ, childhood psychosocial adversity correlated with poorer cognitive performance, and the three demographic variables did not correlate with one another.
Predictors of Lifetime Drinking
Logistic regression models found that those performing better on the TMT B-A were more likely to be lifetime drinkers, even after the addition of covariates in the model (b = −2.48, p = .017). A significant association of PIQ (b = 0.14, p = .005) with lifetime drinking in the univariate model was reduced to nonsignificant with the addition of covariates (p = .73). Neither VIQ nor CPE scores were significantly associated with lifetime drinking, with or without covariates.
Predictors of AUD Symptoms
Table 2 shows results of the symptom count portion of the ZINB models for each cognitive predictor alone and with the three demographic covariates.
Childhood Cognitive Predictors of Lifetime Alcohol Use Disorder Symptom Count With and Without Covarying for Gender, Childhood Psychosocial Adversity, and Muslim Religion
Intellectual ability
In univariate models, lower scores on each IQ scale predicted AUD symptoms (ps < .04), but only VIQ remained a significant predictor with covariates (PIQ reduced to p = .09). We found one significant VIQ × Male interaction (b = 0.05, p = .017), which we probed by rerunning the model separately for each gender. Lower VIQ was a stronger predictor of AUD symptoms for females (b = −0.53, p < .001) than for males (b = −0.08, p = .07).
Trail Making Test
Models including nonlinear terms revealed a significant negative effect of (TMT B-A)2 on AUD symptoms (b = 0.07, p < .05). We probed this effect by reestimating the model with the TMT B-A distribution trichotomized into three groups (fast = < −1 SD; midrange = between −1 and +1 SD; slow = > 1 SD). The relationship of TMT B-A with AUD symptom count differed in both direction and magnitude across the three groups (fast b = −1.13, p = .79; midrange b = 0.94, p = .55; and slow b = 2.61, p = .05). Only for those in the slow range was there indication of TMT B-A being predictive of AUD symptoms.
Academic achievement
Lower CPE scores were predictive of higher AUD symptoms, with (p = .029) and without (p = .012) covariates included in the model.
Multiple cognitive predictors
Lastly, we entered the two cognitive variables that were significant in linear models, VIQ and CPE scores, simultaneously in ZINB models with and without covariates. When entered together, neither predicted AUD symptoms (ps > .24), indicating the portion of each of these associated with AUD symptoms may be shared.
DiscussionThis study examined cognitive abilities and academic achievement in a general cohort, non-Western sample of 11-year-old youth as predictors of alcohol involvement over approximately the next 25 years. Lifetime drinking was predicted only by better childhood performance on the TMT B-A once demographic covariates were taken into account. Lower verbal abilities and academic achievement were linearly associated with subsequent alcohol problems, whereas the relationship between poorer performance on the TMT B-A and alcohol problems only emerged at the lower end of the performance range. All of these associations were found prospectively in a general sample of youth tested prior to the typical onset of drinking in this society (99% of the sample had not consumed a full drink), indicating these associations existed prior to alcohol use and were not merely consequences of consumption.
Being a lifetime drinker was not strongly linked to childhood cognitive measures in this Mauritian sample after accounting for demographic covariates. Studies of twin samples have found that initiation of alcohol use is more strongly influenced by environmental factors than genetic factors (e.g., Heath, Meyer, Eaves, & Martin, 1991; Rhee et al., 2003), and thus factors associated with lifetime drinking status may vary more across cultures. One environmental explanation that has been proposed for the higher levels of drinking seen among those with higher intellectual ability and achievement in Western societies is that this increases the opportunity for alcohol use (e.g., Johnson et al., 2009; Slutske et al., 2004). On Mauritius, however, higher academic achievement does not typically place young adults in a more risky drinking environment, given that higher education is not linked to moving away from one’s family of origin, nor does high academic achievement necessarily allow one to pursue higher education. Thus, our findings are consistent with the idea that exposure to drinking environments contributes to the link between childhood cognitive performance and lifetime drinking. Additional examination of differences in drinking norms, contexts, and accessibility across societies may further elucidate unique environmental components of this relationship.
On the other hand, both childhood verbal intelligence and academic achievement were predictive of lifetime alcohol-related problems, even after accounting for gender, childhood psychosocial adversity, and Muslim religion in this Mauritian sample. These results are consistent with other prospective cohort studies in Western societies (e.g., Batty et al., 2006; Windle & Blane, 1989), providing further evidence that poor cognitive performance in childhood is a robust predictor of later alcohol problems across cultures.
In our sample, verbal intelligence was a stronger predictor of lifetime alcohol problems in females than in males. The specificity of the relationship of lower verbal abilities with alcohol problems in our female sample suggests the possibility of a pathway to alcohol problems that is distinct from the deviance proneness pathway (which is more associated with behavioral undercontrol in males; Sher, 1991) that may operate more through other factors such as social skills and judgment (see Maggs, Patrick, & Feinstein, 2008; Windle & Blane, 1989). Further examination of gender differences in models that include social, peer, and situational factors may help explain the association between verbal abilities and alcohol-related problems.
Poor academic achievement in childhood was found to be a significant predictor of lifetime alcohol problems in both males and females. This relationship remained significant after covarying for psychosocial adversity, even though previous reports with this sample have shown psychosocial adversity to be associated with lower IQ scores (Liu et al., 2004). In Mauritius, primary education is mandated, and the children in our cohort had relatively uniform educational opportunities up through age 11 years, regardless of psychosocial adversity. A mediational pathway cannot be established with these data, but our findings indicate that poor academic achievement by the end of primary school is a risk factor for subsequent alcohol problems that is not simply accounted for by concurrent psychosocial adversity.
The measure of TMT B-A exhibited a curvilinear relationship with lifetime alcohol problems. More severe deficits served as a risk factor, but stronger performance did not add any protection against developing alcohol problems. Our findings suggest that, within a general sample, the link between poor functioning on the TMT B-A in childhood and subsequent alcohol problems emerges only for those in the lowest performance range. The deviance proneness model hypothesizes that underlying behavioral dysregulation manifests in childhood as attention and cognitive deficits; the TMT B-A difference score, a measure of complex divided attention and sequencing (Strauss et al., 2006), may be assessing abilities that are indicators of this dysregulation construct. Future research examining general samples that include individuals on the tail ends of performance will improve our understanding of the specificity of various cognitive measures as precursors to alcohol problems as well as their relationships with other measures of behavioral dysregulation.
Our findings should be interpreted along with the limitations of the study, including the use of a limited number of measures to assess cognitive performance in a culture for which they were not designed, attrition as is the case in any longitudinal study, and the inability to speak to mechanisms for how cognitive deficits in childhood lead to alcohol problems by adulthood. Despite these limitations, this study provides evidence that childhood cognitive performance is not strong a predictor of lifetime alcohol use in this society, indicating environmental specificity of previously found relationships in Western societies, but that childhood cognitive deficits are risk factors for subsequent alcohol problems in this novel east African cultural context, providing further evidence of the generalizability of this relationship across societies.
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Submitted: July 10, 2014 Revised: September 30, 2014 Accepted: October 6, 2014
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Source: Psychology of Addictive Behaviors. Vol. 29. (2), Jun, 2015 pp. 365-370)
Accession Number: 2015-03022-001
Digital Object Identifier: 10.1037/adb0000043
Record: 32- Title:
- Childhood family characteristics and prescription drug misuse in a national sample of Latino adults.
- Authors:
- Vaughan, Ellen L.. Department of Counseling and Educational Psychology, Indiana University Bloomington, Bloomington, IN, US, elvaugha@indiana.edu
Waldron, Mary. Department of Counseling and Educational Psychology, Indiana University Bloomington, Bloomington, IN, US
de Dios, Marcel A.. Department of Health Disparities Research, University of Texas M. D. Anderson Cancer Center, Houston, TX, US
Richter, James. Department of Counseling and Educational Psychology, Indiana University Bloomington, Bloomington, IN, US
Cano, Miguel Ángel. Department of Epidemiology, Robert Stempel College of Public Health & Social Work, Florida International University, Miami, FL, US - Address:
- Vaughan, Ellen L., Department of Counseling and Educational Psychology, Indiana University Bloomington, 201 North Rose Avenue, Bloomington, IN, US, 47405-1006, elvaugha@indiana.edu
- Source:
- Psychology of Addictive Behaviors, Vol 31(5), Aug, 2017. pp. 570-575.
- NLM Title Abbreviation:
- Psychol Addict Behav
- Page Count:
- 6
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- Bulletin of the Society of Psychologists in Addictive Behaviors; Bulletin of the Society of Psychologists in Substance Abuse
- Other Publishers:
- US : Educational Publishing Foundation
Society of Psychologists in Addictive Behaviors - ISSN:
- 0893-164X (Print)
1939-1501 (Electronic) - Language:
- English
- Keywords:
- Hispanic/Latino, prescription drug misuse, parental alcoholism, parental divorce, parental death
- Abstract:
- Prescription drug misuse is a growing public health concern and has been understudied in Latino populations. The current study tests the relationships between childhood and family characteristics and prescriptions drug misuse among adult Latinos. A subsample of 8,308 Latinos from the National Epidemiologic Survey on Alcohol and Related Conditions (NESARC) were examined. Logistic regression analyses tested associations between parental alcoholism, parental divorce before age 18, and parental death before age 18 and prescription drug misuse and prescription drug use disorder. Parental alcoholism and parental divorce increased the odds of both prescription drug misuse and use disorder. Parental death increased the odds of prescription drug use disorders. The results have important implications for understanding the complex associations between family psychosocial history and prescription drug misuse. (PsycINFO Database Record (c) 2017 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Children of Alcoholics; *Drug Abuse; *Prescription Drugs; *Family History; *Latinos/Latinas; Death and Dying; Divorce; Parents
- PsycINFO Classification:
- Substance Abuse & Addiction (3233)
- Population:
- Human
Male
Female - Location:
- US
- Age Group:
- Adulthood (18 yrs & older)
- Tests & Measures:
- Parental Alcoholism Measure [Appended]
- Methodology:
- Empirical Study; Quantitative Study
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- First Posted: Apr 24, 2017; Accepted: Mar 19, 2017; Revised: Mar 6, 2017; First Submitted: Oct 19, 2016
- Release Date:
- 20170424
- Correction Date:
- 20170807
- Copyright:
- American Psychological Association. 2017
- Digital Object Identifier:
- http://dx.doi.org/10.1037/adb0000278
- PMID:
- 28437122
- Accession Number:
- 2017-17890-001
- Persistent link to this record (Permalink):
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- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2017-17890-001&site=ehost-live">Childhood family characteristics and prescription drug misuse in a national sample of Latino adults.</A>
- Database:
- PsycINFO
Childhood Family Characteristics and Prescription Drug Misuse in a National Sample of Latino Adults / BRIEF REPORT
By: Ellen L. Vaughan
Department of Counseling and Educational Psychology, Indiana University Bloomington;
Mary Waldron
Department of Counseling and Educational Psychology, Indiana University Bloomington
Marcel A. de Dios
Department of Health Disparities Research, University of Texas M. D. Anderson Cancer Center
James Richter
Department of Counseling and Educational Psychology, Indiana University Bloomington
Miguel Ángel Cano
Department of Epidemiology, Robert Stempel College of Public Health & Social Work, Florida International University
Acknowledgement: A poster using the broader National Epidemiologic Survey on Alcohol and Related Conditions data looking at associations between parental divorce and death and prescription drug misuse was presented at the American Psychological Association in 2010. The results of the current study focusing on the Latino subsample have not been previously disseminated.
Note: John M. Roll served as the action editor for this article.
The Centers for Disease Control and Prevention ([CDC]; 2016) report that overdoses from prescription drugs resulting in death increased rapidly from 1999 to 2014. The CDC analyzed the morbidity underlying these overdoses and found that number of emergency room visits for nonmedical use of opioid analgesics increased by 111% between 2004 and 2008 (Cai, Crane, Poneleit, & Paulozzi, 2010). Emergency room visits related to nonmedical benzodiazepine use increased 89% from 2004 to 2008. The misuse of prescription drugs is a public health problem that permeates the range of communities from urban cities to rural communities (Substance Abuse and Mental Health Services Administration, Center for Behavioral Health Statistics and Quality, 2013). Data from the National Survey on Drug Use and Health (Substance Abuse and Mental Health Services Administration [SAMHSA], 2015) indicate that approximately 6.5 million (2.5%) Americans use psychotherapeutic drugs for nonmedical use each month.
Research investigating prescription drug misuse among Latino populations is sparse and needed. Latinos represent a large and growing population in the United States (Stepler & Brown, 2016). It has been noted that because the population is young, Latinos are at increased risk for substance use (Volkow, 2006). Among Latino eighth graders, the rates of use for various prescription drugs such as hydrocodone are on par with or exceed use of these substances by non-Latino, White youth (Johnston, O’Malley, Miech, Bachman, & Schulenberg, 2016). The rates of use fall by 12th grade, but this may be due to high rates of dropout and school exclusion by minority youth. In adulthood and older adulthood, the rates of prescription drug misuse among Latinos is also concerning (SAMHSA, 2015). For example, a recent study showed that, among adults age 65 and older, Latinos having a greater odds of misusing prescription drugs than non-Latino Whites or African Americans (Moore et al., 2009).
The empirical literature addressing correlates of substance use among Latinos has often focused on important cultural and contextual factors. Familismo, which reflects the cultural saliency of family for Latinos, remains a powerful cultural value for Latinos (Santiago-Rivera, Arredondo, & Gallardo-Cooper, 2002). Thus, prevention and intervention efforts with Latinos are often family focused (e.g., Familias Unidas; Coatsworth, Pantin, & Szapocznik, 2002). Family factors have been related to substance use among Latino adolescents and emerging adults (Knight et al., 2010). There are a number of studies linking illicit substance use, including nonprescribed use of prescription drugs, to parental alcoholism and parental divorce in predominantly White, non-Latino samples (e.g., Waldron, Grant, et al., 2014, Waldron, Vaughan, et al., 2014). However, little is known about parental alcoholism and parental divorce before the age of 18 in Latino families and their relationship to prescription drug misuse, specifically opiates, tranquilizers, and benzodiazepines. To our knowledge, there are no published studies of parental death before the age of 18 and risk of prescription drug misuse, regardless of racial/ethnic background.
Research testing associations between family characteristics and prescription drug misuse among Latinos has had some growth, but has largely focused on adolescents. For example, Ford and Rigg (2015) found that stronger bonds with parents reduced the odds of prescription pain reliever misuse among Latino adolescents. In another study, perceived parental disapproval of marijuana and alcohol use decreased the odds of prescription drug misuse among Latinos (Conn & Marks, 2014). Two studies report associations with family structure, including parental separation or divorce. Using Monitoring the Future data, Harrell and Broman (2009) did not find an association between family structure (e.g., single parenthood and stepfamily vs. two-parent household) and prescription drug misuse among Latino adolescents. Barrett and Turner (2006) found that less problematic adolescent substance use was associated with intact mother-father families than single-parent families. However, those single-parent families that had at least one additional relative living in the household did not show the increased risk for substance use among adolescents. While this study had large subsample of Latino youth, the authors did not assess these specific relationships stratified by racial or ethnic groups.
Parental alcoholism has not been studied in relation to prescription drug misuse among Latino samples. Such an omission is surprising given long-documented research on children of alcoholics and their increased risk for early and problem involvement across a range of substance classes (Sher, 1991; Windle & Searles, 1990), including prescription drugs (Tucker et al., 2015). Information on parental alcoholism and both parental divorce and parental death during childhood is important to understanding the associations between family context and prescription drug misuse in an understudied sample of Latinos. In addition, the current study will also include nativity as an important covariate for Latino populations. Previous research has indicated that Latinos born outside of the United States have lower rates of substance use disorders (Alegria, Canino, Stinson, & Grant, 2006). The aim of the current study is to test association between family characteristics prior to age 18 and prescription drug misuse in a national sample of Latino adults. Important demographic covariates such as biological sex and nativity will be included in models testing these associations.
MethodThis study is a secondary data analysis of data from wave one of the NESARC. The NESARC is a representative sample of noninstitutionalized adults in the United States and District of Columbia (Chen et al., 2006; Grant & Dawson, 2006). Participants’ data are gathered through in-home computer assisted interviews. The primary goal of NESARC is to ascertain the prevalence of alcohol use disorders and related problems. Data are collected on a broad spectrum of substance use behaviors including use of prescription drugs without a prescription. Review by the institutional review board determined that the research was exempt from review due to the publically available and deidentified nature of the data.
Participants
Participants for this secondary data analysis were self-identified Latino adults who participated in wave one of the NESARC (n = 8,308). The sample was nearly evenly split between men and women (49.1% female) and over half of the sample was born outside of the United States (57.3%). With respect to prescription drug misuse, 4.9% of the sample reported lifetime prescription drug misuse and 1.4% reported lifetime prescription drug use disorder. More detailed demographic information can be found in Table 1.
Frequencies
Measures
Demographic variables
Demographic variables included biological sex, marital status, income, and nativity. Biological sex (female = 0; male = 1), marital status (not married = 0; married = 1), and nativity (0 = born outside of the U.S.; 1 = born in the U.S.) were all dummy coded. Personal income was a four-level categorical variable representing, 0 = $0–$19,999, 1 = $20,000–$49,999, 2 = $50,000–$79,999, and 3 = $80,000 and up.
Childhood family characteristics
Participants were asked whether they had experienced a number of family transitions prior to the age of 18. For the current study, we tested whether participants experienced divorce of a parent (0 = no, 1 = yes) and whether the participant experienced the death of a parent (0 = no, 1 = yes).
Parental alcoholism
Two items assessed maternal and paternal alcoholism. Participants were asked, “Has your blood or natural <parent> been an alcoholic or problem drinker at any time in <his/her> life?”. These two items were combined and coded to indicate whether or not the participant’s parent had parental a history of alcoholism (0 = no, 1 = yes).
Substance use disorders
Alcohol use disorder was coded as lifetime history of Diagnostic and Statistical Manual of Mental Disorders (4th ed.; DSM–IV; American Psychiatric Association, 1994) alcohol abuse or dependence (0 = no, 1 = yes). Nicotine dependence is based upon DSM–IV criteria and reflects lifetime nicotine dependence (0 = no, 1 = yes).
Prescription drug misuse and disorder
Lifetime prescription drug misuse was coded using three items, whether the participant had ever used (a) sedatives, (b) tranquilizers, or (c) opioids (heroin is asked separately; [0 = no, 1 = yes]). Participants were instructed to respond about use that was “on your own-that is either without a doctor’s prescription; in greater amounts, more often or longer than prescribed; or for a reason other than a doctor said you should use them.” Prescription drug use disorder was coded as lifetime history of DSM-IV sedative, tranquilizer, or opioid (excluding heroin) abuse or dependence (0 = no, 1 = yes).
Data Analytic Plan
Preliminary descriptive analyses and logistic regression analyses were conducted for the current study. In order to account for the sampling design, the complex samples module for SPSS 24 was used for data analysis. First, a series of unadjusted analyses were conducted to test bivariate associations between each family variable of interest and covariates with prescription drug misuse. The same unadjusted analyses were conducted to test bivariate associations between each family variable of interest and covariates with prescription drug use disorders. Next, an overall logistic regression model was tested using all significant variables from unadjusted bivariate analyses; in these analyses, conducted separately for prescription drug misuse and prescription drug disorders, the following independent variables were modeled: biological sex, income, nativity, marital status, parental alcoholism, parental divorce before age 18, and parental death before age 18.
ResultsFor lifetime prescription drug misuse, unadjusted analyses were statistically significant for all variables with the exception of parental death before age 18 (Table 2). In adjusted analyses, the overall model was statistically significant, Wald F = 578.75, p < .001 and explained between 5.7% (Cox and Snell) and 17.7% (Nagelkerke) of the variance in prescription drug misuse. Parental alcoholism (odds ratio [OR] = 1.19, 95% CI [1.07, 1.32]) and parental divorce (OR = 1.55, 95% CI [1.41, 1.71]) before the age of 18 both increased the odds of prescription drug misuse. Lifetime alcohol use disorder (OR = 3.85, 95% CI [3.46, 4.29]) and nicotine dependence (OR = 3.48, 95% CI [2.91, 4.20]) were both associated with greater odds of prescription drug misuse. In adjusted analyses, women had greater odds of prescription drug misuse (OR = 1.25, 95% CI [1.15, 1.37]). Participants born outside of the United States (OR = .66, 95% CI [.58, .74]) and those married or living as married (OR = .68, 95% CI [.60, .76]) had lower odds of prescription drug misuse. Compared with those with incomes greater than $80,000, those with lower income had reduced odds of prescription drug misuse (see Table 2 for ORs by income group).
Unadjusted and Adjusted Odds Ratios
The next series of analyses tested the associations between the family variables of interest and prescription drug use disorder. All variables were significantly related to the outcome and, thus, were retained in adjusted analyses. The overall model was statistically significant, Wald F = 1,873.24, p < .001 and explained between 3.4 (Cox and Snell) and 26.4% (Nagelkerke) of the variance in prescription drug use disorder. Parental alcoholism (OR = 1.35, 95% CI [1.07, 1.71]), parental divorce (OR = 2.43, 95% CI [1.96, 2.99]), and death of a parent (OR = 1.63, 95% CI [1.22, 2.19]) all increased the odds of a prescription drug use disorder. Lifetime alcohol use disorder (OR = 5.92, 95% CI [5.26, 6.67]) and nicotine dependence (OR = 4.57, 95% CI [3.87, 5.46] were both associated with greater odds of prescription drug misuse. Women (OR = .77, 95% CI [.64, .92]), participants born outside of the United States (OR = .52, 95% CI [.44, .62]), and those married or living as married (OR = .53, 95% CI [.41, .68]) had lower odds of a prescriptions drug use disorder. Like lifetime prescription drug misuse, those with lower incomes had lower odds of prescription drug use disorder when compared with those with incomes greater than $80, 000 (see Table 2 for odds ratios by income group).
DiscussionThe aim of the current study was to test the associations between family characteristics prior to age 18 and prescription drug misuse and prescription drug use disorder in a national sample of Latino adults. Prescription drug misuse is a critical public health problem and, historically, has been studied less than other substance classes such as alcohol, tobacco, and marijuana. Understanding risk and protective factors for prescription drug use is critical for the development of prevention and intervention for Latino populations. Family is a core developmental context and Familismo represents an important Latino cultural value (Santiago-Rivera et al., 2002). With respect to prescription drug misuse, it is not clear whether family variables are related in the same ways as for other substances given that attitudes regarding prescription drug use may be more permissive than for other drugs.
Results of the current study underscore the role that family disruption may play in prescription drug misuse behaviors. Parental alcoholism and parental divorce before the age of 18 both increased the odds of any prescription drug misuse as well as prescription drug use disorder even when accounting for other substance use disorders. These results are inconsistent with the adolescent literature which found no relationship between family structure and prescription drug misuse (Harrell & Broman, 2009). There are a number of reasons why this may have occurred. The NESARC is a study of adults, and thus, the prevalence of prescription drug use is greater and allowed for such associations to emerge later in participants’ lives, when access to prescription drugs may be more likely. Barrett and Turner (2006) found that intact family structure was associated with reduced risk for substance use among a sample of young adults with a large subsample of Latinos. The results of the current study are consistent with their study. With respect to parental alcoholism, the results are consistent with other research that has found broad associations between parental alcoholism and substance use disorders (Mellentin et al., 2016) and parental substance use and adolescent prescription drug misuse (Tucker et al., 2015). Death of a parent before the age of 18 increased the odds of a prescription drug use disorder. This result is consistent with previous research using a Caucasian sample of male twins that found associations between parental death and the development of alcohol use disorders (Otowa, York, Gardner, Kendler, & Hettema, 2014).
Of note, a number of control variables were also significantly related to prescription drug misuse and prescription drug use disorders in this Latino subsample. Alcohol and nicotine use disorders were robust predictors of prescription drug misuse. This finding is not surprising, given high rates of co-occurring substance use and substance use disorders (Falk, Yi, & Hiller-Sturmhöfel, 2006, 2008). Latinas were at lower odds for lifetime prescription drug disorders, but not lifetime misuse. Consistent with previous research using the NESARC that found a lower lifetime prevalence of substance use disorders among those born outside the United States (Alegria et al., 2006), participants who were born outside of the United States were also at lower odds for prescription drug misuse and disorders. One potential explanation is that those who are born outside of the United States may hold traditional values that do not promote substance use.
Participants with lower incomes also had lower odds of prescription drug misuse and disorder. More specifically, compared with those participants who reported family incomes greater than $80,000, those in every other income category had lower odds of prescription drug misuse and prescription drug misuse disorders. These results are contrary to other NESARC studies that include all racial and ethnic groups (e.g., Martins, Keyes, Storr, Zhu, & Chilcoat, 2009; Wu, Woody, Yang, & Blazer, 2010). This finding may be connected to the focus on Latino participants in the study. Lower incomes may be a proxy indicator for less acculturation to dominant United States culture which has been associated with lower rates of substance use among Latinos (Zemore, 2007). Further, while not the case for other racial and ethnic groups and prescription drug access, lower income for Latino groups may be associated with poorer access to both health care and the financial means to obtain such substances. This represents an important area of further research. Finally, consistent with previous research, participants who are married or living as married had lower odds of prescription drug misuse and disorder than those with other marital statuses. It may be that being married or in a marriagelike relationship confers a greater level of social support and connectedness to reduce risk for prescription drug misuse.
The current study should be understood in the context of its strengths and limitations. First, the NESARC is a national representative sample allowing for greater generalizability of findings to the larger Latino population in the United States. This is important, given that many studies of Latinos are from geographically restricted areas. Second, there has been a growing interest in prescription drug misuse and research is needed to identify important risk and protective factors among understudied populations such as the Latino population. The aim of the current study was to focus in on family factors as an important developmental and cultural context for this population and to tests these variables with respect to prescription drug misuse and disorder in a population of adults.
There are limitations of the current study also worth noting. The results are correlational in nature and do not allow for causal conclusions. Family factors account for a small amount of the variance in prescription drug misuse. That said, this is not atypical in risk and protective factor research for substance use and use disorders. The factors that contribute to prescription drug use come from multiple and often related contexts. The aim of the current study was to test a group of variables (family characteristics) that are culturally relevant for Latinos to inform prevention and intervention. In addition, the current study was not able to test other cultural variables such as acculturation and enculturation as the NESARC does not have such variables. Due to low prevalence, particularly for the prescription drug use disorder, the results of the current study did not allow for analyses for separate drug classes used in the composite variables.
Future research in this area should look at childhood family factors using longitudinal methods as well as test important mediators and moderators of these relationships. In addition, the roles that culture and cultural values play in substance use behaviors is relevant to Latinos living in the United States. Current models of acculturation are tuned into behavioral and values shifts as well as the receiving environment for Latino populations born outside of the United States (Schwartz, Unger, Zamboanga, & Szapocznik, 2010). These cultural contexts are influential in drug use attitudes and behaviors and are an important area of further research with respect to prescription drug misuse. For example, future research might test how family structure (including parental divorce) might moderate the relationship between the Latino cultural value of Familismo and prescription drug misuse. In sum, the relationships between family structure, family related values and substance use behaviors among Latinos are complex and warrant further research to inform the development of prevention and intervention strategies.
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Submitted: October 19, 2016 Revised: March 6, 2017 Accepted: March 19, 2017
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Source: Psychology of Addictive Behaviors. Vol. 31. (5), Aug, 2017 pp. 570-575)
Accession Number: 2017-17890-001
Digital Object Identifier: 10.1037/adb0000278
Record: 33- Title:
- Childhood trauma and personality disorder criterion counts: A co-twin control analysis.
- Authors:
- Berenz, Erin C.. Department of Psychiatry, Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, VA, US, ecberenz@vcu.edu
Amstadter, Ananda B.. Department of Psychiatry, Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, VA, US
Aggen, Steven H.. Department of Psychiatry, Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, VA, US
Knudsen, Gun Peggy. Division of Mental Health, Norwegian Institute of Public Health, Oslo, Norway
Reichborn-Kjennerud, Ted. Division of Mental Health, Norwegian Institute of Public Health, Oslo, Norway
Gardner, Charles O.. Department of Psychiatry, Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, VA, US
Kendler, Kenneth S.. Department of Psychiatry, Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, VA, US - Address:
- Berenz, Erin C., Virginia Institute for Psychiatric and Behavioral Genetics, VA Commonwealth University, Department of Psychiatry, 800 E. Leigh Street, P.O. Box 980126, Richmond, VA, US, 23298-0126, ecberenz@vcu.edu
- Source:
- Journal of Abnormal Psychology, Vol 122(4), Nov, 2013. pp. 1070-1076.
- NLM Title Abbreviation:
- J Abnorm Psychol
- Page Count:
- 7
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- The Journal of Abnormal and Social Psychology; The Journal of Abnormal Psychology; The Journal of Abnormal Psychology and Social Psychology
- ISSN:
- 0021-843X (Print)
1939-1846 (Electronic) - Language:
- English
- Keywords:
- co-twin control analysis, personality disorders, stress, trauma, twin study, family factors, genetic factors, environmental factors
- Abstract:
- Correlational studies consistently report relationships between childhood trauma (CT) and most personality disorder (PD) criteria and diagnoses. However, it is not clear whether CT is directly related to PDs or whether common familial factors (i.e., shared environment and/or genetic factors) better account for that relationship. The current study used a cotwin control design to examine support for a direct effect of CT on PD criterion counts. Participants were from the Norwegian Twin Registry (N = 2,780), including a subset (n = 898) of twin pairs (449 pairs, 45% monozygotic [MZ]) discordant for CT meeting DSM–IV Posttraumatic Stress Disorder Criterion A. All participants completed the Norwegian version of the Structured Interview for DSM–IV Personality. Significant associations between CT and all PD criterion counts were detected in the general sample; however, the magnitude of observed effects was small, with CT accounting for no more than approximately 1% of variance in PD criterion counts. A significant, yet modest, interactive effect was detected for sex and CT on Schizoid and Schizotypal PD criterion counts, with CT being related to these disorders among women but not men. After common familial factors were accounted for in the discordant twin sample, CT was significantly related to Borderline and Antisocial PD criterion counts, but no other disorders; however, the magnitude of observed effects was quite modest (r2 = .006 for both outcomes), indicating that the small effect observed in the full sample is likely better accounted for by common genetic and/or environmental factors. CT does not appear to be a key factor in PD etiology. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Early Experience; *Etiology; *Personality Disorders; *Trauma; Environment; Genetics; Twins
- Medical Subject Headings (MeSH):
- Adult; Case-Control Studies; Child; Child Abuse; Diseases in Twins; Educational Status; Female; Humans; Life Change Events; Male; Norway; Personality Disorders; Regression Analysis; Sex Factors; Wounds and Injuries; Young Adult
- PsycINFO Classification:
- Personality Disorders (3217)
- Population:
- Human
Male
Female - Location:
- Norway
- Age Group:
- Adulthood (18 yrs & older)
- Tests & Measures:
- Structured Interview for DSM–IV Personality
Structured Interview for DSM–IV Personality: Norwegian version
Munich-Composite International Diagnostic Interview: Norwegian version
Munich Composite International Diagnostic Interview - Grant Sponsorship:
- Sponsor: National Institutes of Health
Grant Number: MH-068643
Recipients: No recipient indicated
Sponsor: Norwegian Research Council, Norway
Recipients: No recipient indicated
Sponsor: Norwegian Foundation for Health and Rehabilitation, Norway
Recipients: No recipient indicated
Sponsor: Norwegian Council for Mental Health, Norway
Recipients: No recipient indicated
Sponsor: European Commission, Europe
Grant Number: QLG2-CT-2002-01254
Other Details: Under the program “Quality of Life and Management of the Living Resources” of the Fifth Framework Program.
Recipients: No recipient indicated - Methodology:
- Empirical Study; Interview; Quantitative Study; Twin Study
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- Accepted: Jul 25, 2013; Revised: Jul 23, 2013; First Submitted: Sep 16, 2012
- Release Date:
- 20131223
- Correction Date:
- 20150216
- Copyright:
- American Psychological Association. 2013
- Digital Object Identifier:
- http://dx.doi.org/10.1037/a0034238
- PMID:
- 24364608
- Accession Number:
- 2013-44247-012
- Number of Citations in Source:
- 41
- Persistent link to this record (Permalink):
- http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2013-44247-012&site=ehost-live
- Cut and Paste:
- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2013-44247-012&site=ehost-live">Childhood trauma and personality disorder criterion counts: A co-twin control analysis.</A>
- Database:
- PsycINFO
Childhood Trauma and Personality Disorder Criterion Counts: A Co-twin Control Analysis
By: Erin C. Berenz
Department of Psychiatry, Virginia Institute of Psychiatric and Behavioral Genetics, Virginia Commonwealth University;
Ananda B. Amstadter
Department of Psychiatry, Virginia Institute of Psychiatric and Behavioral Genetics, Virginia Commonwealth University
Steven H. Aggen
Department of Psychiatry, Virginia Institute of Psychiatric and Behavioral Genetics, Virginia Commonwealth University
Gun Peggy Knudsen
Division of Mental Health, Norwegian Institute of Public Health, Oslo, Norway
Ted Reichborn-Kjennerud
Division of Mental Health, Norwegian Institute of Public Health, Oslo, Norway and The Institute of Psychiatry, University of Oslo, Norway
Charles O. Gardner
Department of Psychiatry, Virginia Institute of Psychiatric and Behavioral Genetics, Virginia Commonwealth University
Kenneth S. Kendler
Department of Psychiatry, Virginia Institute of Psychiatric and Behavioral Genetics and Department of Human and Molecular Genetics, Virginia Commonwealth University
Acknowledgement: Supported in part by NIH Grant MH-068643 and grants from the Norwegian Research Council, the Norwegian Foundation for Health and Rehabilitation, the Norwegian Council for Mental Health, and the European Commission under the program “Quality of Life and Management of the Living Resources” of the Fifth Framework Program (QLG2-CT-2002-01254).
Stressful and potentially traumatic life events have been implicated in theories of psychopathology etiology and maintenance (Cicchetti & Cohen, 1997; Linehan, 1993). Exposure to potentially traumatic events during childhood has been related to a number of personality disorder (PD) diagnoses and symptoms (Battle et al., 2004), with Borderline PD receiving the most empirical attention. For example, individuals with clinical or subclinical Borderline PD symptoms are more likely to endorse having experienced childhood abuse compared to nonclinical controls (Bandelow et al., 2005; Laporte, Paris, Guttman, & Russell, 2011; Sansone, Hahn, Dittoe, & Wiederman, 2011). Additionally, etiologic theories of Borderline PD highlight the importance of the role of childhood trauma (CT; Linehan, 1993).
Associations between CT and other PDs also have been documented. For example, childhood maltreatment and abuse are related to Schizotypal PD symptoms (Berenbaum, Thompson, Milanek, Boden, & Bredemeier, 2008; Powers, Thomas, Ressler, & Bradley, 2011), specifically paranoia and unusual perceptual experiences (Steel, Marzillier, Fearon, & Ruddle, 2009). Childhood abuse and witnessing domestic violence have been associated with greater self-reported antisocial behaviors in adolescence (Sousa et al., 2011) and Antisocial PD symptoms in adulthood (Bierer et al., 2003). Other studies have reported broad associations between childhood abuse and the majority of categories of PD symptoms (Battle et al., 2004; Tyrka, Wyche, Kelly, Price, & Carpenter, 2009), even when participants were selected on the basis of having no history of Axis I psychopathology (Grover et al., 2007). Furthermore, childhood abuse and maltreatment are prospectively related to a range of PD symptoms and diagnoses (Cohen, Crawford, Johnson, & Kasen, 2005; Johnson, Cohen, Brown, Smailes, & Bernstein, 1999).
It is tempting to assume that the observed associations between CT and PDs are causal. Although associations have been well documented, a direct effect of CT on PDs has not been established. An alternative explanation is that common mechanisms explain significant covariation between CT and features of PDs (Chapman, Leung, & Lynch, 2008; New et al., 2009). The observed relationship between CT and PDs could also result from common environmental factors (e.g., stressful family environments) and/or shared genetic factors that predispose to both (Button, Scourfield, Martin, Purcell, & McGuffin, 2005; McGuigan & Pratt, 2001; Sartor et al., 2012). Indeed, likelihood of exposure to traumatic events is moderately influenced by genetic factors (Kendler & Baker, 2007), and it is possible that some of these same factors play a role in the development of PDs. Unique environmental influences may also play a role in the CT−PD relationship (e.g., impact of parental reactions on the trauma-exposed child; Nugent, Ostrowski, Christopher, & Delahanty, 2007). Individuals with PDs compared to those without PDs may also be more likely to report a history of CT because of greater negative emotionality, which may bias retrospective reporting (Hardt & Rutter, 2004).
Genetically informative samples consisting of twins who are discordant for CT may provide some insight into the role of CT in PDs, given that shared genetic and environmental factors may be accounted for statistically (Kendler & Campbell, 2009). For example, as traumatic events have been shown to correlate with multiple family background risk factors that are shared by twins (e.g., interpersonal loss, family discord, economic adversity), these factors are statistically controlled for in this model. Without sufficient control in epidemiological samples, which necessitate the measurement of all confounding factors, some of which are unknown, the clustering of traumatic events would likely serve to overestimate the association between CT and PDs. In the one study to our knowledge that addresses trauma and PDs using this design, Bornovalova and colleagues (2013) found that the relationship between childhood abuse and adult Borderline PD traits was likely noncausal and better accounted for by genetic factors. However, this study utilized a questionnaire to assess Borderline traits and did not assess a full range of CT events or PD categories. In fact, the heterogeneity of the assessment and definition of CT in the literature more broadly is problematic. Most notably, many studies fail to assess DSM–IV PTSD Criterion A for trauma exposure (Battle et al., 2004), resulting in a variable that could be assessing stressful life events or negative aspects of the family environment more generally. Past research also focuses largely on child maltreatment and abuse, without assessment or inclusion of other forms of CT that fall within the scope of DSM–IV Criterion A events (Grover et al., 2007). The current investigation sought to address several outstanding limitations of the existing literature by utilizing a genetically informed sample, employing a conservative definition of CT (i.e., DSM–IV PTSD Criterion A), allowing for inclusion of a broad range of CT event types, and using a clinical interview to assess CT and PD criterion counts.
The first aim of the current study was to detect and quantify an association between CT and PD criterion counts in a large sample of adult Norwegian twins obtained from the Norwegian Twin Registry (NTR). It was hypothesized that CT would evidence significant associations with PD criterion counts, above and beyond the effects of age, education level, and participant sex. Given that past studies consistently evidence sex differences in PD prevalence and expression (Torgersen, Kringlen, & Cramer, 2001; Verona, Sprague, & Javdani, 2012), we also evaluated an interaction between CT and participant sex in relation to PD criterion counts. The second aim of the study was to clarify the nature of an association between CT and PD criterion counts in a sample of twins discordant for CT. Specifically, we aimed to evaluate whether CT exerted a direct (i.e., potentially causal) or indirect (i.e., better accounted for by shared environmental and/or genetic factors) effect on PD criterion counts.
Method Sample and Assessment Method
The Norwegian National Medical Birth Registry, established on January 1, 1967, receives mandatory notification of all live births. The NTR identified and recruited twins from the registry, with participants completing questionnaire studies in 1992 (including twins born between 1967 and 1974) and 1998 (including twins born between 1967 and 1979). Of the 6,442 eligible twins that agreed to be contacted again after the second questionnaire, approximately 44% (2,794 twins) participated in an interview study initiated in 1999 (Tambs et al., 2009). This sample also included 68 twin pairs who had not completed the second questionnaire study, but were still recruited (due to technical problems). Data for the current investigation included all participants who completed the interview study and had complete data on PD criterion counts and CT (N = 2,780). This included a subset (n = 616) of twin pairs (46% monozygotic [MZ]) that were discordant for CT. Participants in the general sample (63.5% women) had a mean age of 28.2 (SD = 3.9) at the time of the interview and reported approximately 14.9 years of education (SD = 2.6).
Approval was received from The Norwegian Data Inspectorate and the Regional Ethical Committee approved the study. All participants provided written informed consent. Interviewers were primarily senior clinical psychology graduate students at the end of their 6-year training course (including at least 6 months of clinical practice; 75%) with the remainder (25%) being experienced psychiatric nurses, with the exception of two medical students. The interview training, conducted by one psychiatrist and two psychologists, consisted of a formal presentation on personality disorders, in-class demonstrations of the interview, multiple supervised role plays and test interviews, and group discussion of possible problems and scoring issues. The interviews, mostly face-to-face, were carried out between June, 1999 and May, 2004. For practical reasons, 231 interviews (8.3%) were done by phone. A different interviewer interviewed each twin in a pair.
Assessment of PDs
A Norwegian version of the Structured Interview for DSM–IV Personality (SIDP-IV; Pfohl, Blum, & Zimmerman, 1995), a comprehensive semistructured diagnostic interview, was used to assess all 10 DSM–IV PDs. The SIDP-IV has been successfully used in previous large-scale studies in Norway (Helgeland, Kjelsberg, & Torgersen, 2005; Torgersen et al., 2001). The SIDP-IV contains nonpejorative questions organized into topical sections rather than by individual PD to improve interview flow, and uses the “5-year rule,” meaning that behaviors, cognitions, and feelings that predominated for most of the past 5 years are judged to be representative of an individual’s personality. Each DSM–IV criterion is scored on a 4-point scale (0 = absent, 1 = subthreshold, 2 = present, or 3 = strongly present). Only the A criterion was assessed for Antisocial PD, given the 5-year assessment timeframe (i.e., Criterion C—presence of conduct disorder prior to age 15—was not assessed).
Given the low base rate of PDs, ordinal symptom counts were created that reflect the number of positively endorsed criteria for each disorder. Results from multiple threshold tests of these 10 PDs indicate that the four response options scored as successive integers represent increasing levels of “severity” on a single continuum of liability (Reichborn-Kjennerud et al., 2007; Torgersen et al., 2008). Because few individuals endorsed (scored 2 or greater) most of the criteria for individual PDs, high criterion counts were infrequent. These low frequency, high symptom counts were collapsed so that variation for all PDs was represented as six ordinal categories. This approach has been successfully utilized in past research using the current sample (Reichborn-Kjennerud et al., 2007). Previous studies using the current data have reported high interrater reliability (range of intraclass correlations for endorsed criterion counts = .81−.96) for the assessed PDs obtained by two raters (one psychologist and one psychiatrist interview trainer) scoring 70 audiotaped interviews (Kendler et al., 2008).
Assessment of CT
A Norwegian computerized version of the Munich-Composite International Diagnostic Interview (M-CIDI; (Wittchen & Pfister, 1997), a comprehensive structured diagnostic interview assessing Axis I diagnoses, was administered. The M-CIDI has good test−retest and interrater reliability (Wittchen, 1994; Wittchen, Lachner, Wunderlich, & Pfister, 1998). In the PTSD module of the M-CIDI, participants were asked if they had personally experienced or witnessed any of the following traumas: 1) a terrible experience at war, 2) serious physical threat (with a weapon), 3) rape, 4) sexual abuse as a child, 5) a natural catastrophe, 6) a serious accident, 7) being imprisoned, taken hostage, or kidnapped, or 8) another event. We defined CT as an event occurring before the age of 17 that met DSM–IV PTSD Criteria A1 (i.e., “the person experienced, witnessed, or was confronted with an event or events that involved actual or threatened death or serious injury, or a threat to the physical integrity of self or others) and A2 (i.e., “the person’s response involved intense fear, helplessness, or horror”; American Psychiatric Association, 1994). Approximately 17% (n = 467) of the total sample met these criteria.
Individuals’ worst CT were: 35.0% childhood sexual assault, 15.8% rape, 13.1% an accident, 12.6% an “other” traumatic event, 10.9% physical threat to oneself, 10.9% witnessing a traumatic event, 1.1% a natural disaster, and 0.5% being held hostage.
Data Analytic Plan
Analyses for the current study were conducted in SAS. First, the association between CT and PD criterion counts was examined in the general sample. A series of linear regression models was conducted, with age, education level, participant sex (1 = male, 2 = female), and CT (1 = no CT, 2 = CT) entered in level 1. To examine potential sex differences in the relationship between CT and PD criterion counts, the interaction of participant sex and CT was entered at level 2. A square root transformation was conducted for all PD criterion counts prior to inclusion in the regression models.
Second, a series of fixed effects regressions was conducted to examine CT−PD criterion count relationships among the subset of twin pairs discordant for CT, with twin pair serving as the fixed between-groups factor. This method allows for statistical control of unobserved between-family variation (e.g., genetic factors, family stressors, parenting style, socioeconomic factors, etc.). The relationships between CT and PD criterion counts were compared in the general and discordant twin samples. Based on such comparison, one may determine whether the observed relationship between CT and PD criterion counts in the general sample is likely direct or indirect (i.e., better accounted for by familial factors). Specifically, if the magnitude of the relationship between CT and PD criterion counts were comparable in the general and discordant twin samples, the effect is likely to be direct. If the relationship is significantly lesser in magnitude or nonexistent in the discordant twin sample, the effect is likely to be indirect, or accounted for by shared familial factors. The current investigation was not sufficiently powered to examine discordant MZ and DZ twin pairs separately, which would allow for speculation regarding whether shared environmental or genetic factors were responsible for observed indirect effects. For a more comprehensive overview of the cotwin control design, see K.S. Kendler et al.(1993). A total of 20 regression models (10 in the full sample, 10 in the discordant twin subsample) were conducted. Bonferroni correction for multiple testing indicated statistical significance at p = .003.
Results Descriptive Statistics and Zero-Order Correlations (General Sample)
See Table 1. Sex was significantly related to CT, with men being more likely to endorse a CT. Men also were more likely to endorse a greater number of criteria for Narcissistic, Antisocial, and Obsessive-Compulsive PDs, while women were more likely to endorse criteria for Schizotypal, Histrionic, Borderline, and Dependent PDs. Age and years of education were significantly related to various PD criterion counts with no particular pattern being observed. CT was significantly, yet modestly, related to a greater number of criteria for all PDs, with the exception of Avoidant PD.
Descriptive Statistics and Zero-Order Correlations in the Total Sample
Trauma Exposure and PD Criterion Counts
See Table 2 for regression statistics for the main effect of CT on PD criterion counts in the full and discordant twin samples. CT was significantly related to all PD criterion counts in the full sample, after covarying for sex, age, and education level, with the exception of Schizoid, Avoidant, and Dependent PDs. The greatest effects were observed for Borderline (r2 = .013), Obsessive-Compulsive (r2 = .008), Schizotypal (r2 = .007), and Antisocial PDs (r2 = .007; see Figure 1 for a comparison of effect sizes for all PD criterion counts). An interaction between CT and sex was statistically significant for Schizoid (β = .25, t = 2.95, p = .003) and Schizotypal PDs (β = .26, t = 3.16, p = .002). CT was significantly, yet modestly, related to Schizoid and Schizotypal PD criterion counts among women (β = .10, t = 4.10, sr2 = .01, p < .001; and β = .13, t = 5.31, sr2 = .02, p < .001, respectively) but not men (β = −.01, t = −.44, p = .661; and β = .01, t = .42, p = .678, respectively).
Childhood Trauma Predicting Personality Disorder Criterion Counts
Figure 1. Magnitude of relationship between childhood trauma and personality disorder criterion counts. Note: * p < .003, indicating statistical significance after Bonferroni correction for multiple testing within the indicated sample; analyses in the full sample represent the effect of childhood trauma on criterion counts above and beyond the variance accounted for by participant age, education level, and sex.
Results of the fixed effects regressions indicated that after accounting for family and genetic factors in the discordant twin sample, the relationship between CT and PD criterion counts was quite modest (see Table 2). Overall, the magnitude of the effect of CT on PD criterion counts was quite small prior to accounting for familial liability, not accounting for more than 1% of variance for any given disorder, and upon controlling for familial factors, the relationship between CT and PD criterion counts was essentially nonexistent (see Figure 1 for a comparison of effect sizes in the two samples). The potential exceptions to this pattern are for Borderline and Antisocial PD criterion counts, for which CT appears to account for a slight proportion of unique variation (<1%) beyond shared familial liability.
DiscussionOur first aim was to examine associations between CT and PD criterion counts in the general sample. Consistent with past work, CT was significantly associated with the majority of PD criterion counts, after accounting for sex, age, and education (Battle et al., 2004; Grover et al., 2007; Tyrka et al., 2009). Significant associations were not detected for Schizoid, Avoidant, or Dependent PDs. The strongest associations were detected for Borderline, Obsessive-Compulsive, Schizotypal, and Antisocial PD criterion counts; however, CT accounted for less than 2% of variance in these disorders, even without the inclusion of rigorous covariates (e.g., life stressors, parenting style, etc.). In contrast, trait-level neuroticism accounts for approximately 45% of variance in Borderline PD traits prior to accounting for shared genetic factors (Distel et al., 2009).
The relationship between CT and Schizoid and Schizotypal PD criterion counts varied by sex, with a significant trauma−PD association being detected among women but not men. It is possible that CT is associated with factors promoting a decreased capacity for or interest in close relationships among women. However, the magnitude of effects was consistent with the pattern observed in the full sample, with CT accounting for no more than 2% of variance in these disorders. Prior to controlling for the role of familial factors, CT appears to exert quite modest influence on PD criterion counts.
Second, we sought to explain the nature of the observed effects through the use of a cotwin control design, which relies on comparing the magnitude of effects in the general sample with that observed in twins discordant for CT. The relationships between CT and Antisocial and Narcissistic PDs were comparable in the two samples. It is possible that the very small (i.e., r2 < .01) effect of CT is causal in nature for these disorders. CT also was significantly related to Borderline PD criterion counts in the discordant twin sample; although, the magnitude of the effect in the discordant twins was 50% of that observed in the full sample, suggesting that a substantial portion of the relationship is likely better accounted for by familial factors rather than a direct effect of CT. The role of CT in the remaining PD criterion counts was essentially nonexistent in the discordant twin sample. It does not appear that CT plays a substantial role in the development of PD symptoms.
These findings are at odds with existing theories and clinical intuition regarding PD etiology, particularly in the case of Borderline PD. For example, one review paper on CT in Borderline PD concluded that, “the evidence suggests that childhood trauma should be included in a multifactorial model of BPD” (Ball & Links, 2009). It is worth noting that the studies reviewed by the authors were purely correlational and did not account for the role of familial factors. Marsha Linehan cites CT as a “prototypic invalidating experience” contributing to a biosocial model of Borderline PD etiology, with an entire stage of treatment being devoted to addressing CT and reducing trauma-related behaviors in Dialectical Behavior Therapy (Linehan, 1993). Finally, there has been empirical interest in discovering a biological link between CT and PDs. It has been suggested that CT may lead to neurobiological alterations (e.g., disruption of serotonin function), which in turn may lead to emotional and behavioral deficits in PDs (Lee, 2006). The current findings would suggest that focus on CT as a key risk factor in PDs may not be particularly fruitful. Indeed, this study replicates and extends the only other investigation to our knowledge that addresses CT and PDs using a discordant twin design, in which little to no direct relationship between CT and Borderline PD symptoms was detected (Bornovalova et al., 2013). Investigation of traumatic events in the etiology of Axis I psychopathology using genetically informed designs is needed.
The current project has a number of limitations. First, the base rate of high criterion counts in the current sample was quite low, preventing analysis at the diagnostic level. Future studies utilizing large samples and diverse methodologies are needed. Second, this study was underpowered to examine trauma type or severity in relation to PD criterion counts. Similarly, this study did not have data on familial response to CT. Parental response to trauma may influence the trauma-exposed child’s unique environment. For example, parental posttraumatic stress disorder symptoms and general parental distress may exert unique influence on children’s reactions to trauma (Nugent et al., 2007). Future studies investigating trauma characteristics and responses of the individual and his or her family to CT may be useful. Third, the ethnic and age composition of the current sample is relatively homogenous. Fourth, this study relied on retrospective reporting of CT. However, one would expect this reporting method to inflate the relationship between CT and PD symptoms; therefore, it is possible that the very modest effects detected in the current sample are upwardly biased by recall effects. Fifth, this study did not include a social desirability measure, which may be useful for future investigations in order to account for potential reporting bias. Sixth, this study was underpowered to analyze MZ and DZ twins separately, which would have provided additional information on whether genetic or shared environmental factors accounted for the relationship between CT and PD criterion counts. Finally, this study did not assess for a history of conduct disorder symptoms as related to Antisocial PD. Despite these limitations, the current study provides novel data on the relationship between CT and PD criterion counts.
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Submitted: September 16, 2012 Revised: July 23, 2013 Accepted: July 25, 2013
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Source: Journal of Abnormal Psychology. Vol. 122. (4), Nov, 2013 pp. 1070-1076)
Accession Number: 2013-44247-012
Digital Object Identifier: 10.1037/a0034238
Record: 34- Title:
- Chronic sleep disturbances and borderline personality disorder symptoms.
- Authors:
- Selby, Edward A.. Department of Psychology, Rutgers University, Piscataway, NJ, US, edward.selby@rutgers.edu
- Address:
- Selby, Edward A., Department of Psychology, Rutgers University, Tillett Hall, 53 Avenue East, Piscataway, NJ, US, 08854, edward.selby@rutgers.edu
- Source:
- Journal of Consulting and Clinical Psychology, Vol 81(5), Oct, 2013. pp. 941-947.
- NLM Title Abbreviation:
- J Consult Clin Psychol
- Page Count:
- 7
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- Journal of Consulting Psychology
- Other Publishers:
- US : American Association for Applied Psychology
US : Dentan Printing Company
US : Science Press Printing Company - ISSN:
- 0022-006X (Print)
1939-2117 (Electronic) - Language:
- English
- Keywords:
- borderline personality disorder, emotion dysregulation, fatigue, insomnia, sleep, chronic sleep disturbances
- Abstract:
- Objective: Few studies have examined the experience of chronic sleep disturbances in those with borderline personality disorder (BPD), and further establishing this association may be pertinent to enhancing current treatments, given the relevance of sleep to emotion regulation and stress management. Method: Data were analyzed (N = 5,692) from Part II of the National Comorbidity Survey–Replication (NCS-R) sample (Kessler & Merikangas, 2004), which assessed personality disorders and sleep problems. Rates of chronic sleep disturbances (difficulty initiating sleep, difficulty maintaining sleep, and waking earlier than desired), as well as the consequences of poor sleep, were examined. Indices for BPD diagnosis and symptoms were used in logistic and linear regression analyses to predict sleep and associated problems after accounting for chronic health problems, Axis I comorbidity, suicidal ideation over the last year, and key sociodemographic variables. Results: BPD was significantly associated with all 3 chronic sleep problems assessed, as well as with the consequences of poor sleep. The magnitude of the association between BPD and sleep problems was comparable to that for Axis I disorders traditionally associated with sleep problems. BPD symptoms interacted with chronic sleep problems to predict elevated social/emotional, cognitive, and self-care impairment. Conclusions: Sleep disturbances are consistently associated with BPD symptoms, as are the daytime consequences of poor sleep. There may also be a synergistic effect where BPD symptoms are aggravated by poor sleep and lead to higher levels of functional impairment. Sleep in patients with BPD should be routinely assessed, and ameliorating chronic sleep problems may enhance treatment by improving emotion regulation and implementation of therapeutic skills. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Borderline Personality Disorder; *Sleep Disorders; *Symptoms; Emotional Regulation; Fatigue; Insomnia
- Medical Subject Headings (MeSH):
- Adult; Borderline Personality Disorder; Chronic Disease; Comorbidity; Female; Health Surveys; Humans; Male; Single-Blind Method; Sleep Wake Disorders; United States
- PsycINFO Classification:
- Psychological & Physical Disorders (3200)
- Population:
- Human
- Age Group:
- Adulthood (18 yrs & older)
- Tests & Measures:
- International Personality Disorder Examination
World Health Organization Disability Assessment Schedule II
Composite International Diagnostic Interview DOI: 10.1037/t02121-000 - Grant Sponsorship:
- Sponsor: Brain and Behavior Research Foundation
Recipients: No recipient indicated
Sponsor: Families for Borderline Personality Disorder Research
Other Details: early investigator grant
Recipients: Selby, Edward A. (Prin Inv)
Sponsor: National Institute of Mental Health
Grant Number: U01-MH60220
Recipients: No recipient indicated
Sponsor: National Institute on Drug Abuse
Recipients: No recipient indicated
Sponsor: Substance Abuse and Mental Health Services Administration
Recipients: No recipient indicated
Sponsor: Robert Wood Foundation
Grant Number: 044708
Recipients: No recipient indicated
Sponsor: John W. Alden Trust
Recipients: No recipient indicated - Methodology:
- Empirical Study; Quantitative Study
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- First Posted: Jun 3, 2013; Accepted: Apr 29, 2013; Revised: Feb 15, 2013; First Submitted: Sep 20, 2012
- Release Date:
- 20130603
- Correction Date:
- 20160616
- Copyright:
- American Psychological Association. 2013
- Digital Object Identifier:
- http://dx.doi.org/10.1037/a0033201
- PMID:
- 23731205
- Accession Number:
- 2013-19431-001
- Number of Citations in Source:
- 25
- Persistent link to this record (Permalink):
- http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2013-19431-001&site=ehost-live
- Cut and Paste:
- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2013-19431-001&site=ehost-live">Chronic sleep disturbances and borderline personality disorder symptoms.</A>
- Database:
- PsycINFO
Chronic Sleep Disturbances and Borderline Personality Disorder Symptoms / BRIEF REPORT
By: Edward A. Selby
Rutgers University;
Acknowledgement: Support for this project was provided, in part, by the Brain and Behavior Research Foundation and the Families for Borderline Personality Disorder Research with an early investigator grant (Edward A. Selby, principal investigator). The National Comorbidity Survey–Replication (NCS-R) was supported by National Institute of Mental Health Grant U01-MH60220, with supplemental support from the National Institute on Drug Abuse, the Substance Abuse and Mental Health Services Administration, the Robert Wood Foundation (Grant 044708), and the John W. Alden Trust. The views and opinions expressed in this report are those of the author and should not be construed to represent the views of any sponsoring organizations, agencies, or the U.S. government. A complete list of NCS publications and the full text of all NCS-R instruments can be found at http://www.hcp.med.harvard.edu/ncs. The NCS-R is carried out in conjunction with the World Health Organization World Mental Health (WMH) Survey Initiative.
Although not traditionally thought of as a disorder associated with sleep disturbances, there is growing evidence that those with borderline personality disorder (BPD) experience a variety of problems with sleep, including increased sleep onset latency and low sleep efficiency during polysomnography assessments (Bastien, Guimond, St-Jean, & Lemelin, 2008), abnormal sleep architecture (Battaglia, Strambi, Bertella, Bajo, & Bellodi, 1999), and nightmares (Asaad, Okasha, & Okasha, 2002; Selby, Ribeiro, & Joiner, in press). Sleep problems are clinically pertinent to BPD, as they are linked to functional impairment (Roth et al., 2006) and emotion dysregulation (Zohar, Tzischinsky, Epstein, & Lavie, 2005). To date, minimal research has systematically examined sleep disturbances in BPD, particularly chronic sleep problems. Chronic sleep problems involve difficulty sleeping most nights for an extended period of time (often lasting weeks to months), as opposed to acute sleep problems, which may last for a few days or arise intermittently, and can lead to major problems in daily functioning (Simon & Von Korff, 1997). Research is also lacking on BPD and the daytime consequences of chronic sleep problems, such as excessive daytime sleepiness, poor sleep-related fatigue, and difficulties engaging in activities due to poor sleep. Importantly, BPD may increase vulnerability to sleep problems, due to issues such as emotion dysregulation, and poor sleep may result in elevated daytime functional impairment.
Improving our understanding of sleep disturbances in BPD is also relevant to improving our interventions. At present, dialectical behavior therapy (DBT; Linehan, 1993) is the only psychotherapy for BPD that specifically addresses sleep problems. Improving the sleep of patients with BPD may aid in improving their ability to manage stressful situations, employ coping skills, and improve overall levels of energy and positive affect. In turn, improving the ability to manage stress may further reduce sleep problems (Carlson & Garland, 2005). Those with BPD also frequently experience suicidal ideation, the experience of which has itself also been linked to sleep problems (Sjöström, Wærn, & Hetta, 2007; Wojnar et al., 2009). This makes it important to determine if the sleep problems of those with BPD also occur beyond the context of suicidal ideation.
Previous studies on sleep and BPD have involved small samples, often during acute polysomnography studies, and none to date have examined BPD and chronic sleep problems in a large epidemiological sample. One advantage to using such a sample is reduced treatment-seeking bias and increased understanding of how these issues affect people in the community at-large. Another advantage is the ability to control for Axis I disorders that are intertwined with acute and chronic sleep problems (e.g., depression, anxiety disorders)—an important issue given the role of comorbidity in BPD (Lenzenweger, Lane, Loranger, & Kessler, 2007). The purpose of the present study was to examine chronic sleep disturbances, poor sleep-related consequences, and social/emotional and cognitive impairment as a function of poor sleep in those exhibiting BPD symptoms with the National Comorbidity Survey–Replication (NCS-R; Kessler & Merikangas, 2004).
Method Sample and Diagnostic Assessment
The NCS-R was a nationally representative, institutional review board–approved survey of adults age 18 and older designed to involve multistage clustered area probability sampling and conducted between 2001 and 2003. Overall, the survey consisted of 9,282 respondents, with an overall response rate of 70.9%. All respondents in the NCS-R completed the Part I diagnostic interview, which consisted of the World Health Organization (WHO) Composite International Diagnostic Interview (CIDI 3.0; Kessler & Üstün, 2004) and included diagnostic information on anxiety disorders, mood disorders, and substance use disorders. Blinded clinical reappraisals of a probability subsample of the NCS-R indicated good concordant validity between the Axis I diagnosis (Kessler et al., 2005) according to the Diagnostic and Statistical Manual of Mental Disorders (4th ed.; DSM–IV;American Psychiatric Association, 1994) and the CIDI diagnosis. In addition to completing assessment the CIDI, a probability subsample of respondents (N = 5,692), with and without DSM–IV diagnoses, also received the NCS-R Part II interview, which also assessed personality disorders and sleep problems; only NCS-R Part II data were used in this study.
BPD Symptoms
Respondents completed eight questions taken from the International Personality Disorder Examination (IPDE) screening questionnaire (Loranger et al., 1994) designed to measure BPD symptoms. These items have been used extensively in examining personality disorders (e.g., Lenzenweger, 1999; Lenzenweger et al., 2007) and have been found to significantly predict personality disorder diagnoses when the IPDE was clinician-administered (Loranger, 1999; Loranger et al., 1994). Furthermore, the more IPDE items were endorsed, the higher was the probability that a personality disorder diagnosis was obtained using structured clinical interviews (Lenzenweger, Loranger, Korfine, & Neff, 1997). Items were rated with a dichotomous yes (1) or no (0) answer, and all were summed to create a continuous measure of BPD (α = .74). Importantly, clinical reappraisal interviews were previously conducted by phone on a probability subsample of 214 respondents from Part II of the NCS-R and screened positive for personality disorder symptoms, and clinical reassessments with the IPDE were highly correlated (r > .90) with these items (Lenzenweger et al., 2007). Of note, because the NCS-R included a separate suicidal behavior assessment, the IPDE BPD question on suicidal and self-injurious behavior was not included. As a result of this missing criterion, three indices of BPD were generated for use in this study: (1) a dichotomous measure for those endorsing 5+ diagnostic criteria for BPD (to obtain odds ratios), (2) a continuous measure of BPD symptoms, and (3) a dichotomous BPD diagnosis (5+ symptoms endorsed) including occurrence of a lifetime suicide attempt (from the suicide assessment below).
Regarding the distribution of BPD symptoms, Selby (2013) found that the base rates (BRs) of BPD symptoms in this sample, as well as the endorsement of those symptoms by those with BPD (diagnostic sensitivity; DS), were well represented: intense anger (BR = 23%, DS = 73%), affective instability (BR = 30%, DS = 90%), chronic emptiness (BR = 21%, DS = 83%), identity disturbance (BR = 19%, DS = 73%), stress-related paranoia (BR = 17%, DS = 69%), avoiding abandonment (BR = 12%, DS = 60%), impulsivity (BR = 40%, DS = 89%), and unstable relationships (BR = 15%, DS = 60%). Thus, those with BPD in this sample exhibited the full spectrum of BPD symptoms.
Chronic Sleep Problems
Three common sleep problems assessed during Part II of the NCS-R were used (1) delayed sleep onset latency (SOL; “Nearly every night it took you two hours or longer before you could fall asleep”), (2) amount of time spent awake after sleep onset (WASO; “You woke up nearly every night and took an hour or more to get back to sleep”), and (3) waking earlier in the morning than desired (EMA; “You woke up nearly every morning at least two hours earlier than you wanted to”). Each was prefaced as happening for “periods lasting two weeks or longer in the past 12 months.” All were consistent with DSM–IV definitions for the associated sleep problem and have been reported on previously (Roth et al., 2006).
Poor Sleep Consequences
In addition to chronic sleep problems, this study examined the consequences of poor sleep using three assessments: (1) problems “feeling sleepy during the day,” (2) feeling fatigued during the day due to “poor sleep,” and (3) frequency of being “too tired to complete daily activities” as a result of poor sleep. All questions were assessed in the same section as the above questions about chronic sleep problems. The first question, on the experience of daytime sleepiness most days for two or more weeks in the past 12 months, was coded as yes (1) or no (0). The second question, relating to daytime fatigue as a result of poor sleep, and the third question, regarding frequency of being too tired to carry out daily activities—both of which were asked in terms of the frequency of the problem during the worst month in the past year—were rated on 4-point Likert scales. These two questions were originally rated 1 (never), 2 (rarely), 3 (sometimes), and 4 (often). In order to simplify the presentation of results and present odds ratios, these items were recoded to reflect that 4 (often experience of this problem) was recoded as (1), and all other ratings were coded as (0).
Functional Impairment
Six outcomes were utilized from the World Health Organization Disability Assessment Schedule II (WHO-DAS II; Chwastiak & Von Korff, 2003), a 36-item measure for general disability impairment. Respondents reported the severity of each problem over the last 30 days regarding the following: (1) days having been totally unable to work or carry out daily activities (days out of role), (2) days able to carry out normal activities but with reduced workload or inhibited productivity (reduced work quality), (3) difficulty caring for oneself with activities such as hygiene (self-care), (4) difficulties with physical mobility, (5) cognitive impairment (e.g., difficulty remember things), and (6) difficulty with social and emotional role performance (e.g., controlling emotions when around other people). The complex method (Chwastiak & Von Korff, 2003), which involves item response theory, was used to score each scale, and this resulted in a continuous scale range from 0 (No disability) to 100 (Full disability).
Comorbid DSM–IV Disorders
All respondents were rated for Axis I diagnoses with the CIDI, which has good concordance with other structured clinical interviews (Kessler & Üstün, 2004; Kessler et al., 2005). The following disorders, which were present over the last 12 months, were included as covariates in all analyses: major depressive disorder (MDD), dysthymia, manic episode, alcohol and drug dependence, panic disorder (with and without agoraphobia), generalized anxiety disorder (GAD), and posttraumatic stress disorder (PTSD). Each of these disorders has sleep problems as a symptom or has been linked to sleep problems, and their inclusion allowed for benchmarks to compare the association between BPD and sleep problems to. To examine comorbidity, variables were generated to indicate presence (1) or absence (0) of any two or three Axis I disorders.
Suicidality
As a part of the CIDI, all respondents completed a suicide risk assessment that included lifetime history of suicidal behavior as well as suicidal behavior in the last 12 months. Those who had “seriously thought about committing suicide” were coded and used as a covariate in analyses, and the presence of a lifetime suicide attempt was also used to generate one of the BPD indices described above.
Sociodemographic Control Variables
The following variables from the NCS-R were included in all analyses due to their potential impact on sleep problems: age, sex, race–ethnicity, education level, marital status, occupational status, family income level, and number of preschool children living at home. The relationships between covariates and personality disorders and sleep problems has previous been reported on (Lenzenweger et al., 2007; Roth et al., 2006). Also included was the presence or absence of any one of the following chronic health conditions endorsed, due to potential sleep interference: arthritis, neck or back ache, headaches, chronic pain, chronic allergies, stroke, and heart disease.
Data Analytic StrategyLogistic regression analyses were used to examine sleep problems and consequences, with the coefficients and their standard errors exponentiated as odds ratios with 95% confidence intervals. All analyses included medical and sociodemographic control variables, comorbid Axis I disorders, and presence of suicidal ideation in the last year; adjusted odds ratios (AORs) were presented. Finally, the interactions between BPD symptoms and chronic sleep problems in predicting functional impairment were examined with linear regression. Because the NCS-R was a complex sample involving clustering, stratification, and weighting specific to Part II, in order to adjust for potential differences in probability of selection for the sample, data analysis had to account for these procedures. Accordingly, standard errors of the logistic regression and linear regression analyses were adjusted for these sampling and weighting procedures with the COMPLEX function of the MPlus statistical program (Muthén & Muthén, 2008–2010).
Results Prevalence of Sleep Problems and Borderline Personality Disorder
A total of 63% of those meeting diagnostic criteria for BPD (5+ symptoms endorsed) reported having at least one of the sleep problems assessed. The average duration for sleep problems for those with BPD was 19.9 weeks (SD = 21.6), which was significantly more than for those without BPD (M = 8.9, SD = 17.2), F(1, 6590) = 45.4, p < .01, d = 0.60. As seen in Table 1, those with BPD reported significant experiences with delayed SOL (AOR = 1.8, wald = 33.3, p < .01), WASO (AOR = 1.9, wald = 41.3, p < .01), and EMA (AOR = 2.3, wald = 64.8, p < .01). BPD diagnosis was also a significant predictor of having all three chronic sleep problems (27% of those with BPD; wald = 37.75, p < .01, AOR = 2.1). BPD associations with each of the chronic sleep problems were similar in magnitude to those of disorders traditionally linked to sleep problems or involving sleep problems in diagnostic criteria (e.g., GAD, MDD, PTSD).
Multivariate Logistic Regression Analyses Predicting Sleep Problems (N = 5,692)
Borderline Personality Disorder and Poor Sleep-Related Consequences
Approximately 66% of those with BPD reported having at least one consequence over the last 12 months. BPD diagnosis (see Table 2) demonstrated clear and consistent associations, beyond covariates, with sleepiness during the day (AOR = 2.0, wald = 51.2, p < .01), daytime fatigue due to poor sleep (AOR = 2.1, wald = 43.4, p < .01), and being too tired to complete daily activities (AOR = 1.9, wald = 12.8, p < .01). BPD diagnosis was also a significant predictor of having all three poor sleep consequences (6% of those with BPD; wald = 7.5, p < .01, AOR = 2.1). As with chronic sleep problems, BPD diagnosis had AORs similar in magnitude to those of many Axis I disorders traditionally associated with sleep problems.
Multivariate Logistic Regression Analyses Predicting Poor Sleep Consequences (N = 5,692)
Functional Impairment Associated With Poor Sleep and Borderline Personality Symptoms
BPD interacted with sleep problems (see Table 3) to indicate more problems with self-care (β = .08, t = 3.0, p < .01), cognitive impairment (β = .17, t = 7.1, p < .001), and social/emotional impairment (β = .19, t = 7.9, p < .001) after accounting for key covariates. Figure 1 indicates worse impairments for those with more BPD symptoms who exhibited more sleep problems for these areas of functioning. No significant interactions were found for days out of role, role impairment, or decreased physical mobility.
BPD and Chronic Sleep Problems Predicting Functional Impairment
Figure 1. Interaction between BPD symptoms and chronic sleep problems on functional impairment on the World Health Organization Disability Assessment Schedule II. Low and high levels refer to one standard deviation below and above the mean, respectively. BPD = borderline personality disorder (referring to BDP symptoms); SLP = sleep (referring to the number of sleep problems).
DiscussionThe current study found a clear and consistent report of chronic sleep disturbances in those with BPD, even after accounting for key covariates, with many experiencing delayed SOL, increased WASO, and increased EMA most days for at least 2 weeks over the last year. Interestingly, the magnitudes of the AORs for BPD on chronic sleep problems was similar to those for other Axis I disorders often associated with sleep disturbance and with sleep-related criteria (i.e., MDD, GAD). BPD was also significantly associated, due to problems sleeping, with increased daytime sleepiness, fatigue, and feeling too tired to complete daytime activities. Potential reasons for the association between BPD and sleep problems, beyond Axis I contributions, may be that BPD psychopathology increases vulnerability to sleep problems because of emotion dysregulation or rumination (Selby & Joiner, 2009) or because of interpersonal conflicts during the day. Such experiences during the day may translate into difficulty falling asleep or frequent awakenings due to preoccupation with the problems or increased arousal. They may also experience frequent nightmares on days with more emotion dysregulation (Selby et al., in press).
Findings also indicated that when those with BPD have sleep problems they might experience increased difficulties with emotion dysregulation, problems in social relationships or self-care, and memory problems. These interactions suggest that when both problems are present a positive feedback loop may arise where BPD symptoms may contribute to poor sleep and poor sleep aggravates symptoms of BPD. However, there may be differential effects of sleep problems on aggravating BPD symptoms, and some may be more affected than others. For example, despite the finding that many with BPD reported feeling too tired to complete daily activities due to poor sleep, BPD symptoms did not interact with sleep problems to predict number of days out of role or reduced role quality, nor did the interaction predict decreased physical mobility. However, those with elevated BPD symptoms and sleep problems reported more cognitive and social/emotional impairment. These findings indicated that when those with BPD are experiencing sleep problems, they may have increased problems with issues such as emotion dysregulation, social relationships, and remembering things (potentially impacting techniques and skills learned in therapy). Furthermore, a significant interaction was also found for those with BPD and sleep problems in predicting decreased ability for self-care, and reduced self-care also likely worsens problems with emotional, social, and cognitive regulation. Future research should compare those with BPD who do and do not have chronic sleep problems to determine if there are significant group differences in intensity or duration of specific BPD symptoms or if symptoms are exacerbated at a more general level.
Important strengths of the current study to consider include use of a large, nontreatment-seeking sample and controlling for medical, Axis I, and sociodemographic variables associated with sleep problems. One primary limitation involved cross-sectional assessment of sleep problems over the last year (e.g., potential recall bias), and there was some temporal inconsistency between the indices measured over the course of the last year (sleep problems) and the indices measured over the last 30 days (functional impairment). Longitudinal studies are needed to determine if BPD increases vulnerability to sleep problems or if sleep problems simply aggravate BPD symptoms. Further, all indices in this study were self-report, and further studies with sleep diary monitoring and/or polysomnography studies may be needed to replicate and extend these findings and ascertain more precise assessments of chronic sleep problems in BPD. Another limitation was that BPD was assessed with IPDE screening items, and findings should be replicated in samples where BPD is assessed with structured clinical interviews. Finally, other factors may be involved in the BPD association with poor sleep, such as being hypervigilant or worried about sleep problems, poor sleep environment, or sleep state misperceptions.
Clinically, monitoring of sleep problems and associated impairment may be important in therapy, and it may be beneficial to thoroughly cover facets of sleep hygiene as a routine part of therapy for those with BPD. Although some therapies for BPD integrate sleep hygiene to some extent (e.g., the emotion regulation module of DBT; Linehan, 1993), many therapies may overlook this issue. It may be the case that treating sleep will help reduce negative emotion in those with BPD, and such daytime improvements may further improve overall sleep quality. Finally, sleep hygiene alone may not be enough to treat some BPD patients with chronic sleep problems, and some patients may also benefit from additional cognitive behavior therapy for insomnia (Edinger & Means, 2005).
Footnotes 1 Diagnostic efficiency analyses of the IPDE BPD screening items have indicated adequate sensitivity (all >.60), specificity (all >.66), negative predictive power (all >.95), and total predictive value (all >.68); however, all were somewhat low on positive predictive power (range .24–.53), indicating that endorsing only one symptom was not a strong indicator for the presence of a BPD diagnosis and supporting the notion that multiple items needed to be endorsed for a diagnosis (Selby, 2013).
2 The main effects of BPD diagnosis on the six indices of impairment measured by the WHO-DAS II have previously been reported on, with significant effects found for BPD diagnosis on mobility, cognition, days out of role, diminished role quality, and problems with social and emotional functioning (Lenzenweger et al., 2007). However, the current study builds on these findings by examining the interaction between a continuous predictor of BPD symptoms and the number of chronic sleep problems assessed, and in this study the WHO-DAS II scales were left as continuous variables rather than dichotomized as in the previous study.
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Submitted: September 20, 2012 Revised: February 15, 2013 Accepted: April 29, 2013
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Source: Journal of Consulting and Clinical Psychology. Vol. 81. (5), Oct, 2013 pp. 941-947)
Accession Number: 2013-19431-001
Digital Object Identifier: 10.1037/a0033201
Record: 35- Title:
- Clinical utility of the Cross-Cultural (Chinese) Personality Assessment Inventory (CPAI-2) in the assessment of substance use disorders among Chinese men.
- Authors:
- Cheung, Fanny M.. Department of Psychology, The Chinese University of Hong Kong, Shatin, Hong Kong, fmcheung@cuhk.edu.hk
Cheung, Shu Fai, ORCID 0000-0002-9871-9448. Department of Psychology, University of Macau, Taipa, Macao
Leung, Freedom. Department of Psychology, The Chinese University of Hong Kong, Shatin, Hong Kong - Address:
- Cheung, Fanny M., Department of Psychology, The Chinese University of Hong Kong, Shatin, Hong Kong, fmcheung@cuhk.edu.hk
- Source:
- Psychological Assessment, Vol 20(2), Jun, 2008. pp. 103-113.
- NLM Title Abbreviation:
- Psychol Assess
- Page Count:
- 11
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- Psychological Assessment: A Journal of Consulting and Clinical Psychology
- ISSN:
- 1040-3590 (Print)
1939-134X (Electronic) - Language:
- English
- Keywords:
- Cross-Cultural Chinese Personality Assessment Inventory, CPAI-2, substance use disorders, Chinese men
- Abstract:
- This study examined the clinical utility of the Cross-Cultural (Chinese) Personality Inventory (CPAI-2) in differentiating the personality characteristics of Chinese men with substance use disorders from other psychiatric patients and normal control participants. The CPAI-2 profile of 121 Chinese men with substance use disorders was contrasted with that of a matched psychiatric comparison group (n = 172) and a normal comparison group (n = 187). Multivariate analyses of variance and logistic regression results supported the utility of the CPAI-2 clinical scales, especially Pathological Dependence, Antisocial Behavior, and Depression, for assessing substance use disorders. The Pathological Dependence scale (cutoff T score of 64) achieved good sensitivity and specificity. Apart from the universal personality traits related to neuroticism, conscientiousness, and agreeableness found in Western studies, the indigenously derived CPAI-2 personality scales, including Family Orientation and Harmony, highlighted deficits in social adjustment and interpersonal relationship as important cultural features in the personality characteristics of these participants. The study provided a cross-cultural extension to research on the relationship between personality and substance use disorders and could assist clinicians in considering culturally relevant treatment approaches. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Chinese Cultural Groups; *Cross Cultural Psychology; *Drug Abuse; *Human Males; *Personality Measures; Substance Use Disorder
- Medical Subject Headings (MeSH):
- Adolescent; Adult; Analysis of Variance; China; Cross-Cultural Comparison; Diagnosis, Differential; Humans; Male; Middle Aged; Personality Assessment; Personality Disorders; Predictive Value of Tests; Sensitivity and Specificity; Substance-Related Disorders
- PsycINFO Classification:
- Personality Scales & Inventories (2223)
Personality Traits & Processes (3120) - Population:
- Human
Male
Inpatient - Location:
- China; Hong Kong
- Age Group:
- Adulthood (18 yrs & older)
Young Adulthood (18-29 yrs)
Thirties (30-39 yrs)
Middle Age (40-64 yrs) - Tests & Measures:
- Cross-Cultural (Chinese) Personality Inventory
- Grant Sponsorship:
- Sponsor: Hong Kong Government, Research Grants Council Earmarked, Hong Kong
Grant Number: CUHK4326/01H; CUHK4333/00H
Recipients: No recipient indicated - Methodology:
- Empirical Study; Quantitative Study
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- Accepted: Feb 29, 2008; Revised: Feb 19, 2008; First Submitted: Feb 21, 2006
- Release Date:
- 20080616
- Correction Date:
- 20151207
- Copyright:
- American Psychological Association. 2008
- Digital Object Identifier:
- http://dx.doi.org/10.1037/1040-3590.20.2.103
- PMID:
- 18557687
- Accession Number:
- 2008-06771-002
- Number of Citations in Source:
- 50
- Persistent link to this record (Permalink):
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- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2008-06771-002&site=ehost-live">Clinical utility of the Cross-Cultural (Chinese) Personality Assessment Inventory (CPAI-2) in the assessment of substance use disorders among Chinese men.</A>
- Database:
- PsycINFO
Clinical Utility of the Cross-Cultural (Chinese) Personality Assessment Inventory (CPAI–2) in the Assessment of Substance Use Disorders Among Chinese Men
By: Fanny M. Cheung
Department of Psychology, The Chinese University of Hong Kong, Shatin, Hong Kong;
Shu Fai Cheung
Department of Psychology, University of Macau, Taipa, Macau
Freedom Leung
Department of Psychology, The Chinese University of Hong Kong, Shatin, Hong Kong
Acknowledgement: This study was funded partly by Hong Kong Government Research Grants Council Earmarked Grant Projects CUHK4326/01H and CUHK4333/00H. We thank Sammy Cheung (Hong Kong), Long Cui (Jilin), Jingli Gan (Henan), Changqing Jiang (Beijing), Hanning Li (Guangxi), Qijun Li (Nanjing), Fang Lin (Fuzhou), Zhiling Liu (Beijing), Zeqing Wang (Beijing), Fengyan Zhu (Beijing), and Nan Zhang (Chengdu) for data collection from the psychiatric participants in China and Hong Kong. We thank Man Yee Ho for assistance in statistical analyses of the present study.
In Western studies of the personality characteristics of people with substance use disorders, the most commonly used personality measures are the Minnesota Multiphasic Personality Inventory and the Minnesota Multiphasic Personality Inventory—2 (MMPI & MMPI–2; Butcher, Dahlstrom, Graham, Tellegen, & Kaemmer, 1989; Rouse, Butcher, & Miller, 1999). Specific scales have been developed to assess alcohol and drug problems, including the MacAndrew Alcoholism Scale—Revised (MAC–R; Butcher et al., 1989), the Addiction Potential Scale (APS; Weed, Butcher, McKenna, & Ben-Porath, 1992), and the Addiction Acknowledgment Scale (AAS; Weed et al., 1992). In particular, the AAS has been found to be the most accurate at identifying current substance use disorders (Clements & Heintz, 2002; Stein, Graham, Ben-Porath, & McNulty, 1999). While many of the MMPI–2 clinical scales are elevated among individuals with substance use disorders (Weybrew, 1996), typical code types shared by different subgroups included elevations on Scales 2 (Depression), 4 (Psychopathic Deviance), and 8 (Schizophrenia; Donovan, Soldz, Kelley, & Penk, 1998). Similar patterns have been found with a Chinese sample (Guan, Tang, Xue, & Zhou, 2002). Among specific subtypes of substance use, heroin users were characterized by hostility, depression, and alienation, whereas polydrug users were most disturbed by paranoid thinking, anxiety, and withdrawal (Donovan et al., 1998; Weybrew, 1996). MMPI–2 profiles distinguished between persons with substance use disorders who had moderate elevation on the clinical scales with a single elevation on the Psychopathic Deviance scale and those who had pervasive psychological distress in addition to the substance use problems. The MMPI–2 profiles of the latter group had multiple clinical elevations (Tran, Bux, Haug, Stitzer, & Svikis, 2001).
The Millon Clinical Multiaxial Inventory (MCMI; Millon, 1983) is another clinical instrument that has widely been used to assess persons with substance use disorders (e.g., Craig, 2000; Craig, Bivens, & Olson, 1997; Craig & Weinberg, 1992; Matano, Locke, & Schwartz, 1994; McMahon, Malow, & Penedo, 1998). Similar to MMPI–2 studies, studies using the MCMI have also found that persons with substance use disorders display greater emotional and characterological problems than the control groups. For example, both alcohol and drug use disorders were associated with significant emotional distress such as anxiety and depression (Craig & Weinberg, 1992). Moreover, a significant proportion of persons with substance use disorders were found to have personality disorders (Craig, 2000). The modal MCMI–III personality profile for persons with substance use disorders is represented by a single elevation on the antisocial scale (Craig, 2000; Craig et al., 1997).
Researchers using other instruments designed not solely for clinical assessment have also identified the association of substance use disorders with depression, anxiety, and antisocial behaviors. For example, using an alternative five-factor measure, Ball (1995, 2002) found that the impulsive sensation-seeking, neuroticism-anxiety, and aggression-hostility scales of the Zuckerman-Kuhlman Personality Questionnaire ( Zuckerman, Kuhlman, Thornquist, & Kiers, 1991) were associated with greater cocaine use and psychiatric severity. On the Multidimensional Personality Questionnaire (Tellegen, 1982), alcoholism was primarily characterized by negative emotionality, whereas drug use disorder was characterized by behavioral disinhibition (McGue, Slutske, & Iacono, 1999).
In addition to clinical scales, measures of normal personality dimensions have been used to examine the etiology, course, and risk factors of substance use disorders. Studies of normal personality traits of persons with substance use disorders using the NEO Personality Inventory—Revised (NEO-PI–R; Costa & McCrae, 1992) showed that these individuals are more neurotic and less conscientious and agreeable (McCormick, Dowd, Quirk, & Zegarra, 1998). Results from these personality studies have identified some common personality characteristics associated with substance use disorder, with variations among subtypes. The most common personality characteristics include rebelliousness or antisocial behavior, impulsivity, acting out behavior, alienation. and hostility on the one hand and emotional instability, depression. and overall psychological disturbance on the other hand.
CPAI–2 as a Culturally Relevant Personality MeasurePersonality assessment in Asia has generally relied on English-language measures developed in Western countries that have been translated and adapted for local use. Cross-cultural psychologists have noted gaps in culturally relevant constructs in these translated measures and have argued for the use of indigenously derived measures (Cheung, Cheung, Wada, & Zhang, 2003; Cheung & Leung, 1998). The Chinese Personality Assessment Inventory (CPAI; Cheung et al., 1996) was developed using a combined emic–etic approach to address this need. It includes personality characteristics that are etic, or culturally universal, as well as emic, or specifically salient in the Chinese culture (Van de Vijver & Leung, 1997).
Cheung, Kwong, and Zhang (2003) demonstrated the clinical utility of the CPAI in differentiating male prisoners from a random sample of normal males in Hong Kong and participants with psychiatric disorders from a sample of normal adults in mainland China. Multivariate analysis of variance (MANOVA) results also indicated the CPAI clinical and personality scales significantly differentiate among participants with different psychiatric diagnoses (bipolar, schizophrenia, and neurotic disorders).
Cheung, Cheung, and Zhang (2004a) examined the convergent validity of the CPAI by comparing its patterns of correlations with the Chinese version of the MMPI–2 (Cheung, Zhang, & Song, 2003). Many of the CPAI clinical scales had higher correlations with the MMPI–2 clinical and content scales measuring similar psychopathologies than with those measuring dissimilar ones. However, only moderate correlations were obtained between the CPAI and MMPI–2 scales assessing hypomania, paranoia, antisocial behavior, and substance use disorders. Discrepancies were also found between the CPAI clinical scales and some of the MMPI–2 clinical scales that were generally elevated even among Chinese normal samples, including Scales 2 (Depression), 7 (Psychasthenia), and 8 (Schizophrenia). Notwithstanding the basic convergence of the two instruments, these cross-cultural differences suggested the need for culturally sensitive measures using local norms.
Objectives of This StudyA large-scale research project involving different categories of psychiatric patients in Mainland China and Hong Kong was conducted to examine the clinical utility of the revised version of the CPAI (CPAI–2; Cheung, Cheung, & Zhang, 2004b; Cheung et al., 2008). The present article reports the clinical utility of the CPAI–2 personality and clinical scales in the assessment of Chinese people with substance use disorders. In particular, we investigated the added value of the emic, or indigenously derived, scales of the CPAI–2 in contributing to the prediction of substance use disorders among Chinese people.
Western clinical studies have suggested that the major personality features of substance use disorders are associated with negative emotionality and behavioral disinhibition. Some of the CPAI–2 clinical scales cover these personality features. In our present study, we focused on the Pathological Dependence scale (PAT) and four other CPAI–2 clinical scales, namely, Depression (DEP) and Anxiety (ANX), which reflect negative emotionality, and Antisocial Behavior (ANT) and Hypomania (HYP), which are related to behavioral disinhibition.
In addition to the clinical scales, we also examined the utility of the CPAI–2 personality scales related to neuroticism, conscientiousness, and agreeableness. In a joint factor analysis of the CPAI–2 and the NEO Five-Factor Inventory (NEO-FFI; Costa & McCrae, 1992) in a Chinese sample, the CPAI–2 Emotionality (EMO) and Pessimism (low end on the Optimism vs. Pessimism [O-P] scale) scales loaded with the NEO-FFI Neuroticism factor, while the Responsibility (RES) and Meticulousness (MET) scales loaded with the NEO-FFI Conscientiousness factor (Cheung et al., 2008). Three CPAI–2 Accommodation factor scales, namely, Defensiveness (DEF), Graciousness versus Meanness (G-M) and Veraciousness versus Slickness (V-S), were also found to be associated with the NEO-FFI Agreeableness factor in the joint factor analysis.
Apart from those etic, or universal, CPAI–2 scales associated with substance use disorders in Western studies, we further explored the utility of other indigenously derived scales that were relevant in the Chinese cultural context. We included normal personality scales measuring personality characteristics and interpersonal relationships that have been found to be important risk factors of mental health and adjustment among Chinese people (Cheung, Gan, & Lo, 2005), including Harmony (HAR) and Family Orientation (FAM).
Specifically, we hypothesized that the PAT scale, which was designed to assess various forms of addictive behavior, should differentiate between normal adults and persons with substance use disorders. We expected the substance use disorders group to score higher on clinical scales associated with behavioral disinhibition and negative emotionality, including ANT, HYP, DEP, and ANX. In terms of the normal personality scales on the CPAI–2, we hypothesized that compared with the normal population, the substance use disorders group would score higher on EMO and lower on O-P, RES, and MET from the Dependability factor. We further hypothesized that compared with their normal counterparts, the substance use disorders group would score higher on DEF and lower on G-M and V-S from the Accommodation factor.
Extending from the research literature on the relationship between agreeableness and substance use disorders as well as from the literature on Chinese mental health, we speculated that the substance use disorders group would score lower on the CPAI–2 HAR and FAM scales. Although we did not find direct parallels of these scales in the literature, we speculated that in the Chinese cultural context emphasizing harmony, social propriety, and family relationship, these scales would discriminate between persons with substance use disorders and their normal counterparts.
To demonstrate the specificity of the CPAI–2 clinical scales, we attempted to discriminate the personality profiles of the substance use disorders group from those of persons with other psychiatric diagnoses who did not have a substance use problem. Since we expected behavioral disinhibition to be a major feature discriminating between the two groups, we hypothesized that the substance use disorders group would score higher on the PAT, ANT, and HYP scales than the psychiatric comparison group, while the two groups might share some of the symptoms measured by the other clinical scales related to negative emotionality. In terms of normal personality characteristics, we expected that persons with substance use disorders would be more defensive, more hostile, and less trustworthy, and so, their scores on DEF and G-M would be higher and their scores on V-S would be lower than persons with other psychiatric diagnoses. In addition to comparing the mean differences among the substance use disorders group, the normal comparison group, and the psychiatric comparison group on the aforementioned scales, logistic regression was also conducted to identify the most salient predictors differentiating the substance use disorders group from the other two comparison groups.
MethodIn a large-scale project on the clinical utility of the CPAI–2, we recruited the participation of 10 psychiatric hospitals/clinics in China and two psychiatric hospitals/clinics in Hong Kong. The mainland Chinese hospitals were selected to cover different geographical locations over China. The cities/regions included Beijing, Chengdu, Fuzhou, Guangxi, Henan, Hong Kong, Jilin, and Nanjing. Five categories of psychiatric disorders comprising the most common types of patients found in psychiatric hospitals and clinics in China and Hong Kong were targeted in the clinical utility project: schizophrenic disorders, bipolar disorders, depressive disorders, anxiety and other neurotic disorders, and substance use disorders. In mainland China, the diagnoses were made according to the Chinese Classification of Mental Disorders (CCMD–2) set by the Neuropsychiatry Branch of the Chinese Medical Association (1989) and the Chinese Journal of Nervous and Mental Disease Editorial Committee (1986). References were also made to the revised fourth edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM–IV–TR; American Psychiatric Association, 2000), which was the primary basis of diagnosis used in Hong Kong. There was substantial agreement among diagnoses based on the CCMD–2 and the DSM systems (Zheng, Lin, Zhao, Zhang, & Young, 1994). Prior to commencing the data collection, the doctors in charge of the project at each hospital participated in a training meeting to ensure standardization of the diagnosis, research protocol, and procedure. The present study focused on the subgroup of participants with substance use disorders and their comparison with a matched group of participants who had other psychiatric diagnoses but who did not have any diagnosis of substance use disorder.
Participants
From the total sample of 1,749 participants with psychiatric diagnoses, there were 149 participants with substance use disorders as the primary diagnosis. Opioid was the major substance used by this group of participants (71%), with the remaining participants being addicted to alcohol (29%). Opioid was the most dominant drug found in trends of substance use problems in China (Chen & Huang, 2007) and in Hong Kong (CRDA 56th Report, 2007). Given that these participants were recruited from the substance use treatment units at psychiatric hospitals and clinics, they were likely to be at the more severe end of the distribution of persons with substance use problems in the Chinese community. Because of case screening in this study, none of the selected cases was given a secondary psychiatric diagnosis by the attending doctor, although comorbidity of substance use disorders and psychiatric disorders is common in the United States (Regier et al., 1990).
The medical personnel explained the purpose of the research project to the participants at the time when they were invited to join the study. They were told that they could withdraw from the study at any time. They were reassured that participation in the study was not related to their treatment. All participants took part in the study on a voluntary basis, with informed consent. As gender differences were found in some of the CPAI–2 scales (Cheung et al., 2004b), the substance use disorders participants were separated by gender. Given the small number of female cases, only 121 male patients (108 from China and 13 from Hong Kong) were selected as the target group for this study (substance use disorders group). The age of the participants ranged from 18 to 61 years, with the mean at 34.7 years (SD = 9.6). Over half (59%) of the participants had completed junior high school, 26% had a senior high education, and 7% had tertiary education. In terms of marital status, 36% of the substance use disorder group were single, 45% were married, and 18% were divorced. Among this group of participants, 95% were inpatients, and 5% were outpatients, which may suggest that they were more severe cases.
For comparison with the normal adults, we extracted a subgroup of male participants from the CPAI–2 normative sample (normal comparison group) that approximated the age distribution and educational level of the substance use disorder participants. The original normative sample was collected from six main regions in mainland China, using a quota sampling approach, and from Hong Kong, using a random sampling of household (see Cheung et al., 2004b, 2008). The normal comparison group used in this study, which was extracted from the normative sample and matched by age and educational level, consisted of 187 Chinese males from China and Hong Kong. The mean age of the normal comparison group was 34.8 years (SD = 9.4), with a range of 18 to 61 years. Six percent of this group had a primary level education, 59% had completed junior high school, while another 30% had a senior high education. The marital status of the normal comparison group consisted of 27% single men, 65% married men, and less than 2% divorced men.
To differentiate the participants with substance use disorders from participants with other psychiatric disorders, we selected 172 male participants from the remaining psychiatric sample as the psychiatric comparison group. The psychiatric comparison group was matched with the substance use disorders group for age and education. For this comparison group, we selected only the cases that did not have a primary or a secondary diagnosis of substance use disorders. The mean age of the psychiatric comparison group was 34 years (SD = 9.8), with a range from 18 to 61 years. Half of the psychiatric comparison group had completed junior high school, another 38% had a senior high education, and 9% had tertiary education. In terms of marital status, 50% of this group were single, 38% were married, and 7% were divorced. The primary Axis 1 diagnoses of these participants included schizophrenic disorder (53%), bipolar disorder (16%), depressive disorder (15%), and anxiety disorder (16%); comorbidity other than substance use disorders was present when secondary diagnoses were also considered. Among this comparison group, 86% were inpatients and 14% were outpatients.
Measures
The CPAI–2 (Cheung, Leung, Song, & Zhang, 2001) is a paper-and-pencil questionnaire consisting of 541 items that are self-report statements to which the participants respond in a yes–no format. The CPAI–2 was standardized using a representative sample of 1,911 respondents (1,575 from China and 336 from the Hong Kong special administrative region) aged 18 to 70 years. The factor structure of the CPAI–2 is similar to that of the original CPAI (Cheung et al., 2008). The raw scores of each scale are converted into T scores based on the means and standard deviations of the normative sample in the original CPAI–2 standardization study (Cheung et al., 2004b). Similar to the MMPI–2, the CPAI–2 T score has a mean of 50 and a standard deviation of 10. The full set of CPAI–2 (Form A) consisting of 28 personality scales, 12 clinical scales, and 3 validity scales was used in this study. The names of the scales of the CPAI–2 are listed in the Appendix. To screen out invalid protocols on the CPAI–2 in research studies, we deleted cases that indicated random responses, response inconsistency, and extremely high infrequency scores, using the same screening criteria for selecting the normative sample in the restandardization of the CPAI–2 (see Cheung et al., 2008).
Procedure
The doctor in charge of the project at each hospital/clinic supervised the screening of all psychiatric participants for the designated diagnostic categories. On the basis of unstructured clinical observations using the standard diagnostic classification systems adopted in China and Hong Kong (using CCMD–2 and DSM–IV–TR), the screening doctor completed a standardized diagnostic form indicating the primary and, if applicable, the secondary diagnoses of the participants, as well as the severity of the symptoms associated with the diagnoses. The two groups of participants at the hospitals/clinics completed the CPAI–2 individually or in small groups, with two short breaks between sessions to ensure that they maintained their concentration.
Statistical Analyses
The T scores of the substance use disorders group, the normal comparison group, and the psychiatric comparison group on the CPAI–2 scales were compared using MANOVA. To test for significant univariate group effects, two contrasts were employed: (a) normal comparison group versus substance use disorders group and (b) psychiatric comparison group versus substance use disorders group. To control for the Type I error rate of these specific tests, we used a more stringent alpha level of .01.
In addition to identifying scales that differentiated the substance use disorders group from the two comparison groups, we also examined the most salient predictors using regression analysis. Since there were three groups, we adopted polytomous logistic regression (Kleinbaum & Klein, 2002) to predict the membership of each case. The substance use disorders group was used as the reference group in the analysis. The selection of predictors involved three steps. Our choice of predictors was based on the hypotheses presented in the introductory section.
In Step 1, we included PAT as the predictor. Since PAT was developed specifically to assess various forms of addictive behaviors, a significant prediction in this step would support the validity of this scale. In Step 2, stepwise selection procedure was used to identify clinical scales that significantly increased prediction after PAT was included. This step included the four CPAI–2 clinical scales that we specified in the original hypotheses: DEP, ANX, ANT, and HYP. In Step 3, stepwise selection was used to identify significant predictors from other CPAI–2 normal personality scales specified in our hypotheses to identify the universal and culturally relevant personality predictors found to be associated with substance use disorders. Candidate variables in this step included EMO and O-P, which are related to neuroticism; RES and MET, which are related to conscientiousness; DEF, V-S, and G-M, which are related to agreeableness; and the two indigenously derived scales, HAR and FAM. Since we lacked a theoretical basis for the relative importance and the interrelationship of variables in this step and given the large number of candidate variables, we adopted a more stringent significance level of .001 in this step to lessen the problem of Type I error.
As the PAT scale was designed for the purpose of identifying various forms of addictive behavior, we further examined the optimal cutoff T score on the PAT for differentiating the substance use disorders group from the other two comparison groups, using the receiver operating characteristic (ROC) curve.
ResultsThe mean T scores of the substance use disorders, normal comparison, and psychiatric comparison groups on the CPAI–2 clinical and personality scales are presented in Table 1.
CPAI–2 T Score Means and MANOVA Results for the Substance Use Disorders, Psychiatric Comparison, and Normal Comparison Groups
CPAI–2 Profile
On the clinical scales, the substance use disorders group obtained the highest T score on PAT, reaching a mean score over two standard deviations above the norm, followed by ANT and Paranoia (PAR), both reaching 1.5 standard deviations above the norm. On the personality scales, the mean scores of the substance use disorders group all fell within one standard deviation of the norm, with relatively lower scores on the following scales: O-P and FAM from the Dependability factor. These participants also scored high on the DEF and low on the V-S scales from the Accommodation factor.
MANOVA Results
Results from MANOVA comparisons among the substance use disorders group, the normal comparison group, and the psychiatric comparison group showed a significant main effect of psychiatric diagnoses on the CPAI–2 clinical scales, Wilks's lambda F(24, 872) = 11.35, p < .001, partial η2 = .238. Table 1 presents the MANOVA results and the univariate group comparison.
Compared with the matched sample of normal male adults, the participants with substance use disorders reported more psychological disturbance, scoring higher on all the CPAI–2 clinical scales. The univariate group comparison showed that the greatest mean score differences between the two groups were found on the PAT, ANT, PAR, DEP, and ANX scales. Comparison between the substance use disorders group and the psychiatric comparison group showed that participants with substance use disorders scored higher on DEP, PAT, HYP, ANT, Distortion of Reality, and PAR. Further analyses on the univariate comparison between the normal comparison group and the psychiatric comparison group showed that they did not differ on PAT and ANT. The difference on PAT and ANT between the substance use disorders group and the two comparison groups, as well as the lack of difference between the two comparison groups themselves, provide support for the discriminant validity of these two scales.
Similar analyses on CPAI–2 normal personality scales were conducted. Results showed significant differences in the overall pattern of personality among the three groups, Wilks's lambda F(54, 866) = 4.73, p < .001, partial η2 = .228. As expected, contrasts between the substance use disorders group and the normal comparison group on the CPAI–2 personality scales in the univariate comparison showed that the substance use disorders participants scored significantly higher on EMO and lower on O-P, RES, and MET. As hypothesized, the substance use disorders participants also scored significantly higher on DEF and lower on G-M and V-S. For the two indigenous scales, HAR and FAM, the substance use disorders participants scored lower than their normal counterparts, as expected.
In addition to the hypothesized differences, the substance use disorders participants also scored significantly higher on the Face and the Self versus Social Orientation (S-S) scales. They scored significantly lower on the Enterprise, the Logical versus Affective Orientation, the Practical Mindedness (PRA), the Interpersonal Tolerance, and the Internal versus External Locus of Control (I-E) scales. (See the Appendix for a description of these scales.)
Univariate comparison between the substance use disorders group and the psychiatric comparison group showed that the two groups obtained significantly different scores on most of the Accommodation factor scales reflecting less agreeableness and on the Dependability factor scales associated with lower conscientiousness. Compared with the psychiatric comparison group on these two factors, the substance use disorders group was more defensive (high DEF), meaner (low G-M), and more slick (low V-S). The results were consistent with our hypotheses. Moreover, the substance use disorders group was also more self-oriented (high S-S), less practical minded (low PRA), less meticulous (low MET), more inclined toward external locus of control (low I-E), and less family oriented (low FAM) than the psychiatric comparison group. The substance use disorders participants also scored higher on the Novelty and Leadership scales. As hypothesized, these two groups did not differ on EMO and O-P, which reflects negative emotionality.
Polytomous Logistic Regression
To identify the most salient CPAI–2 predictors, we conducted polytomous logistic regression to discriminate the three groups on the basis of the hypotheses set out in the introductory section. A summary of the results of the logistic regression analysis is presented in Table 2. Only the scales that remain in the final model are reported below.
Polytomous Logistic Regression Analysis Using CPAI–2 Scales to Differentiate Among the Substance Use Disorders, Psychiatric Comparison, and Normal Comparison Groups
In Step 1, PAT was included as a significant predictor, as hypothesized. In Step 2, the significant predictors were ANT and DEP. In Step 3, only O-P was entered by the stepwise procedure. None of the other personality scales explored in the subsequent step were included in the final model. The parameter estimates for the final model and the effect-size measure of the model's predictive power in each step (the Nagelkerke index) are presented in Table 2. In the CPAI–2 T-score metric, one unit represents 0.1 standard deviation in the normative sample. We present the odds ratio corresponding to a change in ten units, that is, one standard deviation.
As shown in Table 2, PAT was the strongest predictor. Cases with higher PAT scores were more likely to be assigned to the substance use disorders group than to the other two groups. Moreover, DEP and O-P, in addition to PAT, significantly differentiated the substance use disorders group from the normal comparison group. Cases with higher DEP scores (more depressive symptoms) or lower O-P scores (more pessimistic) were more likely to be assigned to the substance use disorders group than to the normal comparison group. In the differentiation between the substance use disorders group and the psychiatric comparison group, ANT contributed significant predictive value beyond that of PAT. Cases with higher ANT scores (more self-reported antisocial behaviors) were more likely to be assigned to the substance use disorders group than to the psychiatric comparison group.
As PAT was the strongest predictor, the ROC curves were examined to identify the optimal PAT cutoff T scores for differentiating the substance use disorders participants from members in the other two groups. The sensitivity and specificity for selected cutoff scores are presented in Table 3. As shown in the table, when using the PAT scale to differentiate substance use disorders participants from normal male adults, the optimal cutoff T score was 64. The sensitivity of the PAT scale was 73%, and the specificity was 80%. When using the PAT scale to differentiate substance use disorders participants from other psychiatric participants without substance use problems, the optimal cutoff T score on the PAT was also 64, with sensitivity at 73% and specificity at 77%.
Sensitivity and Specificity for Different Cutoff Scores of the Pathological Dependence (PAT) Scale
DiscussionResults from both the MANOVA and the logistic regression analysis in this study showed that the emic and etic CPAI–2 clinical and personality scales were able to discriminate men with substance use disorders from both normal male adults in the community and men with other psychiatric diagnoses. The clinical profile of the substance use disorders participants was characterized by elevated scores on the CPAI–2 PAT, ANT, and DEP scales.
In particular, the PAT achieved good sensitivity and specificity in differentiating the substance use disorders group from the psychiatric comparison group and the normal comparison group. PAT consists of items on a range of addictive behaviors, including substance use, alcoholism, smoking, and pathological gambling, which reflect common problems of pathological dependence in the Chinese cultural context. The support for the validity of the CPAI–2 PAT scale in assessing addiction is especially important, as the original version of this scale on the CPAI did not correlate with the MMPI–2, MAC–R, APS, and AAS in a normal sample of Chinese students (Cheung et al., 2004a). The pattern of elevations of the CPAI–2 clinical scales obtained in this study converge with similar elevations found in MMPI–2 profiles of persons with substance use disorders (Donovan et al., 1998; Guan et al., 2002; Tran et al., 2001; Weybrew, 1996). The clinical features found in this group of participants are also supported by other personality studies (Allen, Moeller, Rhoades, & Cherek, 1998; Ball, 1995; Morey, 1991; Parker, Daleiden, & Simpson, 1999) which showed that substance use disorders are associated with other psychosocial problems. The major features of behavioral disinhibition and negative emotionality in substance use disorders were confirmed in this study.
The inclusion of the CPAI–2 normal personality scales augment clinical assessment by identifying long-standing personality traits associated with the clinical profiles that might have implications on treatment. In terms of personality traits, the distinctive features of the CPAI–2 profile of the Chinese men with substance use disorders were characterized by poor dependability, emotional instability, and social maladjustment relative to the cultural norms. Although, on the surface, the substance use disorders participants might appear to be assertive and active, their lack of structure and responsibility hampered their social adjustment. They were less trustworthy, tended to be unrealistic, and were likely to attribute causes of events to external factors. They tended to be self-centered and had more conflicts with their family and other people. Some of these CPAI–2 personality correlates confirm the patterns of negative emotionality and behavioral disinhibition found in studies of American samples of persons with substance use disorders using normal personality scales (Allen et al., 1998; Ball, 1995; Henderson, Galen, & DeLuca, 1998; McCormick et al., 1998; McGue et al., 1999).
The indigenously derived CPAI–2 personality correlates also highlight culturally salient dimensions for understanding the personality traits of persons with substance use disorders in the Chinese context. In particular, the low score on the FAM scale reflects the breakdown in traditional family support, which constitutes an anchor for a person's stability. A low score on V-S and a high score on DEF reflect the tendency to smooth over flaws and to protect oneself through defense mechanisms, including rationalization, externalization of blame, and self-enhancement. In a cultural norm that emphasizes harmony and reciprocity in social exchange, the participants' lower scores on the G-M and the HAR scales suggest a disruptive pattern in social relationships. Although the indigenously derived personality scales did not contribute unique value to the diagnostic prediction in this study, these cultural contexts and patterns of behaviors might provide useful information for clinicians in understanding the psychopathology of and considering treatment approaches for Chinese persons with substance use disorders.
While establishing the utility of the CPAI–2 in assessing substance use disorders in this study, we recognize the limitations of the cross-sectional design in which only the attending clinician's unstructured diagnostic classification, albeit based on a standard diagnostic taxonomy, is used as the criterion variable (Sutker & Allain, 1988). The focus on the primary diagnosis by the clinicians also limited the classification of comorbid cases. There were no other outcome variables included in this study. We note that the reliability coefficients of some of the personality scales were generally lower than those of the clinical scales, given the broader bandwidth of these personality constructs. This may limit the validity of the findings related to the personality scales. Given the relatively small sample of participants with substance use disorders, we selected only male participants with primarily opioid and alcohol dependence and did not divide the samples into subtypes of substance use disorders. The small sample size did not allow us to cross-validate the present findings.
ConclusionIn the United States, there is an increasing recognition of the need for more culturally relevant and valid psychological assessment given the cultural diversity in the American population and the large influx of immigrants, especially from Asia (Cheung, Leong, & Ben-Porath, 2003). This study provides an extension of the findings from previous studies that adopted mostly Western-based personality tests to evaluate the relationship between personality and substance use disorders to a non-Western setting by using a culturally sensitive measure. The results illustrate the utility of the CPAI–2 in the assessment of substance use disorders in Chinese societies. The CPAI–2 etic, or culturally universal, scales demonstrate convergent personality correlates that have been found in other Western studies. The emic, or indigenously derived, scales offer culturally relevant information to understand maladjustment in a Chinese cultural context beyond that obtained from translated instruments. Given the cross-cultural congruence found in the CPAI personality structure of Chinese American samples in the United States and in other Asian samples (Cheung, Cheung, Leung, Ward, & Leong, 2003; Lin & Church, 2004), we expect the CPAI–2 to provide clinicians with a culturally sensitive assessment measure for other ethnic Asian clients.
This study is the first in a series reporting the clinical utility of the CPAI–2 with different groups of persons with psychiatric disorders. As a comprehensive personality inventory that includes both clinical and normal personality scales, the CPAI–2 provides clinicians with a tool for a broad spectrum of personality assessment that is relevant to an ethnic Chinese population. Further studies are needed to extend the validation of CPAI–2 for substance use to the assessment of female samples and subtypes of substance use disorders and to predict treatment outcome.
Footnotes 1 On the CPAI–2 clinical scales, a high T score of 60 suggests deviance on that dimension, similar to the trend we found on the Chinese MMPI–2 and the original CPAI (Cheung, Kwong, & Zhang, 2003; Cheung, Zhang, & Song, 2003). Theoretically, we do not propose a cutoff score for the personality scales as these scales are designed to be descriptive. For the purpose of interpretation, on personality scales with a bipolar dimension, for example, Extraversion versus Introversion, a T score above the mean of 50 indicates more of the personality characteristic listed first in the scale label (i.e., extraversion) as compared with the standardization sample; a score below 50 indicates more of the personality characteristic listed second in the scale label (i.e., introversion). Deviation from the mean of 50 on these personality scales is meant to be descriptive to help researchers understand the meaning of the score on a particular scale.
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APPENDIX APPENDIX A: Scales of the Cross-Cultural (Chinese) Personality Assessment Inventory (CPAI–2) With Brief Descriptions
Social Potency Factor
Novelty (NOV): Like trying new things and facing new challenges.
Diversity (DIV): Try out new ways of handling tasks.
Divergent Thinking (DIT): Deal with issues or problems from various perspectives.
Leadership (LEA): Take the initiative to lead, influence others, and make decisions in a group.
Logical versus Affective Orientation (L-A): High score: logical and analytic in thinking and behavior; low score: sentimental and intuitive orientation.
Aesthetics (AES): Value and enjoy art and music.
Extraversion versus Introversion (E-I): High score: sociable and socially comfortable; low score: prefer to be quiet and solitary.
Enterprise (ENT): Explore the unbeaten paths and dare to take risks.
Dependability Factor
Responsibility (RES): Dedicated, persistent, and can be relied upon to carry out tasks.
Emotionality (EMO): Emotionally stable, in control of emotions.
Inferiority versus Self-acceptance (I-S): High score: poor self-esteem; low score: self-confident.
Practical Mindedness (PRA): Realistic and pragmatic, focus on substance rather than form.
Optimism versus Pessimism (O-P): High score: energetic and positive outlook; low score: hold grievances, low spirited.
Meticulousness (MET): Cautious, orderly, and pay attention to details.
Face (FAC): Excessive attention to social recognition, concern for maintaining self-respect in social relationships.
Internal versus External Locus of Control (I-E): High score: attribute to internal factors in explaining success and failure; low score: attribute to external factors, luck and fate.
Family Orientation (FAM): Value family bonding and have strong family ties.
Accommodation Factor
Defensiveness (Ah-Q Mentality) (DEF): Cultural pattern of defense mechanisms, including rationalization, externalization of blame, self-enhancement, and belittling others' achievements.
Graciousness versus Meanness (G-M): High score: bear no grudges, treat others leniently; low score: overly critical of others, retaliatory, and calculating.
Interpersonal Tolerance (INT): Accept diversity and tolerate differences in people.
Self versus Social Orientation (S-S): High score: independent and unwilling to join cooperative activities; low score: collectivistic orientation, a team player.
Veraciousness versus Slickness (V-S): High score: truthful, adhere to principles; low score: boastful, suave, and superficial.
Interpersonal Relatedness Factor
Traditionalism versus Modernity (T-M): High score: endorse traditional beliefs, customs, and values; low score: challenge traditional ideas, endorse individual freedom.
Renqing (Relationship Orientation) (REN): Adhere to the cultural norms of interpersonal interaction, such as courteous rituals, reciprocal exchange of resources, maintaining and utilizing useful social ties.
Social Sensitivity (SOC): Sensitive to how others feel and react.
Discipline (DIS): Rigid and disciplined as opposed to flexible and adaptable.
Harmony (HAR): Maintain inner peace and contentment, avoid conflict and competition.
Thrift versus Extravagance (T-E): High score: endorse traditional value of frugality; low score: materialistic tendency and high consumption.
Among the clinical scales, the Inferiority versus Self-acceptance (I-S) scale has been designed to be used as both a personality and a clinical scale if the two sets of scales are used separately. Descriptions of the symptoms or behaviors reported in the remaining 11 clinical scales are as follows:
Emotional Problem Factor
Depression (DEP): Gloomy, lethargic, lack confidence, self-reproaching.
Physical Symptoms (PHY): Frail, frequent dizziness, headache, muscular cramps, or insomnia.
Anxiety (ANX): Excessively worried, restless, unable to focus attention.
Somatization (SOM): Express distress through somatic presentation, lack insight into psychological problem.
Behavioral Problem Factor
Hypomania (HYP): Impulsive, bursting with energy, excitement seeking.
Antisocial Behavior (ANT): Rebellious, undisciplined, reject social and legal norms.
Need for Attention (NEE): Dependent, self-centered, histrionic.
Pathological Dependence (PAT): Addicted to drinking, smoking, gambling, or drugs.
Distortion of Reality (DIR): Bizarre thought patterns, visual and auditory hallucinations.
Paranoia (PAR): Sensitive, fuss over minor problems, self-aggrandizing.
Sexual Maladjustment (SEM): Uncomfortable with sexuality, suffer sexual dysfunctions.
Infrequency (INF): Answering in an opposite manner to the majority of respondents, and may reflect peculiar behavioral patterns.
Good Impression (GIM): Attempt to gain positive evaluation by exaggerating good qualities and concealing weaknesses.
Response Consistency Index (RCI): Extremely low scores indicate answering in a careless and inconsistent manner.
Submitted: February 21, 2006 Revised: February 19, 2008 Accepted: February 29, 2008
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Source: Psychological Assessment. Vol. 20. (2), Jun, 2008 pp. 103-113)
Accession Number: 2008-06771-002
Digital Object Identifier: 10.1037/1040-3590.20.2.103
Record: 36- Title:
- Cognitive behavioral therapy for adherence and depression (CBT-AD) in HIV-infected injection drug users: A randomized controlled trial.
- Authors:
- Safren, Steven A.. Massachusetts General Hospital, MA, US, ssafren@partners.org
O'Cleirigh, Conall M.. Massachusetts General Hospital, MA, US
Bullis, Jacqueline R.. Harvard Medical School, Boston, MA, US
Otto, Michael W.. Department of Psychology, Boston University, Boston, MA, US
Stein, Michael D.
Pollack, Mark H.. Massachusetts General Hospital, MA, US - Address:
- Safren, Steven A., MGH Behavioral Medicine, One Bowdoin Square, 7th Floor, Boston, MA, US, 02114, ssafren@partners.org
- Source:
- Journal of Consulting and Clinical Psychology, Vol 80(3), Jun, 2012. pp. 404-415.
- NLM Title Abbreviation:
- J Consult Clin Psychol
- Page Count:
- 12
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- Journal of Consulting Psychology
- Other Publishers:
- US : American Association for Applied Psychology
US : Dentan Printing Company
US : Science Press Printing Company - ISSN:
- 0022-006X (Print)
1939-2117 (Electronic) - Language:
- English
- Keywords:
- HIV/AIDS, adherence, antiretroviral therapy (ART), depression, substance abuse, cognitive behavioral therapy for adherence and depression, injection drug users
- Abstract:
- Objective: Depression and substance use, the most common comorbidities with HIV, are both associated with poor treatment adherence. Injection drug users comprise a substantial portion of individuals with HIV in the United States and globally. The present study tested cognitive behavioral therapy for adherence and depression (CBT-AD) in patients with HIV and depression in active substance abuse treatment for injection drug use. Method: This is a 2-arm, randomized controlled trial (N = 89) comparing CBT-AD with enhanced treatment as usual (ETAU). Analyses were conducted for two time-frames: (a) baseline to post-treatment and (b) post-treatment to follow-up at 3 and 6 months after intervention discontinuation. Results: At post-treatment, the CBT-AD condition showed significantly greater improvement than ETAU in MEMS (electronic pill cap) based adherence, γslope = 0.8873, t(86) = 2.38, p = .02; dGMA-raw = 0.64, and depression, assessed by blinded assessor: Mongomery-Asberg Depression Rating Scale, F(1, 79) = 6.52, p < .01, d = 0.55; clinical global impression, F(1, 79) = 14.77, p < .001, d = 0.85. After treatment discontinuation, depression gains were maintained, but adherence gains were not. Viral load did not differ across condition; however, the CBT-AD condition had significant improvements in CD4 cell counts over time compared with ETAU, γslope = 2.09, t(76) = 2.20, p = .03, dGMA-raw = 0.60. Conclusions: In patients managing multiple challenges including HIV, depression, substance dependence, and adherence, CBT-AD is a useful way to integrate treatment of depression with an adherence intervention. Continued adherence counseling is likely needed, however, to maintain or augment adherence gains in this population. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Cognitive Behavior Therapy; *Drug Therapy; *HIV; *Major Depression; *Treatment Compliance; AIDS; Antiviral Drugs; Drug Abuse; Intravenous Drug Usage
- Medical Subject Headings (MeSH):
- Aged; Antiretroviral Therapy, Highly Active; Cognitive Therapy; Depressive Disorder; Female; HIV Infections; Humans; Male; Medication Adherence; Middle Aged; Substance Abuse, Intravenous; Substance-Related Disorders; Treatment Outcome
- PsycINFO Classification:
- Health & Mental Health Treatment & Prevention (3300)
- Population:
- Human
Male
Female - Age Group:
- Adulthood (18 yrs & older)
Young Adulthood (18-29 yrs)
Thirties (30-39 yrs)
Middle Age (40-64 yrs)
Aged (65 yrs & older) - Tests & Measures:
- Clinical Global Impression
Beck Depression Inventory DOI: 10.1037/t00741-000
Montgomery-Asberg Depression Rating Scale DOI: 10.1037/t04111-000
Mini International Neuropsychiatric Interview DOI: 10.1037/t18597-000 - Grant Sponsorship:
- Sponsor: National Institute on Drug Abuse
Grant Number: Grant R01DA018603
Recipients: Safren, Steven A. - Conference:
- International Conference on HIV Treatment (IAPAC), 5th, May, 2010, Miami, FL, US
- Conference Notes:
- Portions of this article were presented at the aforementioned conference.
- Methodology:
- Empirical Study; Quantitative Study
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- First Posted: Apr 30, 2012; Accepted: Feb 7, 2012; Revised: Feb 6, 2012; First Submitted: May 9, 2011
- Release Date:
- 20120430
- Correction Date:
- 20130715
- Copyright:
- American Psychological Association. 2012
- Digital Object Identifier:
- http://dx.doi.org/10.1037/a0028208
- PMID:
- 22545737
- Accession Number:
- 2012-10794-001
- Number of Citations in Source:
- 56
- Persistent link to this record (Permalink):
- http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2012-10794-001&site=ehost-live
- Cut and Paste:
- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2012-10794-001&site=ehost-live">Cognitive behavioral therapy for adherence and depression (CBT-AD) in HIV-infected injection drug users: A randomized controlled trial.</A>
- Database:
- PsycINFO
Cognitive Behavioral Therapy for Adherence and Depression (CBT-AD) in HIV-Infected Injection Drug Users: A Randomized Controlled Trial
By: Steven A. Safren
Massachusetts General Hospital;
Harvard Medical School;
Conall M. O'Cleirigh
Massachusetts General Hospital;
Harvard Medical School
Jacqueline R. Bullis
Harvard Medical School;
Department of Psychology, Boston University
Michael W. Otto
Department of Psychology, Boston University
Michael D. Stein
Butler Hospital and Brown University
Mark H. Pollack
Massachusetts General Hospital;
Harvard Medical School
Acknowledgement: Funding for this project came from National Institute on Drug Abuse Grant R01DA018603 to Steven A. Safren. Clinical Trial Registration: Skills Based Counseling for Adherence and Depression in HIV+ Methadone Patients (NCT00218634; http://clinicaltrials.gov/ct2/show/NCT00218634). Portions of this article were presented at the fifth International Conference on HIV Treatment (IAPAC), May 2010, Miami, Florida.
Safren has served as a consultant for Dimangi and ISA Associates on technology-based adherence interventions and receives royalties from various book publishers, including Oxford University Press and Guilford Press. O'Cleirigh and Bullis have no disclosures to report. In the last 2 years, Otto has served as a consultant for MicroTransponder, Inc., and received research support from Shering Plough (Merck) as well as royalties from various book publishers. Pollack has served as a consultant for Eli Lilly, Medavante, Otsuka, and Targia Pharmaceuticals. In addition, he has received several grants from Bristol Myers Squibb, Euthymics, Forest Laboratories, and GlaxoSmithKline. Last, he has received equities from Medavante, Mensante Corporation, Mindsite, Targia Pharmaceutical Royalty/patent: SIGH-A, SAFER interviews. Stein has served as a consultant for Gilead, Bristol Myers Squibb, and visualmd.com.
We thank the many members of the study team who made this study a success. This includes (but is not limited to) Giselle Perez, Susan Michelson, Laura Reilly, Jessica Graham, Pamela Handelsman, Nicholas Perry, Nafisseh Souroudi, Jeffrey Gonzalez, Joseph Greer, Robert Knauz, Jonathan Lerner, Deb Herman, Luis Serpa, Jared Israel, Lauren McCarl, Allison Applebaum, Ellen Hendriksen, Christina Psaros, Lara Traeger, Sarah Markowitz, Carla Berg, the staff at BayCove, Community Substance Abuse Center, Habit OpCo, Roger Weiss, Kenneth Mayer, Robert Malow, and most importantly the study participants.
HIV continues to be a major public health concern in the United States, with no decline in rates of new infections and prevalence growing steadily (Centers for Disease Control and Prevention, 2008). The two most prevalent and interfering psychosocial comorbidities of HIV infection are clinical depression and substance use (Berger-Greenstein et al., 2007; Bing et al., 2001; Ruiz Perez et al., 2005). In a nationally representative probability sample of 2,864 adults who participated in the HIV Care Services and Utilization Study, 12-month prevalence rates for major depression, substance use without dependence (excluding marijuana use), and substance dependence were estimated as 36%, 25%, and 12.5%, respectively (Bing et al., 2001). Clinical depression and problematic substance use not only can cause significant distress and functional impairment but also can interfere with HIV treatment and care; both conditions have consistently been associated with poor antiretroviral therapy (ART) adherence (Catz, Kelly, Bogart, Benotsch, & McAuliffe, 2000; DiMatteo, Lepper, & Croghan, 2000; Lucas, Cheever, Chaisson, & Moore, 2001; Lucas, Gebo, Chaisson, & Moore, 2002; Paterson et al., 2000; Safren et al., 2001). A recent meta-analysis of 99 independent samples revealed a significant relationship between depression and adherence to HIV medications (Gonzalez, Batchelder, Psaros, & Safren, 2011).
Individuals with injection drug use (IDU) histories continue to compose a large proportion of individuals living with HIV in the United States (Centers for Disease Control and Prevention, 2010). The most recent estimates available from 37 states with confidential name-based HIV-infection reporting suggest that approximately 12% of men and 15% of women living with HIV in 2008 acquired HIV through IDU (Centers for Disease Control and Prevention, 2010). However, these estimates only account for HIV-infection directly attributable to IDU and thus do not reflect the secondary impact of transmission through sexual contact with a partner who acquired HIV through IDU.
In addition to the importance of adherence for self-care and optimization of the benefits of ART, adherence may be important in the transmissibility of HIV. HIV transmission is highly dependent on the amount of HIV viral load present in any given individual's blood and genital secretions (Hull & Montaner, 2011). HIV viral load can be reduced to an undetectable level through successful antiretroviral therapy, which seems to significantly reduce transmission risk between HIV-serodiscordant partners (Attia, Egger, Muller, Zwahlen, & Low, 2009). Accordingly, increased adherence to ART among opioid-dependent individuals living with HIV may also provide a secondary public health benefit of contributing to HIV prevention efforts (Hull & Montaner, 2011) and is a part of new emerging “test, treat, and retain” strategies.
Injection drug users living with HIV face multiple changes to successful HIV treatment, including a poorer virological response to ART compared with other HIV-infected populations, poor adherence to ART, and increased rates of attrition during interventions (Keiser et al., 2012; Weber et al., 2009). Research suggests that HIV-infected individuals currently receiving treatment for IDU or opioid dependence continue to struggle with adherence to ART (Weber et al., 2009). Avants, Margolin, Warburton, Hawkins, and Shi (2001), for example, found that more than a third of HIV-positive patients receiving methadone maintenance treatment reported less than 80% adherence to their HIV medication regimens, a rate that potentially increases the risk of developing a drug-resistant strain of the virus. Even when ART doses were directly administered and supervised in a methadone clinic-based program, continued substance use was associated with an increased risk of nonadherence and intervention dropout (Lucas et al., 2007).
HIV-positive individuals are also at an increased risk for major depression, with prevalence rates suggesting that twice as many HIV-positive individuals suffer from depression than demographically matched HIV-negative individuals (Ciesla & Roberts, 2001). Among a sample of triply diagnosed patients with HIV, substance abuse, and psychiatric illness, Berger-Greenstein et al. (2007) reported that over 70% of participants met criteria for major depression; self-reported depressive symptoms were also significantly related to worse HIV medication adherence and lower CD4 cell count. A prospective observational study by Riera et al. (2002) reported that among 202 HIV-positive patients, depression and methadone maintenance treatment were independent predictors of poor adherence to antiretroviral medications. During an evaluation of depressive symptoms and symptomatic response among HIV-infected injection drug users who were enrolled in a randomized controlled trial of directly observed ART, improvements in depression over 6 months were associated with increases in CD4 cell count and adherence, whereas worsening in depression was associated with active drug use and increases in plasma viral RNA levels (Attia et al., 2009; Hull & Montaner, 2011; Springer, Chen, & Altice, 2009).
Maintaining excellent adherence can be a difficult and challenging process for a sizeable proportion of individuals living with HIV. Poor adherence decreases the benefits of ART, as well as chances of prolonged survival (e.g., García de Olalla et al., 2002; Thompson et al., 2010). To address these barriers, there is an emerging evidence base for the efficacy of interventions for ART adherence (Amico, Harman, & Johnson, 2006; Simoni, Amico, Pearson, & Malow, 2008; Simoni, Frick, Pantalone, & Turner, 2003; Simoni, Pearson, Pantalone, Marks, & Crepaz, 2006). However, to date, most interventions have produced only modest effects, have focused directly on adherence, and have not addressed psychosocial comorbidities that may moderate the degree to which the interventions would be successful. When one considers the symptoms of a depressive episode (e.g., persistent sad mood, loss of interest, concentration problems, low energy, feelings of excessive worthlessness/guilt), it is not difficult to see how these symptoms could interfere with the acquisition or use of skills necessary to improve adherence and could potentially minimize the efficacy of adherence training interventions that do not directly treat depression.
Our prior work involved developing (Safren et al., 2004) and initially testing (Safren et al., 2009) cognitive behavioral therapy for adherence and depression (CBT-AD) in HIV. We showed in a crossover design that integrating adherence counseling using our Life-Steps protocol (Safren et al., 2001) with CBT for depression was successful at both increasing adherence and reducing depression in individuals with HIV and depression (Safren et al., 2009). Although individuals with active substance use were excluded from this initial study, HIV infection due to IDU accounts for 18.5% of cases of HIV among adults in the United States (Centers for Disease Control and Prevention, 2008, 2010). As articulated above, these individuals may be particularly at risk for depression due to the multiple stressors involved with managing comorbid HIV-infection and opioid dependence. Triply diagnosed individuals—those with HIV, clinical depression, and substance use (e.g., opioid abuse/dependence)—represent a population uniquely at risk for nonadherence. Accordingly, in designing this trial, a priori, we sought to examine the degree to which intervening on depression would assist the ability to benefit from evidenced-based adherence counseling.
The primary objective of the current study was to test, in a randomized controlled trial, CBT-AD in patients with HIV, depression, and opioid dependence who were undergoing treatment for their substance use disorder. We hypothesized that those who were assigned to the CBT-AD condition would have better adherence (primary outcome), decreased depression, and improved biological outcomes (e.g., decreased viral load and increased CD4+ lymphocyte counts) than the comparison group (enhanced treatment as usual [ETAU]) and that these gains would be maintained over the 9-month follow-up period.
Method Study Subjects and Setting
Enrollment occurred between July of 2005 and October of 2008 and included (89 randomized) individuals between the ages of 18 and 65 years who were HIV-seropositive, prescribed antiretroviral therapy for HIV, endorsed a history of injection drug use, were currently enrolled in opioid treatment for at least 1 month, and met criteria for a diagnosis of current or subsyndromal depressive mood disorder (72 major depressive disorder; one dysthymia; 16 bipolar disorder, most recent episode depressed). Subsyndromal depression (n = 10) was defined as a past history of major depression, with a current level of residual symptoms (Clinical Global Impression [CGI—see Measures section] of at least 2) that did not meet diagnostic threshold (i.e., due to antidepressant therapy).
Treatment for opioid dependence varied, with the majority of participants having received methadone (70%, n = 63) and the remainder receiving suboxone therapy (5.6%, n = 5), group (4.5%, n = 4) or individual substance abuse counseling (7.9%, n = 7), active participation in Narcotics Anonymous (4.5%, n = 4), or other active substance abuse treatment (6.7%, n = 6). There were no significant differences in type of substance abuse treatment between the experimental and control conditions.
Excluded individuals were those with any active untreated or unstable major mental illness that would interfere with study participation (e.g., active mania or psychosis), inability or unwillingness to provide informed consent, or current participation in cognitive behavioral therapy (CBT) for depression.
Participant demographics are depicted in Table 1, and raw study-related outcomes, including baseline, appear in Table 2 (note that analyses and graphs used general linear modeling [GLM] and hierarchical linear modeling [HLM] adjusted scores). The study sample was of a predominately lower socioeconomic status, with only 4% working or being in school full-time and 67% on disability. There were no differences on baseline demographic variables across conditions. There was a baseline difference for CD4 count, with the CBT-AD group having a higher CD4 count than the comparison condition, t(87) = 2.76), p < .01—see Table 2. Hence, baseline levels were covaried in longitudinal analyses.
Sociodemographic Characteristics of Participants
Unadjusted Mean Descriptive Scores for Outcomes Across Conditions and Time
The sample had substantial psychosocial comorbidity, with 62% having at least one additional Diagnostic and Statistical Manual of Mental Disorders (4th ed.; DSM–IV; American Psychiatric Association, 1994) diagnosis besides depression and substance abuse disorder (see Table 2). Sixty-five percent of the randomized sample had recent illicit substance use as assessed by a combination of toxicology screening and self-report at baseline. During the clinician-administered assessments administered at baseline, participants were asked to report any substance use over the past 30 days. Approximately one third (30.3%) of randomized participants reported polysubstance use during the past 30 days, 23.6% reported alcohol use; 5.6% reported alcohol use to intoxication; 25.8% reported heroin use; 75.3% reported methadone use; 23.6% reported either opiate or analgesic use; 37.5% reported either sedative, hypnotic, or tranquilizer use; 25.8% reported cocaine use; 16% reported cannabis use; and 1.1% reported hallucinogen and inhalant use over the past 30 days; there was no reported amphetamine or barbiturate use. In addition to the clinician-administered assessment of substance use over the past 30 days, participants also provided a saliva sample for a toxicology screen. Of the 89 participants randomized, 77.2% tested positive for methadone use, 22.5% for cocaine use, 12.7% for opiate use, 8.8% for benzodiazepine use, 5% for cannabis use, and 1.3% for amphetamine and barbiturate use. There were no significant differences in reported substance use or toxicology results based on randomization condition.
For the first participants (n = 40), all study visits took place at one of four methadone clinics in the greater Boston area. Recruitment was later expanded for two major reasons: (a) we discovered that some potential participants were not comfortable referring themselves or being seen for the study at the methadone clinics despite measures to protect confidentiality about being in an HIV study, and (b) during the time of the study, more options became available for treatment of opioid dependence, such as suboxone. Seventy percent were on methadone at baseline, 6% suboxone, 7% Narcotics Anonymous/Alcoholics Anonymous only, and 18% counseling (individual or group); 56% were on an antidepressant medication at study entry Hence, participants were then recruited through community outreach and HIV clinics at Massachusetts General Hospital (MGH; n = 8) and Rhode Island Hospital (n = 9). The remaining participants (n = 32) were referred by other study participants or through community outreach and recruitment flyers posted in other HIV care or substance abuse (including additional methadone clinic) settings but were seen at an MGH-based research clinic. This adaptive trial design allowed us to keep up with the ever-changing epidemic as the trial was in process.
After a complete description of the study was provided to the participants, study clinicians obtained written informed consent. All study procedures were approved by the Institutional Review Boards at Massachusetts General Hospital (MGH) in Boston, MA and at Rhode Island Hospital in Providence, RI.
Study Design and Procedures
Study visits
After an initial evaluation to determine study eligibility and a 2-week period during which participants started using the electronic pill caps, there were four major study assessment visits: T1 was the baseline assessment; T2 was the post-treatment outcome, which happened at end of intervention for those in the experimental arm (approximately 3 months after baseline for participants in both arms); T3 was a 3-month follow-up (occurring 6 months from baseline); and T4 was a 9-month follow-up (occurring 12 months from baseline). These assessment points were chosen so that we could have four major outcome assessments over the course of 1 year of study involvement, allowing for longitudinal analyses for the follow-up assessments. The post-treatment assessment was at approximately 3 months, and that was selected to allow participants in the CBT-AD condition enough time to come to nine treatment sessions, accounting for issues such as snow and life events that might not allow for coming consistently every week. The 3-month (post-treatment) assessment was to examine acute outcomes during the treatment, right after it was discontinued. The T3 and T4 outcomes were to examine short-term and long-term maintenance of gains, as well as have time to see adherence or depression-based changes in any biological outcomes.
These assessments included electronic pill cap evaluations (Medication Event Monitoring System [MEMS], manufactured by AARDEX) for adherence, assessment of depression by self-report and an independent assessor blinded to study condition, as well as HIV plasma RNA and CD4+ lymphocyte counts either drawn for the study or abstracted from participants' medical records if collected in the month prior to the assessment. Samples acquired during the baseline assessment with a viral load of over 1,000 copies per milliliter were tested for genotypic resistance. Participants received $50.00 for the major assessments (e.g., baseline and three follow-up visits) and $25.00 for the weekly visits during the acute study period.
Randomization
Study coordinators randomly assigned participants at their first visit after the baseline in blocks of two, stratified by biological sex, depression severity (current major depression or residual symptoms only), and adherence (baseline MEMS-based adherence above or below 80%). Assignment to study condition (CBT-AD or ETAU) was concealed from both study therapists and participants until the conclusion of the first counseling visit (see below).
Assessment Measures
Primary outcome: Adherence
MEMS caps recorded each instance of bottle opening, monitoring the antiretroviral medication that the participants considered the most difficult to remember or the dose taken most frequently. To account for doses that participants may have taken without opening the pill cap (e.g., took out afternoon doses when they opened the pill bottle in the morning), we counted a dose as taken if participants could recall specific instances when they took their medications but did not use the cap (Liu et al., 2001, 2006; Llabre et al., 2006). A dose was considered missed if it was not taken within a 2-hr window of the designated time. If participants were using a pill-box prior to entering the study, we encouraged them to monitor a pill that is taken concurrently with another, with one going in the pill box and the other going in the bottle, so that the function of the pill-box (i.e., knowing if a pill has been taken/organization) could be maintained. In both conditions, if there were discrepancies between self-report and MEMs data, research assistants (RAs) or therapists would interview participants further to determine whether their cap should be replaced and/or try to figure out what may have caused this discrepancy.
For the acute outcome (baseline to post-treatment), adherence was operationalized as the percentage of MEMS-based adherence since the last visit; visits were scheduled weekly or, at most, every 2 weeks. For the follow-up longitudinal analyses, we used adherence in the past 2 weeks. This is consistent with our prior studies that used MEMs, and balances having an adequate sampling of time with not overlapping too much with the time that the participants were still potentially improving from the intervention (Safren, Hendriksen, DeSousa, Boswell, & Mayer, 2003; Safren et al., 2004; Safren et al., 2009).
Clinician-Administered Assessments
Enrollment visit
The initial evaluation to establish study eligibility included a diagnostic evaluation of DSM–IV diagnoses using the Mini International Neuropsychiatric Interview (MINI; Sheehan et al., 1998), one of the most widely used diagnostic assessments. This evaluation was completed by one of the study therapists and was presented for review and diagnostic consensus by the study team.
Independent assessments
An independent assessor (IA), who remained blind to study condition, conducted the clinician-administered outcome assessments. The IA visits included administration of (a) the Montgomery-Asberg Depression Rating Scale (MADRS; Montgomery & Asberg, 1979), and (b) a rating of global distress and impairment for depression and substance abuse using the Clinical Global Impression (CGI; National Institute of Mental Health, 1985) for severity (e.g., 1 = not ill to 7 = extremely ill). The MADRS and CGI scores were regularly reviewed through audiotape supervision with another blinded assessor. Matching the number of study visits between the two conditions helped preserve the blinding (e.g., if the IA saw the participant in the waiting room frequently, the participant could be in either condition). Additionally, participants were reminded before and during the IA visits not tell the assessor which study condition they were in.
Participant Measure of Depression
Participants completed the self-reported Beck Depression Inventory—Short Form (BDI-SF; Beck & Beck, 1972) during each visit. This measure was designed for use with medical populations, removing many of the somatic symptoms of depression that might be confounded with medication side-effects or physical functioning.
Biological Outcome Measures
At the major study assessment visits, participants who did not have an HIV plasma RNA or CD4+ lymphocyte test in the prior month accessible through clinic chart review provided blood for testing.
Intervention Conditions
Participants in both the treatment (CBT-AD) and comparison (ETAU) conditions received a single-session intervention on HIV medication adherence (Life-Steps), which involved 11 informational, problem-solving, and cognitive behavioral steps (Safren, Otto, & Worth, 1999). In each step, participants and the clinician define the problem, generate alternative solutions, make decisions about the solutions, and develop a plan for implementing them. Participants also received adherence tools such as assistance with a schedule and a cue-dosing watch that could sound two alarms per day. To enhance treatment as usual, they also had a letter mailed to their medical providers documenting the participant's depression or other psychiatric disorders and suggesting that these conditions should continue to be assessed or treated.
In addition to this, those assigned to the experimental condition also received eight sessions of CBT-AD (Safren, Gonzalez, & Soroudi, 2007a, 2007b). Accordingly, this was nine sessions total, with Life-Steps being Session 1, followed by eight CBT-AD sessions. This approach integrated continued adherence counseling with traditional CBT techniques for the treatment of depression. Module 1 (≈1 session; average in this study = 1.0 session) and provided psychoeducation about HIV and depression and a motivational interviewing (MI) exercise designed to set the stage for behavioral change. The MI exercise involved examining the pros and cons of changing and not changing self-care behaviors, as well as a discussion of a metaphor about treatment. Module 2 (≈1 session; average in this study = 1.2 sessions across participants) focused on behavioral activation and activity scheduling, which was designed to increase regularly occurring activities that involve pleasure and mastery. Module 3 (≈3 sessions; average in this study = 2.4 sessions across participants), cognitive restructuring, involved training in adaptive thinking, such as identifying and restructuring negative automatic thoughts. Module 4 (≈2 sessions; average in this study = 1.0 sessions across participants), problem-solving, involved training in selecting an action plan for problems and breaking this plan into manageable steps (Nezu, Nezu, Felgoise, McClure, & Houts, 2003). Module 5 (≈1 session; average in this study = 1.0 session across participants), relaxation, involved training in progressive muscle relaxation and diaphragmatic breathing. Some participants had a review session as needed (average = 0.4 sessions across participants). Sessions were approximately 50 min long and occurred weekly, with the goal of completion in approximately 3 months. A more detailed description of the intervention can be found in our published manuals (Safren et al., 2007a, 2007b). Flexibility in the number of sessions devoted to any module was permitted to address the complexity and variability of issues facing participants with HIV, depression, and intravenous drug use histories.
Study interventionists included clinical psychologists, psychology pre- and post-doctoral fellows in clinical psychology, and one master's-level psychologist. Training involved didactic learning from the modules and supervision using audio-recordings of sessions. To maximize therapist adherence to the intervention, all sessions were audio-recorded for monitoring and supervision. Interventionists met with a clinical supervisor on a weekly basis for clinical supervision where cases and interventions were discussed. For new interventionists, all sessions were listened to by the clinical supervisor, for at least the clinician's first participant, for feedback purposes. On an ongoing basis, one therapist per week was assigned an audiotape (of a different therapist's session) to review and complete a checklist for interventionist adherence, including whether the specific components of the modules of treatment were, in fact, delivered. Traditional monitoring of treatment fidelity, as typically done in randomized controlled trials of psychological treatments with circumscribed samples (usually individuals meeting criteria for one psychological disorder and receiving treatment for that disorder), was not possible for this study population of triply diagnosed individuals, because urgent life events often occurred and were addressed in treatment. Hence, the supervision sessions and fidelity ratings were used to develop a process for future research. This balanced the need for therapists to adhere to the general principals of CBT and intervene with respect to self-care behaviors, while being flexible regarding the order of the manualized modules, fitting the manualized modules to the clients' needs, and providing CBT strategies to assist with depression and self-care, regardless of what particular chapter would have been next in the treatment protocol. Accordingly, by the end of this process, approximately 5% of sessions were rated using the system that was developed during the study. A future article will more fully detail these data, and a current trial that seeks to examine active components of treatment (e.g., that compares this intervention to a credible, time-matched, active control treatment with both fidelity and contamination ratings) is currently underway.
After the Life-Steps adherence counseling session, participants in the ETAU condition also had eight study visits before the post-treatment assessment (to make the nine sessions total). During these visits, participants had their MEMS cap downloaded, completed the BDI-SF, and received the same number and timing of visits as those in the CBT-AD condition.
Statistical Analyses
Our prior study had an effect size of 1.0 for the primary MEMS-based adherence intent to treat outcome (Safren et al., 2009). That study, however, did not include those with comorbid substance dependence and hence we powered the study for an effect size of d = 0.8, which yielded a goal of approximately 100 participants using analysis of variance for MEMS-based adherence. Longitudinal modeling, however, as described next, allows for greater power using all available data. The actual sample size included 89 randomized participants.
HLMs (with HLM 6.06 software) were used to evaluate acute study outcomes when there were at least three data points (Raudenbush, Bryk, Cheong, & Congdon, 2004). This included MEMS-based adherence during the pre-post treatment phase, which was collected at each study visit; self-reported depression during the pre-post treatment phase, which was also collected at each study visit; and follow-up analyses for all study outcomes.
Repeated measures (GLMs) were used for pre-post assessments of depression by independent assessors (e.g., MADRS and CGI) and pre-post biological markers of HIV disease (e.g., plasma RNA viral copies and CD4 cell count), as there were only two assessment time points, and HLM or other mixed effects modeling could not be used. In these analyses, study condition assignment was the between-subjects factor. This was part of the a priori analysis plan: to first examine pre-post outcomes and second examine maintenance of any gains and biological endpoints over the longitudinal follow-up.
For pre-post HLM analyses where we had repeated measures (MEMS-based adherence and BDI), the Level 1 HLM model included the time variable (weeks since baseline), which provided the structure of the model for the outcome variable of interest. The Level 2 model tested the significance of the treatment effect and is estimated from the significance of the slope (gamma coefficient) associated with the random assignment variable (CBT-AD or ETAU). As there were significant differences between the randomly assigned conditions on CD4 cell number at study entry baseline, CD4 cell number was controlled in each of the main outcomes analyses (see footnote 1).
For maintenance of treatment effects HLM analyses, to model the slope in study outcomes across the follow-up assessments using baseline values, the Level 1 model included time, and the Level 2 model tested study condition, controlling for pre-randomization levels. Treatment effects on log viral load and CD4 cell count change across the follow-up time period were estimated by controlling for resistance to at least one antiretroviral medication in the Level 2 model.
For the HLM models, all continuous measures in the Level 2 model were centered about their group means, and all dichotomous variables were coded 1/0. Model parameters were estimated using full maximum-likelihood estimation with robust standard errors. In all analyses that used HLM, unconstrained models were run to confirm significant individual variation about the slope and intercept before accounting for random assignment. For all analyses, the Type I error rate adopted was a p of .05. Effect sizes for HLM growth estimates were calculated for statistically significant outcomes using the formula dGMA-raw = γ11(time)/SDraw, in line with current recommendations for communicating effect magnitude (Feingold, 2009; Raudenbush & Liu, 2001).
For all analyses involving comparing the two study arms, analyses presented controlled for baseline CD4 cell count differences between groups (there were no other baseline differences between the two study conditions). When the same analyses were conducted not controlling for baseline CD4 cell count differences between groups, the pattern of results were the same (see footnote 1).
Results Participant Characteristics
Participant flow throughout the duration of the study is depicted in Figure 1. Ninety-one percent of those randomized were retained for the acute outcome assessment (n = 86), and 84% (n = 79) returned to at least one post-treatment follow-up and hence could be used for follow-up analyses. Although there was a raw number greater loss to attrition at the final assessment point in the ETAU condition (eight out of 44 participants), compared with the CBT-AD condition (15 out of 45 participants), this difference was not statistically significant. Raw scores for study outcomes are presented in Table 2 by study condition (note that HLM and GLM analyses use adjusted scores). At baseline, 11.2% of the sample collapsed across condition had genotypic resistance. There were no study-related adverse events that occurred during the study.
Figure 1. Consolidated Standards of Reporting Trials (CONSORT) participant flow chart. IDU = injection drug use; CBT-AD = cognitive behavioral therapy for adherence and depression; ETAU = enhanced treatment as usual.
Baseline to Post-Treatment Outcomes
Adherence (MEMS)
There was a significant upward slope in MEMS-based adherence, γslope = 0.47, t(88) = 2.16, p = .033, during the treatment period, indicating improved adherence for the study participants as a whole. In addition, there was significant individual variation about the slope, ρslope = 2.35, df(87), χ2 = 203.01, p < .001, providing the justification for conducting the analysis by randomized group (Level 2 analysis). When adding treatment condition, and, as a covariate, baseline CD4, to the model, the increase in adherence was significantly greater over time in the CBT-AD condition (11.8 percentage points) than in the comparison condition (0.5 percentage points), γslope = 0.887, t(86) = 2.38, p = .02, dGMA-raw = 0.64 (see Figure 2).
Figure 2. Pre-post outcomes: Longitudinal (hierarchical linear modeling) analysis of MEMS-based adherence and depression (BDI-SF). CBT-AD = cognitive behavioral therapy for adherence and depression; ETAU = enhanced treatment as usual; MEMS = Medication Event Monitoring System; BDI-SF = Beck Depression Inventory—Short Form (Beck & Beck, 1972). Data points are adjusted scores using MEMS-based adherence and BDI-SF scores for the time since prior visit.
Depression rated by independent assessor
There were significantly greater reductions in depression for the CBT-AD condition relative to the comparison condition for both the MADRS, F(1, 78) = 9.72, p = < .01, d = 0.55, and CGI, F(1, 78) = 17.14, p < .001, d = 0.85 (see Table 2). These analyses used baseline CD4 as a covariate.
Self-reported depression (BDI-SF)
There was a significant decreasing slope, γslope = –0.23, t(88) = –3.52, p < .01, for self-reported depression over time. When accounting for treatment condition, and controlling for baseline CD4 as a covariate, those in the CBT-AD condition experienced a significant estimated reduction in depression symptoms (5.1 points on the BDI-SF) compared with a nonsignificant change in the control condition (<1 point on the BDI-SF), γslope = –0.320, t(86) = –2.39, p = .02, dGMA-raw = 0.63.
Biological outcomes
Although treatment effects on HIV viral load and CD4 count were more likely to emerge only at follow-up, acute effects (e.g., baseline to post-treatment) were examined and are presented in Table 2 for consistency. Controlling for medication resistance, randomized effects were not significant for either CD4 cells, F(1, 74) = 1.92, p = .32, d = 0.10, or for log viral load, F(1, 71) = 1.08, p = .30, d = 0.20, with the additional control for baseline CD4.
Post-Intervention Follow-Up Assessments at 3 and 9 Months
Adherence (MEMS)
MEMS-based adherence gains acquired across CBT-AD treatment were not maintained during follow-up as evidenced by the significant downward slope for MEMS-based adherence, γslope = –0.294, t(79) = –3.24, p < .01, that did not differ by study condition, γslope = 0.20, t(76) = 1.23, p = .22, dGMA-raw = 0.21 (see Figure 3). Baseline CD4 differences were controlled for as a covariate in these analyses.
Figure 3. Follow-up outcomes: Analysis of MEMS-based adherence and depression (BDI-SF). CBT-AD = cognitive behavioral therapy for adherence and depression; ETAU = enhanced treatment as usual; MEMS = Medication Event Monitoring System; BDI-SF = Beck Depression Inventory—Short Form (Beck & Beck, 1972); F/U = follow-up. Follow-up outcomes used hierarchical linear modeling adjusted scores for prior 2 weeks MEMS-based adherence.
Clinician-assessed depression (MADRS and CGI)
Depression gains as assessed by blinded clinicians were maintained, which was demonstrated on the CGI by a trend for continued improvement during the follow-up time period, γslope = –0.008, t(80) = –1.93, p = .06, with no differential improvement by condition over follow-up after acute treatment ended, γslope = 0.011, t(78) = 1.59, p = .12, dGMA-raw = 0.51. Similarly, the MADRS also demonstrated a trend for a continued improvement for the group as a whole, γslope = –0.052, t(80) = –1.69, p = .09, with no differential improvement by condition, γslope = 0.078, t(78) = 1.47, p = .14, dGMA-raw = 0.61. These analyses had baseline depression scores and baseline CD4 as covariates.
Self-reported depression (BDI-SF)
Improvements in depression acquired during the CBT-AD intervention and assessed via self-report were maintained during follow-up, as evidenced by no significant change in self-reported depressive symptoms for the whole sample, γslope = 0.03, t(73) = –1.27, p = .21, or by group assignment when controlling for baseline BDI and CD4 differences, γslope = –0.01, t(72) = –0.30, p = .76, dGMA-raw = 0.13.
Biological outcomes (HIV viral load, CD4 cell count)
There was no significant change in log viral load over the follow-up time period for the group as a whole, γslope = –0.0015, t(75) = –0.40, p = .69, or based on group assignment, γslope = 7.28 × 10−x4, t(74) = –0.165, p = .87, dGMA-raw = 0.02, controlling for baseline log viral load, CD4 cell number, and resistance. There were also no differences across the two conditions in the percentage of participants who attained a suppressed viral load, γslope = 5.0 × 10−x5, t(74) = –0.002, p = .98, dGMA-raw < 0.01.
Over the follow-up period, the slope of CD4 cell number was nonsignificant for the entire sample, γslope = 0.590, t(79) = 1.08, p = .29. When condition was added to the model, there was a significant increase in CD4 cells in the CBT-AD condition compared with the control condition, controlling for baseline CD4 and medication resistance, 61.2 CD4 cell increase versus 22.4 CD4 cell decrease, γslope = 2.09, t(76) = 2.20, p = .03, dGMA-raw = 0.60. Note these differences were also significant without controlling for baseline CD4 and medication resistance.
DiscussionThe current study examined the use of a time-limited intervention (CBT-AD) addressing both adherence and clinical depression in a sample of triply diagnosed individuals with HIV. The intervention had acute and significant effects on both adherence and depression during the time in which the intervention was being delivered. MEMS-based adherence in the CBT-AD group improved approximately 11.8% from baseline and 11.3% over the comparison condition during treatment. The magnitude of this effect is potentially clinically significant in that it has been suggested that a 10% change in adherence can result in improved HIV outcomes (Bangsberg, 2006; Liu et al., 2001). However, after the intervention ended, MEMS-based adherence decreased, whereas intervention-related improvements in depression remained relatively stable. By way of contrast, our prior study of CBT-AD with depressed HIV-infected participants who were not also struggling with IDU histories and substance dependence showed sustained effects on both adherence and depression, and improvements in viral load over time (Safren et al., 2009). As such, it appears that our intervention was not resilient to the psychosocial challenges magnified by the context of opioid dependence, HIV, and depression. It is not clear which aspect or aspects of substance dependence (e.g., lapses in substance use, neuropsychological impairment, psychosocial stress, schedule disruptions, altered motivation, altered meaning of pill taking) may have contributed to this loss of efficacy for the adherence but not depression outcomes, and moderators will be explored in future secondary articles. Our findings indicate that, for triply diagnosed individuals, continued adherence counseling may be necessary to maintain or potentially augment adherence gains, even when depression symptoms improve.
With respect to the depression findings, the average depression score on the MADRS for those in the CBT-AD conditions at baseline was in the range for “moderate” severity and only “mild” at the 12-month follow-up assessment. This 40% decrease in symptoms at post-treatment, which was maintained at 12 months, represents a clinically meaningful reduction (Müller, Himmerich, Kienzle, & Szegedi, 2003; Robertson, 1983).
Differences in viral load did not emerge across the two conditions over time; however, there were differential improvements in CD4 in those who received CBT-AD versus those who received ETAU when statistically controlling for baseline group differences. Although preliminary, this finding is consistent with the results of other HIV-related psychosocial interventions demonstrating that psychosocial interventions can directly improve biological indicators of HIV pathogenesis, including CD4 cells counts (Petrie, Fontanilla, Thomas, Booth, & Pennebaker, 2004) and HIV viral load (Antoni et al., 2006). This finding, that there were differences in depression and CD4 but not long-term differences in adherence or viral load is noteworthy but certainly requires replication. Despite the absence of viral load change, it is possible that our participants still reaped some psychoneuroimmunological benefit due to sustained reductions in depression as evidenced by an increase in CD4 lymphocyte count over time, which occurred after covarying out baseline differences. The reduction of the immunosuppressive effects of depression-related dysregulation of the catecholaminergic (norepinephrine) or HPA (cortisol) axis may support sustained increases in CD4 cell counts (Leserman, 2003). In fact, Antoni et al. (2006) reported that treatment-related decreases in HIV viral load were mediated by reductions in depressed mood. The preliminary finding that treating depression is associated with improved immunity is also consistent with noninterventional studies showing a relationship between depressive symptoms and change in CD4 cells over time (Ickovics et al., 2001; Ironson et al., 2005; Leserman, 2008). Future secondary analyses will examine mediational or other potential pathways with these data. This immunological finding requires replication, as it is possible that there are other explanations for these results, such as regression to the mean after different values at baseline. Additional randomized controlled trials of interventions that improve depression on immunologic outcomes are required to examine this further. With respect to the absence of viral load findings, it is possible that the ability to detect viral load differences was limited because of the relatively low average viral load levels at baseline.
It is noteworthy that prior studies have found short-term ART adherence gains simply as a result of using a MEMS cap for monitoring without additional intervention, gains sometimes lasting up to 40 days (Deschamps, Van Wijngaerden, Denhaerynck, De Geest, & Vandamme, 2006; Wagner & Ghosh-Dastidar, 2002); although our post-treatment outcome assessment target time window was 90 days. Accordingly, it is possible that the first set of MEMS-based adherence assessments (at baseline—first 2 weeks) represent an improvement over what their true baseline adherence would have been. Hence, decreases in MEMS-based adherence over time to the final follow-up may be due to the waning effects of adherence monitoring with MEMS on adherence over time. The randomized design with the post-treatment outcome being approximately 3 months after randomization (i.e., more than 40 days) showed differences between the intervention and comparison conditions at post-treatment, indicating that it is unlikely that there would be a difference in a sensitization effect to MEMS-based adherence across the two study conditions.
There were eight out of 44 individuals lost to follow-up in the CBT-AD condition and 15 out of 45 from the ETAU condition. Although this was not statistically significant, it raises an issue that may be common to adherence interventions. More specifically, if depression is associated with adherence, and those who are adherent to study participation are more likely to also be adherent to HIV medications, it is possible that attrition is associated with poorer adherence and more severe depression. In this scenario, the actual treatment effect would be greater than what was observed, because the control participants who dropped out would be those with worse depression and adherence. Conversely, it is also possible that control participants who dropped out did so because they did not perceive benefit from participation in the study. Our clinical experience meeting with these participants, however, suggests that that the latter was not the case.
There are several additional limitations to the present study that should be noted. First, although MEMS are an objective indicator of adherence, it is possible that adherence was underestimated if the MEMS cap was not used. Accordingly, we asked participants at each assessment whether they recalled taking pills without using the cap and used a corrected adherence score (Liu et al., 2001, 2006; Llabre et al., 2006). Second, as part of a program of research to test a treatment, and to examine whether treating depression is necessary to benefit from an adherence intervention before trying to dismantle the “active ingredients,” the comparison group was not attention-matched. Although CBT-AD and ETAU participants came for the same number of visits and had the same incentive payments, we do not know if the positive acute adherence outcomes are related to specific elements of CBT-AD beyond the Life-Steps adherence counseling. Third, despite procedures to ensure confidentiality, we discovered that individuals in methadone treatment centers may have been reluctant to refer themselves to the study because of the fear of having their HIV status “outed.” Recruitment efforts that were expanded to address this barrier threatened internal validity, but this concern was balanced by achieving greater participant heterogeneity and therefore generalizability. Fourth, the participant incentives may have driven participation, and generalization of findings to opioid-stabilized patients who are less likely to come for such counseling may be limited. Fifth, we had hoped for a sample size of 100, and only 89 could be randomized because of the complexities of recruitment. Finally, given the complexity of the population, monitoring fidelity to the intervention was a work in progress throughout the study. Accordingly, by the later half of the study, we had developed a fidelity monitoring checklist that allowed for therapist flexibility when emergent life events would occur, with the ability to change the order of modules and/or utilize the treatment module most relevant to an emergent concern when it would occur. Although this can be seen as a threat to internal validity, it conversely increases the external validity of the intervention, as this is what clinicians would likely do in real-world clinical practice.
Future research should examine the cost-effectiveness of the intervention as part of a larger effectiveness trial involving substance abuse treatment programs that have high numbers of HIV-infected patients in their practice base. Accordingly, if substance abuse counselors or less trained interventionists could integrate this intervention into their current counseling, there would not be incremental costs, and adherence counseling could be maintained, potentially allowing for sustained benefits.
The findings, however, suggest that CBT-AD is a potentially useful strategy for increasing adherence and decreasing depression in HIV-positive patients with a history of IDU who are in substance abuse treatment. Adherence gains were only present during the time that the treatment was ongoing, suggesting that booster, additional, or continued adherence counseling sessions may be needed to sustain improvements in adherence outcomes in this population of individuals struggling with multiple comorbidities. It appears that this intervention, integrated into substance use counseling in methadone or other drug treatment programs, would be beneficial for individuals with HIV and substance abuse disorders. For depression, this intervention resulted in sustained improvement, and CD4 cell counts increased in the CBT-AD condition compared with the control condition over time. Whereas prior studies have found correlational associations of depression to CD4, this is the first study to suggest a possible effect when depression was successfully treated.
Footnotes 1 We thank the anonymous reviewers for suggesting that we control for CD4 in all outcome analyses, as CD4 differed in magnitude between study arms. Analyses were initially conducted without controlling for baseline CD4, and the pattern of results was the same when controlling for and when not controlling for CD4 in these analyses.
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Submitted: May 9, 2011 Revised: February 6, 2012 Accepted: February 7, 2012
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Source: Journal of Consulting and Clinical Psychology. Vol. 80. (3), Jun, 2012 pp. 404-415)
Accession Number: 2012-10794-001
Digital Object Identifier: 10.1037/a0028208
Record: 37- Title:
- Comparing criterion- and trait-based personality disorder diagnoses in DSM-5.
- Authors:
- Yam, Wern How. Department of Psychology, University at Buffalo, The State University of New York, Buffalo, NY, US, whyam@buffalo.edu
Simms, Leonard J.. Department of Psychology, University at Buffalo, The State University of New York, Buffalo, NY, US - Address:
- Yam, Wern How, Department of Psychology, Park Hall 219, University at Buffalo, The State University of New York, Buffalo, NY, US, 14221, whyam@buffalo.edu
- Source:
- Journal of Abnormal Psychology, Vol 123(4), Nov, 2014. pp. 802-808.
- NLM Title Abbreviation:
- J Abnorm Psychol
- Page Count:
- 7
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- The Journal of Abnormal and Social Psychology; The Journal of Abnormal Psychology; The Journal of Abnormal Psychology and Social Psychology
- ISSN:
- 0021-843X (Print)
1939-1846 (Electronic) - Language:
- English
- Keywords:
- DSM-5, classification, assessment, Section III, personality disorder
- Abstract:
- In the recent Diagnostic and Statistical Manual of Mental Disorders (DSM-5), the official personality disorder (PD) classification system remains unchanged. However, DSM-5 also includes an alternative hybrid categorical-dimensional PD system in Section III to spur additional research. One defining feature of the alternative system is the incorporation of a trait model with PD-specific trait configurations, but relatively little work has evaluated how these traits map onto official PD diagnoses or their implications for diagnosis rates. To that end, we compared official PD criteria to Section III PD traits in a sample of current or recent psychiatric patients. We (a) evaluated the extent to which PD traits predicted traditional PD criterion counts, and (b) computed trait-based diagnosis rates and compared them to those reported in several published outpatient and epidemiological samples. Overall, PD traits generally predicted PD criterion counts, but with less than ideal specificity. In addition, we identified differences in diagnosis rates across approaches. These results provide some support for the Section III approach, but they also identify important areas in need of refinement and future study before the field could reasonably switch to a hybrid PD classification approach like that in Section III. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Diagnostic and Statistical Manual; *Personality Disorders; *Personality Traits; Criterion Referenced Tests
- PsycINFO Classification:
- Personality Disorders (3217)
- Population:
- Human
Male
Female - Location:
- US
- Age Group:
- Adulthood (18 yrs & older)
- Tests & Measures:
- Structured Clinical Interview for DSM–IV Axis II Disorders Personality Questionnaire
Personality Inventory for DSM-5 DOI: 10.1037/t30042-000 - Grant Sponsorship:
- Sponsor: National Institute of Mental Health, US
Grant Number: R01MH080086
Recipients: Simms, Leonard J. - Methodology:
- Empirical Study; Quantitative Study
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- First Posted: Aug 11, 2014; Accepted: Jul 7, 2014; Revised: Jun 24, 2014; First Submitted: Sep 30, 2013
- Release Date:
- 20140811
- Correction Date:
- 20141110
- Copyright:
- American Psychological Association. 2014
- Digital Object Identifier:
- http://dx.doi.org/10.1037/a0037633
- Accession Number:
- 2014-32647-001
- Number of Citations in Source:
- 31
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- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2014-32647-001&site=ehost-live">Comparing criterion- and trait-based personality disorder diagnoses in DSM-5.</A>
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Comparing Criterion- and Trait-Based Personality Disorder Diagnoses in DSM-5 / BRIEF REPORT
By: Wern How Yam
Department of Psychology, University at Buffalo, The State University of New York;
Leonard J. Simms
Department of Psychology, University at Buffalo, The State University of New York
Acknowledgement: This study was supported by a research grant to L. J. Simms from the National Institute of Mental Health (No. R01MH080086).
Substantial evidence has accumulated showing the limitations of the Diagnostic and Statistical Manual of Mental Disorders (DSM) classification system for personality disorder (PD) and the advantages of understanding PD from a trait-dimensional perspective (e.g., Clark, 2007; Livesley, 2007; Trull & Durrett, 2005; Widiger & Samuel, 2005; Widiger & Trull, 2007). Despite these advances, the official PD classification remains unchanged in the revised DSM manual (DSM-5; American Psychiatric Association [APA], 2013). However, the Personality and PD workgroup proposed an alternative hybrid PD classification system that combines dimensional and categorical elements, reflecting a compromise between those favoring a fully trait-based system (e.g., Clark, 2007; Widiger & Simonsen, 2005) and those favoring the polythetic categorical system embodied in the official nosology since 1980 (e.g., Gunderson, 2010; Zimmerman, 2012). To spur research into this alternative, DSM-5 lists the hybrid approach in Section III, but relatively little is known about how Section III PDs compare to their traditional counterparts. Thus, our principal aim was to compare the trait-based elements of the alternative approach to the criterion-based elements of the traditional PD classification system in terms of diagnostic validity and diagnosis rates.
The Section III approach retains 6 of the 10 traditional PDs—antisocial, avoidant, borderline, narcissistic, obsessive–compulsive, and schizotypal PDs. PDs are conceptualized as significant impairments in personality functioning coupled with pathological personality traits that are assessed across seven diagnostic criteria (APA, 2013). Criterion A evaluates personality functioning, comprising self- and interpersonal impairments unique to each PD. Criterion B includes 25 personality traits—nested within five broad domains (negative affect, detachment, antagonism, disinhibition, and psychoticism)—that can be present in prespecified configurations associated with each retained PD or in idiosyncratic configurations (i.e., PD trait-specified [PD-TS]). For example, a diagnosis of borderline PD requires at least four of the seven specified traits—anxiousness, depressivity, emotional lability, hostility, impulsivity, risk-taking, and separation insecurity—with impulsivity, risk-taking, or hostility required. Criteria C to G include inclusion and exclusion criteria similar to the official system.
Given the novelty of the Section III approach, relatively little has been published evaluating its utility and functioning vis-à-vis the official criterion-based model. However, two recent papers reported limited support for the particular trait-to-PD mappings suggested in Section III (Few et al., 2013; Hopwood et al., 2012). In both cases, although evidence showed that the Section III traits generally predicted official PD criterion counts, the specified trait-to-PD relations showed limited specificity, with (a) nonspecified traits sometimes adding significantly to the prediction of certain PDs and (b) specified traits often relating to multiple PDs. Although the nonspecificity of traits is not surprising given the known patterns of comorbidity among the traditional PDs, these findings nonetheless raise important questions about the adequacy of the Section III trait configurations. Moreover, inconsistencies across the studies suggest that additional work is needed before strong conclusions are possible regarding how best to map PD traits onto traditional PDs.
A related question not adequately addressed in Section III is the clinical threshold required for traits, which has important diagnostic implications. Notably, no specific thresholds are provided in Section III except that trait elevation could be assessed by comparison to “population norms and/or clinical judgment” (p. 774; APA, 2013); the use of normative samples also has been suggested previously as a means of interpreting trait scores via standardized T scores (Miller, 2012). Samuel, Hopwood, Krueger, Thomas, and Ruggero (2013) recently studied the effect of the proposed clinical thresholds on PD diagnosis rates in a large sample of undergraduates. Using norms reported by Krueger, Derringer, Markon, Watson, and Skodol (2012), Samuel et al. reported markedly lower diagnosis rates for trait-based diagnoses relative to the traditional criterion-based diagnoses. Although provocative, the data of Samuel et al. were collected in undergraduates; extension to patient samples is needed to study the generalizability of their results to more ecologically valid samples.
Although the use of trait configurations as a dimensional bridge to the traditional criterion-based approach is not a new practice (e.g., Miller, 2012), an issue in need of further study is the extent of classification convergence across models. Morey and Skodol (2013) examined classification convergence through mental health clinicians who rated a prior patient using the trait- and criterion-based models. Results showed general support for classification convergence, with correlations between DSM-5 and DSM–IV criterion counts yielding a Mdn correlation of .75. However, Morey and Skodol’s approach was limited to clinician reports of patient symptomatology. A study reporting Section III diagnosis estimates and classification convergence from the patient perspective would meaningfully extend the literature.
In sum, more work is needed to improve our understanding of intermodel convergence and diagnosis rates across PD classification approaches, with the aim of informing future revisions to PD nosology. As such, the goals of the study presented here were to study (a) how strongly Section III PD traits predict traditional PD criterion counts, (b) whether any nonspecified PD trait incrementally predicts PD criterion counts, and (c) Section III diagnosis rates in a large psychiatric sample.
Method Participants and Procedures
Participants—recruited by distributing flyers at mental health clinics across western New York—were eligible to participate if they reported psychiatric treatment within the past 2 years. The final sample included 628 participants, 454 of who completed the measures needed for this study—M age = 42.0 years (SD = 12.6), 65% female, 68% Caucasian. This subsample differed from the full sample in terms of race, χ2(4, N = 624) = 28.75, p < .01, and age, t(626) = 3.68, p < .01, but not sex, χ2(1, N = 627) = 1.56, p = .21. Those excluded were more likely to be African American and older. Eligible participants attended a 4-hr session and completed self-report measures using computers in privacy carrels. For the study presented here, we analyzed responses to the Personality Inventory for DSM-5 (PID-5; Krueger et al., 2012) and Structured Clinical Interview for DSM–IV Axis II Disorders Personality Questionnaire (SCID-II-PQ; First, Spitzer, Gibbon, & Williams, 1995). Participants were compensated $50 plus transportation costs. Procedures were approved by the Social and Behavioral Sciences Institutional Review Board at the University at Buffalo.
Measures
The PID-5 assesses the 25 maladaptive traits proposed in Section III. It includes 220 self-report items rated on a 4-point scale ranging from 0 (very false or often false) to 3 (very true or often true). Higher scale scores are indicative of greater pathology. Internal consistencies in the current study averaged .87, range = .75 to .96, across traits. Recent studies have supported the construct validity of the PID-5 (Anderson et al., 2013; Hopwood et al., 2013; Wright & Simms, 2014).
The SCID-II-PQ is a self-report measure composed of 119 items rated dichotomously, which typically is administered before the SCID-II interview to shorten the interview period, that map onto DSM–IV PD criteria. Internal consistencies in the current study averaged .69, range = .50 to .81, across PD criterion counts. Past work has supported the use of the SCID-II-PQ as a standalone measure of PD features (Ekselius, Lindström, von Knorring, Bodlund, & Kullgren, 1994; Jacobsberg, Perry, & Frances, 1995; Piedmont, Sherman, Sherman, & Williams, 2003).
Data Analyses
Analyses were restricted to the six retained Section III PDs. Analyses first were conducted to evaluate the extent to which Section III traits predicted PD criterion counts. SCID-II-PQ criterion counts were correlated with PID-5 traits, followed by multiple regressions with hierarchical and stepwise components. SCID-II-PQ criterion counts were regressed on separate blocks of specified and nonspecified traits. In the first block of each analysis, specified PID-5 traits were included as predictors of a single SCID-II-PQ criterion count. The second block—using a stepwise selection process in which criteria for entry and retention in the regression model were set at p < .01—included all remaining nonspecified traits. These procedures permitted us to study the predictive power of PID-5 traits individually and in the context of other traits vis-à-vis SCID-II-PQ criterion counts. Given the many tests conducted, we adopted a moderately conservative significance threshold of p < .01 across all analyses.
Second, analyses were conducted to study how the Section III trait model affects diagnostic rates relative to the official criterion-based model. Traditional criterion-based diagnostic rates were obtained from previously published psychiatric (Alnaes & Torgersen, 1988; Zimmerman, Rothschild, & Chelminski, 2005) and epidemiological samples (Grant et al., 2003; Torgersen, 2005; Trull, Jahng, Tomko, Wood, & Sher, 2010) whereas Section III PD diagnoses in our sample were scored using the specific DSM-5 PD trait configurations. For the latter, T scores (i.e., standardized scores in which M = 50 and SD = 10) were computed using U.S. representative norms presented by Krueger et al. (2012). Consistent with the general personality assessment literature, we considered a T score of 65 or greater to reflect clinical significance. χ2 tests then were conducted to examine significant differences in diagnosis rates across studies.
Results Convergence Between Traits and PD Criterion Counts
Table 1 presents correlations between SCID-II-PQ criterion counts and PID-5 traits. Results revealed strong relations between PID-5 traits and SCID-II-PQ criterion counts. Across diagnoses, PD-specified traits generally correlated moderately to strongly with SCID-II-PQ criterion counts, Mdn r = .43, range = .13 to .66. However, it is important to note that 5 of 30 PD-specified traits showed relatively weak correlations with their respective SCID-II-PQ criterion counts, including intimacy avoidance for avoidant PD, risk-taking for borderline PD, intimacy avoidance and restricted affectivity for obsessive–compulsive PD, and restricted affectivity for schizotypal PD. In addition, many nonspecified traits correlated weakly to moderately with SCID-II-PQ criterion counts, Mdn r = .26, range = .01 to .57. Across PDs, 3, 11, 12, 15, 1, and 11 nonspecified PID-5 traits correlated at least moderately with SCID-II-PQ criterion counts for antisocial, avoidant, borderline, narcissistic, obsessive–compulsive, and schizotypal PDs, respectively. Thus, most PD-specified traits correlated as expected, but many additional traits also related significantly across PDs, suggesting that the DSM-5 trait specifications may be incomplete in some cases.
Zero-Order Correlations Between SCID-II-PQ Criterion Counts and PID-5 Traits
Hierarchical regressions, in which corresponding SCID-II-PQ criterion counts were regressed on blocks of specified and nonspecified traits, then were conducted to determine the strongest trait predictors of each PD in the context of other traits. The average tolerance and variance inflation factors across the regressions were greater than .10 (M = .55, SD = .14) and lower than 10 (M = 1.97, SD = .59), respectively, indicating that multicollinearity did not exert undue influence on parameter estimates. Regression results (see Table 2) indicated that all PD-specified trait blocks significantly predicted their corresponding SCID-II-PQ criterion counts, Mdn R2 = .33, range = .21 (antisocial) to .54 (borderline). The stepwise addition of nonspecified traits showed incremental prediction of all criterion counts except obsessive–compulsive PD, Mdn ΔR2 = .03, range = .00 (obsessive–compulsive) to .12 (narcissistic).
SCID-II-PQ Criterion Counts Regressed on PID-5 Specified and Nonspecified Traits
For Block 1, PD-specified traits varied in the extent to which they predicted their respective SCID-II-PQ criterion counts. Across PDs, 15 of the 30 predicted traits were significant predictors of their corresponding criterion counts. For antisocial PD, only callousness predicted the corresponding criterion count. Three of four specified traits for avoidant PD (anhedonia, anxiousness, and withdrawal) predicted the corresponding criterion count. For borderline PD, four of seven specified traits (depressivity, emotional lability, hostility, and impulsivity) predicted the matching criterion count. Both specified traits for narcissistic PD (attention seeking and grandiosity) predicted the corresponding criterion count. For obsessive–compulsive PD, only rigid perfectionism predicted the matching criterion count. Finally, four of six specified schizotypal PD traits (eccentricity, restricted affectivity, unusual beliefs and experiences, and withdrawal) predicted the corresponding criterion count, with restricted affectivity predicting negatively.
Block 2 stepwise selection procedures added nonspecified traits to all but one model (obsessive–compulsive PD). Furthermore, the standardized parameter estimates indicated that specified traits for each PD still varied in how they predicted SCID-II-PQ criterion counts. Of the 15 significantly predicting specified traits in Block 1, 12 remained significant after accounting for additional nonspecified traits. For antisocial PD, one specified trait (callousness) and three nonspecified traits (grandiosity, submissiveness, and unusual beliefs/experiences) predicted the corresponding criterion count, with grandiosity and submissiveness predicting negatively. For avoidant PD, two specified traits (anxiousness and withdrawal) and three nonspecified traits (grandiosity, manipulativeness, and separation insecurity) predicted the corresponding criterion count, with grandiosity and manipulativeness predicting negatively. For borderline PD, three specified traits (depressivity, emotional lability, and impulsivity) and two nonspecified traits (submissiveness and suspiciousness) predicted the corresponding criterion count, with submissiveness predicting negatively. For narcissistic PD, both specified traits (attention seeking and grandiosity) and two nonspecified traits (hostility and suspiciousness) predicted the corresponding criterion count. Finally, for schizotypal PD, three specified traits (eccentricity, unusual beliefs and experiences, and withdrawal) and one nonspecified trait (emotional lability) predicted the corresponding criterion count.
Diagnosis Rates
Table 3 presents diagnosis rates for the Section III trait approach in our patient sample and traditional PDs in published outpatient and epidemiological samples. Results showed a mixed picture. Compared with the outpatient samples, our trait-based diagnosis rates for avoidant and obsessive–compulsive PDs were lower, diagnosis rates for borderline and narcissistic PDs were comparable, and antisocial and schizotypal PDs yielded mixed results, with trait-based diagnosis rates either higher or comparable to their traditional counterparts. Compared with the epidemiological samples, trait-based diagnosis rates were higher than their traditional counterparts in all cases except antisocial and obsessive–compulsive PDs.
PD Diagnosis Rates (%) in the Current Study and Published Samples
DiscussionWe compared the trait- and criterion-based approaches to PD classification in DSM-5 to study (a) the performance of the PD-specified traits in predicting official criteria, (b) whether additional nonspecified traits predicted official criteria, and (c) diagnosis rates of the proposed trait configurations relative to other published outpatient and epidemiological samples. We showed that PD-specified and nonspecified traits generally predicted traditional PD criteria. However, the results also revealed problems with specificity, with some PD-specified traits predicting multiple traditional PDs and some nonspecified traits incrementally predicting traditional PDs, even after accounting for PD-specified traits. The findings also demonstrated discrepancies across models in diagnosis rates. Taken together, the results suggest that the Section III trait model will require further study and, perhaps, modifications before the Section III system can be considered a parallel replacement for the current PD nosology.
Associations Between Traits and Types
Consistent with recent literature (Few et al., 2013; Hopwood et al., 2012; Morey & Skodol, 2013; Samuel et al., 2013), our results suggest that traits relate broadly to official criterion counts. However, PD-specific trait findings were more mixed. Although all PDs were predicted by one or more traits, some PD-specified traits did not behave as expected. In particular, the regressions revealed some results that either (a) failed to conform to the predictions of Section III trait specifications or (b) did not appear particularly meaningful. In some cases, nonspecified traits incrementally predicted a given PD in conceptually meaningful ways. For example, hostility incrementally predicted narcissistic PD after accounting for grandiosity and attention-seeking, a finding that is consistent with models of pathological narcissism (e.g., Pincus et al., 2009). In other cases, nonspecified traits showed evidence of incremental prediction for less clear reasons. For example, submissiveness correlated positively with borderline PD but showed an opposite pattern in the regression results, a finding that suggests that suppression could be causing some spurious effects (e.g., see Beckstead, 2012). Finally, likely because of overlapping variance across traits, some PD-specified traits that correlated significantly with their corresponding PD failed to show the same pattern in the regressions. For example, all seven PD-specified traits for borderline PD were significant in the correlation analyses, but only four of seven (depressivity, emotional lability, hostility, and impulsivity) were significant predictors in the regressions. Results such as these suggest that more parsimonious subsets of traits may more efficiently represent some PDs.
Nonetheless, these results provide an empirical basis for considering possible revisions to the Section III trait specifications. Of course, replication is needed, especially for results that lack a clear conceptual basis. Furthermore, because the PID-5 represents only one instantiation of Section III traits, other assessment tools (e.g., the Computerized Adaptive Test of Personality Disorder; see Simms et al., 2011) could be used to determine whether the observed effects generalize beyond the PID-5. Taken together, these findings echo the critique that empirically supported conceptualizations of traits that personify PD in Section III are lacking (Zimmerman, 2012). In guiding future work, one suggested data-driven approach to identifying traits for a given PD would involve selecting traits that are above a minimum convergent correlation but that also exhibit discriminant validity relative to other PDs (Hopwood et al., 2012). Such an approach would identify an empirical set of traits to define each PD while also addressing the problem of diagnostic overlap that has plagued the traditional criterion-based approach (e.g., Clark, 2007; Westen & Shedler, 1999).
Diagnosis Rates Across Approaches
Our results revealed notable diagnosis rate differences across approaches. Relative to traditional diagnosis rates in published outpatient samples, the trait-based elements of Section III resulted in a mixed picture, with some trait-based diagnoses being more, less, or equivalently diagnosed. Notably, similar to Samuel et al. (2013), our trait-based diagnosis rates likely represent overestimates to the extent that other inclusion and exclusion criteria were not considered (e.g., evidence of impairment, exclusions due to substance abuse or general medical conditions, etc.). As such, although further study is needed to examine this point, it is quite likely that the Section III diagnoses that account for all impairment, inclusion, and exclusion criteria will be more conservative than comparable traditional PD diagnoses. Such shifts in prevalence rates may have a large effect on the broader mental health-care system. The relative diagnostic conservatism of the Section III trait-based approach likely will lead to a surge in subthreshold diagnoses and, consequently, an increase in PD-TS diagnoses.
Of course, the traditional approach is not without a similar dynamic. Coverage inadequacies are a weakness of the official PD nosology (Widiger & Trull, 2007), likely resulting in the excessive PD Not Otherwise Specified (NOS) diagnoses reported to be among the most prevalent in psychiatric samples (e.g., Zimmerman et al., 2005). Therefore, both approaches come with a generic, nonspecific option for diagnosing PD. However, the PD-TS diagnosis has the advantage of specifying in a standardized way the exact traits that are impairing to a given patient, which could lead to the development of trait-based treatment approaches.
Limitations and Conclusion
This work extends the PD literature in important ways but is not without limitations. First, although we studied a large sample of current or recent psychiatric patients, which is an important extension of previous work in this area, all participants were limited geographically and mainly were Caucasian and African American, which may limit the generalizability of our findings. Second, as noted above, our diagnosis rates are limited to the extent that they considered only the trait-based elements of the Section III approach (i.e., personality functioning [Criterion A] was not considered). As such, similar to Samuel et al. (2013), our reported PD diagnosis rates should not be considered definitive population base rates. Additional work is needed to fully understand how these competing approaches compare when all features and impairment consequences of the Section III approach are considered. Third, although multiple analytical approaches were used, our data were limited self-report responses. Future studies will benefit from considering other assessment methods (e.g., clinical interviews).
In summary, we extended the relatively new literature on the alternative DSM-5 Section III PD classification approach by comparing the Section III trait approach to the traditional criterion-based approach in a psychiatric sample. Although we found general support for traits in predicting traditional PD criteria, problems with trait specificity and overlap emerged at the level of individual PDs. We also found differences in diagnosis rates across systems that carry implications for the broader mental health field. To that end, more research is needed to improve the validity of the Section III trait model, with the end goal of a PD diagnostic system that is empirically supported and compelling to researchers and clinicians.
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Submitted: September 30, 2013 Revised: June 24, 2014 Accepted: July 7, 2014
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Source: Journal of Abnormal Psychology. Vol. 123. (4), Nov, 2014 pp. 802-808)
Accession Number: 2014-32647-001
Digital Object Identifier: 10.1037/a0037633
Record: 38- Title:
- Connecting clinical and actuarial prediction with rule-based methods.
- Authors:
- Fokkema, Marjolein. Faculty of Psychology and Education, VU University Amsterdam, Amsterdam, Netherlands, m.fokkema@vu.nl
Smits, Niels. Faculty of Psychology and Education, VU University Amsterdam, Amsterdam, Netherlands
Kelderman, Henk. Faculty of Psychology and Education, VU University Amsterdam, Amsterdam, Netherlands
Penninx, Brenda W. J. H.. Department of Psychiatry, VU University Medical Center Amsterdam, Amsterdam, Netherlands - Address:
- Fokkema, Marjolein, Department of Psychology and Education, Vrije Universiteit Amsterdam, Room 2B73, Van der Boechorststraat 1, 1081BT, Amsterdam, Netherlands, m.fokkema@vu.nl
- Source:
- Psychological Assessment, Vol 27(2), Jun, 2015. pp. 636-644.
- NLM Title Abbreviation:
- Psychol Assess
- Page Count:
- 9
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- Psychological Assessment: A Journal of Consulting and Clinical Psychology
- ISSN:
- 1040-3590 (Print)
1939-134X (Electronic) - Language:
- English
- Keywords:
- actuarial prediction, clinical judgment, decision making, linear models, rule-based method
- Abstract:
- Meta-analyses comparing the accuracy of clinical versus actuarial prediction have shown actuarial methods to outperform clinical methods, on average. However, actuarial methods are still not widely used in clinical practice, and there has been a call for the development of actuarial prediction methods for clinical practice. We argue that rule-based methods may be more useful than the linear main effect models usually employed in prediction studies, from a data and decision analytic as well as a practical perspective. In addition, decision rules derived with rule-based methods can be represented as fast and frugal trees, which, unlike main effects models, can be used in a sequential fashion, reducing the number of cues that have to be evaluated before making a prediction. We illustrate the usability of rule-based methods by applying RuleFit, an algorithm for deriving decision rules for classification and regression problems, to a dataset on prediction of the course of depressive and anxiety disorders from Penninx et al. (2011). The RuleFit algorithm provided a model consisting of 2 simple decision rules, requiring evaluation of only 2 to 4 cues. Predictive accuracy of the 2-rule model was very similar to that of a logistic regression model incorporating 20 predictor variables, originally applied to the dataset. In addition, the 2-rule model required, on average, evaluation of only 3 cues. Therefore, the RuleFit algorithm appears to be a promising method for creating decision tools that are less time consuming and easier to apply in psychological practice, and with accuracy comparable to traditional actuarial methods. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Decision Making; *Linear Regression; *Medical Diagnosis; *Prediction; *Test Interpretation; Models
- PsycINFO Classification:
- Tests & Testing (2220)
- Population:
- Human
Male
Female - Age Group:
- Adulthood (18 yrs & older)
Young Adulthood (18-29 yrs)
Thirties (30-39 yrs)
Middle Age (40-64 yrs)
Aged (65 yrs & older) - Tests & Measures:
- Fear Questionnaire-15-item version
Beck Anxiety Inventory--21-item version
Inventory of Depressive Symptomatology–-Self-Report--30-item Version DOI: 10.1037/t20431-000 - Methodology:
- Empirical Study; Longitudinal Study; Interview; Mathematical Model; Quantitative Study
- Supplemental Data:
- Other Internet
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- First Posted: Feb 2, 2015; Accepted: Nov 11, 2014; Revised: Oct 17, 2014; First Submitted: May 22, 2014
- Release Date:
- 20150202
- Correction Date:
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Connecting Clinical and Actuarial Prediction With Rule-Based Methods
By: Marjolein Fokkema
Faculty of Psychology and Education, VU University Amsterdam;
Niels Smits
Faculty of Psychology and Education, VU University Amsterdam
Henk Kelderman
Faculty of Psychology and Education, VU University Amsterdam and Faculty of Social Sciences Institute of Psychology, Leiden University
Brenda W. J. H. Penninx
Department of Psychiatry and EMGO Institute for Health and Care Research, VU University Medical Center Amsterdam
Acknowledgement:
Since publication of Paul Meehl’s “disturbing little book” (Meehl, 1954, 1986), the performance of clinical versus actuarial prediction methods has been a topic of debate and research in psychology (e.g., Ægisdóttir et al., 2006; Dawes, Faust, & Meehl, 1989; Grove & Meehl, 1996; Grove, Zald, Lebow, Snitz, & Nelson, 2000). In line with Meehl’s (1954) findings, two more recent meta-analyses comparing the accuracy of both prediction methods have shown actuarial prediction to be on average 10% to 13% more accurate (Ægisdóttir et al., 2006; Grove et al., 2000). Despite this evidence, some authors have noted a limited use of actuarial prediction methods in clinical practice (e.g., Bell & Mellor, 2009; Kleinmuntz, 1990). Whereas some have attributed this to the limited value of current actuarial prediction methods for clinical practice (Garb, 1994, 2000), other authors have attributed it to the high demands actuarial methods place on clinicians’ time, information, and computational power (Katsikopoulos, Pachur, Machery, & Wallin, 2008; Kleinmuntz, 1990). In any case, there has been a call for the development of new actuarial prediction methods for clinical practice (Garb, 1994, 2000; Spengler, 2012). In this paper, we propose an actuarial prediction method that involves less testing and computation when applied in clinical practice, and with predictive power that may well compete with that of more established actuarial methods based on linear main effects (LME) models.
It is interesting that actuarial prediction methods traditionally used are generally restricted to LME models. For example, the majority of studies included in Ægisdóttir et al. (2006) that provided explicit descriptions of their data-analytic approach, used linear regression, logistic regression, or linear discriminant analysis for actuarial prediction. Although LME models may often dominate psychologists’ data-analytic toolbox, they have three drawbacks. First, LME models do not seem to resemble the reasoning process of human decision makers in clinical practice. Many authors have found the weighing of cues in human judgment (e.g., Dhami, 2003; Gigerenzer & Goldstein, 1996; Green & Mehr, 1997), and more specifically, judgment by psychologists (e.g., Ganzach, 1995, 1997, 2001; Steadman et al., 2000), to be nonlinear. Psychologists are thus unlikely to make decisions by weighing the values of a large number of variables, as in LME models. Instead, they are likely to use only a small number of cues, and the weights of cues may be dependent on other cue values (e.g., Brannick & Brannick, 1989). Second, LME models may provide clinicians with risk factors, but they do not provide direct identification of patients who are at high risk. LME models require calculation of a risk index, by multiplying values of predictor variables by their weights, summing these, and comparing this index to a given cut-off value for deciding whether a patient is at risk. Such calculations are cumbersome to perform, and such models may not conform with clinical reasoning (e.g., Marshall, 1995). In addition, these computations can only be made after all cues are evaluated, although for many patients, evaluation of only a subset of cues may suffice to make a decision. Third, from a data-analytic perspective, LME models may not provide the most accurate or informative results (e.g., Breiman, 2001; Hastie, Tibshirani, & Friedman, 2009). For example, assumptions of normality underlying LME models may be violated in many applications, and LME models are unable to capture potential interaction effects between predictor and outcome variables.
In the current paper, we aim to introduce rule-based methods as a tool for actuarial prediction in clinical practice, that does not suffer from the drawbacks described earlier. The results of rule-based methods may show closer resemblance to the reasoning of psychologists working in applied settings, and allow for direct identification of high- and/or low-risk patients. Due to their interpretability and flexibility, rule-based methods have already gained popularity in the areas of machine learning and data mining (Fürnkranz, Gamberger, & Lavrač, 2012). Furthermore, the decision rules resulting from the application of rule-based methods can be represented as fast and frugal trees (FFTs; Martignon, Vitouch, Takezawa, & Forster, 2003): graphically represented decision tools, developed within the area of heuristic decision making (Gigerenzer & Goldstein, 1996; Gigerenzer, Todd, & the ABC Research Group, 1999). Therefore, we believe rule-based methods may be a promising tool for the application of actuarial prediction in clinical practice.
In what follows, we describe FFTs and rule-based learning algorithms. In the Illustration section, we describe the application of RuleFit (Friedman & Popescu, 2008), a rule-based learning algorithm, to a clinical prediction problem. With RuleFit, we derive simple rules for prediction of the course of depressive and anxiety disorders, using a dataset from the Netherlands Study of Depression and Anxiety (NESDA; Penninx et al., 2011). To assess the performance of the rule-based model, we compare its efficiency and accuracy to that of an LME-based prediction model, originally applied to the data. In the Discussion section, we describe the advantages and disadvantages of a rule-based approach to clinical prediction problems.
FFTsKatsikopoulos et al. (2008) suggested fast and frugal heuristics as a means to “bridge the clinical-actuarial divide” (p. 443). Fast and frugal heuristics (Gigerenzer & Goldstein, 1996) are simple, nonlinear decision rules, which evaluate only a small number of binary input variables, or cues. One of those heuristics is the FFT: A decision tree that evaluates a limited number of cues in a very straightforward manner. By definition, an FFT that evaluates m cues has m + 1 exit nodes, with one exit node for each of the first m – 1 cues and two exit nodes for the last cue (Martignon, Katsikopoulos, & Woike, 2008; Martignon et al., 2003). For example, suppose we want to use the two anxiety items of the five-item Mental Health Inventory (MHI) to assess whether a respondent is at risk for having an anxiety disorder (Cuijpers, Smits, Donker, Ten Have, & de Graaf, 2009; Ware & Sherbourne, 1992). In Figure 1, a (fictitious) FFT for deciding whether a respondent is at risk, using MHI anxiety items, is depicted. This FFT evaluates two cues, and has three exit nodes. When the answer to the first question or cue (“In the last month, did you feel calm and peaceful?”) is “yes,” we may immediately decide that this respondent is not at risk for anxiety disorder (see Figure 1). However, when the answer is “no,” interviewing may be continued by presenting the second question or cue to the respondent (“In the last month, did you consider yourself to be a very nervous person?”). If the answer is “no,” we may decide this respondent is not at risk. In a similar vein, when the answer is “yes,” we may decide that this patient is at risk for anxiety disorder (see Figure 1).
Figure 1. Example fast and frugal tree for at risk and not at risk anxiety disorder classification.
FFTs offer several advantages as decision-making tools: They require evaluation of only a limited number of cues. In many instances, not every cue in the FFT has to be evaluated because an exit node is reached early in the tree. Although FFTs require less information for prediction of new classes, their accuracy has been shown to be only slightly lower than that of more complex models based on the same dataset (Jenny, Pachur, Williams, Becker, & Margraf, 2013; Martignon et al., 2008; Smith & Gilhooly, 2006). Finally, the graphical representation of FFTs allows for fast and straightforward application in practical decision making.
Current algorithms for creating FFTs, however, have some limitations as well. First, although the graphical tree structure of FFTs appears to convey interaction effects, the algorithms described in Martignon et al. (2003) only optimize overall diagnostic accuracy. The algorithms order cues based on overall sensitivity or specificity of cues, while potential interactions between cues are not taken into account. Second, the algorithms do not provide a method for variable selection: Cues to be included in the FFT are selected by the user, prior to application of the algorithms. Third, the current algorithms can only deal with dichotomous cues, while in many prediction problems input variables may be ordinal or continuous.
Classification and Regression TreesIn contrast to the current FFT algorithms, the classification and regression tree (CART) algorithm of Breiman, Friedman, Olshen, and Stone (1984) is able to deal with large numbers of input variables, with categorical, ordinal, and continuous in- and output variables, and is able to capture interaction effects as well. The CART algorithm creates a decision tree, by partitioning observations into increasingly smaller subgroups, whose members are increasingly similar with respect to an outcome variable. Partitions, or splits, are made using one input variable at a time: In every node, the algorithm selects the variable and splitting point that separate the observations into two subsets for which the distributions of the outcome variable are most different. The result is a decision tree, consisting of branches and nodes. This tree can be used for prediction, by “dropping” new observations down the tree (Breiman et al., 1984). For a more extensive description of the CART algorithm, see Berk (2006) or Strobl, Malley, and Tutz (2009).
Gigerenzer and Goldstein (1996) and Gigerenzer et al. (1999) suggested CART as a powerful algorithm for the creation of simple decision-making tools because CART trees, like FFTs, evaluate one cue at a time to arrive at a final decision. However, the ease with which a decision tree can be communicated or interpreted, diminishes with the number of nodes and branches within a tree (Elomaa, 1994; Quinlan, 1987b). For example, the tree in Figure 1 is easy to comprehend, as it consists of only one branch, and evaluates only two cues. However, a decision tree consisting of many branches, six for example, would be much more difficult to comprehend or communicate. Therefore, CART trees may need to be simplified to improve their usability and communicability.
Rule-Based MethodsOne way to simplify decision trees is to convert their branches to decision rules, which are easier to communicate and use (Elomaa, 1994; Quinlan, 1987a, 1987b). Decision rules are statements of the form if [condition]–then [decision] (Dembczyński, Kotɫlowski, & Sɫlowiński, 2010). Likewise, decision rules used for prediction can be formulated as if [condition]–then [prediction]. The condition specifies a set of values of input variables, and the prediction specifies the expected value of the output variable, when an observation satisfies the specified condition. These rules are conjunctive: Every one of the arguments has to be met, and if any single condition is not met by an observation, the rule does not apply to the observation.
Prediction rules can be represented as an FFT, and vice versa. For example, the FFT in Figure 1 represents the prediction rule: If Q1 = “no” and Q2 = “yes”; then “At risk.” Several algorithms for rule induction have been developed, with the large majority aimed at (binary) classification (e.g., Cohen & Singer, 1999; Dembczyński et al., 2010; Frank & Witten, 1998; Indurkhya & Weiss, 2001; Quinlan, 1993). The RuleFit algorithm of Friedman and Popescu (2008) can deal with both classification and regression problems and is therefore preeminently suited for prediction problems in clinical psychology, as these may involve categorical as well as continuous outcome variables.
RuleFit AlgorithmRuleFit is a so-called ensemble method (e.g., Berk, 2006): It combines the predictions of multiple simple prediction functions to make a final prediction. The RuleFit model, as most learning ensembles, takes the form
where F(x) is the linear predictor in a generalized linear model, M is the size of the ensemble, and fm(x) denotes ensemble member m. Ensemble members can be any function of the input variables x; in the case of RuleFit the functions are decision rules. The predictions of the ensemble are a linear combination of the predictions of the ensemble members, with a0, . . . , aM representing weight coefficients. RuleFit derives an ensemble of prediction rules in two stages: First, it generates a large initial ensemble of decision rules fm(x), and second, it estimates the weight coefficients a0, . . . , aM for the final ensemble.
Stage 1: Rule Generation
To generate a large initial ensemble of decision rules, RuleFit draws a large number of subsamples of predetermined size of the training dataset, and grows a CART tree on each of the subsamples. Larger subsample size results in more similar subsamples, more similar CART trees, and more similar decision rules. The size of every CART trees grown is determined by a random draw from an exponential distribution, of which the mean is determined by the user. The minimum tree size is two; setting the average tree size to values > 2 allows for the detection of interaction effects.
The learning rate of the ensemble can be controlled by setting a shrinkage parameter. This parameter determines the weight given to previously induced ensemble members, when learning new ensemble members. Instead of fitting a tree directly to the data in the current subsample, the tree is fitted to the residual of the predictions of previously induced trees, weighted by the shrinkage parameter. Setting the weight parameter to 0 minimizes the influence of previously induced ensemble members, and results in the tree being fit directly to the data in the current subsample. Setting the weight parameter to 1 maximizes the influence of previously induced ensemble members, and resembles boosting (e.g., Schapire, 2003). Friedman and Popescu (2003) found a shrinkage parameter value of 0.01 to provide the best results.
After growing a CART tree, every node of the tree is included as a decision rule in the initial ensemble. To illustrate, an example of a decision tree from Fokkema, Smits, Kelderman, Carlier, and van Hemert (2014) is presented in Figure 2. Figure 2 represents a classification tree for predicting major depressive disorder diagnoses using total scores on four subscales of a mood and anxiety symptoms questionnaire: anhedonic depression (AD), general distress - depression (GDD), (general distress - anxiety (GDA), and (general distress - mixed (GDM). The tree has a total of 15 nodes, and therefore provides 15 decision rules. For example, Node 10 in Figure 2 can be represented as the rule r10(x) = I(AD > 76) × I(AD ≤ 81) × I(GDM ≤ 44), where I is an identity function, taking a value of 0 when the condition is not met, and taking a value of 1 when the condition is met. Likewise, Node 3 can be represented as the rule r3(x) = I(AD ≤ 76) × I(GDD ≤ 25). Rules rm(x) take a value of 1 when all the conditions of the rules are met; and a value of 0 when any of the conditions of the rules are not met.
Figure 2. Example decision tree from Fokkema, Smits, Felderman, Carlier, and van Hemert (2014). From “Combining Decision Tress and Stochastic Curtailment for Assessment Length Reduction of Test Batteries Used for Classification,” by M. Fokkema, N. Smits, H. Felderman, I. Carlier, and A. van Hemert, 2014, Applied Psychological Measurement, 38, p. 11. Copyright 2014 by Sage.
Stage 2: Weight Estimation
To improve interpretability and counter overfitting, RuleFit creates a final ensemble of prediction functions by applying a sparse regression of the output variable on the decision rules, in the second stage. Compared to ordinary least squares (OLS), sparse regression methods shrink the coefficients of predictors’ variables to values close to or equal to zero. This offers two major advantages: lower expected prediction error, due to lower variance of the coefficient estimates, and better interpretability, due to the smaller number of predictors with nonzero coefficients (Hastie et al., 2009).
One of four sparse regression methods can be used: ridge, elastic net, lasso, or forward stage-wise regression. Each proves a different level of sparsity of the final ensemble (e.g., Hastie, Taylor, Tibshirani, & Walther, 2007; Tibshirani, 1996; Zou & Hastie, 2005). Ridge regression generally shrinks coefficients to smaller, more similar, but nonzero values, compared to the OLS solution. Lasso regression generally shrinks coefficients to smaller values than the OLS solution; it may also shrink coefficient estimates to zero, therefore providing sparser models than with ridge regression. Elastic net regression provides a hybrid of ridge and lasso regression.
The forward stage-wise regression algorithm initializes by setting coefficients am of all prediction functions fm(x) to zero. Then, the coefficient of the prediction function most strongly correlated with the outcome variable is increased (or decreased, depending on the sign of the correlation) in very small steps (e.g., 0.01). The coefficient of the prediction function is increased (or decreased) in this way, until another prediction function has an equally strong or stronger correlation with the current residual. Then, the coefficient of that prediction function is increased (or decreased), until another prediction function has an equally strong or stronger correlation with the current residual. This process continues until no predictor has any correlation with the residual anymore. Forward stage-wise regression is preferable in the case of large numbers of correlated predictors, and yields very sparse models (Hastie et al., 2007), thus improving interpretability.
In short, RuleFit creates a large initial ensemble of prediction rules in the first stage, and selects only those rules that improve predictive accuracy in the second stage. This provides the user with a relatively small rule ensemble that can be easily interpreted and applied.
IllustrationTo illustrate the use of the RuleFit algorithm, we apply it to a dataset that was used by Penninx et al. (2011) to find predictors of the course of depressive and anxiety disorders. Penninx et al. (2011) used logistic regression analysis to find sociodemographic and clinical characteristics that predicted psychiatric status (i.e., presence of a depressive or anxiety disorder) after 2 years. Prediction of the course of these disorders is important, as it offers support for individualized care approaches, in which intensive treatment strategies are reserved for patients at high risk for a chronic course of the disorder.
It should be noted that baseline sociodemographic and clinical characteristics can be expected to only partially explain 2-year psychiatric status because other (e.g., environmental, genetic, neurobiological, and personality) characteristics also exert influence on the course of depressive and anxiety disorders. Sociodemographic and clinical information, however, is readily available to clinicians, and therefore provides a good starting point for course prediction. In addition, definitions of chronicity require a 2-year time period (Scott, 1988), although predictions over a 2-year time period may be expected to be of lower accuracy than predictions over smaller time periods. Any model for the prediction of 2-year psychiatric status can therefore be expected to have somewhat limited predictive accuracy, but may nevertheless offer valuable decision-making tools for the allocation of limited health care resources.
Sample
Penninx et al. (2011) identified predictors of the course of depressive and anxiety disorders using data from the NESDA (Penninx et al., 2008). This longitudinal study included 2,981 respondents, aged 18 through 65 years. Penninx et al. (2011) used baseline characteristics of respondents with depressive and/or anxiety disorder, to predict psychiatric status (i.e., presence of depressive and/or anxiety disorder) after 2 years. Therefore, analyses were performed on data from respondents who had a current depressive and/or anxiety disorder at baseline and participated in the follow-up after 2 years (N = 1,209). In this sample, mean age was 42.1 years and 66% were women. The NESDA study protocol was centrally approved by the Medical Ethics Review Board, and all respondents provided written informed consent. Further descriptions about the sample can be obtained from Penninx et al. (2011).
The logistic regression analysis of Penninx et al. (2011) included 20 predictor variables, consisting of sociodemographic variables, psychiatric indicators, and treatment indicators. Penninx et al. (2011) found comorbidity of depressive and anxiety disorders, age, agoraphobia, symptom duration, severity of depressive symptoms, severity of anxiety symptoms, and age at disorder onset to be significant predictors of psychiatric status after 2 years. The area under the receiver operating characteristic curve (AUC) for the logistic regression model incorporating all predictor variables, using the same data for estimation and evaluation of the model, was .72 (Penninx et al., 2011).
Outcome and Predictor Variables
In the following paragraphs, we provide a brief description of the variables relevant for prediction of 2-year psychiatric status. More detailed descriptions of the variables can be obtained from Penninx et al. (2011).
The outcome variable, psychiatric status after 2 years, was based on the presence of a Diagnostic and Statistical Manual of Mental Disorders (4th ed.; DSM–IV; American Psychiatric Association, 1994) depressive and/or anxiety disorder at 2-year follow-up (within a 6-months recency period), as assessed by the Composite International Diagnostic Interview (version 2.1; CIDI; World Health Organization, 1997).
Baseline psychiatric status was assessed by means of the baseline CIDI interview, and distinguished between three mutually exclusive categories: pure depression, pure anxiety, and comorbid depression and anxiety. Type of depressive disorder was assessed by means of the baseline CIDI interview as well, and distinguished between three mutually exclusive categories: first episode major depressive disorder (MDD), recurrent MDD, dysthymia. Type of anxiety disorder was assessed by means of the baseline CIDI interview, and distinguished between panic disorder, social phobia, generalized anxiety disorder, and agoraphobia without panic disorder. Age of onset of the index disorder was assessed by means of the baseline CIDI.
Duration of depressive and anxiety symptoms was assessed by means of the baseline Life Chart Interview (LCI; Lyketsos, Nestadt, Cwi, Heithoff, & Eaton, 1994). LCI anxiety and depression scores represent the percentage of time in which symptoms of anxiety or depressive disorder were present, during the 4 years before baseline. As the LCI provides separate indicators for anxiety and depression symptoms, the maximum value of both indicators was taken for every respondent. Severity of depressive symptoms was assessed by means of the 30-item Inventory of Depressive Symptomatology (IDS; Rush, Gullion, Basco, Jarrett, & Trivedi, 1996). Severity of anxiety symptoms was assessed by means of the 15-item Fear Questionnaire (FQ; Marks & Mathews, 1979) and the 21-item Beck Anxiety Inventory (BAI; Beck, Epstein, Brown, & Steer, 1988). Sociodemographic characteristics (age, gender, education in years) were obtained with self-report questions.
Analytic Models and Software
To replicate the study by Penninx et al. (2011), and to provide a benchmark based on an LME model, for evaluating the accuracy and efficiency of rule-based prediction of psychiatric status in 2 years, we performed logistic regression (LR) analysis in R (R Development Core Team, 2010). In LR analysis, observations with missing values are deleted (listwise deletion); therefore, we created five imputed datasets using the mi package (Su, Yajima, Gelman, & Hill, 2011) in R, and LR results were pooled across the five imputed datasets.
To identify decision rules for predicting psychiatric status after 2 years, we used the R implementation of the RuleFit algorithm (Friedman & Popescu, 2012; which can be freely downloaded from http://statweb.stanford.edu/~jhf/R-RuleFit.html). The RuleFit algorithm handles missing data by using all values that are not missing, so multiple imputation was not necessary for the rule ensemble. RuleFit has a number of settings that can be used to control the complexity of the final ensemble (Friedman & Popescu, 2008, 2012). In the current study, we used the default settings of the program, with two exceptions: The model type was set to generate rules only (no linear functions), and forward stagewise regression was selected for creating the final ensemble.
Evaluation of Performance
Predictive accuracy of the rule ensemble and the logistic regression model was assessed by calculating the AUC. The AUC represents the area under the receiver operating characteristic (ROC) curve for a given model. The ROC curve plots the true positive rate against the false positive rate, for several cutoff values of the class probabilities derived from a given model. AUC values reflect the probability that a randomly chosen observation from the positive class, has a higher model derived probability of belonging to that class, than a randomly selected observation from the negative class (e.g., Kraemer & Kupfer, 2006). An AUC of 1.0 represents perfect classification accuracy, whereas an AUC of 0.5 represents classification accuracy equal to random guessing.
In addition, correct classification rates, sensitivities, and specificities were calculated for the LR model and the rule ensemble. The correct classification rate represents the proportion of cases correctly classified. Sensitivity represents the correct classification rate among positively labeled cases, and specificity represents the correct classification rate among negatively labeled cases. Correct classification rate, sensitivity, and specificity for a given model may vary, according to the threshold of the model derived probabilities selected for classifying cases as positive or negative. Thresholds were selected so as to provide equal sensitivity for both models, allowing for straightforward comparison in terms of specificity and correct classification rate. The sensitivity was selected to be the value that maximized the sum of the weighted sensitivity and specificity in the RuleFit model.
All measures of predictive accuracy were estimated by means of 10-fold cross validation (CV). Ten-fold CV provides a more accurate and less optimistic estimate of performance of a predictive model than evaluation of performance with the same data that was used for estimation of the model (Hastie et al., 2009). With 10-fold CV, the original dataset is split into 10 random, equally sized subsets or folds. For each fold k, the model is retrained, using the observations in the other nine folds. Then, the prediction error is evaluated using the observations in fold k. This process is repeated for every fold, and the estimated prediction error is averaged over the 10 folds. This procedure yields a more realistic estimate of future prediction error of a model because it does not use the same data for building the model and estimating predictive accuracy. Note that the final model is built using the complete dataset.
To evaluate the efficiency of the RuleFit ensemble, we calculated the number of cues that required evaluation to arrive at a final decision for every respondent in the dataset.
RuleFit Ensemble for Prediction of 2-Year Psychiatric Status
Of the 1,209 respondents in the dataset, 61.5% had a depressive and/or anxiety disorder at 2-year follow-up. Due to this, for patients with a current depressive or anxiety disorder, the a priori odds of having the same psychiatric status 2 years later were 1.60.
The RuleFit ensemble, with rules selected by forward stagewise regression, comprised only two prediction rules. The first rule of the ensemble applied to respondents with an IDS score > 13.50, and anxiety and depressive symptoms for at least 35.9% of the time, over the past 4 years. This rule had a coefficient of 1.330, representing the estimated increase in the log odds for having the same psychiatric status after 2 years, when the rule applies. The coefficient indicates that respondents meeting the conditions of this rule had an increased risk of having the same psychiatric status in 2 years: their odds increased by factor e1.330 = 3.78. Therefore, we formulated it as the following prediction rule: if (IDS score > 13.50 and symptom duration > 35.9%), then (high risk). This rule is represented as an FFT in the upper panel of Figure 3.
Figure 3. Two fast and frugal trees for prediction of psychiatric status after two years: high risk (upper panel) and low risk (lower panel).
The second rule applied to respondents with a BAI score < 9.50, and no comorbid disorder. The second rule had a coefficient of −0.843, the sign indicating that respondents meeting the conditions of this rule had a lowered risk of having the same psychiatric status in 2 years. For those respondents, the odds of having a depressive or anxiety disorder after 2 years decreased; they were multiplied by e–0.843 = 0.43. The second prediction rule was formulated as: if (BAI score < 9.50 and no comorbid disorder), then (low risk). It is represented as an FFT in the lower panel of Figure 3. Note that a health care worker interested in identifying those who have a high risk of chronicity may only be interested in the first rule.
Table 1 presents distributions and estimated probabilities for the two prediction rules. The first rule applied to 24.4% of respondents (see Table 1). Patients matching the conditions of this rule, and not the conditions of the second rule, have a high risk of having the same psychiatric status in 2 years time, with estimated odds of 6.57. The second rule applied to 38.0% of respondents (see Table 1). Respondents meeting the conditions of this rule, and not the conditions of the first rule, have a low risk of having the same psychiatric status in 2 years time, with estimated odds of 0.75.
Frequencies and Estimated Probabilities of High- and Low-Risk Rules
For respondents who did not meet the conditions of either rule (40.8% of respondents; Table 1), the estimated odds were 1.74, which is slightly higher than the a priori odds of having the same psychiatric status in 2 years time. A small proportion (3.2%; Table 1) of respondents met the conditions of the high risk, as well as the low risk rule. For those respondents meeting the conditions of both rules, the estimated odds were 2.83, indicating that they had an elevated risk of having the same psychiatric status in 2 years time. Therefore, for the 24.4% of patients meeting the conditions of the high risk rule, it may not be necessary to evaluate further cues whether they met the conditions of the low risk rule, as they had at least an elevated risk of having the same psychiatric status after 2 years.
Efficiency of RuleFit Ensemble
The two rules in the RuleFit ensemble required evaluation of at most four cues. For most respondents, however, cue evaluation can be halted earlier. In the sample of 1,209 respondents, the median total number of evaluated cues was 3.0, and the mean was 2.99 cues (SD = 0.545). Halting cue evaluation after the first rule, for respondents who met the criteria of the first rule, would result in a further reduction in the average number of cues to be evaluated, from 2.99 to 2.67.
Predictive Performance of RuleFit Ensemble and Comparison With LR
The full logistic regression model incorporated 20 predictor variables, of which five were significant predictors of 2 year psychiatric status (i.e., had p values < .05). These were age, IDS score, duration of anxiety and depressive symptoms according to the LCI, age of onset of the index disorder, and BAI score. The AUC for the full model including all 20 predictor variables, as assessed by 10-fold CV, was .689 (see Table 2).
Predictive Performance of the Logistic Regression Model With Four Predictor Variables and the RuleFit Ensemble, Based on Ten-Fold Cross Validation
The accuracy of the RuleFit model was similar to that of the LR model. Based on 10-fold CV, the AUC for the RuleFit model was .686 (see Table 2). With sensitivity set equal to .782 for both models, the RuleFit ensemble provided specificity of .447, which was slightly lower than the specificity of .463 for the LR model. Correct classification rates were very similar between the RuleFit ensemble and the LR model: .653 and .659, respectively (Table 2).
Summary
The RuleFit algorithm, using forward stagewise regression for selecting the final ensemble, produced two simple decision rules for prediction of psychiatric status of respondents with a current depressive or anxiety disorder. Although the course of psychiatric disorders is determined by many other than sociodemographic and clinical characteristics, these two rules provide a good starting point for course prediction in clinical practice. Although the RuleFit ensemble required evaluation of only three cues, on average, to make a prediction, its accuracy was very similar to that of a logistic regression model comprising 20 predictor variables.
DiscussionIn the Illustration, we showed that the RuleFit algorithm can provide simple rule ensembles, which may prove highly usable for psychologists working in applied settings. We found the predictive accuracy of rule ensembles to be competitive with that of an LME model that would usually be applied for actuarial prediction.
Unlike LME models, rule ensembles are able to convey interaction effects. This not only allows for a more flexible representation of the relationship between predictor variables and the outcome; it also produces results that are more efficient in decision making. Whereas logistic regression models require the values of all (significant) predictor variables to be taken into account for making a prediction, decision rules require evaluation of only a limited number of cues.
This is reminiscent of sequential testing, introduced by Cronbach and Gleser (1965), in which the aim was to collect new information at every stage of testing; attributes that were redundant given previous outcomes, were neglected. In clinical diagnosis, sequential testing may provide substantial reductions in respondent burden and clinicians’ time needed for making a decision. For example, Fokkema et al. (2014) demonstrated that sequential testing in clinical diagnosis may provide assessment length reduction of about 50%. In the current study, we found that the number of cues to be evaluated could be reduced by 25% to 33%.
The tree-based representation offers an additional improvement in the practical applicability of prediction rules. Applicability may be a key factor in advancing the use of actuarial prediction in clinical practice, as well-validated prediction rules may be available, but still rarely used in practice, possibly due to their complexity. For example, for the Outcome Questionnaire–45 (Lambert, Hansen, & Finch (2001)), regression rules for predicting which patients will have a poor treatment response have been proven effective in predicting and improving outcomes, but are rarely used in practice (Hannan et al., 2005; Lambert et al., 2003). Although complexity of the rules and computations required for making predictions may be inconsequential when tests are administered by computer, in practice tests may often be administered in paper-and-pencil versions, or computerized administration and scoring routines may be unavailable to practitioners.
In the current study, we used an ensemble method to derive decision rules. The use of ensemble methods is advantageous because the predictions of ensemble methods are more accurate than any of their constituent members (Dietterich, 2000; Berk, 2006). However, ensembles consisting of many prediction functions may be difficult for humans to use and interpret. For the RuleFit algorithm, the sparsity of settings can be used to adjust the complexity of the final ensemble. The current study, in line with Hastie et al. (2007), indicates that the use of forward stagewise regression to select and determine the weights of prediction rules, provides an ensemble of interpretable size.
In addition to a small number of prediction rules in the final ensemble, it may be desirable for the decision rules in an ensemble to be noncompensatory (Einhorn, 1970; Martignon et al., 2008). A model is noncompensatory if the effect of more important variables cannot be compensated for by variables of lesser importance (see Martignon et al., 2008 for a more precise definition of noncompensatory models). This results in more efficient decision making: Whenever the conditions of a rule have been met, checking the conditions of further rules is unnecessary for making a final decision. In the current study, the RuleFit model was noncompensatory: Meeting the conditions of the first rule resulted in a higher risk, regardless of whether the conditions of the second rule were met. However, the RuleFit algorithm does not necessarily provide noncompensatory models, and may provide compensatory models in other instances.
Some authors have criticized the use of CART based methodologies for deriving decision rules (e.g., Marshall, 1995, 2001). For example, they argued that some of the decision rules derived from CART trees may be redundant. The RuleFit algorithm counters this issue by the use of sparse regression to determine the weights of the rules. However, some objections, such as for example, the data-driven nature of machine learning methodologies, remain valid. Therefore, in application and interpretation of the results of rule-based methods, as with all data-analytic methods, predictive accuracy of decision rules should not be confused with biological meaning or diagnostic interpretation.
In conclusion, the current study has shown rule-based methods to be a promising tool for the development of actuarial prediction methods that are easily applicable, efficient, and accurate for clinical decision making.
Footnotes 1 In the remainder of this article, we distinguish between the algorithms to derive FTTs and graphical representations of FFTs. The term FFT will be used to denote the graphical tree representation, whereas algorithms to derive FFTs will be explicitly denoted as such.
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Submitted: May 22, 2014 Revised: October 17, 2014 Accepted: November 11, 2014
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Source: Psychological Assessment. Vol. 27. (2), Jun, 2015 pp. 636-644)
Accession Number: 2015-03435-001
Digital Object Identifier: 10.1037/pas0000072
Record: 39- Title:
- Coping-motivated marijuana use correlates with DSM-5 cannabis use disorder and psychological distress among emerging adults.
- Authors:
- Moitra, Ethan. Warren Alpert Medical School of Brown University, Department of Psychiatry and Human Behavior, RI, US, ethan_moitra@brown.edu
Christopher, Paul P.. Warren Alpert Medical School of Brown University, Department of Psychiatry and Human Behavior, RI, US
Anderson, Bradley J.. Butler Hospital, General Medicine Research Unit, Providence, RI, US
Stein, Michael D.. Warren Alpert Medical School of Brown University, Department of Medicine, RI, US - Address:
- Moitra, Ethan, Brown University, Department of Psychiatry and Human Behavior, Box G-BH, Providence, RI, US, 02912, ethan_moitra@brown.edu
- Source:
- Psychology of Addictive Behaviors, Vol 29(3), Sep, 2015. Marijuana Legalization: Emerging Research on Use, Health, and Treatment. pp. 627-632.
- NLM Title Abbreviation:
- Psychol Addict Behav
- Page Count:
- 6
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- Bulletin of the Society of Psychologists in Addictive Behaviors; Bulletin of the Society of Psychologists in Substance Abuse
- Other Publishers:
- US : Educational Publishing Foundation
Society of Psychologists in Addictive Behaviors - ISSN:
- 0893-164X (Print)
1939-1501 (Electronic) - Language:
- English
- Keywords:
- marijuana, DSM-5, motives, coping, emerging adults
- Abstract:
- Compared to other age cohorts, emerging adults, ages 18–25 years, have the highest rates of marijuana (MJ) use. We examined the relationship of using MJ to cope with negative emotions, relative to using MJ for enhancement or social purposes, to MJ-associated problems and psychological distress among emerging adults. Participants were 288 community-dwelling emerging adults who reported current MJ use as part of a 'Health Behaviors' study. Linear and logistic regressions were used to evaluate the adjusted association of coping-motivated MJ use with the fifth edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5) cannabis use disorder, MJ-related problem severity, depressive symptoms, and perceived stress. After adjusting for other variables in the regression model, using MJ to cope was positively associated with having DSM-5 cannabis use disorder (OR = 1.85, 95% CI [1.31, 2.62], p < .01), MJ problem severity (b = .41, 95% CI [.24, .57], p < .01), depression (b = .36, 95% CI [.23, .49], p < .01), and perceived stress (b = .37, 95% CI [.22, .51], p < .01). Using MJ for enhancement purposes or for social reasons was not associated significantly with any of the dependent variables. Using MJ to cope with negative emotions in emerging adults is associated with MJ-related problems and psychological distress. Assessment of MJ use motivation may be clinically important among emerging adults. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Coping Behavior; *Diagnostic and Statistical Manual; *Distress; *Marijuana Usage; *Mental Disorders; Cannabis; Marijuana; Motivation; Social Behavior
- Medical Subject Headings (MeSH):
- Adaptation, Psychological; Adolescent; Adult; Anxiety; Depression; Diagnostic and Statistical Manual of Mental Disorders; Female; Humans; Linear Models; Logistic Models; Male; Marijuana Abuse; Marijuana Smoking; Motivation; Smoking; Stress, Psychological; Young Adult
- PsycINFO Classification:
- Substance Abuse & Addiction (3233)
- Population:
- Human
Male
Female - Location:
- US
- Age Group:
- Adulthood (18 yrs & older)
Young Adulthood (18-29 yrs) - Tests & Measures:
- Fagerström Test for Nicotine Dependence
Marijuana Problem Scale
Perceived Stress Scale–4
Reasons for Drinking Measure
Reasons for Marijuana Use Measure
Structured Interview for the DSM-IV
Timeline Follow-Back Measure
Patient Health Questionnaire-9 DOI: 10.1037/t06165-000 - Grant Sponsorship:
- Sponsor: National Institute on Alcohol Abuse and Alcoholism
Grant Number: R01 AA020509
Recipients: No recipient indicated - Methodology:
- Empirical Study; Quantitative Study
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- First Posted: Apr 27, 2015; Accepted: Mar 9, 2015; Revised: Mar 2, 2015; First Submitted: Dec 8, 2014
- Release Date:
- 20150427
- Correction Date:
- 20150928
- Copyright:
- American Psychological Association. 2015
- Digital Object Identifier:
- http://dx.doi.org/10.1037/adb0000083
- PMID:
- 25915689
- Accession Number:
- 2015-17753-001
- Number of Citations in Source:
- 39
- Persistent link to this record (Permalink):
- http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2015-17753-001&site=ehost-live
- Cut and Paste:
- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2015-17753-001&site=ehost-live">Coping-motivated marijuana use correlates with DSM-5 cannabis use disorder and psychological distress among emerging adults.</A>
- Database:
- PsycINFO
Coping-Motivated Marijuana Use Correlates With DSM-5 Cannabis Use Disorder and Psychological Distress Among Emerging Adults
By: Ethan Moitra
Department of Psychiatry and Human Behavior, Warren Alpert Medical School of Brown University;
Paul P. Christopher
Department of Psychiatry and Human Behavior, Warren Alpert Medical School of Brown University and Butler Hospital, Providence, Rhode Island
Bradley J. Anderson
General Medicine Research Unit, Butler Hospital
Michael D. Stein
Department of Medicine and Department of Health Services, Policy, and Practice, Warren Alpert Medical School of Brown University and Butler Hospital
Acknowledgement: This study was funded by NIAAA Grant R01 AA020509. Trial registered at clinicaltrials.gov as NCT01473719.
Evolving social attitudes toward marijuana (MJ) have led to legalization of its use for medical and recreational purposes in some U.S. states. Over the past 30 years, disapproval of MJ use has decreased across birth cohorts (Keyes et al., 2011), with emerging adults (ages 18–25 years) being most accepting of use. Compared to other age cohorts, emerging adults (Arnett, 2001) also have the highest rates of MJ use, and substance use disorders peak during this period (Substance Abuse and Mental Health Services Administration [SAMHSA], 2013). Indeed, studies have shown that nearly 10% of college-based emerging adults meet criteria for an MJ use disorder (Caldeira, Arria, O’Grady, Vincent, & Wish, 2008; Caldeira et al., 2009).
Reasons for substance use can vary between and within individuals (Cooper, 1994). Common motives are to cope with negative emotions or distress (e.g., “to forget my worries”), to conform (e.g., “because I felt pressure from others who do it”), for enhancement purposes (e.g., “because I like the feeling”), for expansion (e.g., “to expand my awareness”), and for social purposes (e.g., “it’s what I do with friends”). Conformity-motivated use is driven by a desire to reduce social exclusion, enhancement-motivated use is described as being driven by a desire for excitement or joy, expansion-motivated use relates to seeking cognitive or perceptual enhancement of experiences, and social-motivated use seeks to facilitate social cohesion (Simons, Correia, Carey, & Borsari, 1998; Simons, Gaher, Correia, Hansen, & Christopher, 2005). According to the stress-coping model (Wills & Shiffman, 1985), people may also consume substances as a coping response to stress, with the substance being used to engender positive affect and/or decrease an aversive mood.
Enhancement, expansion, and social motives are positively associated with MJ use but less related to negative outcomes such as MJ-related problems or psychological distress (Bonn-Miller, Zvolensky, & Bernstein, 2007; Brodbeck, Matter, Page, & Moggi, 2007). However, individuals with MJ use disorders have higher rates of enhancement-motivated MJ use compared to more casual MJ users (Bonn-Miller & Zvolensky, 2009). Although using MJ to conform is associated with social anxiety symptoms (Buckner, Bonn-Miller, Zvolensky, & Schmidt, 2007), it has been found to negatively correlate with recent MJ use (Bonn-Miller, Zvolensky, & Bernstein, 2007).
Although most emerging adults report using MJ primarily for enhancement or social reasons (Lee, Neighbors, & Woods, 2007), those who endorse greater MJ use to cope with distress may represent a subgroup trying to manage more severe mental health problems and, in doing so, may be at risk for MJ-related problems. Emerging adults may be more likely than other adults to use MJ to cope with psychological distress (Buckner, 2013). Using MJ to cope with distress is associated with negative affect, anxious arousal, and depressive symptoms (Beck et al., 2009; Mitchell, Zvolensky, Marshall, Bonn-Miller, & Vujanovic, 2007). Among emerging adults with a history of trauma, coping-motivated use, but not other motives, is associated with posttraumatic stress symptoms (Bonn-Miller, Vujanovic, Feldner, Bernstein, & Zvolensky, 2007). Using MJ to cope with negative emotions is also uniquely associated with emotional dysregulation (Bonn-Miller, Vujanovic, & Zvolensky, 2008) and social anxiety symptoms (Buckner et al., 2007) relative to other motives. Although these studies indicate that emerging adults who use MJ to cope experience psychological distress, they are limited by the exclusion of individuals with current Axis I psychopathology (Bonn-Miller, Vujanovic, et al., 2007) and restriction to college students (Buckner et al., 2007). A more representative sample of emerging adults who use MJ to cope with distress is needed to better understand the relationship among these factors.
Coping-motivated MJ use in emerging adults is also associated with more persistent use (Patrick, Schulenberg, O’Malley, Johnston, & Bachman, 2011; Patrick, Schulenberg, O’Malley, Maggs, et al., 2011; Titus, Godley, & White, 2007). Persistent use can lead to MJ-related problems, particularly for individuals who start using earlier in life (Anthony & Petronis, 1995). Among emerging adult MJ users, those who use to cope with distress are at increased risk for MJ-related problems (Buckner, 2013; Lee et al., 2007) and are more likely to meet criteria from the fourth edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-IV; American Psychiatric Association, 2000) for MJ dependence (Bonn-Miller & Zvolensky, 2009). Yet the DSM-IV had inadequate clinical utility in discriminating MJ problem severity among emerging adults (Martin, Chung, Kirisci, & Langenbucher, 2006). To our knowledge, no study has investigated the link between using MJ to cope with distress and cannabis use disorder as defined in the fifth edition of the DSM (DSM-5; American Psychiatric Association, 2013).
In this study, we examined the association of motivations for using MJ (social, enhancement, and coping) in emerging adults with four measures of MJ-related problem severity and psychological distress: (a) meeting DSM-5 criteria for cannabis use disorder, (b) MJ-related problem severity, (c) depressive symptomatology, and (d) perceived stress. We hypothesized that coping-motivated use would be more strongly associated with these adverse outcomes than social- or enhancement-motivated use.
Method Participants
Participants were recruited for a large study on health behaviors among emerging adults who use MJ or alcohol through advertisements online, in local college newspapers, on public transportation, and on commercial radio in Rhode Island. After a telephone screen, eligible individuals were invited for a compensated ($40) in-person interview and free sexually transmitted infection testing. The study was approved by the Butler Hospital Institutional Review Board.
Eligibility criteria included being 18–25 years old, drinking alcohol and/or using MJ in the past month, being sexually active in the past 6 months, not having suicidal ideation in the past 2 weeks, and living within 30 minutes of the research site. Of the 1,621 individuals screened by phone, 689 were ineligible. The remaining 932 eligible persons were invited for an interview, and 533 were either not interested or did not keep a scheduled baseline appointment. In total, 399 individuals completed baseline interviews, after which 17 persons were found to be ineligible. For the present analysis, we included data only from individuals who reported using MJ in the past 30 days (n = 288).
Measures
Frequency of cigarette smoking
Frequency of cigarette smoking was assessed using an item from the Fagerström Test for Nicotine Dependence (Heatherton, Kozlowski, Frecker, & Fagerström, 1991): “How many cigarettes do you smoke per day?”
Marijuana Problem Scale
The Marijuana Problem Scale (MPS; Stephens, Roffman, & Curtin, 2000; Stephens et al., 2004) is a reliable and valid measure (score range 0–48) of 19 problems directly related to MJ use, ranging from losing a job to having withdrawal symptoms to having problems in one’s family.
Patient Health Questionnaire–9
The Patient Health Questionnaire–9 (PHQ-9; Kroenke, Spitzer, & Williams, 2001) is a validated and reliable nine-item measure (score range 0–27) of depressive symptoms.
Perceived Stress Scale–4
The Perceived Stress Scale–4 (PSS-4; Cohen, Kamarck, & Mermelstein, 1983) is a four-item measure (score range 0–16) that assesses the degree to which individuals perceive their environment and experiences as stressful.
Reasons for Marijuana Use
We adapted the Reasons for Drinking measure developed by Cooper, Russell, Skinner, and Windle (1992) for this study to examine MJ use motives. This measure has three subscales: (a) coping (sample item, “Because it helped when you felt depressed or nervous”), (b) enhancement (“Because it’s exciting”), and (c) social (“To be sociable”). The Reasons for Marijuana Use subscales had a possible range of 1–4, corresponding to never/almost never, sometimes, often, and almost always. In this sample, internal consistency reliabilities were .84, .84, and .73 for the coping, enhancement, and social scales, respectively. Product-moment correlations between the subscales ranged from .45–.47.
Structured Interview for the DSM-IV—cannabis abuse and dependence modules
The Structured Interview for the DSM-IV (First, Spitzer, Gibbon, & Williams, 1996) is the most widely used, reliable, and well-validated, structured clinical assessment tool for DSM-IV diagnostic criteria. To assess the craving criterion under DSM-5 defined cannabis use disorder, we asked all participants, “In the past 3 months, have you often had cravings or strong desires or urges to use marijuana?” Participants endorsing two or more of the abuse or dependence items, including our additional craving question, met criteria for DSM-5 cannabis use disorder. Severity of cannabis use disorder was coded by number of criteria endorsed: no disorder = zero or one symptom, mild = two or three symptoms, moderate = four or five symptoms, and severe = six or more symptoms (Hasin et al., 2013).
Timeline Follow-Back Measure
The Timeline Follow-Back Measure (TLFB; Sobell & Sobell, 1996) is a semistructured interview that uses a calendar-guided approach (Fals-Stewart, O’Farrell, Freitas, McFarlin, & Rutigliano, 2000) and assesses alcohol and MJ use in the past 30 days.
Data Analysis
Descriptive statistics summarize the characteristics of the sample. Our primary focus is on the associations of using MJ for coping, socialization, and enhancement with indicators of MJ use severity and problems. We also examined associations with measures of psychological well-being. Background characteristics included as covariates were age, gender, ethnoracial group, employment status, education, alcohol use frequency (past 30 days), MJ use frequency (past 30 days), and number of cigarettes smoked per day (past 30 days). Associations were estimated in a seemingly unrelated regression framework (Zellner, 1962) using Mplus 5.1 (Muthén & Muthén, 2008). This method assumes error terms are correlated across equations and parameter estimates are more efficient than equation-by-equation estimation. The interpretation of estimated coefficients is identical to single-equation regression models. Prior to analysis, all continuous variables were standardized to zero-mean and unit variance. For the equations with continuous dependent variables (PHQ-9, PSS-4, and MPS), the coefficients reported for continuous factors are fully standardized regression coefficients, and the coefficients for the categorical factors are y-standardized. Associations with meeting criteria for DSM-5 cannabis use disorder are reported as odds ratios. Parameters and inferential statistics were estimated using maximum likelihood with robust standard errors (MLR in Mplus). All above-described covariates and the three motivation-to-use MJ subscales were entered simultaneously in the multivariate models. An indicator variable contrasting non-Latino Whites to all other racial or ethnic identifications was used in analyses.
ResultsOf the 288 emerging adults who reported MJ use in the past 30 days, mean age was 21.2 (SD = 2.1) years, 135 (51.7%) were male, and 187 (64.9%) were non-Latino White (see Table 1). On average, participants used alcohol and MJ on 26.7% (SD = 18.0%) and 52.5% (SD = 38.1%) of TLFB days, respectively. More than two thirds (70.5%) reported no cigarette smoking in the 30 days prior to baseline.
Background Characteristics and Descriptive Statistics (n = 288)
After adjusting for other variables in the model, including using MJ for enhancement and social reasons, using MJ to cope with distress was positively and significantly associated with meeting DSM-5 diagnostic criteria for cannabis use disorder (OR = 1.85, 95% CI [1.31, 2.62], p < .01). As a supplementary analysis, we estimated a parallel ordinal logit regression model in which cannabis use disorder severity was regressed on the reasons to use indices and all covariates described in Table 2; results were consistent with those reported for the dichotomized outcome. Using MJ to cope was associated positively and significantly with cannabis use disorder severity (OR = 1.59, 95% CI [1.15, 2.19], p < .05). Neither using for social reasons (OR = 1.46, 95% CI [0.98, 2.18], p > .05) nor using for enhancement (OR = 0.94, 95% CI [0.66, 1.34], p > .05) was associated significantly with cannabis use disorder severity. Results were the same when analyzing the unique associations of the three reasons for using MJ and cannabis use disorder based on number of criteria endorsed.
Seemingly Unrelated Regression Model Estimating the Adjusted Association of Using Marijuana to Cope, to Socialize, and for Enhancement on Various Measures (n = 288)
Using MJ to cope was also significantly associated with MJ problem severity (b = .41, 95% CI [.24, .57], p < .01), depressive symptomatology (b = .36, 95% CI [.23, .49], p < .01), and perceived stress (b = .37, 95% CI [.22, .51], p < .01; see Table 2). These multivariate models estimated the effects of the other reasons for using MJ subscales, revealing that using MJ to socialize or for enhancement purposes was not uniquely associated significantly with any of the dependent variables (see Table 2).
DiscussionThis study found that among emerging adults who use MJ, use to cope with distress is positively and significantly associated with having a DSM-5 cannabis use disorder. Using MJ for enhancement or social purposes did not uniquely account for a significant proportion of variance in this outcome. Moreover, coping-motivated use, but not social- or enhancement-motivated use, is associated with MJ-related problems in this group. Using MJ to cope with negative emotions among emerging adults also appears to be uniquely associated with psychiatric symptoms, as measured by severity of depressive symptoms and degree of perceived stress, consistent with prior research (Mitchell et al., 2007). These data are the first to demonstrate the confluence of cannabis use disorder, MJ-related problems, and psychiatric symptoms in the same sample. In addition, a significant shortcoming of previous work was the exclusion of individuals who met DSM diagnostic criteria for Axis I psychopathology (Bonn-Miller, Vujanovic, et al., 2007; Bonn-Miller et al., 2008; Bonn-Miller & Zvolensky, 2009), a meaningful omission given the concern for mental health issues in these individuals.
The acceptability of MJ use is growing in emerging adults (Keyes et al., 2011), a high-risk group for substance use disorders (SAMHSA, 2013). Although prior studies have shown an association between coping-motivated MJ use and a variety of negative psychological factors (e.g., Mitchell et al., 2007), little research has compared the relationship of coping-motivated use, relative to social- and enhancement-based use, to MJ-related problems and psychological variables. These are also the first results linking using MJ to cope with psychological distress to the newly defined DSM-5 cannabis use disorder. This new diagnostic category represents an important streamlining of the DSM’s cannabis abuse and dependence diagnoses while incorporating a severity dimension. This new approach is particularly relevant to clinicians working with emerging adults, because the DSM-IV classification system poorly quantified severity of use in this age group (Martin et al., 2006).
This study had limitations. First, our primary measure of MJ use motives was adapted from an alcohol scale. Moreover, we did not measure conformity- or expansion-motivated use; these would be important to include in future research. Second, although coping-motivated use was significantly associated with negative psychological variables, our estimated standardized effect sizes suggest that coping-motivated use might not be the only factor associated with these outcomes. Third, the sample was limited to emerging adults who had past-month MJ use, were not seeking treatment, and were sexually active. Although the sample had the strengths of not excluding those with Axis I psychopathology, being ethnically/racially diverse, being nearly half female, and including a substantial number of emerging adults not currently in school (41%), it was not an epidemiological sample. Furthermore, > 50% of potential participants declined to participate in the study. Fourth, we did not use a diagnostic measure to assess the presence of major depressive disorder. Fifth, MJ-induced anxiety is one of the most commonly reported acute symptoms of MJ use (Crippa et al., 2009). Thus, it is possible that self-reported use of MJ to cope with distress is confounded by MJ-triggered symptoms. Finally, given the cross-sectional nature of the data, we were unable to examine the temporal relationship between coping-motivated use and psychological distress. Still, our findings suggest that assessment of use of MJ to manage psychological distress may be clinically important and, if found, signal the importance of a broad and careful mental health assessment.
These results raise the question about how coping-motivated MJ use might improve or worsen one’s well-being. Although MJ may be perceived as beneficial in ameliorating symptoms of emotional distress, long-term MJ use for these purposes has been associated with deleterious consequences (Patrick, Schulenberg, O’Malley, Johnston, et al., 2011). More longitudinal research will be needed to examine if, despite being used with the intention of mitigating distress, coping-motivated use may actually worsen psychological health.
ConclusionsMJ use is becoming more socially acceptable and common in emerging adults. These results help clinicians identify MJ-using individuals who are likely to also have psychological distress symptoms. It appears that there is an important subset of emerging adults who use MJ for coping purposes, and these individuals are at risk for a variety of MJ-related problems. Clinicians working with patients who use MJ to cope with negative emotions face the challenge of confronting misconceptions about the perceived benefit of using MJ to “treat” distress. If MJ users continue to be reluctant to engage in drug counseling or to reduce use, despite having substance-related problems, clinicians must become more open to providing treatment such as alternative coping strategies for what these users might be more motivated to change, namely, their symptoms of psychological distress.
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Submitted: December 8, 2014 Revised: March 2, 2015 Accepted: March 9, 2015
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Source: Psychology of Addictive Behaviors. Vol. 29. (3), Sep, 2015 pp. 627-632)
Accession Number: 2015-17753-001
Digital Object Identifier: 10.1037/adb0000083
Record: 40- Title:
- Correlates of engaging in drug distribution in a national sample.
- Authors:
- Stanforth, Evan T.. Department of Educational and Psychological Studies, University of Miami, Coral Gables, FL, US, e.stanforth@miami.edu
Kostiuk, Marisa. Department of Counseling Psychology, University of Denver, Denver, CO, US
Garriott, Patton O.. Department of Counseling Psychology, University of Denver, Denver, CO, US - Address:
- Stanforth, Evan T., Department of Educational and Psychological Studies, University of Miami, 5202 University Drive, Coral Gables, FL, US, 33146, e.stanforth@miami.edu
- Source:
- Psychology of Addictive Behaviors, Vol 30(1), Feb, 2016. pp. 138-146.
- NLM Title Abbreviation:
- Psychol Addict Behav
- Page Count:
- 9
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- Bulletin of the Society of Psychologists in Addictive Behaviors; Bulletin of the Society of Psychologists in Substance Abuse
- Other Publishers:
- US : Educational Publishing Foundation
Society of Psychologists in Addictive Behaviors - ISSN:
- 0893-164X (Print)
1939-1501 (Electronic) - Language:
- English
- Keywords:
- drug distribution, selling drugs, illicit substance use
- Abstract:
- In this study, we examined self-reported behaviors and characteristics of individuals involved in drug distribution to identify correlates of engaging in drug-distribution behaviors. Correlates of interest included demographic characteristics, substance-use patterns, psychological impairment, and criminal involvement. Data from the 2012 National Survey on Drug Use and Health (U.S. Department of Health and Human Services, Substance Abuse & Mental Health Services Administration, 2013) were used for analyses (N = 55,108). A logistic regression analysis distinguished those who have sold drugs from those who have not sold drugs to identify correlates of engaging in drug distribution. Results showed that recency of substance use, severity of substance use, criminal activity, mental health diagnoses, substance-use treatment, and arrest history were all significantly associated with distribution behaviors. Findings indicate the importance of accounting for the heterogeneous characteristics of individuals involved in distribution behaviors when considering treatment options or criminal proceedings. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Illegal Drug Distribution; *Mental Health; *Statistical Correlation; Criminal Behavior; Demographic Characteristics; Diagnosis; Drug Usage; Legal Arrest; Treatment
- PsycINFO Classification:
- Psychological & Physical Disorders (3200)
- Population:
- Human
Male
Female - Location:
- US
- Age Group:
- Childhood (birth-12 yrs)
School Age (6-12 yrs)
Adolescence (13-17 yrs)
Adulthood (18 yrs & older)
Young Adulthood (18-29 yrs)
Thirties (30-39 yrs)
Middle Age (40-64 yrs)
Aged (65 yrs & older) - Tests & Measures:
- 2012 National Survey on Drug Use and Health
- Methodology:
- Empirical Study; Qualitative Study; Quantitative Study
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- First Posted: Oct 26, 2015; Accepted: Aug 19, 2015; Revised: Aug 18, 2015; First Submitted: May 18, 2015
- Release Date:
- 20151026
- Correction Date:
- 20160215
- Copyright:
- American Psychological Association. 2015
- Digital Object Identifier:
- http://dx.doi.org/10.1037/adb0000124
- PMID:
- 26502336
- Accession Number:
- 2015-48437-001
- Number of Citations in Source:
- 27
- Persistent link to this record (Permalink):
- http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2015-48437-001&site=ehost-live
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- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2015-48437-001&site=ehost-live">Correlates of engaging in drug distribution in a national sample.</A>
- Database:
- PsycINFO
Record: 41- Title:
- Correlates of recent drug use among victimized women on probation and parole.
- Authors:
- Golder, Seana. Kent School of Social Work, University of Louisville, Louisville, KY, US, seana.golder@louisville.edu
Hall, Martin T.. Kent School of Social Work, University of Louisville, Louisville, KY, US
Engstrom, Malitta. School of Social Policy and Practice, University of Pennsylvania, PA, US
Higgins, George E.. Department of Justice Administration, University of Louisville, Louisville, KY, US
Logan, TK. Department of Behavioral Science and the Center on Drug and Alcohol Research, University of Kentucky, KY, US - Address:
- Golder, Seana, Kent School of Social Work, University of Louisville, Louisville, KY, US, 40292, seana.golder@louisville.edu
- Source:
- Psychology of Addictive Behaviors, Vol 28(4), Dec, 2014. pp. 1105-1116.
- NLM Title Abbreviation:
- Psychol Addict Behav
- Page Count:
- 12
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- Bulletin of the Society of Psychologists in Addictive Behaviors; Bulletin of the Society of Psychologists in Substance Abuse
- Other Publishers:
- US : Educational Publishing Foundation
Society of Psychologists in Addictive Behaviors - ISSN:
- 0893-164X (Print)
1939-1501 (Electronic) - Language:
- English
- Keywords:
- women, victimization, probation, parole, drug use, psychological distress
- Abstract:
- Guided by the Comprehensive Health Seeking and Coping Paradigm (CHSCP; Nyamathi, 1989), the present research sought to examine associations between victimization, psychological distress, lawbreaking and recent drug use (past 12 months) among 406 victimized women on probation and parole. Bivariate differences between women who reported recent drug use and those who did not report recent use were compared across the 4 domains of the CHSCP (sociodemographic characteristics, personal resources, lifetime victimization, dynamic crime and drug factors). Variables significantly related to recent drug use at the bivariate level were retained in the multivariate analysis. The final multivariate model, using stepwise logistic regression via backward elimination, retained five candidate variables indicating women who recently used drugs, were younger, were not sexually victimized as children, began using drugs before they were 13 years of age, were on probation, and had engaged in more recent lawbreaking. The final model accounted for approximately 30% of the variance in drug use over the past 12 months. Implications for intervention and future research are discussed. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Drug Usage; *Female Criminals; *Parole; *Probation; Distress; Victimization
- Medical Subject Headings (MeSH):
- Adaptation, Psychological; Adult; Crime; Crime Victims; Criminals; Drug Users; Female; Humans; Middle Aged; Sexual Behavior; Stress, Psychological; Substance-Related Disorders
- PsycINFO Classification:
- Criminal Behavior & Juvenile Delinquency (3236)
- Population:
- Human
Female - Location:
- US
- Age Group:
- Adulthood (18 yrs & older)
- Grant Sponsorship:
- Sponsor: National Institute on Drug Abuse
Grant Number: R01DA027981
Recipients: No recipient indicated - Methodology:
- Empirical Study; Interview; Quantitative Study
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- Accepted: Aug 19, 2014; Revised: Aug 7, 2014; First Submitted: Aug 20, 2013
- Release Date:
- 20141222
- Copyright:
- American Psychological Association. 2014
- Digital Object Identifier:
- http://dx.doi.org/10.1037/a0038351
- PMID:
- 25528050
- Accession Number:
- 2014-56246-004
- Number of Citations in Source:
- 101
- Persistent link to this record (Permalink):
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- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2014-56246-004&site=ehost-live">Correlates of recent drug use among victimized women on probation and parole.</A>
- Database:
- PsycINFO
Correlates of Recent Drug Use Among Victimized Women on Probation and Parole
By: Seana Golder
Kent School of Social Work, University of Louisville;
Martin T. Hall
Kent School of Social Work, University of Louisville
Malitta Engstrom
School of Social Policy and Practice, University of Pennsylvania
George E. Higgins
Department of Justice Administration, University of Louisville
TK Logan
Department of Behavioral Science and the Center on Drug and Alcohol Research, University of Kentucky
Acknowledgement: The research described here was supported, in part, by a grant from the National Institute on Drug Abuse (R01DA027981). Special thanks to all the women who have participated in this research. Additional gratitude is expressed to Robin Cook, Amy Brooks, and the Kentucky Department of Corrections, Division of Probation and Parole, for their assistance.
Drug use is a serious threat to public health and functioning among women in the criminal justice system. In fact, drug use and its associated legal penalties have fueled the growth of women in the justice system over the past 30 years. In 1980, as the War on Drugs accelerated, 13,258 women were incarcerated in state or federal prison. By 2011, that number had risen to 111,387, representing a staggering 740% increase in the number of women in prison in the United States (Carson & Sabol, 2012; The Sentencing Project, 2012). Although attention most frequently focuses on individuals who are incarcerated, the majority of justice-involved women are supervised in the community (e.g., probation, parole). Thus, as the number of women who are incarcerated has grown, so too has the number of women involved in community-based sanctions. In 1990, approximately 481,000 women were on probation in the United States. By 2011, that number had increased to nearly 1 million (Glaze, 2002; Department of Justice, 2001; Maruschak & Parks, 2012). The most recent data available indicate that 1 in every 89 women in the U.S. is under some form of correctional authority, with approximately 85% assigned to probation or parole (Pew Center on the States, 2009).
Drug-related offenses and/or drug-involvement are common among the majority of justice-involved women. Female jail and prison inmates are more likely than their male counterparts to be incarcerated for a drug offense (Carson & Sabol, 2012; James, 2004). Among women, approximately 29% of jail inmates and 25% of state prisoners have been charged with drug offenses, compared with 24% and 17%, respectively, for men in jail and state prisons (Carson & Sabol, 2012; James, 2004). In addition to the high prevalence of drug-related charges, many women in the justice system also use drugs. Data from the Arrestee Drug Abuse Monitoring Program (ADAM) found that among female arrestees, 63% tested positive for use of at least one of the following substances: cocaine, marijuana, opiates, methamphetamine or PCP (National Institute of Justice, 2003). Additionally, in a recent study of female jail detainees from across the U.S., 83% experienced a substance use disorder at some point in their lives and 53% met the diagnostic criteria for a substance use disorder in the past year (Lynch, DeHart, Belknap, & Green, 2012). In comparison, only 5.9% of females over age 12 in the general population report any substance use or dependence in the past year (5.0% for alcohol and 2.0% for other drugs; Substance Abuse and Mental Health Services Administration, 2010).
Research across numerous populations has found a consistent association between women’s substance use and psychological distress (e.g., depression, anxiety, PTSD; Chilcoat & Breslau, 1998a; Chilcoat & Breslau, 1998b; Conway, Compton, Stinson, & Grant, 2006; Cottler, Compton, Mager, Spitznagel, & Janca, 1992; Dansky et al., 1995; DeHart, Lynch, Belknap, Dass-Brailsford, & Green, 2013; Hien et al., 2010; Jané-Llopis & Matytsina, 2006; Kessler, Chiu, Demler, Merikangas, & Walters, 2005; Schiff, El-Bassel, Engstrom, & Gilbert, 2002; Watkins et al., 2004). Seminal epidemiological research documents higher prevalence of psychological distress among justice-involved women than among women in the general population (Jordan, Schlenger, Fairbank, & Caddell, 1996; Teplin, Abram, & McClelland, 1996). For example, between 13% and 17% of women in jail and prison, respectively, are found to have major depressive disorder (Jordan et al., 1996; Teplin et al., 1996). These rates are two to three times higher than rates among women in the general population. High rates of PTSD are also found among female jail detainees; 22.3% of the women in one study met the lifetime diagnostic criteria for PTSD (Teplin et al., 1996). In a more recent national survey, it was found that 43% of all female jail inmates had a serious mental illness such as depression (28%), bipolar disorder (15%), or schizophrenia (4%), whereas 53% had a lifetime diagnosis of PTSD (Lynch et al., 2012). Incarcerated women with mental health problems are three times more likely to experience addiction than those without mental health problems (James & Glaze, 2006). Other data indicate that rates of co-occurring substance use and psychological distress (e.g., PTSD, any serious mental illness) range from 39% to 46% among female jail detainees (Lynch et al., 2012). The combination of substance use and psychological distress places justice-involved women at higher risk for other negative life events: unemployment, homelessness, increased criminal justice involvement, and victimization (Baillargeon et al., 2010; Engstrom, El-Bassel, Go, & Gilbert, 2008; James & Glaze, 2006; Marquart, Brewer, Simon, & Morse, 2001).
Victimization across the life span is highly prevalent among women involved in the justice system. Although estimates vary depending on the sample, measures, and method of data collection, data indicate that between 60% and 99% of women in the criminal justice system have experienced some form of physical, sexual and/or psychological victimization in their lives (McDaniels-Wilson & Belknap, 2008; Reichert, Adams, & Bostwick, 2010). Lawbreaking and incarceration have been linked empirically and theoretically to women’s experiences of victimization (Bloom, Owen, & Covington, 2003; Browne, Miller, & Maguin, 1999; McDaniels-Wilson & Belknap, 2008; Reichert et al., 2010; Salisbury & Voorhis, 2009; Tripodi & Pettus-Davis, 2013; Widom, 1995; Widom & Ames, 1994). For example, childhood abuse and neglect increase the likelihood of arrest as a juvenile by 59%, as an adult by 28%, and for a violent crime by 30%, regardless of gender (Widom & Ames, 1994). For females in particular, those who were abused or neglected in childhood are 73% more likely than a comparison group of women to be arrested for property, alcohol, drug, violent and misdemeanor charges such as disorderly conduct, curfew violations or loitering (Widom & Ames, 1994).
An increasing body of research across diverse populations of women suggests that the association between victimization and subsequent criminal justice involvement is influenced by substance use and psychological distress (Daly, 1992–1993; DeHart et al., 2013; Salisbury & Voorhis, 2009). However, research has yet to fully examine the complex relationships between various types of victimization (e.g., childhood abuse, adult intimate partner violence (IPV), and adult nonintimate partner violence [NIPV]), psychological distress, and substance use among justice-involved women specifically. In fact, most of the research to date on justice-involved women has been descriptive in nature, highlighting prevalence rather than illuminating the relationships among these factors (for notable exceptions, see Salisbury & Voorhis, 2009; Tripodi & Pettus-Davis, 2013). Given the ways in which substance use contributes to the risk of criminal justice involvement, especially in the context of the War on Drugs, it is particularly important to understand correlates of drug use in order to design effective services that can improve well-being and reduce drug use and its multifaceted consequences for women.
In order to address this gap, the present study sought to increase understanding of the associations between different types of victimization, psychological distress, lawbreaking activity and recent drug use among women in the criminal justice system. This study was guided by an adaptation of the Comprehensive Health Seeking and Coping Paradigm (CHSCP; Nyamathi, 1989); research guided by the CHSCP has made important contributions to understanding the mechanisms associated with behaviors such as substance use and HIV risk behaviors among adults experiencing poverty and other risks (Nyamathi, Flaskerud, & Leake, 1997; Nyamathi, Keenan, & Bayley, 1998; Nyamathi et al., 1999; Nyamathi, Leake, Keenan, & Gelberg, 2000; Nyamathi et al., 2012; Nyamathi, Stein, & Bayley, 2000; Nyamathi, Stein, & Swanson, 2000). Within this framework, women’s engagement in high-risk behavior, such as recent drug use, is conceptualized as a function of a multidimensional health-seeking process that is characterized as a transaction between the individual and her environment at multiple systemic levels (Nyamathi, 1989). In the present study, sociodemographic variables and indicators of personal resources (a theoretical construct that includes measures of psychological distress, self-esteem and impulsivity), victimization, and dynamic drug and crime factors are identified as potential domains that influence women’s engagement in recent drug use. The CHSCP was used to guide the selection of relevant behavioral domains and their operationalization (e.g., personal resources) and adapted to include domains that address the unique circumstances of justice-involved women (e.g., victimization, dynamic drug and crime factors).
Although theoretically guided, this research is exploratory and not driven by a priori hypotheses about relationships among domains; rather, this research seeks to provide the necessary evidence upon which to build well-defined, population-specific models of behavior. Thus, the present study addressed the following research question: what factors (i.e., sociodemographic characteristics, personal resources, type of victimization, and dynamic drug and crime factors) are associated with recent drug use among victimized women on probation and parole? In a population where drug use and victimization are highly prevalent, findings from the present study will allow practitioners and policymakers to more easily identify and assist women at higher risk for continued substance use, and potentially, further criminal justice involvement.
Method Participants and Procedures
The sample included 406 women on probation and/or parole in Jefferson County, Kentucky. Jefferson County is a large, urban area that includes Louisville. Recruitment methods included face-to-face recruitment at all probation and parole offices located within the county; direct mailings to women on probation and parole in Jefferson County; advertisements in the local newspaper; the website Craigslist; public access TV; fliers posted in a variety of public locations (e.g., bus stops, convenience stores, apartment complexes); community-based organizations; government agencies; health care facilities; and community outreach by study personnel.
To be eligible for participation, women had to meet the following criteria: a) be on probation and parole in Jefferson County; b) be at least 18 years of age; c) report that when they had sex they either had sex with men only or with both women and men (women who had been recently incarcerated were asked about the year before incarceration); and d) report lifetime experience of physical and/or sexual victimization as a child and/or an adult from a parent/caretaker, intimate partner, and/or nonintimate partner (e.g., stranger, acquaintance). Screening for eligibility was conducted by telephone (90%) and in person (10%). Eighty-one percent of the women screened were eligible to participate. Women who were screened reported learning about the study from the following sources (participants could identify more than one source): direct mail (33%); word of mouth (e.g., a probation officer, mother, friend; 33%); fliers posted in public locations (15%); community-based organization (11%); direct contact with study personnel (9%); and newspaper/radio/Internet (2%). The most common reasons for ineligibility were not being on probation or parole, no history of victimization, and reporting only female sexual partners.
Before the interview, the women were consented using the University of Louisville Institutional Review Board approved consent form; an National Institutes of Health (NIH) Certificate of Confidentiality was obtained and documented in the consent form. All interviews were administered face-to-face by trained female staff using audio computer-assisted interviewing (ACASI; Nova Research Company, 2003) on laptop computers; on average, interviews lasted 2 and a half to 3 hrs. Participants used headphones to listen as the survey items were read to them by the computer (questions and response options were also simultaneously displayed on the computer screen); participants entered their own responses directly into the computer. Questionnaires were password-protected and response data were encrypted so that unauthorized users were unable to view, export or modify collected data without the correct password. Participants were debriefed and compensated $35 for their time.
Measures Independent Variables
Sociodemographic characteristics
Respondents’ age was provided in years. Two categories were used to describe the race/ethnicity of the participants: African American, multiracial, or other racial/ethnic background and White. Intimate partner status was assessed by three categories indicating whether a respondent reported being: single; married or cohabiting with a male sexual partner; or divorced, separated or widowed at the time of the interview. Three categories described educational attainment: less than a high school diploma/GED; high school diploma/GED; greater than a high school diploma/GED. Current employment status was dichotomous, working (1) or not working (0), and women were asked if they considered themselves homeless (yes = 1; no = 0).
Type of victimization
Victimization was operationalized by eight dichotomous variables (yes = 1; no = 0) reflecting whether a woman reported experiencing any of the following types of victimization: childhood psychological abuse, childhood physical abuse, childhood sexual abuse, psychological IPV, physical IPV, sexual IPV, physical NIPV, and sexual NIPV. This operationalization is consistent with a growing body of research that examines different types of childhood and adult victimization simultaneously (Alvarez et al., 2009; McGuigan & Middlemiss, 2005).
Variables comprised items adapted from the National Crime Victimization Survey, the Revised Conflict Tactics Scale and Tolman’s Psychological Maltreatment of Women Inventory (Straus, Hamby, Boney-McCoy, & Sugarman, 1996; Tjaden & Thoennes, 1998, 2000; Tolman, 1999; Tolman, 1989; a complete list of items is available from the first author) and have been used in prior research (Golder & Logan, 2010, 2011; Golder & Logan, 2014; Logan & Leukefeld, 2000; Logan, Walker, & Leukefeld, 2001). Childhood victimization assessed abuse by a parent and/or other caretaker when a woman was 18 or younger; IPV referred to perpetration of violence by individuals “like a boyfriend and/or husband” whereas NIPV was defined as victimization perpetrated by a stranger, acquaintance or relative (other than guardians/parents or spouses).
Psychological abuse in childhood and in the context of IPV captured a range of potentially psychologically abusive experiences (e.g., “insulted, shamed or humiliated you in front of others”; “withhold food from you as a punishment”). Physical childhood abuse assessed whether respondents had ever been physically hurt on purpose, beat up, or attacked with a weapon; physical IPV and NIPV assessed whether the corresponding person had ever: physically hurt her on purpose; caused her to have an accident; beat her up; used a knife, gun or some other thing (like a club or bat) to get something [from her]; and/or attacked her with a weapon. Childhood, IPV and NIPV sexual victimization were assessed by questions asking respondents if they had ever been forced or threatened to: do “sexual things other than sexual intercourse (e.g., petting, oral sex)”; “have sexual intercourse but it did not actually occur”; and/or “have sexual intercourse and it actually happened.”
Personal resources
Three areas were evaluated: psychological distress, self-esteem and impulsivity. Psychological distress was operationalized by two variables (general psychological distress; PTSD). General psychological distress was measured by the Global Severity Index (GSI) of the Brief Symptom Inventory (BSI; Derogatis, 1993), which yields a summary score that combines the nine symptom domains of the BSI. The individual is asked to describe her degree of distress for each psychological symptom during the past seven days; higher scores indicate higher levels of psychological distress/symptoms (range 0–4). Raw scores above .62 on the GSI indicate levels of reported psychological distress that exceed those of 84% of the national population of women and are considered to surpass the clinical threshold for presence of a mental health problem (Derogatis, 1993; Golder & Logan, 2010; Potter & Jenson, 2003). Alpha reliability for the present scale was .97.
PTSD was measured by the 49-item Posttraumatic Stress Diagnostic Scale (PDS; Foa, 1995; Foa, Cashman, Jaycox, & Perry, 1997). A single variable assessed whether or not (yes = 1; no = 0) the woman currently met the DSM–IV diagnostic criteria for PTSD. The PDS is found to be reliable, valid and have good diagnostic performance (Foa, 1995; Griffin, Uhlmansiek, Resick, & Mechanic, 2004; Powers, Gillihan, Rosenfield, Jerud, & Foa, 2012).
Self-esteem was measured by the Rosenberg Self-esteem Scale (Rosenberg, 1965). The scale has a possible range of 0 to 30, with higher scores reflecting lower self-esteem (alpha reliability in the present study = .87.). Lastly, impulsivity was measured by the Barratt Impulsiveness Scale-11, a widely used and well-validated personality measure (Patton, Stanford, & Barratt, 1995). The measure consists of 30 statements; representative items include “I don’t pay attention” and “I do things without thinking.” The response options range from 1 = “Rarely/Never” to 4 = “Almost Always/Always”; higher scores reflect higher levels of impulsivity (possible range = 30 to 120; alpha in the present study = .87).
Dynamic drug and crime involvement factors
Eight variables operationalized this construct. Previous studies have demonstrated that early substance use—even when such use does not meet criteria for a substance use disorder—is associated with an increased risk of adult illegal activity or criminality (Stenbacka & Stattin, 2007). In the current study, early drug use was operationalized as a dichotomous variable indicating whether or not a woman used any of 10 substances (i.e., marijuana, cocaine, crack, heroin, opiates other than heroin, hallucinogens, sedatives/tranquilizers/barbiturates, methamphetamine, club drugs and prescription drugs) before the age of 13 (yes = 1; no = 0); prior research has established the age of 13 as an appropriate cutpoint for early initiation of substance use (Grant & Dawson, 1998). Participants were asked whether they had ever been in alcohol or drug treatment (yes = 1; no = 0) and the total number of lifetime treatment episodes.
Correctional status was assessed by asking women to indicate whether they were on probation, parole or both. Questions adapted from the Addiction Severity Index (McLellan et al., 1992) assessed the number of days a woman reported being in a controlled environment and/or halfway house/recovery home in the past 12 months. Women reported the total number of days they had been incarcerated in the past 5 years.
Finally, two variables measured the number of lawbreaking activities a woman reported over her lifetime and over the past 12 months (possible range 0 to 8). Women were asked about their engagement in eight separate lawbreaking behaviors (e.g., “Purposely damaged or destroyed something that did not belong to you?”; “Knowingly received, bought or sold stolen goods”). Questions involving drug-involved lawbreaking were omitted to avoid overlap with the dependent variable.
Dependent Variable
Recent drug use
Because even a single episode of drug use can have significant implications for women on probation and parole, a dichotomous variable (yes = 1; no = 0) was used to assess whether a respondent reported use of any of the 10 substances identified above during the past 12 months.
Data Analysis
To address the primary research question, bivariate and multivariate analyses were conducted. Bivariate analyses were used to gain a better understanding of which independent variables best represented the theoretically specified domains under consideration. First, differences between women who reported recent drug use and those who did not report recent use were compared across the four domains of the CHSCP (sociodemographic characteristics, personal resources, lifetime victimization, dynamic crime and drug factors). Variables that evidenced a significant relationship with the dependent variable in this step were retained for inclusion in the multivariate analysis. In addition, bivariate correlations are presented for these variables, providing a measure of effect size (i.e., correlation coefficient) and a means for assessing for high multicollinearity. Finally, multivariate analysis using logistic regression was used to identify the best fitting model; tolerance statistics are presented for each variable in the model to allow for examination of multicollinearity. The overall analysis strategy is consistent with a theory-guided, model-building approach and the research question being addressed (Tabachnick & Fidell, 2001).
Results Descriptive Findings
Means or percentages, standard deviations, and range for all variables are reported in Table 1. Briefly, the women were on average 37 years of age, slightly more than half were White (50.5%), and 44.6% reported being single, not living with a male partner. Educational attainment varied; slightly more than 27% reported less than a GED or high school diploma whereas 32% reported more than a GED or high school diploma. Twenty-nine percent of the women reported working part- or full-time, and 34% considered themselves homeless. Select results for Personal Resources showed that 68.7% of the women in this sample had a level of general psychological distress that was greater than the cutoff for clinical significance and 48.5% of the women met the diagnostic criteria for PTSD.
Sample Characteristics and Between-Group Differences for Women Who Do and Do Not Report Recent Drug Use (Past 12 Months)
Approximately 70% of the women reported experiencing some form of physical and/or childhood sexual victimization, slightly more than 90% reported ever experiencing any physical and/or sexual IPV, and 72% reported physical and/or sexual NIPV sometime in their lives (data not reported in table). Rates of childhood victimization ranged from 38.7% (sexual) to 75.1% (psychological), whereas the prevalence of different types of IPV in the past 12 months ranged from 9.1% (sexual) to 49.0% (psychological). Slightly more than 7% of women reported sexual NIPV and 10.8% reported physical NIPV in the past 12 months. Approximately 42% of the women had engaged in drug use before the age of 13. Two-thirds of the participants had been in drug treatment sometime in their lives. The majority of women were on probation (75.6%); they had spent approximately 47 days in a controlled environment in the past 12 months and 286 days incarcerated in the past 5 years. On average, the women had engaged in almost four (3.78) different lawbreaking behaviors over their lives and about two (1.84) lawbreaking behaviors in the past year.
Approximately 46% of the women reported recent drug use. Regarding the types of substances that were used in the past 12 months, marijuana was the most common (27.9%) followed by opiates other than heroin (19.8%), cocaine (18.0%), sedatives/tranquilizers/barbiturates (17.0%), crack (14.8%), prescription drugs (13.1%), heroin (6.2%), club drugs (2.7%) and hallucinogens (.7%; data not reported in table).
Bivariate Analysis
Significant associations were found between 14 of the independent variables and the dependent variable. Women who used drugs were more likely to be younger than women who did not use drugs in the past 12 months and to report higher levels of general psychological distress (1.29 compared with 1.08) and PTSD (54.5% compared with 43.4%). With the exception of childhood psychological and physical victimization, there were statistically significant differences on all the measures of victimization between women who reported recent drug use and those who did not report recent drug use. Generally, victimization was associated with drug use; however, women who reported experiencing childhood sexual victimization were less likely to report recent drug use (33.2% compared with 43.4%). Regarding the dynamic drug and crime factors, women who used drugs recently were more likely to report initiating drug use at an early age (52.9% compared with 32.4%) and to have been in drug treatment in the past 12 months (46.0% compared with 35.2%). However, there were no significant differences in the number of times each group of women reported being in drug treatment. Finally, women who used drugs recently were more likely to be on probation (88.2%), to have spent fewer days incarcerated in the past 5 years (an average of 237.10 days in the past 5 years as compared with 327.94 days), and to have engaged in more lawbreaking behaviors across the two measured periods (4.18 and 2.42 ever and in the past 12 months, respectively). The correlation matrix is found in Table 2; there is no evidence of high multicollinearity among the variables (Allison, 2012).
Bivariate Correlation Matrix
Multivariate Analysis
The test of the final model, against a constant-only model was statistically reliable, chi-square (14, N = 399) = 102.001, p ≤ .01, indicating that the predictors, as a set, reliably distinguished between women who used drugs in the past 12 months and those who did not (−2LL = 446.237). The final model accounted for 30% (Nagelkerke R2: .303) of the variance in drug use over the past 12 months (Tabachnick & Fidell, 2001). The overall prediction success for this model was 71.8%, which was greater than the proportional by chance accuracy rate of 62.75%, thus supporting the utility of the model. More specifically, 77.5% of the women who reported not using drugs in the past 12 months and 65.2% of those who did use drugs were correctly predicted by the model.
Table 3 shows the regression coefficients, standard errors (SE), odds ratios (OR), 95% confidence intervals (CI), and tolerance for the final model. Five variables in the model reached the conventional level of significance: age, childhood sexual victimization, early drug use, correctional status, and lawbreaking (past 12 months). There was an inverse relationship (OR = .96; 95% CIs [.94, .99] between age and the dependent variable: older age was associated with slightly reduced odds of drug use in the past year. Similarly, there was an inverse relationship between childhood sexual victimization and recent drug use such that women who were sexually victimized as children were considerably less likely to report recent drug use than women who had not experienced this type of victimization (OR = .56; 95% CI = [.34, .91] Early drug use (OR = 2.63; 95% CI [1.60, 4.32] and engaging in more lawbreaking behaviors during the past 12 months (OR = 1.75; 95% CI [1.32, 2.31] were associated with recent drug use. Finally, correctional status was inversely related to the dependent variable (OR = .26; 95% CI [.13, .50] the odds of women on parole reporting drug use in the past 12 months were less than those of women on probation. Confirming the interpretation of the bivariate correlations, tolerance values were all well above .10; larger values (i.e., those closer to 1.0) are indicative of fewer problems with multicollinearity (Denis, 2011; Regression with SPSS Chapter 2: Regression Diagnostics).
Logistic Regression Model Predicting Recent Substance Use (Past 12 Months)
DiscussionDrug use plays a major role in women’s involvement in the criminal justice system. Identifying and understanding factors associated with this high-risk behavior is essential in order to address it. This is the first known study to elucidate the associations between types of victimization, psychological distress and recent drug use among women on probation and parole. When controlling for confounding factors, the multivariate findings indicate that women who recently used drugs were younger, were not sexually victimized as children, began using drugs before they were 13 years of age, were on probation and had engaged in more recent lawbreaking. In terms of the CHSCP framework informing the study, these findings suggest that domains related to sociodemographic characteristics, victimization, drug use, illegal activity and correctional system status are salient correlates in recent drug use among women on probation and parole involved in this study. In order to further consider the relationships identified in the multivariate analysis, each of the significant predictors is considered in turn.
The association between age and substance use in the general population is well established. Research has identified an age-related pattern of drug and alcohol use that begins in adolescence, peaks in young adulthood, and then declines (Bachman, Wadsworth, O’Malley, Johnston, & Schulenberg, 1997; Chassin, Fora, & King, 2004; Chen & Kandel, 1995; van Lier, Vitaro, Barker, Koot, & Tremblay, 2009; Roettger, Swisher, Kuhl, & Chavez, 2011; Tobler & Komro, 2010). This pattern is evidenced in the most recent National Survey on Drug Use and Health: rates of substance dependence or abuse were higher among younger adults (age 18 to 25; 18.6%) than among youth (age 12 to 17; 6.9%) or adults 26 and older (age 26 and older; 6.3%; Substance Abuse and Mental Health Services Administration, 2012). Notwithstanding the general relationship between age and substance use, methodological advances have highlighted the heterogeneity found among people who use drugs and have contributed to a more nuanced understanding of different patterns of substance use across the life course (Guo et al., 2002; Hser, Anglin, Grella, Longshore, & Prendergast, 1997; Hser, Huang, Brecht, Li, & Evans, 2008; Kertesz et al., 2012). Research focused on adults with substance use histories suggests that the relationship between age and drug use behaviors is complex and does not necessarily decline with age (for discussion see Engstrom, Shibusawa, El-Bassel, & Gilbert, 2011). In fact, research with 60 incarcerated women in Kentucky found similar rates of recent substance use among older and younger women (Stanton, Walker, & Leukefeld, 2003).
Relatedly, there is a growing body of research that has identified a variety of distinct subgroups and trajectories among people who use drugs, with certain attention given to age at initiation (Chassin et al., 2004). In particular, early onset of drug use (as well as illegal activity) is associated with more severe and persistent drug use patterns (Hser et al., 2008; Hser, Longshore, & Anglin, 2007). Consistent with these findings, the present study found that odds of recent drug use were 2.45 times as large for women who used drugs before the age of 13 than for women who did not experience early drug use. The aforementioned results, together with research indicating that people with younger age of drug initiation and more involvement in lawbreaking may be less responsive to treatment (Grella & Lovinger, 2011), suggest that victimized women on probation and parole who initiated early drug use may be at particularly high risk for ongoing substance use and may require innovative outreach and intervention strategies. One approach that may hold promise for adaptation is the Comprehensive Gang Model. This model, which has been tested and refined in more than 20 communities in the U.S. since the 1990s, employs a comprehensive set of strategies that address both structural/community-level issues and individual-level approaches to address a wide range of needs (i.e., psychological distress, educational attainment) among gang-involved youth (Office of Juvenile Justice and Delinquency Prevention, 2010; Spergel, 1995). The specific strategies include: community mobilization involving citizens, former gang members (in the case of adaptation, these strategies would focus on justice-involved women rather than gang members), community groups and agencies in the coordination of community-based programming; opportunity for justice-involved youth to engage in education, training and employment programs; social intervention focused on linking justice-involved youth and their families to needed services; use of close supervision and monitoring by the criminal justice system, as well as community-based organization; and development and implementation of policies and procedures that capitalize on available and existing resources. Unfortunately, evidence-supported prevention programs such as the Comprehensive Gang Model have not been widely adopted in the U.S.
The relationship between child sexual abuse and later substance use among women is well documented (Gutierres & Puymbroeck, 2006; Ireland & Widom, 1994; Widom, Marmorstein, & White, 2006). As such, it is not unexpected that rates of child sexual victimization and recent drug use among this sample appear high. For example, the prevalence of child sexual abuse among a national sample of older adolescents indicates that among females 17 and younger, 26.6% and 5.5% report an incident of sexual abuse/assault by any type of perpetrator or family member, respectively (Finkelhor, Shattuck, Turner, & Hamby, 2014). In comparison, 38.7% of the women in the present sample reported experiencing childhood sexual victimization perpetrated by a parent and/or caretaker. Similarly, among all women 12 and over, only 12.5% report any past year use of illicit substances compared with 46% of the women in this study (Substance Abuse & Mental Health Services, 2009, 2010). Although the current study found high prevalence of childhood sexual victimization and recent drug use among participants, it also found an inverse relationship between childhood sexual victimization and recent drug use. Among women in this study, experiencing childhood sexual abuse was associated with a 44% reduction in odds of recent drug use. Thus, the relationship between these factors among this sample of highly victimized women appears complicated and may reflect the particular roles of current victimization, psychological distress and other factors in recent drug use. Future research may examine differences between the women who have been victimized sexually as children and those who have not in order to better understand these relationships. More specifically, research should determine if there are groups of women who have distinct profiles based on indicators of lifetime violence and how those profiles are associated with drug use and other indicators of psychosocial functioning.
Finally, two dynamic drug and crime factors were also significant predictors of recent drug use: correctional status and engaging in more recent lawbreaking behavior. The odds were greater that probationers, as compared with parolees, would report recent drug use (OR = .26; 95% CI [.14, .46] It may be that this finding indicates the presence of different trajectories of drug use (and/or lawbreaking behaviors) captured by the categorization/assignment of women to probation and parole. In fact, probation and parole represent different points on the criminal justice continuum (Center for Substance Abuse Treatment, 2005). As such, entry into, conditions of, and consequences for failure to comply with conditions may differ by type of supervision, thus influencing women’s engagement in drug-using behavior. For example, a common condition of both probation and parole is the prohibition of drug use. For parolees, who have experienced a period of incarceration, the risk of revocation and subsequent reincarceration that is associated with drug use may act as an inhibiting factor. In contrast, women on probation may or may not have experienced a sustained period of incarceration. Thus the possibility of incarceration may be less threatening and may exert less influence on their decision to use drugs. Future longitudinal research will provide the opportunity to examine this supposition and provide data on associations between correctional status/involvement and long-term patterns of substance use among this population.
Similarly, the association between recent lawbreaking and recent drug use appears to reflect the complicated, multidimensional relationship between lawbreaking and drug use. Specifically, the relationship between drug use and lawbreaking has been characterized as reciprocal in nature, such that there is a “multiplier” effect through which more engagement in one is associated with more engagement in the other (Prendergast, Huang, & Hser, 2008). Again, longitudinal research that provides an opportunity to track trajectories of both substance use and lawbreaking is needed to further clarify these complex relationships. Although there remains a need for future investigations, decades of research have consistently found that “substance abuse treatment offers the best strategy for interrupting the drug abuse/criminal justice cycle” (National Institute on Drug Abuse, 2012, p. 13), particularly in the context of current drug-related legislation and criminal penalties in the U.S. This large body of research, together with the findings from the current study, underscore the critical need to identify effective strategies to engage more justice-involved women in substance use treatment that is both trauma-informed and guided by principles of effective treatment for people involved in illegal activity and the criminal justice system (Marlowe, 2003; Miller & Najavits, 2012).
These findings should be considered in light of the limitations. Although it is estimated that the sample comprised approximately one-fifth of all women on probation and parole in Jefferson County at the time recruitment was initiated (Kentucky Department of Corrections, Division of Probation and Parole, personal communication, November 5, 2010), the generalizability is limited by nonrandom sampling. Only victimized women were included in this sample; thus, comparisons between nonvictimized and victimized women were not possible. However, given the prevalence of victimization among justice-involved women, the most relevant questions are not limited to differences between victimized and nonvictimized women. Rather, research that helps elucidate the heterogeneous nature of victimization (i.e., type of victimization as measured in the current study, nature of relationships involving violence, severity of violence and so on . . .) and the subsequent effects on behavior are also necessary to inform development of targeted interventions for this population. Notwithstanding, these results are not generalizable to all women on probation and parole. Women who reported only having sex with other women were excluded from participation. Intimate partner violence between same-gender female partners is an important and understudied issue. The dynamics of intimate partner violence between same-gender partners may be similar to and/or distinct from violence between opposite-gender partners; however, this empirical question/issue was outside the focus of the present study. Additionally, there was concern that inclusion of women who only had sex with other women would yield a subsample that was too small for meaningful analysis; examination of screening data indicated that only 4% of all women screened were excluded because they reported only same-gender sexual behavior. Further attention to this group of justice-involved women is an important priority for future research. Dichotomizing race/ethnicity does not allow for in-depth assessments of potential race- and ethnicity-related differences in recent drug use; future research would benefit from sampling and recruitment strategies that support adequate statistical power to conduct meaningful analysis in this area. Similarly, although the detection of any drug use is reason for violation and potential incarceration for individuals on probation and parole, measuring recent drug use without consideration of the quantity/frequency of use may not capture severity, or allow consideration of other indices of problematic use, including patterns and consequences of use. Further, the cross-sectional design limits the ability to determine temporal ordering of the variables; as such, inferences regarding directionality of associations with drug use should be viewed cautiously. This study relied on self-reports of sensitive information that may have yielded underreports of some behaviors; however, this risk was minimized with the use of ACASI technology, which enhances reporting of potentially sensitive information (Wolff & Shi, 2012).
Despite its limitations, this study makes important contributions to our understanding of recent drug use among women on probation and parole. The CHSCP offers a broad theoretical framework from which to examine complex relationships between sociodemographic characteristics, victimization, psychological distress, lawbreaking activity, justice system involvement, and drug use among this population. As such, it assists with informing multidimensional intervention strategies that may provide key avenues to reduced drug use among this population of women. Most notably, the findings of the present study highlight the potential value of services that can effectively address risks associated with younger age, early initiation of drug use, probation status, and ongoing involvement in lawbreaking activity among women on probation and parole with histories of victimization.
Footnotes 1 The 2000 ADAM report marks the last time data were available for female arrestees. In the case of female and male arrestees, the number of men and women testing positive for these substances at arrest were almost identical, with 64% of men testing positive.
2 A total of 7.6% of the respondents reported being Latina, Native American, Asian, multiracial or other racial/ethnic identity; the majority of these individuals (3.2%) identified as multiracial. Because the individual categories were too small for meaningful analysis, the groups were collapsed into a single group.
3 Prescription drug misuse was operationalized as ever using “prescription drugs that were not prescribed to you, in excess of what was prescribed for you, and/or for recreational purposes.”
4 The proportional by chance accuracy rate was computed by calculating the proportion of cases for each group (i.e., those cases that are correctly predicted and those that are incorrectly predicted) in the classification table at Step 0 and then squaring and summing the proportion of those cases for each group (0.4632 + 0.5372 = 0.502). Based on these calculations, the proportional by chance accuracy criteria is 62.75% (1.25 × 50.2% = 62.75%; Bayaga, 2010).
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Submitted: August 20, 2013 Revised: August 7, 2014 Accepted: August 19, 2014
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Source: Psychology of Addictive Behaviors. Vol. 28. (4), Dec, 2014 pp. 1105-1116)
Accession Number: 2014-56246-004
Digital Object Identifier: 10.1037/a0038351
Record: 42- Title:
- Correspondence between self-report and interview-based assessments of antisocial personality disorder.
- Authors:
- Guy, Laura S.. Department of Psychology, Simon Fraser University, Burnaby, BC, Canada, lguy@sfu.ca
Poythress, Norman G.. Florida Mental Health Institute, University of South Floridam, Tampa, FL, US
Douglas, Kevin S.. Department of Psychology, Simon Fraser University, Burnaby, BC, Canada
Skeem, Jennifer L.. Department of Psychology and Social Behavior, University of California, Irvine, CA, US
Edens, John F.. Department of Psychology, Texas A&M University, College Station, TX, US - Address:
- Guy, Laura S., Department of Psychology, Simon Fraser University, RCB 5246, 8888 University Drive, Burnaby, BC, Canada, V5A 1S6, lguy@sfu.ca
- Source:
- Psychological Assessment, Vol 20(1), Mar, 2008. pp. 47-54.
- NLM Title Abbreviation:
- Psychol Assess
- Page Count:
- 8
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- Psychological Assessment: A Journal of Consulting and Clinical Psychology
- ISSN:
- 1040-3590 (Print)
1939-134X (Electronic) - Language:
- English
- Keywords:
- antisocial personality disorder, Personality Diagnostic Questionnaire-4, Personality Assessment Inventory, Structured Diagnostic Clinical Interview-II, prisoners
- Abstract:
- Antisocial personality disorder (ASPD) is associated with suicide, violence, and risk-taking behavior and can slow response to first-line treatment for Axis I disorders. ASPD may be assessed infrequently because few efficient diagnostic tools are available. This study evaluated 2 promising self-report measures for assessing ASPD--the ASPD scale of the Personality Diagnostic Questionnaire-4 (PDQ-4; S. E. Hyler, 1994) and the Personality Assessment Inventory (PAI; L. Morey, 1991, 2007)--as well as the ASPD module of the Structured Clinical Interview for DSM-IV Axis II (SCID-II; M. B. First, R. L. Spitzer, M. Gibbon, J. B. W. Williams, & L. S. Benjamin, 1997). The measures were administered to 1,345 offenders in court-mandated residential substance abuse treatment programs and prisons. PDQ-4 and PAI scores related strongly to SCID-II symptom counts (rs = .67 and .51, respectively), indicating these measures convey useful clinical information about the severity of offenders' ASPD pathology. The dimensional association between the measures was relatively invariant across gender, race, and site, although differences in mean scores were observed. Levels of agreement of the SCID-II with the PDQ-4 (κ = .31) and PAI (κ = .32) in classifying participants as ASPD was limited. Alternative thresholds for both self-report measures were identified and cross-validated. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Antisocial Personality Disorder; *Personality Measures; *Prisoners; *Psychological Assessment; Self-Report; Structured Clinical Interview
- Medical Subject Headings (MeSH):
- Adult; Antisocial Personality Disorder; Female; Humans; Interview, Psychological; Male; Personality Assessment; Personality Inventory; Prisoners; Reproducibility of Results; Self-Assessment; Severity of Illness Index; Substance-Related Disorders; Surveys and Questionnaires
- PsycINFO Classification:
- Clinical Psychological Testing (2224)
Personality Disorders (3217) - Population:
- Human
Male
Female - Location:
- US
- Age Group:
- Adulthood (18 yrs & older)
Young Adulthood (18-29 yrs)
Thirties (30-39 yrs)
Middle Age (40-64 yrs) - Tests & Measures:
- Antisocial Personality Disorder Scale
Personality Assessment Inventory DOI: 10.1037/t03903-000
Structured Clinical Interview for DSM-IV Axis II Personality Disorders
Personality Diagnostic Questionnaire-4+ DOI: 10.1037/t07759-000 - Grant Sponsorship:
- Sponsor: National Institute of Mental Health
Grant Number: 1 RO1 MH63783-01A1
Date: 2002 - 2005
Other Details: Personality Features in Social Deviancy
Recipients: No recipient indicated
Sponsor: Michael Smith Foundation for Health Research, Career Scholar Funding Program
Recipients: Douglas, Kevin S. - Methodology:
- Empirical Study; Interview; Quantitative Study
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- Accepted: Nov 8, 2007; Revised: Nov 7, 2007; First Submitted: Mar 14, 2007
- Release Date:
- 20080303
- Correction Date:
- 20120827
- Copyright:
- American Psychological Association. 2008
- Digital Object Identifier:
- http://dx.doi.org/10.1037/1040-3590.20.1.47
- PMID:
- 18315398
- Accession Number:
- 2008-02315-005
- Number of Citations in Source:
- 37
- Persistent link to this record (Permalink):
- http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2008-02315-005&site=ehost-live
- Cut and Paste:
- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2008-02315-005&site=ehost-live">Correspondence between self-report and interview-based assessments of antisocial personality disorder.</A>
- Database:
- PsycINFO
Correspondence Between Self-Report and Interview-Based Assessments of Antisocial Personality Disorder
By: Laura S. Guy
Department of Psychology, Simon Fraser University;
Norman G. Poythress
Florida Mental Health Institute, University of South Florida
Kevin S. Douglas
Department of Psychology, Simon Fraser University;
Mid-Sweden University, Sundsvall, Sweden
Jennifer L. Skeem
Department of Psychology and Social Behavior, University of California, Irvine
John F. Edens
Department of Psychology, Texas A&M University
Acknowledgement: This research was supported by National Institute of Mental Health Grant 1 RO1 MH63783-01A1—Personality Features in Social Deviancy (2002–2005)—and support given to Kevin S. Douglas by the Career Scholar funding program of the Michael Smith Foundation for Health Research. We acknowledge and appreciate the assistance and cooperation of the following agencies in collecting data for this research—Drug Abuse Comprehensive Coordinating Office (DACCO), Tampa, FL; Florida Department of Corrections; Gateway Foundation, Huntsville, TX; Nevada Department of Prisons; Odyssey House, Salt Lake City, UT; Operation PAR, Pinellas Park, FL; Oregon Department of Corrections; Utah Department of Corrections; Volunteers of America, Portland, OR; Westcare, Harris Springs, NV—but none of the opinions or conclusions expressed in this article reflect any official policy or position of any of these institutions.
In the current diagnostic nomenclature, individuals who commit repeated criminal and/or other antisocial acts most often fall under the diagnostic label of antisocial personality disorder (ASPD). Establishing efficient and accurate procedures for diagnosing ASPD is important because the diagnosis is associated with several significant outcomes such as future involvement in antisocial behavior and engaging in risk-taking behaviors that have intentional (e.g., suicide attempts; Hills, Cox, McWilliams, & Sareen, 2005) and unintentional (e.g., motor vehicle crashes; see Nordstrom, Zwerling, Stromquist, Burmeister, & Merchant, 2001) consequences. A diagnosis of ASPD also has implications for intervention efforts, as personality disorders can complicate the first-line treatment of major mental disorders, thereby slowing rates of response (e.g., Kopta, Howard, Lowry, & Beutler, 1994; Shea, Widiger, & Klein, 1992). Moreover, reliable and valid diagnostic procedures are critical to informing decisions in the management of people with ASPD in both clinical (e.g., hospital release decisions) and correctional (e.g., sentencing, parole) settings.
Consistent with the lack of consensus regarding the construct validity of personality disorders (PDs; e.g., Clark, Livesley, & Morey, 1997), no single assessment tool or approach for diagnosing PDs is regarded as “universally accepted.” Both self-report questionnaires and clinical interviews present advantages and disadvantages. Structured interviews typically are resource intensive, often requiring interviewers with advanced clinical training to administer a lengthy interview to one individual at a time. The comparative efficiency of self-report measures, which may be relatively brief and, in some contexts, administered to groups of individuals by a technician (although subsequently interpreted by a trained clinician), has encouraged a number of investigators to develop measures of this type. On the other hand, shortcomings associated with self-report measures include respondents' idiosyncratic understanding of items, inability to clarify state versus trait issues, and inability to appraise the validity of the basis upon which respondents endorsed criteria (see Smith, Klein, & Benjamin, 2003). It would seem that best clinical practice would draw on the advantages of both assessment approaches such that a reliable and valid self-report tool could be used to augment diagnostic and symptom information gathered during an interview. However, following such an approach requires demonstrated concordance between measures. In addition, it is important to examine the concordance of self-report questionnaires with clinical interviews because, historically, they have tended to overdiagnose PDs (Guthrie & Mobley, 1994).
The present study investigated the convergence of two subscales from self-report measures with differing conceptual and empirical bases of development—the ASPD scale of the Personality Diagnostic Questionnaire-4 (PDQ-4; Hyler, 1994) and the Antisocial Features (ANT) scale of the Personality Assessment Inventory (PAI; Morey, 1991, 1996)—with the ASPD module of one of the most well known and often-used semistructured interviews: the Structured Clinical Interview for DSM–IV Axis II (SCID-II; First et al., 1997). The PDQ-4 is a forced choice questionnaire whose items closely track the DSM–IV diagnostic criteria. The PDQ-4 has been used in studies of psychopathology in clinical samples (e.g., borderline personality; see van den Bosch, Verheul, Schippers, & van den Brink, 2002) and in studies of the relation between PD and measures of general personality constructs in nonreferred samples (e.g., Fossati et al., 2004). Several studies have focused on the ASPD scale of the PDQ-4 with offenders and individuals with other problem behaviors, including forensic hospital inpatients (e.g., Whyte, Fox, & Coxell, 2006) and prisoners (e.g., Tye & Mullen, 2006). Of the studies cited that investigated justice-involved samples, only Blackburn, Donnelly, Logan, and Renwick (2004) reported the internal consistency of the ASPD scale (α = .88). Chance-corrected categorical agreement between the PDQ-4 ASPD scale and criterion diagnoses based on semistructured clinical interview and records review has been fair to poor in the few studies published thus far (e.g., Davison, Leese, & Taylor, 2001).
The PAI is a personality inventory that was designed to assess critical clinical variables. It has been used in studies covering a wide array of populations (see Morey, 1991, 2007). The ANT scale was developed to measure symptoms of antisocial personality disorder and psychopathy. ANT consists of three conceptually distinct subscales whose item content taps disparate facets of antisociality: ANT-A (Antisocial Behaviors), which assesses a history of conduct problems and criminality; ANT-S (Stimulus Seeking), which reflects a tendency toward thrill-seeking and low boredom tolerance; and ANT-E (Egocentricity), which taps a self-centered, callous, remorseless interpersonal style. Compared with that for the PDQ-4, the body of research that supports the validity of the PAI for use in forensic and correctional settings is more developed (for reviews, see Edens & Ruiz, 2005; Morey, 1991, 2007). Some research has been completed on the diagnostic concordance between clinical interviews and some PAI scales, but little research to date has investigated the concordance of the ANT scale with interview-based measures of ASPD.
Although several clinical inventories are available for assessing ASPD, and commentators have noted that “there is little empirical justification for preferring one interview for ASPD over others” (Lilienfeld, Purcell, & Jones-Alexander, 1997, p. 66), the SCID-II was used in the present study as the criterion because it is widely used and is among the most well researched semistructured interview tools for use by clinicians. In a review of the convergence between structured interviews and questionnaires assessing PDs, median kappas across PD categories for measures from 19 studies indicated that the SCID-II evidenced slightly higher rates of convergence with self-report questionnaires relative to other interview guides (Clark et al., 1997).
The present study fills an important gap in research on the assessment of ASPD with offenders. Using two large samples of offenders drawn from prisons and residential substance abuse treatment programs, we report on the psychometric properties of the PDQ-4 ASPD and PAI ANT scales and their diagnostic efficiency against the SCID-II as a criterion measure. Building a solid body of empirical knowledge about the degree of concordance between self-report measures and clinical interviews is important because it may inform decision making regarding effective allocation of typically scarce human and financial resources. Another important aim of the present project was to evaluate whether there are differences in reliability and diagnostic efficiency across race and gender, issues which to our knowledge have not been explored using the PAI with offenders. Although no systematic investigation of these issues has occurred with the PDQ-4 either, researchers (Cale & Lilienfeld, 2002) have reported a large gender difference on scores on the PDQ-4+ ASPD scale (Cohen's d = 0.86). To ensure the fair use and unbiased application of psychological measures, one must evaluate whether they operate differentially across groups of people, especially given that minorities are overrepresented in the criminal justice system (Federal Bureau of Prisons, 2007) and comparatively less research has been conducted with female offenders (Blanchette & Brown, 2006).
Method Participants
Participants were 1,345 offenders either incarcerated in prisons (n = 678) or court-ordered to participate in substance-related residential treatment (n = 667). They were recruited from state prisons and residential drug treatment sites in Florida, Nevada, Oregon, Texas, and Utah. Eligibility criteria for study inclusion were: (1) Black or White, (2) English speaking, (3) estimated screening IQ of at least 70, and (4) not receiving psychotropic medication for active symptoms of psychosis. The primary target age range was 21–40 years, although 109 participants over 40 years were recruited and retained in analyses. The mean age was 31.40 years (SD = 6.69). Most participants were men (83%) and White (66%; 34% Black; 10% additionally identified themselves as of Hispanic ethnicity).
Measures
Structured Clinical Interview for DSM–IV Axis II (SCID-II) Antisocial Personality Disorder (ASPD) Scale (First et al., 1997)
The ASPD module assesses the 22 possible symptom indicators of DSM–IV criteria for ASPD. It yields both dimensional and categorical scores. SCID-II scores in the present study were based on information obtained during the interview and from a detailed review of participants' institutional files. SCID-II assigns a diagnosis of ASPD if at least two items from the conduct disorder criteria and at least three items from the adult criteria are endorsed. High levels of interrater reliability (e.g., Maffei et al., 1997) and high concurrent validity for consensus diagnoses of ASPD (e.g., Skodol, Rosnick, Kellman, Oldham, & Hyler, 1988) have been demonstrated for the ASPD module.
For the present sample, interrater reliability of the SCID-II was determined through observation of SCID-II interviews of study participants by research assistants (RAs), who were advanced-level clinical psychology graduate students, in addition to the requisite file reviews. All such observations were done by one of the current authors (Kevin S. Douglas), whose SCID-II scores were treated as the “criterion” against which RA scores were measured. Kevin S. Douglas traveled to each site approximately every 6 months and observed two cases per visit, for a maximum of six visits. Given some minor variations in this general procedure, there were a total of 51 live interrater reliability cases (3.8% of the sample). Concordance was excellent for ASPD diagnoses (κ = .74; n = 50). Interrater reliability for total symptom count also was high: ICC1, which is a measure of agreement that is acceptable for noncategorical data (Bartko & Carpenter, 1976; Cicchetti & Sparrow, 1981), was .86 (n = 46). For the 22 items comprising the SCID-II ASPD module for the present sample, internal consistency (α) was .83 and the mean interitem correlation (MIC) was .18.
Personality Diagnostic Questionnaire-4 (PDQ-4) ASPD Scale (Hyler, 1994)
The PDQ-4 ASPD scale consists of 22 true–false self-report items (one for each DSM–IV ASPD criterion). To meet diagnostic threshold for ASPD on the PDQ-4, at least three items from the conduct disorder criteria and at least four items from the adult criteria need to be endorsed (personal communication, S. Hyler, November 9, 2006). Participants were classified into ASPD positive and negative groups based on these parameters, which were used in the analyses reported below. Internal consistency for the PDQ-4 was good (α = .85); MIC was .20.
Personality Assessment Inventory: Antisocial Features Scale (PAI ANT, Morey, 1991, 2007)
Noted above, ANT was designed to tap the core affective, interpersonal, and behavioral features that traditionally have been associated with psychopathy and antisocial personality. Internal consistency for ANT among offender samples typically has been good (e.g., α = .82; Edens & Ruiz, 2005). In terms of validity data, ANT typically correlates moderately to highly with other measures of antisocial and psychopathic traits. ANT has also been shown to predict various types of theoretically relevant forms of social deviance (e.g., institutional adjustment difficulties) among offenders (for recent reviews, see Edens and Ruiz, 2005; Morey, 2007). Although there is no specific diagnostic cutoff that is recommended in the PAI manual, scores greater than or equal to 70 T frequently have been examined in group-level analyses (Edens & Ruiz, 2005) and are indicative of individuals who should be “impulsive and hostile, perhaps with a history of reckless and/or antisocial acts” (Morey, 2007, p. 42). Accordingly, 70 T was used as the primary cutoff for classification into the ANT-defined ASPD group in the present study. For the 24 items comprising ANT, internal consistency (α) was .75 and MIC was .11 (based on a subset of approximately 700 participants who were available for these item-level analyses).
Procedure
Data were collected as part of a larger study in which RAs were trained in the administration of the study protocol prior to data collection. At each site, RAs randomly selected potential participants from lists of individuals who met inclusion criteria for the study. Enrollment interviews were conducted in a private room, and informed consent was obtained through procedures approved by a university institutional review board. Next, an IQ screening test was administered; participants whose estimated IQ was below 70 were excused from the study (n = 6). A reading ability test was administered to participants who did not meet certain educational history requirements and who had difficulty reading the first few items of the PAI. The larger study protocol included several additional measures (not described here), took on average 4.5 hr to complete, and typically was administered in two sessions. Also, a detailed review of each participant's institutional files was completed. At the end of protocol administration, $20 was deposited into the agency account of each participant, unless reimbursement was prohibited by the agency's policies (one site).
The participants included in analyses (N = 1,345) are those for whom there were complete data on the PDQ-4, SCID-II, PAI ANT, and PAI Inconsistency and Infrequency scales. These two PAI validity scales were used to evaluate participants' vulnerability to careless or random responding (data for the PDQ-4 validity indices were not available because only the ASPD scale was administered). Consistent with other research, participants were retained for analyses if they obtained Inconsistency and Infrequency scores below 80 T (see Edens & Ruiz, 2005). Individuals were also excluded if they did not complete or were not administered the PDQ-4, SCID-II, or PAI (n = 225). Of the remaining 1,516 individuals, 171 were screened out because data were missing for one or more PDQ-4 or SCID-II items or the PAI scores were invalid, resulting in the final sample size of 1,345 participants. There were no statistically significant differences in age or gender between the 171 excluded participants and those retained. However, significantly more of the excluded participants were recruited from prisons (n = 112) than from drug treatment facilities (n = 59; χ2 = 13.84, p < .001).
ResultsThe primary aim of the present study was to evaluate the psychometric properties of the PDQ-4 ASPD and PAI ANT scales and their diagnostic efficiency relative to the SCID-II ASPD module. We examined concordance at dimensional and categorical levels and calculated traditional indices of efficiency, including sensitivity, specificity, positive predictive power (PPP), negative predictive power (NPP), overall hit rate, and receiver operating characteristic analyses.
First, in terms of descriptive information and bivariate correlations between measures, mean total symptom counts for the 22 items on the PDQ-4 (M = 7.98, SD = 4.65) and SCID-II (M = 7.42, SD = 4.37) ASPD scales were significantly different, t(1344) = 5.57, p ≤ .01; Cohen's d = 0.12. The mean ANT T score was 70.93 (SD = 11.90). The zero-order correlations between the measures' total scores were large and all statistically significant (p < .01): SCID-II/PDQ-4 (.67); SCID-II/PAI (.51); and PDQ-4/PAI (.66). The SCID-II and PAI classified similar proportions of the sample with ASPD (SCID-II: n = 744, 55%; PAI: n = 721, 54%), whereas the PDQ-4 classified fewer participants into this category (n = 456, 34%). Rates of diagnostic agreement (kappa) between the measures were: SCID-II/PDQ-4 (.31); SCID-II/PAI (.32); and PDQ-4/PAI (.40). Generally, kappa values of .75 and greater are considered to reflect excellent agreement; .60–.74, good agreement; .40–.59, fair agreement; and .00–.39, poor agreement (Cicchetti & Sparrow, 1981).
Diagnostic efficiency statistics are presented in Table 1 (for the PDQ-4) and Table 2 (for the PAI). In these analyses, classifications of PDQ-4 ASPD and PAI ANT were compared with classifications of SCID-II ASPD, which was treated as the criterion. We used tests of the equality between proportions to gauge whether the classification indices (sensitivity, specificity) of the measures differed from one another. These tests revealed that whereas the PAI was significantly more sensitive than the PDQ-4 (χ2 = 18.71, p < .01), the PDQ-4 was significantly more specific than ANT (χ2 = 11.99, p < .01).
Diagnostic Agreement and Efficiency Between PDQ-4 and SCID-II ASPD Scales
Diagnostic Agreement and Efficiency Between PAI ANT and SCID-II ASPD Scales
PPP for the PDQ-4 (.79) was higher than for ANT (.70), and NPP for ANT (.62) was higher than for the PDQ-4 (.57). The hit rates, which are the proportion of correct decisions, for the PDQ-4 and ANT were comparable (.64 and .66, respectively). Area under the curve (AUC) values in the tables represent the likelihood that a randomly selected individual with SCID-II-defined ASPD will have a higher PDQ-4 score (i.e., symptom count) or ANT T score than that of a randomly selected individual without ASPD. The AUC for the PDQ-4 (.80, SE = .01) was significantly larger than the AUC for ANT (.72, SE = .01); z = 4.83, p < .01.
Given the rather modest rates of diagnostic concordance, we investigated alternative PDQ-4 decision thresholds for ASPD diagnostic classification. The PDQ-4, as mentioned, requires the identification of at least three conduct disorder symptoms and four adult symptoms for ASPD to be considered present. Given that there was a stronger association in the present sample between SCID-II total scores and the number of conduct disorder symptoms endorsed on the PDQ-4 (r = .67, p < .01) compared with the number of adult symptoms (r = .45, p < .01), it is possible that different combinations of symptom counts (e.g., four conduct disorder and two adult symptoms) would provide increased concordance between the SCID-II and PDQ-4. To examine the impact on diagnostic concordance between the two measures when different numbers of adult (Criterion A) and conduct disorder (Criterion C) symptoms are endorsed on the PDQ-4, we calculated the chance-corrected agreement (kappa) between the measures' classifications for all 70 permutations of adult and conduct disorder symptoms.
We first split the sample randomly to create derivation (n = 668) and “holdout,” or cross-validation (n = 677), subgroups. The best item combination in the derivation sample yielded a poor to fair level of agreement (κ = .40), based on endorsement of four or more conduct disorder items and three or more adult items on the PDQ-4. When this symptom combination was tested in the holdout sample, agreement continued to be fair (κ = .43). As a point of comparison, kappa values in the derivation and holdout samples using the recommended PDQ-4 criteria (i.e., three or more conduct disorder and four or more adult items) were poor (κ = .25 and κ = .36, respectively). Compared with these PDQ-4 criteria, the alternative criteria tested (i.e., four or more conduct disorder and three or more adult items) also yielded higher values for the overall hit rate (.70 vs. .64), sensitivity (.63 vs. .48), and NPP (.63 vs. .57). Specificity was lower (.80 vs. .85), and PPP remained unchanged (.79).
An alternative basis for deriving cut scores that did not take into account the number of conduct disorder and adult symptoms on the PDQ-4 also was investigated. Using the total set of 22 PDQ-4 items, we formed dichotomous groups of ASPD/no ASPD for cut scores ranging from 5 to 10. Rates of agreement were then calculated between the SCID-II and PDQ-4 using these six cut scores. Kappa values for the PDQ-4 cut scores of 5 through 10 were .36, .41, .46, .42, .39, and .42, respectively. The cut score of 7, which yielded the largest kappa and best overall hit rate (.73) in the derivation sample, produced the highest rate of diagnostic concordance in the holdout sample (κ = .49). Thus, irrespective of whether the PDQ-4 cut score is attained by endorsement of at least three adult and four conduct disorder items (Hyler's criteria; personal communication) or vice versa (the criteria proposed herein), there is consistency in that at least seven items should be endorsed.
We also examined, as an additional approach to investigating cut scores that did not take into account the number of conduct disorder and adult symptoms on the PDQ-4, the utility of using either only conduct disorder or only adult symptoms. The mean number of conduct disorder and adult symptoms endorsed in the entire sample was 4.91 (SD = 3.50) and 3.07 (SD = 1.75), respectively. First, by examining only the 15 PDQ-4 conduct disorder items and using the total SCID-II score as the criterion, we obtained the largest kappa value in both the total (κ = .45) and derivation (κ = .47) samples for the cut score of 4 (overall HR in the total and derivation samples were .73 and .74, respectively). In the holdout sample, this cut score yielded a kappa of .43 (HR = .72). Second, by examining only the seven PDQ-4 adult symptom items, we obtained the largest kappa value in both the total (κ = .27) and derivation (κ = .23) samples for the cut score of 3 (overall HR in the total and derivation samples were .64 and .62, respectively). In the holdout sample, this cut score yielded a kappa of .31 (HR = .66).
The second aim of this project involved “testing the boundaries” of generalization of the main findings. Specifically, we evaluated whether there were differences in reliability and diagnostic efficiency across race and gender. We also investigated the potential impact of the site from which participants were drawn (substance abuse treatment facilities or prisons). As a first step in evaluating performance of the measures across these groups, we compared the measures' total scores across these domains. Gender differences were observed, with men scoring significantly higher than women on the PDQ-4 (men: M = 8.10, SD = 4.65; women: M = 7.38, SD = 4.61), t(1343) = 2.12, p = .03; d = 0.16, and SCID-II (men: M = 7.69, SD = 4.37; women: M = 6.06, SD = 4.15), t(1343) = 5.14, p < .01; d = 0.38, but not on ANT (men: M = 71.15, SD = 11.89; women: M = 69.81, SD = 11.94), t(1343) = 1.54, p = .13; d = 0.11. Significant differences across race also were observed: Whereas Whites had higher ANT scores (M = 72.03, SD = 12.10) than did Blacks (M = 68.89, SD = 11.24), t(1321) = 4.60, p < .01; d = 0.27, Blacks had higher SCID-II scores (M = 7.79, SD = 4.42) than did Whites (M = 7.22, SD = 4.31), t(1321) = 2.27, p = .02; d = 0.13. PDQ-4 scores did not differ significantly between the two groups (Whites: M = 8.17, SD = 4.70; Blacks: M = 7.65, SD = 4.55), t(1321) = 1.93, p = .05; d = 0.11. Scores on the ASPD scales of the two self-report measures were significantly higher in the treatment sample compared with the prison sample. Mean scores on the PDQ-4 were: treatment (M = 8.49, SD = 4.74); prison (M = 7.48, SD = 4.51), t(1343) = 4.00, p < .01; d = 0.22. Mean scores on ANT were: treatment (M = 73.30, SD = 11.70); prison (M = 68.60, SD = 11.65), t(1343) = 7.38, p < .01; d = 0.40. However, no significant differences between site samples were observed for the interview-based SCID-II (treatment: M = 7.32, SD = 4.30; prison: M = 7.52, SD = 4.44), t(1343) = 0.83, p = .41; d = 0.05. We note that all significant differences were small in magnitude, according to Cohen's recommended interpretations of standardized mean differences (Cohen, 1992).
Tables 1 (PDQ-4) and 2 (PAI) present indicators of the correspondence between the SCID-II and the two self-report measures across the various groups. Based on tests of the differences between independent correlations, there were no significant differences in diagnostic agreement between the SCID-II and either the PDQ-4 or ANT as a function of gender, race, or referral sample (prison vs. substance abuse; see Tables 1 and 2). Similar results were obtained when AUC values were examined. For the PDQ-4, AUCs across groups ranged from .78 (for Blacks) to .83 (for women and participants at drug treatment sites), and all were statistically significant (SEs ranged from .01 to .03). AUC values for ANT were comparatively smaller and ranged from .70 (Blacks) to .73 (Whites and prisoners); all were statistically significant as well (SEs ranged from .01 to .04). There were no significant differences in AUC values between the subgroups (i.e., gender, race, or referral) for either self-report questionnaire.
Results for the PDQ-4 (see Table 1) revealed good specificity but limited sensitivity. To test whether sensitivity and specificity differed significantly across groups, we computed the equality of the proportions of ASPD classifications for each group. There were no significant differences in sensitivity between women and men or between Blacks and Whites. However, sensitivity was significantly higher among drug treatment participants compared with prisoners (χ2 = 11.39, p < .01). Analyses of specificity indicated that although no significant differences were observed across race or site groups, specificity was significantly higher for women compared with men (χ2 = 5.86, p = .02). Variation within groups was observed for both overall hit rates and AUC values.
For ANT (see Table 2), the expected tradeoff between specificity and sensitivity was smaller compared with that observed for the PDQ-4. Tests of the equality of proportions for sensitivity revealed significant differences across all the groups. Compared with their counterpart group, ANT more frequently correctly classified the following groups as having ASPD: men (χ2 = 4.73, p = .03), Whites (χ2 = 4.09, p = .04), and drug treatment participants (χ2 = 7.29, p = .01). Analyses for specificity yielded significant differences for gender, with women more often than men being correctly classified by ANT as not having ASPD (χ2 = 5.10, p = .02), and for site, with prisoners more often than treatment participants being correctly classified by ANT as not having ASPD (χ2 = 5.87, p = .02). Specificity rates did not differ significantly for racial subgroups. Table 2 also indicates substantial invariance across groups for overall hit rates and AUC values.
DiscussionOur first objective was to evaluate the psychometric properties of the APSD scales of two self report measures—the PDQ-4 and PAI—and their diagnostic efficiency compared with the SCID-II ASPD scale. Results indicated that all three measures were related strongly at the dimensional (i.e., symptom count) level, particularly when one considers that the strength of the relationship between the two is constrained by the reliability of the scales themselves. At the diagnostic level, however, our results were less supportive of the concordance between measures. Rates of categorical agreement with the SCID-II were comparably poor for the PDQ-4 (κ = .31) and ANT (κ = .32), and results did not support the interchangeability of the interview-based SCID-II with self-report measures. Introducing method variance clearly had an impact, as the rate of categorical agreement between the two self-report measures was still limited, but higher (κ = .40) than the rate for either measure with the SCID-II. The diagnostic discordance between the PDQ-4 and SCID-II is not surprising given the differential symptom requirements to make diagnoses of ASPD across the two instruments. Although self-reports tend to overdiagnose personality disorders (Guthrie & Mobley, 1994), the PDQ-4 criteria are more stringent than SCID-II criteria in that they require two additional symptoms (one child, one adult) to diagnose ASPD. Nevertheless, despite the higher thresholds used by the PDQ-4, our finding that the SCID-II yielded a higher proportion of ASPD cases than did the PDQ-4 was not expected in light of research demonstrating that self-report measures tend to overdiagnose. Similarly, the finding that ANT (using 70 T as a cutoff) and the SCID-II classified similar proportions of participants as having ASPD was unexpected.
From a psychometric perspective, a lack of association in dimensional scores would have been more problematic than the observed discordance in diagnostic classifications. That is, although dichotomous diagnoses are attractive from a clinical perspective, the practice of assigning diagnoses assumes the presence of taxonicity (i.e., that there is a genuine demarcation between the presence versus absence of the disorder), and this is not supported by empirical evidence (Marcus, Lilienfeld, Edens, & Poythress, 2006). Additional challenges to attaining high rates of diagnostic concordance for personality disorders have been described, such as the failure to demonstrate that personality disorders correspond to discrete psychiatric entities and the lack of consistent, robust evidence of the longitudinal stability of diagnostic status (see McCrae et al., 2001, for an informed discussion). In addition, and of particular importance to the present study, the use of clinical ratings as the criterion against which the accuracy of self-report measures is compared may be problematic in light of research (Clark et al., 1997) indicating low agreement among psychiatrists' ratings of personality disorders when using different interviews (although the “clinical” ratings in the current study were collected for research purposes and evidenced high reliability).
Traditionally, screening tools are designed to “screen in” for further assessment individuals who may have a given disorder. Applying the PDQ-4 criteria yielded poor sensitivity and low negative predictive power. These are undesirable features in a screening tool designed to identify offenders with potential ASPD. However, the high specificity and positive predictive power indicate that a favorable application of the PDQ-4 using the recommended cutoff value might be to “screen out” individuals who do not have ASPD. This might be a desirable attribute in certain clinical contexts. For example, one might wish to use the PDQ-4 to identify persons who are unlikely to have ASPD and therefore are appropriate for inclusion in interpersonal psychotherapy groups (Yalom & Leszck, 2005). In contrast to the PDQ-4, the PAI was significantly more sensitive and less specific, which suggests that it would be a preferable measure compared with the PDQ-4 for identifying persons in need of more detailed assessment.
Should one's goal be to use the PDQ-4 in the more traditional manner to “screen in” offenders who may have ASPD, however, one may wish to use the alternative criteria we identified to better distinguish between SCID-II-defined ASPD and non-ASPD cases. When the number of conduct disorder and adult symptoms was considered, optimal classification was achieved at four or more child and three or more adult symptoms, which also cross-validated well in the holdout sample. Unfortunately, however, the level of diagnostic agreement was raised only from “poor” (recommended criteria) to “fair” (alternative criteria)—even when no constraints were imposed on the specific number of conduct disorder and adult symptoms required to meet ASPD diagnostic criteria. As such, it seems that the PDQ-4 performs better as a dimensional tool than as a categorical tool. This finding is consistent with reviews (e.g., Lilienfeld et al., 1997) concluding that although diagnostic concordance between interview and self-report measures of ASPD generally is poor, the association between measures appears more robust when the total symptom count is considered. Knowledge about the severity of an individual's personality pathology could be informative in several clinical decision-making contexts—and would entail a dimensional application of the PDQ-4. To the extent that dimensional models of personality disorder may be considered in revisions of the DSM (Trull, Tragesser, Solhan, & Schwartz-Mette, 2007; Widiger & Trull, 2007), our findings suggest that the PDQ-4 may be a useful clinical tool.
Our second objective was to examine the degree to which our findings using the total sample would hold up across referral site, gender, and race. When mean scores on each of the three tests were examined, several significant differences were observed. However, it is important to note that statistical significance generally is easily attainable when studying samples as large as was investigated in the present study. Moreover, all of the effect sizes obtained would be characterized as small in magnitude (Cohen, 1992). Also, and importantly, although we observed some mean differences in scores, the degree of dimensional association between the self-report measures and the SCID-II appeared to be relatively invariant across the three subgroups. We must therefore consider the possibility that, in addition to one interpretation of our results as indicating “genuine” differences in mean scores, the results are due to bias of some sort (see Widiger, 1998).
Bias regarding the application of the diagnostic criteria can result either from clinicians failing to adhere to DSM criteria or failing to apply the criteria equally to different members of a group (e.g., Blacks and Whites). Our finding that men had higher mean scores on the PDQ-4 and SCID-II than did women is consistent with research indicating that prevalence rates of ASPD are relatively higher among men (American Psychiatric Association, 2000). However, this finding—especially given that the largest mean difference for gender was observed for the interview-based SCID-II—also is consistent with research documenting the presence of interviewer bias. For example, Garb (1997) reviewed empirical literature indicating that clinicians may be less likely to perceive women as violent or antisocial. Our finding that Blacks were given higher ratings than Whites on an interview-based measure also is consistent with Garb's conclusions that clinicians may be more likely to perceive Blacks as violent or antisocial.
Although we are unable to offer a definitive explanation for the differential findings across groups, it is important to document and pursue them in future work that focuses specifically on different forms of potential gender and racial bias. Work in this area is critical, especially given that much more research on psychological assessment in correctional and forensic settings has been conducted with men than with women (Blanchette & Brown, 2006) and minorities are overrepresented in U.S. prisons (Federal Bureau of Prisons, 2007).
Strengths of this study are the use of large and diverse samples across several settings, comparisons across heretofore unexamined subgroups of practical and theoretical interest, and use of trained examiners with evidence of high interrater reliability. Various limitations should be noted, however. For example, we examined only two primary racial groups (Blacks and Whites), which limits the generalizability of our findings to other groups (e.g., Asians, Native Americans). Another limitation of the present study pertains to our use of “offender” samples, which raises questions as to how well the self-report measures would work as a diagnostic tool in samples where the base rates of ASPD would be appreciably lower (e.g., epidemiological studies of community samples) and the potential for false positives (versus false negatives) would be much more likely. These caveats notwithstanding, the current results showed strong concordance of the PDQ-4 and ANT with the SCID-II at the dimensional level but limited correspondence diagnostically.
Footnotes 1 The PDQ-4+ includes the same items that assess the established PDs as the PDQ-4 but differs from the PDQ-4 by having additional items that assess two experimental personality disorders.
2 According to Cohen (1992), small, moderate, and large d values are .20, .50, and .80, respectively.
3 Correlations between the ANT subscales were as follows: ANT-A/ANT-E (.47); ANT-A/ANT-S (.52); ANT-E/ANT-S (.51), all ps ≥ .001.
4 Because the PAI manual discusses scores of 60 T and 82 T as meaningful breaking points, we also investigated the diagnostic efficiency within the total sample using these cut scores in addition to the cut score of 70 T, which was used in all analyses. Diagnostic agreement was lower using 60 T and 82 T (κ = .21 and .18, respectively). Results of utility analyses for 60 T and 82 T, respectively, were as follows: sensitivity (.93, .29); specificity (.27, .92); PPP (.61, .80); NPP (.76, .51); HR (.64, .56).
5 Aside from ANT, the PAI provides an additional method of measuring antisocial features that has not been the focus of much research to date. Using data from the clinical normative group (Morey, 1991), Morey (1996) developed a logit function for estimating ASPD: .044(Antisocial Behaviors) + .017(Aggression–Physical Aggression) – .008(Antisocial Egocentricity) – .002 (Antisocial Stimulus Seeking) – .028 (Anxiety-Affective) + 1.85. The PAI clinical interpretive software uses a cut of 2 probability value on this function to flag ASPD as a diagnostic consideration (personal communication, Leslie C. Morey, August 1, 2007). With this probability as a cut score in our derivation sample, 36% (n = 241) of participants were classified as having ASPD. The categorical rate of agreement with SCID-II diagnosis was κ = .34. The correlations between the continuous variable created by the logit function and total scores on SCID-II, PDQ-4, and ANT were .46, .49, and .52, respectively (all ps < .01). When this cut score was applied in the holdout sample described later in the text, 38% (n = 254) of participants were classified as having ASPD and kappa decreased to .25. The correlations between the continuous variable created by the logit function and total scores on SCID-II, PDQ-4, and ANT were .48, .52, and .55, respectively (all ps < .01). Compared with the ANT cut score of 70 T (see Table 2), the cross-validated logit function yielded a similar overall hit rate (.66 for 70 T vs. .61 for the function). Other diagnostic efficiency results for the logit function in the holdout sample were: sensitivity (.49), specificity (.77), PPP (.72), NPP (.55). Given that the logit function at best performed comparably to ANT in isolation, the subsequent analyses reported below focus solely on the performance of ANT.
6 Indeed, supplementary analyses indicated that diagnostic concordance was increased when the same threshold was used for both measures. When the PDQ-4 criteria (i.e., four or more adult symptoms and three or more child symptoms) were applied to the SCID-II, κ = .33. When the SCID-II criteria (i.e., three or more adult symptoms and two or more child symptoms) were applied to the PDQ-4, κ = .37.
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Submitted: March 14, 2007 Revised: November 7, 2007 Accepted: November 8, 2007
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Source: Psychological Assessment. Vol. 20. (1), Mar, 2008 pp. 47-54)
Accession Number: 2008-02315-005
Digital Object Identifier: 10.1037/1040-3590.20.1.47
Record: 43- Title:
- Daily associations between PTSD, drinking, and self-appraised alcohol-related problems.
- Authors:
- Wilson, Sarah M., ORCID 0000-0002-1028-6028. Mid-Atlantic Mental Illness Research Education and Clinical Center (MIRECC), Durham, NC, US, sarah.wilson@duke.edu
Krenek, Marketa. VISN 20 MIRECC, VA Puget Sound Health Care System, Seattle, WA, US
Dennis, Paul A.. Department of Psychiatry and Behavioral Sciences, Duke University Medical Center, Durham, NC, US
Yard, Samantha S.. VA Puget Sound Health Care System, Seattle, WA, US
Browne, Kendall C.. VISN 20 MIRECC, VA Puget Sound Health Care System, Seattle, WA, US
Simpson, Tracy L.. Center of Excellence in Substance Abuse Treatment & Education (CESATE), VA Puget Sound Health Care System, Seattle, WA, US - Address:
- Wilson, Sarah M., Mid-Atlantic MIRECC, Durham VA Health Care System, 508 Fulton Street, Durham, NC, US, 27705, sarah.wilson@duke.edu
- Source:
- Psychology of Addictive Behaviors, Vol 31(1), Feb, 2017. pp. 27-35.
- NLM Title Abbreviation:
- Psychol Addict Behav
- Page Count:
- 9
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- Bulletin of the Society of Psychologists in Addictive Behaviors; Bulletin of the Society of Psychologists in Substance Abuse
- Other Publishers:
- US : Educational Publishing Foundation
Society of Psychologists in Addictive Behaviors - ISSN:
- 0893-164X (Print)
1939-1501 (Electronic) - Language:
- English
- Keywords:
- longitudinal, alcohol use disorder (AUD), posttraumatic stress disorder (PTSD), psychiatric comorbidities, interactive voice recognition (IVR)
- Abstract:
- Alcohol dependence (AD) and posttraumatic stress disorder (PTSD) are highly comorbid, yet limited research has focused on PTSD and daily drinking as they relate to self-appraised alcohol-related problems. In treatment contexts, patients’ appraisals of alcohol-related problems have implications for assessment, intervention strategies, and prognosis. This study investigated the moderating effect of within-person (daily symptoms) and between-person (overall severity) differences in PTSD on the association between daily drinking and same-day alcohol-related problems. Participants with comorbid AD and PTSD (N = 86) completed 1 week of Interactive Voice Recognition data collection, and logistic and γ-adjusted multilevel models were used to estimate odds and magnitude of self-appraised alcohol-related problems. Results revealed that both within-person and between-person PTSD moderated the association between number of drinks and severity of self-appraised problems. As within-person and between-person PTSD symptoms increased, there was a weaker association between number of drinks consumed and perceived alcohol-related problems. Contrasts further revealed that on nondrinking and light-drinking days, PTSD (both daily symptoms and overall severity) was positively associated with ratings of alcohol-related problems. However, PTSD was not associated with alcohol-related problems on heavier drinking days. In conclusion, more severe PTSD is associated with a less directly contingent relationship between drinking quantity and perceived alcohol-related problems. These findings suggest the importance of further investigations of this moderating effect as well as clinical treatment of comorbid AD and severe PTSD with functional analysis of drinking. (PsycINFO Database Record (c) 2017 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Alcohol Drinking Patterns; *Alcoholism; *Comorbidity; *Posttraumatic Stress Disorder; Self-Report
- PsycINFO Classification:
- Psychological Disorders (3210)
- Population:
- Human
Male
Female - Location:
- US
- Age Group:
- Adulthood (18 yrs & older)
- Tests & Measures:
- Form-42
Form 90 DOI: 10.1037/t03952-000
Hamilton Depression Inventory DOI: 10.1037/t41553-000
Clinician-Administered PTSD Scale DOI: 10.1037/t00072-000
Structured Clinical Interview for DSM-IV Axis I Disorders
PTSD Symptom Scale-Interview Version DOI: 10.1037/t05176-000 - Grant Sponsorship:
- Sponsor: NIH/NIAAA, US
Grant Number: R21AA17130-01
Recipients: Simpson, Tracy L. (Prin Inv)
Sponsor: Center of Excellence in Substance Abuse Treatment and Education (CESATE)
Recipients: Simpson, Tracy L.
Sponsor: VISN 20 and VISN 6 Mental Illness Research, Education, and Clinical Centers (MIRECC), US
Recipients: No recipient indicated
Sponsor: Department of Veterans Affairs, Office of Academic Affiliations, Advanced Fellowship Program in Mental Illness Research and Treatment, US
Recipients: Wilson, Sarah M.; Krenek, Marketa; Yard, Samantha S.; Browne, Kendall C.
Sponsor: VA Puget Sound Health Care System, US
Recipients: No recipient indicated - Conference:
- Meeting of the International Society for Traumatic Stress Studies, 2015
- Conference Notes:
- Findings from the current study were disseminated previously as a poster presentation at the aforementioend conference.
- Methodology:
- Empirical Study; Longitudinal Study; Quantitative Study
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- First Posted: Jan 9, 2017; Accepted: Nov 3, 2016; Revised: Nov 1, 2016; First Submitted: Oct 8, 2015
- Release Date:
- 20170109
- Correction Date:
- 20170206
- Digital Object Identifier:
- http://dx.doi.org/10.1037/adb0000238
- PMID:
- 28068120
- Accession Number:
- 2017-00752-001
- Persistent link to this record (Permalink):
- http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2017-00752-001&site=ehost-live
- Cut and Paste:
- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2017-00752-001&site=ehost-live">Daily associations between PTSD, drinking, and self-appraised alcohol-related problems.</A>
- Database:
- PsycINFO
Daily Associations Between PTSD, Drinking, and Self-Appraised Alcohol-Related Problems
By: Sarah M. Wilson
Mid-Atlantic Mental Illness Research Education and Clinical Center (MIRECC), Durham, North Carolina and Durham VA Health Care System, Durham, North Carolina;
Marketa Krenek
VISN 20 MIRECC, VA Puget Sound Health Care System, Seattle, Washington
Paul A. Dennis
Department of Psychiatry and Behavioral Sciences, Duke University Medical Center and Durham VA Health Care System, Durham, North Carolina
Samantha S. Yard
VA Puget Sound Health Care System
Kendall C. Browne
VISN 20 MIRECC, VA Puget Sound Health Care System and Department of Psychiatry & Behavioral Sciences, University of Washington
Tracy L. Simpson
Center of Excellence in Substance Abuse Treatment & Education (CESATE), VA Puget Sound Health Care System and Department of Psychiatry & Behavioral Sciences, University of Washington
Acknowledgement: This study was supported in part by a grant from NIH/NIAAA award (R21AA17130-01; PI: TLS) and by resources from the Center of Excellence in Substance Abuse Treatment and Education (CESATE; TLS), the VISN 20 and VISN 6 Mental Illness Research, Education, and Clinical Centers (MIRECC), the U.S. Department of Veterans Affairs Office of Academic Affiliations, Advanced Fellowship Program in Mental Illness Research and Treatment (SMW, MK, SSY, and KCB), and the VA Puget Sound Health Care System, Seattle, WA. The views expressed in this article are those of the authors and do not represent the views of the Department of Veteran Affairs or the United States government. Findings from the current study were disseminated previously as a poster presentation at the meeting of the International Society for Traumatic Stress Studies (Wilson, Krenek, Browne, Yard, & Simpson, 2015). The data used in the current study have yielded previously published articles (Browne, Wray, Stappenbeck, Krenek, & Simpson, 2016; Krenek, Lyons, & Simpson, 2016; Lehavot, Stappenbeck, Luterek, Kaysen, & Simpson, 2014; Stappenbeck, et al., 2015; Simpson, Stappenbeck, Luterek, Lehavot, & Kaysen, 2014).
Alcohol use disorder (AUD) and posttraumatic stress disorder (PTSD) often co-occur. Among individuals with PTSD, AUD is quite common, with estimates of lifetime prevalence ranging from 22–75% and with population-level estimates at 42% (Grant et al., 2015; Jacobsen, Southwick, & Kosten, 2001; Pietrzak, Goldstein, Southwick, & Grant, 2011). Similarly, individuals with lifetime AUD are at increased risk for lifetime PTSD (Goldstein et al., 2016). Much of the impairment associated with comorbid PTSD and AUD manifests with regard to difficulties in relationships, occupational impairment, legal problems, and health concerns (Blanco et al., 2013; Drapkin et al., 2011). For example, both PTSD and AUD increase the risk for health-related problems and all-cause mortality (Boscarino & Figley, 2009; Chwastiak, Rosenheck, Desai, & Kazis, 2010; Pacella, Hruska, & Delahanty, 2013), and their comorbidity is associated with greater physical health concerns and lower quality of life than seen for those with one or the other disorder (Evren et al., 2011; Kaysen et al., 2008). Although PTSD and AUD often exacerbate each other (Marshall et al., 2012; Oslin, Cary, Slaymaker, Colleran, & Blow, 2009; Wolitzky-Taylor, Bobova, Zinbarg, Mineka, & Craske, 2012), limited research has addressed the interplay between PTSD symptoms, drinking, and alcohol-related problems.
There is evidence that PTSD symptomatology is associated with AUD severity in treatment-seeking populations, and moreover this effect is not explained by quantity of alcohol consumed. In a sample of treatment-seeking U.S. veterans, those with comorbid PTSD and AUD were compared with individuals with AUD alone (Fuehrlein, Ralevski, O’Brien, Jane, Arias, & Petrakis, 2014). Although they had significantly fewer drinks per day and fewer heavy drinking days, participants with comorbid AUD/PTSD presented with significantly higher alcohol dependence scores (Fuehrlein et al., 2014). As this finding suggests, those with comorbid PTSD have AUD symptoms that may not be completely contingent upon quantity of alcohol consumed. In another study of treatment-seeking veterans, despite having fewer years drinking and no difference in current drinking quantity/frequency, those with comorbid PTSD/AUD reported greater alcohol dependence (AD; Petrakis et al., 2006). Taken together, these findings suggest that among individuals with AUD, comorbid PTSD may exacerbate alcohol’s impact on alcohol-related problems.
Building upon cross-sectional evidence of elevated alcohol problems in those with comorbid PTSD/AUD, Gaher and colleagues (2014) used experience sampling to examine within-person and between-person relationships between PTSD, drinking, and self-rated drinking problems. To examine daily and overall relationships with alcohol-related problems, it was necessary to statistically disaggregate effects of within-person PTSD and drinking (daily levels) from between-person PTSD and drinking (overall levels). In the study, within-person PTSD and drinking were assessed multiple times per day, and between-person PTSD was assessed by averaging each participant’s responses over the 2 weeks of monitoring. The authors showed that after controlling for the effects of drinking, both within-person and between-person PTSD independently affect self-ratings of alcohol-related problems. Within-person daily PTSD symptoms were associated with increased alcohol-related problems reported later the same day after controlling for quantity of alcohol consumed. Similarly, those with more severe between-person PTSD tended to report more alcohol-related problems after controlling for alcohol consumption.
Despite evidence that PTSD affects alcohol-related problems after controlling for drinking quantity, it remains unknown whether PTSD moderates the relationship between drinking amount and perceived alcohol-related problems. If PTSD severity does have a moderating effect on the relationship between drinking quantity and self-ratings of alcohol-related problems in treatment-seeking populations with comorbid PTSD/AUD, this could potentially affect PTSD/AUD treatment strategies. According to prevailing theoretical orientations to AUD treatment, behavior change is made possible in part through awareness and accurate assessment of alcohol-related problems (Donovan, 2003; Prochaska & Velicer, 1997). For individuals entering alcohol treatment, the more accurate they are in their self-appraisal of their pretreatment alcohol-related problems, the more positive their treatment outcome (Sawayama et al., 2012). Given that those with unremitting PTSD fare worse in AUD treatment outcome (Read, Brown, & Kahler, 2004), it is possible that PTSD contributes to this disparity by either exacerbating alcohol-related problems or disrupting accurate self-rating of alcohol-related problems.
The Present StudyWhile the aforementioned evidence clarifies that PTSD symptomatology adds unique variance to daily self-ratings of alcohol-related problems, we currently do not know whether PTSD symptoms moderate the cross-sectional association between drinking behavior and self-reports of alcohol-related problems. Varying levels of daily and overall PTSD symptom severity may differentially interact with heavy and lighter alcohol consumption to inform alcohol-related problem ratings. Both within person daily levels of PTSD and between person overall levels of PTSD could interact with daily drinking amounts. Thus, the present study used pretreatment daily monitoring data from an experimental treatment study (Simpson et al., 2014) to test the hypothesis that the association between daily drinking and same-day alcohol-related problems varies as a function of within-person PTSD (same-day symptoms) and between-person PTSD (overall severity).
Method Participants
Study participants were adults with concurrent diagnoses of AD and PTSD who indicated a desire to decrease alcohol use as part of a larger brief intervention study registered through ClinicalTrials.gov (Protocol #: NCT00760994). Briefly, study inclusion criteria were the following: (a) at least 18 years of age, (b) current AD diagnosis as defined by the Diagnostic and Statistical Manual of Mental Disorders, fourth edition, text revision (DSM–IV–TR; American Psychiatric Association, 2000), (c) alcohol use within the past 2 weeks, (d) current DSM–IV PTSD diagnosis, (e) capacity to provide informed consent, and (f) telephone access. Exclusion criteria were as follows: (a) history of delirium tremens or seizures, (b) opiate use or chronic treatment with opioid-containing medications during the past month, (c) Antabuse or naltrexone treatment, (d) alcohol withdrawal symptoms at initial consent, (e) acute suicidality/homicidality with intent/plan, or (f) psychosis. See Simpson et al. (2014), for further information on study participants and full inclusion/exclusion criteria. Data were excluded from the present analysis for participants with less than 50% adherence to daily monitoring to minimize nonresponse bias.
Ninety-two participants met the study entry criteria, and the final sample consisted of those who had at least 50% adherence on the daily monitoring protocol. Three participants were excluded because they did not complete monitoring days, and an additional three were excluded because they had less than 50% adherence on daily monitoring, yielding a final sample size of 86. Participants were on average 44.7 years of age (SD = 11.0), and ages ranged from 21 to 63. Ethnic group breakdown was as follows: 43.0% African American, 39.5% European American, 4.7% Hispanic/Latino, 4.7% American Indian, 1.2% Asian American, and 7.0% other ethnicity or missing. Almost half of the study sample was women (49.0%) and just over a quarter were veterans (25.6%). Only 13.1% were currently married, nearly a quarter were homeless (23.3%), and 12.8% were employed at least part-time, 10.5% were students, 45.3% were retired or disabled, and 31.4% were unemployed.
Procedure
Participants were recruited through newspaper advertisements and flyers. Participants completed an initial phone screen and then came into the lab where they provided written informed consent, underwent further screening for study inclusion, and a baseline assessment consisting of interview and self-report measures. They were compensated $30 for completing the baseline assessment. Participants received instruction on the telephone daily Interactive Voice Response (IVR) protocol. All study procedures were approved by the VA Puget Sound Health Care System Human Subjects Division Internal Review Board.
Participants completed self-report measures daily by calling a designated telephone number and answering prompts with IVR. Compliance was tracked automatically, and participants who did not call were personally contacted within two business days to collect data (10% of calls were collected verbally). Compensation was $1 for every completed day of monitoring, with a $10 bonus for seven consecutive monitoring days or a $7 bonus for six consecutive days. The time interval of IVR data collection was targeted for the 7 days before receipt of a brief intervention but this pretreatment baseline period ranged from 6 to 20 days because of scheduling difficulties. For additional information regarding procedures, see Simpson et al. (2014).
For the 86 participants who completed IVR monitoring, available observations ranged from 4 to 16 days (M = 7.3, SD = 2.6), with 659 total possible observations for all participants. The final IVR dataset, including all entries with available outcome data, contained 620 daily observations.
Measures
Screening assessment
Inclusion and exclusion criteria were assessed with the following measures: Hamilton Depression Inventory to assess suicidality (Hamilton, 1960); Structured Clinical Interview for DSM–IV Axis I Disorders to assess alcohol dependence, opiate use, and psychotic disorder (First, Gibbon, Spitzer, & Williams, 1995); PTSD Symptom Scale-Interview Version (PSS-I) to ascertain past-month PTSD diagnosis (Foa, Riggs, Dancu, & Rothbaum, 1993); and Form-42 (adapted from Form-90; Miller & Del Boca, 1994) to ensure alcohol use within the past 2 weeks. For additional information regarding these measures, see Simpson et al., 2014.
Baseline demographics
Participants were asked to identify age, gender, and veteran status as demographic covariates, and gender and veteran status were coded dichotomously (veteran status, 0 = nonveteran, 1 = veteran; gender, 0 = male, 1 = female).
Baseline PTSD
Participants were rated on baseline PTSD symptoms using the PSS-I, which is a 20- to 30-min, 17-item semistructured interview assessing DSM–IV symptoms of PTSD. The interviewer rates the severity of each symptom on a scale ranging from 0 (not at all) to 3 (5 or more times per week/very much), which yields a total score ranging from 0 to 51. The PSS-I shows excellent validity when compared to the Clinician-Administered PTSD Scale (Foa & Tolin, 2000). The PSS-I had good internal reliability in the current sample (α = .81).
Daily IVR self-report
Participants self-reported alcohol intake, PTSD symptoms, and alcohol-related problems daily using IVR telephone monitoring. Day of week (weekday = 0, weekend day = 1) was also recorded. Use of daily self-report IVR minimizes recall bias and underreporting of drinks per day (Searles, Helzer, Rose, & Badger, 2002).
IVR drinking
Participants were queried regarding the number of standard drinks consumed the day prior (beer, wine, and liquor, respectively). The number of each type of standard drink consumed each day was summed to yield a total drinks per day variable. Abnormally high values on this measure were verified verbally with participants. This methodology has been previously validated against retrospective self-report (Krenek, Lyons, & Simpson, 2016).
IVR PTSD symptoms
As described in Simpson et al., 2014, items assessing daily PTSD symptomatology were adapted from the PCL-C (King, Leskin, King, & Weathers, 1998). Items included three re-experiencing symptoms (intrusive thoughts, nightmares, and upset because of reminders), two avoidance symptoms (avoidance of thoughts and feelings associated with event, avoidance of trauma reminders), three emotional numbing symptoms (loss of interest, feeling detached, and emotionally numb), and four hyperarousal symptoms (concentration problems, alertness/vigilance, exaggerated startle, and anger/irritability). Items were chosen based on those that load most strongly in factor analytic studies of PTSD (e.g., King et al., 1998; Krause, Kaltman, Goodman, & Dutton, 2007; Palmieri, Weathers, Difede, & King, 2007) and that were likely to vary daily. Participants indicated how bothered they were on the previous day by each symptom on 9-point scales ranging from 0 = not at all to 8 = all the time. Items were averaged to create a daily IVR PTSD severity score (α = .94).
IVR alcohol-related problems
Self-appraised alcohol-related problems were assessed with a single item, “Yesterday, to what extent did you experience any negative consequences or problems related to your drinking?” Participants indicated a response on a 9-point scale ranging from 0 = none at all to 8 = worse ever. This single-item method of assessing daily alcohol-related problems was adapted from Searles and colleagues (2000).
Data Analytic Approach
Missingness was assessed for any associations with key variables at the between-subjects level (gender, veteran status, ethnicity, and baseline PTSD severity), and the within-subject level (weekend/weekday). Given nesting within the IVR data (daily self-reports nested within person), multilevel modeling (MLM) was used to analyze the data. The continuous outcome variable, daily alcohol-related problems, was zero-inflated (48.2% of responses were 0) and overdispersed (M = 2.16, SD = 2.53), corresponding to a zero-inflated gamma distribution. As such, we modeled daily alcohol-related problems via a two-part analysis. First, we used logistic MLM to model the odds of reporting any alcohol-related problems on a given day as a function of whether or not alcoholic drinks were consumed that day and PTSD symptom severity. Then, we used gamma-adjusted MLM to model nonzero alcohol problem ratings as a function of number of alcoholic drinks consumed and PTSD symptom severity.
Given that PTSD symptom severity data were collected longitudinally over a period of several monitoring days, it was possible to disaggregate within-person and between-person effects of PTSD symptom severity on alcohol problems (Curran & Bauer, 2011). To do so, we mean-standardized (i.e., z-scored) PTSD severity to statistically partial out effects of within-person daily PTSD symptoms opposed to overall, between-person PTSD symptoms over the entire monitoring period. Within-person PTSD severity was person-mean standardized (PMS) to capture the extent to which PTSD symptoms deviated from each participant’s personal mean on each day of monitoring. In other words, PMS PTSD reflects how mild/severe the participants’ PTSD symptoms were each day compared with their own personal average. We calculated between-person PTSD by grand-mean standardizing (GMS) each person’s overall PTSD scores. GMS PTSD, therefore, quantified the relative severity of each participant’s PTSD over the entire IVR monitoring period compared with others in the sample.
In each of the two models, we modeled the main effects of drinking and PTSD symptom severity on alcohol problems, and their interactions. In the logistic analysis, we examined the cross-level interaction between GMS PTSD (between-person differences in PTSD severity) and number of drinks consumed that day (Number of Drinks × Overall PTSD). We also examined the within-level interaction between PMS PTSD (within-person differences in PTSD) and number of drinks consumed that day (Number of Drinks × Daily PTSD). In the gamma model, we examined the interaction between GMS PTSD and number of drinks consumed that day (Number of Drinks × Overall PTSD) as well as the interaction between PMS PTSD and number of drinks consumed that day (Number of Drinks × Daily PTSD). In addition to these variables, veteran status, gender, age, time (days since beginning IVR monitoring), and weekend day versus weekday were covaried in both models. Logistic and gamma-adjusted MLM were conducted using PROC GLIMMIX, available in SAS (Version 9.4).
ResultsPrior preliminary analyses did not yield any significant differences between those included (n = 86) and excluded (n = 6) from the analyses with regards to baseline demographic characteristics or baseline PTSD symptomatology (see Simpson et al., 2014). Regarding missing IVR observations, there were no statistically significant patterns of missingness with regard to gender, veteran status, ethnicity, baseline PTSD severity, or weekend/weekday. At baseline, the average PSS-I score was 29.5 (SD = 9.2). See Table 1 for within-person and between-person variable mean values and SDs for the entire monitoring period. As a preliminary step, we explored within-person and between-person variability in IVR PTSD symptoms over the monitoring period by calculating an intraclass correlation (ICC) for the normally distributed IVR PTSD variable (Hoffman & Stawski, 2009).The ICC (0.695) indicated that 69.5% of the variability in PTSD symptoms was at the between-person level and 30.5% of the variability was at the within-person level.
Between-Subject and Within-Subject Descriptive Statistics, N = 620 Observations Among N = 86 Participants
Results from the logistic and gamma multilevel models are displayed in Table 2. According to the logistic model, the odds of reporting any alcohol-related problems increased when any alcohol was consumed, and individuals with higher between-person GMS PTSD severity were more likely to report having had an alcohol-related problem. Neither of the interactions between PTSD symptom level (PMS and GMS) and number of drinks consumed were significant.
Multilevel Models of Daily Alcohol-Related Problems
Turning to the gamma model, the main effects of number of drinks consumed, daily PMS PTSD symptom scores, and between-person GMS PTSD severity were each positively associated with number of alcohol-related problems reported. These main effects were qualified by significant interactions. According to the Number of Drinks × Daily PTSD interaction, the effect of number of drinks on alcohol-related problems varied as a function of daily PTSD level. At low levels of daily PTSD (1 SD below the mean) the multiplicative effect of each additional drink on self-reported problems was 2.03 (p < .001), whereas it was 1.87 (p < .001) at high levels of daily PTSD (1 SD above the mean). Contrasts in number of alcohol-related problems reported by daily PTSD level were most evident when few drinks were consumed. On days when more drinks consumed, there was less contrast in alcohol-related problems reported by daily PTSD level. There was a small effect of within-person daily PTSD on ratings of alcohol-related problems on nondrinking days (Cohen’s d = 0.25, p = .024) and on light drinking days (defined as three drinks consumed; Cohen’s d = 0.24, p = .045). There was no significant effect of within-person PTSD on self-rated problems for heavy drinking days (defined as nine drinks consumed; Cohen’s d = 0.05, p = .68).
The moderating effect of PTSD was even more pronounced in the Number of Drinks × Overall PTSD interaction (see Figure 1). According to that, the effect of number of drinks on alcohol-related problems varied as a function of overall PTSD severity level. As seen in Figure 1, at low levels of between-person PTSD (1 SD below the mean), the multiplicative effect of each additional drink on self-reported problems was 2.29 (p < .001), whereas it was 1.65 (p < .001) at high levels of PTSD (1 SD above the mean). Thus, the association between number of drinks consumed and self-rated alcohol-related problems was weaker for those with more severe between-person PTSD. The greatest contrasts in reported alcohol-related problems by individual differences in PTSD symptom level occurred on nondrinking days (Cohen’s d = 0.45, p < .001) and light-drinking days (Cohen’s d = 0.30, p = .007). However, as the number of drinks increased, there was less of an effect of between-person differences in PTSD symptom level on number of alcohol-related problems reported. At high levels of drinking (i.e., nine drinks), there was a trend toward a small inverse association between PTSD severity and alcohol-related problems, but this effect was not significant (Cohen’s d = 0.16, p = .13). Generally, at higher levels of between-person PTSD severity, there was a weaker association between quantity of alcohol consumed and self-rated alcohol-related problems.
Figure 1. Plot of modeled effects on alcohol-related problems by number of drinks consumed and between-person overall PTSD severity. Low and high PTSD scores were derived by calculating 1 SD offsets from the mean. Effect sizes and corresponding p values reflect contrasts between mean PTSD symptom levels and 1 SD offsets. PTSD = posttraumatic stress disorder; GMS = grand-mean standardized.
DiscussionThis study examined the daily effects of PTSD severity and alcohol consumption on alcohol-related problems among treatment-seeking men and women with co-occurring PTSD and alcohol dependence. Consistent with prior research (Gaher et al., 2014), we found that daily variability in PTSD severity, overall between-person PTSD severity, and alcohol consumption were all broadly predictive of alcohol-related problems. Our research extends these findings by providing evidence for moderating effects of both overall PTSD severity and daily PTSD symptom variability on self-appraised alcohol-related problems. We found that on nondrinking days and moderate drinking days (three drinks consumed), greater daily within-person PTSD symptom severity was associated with greater alcohol-related problems. Additionally, compared with participants with lower overall between-person PTSD scores, participants with higher overall PTSD severity reported higher ratings of alcohol-related problems on nondrinking and moderate-drinking days. This pattern was not apparent, however, on heavier drinking days, when neither within- nor between-person PTSD were associated with degree of alcohol-related problems. Given the strength of the moderating effect of between-person PTSD, these results suggest that for those with more severe PTSD there is a less contingent association between how much they drink on a given day and the extent to which they report negative consequences from drinking on nondrinking and moderate-drinking days.
These results may help to shed light on the prior research indicating that treatment-seeking individuals with comorbid PTSD and AUD, compared with those with AUD only, report greater alcohol dependence severity despite comparable or lower levels of consumption (Fuehrlein et al., 2014; Petrakis et al., 2006). In the present study, the weaker association between consumption and alcohol-related problems among those with higher levels of PTSD may be due in part to the bidirectional relationship between PTSD and negative coping strategies (Read, Griffin, Wardell, & Ouimette, 2014), suggesting that those with higher levels of PTSD may engage in negative coping strategies that lead to negative alcohol-related experiences regardless of quantity of alcohol consumed. Similarly, individuals with high levels of PTSD may experience negative effects from even small amounts of alcohol given positive associations between impulsivity/emotion dysregulation, and problematic alcohol use among those with PTSD (Schaumberg et al., 2015; Tripp & McDevitt-Murphy, 2015). It is noteworthy that among individuals with high levels of PTSD, the mean score for alcohol-related problems fell in the middle of the scale, suggesting that the weaker association between alcohol use and problems cannot be explained by a measurement ceiling effect.
It is also possible that alcohol craving or abstinence effects (i.e., withdrawal, hangover) were particularly detrimental to participants with higher PTSD on days with low or no alcohol use. Prior research has shown that the relationship between PTSD and alcohol-related consequences is mediated by alcohol craving (Tripp et al., 2015), and that exposure to trauma cues is linked to increased alcohol craving (Coffey et al., 2002). If those with higher PTSD have higher levels of craving on low- or nondrinking days, this could explain their self-rating of alcohol problems during days when they are not drinking as heavily. Indeed, both overall PTSD severity and daily increases in PTSD symptoms were associated with moderately high ratings of alcohol-related problems on nondrinking days. Additionally, experiences of hangover or alcohol withdrawal may be more aversive for those with severe PTSD, or conversely may temporarily exacerbate within-person PTSD symptoms.
Additionally, individuals with severe PTSD often have cognitive biases that may affect their accurate appraisal of alcohol-related problems. A core component of the disorder is a tendency to view experiences in a negative light and appraise situations as inherently dangerous (Vythilingam et al., 2007). Additionally, those with PTSD show memory bias toward trauma- and threat-related stimuli (Paunovic, Lundh, & Öst, 2002). Thus, a person with higher PTSD symptoms may recall a relatively minor incident that occurred after a single drink (e.g., an argument) as more distressing or problematic than someone with lower levels of PTSD. Similarly, this person may find it difficult to distinguish or attend accurately to the nuances between distinct incidences of varying importance or extremity, thus remembering them all with a similar valence. Additionally, given some particularly high responses with regards to drinking quantity, it is possible that memory bias also affected reporting of drinking patterns.
An interesting find was that the results indicated that between-person differences in PTSD more strongly moderated the association between alcohol consumption and problems than intraperson variability in PTSD. One possible explanation for this result is that the individual variability in PTSD severity was low. However, it is also possible that compared with daily PTSD, overall PTSD severity is a more sensitive marker for the negative impact of PTSD on functioning and perceived effects of alcohol use. This second explanation makes sense in the context of evidence for latent classes of PTSD that differ based upon overall, chronic symptomatology (e.g., Cloitre, Garvert, Weiss, Carlson, & Bryant, 2014; Galatzer-Levy, Nickerson, Litz, & Marmar, 2013).
Amplified alcohol-related problems on nondrinking and moderate-drinking days among those with more severe PTSD could be an important factor in treatment for individuals with comorbid PTSD and AUD. For example, individuals with more severe PTSD who overestimate alcohol-related problems may feel greater motivation to seek out and adhere to treatment. On the other hand, this overestimation may lead to lower self-efficacy for treatment success, which could partially explain the poor treatment outcomes often seen for this group (Saxon & Simpson, 2015). Additionally, this pattern of highly rated drinking problems on low-drinking days could be symptomatic of attentional biases that interfere with the accurate self-assessment that contributes to successful PTSD and AUD treatment (Donovan, 2003; Prochaska & Velicer, 1997). In other words, if a patient’s attentional bias causes them to consistently overestimate the role of alcohol in their life problems, this may interfere with their ability to make and achieve behavior change goals. Ultimately, treatment to address both PTSD and AUD may require interventions that support accurate assessment of drinking consequences either to increase motivation to engage in treatment or to clarify the impact of treatment as it (and its corresponding gains) occurs, or both.
These results should be viewed in the context of some study limitations. First, findings from this study were specific to a treatment-seeking sample, who may already be making efforts to reduce alcohol consumption. Thus, findings may not generalize to individuals with comorbid PTSD/AUD who are not seeking treatment. Additionally, all measures were self-report, which increases the likelihood of misinterpretation and other biases. Also, alcohol-related problems were assessed with a single item that asked about participants’ subjective experience of problems rather than the number of problems or specific types of problems. Given that this measure did not specify whether problems were because of active use or because of withdrawal, a more detailed measure of alcohol-related problems should be included in future studies. Measures also did not include consumption of other illicit substances, such as marijuana, opiates, cocaine, and methamphetamines. In addition, though only 1-day prior, retrospective reporting of PTSD symptoms, alcohol use, and problems, may have resulted in recall bias. However, questioning respondents at the end of the day would have increased the risk of missing consumption data and alcohol-related problems occurring later in the evening or early morning. In addition, we were only able to monitor participants for 1 week before receipt of a brief intervention; future research should use a longer monitoring period to better assess within-person changes and to determine whether these findings are maintained across time. Furthermore, given our sample of treatment seeking individuals with comorbid PTSD and alcohol dependence, results must be generalized as such. However, a strength of the study is that the sample included both veterans and civilians with almost equal distribution across gender. Finally, future research should include comparison groups with other psychological disorders or alcohol dependence only to parse out the influence of PTSD-specific symptomatology.
Despite these limitations, the results of the present study could provide guidance for future investigations on this important topic, including examination of potential mediators. For instance, cognitive symptoms such as catastrophizing or negative rumination may explain the weaker association between consumption and problems for those with more severe PTSD, as discussed above. Alternatively, negative coping processes (e.g., avoidance and anger reactivity) that are common components of PTSD may be contributing to this effect by compounding or prolonging alcohol’s effects. Additionally, gender may also moderate the effect of alcohol consumption and PTSD symptoms on self-ratings of alcohol-related problems. Further research should include consideration of coping mechanisms to increase our understanding of alcohol use and treatment in the context of PTSD.
Footnotes 1 Previously published findings from this dataset showed modest lagged effects of posttraumatic stress disorder (PTSD) on next-day number of drinks (Simpson et al., 2014). In the current study, additional models not presented in current results demonstrated that there were no lagged effects of PTSD on alcohol-related problems. We ran models of lagged effects of PTSD on alcohol-related problems (controlling for same-day PTSD) and found no lagged effects of intraindividually varying PTSD symptoms on alcohol-related problems in either the logistic (p = .90) or gamma models (p = .86).
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Submitted: October 8, 2015 Revised: November 1, 2016 Accepted: November 3, 2016
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Source: Psychology of Addictive Behaviors. Vol. 31. (1), Feb, 2017 pp. 27-35)
Accession Number: 2017-00752-001
Digital Object Identifier: 10.1037/adb0000238
Record: 44- Title:
- Depressive symptoms, religious coping, and cigarette smoking among post-secondary vocational students.
- Authors:
- Horton, Karissa D.. Department of Kinesiology and Health Education, The University of Texas at Austin, Austin, TX, US
Loukas, Alexandra. Department of Kinesiology and Health Education, The University of Texas at Austin, Austin, TX, US, alexandra.loukas@austin.utexas.edu - Address:
- Loukas, Alexandra, Department of Kinesiology and Health Education, The University of Texas at Austin, D3700, Austin, TX, US, 78712, alexandra.loukas@austin.utexas.edu
- Source:
- Psychology of Addictive Behaviors, Vol 27(3), Sep, 2013. pp. 705-713.
- NLM Title Abbreviation:
- Psychol Addict Behav
- Page Count:
- 9
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- Bulletin of the Society of Psychologists in Addictive Behaviors; Bulletin of the Society of Psychologists in Substance Abuse
- Other Publishers:
- US : Educational Publishing Foundation
Society of Psychologists in Addictive Behaviors - ISSN:
- 0893-164X (Print)
1939-1501 (Electronic) - Language:
- English
- Keywords:
- cigarette smoking, depressive symptoms, religious coping, stress, vocational student, racial differences
- Abstract:
- Depressive symptoms are associated with increased levels of cigarette smoking, yet not every individual experiencing depressive symptoms smokes. This study examined whether religious coping moderated the impact of depressive symptoms on past 30-day cigarette use among a racially/ethnically diverse sample of 963 postsecondary vocational students (46.8% women; mean age = 25 years). Results from negative binomial regression analyses indicated that depressive symptoms increased the likelihood of cigarette smoking (quantity−frequency measure of cigarette use) for female students, whereas positive religious coping decreased the likelihood of smoking for female students. Consistent with religious coping theory and as expected, negative religious coping moderated the depressive symptoms-smoking relationship such that negative religious coping exacerbated the impact of depressive symptoms on cigarette smoking among females. Positive religious coping also moderated the depressive symptoms-cigarette smoking relationship for females. However, contrary to expectations, high levels of positive religious coping exacerbated the likelihood of cigarette smoking among females with high levels of depressive symptoms. Surprisingly, neither depressive symptoms nor positive or negative religious coping contributed to the likelihood of males’ smoking. Study limitations and suggestions for directions in future research are discussed. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Coping Behavior; *Major Depression; *Religion; *Symptoms; *Tobacco Smoking; Racial and Ethnic Differences; Vocational School Students
- Medical Subject Headings (MeSH):
- Adaptation, Psychological; Adolescent; Adult; Depression; Female; Humans; Male; Regression Analysis; Religion and Psychology; Severity of Illness Index; Sex Factors; Smoking; Students; Vocational Education; Young Adult
- PsycINFO Classification:
- Substance Abuse & Addiction (3233)
- Population:
- Human
Male
Female - Location:
- US
- Age Group:
- Adulthood (18 yrs & older)
Young Adulthood (18-29 yrs) - Tests & Measures:
- Brief RCOPE Measure
National College Health Risk Behavior Survey
Center for Epidemiologic Studies Depression Scale
Brief COPE Inventory DOI: 10.1037/t04102-000 - Grant Sponsorship:
- Sponsor: National Cancer Institute
Grant Number: R03CA130589
Recipients: Loukas, Alexandra
Sponsor: American Association for Health Education, US
Other Details: Will Rogers Institute Fellowship
Recipients: Horton, Karissa D. - Methodology:
- Empirical Study; Quantitative Study
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- First Posted: Dec 31, 2012; Accepted: Oct 16, 2012; Revised: Jul 18, 2012; First Submitted: Dec 22, 2011
- Release Date:
- 20121231
- Correction Date:
- 20130923
- Copyright:
- American Psychological Association. 2012
- Digital Object Identifier:
- http://dx.doi.org/10.1037/a0031195
- PMID:
- 23276324
- Accession Number:
- 2012-34896-001
- Number of Citations in Source:
- 46
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- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2012-34896-001&site=ehost-live">Depressive symptoms, religious coping, and cigarette smoking among post-secondary vocational students.</A>
- Database:
- PsycINFO
Depressive Symptoms, Religious Coping, and Cigarette Smoking Among Post-Secondary Vocational Students
By: Karissa D. Horton
Department of Kinesiology and Health Education, The University of Texas at Austin
Alexandra Loukas
Department of Kinesiology and Health Education, The University of Texas at Austin;
Acknowledgement: Karissa D. Horton is now the Cofounder and Principal Consultant at Limetree Research, LLC.
This research was supported by Grant #R03CA130589 from the National Cancer Institute, awarded to the second author, and an American Association for Health Education Will Rogers Institute Fellowship, awarded to the first author.
Although the rate of cigarette smoking by adults in the United States is now half of what it was in 1964 (U.S. Department of Health, 1964), 19.3% of U.S. adults 18 years and older report that they smoke cigarettes (Schiller, Lucas, Ward, & Peregoy, 2012). Tobacco use remains the leading cause of preventable death in the U.S. (Mokdad, Marks, Stroup, & Gerberding, 2004) and individuals dealing with depressive symptoms tend to smoke more than their nondepressed peers (Anda et al., 1990; Kenney & Holahan, 2008). A study of the historical trends of the relationship between depression and cigarette smoking suggests that the depression−cigarette use relationship has only become apparent in recent years as the prevalence rate of cigarette smoking declined and those who continue to smoke are more likely to be depressed (Murphy et al., 2003).
Depressive symptoms are a significant cause of stress with the potential to spin out of control if an individual does not engage in the necessary efforts to cope with their situation (Smith, McCullough, & Poll, 2003). Tomkins’ (1966) negative affect model for smoking suggests that some may smoke to reduce their negative affect enough to deal with and get to the root of their stress, whereas others may use cigarettes to completely sedate themselves without facing their stressors. Perhaps as Murphy et al. (2003) noted, the relief associated with the reduction of depressive symptoms is so beneficial that depressed individuals continue to smoke cigarettes despite the well-known harmful health effects. Yet, not every individual who experiences depressive symptoms smokes, suggesting that other variables may moderate the relationship between depressive symptoms and cigarette use. Therefore, this study examined the unique role of religious coping, over and above nonreligious coping, as a potential moderator of the relationship between depressive symptoms and cigarette use among a racially/ethnically diverse sample of students enrolled in 2-year postsecondary vocational schools.
The more religiously involved an individual is, the less likely she or he is to smoke cigarettes (see Koenig, McCullough, & Larson, 2001). This association has been reported in samples of college students (Oleckno & Blacconiere, 1991), young adults (Whooley, Boyd, Gardin, & Williams, 2002), and older adults (Koenig et al., 1998). Although these studies indicate an inverse relationship between the institutional aspects of religious involvement (e.g., religious service attendance) and cigarette use (see Koenig et al., 2001), relatively less research has examined the unique role of more personal measures of religiousness, such as religious coping, on cigarette use. Nonetheless, drawing on religious coping theory and existing research (Koenig et al., 2001), it is likely that individuals who use positive religious coping may turn to a “divine other” (e.g., God of monotheistic faiths) for support in times of stress and in this way reduce reliance on smoking, whereas those who engage in negative religious coping may not only lack that sense of support but feel a sense of abandonment that increases the possibility of smoking. From this perspective positive religious coping should be directly inversely associated with smoking, whereas negative religious coping should be positively associated with smoking.
Religious CopingReligious coping can be defined as specific means through which some individuals incorporate “a full range of behaviors, emotions, cognitions, and relationships” to deal with a variety of stressful situations (Pargament, Tarakeshwar, Ellison, & Wulff, 2001, p. 498). Positive religious coping includes a search for a spiritual connection, a collaborative relationship with a divine other, and seeking support from a divine other (Pargament, 1999). Conversely, negative religious coping includes a sense that a divine other is punishing oneself for sins as well as a sense of abandonment by a divine other (Pargament, 1999). A variety of health-related outcomes associated with positive and negative religious coping vary in expected ways. For example, positive religious coping is associated with better stress-related adjustment compared with negative religious coping, which is associated with more adjustment problems (Pargament, Smith, Koenig, & Perez, 1998). Personal measures of religiousness may play a particularly salient role for individuals who experience decreased levels of control in their life. Prayer, for example, is a common coping strategy that individuals draw upon when dealing with stress (see Pargament, 1997).
Religious Coping as a Moderator of Depressive Symptoms and Cigarette UseIn addition to the direct effects of positive and negative religious coping on cigarette use, we anticipate that both types of religious coping will moderate the association between depressive symptoms and current cigarette use. Feeling a sense of support from a divine other (positive religious coping) may buffer, or lessen the deleterious impact of depressive symptoms on cigarette use. Depressed individuals are more likely than nondepressed individuals to become socially withdrawn (see review by Tse & Bond, 2004), exhibit excessive reassurance seeking (Joiner & Metalsky, 1995), and express neediness for emotional support. Coyne’s (1976) interactional theory of depression suggests that the increased demand on these relationships often leads to diminished levels of support for, and even rejection of, the depressed individual (see Joiner & Metalsky, 1995). Thus, individuals lacking in social support from their “real” relationships may particularly gain from the support, and the sense of control over stressful situations, provided by a collaborative relationship with a perceived divine other. This relationship with a divine other may rival the intensity of relationships with family and friends (Pollner, 1989). As noted by Pollner (1989, p. 93), the divine is often personified as one who will convey a sense of support as well as guidance for individuals, particularly during stressful circumstances. A supportive relationship with a divine other may diminish a depressed individual’s need to smoke cigarettes by filling the void of social support that those with depressive symptoms often experience. In this way, a relationship with a divine other may make up for a lack of relationship or support from family and friends.
Unlike the buffering role of positive religious coping, negative religious coping may exacerbate the influence of depressive symptoms on cigarette smoking. Unsupportive social relationships coupled with feelings of abandonment and/or punishment by a divine other may decrease one’s sense of purpose and meaning in life and worsen existing feelings of negative affect, all of which may increase cigarette smoking among those with depressive symptoms. For example, compared with their peers who reported lower levels of negative religious coping, individuals living with HIV/AIDS who reported higher utilization of negative religious coping strategies subsequently reported significantly higher levels of depressive symptoms and more symptoms associated with HIV/AIDS (Trevino et al., 2010).
Occupational Status and Sense of Control Through a Divine OtherIndividuals in blue-collar occupations, which rank low on occupational prestige, tend to lack autonomy in their daily work and a sense of control at their job, which increase their risk for experiencing depressive symptoms (Link, Lennon, & Dohrenwend, 1993). These individuals are also more likely than white-collar workers to have higher rates of current smoking (Barbeau, Krieger, & Soobader, 2004). Blue-collar workers may lack the coping resources typically afforded only to those with higher levels of socioeconomic status and thus, may possibly gain a sense of control through a relationship with a divine other (Pollner, 1989). In this way, religious coping could be a coping resource that fills a specific need for blue-collar workers who perceive a lack of control in their life. Vocational students enrolled in postsecondary 2-year vocational/technical school programs, which prepare students for blue-collar occupations (e.g., welding, air-conditioning), also report higher smoking rates than the general adult population (Loukas, Murphy, & Gottlieb, 2007), making vocational students an excellent population for the current study.
Gender Differences Across Study VariablesA discussion of depressive symptoms, cigarette smoking, and religious coping would not be complete without examination of gender differences. Women are twice as likely as men to suffer from depressive symptoms (Nolen-Hoeksema, 2001) and women report more cigarette smoking in response to negative affect (Brandon & Baker, 1991). It follows, then, that depressive symptoms may predict women’s smoking outcomes to a greater degree than men’s. Women report more religious involvement than men across a variety of indicators (see Beit-Hallahmi & Argyle, 1997). Likewise, women’s stronger propensity to rely upon and cultivate relationships with others, including a divine other (Pollner, 1989), increases the likelihood that the direct and moderating influence of religious coping will be greater for them compared with men. Moreover, societal norms tend to suggest that women are more reliant upon others to deal with the stressors they face, whereas men are expected to be more self-reliant when working through their problems. So, in this sense, women may be more likely than men to seek out, engage in, and benefit from a collaborative relationship with a divine other, whereas men may be more likely to deal with their problems without such a reliance on a divine other (Maynard, Gorsuch, & Bjorck, 2001).
Study HypothesesFindings in the literature indicate that religious and nonreligious coping are correlated and that even among those who draw on religious coping to deal with stress, nonreligious coping is also a fundamental part of their coping repertoire (Pargament, 1997). To provide a conservative test of the impact of religious coping on smoking, the overlapping variance associated with nonreligious problem-focused (active coping, use of instrumental social support) and emotion-focused coping (denial, self-blame) was removed. Thus, we tested whether or not religious coping makes a unique contribution to cigarette smoking over and above the contribution of nonreligious coping. Moreover, because age and financial stress are positively correlated with cigarette smoking (Siahpush, Borland, & Scollo, 2003) and because cigarette smoking outcomes vary by race (Kiviniemi, Oromo, & Giovino, 2011), this study also controlled for these variables. In summary, the current study examined whether positive and negative religious coping moderated the relationship between depressive symptoms and the likelihood of current cigarette use, over and above the contributions of age, race, financial stress, and nonreligious problem- and emotion-focused coping. We hypothesized that:
1. Depressive symptoms, negative religious coping, and emotion-focused nonreligious coping would be directly associated with higher levels of past 30-day cigarette use, and positive religious coping and problem-focused nonreligious coping would be directly associated with lower levels of current cigarette use.
2. Positive religious coping would buffer or lessen the impact of depressive symptoms on past 30-day cigarette use, whereas negative religious coping would exacerbate or worsen the negative impact of depressive symptoms on past 30-day cigarette use.
Method Participants
Study participants were drawn from a larger study comprised of a convenience sample of 1,120 students recruited from 81 required introductory- and advanced-level classes at two 2-year public colleges in Texas. Student enrollment at the participating schools was 2,590 and 9,582, respectively. The schools offer programs in areas including automotive technology, air-conditioning repair, allied health, business management, computer-aided drafting, medical technology, repair and manufacturing technology, and vocational nursing. Of the 1,434 students enrolled in the 81 classes, 1,131 were in class during the survey administration and 1,120 (78% of enrolled students) volunteered to complete the 117-item anonymous self-report Vocational Student Tobacco Use Survey. Students with any missing data were excluded from the analyses (n = 157); thus, the final sample size was 963.
Of the 963 students who had complete data, 46.8% were women, 53.2% were men; 38.5% were White, 23.9% were African American, 32.8% were Hispanic, 4.7% reported another race/ethnicity, and 10 participants did not provide their race/ethnicity. With regard to age (M = 25.30, SD = 8.59) 60.6% of the participants were aged 18 to 24, 25.6% were 25 to 34, 13.8% were older than 35, and 11 participants did not complete this item. Although respondents did not provide their religious affiliation, county-level data assessed in 2000 indicated that in the first county where data were collected religious affiliation was: Evangelical Protestant, 29.9%; Catholic, 25%; Mainline Protestant, 8.4%; Other; 1.1%; Orthodox, 0.2%; and Unclaimed, 35%. In the second county, religious affiliation was: Catholic, 41.2%, Evangelical Protestant, 16%; Mainline Protestant, 6.1%; Other, 1.8%; Orthodox, .09%, Unclaimed, 34.9% (Association of Statisticians of American Religious Bodies, 2000).
Procedure
Upon approval from the Institutional Review Boards (IRBs) at the University conducting the study and the participating college with an IRB, the researchers scheduled administration of the voluntary and anonymous survey during 25 minutes of class time. A total of 40 classes participated in data collection at the first school (30 classes fall 2007, 10 classes spring 2008). At the second school, a total of 41 classes (20 classes fall 2007, nine classes spring 2008, 12 classes summer 2008) participated in data collection. Across both schools, the survey was administered in 81 classes, from fall 2007 to summer 2008.
Measures
Demographic covariates
Students reported basic demographic information including gender, age, and race/ethnicity. One item from Pearlin, Menaghan, Lieberman, and Mullan’s (1981) measure of economic strain was used to assess students’ perceived financial stress. This item measured students’ financial situation “at the end of the month” (1 = some money left over; 2 = just enough to make ends meet; 3 = not enough money to make ends meet). A high score indicates higher levels of perceived financial stress.
Nonreligious coping covariates
Eight items (four subscales) from the 28-item Brief COPE (Carver, 1997) were used to assess how often students engaged in four different types of nonreligious coping strategies. Students were asked to “think about how you try to understand and deal with major problems in your life. To what extent is each involved in the way you cope?” The four types of nonreligious coping were measured with two items each. Two items measured active coping (e.g., “I’ve been concentrating my efforts on doing something about the situation I’m in.”); two items measured instrumental support (e.g., “I’ve been trying to get advice or help from other people about what to do.”); two items measured denial (e.g., “I’ve been refusing to believe that it has happened.”); and two items measured self-blame (e.g., “I’ve been blaming myself for things that happened.”). Each item was scored on a scale from 0 to 3 and a high score indicated greater use of a coping strategy. Based on an exploratory factor analysis and previous research (Carver, Scheier, & Weintraub, 1989), two common types of nonreligious coping—problem-focused (active coping, using instrumental support) and emotion-focused (denial, self-blame) coping—served as control variables in this study. In this sample, Cronbach’s alpha for problem-focused coping was 0.87 and 0.84, for women and men, respectively. Cronbach’s alpha for emotion-focused coping was 0.85 and 0.87, for women and men, respectively.
Religious coping
Five items were selected from the 6-item Brief RCOPE (Pargament, 1999) to assess how often (0 = a great deal to 3 = not at all) students engage in both positive and negative forms of religious coping when dealing with a major life problem. Prior to answering the religious coping items, students were asked to “think about how you try to understand and deal with major problems in your life. To what extent is each of these involved in the way you cope?” Three items measured positive religious coping (e.g., “I work together with God as partners to get through the hard times.”) and two items measured negative religious coping (e.g., “I feel that stressful situations are God’s way of punishing me for my sins or lack of spirituality.”). The self-directing negative religious coping item (“I try to make sense of the situation and decide what to do without relying on God.”) is considered to be conceptually distinct from the other negative religious coping items and thus, was removed. That is, whereas the two remaining negative religious coping items consider the individual’s relationship with God, the self-directing negative religious coping item taps into the individual’s actions apart from a relationship with God and in this way may be more similar to other nonreligious coping strategies. Each item was reverse coded so that higher scores indicate greater use of positive and negative religious coping strategies.
Although past research studies generally report good reliability for positive religious coping (e.g., α = .81), Cronbach’s alpha tends to be lower for negative religious coping (e.g., α = .58; see Pargament et al., 2001). Consistent with Pargament’s study, the Cronbach’s alpha for the 3-item positive religious coping subscale was 0.86 and 0.90, for women and men, respectively, whereas Cronbach’s alpha for the 2-item negative religious coping subscale was 0.59 and 0.65, for women and men, respectively.
Depressive symptoms
The Center for Epidemiologic Studies Depression Scale (CES-D; Radloff, 1977) is a 20-item measure of the frequency and severity of depressive symptoms occurring in the past week. Students indicated how often in the past week they experienced symptoms of the following four subscales: somatic complaints, depressive affect, positive affect, and interpersonal problems. Each item (e.g., “I felt that I could not shake off the blues even with help from my family or friends.”) is scored on a scale ranging from 0 = rarely or none of the time; less than 1 day to 3 = most or all of the time; 5 to 7 days; therefore, a high score indicates elevated levels of depressive symptomatology. Previous studies have validated the four-factor structure of the CES-D in adult samples (Golding & Aneshensel, 1989) and provided evidence of reliability for this well-established measure of depressive symptoms. Cronbach’s alpha for the CES-D measure was 0.85 and 0.83 for women and men, respectively, indicating that the measure has good internal consistency reliability in the current sample.
Quantity−frequency of current cigarette use
Current cigarette use was measured using two items from the National College Health Risk Behavior Survey (Centers for Disease Control & Prevention, 1997). One item measured frequency of current cigarette use (e.g., “During the past 30 days, on how many days did you smoke cigarettes?”). Students identified how often they smoke cigarettes on a scale ranging from 0 = 0 days to 6 = all 30 days. One item measured quantity of current cigarette use (e.g., “During the past 30 days, on the days you smoked, how many cigarettes did you smoke per day?”). Students identified the quantity of current cigarette use on a scale ranging from 0 = I did not smoke cigarettes during the past 30 days to 6 = more than 20 cigarettes per day. Similar to Schleicher, Harris, and Catley (2009), the two current cigarette use items were multiplied together to create a quantity−frequency variable for cigarette use, which provided a count of the quantity−frequency of cigarettes smoked during the 30 days prior to the survey and ranged from 0 to 36.
Missing Data Analysis
Data from 157 students were removed from the current sample because they were missing substantial amounts of data. A series of analyses were conducted to examine if the data from these 157 students differed from the 963 that were retained for study analyses, potentially resulting in an exclusion bias. The nonparametric Mann–Whitney U test indicated that the distribution of scores on the quantity−frequency of cigarette smoking variable did not differ between the two groups (p = .87). Similarly, independent samples t tests indicated that there were no differences between the two groups on positive religious coping [t(1040) = 1.79, p = .08], negative religious coping [t(1038) = 1.17, p = .24], or depressive symptoms [t(1083) = 1.42, p = .16]. Thus, removal of 157 students’ data from the current sample did not result in exclusion bias.
Data Analysis
Negative binomial regression was used to examine the unique contributions of positive and negative religious coping to the quantity−frequency of past 30-day cigarette use after accounting for the contributions of the covariates (age, race, perceived financial stress, problem- and emotion-focused coping). Negative binomial regression is the preferred statistical method when the dependent variable represents a count of events that are non-normally distributed (Hox, 2010). In the present study, 70.2% of participants received a 0 (indicating they were nonsmokers) on the quantity−frequency variable; thus, this count variable was non-normally distributed.
To determine if the association between depressive symptoms and current cigarette use varied (was moderated) by use of positive and negative religious coping, two separate two-way interactions between depressive symptoms and religious coping were tested. In order to determine the nature of a significant interaction, the methods outlined by Aiken and West (1991) were used. Specifically, the relationship between depressive symptoms and quantity−frequency of past 30-day cigarette use was examined at high (1 SD above the mean value) and low (1 SD below the mean value) levels of the positive or negative religious coping variable. In order to prevent problems with multicollinearity, the nonreligious coping (problem- and emotion-focused) covariates and the depressive symptoms and religious coping (positive and negative) predictor variables were each mean-centered (Aiken & West, 1991).
Each of the covariates (age, race, financial stress, problem- and emotion-focused coping) and all three predictor variables (depressive symptoms, positive and negative religious coping) were entered simultaneously in model one. Both of the two-way interaction terms (depressive symptoms x positive religious coping; depressive symptoms x negative religious coping) were tested independently in model two, with all covariates and main effects included.
Results Preliminary Analyses
Given that the comorbidity of cigarette smoking and depression vary across males and females (Husky, Mazure, Paliwal, & McKee, 2008), Box’s M test was used to determine the homogeneity of variance−covariance matrices of the study variables across gender in this study. Because Box’s M test was significant (Box’s M = 58.67, df = 36, p = .01) indicating that the variance−covariance matrices varied across gender, all subsequent models were run separately for male and female students.
Mean Differences Across Gender
Mean differences across gender in the study variables were examined for descriptive purposes and are presented in Table 1. With the exception of age, race, and emotion-focused nonreligious coping, results indicated a statistically significant difference at the p < .05 level in scores for each study variable across gender. Female students reported significantly more perceived financial stress, depressive symptoms, positive religious coping, and problem-focused coping than did male students. However, male students reported significantly more negative religious coping and quantity−frequency of past 30-day cigarette use than did female students.
Means, Standard Deviations, and Zero-Order Correlations (R) for Male (n = 512) and Female (n = 451) Students’ Study Variables
Zero-Order Correlations
Prior to testing the study hypotheses, zero-order correlations between the independent and dependent variables for males and females were examined. As shown in Table 1, compared with their non-White peers, both White male and White female students were more likely to smoke cigarettes in the past month. Perceived financial stress, depressive symptoms, and problem-focused nonreligious coping were each associated with increased smoking among female students. Lastly, positive religious coping was associated with decreased cigarette smoking in the past month among female students.
Main Effects
The negative binomial regression, for model one, predicting quantity−frequency of past 30-day cigarette use from the covariates, depressive symptoms, and positive and negative religious coping was statistically significant for male, χ2(8) = 49.08, p < .001, and female, χ2(8) = 159.27, p < .001, students (see Table 2, model one). Among the covariates, age and race were both uniquely associated with past 30-day cigarette use for male and female students, whereas financial stress and emotion-focused coping were uniquely associated with past 30-day cigarette use, but only for female students. Among the predictor variables, depressive symptoms and positive religious coping were significantly associated with quantity−frequency of past 30-day cigarette use, but for females only.
Negative Binomial Regression Analysis for Variables Predicting Quantity-Frequency of Past 30-Day Cigarette Use for Male (n = 512) and Female (n = 451) Students
Given that the negative binomial regression model is log-linear, it is possible to convert the regression coefficients into the predicted multiplicative effect of a 1-unit change in the variable of interest (i.e., depressive symptoms) on the count of cigarette use, holding all other variables constant (Coxe, West, & Aiken, 2009). For example, the exponentiation of the regression coefficient for financial stress (e0.37 = 1.45) indicates the multiplicative difference in quantity−frequency of cigarettes smoked based on a female student’s financial stress. Thus, a female student with a perceived financial stress score of 3 is expected to have a quantity−frequency score for cigarettes smoked that is, on average, 1.45 times greater than a student with a perceived financial stress score of 2. Examination of the exponentiation of the regression coefficients for the other covariates indicate that for both males and females, a student who is 25-years-old is expected to have a quantity−frequency score that is, on average, 1.02 times greater than a 24-year-old student of the same gender and that White male and female students are expected to have quantity−frequency scores that are, on average, 1.92 and 2.61 times greater than their non-White counterparts, respectively. In addition, a female with an emotion-focused coping score of 9 is expected to have a quantity–frequency score for cigarettes smoked that is, on average, 1.05 times greater than a female with an emotion-focused coping score of 8.
Regarding the exponentiation of the regression coefficients for the female depressive symptoms and positive religious coping predictor variables (see Table 2), results indicate that a student with a depressive symptoms score of 20 is expected to have a quantity−frequency score for cigarettes smoked that is, on average, 1.03 times greater than a student with a depressive symptoms score of 19. Furthermore, a 1-unit increase in positive religious coping causes the expected quantity−frequency score for cigarettes smoked by female students to decrease by a factor of 0.93.
Interaction Effects
As shown in Table 2, model two, four two-way interactions were significant: two between depressive symptoms and positive religious coping, one for males, χ2(9) = 57.08, p < .001, and one for females, χ2(9) = 167.80, p < .001, and two between depressive symptoms and negative religious coping, one for males, χ2(9) = 55.01, p < .001, and one for females, χ2(9) = 171.87, p < .001. Probing the significant 2-way interaction between depressive symptoms and negative religious coping for female students revealed that depressive symptoms were significantly associated with cigarette smoking during the past month at high level of religious coping (β = .05, p < .001), but not low levels of religious coping (β = .01, p > .05; see Figure 1). As expected, findings indicated that negative religious coping exacerbated the influence of depressive symptoms on the likelihood of female students’ past 30-day cigarette use. On the other hand, probing male students’ depressive symptoms x negative religious coping interaction revealed that depressive symptoms were not associated with quantity−frequency of past 30-day cigarette smoking at high levels of negative religious coping (β = .003, p > .10), but were marginally associated with past month smoking at low levels of negative religious coping (β = −02, p = .086). Given the nonsignificant and marginal associations, this finding is not discussed further.
Figure 1. Examining the depressive symptoms × Negative Religious Coping interaction for female students.
Probing the significant depressive symptoms x positive religious coping interaction for female students indicated that depressive symptoms were significantly associated with quantity−frequency of past 30-day cigarette use at high levels of positive religious coping (β = .05, p < .001), but not low levels of positive religious coping (β = .003, p > .05). Although unexpected, these results indicate that high levels of positive religious coping exacerbated the influence of depressive symptoms on the likelihood of females’ current cigarette use. However, examination of the point estimates in Figure 2 indicate that high levels of positive religious coping may be protective for female students reporting low levels of depressive symptoms, but the protective effect is no longer present for those females with high levels of depressive symptoms. For males, probing the depressive symptoms x positive religious coping interaction revealed that depressive symptoms were marginally associated with quantity−frequency of past month cigarette use at high (β = .02, p = .08) and low (β = −.02, p = .07) levels of positive religious coping. Given these findings, this interaction is not further discussed.
Figure 2. Examining the depressive symptoms × Positive Religious Coping interaction for female students.
DiscussionDespite the growing amount of research examining the influence of religious involvement on various health behaviors, our knowledge of the influence of religious coping on the association between depressive symptoms and cigarette use is lacking. This study extends existing research by examining religious coping as a moderator of the relationship between depressive symptoms and the likelihood of past 30-day cigarette use, while controlling for the influence of nonreligious coping and other covariates in a racially/ethnically diverse sample of vocational/technical school students. In partial support of Hypothesis 1, findings indicated that depressive symptoms were associated with an increased likelihood of cigarette use among female students, whereas positive religious coping decreased the likelihood of female students’ cigarette use, even after controlling for demographic variables and nonreligious coping. Corroborating theory on the role of religious coping, results also showed that negative religious coping exacerbated the influence of depressive symptoms on the likelihood of cigarette use for females. Unexpectedly, however, positive religious coping also exacerbated the influence of depressive symptoms on the likelihood of female cigarette use.
Consistent with Tomkins’ (1966) negative affect model for smoking and prior research (Kenney & Holahan, 2008), depressive symptoms were associated with an increased likelihood of current cigarette use, but only among female students. Past studies report that women have higher rates of depression/depressive symptoms than men (see Nolen-Hoeksema, 2001) and are more likely to use tobacco when dealing with feelings of negative affect (Brandon & Baker, 1991). Moreover, this finding supports those of others showing that depression (Husky et al., 2008) is more strongly associated with cigarette smoking in women than men. The positive association between depressive symptoms and smoking lends itself to the paradox of coping and substance (cigarette) use noted by Brandon, Herzog, Irvin, and Gwaltney, 2004: cigarette smoking functions both as a way to deal with one’s stress and a deleterious health outcome associated with other unproductive efforts to deal with stress.
Additional findings indicated that in comparison with their counterparts, female vocational students who used positive religious coping were less likely to report current cigarette use. Just as Tomkins’ (1966) negative affect model for smoking suggests that some smoke to relieve their stress, findings from this study suggest that female vocational students may use positive religious coping to deal with their stress, but without the health risks of smoking. It is likely that individuals who see themselves as working together with a divine other to deal with their problems may be able to better adapt to their stressful situations (see review by Koenig et al., 2001). These findings add to the growing literature showing the beneficial influence of various aspects of religion on females’ health behaviors (Koenig et al., 2001).
Contrary to the findings for positive religious coping, negative religious coping was not directly associated with past 30-day smoking. However, results confirmed our expectations that negative religious coping would interact with depressive symptoms and exacerbate its impact on the likelihood of current cigarette use. Specifically, female students who reported high levels of depressive symptoms and also engaged in high levels of negative religious coping had an increased likelihood of current cigarette use. Individuals who experience depressive symptoms often exhibit negative emotions and behaviors that turn others away from them, eroding their network of support (Coyne, 1976). Therefore, female students who have strained social relationships associated with depressive symptoms may also feel that God has abandoned them and that their stress is a result of God punishing them. For this reason, female students may be at increased risk for smoking cigarettes.
Unexpectedly, high levels of positive religious coping exacerbated the detrimental impact of depressive symptoms on the likelihood of female students’ current cigarette smoking. In particular, although women with low levels of depressive symptoms may have benefited from the use of positive religious coping, women with high levels of depressive symptoms did not. These findings are partially inconsistent with the direct salutary contribution of positive religious coping to female students’ cigarette use and with our expectation that positive religious coping would buffer the impact of depressive symptoms on current smoking. However, results are consistent with previous findings that positive religious coping exacerbated the influence of perceived racial/ethnic discrimination on the likelihood of African American students’ current cigarette use (Horton & Loukas, 2013). According to Pargament (1997), overreliance on a divine other to assist with or take control of a situation, particularly when an individual needs to take action to deal with their stress, can have harmful consequences. It is possible then that female students with high levels of depressive symptoms may cede personal control over their situation and rely too much on a divine other to get them through difficult situations (Pargament, 1999). Overreliance on positive religious coping to deal with the stress of depressive symptoms may also indicate female students’ inability to assess the best way to deal with their situation. This explanation supports Pargament’s warning of the potential breakdown in the coping process that can occur when the use of religious and nonreligious coping resources are not coordinated appropriately to address a stressful situation (Pargament, 1997). Another explanation for this unexpected finding may be that individuals tend to use all available coping resources, whether positive or negative, to deal with elevated levels of distress associated with depressive symptoms (Coyne, Aldwin, & Lazarus, 1981).
Surprisingly, neither depressive symptoms nor religious coping contributed to the likelihood of males’ smoking. The lack of findings for male students may be due in part to males’ lower mean scores for depressive symptoms and for positive religious coping. The lack of findings for males may also be attributed to the fact that the strength of association between affective disorders and cigarette smoking is weaker for males than females (Brandon & Baker, 1991). Regarding religious involvement, other variables may be better predictors of cigarette smoking for males and should be pursued in future studies. Given that men tend to be less collaborative and less religiously involved than women (Pew Research Center, 2008), subsequent studies should consider the roles of self-directing religious coping (dealing with one’s stress independent of a divine other) and spiritual discontent (feelings of anger and distancing oneself from a divine other) in male vocational school students’ smoking.
The limitations of the current study must be taken into account when interpreting the findings. The cross-sectional study design limits our ability to examine the temporal relationships between the study variables. Future studies should assess both the short- and long-term influences of religious coping on the relationship between depressive symptoms and cigarette smoking using full measures of religious and nonreligious coping. Due to space limitations, a brief form of the religious coping measure and selected subscales of the nonreligious coping measure were used in the present study. Another limitation is the low internal consistency reliability of the 2-item negative religious coping subscale, which may have influenced the study findings. The low internal consistency reliability of negative religious coping may be due to the use of two diverse items measuring struggle with a divine other. Subsequent studies using measures that tap into other more specific aspects of negative religious coping should be conducted to determine if the pattern of findings reported in the present study is replicated. Additionally, the absence of data on the religious affiliation and level of organizational religious involvement of the study participants limits the ability to assess the influence of institutional aspects of religiousness on the study findings. Finally, findings are limited to the examination of religious coping by those affiliated with a monotheistic faith and our measures of religious coping do not include assessment of spiritual coping outside of the influence of organized religion. Future studies should, therefore, examine religious coping for individuals of other faiths and incorporate examination of the role of spirituality in the association between depressive symptoms and smoking.
Notwithstanding these limitations, this study contributes to the literature in several areas, including religious coping, cigarette smoking, and vocational students training to work in blue-collar occupations. This research demonstrated that the direct and moderating influences of religious coping on the likelihood of cigarette smoking occurred in ways that were expected and unexpected. As expected, findings indicated that positive religious coping was associated with a decreased likelihood of female students’ cigarette use and negative religious coping exacerbated the influence of high levels of depressive symptoms on the likelihood of females’ smoking. Although unexpected, this research also suggests that overreliance on positive religious coping increased the likelihood of cigarette smoking for females who reported high levels of depressive symptoms. Simultaneous examination of both the personal (i.e., religious coping, attachment to God) and organizational (i.e., religious affiliation and religious service attendance) measures of religious involvement, depressive symptoms, and cigarette use is needed for a finer-grained analysis of these associations.
There are practical implications to the finding that religious coping is relevant to the female vocational students participating in this study. For females to whom religion is important, religious coping serves as an addition to their repertoire of existing coping resources from which they can draw upon. In particular, such women may also benefit from the social support of those within their religious community and the proscriptive norms of their faith that discourage harmful health behaviors such as smoking (Koenig et al., 2001). The implication of these findings for women who are currently trying to quit smoking, is that in addition to other resources, religious coping may contribute to decreases in smoking.
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Submitted: December 22, 2011 Revised: July 18, 2012 Accepted: October 16, 2012
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Source: Psychology of Addictive Behaviors. Vol. 27. (3), Sep, 2013 pp. 705-713)
Accession Number: 2012-34896-001
Digital Object Identifier: 10.1037/a0031195
Record: 45- Title:
- Detecting emotional expression in face-to-face and online breast cancer support groups.
- Authors:
- Liess, Anna. Stanford University School of Medicine, Stanford, CA, US
Simon, Wendy. Stanford University School of Medicine, Stanford, CA, US
Yutsis, Maya. Stanford University School of Medicine, Stanford, CA, US
Owen, Jason E.. Department of Psychology, Loma Linda University, Loma Linda, CA, US
Piemme, Karen Altree. Stanford University School of Medicine, Stanford, CA, US
Golant, Mitch. The Wellness Community-National, Santa Monica, CA, US
Giese-Davis, Janine. Stanford University School of Medicine, Stanford, CA, US, jgiese@stanford.edu - Address:
- Giese-Davis, Janine, Department of Psychiatry and Behavioral Sciences, 401 Quarry Road, Room 2318, MC#5718, Stanford, CA, US, 94305, jgiese@stanford.edu
- Source:
- Journal of Consulting and Clinical Psychology, Vol 76(3), Jun, 2008. pp. 517-523.
- NLM Title Abbreviation:
- J Consult Clin Psychol
- Page Count:
- 7
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- Journal of Consulting Psychology
- Other Publishers:
- US : American Association for Applied Psychology
US : Dentan Printing Company
US : Science Press Printing Company - ISSN:
- 0022-006X (Print)
1939-2117 (Electronic) - Language:
- English
- Keywords:
- emotional expression, text analysis, breast cancer, group therapy, video coding
- Abstract:
- Accurately detecting emotional expression in women with primary breast cancer participating in support groups may be important for therapists and researchers. In 2 small studies (N = 20 and N = 16), the authors examined whether video coding, human text coding, and automated text analysis provided consistent estimates of the level of emotional expression. In Study 1, the authors compared coding from videotapes and text transcripts of face-to-face groups. In Study 2, the authors examined transcripts of online synchronous groups. The authors found that human text coding significantly overestimated Positive Affect and underestimated Defensive/Hostile Affect compared with video coding. They found correlations were low for Positive Affect but moderate for negative affect between Linguistic Inquiry Word Count (LIWC) and video coding. The implications of utilizing text-only detection of emotion are discussed. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Breast Neoplasms; *Emotional States; *Emotionality (Personality); *Measurement; *Support Groups; Group Psychotherapy; Internet; Videotapes; Written Communication
- Medical Subject Headings (MeSH):
- Adult; Affect; Aged; Automatic Data Processing; Breast Neoplasms; Facial Expression; Female; Humans; Internet; Middle Aged; Psychotherapy, Group; Signal Detection, Psychological; Social Support; Videotape Recording
- PsycINFO Classification:
- Research Methods & Experimental Design (2260)
Behavioral & Psychological Treatment of Physical Illness (3361) - Population:
- Human
Female - Location:
- US
- Age Group:
- Adulthood (18 yrs & older)
Thirties (30-39 yrs)
Middle Age (40-64 yrs) - Grant Sponsorship:
- Sponsor: Stanford School of Medicine, US
Other Details: medical scholars project
Recipients: Liess, Anna
Sponsor: California Breast Cancer Research Program, US
Grant Number: 1FB-0383; 4BB-2901; 9IB-0191; 5JB-0102
Recipients: No recipient indicated
Sponsor: Kozmetsky Global Collaboratory
Recipients: No recipient indicated
Sponsor: National Institute on Aging/National Cancer Institute, US
Grant Number: AG18784
Other Details: Program Project, David Spiegel
Recipients: No recipient indicated - Conference:
- The Society of Behavioral Medicine Annual Meeting, 25th, Mar, 2004, Baltimore, MD, US
- Conference Notes:
- Portions of this article were presented at the aforementioned meeting, and at The Society of Behavioral Medicine 23rd Annual Meeting, April 3-6, 2002, in Washington, DC.
- Methodology:
- Empirical Study; Quantitative Study
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- Accepted: Feb 20, 2008; Revised: Feb 5, 2008; First Submitted: May 9, 2007
- Release Date:
- 20080609
- Copyright:
- American Psychological Association. 2008
- Digital Object Identifier:
- http://dx.doi.org/10.1037/0022-006X.76.3.517
- PMID:
- 18540745
- Accession Number:
- 2008-06469-016
- Number of Citations in Source:
- 17
- Persistent link to this record (Permalink):
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Detecting Emotional Expression in Face-to-Face and Online Breast Cancer Support Groups
By: Anna Liess
Stanford University School of Medicine
Wendy Simon
Stanford University School of Medicine
Maya Yutsis
Stanford University School of Medicine
Jason E. Owen
Department of Psychology, Loma Linda University
Karen Altree Piemme
Stanford University School of Medicine
Mitch Golant
The Wellness Community—National, Santa Monica, California
Janine Giese-Davis
Stanford University School of Medicine;
Acknowledgement: This research was conducted in partial fulfillment of a medical scholar's project funded through the Stanford School of Medicine to Anna Liess. Additionally part of the research presented was conducted in partial fulfillment of a Stanford undergraduate Honor's Thesis in Biology by Wendy Simon, both under the mentorship of Janine Giese-Davis. The research was supported in part by California Breast Cancer Research Program Grants 1FB-0383, 4BB-2901, and 9IB-0191, 5JB-0102, the Kozmetsky Global Collaboratory, and NIA/NCI Program Project AG18784 to David Spiegel. Portions of this article were presented at The Society of Behavioral Medicine 25th Annual Meeting, March 24–27, 2004, in Baltimore, Maryland, and The Society of Behavioral Medicine 23rd Annual Meeting, April 3–6, 2002, in Washington, DC. We would like to acknowledge Morton Lieberman, principal investigator of the online study; Helena C. Kraemer, biostatistician; coding lab programmers Suzanne Twirbutt and Theo Chakkapark; linguist Alex Gruenstein; coders Kristina Roos and Kinsey McCormick; the rest of the emotion coders at The Emotion Coding Lab—Stanford (Giese-Davis.com); and the women with breast cancer who agreed to be videotaped and who participated in our studies.
Women with breast cancer experience reductions in distress and pain, a decrease in emotional suppression, and increased restraint of hostility after participation in face-to-face (F2F) groups that facilitate emotional expression (Classen et al., 2001; Giese-Davis et al., 2002). They also experience decreased distress, greater heart-rate habituation to writing, and declines in physical symptoms while expressing emotions in written text (Low, Stanton, & Danoff-Burg, 2006). Online synchronous groups (OSGs) for breast cancer combine group support and written text and may similarly decrease distress and pain (Lieberman et al., 2003; Winzelberg et al., 2003) but may lead to increased emotional suppression (Lieberman et al., 2003).
Detecting emotional expression in these groups may be important for therapists and researchers testing whether expression mediates outcomes. Researchers often analyze expression in text transcripts of therapy rather than use video analysis (Low et al., 2006). Likewise, in OSGs, therapists have only text on which to rely for emotional cues. Detecting emotion in text for either would be compromised if some emotion categories rely heavily on non-verbal channels.
Early research on emotion communication channels (i.e., voice, face, text content) indicated that people watching videotapes of social interactions were significantly more accurate (those reading a transcript not exceeding chance levels) when asked interpretive questions (Archer & Akert, 1977). Detecting deceptive negative affect relies on vocal, facial, and text content cues (O'Sullivan, Ekman, Friesen, & Sherer, 1985). However, honest positive affect is difficult to detect in the absence of vocal (Krauss, Appel, Morency, Wenzel, & Winton, 1981), facial, or body cues (O'Sullivan et al., 1985). Since a host of computer-mediated interventions for cancer patients (e-mail, real-time text correspondence, online forums, and electronic support groups; Davison, Pennebaker, & Dickerson, 2000) are now in common practice, we thought it timely to investigate detection of emotional expression comparing human coding of videotape and text transcripts of F2F groups as well as human and text analysis (Pennebaker & Francis, 1999) of F2F groups and OSGs. By using trained human coders and strict reliability standards, we believe we offer the best-case scenario for detection of expression in both videotape and text.
Detecting emotional expression in therapy is a complex process using (a) observational coding from videotape (Giese-Davis, DiMiceli, Sephton, & Spiegel, 2006; Giese-Davis, Piemme, Dillon, & Twirbutt, 2005), (b) coding of transcripts (Grabhorn, 1998), (c) automated text analysis (Low et al., 2006), or (d) content analysis (LaBarge, Von Dras, & Wingbermuehle, 1998). Regardless of method, affect constructs are often labeled with the same words (e.g., positive, negative, or defensive affect) even when assumptions differ dramatically. Trained raters may be in the best position to accurately identify expression in therapy contexts, but instead automated text analysis is increasingly used. The use of these programs assumes that words carry psychological information independent of context (Krippendorf, 2004). Because expression is highly contextual, irony, sarcasm, and anxiety may be missed by text analysis (Davison et al., 2000). Few studies have compared methods. In our two studies, we compare levels of emotion detected from human coding of video versus text (Study 1) in F2F groups and whether text analysis correlates with human coding of similar constructs (Study 1 and 2) in F2F groups and OSGs.
- Hypothesis 1: Differences in levels of affect.
- Due to greater reliance on non-verbal cues, human text coding will underestimate some video-coding categories: validation, high and low affection (included in Positive Affect), high fear, sadness, direct anger (included in Primary Negative Affect), tension, micro-moment contempt, belligerence, disgust, and stonewalling (included in Defensive/Hostile Affect). However, because some categories may be overestimated by text coding, our tests are two-tailed (e.g., interest, which is coded from video when there is a genuine positive voice tone, not just when a woman asks a question). Our study was powered to test summary variables.
- Hypothesis 2: Multimethod–multitrait consistency.
- Though levels of video and text may differ, strong convergent correlations will indicate that coders utilized similar cues. The strength of correlations among categories will replicate within each method (Kenny & Campbell, 1989). These analyses are necessary to examine whether constructs are consistent across methods.
- Exploration: Human and automated text analysis.
- Human coding will correlate significantly with text analysis of similar constructs. These correlations will be larger in OSGs than F2F groups because communication must rely on clarity in text rather than on non-verbal cues.
Method Therapy Model
Both studies were community/research collaborations (March 8, 2000–January 16, 2002) between The Wellness Community (TWC), Stanford University, and the University of California San Francisco (UCSF). In Study 1, we selected women randomized to TWC in a study comparing supportive–expressive (SET) groups with TWC groups. In Study 2, women participated in TWC OSGs (Lieberman et al., 2003).
TWC offered free F2F therapy in Study 1 as part of their ongoing support programs serving over 5,000 cancer patients each week. Groups of 12 participated in weekly, 2-hr groups led by one therapist for 16 weeks. In TWC's “patient-active” model, therapists encourage patients to (a) become empowered; (b) partner with physicians; (c) access resources; (d) make active choices in their recovery; and (e) reduce unwanted aloneness, loss of control, and loss of hope. In Study 2, TWC offered free OSGs where groups of 8 participated in two non-randomized 90-min groups (4 total) led by one therapist for 16 weeks. Women could access a private, 24-hr, TWC-based newsgroup by cohort. The same therapy model was used for both studies.
Participants
Recruitment for both studies was through collaborating community organizations and general advertising, and for Study 2 through online postings on breast-cancer-related sites. Women were eligible if they were over 18 years old, diagnosed with physician-confirmed primary breast cancer (Stages I–III, without metastasis or recurrence), English-literate, less than 18-months posttreatment, and had not attended more than 8 support-group sessions. Women lived in the San Francisco East Bay in Study 1 and in California and throughout the United States in Study 2. Study procedures were approved by institutional review boards at Stanford and UCSF. Participants signed written informed consent and physician contact consents. In Study 1, women could earn up to $40 for completing questionnaires, but no payment was given in Study 2.
In Study 1, of 108 women contacted, 92 consented, 66 completed baseline data, 45 were randomized at the Walnut Creek site (22 to TWC, 23 to SET) and 18 were randomized to a second community site in San Francisco (10 to The Cancer Support Community, 8 to SET). Some attrition occurred due to time delays associated with block randomization. Of 22 women randomized to TWC, 20 attended sessions in two groups. A videographer taped each session, focusing on the woman speaking. In Study 2, women registered at www.twc-chat.org which provided information, consent details, and an invitation to participate. Of 67 women recruited, 35 did not participate due to scheduling difficulties, 32 consented and completed online measures at baseline, and 26 women completed the 16-week measures. We randomly selected 2 of 4 groups (N = 16). The OSG closely mimics F2F group interactions. Demographic and medical variables for both studies are in Table 1. Final sample for Study 1 is N = 20 and for Study 2 is N = 16.
Demographic and Medical Characteristics for Primary Breast Cancer Patients Participating in F2F Breast Cancer Support Groups (N = 20) and in TWC OSGs (N = 16)
Human and Automated Coding
For each study, we coded Sessions 2, 6, 10, and 15 for two groups. For F2F groups, we coded each participant's expression by using Specific Affect (SPAFF) for Breast Cancer (Giese-Davis et al., 2005) for Videotape and for Text (Giese-Davis et al., 2005) following professional transcription. For OSGs, we coded transcripts by using SPAFF for Text. We also conducted Linguistic Inquiry Word Count (LIWC) text analysis (Pennebaker & Francis, 1999) for each participant-by-session segment. We used mean scores across four sessions per woman for analyses.
We used SPAFF for Breast Cancer and SPAFF for Text (Giese-Davis et al., 2005) for human coding and tested hypotheses with Positive Affect (affection, affection with touch, interest, validation, genuine humor, and excitement), Primary Negative Affect (direct anger, low and high sadness, verbalized and high fear), and Defensive/Hostile Affect (tension, tense humor, whining, disgust, micro-moment contempt, verbalized contempt, domineering, and belligerence; Giese-Davis et al., 2005). For video, we coded 1 woman at a time at least twice (68 person-by-tape segments: Mean kappa = 0.70, SD = 0.09). For a kappa of .60 or higher, a coin toss determined which coder's data we used (50 segments). If kappa was below .55, it was recoded (9-by-3 and 3-by-5 coders). Six segments (kappa between .55 and .60) were consensus coded to maintain thresholds. We gave a transcriptionist the timing of speaking turns so that video and text segments were comparable in hours:minutes:seconds:frames. The median correlation between two coders of each F2F transcript was 0.54 (SD = 0.11) for percent time and 0.66 (SD = 0.14) for word count. Either coder's work provided the same magnitude of results. We used consensus coded OSG transcripts because median correlations were 0.58 (SD = 0.27) for percent time but 0.38 (SD = 0.34) for word count. Each separate emotional expression in a stream of data over time has a duration in seconds that we used to calculate percent-time data.
We used LIWC text analysis, which automatically matches each word to 1 or more of 82 language dimensions (Pennebaker & Francis, 1999). Summary scores are the number of words matching a dimension divided by total number of words. Current analysis focused on two dimensions: (a) Expression of Emotion included Positive Emotion (happy), Positive Feeling (joy, love), Optimism (pride, certainty), Assents (yes, OK), Question Marks, Negative Emotion (range of negative words), Sadness (grief, cry), Anger (pissed, hate), Anxiety (nervous, tense, afraid), and Negations (no, not); (b) Cognitive Mechanisms included Inhibition (always, never), Tentativeness (perhaps, might), and Discrepancy (should, would).
Data Analysis
We utilized percentages to assure comparability across methods (F2F groups ran for 2 hr while OSGs ran for 1.5 hr). We utilized percent time to compare video coding (which typically uses mean duration of time; Giese-Davis et al., 2006), with SPAFF text percent time. Time in transcripts was calculated as total time for each utterance divided by number of words. SPAFF text percent word count was used for correlations with LIWC (Table 2).
Type of Coding Measurement by Study Question
In Study 1, to compare methods of human coding, we used the non-parametric Friedman test (due to non-normal distributions) to compare summary variables for three related samples: SPAFF video percent time, SPAFF text percent time, and SPAFF text percent word count (Table 2 and 3; Figure 1). In the analysis of emotion, two measurement traditions exist: one based on duration of affect, one on word count. Because no prior comparisons give an indication of level differences between methods, we compared all three. If significant, we examined three pairwise comparisons with Wilcoxon signed ranks tests. To examine convergent correlations within and between methods, we utilized Spearman correlations (Table 3). We also explored whether the associations among SPAFF categories were consistent whether coded by video or text with a multitrait–multimethod matrix and Kenny and Campbell's method (Campbell & Fiske, 1959; Kenny & Campbell, 1989). If variables within both methods are similar, the same patterns should emerge in all hetero-trait triangles, both within the mono-method triangle (Table 4, italicized numbers) and hetero-method triangle (Table 4, bold numbers). For instance, the size of correlation between variables 1 and 2 in method A ought to be similar to the correlation between variables 1 and 2 in method B. Lastly, we explored correlations among LIWC and SPAFF categories thought to represent similar constructs (Table 5) in F2F groups and OSGs.
Median, 25th, and 75th Percentiles for Specific Affect Summary Codes for Videotape and Text in Women With Primary Breast Cancer (N = 20)
Figure 1. Graphed are box-and-whisker plots for each summary measure of each affect coded for Study 1 from videotape (Specific Affect for Breast Cancer) and transcripts of the videotapes (Specific Affect for Text): video percent time, transcript percent time, and transcript percent word count. Bottom line on whisker = the smallest observation; bottom line on box = lower quartile; middle line on box = median; top line on box = upper quartile; top line on whisker = largest observation; circles = mild outlier; stars = extreme outlier.
Spearman Correlations Among Emotion Categories for Specific Affect for Breast Cancer Video (Percent Time) and Specific Affect for Text (Percent Time)
Spearman Correlations Between SPAFF Video Coding and SPAFF Text Word Count With LIWC Affect Categories
Results Similarity Between Coding Methods in F2F Groups (Table 3, Figure 1)
Significantly more Positive Affect was coded in transcripts than in videotapes. Levels of Primary Negative Affect were generally low and were not different between transcripts and videotape. Significantly less Defensive/Hostile Affect was coded in transcripts than in videotapes. We had no hypotheses about Neutral and Constrained Anger, but we present these for the possible interest of readers.
For Positive Affect, video percent time was significantly lower than both text percent time (z = –3.65, p < .001) and percent word count (z = –2.14, p = .03). Text percent time was significantly higher than percent word count (z = –3.11, p = .002). For Defensive/Hostile Affect, video percent time was significantly higher than either text percent time (z = –3.43, p = .001) or text percent word count (z = –3.81, p < .001). Text percent time and word count did not differ.
Multimethod–Multitrait Consistency
Results indicate that emotion constructs are consistent across methods except for Defensive/Hostile Affect. We found strong convergent validity (Table 4, bold italicized numbers) between methods for video and text percent time: Neutral, Positive Affect, Primary Negative Affect, and Constrained Anger, but not for Defensive/Hostile Affect. We found similar patterns of correlation levels among emotion categories other than those with Defensive/Hostile Affect. For instance, in both the mono-method triangles (Table 4, italicized numbers) for video (emotion categories 1–5) and text coding (emotion categories 6–10), and the hetero-method video- and text-coding block (Table 4, bold numbers), Primary Negative Affect is moderately negatively correlated with Positive Affect, rs = –.24, –.39, and –.27, respectively.
SPAFF and LIWC Correlations (Table 5)
Positive affect variables from LIWC does not correlate significantly with SPAFF Positive Affect coded from video (r = –.34 to .19) or text (r = –.29 to .28) in F2F groups. In OSGs, where participants use emoticons to increase clarity, correlations between SPAFF and LIWC are higher but not significant for Positive Emotion, Positive Feeling, and Questions Marks. There are four negative correlations. Negative affect variables from LIWC correlates moderately with SPAFF F2F video and text Primary Negative Affect (r = .01 to .60, three significant) and Constrained Anger (r = .08 to .74, four significant), indicating that they may measure similar constructs. For negative affect variables in OSGs, SPAFF and LIWC correlations are not significant, and three are negative. LIWC variables thought to be similar correlate moderately with SPAFF F2F video and text Defensive/Hostile Affect (r = –.32 to .46). For Defensive/Hostile Affect in OSGs, SPAFF and LIWC correlations are moderate but not significant.
DiscussionWe compared levels of affect detected by human coders in F2F TWC groups and found that text coding overestimated Positive Affect and underestimated Defensive/Hostile Affect compared with video coding. Differences are likely because text cannot convey intonation, facial expression, and body posturing. We also examined correlations between human coding and automated text analysis in TWC F2F groups and OSGs, finding significant positive correlations for Primary Negative Affect, Constrained Anger, and Defensive/Hostile Affect, but none for Positive Affect.
Our research indicates that genuine positive affect is difficult to judge accurately from text in OSG and F2F groups and supports an earlier finding that accurate assessment of positive affect is more related to voice tone than content (O'Sullivan et al., 1985). We were surprised that significantly more Positive Affect was detected by coders in text than videotape and that text analysis correlated so poorly with human coding. An examination of video and text segments of mismatches indicated that a statement may seem positive on paper but may be interpreted as Defensive/Hostile Affect or Primary Negative Affect in the presence of defensive body posturing, a raised voice, or tears in the eyes. One can only speculate about how the probable increase in perception of positive affect and lack of cues for defensive/hostile affect might affect OSGs.
Non-verbal cues may be crucial for detecting Defensive/Hostile Affect because human coders identified significantly less from text than videotape, and convergent validity was low. If, like our coders, OSG therapists cannot detect hostility accurately, a lasting impact on participants' emotion regulation may be curtailed.
Rates of Primary Negative Affect detected in video and text were similar, and correlations between human coding and text analysis were higher, implying that Primary Negative Affect is conveyed to a greater extent through content. We found equally low levels in both F2F groups and OSGs.
This study was limited by small sample sizes, and the lack of economic and cultural diversity may have restricted the range of affect. Future research could randomize women to F2F groups versus OSGs so that statistical comparison is possible. However, these preliminary studies indicate that video and text methods of detecting emotion yield substantially different results.
We highlight ways in which the interpretation of written text can be changed by non-verbal cues in the following example. A common training technique for SPAFF is to ask the trainee simply to open a book to a random sentence. The trainee reads that sentence with the facial muscle movement, voice tone, and body movement of each one of the 20+ expressions coded by SPAFF. The same words can thus convincingly be used to convey emotional expressions as varied as validation, contempt, domineering, sadness, affection, and joy. Given that written text can be interpreted so broadly, examination of the coherence between methods of coding emotional expression, and caution when interpreting words in text as evidence of a particular emotion, seem crucial.
Based on these results, we recommend that researchers use caution when assessing computer-based and electronic psychotherapy services that depend solely on text. We also recommend that OSG therapists receive training to increase attention to cues for emotion in the absence of voice tone, facial movement, and body movement. OSG therapists may need to frequently double check their perception of affect to counteract their own tendency to see greater positive affect than is warranted (as did our human text coders) in order for these groups to be effective.
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Campbell, D. T., & Fiske, D. W. (1959). Convergent and discriminant validation by the multitrait–multimethod matrix. Psychological Bulletin, 56(2), 81–105.
Classen, C., Butler, L. D., Koopman, C., Miller, E., Dimiceli, S., Giese-Davis, J., et al. (2001). Supportive–expressive group therapy and distress in patients with metastatic breast cancer: A randomized clinical intervention trial. Archives of General Psychiatry, 58(5), 494–501.
Davison, K. P., Pennebaker, J. W., & Dickerson, S. S. (2000). Who talks? The social psychology of illness support groups. American Psychologist, 55(2), 205–217.
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Giese-Davis, J., Koopman, C., Butler, L. D., Classen, C., Cordova, M., Fobair, P., et al. (2002). Change in emotion regulation-strategy for women with metastatic breast cancer following supportive–expressive group therapy. Journal of Consulting and Clinical Psychology, 70(4), 916–925.
Giese-Davis, J., Piemme, K. A., Dillon, C., & Twirbutt, S. (2005). Macro-variables in affective expression in women with breast cancer participating in support groups. In J.Harrigan, K. R.Scherer, & R.Rosenthal (Eds.), Nonverbal behavior in the affective sciences: A handbook of research methods (pp. 399–445). Oxford, England: Oxford University Press.
Grabhorn, R. (1998). Affective experience in a case of group therapy with psychosomatic inpatients. Psychoanalytic Inquiry, 18(3), 490–511.
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Krauss, R. M., Appel, W., Morency, N., Wenzel, C., & Winton, W. (1981). Verbal, vocal, and visible factors in judgments of another's affect. Journal of Personality and Social Psychology, 40(2), 312–320.
Krippendorf, K. (2004). Content analysis. Thousand Oaks, CA: Sage.
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Submitted: May 9, 2007 Revised: February 5, 2008 Accepted: February 20, 2008
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Source: Journal of Consulting and Clinical Psychology. Vol. 76. (3), Jun, 2008 pp. 517-523)
Accession Number: 2008-06469-016
Digital Object Identifier: 10.1037/0022-006X.76.3.517
Record: 46- Title:
- Detecting well-being via computerized content analysis of brief diary entries.
- Authors:
- Tov, William. Singapore Management University, Singapore, Singapore, williamtov@smu.edu.sg
Ng, Kok Leong. Singapore Management University, Singapore, Singapore
Lin, Han. Division of Psychology, Nanyang Technological University, Singapore
Qiu, Lin. Division of Psychology, Nanyang Technological University, Singapore - Address:
- Tov, William, School of Social Sciences, Singapore Management University, 90 Stamford Road, Level 4, Singapore, Singapore, 178903, williamtov@smu.edu.sg
- Source:
- Psychological Assessment, Vol 25(4), Dec, 2013. pp. 1069-1078.
- NLM Title Abbreviation:
- Psychol Assess
- Page Count:
- 10
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- Psychological Assessment: A Journal of Consulting and Clinical Psychology
- ISSN:
- 1040-3590 (Print)
1939-134X (Electronic) - Language:
- English
- Keywords:
- content analysis, emotion, linguistic analysis, satisfaction, well-being, Linguistic Inquiry and Word Count, computerized content analysis, brief diary entries
- Abstract:
- Two studies evaluated the correspondence between self-reported well-being and codings of emotion and life content by the Linguistic Inquiry and Word Count (LIWC; Pennebaker, Booth, & Francis, 2011). Open-ended diary responses were collected from 206 participants daily for 3 weeks (Study 1) and from 139 participants twice a week for 8 weeks (Study 2). LIWC negative emotion consistently correlated with self-reported negative emotion. LIWC positive emotion correlated with self-reported positive emotion in Study 1 but not in Study 2. No correlations were observed with global life satisfaction. Using a co-occurrence coding method to combine LIWC emotion codings with life-content codings, we estimated the frequency of positive and negative events in 6 life domains (family, friends, academics, health, leisure, and money). Domain-specific event frequencies predicted self-reported satisfaction in all domains in Study 1 but not consistently in Study 2. We suggest that the correspondence between LIWC codings and self-reported well-being is affected by the number of writing samples collected per day as well as the target period (e.g., past day vs. past week) assessed by the self-report measure. Extensions and possible implications for the analyses of similar types of open-ended data (e.g., social media messages) are discussed. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Emotions; *Linguistics; *Well Being; *Words (Phonetic Units); *Journal Writing; Computer Software; Content Analysis; Life Satisfaction
- Medical Subject Headings (MeSH):
- Anxiety; Depression; Emotions; Female; Humans; Life Change Events; Linguistics; Male; Medical Records; Natural Language Processing; Psychometrics; Quality of Life; Reproducibility of Results; Singapore; Software; Students; Writing; Young Adult
- PsycINFO Classification:
- Research Methods & Experimental Design (2260)
Personality Traits & Processes (3120) - Population:
- Human
Male
Female - Location:
- Singapore
- Age Group:
- Adulthood (18 yrs & older)
- Tests & Measures:
- Quality of Life Inventory DOI: 10.1037/t03748-000
- Grant Sponsorship:
- Sponsor: Singapore Management University, Office of Research, Singapore
Grant Number: 08-C242-SMU-020 and 09-C242-SMU-019
Other Details: Internal Research Grants
Recipients: No recipient indicated - Methodology:
- Empirical Study; Quantitative Study
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- First Posted: Jun 3, 2013; Accepted: Apr 12, 2013; Revised: Apr 9, 2013; First Submitted: Jul 25, 2012
- Release Date:
- 20130603
- Correction Date:
- 20131209
- Copyright:
- American Psychological Association. 2013
- Digital Object Identifier:
- http://dx.doi.org/10.1037/a0033007
- PMID:
- 23730828
- Accession Number:
- 2013-19093-001
- Number of Citations in Source:
- 39
- Persistent link to this record (Permalink):
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- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2013-19093-001&site=ehost-live">Detecting well-being via computerized content analysis of brief diary entries.</A>
- Database:
- PsycINFO
Detecting Well-Being via Computerized Content Analysis of Brief Diary Entries
By: William Tov
School of Social Sciences, Singapore Management University, Singapore;
Kok Leong Ng
School of Social Sciences, Singapore Management University, Singapore
Han Lin
Division of Psychology, Nanyang Technological University, Singapore
Lin Qiu
Division of Psychology, Nanyang Technological University, Singapore
Acknowledgement: This research was supported by Internal Research Grants 08-C242-SMU-020 and 09-C242-SMU-019 from the Office of Research at Singapore Management University. We thank Euodia Chua, David Koh, Sharon Koh, Ciccy LV, Winnie Yeo, and Jose Yong for their assistance in processing the data.
Diary studies have made important contributions in both clinical (Thiele, Laireiter, & Baumann, 2002) and social–personality (Bolger, Davis, & Rafaeli, 2003) psychology. Diary methodology enables researchers to assess the ongoing experience of participants in their natural environment while mitigating potential biases in recall (Shiffman, Stone, & Hufford, 2008). Although most diary studies employ closed-ended items such as checklists and rating scales, open-ended items have also been useful—particularly in allowing participants to record personally meaningful thoughts and experiences. Lavallee and Campbell (1995) suggested that such experiences, though idiosyncratic and subjective, may correlate more strongly with measures of stress and emotional well-being than the objective items that tend to make up event checklists. The content of open-ended responses can be analyzed for particular themes. For instance, Craske, Rapee, Jackel, and Barlow (1989) asked people with generalized anxiety disorder (GAD) to record their most significant worry episodes over a 3-week period. Content analyses revealed that GAD participants reported more concerns about their health and less concerns about financial issues than did control participants. Similarly, Lavallee and Campbell’s (1995) participants described a negative event twice a day for 2 weeks. These written descriptions were then coded for the degree of self-focus. The authors showed that negative events were more likely to induce self-focus if they were related to important goals.
Despite the unique insights afforded by open-ended responses, a major drawback is the time required to develop a coding scheme and train research assistants to code the data accurately. The problem is further compounded in diary studies due to the potentially large volume of responses. Over the past decade, computerized programs such as the Linguistic Inquiry and Word Count (LIWC; Pennebaker, Booth, & Francis, 2011) have greatly facilitated the content analysis of written responses. LIWC analyzes written samples by counting words that fall into various categories (e.g., social, achievement, future tense, and so on) as defined by an internal dictionary. Because writing samples vary in length, word counts in each category are taken as a percentage of the total number of words in the sample.
Computerized content analysis can handle large volumes of open-ended responses at greatly reduced speeds, without sacrificing consistency in coding. Despite the promise of such methods, little is known about the applicability of LIWC to the sort of brief, written entries that are collected in diary studies. To date, many researchers have used LIWC to analyze narratives or descriptions of singular experiences (for a review, see Tausczik & Pennebaker, 2010). However, the structure of diary data may pose unique challenges. LIWC counts words in various categories but fails to consider the context in which they appear. Thus, two phrases like “I had a really good time” and “I didn’t have a good time” are coded equally for positive emotion. Though these shifts in meaning are no doubt present in written narratives, they may occur with increased frequency in brief diary entries due to the greater variety of topics, contexts, and, hence, word meanings that these data may capture. A similar issue applies to text messages from pagers and social media sites that have been analyzed by previous researchers (e.g., Back, Küfner, & Egloff, 2010; Golder & Macy, 2011).
In the present research, we evaluated the validity of using LIWC to detect the subjective well-being of participants in two diary studies. Subjective well-being consists of positive emotion, negative emotion, global life satisfaction, and satisfaction with specific life domains (Diener, Suh, Lucas, & Smith, 1999). Therefore, we also examined the ability of LIWC to code for life-content themes—topics concerning family, friends, work, health, leisure, and financial matters. As we will discuss later, the correspondence between LIWC codings and self-reported experience deserves more attention than it has received in the past. Moreover, such an assessment is timely as past studies relied on an older version of LIWC (Pennebaker, Francis, & Booth, 2001). In 2007, the LIWC dictionary was revised with several rarely used categories removed (e.g., optimism, grooming, and television) and new ones added (e.g., health, negations, inhibitions, and discrepancy; Pennebaker, Chung, Ireland, Gonzales, & Booth, 2007). This change resulted in a recategorization of some words. For example, the word work was coded into the present tense, occupation, job, and achievement categories in LIWC2001 but was recategorized into the work and achievement categories in LIWC2007. For several categories, word coverage also expanded from LIWC2001 to LIWC2007: positive emotions (from 265 to 407), negative emotions (from 345 to 500), anxiety (from 61 to 91), anger (from 120 to 185), sadness (from 72 to 101), friend (from 29 to 37), and leisure (from 103 to 228). Therefore, LIWC2007 may provide a more comprehensive analysis of word use than LIWC2001.
Validity of LIWC Emotion Codings as Measures of Emotional ExperienceA major application of LIWC is quantifying the emotional content of written expression (for a review, see Tausczik & Pennebaker, 2010). Researchers have validated such applications in various ways. In a series of experiments, participants either wrote about a personal experience or described film clips that were emotionally positive, negative, or neutral (Kahn, Tobin, Massey, & Anderson, 2007). Those in the positive (negative) group used more positive (negative) emotion words than those in the other two groups. Bantum and Owen (2009) analyzed messages from an online discussion board for breast cancer patients. They compared LIWC emotion codings with those made by human coders. Overall, LIWC detected a high proportion (> 77%) of positive and negative emotion words identified by human coders. Nevertheless, false-positive rates were also substantial (from 57% to 76%). Thus, there are occasional discrepancies between the degree of emotionality estimated by LIWC and the emotional meaning perceived by human readers. A particularly acute example is provided by Back et al.’s (2010) analysis of text messages sent during the September 11th attacks. The frequency of anger words counted by LIWC rose sharply as events unfolded during the day. However, upon closer inspection (Pury, 2011), a large number of “angry” comments came from an automated technical message sent repeatedly to a single pager. The message contained the word critical, which is coded by LIWC as an anger word even though the message did not actually express human emotion.
Researchers have also examined the correspondence between LIWC emotion codings and self-reported emotional experience. Here the evidence is inconclusive. In Kahn et al.’s (2007) study, LIWC positive emotion correlated with self-rated amusement after watching a comedy clip, but not with positive mood assessed by the Positive Affect and Negative Affect Schedule (PANAS; Watson, Clark, & Tellegen, 1988). Moreover, LIWC negative emotion did not correlate significantly with either self-reported sadness or negative mood (PANAS) after watching a sad film clip. Alvarez-Conrad, Zoellner, and Foa (2001) examined rape narratives from women suffering from posttraumatic stress disorder. LIWC negative emotion correlated with self-reported anger but not with anxiety or depression. Mehl (2006) also failed to observe a relation between LIWC negative emotion and scores on the Beck Depression Inventory–Short Form (Beck, Rial, & Rickels, 1974). Finally, LIWC emotion codings have not consistently correlated with personality traits that are commonly associated with positive and negative affectivity such as extraversion and neuroticism (Kahn et al., 2007; Mehl, Gosling, & Pennebaker, 2006; but see Pennebaker & King, 1999, for an exception).
The lack of consistent associations between LIWC emotion codings and self-reported emotion is somewhat surprising, given the large number of studies in which the program has been used to examine emotional expression (Tausczik & Pennebaker, 2010). Recently, Golder and Macy (2011) used LIWC to analyze the emotional content of over 500 million messages (“tweets”) from the social media site Twitter. They observed fluctuations in LIWC positive and negative emotions throughout the course of the day, which they partly attributed to the effects of circadian rhythm on mood. However, this conclusion assumes that the use of emotion words truly expresses the internal experience of the writer. Unfortunately, the current literature does not clearly support such an assumption.
We suggest three reasons why LIWC codings have not consistently correlated with self-reported emotion. One factor is the nature of the writing sample. Whereas autobiographical narratives by definition must refer to personal experiences, the same cannot be said of messages from pagers, discussion boards, and social media that other researchers have examined (Back et al., 2010; Bantum & Owen, 2009; Golder & Macy, 2011). Such messages often contain comments on a range of topics that have little to do with the sender’s emotional experience. Second, the number of writing samples collected affects how well they represent the emotional experience of the writer. Narratives about particular events—even if autobiographical (e.g., Alvarez-Conrad et al., 2001; Kahn et al., 2007)—may not capture the full range of experiences that shapes the writer’s emotional life. Mehl et al.’s (2006) study is noteworthy in this regard. Participants wore an audio recorder that randomly sampled sounds from their daily life four or five times per hour, over a 2-day period. The audio samples were transcribed and coded by LIWC. Despite the fine-grained temporal resolution of these data, LIWC emotion codings did not correlate with broad personality traits such as extraversion or neuroticism. Thus, a third factor may be the match between the event-sampling period and the target period assessed by the self-report measure. Though the samples collected by Mehl et al. (2006) were highly representative of participants’ experience during the 2-day period, participants did not rate how they felt during the same period. LIWC codings might correlate more strongly with the latter measures than with the personality scales examined by Mehl et al.
In the present research, participants described everyday personal experiences for a period of weeks. They also reported their emotions concurrently throughout the event-sampling period; retrospectively at the end of the study; and in general, providing us with, global trait-based measures of their affective dispositions. We expected LIWC codings to correlate most strongly with concurrent measures (when target and sampling period were closely aligned); and least strongly with global measures (when the target period extended beyond the sampling period).
LIWC Codings of Life ContentIn addition to emotional content, LIWC codes for life-content themes like work and family. We present a novel application of such codings to open-ended diary data: detecting satisfaction in specific life domains. A unique feature of short written entries is that they typically refer to a single event, providing a microcontext for the words that are used. Thus, it may be possible to detect positive (negative) events in specific areas of life by coding for the co-occurrence of positive (negative) words and a particular life-content word (e.g., work). To examine the validity of this approach for detecting satisfaction, we correlated these event codings (e.g., positive work events) with self-reported satisfaction in each domain.
Life content provides more detail about a person’s quality of life. An expanded view of subjective well-being not only includes emotional experience and life satisfaction, but satisfaction with specific life domains as well (Diener et al., 1999). Such information is often of interest to counselors and clinicians who wish to know not simply if their clients are feeling well but also whether they are encountering difficulties in specific areas. For example, the Quality of Life Inventory (Frisch, Cornell, Villanueva, & Retzlaff, 1992) assesses satisfaction in 17 domains including work, friends, health, recreation, and standard of living. Moreover, well-being covaries more with positive and negative experiences in some domains than in others (Stone, 1987). A quick method of coding life-content information would enrich the analysis of open-ended diary entries and broaden quality of life research by opening up new sources of data for investigation (e.g., social media and online blogs). To our knowledge, this is one of the first studies to make use of LIWC health, money, and leisure categories and one of a handful in which LIWC family, friends, and work categories have been used (see Tausczik & Pennebaker, 2010).
We evaluated the validity of using LIWC emotion and life-content codings as indicators of well-being. Our analyses relied on data from two diary studies that were previously described by Tov (2012). In Study 1, participants described two events each day for 21 days. They also reported their emotional experience and satisfaction in various domains on a daily basis. In Study 2, similar measures were collected twice a week for 2 months. Retrospective and global measures were obtained at the end of both studies. Given the autobiographical nature of the writing samples, the relatively large number of samples per person, and the concurrent reports of emotion and domain satisfactions that were collected, these data provide a rich assessment of the potential and limitations of LIWC. In addition, previous LIWC studies have relied heavily on American samples (one exception is Golder & Macy, 2011). In contrast, our participants were English-speaking students in Singapore, thus affording an assessment of how well LIWC emotion codings capture the self-reported emotional experience of a non-Western sample.
We divided our presentation into two major sections. First, we focused on LIWC emotion codings and evaluated their correspondence with self-reported emotional experience. In the second section, we evaluated the potential of using LIWC content codings to detect satisfaction with specific life domains.
LIWC Emotion Codings and Self-Reported Emotional Experience Study 1
Method
Participants
Students at Singapore Management University (SMU) were recruited for a 3-week daily diary study. The final sample consisted of 206 participants (121 females) with a mean age of 21.6 years. The majority of the sample (82.5%) was ethnically Chinese. All participants were fluent in spoken and written English as this is the language of instruction at SMU. For more details on the samples for Studies 1 and 2, see Tov (2012).
Measures and procedure
Each night for 21 days, participants logged into a website (between 9 p.m. and 3 a.m.) and completed a short survey. They rated the extent to which they had experienced positive (happy, pleased, proud, affectionate, relaxed, cheerful) and negative emotions (sad, angry, stressed, depressed) during the day from 0 (not at all) to 6 (extremely). Emotion terms were selected to reflect a range of arousal levels (Russell, 1980). The response scale was adapted from the PANAS (Watson et al., 1988). For each participant, responses were aggregated across all daily surveys (M = 19.27). We combined the positive emotion items but separately examined the negative emotion items and their correlation with LIWC sadness, anger, and anxiety. The reliability of the concurrent emotion scores, aggregated across the 21 days (Raudenbush & Bryk, 2002), was high: positive emotion = .94, sad = .91, angry = .92, stressed = .93, and depressed = .92. Finally, participants reported two events (one positive, one negative) that occurred during the day. A total of 7,703 events were reported. Each participant averaged 48.26 characters (SD = 26.07) or 9.23 words (SD = 5.02) per event.
After the 21-day sampling period, participants attended a final survey session. They were asked to retrospect over the previous 3-weeks and rate (0 = not at all; 6 = extremely) the extent to which they had experienced positive emotions (using the same six items from the daily survey; α = .85) and negative emotions (sad, upset, ashamed, angry, stressed, and depressed; α = .88). They also rated from 1 to 7 their satisfaction during this period (i.e., their level of satisfaction—dissatisfaction and how terrible—excellent the period was; α = .83). Next, they rated the extent to which they experienced positive and negative emotions in general using the same emotion terms from the retrospective measures. Finally, participants completed the Satisfaction with Life Scale (SWLS; Diener, Emmons, Larsen, & Griffin, 1985). All global well-being scores were reliable (αGlobalPositive = .85; αGlobalNegative = .88; αSWLS = .87).
LIWC emotion codings
We were interested in how well LIWC emotion codings captured the self-reported emotional experience of participants over the entire sampling period. Therefore, LIWC word counts for each emotion category were computed as a percentage of the total number of words written by each participant, across all events. Essentially, we combined all the event descriptions written by a participant into a single writing sample to obtain an overall estimate of emotional expression during the 3-week period.
Results and discussion
Descriptive statistics are presented in Table 1. LIWC positive emotion was significantly higher than LIWC negative emotion, t(205) = 15.61, p < .001, d = 1.41. Similarly, self-reported positive emotions were experienced to a greater extent than negative emotions (all ps < .001). One exception was stress, which was fairly common at moderate levels. Retrospective satisfaction correlated with retrospective positive (r = .60) and negative (r = –.52) emotion. Similarly, global life satisfaction correlated with global positive (r = .55) and negative (r = –.35) emotion. All correlations were significant (p < .002) and are consistent with theories of subjective well-being (Diener et al., 1999). Next, we examined the correlation between LIWC emotion codings and self-reported emotion.
Means and Standard Deviations for Linguistic Inquiry and Word Count Emotion Codings and Self-Report Measures
Daily self-reported emotion
LIWC positive emotion was associated with higher levels of daily positive emotion and lower levels of sadness and stress (see Table 2). LIWC negative emotion was associated with greater daily sadness, anger, and depression. Similarly, LIWC anger and anxiety correlated with daily negative emotions. Given that negative emotions tend to covary (Diener & Iran-Nejad, 1986), we examined how LIWC anger and anxiety correlated with daily anger and stress, controlling for all other self-report measures. These analyses revealed that LIWC anger was uniquely related to daily anger (r = .17, p = .02) above and beyond daily anxiety, sadness, depression, and positive emotion. However, LIWC anxiety was not uniquely related to daily stress (r = .07, p = .34) after all other daily emotions had been controlled.
Correlations Between Linguistic Inquiry and Word Count Emotion Codings and Concurrent Emotion Measures
Retrospective and global self-reported well-being
LIWC positive emotion correlated with retrospective and global positive emotion (Table 3). Similarly, LIWC negative emotion correlated with retrospective and global negative emotion. With one exception, the satisfaction measures generally did not correlate with LIWC emotion codings. We also compared the correlations of LIWC emotion codings with concurrent, retrospective, and global self-report emotion measures. For these analyses, daily sadness, anger, anxiety, and depression were averaged into a single measure of concurrent negative emotion (α = .89). We expected LIWC emotion codings to correlate more strongly with concurrent than with global self-report measures. Contrary to our prediction, the size of the correlation between LIWC emotion and self-reported emotion did not significantly differ among the three target periods (all ps > .12).
Correlations Between Linguistic Inquiry and Word Count Emotion Codings and Well-Being Measures
The results of Study 1 provide important evidence that LIWC emotion codings correspond with self-reported emotional experience. Participants who used positive (negative) emotion words in their event descriptions also experienced positive (negative) emotions as assessed by the concurrent measures. Moreover, LIWC emotion codings predicted how participants remembered feeling during the 3-week period and how they reported feeling in general. One exception is LIWC sadness, which was unrelated to daily sadness and depression.
Study 2
We attempted to replicate the findings of Study 1 using data from an 8-week diary study. These data were previously collected for a separate study on well-being and memory (Tov, 2012). Self-report measures of well-being were collected concurrently (each week during the event-sampling period), retrospectively (at the end of the sampling period), and at a global level (3 weeks later). Unlike Study 1, events were reported only twice a week to reduce participant burden. Replication in Study 2 would suggest that the detection of emotional experience via LIWC is robust across target periods and event-sampling frequency.
Method
Participants
SMU students were recruited for a diary study spanning 4 months. The final sample consisted of 139 students (91 women). On average, students were 21.3 years old, and 75.5% were ethnically Chinese. All participants were fluent in spoken and written English.
Measures and procedure
Twice a week for 8 weeks, participants logged into a website to complete a short survey. On Wednesdays, they reported two events (one positive, one negative) that occurred during the period of Sunday through Tuesday. On Sundays, they reported two events that occurred during the period of Wednesday through Saturday. A total of 4,073 events were reported. Each participant averaged 48.28 characters (SD = 18.99) or 9.08 words (SD = 3.71) per event.
On Sundays, participants rated the extent to which they experienced positive (happy, pleased, relaxed, and cheerful) and negative (angry, sad, and stressed) emotions during the week from 0 (not at all) to 6 (extremely). As in Study 1, we averaged the positive emotion items but examined the negative emotions separately. Responses were aggregated across the 8 weeks of the study (M = 7.94). The reliability of these aggregated scores (Raudenbush & Bryk, 2002) was adequate: positive emotion = .84, sad = .80, angry = .82, and stressed = .83.
At the end of the 8-week diary period, participants retrospectively rated their positive emotions (happy, pleased, relaxed, and cheerful; α = .83), negative emotions (angry, sad, stressed, and upset; α = .81), and satisfaction (satisfied—dissatisfied; terrible—excellent; α = .81) over the past 2 months of the study. Response scales were identical to Study 1. Three weeks later, participants attended a final session and completed global measures of emotional experience (using identical items; αGlobalPositive = .86, αGlobalNegative = .74), and the SWLS (α = .87).
Results and discussion
Descriptive statistics are presented in Table 1. As in Study 1, both LIWC codings and self-report measures suggested that positive emotions were experienced to a greater extent than negative emotions (all ts ≥ 4.322, ps < .001). The lone exception was average weekly stress, which was experienced at similar levels as positive emotions (p = .41). Retrospective satisfaction correlated .65 and –.35 with retrospective positive and negative emotion, respectively; global satisfaction correlated .51 and –.29 with global positive and negative emotion, respectively. All correlations were significant (p < .001).
Weekly self-reported emotion
Although LIWC negative emotion correlated with weekly negative emotion, LIWC positive emotions did not correlate with weekly positive emotion (Table 2). As in Study 1, LIWC anxiety and anger were correlated with multiple measures of weekly negative emotion. However, even after controlling for all other self-reported emotions, LIWC anxiety and anger were uniquely related to weekly stress (r = .18) and anger (r = .23), respectively (ps < .05). In contrast, LIWC sadness was unrelated to weekly sadness.
Retrospective and global self-reported well-being
LIWC negative emotion correlated with retrospective negative emotion and marginally with global negative emotion (p = .07; see Table 3). No other correlations were observed. We also examined whether LIWC emotion codings correlated more strongly with concurrent than with either retrospective or global self-report measures. Weekly sadness, anger, and stress were averaged into a single concurrent score (α = .82). No significant differences between correlations were observed (all ps > .08).
Study 2 replicated the correlation between LIWC and self-report measures for negative emotions only. In both studies, all scores derived from self-report measures were reliable (≥ .74). The correlations between self-reported satisfaction and emotion support their validity as well-being measures. Given this finding, two other factors can be considered. First, events were sampled less frequently in Study 2 than in Study 1. In Study 1, we collected 42 events over 21 days (two events/day). In Study 2, however, 32 events were collected over 56 days (0.57 events/day). Second, the target period for the concurrent self-report measure was broader in Study 2 (past week) than in Study 1 (past day). Thus, in Study 2, the target period was not covered by as many events as in Study 1. This increases the likelihood that unreported events impinged on participants’ overall emotional experience during the past week. Also, positive events reported at midweek may not have been recalled at the end of the week, when self-reports were collected. Despite the reduced coverage in Study 2, LIWC negative emotions correlated with concurrent and retrospective emotions. We offer some possible explanations in the General Discussion.
LIWC Life-Content Codings and Self-Reported Domain SatisfactionNext we evaluated a method for detecting satisfaction in specific life areas using LIWC life-content codings. We assumed that people are more (less) satisfied in a given domain if they frequently report positive (negative) events in that domain. The data are taken from Studies 1 and 2. In the following, we describe the procedure for coding the co-occurrence of words indicating (a) the valence of an event and (b) its relevance for a particular domain (e.g., family).
Method
Identifying valence-relevant words
As participants were instructed to provide one positive and one negative event when they wrote their diary entries, the valence of each event was already known. However, we sought to develop an approach that could be applied to other types of data. In social media, for example, the valence of a message is unknown and must somehow be inferred from its content. Therefore, we examined the LIWC2007 dictionary and identified categories that could disambiguate the valence of events.
A major impetus for our coding procedure was to extend applications of LIWC beyond coding for emotional content. Thus, we were interested in words that distinguished the valence of an event, regardless of whether they expressed emotion. It is possible to cognitively evaluate an event as negative or positive without a strong emotional reaction. A similar distinction is made between satisfaction judgments and emotional experience (Diener et al., 1999). Moreover, an approach that relies only on emotion words is problematic because negative emotion words were less frequent than positive emotion words (see Table 1). As a result, fewer negative events would be coded if only negative emotion words are used to identify them. In addition to LIWC positive and negative emotion, we selected three categories: negations (e.g., aren’t, cannot, did not), inhibition (e.g., avoid, block), and discrepancy (e.g., could’ve, mistake). These categories generally imply that an event did not occur in a desirable manner.
We submitted each event description to LIWC and obtained codings on the five valence-relevant word categories. To determine how well the five categories distinguished between events that were actually positive or negative (as specified by participants), we entered them simultaneously as predictors in a logistic regression model. All categories significantly predicted valence. The raw regression coefficients (Study 1/Study 2) were positive emotion (b = .13/.10); negative emotion (b = –.19/–.22); negations (b = –.25/–.28); discrepancy (b = –.06/–.07); and inhibition (b = –.12/–.14), all ps ≤ .001. Hence, an event was coded as negative if it (a) contained any “negative words” (i.e., negative emotion, negations, inhibitions, or discrepancies) and (b) did not contain a positive emotion word. An event was coded as positive if it contained positive emotion and did not contain negative words. Thus, this coding procedure results in mutually exclusive categories: an event can only be coded as positive, negative, or neither.
Coding for the co-occurrence of domain-relevant words and valence
To determine which domain an event was relevant to, we employed six categories in the LIWC2007 dictionary: family, friends, work, money, health, and leisure. We then coded for the co-occurrence of valence and domain-relevant words. For example, a negative event that contained a leisure word was coded as a negative leisure event (e.g., “Movie ticket seller tried to fool me”). A positive event that contained a leisure word was coded as a positive leisure event (e.g., “My swimming skills improved a lot”).
Self-reported domain satisfaction
On each day of Study 1, participants rated from 1 (extremely dissatisfied) to 7 (extremely satisfied) their satisfaction with various life domains (family, friends, health, leisure time, financial situation, grades/academic performance, what was learned in courses, and campus activities). The latter three items were averaged into an index of academic satisfaction. Using formulas provided by Shrout and Lane (2012), we estimated the reliability of the daily academic satisfaction score to be R1R = .66.
Similar items were administered in Study 2, with two differences. First, participants rated their satisfaction with reference to the past week (instead of the past day, as in Study 1). Second, we replaced campus activities with progress in completing assignment/projects. This item was combined with learning and grades to obtain an index of academic satisfaction (R1R = .60).
Results and Discussion
First, we examined the percentage of events that participants reported in each domain (Table 4). On average, around 14%–15% of events were negative work-related events. In the context of our college student sample, work largely referred to school work (e.g., projects, exams, and assignments). Across both studies, negative work and health events were more frequently reported than positive events in these domains. In contrast, positive friend and leisure events were more frequently reported than negative events.
Average Percentage of Events Reported by Participants in Each Domain by Valence
These differences may reflect the nature of the various domains. For example, a student can spend countless hours every day studying and working on projects. These activities may appear less pleasant than socializing or even sleeping, and the payoff may not be evident until weeks later (e.g., getting an A on the midterm). Thus, in the work domain, negative events are more frequent than positive events. Negative health events may not be more frequent than positive health events but are often more noteworthy. The words coded by LIWC health also tend to be negative (e.g., pain, ache, sick). Even positive health words like heal presuppose a negative condition. Time spent in leisure or with friends, on the other hand, may be more frequently positive because it often involves activities or people whose company is enjoyed the most. When we inspected the negative leisure events, we observed that the negativity often did not stem from the leisure activity itself (e.g., “Conquered fear of swimming in the sea”).
Next, we examined how well LIWC-derived event codings corresponded to self-reported satisfaction in each domain. In Study 1, as participants reported two events per day, we summed the event codings across events reported on the same day. We then analyzed the association between these daily event frequencies and daily domain satisfaction through multilevel regression models with daily measures nested within participants. In Table 5, each coefficient represents the change in satisfaction associated with reporting a single event in that domain. Thus, on days when a positive family event was reported, daily family satisfaction increased by 0.64 points. Positive events predicted daily satisfaction with family, friends, academics, and leisure. Negative events predicted daily satisfaction with family, financial situation, and health.
Multilevel Regression Coefficients Predicting Domain Satisfaction From Event Frequencies Derived From Linguistic Inquiry and Word Count
In Study 2, participants reported four events per week. Therefore, event codings were summed across events reported in the same week. These weekly frequencies were entered as predictors of weekly domain satisfaction in multilevel regression models. Overall, the coefficients in Study 2 were smaller than those in Study 1. These differences might be due to the target period of the self-report measures. For example, a single positive friend event was associated with a 0.40 increase in friend satisfaction on the particular day it was reported (Study 1) but with only a 0.15 increase in friend satisfaction over the entire week (Study 2). Because more events transpire during the course of a week (vs. a day), the effect of any one event is diminished. Nevertheless, positive friend and work events and negative health events still predicted weekly satisfaction, replicating our results in Study 1.
Although negative work events were more frequently reported than positive work events, the latter and not the former predicted academic satisfaction in both studies. This may reflect the results-oriented nature of school work. Long hours of studying and occasional setbacks are experienced as stressful and unpleasant, but a single positive outcome (e.g., a good grade on a project) can make the time invested worthwhile. Furthermore, though work is typically believed to be unpleasant, it also provides a sense of challenge and opportunities to experience flow (Csikszentmihalyi & LeFevre, 1989).
General DiscussionUsing data from two diary studies, we have provided important validity evidence for old and new applications of LIWC. For example, LIWC negative emotion not only predicted how negatively participants were feeling when they wrote their diary entries (concurrently) but also how negatively they remembered feeling over the entire diary period (retrospectively). LIWC anger and anxiety also correlated with self-reported emotion. In addition, we introduced a method for combining LIWC codings to estimate the frequency of positive and negative events in six life domains. We showed that these LIWC-derived event frequencies corresponded with how satisfied participants were with friends, academics, and health. Though further studies are needed to validate this approach, co-occurrence codings offer a promising way to extract information that is more specific than the emotional content of open-ended responses.
Although the results are encouraging in many respects, the inconsistencies between Studies 1 and 2 are informative. Generally, the correspondence between LIWC and self-reported well-being was stronger in Study 1 than in Study 2. In Study 1, LIWC positive and negative emotion predicted concurrent, retrospective, and global self-report measures. Furthermore, in all six life domains, self-reported satisfaction correlated with either positive or negative LIWC-derived event frequencies. However, in Study 2, LIWC positive emotion was unrelated to self-reported emotion, and event frequencies had smaller effects on domain satisfaction.
As we noted previously, a major difference between Studies 1 and 2 is the density with which the self-report target period was covered by the diary entries. In Study 1, participants rated their emotion each day and reported two events per day. In Study 2, participants rated their emotion over the past week. Not only was the target period broader, but the coverage density was sparser (0.57 events per day). The reduced coverage in Study 2 may have exacerbated two sources of error that weakened the relation between LIWC and self-reported well-being. First, events reported on Wednesdays may not have been recalled when self-report items were assessed on Sundays. Second, the self-report measures may have been influenced by additional events that were not measured because participants could only report four events per week. These observations have both theoretical and methodological implications for future researchers.
The greater correspondence between LIWC and daily self-report measures (Study 1)—relative to weekly self-report measures (Study 2)—suggests that LIWC codings of diary entries tend to reflect momentary feelings and attitudes more than stable affective traits. The use of words that describe particular emotions or life domains can reveal how participants currently feel about a specific experience without indicating how participants feel more generally. This accords with the purpose of diary studies: to capture ongoing or recent experiences. We believe that the discrepancy between Studies 1 and 2 underscores the importance of coverage density. On this basis, a few recommendations can be made to future researchers who wish to estimate emotional experience or attitudes using brief open-ended diary entries or other similar types of data. In such cases, researchers must consider the target period they wish to generalize to (e.g., past day, week, or month). Researchers should then ensure that they obtain a sufficient number of writing samples to cover the target period. Our findings suggest that 0.57 samples per day may be too few (particularly for assessing positive emotion and domain satisfaction). More consistent results may be obtained with at least two samples per day. Moreover, although LIWC codings of diary entries tend to reflect momentary feelings, it may still be possible to obtain measures of general attitudes and affective traits if coverage is sufficiently dense over an extended period of time. In Study 1, with writing samples collected each day for 3 weeks, both LIWC positive and negative emotion correlated with global self-reports.
Apart from the issue of coverage density, it is noteworthy that LIWC negative emotion consistently correlated with both concurrent and retrospective negative emotional experience across both studies. This pattern might reflect a negativity bias in the processing and memory of emotional events (Baumeister, Bratslavsky, Finkenauer, & Vohs, 2001). Negative experiences tend to be more differentiated than positive experiences in that there are more words to describe the former than the later (Rozin & Royzman, 2001). This asymmetry is mirrored in the greater number of words contained in LIWC negative emotion (500) than in LIWC positive emotion (407). Consequently, negative experiences may result in more distinctive written descriptions compared with positive experiences. Furthermore, negative arousal may enhance attention, encoding, and subsequent retrieval of the specific details of a negative event (Kensinger, 2009). Arousal-enhanced processing might also explain why LIWC anger and anxiety tended to correlate consistently with self-reported anger and stress. Fear (which is coded by LIWC anxiety) and anger may implicate fight-or-flight arousal mechanisms, rendering such experiences more memorable. In contrast, both sadness and general positive emotions can range from high to low arousal—producing inconsistent enhancements in attention and encoding.
Limitations
The participants in our study were explicitly instructed to write positive and negative events. It is fair to ask whether such instructions artificially sensitized participants to the valence of their experiences and inflated the detectability of emotions through word use. We think this is unlikely given that participants reported an equal number of positive and negative events. If our instructions inflated participants’ use of emotion words, LIWC positive and negative emotion should be equally frequent. Instead, LIWC positive emotion was higher than LIWC negative emotion, a pattern that is consistent with the observation that self-reported positive emotions are more frequent than negative emotions in large cross-national surveys (Diener & Tov, 2009).
Given the low frequency of LIWC sadness, anxiety, and anger, it is possible that a lack of variation in these measures attenuated their correlation with self-reported negative emotion. The standard deviations for these LIWC categories were smaller than those observed for the broader positive and negative emotion categories (see Table 1). Nevertheless, we observed significant correlations between LIWC and self-reported anger and anxiety, but not sadness—even though there was more variation in the latter. Still, we acknowledge that greater variation in these LIWC categories could result in a stronger correspondence with self-report measures.
The lack of correspondence between LIWC sadness and self-reported sadness could also be due to the particular design of our study. Our participants had to describe their experiences in a relatively concise manner. It is unknown whether brevity invites a certain style of verbal processing that makes sadness less detectable. In contrast, Rodriguez, Holleran, and Mehl (2010) instructed participants to write about their personality in a stream-of-consciousness manner. They found that LIWC sadness correlated substantially with self-reported depression. Furthermore, the effect was observed when participants were told to write an entry for a private diary but not when they wrote for an online blog post that could be read by others. Perhaps private, stream-of-consciousness writing provides better cues for the detection of sadness and depression because such writing is conducive to rumination. Other forms of writing might yield more reliable linguistic cues for positive emotion. Clearly, more research is needed on how the communicative context of writing affects emotional expression and word use.
We introduced a coding method for identifying positive and negative events in specific life domains. This approach revealed that positive events were more frequently reported in some domains, whereas negative events were more frequently reported in others. The method is fairly simple to apply, but it requires caution in its use and interpretation. Our approach relies on four LIWC categories (negative emotion, negation, inhibition, and discrepancy) to identify negative events, but only one category (positive emotion) to identify positive events. As there are more words across the four categories of “negative words” than there are in LIWC positive emotion, it is possible that more negative events are identified than positive events simply because a wider net is cast for the former than the latter. In the present studies, this potential bias was partly offset by the lower frequency of negative words relative to positive emotion words. Future researchers should take note of these potential biases and consider ways to mitigate them if necessary (e.g., by identifying more cues for positive events).
Possible Extensions and Applications
We focused on the validity of LIWC codings as estimates of well-being during a particular target period. However, diary studies also permit the study of within-person, momentary changes in feelings. We did not evaluate the validity of using LIWC codings to track daily or weekly changes in well-being because our data were suboptimal for such analyses. To estimate daily change reliably would require a greater number of writing samples per day than the two events our participants reported each day in Study 1 (see Shrout & Lane, 2012, for relevant formulas). This is especially important given that LIWC codings are affected by the length of the writing sample. A single instance of the word happy results in a higher score for LIWC positive emotion when the entry contains just three words instead of 30. The more samples there are per day, the more such sources of error will be minimized. Alternatively, all writing samples from a single day might be combined into a longer sample to reduce the noise associated with extremely brief entries. We took a similar approach by combining all diary entries collected over the target period into a single writing sample reflecting that period.
Although we evaluated LIWC in the context of short, open-ended diary responses, the present findings may be applicable to social media messages. There has been growing interest in measuring well-being from social media (e.g., Burke, Marlow, & Lento, 2010; Kramer, 2010). Social media messages provide numerous writing samples that can be retrieved in an unobtrusive manner. However, findings thus far have been inconsistent when emotion word counts have been correlated with self-reported life satisfaction (Kramer, 2010; Wang, Kosinski, Stillwell, & Rust, in press). Our analyses suggest that emotion word counts are more consistently related to self-reported emotional well-being (especially negative emotion) than broad cognitive well-being (i.e., life satisfaction). Moreover, as we noted, diary entries are more likely to reflect momentary feelings than global feelings and attitudes unless the coverage density is sufficiently high. In social media, emotional experiences are often shared in real time; thus, they are likely to reflect momentary feelings. Although social media studies often include large numbers of participants, this does not fully address the issues highlighted by our research. If only two or three writing samples are obtained per person and a global measure of well-being is administered, then it should not be surprising if correlations between word counts and self-reported well-being are low—even if thousands of participants are examined.
In addition, the coding of social media might be improved by using the co-occurrence method to distinguish personal from nonpersonal events. Positive personal events might contain positive emotion words and first-person pronouns (e.g., I, my, me). Other information could also be coded. On Facebook, messages often include information such as “place of check-in” and friends who were “tagged.” This information could be coded and combined. A check-in at a conference hall might signal an academic event; tagging might signal a social event. Despite these potential applications, a remaining challenge in utilizing social media is that people tend to selectively present their emotions (Qiu, Lin, Leung, & Tov, 2012). Thus, additional validation studies will be required to evaluate the usefulness of our approach in these contexts.
The present studies provided a rich analysis of the validity of LIWC codings as measures of well-being. Our results were generally encouraging but also highlighted certain methodological considerations that could improve future applications. Given that the sample consisted of non-Western, English-speaking students, the present research provides preliminary support for the robustness of the LIWC2007 English dictionary across cultures. We hope the co-occurrence methodology introduced in this article offers researchers new ways of extracting information from open-ended responses. The present research took advantage of the unique structure of diary data. How this approach fares with other types of data remains to be seen.
Footnotes 1 Unfortunately, other than LIWC positive emotion, we were unable to identify additional categories that predicted positive events. One potential category in the LIWC2007 dictionary was assent words (e.g., yeah, okay, absolutely, and so on). However, many of these words overlap with LIWC positive emotion and did not predict valence when both categories were entered into a logistic regression.
2 An important caveat of our procedure is that the stem friend* is itself coded by LIWC as a positive emotion word. Because we only coded negative events that did not contain any positive emotion words, the frequency of negative friend events is somewhat underestimated. Thus, “quarreled with a friend” would not be coded as a negative event. When we recoded the negative events to include those entries that contained the stem friend*, the frequency of negative friend events increased in Studies 1 (M = 2.96) and 2 (M = 4.58). However, positive friend events were still more frequent in both studies, ts > 9.40, ps < .001.
3 We also recoded the events using only two categories (negative emotion and negations) to identify negative events. Although this procedure actually led to more positive events than negative events being identified overall, negative events were still more frequently reported in the domains of work and health.
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Submitted: July 25, 2012 Revised: April 9, 2013 Accepted: April 12, 2013
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Source: Psychological Assessment. Vol. 25. (4), Dec, 2013 pp. 1069-1078)
Accession Number: 2013-19093-001
Digital Object Identifier: 10.1037/a0033007
Record: 47- Title:
- Detection of aberrant responding on a personality scale in a military sample: An application of evaluating person fit with two-level logistic regression.
- Authors:
- Woods, Carol M.. Department of Psychology, Washington University in St. Louis, St. Louis, MO, US, cwoods@artsci.wustl.edu
Oltmanns, Thomas F.. Department of Psychology, Washington University in St. Louis, St. Louis, MO, US
Turkheimer, Eric. Department of Psychology, University of Virginia, Charlottesville, VA, US - Address:
- Woods, Carol M., Department of Psychology, Washington University in St. Louis, Campus Box 1125, St. Louis, MO, US, 63130, cwoods@artsci.wustl.edu
- Source:
- Psychological Assessment, Vol 20(2), Jun, 2008. pp. 159-168.
- NLM Title Abbreviation:
- Psychol Assess
- Page Count:
- 10
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- Psychological Assessment: A Journal of Consulting and Clinical Psychology
- ISSN:
- 1040-3590 (Print)
1939-134X (Electronic) - Language:
- English
- Keywords:
- person fit, aberrant responding, personality assessment, personality disorders
- Abstract:
- Person-fit assessment is used to identify persons who respond aberrantly to a test or questionnaire. In this study, S. P. Reise's (2000) method for evaluating person fit using 2-level logistic regression was applied to 13 personality scales of the Schedule for Nonadaptive and Adaptive Personality (SNAP; L. Clark, 1996) that had been administered to military recruits (N = 2,026). Results revealed significant person-fit heterogeneity and indicated that for 5 SNAP scales (Disinhibition, Entitlement, Exhibitionism, Negative Temperament, and Workaholism), the scale was more discriminating for some people than for others. Possible causes of aberrant responding were explored with several covariates. On all 5 scales, severe pathology emerged as a key influence on responses, and there was evidence of differential test functioning with respect to gender, ethnicity, or both. Other potential sources of aberrancy were carelessness, haphazard responding, or uncooperativeness. Social desirability was not as influential as expected. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Personality Disorders; *Personality Measures; *Responses
- Medical Subject Headings (MeSH):
- Adolescent; Adult; Ethnic Groups; Female; Humans; Male; Military Personnel; Personality Assessment; Personality Disorders; Psychometrics; Regression Analysis; Reproducibility of Results; Self Disclosure; Sex Distribution; Temperament
- PsycINFO Classification:
- Personality Scales & Inventories (2223)
Personality Traits & Processes (3120) - Population:
- Human
Male
Female - Location:
- US
- Age Group:
- Adulthood (18 yrs & older)
Young Adulthood (18-29 yrs) - Tests & Measures:
- Schedule for Nonadaptive and Adaptive Personality DOI: 10.1037/t07079-000
Multi-Source Assessment of Personality Pathology DOI: 10.1037/t05155-000 - Methodology:
- Empirical Study; Quantitative Study
- Supplemental Data:
- Other Internet
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- Accepted: Feb 5, 2008; Revised: Jan 29, 2008; First Submitted: Jun 14, 2007
- Release Date:
- 20080616
- Correction Date:
- 20140616
- Copyright:
- American Psychological Association. 2008
- Digital Object Identifier:
- http://dx.doi.org/10.1037/1040-3590.20.2.159; http://dx.doi.org/10.1037/1040-3590.20.2.159.supp(Supplemental)
- PMID:
- 18557693
- Accession Number:
- 2008-06771-008
- Number of Citations in Source:
- 54
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Detection of Aberrant Responding on a Personality Scale in a Military Sample: An Application of Evaluating Person Fit With Two-Level Logistic Regression
By: Carol M. Woods
Department of Psychology, Washington University in St. Louis;
Thomas F. Oltmanns
Department of Psychology, Washington University in St. Louis
Eric Turkheimer
Department of Psychology, University of Virginia
Acknowledgement:
Person fit is the degree to which an item response model fits for an individual examinee (Meijer, 1996; Meijer & Sijtsma, 1995, 2001; Reise, 1995; Reise & Flannery, 1996; Tellegen, 1988). For example, in a general sample of people, the probability of responding “true” to items about personality or psychopathology is expected to decrease as items become more severe (i.e., as these items measure more extreme personality or psychopathology). Thus, in the general population, fewer people should endorse an item such as “I avoid important activities because of my anxiety” (more extreme anxiety) than an item such as “I get nervous before speaking in front of an audience” (less extreme anxiety). This pattern of decreasing endorsement with increasing severity, also called consistency (Tellegen, 1988) or scalability (Reise & Waller, 1993), should hold for each person on all scale items.
For dichotomously scored items, a perfectly scalable individual endorses all items with severity below his or her level of the latent variable, θ, and endorses no items with higher severity. This endorsement pattern is also called a perfect Guttman scale (Guttman, 1950). For example, when items are ordered by severity (e.g., −2.52, −1.50, −1.32, −1.17, −1.08, −1.05, −0.98, −0.18, 0.05, 0.18, 0.20, 0.47, 0.79, 1.18, 1.20, 1.70, 1.80, 3.91), the perfectly scalable pattern of responses for someone with θ = 1.10 is 111111111111100000 (1 = endorsement). The more responses deviate from this pattern of decreasing endorsement with increasing severity, the poorer the person fit. Poor person fit can be caused by, for example, carelessness, malingering, or uncooperativeness. See Reise and Waller (1993) for examples of more and less scalable response patterns observed empirically for the Social Closeness scale of the Multidimensional Personality Questionnaire (Tellegen, 1982).
Many methods for the assessment of person fit have been developed (see review by Meijer & Sijtsma, 2001). In the present study, we illustrate one method introduced by Reise (2000) that appears to have been applied to empirical data only once before (Woods, 2008). The method is somewhat complicated, which may be why it has been infrequently applied. As with most person-fit methods, one first estimates item parameters and each person's θ using item response theory (IRT). Reise's (2000) method requires subsequent application of two-level logistic regression. A key advantage is that the approach permits not only identification of aberrant responding but modeling of hypothesized causes of person-fit heterogeneity. Continuous or categorical person-level covariates are easily added to the model to explain the heterogeneity.
In the research presented here, we used Reise's (2000) method to test for aberrant responding to true/false self-report items about personality pathology on 15 trait and temperament scales that form the core of the Schedule for Nonadaptive and Adaptive Personality (SNAP; Clark, 1996). If significant person-fit heterogeneity was observed on a SNAP scale, we have included several person-level covariates to help identify sources of the aberrancy.
Two-Level Logistic Regression for Person Fit The Person Response Function
In Reise's (2000) approach, logistic regression is used for estimation of the parameters of a person response function (PRF; Lumsden, 1978; Nering & Meijer, 1998; Sijtsma & Meijer, 2001; Trabin & Weiss, 1983; Weiss, 1973, as cited in Sijtsma & Meijer, 2001). The PRF shows the relationship between item endorsement and item severity for each person. In contrast to an item response function (IRF), which indicates how well an item discriminates among persons with higher and lower levels of θ, a PRF indicates how well a person discriminates among items of varying severity. An IRF shows how the probability of responding “true” depends on θ for one item, whereas a PRF shows how the probability of responding “true” depends on item severity for one person.
Figure 1 plots the PRF for one of the best fitting and one of the worst fitting response patterns we observed for the SNAP Exhibitionism scale. The line with dots as the plotting symbol has a large negative slope (−2.22, intercept = −0.11), which indicates decreasing endorsement with increasing severity. With items ordered by estimated severity parameters (βi = −1.58, −1.05, −0.79, −0.79, −0.56, −0.29, −0.16, −0.15, −0.12, 0.03, 0.16, 0.3, 0.43, 0.53, 0.72, 1.34), this man's responses were 1111111100000000; his θ was estimated to be −0.06. The other line in Figure 1 (with crosses as the plotting symbol) shows the PRF for a more aberrant responder, for whom the relation between endorsement and severity was weak (PRF slope = −0.37, intercept = −0.58). For this woman, the response pattern was 0101101000000101 and the estimated θ was −0.31.
Figure 1. Two empirical person response functions (PRFs) for the SNAP Exhibitionism scale. The curve with dots for the plotting symbol shows a strong inverse relationship between item endorsement probability and item severity (PRF slope = −2.22). The curve with crosses for the plotting symbol shows a weak endorsement–severity relationship (PRF slope = −0.37). Each dot or cross corresponds to an estimated severity parameter for each of the 16 Exhibitionism items.
As is explained subsequently, βi and θ are estimated (in the same metric) with IRT, and PRF slopes and intercepts are estimated with logistic regression. We plotted these example PRFs using the logistic regression equation solved for the probability that the response (uij) to item i from person j is “1,”
with the slope (b1) and intercept (b0) parameters given above and βi used as the predictor (xi).
To understand the range of magnitude for a PRF slope, we can exponentiate the range and interpret in terms of odds ratios. For the best fit pattern above, the odds of item endorsement are multiplied by exp(−2.22) = 0.11 for every one-unit increase in item severity. For the worst fit pattern, the odds of item endorsement are multiplied by exp(−0.37) = 0.69 for every one-unit increase in item severity. One unit of item severity is one standard deviation of θ. When there is absolutely no relation between endorsement and severity, the PRF slope is 0, and the odds of item endorsement are unchanged as severity increases [exp(0) = 1].
The odds of responding “1” versus “0” for any particular value of βi can be obtained by plugging that value into
Because exp(0) = 1, the odds of responding “1” versus “0” are equal when
For patterns that match the model, the odds are equal when βi = θ. For the best fit pattern above, the odds of “1” versus “0” are equal when βi = −0.05, which is nearly equal to this respondent's estimate of θ(−0.06). These odds do not hold for patterns with poor fit: For the worst fit pattern above, the odds are equal when βi = −1.57 (estimated θ was −0.31).
If the PRF slope estimate is about the same for all people, then either the scale discriminates fairly well and about equally for all individuals or the scale has poor measurement properties, so that aberrant responding cannot be detected. Scales discriminate better when the items are highly related to one another and to θ and when values of βi are spread over a relatively wide range of θs. If a scale lacks these good properties for most people, aberrant responding is not detectable.
If a scale has good properties for most people, it is possible to identify persons for whom the scale is less discriminating. If PRF slopes vary significantly over persons, as they did for the Exhibitionism scale, the scale is not equally discriminating for all individuals; thus, the item response model does not fit equally well for all persons. Individuals for whom fit is worst are identified on the basis of their estimated PRF slope (slopes closest to 0 are worst). Reise's (2000) approach stands out among PRF-based methods, because it provides sound methods for the estimation of PRF slopes for individuals.
IRT Precedes Logistic Regression
Using Reise's (2000) method, one fits an item response model to the data prior to the logistic regression analyses to estimate the severity (βi) of each item (i.e., threshold parameter) and θ for each person. This method constitutes a typical application of IRT (see introductions by Embretson & Reise, 2000, or Thissen & Wainer, 2001) and can be carried out with standard software, such as BILOG-MG (Zimowski, Muraki, Mislevy, & Bock, 2003) or MULTILOG (Thissen, 1991).
In the present analysis, we use a variation of classic IRT to obtain estimates of βi and θ. Implicit in classic IRT is the assumption that θ is normally distributed in the population of people. This is unlikely to be true for all variables. Ramsay-curve IRT (RC–IRT; Woods, 2006a, 2007; Woods & Thissen, 2006) can be used for estimation of the θ distribution simultaneously with the item parameters. If the latent distribution is approximately normal, the answers match those from standard software, such as MULTILOG. If the distribution is not approximately normal, RC–IRT provides more accurate item parameter estimates than do methods that assume normality (Woods, 2006a, 2007; Woods & Thissen, 2006). RC–IRT is used for the present analyses.
Two-Level Logistic RegressionIn general, logistic regression is a variant of linear regression that is adapted to be appropriate for a categorical (binary, ordinal, or nominal) outcome variable. In the study reported here, we employed logistic regression for a binary outcome (refer to Agresti, 1996, 2002; Collett, 2003; or Hosmer & Lemeshow, 2000). In the models to be fitted, each true/false response to a SNAP item is an outcome, and the βi of that item is used as the predictor. The coefficient from this regression is the PRF slope that reflects the relationship between item response and item severity.
However, the PRF slope is potentially different for different people. A logistic regression model that permits the PRF slope to vary over individuals (i.e., that permits it to be treated as random rather than fixed) has two levels (references on multilevel models include Raudenbush & Bryk, 2002, and Snijders & Bosker, 1999). The first level is the regression of item response on item severity described above, with the slope treated as random. The error variance of this slope indicates the degree to which it varies over persons.
If there is statistically significant variability in PRF slopes, the PRF slope can be treated as an outcome in a second level of the model, with person-level predictors entered to explain its variability. However, only the systematic portion of its variability is explainable. A reliability coefficient that is a function of the Level 2 prediction error variance and the Level 1 residual variance (Woods, 2008, gives details) will be used to approximate the proportion of variability in the PRF slopes that could potentially be explained by covariates. Reliability coefficients are interpreted as the proportion of the total variance that is reliable.
The intercept from the Level 1 regression may also vary over persons. In the present context, the random intercept is analogous to θ, so it is expected to vary significantly over people and to be completely explainable by IRT scores. If it behaves otherwise, this is an indication that some of the IRT assumptions are violated (see Reise, 2000, or Woods, 2008, for further explanation). No evidence of this type of violation appeared in our analyses, so the intercept is not discussed further.
Covariates
Gender and ethnicity will be entered as covariates, because systematic differences in responding are commonly observed among demographic groups. If group membership is a strong predictor of person fit, this can be interpreted as evidence of differential test functioning (DTF; Shealy & Stout, 1993). DTF occurs when a scale has different measurement properties for one group versus another, controlling for true mean differences in the trait being measured. When differences in measurement properties occur for individual items, it is called differential item functioning (DIF; Camilli & Shepard, 1994; Holland & Wainer, 1993; Millsap & Everson, 1993).
Sometimes, even if many items function differently between groups, DIF cancels out at the scale level, so DTF is trivial or nonexistent. Thus, DIF does not always imply DTF. If one is interested in scale-level functioning, it makes sense to test for DIF only when there is evidence of DTF. In the present research, DTF is tested as part of the more general person-fit assessment. Evidence of DTF implies DIF, so, if we find DTF, subsequent analyses beyond the scope of the present research will be warranted to identify differentially functioning items.
Another set of covariates includes scores on three SNAP validity scales, which were designed to identify certain sources of invalid responding. Although it was unclear what types of invalidity to expect, there were probably special demand characteristics associated with our sample of Air Force recruits who were tested at the end of basic military training. They were told that their SNAP responses would not be shared with the Air Force. Nevertheless, they may have worried that their scores would influence the next step of their careers. These circumstances may have been especially likely to elicit socially desirable responding.
Additionally, it was unclear how seriously recruits responded to the task, so there may have been aberrancy due to carelessness, uncooperativeness, or haphazard responding, and some recruits may have suffered from a distressed mood or personality pathology. Prior to basic training, recruits were screened for obvious mental disorders by the military, but there was no professionally administered standardized battery that screened for major mental disorders. After training (when recruits filled out the SNAP), neither military screening nor professionally administered standardized assessment was carried out. Recruits who were seriously disturbed could have had elevated scores on the SNAP validity scale designed to detect malingering.
If higher validity-scale scores are associated with more aberrant PRF slopes, the source (e.g., social desirability) is implicated as a contributor to poor person fit. However, scores on validity scales do not definitively clarify the source of aberrant responding. Additional information about individuals is needed (Piedmont, McCrae, Riemann, & Angleitner, 2000). For example, researchers must thoroughly evaluate mental health status to distinguish between malingering and true pathology. Haphazard or careless responding to validity-scale items could also produce misleading scores on validity scales. Nevertheless, a significant relationship between person-fit heterogeneity and a particular validity scale would provide a hypothesis about aberrant responding that could be investigated in future research.
The last two covariates were nominations from peers regarding the extent to which each recruit exhibited features of obsessive-compulsive personality disorder (OCPD) or borderline personality disorder (BPD). We examined these specific types of personality disorder because they are likely to have interesting (and opposite) effects on SNAP scores. People who exhibit features of OCPD are preoccupied with orderliness and perfection. People who exhibit features of BPD are markedly impulsive, are emotionally erratic, and have unstable images of themselves and other people. If aberrant responding is partially due to carelessness, recruits with more nominations for features of OCPD might be less likely to give aberrant response patterns. Conversely, if aberrant responding is partially produced by the presence of serious personality pathology, recruits with more nominations for features of BPD might be more likely to give aberrant responses.
Serious personality pathology can, of course, manifest in other forms. We could have used peer ratings of antisocial, histrionic, or narcissistic traits, but ratings for these disorders were highly correlated with those for BPD. Multicollinearity among covariates was avoided by the use of just one measure of severe pathology. Furthermore, the extreme impulsivity and disturbed self-image associated with BPD seem particularly relevant to aberrant response patterns that are the topic of these analyses.
The use of peer nominations for pathological personality features allowed us to avoid basing our conclusions about personality features on another self-report measure that would share the same unique perspective or possible biases reflected in participant self-descriptions on the SNAP. Studies of people with and without mental disorders point to the conclusion that there is, at best, only a modest correlation between the ways in which people describe their own personalities and the ways in which they are perceived by others (Biesanz, West, & Millevoi, 2007; Clark, 2007; Watson, Hubbard, & Weise, 2000).
Method Participants and Data Collection
The sample consisted of 2,026 Air Force recruits (1,265 male, 761 female) who were completing basic military training at Lackland Air Force Base in San Antonio, Texas. Most were between 18 and 25 years of age (Mdn = 19 years). They self-identified as White (1,305), African American (348), Hispanic (75), Asian (68), or Native American (17), with the remaining 213 classified as “other.” No standardized assessment battery was administered as part of this study to screen for major mental disorders (e.g., schizophrenia, substance use disorders, or mood disorders). However, recruits had been screened by the military for obvious mental disorders when they enlisted and, again, when they entered basic training.
As part of a larger study (see Oltmanns & Turkheimer, 2006; Thomas, Turkheimer, & Oltmanns, 2003), data were collected by computer from demographically heterogeneous training groups called “flights.” All recruits are assigned to a flight at the beginning of basic training, and the members of each flight do virtually everything together for 6 weeks. All participants completed self-report items of the SNAP (Clark, 1996) at an individual computer terminal and then rated peers in their flight using the Multisource Assessment of Personality Pathology (MAPP; Oltmanns & Turkheimer, 2006).
The SNAP
Traits and temperaments
The SNAP consists of 375 true/false questions about trait dimensions related to normal personality traits and features of personality disorders. In this analysis, person fit was evaluated for 15 scales that measure traits or temperaments. The temperament scales are Disinhibition (35 items), Negative Temperament (28 items), and Positive Temperament (27 items). The trait scales (and number of items) include Aggression (20), Dependency (18), Detachment (18), Eccentric Perceptions (15), Entitlement (16), Exhibitionism (16), Impulsivity (19), Manipulativeness (20), Mistrust (19), Propriety (20), Self-Harm (16), and Workaholism (18).
Validity
Three SNAP validity scales were used: Rare Virtues (12 items), Deviance (20 items), and Variable Response Inconsistency (VRIN; 22 items). Rare Virtues consists of highly socially desirable behaviors (e.g., “I have never made a promise that I didn't keep”) that are rarely true, so high scores often reflect a naive “fake good” response set. Deviance is the opposite: Items reflect severe pathology rarely endorsed by normal participants, so high scores may indicate “faking bad,” including malingering. Persons who experience severe pathology also have high Deviance scores (Clark, 1996).
VRIN is designed to identify inconsistent responding while controlling for item content. VRIN consists of pairs of content-matched items keyed in the same direction, so it is inconsistent to answer “true” to one item and “false” to the other (e.g., “People say I drive myself hard” and “I've been told that I work too hard”). High VRIN scores can be due to carelessness, haphazard responding, or uncooperativeness (Clark, 1996). Summed scores (i.e., sums of 0/1 item scores) for each validity scale were used as covariates for the person-fit analyses. We used observed summed scores instead of IRT scores, because it was unclear whether a validity scale was measuring a latent trait.
The MAPP
The MAPP consists of 103 personality traits presented one at a time. Most items are based on features of 10 personality disorders listed in the Diagnostic and Statistical Manual of Mental Disorders (4th ed.; American Psychiatric Association, 1994); 10 MAPP subscales correspond to these disorders. Recruits were asked to identify at least one member of their flight or training group who exhibited each trait and to indicate the extent to which the target person demonstrated that trait (0 = never, 1 = sometimes, 2 = usually, 3 = always). Item scores for each target are the mean rating over judges, and scale scores are the mean of item scores. Scores for the BPD and OCPD scales were used for the present research.
Item Response Analysis
Although the two-parameter-logistic (2PL; Birnbaum, 1968) IRF is theoretically appealing for personality items, the three-parameter-logistic IRF (3PL; Birnbaum, 1968) sometimes fits significantly better than does the 2PL (e.g., Reise & Waller, 2003). The 3PL includes a third parameter for each item (g), which is the lower asymptote of the IRF and interpreted as a guessing parameter for items with correct answers. For personality items for which endorsement indicates more of a positive trait, g is sometimes interpretable as a social desirability parameter (Zumbo, Pope, Watson, & Hubley, 1997). Reise and Waller (2003) discussed alternative interpretations for positive items and items for which endorsement indicates more of a negative or pathological trait.
In a simulation study, Woods (in press) found that the likelihood ratio chi-square test that compared the 2PL and 3PL IRFs with the θ distribution fixed at normal correctly pointed toward the 3PL, even when the θ distribution was actually nonnormal. This finding informed the analytic strategy used in the present study. For each SNAP scale, the 2PL and the 3PL were compared with normal θ and RC–IRT (Woods & Thissen, 2006; Woods, 2006a, 2007) was then carried out with the preferred IRF. The 3PL was preferred for a given scale if (a) the chi-square test was significant and (b) the Bayesian information criterion (BIC; Schwarz, 1978) was smaller for the 3PL than for the 2PL. Because the BIC imposes a penalty for the number of parameters estimated, using it with the chi-square test helps to balance parsimony with good fit.
We carried out RC–IRT using the RCLOG (Version 2; Woods, 2006b) program. Expected a posteriori (EAP) estimates of θ for each person and estimates of βi for each item were saved for use in the logistic regression models. No other item parameters from the IRT analyses were used in subsequent analyses, because PRFs specify relationships only between βi and item endorsement. Although we used no other item parameters for the two-level logistic regression, the IRF must be specified as accurately as possible, because 2PL-based estimates of βi are not, in general, equivalent to 3PL-based estimates of βi.
Person-Fit Analysis
Two-level logistic regression was carried out with maximum likelihood estimation, with standard errors approximated by first-order derivatives (the MLF estimator) implemented in the Mplus program (Version 4.12; Muthén & Muthén, 2006). We computed reliability coefficients with SAS software using formulas given in Woods (2008). In all models, the intercept was treated as a fixed function of EAPs, because, compared with treating the intercept as random, this method improves estimation and convergence (Woods, 2008).
In the first model (Model 1), no predictors of the random PRF slope were included. If statistically significant variability in PRF slopes was observed in Model 1, which was at least partially systematic (based on the reliability coefficient), a second model (Model 2) that included 11 person-level covariates (described below) was fitted. We estimated PRF slopes for individuals from Model 2 using empirical Bayes methods (Morris, 1983; Snijders & Bosker, 1999, pp. 58–63) implemented in Mplus.
Covariates were scores for the three validity scales, peer-rated BPD and peer-rated OCPD, gender (1 = male, 2 = female), and race. We used five binary variables to code the nominal six-level race variable with White as the reference group. For each SNAP scale, we compared the global fit of models with and without covariates using Akaike's information criterion (AIC; Akaike, 1973) and the BIC. Both are functions of the optimized log likelihood, with a penalty for the number of parameters (AIC) or the number of parameters and the sample size (BIC). Smaller values are preferred.
Results Descriptive Statistics on SNAP Scales
Means and standard deviations (SDs) of summed scores (i.e., the sum of all 0/1 coded item scores) on each scale are given in Table 1. Normative values from a college sample given in the SNAP manual (Clark, 1996) are listed for comparison. Notice that the military sample is much larger and is about 62% male, whereas the college sample is about 35% male. In general, means for the military sample were lower on most scales—markedly so on Disinhibition—but were higher on Propriety and Workaholism.
Means (SDs) of Summed Scores for SNAP Personality and Validity Scales
Recruits had just spent an intense, 6-week period in an authoritarian environment in which cleaning and organizing were highly valued activities. People who did not follow the strict rules were likely to be punished by training instructors and chastised by peers. Those who score low on Disinhibition are “serious people who believe in doing things in proper order and following rules of all kinds” (Clark, 1996). Higher scorers on Propriety are “greatly concerned with proper standards of conduct” (Clark, 1996), and higher scorers on Workaholism are “perfectionists… who enjoy work more than play” (Clark, 1996). Recruits may have been (a) more perfectionistic and concerned about proper conduct than others when they enlisted, (b) particularly likely to endorse items about these characteristics immediately after basic training, or (c) both.
Another interesting difference between the samples is that military women scored lower than did military men on the Dependency scale but that college women scored higher than did college men (as did college women in another sample described by Oltmanns & Turkheimer, 2006). Perhaps women who self-select into the military and survive basic training tend to be particularly independent. A final observation is that recruits scored higher on Rare Virtues, so they may have been more influenced by social desirability than were members of the normative group. However, the maximum score on Rare Virtues is 12, so the recruits' scores were not particularly high.
RC–IRT
The 2PL was preferred to the 3PL for all scales. Although 12 of the 15 chi-square tests were significant (α = .05), the BIC was always smaller for the 2PL. The difference between BICs for each scale was between 0.18% and 11.04% of the 3PL BIC value.
RC–IRT was carried out using the 2PL for all scales. The θ distribution was approximately normal (skewness [s] = 0; kurtosis [k] = 3) for the majority of the scales but was nonnormal for Dependency (s = −0.65, k = 5.07), Entitlement (s = 2.32, k = 13.30), Exhibitionism (s = 1.30, k = 9.14), and Workaholism (s = −0.71, k = 6.37). Complete details of the RC–IRT analyses are available upon request from Carol M. Woods but are not given here because they are not the present focus. The purpose of the RC–IRT analysis was to obtain EAPs and estimates of βi for use in the logistic regression models.
Scales With Poor Item Properties
The majority of items on the Propriety and Manipulativeness scales discriminated poorly. For Propriety, discrimination parameters (ais) ranged from 0 to 1.59, and the average over items was 0.81 (SD = 0.62). For Manipulativeness, ais ranged from 0.18 to 2.31 (M = 1.35, SD = 0.50). Additionally, all βi estimates for Manipulativeness were positive; they ranged from 0.61 to 3.49, plus β63 = 9.59. This large severity parameter for Item 63, “People who try to get out of doing something by pretending to need help are probably lazy, not clever,” resulted from the combination of a high endorsement proportion (.86) with poor discrimination (ai = 0.18).
The probability of responding “true” to a weakly discriminating item is about the same for all respondents, so it is impossible even to define aberrancy. It is also difficult to detect aberrancy on scales on which βi is concentrated in a narrow range of θ, because the restricted range limits the degree to which βi could possibly covary with endorsement probability. Because aberrancy is difficult to define for poorly discriminating scales and those with narrow-ranging βi, person fit was not assessed for Propriety and Manipulativeness.
Two-Level Logistic Regression Model 1
Homogeneous PRF slopes for personality scales
The variance of the random PRF slope (b1) was .00 or .01 and was statistically nonsignificant for eight personality scales. Because the slope did not vary over individuals, the mean (i.e., Level 2 intercept) is a useful summary statistic. Mean PRF slopes were Self-Harm, −1.62; Aggression, −1.39; Eccentric Perceptions, −1.37; Dependency, −1.35; Impulsivity, −1.29; Mistrust, −1.25; Detachment, −1.14; and Positive Temperament, −1.12. The negative slopes are consistent with the usual definition of good person fit: Item endorsement decreased as item severity increased.
Heterogeneous PRF slopes for personality scales
The variance of b1 was nonzero and was statistically significant for five personality scales: Negative Temperament, Disinhibition, Workaholism, Exhibitionism, and Entitlement. Results from Model 1 are given in Tables 2 and 3, which display indices of global model fit (AIC and BIC), the variance of b1(τ) and its standard error (SE), the corresponding reliability coefficient (Λ), and the Level 2 intercept (γ0) with SE. Reliability coefficients indicated that between 36% and 80% of the person-fit heterogeneity was systematic and could be explained by covariates.
Person-Fit Analysis for Negative Temperament, Disinhibition, and Workaholism
Person Fit Analysis for Exhibitionism and Entitlement
Two-Level Logistic Regression Model 2
Model 2 was fitted to data for the five personality scales that had significant heterogeneity on the basis of Model 1. As shown in Tables 2 and 3, the AIC was smaller for each scale when covariates were included, which indicated better fit. For Exhibitionism, the BIC increased when covariates were added to the model (indicating worse fit), because the BIC rewards parsimony more than does the AIC. However, for every scale, the addition of covariates reduced τ; this result indicated that the covariates explained at least some of the heterogeneity. Reliability coefficients showed that 19%–50% of the remaining unexplained variance was systematic.
For each scale, Level 2 regression parameters for each covariate (with SEs) are listed in Tables 2 and 3. An asterisk marks those results that significantly predicted PRF slopes (α = .05). Below, we describe covariates that significantly predicted aberrant responding. We also mention some nonsignificant effects in which the mean PRF slope was nontrivially more aberrant for Hispanic (n = 75) or Asian (n = 68) recruits than for White recruits. Though not statistically reliable with present sample sizes, these observations lead to hypotheses to be explored in future research.
Negative Temperament
For Negative Temperament, responses from recruits rated by their peers as higher in BPD were significantly more aberrant: With a 1 SD increase in BPD score, the PRF slope increased by 0.58. Greater aberrancy was also predicted by higher Deviance scores. These two effects suggest that aberrancy might have been due in part to the presence of pathology. Higher Deviance scores could be due to malingering rather than to true pathology, but malingering seems unlikely in this sample. Higher Rare Virtues scores significantly predicted more aberrant PRF slopes, which suggests that socially desirable responding might have produced some aberrancy. Mean PRF slopes were significantly more aberrant for African American versus White recruits and for Asian versus White recruits (the latter difference was not statistically significant). The ethnicity effects can be interpreted as evidence of DTF.
Disinhibition
For Disinhibition, some results were the same as those for Negative Temperament: Aberrancy was significantly greater for recruits with higher Deviance scores and for recruits rated by their peers as higher in BPD. Thus, the presence of pathology was again implicated as a potential cause of aberrant responding. Interestingly, Rare Virtues predicted aberrancy, but the direction of the effect indicated that more aberrancy was accompanied by less socially desirable responding. Higher VRIN scores were associated with greater aberrancy, a result that suggests carelessness, haphazard responding, or uncooperativeness as possible influences. Perhaps one of these influences explains the unexpected direction of the effect for Rare Virtues. The fact that mean PRF slope was significantly more aberrant for men than for women can be interpreted as DTF.
Workaholism
Higher Deviance and VRIN scores predicted greater aberrancy for Workaholism, and the mean PRF slope was significantly more aberrant for female than for male recruits. It is surprising that Rare Virtues appeared to be unrelated to aberrant responding on Workaholism.
Exhibitionism
Many covariates significantly predicted aberrancy on Exhibitionism. As observed for other scales, greater aberrancy was predicted by higher peer-rated BPD and higher Deviance scores. This result implicates pathology as an explanation. Higher VRIN scores and lower peer-rated OCPD were associated with more aberrant PRF slopes, which is consistent with the possibility that aberrancy was partially due to carelessness (and that people with higher levels of OCPD traits were less careless). Higher scores on Rare Virtues predicted greater aberrancy and suggested that social desirability may have influenced responses. Finally, there was substantial evidence of DTF: The mean PRF slope was more aberrant for women, African Americans, Asians, and Hispanics compared with men and Whites (effects for Asians and Hispanics were not statistically significant).
Entitlement
The finding that higher scores on Deviance and VRIN were associated with greater aberrancy on Entitlement suggests pathology, carelessness, haphazard responding, or uncooperativeness as possible explanations. There was also evidence of DTF: Significant differences in the mean PRF slope indicated greater aberrancy for Asians versus Whites and for Whites versus African Americans.
Correlations Among Person Fit Across Scales
Table 4 lists Pearson correlations among PRF slope estimates for the five scales with significant heterogeneity. Slopes for all scales correlated significantly with one another, though the strength of the relationship varied from .17 to .63. These results indicate that individuals who responded aberrantly to one scale tended to respond aberrantly to the other four scales as well.
Pearson Correlations Among PRF Slopes for SNAP Scales With Significant Heterogeneity
DiscussionWe used Reise's (2000) method to evaluate person fit for 13 personality scales from the SNAP (Clark, 1996). Two scales (Propriety and Manipulativeness) were not assessed, because the measurement properties appeared to be poor for most people. Person-fit heterogeneity was observed for 5 of the 13 scales and indicated that the 5 scales discriminated better for some people than for others. Our finding that some heterogeneity was predictable from covariates leads to hypotheses about what might have caused the aberrant responding. Our results are limited by the possibility of measurement error in the scales, covariates, or both, and the explanations we offer are speculative.
A more thorough understanding of the aberrant responding requires additional information from, for example, clinical interviews. Meijer, Egberink, Emons, and Sijtsma (in press) used ancillary information in a study focused on person fit for a scale about children's self-perceptions: In addition to utilizing additional information about the children's personal and emotional well-being, they readministered the scale to aberrant responders to check reliability and interviewed teachers about aberrant responders. In the present study, we explored sources of person fit using peer ratings of BPD and OCPD, scores on three validity scales, gender, and ethnicity. Information provided by peers is particularly useful in this regard, because self-report and informant-report measures provide rather different perspectives on personality pathology (Furr, Dougherty, Marsh, & Mathias, 2007; Miller, Pilkonis, & Clifton, 2005).
Explanations for Aberrant Responding on the SNAP
Severe personality pathology may partly explain aberrant responding. Higher scores on Deviance significantly predicted more aberrant PRF slopes for all five scales. Elevated Deviance scores could indicate feigned pathology. Recruits who were having a particularly difficult time adjusting to the demands of military life may have been trying to “fake bad” in order to find a way out of the commitment they had made when they enlisted.
It is also possible that some recruits suffered from pathology that was not detected by the military during initial screenings or that had been catalyzed by basic training. BPD characteristics, such as extreme impulsiveness, emotional instability, and identity disturbance, were discernible by peers, and higher levels of peer-rated BPD traits predicted aberrant responding for Negative Temperament, Disinhibition, and Exhibitionism. The fact that peer scores for BPD characteristics were found to be associated with aberrant responding on these SNAP scales provides support for the argument that informants are able to provide valid or meaningful data regarding personality pathology, even when those scores are discrepant from the image provided by self-report measures (Fiedler, Oltmanns, & Turkheimer, 2004; Klein, 2003).
Carelessness, haphazard responding, uncooperativeness, or some combination of these influences accounted for some of the aberrancy. Higher VRIN scores were associated with more aberrant PRF slopes for all scales except Negative Temperament. One unique feature of this scale (compared with the other four) is that all constituent items are near the end of the 375-item SNAP (Item 241 and higher). Could recruits have become more careful, deliberate, or cooperative near the end? Alternatively, haphazard responses on the other scales may have been due to confusion, and items on the Negative Temperament scale may have been less confusing. Future research should explore these possibilities.
Scores on Rare Virtues were less related to aberrant responding than we had expected. This may be because Rare Virtues is not a particularly useful measure of social desirability or because the recruits were not as concerned about presenting themselves favorably as we had hypothesized. We used summed scores rather than IRT scores for validity scales, because it was unclear whether a validity scale really measures a latent variable. However, as a sensitivity analysis, we refitted the five 2-level logistic regression models with EAPs rather than summed scores for the validity scales. Rare Virtues EAPs were about as predictive as were summed scores (full results from the sensitivity analyses are available upon request from Carol M. Woods). It seems that the construct validity of validity scales is rarely, if ever, examined empirically (Piedmont et al., 2000). Insightful psychometric evaluations of validity scales would be helpful.
All five personality scales showed evidence of DTF with respect to gender, ethnicity, or both. It would be useful to explore possible causes for these group differences, because there are surely other variables (perhaps continuous variables) for which group membership is a proxy. Also, DTF implies DIF, and it would be useful to know which items function differently. Many methods for DIF testing (reviewed by Millsap & Everson, 1993) exist and could be applied to these SNAP scales in future research.
Limitations
Aberrant responding cannot always be detected. Power is best for longer scales that are composed of items that are highly related to one another and to θ and that have relatively widely varying severity levels. Scales with poor measurement properties may appear free of person-fit heterogeneity when, in fact, aberrancy cannot be defined. In the present study, Propriety and Manipulativeness seemed to have poor properties, such that assessing person fit seemed futile. On the basis of our IRT analyses, the other SNAP scales had acceptable measurement properties, so it is encouraging that person-fit heterogeneity was observed for only 5 out of 13 of them. Because person fit is sample dependent, results may differ for samples of college students, patients, community volunteers, or even other samples of Air Force recruits. Replication studies are warranted.
Another limitation inherent in person-fit assessment is that an item response model must first be fitted to data for all respondents. Because it is not possible to know in advance which response patterns are aberrant, they are all combined for IRT. In a simulation study focused on Reise's (2000) method, Woods (2008) found that the presence of aberrant responders produced bias in the IRT results and PRF slopes in a direction that blurred the distinction between aberrant and nonaberrant responders. It would be interesting to test an iterative-purification variation of Reise's method, wherein IRT and two-level logistic regression are repeated several times, with 5% (or some other percentage) of the most aberrant respondents being eliminated each time.
Conclusion
Despite the limitations inherent in person-fit assessment, we believe that Reise's (2000) method can be a useful tool for improving psychological measurement. PRF estimates can help identify people who may have provided invalid data or who may need special attention (e.g., if they suffer from severe pathology). It is sometimes necessary to eliminate aberrant responders from a data set, but this should never be done on the basis of person-fit statistics alone. As much ancillary information as possible is needed if we are to understand what appears to be aberrant responding. Our assessment of the SNAP has raised many questions about variables that may influence response to self-report questionnaires. Exploration of these questions is important, if psychological measurement is to continue to improve.
Footnotes 1 The RCLOG computer program is freely available upon request from Carol M. Woods.
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Submitted: June 14, 2007 Revised: January 29, 2008 Accepted: February 5, 2008
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Source: Psychological Assessment. Vol. 20. (2), Jun, 2008 pp. 159-168)
Accession Number: 2008-06771-008
Digital Object Identifier: 10.1037/1040-3590.20.2.159
Record: 48- Title:
- Developing a theory driven text messaging intervention for addiction care with user driven content.
- Authors:
- Muench, Frederick. Department of Psychiatry, Columbia University College of Physicians and Surgeons, New York, NY, US, fm2148@columbia.edu
Weiss, Rebecca A.. Department of Psychology, Fordham University, NY, US
Kuerbis, Alexis. Research Foundation for Mental Hygiene, Inc, New York, NY, US
Morgenstern, Jon. Department of Psychiatry, Columbia University College of Physicians and Surgeons, Columbia University, NY, US - Address:
- Muench, Frederick, Columbia University College of Physicians and Surgeons, and Mobile Health Interventions, Department of Psychiatry, Columbia University, 3 Columbus Circle, Suite 1404, New York, NY, US, 10019, fm2148@columbia.edu
- Source:
- Psychology of Addictive Behaviors, Vol 27(1), Mar, 2013. pp. 315-321.
- NLM Title Abbreviation:
- Psychol Addict Behav
- Page Count:
- 7
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- Bulletin of the Society of Psychologists in Addictive Behaviors; Bulletin of the Society of Psychologists in Substance Abuse
- Other Publishers:
- US : Educational Publishing Foundation
Society of Psychologists in Addictive Behaviors - ISSN:
- 0893-164X (Print)
1939-1501 (Electronic) - Language:
- English
- Keywords:
- behavior change, mobile phone, substance abuse treatment, text message, treatment development, intervention, addiction care, user driven content
- Abstract:
- The number of text messaging interventions designed to initiate and support behavioral health changes have been steadily increasing over the past 5 years. Messaging interventions can be tailored and adapted to an individual's needs in their natural environment—fostering just-in-time therapies and making them a logical intervention for addiction continuing care. This study assessed the acceptability of using text messaging for substance abuse continuing care and the intervention preferences of individuals in substance abuse treatment in order to develop an interactive mobile text messaging intervention. Fifty individuals enrolled in intensive outpatient substance abuse treatment completed an assessment battery relating to preferred logistics of mobile interventions, behavior change strategies, and types of messages they thought would be most helpful to them at different time points. Results indicated that 98% participants were potentially interested in using text messaging as a continuing care strategy. Participants wrote different types of messages that they perceived might be most helpful, based on various hypothetical situations often encountered during the recovery process. Although individuals tended to prefer benefit driven over consequence driven messages, differences in the perceived benefits of change among individuals predicted message preference. Implications for the development of mobile messaging interventions for the addictions are discussed. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Addiction; *Drug Abuse; *Electronic Communication; *Intervention; Behavior Change; Health Care Psychology; Cellular Phones; Text Messaging
- Medical Subject Headings (MeSH):
- Adult; Aftercare; Behavior, Addictive; Female; Humans; Male; Middle Aged; Remote Consultation; Substance-Related Disorders; Text Messaging
- PsycINFO Classification:
- Drug & Alcohol Rehabilitation (3383)
- Population:
- Human
Male
Female - Location:
- US
- Age Group:
- Adulthood (18 yrs & older)
Young Adulthood (18-29 yrs)
Thirties (30-39 yrs)
Middle Age (40-64 yrs) - Tests & Measures:
- Primary Appraisal of Harm Measure
- Grant Sponsorship:
- Sponsor: National Institute on Drug Abuse
Grant Number: R43 DA029359-01A1
Recipients: Muench, Frederick - Methodology:
- Empirical Study; Quantitative Study
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- First Posted: Sep 10, 2012; Accepted: Aug 13, 2012; Revised: Jul 20, 2012; First Submitted: Dec 19, 2011
- Release Date:
- 20120910
- Correction Date:
- 20151207
- Copyright:
- American Psychological Association. 2012
- Digital Object Identifier:
- http://dx.doi.org/10.1037/a0029963
- PMID:
- 22963375
- Accession Number:
- 2012-24205-001
- Number of Citations in Source:
- 34
- Persistent link to this record (Permalink):
- http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2012-24205-001&site=ehost-live
- Cut and Paste:
- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2012-24205-001&site=ehost-live">Developing a theory driven text messaging intervention for addiction care with user driven content.</A>
- Database:
- PsycINFO
Developing a Theory Driven Text Messaging Intervention for Addiction Care With User Driven Content
By: Frederick Muench
Department of Psychiatry, Columbia University College of Physicians and Surgeons, and Mobile Health Interventions, Department of Psychiatry, Columbia University, New York, NY;
Rebecca A. Weiss
Department of Psychology, Fordham University
Alexis Kuerbis
Research Foundation for Mental Hygiene, Inc., New York, NY, and Department of Psychiatry, Columbia University College of Physicians and Surgeons, Columbia University
Jon Morgenstern
Department of Psychiatry, Columbia University College of Physicians and Surgeons, Columbia University
Acknowledgement: The study was funded through a grant from the National Institute on Drug Abuse (R43 DA029359-01A1; Muench). Frederick Muench was founder of Mobile Health Interventions, which received the SBIR to develop the messaging intervention. This paper describes a treatment development process and not the efficacy of a product or service. Dr. Muench is currently President of Mobile Health Interventions, but the company is in the process of being acquired by a non-profit for which Dr. Muench will remain a consultant. His compensation is fixed and is not dependent on revenue or sales.
The mobile phone is considered by many to be the next frontier in health behavior change because it is a vehicle to deliver personal, intelligent, and adaptive health information anywhere in real time (Patrick, Griswold, Raab, & Intille, 2008; Riley et al., 2011). There is a growing body of research on text messaging for a range of health and mental health problems such as diabetes, asthma, obesity and weight loss, tobacco dependence, bulimia, and even for increasing levels of daily functioning for those with schizophrenia (Cole–Lewis & Kershaw, 2010; Fjeldsoe, Marshall, & Miller, 2009; Heron & Smyth, 2010; Krishna, Boren, & Balas, 2009; Pijnenborg et al., 2010). This is due in part to text messaging or short message service (SMS) being the most accessible and common form of mobile communications. Over 95% of phones are SMS capable, and more text messages are being sent and received every day than mobile phone calls. In addition, although there is a large discrepancy in home Internet access between high and low income households and ethnicities, this is not the case with mobile phone use (Pew Internet & American Life Project, 2010), highlighting that SMS is a particularly useful intervention medium for disenfranchised population groups such as substance users (Fjeldsoe et al., 2009; Heron & Smyth, 2010).
Mobile interventions are particularly well suited for addictions continuing care because they can be utilized with individuals in their natural environment and adapt to their needs in real time using just-in-time therapies. Similar strategies have been used in the empirically supported phone-based continuing care literature (e.g., extended case monitoring, assertive continuing care, recovery management check-ups, early warning signs relapse prevention and concurrent recovery monitoring), which focus on minimal contact over longer periods of time, adapting to the current needs of individuals (McKay, 2009). Despite the promise of SMS interventions for addiction and the encouraging results from tobacco cessation studies (Free et al., 2011), there have been no published studies using SMS for addiction continuing care, nor any studies examining methods to develop effective mobile interventions.
More than other mediums of intervention development, our current behavioral theories may not adequately capture the capabilities of behavior change interventions using mobile phones across disorders. Riley and colleagues (2011) highlight that mobile technology's greatest strength is that it offers the opportunity to use dynamic interactive real-time therapies and that our current behavior change intervention theories, possibly aside from self-regulation theory (Carver & Scheier, 1998), need to be adjusted to account for real-time adaptations at the moment level. Numerous studies have revealed that individuals have very different needs at different times of the change process (Herd & Borland, 2009) and that a range of psychological processes have varying importance throughout the change process (Prochaska & Velicer, 1997; Rothman, Baldwin, & Hertel, 2004). Short message service interventions for smoking cessation have integrated these important just-in-time therapies for participants by providing tailored messages based on participant characteristics and their quit date as well as just-in-time adaptations to care, based on ones current level of functioning (e.g., Brendryen, Kraft, & Schaalma, 2010; Rodgers et al., 2005). Understanding these specific and critical events, as well as in-the-moment decision making, can shape future mobile interventions. With careful understanding, interventions can be individually tailored to each person's needs and specific circumstances.
More than most delivery mediums, mobile interventions are by nature multiple, repeated, brief interventions. Each brief contact can be considered a microintervention to foster a positive cognitive, affective, or behavioral reaction in the moment. Therefore, creative strategies should be used to further develop interventions that move the field toward using these new mediums. For example, SMS interventions can borrow from the large public health literature on tailored benefit driven (gain framed) versus consequence driven (loss framed) messaging, as well as from the decisional balance literature to push specific types of tailored messaging. There is evidence that health messages that are congruent to underlying motivational orientation or personality style are more effective than those that are incongruent (Rimer & Kreuter, 2006; Noar, Benac, & Harris, 2007; Williams–Piehota, Schneider, Pizarro, Mowad, & Salovey, 2004). Additionally, the foundational literature on matching change processes to readiness to change (Prochaska, DiClemente, Velicer, & Rossi, 1993) can be replicated through existing messaging interventions (Brendryen, Kraft, & Schaalma, 2010).
Despite the growing empirical support for mobile interventions targeting general behavioral changes, there is little information on the acceptability of text messaging systems for those with substance abuse problems, particularly for those in addiction treatment. Because addiction carries such stigma, there is a need to understand the acceptability of mobile interventions among the individuals it might serve. This includes understanding what types of messaging are most appropriate; when messages might be perceived as most helpful; what concerns exist regarding using drug-related terms in the intervention content; how many individuals have unlimited messaging plans; and whether individuals are interested in real-time support and social networking as part of mobile interventions. When combined with the lack of research on developing empirically guided mobile content, there is a need to perform foundational research on mobile intervention development for addictive disorders. To help achieve this aim, we performed an exploratory study consisting of a one-time assessment to examine self-reported user acceptance of integrating an interactive SMS system into addiction care. In addition to inquiring about user preferences in relation to messaging logistics, the assessment included questions about the types of messages individuals may prefer receiving (consequence based vs. benefit based). Participants were also asked to generate their own messages in relation to different situations during the change process that they perceived would be most helpful for themselves. These user-generated messages were collected to help develop intervention text messages for specific situations (e.g., treatment entry vs. high craving/risk for relapse). Due to our interest in using messaging as a continuing care intervention for behavioral maintenance, the assessment battery was focused on acceptability, feasibility, and preferences related to messages that would be received in throughout the treatment process, from entry to aftercare. It is important to note that we did not send actual messages to participants but rather obtained this preliminary information through a one-time self-report assessment to eventually develop a pilot continuing care intervention program. The study was approved by the Bronx Lebanon Hospital Institutional Review Board.
Method Participants
Participants included 50 individuals (41 men, 9 women), ranging in age from 26 to 64 years old (mean [M] = 43.5, standard deviation [SD] = 8.1), who were enrolled in intensive outpatient substance abuse treatment at an inner-city clinic. Potential participants were recruited through flyers placed in the treatment program and were instructed to contact their counselor if interested in the study. To be included, participants needed to be fluent in English, read at least at an 8th grade level, be drug free for 30 days and be referred by their primary counselor to participate. Participants were excluded if they were unable to comprehend the consent form quiz, or if their primary counselor reported the presence of a mental or psychiatric disorder that would inhibit completion of the assessment. Three participants were excluded due to language barriers.
Roughly half of the sample was African American (n = 27, 54.0%), 40.0% were Hispanic (n = 20), 4.0% were Caucasian (n = 2), 2.0% were Asian (n = 1), and 2.0% were biracial (n = 1). The majority were unemployed (n = 40; 80.0%), 4% were employed full-time (n = 2), and 4.0% were either employed part-time or students (n = 2), and 12% (n = 6) individuals chose not to respond regarding employment status. Individuals were drug free for an average of 98 days (SD = 104) at the time of the study. Individuals reported the substances that caused the greatest consequences in their lives were alcohol (n = 32; 64%), cocaine (n = 30; 60%), and heroin (n = 18, 32%).
Procedure
Participants who met eligibility criteria were given a copy of the consent form by their primary counselor prior to their attendance at one of four group meetings. Upon arrival at the meeting, the consent form was reviewed, and participants were further screened by the experimenter and given an overview of the 90-minute session. In the group setting, participants independently completed three self-report questionnaires. Questions were related to mobile adoption, preferences for logistical integration of mobile interventions for continuing care, process variables, and preferences for which of two messages (gain framed vs. loss framed) they would rather receive. They were also asked to write messages they thought would be most helpful to receive during different periods of the change process. Then, as part of the larger treatment development study and not included in this manuscript, they participated in a structured focus group where they were presented with different types of experimenter generated messages and asked to discuss with the group which messages they thought would be most helpful and which ones would be least helpful to receive.
Assessment
Participants completed three separate questionnaires. The first questionnaire was specifically designed to inquire about the acceptability and feasibility of an interactive SMS system for addiction care. Domains covered in this questionnaire included demographics, cell phone usage and features, acceptability, and helpfulness of SMS for addiction care, and preferences for specific types of features within a system. The second questionnaire consisted of items intended to assess for specific participant characteristics covering a wide range of domains (e.g., demographics, treatment history, harm appraisals) that could guide text messages. These items were taken from a questionnaire specifically designed for a web-based assessment and adapted for the current study. For the current study, we focused only on the single-item process variables including overall harm from substance use and benefits to being drug free, derived from the Primary Appraisal of Harm Measure (PAM: Morgenstern, Labouvie, McCrady, Kahler, & Frey, 1997), self-efficacy and commitment/readiness derived from readiness rulers (Heather, Smailes, & Cassidy, 2008; Sobell & Sobell, 1995). The response format for each item was a 7-point Likert-type scale ranging from not at all (1) to extremely (7).
The third questionnaire focused on participants' preferences for certain types of messages. This questionnaire was comprised of two domains. The first domain consisted of six experimenter developed messages that were nearly identical in their wording, except for modifications designed to either highlight the benefits of changing or the consequences of not changing. We then asked participants to choose the message they preferred of the two similarly worded items. In the second domain, participants were asked to create messages (i.e., 160 characters or less) that they felt would be most helpful at four different time periods of recovery, including treatment entry, after 90 days being drug free, when an individual was at risk for relapse, and if an individual had relapsed.
In an attempt to classify the messages written by participants for each researcher-defined time period (e.g., at risk for relapse), we used the taxonomy of behavior change techniques developed by Abraham and Michie (2008) and the processes of change developed by Prochaska and DiClemente (1984; Prochaska, DiClemente, & Norcross, 1992). Because several of the techniques and processes, such as prompt self-monitoring of behavior, are inherent in text messaging (i.e., getting a relevant text message itself is a prompt, regardless of the content of the text) or were not relevant (e.g., model the behavior or agree on behavioral contract, both of which are described as physical actions), we only used a subset of strategies primarily taken from the processes of change. Messages written by participants for each of four situations (treatment entry, 90 days being drug free, at risk for relapse, and actual relapse) were coded into 10 behavior change techniques including messages that fostered helping relationships, self and environmental reevaluation, motivation/self-liberation, spirituality, general encouragement/self-efficacy, cognitive reframing, behavioral counterconditioning, consciousness raising, reinforcement management, and stimulus control. Helping relationships included anything that fostered social support, whether from a group (e.g., get to a meeting) or an individual (e.g., call a friend now). During the coding process, we added another category called Alcoholics Anonymous (AA) “pearl” due to the number of individuals writing messages using slogans such as “one day at a time” or “easy does it.” Although these could be seen as a cognitive reframe, we decided to keep them as their own category to differentiate between these and other types of cognitive reframing.
Statistical Analyses
A dichotomous variable was created regarding a preference for messages concerning the benefits versus the consequences of treatment, based on a participant's majority preference (i.e., at least 2/3 of benefit or consequence messages were selected). This was used to run univariate analysis of variance (ANOVA) among the five process variables, and all significant relationships were regressed on the dichotomous preference variable using logistic regression. For user generated messages, two researchers independently coded all text messages using the taxonomy described above. Each was given a definition and example of a message for each category. Agreement was dichotomous (yes/no). In cases where there was disagreement about the categorization of a message, the message was discussed and subsequently placed either in a category, based on agreement between both coders or marked as uncoded and excluded from the analyses. Of the 317 messages provided by the participants, 8 (2.5%) messages were left uncoded and dropped due to either rater disagreement about the proper classification or because they did not make sense in the context of the assignment. The remaining 309 messages were coded by two reviewers, resulting in 89% agreement. Figure 1 presents the percentage of messages corresponding to each type of behavior change strategy by situation.
Figure 1. Preferred behavior change strategy based on stage of treatment.
Results Participant Characteristics and Feasibility of Text Messaging System
The majority of participants owned at least one phone in the past year (n = 45, 90.0%), with an average of 2.9 phones (SD = 2.4; Range: 0–15) in the past 3 years. About half of the participants had additional features on their phones, most notably, 62% (n = 31) could send an attachment in a text, 56% (n = 28) had access to the Internet, 46% (n = 23) could download applications, 48% (n = 24) had a camera, 40% (n = 20) had audio recording, and 32% (n = 16) had video recording and playback. Roughly half of those who knew of their messaging plan had unlimited text messages (n = 22, 48.0%). Some individuals received an overwhelming number of text messages per week (e.g., 900). Therefore the top 5% of responses were excluded to provide a more representative mean. Excluding the top 5%, participants received a mean of 17.3 (SD = 27.5) texts in the past week.
Interest in Using the System
Almost all participants (n = 49, 98%) reported they would be interested in using an interactive text messaging system during and after treatment. Thirty-four percent (n = 17) of individuals thought the system would be most helpful at treatment entry, 22% (n = 11) during later stages of treatment and 44% (n = 22) following treatment. Almost two thirds (62%) reported they would prefer receiving at least one message daily, as compared with 14% who reported preferring weekly messages. When asked about whether they would use certain system features, 80% (n = 40) reported they would inform the system of a lapse to drug use, 84% (n = 42) would send a “help message” if they were in a high-risk situation for using drugs and looking for guidance, 78% (n = 39) would want their counselor alerted if at risk for relapse, and 96% (n = 48) would want a friend alerted in this situation. Sixty percent (n = 30) of individuals reported no concerns with the use of addictions terms (e.g., using, drugs) in SMS messages.
Message Preferences
Table 1 presents the results of the user messaging preferences comparing consequence driven versus benefit driven messages. Individuals tended to prefer messages about the benefits of changing (64%) versus the consequences of not changing (34%). However, as noted in Table 1, one message about imagining the benefits of changing versus the consequences of not changing was evenly endorsed. To understand whether process variables predicted message preferences, we compared scores on the four process rulers (readiness for change, benefits of changing, harm of past use, and confidence to change) for those who preferred benefit driven messages to those who preferred consequence driven messages using univariate ANOVA. Individuals who preferred benefit driven messages had significantly higher ratings for perceived benefits to being drug free, F(1, 45) = 8.76, p < .05, and readiness for change, F(1, 45) = 4.84, p < .05. There were no significant differences between groups on ratings of self-efficacy or harm from past use. Step-wise logistic regression revealed that the level of benefit from being drug free mediated the relationship between readiness for change and choosing benefit driven messages accounting for 12.5% of the variance, R2 = .125, incremental F(2, 44) = 4.28, p < .05.
Preference for Benefit Versus Consequence Focused Messages
Only those strategies that had approximately a 5% endorsement for at least one situation were included in Figure 1. Those excluded were stimulus control and reinforcement management. Overall, helping relationships was most frequently endorsed at all time points with significant increases during risk for a lapse (30%) and following a lapse (53%). Motivational messages (self-liberation) followed an opposite trend, with higher values early in the change process and at the 90-day mark. As could be expected, general encouragement/efficacy messages spiked at the 90-day mark, reinforcing success; reevaluation messages were written early in the change process and during risk for a lapse. The AA pearls accounted for about 15% of messages for all periods aside from following a lapse (6.1%). Spirituality messages remained fairly constant across situations at just under 5%.
DiscussionThe current exploratory study revealed that individuals in substance abuse treatment would be interested in receiving text messages that support their recovery and that the messages individuals believed would be most helpful to them differed depending on the point in the change process. Although participants reported that benefit driven messages were perceived as more helpful than consequence driven messages, these preferences differed based on their ratings of their personal perceived benefits for change and readiness for change, suggesting that message tailoring to preferences and readiness for change can be a useful undertaking, a finding consistent with the computer-based intervention literature.
The use of SMS in continued care appears feasible, as 90% of even the most disenfranchised individuals in substance abuse treatment had mobile phones and expressed willingness to use SMS as a continuing care intervention. Although nearly all phones are SMS capable, making it a widely disseminated and far-reaching medium for intervention, it is striking that nearly half of the sample had smart phones, an adoption rate that is higher than the general U.S. adoption rate at the time of the study (Neilsen, 2011). This highlights the possibility of expanding mobile web features, tracking applications, and adding audio and video messaging to mobile intervention development with disenfranchised populations. It was also notable that 78% of individuals wanted a counselor alerted and 96% would want a friend alerted if they were at risk for relapse, validating current trends in increasing social networking and support through technology as an important component of behavior change interventions. Moreover, there was a high level of endorsement for using help and crisis messaging, a component that would be proactively initiated by the user. These results emphasize a need and role for both proactive and reactive pushing of intervention content.
It is worth mentioning that participants' desires may differ from clinicians' recommendations. In a separate survey (Muench & Weiss, 2011), we examined the preferences of 34 addiction treatment providers and found that though 87% would use a similar system as part of their care, providers were less likely to prefer the instant alert option, with only 8% wanting to be alerted to a possible relapse in the moment. However, 80% would be interested in some type of alert, with 31% opting for an e-mail, and 40% desiring an alert the next working day. Interestingly, this former survey also revealed that only 11% of providers believed the system would be most beneficial after treatment, as compared with 44% of clients, stressing the importance of taking both provider and client perspectives into account when developing systems that extend the reach of care.
One concern that may inhibit effectiveness was that individuals had an average of 2.9 cell phones in the last year. A recent study revealed that service interruptions and length of time with the same phone number was the best predictor of outcome in an SMS intervention study of oral contraceptive use in a low-income inner-city sample of women (Castaño, Bynum, Andrés, Lara, & Westhoff, 2012). These potential obstacles to continued and consistent phone service suggest the need for proactive techniques to improve engagement in messaging and other phone related mobile interventions, such as collateral phone numbers when client phones may be inactive. That only 60% of participants approved of using language that could potentially identify them as an individual in recovery suggests that mobile interventions need to take the privacy needs of individuals into account and that more research is needed in developing acceptable mobile interventions for those attempting to change addictive behaviors. For example, in our mobile intervention development study for problem drinkers, we are developing “mirrored” messages in which participants can choose a code word (e.g., coffee, soda) to replace the word alcohol in messages for confidentiality purposes. Overall, despite some barriers to effective integration, the user characteristics and preferences found in the current study indicate that mobile interventions for the addictions are very promising.
General preferences for benefit-driven messages correspond to the general message tailoring literature. For example, a metaanalytic review of disease prevention messages found that gain-framed appeals, which emphasize the advantages of compliance with the communicator's recommendation, are slightly more persuasive than loss-framed appeals, which emphasize the disadvantages of noncompliance (O'Keefe & Jensen, 2006). In addition, individuals were drug free for over 3 months, which corresponds to the theory that benefit driven interventions may be more efficacious for individuals in maintenance stages (Rothman et al., 2004). However, higher scores on the benefits of being drug free were the uniquely associated with preferring benefit driven messages, emphasizing the need for tailored messaging programs based on decision support paradigms. These findings correspond to the health promotion literature on messaging that is congruent with motivational styles being more efficacious than incongruent messaging (e.g., Mann, Sherman, & Updegraff, 2004) and therefore suggests that there is a base from which to draw tailoring content for messaging interventions.
User generated content that related to researcher defined points during the treatment and recovery process revealed interesting information as to the strategies individuals in drug treatment think they may use. There were some logical patterns in the data, such as the spikes in messages that fostered social support during times of crisis; messages that encouraged individuals to change early in the change process; messages that trigger self and other reevaluation during transition or decision points (e.g., early in treatment and at risk for relapse); and messages that encourage self-efficacy when milestones are met (e.g., 90 days clean). We were surprised to find that some techniques associated with positive outcomes in the addiction treatment process literature, such as avoiding high-risk situations, were not written about more often across periods of recovery.
As noted earlier, each contact in a mobile intervention should be considered a stand-alone intervention that is a part of a larger whole. Messages should be designed to intervene with the user in their environment, in response to the particular time and place both physically and in their recovery using ecological momentary interventions. Self-regulation theory highlights that very different techniques may be more effective or used more depending on the level of arousal or at different time points (e.g., cognitive reappraisals for lower arousal and distraction for higher arousal; Sheppes & Gross, 2010). Results of the current study stress that individuals know that reappraisal or reevaluation and helping relationships are important when one might have high craving, but that once a lapse has taken place, social support is the crucial factor. Including user driven content has numerous advantages but has not been utilized often in the treatment development literature. Coding these messages based on empirically derived behavior change techniques can help determine the active ingredients and preferences of the individuals receiving these mobile interventions.
Overall, results highlight the promise of using mobile interventions for addiction care and underscore the need for empirically tested models for this new medium. Mobile applications for addictions are readily available in smart phone app stores, and whereas some are based on empirically sound intervention strategies, very few are using the available theoretical underpinnings of effective treatments (Cohn, Hunter–Reel, Hagman, & Mitchell, 2011). The mobile phone can enhance many techniques such as: relapse prevention, tailored individualized messaging, self-regulation in the moment, increasing salience of therapeutic cues and social support alerts—all of which have been theorized or known to be effective mechanisms of change. Moreover, the mobile phone allows for new theoretically based interventions which have been explored less in the addictions field, such as self-modeling interventions through user generated content (e.g., record me when I am motivated and play it back later; Muench, Tryon, Travaglini, & Morgenstern, 2006). Finally, more research is needed to understand both how user preferences will drive intervention development, as well as which types of messages are effective for which types of individuals.
This exploratory study had several limitations and results should be viewed as preliminary until further research is conducted. The intent of this study was to begin to establish a foundation of knowledge about the feasibility and acceptability of an SMS system for addiction continuing care and to begin to develop the content for a pilot intervention using such a system. Its aim was not to pilot test the efficacy of the system. The primary limitation is that this study did not examine the preferences of these messages in a real world setting where individuals could rate the benefits to measure preferences in certain situations. Future research should include sending the types of messages individuals find helpful in vivo at various points during treatment, and/or to test different types of messages (e.g., self-written vs. gain framed) and compare outcomes. Although this study only examined preferences, it is still useful to understand what individuals in treatment prefer. Nonetheless, there may be a discrepancy between what is objectively most effective for patients and what is perceived as most effective by patients. However, even if this discrepancy exists, it is likely that once a patient returns to his or her home environment (or is allowed to leave a legally required treatment program), he or she will continue to engage in strategies perceived as effective. A second limitation is that our sample skewed male, and our population was a disenfranchised inner-city treatment population potentially limiting the generalizability of these findings. Our sample was too small to examine gender differences or race/ethnicity differences, but such characteristics could have a profound impact on which messages are preferred by users. Future studies can improve on these limitations and test the real world efficacy of mobile interventions with substance abusing populations.
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Submitted: December 19, 2011 Revised: July 20, 2012 Accepted: August 13, 2012
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Source: Psychology of Addictive Behaviors. Vol. 27. (1), Mar, 2013 pp. 315-321)
Accession Number: 2012-24205-001
Digital Object Identifier: 10.1037/a0029963
Record: 49- Title:
- Development and evaluation of the Marijuana Reduction Strategies Self-Efficacy Scale.
- Authors:
- Davis, Alan K.. Department of Psychology, Bowling Green State University, Bowling Green, OH, US, akdavis@bgsu.edu
Osborn, Lawrence A.. Department of Psychology, Bowling Green State University, Bowling Green, OH, US
Leith, Jaclyn. Department of Psychology, Bowling Green State University, Bowling Green, OH, US
Rosenberg, Harold. Department of Psychology, Bowling Green State University, Bowling Green, OH, US
Ashrafioun, Lisham. Department of Psychology, Bowling Green State University, Bowling Green, OH, US
Hawley, Anna. Department of Psychology, Bowling Green State University, Bowling Green, OH, US
Bannon, Erin E.. Department of Psychology, Bowling Green State University, Bowling Green, OH, US
Jesse, Samantha. Department of Psychology, Bowling Green State University, Bowling Green, OH, US
Kraus, Shane, ORCID 0000-0002-0404-9480. Department of Psychology, Bowling Green State University, Bowling Green, OH, US
Kryszak, Elizabeth. Department of Psychology, Bowling Green State University, Bowling Green, OH, US
Cross, Nicole. Department of Psychology, Bowling Green State University, Bowling Green, OH, US
Carhart, Victoria. Department of Psychology, Bowling Green State University, Bowling Green, OH, US
Baik, Kyoung-deok. Department of Psychology, Bowling Green State University, Bowling Green, OH, US - Address:
- Davis, Alan K., Department of Psychology, Bowling Green State University, 822 E. Merry Avenue, Bowling Green, OH, US, 43403, akdavis@bgsu.edu
- Source:
- Psychology of Addictive Behaviors, Vol 28(2), Jun, 2014. pp. 575-579.
- NLM Title Abbreviation:
- Psychol Addict Behav
- Page Count:
- 5
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- Bulletin of the Society of Psychologists in Addictive Behaviors; Bulletin of the Society of Psychologists in Substance Abuse
- Other Publishers:
- US : Educational Publishing Foundation
Society of Psychologists in Addictive Behaviors - ISSN:
- 0893-164X (Print)
1939-1501 (Electronic) - Language:
- English
- Keywords:
- marijuana, reduction strategies, self-efficacy, university students, Marijuana Reduction Strategies Self-Efficacy Scale, psychometric properties
- Abstract:
- To evaluate several psychometric properties of a questionnaire designed to assess college students’ self-efficacy to employ 21 cognitive–behavioral strategies intended to reduce the amount and/or frequency with which they consume marijuana, we recruited 273 marijuana-using students to rate their confidence that they could employ each of the strategies. Examination of frequency counts for each item, principal components analysis, internal consistency reliability, and mean interitem correlation supported retaining all 21 items in a single scale. In support of criterion validity, marijuana use-reduction self-efficacy scores were significantly positively correlated with cross-situational confidence to abstain from marijuana, and significantly negatively correlated with quantity and frequency of marijuana use and marijuana-related problems. In addition, compared with respondents whose use of marijuana either increased or remained stable, self-efficacy was significantly higher among those who had decreased their use of marijuana over the past year. This relatively short and easily administered questionnaire could be used to identify college students who have low self-efficacy to employ specific marijuana reduction strategies and as an outcome measure to evaluate educational and skill-training interventions. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Drug Usage; *Marijuana; *Psychometrics; *Questionnaires; *Self-Efficacy; College Students
- Medical Subject Headings (MeSH):
- Adolescent; Adult; Cannabis; Female; Humans; Male; Marijuana Abuse; Psychometrics; Reproducibility of Results; Self Efficacy; Students; Surveys and Questionnaires; Young Adult
- PsycINFO Classification:
- Clinical Psychological Testing (2224)
Substance Abuse & Addiction (3233) - Population:
- Human
Male
Female - Location:
- US
- Age Group:
- Adulthood (18 yrs & older)
Young Adulthood (18-29 yrs) - Tests & Measures:
- Marijuana Reduction Strategy Self-Efficacy Scale
Marijuana Refusal Self-Efficacy Questionnaire
Rutgers Marijuana Problem Index - Methodology:
- Empirical Study; Quantitative Study
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- Accepted: Feb 24, 2014; Revised: Jan 29, 2014; First Submitted: Apr 14, 2013
- Release Date:
- 20140623
- Copyright:
- American Psychological Association. 2014
- Digital Object Identifier:
- http://dx.doi.org/10.1037/a0036665
- PMID:
- 24955675
- Accession Number:
- 2014-24742-015
- Number of Citations in Source:
- 20
- Persistent link to this record (Permalink):
- http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2014-24742-015&site=ehost-live
- Cut and Paste:
- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2014-24742-015&site=ehost-live">Development and evaluation of the Marijuana Reduction Strategies Self-Efficacy Scale.</A>
- Database:
- PsycINFO
Development and Evaluation of the Marijuana Reduction Strategies Self-Efficacy Scale / BRIEF REPORT
By: Alan K. Davis
Department of Psychology, Bowling Green State University;
Lawrence A. Osborn
Department of Psychology, Bowling Green State University
Jaclyn Leith
Department of Psychology, Bowling Green State University
Harold Rosenberg
Department of Psychology, Bowling Green State University
Lisham Ashrafioun
Department of Psychology, Bowling Green State University
Anna Hawley
Department of Psychology, Bowling Green State University
Erin E. Bannon
Department of Psychology, Bowling Green State University
Samantha Jesse
Department of Psychology, Bowling Green State University
Shane Kraus
Department of Psychology, Bowling Green State University
Elizabeth Kryszak
Department of Psychology, Bowling Green State University
Nicole Cross
Department of Psychology, Bowling Green State University
Victoria Carhart
Department of Psychology, Bowling Green State University
Kyoung-deok Baik
Department of Psychology, Bowling Green State University
Acknowledgement:
Marijuana is the most commonly used illegal drug in the United States, including among university students (Johnston, O’Malley, Bachman, & Schulenberg, 2012). Although many students use marijuana with few, if any, harmful consequences, excessive use of marijuana is associated with a variety of health, social, and academic problems (Bell, Wechsler, & Johnston, 1997; Caldeira, Arria, O’Grady, Vincent, & Wish, 2008; Hammersley & Leon, 2006; Lee, Neighbors, Kilmer, & Larimer, 2010; Shillington & Clapp, 2001). The health and welfare of university students who use marijuana regularly could be maintained or improved if they employed cognitive–behavioral self-control strategies to reduce the amount and/or the frequency of consumption (e.g., Roffman & Stephens, 2012; Rooke, Copeland, Norberg, Hines, & McCambridge, 2013).
Despite the advantages and potential effectiveness of employing cognitive–behavioral self-control strategies to reduce marijuana use, there is little research examining the extent to which these strategies are employed and the barriers that impede their implementation. One factor that may influence the implementation of reduction strategies is self-efficacy (Bandura, 1977). Although investigators have developed abstinence self-efficacy questionnaires to evaluate drug takers’ confidence to refrain from using a target drug in various contexts (Annis & Martin, 1985; Sklar, Annis, & Turner, 1997), we could find no instrument designed to assess users’ self-efficacy to employ specific strategies intended to reduce their use of marijuana.
Therefore, we designed the present study to develop and evaluate several key psychometric properties of a self-administered questionnaire to measure marijuana users’ self-efficacy to employ various self-control skills to reduce how much and how often they consume marijuana as an alternative or complement to measuring only their confidence to abstain. We recruited a sample of university students who regularly consumed marijuana to assess factor structure, internal consistency reliability, unidimensionality, and criterion validity of the measure. Specifically, although we view confidence to employ use-reduction strategies and confidence to refrain from using marijuana across different circumstances as different types of self-efficacy, we hypothesized that use-reduction self-efficacy would be significantly but only moderately correlated with self-efficacy to refrain from marijuana use. In addition, we expected that lower self-efficacy to employ use-reduction strategies would be significantly correlated with more frequent consumption of marijuana and experience of more marijuana use-related problems. As another evaluation of criterion validity, we tested the assumption that those who had decreased their marijuana use over the past year would report higher self-efficacy to employ use-reduction strategies compared with those whose consumption had remained stable or increased.
Method Procedure and Respondents
Following approval of the project by the institutional review board in the spring of 2012, we sent a recruitment e-mail (with a 1-week follow-up reminder e-mail) to a random sample of 8,000 students, which was approximately 50% of the undergraduates enrolled in the Midwestern public university from which respondents were recruited. The e-mail overture provided a short description of the study, including the eligibility requirements (i.e., at least 18 years of age; used marijuana at least once per month in each of the last 6 months), compensation (i.e., opportunity to win one of two $50 gift certificates from an online retailer), and a web link to the study materials, which were hosted by a commercial survey designer (www.surveygizmo.com). To help ensure anonymity, the survey (which took approximately 30 min to complete) was programmed to automatically provide a gift certificate to the two randomly selected winners immediately after they completed the questionnaires.
Of the 479 individuals who clicked the link to the study materials, 326 submitted survey responses and 300 of these met the eligibility requirements. Of the 300 eligible respondents, 273 were retained because they completed every item on the measure of strategy-specific self-efficacy (in anticipation of list-wise deletion on several SPSS analyses). Evaluation of demographic characteristics of the sample and the larger student body from which they were recruited revealed that 84% of the sample identified themselves as Caucasian, as did 77% of the study body; 93% of the sample were between the ages of 18 and 24, as were 90% of the study body; 45% of our sample were female, as were 55% of the study body; and 36% of the sample lived on campus, as did 38% of the student body. In addition, the sample reflected the full range of years at university (first year = 18%, second year = 26%, third year = 26%, fourth year = 20%, fifth year or higher = 10%). Table 1 provides detailed information on marijuana and other drug use history of the sample.
Marijuana Use and Other Drug Use Characteristics
Measures
Marijuana Reduction Strategy Self-Efficacy Scale (MJ-RS-SES)
To develop the MJ-RS-SES, we (a) created a pool of cognitive–behavioral strategies that an individual might employ to reduce his or her consumption of marijuana and (b) modified items on questionnaires designed to assess past use of (Martens, Pederson, Labrie, Ferrier, & Cimini, 2007) and current self-efficacy to employ (Bonar et al., 2011; Kraus et al., 2012) alcohol reduction strategies. After deleting redundant items and rephrasing potentially ambiguous items, we compiled a list of 21 strategies that comprised the working draft of the MJ-RS-SES (see Table 2 for the list of items). The instructions at the top of the questionnaire asked the respondent to rate his or her current confidence, on an 11-point scale from 0% (not at all confident) to 100% (completely confident) in increments of 10, that he or she COULD use each of the listed strategies to reduce his or her marijuana consumption. Readability statistics indicated that the questionnaire items are easily readable (Flesch-Kincaid Grade Level = 2.6; Flesch Reading Ease = 86.3 on a scale of 0 [most difficult] to 100 [easiest]).
Items, Component Loadings, and Item Means and SDs on the Marijuana-Reduction Strategies-Self-Efficacy Scale (MJ-RS-SES; N = 273)
Marijuana Refusal Self-Efficacy Questionnaire (MRSEQ)
This questionnaire was based in part on a previously published measure assessing refusal self-efficacy for alcohol (Oei, Hasking, & Young, 2005; Young, Oei, & Crook, 1991). Specifically, we modified the instructions to ask about respondents’ perceived self-efficacy to abstain from using marijuana in each of 13 social, emotional, and environmental situations in which marijuana could be consumed. Respondents were asked to rate how confident they were that they could refuse marijuana in each of the listed situations on an 11-point scale from 0% (no confidence, cannot refuse) to 100% (extreme confidence, certain can refuse) in increments of 10. Internal consistency reliability in the current sample was .87.
Rutgers Marijuana Problem Index (RMPI)
This questionnaire (Simons, Correia, Carey, & Borsari, 1998) asks respondents to rate the frequency with which they have experienced each of 17 specific marijuana-related problems over the last year using a 5-point scale ranging from 0 (never) to 4 (10 or more times). Research has supported the internal consistency reliability, predictive validity, and criterion validity of this measure (Lee, Neighbors, Hendershot & Grossbard, 2009; Simons et al., 1998). Internal consistency reliability in the current sample was .85.
Marijuana Use History
We designed this questionnaire to assess the frequency, durThis questionnaire was designeation, and stability of marijuana use, previous attempts to reduce and quit using marijuana, typical location of consumption, typical means of consumption, forms of marijuana consumed, and current experience of intoxication.
Drug Use History
We designed this questionnaire to assess respondents’ use of drugs other than marijuana. Respondents were asked to indicate (i.e., yes or no) whether they had ever used any of the listed substances (i.e., cocaine, heroin, hallucinogens, ecstasy/MDMA, amphetamines, prescription opiates, tranquilizers, sedatives, inhalants, and Spice/K2) and whether they had used these substances in the past 3 months.
Demographics
We designed this questionnaire to assess basic demographic data including age, gender, ethnicity, year in college, and residence on or off campus.
Results Item Reduction
As the initial step in the evaluation of the MJ-RS-SES, we examined the response frequencies for each of the 21 strategies to identify any “unbalanced” frequencies—that is, 75% or more of respondents endorsed that they had either no or very little confidence (0% or 10% ratings) or had very high confidence (90% or 100% ratings) that they could use that strategy. None of the items on the MJ-RS-SES were unbalanced. Next, we examined the item-total correlations to identify items for potential elimination. No items met Ferketich’s (1991) criterion (r < .30) for elimination (item-total rs ranged from .51 to .82).
Principal Components, Unidimensionality, and Internal Consistency Reliability
Next, we conducted a principal components analysis using data from the 273 respondents who rated their confidence on every one of the 21 items of the MJ-RS-SES. The solution was not rotated because we had no a priori basis for assuming the analysis would yield multiple components. This analysis yielded three components with eigenvalues greater than 1.0; however, the scree plot showed obvious flattening after the first component (eigenvalue = 11.3; 53% of variance accounted for) with relatively small eigenvalues (1.5 and 1.1) and proportions of variance (7% and 5%) accounted for by the subsequent two components. In addition, examination of the component loadings (Table 2) revealed that all 21 items had their highest loading on the first component (loadings ranged from .49 to .82), with few items cross-loading on components 2 and 3.
Next, we calculated the mean interitem correlation to evaluate the “unidimensionality” of the 21-item scale. Based on Clark and Watson (1995), we interpreted the resulting coefficient (mean r = .50, interitem rs ranged from .17 to .85) as support for the unidimensionality of the scale. In addition, internal consistency reliability was notably high across the 21 items (α = .96), perhaps in part because of the large number of items on the scale. We interpreted these findings as indicating that the 21 items on the MJ-RS-SES comprise a single scale.
Base Rates of Self-Efficacy to Employ Specific Marijuana Reduction Strategies
As examination of Table 2 reveals, 20 of the 21 items had means indicating that, on average, respondents reported being moderately confident that they could employ these strategies. However, the relatively large standard deviations also indicate that confidence varied considerably with some students having relatively low and others having relatively high self-efficacy to employ each of these 20 strategies. Furthermore, even that one item (“Dilute marijuana you are about to use with tobacco”) with a notably low mean rating (M = 27.7) had a large standard deviation (SD = 36.7).
Evaluation of Criterion Validity
First, we assessed the association of self-efficacy to employ specific marijuana reduction strategies with self-efficacy to abstain from marijuana across various circumstances. As expected, MJ-RS-SES scores were positively correlated with MRSEQ scores, r(271) = .62, p < .01. Second, we assessed the association of reduction strategy self-efficacy with the experience of marijuana-related problems and with quantity and frequency of marijuana consumption. As expected, MJ-RS-SES scores were significantly, albeit less strongly, negatively correlated with marijuana problems, r(271) = −.28, typical number of joints smoked per week, r(271) = −.37, and typical number of days of marijuana use per month, r(266) = −.32, (all ps < .01). Third, an analysis of variance (ANOVA) revealed that mean MJ-RS-SES scores varied as a function of stability of marijuana use over the past 12 months, F(2, 269) = 3.62, p < .05, ηp2 = .03. Specifically, those 82 individuals who reported a decrease in their marijuana use over the past year reported higher self-efficacy (M = 66.8, SD = 22.2) to employ reduction strategies than those 145 individuals who reported that their marijuana use had stayed the same (M = 57.7, SD = 27.5) and those 45 who reported an increase in their marijuana use over the past 12 months (M = 56.9, SD = 27.6).
Association of MJ-RS-SES With Current Intoxication
Although it was not a test of validity per se, we conducted an independent samples t-test to examine whether there were mean differences in self-efficacy as a function of endorsement of intoxication (i.e., yes or no) while completing the survey. Those 43 respondents who endorsed that they were intoxicated while completing the study materials had significantly lower self-efficacy (M = 52.0, SD = 30.8) that they could employ the use-reduction strategies, t(269) = −2.3, p < .05, d = .28, compared with those 228 who stated they were not intoxicated while completing the study materials (M = 61.9, SD = 25.2).
DiscussionIn the present study, we recruited 273 undergraduates who were regular users of marijuana to complete a web-administered measure of their self-efficacy to employ 21 marijuana use-reduction strategies. Based on the distribution of confidence ratings, interitem correlations, principal components analysis, and internal consistency reliability, we decided not to delete any strategies (which helps increase content validity of the questionnaire) and concluded that all 21 items comprise a single scale. Furthermore, the readability statistics indicate that the questionnaire should be easily understood both by university students and by younger and less educated respondents.
This initial evaluation of the MJ-RS-SES supported several aspects of criterion validity; specifically, reduction strategy self-efficacy scores were significantly positively correlated with self-efficacy to refrain from use and significantly negatively associated with quantity and frequency of marijuana use, marijuana-related problems, and having increased one’s use of marijuana over the past year. We also found that self-efficacy was significantly lower among those who reported being intoxicated compared with those who were not. Whether those who were intoxicated have lower self-efficacy because intoxication during participation is a proxy for an unwillingness or inability to employ self-control skills, or whether this difference is an outcome of answering the items while intoxicated, awaits further research using a within-subjects design in which students report their confidence to use these strategies while and while not intoxicated.
We note that the MJ-RS-SES and our study of its psychometric properties have several limitations. For one, not all of the 21 strategies will apply in the many different contexts in which students use marijuana (e.g., using when alone vs. with others; using with friends vs. with strangers; using highly potent vs. less potent marijuana). Furthermore, we did not include an open-ended question asking respondents if they used any other strategies, and we recognize some may employ personally unique reduction strategies not currently listed on the MJ-RS-SES. Another limitation is that respondents’ ratings of their self-efficacy may be inaccurate depending on how insightful and truthful they are regarding their confidence to employ these self-control strategies.
We also note that outlining the inclusion criteria and incentive in the e-mail invitation (considered part of informed consent by our university institutional review board), could have encouraged respondents to misrepresent their marijuana use in order to meet eligibility requirements to qualify to win one of the gift cards. Moreover, we created the MRSEQ because no such questionnaire existed for marijuana and we wanted to ask respondents to rate their confidence to abstain in specific marijuana-related contexts. In addition, we developed our own marijuana use history questionnaire because we wanted to assess additional information—such as respondents’ stability of use, previous attempts to reduce and quit using marijuana, typical means and location of consumption, and current experience of intoxication—that are not typically included on such measures.
Another potential limitation of the study was the relatively low response rate given the total number of e-mail overtures sent. Given recently published nationwide data (Johnston et al., 2012) showing that the prevalence of past-month marijuana use among college students was 19.4%, and our requirement that respondents had to use marijuana at least once per month for the past 6 months, our potential respondent pool was unlikely to be larger than 1,552 monthly marijuana users out of 8,000 university students to whom the e-mail overture was sent. In addition, the time demands of the survey, the limited number of incentives offered, and our having sent the recruitment email only twice, could have decreased the number of potential respondents who decided to participate. Although our sample of marijuana users was notably similar to the campus at large in terms of age, ethnicity, and gender, it may not be fully representative of the population of those who use marijuana.
A combination of intrapersonal, peer, societal, and legal influences may result in more students electing to seek assistance to reduce their use of marijuana as an alternative to either abstinence or excessive consumption. Therefore, despite the limitations outlined above, a self-report questionnaire of self-confidence to employ a variety of marijuana use-reduction strategies has several possible applications. For example, the questionnaire could be used as an outcome measure to assess the degree to which education and prevention interventions impact reported confidence to employ specific reduction strategies, perhaps especially among students who are more likely to experience negative marijuana-related consequences. In addition, for students who are naïve about specific self-control strategies, rating one’s confidence to employ each strategy listed on the MJ-RS-SES might itself serve as a brief intervention that informs young people of specific strategies they could employ to reduce their consumption of marijuana. This last application warrants its own evaluation using a sample that has little knowledge of these skills.
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Submitted: April 14, 2013 Revised: January 29, 2014 Accepted: February 24, 2014
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Source: Psychology of Addictive Behaviors. Vol. 28. (2), Jun, 2014 pp. 575-579)
Accession Number: 2014-24742-015
Digital Object Identifier: 10.1037/a0036665
Record: 50- Title:
- Development and preliminary validation of the Level of Care Index (LOCI) from the Personality Assessment Inventory (PAI) in a psychiatric sample.
- Authors:
- Sinclair, Samuel Justin. Department of Psychiatry, Massachusetts General Hospital, Boston, MA, US, SJSinclair@Partners.org
Slavin-Mulford, Jenelle. Department of Psychiatry, Massachusetts General Hospital, Boston, MA, US
Antonius, Daniel. School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, NY, US
Stein, Michelle B.. Department of Psychiatry, Massachusetts General Hospital, Boston, MA, US
Siefert, Caleb J.. Department of Behavioral Sciences, University of Michigan—Dearborn, Dearborn, MI, US
Haggerty, Greg. Von Tauber Institute for Global Psychiatry, Nassau University Medical Center, East Meadow, NY, US
Malone, Johanna C.. Department of Psychiatry, Massachusetts General Hospital, Boston, MA, US
O'Keefe, Sheila. Department of Psychiatry, Massachusetts General Hospital, Boston, MA, US
Blais, Mark A.. Department of Psychiatry, Massachusetts General Hospital, Boston, MA, US - Address:
- Sinclair, Samuel Justin, Department of Psychiatry, Massachusetts General Hospital, One Bowdoin Square 7th Floor, Boston, MA, US, 02114, SJSinclair@Partners.org
- Source:
- Psychological Assessment, Vol 25(2), Jun, 2013. pp. 606-617.
- NLM Title Abbreviation:
- Psychol Assess
- Page Count:
- 12
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- Psychological Assessment: A Journal of Consulting and Clinical Psychology
- ISSN:
- 1040-3590 (Print)
1939-134X (Electronic) - Language:
- English
- Keywords:
- LOCI, Level of Care Index, PAI, personality assessment inventory, psychiatric hospitalization, mental health service utilization, test development, test validation
- Abstract:
- Research over the last decade has been promising in terms of the incremental utility of psychometric tools in predicting important clinical outcomes, such as mental health service utilization and inpatient psychiatric hospitalization. The purpose of this study was to develop and validate a new Level of Care Index (LOCI) from the Personality Assessment Inventory (PAI). Logistic regression was initially used in a development sample (n = 253) of psychiatric patients to identify unique PAI indicators associated with inpatient (n = 75) as opposed to outpatient (n = 178) status. Five PAI variables were ultimately retained (Suicidal Ideation, Antisocial Personality–Stimulus Seeking, Paranoia–Persecution, Negative Impression Management, and Depression–Affective) and were then aggregated into a single LOCI and independently evaluated in a second validation sample (n = 252). Results indicated the LOCI effectively differentiated inpatients from outpatients after controlling for demographic variables and was significantly associated with both internalizing and externalizing risk factors for psychiatric admission (range of ds = 0.46 for history of arrests to 0.88 for history of suicidal ideation). The LOCI was additionally found to be meaningfully associated with measures of normal personality, performance-based tests of psychological functioning, and measures of neurocognitive (executive) functioning. The clinical implications of these findings and potential utility of the LOCI are discussed. (PsycINFO Database Record (c) 2017 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Personality Measures; *Test Construction; *Test Validity; Health Care Utilization; Psychiatric Hospitalization
- Medical Subject Headings (MeSH):
- Adolescent; Adult; Aged; Aged, 80 and over; Female; Humans; Inpatients; Male; Mental Disorders; Middle Aged; New England; Outpatients; Personality Assessment; Personality Inventory; Pilot Projects; Psychiatric Status Rating Scales; Psychometrics; Young Adult
- PsycINFO Classification:
- Tests & Testing (2220)
Psychological & Physical Disorders (3200) - Population:
- Human
Male
Female
Inpatient
Outpatient - Location:
- US
- Age Group:
- Adulthood (18 yrs & older)
Young Adulthood (18-29 yrs)
Thirties (30-39 yrs)
Middle Age (40-64 yrs)
Aged (65 yrs & older)
Very Old (85 yrs & older) - Tests & Measures:
- Level of Care Index
NEO Five-Factor Inventory
Social Cognition and Object Relations Scale-Global Rating Method
Trail Making Test—Part B
Suicide Potential Index
Violence Potential Index
California Personality Inventory
Millon Clinical Multiaxial Inventory
Psychological Inventory of Personality and Symptoms
Delis-Kaplan Executive Function System DOI: 10.1037/t15082-000
Personality Assessment Inventory DOI: 10.1037/t03903-000
Stroop Neuropsychological Screening Test
Minnesota Multiphasic Personality Inventory
Thematic Apperception Test (TAT)
Wisconsin Card Sorting Test DOI: 10.1037/t31298-000 - Methodology:
- Empirical Study; Interview; Quantitative Study
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- First Posted: Mar 4, 2013; Accepted: Jan 24, 2013; Revised: Jan 23, 2013; First Submitted: May 24, 2012
- Release Date:
- 20130304
- Correction Date:
- 20170216
- Copyright:
- American Psychological Association. 2013
- Digital Object Identifier:
- http://dx.doi.org/10.1037/a0032085
- PMID:
- 23458082
- Accession Number:
- 2013-07336-001
- Number of Citations in Source:
- 62
- Persistent link to this record (Permalink):
- http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2013-07336-001&site=ehost-live
- Cut and Paste:
- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2013-07336-001&site=ehost-live">Development and preliminary validation of the Level of Care Index (LOCI) from the Personality Assessment Inventory (PAI) in a psychiatric sample.</A>
- Database:
- PsycINFO
Development and Preliminary Validation of the Level of Care Index (LOCI) From the Personality Assessment Inventory (PAI) in a Psychiatric Sample
By: Samuel Justin Sinclair
Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School;
Jenelle Slavin-Mulford
Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School
Daniel Antonius
School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, and Department of Psychiatry, New York University School of Medicine
Michelle B. Stein
Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School
Caleb J. Siefert
Department of Behavioral Sciences, University of Michigan—Dearborn
Greg Haggerty
Von Tauber Institute for Global Psychiatry/Nassau University Medical Center
Johanna C. Malone
Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School
Sheila O’Keefe
Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School
Mark A. Blais
Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School
Acknowledgement: Jenelle Slavin-Mulford is now at Department of Psychology, Georgia Regents University.
The last 20 years has seen a major shift in the mental health field, particularly in terms of the practices surrounding inpatient psychiatric hospitalization. Changes in managed care have altered the nature of psychiatric hospitalization, resulting in increased focus on symptom stabilization, shorter length of stays (LOSs), reduced daily census, and greater reliance on community outpatient mental health services for maintenance treatment (Averill, Hopko, Small, Greenlee, & Varner, 2001; Hopko, Lachar, Bailley, & Varner, 2001; Rocca et al., 2010). In light of these changes, longer term mental health facilities have generally been replaced by acute psychiatric hospitals, which function specifically to meet the objectives above and to reduce operating costs. As a result, rates of inpatient psychiatric admission declined in the 1970s and 1980s (Blader, 2011).
In spite of these trends, research over the last decade suggests an increase in inpatient psychiatric admissions for some populations. Using data from the annual National Hospital Discharge Survey, Blader (2011) examined rates of inpatient psychiatric admissions between 1996 and 2007 and reported that for children, adolescents, and adults, psychiatric discharge rates increased; only the elderly saw a decline. Similarly, the total number of inpatient days increased for children and adolescents, but decreased for the elderly. Blader (2011) further reported that the nature of psychopathology resulting in admission has grown more acute and that psychiatric admissions through the emergency room increased. In contrast, rates of hospitalization for more chronic conditions involving less immediate risk (such as anxiety disorders) have been steadily decreasing. This increase in focus on acute distress to determine need for hospitalization likely reflects the more stringent admissions criteria that currently exist. In light of these recent trends, new research is necessary to better predict and proactively treat risk factors for psychiatric hospitalization.
Various approaches have been used over the last few decades to identify people at risk for increased level of psychiatric care. Most studies have focused predominantly on clinician-based judgments and semistructured interviews (Averill et al., 2001; Colasanti et al., 2010; Hopko et al., 2001; Perlick, Rosenheck, Clarkin, Sirey, & Raue, 1999), as well as other clinical and demographic risk factors (Kolbasovsky, Reich, & Futterman, 2007; Miret et al., 2011; Olfson, Ascher-Svanum, Faries, & Marcus, 2011). More recently, these prediction models have also begun to include the use of psychometric data (Averill et al., 2001). The clinician-administered Brief Psychiatric Rating Scale (BPRS; Overall & Gorham, 1962) is one specific psychometric tool that has received considerable attention over the last decade, particularly in terms of its ability to predict acute psychiatric admission and LOS (Colasanti et al., 2010; Hopko et al., 2001; Perlick et al., 1999; Rocca et al., 2010). However, although valuable, clinician-administered evaluations are not always feasible given resource and time limitations, and there has been a concurrent increase in use of self-report measures of psychopathology and personality as alternatives to clinician-administered scales, given the fact that such measures require minimal staff resources and often possess strong psychometric properties (Williams, Weiss, Edens, Johnson, & Thornby, 1998).
Results have varied somewhat in terms of the incremental validity of self-report measures in predicting important clinical outcomes beyond that predicted by demographic information and clinician assessments (Clements, Murphy, Eisen, & Normand, 2006). In part, this reflects the narrow scope of measures used to assess psychological functioning, and the fact that important clinical/cognitive domains (e.g., impulsivity, confusion) are not always adequately represented. In contrast, other approaches have used multiple broadband measures of psychopathology (e.g., the Minnesota Multiphasic Personality Inventory, California Personality Inventory, the Millon Clinical Multiaxial Inventory, and the Psychological Inventory of Personality and Symptoms) to predict rates of psychiatric hospital utilization with some success (Williams et al., 1998). However, the burden on patients to complete larger test batteries is high.
Another complicating issue with respect to this overall body of research is that the results have varied considerably in terms of specific variables that predict increased level of care and mental health service utilization. Although variables including suicide and violence risk, psychosis and paranoia, depression, prior psychiatric hospitalization, mania, posttraumatic stress, alcohol and drug abuse, and Axis II personality traits have all been found to be associated with psychiatric hospitalization and LOS, the combination and relative contribution of these factors have varied markedly across studies (Averill et al., 2001; Colasanti et al., 2010; Hopko et al., 2001; Kolbasovsky et al., 2007; Miret et al., 2011; Olfson et al., 2011; Perlick et al., 1999). Given this variability in findings, it has been more difficult to develop prediction models that are generalizable across settings and populations.
Some have argued this reflects differences both in terms of the methods used (i.e., historical/demographic information, clinician-based judgments, clinician-administered psychometric tests, and self-report measures of psychological/personality functioning) and samples under study (Hopko et al., 2001). With respect to the latter point, although many of these studies have focused on mixed psychiatric samples (e.g., Averill et al., 2001; Blais et al., 2003; Clements et al., 2006; Hopko et al., 2001; Kolbasovsky et al., 2007; Rocca et al., 2010), others have been disorder-specific and focused on discrete conditions such as schizophrenia and bipolar disorder (Colasanti et al., 2010; Hoblyn, Balt, Woodard, & Brooks, 2009; Jackson, Fein, Essock, & Mueser, 2001; Olfson et al., 2011; Perlick et al., 1999). In light of these inconsistencies, additional work is needed to refine these prediction models, which themselves may be specific to certain contexts (e.g., acute psychiatric hospitals).
Assessing Treatment-Related Factors With the Personality Assessment InventoryThe Personality Assessment Inventory (PAI; Morey 1991, 2007) is a more recent broadband measure of psychological functioning that has seen increased use over the last several decades in multiple contexts. The PAI allows clinicians to quickly assess patients across a number of relevant clinical domains (e.g., depression, anxiety, mania, psychosis, etc.), as well as with respect to interpersonal style and factors that may impact the course of treatment (e.g., suicidal ideation and aggression). The PAI has been used across a variety of clinical (Siefert, Sinclair, Kehl-Fie, & Blais, 2009; Sinclair et al., 2010, 2009), forensic (Edens, Cruise, & Buffington-Vollum, 2001; Morey & Quigley, 2002; Walters & Duncan, 2005), and medical settings (Corsica, Azarbad, McGill, Wool, & Hood, 2010; Wagner, Wymer, Topping, & Pritchard, 2005), and its utility is well documented.
In addition to the core scales, Morey (1991, 2007) developed a number of supplemental indices to assess for factors including suicide and violence potential, defensiveness, malingering, and treatment process, among others. This additional work was done to maximize the potential of the PAI to not only measure discrete clinical constructs but also evaluate for other important factors associated with treatment process. In short, different profile configurations that have been shown in the literature to relate to these overarching constructs were aggregated into single indices to assess for these phenomena specifically. For example, the Suicide (SPI) and Violence (VPI) Potential Indices were developed by identifying the 20 features of the PAI profile found to be most associated with suicide and violence risk, respectively. Independent research has supported the validity and psychometric adequacy of these indices (Hopwood, Baker, & Morey, 2008; Sinclair et al., 2012).
Study PurposeResearch over the last decade has been promising in terms of the incremental utility of psychometric tools in predicting important clinical outcomes, such as mental health service utilization and inpatient psychiatric hospitalization. Although the specific variables found to be predictive of these outcomes have varied (likely as a function of the different tools used and samples under study), newer broadband instruments of psychological functioning such as the PAI allow for added potential in refining these prediction models. Toward these ends, the purpose of this study was to develop a new index for the PAI (the Level of Care Index, or LOCI) to assess the potential need for increased level of psychiatric care. Adopting methods used by Morey (1991, 2007) to construct PAI supplemental indices, in the current study we sought to initially identify PAI profile variables demonstrating empirical associations with inpatient (as opposed to outpatient) level of care in an initial development sample. Second, these variables were then aggregated into a single index and validated in an independent sample.
The validity of the LOCI was assessed using a multimethod approach. First, the LOCI was evaluated in terms of how effectively it differentiated psychiatric inpatients from outpatients in an independent (validation) sample. Next, associations between the LOCI and a number of life event variables that are associated with increased level of care (e.g., history of psychiatric hospitalization, history of suicide attempts, etc.) were evaluated. Third, the relationships between the LOCI and a measure of normal personality (the NEO Five-Factor Inventory [NEO-FFI]) and a performance-based measure of psychological functioning (Social Cognition and Object Relations Scale-Global Rating Method [SCORS-G], as applied to the Thematic Apperception Test [TAT; Murray, 1943]) were assessed.
Finally, in light of research that has demonstrated a relationship between neuropsychological dysfunction (particularly in terms of executive functioning) and suicide and violence potential (which are also identified risk factors for psychiatric hospitalization; Sinclair et al., 2012), the relationship between the LOCI and measures of executive functioning were examined. Specifically, because executive dysfunction has been found to be associated with greater levels of impulsivity, which in turn may predispose someone to self- or other-harming behaviors that subsequently place them at increased risk for psychiatric hospitalization (Harkavy-Friedman et al., 2006; Jollant et al., 2005; Marzuk, Hartwell, Leon, & Portera, 2005; Morgan & Lilienfeld, 2000; Westheide et al., 2008), we hypothesized that LOCI scores would be elevated in groups with executive functioning impairment.
Method Participants
Study participants were 593 psychiatric patients referred for psychological and neuropsychological assessment within an academic medical setting in the northeast United States. Of these, 505 participants completed a valid PAI as part of their evaluation using validity criteria (Inconsistency ≥ 73; Infrequency ≥ 75; Negative Impression Management ≥ 92; Positive Impression Management ≥ 68) specified by Morey (1991, 2007). Within this analytic sample, 356 (70%) patients underwent assessment in an outpatient psychology assessment center, and 149 (30%) completed the evaluation as part of their care on an acute inpatient psychiatric unit. The mean age for the total sample was 42.3 (SD = 15.1; range = 18–92), and there was no significant difference in age between outpatients (M = 42.9, SD = 15.4; range = 18–92) and inpatients (M = 40.9, SD = 14.2; range = 18–81). The overall sample was evenly split in terms of gender (54% male) and identified their race/ethnicity as predominantly Caucasian (88% White), and the average education level was 14.5 years (SD = 2.9). No significant differences were found between outpatients and inpatients in terms of gender, race/ethnicity, or level of education.
All study participants were receiving psychiatric care at the time of the evaluation and were referred by their respective outpatient or inpatient providers for psychological and/or neuropsychological testing to assist with diagnosis and treatment planning. As part of the referral process, preevaluation psychiatric diagnoses were acquired from either the referring provider or the patient’s hospital medical record. Primary psychiatric diagnoses for the overall sample included major depressive disorder (52%), bipolar disorder (13.0%), anxiety disorders (13%), cognitive disorders such as attention-deficit/hyperactivity disorder (6%), substance abuse disorders (5%), primary psychotic disorders (4.0%), and other Axis I disorders (7%). Table 1 presents demographic information across each of the subsamples used in the current study.
Demographic and Diagnostic Characteristics of the Development (n = 253) and Validation (n = 252) Samples
Procedure
All evaluations were performed by licensed clinical psychologists, or unlicensed psychology trainees (i.e., predoctoral psychology interns or postdoctoral fellows) who conducted the assessments under direct supervision. All data acquired as part of these evaluations were entered into a deidentified data repository that was approved by the hospital’s Institutional Review Board. These data included patient demographic information: preassessment clinical and diagnostic information that was collected from the referring provider and patient’s medical record; information about the patient’s history that was collected as part of a semistructured clinical interview; and all psychological and neuropsychological test scores. Although the test batteries for these patients varied, the PAI (Morey, 1991, 2007) was a core measure administered to all patients.
Measures
The PAI
The PAI is a 344-item broadband, self-report measure of general psychological functioning that was designed to assess major domains of psychopathology as well as factors that may impact treatment and interpersonal style (Morey, 1991, 2007). The PAI contains four validity scales that have been shown to be sensitive in detecting inconsistent/random responding as well as positive and negative response styles (Morey, 1991, 2007; Sellbom & Bagby, 2008). It additionally contains 11 embedded clinical scales developed to assess for the following domains of psychopathology: somatic complaints, anxiety, anxiety-related disorders, depression, mania, paranoia, schizophrenia, borderline features, antisocial features, alcohol problems, and drug problems. The PAI also includes five treatment consideration scales designed to assess factors such as suicidal ideation, aggression, situational stress, reduced social support, and amenability to treatment. Finally, it contains two scales assessing interpersonal style (dominance and warmth; Morey, 1991, 2007). The same 4-point Likert-scale is used for all items (False, Slightly True, Mainly True, Very True). Overall, research on the underlying psychometric properties of the PAI has been quite promising (Boone, 1998; Braxton, Calhoun, Williams, & Boggs, 2007; Deisinger, 1995; Holden, 2000; Morey, 1991, 2007; Siefert et al., 2009; Sinclair et al., 2010, 2009). Normative data from both nonclinical and clinical populations are used in score/profile interpretation. Across a number of studies, the PAI has been found to be a reliable and valid measure of general psychological functioning (Morey, 1991, 2007; Siefert et al., 2009; Sinclair et al., 2010, 2009).
The NEO-FFI
The NEO-FFI is a 60-item measure that was designed to assess the “Big Five” dimensions of normal personality: Neuroticism, Extraversion, Openness, Agreeableness, and Conscientiousness. The psychometric properties of the NEO-FFI have generally been found to be excellent, with moderate to high internal consistency reliability for all five scales and evidence for construct validity (Costa & McCrae, 1992; McCrae & Costa, 2004). All items are rated along a 5-point response scale ranging from 1 (strongly agree) to 5 (strongly disagree), and scales are then scored reflecting these overarching personality dimensions. In the current study, the NEO-FFI was administered to 144 outpatients in the validation sample.
The SCORS-G
The SCORS-G (Stein, Hilsenroth, Slavin-Mulford, & Pinsker, 2011; Stein, Slavin-Mulford, Sinclair, Siefert, & Blais, 2012; Westen, 1995) is an assessment technique that evaluates the quality of object relations in narrative data. Clinicians are trained in the SCORS-G method and rate participants’ narrative material along eight core dimensions: (1) Complexity of Representations assesses how well people are able to see and interpret the internal states of self and others; (2) Affective Quality of Representations evaluates a person’s expectations of others in the context of relationships from an affective standpoint; (3) Emotional Investment in Relationships assesses a person’s capacity for intimacy and emotional reciprocity; (4) Emotional Investment and Values in Moral Standards looks at how well a person uses abstract thought when considering issues of morality and compassion for others; (5) Understanding of Social Causality evaluates how well a person understands the emotions, motivations, and behavior of themselves and others; (6) Experience and Management of Aggressive Impulses (AGG) assesses the extent to which a person is able to modulate aggressive impulses in an appropriate way; (7) Self-Esteem evaluates the person’s self-concept; and (8) Identity and Coherence of Self looks at the integration and cohesiveness of a person’s sense of self, versus how fragmented and disjointed it may be.
In the current study, the SCORS-G method was applied to seven core cards (Cards 1, 2, 3BM, 4, 13MF, 12M and 14) from the TAT (Murray, 1943), which were administered to a subsample of 53 outpatients in the validation sample (Stein et al., 2012). The following standardized set of instructions was presented to the patient:
Now I’m going to show you some cards with pictures on them. The pictures are of people in various situations and what I want you to do is make up a story around the picture. Like all stories, yours should have a beginning, middle, and ending. Tell me what led up to the picture, how it turns out, and what the people feel and think. Here’s the first one. Make up a story around this picture.
Two independent raters then scored TAT narratives for these eight core domains using a 7-point likert scale, where lower scores (e.g., 1 or 2) are indicative of greater pathology and higher scores (e.g., 6 or 7) suggest healthy responses. The two independent raters used in the current study were considered to be experts in the SCORS-G system, and both previously completed manualized training on the SCORS-G (Hilsenroth, Stein, & Pinsker, 2007; Stein et al., 2011; Westen, 1995). Interrater reliability for these raters have been found to be good (>0.60) to excellent (>0.74) in prior research (Eudell-Simmons, Stein, DeFife, & Hilsenroth, 2005; Pinsker-Aspen, Stein, & Hilsenroth, 2007; Slavin, Stein, Pinsker-Aspen, & Hilsenroth, 2007; Stein, Pinsker-Aspen, & Hilsenroth, 2007). Prior to this study, a subsample of 22 TAT narratives were scored by both raters, and differences beyond 1 point were reviewed and reconciled as a means of calibrating ratings for the current study. Following this, both raters independently rated all 371 TAT narratives (seven narratives/TAT cards per individual) used in the current study. A recent study by Stein and colleagues (2012) documented the construct validity of the SCORS-G, particularly in terms of its relationship with measures of psychopathology, intellectual/cognitive functioning, and normal personality.
Executive functioning measures
Executive functioning was evaluated in the current study using two commonly used tests: the Trail Making Test—Part B (Reitan & Wolfson, 1985) and the Stroop Neuropsychological Screening Test (Trenerry, Crosson, DeBoe, & Leber, 1989). The Stroop and Trail Making Test—Part B are commonly used screening tests of cognitive flexibility and inhibition and are frequently administered to patients in more acute settings to assess executive functioning. The Trail Making Test—Part B requires an examinee to draw lines through numbers and letters in alternating and ascending fashion, and is considered to be a sensitive measure of set-shifting and cognitive flexibility. Score estimates were derived on the basis of the amount of time it took examinees to complete the test, using normative data provided by Tombaugh (2004). Both test–retest (Dikmen, Heaton, Grant, & Temkin, 1999) and interrater reliability (Fals-Stewart, 1991) for Trail Making Test—Part B have been found to be excellent. The validity of Trail Making Test—Part B in evaluating neurological integrity is also well documented (Reitan & Wolfson, 2004). The Stroop Neuropsychological Screening Test requires examinees to first read a list of 112 words that are written in different ink colors during the first trial, and then in a second trial they are asked to name the color of the ink the word is written in (which requires that they inhibit the natural response of reading the word and provide a dissonant response). The number of correct responses (minus the incorrect responses) within a 2-min time frame is then used to derive a person’s test score, using normative data provided by Trenerry et al. (1989). Test–retest reliability for the Stroop has been found to be high at 0.90, and the instrument has been found to effectively differentiate those without brain damage from brain-damaged groups (Trenerry et al., 1989). Raw scores for both tests were converted into standard scores using existing normative data for each instrument to have a mean of 100 and standard deviation of 15.
ResultsA random number generator was first used to split the total sample into two subsamples to initially develop and then independently validate the LOCI. An even number of outpatients and inpatients were distributed within both samples. The development sample (DS) contained 253 total patients, 178 of whom were outpatients and 75 were inpatients. The validation sample (VS) contained 252 patients, 178 of whom were outpatients and 74 were inpatients. No significant differences in age, gender, race/ethnicity, or education were found between DS or VS. Preevaluation diagnoses were generally distributed evenly across the DS and VS samples. Table 1 presents diagnostic and demographic information for both inpatients and outpatients in the development and validation samples, respectively.
Logistic regression was subsequently employed using the development sample (n = 253) to evaluate which of the PAI scales and subscales were the best predictors of inpatient level of care. As a means of maximizing predictive power, all PAI scales (i.e., validity, clinical, treatment consideration, and interpersonal) were entered into the regression model. In cases in which PAI scales also contained subscales (e.g., Depression–Affective, Cognitive, Physiological), the latter subscales were entered in lieu of full scales as a means of improving precision. In total, 43 PAI scales were entered in a stepwise fashion to determine which variables significantly (i.e., p < .05) predicted inpatient level of care; the results of this analysis are presented in Table 2. Of the 43 PAI variables that were entered, nine were found to be significant predictors of inpatient status, explaining 39% of the variance in the target variable. These variables include Negative Impression Management (NIM), Suicidal Ideation (SUI), Somatic Complaints–Somatization (SOM-S), Depression–Affective (DEP-A), Paranoia–Persecution (PAR-P), Borderline Features–Negative Relationships (BOR-N), Antisocial Personality–Stimulus Seeking (ANT-S), Antisocial Personality–Egocentricity (ANT-E), and Schizophrenia–Thought Disorder (SCZ-T).
Predicting Inpatient Level of Care With the PAI Using Logistic Regression in the Development Sample (n = 253)
Next, each of these nine individual PAI scales were then evaluated separately in the development sample (n = 253) in terms of their relationship with level of care (inpatient vs. outpatient) using analysis of covariance (ANCOVA). All ANCOVA models were constructed to control for the effects of age, gender, education, and race/ethnicity. This was done to assess each predictor’s linear relationship with the target variable, after accounting for demographic variables known to be associated with risk for hospitalization (e.g., Blais et al., 2003; Miret et al., 2011; Olfson et al., 2011; Unick et al., 2011). Adjusted mean PAI scale scores and Cohen’s d estimates were both estimated to evaluate whether differences across groups were statistically significant, and also to assess the magnitude of the effect. Cohen (1988) suggested d values of 0.2 as indicating a small effect, 0.5 as a moderate effect, and 0.8 and greater as a large effect.
Table 3 presents adjusted means for these nine PAI indicators for inpatients and outpatients in the development sample (n = 253). As can be seen, only five (NIM, SUI, DEP-A, PAR-P, and ANT-S) of the original nine PAI scales were found to be significantly associated with level of care after controlling for other demographic variables. Cohen’s d estimates for these five scales ranged from 0.28 (PAR-P) to 0.68 (SUI), with a median of 0.48 (DEP-A). As hypothesized, inpatients scored significantly higher on all five of these scales, as compared with outpatients.
Adjusted Mean (SE)a PAI Scale Scores Across Treatment Setting in the Development Sample (n = 253)
Using a method similar to Morey (1991, 2007), these five scales were then aggregated into a single index. However, as opposed to dichotomizing score values based on a specific threshold (e.g., a T score of “60,” which is the method Morey used in developing the PAI supplemental indices; Morey 1991, 2007), variables in the current study were coded in terms of their relative distance from the mean, using a methodology employed with neuropsychological test variables for deriving “impairment scores” (see, e.g., Harvey, Keefe, Patterson, Heaton, & Bowie, 2009). This was done to maximize the sensitivity and reliability of the overall index. Specifically, Tscores less than 50 were assigned an index score of “0”; T scores between 50 and 59 were assigned a score of “1”; T scores between 60 and 69 were assigned a score of “2”; T scores between 70 and 79 were assigned a score of “3”; T scores between 80 and 89 were assigned a score of “4”; T scores between 90 and 99 were assigned a score of “5”; and T scores greater than or equal to 100 were assigned a score of “6.”
The only exception to this was NIM. Because research (e.g., Morey, 1991, 2007) has demonstrated that NIM values ≥ 92 are associated with random completion of the PAI and raise concerns about test validity, the current study was limited to valid protocols only using criteria specified by Morey (2007). Given this, T scores less than 40 were assigned an index score of “0”; T scores between 40 and 49 were assigned a score of “1”; T scores between 50 and 59 were assigned a score of “2”; T scores between 60 and 69 were assigned a score of “3”; T scores between 70 and 79 were assigned a score of “4”; T scores between 80 and 89 were assigned a score of “5”; and T scores of 90+ were assigned a score of “6.” The overall LOCI score was then calculated by summing these values for all five PAI scales, with scores ranging from 0 to 30.
Although there are multiple approaches to scoring the LOCI that could be considered (e.g., using “optimal” weights, rounded weights, or unit weights, etc.), the method detailed above was chosen primarily for ease of use and interpretation. This is important given that the LOCI may be of particular interest in applied clinical settings, where more complex aggregating/weighting methods would in all likelihood limit the utility of the index. Furthermore, this approach is generally consistent with how other PAI supplemental indices (e.g., SPI and VPI indices) are scored (i.e., using integer composites). In light of these practical issues, and fact that the LOCI was found to be highly correlated with the mean of the five scales comprising the index (r = .991; p < .0001) in the validation sample (which would suggest minimal loss of information when compared with the combination of continuous scores), this approach seemed justified.
Table 4 presents adjusted mean LOCI scores and effect sizes (Cohen’s d) across psychiatric groups differing in level of care, as well as across a variety of risk factors for psychiatric hospitalization in the independent validation sample (n = 252). As is detailed in Table 4, LOCI scores were found to be significantly higher for the inpatient (M = 10.4; SE = 0.6) group as compared with outpatients (M = 7.8; SE = 0.4), and the effect size was moderate in magnitude (d = 0.55) even after controlling for other covariates. As is also presented in Table 4, those with a history of psychiatric admissions, suicide attempts, suicidal ideation, and history of self-harming behaviors (e.g., cutting, burning) all had significantly higher LOCI scores as compared to those without, with effect sizes in the moderate to large range (range of ds = 0.55–0.88). Similarly, those with a history of violent behavior (M = 10.9; SE = 0.9) and arrest record (M = 10.3; SE = 0.7) were also found to have significantly higher LOCI scores as compared with those without (M = 8.2; SE = 0.3; M = 8.1; SE = 0.4, respectively), with effect sizes in the moderate range (d = 0.55 and 0.46, respectively). Finally, LOCI scores were also significantly elevated in populations with a history of psychosis (d = 0.62) and mania (d = 0.39).
Adjusted Mean (SE)a Level of Care Index (LOCI) Scores Across Psychiatric Groups in the Validation Sample (n = 252)b
Table 5 presents adjusted mean LOCI scores in the validation sample (n = 252) for groups varying in the number of past psychiatric hospitalizations and suicide attempts after controlling for the effects of age, gender, race/ethnicity, and education. Multiple prior hospitalizations and suicide attempts were associated with significantly elevated LOCI scores, as compared with those with only one or no prior hospitalizations/suicide attempts. Similarly, post hoc tests indicated that all pairwise LOCI comparisons were statistically significant, suggesting varying levels of risk factors across these groups.
Adjusted Mean (SE)a Level of Care Index (LOCI) Scores Across Psychiatric Groups in the Validation Sample (n = 252)b
As a means of evaluating the incremental validity of the LOCI, a series of hierarchical logistic regressions were conducted in the validation sample (n = 252) to determine whether the LOCI added unique predictive value above and beyond other PAI indices, including Mean Clinical Elevation (MCE), the SPI, and the VPI. In each step, the latter PAI index was entered in the first block followed by the LOCI in the second block. As is demonstrated in Table 6, the LOCI added significantly to the prediction of level of care after accounting for MCE, SPI, and VPI, supporting the incremental validity of the index.
Incremental Validity of the LOCI in Predicting Inpatient Level of Care After Controlling for Mean Clinical Elevation, Suicide Potential, and Violence Potential in the Validation Sample (n = 252)
Using a multimethod approach to construct validation, the LOCI was also evaluated in the validation sample in terms of its relationship with a measure of normal personality (NEO-FFI), ratings from the SCORS-G, and performance on neuropsychological measures of executive functioning. Results indicate that the LOCI was positively correlated with Neuroticism (r = .65; p < .001), and negatively correlated with Extraversion (r = −.40; p < .001), Agreeableness (r = −.51; p < .001), and Conscientiousness (r = −.31; p < .001). No meaningful relationship was found between the LOCI and Openness (r = .03; p = .697). Similarly, Pearson correlations between the LOCI and SCORS-G variables were evaluated for a subset of 53 outpatients in the validation sample who completed the TAT as part of their evaluation. Results indicate that the LOCI was significantly (negatively) associated with Affective Quality of Representation (r = −.50; p < .0001), Emotional Investment in Values and Moral Standards (r = −.34; p < .05), Experience and Management of Aggressive Impulses (r = −.42; p < .005), Self Esteem (r = −.51; p < .001), and Identity and Coherence of Self (r = −.45; p < .001).
Given research (Sinclair et al., 2012) that has shown a relationship between executive dysfunction and risk factors for psychiatric hospitalization (e.g., suicide and violence potential), the relationship between the LOCI and measures of executive functioning were examined in a subsample of 174 outpatients in the validation sample who completed the Trail Making Test—Part B and the Stroop Neuropsychological Screening Test as part of their evaluation. Patients were classified into “impairment” groups depending on whether they exhibited impairment on either measure of executive functioning, which was defined here as a standard score below 70 (similar to how other studies have categorized cognitive deficit; see Lezak, Howieson, & Loring, 2004). On the basis of this definition, 72 patients were classified as having evidence of impairment, whereas 102 did not. As hypothesized, results indicate that significantly higher LOCI scores were observed in groups manifesting evidence of executive dysfunction (M = 8.9; SE = 0.6) as compared with those who did not (M = 7.4; SE = 0.4), and the overall effect size was small to moderate (d = 0.31) even after controlling for the effects of age, gender, race/ethnicity, and education.
Finally, Table 7 presents the percentages of inpatients and outpatients across five LOCI score ranges in the overall sample (N = 505). As expected, there was a higher proportion (81%) of outpatients falling within the lowest LOCI (0–4) score range, and a larger percentage of inpatients (67%) falling within the highest LOCI score range (20+). Furthermore, as LOCI scores increased, the percentage of inpatients also increased, whereas the overall percentage of outpatients decreased. The results provide further support for the construct validity of the LOCI.
Treatment Setting Classification Statistics for LOCI Score Ranges in the Total Sample (N = 505)
DiscussionThe last few decades has seen a major shift in terms of the practices surrounding inpatient psychiatric hospitalization, with increased focus on rapid symptom stabilization and reduced LOS, and greater reliance on outpatient care systems for longer term management (Averill et al., 2001; Hopko et al., 2001; Rocca et al., 2010). However, there is also recent evidence suggesting an increase in inpatient psychiatric service utilization for some populations (Blader, 2011). As a result of these trends, newer research is necessary to more effectively predict and proactively treat risk factors for psychiatric hospitalization. Although approaches to this have varied considerably and relied on methods including clinician-based judgments and semistructured interviews, research has begun to demonstrate the incremental validity of psychometric data in making these predictions (Averill et al., 2001). Toward these ends, broadband self-report measures of psychopathology and personality such as the PAI have seen increased use in clinical decision making (Edens et al., 2001; Morey & Quigley, 2002; Walters & Duncan, 2005). Thus, developing scales that can help in identifying patients requiring increased level of care is likely to be of benefit. As a means of contributing to this growing body of research, the purpose of this study was to develop and present preliminary validity data for a new supplemental index for the PAI that would allow clinicians to identify patients who may require a higher level of psychiatric care—termed here the LOCI.
In an initial development sample of psychiatric inpatients and outpatients, nine PAI scales were initially found to be predictive of inpatient (as compared with outpatient) level of care and accounted for 39% of the variance in the target variable (NIM, SUI, SOM-S, DEP-A, PAR-P, BOR-N, ANT-E, ANT-S, SCZ-T). However, only five of these scales were retained, after accounting for the effects of age, gender, race/ethnicity, and education (NIM, SUI, DEP-A, PAR-P, and ANT-S). Of note, whereas other studies have typically only identified certain clusters of these variables (psychotic process, depression, and suicidality, to name a few) (Averill et al., 2001; Colasanti et al., 2010; Hopko et al., 2001; Kolbasovsky et al., 2007; Miret et al., 2011; Olfson et al., 2011; Perlick et al., 1999), the five scales concurrently identified in the present study reflect a range of internalizing (depression, suicidal ideation), externalizing (stimulus-seeking behaviors), and reality-impairing (paranoid ideation) pathology. This likely reflects both the fact that the sample under study was mixed and contained a range of psychiatric disorders, and also that there is a complex array of risk factors associated with increased level of care across different psychiatric groups. Given the range of variables included in the LOCI, the index may have greater potential in terms of identifying patients who are at risk across multiple psychiatric domains.
Using these five PAI indicators, an aggregated LOCI index was then developed, and the initial findings (assessed here in an independent clinical sample using a multimethod approach) are promising. As hypothesized, the LOCI effectively differentiated psychiatric inpatients from outpatients in an independent validation sample with a moderate effect observed. Furthermore, there was a meaningful relationship between LOCI score categories and the percentages of inpatients/outpatients, where higher score ranges were associated with inpatient treatment status. Furthermore, the LOCI demonstrated meaningful relationships with a number of life event variables that are risk factors for psychiatric hospitalization (e.g., history of psychiatric admission, number of prior admissions, etc.), with effect sizes in the moderate to large range. Speaking to the point above regarding the range of domains covered by the LOCI, the index was also effective in differentiating groups with known internalizing (e.g., history of suicide attempts, suicidal ideation, etc.) and externalizing (e.g., history of violence, arrests, etc.) risk factors for involuntary admission. In contrast to research focusing on only one or the other to predict inpatient utilization (Goethe, Dornelas, & Gruman, 1999; Miret et al., 2011; Viinamäki et al., 1998), the current findings suggest that components of both may be associated with increased risk for hospitalization and that the LOCI may represent a more integrated method of assessing these different factors. The validity of the LOCI was also demonstrated via findings linking LOCI scores to key clinical variables and personality features associated with risk for hospitalization.
Following on these last points, there have been numerous studies that have demonstrated the effect of past psychiatric hospitalization on the likelihood of future admission (Goethe et al., 1999; Hopko et al., 2001; Miret et al., 2011; Perlick et al., 1999; Viinamäki et al., 1998). As such, the ability of the LOCI to effectively differentiate groups with no prior hospitalization with those who have been hospitalized once and those more than once is further evidence of its validity. That being said, prospective research is necessary to evaluate how well the LOCI is able to predict future admission to the hospital.
Given that other PAI indices (e.g., MCE, SPI, and VPI) are also likely to be related to level of care needs, the incremental validity of the LOCI was assessed by evaluating whether LOCI scores predicted inpatient status above and beyond these three indices separately. The results supported the incremental validity of the LOCI and suggest that this new index contains unique indicators of the need for increased level of care, which extend beyond these other PAI indices. In part, this likely reflects the inclusion of SUI as a component of the LOCI. From a conceptual standpoint, SUI would be expected to influence decisions about level of care, although additional research is necessary to explore this in independent samples.
Given the observed correlations between the LOCI, MCE, SPI, and VPI in our study, further research is also needed to determine how best to use the LOCI in conjunction with other PAI variables for clinical decision making. For example, Kurtz (2010) suggested a clinically meaningful method for combining and interpreting both SUI and SPI and AGG and VPI, despite the high correlations that exist between these related PAI variables. In a similar vein, we believe there are potentially meaningful combinations of MCE and LOCI that may emerge with additional research. For example, in cases in which MCE is low and the LOCI is high, clinicians may be alerted to the need for a more rigorous and potentially different kind of clinical intervention than would be indicated on the basis of distress level alone. However, further research is needed to explore the potential utility of various MCE and LOCI combinations.
Another interesting series of findings from the current study were the relationships between the LOCI and NEO-FFI, where elevated scores on the former were associated with higher Neuroticism, and lower Extraversion, Agreeableness, and Conscientiousness. This pattern may reflect the combination of psychological distress and low social involvement that may be present for many people who are seeking inpatient services. Similarly, the pattern of relationships between the LOCI and SCORS-G variables points to the combination of affective distress, poor management of aggressive impulses and subsequent impulsivity, low self-esteem, and diffuse sense of self—all of which may predispose someone to requiring a higher level of psychiatric care.
Finally, there is recent research suggesting a relationship between neuropsychological dysfunction (particularly in terms of executive functioning) and suicide and violence potential on the PAI, where the former may make someone more vulnerable to impulsive behaviors that are destructive in quality (as a function of not being able to inhibit behavior or manage more complex stressors) (Sinclair et al., 2012). Given that both suicide and violence potential are also strong predictors of psychiatric admission, we expected the LOCI to be elevated in populations manifesting some degree of executive dysfunction, and the results generally supported this, with a small to moderate effect observed.
Taken together, these data indicate that the LOCI may be an effective tool for evaluating an array of risk factors for increased level of care. However, it is important to note that just because someone elevates the index does not mean they require psychiatric hospitalization. Rather, the LOCI is conceptualized here as a tool clinicians may use in making decisions about level of care and the quality (and quantity) of services a patient may require at different points in their care. The clinical decision to hospitalize a patient should always be made on the basis of multiple points of data. Although one should not hospitalize a patient on the basis of LOCI scores alone, the LOCI provides clinicians with another data point to consider in making this difficult decision.
Regardless, the current study is also not without its limitations, not least of which is its cross-sectional design and lack of prospective outcomes data for making predictions. Future research must address this limitation by evaluating whether the LOCI is sensitive in predicting patients who may require a “step up” or “step down” in terms of intensity of care. Additionally, given the mixed nature of the sample in the current study, additional work is needed to determine whether the LOCI functions well in more homogenous groups (e.g., those with a specific psychiatric disorder), particularly given that some of these groups (e.g., those with primary psychotic disorders, for example) were not well represented in the current study.
Another important limitation to consider here is the criterion variables used in validating the LOCI. Although the distinct samples (those being actively treated in inpatient and outpatient settings) used in the current study are one method for evaluating the performance and validity of the LOCI, other methods should also be used to replicate the construct validity of the index. For example, expert clinician ratings (blind to PAI results) of the need for inpatient level of care would provide an even more rigorous criterion for assessing the validity of the LOCI. Similarly, some of the criterion variables used in the current study, particularly those used to assess executive functioning (i.e., Trail Making Test—Part B and the Stroop Neuropsychological Screening Test), were measures of convenience, and further research must replicate these findings using other instruments (e.g., the Wisconsin Card Sorting Test, the Delis Kaplan Executive Functioning System, etc.). This should be a focus of future empirical investigation.
That being said, the sample used in the current study confers some advantages. Although the LOCI is likely to be helpful across an array of clinical contexts, it may in fact provide greater utility in situations in which the decision to hospitalize is more ambiguous, including when clinicians have less experience making these types of decisions (e.g., clinical trainees). Thus, tools are needed precisely when it is not entirely clear whether a patient should be hospitalized or not. Such circumstances are somewhat more likely to arise when deciding whether or not to hospitalize patients with mood, anxiety disorders, and personality disorders. It may be that the LOCI is more suited for mixed psychiatric settings, although this is yet to be determined.
Interestingly, all five of the LOCI indicators are discussed by Morey (1996) as potential factors that should inform decisions about treatment setting—speaking to the validity of the LOCI in another way. For example, Morey (1996) noted that more extreme elevations on DEP-A and PAR-P (also identified in the current study) specifically may signal the presence of affective distress and thought disturbance that may impact someone’s functioning in significant ways and necessitate increased level of care. In our study, NIM elevations in the range assessed was also conceptualized as being another marker of distress. Similarly, elevations on the SUI and ANT-S subscales indicate potential risk factors for self-harm and impulsivity, which in other ways point to the potential need for inpatient level of care. Of note, although all of the LOCI indicators were discussed by Morey (1996) as being important in making decisions about treatment setting, other PAI variables not identified in the current study were also discussed (substance abuse, elevated anxiety, traumatic stress, mania, and psychotic experience). This likely reflects the underlying complexity of factors that may necessitate increased level of care, which would be expected to vary across clinical contexts. As such, indices such as the LOCI, which include indicators from different (internalizing, externalizing, reality-impairing psychopathology) domains, may be of greater benefit in mixed psychiatric settings.
Despite the aforementioned limitations, the results provide preliminary support for the utility of the LOCI in helping clinicians make more global decisions about level of care. Furthermore, the LOCI is composed of indicators of internalizing, externalizing, and reality-impairing pathology, and LOCI scores are linked to explicit and implicit personality features associated with global distress and dysfunction. Although it was developed specifically for aiding clinicians in making decisions about level of care, it may prove useful across an array of settings. Further research is necessary to evaluate the utility of the LOCI for assisting in clinical decisions. As a means of making this process more useful, a simple scoring program is available in Excel from the first author.
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Submitted: May 24, 2012 Revised: January 23, 2013 Accepted: January 24, 2013
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Source: Psychological Assessment. Vol. 25. (2), Jun, 2013 pp. 606-617)
Accession Number: 2013-07336-001
Digital Object Identifier: 10.1037/a0032085
Record: 51- Title:
- Developmental trajectories of clinically significant attention-deficit/hyperactivity disorder (ADHD) symptoms from grade 3 through 12 in a high-risk sample: Predictors and outcomes.
- Authors:
- Sasser, Tyler R.. Department of Psychology, The Pennsylvania State University, University Park, PA, US, tysasser@gmail.com
Kalvin, Carla B.. of Psychology, The Pennsylvania State University, University Park, PA, US
Bierman, Karen L.. of Psychology, The Pennsylvania State University, University Park, PA, US - Address:
- Sasser, Tyler R., Department of Psychology, The Pennsylvania State University, 140 Moore Building, University Park, PA, US, 16802, tysasser@gmail.com
- Source:
- Journal of Abnormal Psychology, Vol 125(2), Feb, 2016. ADHD Across Development: Risk and Resilience Factors. pp. 207-219.
- NLM Title Abbreviation:
- J Abnorm Psychol
- Page Count:
- 13
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- The Journal of Abnormal and Social Psychology; The Journal of Abnormal Psychology; The Journal of Abnormal Psychology and Social Psychology
- ISSN:
- 0021-843X (Print)
1939-1846 (Electronic) - Language:
- English
- Keywords:
- ADHD, aggression, developmental trajectories, adolescent maladjustment
- Abstract (English):
- Developmental trajectories of clinically significant attention-deficit/hyperactivity (ADHD) symptoms were explored in a sample of 413 children identified as high risk because of elevated kindergarten conduct problems. Symptoms of inattention and hyperactivity-impulsivity were modeled simultaneously in a longitudinal latent class analyses, using parent reports collected in Grades 3, 6, 9, and 12. Three developmental trajectories emerged: (1) low levels of inattention and hyperactivity (low), (2) initially high but then declining symptoms (declining), and (3) continuously high symptoms that featured hyperactivity in childhood and early adolescence and inattention in adolescence (high). Multinomial logistic regressions examined child characteristics and family risk factors as predictors of ADHD trajectories. Relative to the low class, children in the high and declining classes displayed similar elevations of inattention and hyperactivity in early childhood. The high class was distinguished from the declining class by higher rates of aggression and hyperactivity at school and emotion dysregulation at home. In contrast, the declining class displayed more social isolation at home and school, relative to the low class. Families of children in both high and declining trajectory classes experienced elevated life stressors, and parents of children in the high class were also more inconsistent in their discipline practices relative to the low class. By late adolescence, children in the high class were significantly more antisocial than those in the low class, with higher rates of arrests, school dropout, and unemployment, whereas children in the declining class did not differ from those in the low trajectory class. The developmental and clinical implications of these findings are discussed. (PsycINFO Database Record (c) 2018 APA, all rights reserved)
- Impact Statement:
- General Scientific Summary—This study supports the notion that clinically significant ADHD symptoms persist into adolescence for some children, but not for others. Children who are more hyperactive or aggressive, or whose parents are inconsistent or ineffective with discipline, are more likely to have clinically significant and stable ADHD symptoms and show more antisocial activities and worse graduation and employment rates in late adolescence. In contrast, children with clinically significant ADHD symptoms who are less hyperactive and aggressive, and who are more socially isolated, tend to show a declining pattern of ADHD symptoms and better functional outcomes. (PsycINFO Database Record (c) 2018 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Adolescent Development; *Aggressive Behavior; *Attention Deficit Disorder with Hyperactivity; *Childhood Development; *Emotional Adjustment; Risk Factors; Symptoms
- Medical Subject Headings (MeSH):
- Adolescent; Adolescent Development; Attention Deficit Disorder with Hyperactivity; Child; Child Development; Female; Humans; Male; Prognosis
- PsycINFO Classification:
- Developmental Disorders & Autism (3250)
- Population:
- Human
Male
Female - Location:
- US
- Age Group:
- Childhood (birth-12 yrs)
School Age (6-12 yrs) - Tests & Measures:
- National Institute of Mental Health’s Diagnostic Interview Schedule for Children
Child Behavior Checklist-Parent Report Form
Child Behavior Checklist-Teacher Report Form
Life Changes Questionnaire
Employment Report Form
Child Behavior Checklist
Parent Questionnaire
Self-Reported Delinquency Scale DOI: 10.1037/t44193-000
Parent Daily Report DOI: 10.1037/t07197-000
Teacher's Report Form DOI: 10.1037/t02066-000
Social Competence Scale DOI: 10.1037/t09698-000 - Grant Sponsorship:
- Sponsor: National Institute of Mental Health
Grant Number: R18 MH48043, R18 MH50951, R18 MH50952, and R18 MH50953
Recipients: No recipient indicated
Sponsor: Center for Substance Abuse Prevention, National Institute on Drug Abuse, National Institute of Mental Health, US
Other Details: also provided support for Fast Track through a memorandum of agreement
Recipients: No recipient indicated
Sponsor: Department of Education
Grant Number: S184U30002
Recipients: No recipient indicated
Sponsor: National Institute of Mental Health
Grant Number: K05MH00797 and K05MH01027
Recipients: No recipient indicated
Sponsor: National Institute on Drug Abuse
Grant Number: DA16903, DA017589, and DA015226
Recipients: No recipient indicated
Sponsor: Institute of Education Sciences
Grant Number: R305B090007
Recipients: Sasser, Tyler R.; Kalvin, Carla B. - Methodology:
- Empirical Study; Longitudinal Study; Quantitative Study
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- Accepted: Aug 14, 2015; Revised: Aug 11, 2015; First Submitted: Jan 15, 2015
- Release Date:
- 20160208
- Correction Date:
- 20180212
- Copyright:
- American Psychological Association. 2016
- Digital Object Identifier:
- http://dx.doi.org/10.1037/abn0000112
- PMID:
- 26854506
- Accession Number:
- 2016-06080-006
- Number of Citations in Source:
- 59
- Persistent link to this record (Permalink):
- http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2016-06080-006&site=ehost-live
- Cut and Paste:
- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2016-06080-006&site=ehost-live">Developmental trajectories of clinically significant attention-deficit/hyperactivity disorder (ADHD) symptoms from grade 3 through 12 in a high-risk sample: Predictors and outcomes.</A>
- Database:
- PsycINFO
Developmental Trajectories of Clinically Significant Attention-Deficit/Hyperactivity Disorder (ADHD) Symptoms From Grade 3 Through 12 in a High-Risk Sample: Predictors and Outcomes
By: Tyler R. Sasser
Department of Psychology, The Pennsylvania State University;
Carla B. Kalvin
Department of Psychology, The Pennsylvania State University
Karen L. Bierman
Department of Psychology, The Pennsylvania State University
Acknowledgement: This work was supported by National Institute of Mental Health (NIMH) Grants R18 MH48043, R18 MH50951, R18 MH50952, and R18 MH50953. The Center for Substance Abuse Prevention and the National Institute on Drug Abuse also provided support for Fast Track through a memorandum of agreement with the NIMH. This work was also supported in part by Department of Education Grant S184U30002, NIMH Grants K05MH00797 and K05MH01027, and National Institute on Drug Abuse (NIDA) Grants DA16903, DA017589, and DA015226. The first two authors were supported by Grant R305B090007 from the Institute of Education Sciences.
We thank the Fast Track project staff and participants and acknowledge the critical contributions and support of the Conduct Problems Prevention Research Group members John Coie, Kenneth Dodge, Mark Greenberg, John Lochman, Robert McMahon, and Ellen Pinderhughes. The views expressed in this article are ours and do not necessarily represent the granting agencies.
Note: Steve S. Lee served as the Guest Editor for this article. —SHG
Attention-deficit/hyperactivity disorder (ADHD) is considered a biologically based but heterogeneous disorder associated with an array of negative outcomes (Barkley, 2006). Although typically conceptualized as chronic, longitudinal research indicates continuity and discontinuity in the course of ADHD (Lahey, Pelham, Loney, Lee, & Willcutt, 2005; Willoughby, 2003). In general, longitudinal studies suggest that inattention persists, whereas hyperactivity-impulsivity (referred to as hyperactivity in the remainder of the article) declines with age (Biederman, Mick, & Faraone, 2000). However, more recent person-oriented analyses suggest more nuanced developmental trajectories (Arnold et al., 2014; Larsson, Dilshad, Lichtenstein, & Barker, 2011). Adding to prior trajectory research, this study modeled inattention and hyperactivity simultaneously to better understand the longitudinal covariation of clinically significant ADHD symptoms across developmental periods (from elementary school through late adolescence). The primary goal of this study was to examine conceptually relevant early child characteristics and family adversity factors that might differentiate children in the developmental trajectories. In addition, late adolescent functioning was explored to enhance understanding of the developmental outcomes associated with the trajectories.
ADHD TrajectoriesA growing body of research highlights important heterogeneity in the developmental course of ADHD. For example, although a majority of children diagnosed with ADHD show chronic difficulties, 20%–50% of children with ADHD no longer meet diagnostic criteria as they move through adolescence, suggesting remission, or at least a marked reduction in symptom severity for some children (Barkley, 2006; Biederman et al., 1996). In recent years, person-oriented analyses have been applied to track the course of ADHD. Studies modeling parent-reported ADHD (inattention and hyperactivity combined) from childhood into mid-adolescence generally document stability over time, revealing chronically elevated ADHD in one (van Lier, van der Ende, Koot, & Verhulst, 2007) or two classes (Malone, Van Eck, Flory, & Lamis, 2010). It is interesting, however, that studies modeling teacher-rated inattention alone reveal more developmental variability, including high, low, increasing, and decreasing trajectories (Pingault et al., 2011; Sasser, Beekman, & Bierman, 2014). Modeling inattention in the absence of hyperactivity, however, does not adequately capture ADHD development. For example, Greven, Asherson, Rijsdijk, and Plomin (2011) found that, despite declines in hyperactivity over time, childhood hyperactivity predicted increased adolescent inattention (controlling for early childhood inattention).
Two studies have compared trajectories of parent-rated inattention and hyperactivity to better understand symptom covariation across time. Following a normative sample across the ages of 9–17, Larsson et al. (2011) found that many children in a stable-high inattention trajectory were also in a declining hyperactivity trajectory, suggesting a “shift” from childhood inattention-hyperactivity to adolescent inattention. Similarly, in children at risk for bipolar disorder from ages 6–12, Arnold et al. (2014) found that, in addition to profiles that were stable (high or low on inattention and hyperactivity), another profile demonstrated decreasing hyperactivity but stable high inattention. These studies suggest that developmental patterns of ADHD might be best understood by allowing for covariation between inattention and hyperactivity. In the present study, longitudinal latent class analysis (LLCA; Collins & Lanza, 2010) permitted for the simultaneous inclusion of clinically significant inattention and hyperactivity symptoms in the same longitudinal model, an enhancement over prior studies that compared separate symptom trajectories. The major focus of this study was to examine child and family risk factors that might differentiate diverging ADHD trajectory patterns (e.g., chronically high vs. declining).
Predicting ADHD Trajectories: Child Characteristics and Family AdversityRecognizing the centrality of cognitive and behavioral self-regulation deficits, models of ADHD development suggest that dysfunction in biologically based regulatory systems precedes ADHD and influences its stability (Barkley, 2006; Campbell, Halperin, & Sonuga-Barke, 2014). Developmental models also recognize that socialization experiences may affect the development of self-regulatory capacities and compensatory skills, thereby altering the course and outcomes of ADHD (Campbell et al., 2014). In particular, high-quality socialization experiences, including positive adult–child interactions and peer relations, appear to facilitate the development of child attention, emotion, and behavior regulation skills (Bernier, Carlson, Deschênes, & Matte-Gagné, 2012; Bierman & Torres, in press). Conversely, inconsistency, nonresponsiveness, or hostility in the socializing environment may impair self-regulatory control and exacerbate child reactivity and impulsivity (Cicchetti, 2002).
It is interesting that a recent review of prospective longitudinal studies of children with ADHD identified risk factors that appear particularly salient in predicting the course of ADHD; among them were the severity of inattention and hyperactivity, concurrent aggression, social isolation, emotional difficulties, and family adversity (Cherkasova, Sulla, Dalena, Pondé, & Hechtman, 2013). These factors may be linked directly with the course of ADHD to the extent that they index dysfunction in biologically based regulatory systems associated with ADHD (Barkley, 2006). In addition, they may affect the developmental course of ADHD indirectly, by increasing or decreasing child exposure to the types of predictable and supportive socialization experiences associated with the development of self-control capacities (Campbell et al., 2014). Evidence supporting the potential influence of each factor is considered briefly in the following sections.
Severity of inattention and hyperactivity
Reflecting the degree of cognitive and behavioral dysfunction, the severity of inattention and hyperactivity in childhood predicts ADHD in adolescence (Cherkasova et al., 2013). More severe inattention undermines school performance and effective social interaction, reducing positive support from teachers and peers (Campbell et al., 2014). Elevated hyperactivity is associated with disruptive and rule-breaking behaviors that increase negative exchanges with parents, teachers, and peers, thereby further fueling emotional reactivity and social alienation (Beauchaine, Hinshaw, & Pang, 2010; Campbell et al., 2014). Among children with ADHD, symptom severity may thus affect the course of the disorder by increasing risk for negative socialization experiences and reducing the positive supports that foster the continued development of self-regulation skills.
Aggression and Social Isolation
An extensive database suggests that comorbid aggression increases the stability of childhood ADHD (Hawes, Dadds, Frost, & Russell, 2013). In addition, aggression has been linked to stable high or increasing trajectories of ADHD relative to low trajectories (Arnold et al., 2014; Sasser et al., 2014; Todd et al., 2008). In the early school years, elevated aggression may reflect heightened temperamental reactivity, serving as a direct index of biologically based liabilities (Vitaro, Brendgen, & Tremblay, 2002). In addition, aggressive behavior greatly increases exposure to coercive exchanges in which peers and adults escalate and reinforce aggressive and impulsive behaviors, undermining the development of self-control (Bierman & Sasser, 2014; Vitaro et al., 2002).
Social isolation is also linked with ADHD, particularly inattention (Willcutt et al., 2012), leading some to suggest that cognitive and temperamental characteristics (low inhibitory or effortful control, low social approach) accrue in some children to yield a pattern of general disengagement (Milich, Balentine, & Lynam, 2001). Children who are disengaged cognitively and socially miss out on key developmental opportunities during the school years, including academic instruction and positive interactions with teachers and peers (Campbell et al., 2014). In consequence, socially isolated children with ADHD may be less likely than socially integrated children to develop competencies that might mitigate their difficulties in later years.
Emotional Difficulties
Characterized behaviorally by irritability and emotional outbursts, emotion dysregulation has received increasing focus as a key factor in the development of ADHD (Shaw, Stringaris, Nigg, & Leibenluft, 2014). Conceptually, by the early school years, elevated emotion dysregulation reflects high levels of temperamental reactivity and negative transactions with caregivers, resulting in difficulty managing strong feelings and coping effectively with frustration or disappointment (Beauchaine, Gatzke-Kopp, & Mead, 2007). Research suggests that children with ADHD and emotion dysregulation are more likely to experience social impairment and more persistent ADHD 4 years later relative to children with ADHD only (Biederman et al., 2012), perhaps as a function of both direct influences and insufficient socialization.
Nearly a quarter of the children with ADHD also express emotional distress, including anxiety and depressed mood (Jarrett & Ollendick, 2008). It has been hypothesized that anxiety or depression may exacerbate problems associated with ADHD by compounding cognitive with emotional difficulties (e.g., Bubier & Drabick, 2009). That said, longitudinal studies have yielded mixed results. Whereas emotional distress (mood or anxiety disorders) differentiated boys with persistent ADHD from those with symptom remission in one study (Biederman et al., 1996), it did not differentiate ADHD trajectories in another (Arnold et al., 2014).
Family Adversity
In addition to child factors, family adversity has been implicated in the course of ADHD (Biederman, Faraone, & Monuteaux, 2002; Counts, Nigg, Stawicki, Rappley, & von Eye, 2005). For example, high and low ADHD trajectories are differentiated by low socioeconomic status (SES), large family size, and single-parent status (Galéra et al., 2011; Larsson et al., 2011; Sasser et al., 2014). Theoretically, exposure to family adversity may maintain or exacerbate ADHD symptoms because of heightened stress and reduced support that directly undermine the development and functioning of self-regulatory systems (Bernier et al., 2012; Cicchetti, 2002). In addition, family adversity may impair parenting and increase negative parent–child interactions. For example, Galéra et al. (2011) found that coercive parenting differentiated children in low versus high ADHD trajectory groups, and Hawes et al. (2013) linked inconsistent parenting with increased ADHD symptoms 1 year later. Together, these studies suggest that low SES, single-parent status, exposure to stressful life events, and ineffective parenting may contribute to chronically high ADHD trajectories.
Validating ADHD Trajectories: Evidence of Differential OutcomesA significant limitation in the earlier literature is a lack of studies examining the link between different developmental patterns of ADHD and later youth outcomes (Pingault et al., 2014; Willoughby, 2003). In general, ADHD significantly increases risk for maladjustment in late adolescence and adulthood, including antisocial activities, school failure, and unemployment (Barkley, 2006). However, only a few studies have validated changes in ADHD by examining developmental outcomes. It is possible that children may show declining patterns of ADHD without necessarily reducing their risk for negative outcomes. For example, Pingault et al. (2011) and Sasser et al. (2014) both found that children with high levels of inattention at school entry experienced significant academic difficulties in the later elementary years, even if their symptoms declined, perhaps because inattention during the early school years impeded acquisition of basic academic skills key for later learning. Links between ADHD trajectories and adolescent antisocial activities or adaptation difficulties (high school dropout, unemployment) are understudied. The current study added to this important database.
Present StudyIn summary, the current study had three research aims. First, longitudinal patterns of clinically significant inattention and hyperactivity were estimated simultaneously using parent ratings collected in Grades 3, 6, 9, and 12. Consistent with prior studies that modeled parallel trajectories of inattention and hyperactivity (Arnold et al., 2014; Larsson et al., 2011), it was anticipated that profiles reflecting stable high and low ADHD symptoms would emerge, as well as profile(s) that reflected discontinuity in inattention and/or hyperactivity. Second, child characteristics (inattention, hyperactivity, aggression, social isolation, emotion dysregulation, and emotional distress) and family adversity (low SES, single-parent status, life stress, inconsistent parenting) were explored as predictors of ADHD trajectories. Predictors were measured in the early school years (kindergarten to Grade 2), when children faced new demands for self-regulation, social interaction, and learning, thereby providing an index of functioning in both home and school contexts (Campbell & Von Stauffenberg, 2008). It was anticipated that elevations in child and family risk factors would be associated with more chronic ADHD profiles. Finally, ADHD trajectories were examined in relation to late adolescent outcomes (antisocial activities, high school dropout, unemployment). It was expected that children with more chronic profiles of ADHD would experience more impairment in late adolescence.
Method Participants
This study included participants of the Fast Track project, a multisite, longitudinal study of children at risk for conduct problems. Children were recruited from 55 schools serving high-risk communities located within four sites (Durham, NC; Nashville, TN; Seattle, WA; and rural PA). Using a multiple-gating screening procedure, all 9,594 kindergarteners across three cohorts (1991–993) were screened for classroom conduct problems by teachers (TOCA-R Authority Acceptance; Werthamer-Larsson, Kellam, & Wheeler, 1991). Children scoring in the top 40% within cohort and site were then screened for home behavior problems by parents, using items from the Child Behavior Checklist (Achenbach, 1991) and similar scales (91% of those eligible participated, n = 3,274). Teacher and parent screening scores were standardized and summed to yield a total severity-of-risk screen score, and children were selected for inclusion into the study based on this screen score, moving from the highest score downward. Deviations were made when a child failed to matriculate in the first grade at a core school (n = 59) or refused to participate (n = 75). The outcome was that 891 high-risk children (ns = 445 for intervention and 446 for control) participated in the Fast Track project. On the kindergarten Teacher’s Report Form of the Child Behavior Checklist (TRF), which provides national norms, the average Externalizing T score (available for 88% of the sample) was 66.4, and 76% of these children scored in the subclinical or clinical range (T scores of 60 or higher). The sample used in this study included participants from the high-risk control group (48% African American, 49% European American, 3% other; 66% male) who did not receive any prevention services. At the first home assessment (end of kindergarten) they were on average 6.5 years (SD = 0.48 years).
Developmental trajectories of clinically significant ADHD symptoms were estimated for 413 children (93% of the high-risk control sample) who had parent ratings of ADHD from at least one assessment (Grades 3, 6, 9, and 12). During trajectory estimation using LLCA, missing data was handled using full information maximum likelihood technique (FIML; Lanza, Dziak, Huang, Wagner, & Collins, 2014). This allowed the inclusion of children who had parent ratings at all four time points (50%), three time points (24%), two time points (11%), or one time point (8%). The 33 children dropped from the study because they lacked parent ratings did not differ significantly from those included on any child or family characteristics studied here. Missing data in the outcome variables (ranging from 14–39% of the sample) was multiply imputed.
Procedures
Parents were interviewed annually at home in the summers by trained research staff. Parents provided informed consent at each time. In the spring of the early elementary years (kindergarten, Grades 1 and 2), research assistants delivered and explained measures to teachers, who completed and returned them to the project. During summer home visits following Grade 12, youth completed computer-administered interviews in which they listened to questions via headphones and responded directly on the computer. Teachers, parents, and youth received financial compensation for study participation. All study procedures complied with American Psychological Association (APA) ethical standards and were approved by the institutional review boards of the participating universities.
Measures
Measures used in the current study are described here, with greater details available at http://www.fasttrackproject.org/data-instruments.php.
ADHD
When children were in Grades 3, 6, 9, and 12, parents completed the computerized version of the National Institute of Mental Health’s Diagnostic Interview Schedule for Children (CDISC; Shaffer & Fisher, 1997), a structured interview designed to assess psychiatric disorders and symptoms defined by the DSM–III–R (for Grade 3; American Psychiatric Association [APA], 1987) or DSM–IV (for Grades 6, 9, and 12; APA, 2000). For the ADHD diagnosis module, the parent responded “yes” or “no” to indicate the presence of each of nine inattention and nine hyperactivity symptoms in the prior 6 months (for Grade 3) or prior year (for Grades 6, 9, and 12). To estimate trajectories of clinically significant ADHD symptoms, inattention and hyperactivity were each scored dichotomously, with the presence of six or more symptoms (in Grades 3, 6, and 9) or 5 or more symptoms (in Grade 12; APA, 2013) scored “1” to indicate severity reaching clinically significant levels or “0” if below that threshold.
Early child characteristics
In the early school years (kindergarten to Grade 2), inattention, hyperactivity, aggression, social isolation, and emotional distress were assessed with the Child Behavior Checklist-Parent Report Form (CBCL-PRF; Achenbach, 1991) and Child Behavior Checklist-Teacher Report Form (CBCL-TRF). Scale scores of inattention, hyperactivity, and aggression were based on narrow-band scales previously validated by the Fast Track project (Stormshak, Bierman, & Conduct Problems Prevention Research Group, 1998). Fifteen items assessed inattention, including cannot finish things, cannot concentrate, inattentive, and does not finish tasks (average α = .66 for parents, α = .95 for teachers). Thirteen items assessed hyperactivity, including hyperactive, fidgets, disturbs others, impulsive, talks out of turn (average α = .75 for parents, α = .95 for teachers). Nine items assessed aggression, including gets in many fights, physically attacks people, threatens, and cruel (average α = .70 for parents, α = .81 for teachers). Social isolation was assessed using a 9-item CBCL narrow-band scale, including prefers to be alone, shy, and withdrawn (average α = .70 for parents, average α = .81 for teachers). Emotional distress was assessed with the anxiety and depression CBCL narrow-band scale, including 14 items, such as lonely, cries, feels worthless, self-conscious, unhappy, and worries (average α = .81 for parents, α = .84 for teachers). Each CBCL item was rated on a 3-point scale (0 = not true, 1 = somewhat/sometimes true, 2 = very/often true). Raw scores were averaged across the three years within rater and divided by the number of items in the scale to represent average item ratings. Emotion dysregulation was assessed with the Emotion Regulation subscale of the Social Competence Scale (Conduct Problems Prevention Research Group, 1995), which included 6 items for parents and 10 items for teachers (e.g., accepts things not going his or her way, copes well with failure, controls temper in a disagreement, appropriately expresses needs and feelings). Each item was rated on a 5-point scale (from 0 = not at all to 4 = very well; average α = .85 for parents, α = .97 for teachers). Scores were reversed to reflect emotion dysregulation and averaged across the 3 years.
Early family adversity
In kindergarten to Grade 2, parents reported on their occupation and educational level, which were scored using Hollingshead’s (1975) system to create 5 levels of SES ranging from 1 = professional/major business to 5 = unskilled labor/service worker. In two-parent families, the codes for SES for each parent were averaged each year, and scores across the 3 years were averaged to reflect family SES. Parents reported on marital status (0 = married, 1 = single parent–separated/divorced, widowed, or never married).
During the interviews, parents completed the Life Stress scale of the Life Changes Questionnaire (Dodge, Bates, & Pettit, 1990), which included 16 items describing stressful life events during the past year (e.g., medical problems with target child, medical problems with family, separation of target child’s parents, financial problems, legal problems, pregnancies). Items represented a selection of common stressors represented on life event checklists (Dohrenwend, 2006), and were rated on a 3-point scale (0 = did not occur, 1 = minor stressor, 2 = major stressor). Scores were averaged across the 3 years (average α = .61). Parents also reported on discipline strategies using the Consistent Discipline subscale of the Parent Questionnaire (Strayhorn & Weidman, 1988). Seven items were rated on a 4-point scale (0 = never to 4 = all the time) to describe consistency and follow-through in limit-setting (e.g., When you give your child a command or order to do something, what fraction of the time do you make sure that your child does it? How often do you think that the kind of punishment you give your child depends on your mood?). Scores were reversed to reflect inappropriate and inconsistent discipline and were averaged across the 3 years (average α = .71).
Late adolescent outcomes
At the end of Grade 12, parents completed the Parent Daily Report (Chamberlain & Reid, 1987), which included an 8-item assessment of antisocial behavior (e.g., physically fight with anyone, tell a lie, take anything that didn’t belong to him/her, purposely destroy property, scream/yell/or shout at anyone, argue or talk back to an adult; α = .73). Youth completed the Self-Reported Delinquency scale (Hawkins, Catalano, & Miller, 1992), responding yes/no to describe delinquent behavior during the past year (e.g., property damage, theft, assault; α = .87). Juvenile arrest data was collected from the court system in the child’s county of residence and surrounding counties through Grade 12. Records included any crime for which the individual had been arrested and adjudicated, with the exception of probation violations or referrals to youth diversion programs for first time offenders. Arrests were categorized into five severity levels, ranging from 1 = status or traffic offenses (e.g., curfew violation, runaway, truancy) to 5 = violent crimes that involve serious harm to others (e.g., aggravated robbery or assault, murder, rape). A “lifetime severity weighted frequency of arrests” index was used in the current study reflecting both the number and severity of offenses for which an individual had been arrested through Grade 12 (Cernkovich & Giordano, 2001).
High school noncompletion was recorded if school records did not indicate a diploma within two years after a nonretained student would have completed Grade 12, and the youth had not passed a high school graduation equivalency test (GED). If school records were missing, participant and parent interviews were used to assess high school graduation. Youth reported on employment status using the Employment Report Form (ERF; Howe & Frazis, 1992) at 2 years after Grade 12. Employment status in the present study was categorized into three levels (0 = full time job, 1 = part time job, 2 = unemployed), with higher scores reflecting unemployment.
Results Analysis Plan
Analyses proceeded in three steps. First, parent ratings of clinically significant ADHD symptoms at Grades 3, 6, 9, and 12 were submitted to LLCA, a mixture model approach for identifying trajectory classes based on categorical observed indicators (Collins & Lanza, 2010). Second, a classify/analyze approach was used to assign children to the best trajectory class and multinomial logistic regression analyses examined early elementary child characteristics and family adversity as predictors of trajectory membership. Finally, ANCOVAs compared the late adolescent outcomes of children in different trajectories.
Descriptive Statistics
Rates of clinically significant levels of hyperactivity were 22.2% (Grade 3), 10.6% (Grade 6), 5.3% (Grade 9), and 5.6% (Grade 12). Rates of clinically significant levels of inattention were 19.6% (Grade 3), 16.5% (Grade 6), 15.8% (Grade 9), and 10.7% (Grade 12). Descriptive statistics for other study variables are shown in Table 1. In early elementary school, rates of child difficulties and family adversity were elevated in this high-risk sample. Teachers rated children as more impaired on each child characteristic than did parents, with the exception of emotional distress. In addition, low SES, high rates of single parenthood (more than half of the sample), and elevated levels of inconsistent parenting and life stress characterized the sample.
Descriptive Statistics for Study Variables
Significant sex and demographic (urban African American, urban European American, and rural European American) differences (p < .05) emerged for several study variables. Boys received higher scores than girls on inattention, Fteachers (1, 445) = 4.67; hyperactivity, Fteachers (1, 445) = 20.77; aggression, Fteachers (1, 445) = 33.06, Fparents (1, 445) = 5.49,; emotion dysregulation, Fteachers (1, 444) = 9.81, Fparents (1, 445) = 3.96; and were more likely to live in single-parent families, F(1, 435) = 4.57. In late adolescence, boys reported higher levels of delinquency, F(1, 407) = 9.77, and higher rates of juvenile arrest, F(1, 407) = 17.42, and school dropout, F(1, 407) = 6.26. Urban African American children received higher scores than urban or rural European American children on teacher-rated inattention, F(2, 442) = 12.81; hyperactivity, F(2, 442) = 23.66; aggression, F(2, 442) = 23.07; social isolation, F(2, 442) = 7.46; emotion dysregulation, F(2, 441) = 25.90; and emotional distress, F(2, 436) = 12.62; and were more likely to live in single-parent families, F(2, 432) = 47.63. In late adolescence, urban African American children had higher rates of juvenile arrests, F(2, 407) = 7.83; high school noncompletion, F(2, 407) = 3.79; and unemployment, F(2, 407) = 5.12. Rural European American children received higher scores on parent-rated emotion dysregulation, F(2, 407) = 3.56, and emotional distress, F(2, 407) = 2.98, than the other demographic groups, and urban and rural European American children experienced elevated levels of life stress relative to urban African American children, F(2, 407) = 8.69. Sex and demographic groups were included as covariates in all subsequent analyses. Correlations among early child characteristics and family adversity are shown in Table 2, among late adolescent outcomes in Table 3, and between early child and family characteristics and emerging adult outcomes in Table 4.
Correlations Among Early Child Characteristics and Family Adversity (Grades K–2)
Correlations Among Late Adolescent Outcomes
Correlations of Early Child and Family Factors With Late Adolescent Outcomes
LLCA
The first step of the analyses was to characterize developmental trajectories, applying PROC LCA Version 1.3.1 (Lanza et al., 2014) parent reports of inattention and hyperactivity, dichotomized at clinically significant thresholds (Grades 3, 6, 9, and 12). To select the appropriate number of trajectory classes, 1,000 iterations of each model were run using randomly generated starting values. Adequate model fit (indicated by a G2 statistic less than the degrees of freedom), and lower levels of the Akaike Information Criteria (AIC), Bayesian Information Criteria (BIC), and adjusted BIC, along with model interpretability, were used to identify the optimal number of classes (Collins & Lanza, 2010). Parameters for LLCA models including 1–6 trajectory classes are shown in Table 5. Models with two (lowest BIC), three (lowest adjusted BIC), and four trajectory classes (lowest AIC) had adequate fit. However, the difference between the AICs of the three and four trajectory class solutions was negligible, and therefore the more parsimonious three trajectory class model was favored as the final LLCA model.
Longitudinal Latent Class Model Parameters
Item-response probabilities are shown in Table 6 and illustrated in Figure 1. A low trajectory class (consistently low levels of inattention and hyperactivity) included 71% of the sample, and a declining trajectory (clinically significant inattention and hyperactivity in third grade, declining below clinical levels in adolescence) included 16%. A third trajectory class (labeled high) included 13% of the sample and was characterized by a high probability of clinically significant hyperactivity in Grade 3, inattention and hyperactivity in Grade 6, and inattention in Grades 9 and 12. As shown in Table 4, there were no statistically significant demographic differences associated with trajectory class membership, with statistically equivalent proportions of males and females, urban African American youth, urban European American youth, and rural European American youth represented in each longitudinal profile.
Longitudinal Latent Classes: Item Response Probabilities and Demographics
Figure 1. Item-response probabilities for longitudinal three-class model of attention-deficit/hyperactivity disorder (ADHD) symptoms.
A classify-analyze approach was used to assign each child to the LLCA trajectory class in which he or she had the highest posterior probability (Lanza et al., 2014). Average posterior probabilities were fairly high (0.95, 0.84, and 0.85 for the low, declining, and high class, respectively) and the proportions of children assigned to each group corresponded closely with the prevalence estimates in the LLCA model, indicating little classification error.
Early Child Characteristics and Family Adversity as Predictors of ADHD Trajectories
To identify factors that differentiated the trajectories, multinomial logistic regression models were estimated for each early elementary child characteristic and family risk. Type III tests provided an omnibus assessment of the contribution of each risk factor (controlling for child sex and site/race); odds ratios (ORs) provided pairwise comparisons of the effect of each risk factor for each trajectory versus the others. First, as shown in the left column of Table 7, relative to those in the low ADHD trajectory, children in the high trajectory were more likely to be rated by teachers and parents as inattentive (ORs = 1.53 and 2.16), hyperactive (ORs = 3.06 and 3.12), aggressive (ORs = 2.24 and 1.82), emotionally dysregulated (ORs = 3.44 and 2.51), and emotional distressed (ORs = 1.53 and 1.52). In fact, only one child characteristic (social isolation) did not differentiate the high and low trajectories. Regarding family adversity, children in the high ADHD trajectory were also more likely to experience greater life stress (OR = 1.44) and more inconsistent parenting (OR = 1.41) than children in the low trajectory.
Regressions Comparing Attention-Deficit/Hyperactivity Disorder (ADHD) Trajectories on Child Characteristics and Family Adversity
Second, as shown in the middle column of Table 7, relative to children in the low ADHD trajectory, children in the declining trajectory were more likely to be rated by parents and teachers as more inattentive (ORs = 1.88 and 2.27), hyperactive (ORs = 1.71 and 2.22), emotionally dysregulated (ORs = 2.17 and 1.54), and socially isolated (ORs = 1.32 and 1.37). It is interesting that only parents (but not teachers) rated children in the declining trajectory as more aggressive (OR = 1.50) and emotionally distressed (OR = 1.45) than children in the low trajectory. Regarding family adversity, children in the declining ADHD trajectory were more likely to experience life stress (OR = 1.69) than children in the low trajectory.
Third, as presented in the right column of Table 7, relative to children in the declining ADHD trajectory, children in the high trajectory were more likely to be rated by teachers (but not parents) as hyperactive (OR = 1.79) and aggressive (OR = 1.90). In addition, parents reported that children in the high trajectory were more emotionally dysregulated (OR = 1.63).
Late Adolescent/Early Adult Outcomes Associated With ADHD Trajectories
To examine group differences in late adolescent outcomes, analyses of covariance (ANCOVAs) controlling for sex, race/site, and early parent-rated aggression were conducted. Results, presented in Table 8, revealed omnibus differences on each of the outcomes assessed. Consistent with expectations, children in the low trajectory demonstrated the best outcomes in late adolescence. Post hoc pairwise comparisons revealed that children in the high ADHD trajectory had significantly higher levels of antisocial behavior (by parent and self-report), arrests, and unemployment compared with children in the low trajectory. Children in the declining and low trajectories did not differ on any late adolescent outcomes. Children in the high trajectory had significantly greater levels of antisocial behavior (by parent report) and higher rates of school dropout than children in the declining group, but the two groups did not differ on self-reported antisocial behavior or unemployment, or juvenile arrests.
Analyses of Covariance (ANCOVAs) Comparing Attention-Deficit/Hyperactivity Disorder Trajectories on Late Adolescent/Early Adult Outcomes
DiscussionAlthough ADHD is often considered a chronic disorder, emerging longitudinal research suggests variability in its developmental course. In this study, LLCA methods identified three developmental trajectories of inattention and hyperactivity (modeled simultaneously) in a high-risk sample of children screened for early conduct problems. Overall, 71% of the sample showed a low trajectory, with no clinically significant levels of inattention or hyperactivity across Grades 3 to 12. The other 29% exhibited clinically significant ADHD symptoms at one or more points in time. This rate is higher than the level of parent-reported ADHD symptoms in normative populations (around 8.8%, Willcutt, 2012), reflecting the high-risk status of this sample. Of these, 16% showed a declining trajectory, with clinically significant levels of inattention and hyperactivity symptoms in Grade 3, declining below clinical levels in late childhood and adolescence (Grades 6, 9, and 12). The other 13% of the sample fell into a high trajectory class characterized by clinical levels of hyperactivity symptoms in Grade 3, inattention and hyperactivity symptoms in Grade 6, and inattention in Grades 9 and 12. A major study goal was to better understand the early elementary risk factors that predicted ADHD symptom trajectories, and which therefore might serve as viable targets for intervention.
The High Trajectory Class
Across studies that have used person-oriented analyses to examine trajectories of ADHD symptoms, many find a profile that is characterized by relatively high, stable symptom levels, reflecting ADHD as a chronic disorder. In the current study, with inattention and hyperactivity symptoms included in the same LLCA, the high trajectory class was characterized by clinically significant levels of hyperactivity during childhood and early adolescence, which declined below the clinically significant threshold in late adolescence. In contrast, inattention reached clinically significant levels in early adolescence and dominated symptom expression in later adolescence. These developmental trends are consistent with findings from prior studies of ADHD symptoms, with hyperactivity declining somewhat over time and inattention remaining relatively stable (Biederman et al., 2000; Willcutt et al., 2012). The pattern found here is also consistent with the findings of Larsson et al. (2011), who compared separate models of inattention and hyperactivity trajectories and suggested that elevated hyperactivity symptoms in childhood are associated with elevated inattention in adolescence. Some researchers have speculated that hyperactivity becomes increasingly internalized with age, manifesting as mental restlessness and distractibility in adolescence (Greven et al., 2011; Weyandt et al., 2003). It is also possible that delays in attention become more pronounced over time as the gap between executive function skill development and increased task demands widens with age (Huizinga, Dolan, & van der Molen, 2006; Willcutt et al., 2012).
In this study, children in the high trajectory class were distinguished from children without clinically significant ADHD symptoms on a host of early childhood characteristics, including elevated inattention and hyperactivity, aggression, emotion dysregulation, and emotional distress. Their parents reported heightened levels of family stress and difficulties with inconsistent and ineffective discipline in the early school years. Children in this high trajectory class had poorer outcomes, including higher rates of antisocial behavior, juvenile arrests, and unemployment than children in the low class, even after controlling for childhood aggression. Considered together, these predictors, trajectories, and outcomes are consistent with negative cascade models of ADHD, in which initial biologically based (i.e., temperamental, cognitive) reactivity and dysregulation contribute to impulsive behaviors and difficulty following rules and routines, as well as a tendency to respond to limit-setting with oppositional or aggressive behavior (Campbell et al., 2014). These early difficulties are exacerbated by inconsistent and ineffective parenting and a lack of positive interpersonal support, which undermine the further development of self-regulation capacities, contribute to poor school adjustment and underachievement, and reinforce antisocial activities (Bierman & Sasser, 2014; Campbell et al., 2014).
The Declining Trajectory Class
Declining trajectory class characteristics
In contrast to the high trajectory class, slightly more than half of the children with elevated ADHD symptoms in childhood (Grade 3) followed a declining trajectory in which their symptoms fell below clinical cut-offs for each of the subsequent time periods. Previous studies modeling inattention symptoms alone have also found declining trajectories (e.g., Pingault et al., 2011; Sasser et al., 2014). The current study revealed a trajectory class characterized by declines in both inattention and hyperactivity. Relative to children in the low trajectory who never exhibited elevated ADHD symptoms, children in the declining trajectory class showed multiple difficulties during the initial school years (kindergarten to Grade 2), including elevated inattention, hyperactivity, social isolation, and emotion dysregulation by both teacher and parent report (relative to the low group). Their parents also reported elevated aggression at home, and elevated levels of life stressors, which reflect events and experiences that undermined family support, such as moves, job changes, interpersonal losses, and medical problems. By late adolescence, not only were their ADHD symptoms improved, but these children fared better than those in the high trajectory class in areas of parent-reported antisocial outcomes and rates of high school completion. Although they were not significantly different from youth in the low ADHD trajectory on any of the late adolescent outcomes studied here, they had intermediate scores between the high and low classes in areas of self-reported antisocial behavior, arrests, and unemployment, suggesting some compromised long-term adjustment.
Declining versus high trajectory class differences
Direct comparisons of the early childhood characteristics of youth who followed a chronic high versus declining trajectory revealed three significant differences. Those who followed a high trajectory pattern were more aggressive and more hyperactive at school (based on teacher report) and more emotionally dysregulated at home (based on parent report) than were children who showed declining symptoms. In addition, although these two groups did not differ significantly on other variables, only the declining class showed elevated social isolation at home and school (relative to the low group), whereas only the high class exhibited elevated emotional distress at school and inconsistent parenting at home (relative to the low group).
These differences are relatively small, and they do not provide definitive information regarding the mechanisms that account for the different developmental pathways experienced by children in the two classes. However, several possibilities exist, which might be explored more fully in future research. First, exposure to stressful life events in early elementary was associated with both high and declining patterns of ADHD, which is consistent with some prior studies that suggest that early family adversity contributes to delays in self-regulatory skill development and thereby may amplify inattentive and hyperactive behavior in early childhood (Bernier et al., 2012; Cicchetti, 2002; Sasser et al., 2014). Theorists have suggested that family adversity might directly increase levels of child emotional distress in ways that distract or overburden regulatory processing and impede executive function maturation in early childhood (Blair & Raver, 2012). In addition, exposure to family life stressors may increase unpredictability and disorganization at home, reducing parental attention, and thereby undermining effective scaffolding of early child self-regulatory development (Sasser et al., 2014).
It is also possible that elevated life stress and biological vulnerabilities contributed to the early ADHD symptoms of children in both the high and declining trajectory classes, but that children in the declining class were more able to benefit from socialization experiences at home and school and thereby showed developmental “catch up” in the later school years. In contrast, children in the high trajectory class, who also experienced inconsistent parenting in addition to elevated life stress, showed more emotional distress at home and more behavioral dysfunction at school, including higher levels of hyperactivity and aggression. The generalization and escalation of hyperactive and aggressive behavior in the school setting may indicate that children with chronic ADHD had greater biological vulnerability and were more impulsive and risk-taking than those in the declining trajectory class; it is also possible that their exposure to inconsistent and ineffective parental discipline in the early years amplified their impulsive and aggressive tendencies (Campbell et al., 2014). These children may have been less amenable to positive socialization efforts at school, and more likely to become enmeshed in coercive interactions with teachers and peers that further undermined self-regulatory skill development, particularly at the transition into adolescence when they gained more autonomy (Beauchaine et al., 2010; Bierman & Sasser, 2014; Cernkovich & Giordano, 2001).
Although social isolation is generally considered a risk factor, it is possible that children in the declining ADHD class, who were more socially withdrawn than children in the low trajectory class, elicited more positive support from teachers and peers than the more socially prominent and disruptive children in the high trajectory class. Considering the poorer late adolescent outcomes of children in the high trajectory, it may be that social isolation also protected children in the declining class from deviant peer influences at the transition into adolescence (Loeber et al., 1993). Future research is needed to explore these or other potential mechanisms associated with declining versus chronic high patterns of ADHD symptoms. Understanding these mechanisms enhances developmental models of the disorder, and may inform areas to target with early intervention.
Limitations
A major strength and unique feature of this study was the availability of repeated parent ratings of ADHD symptoms, which allowed for trajectories that modeled inattention and hyperactivity simultaneously and covered a time period longer than prior studies, from Grade 3 to Grade 12. Additional unique features included data on early child characteristics and family risks that were assessed prior to the trajectories, and a set of important outcomes measured in late adolescence to validate trajectories of clinically significant ADHD symptoms.
At the same time, the study had several limitations. First, the trajectories were based on parent ratings and used dichotomous indicators of clinically significant symptom levels. The availability of repeated parent ratings over time facilitated the modeling of trajectories, but parent ratings are also subject to biases. It is unclear how many of the children rated as having elevated ADHD symptoms in Grade 3 would have been diagnosed with ADHD had a more comprehensive diagnostic evaluation been completed. Parents reported that 22% of the children in the declining trajectory had received “medication to control behavior or attention” by the end of Grade 2 (age 8), whereas 52% of the children in the high trajectory had received medication (compared with just 7% in the low trajectory). This suggests that a relatively greater proportion of the children in the high trajectory received medication evaluations associated with their ADHD symptoms (or other behavior problems). The quality of the medication evaluations was likely variable, but it is possible that more of the children in the high group than in the declining group would have qualified for a full diagnosis of ADHD had more complete assessments been employed in the current study.
In addition, the nature of this sample must be taken into account when interpreting the findings. Children were selected for this sample based on elevated conduct problem behaviors at kindergarten entry. Hence, the study provides rich information regarding the diverging development and outcomes of a subset of children with ADHD symptoms, specifically those with early aggressive and oppositional behaviors. The results may not adequately characterize the development of children with ADHD symptoms who do not show concurrent early conduct problem behavior. In addition, this study focused on risk factors typically associated with conduct problems; future research should also explore temperament and cognitive factors that might also differentiate developmental trajectories. For instance, it is possible that executive function skill development may predict diverging inattention/hyperactivity trajectories.
Third, the design of this study does not make it possible to determine whether or how the amount and kind of treatment experienced by children may have influenced their developmental trajectories. By the end of Grade 2, many of the parents in the sample reported that their children had received some kind of “treatment for emotional or behavioral difficulties” at school or at home (25% of the children in the low group, 49% of the children in the declining group, 80% of the children in the high group). This high rate of service use reflects the high-risk nature of the sample, which was selected for elevated conduct problems. However, the nature and quality of services across these high-risk settings was likely highly variable. The study findings represent developmental trajectories and outcomes that occur given “treatment as usual” in economically disadvantaged communities in four diverse geographical regions of the United States.
Fourth, this study used person-oriented analyses to characterize subgroups within the sample, making it possible to identify classes that showed diverse, nonlinear covariation in clinically significant inattentive and hyperactive symptom patterns over time. While this modeling strategy has many advantages, direct comparisons with other studies are limited by variations in samples, modeling strategies, and measurement that affect the trajectories identified. In this study, inattention and hyperactivity were modeled simultaneously and clinically significant cut-offs were used to better understand developmental variation in disordered levels of ADHD symptoms. In contrast, other studies have modeled symptom severity, which may provide additional information to inform trajectory patterns.
Finally, although this study utilized a strong longitudinal design to examine predictors of discontinuity, it cannot specify causal relationships, because it is possible that other processes beyond those examined in the current analyses contributed to the observed associations.
Clinical Implications and Conclusions
The findings suggest that a developmental perspective may be critical for understanding the clinical course of ADHD. Variable-centered analyses tend to emphasize linear associations across time. In contrast, the person-oriented trajectory model used in this study reveals important nonlinear associations characterizing different developmental profiles of clinically significant ADHD symptoms that may inform clinical assessment and treatment. For example, although hyperactivity symptoms decline over time, the trajectories that emerged in this sample suggest that hyperactivity in childhood may be salient in predicting chronicity, particularly when hyperactivity is observed across the home and school settings, and also when it is accompanied by aggressive behavior. In addition, given their association with differential developmental trajectories in this study, emotional difficulties, including emotion dysregulation and emotional distress, may need more attention in ADHD treatment models that tend to focus primarily on behavioral and cognitive impairments (see Shaw et al., 2014). Recognizing that many children with childhood ADHD improve over time, an important, unanswered question for future research is whether preventive interventions during the early school years designed to target key developmental factors might successfully divert more children with ADHD from the stable high to a declining trajectory class, with corresponding long-term benefits (see Chacko, Wakschlag, Hill, Danis, & Espy, 2009). Future research of this kind is needed to help to fill in the gaps in the existing literature, and illuminate the developmental mechanisms that may underlie diverse developmental trajectories of ADHD symptoms. In turn, a better understanding of the developmental course and processes associated with ADHD trajectories may inform more effective prevention and intervention approaches.
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Submitted: January 15, 2015 Revised: August 11, 2015 Accepted: August 14, 2015
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Source: Journal of Abnormal Psychology. Vol. 125. (2), Feb, 2016 pp. 207-219)
Accession Number: 2016-06080-006
Digital Object Identifier: 10.1037/abn0000112
Record: 52- Title:
- Differentiating bipolar disorder from unipolar depression and ADHD: The utility of the General Behavior Inventory.
- Authors:
- Pendergast, Laura L.. Department of Psychological, Organizational, and Leadership Studies in Education, Temple University, Philadelphia, PA, US, laura.pendergast@temple.edu
Youngstrom, Eric A.. Department of Psychology, University of North Carolina at Chapel Hill, NC, US
Merkitch, Kristen G.. Department of Psychology, Temple University, Philadelphia, PA, US
Moore, Katie A.. Department of Psychology, Temple University, Philadelphia, PA, US
Black, Chelsea L.. Department of Psychology, Temple University, Philadelphia, PA, US
Abramson, Lyn Y.. Department of Psychology, University of Wisconsin—Madison, WI, US
Alloy, Lauren B.. Department of Psychology, Temple University, Philadelphia, PA, US - Address:
- Pendergast, Laura L., Department of Psychological, Organizational, and Leadership Studies in Education, Temple University, Ritter Annex 265, 1301 Cecil B. Moore Avenue, Philadelphia, PA, US, 19122, laura.pendergast@temple.edu
- Source:
- Psychological Assessment, Vol 26(1), Mar, 2014. pp. 195-206.
- NLM Title Abbreviation:
- Psychol Assess
- Page Count:
- 12
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- Psychological Assessment: A Journal of Consulting and Clinical Psychology
- ISSN:
- 1040-3590 (Print)
1939-134X (Electronic) - Language:
- English
- Keywords:
- ADHD, General Behavior Inventory, bipolar disorder, depression, diagnostic efficiency, unipolar depression, attention-deficit/hyperactivity disorder, assessment instruments
- Abstract:
- Adolescence and early adulthood are the peak ages for the onset of unipolar and bipolar mood disorders. Moreover, for most individuals with attention-deficit/hyperactivity disorder (ADHD), symptoms and impairment begin in childhood but persist well into adolescence and adulthood (e.g., Barkley, 2010). Thus, adolescence and early adulthood represent a developmental window wherein individuals can be affected by mood disorders, ADHD, or both. Because treatment protocols for unipolar depression (UPD), bipolar disorder (BD), and ADHD are quite different, it is crucial that assessment instruments used among adolescents and young adults differentiate between these disorders. The primary objectives of this study were to evaluate the predictive and diagnostic validity of General Behavior Inventory (GBI; Depue et al., 1981) scores in discriminating BD from UPD and ADHD. Participants were drawn from adolescent (n = 361) and young adult (n = 614) samples. Based on findings from logistic regression and receiver-operating characteristics analyses, the diagnostic efficiency of the GBI scales range from fair (discriminating UPD from BD) to good (discriminating BD participants from nonclinical controls). Multilevel diagnostic likelihood ratios are also provided to facilitate individual decision making. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Attention Deficit Disorder with Hyperactivity; *Bipolar Disorder; *Diagnosis; *Major Depression; *Psychological Assessment; Inventories
- Medical Subject Headings (MeSH):
- Adolescent; Attention Deficit Disorder with Hyperactivity; Bipolar Disorder; Depressive Disorder; Depressive Disorder, Major; Diagnosis, Differential; Female; Humans; Male; Psychometrics; Statistics as Topic; Surveys and Questionnaires; Young Adult
- PsycINFO Classification:
- Clinical Psychological Testing (2224)
Psychological Disorders (3210) - Population:
- Human
Male
Female - Age Group:
- Adolescence (13-17 yrs)
Adulthood (18 yrs & older)
Young Adulthood (18-29 yrs) - Tests & Measures:
- Schedule for Affective Disorders and Schizophrenia—Lifetime Version
General Behavior Inventory - Grant Sponsorship:
- Sponsor: National Institute of Mental Health
Grant Number: MH52617 and MH77908
Recipients: Alloy, Lauren B. - Methodology:
- Empirical Study; Quantitative Study
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- First Posted: Dec 2, 2013; Accepted: Sep 4, 2013; Revised: Aug 29, 2013; First Submitted: Dec 19, 2012
- Release Date:
- 20131202
- Correction Date:
- 20140224
- Copyright:
- American Psychological Association. 2013
- Digital Object Identifier:
- http://dx.doi.org/10.1037/a0035138
- PMID:
- 24295236
- Accession Number:
- 2013-42119-001
- Number of Citations in Source:
- 92
- Persistent link to this record (Permalink):
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- Cut and Paste:
- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2013-42119-001&site=ehost-live">Differentiating bipolar disorder from unipolar depression and ADHD: The utility of the General Behavior Inventory.</A>
- Database:
- PsycINFO
Differentiating Bipolar Disorder From Unipolar Depression and ADHD: The Utility of the General Behavior Inventory
By: Laura L. Pendergast
Department of Psychological, Organizational, and Leadership Studies in Education, Temple University;
Eric A. Youngstrom
Department of Psychology, University of North Carolina at Chapel Hill
Kristen G. Merkitch
Department of Psychology, Temple University
Katie A. Moore
Department of Psychology, Temple University
Chelsea L. Black
Department of Psychology, Temple University
Lyn Y. Abramson
Department of Psychology, University of Wisconsin—Madison
Lauren B. Alloy
Department of Psychology, Temple University
Acknowledgement: This research was supported by National Institute of Mental Health Grants MH52617 and MH77908 to Lauren B. Alloy.
Bipolar disorder (BD) is an affective condition that affects approximately 4% of the U.S. population (Merikangas et al., 2007) and is often associated with adverse outcomes, including increased use of health services, difficulty with employment and interpersonal relationships, and high rates of suicide attempts (Dennehy et al., 2011; Judd & Akiskal, 2003; Merikangas et al., 2007; Robins & Regier, 1991; Sanchez-Moreno et al., 2009). Pharmacological and psychosocial treatments can considerably reduce BD symptoms and prevent relapse (see Fountoulakis & Vieta, 2008, for a review). However, diagnostic challenges emerge when differentiating BD from other disorders that have a high degree of shared symptomatology, particularly unipolar depression (UPD) and attention-deficit/hyperactivity disorder (ADHD; Galanter & Leibenluft, 2008; Geller, Zimerman, Williams, Bolhofner, & Craney, 2001; Sala, Axelson, & Birmaher, 2009). Actuarial assessment instruments are thought to increase the objectivity and reliability of predictions and have been shown to improve diagnostic certainty and identification of appropriate treatment (Dawes, Faust, & Meehl, 1989). The General Behavior Inventory (GBI; Depue, Krauss, Spoont, & Arbisi, 1989; Depue et al., 1981) is commonly used to assess BD symptoms and has demonstrated robust psychometric properties relative to other instruments (Miller, Johnson, & Eisner, 2009). However, the utility of the GBI in distinguishing BD from other symptomatically similar disorders remains unclear. The purpose of this study is to examine the diagnostic value of GBI scores in differentiating BD from UPD, as well as ADHD.
Overview of Bipolar DisorderBD is characterized by intense and fluctuating states of depression and (hypo)mania—persistent and abnormal periods of elevated, expansive, or irritable mood. There are several BD subtypes delineated largely by the severity and duration of (hypo)manic symptoms. Bipolar I is defined by a history of at least one manic episode that causes significant impairment or hospitalization (a history of depressive episodes may or may not be present), whereas Bipolar II is defined by a history of at least one hypomanic and major depressive episode. Hypomania criteria are comparable to those of mania but to a lesser degree: Rather than significant impairment, hypomania is discerned by a significant change in functioning. Cyclothymia is a chronic disorder defined by alternating periods of potentially brief hypomanic and depressive symptoms, but with the mood dysregulation persisting for 2 or more years (1 year in adolescents). Bipolar disorder not otherwise specified (BDNOS; American Psychiatric Association, 2000) is characterized by (hypo)manic symptoms with or without depressive symptoms that are insufficient in severity, duration, or persistence to meet the full criteria for mania, hypomania, cyclothymia, or depression (American Psychiatric Association, 2000; cf. “other specified bipolar and related disorder,” American Psychiatric Association, 2013, p. 148). Although bipolar spectrum disorders may emerge at any time, research suggests that adolescence may be a particular age of risk for first onset of BD (see Alloy, Abramson, Walshaw, Keyser, & Gerstein, 2006, for a review and synthesis). One retrospective study found that the peak age of onset for BD symptoms was between 15 and 19 years (Lish, Dime-Meenan, Whybrow, Price, & Hirschfeld, 1994). Individuals with BD may develop symptoms in childhood, which often leads to misdiagnosis of more common pediatric disorders, such as ADHD, that have substantial symptomatic overlap (Maniscalco & Hamrin, 2008).
Diagnosis of Bipolar DisorderSeveral studies have demonstrated that individuals with BD may wait between 5 and15 years before a formal diagnosis of BD is made (e.g., Ghaemi, Boiman, & Goodwin, 2000; Ghaemi, Sachs, Chiou, Pandurangi, & Goodwin, 1999; Hirschfeld, Lewis, & Vornik, 2003). Differentiating BD from disorders with similar symptoms, such as UPD and ADHD, is particularly difficult (Galanter & Leibenluft, 2008; Geller et al., 2001; Sala et al., 2009).
BD and UPD are challenging to differentiate (Akiskal, 1995; Bowden, 2005; Ghaemi et al., 2000). Many individuals with BD are initially appropriately diagnosed with major depression because the onset of a depressive episode precedes the onset of a (hypo)manic episode (Ghaemi et al., 1999), particularly for individuals with early-onset BD (Bowden, 2001; Lish et al., 1994). However, individuals with BD often do not perceive symptoms of elevated mood as problematic and may even consider them to be adaptive, decreasing the likelihood of spontaneous reporting to clinicians (Akiskal, 1983; Dunner & Tay, 1993). Moreover, current depression may inhibit recall of (hypo)manic symptoms (Judd et al., 2002). Because patients are less likely to report (hypo)manic symptoms, UPD is commonly misdiagnosed in individuals with BD. Differential diagnosis between BD and UPD has substantial treatment implications. Indeed, one of the primary approaches to treatment for UPD—antidepressant monotherapy—is a frequently used but controversial treatment for BD because its efficacy in treating bipolar depression remains unclear (Pacchiarotti et al., 2011; Sidor & MacQueen, 2011; Undurraga et al., 2012; see Bauer, Ritter, Grunze, & Pfennig, 2012, for a review). Some research has shown that antidepressant monotherapy is ineffective in ameliorating BD depression and in preventing (hypo)mania, and it may increase the risk of serious side effects (Pacchiarotti et al., 2011). Considerations about the safety and efficacy of antidepressant medication use among individuals with BD cannot be given appropriate consideration when the diagnosis itself is wrong (Baldessarini, Vieta, Calabrese, Tohen, & Bowden, 2010; Pacchiarotti et al., 2011). These serious treatment implications necessitate the initiation of assessment techniques aimed at accurate and early diagnosis of BD.
Due to overlapping symptomatology, it is also difficult to differentiate BD from ADHD, particularly among children and adolescents. Core symptoms of pediatric BD, such as irritability, distractibility, and accelerated speech, are also very common among individuals with ADHD and may therefore be of limited utility in differentiating the two groups (Chan, Stingaris, & Ford, 2011; Geller et al., 2002). Both BD and ADHD are often associated with aggression, anxiety, hyperactivity, and mood and sleep disturbances (Asherson, 2005; Cahill, Green, Jairam, & Malhi, 2007; see Skirrow, McLoughlin, Kuntsi, & Asherson, 2009, for a review). It is particularly difficult to differentiate pediatric BD from ADHD when cross-sectional, as opposed to longitudinal, assessment techniques are used.
Another diagnostic complication is the high comorbidity rate between BD and ADHD in younger samples (e.g., Cahill et al., 2007). Rates of comorbidity of ADHD in samples ascertained for BD range as high as 98% (see meta-analysis by Kowatch, Youngstrom, Danielyan, & Findling, 2005). Although rates of BD in samples ascertained for ADHD are often substantially lower, there still is notable co-occurrence that may be attributable to the high base rate of ADHD in most pediatric clinical settings (Galanter & Leibenluft, 2008; Youngstrom, Arnold, & Frazier, 2010). Because ADHD includes similar symptoms and is much more common in most clinical settings, it complicates the accurate detection of BD.
For most affected individuals, symptoms of ADHD persist well into adolescence and adulthood (see Barkley, 2006, for a review). This exacerbates the challenge for clinicians working with patients in emerging adulthood: ADHD continues to manifest longer than described in most training programs and with less known about evidence-based assessment strategies (Barkley, 2010; Wender, 1998). The combination of BD often manifesting earlier than thought and ADHD continuing longer through development than previously thought means that these two competing diagnostic formulations overlap during adolescence and emerging adulthood, creating a clear need for assessment methods that can help identify when either or both conditions are occurring.
Correct differential diagnosis between ADHD and BD is crucial for both research and treatment purposes. If an individual with BD is misdiagnosed as having only ADHD, the best case scenario is merely a delay in initiating treatments that would help stabilize acute mood disturbance (Baldessarini, Tondo, Hennen, & Viguera, 2002). However, untreated BD is associated with high rates of substance misuse, vocational and social disruption, and increased likelihood of risky behavior, accidents, and incarceration (Lopez, Mathers, Ezzati, Jamison, & Murray, 2006; Stewart et al., 2012), as well as progression to more severe BD (e.g., Alloy, Urosevic, et al., 2012). Furthermore, individuals with BD often present with suicidal ideation, whereas those with ADHD do not (Cahill et al., 2007; Geller et al., 2002). Those misdiagnosed with ADHD thus experience a delay in receiving efficacious treatment with mood stabilizers, which have been shown to reduce serious symptoms, including suicidality. Conversely, misdiagnosing someone who has ADHD with BD leads to a dramatically different prescription, with atypical antipsychotics or antiepileptic agents as the front-line treatment and combination treatment using more than one drug simultaneously being common (Yatham et al., 2005). These medications have little or no evidence of efficacy for treating ADHD, yet they will still expose the patient to all the potential side effects, which can be major (Correll, 2008).
Assessment of Bipolar DisorderPoor assessment techniques and instruments are partially responsible for the delay in formal diagnosis of BD (Lish et al., 1994; Mantere et al., 2004; Miller, Johnson, Kwapil, & Carver, 2011). Inasmuch as a BD diagnosis is founded on the frequency, length, and severity of mood and energy disturbance, diagnostic complications may arise when practitioners fail to assess symptoms in a specific and sequential manner. This is often the case in clinical practice as many practitioners use unstructured diagnostic methods, increasing the likelihood of inaccurate diagnosis of BD (Brickman, LoPicolo, & Johnson, 2002; Rettew, Lynch, Achenbach, Dumenci, & Ivanova, 2009; Zimmerman & Mattia, 1999). Actuarial assessment techniques, such as combining behavior-rating scales with statistical prediction rules (Swets, Dawes, & Monahan, 2000), may improve diagnosis of BD because such techniques tend to be more consistent, more accurate, and substantially less prone to cognitive biases (Jenkins, Youngstrom, Washburn, & Youngstrom, 2011).
Structured clinical interviews are considered to be preferred tools in the assessment of BD and could potentially alleviate diagnostic uncertainty. However, structured interviews lack brevity and cost-efficiency—qualities that are in high demand in modern-day clinical practice (Ebesutani, Bernstein, Chorpita, & Weisz, 2012; Starfield et al., 1994). Various behavior checklists have been evaluated as potential actuarial measures for BD (see Johnson, Miller, & Eisner, 2008; Youngstrom, Freeman, & Jenkins, 2009, for reviews). The Child Behavior Checklist (Achenbach, 1991) differentiates BD from ADHD and UPD in youths (Mick, Biederman, Pandina, & Faraone, 2003; Youngstrom, Findling, Calabrese, Gracious, et al., 2004), but it has not been evaluated in young adults for discriminating BD. The Mood Disorder Questionnaire (Hirschfeld, 2002) can distinguish between BD and UPD in adult clinical populations (Miller et al., 2009), but it has not been evaluated for discriminating BD from ADHD in this age range.
Among the numerous assessments for BD available, the GBI has the greatest combined sensitivity (.78) and specificity (.98) and overall most robust psychometric properties (Depue et al., 1989; Klein, Dickstein, Taylor, & Harding, 1989; Miller et al., 2011), as well as small standard errors of measurement (Danielson, Youngstrom, Findling, & Calabrese, 2003). The GBI was specifically created to encompass the major symptoms of BD, including both manic and depressive features (Miller et al., 2009). The Depression scale of the GBI discriminates adolescents with a mood disorder from both those with a nonmood disorder and those with no disorder (Reichart et al., 2005). The GBI also has demonstrated efficacy at case-finding in nonclinical populations (Alloy et al., 2008; Alloy, Urosevic, et al., 2012; Angst & Cassano, 2005; Depue et al., 1989). Additionally, the GBI Hypomanic/Biphasic scale can identify bipolar versus unipolar cases with minimal false positives (Depue et al., 1989). One study also found that the GBI differentiated youths with BD from those with ADHD, oppositional defiant disorder, and conduct disorder (Danielson et al., 2003).
Although substantial research supports the GBI as a useful instrument for identifying BD among both adolescents and adults, studies examining the extent to which scores on the GBI differentiate BD from ADHD and UPD are sparse, and no studies to date have examined the ability of the GBI to differentiate between BD, UPD, and ADHD in young adults. The purpose of this study is to examine the predictive and diagnostic validity of GBI scores in differentiating individuals with BD from nonclinical controls and those with ADHD or UPD in late adolescence and emerging adulthood—the age range where it is clinically crucial to differentiate between persisting ADHD versus emerging BD, while also accurately discerning UPD. We hypothesized that individuals with BD would score significantly higher than nonclinical controls and individuals with ADHD or UPD on the GBI Hypomanic/Biphasic subscale (consistent with findings in adolescents; Danielson et al., 2003). Furthermore, we hypothesized that the BD and UPD groups would score significantly higher on the GBI Depression subscale than the ADHD or nonclinical controls, with the two mood disorder groups showing similar elevations to each other on GBI Depression. In particular, we believed that GBI subscale scores would differentiate individuals with BD from those with ADHD because the items focus on mood and energy and embed the concept of change in functioning and phasic, rather than chronic, presentation. Another objective was to develop multilevel diagnostic likelihood ratios (DLRs) to facilitate assessment and decision making about individual cases (Straus, Glasziou, Richardson, & Haynes, 2011). To our knowledge, this article is the first to examine the GBI’s discriminative validity for separating BD and ADHD in young adults. It is also the first to develop DLRs for interpretation of GBI scores in this age range—making it much more feasible for clinicians to adopt an evidence-based assessment framework for interpreting the GBI (Youngstrom, 2013).
Method Participants
Participants from two samples were included in this study. Participants in Sample 1 were 359 adolescents ages 14–19 years who volunteered to participate in the Teen Emotion and Motivation (TEAM) project (Alloy, Bender, et al., 2012). Participants in Study 2 were 18- to 24-year-old students (n = 614) who participated in the Longitudinal Investigation of Bipolar Spectrum Disorders (LIBS) project (Alloy et al., 2008; Alloy, Urosevic, et al., 2012). The TEAM and LIBS projects were conducted at the same university by the same primary investigators, were very similar methodologically, and employed samples that were comparable in regard to race, sex, and socioeconomic status (see Table 1). Details about sample recruitment and exclusionary criteria are reported elsewhere (Alloy et al., 2008; Alloy, Bender, et al., 2012; Alloy, Urosevic, et al., 2012). Both studies oversampled individuals at risk for bipolar spectrum disorders. Notably, although data from participants with a history of (hypo)mania that started prior to enrolling in Project TEAM were excluded from the main study (see Alloy, Bender, et al., 2012), data from TEAM participants with a history of BD were included in this study.
Demographics of Samples for Logistic Regression and Receiver-Operating Characteristics Analyses
Measures
Diagnostic reference standard: Schedule for Affective Disorders and Schizophrenia—Lifetime Version
The Schedule for Affective Disorders and Schizophrenia—Lifetime Version (SADS-L) is a widely used semistructured diagnostic interview that has long been regarded as a preferred tool in clinical assessment (e.g., Gallagher, 1987). An expanded version of the SADS-L (Exp-SADS-L) was administered to participants in Projects TEAM and LIBS that included extended coverage of mood symptoms. The Exp-SADS-L adds questions and probes to better capture symptoms of a variety of constructs and disorders including depression, hypomania, mania, cyclothymia, eating disorders, ADHD, and acute stress disorder. The Exp-SADS-L was administered by highly trained postdoctoral fellows, doctoral students, and postbaccalaureate research assistants (see Alloy et al., 2008; Alloy, Bender, et al., 2012; Alloy, Urosevic, et al., 2012, for additional details about interviewer training and Exp-SADS-L modifications). All interviewers had at least a bachelor’s degree, had completed over 200 hours of training (i.e., didactics, case vignettes, audiotaped assessments, role playing, and exams), and were blind to participants’ scores on the GBI. Additionally, diagnoses were regularly reviewed by senior diagnosticians and an expert psychiatric diagnostic consultant. In regard to interrater reliability, diagnostic kappa values exceed .96 for mood disorders and .93 for ADHD. Interrater reliability was assessed regularly throughout the project. A subset of the cases (approximately 10%) were independently rated by three senior diagnosticians, and kappa coefficients were calculated based on ratings from the three senior diagnostician as well as the original interviewer.
General Behavior Inventory
The GBI (Depue et al., 1981) taps depressive and hypomanic/manic symptoms in adults, and it has also been validated in children and adolescents (Danielson et al., 2003; Youngstrom, Findling, & Calabrese, 2004; Youngstrom et al., 2005). The GBI includes 73 items on which respondents use a 4-point Likert-type scale to indicate the frequency with which they experience a particular phenomenon (e.g., “Have people said that you looked sad or lonely?”). Higher scores reflect increased pathology. The Depression scale sums 46 of the items, and the Hypomanic/Biphasic scale sums 28 items; internal consistencies for both scales consistently exceeded .90 in prior samples. In this study, alpha coefficients for the (hypo)manic and depressive factors were .93 and .96, respectively, in the adolescent (TEAM) sample and .95 and .98, respectively, in the young adult (LIBS) sample.
Procedures
Eligible participants were contacted and invited to visit the lab. LIBS project participants completed the GBI prior to coming to the lab, whereas Project TEAM participants completed the GBI during their lab visit. All participants received monetary compensation for participation. Written parental consent and youth assent were obtained for participants under age 18, and participants over 18 independently provided written informed consent. All study procedures were approved by the Institutional Review Board at Temple University (Philadelphia, PA).
Data Analyses
Analyses were also conducted on each sample separately, and the findings were comparable (results are available upon request). Logistic regressions included interaction terms for sample, and all interactions were nonsignificant, indicating that the predictions did not change significantly across samples. After establishing that there were no significant differences in GBI performance across samples (all p values ≥ .18), the data were pooled to maximize the precision of DLRs and diagnostic efficiency estimates (Kraemer, 1992). All participants were grouped into four categories based on Exp-SADS-L DSM-IV diagnoses: (a) those with bipolar spectrum disorders (of individuals with bipolar spectrum disorders, 3% had Bipolar I, 50% had Bipolar II, 19% had BDNOS, and 28% had cyclothymia), (b) those with UPD diagnoses (current or past major depressive disorder, dysthymia, depressive disorder NOS, or subthreshold major depressive disorder) but no history of hypomania or mania, (c) those with current or past ADHD (any subtype), and (d) nonclinical controls who did not meet criteria for BD, UPD, or ADHD. Notably, nearly all participants with a past UPD or ADHD diagnosis reported some residual symptoms. These categories are hierarchical and allow comorbidity. For example, cases included in the BD group for analyses might also have depression, ADHD, or other diagnoses (e.g., anxiety disorders, eating disorders, etc.); cases with UPD could have ADHD or other comorbid diagnoses (except bipolar); and the ADHD group could include any comorbidities. This hierarchical arrangement is consistent with prior investigations and maximizes the clinical generalizability of findings relative to more distilled designs (Youngstrom, Meyers, Youngstrom, Calabrese, & Findling, 2006). In this study, 11% of participants with BD also had ADHD, and 8% of participants with UPD also had ADHD.
A series of analyses evaluated the discriminative validity of GBI subscale scores in differentiating individuals with bipolar spectrum disorders from those with UPD, those with ADHD, and those without the aforementioned diagnoses. First, logistic regression analyses evaluated the extent to which the two GBI subscale scores could differentiate between (a) individuals with BD versus no diagnosis (nonclinical controls), (b) those with any mood disorder versus those without (clinical and nonclinical controls), (c) individuals with BD versus those without (clinical and nonclinical controls), (d) individuals with BD versus those with any other diagnosis (i.e., either UPD or ADHD–clinical controls), (e) individuals with BD versus those with UPD, and (f) individuals with BD versus ADHD. The first comparison (BD vs. nonclinical controls) provides a best case scenario for GBI performance (Youngstrom et al., 2006). The second comparison (mood disorder vs. nonmood disorder) provides the extent to which GBI scores can differentiate individuals with mood disorders from those without them—an important first step in assessing BD symptoms. The third and fourth comparisons (BD vs. all controls and BD vs. clinical controls) assess the utility of GBI scores in differentiating between individuals with BD and all controls (i.e., clinical and nonclinical) as well as BD versus clinical controls, respectively. Participants included in the BD versus any diagnosis comparison group are most similar to those typically seen in a clinical setting. The final two comparisons (BD vs. UPD and BD vs. ADHD) examine the utility of GBI scores in contributing to the differential diagnosis of BD and two disorders that are notoriously difficult to differentiate from BD. Logistic regressions also tested whether there was incremental value in combining the GBI scales versus interpreting the more discriminating scale by itself. Subsequently, receiver-operating characteristics (ROC) analyses quantified the relative value of GBI subscale scores in making the aforementioned distinctions (Swets et al., 2000), and the Hanley and McNeil (1983) procedure tested for significant differences in ROC performance. Finally, DLRs quantified the change in odds of BD diagnosis relative to ranges of test score (e.g., low, moderate, high, etc.; Straus et al., 2011).
Results Differentiating Diagnostic Categories
Table 2 presents descriptive statistics, findings from logistic regression analyses, and findings from ROC analyses. In all scenarios, the GBI provided statistically significant differentiation of individuals with BD and those from the respective comparison groups. Specifically, scores from the GBI Hypomanic/Biphasic subscale significantly (p < .001) and uniquely contributed to all diagnostic comparisons, with Nagelkerke R2 estimates ranging from .13 (BD vs. UPD) to .33 (BD vs. nonclinical control). GBI Depression subscale scores significantly and uniquely contributed only when comparing individuals with BD to those with no diagnosis (nonclinical controls) and when comparing those with any mood disorder (UPD or BD) to all other participants (all controls; ADHD or no diagnosis).
Logistic Regression and ROC/AUC Analyses of Diagnostic Differentiation Using General Behavior Inventory Depression and Hypomanic/Biphasic Subscale Scores and Descriptive Statistics
Diagnostic Efficiency Statistics
ROC analyses examined the value of the two GBI subscale scores for distinguishing between diagnostic groups. ROC curves depict the balance between the probability of a true-positive test result (sensitivity) and the probability of a true-negative test result (specificity). The area under the curve (AUC) reflects the diagnostic accuracy of scores, whereby an AUC of 1 would indicate perfect diagnostic accuracy and an AUC of .50 indicates chance levels of diagnostic performance. AUC values for both GBI subscales were significantly better than chance across all four comparison groups (see Table 2). Calculations were conducted to evaluate whether the Hypomanic/Biphasic or Depressive subscale was more effective in differentiating individuals with the target disorder from those without it (Hanley & McNeil, 1983). For most comparison groups, the Hypomanic/Biphasic and Depressive subscales were roughly equal in their ability to differentiate those with the target disorder from those without it. However, the Hypomanic/Biphasic subscale was significantly better than the Depressive subscale in differentiating individuals with BD from those with UPD. ROC curves for the GBI Depressive and Hypomanic/Biphasic subscales are displayed in Figure 1.
Figure 1. Receiver-operating characteristics curves for analyses of General Behavior Inventory subscales. ADHD = attention-deficit/hyperactivity disorder; BD = bipolar disorder; UPD = unipolar depression.
Diagnostic Likelihood Ratios
DLRs capture more detailed diagnostic information for decision making about individual cases. DLRs repackage the older concepts of diagnostic sensitivity and specificity, making it easier to use the information from test results to estimate posterior predictive values (Straus et al., 2011). Using a multilevel approach—estimating the DLR for several intervals of score ranges—preserves more information from the test results (Guyatt & Rennie, 2002). Conceptually, the DLR is the change in the odds of the diagnosis based on the assessment results. The DLR can be combined with the prior probability of the diagnosis by means of a probability nomogram (Jaeschke, Guyatt, & Sackett, 1994) or via applets or online calculators. DLRs of 1.0 mean that the test result is not changing the odds of the diagnosis at all; numbers less than 1.0 indicate that the test result decreases the odds of the diagnosis. Numbers greater than 1.0 indicate increased odds (and probability) of the diagnosis. Suggested rules of thumb are that DLRs between 0.5 and 2 rarely have much impact on the decision-making process, whereas DLRs in the 3 to 7 range are helpful, and values greater than 10 (or smaller than 0.10) are potentially decisive pieces of information (Straus et al., 2011).
We emphasize DLRs for scores on the Hypomanic/Biphasic subscale of the GBI because prior analyses revealed that Hypomanic/Biphasic subscale scores differentiated bipolar spectrum disorders from other diagnostic groups more effectively than did scores on the Depression subscale.
When estimating multilevel DLRs, the goal is to preserve information but not create unnecessary complexity or split to the point that estimates do not behave monotonically (Zhou, Obuchowski, & McClish, 2002). To calculate DLRs, the scores were initially divided into sextiles (the bottom ∼17% of scores were considered very low, then the next ∼17% low, then somewhat low, etc.). However, several categories were found to be redundant (i.e., the confidence intervals for the DLRs overlapped almost completely). Redundant categories were combined, and subsequently, scores on each subscale were divided into three categories: low, moderate, and high.
Table 3 reports the DLRs for the GBI Hypomanic/Biphasic subscale. DLRs also were calculated for a two-step diagnostic process whereby the Depressive subscale was used to determine the presence or absence of a mood disorder and the Hypomanic/Biphasic subscale was used to determine the presence or absence of a bipolar spectrum disorder (see Table 4).
Likelihood Ratios for Scores on GBI Hypomanic/Biphasic Subscale
Likelihood Ratios for Two-Step Classification Process
Overall, increases in odds of BD diagnoses were particularly evident when comparing individuals with BD to those with no diagnosis (nonclinical controls) and to individuals with ADHD. Most notably, when comparing participants with BD to those with ADHD, those with GBI Hypomanic/Biphasic scores of 20 or higher were nearly 5 times more likely to receive bipolar diagnoses using the Exp-SADS-L interview. Conversely, those with very low scores (0 or 1) were 5 times less likely to have a BD based on the semistructured diagnostic interview (DLR = 0.18).
DiscussionThe primary goal of the present study was to evaluate the predictive and diagnostic validity of a promising instrument, the GBI, when attempting to discriminate bipolar spectrum disorders from UPD or ADHD in two samples of adolescents and emerging adults. The GBI has performed well in other age groups, but emerging adulthood is a crucial developmental stage to demonstrate the ability of an instrument to discriminate between BD, depression, and ADHD. On the one hand, adolescence and early adulthood are the peak ages of risk for the onset of mood disorder (Merikangas & Pato, 2009), and on the other hand, ADHD is more likely to persist into this age range than had previously been appreciated (Barkley, 2010). Differentiating these conditions changes the treatment prescription, so it would be valuable to have effective assessment tools validated for this age group. As hypothesized, the GBI provided statistically significant and clinically meaningful discrimination of cases with BD versus the other diagnostic comparison groups. Specifically, the GBI Depression score separated the two groups with mood disorder (BD and UPD) from the comparison groups without mood disorder (ADHD and nonclinical controls), and the Hypomanic/Biphasic score discriminated those with BD from all other groups. Based on ROC analyses, the diagnostic efficiency of the GBI scales ranged from fair, with AUCs ∼.64 for discriminating UPD from BDs, to good (AUC > .80) at discriminating mood disorders from those with no diagnoses. Logistic regressions indicated that the Hypomanic/Biphasic scale was sufficient to capture the diagnostic information. The exception was attempting to discriminate any mood disorder from all others; then, both the Depression and Hypomanic/Biphasic scales added incremental information, suggesting that a sequential interpretation process might be helpful: First, use the Depression scale to determine the presence or absence of mood disorder, and then examine the Hypomanic/Biphasic scale to differentiate whether the mood disorder is bipolar versus a UPD.
The second aim was to develop multilevel DLRs to facilitate clinical decision making about individual cases using the GBI. The DLR values in Tables 3 and 4 show that GBI scores can contribute helpful information in differentiating BDs from all comparison groups, with very low or very high scores changing the odds as much as sixfold. There are several published examples of using DLRs and a probability nomogram or calculator in the assessment of BD as well as ADHD (Frazier & Youngstrom, 2006; Jenkins et al., 2011). Comparing these values to other instruments used to assess BD, the DLRs are comparable to those found using parent report on the GBI to detect bipolar spectrum disorder in children and adolescents (Youngstrom, Findling, Calabrese, Gracious, et al., 2004) and larger than adolescent self-report on the GBI demonstrated in the same sample (Youngstrom, Findling, Calabrese, Gracious, et al., 2004; Youngstrom, Frazier, Findling, & Calabrese, 2008).
Most of the participants in the present samples were older, had a better reading level, and may have had more maturity and insight into their behavior than did the adolescents involved in prior investigations of the GBI. Intriguingly, recent evidence suggests that people with ADHD develop increased self-awareness over time, leading to more valid self-report in adolescence and young adulthood (Barkley, Knouse, & Murphy, 2011). It is less clear that improvements in insight would also apply to BD, and compromised insight is an associated feature of hypomania or mania (Pini, Dell’Osso, & Amador, 2001; Youngstrom, Findling, & Calabrese, 2004). However, the GBI includes item content assessing changes in energy as well as changes in mood, which may be less prone to state-dependent biases in recall (Angst et al., 2011). A second, more methodological explanation could be that the predictor and criterion measure used in the present samples relied on more similar information than the criterion used in the pediatric studies (which included parents in the diagnostic interviews). The Exp-SADS-L that determined diagnoses interviewed the same persons who completed the GBI, combining their self-perceptions with clinical judgment about the content and clinical observations of their mental status and behavior during the interview. Consequently, the present samples had more shared source variance between the predictor and criterion (Campbell & Fiske, 1959). However, the Exp-SADS-L format better approximates the methods that would be viable in clinical practice (e.g., it is unlikely that parents would routinely be involved in diagnostic interviews about young adults).
Because it is possible to convert effect sizes such as Cohen’s d into an estimated AUC from an ROC analysis (Hasselblad & Hedges, 1995), results also indicate that the diagnostic efficiency of the GBI outperforms what would be accomplished by most neurocognitive tasks when trying to separate BD from ADHD, with the possible exception of verbal memory tasks (Walshaw, Alloy, & Sabb, 2010; pooled Cohen’s d for 11 different tests ranged from .31 to .96, median d = .40, and AUC = .61, when comparing cases with BD to healthy controls). The utility of a test takes into account not just its validity but also the costs and benefits associated with its use (Swets et al., 2000). The GBI has several key advantages from the perspective of utility: It is in the public domain and may be used at no charge. It requires no special training to administer or score (although a free Excel worksheet is available to calculate the subscale scores and combine prior probabilities with the DLR). The low costs of training and administration, as well as easy accessibility to the instrument, give the GBI an edge in terms of utility even when neurocognitive or neuroimaging tasks develop to the point that they demonstrate equal diagnostic efficiency. The main drawbacks to the GBI are its length and reading level. The detailed item content requires an 11th or 12th grade reading level, which will not be suitable for use in some clinical settings. Strengths of the present study include that (a) it used a semistructured diagnostic interview administered by highly trained raters to establish the criterion diagnoses, (b) it tested diagnostic efficiency in two samples, (c) it pooled the samples to increase the precision of the DLRs after establishing that the GBI performed similarly in both samples, (d) it used diagnostically heterogeneous samples that included high rates of diagnoses that are challenging to distinguish from BD, and (e) it addressed a key gap in the literature of differentiating between BD versus depression or unremitted ADHD at an age range where little prior work has been done on assessment despite being a developmental epoch where these disorders are highly likely to overlap. Additionally, the use of semistructured versus fully structured interview, along with the inclusion of diagnostically heterogeneous comparison groups, increases the generalizability of results (Youngstrom et al., 2006).
Limitations of the study include reliance on a community sample. Results may not generalize to other clinical settings. Changes in the severity of the mood disorder will affect the sensitivity of the GBI or any other test (Zhou et al., 2002). All other factors being equal, it is easier to detect more severe presentations. The inclusion of cyclothymic disorder and BDNOS in the bipolar group resulted in a more conservative estimate of the sensitivity of the GBI. Similarly, clinical settings will vary widely in terms of the rates of confounding diagnoses that are likely to generate false-positive results on a test (Youngstrom et al., 2006). Samples with high rates of ADHD, severe depression, and psychosis will make it more difficult for the GBI, or any tool, to tease apart BD from the competing diagnoses. Although the present article extends the investigation of the GBI into early adulthood, it does not address the validity of the GBI in late life, which remains another gap in the assessment literature pertaining to BD. The modification and use of the SADS-L as a reference standard for the assessment of ADHD is another limitation of this study. Although DSM-based, clinical interviews, in general, are considered to be preferred tools in assessment of adult ADHD (Adler & Cohen, 2004), there is little evidence supporting the use of this particular clinical interview, the SADS-L, as a diagnostic measure of ADHD.
Overall, the present results provide support for the diagnostic efficiency of the GBI as a method of discriminating between bipolar and other diagnoses in emerging adulthood. Given the prevalence of BD in outpatient and community settings, low GBI scores may be decisive in excluding a bipolar diagnosis. High GBI scores would raise the posterior probability of a BD into a moderate range, warranting additional systematic assessment (Straus et al., 2011; Youngstrom, Jenkins, Jensen-Doss, & Youngstrom, 2012). Future research should evaluate the incremental information that family history (Tsuchiya, Byrne, & Mortensen, 2003), neurocognitive testing (Walshaw et al., 2010), and other sources of information could contribute in tandem with the GBI in evidence-based assessment of BD.
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Submitted: December 19, 2012 Revised: August 29, 2013 Accepted: September 4, 2013
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Source: Psychological Assessment. Vol. 26. (1), Mar, 2014 pp. 195-206)
Accession Number: 2013-42119-001
Digital Object Identifier: 10.1037/a0035138
Record: 53- Title:
- Digit Span is (mostly) related linearly to general intelligence: Every extra bit of span counts.
- Authors:
- Gignac, Gilles E.. School of Psychology, University of Western Australia, Crawley, WAU, Australia, gilles.gignac@uwa.edu.au
Weiss, Lawrence G.. Pearson Clinical & Talent Assessment, San Antonio, TX, US - Address:
- Gignac, Gilles E., School of Psychology, University of Western Australia, 35 Stirling Highway, Crawley, WAU, Australia, 6009, gilles.gignac@uwa.edu.au
- Source:
- Psychological Assessment, Vol 27(4), Dec, 2015. pp. 1312-1323.
- NLM Title Abbreviation:
- Psychol Assess
- Page Count:
- 12
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- Psychological Assessment: A Journal of Consulting and Clinical Psychology
- ISSN:
- 1040-3590 (Print)
1939-134X (Electronic) - Language:
- English
- Keywords:
- memory span, Digit Span, general intelligence, working memory
- Abstract:
- Historically, Digit Span has been regarded as a relatively poor indicator of general intellectual functioning (g). In fact, Wechsler (1958) contended that beyond an average level of Digit Span performance, there was little benefit to possessing a greater memory span. Although Wechsler’s position does not appear to have ever been tested empirically, it does appear to have become clinical lore. Consequently, the purpose of this investigation was to test Wechsler’s contention on the Wechsler Adult Intelligence Scale-Fourth Edition normative sample (N = 1,800; ages: 16 – 69). Based on linear and nonlinear contrast analyses of means, as well as linear and nonlinear bifactor model analyses, all 3 Digit Span indicators (LDSF, LDSB, and LDSS) were found to exhibit primarily linear associations with FSIQ/g. Thus, the commonly held position that Digit Span performance beyond an average level is not indicative of greater intellectual functioning was not supported. The results are discussed in light of the increasing evidence across multiple domains that memory span plays an important role in intellectual functioning. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Digit Span Testing; *Intelligence; *Short Term Memory
- PsycINFO Classification:
- Tests & Testing (2220)
Learning & Memory (2343) - Population:
- Human
- Location:
- US
- Age Group:
- Adolescence (13-17 yrs)
Adulthood (18 yrs & older)
Young Adulthood (18-29 yrs)
Thirties (30-39 yrs)
Middle Age (40-64 yrs)
Aged (65 yrs & older) - Tests & Measures:
- Wechsler-Bellevue Scale
Differential Ability Scales
Wechsler Adult Intelligence Scale--Fourth Edition DOI: 10.1037/t15169-000
WAIS-R (Wechsler Adult Intelligence Scale-Revised) - Methodology:
- Empirical Study; Quantitative Study
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- First Posted: Mar 16, 2015; Accepted: Jan 23, 2015; Revised: Jan 22, 2015; First Submitted: Sep 23, 2014
- Release Date:
- 20150316
- Correction Date:
- 20151214
- Copyright:
- American Psychological Association. 2015
- Digital Object Identifier:
- http://dx.doi.org/10.1037/pas0000105
- PMID:
- 25774642
- Accession Number:
- 2015-11426-001
- Number of Citations in Source:
- 105
- Persistent link to this record (Permalink):
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- Cut and Paste:
- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2015-11426-001&site=ehost-live">Digit Span is (mostly) related linearly to general intelligence: Every extra bit of span counts.</A>
- Database:
- PsycINFO
Digit Span Is (Mostly) Related Linearly to General Intelligence: Every Extra Bit of Span Counts
By: Gilles E. Gignac
School of Psychology, University of Western Australia;
Lawrence G. Weiss
Pearson Clinical & Talent Assessment, San Antonio, Texas
Acknowledgement: Lawrence G. Weiss was involved in the research and development of the WAIS-IV as an employee of Pearson, which is the publisher of numerous psychological tests including the Wechsler scales. We thank Mark Hurlstone and Klaus Oberauer for answering some questions during the preparation of this article.
“Memory span for digits has been underrated as a psychometric test by most clinical psychologists.”—(Jensen, 1970, p. 71)
The assessment of individual differences in memory span has a long history (Blankenship, 1938; Dempster, 1981). Furthermore, the reputation of memory span as an indicator of intellectual functioning has varied substantially over the years, from a poorly regarded cognitive ability considered seriously for exclusion from the Wechsler scales (Matarazzo, 1972) to a construct that may be isomorphic, or nearly so, with general intelligence (g; Colom, Rebollo, Palacios, Juan-Espinosa, & Kyllonen, 2004). Wechsler (1939, 1958; see also Matarazzo, 1972) contended that, beyond an average level of memory span performance, there was little in the way of benefits with respect to intellectual functioning. However, such a postulation does not appear to have ever been tested empirically, despite the fact that it has largely become clinical lore (Fruchter & Fruchter, 1973; Glasser & Zimmerman, 1967; Halstead, 1944; Whimbey & Whimbey, 1975). Consequently, the purpose of this investigation was to evaluate the precise nature of the association between memory span, as measured by Digit Span from the Wechsler Adult Intelligence Scale-Fourth Edition (WAIS-IV; Wechsler, 2008a), and g. If Digit Span scores were observed to share a meaningful and largely linear association with g, then the relatively poor view of the Digit Span test within the clinical assessment community would need to be reconsidered. By contrast, if Digit Span scores were observed to share a nonlinear (quadratic and negative) association with g, then Wechsler’s position would be substantiated. Either position would be an important one to demonstrate empirically.
From an applied clinical perspective, it was considered important to address this question, because Digit Span is one of the 10 core subtests within the Wechsler scales that are used to estimate an individual’s FSIQ. If Digit Span were observed to share a nonlinear association with g, as contended by Wechsler, then perhaps it would be appropriate to consider substituting it for another subtest, one that has a respectable association with g across the whole spectrum of ability.
Digit Span: Description and Historical ReputationHistorically, memory span has been measured across a number of modalities with various stimuli (e.g., verbal, spatial, nonsense words, etc.), however, over time, memory for digits became the preferred method of memory span assessment within the conventional cognitive ability assessments (Blankenship, 1938; Dempster, 1981; Wambach, Lamar, Swenson, Penney, Kaplan, & Libon, 2011). The Wechsler scales have included a measure of memory span for digits, known as Digit Span, since the inception of the Wechsler-Bellevue scale (Wechsler, 1939). With respect to the first four editions of the Wechsler scales (Wechsler, 1939; 1955; 1981; 1997), Digit Span consisted of two subtests: Digit Span Forward and Digit Span Backward. Digit Span Forward requires the participant to recall a series of random single digits in the order with which they were read. The digits are read to the participant at a rate of one per second and the participant must repeat the digits orally. The sequence of digits vary in length from three to nine. By contrast, Digit Span Backward requires the participant to recall the random single digits in the reverse order with which they were read. The sequence of digits varies in length from two to eight. Digit Span Forward has been categorized as a measure of short-term memory (STM) capacity (STMC), whereas Digit Span Backward, which requires the reverse ordering of the digits before recall (i.e., mental manipulation), has been categorized as a measure of working memory capacity (WMC; Oberauer, Süβ, Schulze, Wilhelm, & Wittmann, 2000). In the latest version of the adult Wechsler scale (WAIS-IV; Wechsler, 2008a), an additional measure of WMC, Digit Span Sequencing, has been added to the Digit Span subtest. Digit Span Sequencing requires the participant to recall the randomly presented single digits in ascending numeric order (lowest to highest).
Although memory span tests have been included in the early intelligence batteries (e.g., Stanford-Binet; Terman, 1917), the inclusion of memory span tests at such a time was considered largely unsubstantiated, as there was no obvious link between scores from such tests and school grades (Estes, 1981). Bronner, Healy, Lowe, and Shimberg (1927) recognized some utility associated with Digit Span test scores, however, they ultimately contended that “the value of the whole test has been greatly overestimated” (p. 197). In his description and evaluation of the Digit Span subtest, Wechsler (1939, 1958; see also Matarazzo, 1972) contended that Digit Span test scores were largely independent of g, a view that continues to be expressed in some relatively contemporaneous clinical assessment texts (e.g., Sbordone & Saul, 2000). Perhaps most critically, Wechsler suggested that having an above average memory span ability was of little benefit:
Rote memory more than any other capacity seems to be one of those abilities of which a certain absolute minimum is required, but excesses of which seemingly contribute relatively little to the capacities of the individual as a whole. (Wechsler, 1958, p. 71)
Within the specific context of intellectual functioning assessment in children, Glasser and Zimmerman (1967) expressed a very similar critical view:
[Digit Span] makes the assumption that rote memory is one of those abilities of which a certain absolute minimum is required for all levels of intellectual functioning. However, excesses above this absolute minimum seemingly contribute relatively little to the capacity . . . to function as a whole. It appears then to be one of those abilities which enter into intellectual functioning only as necessary minima.” (p. 96)
Given the perceived relative lack of validity associated with the Digit Span subtest as an indicator of g, it was considered seriously for complete removal from the WAIS (Matarazzo, 1972).
Other critical views include Estes (1974) who categorized Digit Span as a basic associative capacity measure of lower levels of intellectual functioning. Sattler (1965, 1982) referred to Digit Span as a measure of nonmeaningful memory. In the context of the Differential Ability Scales (Elliott, 1990), Hughes and McIntosh (2002) also referred to Digit Span as a measure of nonmeaningful memory. Finally, several sources have contended that Digit Span Forward is not a test of memory span at all, but, instead, a test of elementary attention (Hannay, Howieson, Loring, Fischer, & Lezak, 2004; Hebben & Milberg, 2009; Rapaport, Gill, & Schafer, 1945; Sbordone & Saul, 2000). In light of the above, it seems clear that Digit Span has been largely considered a relatively poor measure of intellectual functioning within much of the clinical assessment literature.
In addition to issues relevant to validity, another psychometric factor that likely helped facilitate Digit Span’s poor reputation was the fact that it was, initially, one of the subtests which yielded the least reliable scores. In the Wechsler-Bellevue (Wechsler, 1939) and the WAIS (Wechsler, 1955), Digit Span was reported to be associated with retest reliabilities of .67 and .68, respectively. Even worse, Digit Span within the WISC (Wechsler, 1949) was reported to be associated with retest reliabilities of .50 to .60 across age groups. McNemar (1942) also noted the relatively low reliability (≈.70) associated with the memory span test scores associated with the Stanford-Binet. Thus, the relatively low initial reliability estimates associated with memory span test scores were not unique to the Wechsler scales.
More important, however, Blackburn and Benton (1957) demonstrated that the reliability of Digit Span test scores could be enhanced by always administering both trials within each digit series, irrespective of whether the participant recalled the first trial correctly. Consequently, within the WAIS-R (Wechsler, 1981) and subsequent Wechsler adult editions, the Digit Span instructions required both trials associated with each item to be administered, until the discontinue rule was reached (failure of both trials of any item). As would be expected based on the results of Blackburn and Benton (1957), Digit Span within the WAIS-R (Wechsler, 1981) was reported to be associated with a respectable retest reliability of .83, which was very comparable with the retest reliabilities associated with the other Wechsler subscales. Digit Span within the WAIS-IV (Wechsler, 2008) was reported to be associated with retest reliability of .83 and an internal consistency reliability of .93. Thus, much more respectable levels of reliability have been achieved with the revised Digit Span subtest in the WAIS-R and later editions. Consequently, the relatively poor view of Digit Span test scores on the grounds of poor reliability is no longer justifiable. Furthermore, as a respectable level of reliability is a necessary (but not sufficient) condition for validity (Bendig, 1952; Mehrens & Lehmann, 1969), it is plausible to consider Digit Span test scores as possibly associated with respectable levels of validity.
Memory Span and Cognitive ScienceIn contrast to the commonly held impression of memory span as a relatively poor indicator of cognitive functioning within much of the intellectual assessment literature, both STM and working memory are highly regarded constructs in the area of cognition (Conway, Jarrold, Kane, Miyake, & Towse, 2007; Hurlstone, Hitch, & Baddeley, 2014). For example, serial recall (of which Digit Span Forward is an operational example) is not considered simply a measure of individual differences in attention or nonmeaningful memory, in contrast to how Digit Span test scores are often considered within the intellectual assessment community (e.g., Hannay, Howieson, Loring, Fischer, & Lezak, 2004; Hebben & Milberg, 2009; Rapaport, Gill, & Schafer, 1945; Sbordone & Saul, 2000). Instead, a large body of empirical and theoretical cognitive research has accumulated to substantiate serial recall as one of the fundamental human memory systems (Jonides, Lewis, Nee, Lustig, Berman, & Moore, 2008). Furthermore, detailed computational models have been proposed to represent the cognitive processes involved with serial recall (e.g., Brown, Neath, & Chater, 2007; Farrell, & Lewandowsky, 2002; Lewandowsky, & Farrell, 2008). More important, although an element of attention may form a part of a model of serial recall (e.g., Burgess & Hitch, 1992; Oberauer & Lewandowsky, 2011), at the core of serial recall models is the capacity to maintain and recall objects in STM (Hurlstone et al., 2014). Thus, the commonly expressed view of serial recall (i.e., Digit Span) in the clinical assessment literature as an indicator of elementary attention or nonmeaningful memory is fundamentally at odds with the cognitive science literature.
Additionally, as recently reviewed by Conway and Kovacs (2013), individual differences in both serial recall (a.k.a., STMC) and WMC have been found to relate substantially to fluid intelligence and g. For example, based on a meta-analytic review of 14 samples (N = 3,168), Kane, Hambrick, and Conway (2005) estimated that WMC and fluid intelligence shared 50% of their true score variance. Because the investigations included in Kane et al. were almost all based on range restricted university samples, Gignac (2014a) estimated the WMC and fluid intelligence latent variable association based on the WAIS-IV (Wechsler, 2008b) normative sample (N = 2, 220). Based on a correlated two-factor model, Gignac (2014a) found that WMC and fluid intelligence shared closer to 60% of their true score variance. As the Digit Span Backward subtest had a standardized loading of .72 on the WMC latent variable, it may be suggested that the correlation between Digit Span Backward and fluid intelligence was .55 (.72 * 77 = .55). Additionally, based on a bifactor model of the WAIS-IV, Gignac (2014a) reported a Digit Span Backward g loading of .58. Thus, working memory capacity, as measured by Digit Span Backward, may be considered a moderately strong correlate of fluid intelligence and g.
In contrast to Digit Span Backward, Digit Span Forward is not typically considered as strong a correlate of fluid intelligence and/or g (e.g., Engle, Tuholski, Laughlin, & Conway, 1999; Jensen & Figueroa, 1975; Prokosch, Yeo, & Miller, 2005). Both theoretically and empirically, the forward recall of objects is considered representative of STMC, as it does not require the manipulation or transformation of information (Oberauer et al., 2000). Consequently, as forward recall does not require the same level of cognitive resources (Li & Lewandowsky, 1995; St Clair-Thompson & Allen, 2013), it is naturally expected to be a less strong correlate of fluid intelligence and g.
In a footnote within the Kane et al. investigation, a moderate meta-analytically derived correlation of .30 between STMC and fluid intelligence was reported, which suggests that only ∼10% of their variance is shared. However, based on the WAIS-IV normative sample correlation matrix (N = 2,200), I estimated the correlation between a fluid intelligence latent variable (Matrix Reasoning, Figure Weights, and Block Design) and the Digit Span Forward observed variable at .51, which suggests that ∼26% of their variance is shared. Furthermore, based on a bifactor model of the WAIS-IV, Gignac (2014a) reported a Digit Span Forward g loading of .46. Thus, although Digit Span Backward is a more substantial correlate of fluid intelligence and g, Digit Span Forward should be considered a moderate correlate, as well. These results are consistent with several other investigations (e.g., Colom, Abad, Rebello, & Chun Shih, 2005; Colom, Flores-Mendoza, Quiroga, & Privado, 2005). Ultimately, as latent variable modeling research has established a substantial (r ≈ .80) association between STMC and WMC (Conway & Kovacs, 2013), virtually any effects relevant to WMC would likely be shared, to some degree, by STMC.
Memory Span and NeuroscienceFrom a more neuroscientific perspective, the nonlinear effect postulated by Wechsler may be suggested to be unlikely. For example, individual differences in intelligence have been suggested to be mediated, in part, by individual differences in the efficiency with which information can be processed by the brain (the neural efficiency hypothesis; Neubauer & Fink, 2009). The evidence in favor of the neural efficiency hypothesis does not suggest that advantages of efficient information processing diminish across the spectrum of ability. In the context of Digit Span, specifically, it will be noted that there is some neuroscientific research that suggests that superior numerical memory span performers use a different part of their brain, in comparison to average performers, when executing a numerical memory span task. For example, Tanaka, Michimata, Kaminaga, Honda, and Sadato (2002) examined the activation of the brain during the completion of a numerical memory span task in a group of abacus experts and a group of controls. The abacus experts achieved a mean digit span of 12.2 and the controls 8.5 (education levels were about the same between the groups; 14.7 and 15.8, respectively). Tanaka et al. found that, in superior performers, the cortical areas relevant to visual spatial skills were predominantly activated during the completion of the digit span task. By contrast, in average performers, the cortical areas relevant to verbal skills (e.g., Broca’s area) were predominantly activated during the digit span task. Thus, superior digit span performers appear to use a different part of the brain, in comparison to more average performers. Consequently, the switch from a verbal to a spatial strategy may involve some change in the strength of the association between memory span and general intelligence. However, it is difficult to hypothesize specifically how the nature of the association might change across the spectrum of ability, if at all.
Study PurposeAlthough the empirical research reviewed above may be argued to be supportive of the notion that memory span is an important indicator of cognitive ability, it does not address the main argument made by Wechsler and others—that memory span is related nonlinearly to g, such that beyond an average level of span there are little or no benefits to intellectual functioning. Consequently, the purpose of this investigation was to examine specifically the nature of the association between Digit Span test scores and general intellectual functioning (FSIQ and g) within the WAIS-IV (Wechsler, 2008) normative sample. In contrast to past factor analytic investigations of the WAIS-IV (e.g., Canivez & Watkins, 2010; Gignac, 2014a; Gignac & Watkins, 2013), the analyses performed in this investigation will be based on the Longest Digit Span Forward (LDSF), Longest Digit Span Backward (LDSB), and Longest Digit Span Sequencing (LDSS) raw data, which will allow for precise and informative estimates of increases in FSIQ/g for each unit increase in memory span. If a nonlinear (quadratic) association were observed, such that beyond an approximate average level of memory span there was no association with g, then Wechsler’s and others’ relatively critical view of Digit Span would be supported. By contrast, if a largely linear association between Digit Span test scores and g were observed, then the relatively favorable view of memory span test scores observed in the cognitive science literature would be supported.
Method Sample and Measure
All of the analyses were based upon a combination of the raw and scaled data associated with the WAIS-IV normative sample (Wechsler, 2008b). The data were made available by the test publisher. The WAIS-IV normative sample was collected based on a stratified sampling strategy to reflect the U.S. census results relevant to gender, age, race/ethnicity, education, and geographic location (Wechsler, 2008b). The Wechsler scales are widely considered valid measures of intelligence (Hunsley & Lee, 2009). Although the total WAIS-IV normative sample is based on 2,200 participants, the participants aged 70 and above did not complete the Letter-Number Sequencing, Figure Weights, and Cancellation subtests (Wechsler, 2008b). Consequently, the total sample size upon which the analyses were performed in this investigation was N = 1,800. Raw scores were made available for the LDSF, LDSB, and LDSS variables. Scaled scores were made available for the remaining 15 WAIS-IV subtests. As, the LDSF, LDSB, and LDSS variables are, in essence, the Digit Span subtest, the overall Digit Span subtest scores were not included in any of the analyses.
An examination of the data revealed that a small number of participants (N = 3) achieved a very low score of two on LDSF. As these participants also scored very low on the remaining subtests, the data were considered valid. However, for the purposes of analyses, a minimum sample size of 10 was considered required for each memory span group. A memory span group corresponded to the individuals in the normative sample who achieved the same longest digit span score on a particular digit span subtest (Forward, Backward, or Sequence). Consequently, in this case, the three scores equal to two on the LDSF variable were Winsorized to a score of three, which yielded N = 12 for the lowest forward memory span group (i.e., span = three). Similarly, only six participant scores were equal to one or less on LDSB. Consequently, these six scores were Winsorized to a score of two which yielded N = 49 for the lowest backward memory span group (i.e., span = two). Finally, only seven participant scores were equal to one or less on LDSS. Consequently, these seven scores were Winsorized to a score of two which yielded N = 31 for the lowest sequence memory span group (i.e., span = two). Thus, the total sample remained 1,800 with some slight adjustments to the very small number of lowest longest digit span scores within the sample.
Data Analysis
The data were analyzed using two strategies: (a) linear and nonlinear contrast analyses, and (b) linear and nonlinear bifactor model analyses. Although both strategies were expected to yield similar results, the first analytic strategy, which was simpler in nature, was considered useful so as to facilitate a more accessible presentation of the results. The second strategy was considered more sophisticated, as it eliminated the impact of measurement error because of the inclusion of latent variables.
With respect to the first strategy, a series of three contrast analyses were performed. In each case, the LDSF, LDSB, and LDSS variables were used as an independent variable. The dependent variable was FSIQ, which is based on the 10 core WAIS-IV subtests. However, so as to avoid the possibility of an auto-correlation between the longest digit span variables and FSIQ, the Digit Span scaled scores, which are typically included within the 10 core WAIS-IV subtests (Wechsler, 2008b), were substituted with the Letter-Number Sequencing subtest scaled scores for the purposes of FSIQ calculation. Within each contrast analysis, the linear, quadratic, cubic, and quartic trends were tested. Effect sizes for each term within the contrast analyses were estimated via reffect2 (Furr, 2004). In the context of this investigation, a reffect2 equal to or less than .01 was considered practically nonsignificant, as it would imply that less than 1% of the variance in FSIQ scores could be accounted for by the pattern of contrast weights specified to reflect a particular trend. Finally, a multiple comparison procedure was also used to test the FSIQ mean differences between all contiguous longest digit span groups (e.g., LDSF 3 vs. LDSF 4; LDSF 4 vs. LDSF 5; LDSF 5 vs. LDSF 6, etc.). As the groups sizes were unequal, and there was the realistic possibility of statistically significant unequal variances, the Games-Howell multiple comparison procedure was chosen, as it is robust to heterogeneity of variances in the presence of unequal sample sizes (Games, Keselman, & Rogan, 1983).
To supplement the relatively simple contrast analyses, a strictly linear bifactor model and a linear plus nonlinear (quadratic) bifactor model was tested. A bifactor model is a completely first-order factor model with direct links between the (typically orthogonal) latent variables and the indicators (Gignac, 2008; Gustafsson & Balke, 1993; Reise, 2012). In this investigation, the bifactor model consisted of one first-order general factor and four nested group factors: Verbal Comprehension, Perceptual Organisation, Working Memory, and Processing Speed (see Figure 1). As the bifactor models included LDSF, LDSB, and LDSS variables, it was considered redundant to include the Digit Span subtest in the bifactor model. Consequently, the bifactor model general factor was defined by 9 core WAIS-IV subtests (i.e., all except Digit Span), the five supplemental WAIS-IV subtests, and, finally, the raw scores associated with LDSF, LDSB, and LDSS.
Figure 1. Bifactor model tested in this investigation. VC = Verbal Comprehension; PO = Perceptual Organization; WM = Working Memory; PS = Processing Speed; VOC = Vocabulary; IN = Information; CO = Comprehension; SIM = Similarities; FW = Figure Weights; MR = Matrix Reasoning; BD = Block Design; VP = Visual Puzzles; PC = Picture Completion; LDSF = Longest Digit Span Forward; LDSB = Longest Digit Span Backward; LDSS = Longest Digit Span Sequencing; LN = Letter-Number Sequencing; AR = Arithmetic; SS = Symbol Search; CD = Coding; CA = Cancellation.
The bifactor model analyses were conducted with Mplus (Muthén & Muthén, 1998–2010), which includes the option to estimate nonlinear (quadratic) factor loadings via numerical integration estimation and robust SEs (MLR; see example 5.7 of the User’s Guide). In this case, as the model was relatively large, the Monte Carlo numerical integration estimation technique was selected. If the linear + nonlinear bifactor model were observed to be associated with a statistically significant improvement in model fit over the linear bifactor model, the data would be suggested to be more consistent with a combination of linear and nonlinear effects. Additionally, if the LDSF, LDSB, and LDSS indicators were observed to be associated with negative, nonlinear loadings, then it would suggest that the strength of the association between memory span and g decreases across the spectrum of ability. To determine if the linear + nonlinear model fit better than the linear only model, the loglikelihood difference test with scaling correction factors was used. Furthermore, the Akaike information criterion (AIC; Akaike, 1987) and Bayesion information criterion (Schwarz, 1978) values were reported. Smaller AIC and BIC values, which include a penalty for model complexity, indicate better fit. All latent variables were constrained to 1.0 for the purposes of model identification. See Reynolds (2013) for more technical details on the performance of a nonlinear confirmatory factor analysis. Finally, latent variable strength was estimated with omega hierarchical (ωh;McDonald, 1999; Zinbarg, Revelle, Yovel, & Li, 2005) and omega specific (ωs;Reise, Bonifay, & Haviland, 2013). Unfortunately, bootstrapping is not possible in conjunction with random numerical integration estimation in Mplus. Consequently, only the omega point estimates were calculated, based on the point estimate standardized factor loadings (see Table 3 in Gignac, 2014b, for an accessible example of the relevant ωh and ωs calculations).
ResultsThe descriptive statistics associated with the longest digit span variables were as follows: LDSF, M = 6.71, SD = 1.32, skew = −.08; LDSB, M = 4.86, SD = 1.39, skew = .34; and LDSS, M = 5.85, SD = 1.32, skew = −.19. The FSIQ scores were associated with a M = 100.04 and SD = 14.98. Thus, the FSIQ data were representative of the normal population, as expected, and were also normally distributed (skew = −.31).
As can be seen in Table 1, there were numerical increases in the FSIQ means across the LDSF, LDSB, and LDSS memory span groups. The assumption of homogeneity of variances was not satisfied across all three LDS variables; consequently, a robust analysis of variance was performed. Based on a series of Welch’s one-way between Groups analysis of variances (ANOVAs), the null hypothesis of equal FSIQ means was rejected for all three longest digit span variables: LDSF, F(6, 133.22) = 85.45, p < .001, ω2 = .22; LDSB, F(6, 344.59) = 110.33, p < .001, ω2 = .27; LDSS, F(7, 217.58) = 107.32, p < .001, ω2 = .29. Thus, between 22% and 29% of the variance in FSIQ scores was accounted for by memory span, which is a large effect size based on Cohen’s (1992) guidelines. As can be seen in Table 2, nearly all of the contiguous memory span levels were associated with statistically significant differences in FSIQ means, based on the Games-Howell multiple comparison procedure. The mean Cohen’s d effects across LDSF, LDSB, and LDSS corresponded to −.64, −.51, and −.55, respectively, which corresponds to a medium sized effect based on Cohen’s (1992) guidelines. In raw score units, the differences in LDSF from 6 to 7 digits, 7 to 8 digits, and 8 to 9 digits corresponded to FSIQ increases of 4.68, 4.51, and 3.51 points, respectively.
Longest Digit Span Descriptive Statistics Across Memory Span Groups (2 to 9)
Contiguous Mean Difference Effect Sizes (Cohen’s d) Across Levels of Longest Digit Spans
Next, a contrast analysis approach was used to examine the patterns in the means. As can be seen in Table 3, the linear and quadratic effects were statistically significant (p < .001) and associated with minimally practically significant effect sizes (reffect2 > .01). However, the linear effects (.13 to .25) were much larger than the quadratic effects (≈.02) across all three longest digit span variables. Although some of the cubic and quartic contrast results were statistically significant (p < .05), none of them were associated with minimally practically significant effect sizes (all < .01). As can be seen in Figure 2, although there was a bend in the pattern of the means indicative of a quadratic effect, the FSIQ means continued to increase across the whole spectrum of memory capacity.
Between-Groups ANOVA Contrast Results
Figure 2. Scatter plots depicting the association between memory span and FSIQ. See the online article for the color version of this figure.
Next, the bifactor model analyses were performed. Because the numerical integration estimation procedure does not provide conventional model-fit statistics, the bifactor model was first tested via maximum likelihood estimation and was found to be associated with acceptable levels of model close-fit, χ2(102, N = 1,800) = 463.01, p < .001, RMSEA = .044, SRMR = .029, CFI = .977, TLI = .969. Furthermore, all of the factor loadings were statistically significant (available upon request). As estimated via numerical integration, the bifactor model that included only linear factor loading terms yielded a log likelihood value of −65,418.822, AIC = 130,971.64, BIC = 131,339.85, scaling correction factor = 1.0532, and 67 freely estimated parameters. Next, again, as estimated via numerical integration, the second bifactor model that included both linear and quadratic factor loading terms yielded a log likelihood value of −65176.59, AIC = 130555.19, BIC = 130789.37, scaling correction factor 1.0668, and 101 freely estimated parameters. Based on the loglikelihood difference test, the bifactor model that included both linear and nonlinear factor loadings was found to be better fitting than the bifactor model that included only linear factor loadings, Trd = 442, df = 34, p < .001. Furthermore, the AIC and BIC values were smaller for the linear + nonlinear bifactor model (ΔAIC = 416.45; ΔBIC = 550.48). Thus, the addition of the nonlinear terms to the bifactor model improved model fit. As can be seen in Table 4, all of the subtests were associated with statistically significant (p < .05) positive linear loadings. Furthermore, the linear general factor (ωh = .84) was found to be particularly strong, whereas, by contrast, the linear nested VC (ωs = .30), PO (ωs = .10), WM (ωs = .26), and PS (ωs = .41) latent variables were found to be relatively weak.
Standardized Loadings Associated With the Bifactor Model: Linear Effects and Nonlinear Effects
Finally, the LDSB, LDSF, and LDSS variables were associated with statistically significant nonlinear standardized loadings of −.09, −.06, and −.12, respectively. The negative nonlinear g loadings imply an association between a subtest and g that is decreasing in strength across the spectrum of cognitive ability. Arguably, loadings of such a small magnitude imply a very weak nonlinear effect, which is consistent with the relatively weak effects associated with the contrast analyses. More important, it will be noted that the vast majority of the WAIS-IV subtests (14 out of 17) evidenced negative, nonlinear associations with g (see Table 4).
DiscussionBased on the results of the contrast analyses, the LDSF, LDSB, and LDSS memory span groups evidenced mean FSIQ differences largely consistent with a linear effect, although there were also small, nonlinear (quadratic) effects, as well. Based on the nonlinear bifactor analysis, LDSF, LDSB, and LDSS were, again, observed to be largely associated with linear, positive effects on g, with additional, small, negative, nonlinear effects with g. However, virtually all of the WAIS-IV subtests were observed to be associated with similar nonlinear, negative, effects with g.
Overall, the results of this investigation do not support Wechsler’s (1939, 1958) view that above average levels of memory span are not beneficial with respect to intellectual functioning, as the association between g and LDSF (.40), LDSB (.48), and LDSS (.51) were principally linear in nature (Figure 2). The magnitude of the memory span bifactor g loadings were noticeably smaller than those reported in Gignac (2014a; Digit Span Forward = .46; Digit Span Backward = .58; Digit Span Sequencing = .63); however, it is important to note that Gignac analyzed the scaled WAIS-IV data, which are based on scoring each subtest item from 0 to 2, rather than simply the longest digit span achieved (i.e., less variability in test scores). Thus, the memory span effects reported in Gignac may be regarded as more representative of the validity of Digit Span test scores as they are actually calculated and interpreted in practice. The longest digit span data were used in this investigation simply to facilitate a more intuitive interpretation of the effects.
From a Cohen’s d perspective, the differences in mean FSIQ across the contiguous LDSF memory span groups was between small and large in magnitude (Cohen, 1992). Although the magnitude of the difference decreased across the spectrum of general ability (i.e., FSIQ), consistent with a nonlinear effect, small to medium FSIQ statistically significant increases were nonetheless observed for LDSF and mostly observed for LDSB and LDSS. Unfortunately, Wechsler (1939, 1958; see also Matarazzo, 1972) did not specify precisely what an absolute minimum memory span was, however, it is plausible to presume that an absolute minimum was not beyond average performance. From the mid- to high-end of the performance spectrum, each unit increase in memory span performance corresponded to an increase of ∼4 IQ points, in this investigation. Across the three memory span indicators, the difference between an approximate average level of memory span performance (five to six) and the highest level of memory span performance (eight to nine) was equal to ∼12.2 IQ points. Arguably, a difference between groups approaching a full SD is substantial, one that would be expected to afford meaningful advantages in life (Gottfredson, 2004; Jensen, 1998; Judge, Ilies, & Dimotakis, 2010). Thus, the clinical lore surrounding the Digit Span subtest should probably be reconsidered, based on the results of this investigation. Although there was a small reduction in the magnitude of the mean FISQ differences from low to high ability, which may be regarded as partial support of Wechsler’s view, such a small effect may be because of a statistical artifact, as described below. From an applied perspective, practitioners should have confidence in the use of the core Digit Span subtest in the estimation of an individual’s FSIQ across the whole spectrum of ability.
It will also be noted that although clinicians are recommended to use the Digit Span subtest and the Arithmetic subtest for the purposes of calculating WM index scores (Wechsler, 2008b), Arithmetic was observed to be associated with a rather weak linear loading of only .13 on the nested WM latent variable. By contrast, Letter-Number Sequencing and Digit Span Backward were found to be associated with respective linear loadings of .56 and .42 on the nested WM latent variable. Consequently, it is suggested that a better representation of working memory capacity could be obtained by a combination of Letter-Number Sequencing and Digit Span Backward, rather than the current recommendation of Digit Span and Arithmetic.
The observation that all three indicators of memory span examined in this investigation evidenced substantial and largely linear associations with g is consistent with the commonly expressed view within the cognitive science literature that memory span is an important attribute of cognitive functioning (Conway & Kovacs, 2013). Precisely how greater memory span ability facilitates greater cognitive ability across a range of diverse tasks, ranging from fluid intelligence tasks to the accumulation and expression of knowledge, remains an active area of research (Wiley, Jarosz, Cushen, & Colflesh, 2011). In the context of explaining why Digit Span correlates with crystallized knowledge subtests, Jensen (1970) speculated that “seemingly small individual differences in immediate memory span, when multiplied over a lifetime of experiences, make for highly significant differences in acquired [knowledge]” (p. 73). Additionally, the ability to make conceptual connections between seemingly disparate pieces of information (e.g., Similarities) may be regarded as an important intellectual capacity (Flynn, 2007). Many breakthroughs in science, for example, may be suggested to be achieved by making connections between several pieces of information, all of which would have to be maintained in memory, simultaneously, during the process of analysis and synthesis. From this perspective, an individual who can maintain eight to nine pieces of information in memory, simultaneously, is arguably at a distinct advantage over an individual who can maintain only three or four.
Although some evidence suggests that Digit Span test scores are relatively insensitive to diffuse head injury (Axelrod, Fichtenberg, Millis, & Wertheimer, 2006; Donders, Tulsky, & Zhu, 2001), there are some case studies that have found selective memory span deficits in those who sustained a focal brain injury such as an ischemia (e.g., Vallat, Azouvi, Hardisson, Meffert, Tessier, & Pradat-Diehl, 2005). Additionally, the contention that Digit Span is simply an indicator of attention may be suggested to be inconsistent with the observation that some brain injured individuals can have intact short-term spatial memory ability but serious verbal serial recall deficits (De Renzi & Nichelli, 1975; Vallar & Baddeley, 1984). Conversely, a deficit in spatial STM can be observed in the presence of intact verbal STM (Hanley, Young, & Pearson, 1991). The reconciliation of such findings with the notion that serial recall is an indicator of attention would require an unlikely model of cognition that included modality specific attentional systems. Ultimately, the notion that Digit Span measures simply attention, rather than a form of memory, is inconsistent with the manner in which serial recall is viewed within the cognitive science literature (Hurlstone et al., 2014). Thus, based on the empirical literature, as well as the results of this investigation, memory span for digits should arguably be viewed as important indicator of short-term verbal memory functioning.
Although the small, negative, nonlinear effects observed in this investigation may be considered, to some degree, consistent with Wechsler’ (1939, 1958) view of Digit Span, it may be suggested that the ostensible partial support may possibly be explained by the well-established psychometric principles of regression toward the mean and standard error of measurement, asymmetry (Daniel, 1999). That is, test scores at the lower- and higher-end of a distribution of scores are known to be associated with less reliability than those test scores at or near the mean of the distribution (Preacher, Rucker, MacCallum, & Nicewander, 2005). As a result, relatively extreme observed test scores are known to regress toward the mean upon retesting (Campbell & Kenny, 1999). Consequently, the phenomenon of regression toward the mean may explain the observation that more substantial group differences in FSIQ were observed in this investigation across the lower memory span groups, whereas progressively smaller group differences in FSIQ were observed across the higher memory span groups.
To avoid confusion, it should be made clear that the participants included in this investigation were not retested on any of the WAIS-IV subtests. Instead, the regression toward the mean effect is hypothesized to operate, principally, at the construct level of g. That is, because all of the WAIS-IV subtests are associated with substantial common variance (i.e., g factor; Canivez & Watkins, 2010; Gignac & Watkins, 2013), the administration of the WAIS-IV battery may be regarded, to some degree, as consistent with a retest administration. Thus, individuals who may have performed particularly poorly on the Digit Span subtest, an indicator of g, would be expected to perform somewhat better on the remaining WAIS-IV subtests, also indicators of g, because of the regression toward the mean effect. Conversely, those who performed particularly well on the Digit Span subtest would be expected to perform somewhat worse on the remaining WAIS-IV subtests. Taken from a different psychometric perspective, the standard error of measurement associated with scores at the lower- and higher-end of the distribution is not symmetric (Daniel, 1999). Instead, the standard error of measurement is asymmetric with greater confidence interval coverage toward the mean, rather than away from the mean (Nunnally & Bernstein, 1994). Consequently, given the effects of regression toward the mean and the asymmetry of the standard error of measurement, the small nonlinear bifactor g loadings reported for LDSF, LDSB, and LDSS may be considered a statistical artifact. Indirect support for such a contention is that small, negative, nonlinear loadings were observed in this investigation for nearly all of the WAIS-IV subtests. Furthermore, based on an additional analysis, the Spearman rank correlation between the reliability of subtest scores and the magnitude of the nonlinear g loading was −.61, (bootstrapped 95% confidence interval [CI] [−.20, −.81]). Thus, greater levels of subtest score reliability were associated with smaller nonlinear g loadings, an observation which is consistent with the fact that the regression toward the mean effect does not occur when test scores are perfectly reliable (Preacher et al., 2005).
Finally, it will be noted briefly that this investigation may be considered relevant to the topic of Spearman’s Law of Diminishing Returns (SLODR), which states that the positive manifold (or g) is greater at lower levels of ability than at higher levels of ability (Deary, & Pagliari, 1991; Jensen, 2003; Spearman, 1927). In this investigation, the nature of the association between memory span scores and FSIQ depicted in the scatter plots within Figure 2 may be suggested to be consistent with SLODR, as the increases in FSIQ means were larger across the lower memory span groups, in comparison to the higher memory span groups. However, again, such an effect may be plausibly explained based on regression toward the mean and the asymmetry of standard error of measurement, (Daniel, 1999). Thus, much of the empirical research ostensibly supportive of SLODR (e.g., Coyle & Rindermann, 2013; Reynolds, 2013; te Nijenhuis & Hartmann, 2006) may be attributable to a statistical artifact. Although, latent variable modeling techniques are emerging to address the regression toward the mean effect (e.g., Marsh & Hau, 2002), none appear to have been proposed in the context of SLODR or the hypothesis relevant to this investigation. Future research in this area is encouraged. It is hypothesized that once the asymmetry of measurement error is controlled and the nonlinear effects between subtests and g may be largely eliminated. It should be emphasized that not all nonlinear effects are suggested here to be because of statistical artifacts. Inverted U-shaped effects would not be explainable from this perspective, nor would nonlinear effects based on scores associated with very high levels of reliability. Finally, nonlinear effects based on scores that do not share a common construct would not apply either.
Limitations
Although the memory span subtests examined in this investigation were found to be associated with respectable levels of validity, there are some minor changes that could be implemented for the purposes of improvement. For example, within the WAIS-IV, an individual’s longest digit span is equal to the number of digits that can be recalled correctly on 50% of trials (Wechsler, 2008b). However, there are only two trials associated with each digit series length (Wechsler, 2008a). Consequently, a 50% success rate implies that the participant needs to recall correctly only one trial. Arguably, enhanced levels of reliability could be achieved with the administration of, say, four or five trials. Enhanced reliability would be particularly advantageous at the lower- and higher-end of the memory distribution of test scores, which are known to be associated with less reliability (Preacher et al., 2005). Consequently, enhanced reliability, particularly at the ends of the distribution of test scores, would be expected to help militate against the regression toward the mean effect hypothesized to have been observed in this investigation. Furthermore, in clinical settings, it would be expected that many cases would perform at the lower (less reliable) end of the spectrum of ability. In neuropsychological settings, as many as 10 trials are recommended to be administered for each digit series length to determine an individual’s longest digit span (Lezak, 2004). In the area of cognition, researchers typically administer between there and five trials per item, with thresholds of 67% to 80% correct to demarcate an individual’s memory span (Conway, Kane, Bunting, Hambrick, Whilhelm, & Engle, 2005). Of course, a distinct disadvantage associated with increasing the number of trials substantially within each digit span series would be a concomitant increase in administration time. Perhaps the ideal would be to move onto an item response theory (IRT) and computer adaptive testing (CAT) framework (van der Linden & Glas, 2000), which would facilitate the administration of a greater number of items/trials closer to an individual’s latent ability relatively quickly.
In addition to the possible advantages associated with administering more trials within a digit series item, it may be beneficial to include at least one more additional digit series of greater length within the Digit Span subtest. That is, with respect to Digit Span Forward, there does appear to be a moderate ceiling effect, as ∼10% of the normal population can recall nine digits (Wechsler, 2008), that is, the largest digit series within the test. Although not reported in the technical manual (Wechsler, 2008), it is highly unlikely that 10% of the population answer correctly the last items within the Vocabulary, Similarities, and Matrix Reasoning subtests, by comparison. Consequently, as range restriction is well-known to attenuate effect sizes (Ghiselli, 1964; Huck, 1992; Sackett & Yang, 2000), it may be suggested that the factor loadings associated with Digit Span reported in this investigation (and others) are, to some degree, smaller than they would otherwise be with the inclusion of an additional digit series at the higher end of the spectrum of ability.
ConclusionIn conclusion, the popularity of memory span individual differences research in the cognitive sciences continues unabated (Conway & Kovacs, 2013). Greater attention on memory span in the clinical assessment community has occurred, as well. For example, the Wechsler scales provide normative sample information for Digit Span Forward and Digit Span Backward, separately. Furthermore, an additional subtest of working memory functioning, Digit Span Sequencing, was added to the WAIS-IV. However, it may be suggested that, overall, the view of memory span within the clinical assessment community is not especially favorable as an indicator of general intellectual functioning, or even memory capacity, in some cases. The results of this investigation suggest that memory span, as measured via Digit Span Forward, Digit Span Backward, and Digit Span Sequencing, are at least moderately good indicators of g. Furthermore, the association is likely best interpreted as largely linear, with every extra bit of memory span counting toward additional intellectual functioning. Consequently, it is perhaps long overdue that Digit Span be regarded as a high quality subtest, as Jensen (1970) suggested nearly 45 years ago.
Footnotes 1 Within the WAIS-IV (Wechsler, 2008a), Digit Span Forward includes an additional item of two trials with two digits.
2 To estimate the association between observed and latent variables within a structural equation model, one can use the path tracing rule (Mulaik & Quartetti, 1997).
3 The method of Winsorizing data is most typically applied in the context of dealing with outliers (Wilcox, 2010). However, it should be made clear that there were no outliers in the WAIS-IV normative sample data based on the interquartile range rule (Hoaglin & Iglewicz, 1987). The method of Winsorizing the lowest longest digit span scores was simply considered to most attractive option in this case, as the lowest scores appeared to be valid and the next lowest scoring groups were not particularly large; consequently, they stood to benefit somewhat by the addition of a few extra cases.
4 As explained by Furr (2004), reffect2 represents the squared correlation between the contrast weights associated with a particular term (in the case of LDSF, e.g., linear: −7, −5, −3, −1, 1, 3, 5, 7; quadratic: −7, −1, 3, 5, 5, 3, −1, −7) and the dependent variable (i.e., FSIQ). From this perspective, the contrast analyses conducted in this investigation may be regarded as very similar to a polynomial regression analysis, and, thus, a relatively powerful statistical approach. The analysis of means approach used in this investigation was considered most useful, in this case, to help specify precisely the possible increases in FSIQ for a given unit change in memory span. By contrast, the beta weights associated with a polynomial regression are typically difficult to interpret (Pedhazur, 1997).
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APPENDIX APPENDIX A: Mplus Syntax Used to Estimate the Bifactor Model Solution
Variable:
Names are
ldsf ldsb ldss bdss siss mrss vcss arss ssss vpss coss cdss lnss fwss inss cass pcss;
ANALYSIS:
ESTIMATOR = MLR;
TYPE = RANDOM;
ALGORITHM = INTEGRATION;
INTEGRATION = MONTECARLO(5000);
MODEL:
g BY vcss* inss coss siss fwss mrss bdss vpss pcss ldsf ldsb ldss lnss arss ssss cdss cass;
vc BY vcss* inss coss siss;
po BY fwss* mrss bdss vpss pcss;
wm BY ldsf* ldsb ldss lnss arss;
ps BY ssss* cdss cass;
g@1;
vc@1;
po@1;
wm@1;
ps@1;
g WITH vc@0 po@0 wm@0 ps@0;
vc WITH po@0 wm@0 ps@0;
po WITH wm@0 ps@0;
wm WITH ps@0;
gxg | g XWITH g;
ldsf-pcss ON gxg;
vcxvc | vc XWITH vc;
vcss inss coss siss ON vcxvc;
poxpo | po XWITH po;
fwss mrss bdss vpss pcss ON poxpo;
wmxwm | wm XWITH wm;
ldsf ldsb ldss lnss arss ON wmxwm;
psxps | ps XWITH ps;
ssss cdss cass ON psxps;
Output: TECH1 TECH8;
Submitted: September 23, 2014 Revised: January 22, 2015 Accepted: January 23, 2015
This publication is protected by US and international copyright laws and its content may not be copied without the copyright holders express written permission except for the print or download capabilities of the retrieval software used for access. This content is intended solely for the use of the individual user.
Source: Psychological Assessment. Vol. 27. (4), Dec, 2015 pp. 1312-1323)
Accession Number: 2015-11426-001
Digital Object Identifier: 10.1037/pas0000105
Record: 54- Title:
- Discrepancy in caregiving expectations predicts problematic alcohol use among caregivers of trauma injury patients six months after ICU admission.
- Authors:
- Kearns, Nathan T., ORCID 0000-0003-3037-3919. Department of Psychology, University of North Texas, Denton, TX, US, nathankearns@my.unt.edu
Blumenthal, Heidemarie. Department of Psychology, University of North Texas, Denton, TX, US
Rainey, Evan E.. Division of Trauma, Critical Care, and Acute Care Surgery, Baylor University Medical Center, Dallas, TX, US
Bennett, Monica M.. Office of Chief Quality Officer, Baylor University Medical Center, Dallas, TX, US
Powers, Mark B.. Division of Trauma, Critical Care, and Acute Care Surgery, Baylor University Medical Center, Dallas, TX, US
Foreman, Michael L.. Division of Trauma, Critical Care, and Acute Care Surgery, Baylor University Medical Center, Dallas, TX, US
Warren, Ann Marie. Division of Trauma, Critical Care, and Acute Care Surgery, Baylor University Medical Center, Dallas, TX, US - Address:
- Kearns, Nathan T., Department of Psychology, University of North Texas, 1155 Union Circle #31128, Denton, TX, US, 76203-5017, nathankearns@my.unt.edu
- Source:
- Psychology of Addictive Behaviors, Vol 31(4), Jun, 2017. pp. 497-505.
- NLM Title Abbreviation:
- Psychol Addict Behav
- Page Count:
- 9
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- Bulletin of the Society of Psychologists in Addictive Behaviors; Bulletin of the Society of Psychologists in Substance Abuse
- Other Publishers:
- US : Educational Publishing Foundation
Society of Psychologists in Addictive Behaviors - ISSN:
- 0893-164X (Print)
1939-1501 (Electronic) - Language:
- English
- Keywords:
- alcohol, caregiver burden, traumatic injury, intensive care unit, family members
- Abstract:
- This prospective study examined the influence of caregiving variables on the development of problematic alcohol use among family members of patients admitted to an urban Level I trauma center. Data were collected from 124 caregivers 48 hrs after initial hospitalization of their family member. The final sample included 81 participants (24.6% male; Mage = 47.8) who completed their follow-up assessment at 6 months. Hierarchical linear and logistic regression analyses assessed increases in consumption and odds of a positive screen for problematic alcohol use in association with caregiver burden, actual time spent in the caregiving role, and caregiving differential (i.e., anticipated time spent caregiving at baseline in relation to actual time caregiving at 6 months). At 6 months, 24.7% of caregivers screened positive for problematic alcohol use. Results uniquely highlighted caregiving differential as a significant predictor of both increases in general alcohol consumption (ΔR2 = .06, p < .01) and odds of screening positive for problematic alcohol use at 6 months (Odds Ratio = 1.05, 95% CI [1.02–1.09]). More specifically, our adjusted model found that providing 10% more time caregiving, relative to expectations at baseline, was associated with an increase in the probability of problematic alcohol use by 22% (95% CI: 8–37%) at 6 months. These results suggest that a discrepancy in expectations regarding anticipated time caregiving and actual time caregiving, rather than solely the amount of caregiving or perceived caregiver burden, may be an important predictor of caregiver alcohol use 6 months after a family member’s ICU hospitalization. (PsycINFO Database Record (c) 2017 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Alcohol Drinking Patterns; *Caregiver Burden; *Caregivers; Expectations; Intensive Care; Traumatic Brain Injury
- PsycINFO Classification:
- Home Care & Hospice (3375)
Drug & Alcohol Usage (Legal) (2990) - Population:
- Human
Male
Female - Location:
- US
- Age Group:
- Adulthood (18 yrs & older)
- Tests & Measures:
- Alcohol Use Disorder Identification Test-Consumption
Caregiver’s Burden Scale - Grant Sponsorship:
- Sponsor: Baylor Health Care System Foundation, Ginger Murchison Traumatic Brain Injury Fund, US
Recipients: No recipient indicated - Conference:
- Society of Critical Care Medication (SCCM) 45th Critical Care Congress, 45th, 2016, Orlando, FL, US
- Conference Notes:
- The data presented in this article were previously presented as an abstract at the aforementioned conference.
- Methodology:
- Empirical Study; Followup Study; Quantitative Study
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- First Posted: May 11, 2017; Accepted: Apr 6, 2017; Revised: Apr 5, 2017; First Submitted: Nov 3, 2016
- Release Date:
- 20170511
- Correction Date:
- 20170612
- Copyright:
- American Psychological Association. 2017
- Digital Object Identifier:
- http://dx.doi.org/10.1037/adb0000282
- PMID:
- 28493754
- Accession Number:
- 2017-20821-001
- Persistent link to this record (Permalink):
- http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2017-20821-001&site=ehost-live
- Cut and Paste:
- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2017-20821-001&site=ehost-live">Discrepancy in caregiving expectations predicts problematic alcohol use among caregivers of trauma injury patients six months after ICU admission.</A>
- Database:
- PsycINFO
Discrepancy in Caregiving Expectations Predicts Problematic Alcohol Use Among Caregivers of Trauma Injury Patients Six Months After ICU Admission
By: Nathan T. Kearns
Department of Psychology, University of North Texas;
Heidemarie Blumenthal
Department of Psychology, University of North Texas
Evan E. Rainey
Division of Trauma, Critical Care, and Acute Care Surgery, Baylor University Medical Center, Dallas, Texas
Monica M. Bennett
Office of Chief Quality Officer, Baylor University Medical Center
Mark B. Powers
Division of Trauma, Critical Care, and Acute Care Surgery, Baylor University Medical Center
Michael L. Foreman
Division of Trauma, Critical Care, and Acute Care Surgery, Baylor University Medical Center
Ann Marie Warren
Division of Trauma, Critical Care, and Acute Care Surgery, Baylor University Medical Center
Acknowledgement: This research was supported in part by the Ginger Murchison Traumatic Brain Injury Fund of the Baylor Health Care System Foundation.
The data presented in this article were previously presented as an abstract at the Society of Critical Care Medication (SCCM) 45th Critical Care Congress in Orlando, Florida, in 2016. This article has not been previously published and has not been submitted elsewhere for publication.
Individuals hospitalized in the intensive care unit (ICU) may be particularly susceptible to issues with alcohol use both before admission and during recovery. Studies of Level I and II trauma centers have reported upward of 52% of patients have positive blood alcohol screens at the time of hospitalization (Cornwell et al., 1998) and that upward of 33% of ICU patients screen positive for problematic alcohol use via self-report and interview assessments (see de Wit, Jones, Sessler, Zilberberg, & Weaver, 2010 for review). Moreover, research has demonstrated that patient alcohol use not only adversely affects their own health, but also the health of their caregivers. For example, Kreutzer and colleagues (2009) found that caregivers of patients who used alcohol excessively experienced significantly more emotional distress. While some research has focused on caregiver-patient relationships, the primary focus of ICU-related research has been on the actual patients and improving their psychological and physiological health (Mitchell, Closson, Coulis, Flint, & Gray, 2000; Robinson, 1991). The effects of ICU admission and recovery on patient caregivers after discharge has been less well-studied.
In the United States, an estimated 34 million family caregivers provide upward of 80% of long-term care to patients (Houser & Gibson, 2008). Previous research indicates risk for maladaptive psychological and behavioral outcomes among these caregivers, following admission of their relative, spouse, or loved one to the ICU. For example, studies show that caregivers suffer from disproportionately elevated anxiety, depression, posttraumatic stress, and complicated grief (Azoulay et al., 2005; Davidson, Jones, & Bienvenu, 2012; Pochard et al., 2005; Warren et al., 2016) and frequently endorse “caregiver burden”—characterized by feelings of helplessness, guilt, anger, and alienation from others (Johnson, Chaboyer, Foster, & van der Vooren, 2001).
Complications that arise from these adverse responses to caregiving may stem from the fact that family members often are assumed to be de facto caregivers, without adequate consultation, preparation, or realistic expectations about what will be required of them after their loved one leaves the hospital (Rotondi, Sinkule, Balzer, Harris, & Moldovan, 2007; Wellard & Street, 1999). Worsening the matter is the fact that medical support and care are typically focused on the patient rather than the caregiver (Blom, Gustavsson, & Sundler, 2013), leaving families to adjust to their new roles on their own. Not only can this affect family members’ ability to be adequate caregivers, it also may hinder their own daily functioning and, subsequently, the recovery and health of the patient if the demands of caregiving exceed their initial expectations without the resources to adjust (McAdam, Fontaine, White, Dracup, & Puntillo, 2012; Verhaeghe, Defloor, Van Zuuren, Duijnstee, & Grypdonck, 2005).
Despite evidence of emotional distress and caregiver burden among family members of ICU patients, little empirical work has been conducted on mechanisms that caregivers use to cope. The limited research that has been done focuses almost exclusively on adaptive coping, such as positive appraisal and acquiring social support (Kosciulek, 1994); however, a substantial and growing literature provides support for a self-medication model of maladaptive coping, whereby individuals use alcohol and other substances as a means of regulating and managing psychological distress (Bolton, Robinson, & Sareen, 2009; Khantzian, 2003). Research demonstrates that elevated posttraumatic stress, anxiety, and depression all may be linked to increased alcohol use in the general population (Conner, Pinquart, & Gamble, 2009; Kushner, Abrams, & Borchardt, 2000), although limited work has examined these links among caregivers, specifically. Rospenda, Minich, Milner, and Richman (2010) found that, in a community sample of caregivers, greater social and emotional burden was significantly associated with increased frequency of drinking, frequency of intoxication, and alcohol use problems. Another study found that family members of intensive care patients reported elevated stress at the time of ICU admission, as well as increased use of alcohol during their loved one’s initial week of hospitalization and throughout their stay in the ICU (Halm et al., 1993). Considering the frequency of caregiver burden endorsements, negative emotions (e.g., helplessness), and prevalence of negative psychological symptoms (i.e., depressive, stress), assessment of caregiver alcohol consumption after discharge from the ICU is a natural, yet missing, next step in the literature. To our knowledge, no study has prospectively investigated problematic alcohol use in a heterogeneous sample of caregivers of ICU patients with a diversity of traumatic injuries and critical care needs.
The present study examined the prevalence and predictors of problematic alcohol use among caregivers after patient admission to the trauma and critical care surgical ICU. More specifically, the current study assessed perceived caregiver burden, actual time spent caregiving, and caregiving differential (i.e., how much time a family member anticipated spending in the caregiver role at admission in relation to actual time spent caregiving) at 6 months post-ICU admission. First, it was hypothesized that each of the caregiving dimensions would positively relate to alcohol use at 6 months after adoption of the caregiving role while controlling for alcohol consumption at admission. Second, it was hypothesized that the effects of caregiving would be robust to the inclusion of several additional relevant covariates (e.g., caregiver age, employment status).
Method Participants
The participants in the current analysis constitute a subgroup of a larger ongoing longitudinal project examining mental health among caregivers of patients admitted to the trauma/critical care ICU of an urban Level I trauma center in the southwestern United States. For the purposes of this study, family members are defined according to the Institute for Patient- and Family-Centered Care as “two or more persons who are related in any way—biologically, legally, or emotionally” (Institute for Patient- and Family-Centered Care, 2015).
A total of 124 participants were screened between March2013 and November 2014. All participants who met the following inclusion and exclusion criteria were approached for screening in the ICU and enrolled consecutively. If multiple family members were present, all family members meeting inclusion/exclusion criteria were approached to participate in the study. Inclusion criteria for participation included (a) both the family member and the patient being 18 years of age or older, (b) the patient being in the trauma/critical care ICU service for longer than 48 hrs with an expected survival greater than 96 hrs, and (c) the family member must anticipate spending time with the patient in a caregiver or supportive role (e.g., emotional, social, financial) after the patient was discharged. Exclusion criteria included the inability to understand written or spoken English at the eighth grade level and the inability to provide at least two forms of contact information for follow up. Participants whose family member had expired prior to 6-month follow-up were excluded from analyses, but remained in the study using a bereavement protocol.
For the final sample, 27 participants who did not complete their 6-month follow-up and 16 participants whose patient expired between the baseline assessment and the 6-month follow-up were excluded. The 81 remaining participants (24.6% male; Mage = 47.8, SD = 13.6) who completed both baseline and 6-month follow-up were retained for primary analyses. Notably, the final sample comprised caregivers of 62 patients, with 13 instances of two caregivers completing their assessment on the same patient and 3 instances of three caregivers completing their assessment on the same patient.
Measures
Participant demographic information was obtained through a standard self-report form administered at baseline, which included age, gender, ethnicity, marital status, education level, employment, and income. As necessary, participant demographic information was extracted or confirmed from the hospital’s trauma registry.
Alcohol use
Problematic alcohol use was measured using the Alcohol Use Disorder Identification Test-Consumption (AUDIT-C). The AUDIT-C is a self-report, three-item screen that has been modified from the original 10-item screen (AUDIT; Babor, Higgens-Biddle, Saunders, & Monteiro, 2001; Bohn, Babor, & Kranzler, 1995; Saunders, Aasland, Babor, de la Fuente, & Grant, 1993). Using a 5-point scale, the AUDIT-C assesses frequency of drinking [0 (Never) to 4 (4 or more times a week)], typical consumption amount [0 (1 or 2 drinks) to 4 (10 or more)], and frequency of binge drinking [0 (Never) to 4 (Daily or almost daily)], with total scores ranging on a scale of 0–12. In men, a score of 4 or more is considered a positive screen for identifying hazardous drinking or potential alcohol use disorder; in women, a score of 3 or more is considered positive (Bradley et al., 2003; Bush, Kivlahan, McDonell, Fihn, & Bradley, 1998). In the current study, scores were examined both as a continuous total, as well as a sex-specific dichotomous variable (i.e., 0 = negative, 1 = positive screen). The AUDIT-C is a recommended screening tool and has been validated to detect risky drinking, alcohol abuse, and dependence (Bush et al., 1998; Frank et al., 2008; Powers et al., 2014; Volk, Steinbauer, Cantor, & Holzer, 1997).
Caregiving
Four aspects of caregiving were assessed: Caregiver Burden, Anticipated Time Caregiving, Actual Time Caregiving, and Caregiving Differential.
Caregiver burden
The Caregiver’s Burden Scale (CBS) was used to assess perceived caregiver burden during the 6-month follow-up. The CBS is a 22-item scale designed to assess subjectively experienced burden by caregiver’s of chronically disabled persons (Lindvall et al., 2014). The items on the CBS assess five factors: general strain, isolation, disappointment, emotional involvement, and environment. Responses are scored on a 4-point, Likert-type scale ranging from 1 (Not at all) to 4 (Often). Total scores are calculated by averaging all 22 items. The CBS has demonstrated adequate to good psychometric properties (Elmståhl, Malmberg, & Annerstedt, 1996).
Anticipated time caregiving and actual time caregiving
The amount of time caregivers anticipated spending in the caregiver role was measured through a single-item, face valid question asked during the baseline assessment. More specifically, participants were asked, “How much time do you anticipate spending in the caregiving role for your patient?” Responses ranged from 0% to 100%, with 0% indicating they would be spending no time in the caregiver role and 100% indicating they would spend all of their time in the caregiver role.
Similarly, the amount of time caregivers actually spent in the caregiver role was measured through a single-item, face valid questions asked during the 6-month follow-up. Using the same 0–100 response format, participants were asked, “What percentage of your time do you spend in a caregiver role for him/her?,” with 0% indicating that they spent no time in the caregiving role and 100% indicating that they spent all of their time in the caregiving role.
Caregiving differential
Caregiving differential was calculated to measure the difference in the amount of the time the caregiver anticipated spending in the caregiving role versus how much time they actually spent in the caregiving role over the course of 6 months. Caregiving differential was calculated by subtracting Anticipated Time Caregiving (at baseline) from Actual Time Caregiving (at 6 months). Scores ranging from −100 to 100, with −100 indicating absolute overestimation of the time they would spent in the caregiving role (i.e., they expected to spent 100% of their time caregiving, but, over the course of 6 months, they actually spent 0% of their time caregiving) and 100 indicating an absolute underestimation of the time they would spend in the caregiving role (i.e., they expected to spent 0% of their time caregiving, but, over the course of 6 months, spent 100% of their time caregiving).
Procedure
Approval was obtained from the medical center’s Institutional Review Board. Researchers identified potential participants for the study from daily trauma census records, referrals from medical personnel (e.g., ICU nursing staff), and biweekly trauma rounds. Participants who met inclusion/exclusion criteria were approached in the waiting room and at bedside, as appropriate (e.g., participants were not immediately approached if the patient or family members were visibly distressed or if medical personnel were attending to the patient). Participants expressing interest in participating in the study were voluntarily consented and enrolled consecutively. If multiple family members were present in the waiting room or at bedside, all family members meeting inclusion/exclusion criteria were approached to participate in the study. Of note, hospital ICU policy typically limits the number of family members in the ICU rooms to 2–3 people at any given time. Baseline measurements were collected during initial inpatient admission to the trauma/critical care ICU. Six-month follow-ups were collected within a 4-week window around the participants’ “due date” (e.g., two weeks before/after 6 months from the date of the baseline assessment). Reminder postcards or emails based on participants’ preference during admission were sent one week prior to the 4-week window opening. Participants were contacted by trained clinical researchers over the telephone using the contact information provided at baseline, with a maximum number of 12 attempts to successfully contact the participant. The same measures were administered during these 6-month follow-up calls as at baseline. A list of community referrals was provided to all participants at baseline and then again if requested at follow-up.
Data Analysis
Analyses were performed using SAS 9.4 (SAS Institute, 2014). All variables were summarized using means and standard deviations for quantitative variables and counts and percentages for nominal variables (e.g., gender). Independent samples t tests and chi-square tests were used, as appropriate, to compare demographic information for participants who screened positive (i.e., scores ≥4 on the AUDIT-C for males, ≥3 for females) for problematic alcohol use at their 6-month follow-up against those who did not screen positive.
For the analyses that looked for associations between AUDIT-C and caregiving, AUDIT-C was used as both continuous and dichotomous. For the continuous analysis, partial Spearman rank correlations (pr) that controlled for baseline AUDIT-C score were used to evaluate associations between AUDIT-C total scores at 6-month follow up, caregiver burden, time spent caregiving at 6-month follow-up, and caregiving differential. Caregiving variables that were significantly associated with AUDIT-C were then evaluated further with a hierarchical linear regression analysis.
The first step of the model included demographic variables that were significantly different between AUDIT-C groups (i.e., negative vs. positive screen) at the α = .10 level; more specifically, age, gender, education (i.e., college degree vs. no college degree), employment status (i.e., unemployed vs. employed) and baseline AUDIT-C score. Caregiving variables that were significantly correlated with AUDIT-C at 6 months (i.e., caregiving differential) were included in the second step of the model. Change in the coefficient of determination (ΔR2) from the first step of the model (i.e., demographics) to the second next step (i.e., caregiving) was assessed, demonstrating how much variation, above and beyond the covariates, that the caregiving variable explained in predicting AUDIT-C at 6-month follow-up.
The analysis for the binary AUDIT-C outcome (i.e., positive screen vs. negative screen) followed a similar approach. First, all caregiving variables were assessed to determine which were significantly associated with a positive screen using logistic regression models that only controlled for baseline AUDIT-C. Caregiving variables that were significant at this step were then evaluated further. As with the previous analyses, a hierarchical logistic regression model was constructed, including significant demographic variables in the first step and caregiving variables in the second step of the model. For these logistic regressions, area under the receiver operating curve (i.e., c-statistic) were calculated to assess the change in the accuracy of the predictability of a positive AUDIT-C screen from the model with no caregiving to the model that included caregiving.
Results Descriptive Statistics
Descriptive statistics for the final sample are presented in Table 1. As can be seen, the sample was predominantly middle-aged (M = 47.8, SD = 13.6), female (75%), white (42%), married (59%), employed (57%), and held a college degree (42%). Relationship to the patient was relatively diverse, with 19 participants indicating that the patient they would be caring for was their spouse, 22 for their parent, 13 for their child, 7 for their sibling, and 20 for someone other than a legal or blood relative (e.g., close friend, domestic partner). In our sample, 22 of the 81 participants (27.2%) that completed their follow-up assessment screened positive for problematic alcohol use at baseline (via the AUDIT-C), while 20 of the 81 (24.7%) screen positive for problematic alcohol use at 6 months. As noted in Table 1, no significant differences were found between participants who completed their 6-month follow-up and those who did not complete their follow-up on any recorded demographic or pertinent nondemographic variables at last assessment.
Comparison of Baseline Characteristics for Those Who Did and Did Not Have 6-Month Follow-Up Data
Preliminary Analyses
In Table 2, the demographic characteristics of 6-month positive screen participants were compared against those with negative screens for problematic alcohol use. Participants in the problematic alcohol use group at 6 months were significantly more likely to be of younger age (p < .001), have scored significantly higher on the AUDIT-C at baseline (p < .001), and have screened positive for problematic alcohol use at baseline (p < .001). Further, participants who were male, had higher education (i.e., having a college degree vs. no college degree), and were employed (i.e., employed vs. not employed) were significantly more likely to screen positive for problematic alcohol use at the α = .10 level. These variables (i.e., gender, education, and employment status) were subsequently included as covariates in the primary analyses, in addition to age and AUDIT-C baseline scores.
Comparison of Baseline Demographics Between Problematic Alcohol Use Groups at 6-Month Follow-Up and Descriptives of 6-Month Caregiving Variables
For the assessment of problematic alcohol use as a continuous variable, a correlation matrix detailing associations between AUDIT-C scores at 6 months and the three caregiving variables, controlling for baseline AUDIT-C scores, is presented in Table 3. Consistent with previous literature, caregiver burden and actual time spent caregiving were significantly associated (pr = .47, p < .001); moreover, caregiver burden and caregiving differential were significantly correlated (pr = .36, p = .001). Importantly, our results indicated that neither caregiver burden (p = .755) nor actual time spend caregiving at 6 months (p = .360) were significantly correlated with caregiver AUDIT-C scores at 6 months. However, caregiving differential was significantly associated with problematic alcohol use (p = .009), such that trending toward underestimation of time spent in the caregiver role related to higher scores on the AUDIT-C at 6 months.
Partial Spearman Correlations Adjusting for AUDIT-C Total Score at Baseline
For the assessment of problematic alcohol use as a dichotomous variable (i.e., positive vs. negative screen at 6 months), a series of logistic regression models assessing odds of a positive screen for each of the caregiving variables, controlling for baseline AUDIT-C screen, were conducted. Neither caregiver burden (Odds Ratio [OR] = 1.03, 95% CI [.99–1.07], p = .162) nor actual time spent caregiving at 6 months (OR = .45, 95% CI [.13–1.58], p = .212) were significantly associated with screening positive for problematic alcohol use. However, consistent with the preliminary analyses for continuous AUDIT-C scores, caregiving differential did significantly increase odds of a positive AUDIT-C screen (OR = 1.04, 95% CI [1.01–1.08], p = .006).
Primary Analyses
With caregiving differential being the only caregiving variable significantly associated with problematic alcohol use, a hierarchical linear regression analysis was conducted for this variable alone. The first step of the model included significant demographic variables (age, gender, education, employment status) and baseline AUDIT-C as covariates (R2 = .45). Caregiving differential was added into the second level, and did significantly improve the overall model (R2 = .51).
Similarly, with caregiving differential being the only caregiving variable that significantly increased odds of screening positive for problematic alcohol use, a hierarchical logistic regression analysis was conducted for this variable alone. The first step of the model included significant demographic variables and baseline AUDIT-C screen as covariates (c-statistic = .88). Caregiving differential was added into the second level of the model. Results indicated that, above and beyond covariates, caregiving differential significantly predicted increased odds of screening positive for problematic alcohol use (OR = 1.05, 95% CI [1.02–1.09], p = .003, Δc-statistic = .07). More specifically, our adjusted model found that providing 10% more time caregiving, relative to expectations at baseline, was associated with an increase in the probability of problematic alcohol use by 22% (95% CI [8–37%]) at 6 months. Results from these analyses are presented in Table 4.
Results From Analyses of Caregiving Differential Predicting Continuous AUDIT-C Score and Odds of a Positive AUDIT-C Screen
DiscussionAlthough limited work has been conducted to prospectively investigate the negative psychological and physiological effects of ICU patient caregiving on family members, some cross-sectional and short-term prospective research suggests an association between increases in ‘burden’ on the caregiver and caregiver alcohol consumption (e.g., Marsh, Kersel, Havill, & Sleigh, 1998). Drawing on work indicating elevated levels of caregiver burden, increased risk of psychological symptoms (e.g., depressive; Pochard et al., 2005), and problematic drinking by caregivers during patient ICU stay (Halm et al., 1993), this study aimed to advance the literature by assessing associations between three pertinent caregiver variables (i.e., perceived caregiver burden, actual time in the caregiving role, and caregiving differential) and caregiver problematic alcohol use 6 months after initial patient admission. Our results suggest that a discrepancy in expected and actual time caregiving, rather than solely the amount of caregiving or perceived caregiver burden, may be an important predictor of caregiver alcohol use 6 months after a family member’s ICU hospitalization. For instance, family members who spent more time in the caregiver role were no more likely than those who spent relatively less time caregiving to endorse problematic levels of drinking (e.g., a positive screen on the AUDIT-C). However, family members who trended toward underestimating time spent caregiving were significantly more likely to screen for problematic alcohol use than those who trended toward overestimating their caregiving role at 6 months. Together, the findings from this study underscore the importance of assessing alcohol use when evaluating the long-term health of caregivers, and calls attention to caregiver expectancies as a potential point of intervention for medical personnel to reduce risk of problematic drinking among caregivers.
The null findings in regard to both caregiver burden and actual time spent in the caregiver role were somewhat surprising given a) previous research indicating an association between caregiver burden and increased alcohol use (Rospenda et al., 2010) and b) established work demonstrating that caregiver burden increases with the amount of time and resources allocated toward providing for the patient (Bugge, Alexander, & Hagen, 1999; Livingston, Brooks, & Bond, 1985; Winstanley, Simpson, Tate, & Myles, 2006). In regard to the null caregiver burden results, it is possible that over the course of 6 months, individuals may learn to adapt to short-term perceptions of social and emotional burden; thus, greater caregiver burden alone is not enough to evidence a positive relation with problematic use at 6 months. However, elevated caregiver burden in combination with an underestimation of time spent in the caregiving role may interrupt or impede the development of adaptive coping and exacerbate risk for long-term development of drinking problems. Of note, caregiver burden and caregiving differential were significantly correlated. However, the current sample was underpowered to adequately test for these more complex relations; future efforts designed a priori to examine such additive and interactive effects are needed.
Sociopsychology “role theory” may provide one explanation for the null results regarding actual time caregiving (e.g., Biddle, 2013). Extensions of this theory to alcohol research suggest that individuals who take on multiple or additional roles are less likely to drink because of the increased demands of those new roles (e.g., less free time; Hajema & Knibbe, 1998; Wilsnack & Wilsnack, 1991). Although not significant, our results generally supported this theory, with preliminary analyses showing a negative association between actual time caregiving and problematic alcohol use at 6 months. These findings suggest that actual time spent caregiving is, at a minimum, not a significant predictor of alcohol use problems and, alternatively, may actually serve as a protective factor within this population, depending on other contextual variables (e.g., anticipated caregiving). Moreover, while Rospenda and colleagues (2010) did find that individuals who experienced increases in social and emotional burden were at increased risk for alcohol use problems, they found no such association between increases in “time-dependent” burden (i.e., the perceived impact caregiving has on the caregiver’s time) and problematic drinking. Collectively, past empirical work and the current findings suggests that time spent in the caregiver role, independent of other factors, may not be a meaningful predictor of subsequent alcohol use.
The finding that caregiving differential was related to problematic alcohol use at 6 months is consistent with the extensive literature concerning the importance of predictability and control in adaptation to stress (e.g., Affleck, Tennen, Pfeiffer, & Fifield, 1987). This work emphasizes perception of control over stressors as a key contributor to adaptive coping, both in terms of acute response (e.g., psychobiological indices; Koolhaas et al., 2011) as well as maintaining factors and mental health outcomes (Feldner, Monson, & Friedman, 2007). Misevaluation of the amount of time and engagement required reflects a break in the continuity of predictability and control over the family members’ caregiving role, which can be especially disruptive and distressful (Thompson, 1981). A growing literature shows that family members experiencing distress in their caregiving role may neglect their own health and well-being (see Johnson et al., 2001 for review); more specifically, those caregivers may be more likely to avoid adaptive coping or health-promoting activities (e.g., therapy, exercise) and engage in maladaptive strategies, such as coping-related alcohol use (Gallant & Connell, 1997; Northouse, Katapodi, Schafenacker, & Weiss, 2012). This interpretation of the data also parallels work examining self-medication as a form of maladaptive coping, whereby hazardous drinking is initiated or maintained in an effort to manage psychological distress (Khantzian, 1997, 2003; Miller, Vogt, Mozley, Kaloupek, & Keane, 2006; Read, Brown, & Kahler, 2004). It is important to note that the current study did not directly examine the perception of control or coping-related alcohol consumption; future efforts assessing these factors, including motives for drinking generally (e.g., Drinking Motives Questionnaire—Revised; Cooper, 1994) and/or specifically (e.g., subsequent to specified caregiving activities), as well as additional factors that may influence these relations (e.g., social or caregiving support, relevant psychological problems) are needed to better understand and, subsequently, intervene in the development of problematic drinking among caregivers.
Together, the current data suggest that providing caregivers with the information necessary to establish realistic expectations regarding their future caregiver role may reduce their long-term risk of problematic alcohol use. However, research on family members’ satisfaction with communication from providers demonstrates that this information often is not adequately disseminated during their time in the hospital. For example, a review of the literature on the needs and experiences of family members in the ICU found that medical personnel (i.e., nurses, doctors) have a tendency to underestimate the needs of patients’ family members, including their desire for accurate and comprehensive information about daily care of patients (Verhaeghe et al., 2005). In an ethnographic review of family issues in home-based care, Wellard and Street (1999) highlighted the ‘normalization’ of providers (e.g., nurses, doctors) assuming that family members are willing and prepared to take on the caregiver role after their loved one leaves the ICU. Collectively, this work highlights the need for structured education to help caregivers better understand the needs of the patient and pushes for the development of collaborative communication strategies among patients, providers, and caregivers to help establish realistic expectations for treatment requirements beyond the hospital stay. Future longitudinal studies should investigate caregivers’ ‘real-time’ or retrospective assessment of the information that was provided to them by nurses, doctors, and practitioners, and whether this communication adequately prepared them for their caregiving role.
Research also has highlighted a lack of support services available for caregivers, particularly those with diverse cultural or linguistic needs (Whittier, Scharlach, & Dal Santo, 2005; Ham, 1999). Work examining the efficacy of relevant support groups indicates positive effect on caregivers’ psychological well-being, coping effectiveness, and reduction in caregiver burden (Chien et al., 2011; Empeño, Raming, Irwin, Nelesen, & Lloyd, 2011; Smith Barusch, & Spaid, 1991). These gains appear to hold constant, regardless of whether the services are provided locally (e.g., on-site support groups) or through telephone support groups (Brown et al., 1999). Moreover, it may be important to continue outreach even after their caregiving role has ceased due to the passing of the patient. One study of family caregivers in palliative care found that bereavement was associated with increased alcohol consumption (Hauser & Kramer, 2004), and another identified that the negative psychological effects of caregiving (e.g., depression, loneliness) persisted for as long as three years after their caregiver responsibilities had ceased (Robinson-Whelen, Tada, MacCallum, McGuire, & Kiecolt-Glaser, 2001).
The provision of proper education and continued support for caregivers of ICU patients requires attention. These findings are an important preliminary step toward understanding the long-term consequence of caregiving as it pertains to the development of problematic alcohol use. Our findings, which echo results and subsequent “calls to action” expressed in similar research, should prompt medical professionals and policy administrators to evaluate the structure and efficacy of their communication with future caregivers before they leave the ICU. At a minimum, medical personnel should strive to establish realistic expectations about the needs of the patient and their role as a caregiver. Further, clinicians and medical practitioners should examine the support services offered to caregivers, both during their time in the ICU and, more importantly, after they have left the structured confines of the hospital.
Limitations
The present study has several limitations. First, despite the use of psychometrically validated measures of caregiver burden and alcohol use, the study may be limited by unavoidable self-report bias inherent in questionnaires. This limitation may also have influenced other predictive measures (e.g., caregiver differential), which were assessed and calculated from self-report, single item questions. However, both the baseline and follow-up assessments were administered by trained clinical researchers who had built rapport with these caregivers during their stay in the ICU and were able to clarify any questions or concerns. Future studies should incorporate the use of standardized measures, as well as in-person interviews and observational methods, when applicable, to help in reducing report biases and allow for the integration of information from different perspectives. For example, future research should consider structured clinical interviews (e.g., SCID-5-RV; First, Williams, Karg, & Spitzer, 2014) or retrospective recall for formal assessment of alcohol consumption (e.g., Alcohol Timeline Follow-Back; Sobell & Sobell, 1992). Second, it is also important to note that a good portion of the variance in alcohol use outcomes at 6 months was not explained by our model, with baseline alcohol use being the largest and most influential predictor of subsequent problematic alcohol use. Interpretation of the findings regarding the role of caregiving expectation should consider these factors. Third, data pertaining to the severity of the patients’ injury were not available. However, severity of injury may have influenced both the anticipation of time spent in the caregiving role and, subsequently, the amount of caregiver burden, caregiving differential scores, and alcohol use. For example, if a patient had a less severe injury, it may have made it more difficult for the caregiver to adequately assess the amount of time they would spend in the caregiving role, which, in turn, influenced their drinking. Further, in regard to intervention, the severity of the patients’ injury may play a critical role in determining the need for caregiver support. For example, it may be that caregivers of patients with more severe injury require more long-term support services, in addition to establishing realistic expectation about caregiving. Future projects targeting caregivers should make sure to include some measure of patient injury severity, such as the Injury Severity Score (ISS; MacKenzie, Shapiro, & Eastham, 1985). Fourth, completed 6-month follow-ups were provided by only 65.3% of caregivers. However, participant baseline characteristics were available for all 124 caregivers, and did not differ significantly on any important demographic (e.g., age, ethnicity) or pertinent nondemographic (e.g., baseline AUDIT-C score) variables as a function of follow-up completion. Moreover, due to this attrition, the final sample available for analyses was relatively small (n = 81); larger scale investigations are required to replicate and extend these findings. Last, despite inclusion criteria stipulating that only caregivers of patients expected to survive for ≥96 hrs were eligible to participate in the study, 16 participants experienced the loss of their loved one at some point between baseline assessment and their 6-month follow-up. While these individuals were given a bereavement protocol in place of the standard follow-up assessment, which included other measures (e.g., assessing complicated grief rather than caregiver burden), they were excluded from the current analyses. Given past research indicating that the negative psychological consequences of caregiving persist even after that role has ceased (Robinson-Whelen et al., 2001), future studies evaluating bereaved caregivers should include measures of alcohol use to assess the prevalence of problematic drinking among those individuals.
Despite these limitations, findings from the present study add to the literature in a number of ways. First, the prospective nature of the study was a considerable strength, allowing the researchers to gauge “real time” assessments of expectations at baseline and control for caregivers’ baseline levels of alcohol use during the analyses. Second, the study included a heterogeneous sample of both caregivers, in terms of their relationship to the patient (e.g., spouse, friend, child), and patients, in terms of the reason for their admission to the ICU (e.g., chronic illness, traumatic injury). Previous research typically has focused on a specific population of caregivers and/or patients, limiting the generalizability and external validity of their findings. Last, because the study focused on caregiver expectations, as opposed to an abstract construct (e.g., emotionality) or a clinical response to trauma (e.g., posttraumatic stress), points for intervention and solutions for ameliorating the underlying issues may be more direct and clear for medical providers and practitioners.
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Submitted: November 3, 2016 Revised: April 5, 2017 Accepted: April 6, 2017
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Source: Psychology of Addictive Behaviors. Vol. 31. (4), Jun, 2017 pp. 497-505)
Accession Number: 2017-20821-001
Digital Object Identifier: 10.1037/adb0000282
Record: 55- Title:
- Do conduct problems and sensation seeking moderate the association between ADHD and three types of stimulant use in a college population?
- Authors:
- Van Eck, Kathryn. Department of Psychology, University of South Carolina, Columbia, SC, US, vaneck.k@gmail.com
Markle, Robert S.. Department of Psychology, University of South Carolina, Columbia, SC, US
Flory, Kate. Department of Psychology, University of South Carolina, Columbia, SC, US - Address:
- Van Eck, Kathryn, Department of Psychology, University of South Carolina, 1512 Pendleton Street, Barnwell College, Columbia, SC, US, 29208, vaneck.k@gmail.com
- Source:
- Psychology of Addictive Behaviors, Vol 26(4), Dec, 2012. pp. 939-947.
- NLM Title Abbreviation:
- Psychol Addict Behav
- Page Count:
- 9
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- Bulletin of the Society of Psychologists in Addictive Behaviors; Bulletin of the Society of Psychologists in Substance Abuse
- Other Publishers:
- US : Educational Publishing Foundation
Society of Psychologists in Addictive Behaviors - ISSN:
- 0893-164X (Print)
1939-1501 (Electronic) - Language:
- English
- Keywords:
- ADHD, conduct problems, sensation seeking, stimulants
- Abstract:
- Evidence suggests that ADHD symptoms predict increased risk for misusing stimulant medication, which may extend to misuse of over-the-counter (OTC) stimulants and illicit stimulants. Conduct-problem (CP) symptoms and sensation seeking (SS) also predict substance use and may enhance risk for stimulant use among college students with ADHD symptoms. Participants, who were undergraduate students aged 18 to 25 years (N = 660; average age = 20.23 years, SD = 1.40; 30% male; 49% non-European American), completed an online survey regarding ADHD symptoms, CP symptoms, SS, and stimulant use (i.e., OTC stimulants, misuse of stimulant medication, and illicit stimulants). Results of logistic regression indicated that SS moderated the association between ADHD symptoms and OTC stimulants. Also, CP moderated the relation between ADHD symptoms and misuse of stimulant medication. Disinhibition, a subscale of SS, also moderated the association between ADHD symptoms and misuse of stimulant medication. Only CP symptoms predicted illicit stimulants. These results suggest that college students with ADHD symptoms display risk for using OTC stimulants, and that disinhibition and CP symptoms increase their risk for misuse of stimulant medication. Implications of these findings are discussed. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Attention Deficit Disorder with Hyperactivity; *CNS Stimulating Drugs; *Conduct Disorder; *Drug Usage; *Sensation Seeking
- Medical Subject Headings (MeSH):
- Adolescent; Adult; Attention Deficit Disorder with Hyperactivity; Central Nervous System Stimulants; Conduct Disorder; Female; Humans; Male; Street Drugs; Students; Substance-Related Disorders; Universities
- PsycINFO Classification:
- Psychological & Physical Disorders (3200)
- Population:
- Human
Male
Female - Location:
- US
- Age Group:
- Adulthood (18 yrs & older)
Young Adulthood (18-29 yrs) - Tests & Measures:
- Current Symptoms Scale
Brief Sensation-Seeking Scale
Substance Use Questionnaire
Self-Reported Delinquency Scale DOI: 10.1037/t44193-000 - Methodology:
- Empirical Study; Quantitative Study
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- First Posted: Mar 19, 2012; Accepted: Jan 7, 2012; Revised: Jan 5, 2012; First Submitted: Jun 11, 2011
- Release Date:
- 20120319
- Correction Date:
- 20161013
- Copyright:
- American Psychological Association. 2012
- Digital Object Identifier:
- http://dx.doi.org/10.1037/a0027431
- PMID:
- 22428861
- Accession Number:
- 2012-06749-001
- Number of Citations in Source:
- 45
- Persistent link to this record (Permalink):
- http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2012-06749-001&site=ehost-live
- Cut and Paste:
- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2012-06749-001&site=ehost-live">Do conduct problems and sensation seeking moderate the association between ADHD and three types of stimulant use in a college population?</A>
- Database:
- PsycINFO
Do Conduct Problems and Sensation Seeking Moderate the Association Between ADHD and Three Types of Stimulant Use in a College Population?
By: Kathryn Van Eck
Department of Psychology, University of South Carolina;
Robert S. Markle
Department of Psychology, University of South Carolina
Kate Flory
Department of Psychology, University of South Carolina
Acknowledgement:
Research indicates that college students with attention-deficit/hyperactivity disorder (ADHD) symptoms (i.e., inattention, impulsivity) experience increased risk for using stimulant drugs (Arria, Garnier-Dykstra, Caldeira, Vincent, O'Grady, & Wish, 2011; Janusis & Weyandt, 2010), especially if they have been diagnosed and prescribed medication later in adolescence (McCabe, Teter, & Boyd, 2006). However, moderators of the relation between ADHD and stimulant use have received little consideration. Further, previous research has focused on prescription stimulant misuse (i.e., using more than prescribed or for reasons other than prescribed) and illicit stimulant use (i.e., cocaine, methamphetamine) , but few studies have assessed the link between ADHD and over-the-counter (OTC) stimulant use (i.e., No-Doz, Vivarin). The only study to date to assess the link between ADHD and nonprescribed, illicit stimulants found that, after controlling for oppositional defiant disorder (ODD), 11–15 year olds with ADHD were twice as likely to consume caffeinated beverages as those without ADHD (Walker, Abraham, & Tercyak, 2010). The present study evaluated the moderating effects of conduct problems (CP) and sensation seeking (SS) on the relation between ADHD and three types of stimulants (i.e., prescription stimulant misuse and illicit and OTC stimulant use). Although tobacco also has stimulant effects, our focus is on the less-studied stimulants mentioned above.
Most previous research on the link between ADHD and stimulant use failed to control for CP. In the current study, CP includes symptoms of conduct disorder (CD) and antisocial personality disorder (APD). Although ODD is sometimes included in the assessment of CP, we chose not to include these symptoms. Research indicates that 82–90% of APD cases meet criteria for CD at least once during ages 13–17, but very few youths who meet criteria for ODD progress to APD without intermediate CD (Loeber, Burke, & Lahey, 2002). Given that CP frequently co-occurs with ADHD and is often linked with substance use (Fergusson, Boden, & Horwood, 2008), failure to control for CP in previous analyses may provide an inaccurate understanding of the link between ADHD and stimulant use. The present study attempts to clarify the association between ADHD and stimulant use for college students by including CP as a moderator.
Several studies indicate that SS is associated with an increased risk for prescription stimulant misuse (Arria, Caldeira, Vincent, O'Grady, & Wish, 2008; Herman-Stahl, Krebs, Kroutil, & Heller, 2007) and illicit stimulant use (e.g., methamphetamine; Herman-Stahl et al., 2007). However, no research has assessed the link between SS and OTC stimulant use or considered the role that SS may play in the risk for stimulant use that college students with ADHD appear to experience. Given that impulsivity is a prominent symptom of ADHD (Barkley, Murphy, & Fischer, 2008) and that individuals with high SS often also display high impulsivity (Hur & Bouchard, 1997), it is likely that ADHD symptoms and SS co-occur. Thus, ADHD and SS may interact such that students who have both high SS and ADHD symptoms are more likely to use stimulants than students who are high on either ADHD symptoms or SS alone.
The Current StudyIn this study, OTC stimulants only included caffeine supplements and not energy drinks. We also did not assess the method used to acquire prescription stimulants (e.g., personal prescriptions, a peer, or a drug dealer). We expected that ADHD symptoms would predict each type of stimulant use. Given that retrospective self-report of childhood ADHD symptoms often has inaccuracies (Mannuzza, Klein, Klein, Bessler, & Shrout, 2002), we used self-report of current symptoms.
We investigated the moderating roles of CP and four subscales of SS (i.e., experience seeking, adventure seeking, disinhibition, and boredom susceptibility). We expected that individuals with both high ADHD and CP symptoms would display the highest risk for using each type of stimulant. We chose to include SS subscales in analyses, given the possibility that the SS subscales may relate differently to ADHD symptoms and stimulant use, which could contribute to differences in moderating effects. Use of only the SS total score could obscure these differences. Given that few studies have examined associations with SS subscales, little research exists to guide hypothesis development. Thus, we chose not to designate specific hypotheses for the moderating effects of SS subscales.
Method Participants and Procedures
A convenience sample of college students (N = 660; 30% male) between the ages of 18 and 25 years (M = 20.23, SD = 1.40) who enrolled in a psychology course at a public university in the South were recruited through class announcements (see Table 1 for sample demographics). After reading an online consent form approved by the university institutional review board and completing the online survey, students received extra credit as compensation.
Sample Demographics
Measures
ADHD symptoms
Participants rated their ADHD symptoms using the Current Symptoms Scale (CSS; Murphy & Barkley, 1996). The 18 items corresponded to diagnostic criteria for ADHD from the Diagnostic and Statistical Manual (4th text rev.; DSM–IV–TR; American Psychiatric Association, 2000). Respondents rated the frequency of ADHD symptoms that they had experienced in the last six months on a scale of 0, never or rarely to 3, very often and repeated items rating ADHD symptoms that they recalled experiencing between the ages of 5 and 12 years. Current ADHD symptoms were highly correlated with report of childhood symptoms (r = .63, p < .001), and rates of diagnosis were comparable, with 8% reporting childhood ADHD symptoms above the clinical cutoff and 9.3% reporting current symptoms in that range. However, given validity concerns regarding retrospective report of ADHD symptoms (Mannuzza et al., 2002), analyses used only current ADHD symptoms. Responses were summed to create inattention and hyperactivity subscales and a total score. Given that inattention and hyperactivity were highly correlated (r = .87, p < .05), we used the total score. Data possessed strong reliability (α = .92). Previous research indicates that data with the CSS is sensitive to symptom change (see Safren et al., 2005; Wilens et al., 1996) and that self-report scores correspond to parent and spousal report (r = .76; Barkley et al., 2008). Research with a normative sample indicates that a current symptoms total score ≥ 27.8 is the clinical cutoff for ADHD for those 17 to 29 years old (Murphy & Barkley, 1996).
Conduct problems
CP symptoms were measured with the Self-Reported Delinquency Scale (SRD; Elliot, Huizinga, & Ageton, 1985). This self-report questionnaire contains 18 items that correspond to the diagnostic criteria of CD in the DSM–IV–TR and provide indication of behaviors associated with APD. The SRD was included in several large-scale studies (Conduct Problems Prevention Research Group, 2010; Loeber, Farrington, Stouthamer-Loeber, & Van Kammen, 1998). Participants answered 1 “yes” or 0 “no” regarding engagement in each behavior in the past 6 months. Responses were summed for a total score of CP (α = .68). The SRD corresponds to court records and parent and teacher reports (Farrington et al., 1996; Pardini, Obradović, & Loeber, 2006).
Stimulant use
Stimulant use was measured with the Substance Use Questionnaire (SUQ), which was used in the Pittsburgh Adolescent Longitudinal Study (Molina & Pelham, 2003). Participants answered 1 “yes” or 0 “no” and four questions were used in analyses: “Have you ever taken any over-the-counter stay-awake pills?”, “Have you ever taken Ritalin, Dexedrine, Adderall, Cylert or other medications more than you were supposed to?”, “Have you ever used cocaine in any form, such as powder, 'crack,' freebase, or coca paste?”, and “Have you ever used amphetamines or uppers more than you were supposed to?” Amphetamines included methamphetamine, speed, ecstasy, and others, although these drugs were not explicitly identified in the question. Report of cocaine and amphetamine use was combined to represent illicit stimulant use; a “yes” on either question was coded 1.
Sensation seeking
The Brief Sensation-Seeking Scale (BSSS; Hoyle et al., 2002) has 8 items in which participants report the degree to which they seek out novel, intense experiences on a scale from 1, strongly disagree to 5, strongly agree. The BSSS has a total score and four subscales (experience seeking, adventure seeking, disinhibition, and boredom susceptibility; α's ranged from 0.62 to 0.78). Construct validity is satisfactory and scores correspond to risk behaviors, including substance use (Hoyle et al., 2002).
Analytic Procedures
SPSS 18.0 was used to conduct hierarchical logistic regressions with ADHD, CP, and the interaction of ADHD and CP symptoms. Hierarchical analyses were also conducted with ADHD, CP, each SS subscale, and the interaction of ADHD and each SS subscale. The dependent variables were the three types of stimulant use (OTC stimulants, misuse of prescription stimulants, and illicit stimulants). We used a Bonferroni adjustment for the number of hierarchical models that were conducted with each type of stimulant use (p < .008). All independent variables were mean centered (Cohen, Cohen, West, & Aiken, 2003). Covariates included sex (0 = male, 1 = female); household income during childhood, ADHD medication status (0 = not taking prescribed ADHD medication, 1 = currently taking prescribed ADHD medication); and race/ethnicity (0 = European American (EA), 1 = non-EA). Analyses for univariate and multivariate outliers revealed no extreme scores.
Only 4.4% of data were missing, which corresponded to 29 participants who chose not to disclose their racial/ethnic background. Cases with missing data only had slightly higher CP symptoms. Given the small proportion of missing data and its lack of association with dependent measures, we used list-wise deletion to manage missing data.
Results ADHD Symptoms and Diagnosis
To provide a complete picture of ADHD symptoms in this sample, we considered the percentage of those who exceeded the clinical cutoff and the distribution of symptoms and diagnosis by gender. We found that 61 students (9.3%) had current symptoms above the clinical cutoff. This rate is higher than prevalence rates of ADHD found in other studies, which range from 2–8% (DuPaul et al., 2001; DuPaul, Weyandt, O'Dell, & Varjao, 2009; Heiligenstein, Conyers, Berns, & Smith, 1998; Lee, Oakland, Jackson, & Glutting, 2008). Men were 1.3 times more likely to report ADHD symptoms in the clinical range, which is comparable to results from a nationally representative sample of adults, where men met criteria for ADHD 1.6 times more frequently than women (Kessler et al., 2006). Overall, symptom severity was equivalent between men and women, but women rather than men who reached the clinical cutoff point had significantly more severe symptoms, t(658) = 3.25, p = .002. However, research conflicts regarding gender differences in ADHD symptom severity (see Barkley et al., 2008; DuPaul et al., 2009; Murphy & Barkley, 1996).
Descriptive Statistics
Descriptive statistics (see Table 2) and correlations (see Table 3) were assessed for continuous variables. Rates of use for each stimulant for the sample are reported in Table 4 in addition to the rates of overlap in use between different types of stimulants. We found that only 2% of the sample had used all three types of stimulants. We also found that a little more than 8% of the sample was taking stimulant medication for ADHD as prescribed by a doctor, which was included as a covariate. ADHD and CP symptoms were related (r = .27, p < .001). ADHD and CP did not show the same associations with all SS subscales, bolstering our decision to evaluate the moderating effects of each SS subscale rather than just using the total score. ADHD symptoms shared a small correlation with boredom (r = .17, p < .001), disinhibition (r = .24, p < .001), and adventure seeking (r = .11, p = .003), but were not related to experience seeking (r = .05, p = .18). Given associations among SS subscales, the effects of each subscale were considered in separate logistic regressions.
Descriptive Statistics
Correlations Among Continuous Variables
Rates of Stimulant Use
OTC Stimulants
Regarding the hierarchical logistic regressions with ADHD, CP, and their interaction (see Table 5), both ADHD (OR = 1.05, Wald χ2 = 14.85, p < .001) and CP symptoms (OR = 1.28, Wald χ2 = 11.44, p = .001) were significantly related to OTC stimulant use; the interaction of ADHD and CP symptoms was not significant. Separate analyses with each SS subscale and the total score revealed that the main effects and their interactions with ADHD symptoms did not reach statistical significance.
Logistic Regressions Assessing Associations Regarding Over-the-Counter Stimulant Use
Misuse of ADHD medication
The main effects of ADHD and CP symptoms were significant (ADHD: OR = 1.05, Wald χ2 = 19.04, p < .001; CP: OR = 1.49, Wald χ2 = 37.27, p < .001; see Table 6), as was their interaction (OR = 0.99, Wald χ2 = 7.29, p = .007; see Figure 1). Results of the interaction indicate that risk for misusing ADHD medications increases as both ADHD and CP symptoms increase, where those high on both symptoms show the highest risk for misusing ADHD medications. Analyses with the SS total score, subscales, and interactions with ADHD symptoms showed that only the main effect of disinhibition (OR = 1.41, Wald χ2 = 10.70, p = .001) and its interaction with ADHD (OR = 0.97, Wald χ2 = 9.20, p = .002) were significant (see Figure 2). Results of this interaction suggest that those with high disinhibition display high stable rates of risk for misusing ADHD medications; however, as ADHD symptoms increase for all groups, risk for misusing ADHD medications approaches the high stable rates of the disinhibition group.
Logistic Regressions Assessing Associations Regarding Misuse of ADHD Stimulant Medication
Figure 1. Conduct Problems Moderates the Association between ADHD Symptoms and Misuse of Stimulant Medication.
Figure 2. Disinhibition Moderates the Association between ADHD Symptoms and Misuse of Stimulant Medication.
Illicit stimulants
Hierarchical logistic regressions used to assess associations of hypothesized variables with illicit stimulants (i.e., amphetamines or cocaine) indicated that only CP-symptoms were significantly related to illicit use (OR = 1.27, Wald χ2 = 8.66, p = .003; see Table 7). No main effects or interactions were statistically significant in analyses with the SS total score, subscales, or their interaction with ADHD symptoms.
Logistic Regressions Assessing Associations Regarding Illicit Stimulants
Some assumptions of logistic regression were tested (see Tabachnick & Fidell, 2007). Because the number of variables included in models for illicit-stimulant use exceeded the rule-of-thumb ratio of one independent variable to every 10 events (Harrell, Lee, & Mark, 1996), models without covariates were also tested. Results were identical to reported findings. Dependent and independent variables also demonstrated sufficient linear associations.
We also ran analyses to identify if SS moderated the relation of CP symptoms and stimulant use. When equivalent tests were run with CP replacing ADHD symptoms in interactions with the SS total score and subscales, the interactions were not statistically significant. CP was slightly skewed, so we reran analyses after log transforming CP; these analyses produced results similar to those reported. We also evaluated gender differences in results in additional models; no significant differences emerged.
DiscussionWith a convenience sample of college students, we evaluated the moderating effects of CP symptoms and SS on the association between ADHD symptoms and lifetime use of three types of stimulants (i.e., OTC stimulants, misuse of stimulant medication, and illicit stimulants). Analyses resulted in three important findings. First, ADHD symptoms had a significant main effect on OTC stimulants and misuse of stimulant medications. Second, although the total score of SS was not a significant moderator, disinhibition moderated the association between ADHD symptoms and misuse of prescription stimulants. Third, CP symptoms only moderated the association between ADHD symptoms and misuse of prescription stimulants.
Rates of stimulant use in this study are comparable to rates of use from surveys of the general college population. Our lifetime-use rate of 24% for misuse of prescription stimulants corresponds to that of several studies with college students, which range from 6.9% to 35.3% (Low & Gedaszek, 2002; McCabe, Knight, Teter, & Wechsler, 2005). Our 9% lifetime-use rate for illicit stimulants corresponds to lifetime-use rates of crack and cocaine specific to college students, which range from 4% to 10% across studies (Johnston, O'Malley, Bachman, & Schulenberg, 2010; Kaperski, Vincent, Caldeira, Garnier-Dykstra, O'Grady, & Arria, 2011). We compared our illicit stimulant-use rate to rates of crack and cocaine use as the rate of illicit stimulant use overall is unclear in many studies.
No research was available for comparison of our OTC stimulant-use rates, but our finding regarding the association between ADHD symptoms and OTC stimulant use (e.g., caffeine pills such as NoDoz and Vivarin) coincides with previous research linking ADHD symptoms to consumption of caffeine drinks (Walker et al., 2010). Although caffeine may improve attention in the short-term, this benefit does not continue with repeated use (Judson & Langdon, 2009), yet many undesirable consequences do persist, including addiction, sleep disturbance, and increased heart rate. Withdrawal may create additional concerns such as lethargy, distractibility, and negative mood (Sigmon, Herning, Better, Cadet, & Griffiths, 2009). Thus, future research should consider outcomes associated with OTC stimulant use for college students with ADHD symptoms.
Students with both ADHD symptoms and disinhibition were more likely to misuse prescription stimulants than students with only ADHD symptoms or disinhibition. These results suggest that behaviors related to “partying” and rule breaking (Hoyle et al., 2002) enhance risk for misusing prescription stimulants for college students with ADHD symptoms (McCabe, Boyd, & Teeter, 2009; Rabiner et al., 2009).
Our findings were also consistent with past research regarding misuse of prescription stimulants (Judson & Langdon, 2009; Rabiner et al., 2009; Upadhyaya et al., 2005). In addition, we found that ADHD symptoms were related to misuse of prescription stimulants even after controlling for prescribed use of stimulant medications. Our rates of prescription stimulant misuse were also highest for students with ADHD and CP symptoms, demonstrating that CP plays an important role in prescription stimulant misuse, just as it does for other types of substance use (Flory & Lynam, 2003). Thus, research in this area must include CP in analyses to accurately represent the association between ADHD and prescription stimulant misuse.
Limitations
Limitations to these analyses include use of a convenience sample, a cross-sectional design, the unknown response rate for the study, and the use of items assessing lifetime use of stimulant use. A specific weakness of the cross-sectional design is that stimulant use may have led to prominent ADHD symptoms rather than ADHD symptoms preceding stimulant use (Levin, Evans, Brooks, & Garawl, 2007). Participants may also not have accurately disclosed stimulant use; with no methods for verifying disclosure rates, the validity of stimulant use cannot be confirmed. We also did not distinguish between different types of prescription stimulant misuse (i.e., misuse of personal medication, acquiring medication from friends with prescriptions). Substance-use questions also did not clarify that some stimulant medications (i.e., Dexedrine and Adderall) were amphetamines. Further, specific amphetamines such as methamphetamine, speed, and ecstasy, were not explicitly identified in the substance use question on amphetamines. Aspects of recruitment (i.e., participation offered only to undergraduates in a psychology course, compensation as extra credit) may have created a selection effect. Finally, given that the percentage of the sample (9.3%) with clinical levels of ADHD symptoms was slightly higher than ADHD prevalence rates for young adults (2–8%; DuPaul et al., 2009), other concerns (i.e., psychological symptoms or disruptions in routine from the transition to college) may have been misidentified as ADHD symptoms.
Clinical Implications
These results have implications for the psychopharmacological and psychotherapeutic treatment of ADHD. Results highlight the need for a comprehensive evaluation of ADHD and CP symptoms prior to providing prescriptions for stimulant medications to college students. For students with a history of misusing stimulant medication, of sharing stimulant mediations, and of abusing other substances, psychotherapeutic approaches may be more effective than medication for managing ADHD symptoms.
Little research exists that explores the motives for stimulant use for college students with and without ADHD. It is possible that students with ADHD symptoms may misuse stimulants to cope with ADHD symptoms, such as disorganization, poor time management, forgetfulness, and distractibility. These students may benefit from psychotherapy oriented toward improving management of these symptoms.
Footnotes 1 For clarification purposes, when referring collectively to all three types of use, we will use the term “stimulant use”. Otherwise we will employ the term “use” for OCT stimulants, “illicit use” for illicit stimulants and “misuse” for prescription stimulants.
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Submitted: June 11, 2011 Revised: January 5, 2012 Accepted: January 7, 2012
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Source: Psychology of Addictive Behaviors. Vol. 26. (4), Dec, 2012 pp. 939-947)
Accession Number: 2012-06749-001
Digital Object Identifier: 10.1037/a0027431
Record: 56- Title:
- Drinking initiation and problematic drinking among Latino adolescents: Explanations of the immigrant paradox.
- Authors:
- Bacio, Guadalupe A.. Department of Psychology, University of California, Los Angeles, Los Angeles, CA, US, gbacio@ucla.edu
Mays, Vickie M.. Department of Psychology, University of California, Los Angeles, Los Angeles, CA, US
Lau, Anna S.. Department of Psychology, University of California, Los Angeles, Los Angeles, CA, US - Address:
- Bacio, Guadalupe A., University of California, Los Angeles, 1285 Franz Hall, Box 951563, Los Angeles, CA, US, 90095-1563, gbacio@ucla.edu
- Source:
- Psychology of Addictive Behaviors, Vol 27(1), Mar, 2013. pp. 14-22.
- NLM Title Abbreviation:
- Psychol Addict Behav
- Page Count:
- 9
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- Bulletin of the Society of Psychologists in Addictive Behaviors; Bulletin of the Society of Psychologists in Substance Abuse
- Other Publishers:
- US : Educational Publishing Foundation
Society of Psychologists in Addictive Behaviors - ISSN:
- 0893-164X (Print)
1939-1501 (Electronic) - Language:
- English
- Keywords:
- Latino/Hispanic, adolescents, alcohol use patterns, immigrant paradox, drinking initiation, immigrant generations
- Abstract:
- Studies indicate that U.S.-born Latino teens exhibit higher rates of alcohol use compared with their foreign-born counterparts. Different hypotheses have been advanced to explain the mechanisms underlying this immigrant paradox, including the erosion of protective cultural factors across generations and increased exposure to risky peer environments in the United States. The present study examined whether the immigrant paradox applies to drinking initiation and problematic drinking among Latino adolescents, and tested whether generational differences in family protective factors and peer risk factors might explain the immigrant paradox. A nationally representative sample of Latino teens (N = 2,482) of Cuban, Mexican, and Puerto Rican origin from 3 immigrant generations (21% first generation, 33% second generation, and 46% third and later generations) was obtained from the National Longitudinal Study of Adolescent Health. Logistic and negative binomial regression models indicated that early drinking initiation and problematic alcohol use were more prevalent among later-generation youth, supporting the immigrant paradox. Erosion of family closeness and increased association with substance-using peers mediated the relationship between generation and alcohol use patterns in this sample. Results provide support for culturally sensitive interventions that target peer perceptions of substance use and bolster protective family values among Latino adolescents. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Adolescent Development; *Alcohol Drinking Patterns; *Immigration; *Generational Differences; *Latinos/Latinas
- Medical Subject Headings (MeSH):
- Adolescent; Adolescent Behavior; Alcohol Drinking; Culture; Emigrants and Immigrants; Family; Female; Hispanic Americans; Humans; Male; Mexican Americans; National Longitudinal Study of Adolescent Health; Prevalence; Puerto Rico; Socioeconomic Factors; United States
- PsycINFO Classification:
- Psychosocial & Personality Development (2840)
- Population:
- Human
Male
Female - Location:
- US
- Age Group:
- Childhood (birth-12 yrs)
School Age (6-12 yrs)
Adolescence (13-17 yrs)
Adulthood (18 yrs & older)
Young Adulthood (18-29 yrs) - Grant Sponsorship:
- Sponsor: National Institutes of Health, National Center on Minority Health and Health Disparities
Grant Number: MD 00508
Recipients: No recipient indicated
Sponsor: Eunice Kennedy Shriver National Institute of Child Health and Human Development
Grant Number: P01-HD31921
Other Details: Add Health, with cooperative funding from 23 other federal agencies and foundations
Recipients: No recipient indicated - Methodology:
- Empirical Study; Quantitative Study
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- First Posted: Oct 1, 2012; Accepted: Aug 14, 2012; Revised: May 8, 2012; First Submitted: Oct 10, 2011
- Release Date:
- 20121001
- Correction Date:
- 20130318
- Copyright:
- American Psychological Association. 2012
- Digital Object Identifier:
- http://dx.doi.org/10.1037/a0029996
- PMID:
- 23025707
- Accession Number:
- 2012-26454-001
- Number of Citations in Source:
- 45
- Persistent link to this record (Permalink):
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- Cut and Paste:
- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2012-26454-001&site=ehost-live">Drinking initiation and problematic drinking among Latino adolescents: Explanations of the immigrant paradox.</A>
- Database:
- PsycINFO
Drinking Initiation and Problematic Drinking Among Latino Adolescents: Explanations of the Immigrant Paradox
By: Guadalupe A. Bacio
Department of Psychology, University of California, Los Angeles;
Vickie M. Mays
Departments of Psychology and Health Services, University of California, Los Angeles, and UCLA Center on Research, Education, Training and Strategic Communication on Minority Health Disparities
Anna S. Lau
Department of Psychology, University of California, Los Angeles
Acknowledgement: This work was supported by funding from the National Institutes of Health, National Center on Minority Health and Health Disparities (MD 00508). We thank Susan D. Cochran, PhD, for her assistance in data management. This research uses data from Add Health, a program project directed by Kathleen Mullan Harris and designed by J. Richard Udry, Peter S. Bearman, and Kathleen Mullan Harris at the University of North Carolina at Chapel Hill, and funded by grant P01-HD31921 from the Eunice Kennedy Shriver National Institute of Child Health and Human Development, with cooperative funding from 23 other federal agencies and foundations. Special acknowledgment is due to Ronald R. Rindfuss and Barbara Entwisle for assistance in the original design. Information on how to obtain the Add Health data files is available on the Add Health website (http://www.cpc.unc.edu/addhealth). No direct support was received from grant P01-HD31921 for this analysis.
Alcohol continues to be the most abused substance among adolescents in the United States. By twelfth grade, 72% of adolescents report having consumed alcohol, 55% report having been drunk, and 25% report binge drinking in the past 2 weeks (Johnston, O'Malley, Bachman, & Schulenberg, 2010b). The public health impact of teen drinking is highlighted by the array of alcohol-related problems reported by young drinkers, such as interpersonal problems, impaired school and work performance, risky sexual behaviors, and drunk driving (Brown et al., 2008; National Institute on Alcohol Abuse & Alcoholism, 2006; Office of the Surgeon General, 2007; Windle & Windle, 2006).
Latino adolescents exhibit the second highest rates of alcohol use, closely following non-Hispanic White teens (Johnston, O'Malley, Bachman, & Schulenberg, 2010a). One of the most consistent factors associated with drinking patterns among Latino teens is nativity. U.S.-born Latino adolescents report higher levels of alcohol use compared with their first-generation immigrant counterparts (Gil, Wagner, & Vega, 2000; Guilamo-Ramos, Jaccard, Johansson, & Turrisi, 2004; Vega & Gil, 1998). Indeed, nativity-based disparities are apparent across many health outcomes, including substance abuse and mental disorders (Alegria et al., 2008). However, immigrants are often exposed to stress or trauma before and during the migration process, commonly settle in impoverished neighborhoods, and confront greater language barriers compared with their U.S.-born counterparts (Guarnaccia & Lopez, 1998; Pumariega, Rothe, & Pumariega, 2005). The advantaged health status of first-generation Latinos has come to be known as the “immigrant paradox” (Markides & Coreil, 1986; Vega & Sribney, 2011).
Although the mechanisms underlying the immigrant paradox are not well understood, the literature has advanced different hypotheses. Proposed explanations include acculturation stress theory, assimilation theory, the healthy immigrant hypothesis, erosion of cultural values, and increased exposure to risky environments. The acculturative stress framework posits that the strain resulting from the challenges that Latino youth encounter as they adapt to the host culture generates stressful situations that elicit substance use as a maladaptive stress management response (Gil et al., 2000). Assimilation theory proposes that as Latino teens assimilate to mainstream culture, their drinking patterns will change to reflect the norms of the host culture (Caetano & Clark, 2003). The healthy immigrant effect explains that healthier people are more likely to successfully immigrate to the United States (Crimmins, Soldo, Kim, & Alley, 2005) and may appear healthier than their U.S.-born counterparts.
Some suggest that erosion of protective features of the culture of origin accounts for increased risk across generations (Barrera, Gonzales, Lopez, & Fernandez, 2004; Mogro-Wilson, 2008). For example, parenting practices and relationships among Latino families are organized by values highlighting the centrality of family integrity. Familismo is a dynamic construct often defined as a normative set of values endorsed by Latinos that encompasses several facets. These include a sense of obligation to provide instrumental support to the family, an edict that family expectations should guide behavior, and an implicit sense that emotional support must be cultivated within the family (Germán, Gonzales, & Dumka, 2009; Sabogal, Marin, Otero-Sabogal, Vanoss Marin, & Perez-Stable, 1987). Orientation toward traditional family values has been found to be protective against externalizing behaviors (Germán et al., 2009; Gonzales et al., 2008), including alcohol and drug use (Castro, Stein, & Bentler, 2009; Gil et al., 2000). However, familismo decreases across generations as Latino teens acculturate, and this decline appears related to increased alcohol use (Gil et al., 2000). As family values change across generations, so, too, may parenting practices. Parental monitoring of adolescents decreases with acculturation among Latino parents (Driscoll, Russell, & Crockett, 2008; Mogro-Wilson, 2008), and decreased monitoring is associated with increased alcohol use among Latino adolescents (Driscoll et al., 2008; Mogro-Wilson, 2008). Thus, the erosion of protective family practices involving closeness and monitoring may explain generational differences in drinking among Latino youth.
Another explanation for the immigrant paradox is that U.S.-born Latino adolescents are disproportionately exposed to environmental conditions that predispose risk, such as substance-using peers (Gil et al., 2000; Lopez et al., 2009; Prado et al., 2009). During adolescence, peer networks become central as teens begin to seek individuation (Brown et al., 2008). Teens are more likely to engage in risky behaviors, including alcohol use, if they associate with deviant peers (Barrera et al., 2004; Brown et al., 2008). It is plausible that immigrant teen social networks present less peer risk than those of U.S.-born Latino youth. Immigrant Latino adolescents are more likely to affiliate with other immigrant youth because of school placements organized by English proficiency (Carhill, Suárez-Orozco, & Paez, 2008) and preferences for Spanish-speaking peers (Carhill et al., 2008). Immigrant and Spanish-speaking Latino youth are less likely to use alcohol (Marsiglia & Waller, 2002). Conversely, U.S.-born Latino teens are more likely to have English-speaking U.S.-born peers who report greater use of alcohol and drugs (Allen et al., 2008). Thus, deviant or substance using peer networks may represent a social risk factor explaining an immigrant paradox in teen drinking.
The first aim of this study was to examine whether the immigrant paradox was present in Latino teens' drinking initiation and problematic drinking using a nationally representative sample of Latino teens. Because most studies evaluating the immigrant paradox have examined nativity, contrasting U.S.-born with foreign-born Latinos, little is known about how drinking patterns among third- and later-generation Latino youth compared with second- and first-generation adolescents. To that end, we examined differences among three generations of Latino youth.
The second aim of this study was to examine the contribution of two hypothesized mechanisms proposed to explain the immigrant paradox, namely, erosion of cultural family practices and increased exposure to risky behaviors. First, the cultural erosion hypothesis was examined using two relevant protective factors, namely, parental monitoring and family closeness, as putative mediators. Second, we examined the role of exposure to risky peer environments using an index of association with substance-using peers as a putative mediator. We predicted that immigrant youth may be less likely than later-generation youth to initiate drinking and experience alcohol-related problems because they benefit from more family closeness, parental monitoring, and prosocial peer networks.
Method Sample and Procedure
The National Longitudinal Study of Adolescent Health (Add Health) is a nationally representative study of health and risk behavior among U.S. adolescents in Grades 7 through 12 (Harris et al., 2008). Add Health utilized a multistage and stratified sampling frame that included all high schools in the United States. A random sample of 80 high schools and their major middle-school feeders were selected for participation. Students completed a self-administered questionnaire during the period 1994 to 1995. A core sample of 12,105 adolescents was selected to participate in home interviews conducted between April and December of 1995. A resident parent, usually the mother, also completed an interview (Harris et al., 2008).
Study Sample
This study used a subsample of Add Health Wave I participants who identified as Latino or Hispanic (N = 2,482); of Mexican (62%), Cuban (18%), and Puerto Rican (20%) origin; who spoke English (53%) or Spanish (47%); and who indicated whether or not they had consumed alcohol in their lifetime. All items were selected from the adolescent interview unless otherwise noted.
Measures
Generational status
Immigrant generation was determined using parent and adolescent responses regarding their respective country of birth. Adolescents who reported being foreign-born were classified as first generation. Teens who reported being U.S.-born and whose parent reported being foreign-born were categorized as second generation. Adolescents who reported being U.S.-born and whose parent reported being also U.S.-born were classified as third and later generation.
National origin
All participants indicated that they were of Latino or Hispanic origin. In addition, to be included in the current study, adolescents self-identified as Mexican/Mexican American/Chicano, Cuban/Cuban American, or Puerto Rican.
Language use at home
Adolescents indicated the usual language spoken at home by choosing English or Spanish.
Parental alcohol use
Parents were asked to indicate how often in the past year they had a drink on a 6-point scale ranging from never (1) to nearly every day (6).
Family socioeconomic status
Parents were asked to indicate their level of educational attainment (less than high school, high school or equivalent, some college, or college graduate and beyond). Parents also reported their annual family income (less than $14,999, $15,000 to $29,999, $30,000 to $44,999, $45,000 to $59,999, and $60,000 or above).
Family structure
Based on teen reports on multiple items regarding household composition, family structure was coded into one of five categories (two biological parents, at least one non-birth parent identified as a parent figure [step, adoptive, grandfather, etc.], single parent, and other [foster home, no identified parent figures, etc.]).
Initiation of drinking
Adolescents indicated whether or not they had ever had a drink of beer, wine, or liquor more than two or three times in their life. The item directed teens to exclude a sip or a taste of someone else's drink.
Problematic alcohol use
For those reporting alcohol initiation, the frequency of alcohol-related problems in the past year was assessed by asking adolescents how many times, as a result of drinking, they “got into trouble with their parents,” “had problems at school or with their schoolwork,” “had problems with friends,” “had problems with someone they were dating,” “did something they later regretted,” “were hung over,” “were sick to their stomach or threw up,” “got into a sexual situation they later regretted,” and “got into a physical fight.” Responses ranged on a 5-point scale from 0 times to 5 or more times in the past year, and were summed for a maximum total of 45 points (α = .85).
Perceived family closeness
Teens were asked, “How much do you feel that…” “your parents care about you,” “people in your family understand you,” “you and your family have fun together,” and “your family pays attention to you”? Answers ranged from not at all (1) to very much (5), for a possible total score of 20 points. Higher scores indicated greater family closeness (α = .76).
Perceived parental monitoring
Adolescents were asked, “Do your parents let you make your own decisions about…” “the time you must be home on weekend nights,” “the people you hang around with,” “what you wear,” “how much TV you watch,” “which TV programs you watch,” “what time you go to bed on week nights,” “what you eat”? Answers were dichotomous for a possible summed total score of seven points. Higher scores indicated greater degree parental monitoring (α = .65).
Association with substance-using peers
Adolescents were asked, “Of your best friends, how many…” “drink alcohol at least once a month,” “smoke a cigarette at least once a day,” and “use marijuana at least once a month”? Answers ranged from 0 to 3, for a total summed score of 9 points. Higher scores indicated greater association with substance-using peers (α = .76).
Missing Data
Missing data ranged from 0% to 32%, depending on the variable, with a mean of 6.21% across all study variables. Items obtained from the parent questionnaire, including family income and parental education, contained the highest percentage of missing data (18% and 32%, respectively). Listwise deletion procedures are not recommended, as this approach may yield biased results; therefore, multiple imputation (MI) was used to estimate missing data values (Rubin, 1987). Missing values were imputed by the command ice (imputation by chained equations; Royston, 2009) in Stata 10 (StataCorp, 2009) using equation models that combined relevant predictors in the data set previously identified using the command pred_eq (Medeiros, 2007). Twenty imputed data sets were created that were then combined using the command mim to generate estimates (Carlin, Galati, & Royston, 2008). MI is commonly used because it yields estimates averaged over the imputed data sets that reflect unbiased parameters and standard errors that take into account the uncertainty of using imputed missing values (Graham, Allison, & Gilreatch, 2007).
Analytic Strategy
First, the relationships between immigrant generation and drinking initiation and problematic alcohol use were examined to establish if the immigrant paradox was prevalent in each of these outcomes. Second, the mediating role of family closeness and parental monitoring, and association with substance-using peers, were tested in separate models to examine if each hypothesized mediator explained generational differences in each drinking outcome. Third, a multimediation model, including all proposed mediators, was conducted to ascertain if each hypothesis explained generational differences in the examined drinking outcomes over and above the others included in the model. Tests of mediation were conducted following the Baron and Kenny (1986) approach. Significance of mediation effects was determined using Sobel tests (Sobel, 1982). All models controlled for adolescents' gender, age, national origin, language used at home, parental alcohol use, family structure, and family socioeconomic status.
Logistic regressions were used to examine the direct and indirect effect of generation on likelihood of drinking initiation during adolescence. Numbers of alcohol-related problems were examined among youth who had started to drink (n = 1,537). Because the variance of this count variable is greater than its mean, negative binomial regression models were used. The alphas obtained in every negative binomial regression conducted were significantly greater than zero, indicating that negative binomial models provided better estimates than would have regular Poisson models. Ordinary least squares regressions were used to examine generational differences in the proposed mediators, all of which are continuous variables. All analyses used the appropriate survey weights to correct for design and sampling effects, as not doing so may yield biased parameter estimates (Chantala & Tabor, 1999). Add Health selected high schools with replacements from the Quality of Education Database as the basis for a stratified cluster sampling (Tourangeau & Shin, 1999) and adjusted individual weights for oversampling. Adolescents for whom weights were missing were excluded from analysis, as recommended by Chantala and Tabor (1999).
Results Sample Characteristics
Table 1 describes the sample used in this study. Twenty-one percent of the adolescents were first-generation immigrants, 33% were second-generation immigrants, and 46% were third-generation immigrants. Participants' age ranged from 11 to 21 years (M = 15.9, SD = 1.7) and 49% were female. Fifty-three percent were living with both biological parents, 48% of parents had not completed high school, and 59% reported a family gross income of $29,000 or less.
Sociodemographic Characteristics by Immigrant Generation Based on Weighted Analyses, Wave I Longitudinal Study of Adolescent Health
Generation and Drinking Initiation
As shown in Table 2, likelihood of alcohol initiation during adolescence increased with generation, F(2, 1000) = 18.58, p < .001. Second-generation teens were 2.77 times more likely and third-generation teens were 3.38 times more likely than first-generation teens to have started drinking. There was no significant difference in drinking initiation between the second and third generations. Age significantly predicted alcohol initiation in the expected direction, t(125) = 8.31, p < .001.
Weighted Odds Ratios (OR) for Each Mediation Model Predicting Drinking Initiation Among Latino Adolescents of Different Immigrant Generations, Wave I Longitudinal Study of Adolescent Health
Mediational Analyses for Drinking Initiation
Table 2 shows the results of mediational analyses for drinking initiation.
Erosion of cultural values hypothesis
Parental monitoring decreased across generations, F(2, 1000) = 3.49, p < .05, but was not significantly related to initiation and thus was not a mediator. Family closeness decreased across generations, F(2, 1000) = 3.43, p < .05, and significantly predicted initiation, t(125.6) = −4.36, p < .001. The effect of generation on initiation was attenuated after parental monitoring and family closeness were added to the model, F(2, 1000) = 14.67, p < .001. Partial mediation was confirmed using the Sobel test when comparing lifetime alcohol use between second and first generations, Z = 1.96, p < .05, and between third- and first-generation teens, Z = 2.06, p < .05.
Exposure to risky peer environment
Association with substance-using peers increased across generations, F(2, 1000) = 14.77, p < .001, and significantly predicted initiation, t(124.2) = 10.89, p < .001. The effect of generation on initiation was attenuated but remained significant after accounting for substance-using peers, F(2, 979.8) = 7.23, p < .001. Sobel tests determined that the effect of generation on drinking initiation was partially mediated by association with substance-using peers (second vs. first, Z = 3.49, p < .05; third vs. first, Z = 4.84, p < .05).
Multimediation
The effect of generation on lifetime alcohol use was attenuated but remained significant after introducing all mediators in the model, F(2, 1000) = 6.37, p < .05. Only family closeness, t(125.5) = −2.47, p < .05, and association with substance-using peers, t(124.4) = 9.79, p < .001, significantly predicted lifetime use. Sobel tests indicated that family closeness was not a significant mediator. However, association with substance-using peers partially mediated the relationship between generation and lifetime use (second vs. first, Z = 3.47, p < .05; third vs. first Z = 4.76, p < .05).
Generation and Problematic Alcohol Use
As shown in Table 3, among the subsample of youth who had initiated drinking, generation significantly predicted number of alcohol-related problems, F(2, 1000) = 5.32, p < .001. Third-generation youth reported a rate of alcohol related problems 1.84 times greater than first-generation teens, t(113.1) = 3.18, p < .001, and 1.48 times greater than second-generation adolescents, t(110.4) = 1.98, p < .05. There were no differences in problematic use between first- and second-generation teens, t(123.1) = 1.06, p > .05. Age was also related to increased rates of problematic alcohol use in the expected direction, t(124.8) = 3.11, p < .05.
Weighted Incidence Rate Ratios (IRR) for Each Mediation Model Predicting Problematic Alcohol Use Among Latino Adolescents of Different Immigrant Generations, Wave I Longitudinal Study of Adolescent Health
Mediational Analyses for Problematic Alcohol Use
Table 3 shows the results of mediational analyses for problematic alcohol use.
Erosion of cultural values hypothesis
There were no generational differences in parental monitoring among drinkers and, as such, it was ruled out as a mediator. Family closeness decreased across generations, F(2, 1000) = 4.90, p < .01, and significantly predicted the number of teen alcohol-related problems, t(122.5) = −4.77, p < .001. The effect of generation on alcohol-related problems was reduced but remained significant after accounting for monitoring and closeness, F(2, 1000) = 3.32, p < .05. Sobel tests determined that family closeness partially mediated the effect of immigrant generation on problematic alcohol use when comparing first- to third-generation adolescents, Z = 3.98, p < .05.
Exposure to risky peer environment
Association with substance-using peers increased across generations among adolescent drinkers, F(2, 1000) = 4.99, p > .05. Association with substance-using peers significantly predicted problematic alcohol use, t(118.3) = 9.95, p < .001. The effect of generation on problematic alcohol use was not significant when association with substance-using peers was introduced in the model, F(2, 1000) = 2.51, p < .001. The effect of generation on problematic alcohol use was fully mediated by substance-using peers (third vs. first, Z = 3.02, p < .05; third vs. second, Z = 2.23, p < .05).
Multimediation
Generation was not significantly related to problematic alcohol use after introducing all mediators in the model, F(2, 1000) = 1.93, p > .05. Family closeness, t(123.7) = −4.33, p < .001, and association with substance-using peers, t(118.6) = 10.50, p < .001, significantly predicted problematic alcohol use. Family closeness fully mediated the effect of generation on problematic alcohol use when comparing third- with first-generation teens, Z = 289, p < .05. Association with substance-using peers fully mediated the effect of generation on problematic alcohol use (third vs. first, Z = 3.16, p < .05; third vs. second, Z = 2.29, p < .05).
DiscussionThe first aim of this study was to examine the prevalence of the immigrant paradox in drinking initiation and problematic alcohol use among Latino adolescents of three immigrant generations. Variants of the immigrant paradox in these drinking patterns were identified. Consistent with previous studies, U.S.-born teens (second and third and later generations) were more likely to initiate drinking compared with immigrant adolescents (Gil et al., 2000; Guilamo-Ramos et al., 2004; Vega & Gil, 1998). However, teens whose parents are U.S.-born (third and later generations) were more likely to experience alcohol-related problems than adolescents whose parents were foreign-born (first and second generations). These findings suggest that nativity and immigrant generation are associated differently with varying drinking outcomes, and the results highlight the importance of assessing generation in addition to nativity when studying alcohol use among Latino teens in the United States. Relying solely on nativity may obscure important similarities and differences among generations of Latino teens. It is possible that assessing only nativity may miss sociocultural processes potentially encompassed by generation, such as acculturation status, enculturation status, or divergent cultural values.
The second aim of the study was to test the contributions of the erosion of cultural values hypothesis and the exposure to risky peer environment hypothesis in explaining generational differences in these drinking patterns. There was support for the hypothesis that the immigrant paradox is partly due to differences in family functioning across generations. Specifically, differences in family closeness across generations, but not parental monitoring, played an important role in explaining generational differences in drinking patterns. It is important to note that the indicator of parental monitoring used had low reliability and may not have captured the ways that parents in this sample exercise parental monitoring. The often-taxing work demands that disadvantaged Latino immigrant parents have to juggle may interfere with their ability to be present in their homes to closely supervise the activities of their offspring, and, as a result, this measure may not be the best indicator of care-giving quality or protective parenting practices.
The negative association of family closeness with alcohol use is consistent with the concept that familismo is protective against deviant behaviors (Castro et al., 2009; Gil et al., 2000; Gonzales et al., 2008). Nonetheless, differences in family closeness did not systematically explain the generational increases in drinking outcomes. Parents of first-generation teens are foreign-born and likely promote familismo more so than parents of third-generation teens who are U.S.-born. Consistently, the greater likelihood of drinking initiation and problematic drinking of third- compared with first-generation teens was partially explained by the generational decline in family closeness. However, decreases in family closeness between the first and second generations did not explain their differences in drinking initiation. Immigrant parents of first- and second-generation youth may support familismo in similar ways, and the increase in drinking initiation between these generations may be better explained by extrafamilial factors such as affiliation with substance-using peers. Similarly, the higher rates of problematic drinking of third- and later- compared with second-generation teens were not explained by differences in family closeness. Although the erosion of family closeness across generations indeed impacts teen alcohol outcomes, it does not fully account for the immigrant paradox in drinking patterns.
Findings support the hypothesis that increased exposure to risky peer environments, through association with substance-using peers, partly explicates the immigrant paradox in drinking among Latino youth. Consistent with other studies (Brown et al., 2008; Lopez et al., 2009; Windle, 2000), adolescents of later generations reported associating with more substance-using peers, and this was related, in turn, to higher likelihood of drinking initiation and problematic alcohol use. However, the effect of association with substance-using peers differed by outcome. Increased association with substance-using peers partially explained the generational increases in drinking initiation. Once adolescents started drinking, risk exposure had a stronger effect such that association with substance-using peers fully mediated the relationship between generation and problematic drinking.
The purported mediators were simultaneously tested as explanations of the generational differences in drinking. Affiliation with substance-using peers was the strongest, albeit partial, explanation of increased drinking initiation among later generations. This robust effect of generation on drinking initiation underlines the importance of continuing to investigate this relationship to inform prevention efforts for Latino adolescents. Similarly, increased association with substance-using peers and decreased family closeness simultaneously explained the significant increase in problematic drinking of third- compared with first-generation teens. These results are consistent with other studies that have found that orientation toward family values buffers the effect of associating with substance-using peers (Germán et al., 2009; Prado et al., 2009). However this study suggests that the protective role of family closeness may be particularly important for first-generation teens in preventing problematic drinking (Wagner, 2003), even after accounting for the strong effects of associating with substance-using peers. It is important to consider that the centrality of family and peer networks changes during adolescence and that the value ascribed to each may differ across generations. It is possible that the comparative advantages of the second and third generations over the first generation, such as speaking English and being U.S. citizens, may decrease the importance that family closeness plays in their development. For these later generations, a better point of intervention might be peer-focused. For first-generation teens, on the other hand, maintaining family closeness may be more adaptive as they enter a new culture and face the adaptation challenges together.
The results from this investigation should be taken with caution due to several limitations. Other plausible explanations for the immigrant paradox were not examined. For instance, greater perceived discrimination associated with nativity and longer residence in the United States (Cook, Alegria, Lin, & Guo, 2009; Córdova & Cervantes, 2010) may also account for the increase in alcohol problems in later-generation Latino youth (Pérez, Fortuna, & Alegria, 2008). Thus, although the putative mediators tested in this study are important, these factors may combine with other risk mechanisms to explain the immigrant paradox.
As a cross-sectional study, it is not possible to determine causality or infer directionality of influence with certainty. For example, the association between family closeness and drinking may signify that teens who drink are more likely to become estranged from their families. Similarly, the directionality of association with substance-using peers may be reversed, such that teens who drink are more likely to select friends who drink. Furthermore, family closeness only approximates one facet of familismo and does not include the other two factors identified by Sabogal and colleagues (1987), namely, sense of obligation to provide support to the family and following family expectations of behaviors. Despite that Sabogal and colleagues (1987) used a diverse sample of Latino individuals of Central American, Cuban, and Mexican origin, it is possible that Latino subgroups may differ in how they interpret and endorse different facets of familismo as a construct. Future studies would benefit from specific instruments that directly measure this cultural construct.
The sample size of our study allowed us to examine only the influence of generation for three major Latino subpopulations of Cuban, Mexican, and Puerto Rican origin. However, our analyses do not speak to possible differences in risk patterns among Latino subgroups. The systematic advantages and disparities between Latinos of Mexican, Puerto Rican, and Cuban origin in language proficiency, migration status, and socioeconomic status may modify how the immigrant paradox in drinking patterns manifests among each group. Moreover, our findings may not be applicable to other Latino populations in the United States. Future studies would benefit from the use of prospective designs with samples that include other Latino subgroups. Limitations notwithstanding, this study represents an important first step toward testing a theory-driven model of alcohol use initiation and alcohol problems in Latino youth.
In sum, drinking initiation and problematic alcohol use among Latino adolescents, as well as the contribution of the tested explanations, differed across generations. This study highlights the importance of assessing beyond the dichotomous indicator of nativity and considering the effect of immigrant generation when studying alcohol use among Latino teens. Further, the results indicate that multiple factors influence alcohol use patterns among Latino adolescents and operate in tandem to explain the immigrant paradox. Findings suggest that effective preventions to delay drinking initiation among Latino teens should target perceptions of peer alcohol and drug use. These results also offer support to culturally sensitive interventions geared at Latino adolescents that bolster family closeness and strengthen perception of family support (Pantin et al., 2009), which may help reduce problematic alcohol use through the transition to adulthood.
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Submitted: October 10, 2011 Revised: May 8, 2012 Accepted: August 14, 2012
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Source: Psychology of Addictive Behaviors. Vol. 27. (1), Mar, 2013 pp. 14-22)
Accession Number: 2012-26454-001
Digital Object Identifier: 10.1037/a0029996
Record: 57- Title:
- Driving after use of alcohol and marijuana in college students.
- Authors:
- McCarthy, Denis M.. Department of Psychological Sciences, University of Missouri- Columbia, Columbia, MO, US, mccarthydm@missouri.edu
Lynch, Andrea M.. Graduate School of Education, Boston College, Boston, MA, US
Pederson, Sarah L.. Department of Psychological Sciences, University of Missouri- Columbia, Columbia, MO, US - Address:
- McCarthy, Denis M., Department of Psychological Sciences, University of Missouri- Columbia, 213 McAlester Hall, Columbia, MO, US, 65211, mccarthydm@missouri.edu
- Source:
- Psychology of Addictive Behaviors, Vol 21(3), Sep, 2007. pp. 425-430.
- NLM Title Abbreviation:
- Psychol Addict Behav
- Page Count:
- 6
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- Bulletin of the Society of Psychologists in Addictive Behaviors; Bulletin of the Society of Psychologists in Substance Abuse
- Other Publishers:
- US : Educational Publishing Foundation
Society of Psychologists in Addictive Behaviors - ISSN:
- 0893-164X (Print)
1939-1501 (Electronic) - Language:
- English
- Keywords:
- drinking & driving, alcohol, marijuana, college students, cognitions
- Abstract:
- Driving after use of marijuana is almost as common as driving after use of alcohol in youth (P. M. O'Malley & L. D. Johnston, 2003). The authors compared college students' attitudes, normative beliefs and perceived negative consequences of driving after use of either alcohol or marijuana and tested these cognitive factors as risk factors for substance-related driving. Results indicated that youth perceived driving after marijuana use as more acceptable to peers and the negative consequences as less likely than driving after alcohol use, even after controlling for substance use. Results of zero-inflated Poisson regression analyses indicated that lower perceived dangerousness and greater perceived peer acceptance were associated with increased engagement in, and frequency of, driving after use of either substance. Lower perceived likelihood of negative consequences was associated with increased frequency for those who engage in substance-related driving. These results provide a basis for comparing how youth perceive driving after use of alcohol and marijuana, as well as similarities in the risk factors for driving after use of these substances. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Alcohol Abuse; *Cognitions; *Driving Under the Influence; *Drug Usage; *Marijuana; College Students
- Medical Subject Headings (MeSH):
- Accidents, Traffic; Adolescent; Adult; Alcoholic Intoxication; Automobile Driving; Cross-Sectional Studies; Culture; Dangerous Behavior; Female; Health Knowledge, Attitudes, Practice; Humans; Male; Marijuana Abuse; Missouri; Peer Group; Students
- PsycINFO Classification:
- Substance Abuse & Addiction (3233)
- Population:
- Human
Male
Female - Location:
- US
- Age Group:
- Adulthood (18 yrs & older)
- Tests & Measures:
- Drinking Styles Questionnaire DOI: 10.1037/t03954-000
- Grant Sponsorship:
- Sponsor: National Institute on Alcohol Abuse and Alcoholism
Grant Number: R03 AA13399
Recipients: McCarthy, Denis M.
Sponsor: National Institute on Alcohol Abuse and Alcoholism
Grant Number: T32 AA13526
Recipients: No recipient indicated - Methodology:
- Empirical Study; Quantitative Study
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- Accepted: Dec 8, 2006; Revised: Dec 7, 2006; First Submitted: Apr 13, 2006
- Release Date:
- 20070917
- Correction Date:
- 20120827
- Copyright:
- American Psychological Association. 2007
- Digital Object Identifier:
- http://dx.doi.org/10.1037/0893-164X.21.3.425
- PMID:
- 17874895
- Accession Number:
- 2007-13102-018
- Number of Citations in Source:
- 35
- Persistent link to this record (Permalink):
- http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2007-13102-018&site=ehost-live
- Cut and Paste:
- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2007-13102-018&site=ehost-live">Driving after use of alcohol and marijuana in college students.</A>
- Database:
- PsycINFO
Driving After Use of Alcohol and Marijuana in College Students
By: Denis M. McCarthy
Department of Psychological Sciences, University of Missouri–Columbia;
Andrea M. Lynch
Lynch Graduate School of Education, Boston College
Sarah L. Pedersen
Department of Psychological Sciences, University of Missouri–Columbia
Acknowledgement: This research was supported by National Institute on Alcohol Abuse and Alcoholism (NIAAA) Grant R03 AA13399 to Denis M. McCarthy and by NIAAA Grant T32 AA13526.
In 2003, motor vehicle accidents were the leading cause of death in college-age youth in the United States (Centers for Disease Control and Prevention, National Center for Injury Prevention and Control, 2003). In 2002, 29% of drivers aged 15 to 20 years killed in traffic accidents were intoxicated (National Highway Traffic Safety Administration, 2003). More than a third (35.5%) of U.S. college student drivers reported drinking and driving in the past month (Wechsler, Lee, Nelson, & Lee, 2003).
Marijuana is the most commonly used illicit drug by U.S. youth. In a nationally representative survey, more than half (53.8%) of those age 18–25 reported lifetime use of marijuana (Office of Applied Studies, 2004). Marijuana was the second most frequently used drug, after alcohol, in samples of reckless drivers (Brookoff, Cook, Williams, & Mann, 1994) and of those involved in vehicle accidents resulting in injury or fatality (Soderstrom, Dischinger, Kerns, & Trifillis, 1995; Terhune et al, 1992). The Monitoring the Future study found that the percentage of U.S. high school seniors who received tickets or had accidents after use of marijuana was comparable to that of alcohol (O'Malley & Johnston, 2003). Rates of self-reported driving after use were also similar for alcohol and marijuana. Given the differences in prevalence of use, these results suggest that youth are relatively more likely to drive after using marijuana than alcohol.
Empirical research has long documented impairment in driving abilities from use of marijuana (Crancer, Dille, Delay, Wallace, & Haykin, 1969; Moskowitz, 1985). Recent studies have demonstrated that marijuana can increase brake latency (Liguori, Gatto, & Robinson, 1998), lateral position errors, and distance variability (Ramaekers, Robbe, & O'Hanlon, 2000; Robbe, 1998) in simulated and closed-road driving tasks.
Considerable research has focused on identifying individual difference factors associated with drinking and driving behavior. For example, disinhibited personality constructs have been correlated with drinking and driving behavior and arrest (Cavaiola, Strohmetz, Wolf, & Lavender 2003; Donovan, Queisser, Umlauf, & Salzberg, 1986; Turrisi, Jaccard, & McDonnell, 1997). Cognitive factors, such as perceived norms (Armitage, Norman, & Connor, 2002), the perceived dangerousness of drinking and driving (Grube & Voas, 1996), and risk appraisal (Gerrard, Gibbons, Benthin, & Hessling, 1996), have also been found to be associated with increased likelihood of drinking and driving in adolescents and young adults.
In contrast, relatively little is known about risk factors and perceptions of driving after use of marijuana. A study of intravenous drug users found that alcohol was rated as the most dangerous drug to use prior to driving, whereas marijuana was rated as the least dangerous (Darke, Kelly, & Ross, 2004). Studies of marijuana users found that they did not perceive marijuana use as affecting their driving ability (Aitken, Kerger, & Crofts, 2000) and perceived driving after use as less impairing than driving after drinking (Terry & Wright, 2005). It is unclear if perceptions are similar for nonusers or if these perceptions are associated with marijuana-related driving.
The present study was designed to improve our understanding of college students' perceptions of driving after use of marijuana. Our first goal was to compare perceptions of driving after marijuana use with perceptions of driving after use of alcohol. Parallel questions assessed normative beliefs, attitudes, and perceived negative consequences for driving after use of alcohol and marijuana. We hypothesized that participants would rate driving after use of marijuana as more acceptable to peers, less dangerous, and less likely to have negative consequences than driving after use of alcohol. Analyses were also conducted controlling for frequency of alcohol and marijuana use.
A second goal was to test cognitions as risk factors for substance-related driving and to evaluate differences in prediction of driving after use of alcohol and marijuana. We hypothesized that greater acceptance by peers, lower perceived dangerousness, and lower perceived probability of negative consequences would be associated with increased likelihood and frequency of self-reported driving after use of alcohol and marijuana. Frequency of use and gender were included as covariates in these analyses.
Method Participants
Participants were recruited from introductory psychology classes at the University of Missouri-Columbia. The sample (N = 599) was 59% women, with a mean age of 18.54 years (SD = 0.86). The sample was primarily Caucasian (87%), with 7% African American, 3% Asian American, and 3% of mixed or other race; 3% reported their ethnicity as Hispanic.
Procedures
Participants were recruited using the introductory psychology subject pool. Data were collected in groups of 10–25. Participants received partial credit toward meeting a research requirement for their introductory psychology course for participating. Procedures were approved by the University of Missouri-Columbia Institutional Review Board.
Measures
Demographic information
A self-report questionnaire was used to collect demographic information, including age, gender, religion, and ethnicity.
Normative beliefs
Drinking and driving cognition questions were adapted from prior studies (Grube & Voas, 1996) and have been used in previous research in our laboratory (McCarthy, Pedersen, Thompsen, & Leuty, 2006; McCarthy, Pedersen, & Leuty, 2005). For normative beliefs, participants were asked how many (0–3) of their three closest friends disapprove of drinking and driving and how many would refuse to ride with a driver who had been drinking. Parallel questions were used to assess normative beliefs about driving after use of marijuana. Items were recoded so that higher scores indicated greater acceptance of substance-related driving. Internal consistency was .80 for alcohol questions and .91 for marijuana questions.
Attitudes
Three questions were used to assess attitudes towards drinking and driving. These questions asked participants how dangerous they think it is to drive within 2 hours of consuming one drink, three drinks, and five or more drinks. Questions used a four-point Likert scale and were coded so that higher scores indicated lower perceived dangerousness. Internal consistency in this sample was .83. For driving after marijuana use, a single question was used, asking how dangerous it is to drive within 2 hours of using marijuana.
Perceived negative consequences
For both alcohol and marijuana, four questions asked participants the likelihood a driver their age would be stopped by police, be breath or drug tested, be arrested, and have an alcohol- or marijuana-related accident. Questions used a four-point Likert scale and were coded so that higher scores indicated lower perceived probability of negative consequences. A mean composite was used for study analyses. Internal consistency was .84 for the alcohol questions and .90 for the marijuana questions.
Alcohol and marijuana use
The Drinking Styles Questionnaire (Smith, McCarthy, & Goldman, 1995) was used to assess alcohol use behavior. This measure has demonstrated good reliability and validity in adolescent and college samples (McCarthy, Miller, Smith, & Smith, 2001; Smith et al., 1995). In the present study, drinker/nondrinker status, past month quantity and frequency of use, and past month frequency of heavy drinking were used as measures of alcohol involvement. Similar questions were used for marijuana use. Questions assessed lifetime use of marijuana, age of first use, and frequency of use in the past year and month.
Driving after substance use
Drinking and driving was assessed with three open-ended questions asking participants to report how many times in the past 3 months they had driven within 2 hours of drinking one drink, three drinks, and five or more drinks. Driving after use of marijuana was assessed with a single question asking how many times participants had driven within 2 hours of smoking marijuana in the past 3 months.
Results Substance Use and Driving Behavior
Table 1 presents descriptive statistics for substance use and driving after use by gender. Comparisons across gender were made using either chi-square or t tests. Men were more likely to report use of marijuana and to drive after use of alcohol or marijuana. Men also reported higher frequency and quantity of alcohol use.
Descriptive Statistics for Alcohol and Marijuana Use and Driving After Use
Forty-three percent of the sample reported driving after drinking, whereas 13% reported driving after use of marijuana. However, these differences may be a function of differences in rates of current use. Of current drinkers, 55% reported driving after alcohol use in the past 3 months, whereas 47% of current marijuana users reported driving after smoking marijuana.
Alcohol and marijuana use were associated, χ2(1, N = 599) = 44.18, p < .01, with current drinkers more likely to use marijuana in the past month (30%) than nondrinkers (2%). There was also an association between driving after marijuana use and driving after one drink, χ2(1, N = 599) = 66.66, p < .01; three drinks, χ2(1, N = 599) = 67.73, p < .01; and five or more drinks, χ2(1, N = 599) = 61.08, p < .01.
Cognitions About Driving After Use
Table 2 presents correlations between substance use and cognitions about driving after use. Greater alcohol use was associated with perceiving drinking and driving as more acceptable to peers and less dangerous. The perception of negative consequences of drinking and driving as less likely was only weakly correlated with greater quantity of alcohol use. For cognitions about driving after marijuana use, frequency of use was associated with all driving cognition variables.
Correlations Between Substance Use and Driving Cognitions
Repeated measures analyses of variance were then used to compare cognitions for driving after use of alcohol with those for marijuana. In each analysis, substance type (marijuana, alcohol) was used as a within-subjects factor and gender as a between-subjects factor. For normative beliefs, there was a significant main effect of substance type, F(1, 597) = 62.17, p < .01; η2 = .10, with participants perceiving their peers as being more accepting of driving after use of marijuana than alcohol. There was a main effect of gender, F(1, 597) = 6.18, p < .05; η2 = .01, with men rating both behaviors as more acceptable. There was no Substance Type × Gender interaction. When frequency of marijuana and alcohol use were added to the analysis, the main effect of substance type was not as strong but remained significant, F(1, 595) = 6.11, p < .05; η2 = .01.
For perceived negative consequences, there was a main effect of substance type, F(1, 597) = 240.54, p < .01; η2 = .29, with participants perceiving negative consequences to be less likely for driving after use of marijuana than alcohol. There was a main effect of gender, F(1, 597) = 11.38, p < .01; η2 = .02, with men rating consequences for both behaviors as less likely. There was no Substance Type × Gender interaction. When controlling for alcohol and marijuana use, the main effects of substance type, F(1, 595) = 9.04, p < .05; η2 = .02, and gender, F(1, 595) = 5.79, p < .05; η2 = .01, remained significant.
We then compared the perceived dangerousness of driving after one drink, three drinks, and five drinks with the perceived dangerousness of driving after use of marijuana. Driving after use of marijuana was rated as more dangerous than driving after one drink, F(1, 597) = 599.29, p < .01; η2 = .51, and slightly more dangerous than three drinks, F(1, 597) = 4.20, p < .05; η2 = .01, but less dangerous than driving after five drinks, F(1, 597) = 366.42, p < .01; η2 = .39. There were significant main effects of gender for each analysis (all ps < .01), indicating that men viewed both behaviors as less dangerous. No Substance × Gender interactions were significant. The pattern of results was the same when frequency of alcohol and marijuana use were included as covariates, with driving after marijuana use rated as more dangerous than driving after one, F(1, 595) = 393.91, p < .01; η2 = .41, and three, F(1, 595) = 15.74, p < .01; η2 = .03, drinks, but less dangerous than after five drinks, F(1, 595) = 54.05, p < .01; η2 = .09.
Cognitions as Predictors of Driving After Use
We then tested whether cognitions were associated with driving after use of alcohol and marijuana. We estimated zero-inflated Poisson regression models using Mplus 3 (Muthén & Muthén, 2004). This model is appropriate when the dependent variable is a count variable with a high proportion of zero values. The dependent variable was number of times driving after use of alcohol or marijuana in the past 3 months. Mplus estimates two components in this type of model. The first, a zero-inflation component, estimates the odds of being in the zero class, or of not engaging in the behavior. This is similar to logistic regression, and an odds ratio is obtained for each independent variable. To simplify reporting, odds ratios were inverted so that higher values indicated greater likelihood of engaging in the behavior. The second component of the model provides a Poisson regression coefficient of the association between the independent variables and frequency of the dependent variable for those able to assume nonzero values. This coefficient is used to calculate the predicted rate of increase in the dependent variable for a one-unit increase in each independent variable (Cohen, Cohen, West, & Aiken, 2003).
For each model, frequency of substance use (either alcohol or marijuana), gender, and all three cognition variables were included as independent variables. For drinking and driving, the pattern of results was the same when each of the three drinking and driving variables (after one, three, or five drinks) was used as the dependent variable. Results are presented for driving after three drinks. For attitudes, perceived danger of driving after three drinks was used, as this variable was most similar to the parallel question for marijuana.
Table 3 presents odds ratios and predicted rate for substance use frequency and cognition variables. Frequency of substance use was associated with engagement and increased frequency of driving after use of either substance. Gender was related only to frequency of driving after use of marijuana. Lower perceived dangerousness and greater perceived peer acceptance were uniquely associated with both increased likelihood and increased frequency of driving after use of either substance. Lower perceived likelihood of negative consequences was associated with increased frequency of driving after use of either substance but not with engagement in either behavior.
Zero-Inflated Poisson Regression Analyses of Driving After Use of Alcohol and Marijuana
DiscussionOne goal of this study was to compare students' perceptions of driving after drinking with those of driving after the use of marijuana. Previous studies (Terry & Wright, 2005) demonstrated that marijuana users perceive driving after smoking marijuana as less impairing than driving after drinking. Our results support this finding, as marijuana use was strongly correlated with cognitions about driving after use. However, our results also indicate that college students in general perceived driving after smoking marijuana as more acceptable to their peers and the negative consequences to be less likely, even after controlling for frequency of use of these substances. When comparing perceived dangerousness of driving after marijuana use to driving after specific amounts of alcohol, youth viewed driving after marijuana use as slightly more dangerous than driving after three alcoholic drinks.
Our results also support substance-related driving cognitions as risk factors for driving after use of either alcohol or marijuana. Despite mean differences between cognitions, results were consistent for driving after use of alcohol and marijuana. Normative beliefs and attitudes had unique associations with both engagement in, and frequency of, driving after use of either substance. For perceived negative consequences, youth who engaged in these behaviors and viewed the negative consequences as less likely reported greater frequency of driving after use.
There are several reasons why youth may perceive driving after use of marijuana as more acceptable and the negative consequences less likely than those of drinking and driving. For over 20 years, the dangers of driving after use of alcohol have been the subject of public advertising campaigns and the focus of legal and public policy changes. Despite research evidence that marijuana impairs driving ability (Ramaekers et al., 2000), similar campaigns have only recently been targeted at driving after use of marijuana. The Office of National Drug Control Policy (2006) has expressed concern about the public image of marijuana as benign and includes information on marijuana's negative effects on driving skills in its youth media campaign.
In general, youth who reported greater involvement with a substance viewed driving after use as less risky. However, although perceived negative consequences were correlated with use of marijuana, these questions were largely not correlated with alcohol involvement. This may indicate that knowledge of the consequences of drinking and driving are not a function of personal use, perhaps due to the broader exposure to the potential consequences of drinking and driving in public discourse and media campaigns.
Differences between perceived negative consequences of driving after use of marijuana and alcohol may also reflect actual differences in legal enforcement between these two substances. The establishment of a per se standard has had a significant impact on reducing drinking and driving behavior (Giesbrecht & Greenfield, 2003). One mechanism by which such policy changes can influence behavior is by altering perceptions about the behavior, such as perceptions of risk and social norms (Greenberg, Morral, & Jain, 2004). In contrast, there is at present no parallel standard for marijuana use, in part due to lack of roadside and definitive testing of marijuana intoxication. Given this, it may be that youth are aware of these differences in enforcement standards, and their perceptions to some extent reflect actual lower probability of receiving negative consequences for driving after use of marijuana.
There are several limitations to the present study. The cross-sectional nature of the data limits inferences about the direction of the association between cognitions and driving behavior. To our knowledge, this study is the first to demonstrate associations between cognitions specific to driving after marijuana use and driving after such use. Finding cross-sectional associations, however, is only a first step toward demonstrating that these factors are important prospective predictors of behavior. Longitudinal studies would be required to examine whether these cognitions influence later substance-related driving behavior, driving behavior influences the development of cognitions, or a combination of both processes.
The sample used was of college students, which limits the generalizablity of findings to other populations. In addition, epidemiological evidence indicates that the prevalence of drinking and driving is higher at large (>10,000 student), public universities (Wechsler et al., 2003). Results of this study may not generalize to college settings with lower drinking and driving rates. The study is also limited by the use of self-report. However, self-report measures of substance-related behavior can be valid in youth, particularly when data collection is confidential or anonymous and when no consequences are associated with the report (Wilson & Grube, 1994).
An additional limitation of this study is that we did not include an assessment of quantity of marijuana use. Unlike alcohol, standardized self-report methods are generally not used to assess the amount of marijuana consumption. Therefore, although questions assessed driving or perceived danger of driving after different amounts of alcohol, parallel questions for marijuana did not specify an amount. This lack of specificity increases error variance due to individual differences in question interpretation. Future studies can use standardized interviews (Brown et al., 1998) to assess quantity of marijuana use and adapt these quantity measures to assess quantity of marijuana used prior to driving.
Results of this study also indicated significant overlap in youth who drive after use of alcohol and use of marijuana. Co-use of alcohol and marijuana prior to driving may be a particularly dangerous behavior, as co-use is associated with greater impairments in driving skills (Lamers & Ramaekers, 2001; Robbe, 1998). Future studies are required to examine youth cognitions and driving behavior associated with co-use of alcohol and marijuana.
The results of this study highlight cognitions about driving after use of marijuana as potential targets of prevention and intervention efforts. For drinking and driving, cognitive factors, such as perceived legal sanctions and normative beliefs, are associated with reduced drinking and driving in offenders receiving treatment (Greenberg et al., 2004). Drinking and driving offenders also cite legal sanctions as their primary motivation for avoiding drinking and driving (Wiliszowski, Murphy, Jones, & Lacey, 1996). Challenging youths' perceptions about the danger and potential negative consequences of driving after marijuana use may be an important technique for reducing this prevalent risk-taking behavior.
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Submitted: April 13, 2006 Revised: December 7, 2006 Accepted: December 8, 2006
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Source: Psychology of Addictive Behaviors. Vol. 21. (3), Sep, 2007 pp. 425-430)
Accession Number: 2007-13102-018
Digital Object Identifier: 10.1037/0893-164X.21.3.425
Record: 58- Title:
- DSM–IV–TR and DSM-5 eating disorders in adolescents: Prevalence, stability, and psychosocial correlates in a population-based sample of male and female adolescents.
- Authors:
- Allen, Karina L.. Telethon Institute for Child Health Research, Centre for Child Health Research, The University of Western Australia, West Perth, WAU, Australia, karina@ichr.uwa.edu.au
Byrne, Susan M.. School of Psychology, The University of Western Australia, WAU, Australia
Oddy, Wendy H.. Telethon Institute for Child Health Research, Centre for Child Health Research, The University of Western Australia, West Perth, WAU, Australia
Crosby, Ross D., ORCID 0000-0001-9131-1629. Department of Clinical Neuroscience, University of North Dakota School of Medicine and Health Sciences, ND, US - Address:
- Allen, Karina L., Telethon Institute for Child Health Research, P.O. Box 855, West Perth, WAU, Australia, 6872, karina@ichr.uwa.edu.au
- Source:
- Journal of Abnormal Psychology, Vol 122(3), Aug, 2013. pp. 720-732.
- NLM Title Abbreviation:
- J Abnorm Psychol
- Page Count:
- 13
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- The Journal of Abnormal and Social Psychology; The Journal of Abnormal Psychology; The Journal of Abnormal Psychology and Social Psychology
- ISSN:
- 0021-843X (Print)
1939-1846 (Electronic) - Language:
- English
- Keywords:
- DSM-5, DSM–IV–TR, Raine Study, eating disorders, prevalence, adolescence, psychosocial correlates
- Abstract:
- The current study aimed to compare the prevalence, stability, and psychosocial correlates of DSM–IV–TR and DSM-5 eating disorders, in a population-based sample of male and female adolescents followed prospectively from 14 to 20 years of age. Participants (N = 1,383; 49% male) were drawn from the Western Australian Pregnancy Cohort (Raine) Study, a prospective, population-based cohort study that has followed participants from prebirth to young adulthood. Detailed self-report questionnaires were used to assess eating disorder symptoms when participants were aged 14, 17, and 20 years. Comparisons between DSM–IV–TR and DSM-5 were conducted using McNemar chi-square tests and Fisher’s exact tests. Changes in eating disorder prevalence over time were considered using generalized estimating equations. Eating disorder prevalence rates were significantly greater when using DSM-5 than DSM–IV–TR criteria, at all time points for females and at age 17 only for males. 'Unspecified'/'other' eating disorder diagnoses were significantly less common when applying DSM-5 than DSM–IV–TR criteria, but still formed 15% to 30% of the DSM-5 cases. Diagnostic stability was low for all disorders, and DSM-5 binge eating disorder or purging disorder in early adolescence predicted DSM-5 bulimia nervosa in later adolescence. Cross-over from binge eating disorder to bulimia nervosa was particularly high. Regardless of the diagnostic classification system used, all eating disorder diagnoses were associated with depressive symptoms and poor mental health quality of life. These results provide further support for the clinical utility of DSM-5 eating disorder criteria, and for the significance of binge eating disorder and purging disorder. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Adolescent Development; *Diagnostic and Statistical Manual; *Eating Behavior; *Psychosocial Factors; Epidemiology
- Medical Subject Headings (MeSH):
- Adolescent; Adult; Depression; Diagnostic and Statistical Manual of Mental Disorders; Feeding and Eating Disorders; Female; Humans; Logistic Models; Male; Prevalence; Prospective Studies; Quality of Life; Sex Factors; Western Australia; Young Adult
- PsycINFO Classification:
- Eating Disorders (3260)
- Population:
- Human
Male
Female - Location:
- Australia
- Age Group:
- Adolescence (13-17 yrs)
Adulthood (18 yrs & older)
Young Adulthood (18-29 yrs) - Tests & Measures:
- Child Eating Disorder Examination
Self-report
Eating Disorder Examination Questionnaire DOI: 10.1037/t03974-000 - Grant Sponsorship:
- Sponsor: National Health and Medical Research Council of Australia
Other Details: career research fellowship
Recipients: Allen, Karina L. - Methodology:
- Empirical Study; Longitudinal Study; Quantitative Study
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- Accepted: Jul 6, 2013; Revised: Jul 5, 2013; First Submitted: Jan 18, 2013
- Release Date:
- 20130909
- Copyright:
- American Psychological Association. 2013
- Digital Object Identifier:
- http://dx.doi.org/10.1037/a0034004
- PMID:
- 24016012
- Accession Number:
- 2013-30852-009
- Number of Citations in Source:
- 47
- Persistent link to this record (Permalink):
- http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2013-30852-009&site=ehost-live
- Cut and Paste:
- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2013-30852-009&site=ehost-live">DSM–IV–TR and DSM-5 eating disorders in adolescents: Prevalence, stability, and psychosocial correlates in a population-based sample of male and female adolescents.</A>
- Database:
- PsycINFO
DSM–IV–TR and DSM-5 Eating Disorders in Adolescents: Prevalence, Stability, and Psychosocial Correlates in a Population-Based Sample of Male and Female Adolescents
By: Karina L. Allen
Telethon Institute for Child Health Research, Centre for Child Health Research, The University of Western Australia, West Perth, Western Australia and School of Psychology, The University of Western Australia, Crawley, Western Australia;
Susan M. Byrne
School of Psychology, The University of Western Australia
Wendy H. Oddy
Telethon Institute for Child Health Research, Centre for Child Health Research, The University of Western Australia
Ross D. Crosby
Department of Clinical Neuroscience, University of North Dakota School of Medicine and Health Sciences and Department of Biostatistics, Neuropsychiatric Research Institute, Fargo, North Dakota
Acknowledgement: We are extremely grateful to the Raine Study participants and their families who took part in this study and to the Raine Study team for cohort management and data collection. The first author is supported by an early career research fellowship from the National Health and Medical Research Council (NHMRC) of Australia. Core funding for the Western Australian Pregnancy Cohort (Raine) Study is provided by the Raine Medical Research Foundation; The University of Western Australia (UWA); the Faculty of Medicine, Dentistry and Health Sciences at UWA; the Telethon Institute for Child Health Research; the Women’s and Infant’s Research Foundation; and Curtin University. Funding for the 14-year Raine Study follow-up was provided by the Raine Medical Research Foundation and NHMRC project grants. Funding for the 17-year follow-up was provided by NHMRC program Grant 35314. Funding for the 20-year follow-up was provided by the Canadian Institutes of Health Research and NHMRC project grants.
It has been established that eating disorders most commonly develop during adolescence (Steinhausen, Gavez, & Metzke, 2005; Stice, Marti, Shaw, & Jaconis, 2009). Under DSM–IV–TR nomenclature (American Psychiatric Association, 2000), current until May, 2013, between 1% and 4% of this age group could be expected to meet criteria for anorexia nervosa (AN) or bulimia nervosa (BN; Hoek, 2006; Hoek & Van Hoeken, 2003), and at least another 5% to meet criteria for an eating disorder not otherwise specified (EDNOS; Isomaa, Isomaa, Marttunen, Kaltiala-Heino, & Bjorkqvist, 2009; Kjelsas, Bjornstrom, & Gotestam, 2004). Conceptualizations of EDNOS have varied considerably, with some studies considering atypical AN and subthreshold BN only, and others including binge eating disorder (BED). Relatively few studies have assessed for purging disorder (PD), which was provisionally defined by Keel and colleagues in 2005 as repeated purging in the absence of objective binge eating, accompanied by the overevaluation of eating, weight, or shape (Keel, Haedt, & Edler, 2005).
One of the limitations of DSM–IV–TR is the overreliance on EDNOS as a diagnostic category (Fairburn et al., 2007). In clinical settings, approximately 50% of those seen for treatment receive a “not otherwise specified” diagnosis when applying DSM–IV–TR criteria (Fairburn et al., 2007; Turner & Bryant-Waugh, 2004). In the community, this proportion rises to over 70% of eating disorder cases (Kjelsas et al., 2004; Machado, Goncalves, & Hoek, 2013; Wade, Bergin, Tiggemann, Bulik, & Fairburn, 2006). Individuals with EDNOS appear to be broadly comparable with individuals with full AN and BN in terms of symptom severity, symptom persistence, and functional impairment (Grilo et al., 2007; Hay et al., 2010; Thomas, Vartanian, & Brownell, 2009). Thus, it is important that these individuals are appropriately recognized in eating disorder diagnostic systems.
Changes to diagnostic criteria in DSM-5 were designed, in part, to reduce the frequency of unspecified eating disorder diagnoses (Walsh, 2009). The fifth edition of the DSM (American Psychiatric Association, 2013) no longer requires amenorrhea for a diagnosis of AN, and AN may be diagnosed if an individual’s behavior indicates fear of weight gain and body image disturbance (e.g., continued self-imposed dietary restriction despite low body weight), even if their self-reported cognitions do not. “Significantly low body weight” has also been more flexibly defined, as “less than minimally normal” for adults, or “less than minimally expected” for children and adolescents (American Psychiatric Association, 2013). For BN, the twice per week frequency requirement for binge eating and purging has been reduced to once per week. Binge eating disorder, an example of EDNOS in DSM–IV–TR, has been recognized as a standalone disorder, and the required frequency of binge eating has also been set at once per week for 3 months, consistent with BN. In DSM–IV–TR, the provisional BED criteria required binge eating 2 days per week for 6 months (American Psychiatric Association, 2000).
The “not otherwise specified” category of DSM-5 has been relabeled to give two separate categories. The first category, Other Specified Feeding or Eating Disorder (OSFED), incorporates specific eating disorder examples not captured by AN, BN, or BED. These include atypical AN, subthreshold BN, subthreshold BED, PD, and night eating syndrome (American Psychiatric Association, 2013). The second category, Unspecified Feeding or Eating Disorder, is intended for cases where insufficient information is available to make a specific eating disorder diagnosis, or where symptoms are genuinely unspecified and do not fit other diagnostic examples (American Psychiatric Association, 2013).
Several studies have compared the prevalence and distribution of DSM–IV–TR and DSM-5 eating disorder diagnoses, using the proposed DSM-5 criteria released ahead of 2013 publication (Walsh, 2009). Results confirm that DSM-5 decreases the use of “unspecified” or “other” eating disorder diagnoses in treatment-seeking (Birgegard, Norring, & Clinton, 2012; Fairburn & Cooper, 2011) and community (Keel, Brown, Holm-Denoma, & Bodell, 2011; Machado et al., 2013; Stice, Marti, & Rohde, 2013) samples. However, research to date has focused almost exclusively on female participants, and only one study (Stice et al., 2013) has used prospective data to compare DSM–IV–TR and DSM-5 prevalence rates over time. Stice, Marti, and Rohde’s (2013) research was conducted with female adolescents (n = 496), meaning that no data are available regarding developmental changes in the prevalence of DSM-5 eating disorders in males.
Stice et al.’s (2013) research is also the only source, to date, of data on the stability of DSM-5 eating disorders over time and on associations between DSM-5 eating disorders and psychological distress. Findings suggest that 1-year remission rates for DSM-5 eating disorders are high (similar to previous reports for DSM–IV–TR eating disorders in the community; Allen, Byrne, Oddy, & Crosby, in press; Stice et al., 2009), that cross-over between DSM-5 BED and BN is relatively common, and that DSM-5 eating disorders are associated with substantial psychosocial impairment (Stice et al., 2013).
The current study aimed to compare the prevalence, stability, and psychosocial correlates of DSM–IV–TR and DSM-5 eating disorders in a population-based sample of male and female adolescents, followed prospectively from 14 to 20 years of age. We have previously reported on DSM–IV–TR disorders in this sample, including prevalence and risk factors at age 14 (Allen, Byrne, Forbes, & Oddy, 2009) and disorder stability from 14 to 20 (Allen et al., in press). For this study, it was hypothesized that:
Hypothesis 1: The prevalence of DSM-5 eating disorders would be significantly greater than the prevalence of DSM–IV–TR eating disorders, for male and female participants.
Hypothesis 2: The proportion of “unspecified” or “other” eating disorder diagnoses would be significantly lower when applying DSM-5 criteria than DSM–IV–TR criteria, for male and female participants.
Hypothesis 3: Eating disorder stability would be low, and diagnostic cross-over would be high, for DSM-5 and DSM–IV–TR eating disorders, to a similar degree over 3-year and 6-year periods.
Hypothesis 4:DSM-5 and DSM–IV–TR eating disorders would show similar associations with depressive symptoms and quality of life.
Method Design and Participants
Data were drawn from the Western Australian Pregnancy Cohort (Raine) Study, a population-based cohort study that has followed participants from prebirth to young adulthood. The Raine Study has been described in detail previously (Allen et al., 2009; Newnham, Evans, Michael, Stanley, & Landau, 1993). In brief, 2,900 women were recruited from the antenatal booking clinics at King Edward Memorial Hospital for women (KEMH), the only public maternity hospital in Western Australia, between May, 1989 and November 1991. Of the 2,900 women enrolled, 2,804 delivered live birth babies. Due to 64 multiple births, the initial cohort included 2,868 children. Children were assessed at birth and 1, 2, 3, 5, 8, 10, 14, 17, and 20 years.
This study had a primary focus on the 14-, 17-, and 20-year follow-ups, when eating disorder data were collected. Eating disorder data were available for 1,598 participants at age 14, 1,242 participants at age 17, and 1,243 participants at age 20. We focused on participants with data at age 14 and at least one of the subsequent follow-ups, giving an effective sample size of 1,383 (49% male). This represents 76% of the participants who completed at least one of the 14 through 20-year assessments (N = 1,878) and 59% of the participants who were eligible for participation in the 14- through 20-year assessments (i.e., not deceased or lost to follow-up prior to age 14; N = 2,344). The mean age of the sample was 14.01 years (SD = 0.19, range = 13.00–15.08) at the 14-year assessment, 16.92 years (SD = 0.24, range = 15.0–18.2) at the 17-year assessment, and 20.01 years (SD = 0.44, range = 19.00–22.08) at the 20-year assessment.
Procedure
Questionnaire packages were posted to adolescents at the 14-, 17-, and 20-year assessments, for at-home completion prior to attendance at a face-to-face assessment session. Height and weight were measured during the face-to-face assessment. Body mass index was calculated using the standard formula (weight [kg]/height [m]2).
Data collection occurred in accordance with Australian National Health and Medical Research Council Guidelines for Ethical Conduct and was approved by the ethics committees of KEMH and Princess Margaret Hospital for Children.
Measures
Eating disorders at 14, 17, and 20 years
Eating disorder symptoms were assessed using 24 self-report items adapted from the Child Eating Disorder Examination (ChEDE; Bryant-Waugh, Cooper, Taylor, & Lask, 1996) and Eating Disorder Examination-Questionnaire (EDE-Q; Fairburn & Beglin, 1994). These items were self-report, as per the EDE-Q, but language was simplified or clarified when there was the possibility of confusion for 14-year-old adolescents. Response options were also simplified. The same four response options were used for all items: 0 = not at all; 1= some of the time (once per week/a few times a month); 2 = a lot of the time (a few times a week); and 3 = most of the time (every day or nearly every day). Participants were asked to be conservative in their answers if they were unsure of the frequency of their behaviors. Questions referred to the previous month and the same items were used at all assessment points. The validity of a simplified EDE-Q rating scale for youth has been established (Goldschmidt, Doyle, & Wilfley, 2007) and support exists for the validity of self-report eating disorder assessment more generally (Berg, Peterson, Frazier, & Crow, 2011, 2012; Berg et al., 2012; Keel, Crow, Davis, & Mitchell, 2002; Mond, Hay, Rodgers, Owen, & Beumont, 2004).
The 24 self-report items assessed for DSM–IV–TR and DSM-5 diagnostic criteria for AN, BN, BED, and PD, with the exception that items referred to 1 month rather than 3 to 6 months. Others have found good convergence between EDE-Q assessment with a 1 month time frame and interview assessment with a 3 to 6 month time frame, in terms of eating disorder detection and classification (Berg et al., 2012). One limitation of the EDE-Q, however, is that it does not assess criterion B of the diagnostic criteria for BED. Specifically, it does not determine whether three of the following symptoms are present: rapid eating, eating until uncomfortably full, eating large amounts when not hungry, eating alone, or feeling disgusted, depressed or guilty after overeating. When these criteria are omitted from diagnostic decision making, the prevalence of BED is inflated (Berg et al., 2012). To address this, we included the overevaluation of weight and shape as a requirement for BED diagnosis. Others have found overevaluation to be strongly associated with eating disorder psychopathology and distress about binge eating in samples of binge eaters (Hrabosky, Masheb, White, & Grilo, 2007; Mond, Hay, Rodgers, & Owen, 2007), and to reliably distinguish between individuals with BED and those who report binge eating without clinical impairment. Operationalized diagnostic requirements are summarized in Table 1. For DSM–IV–TR, the weight threshold for AN was set at the 3rd BMI percentile for age and sex, which is equivalent to weight at least 85% below that expected (a BMI of 17.5 in adults). An EDNOS diagnosis could be received if participants met our criteria for BED (see Table 1); fell just short of meeting full criteria for AN, either by continuing to menstruate or by losing a significant amount of weight without falling below the 3rd BMI percentile; fell just short of meeting full criteria for BN, by binge eating and purging less than twice per week; or reported recurrent purging (approximately weekly) with overevaluation of weight and shape, but without low weight or objective binge eating (see Table 1).
Diagnostic Requirements for DSM-IV-TR and DSM-5 Eating Disorders in the Current Study
For DSM-5, two sets of criteria (“a” and “b”) are presented for AN. The “a” criteria capture participants who endorsed marked fear of weight gain and body image disturbance, with a BMI below the 10th BMI percentile. This corresponds to a BMI of 18.5 in adults, which is the lower end of the World Health Organization’s healthy weight range (Cole, Flegal, Nicholls, & Jackson, 2007) and may be viewed as a marker of “minimally normal” for DSM-5 purposes (American Psychiatric Association, 2013). The “b” criteria for AN capture participants who did not endorse marked fear of weight gain, but who did demonstrate behaviors suggestive of fear of weight gain. This was defined as very low body weight (retaining the 3rd percentile in use for DSM–IV–TR) combined with some acknowledged fear of weight gain, some acknowledged dietary restriction, and marked body image disturbance (see Table 1).
For OSFED, we were in a position to assess PD and atypical AN, but not subthreshold BN or subthreshold BED. We did not assess for Unspecified Feeding or Eating Disorders. For PD, “recurrent purging” was defined as self-induced vomiting or laxative misuse occurring once per week or a few times per month (the same frequency criterion as for DSM-5 BN and BED). For atypical AN, a diagnosis was made if participants had lost considerable weight over the preceding 3 to 4 years, without yet being markedly underweight, and endorsed marked fear of weight gain and body image disturbance.
For atypical AN, it was necessary to consider change in weight between assessment points. To facilitate this, percentage ideal body weight (%IBW) was calculated for each participant at each time point, by dividing actual BMI by a BMI equal to the 50th percentile for age and sex, and then multiplying by 100. At age 14, we considered change in % IBW from age 10 to age 14, by dividing % IBW at 14 by % IBW at 10. The same process was used to consider changes between ages 14 and 17, and ages 17 and 20. On average across all Raine Study participants, % IBW changed by approximately 2% between assessment points, with a standard deviation of 9%–10% (these changes included a decrease of 2% from age 10 to age 14, an increase of 2% from age 14 to age 17, and an increase of 1% from age 17 to age 20). We defined significant weight loss as a reduction in % IBW that was at least two standard deviations more than the sample mean, equating to a reduction of at least 18%–20% IBW over a 3- to 4-year time period.
The mean of items relating to dietary restraint and eating, weight and shape concerns, excluding core diagnostic items (i.e., overevaluation of shape, overevaluation of weight, fear of weight gain, feelings of fatness), was computed as a global index of eating disorder psychopathology. Diagnostic items were excluded so that the index would represent eating disorder psychopathology distinct from diagnostic requirements, facilitating the comparison of dietary restraint and eating, weight and shape concerns across different diagnostic groups.
Depressive symptoms
The self-report Beck Depression Inventory for Youth (BDI-Y; Beck, Beck, & Jolly, 2001) was used to assess depressive symptoms at ages 14 and 17. The BDI-Y is an adolescent adaptation of the adult Beck Depression Inventory-2 (BDI-2) and has well-established psychometric properties (Eack, Singer, & Greeno, 2008). Alpha coefficients in this sample were .97 at age 14 and .94 at age 17.
The 21-item Depression Anxiety Stress Scale (DASS; Lovibond & Lovibond, 1995) was used to assess depressive symptoms at age 20. The DASS has demonstrated reliability and validity in clinical and nonclinical samples (Henry & Crawford, 2005; Ng et al., 2007), and scores on the Depression subscale correlate highly with those on the adult BDI-2. The alpha coefficient for the Depression subscale in this sample at age 20 was .89.
Quality of life
The 12-item Short-Form Health Survey-12 (SF-12) (Ware, Kosinski, & Keller, 1996) was used to assess physical and mental quality of life at age 20. The SF-12 is a reliable, valid, and practical alternative to the longer SF-36 when assessing quality of life (Salyers, Bosworth, Swanson, Lamb-Pagone, & Osher, 2000; Ware et al., 1996). It makes use of norm-based scoring with a population mean of 50 (SD = 10). Quality of life data were not collected at ages 14 or 17.
Statistical Analyses
Preliminary analyses
Independent-samples t tests were used to determine if participants included in this study (N = 1,383) differed in meaningful ways from participants who took part in none (n = 961) or one (n = 495) of the adolescent assessments. Participants were compared on family, parent, and psychosocial variables at ages 5, 8, and 10 years. Adolescent eating disorder symptoms were also compared across participants who provided data at two or more adolescent assessment points and those who took part in only one adolescent assessment.
After data screening, EM imputation using maximum likelihood estimation was used to impute missing eating disorder data for participants who completed two out of three adolescent assessments. Imputation was conducted using established principles and techniques (Kenward & Carpenter, 2007; Schafer & Graham, 2002) and is described below.
Core analyses
All core analyses were conducted for male and female participants separately.
To address Hypotheses 1 and 2, McNemar chi-square tests were used to compare the prevalence of DSM–IV–TR and DSM-5 disorders at ages 14, 17, and 20. Analyses were conducted for all disorders combined and, where numbers permitted, separately for different diagnoses. To complement these analyses, generalized estimating equations were used to examine changes in eating disorder prevalence over the 6-year study period, for DSM–IV–TR and DSM-5 separately. Generalized estimating equations account for correlations within individuals over time. Logistic binomial models were specified with a main effect of time, and the independence working correlation model was used (Wang & Carey, 2003).
To address Hypothesis 3, two sets of analyses were undertaken. First, logistic regression models were used to determine whether the presence of an eating disorder at one time point (e.g., age 14) predicted the presence of a disorder at a later time point (e.g., age 20). If a disorder was stable over time, early incidence of the disorder should predict later incidence of the disorder. Second, and where numbers permitted, cross-over in eating disorder diagnoses was considered. Fisher’s exact tests were used to compare rates of cross-over for each eating disorder diagnosis.
To address Hypothesis 4, nonparametric Kruskal-Wallis and Mann–Whitney U tests were used to compare depressive symptom scores and quality of life scores across DSM–IV–TR and DSM-5 eating disorder diagnoses. Comparisons in global eating disorder symptom scores and BMI were also conducted. These analyses focused on differences within each of the DSM systems (e.g., DSM-5 BN vs. DSM-5 BED vs. no DSM-5 disorder) rather than differences across the DSM systems (e.g., DSM-5 BN vs. DSM–IV–TR BN). This was necessary due to overlap in group membership across DSM–IV–TR and DSM-5 diagnoses.
For DSM-5, we distinguish between atypical AN and PD when discussing OSFED diagnoses.
All analyses were conducted in SPSS Statistics Version 20. Alpha was set at p < .05.
Results Preliminary Analyses
Participant characteristics
Compared with participants included in this study (who completed two or more adolescent assessments), participants who completed no adolescent assessments were significantly more likely to be from single-parent families at 5, 8, and 10 years (p < .001), were significantly less likely to have employed parents at 5, 8, and 10 years (p < .001), had significantly lower family incomes at 5, 8, and 10 years (p < .001), and had significantly higher CBCL Externalizing Problem scores at 5, 8, and 10 years (ps = .001–.007). Results were similar for participants who completed one adolescent assessment, with the exception that this group was not more likely to be from a single parent family than participants who completed two or more adolescent assessments. These findings are consistent with the tendency for socially disadvantaged families to be lost to follow-up over time (Wolke et al., 2009).
When comparing eating disorder data across participants who completed one adolescent assessment (excluded from the current study) and those who completed two or more (included in the study), there were no significant between-groups differences in eating disorder symptom scores, or in the proportion of participants meeting criteria for an eating disorder (ps = .187–.986).
Data imputation
Missing eating disorder data were imputed for participants who completed two of the three adolescent assessments, using EM imputation with maximum likelihood estimation. Data were screened for patterns of missing variables prior to imputation. No evidence was found to suggest that data were not missing at random and Little’s MCAR test was nonsignificant, χ2(1399) = 1376, p = .664. Data were imputed for 281 participants in total (141 participants at age 17 and 140 participants at age 20). The original raw dataset and imputed EM dataset were highly comparable in terms of estimated means and standard deviations for the eating disorder variables, eating disorder prevalence rates, and associations between eating disorder variables, depressive symptoms and quality of life. All subsequent analyses make use of the full, imputed data set.
Hypotheses 1 and 2: Eating Disorder Prevalence Rates
Male participants
Prevalence rates for DSM–IV–TR and DSM-5 eating disorders in males are shown in Figures 1a and 1b, respectively. When comparing total prevalence rates, there were no significant differences between DSM–IV–TR and DSM-5 at age 14 (McNemar χ2 = 0.50, p = .480) or age 20 (McNemar χ2 = 1.33, p = .248). Rates were significantly higher when using DSM-5 than DSM–IV–TR criteria at age 17 (McNemar χ2 = 8.10, p = .004; see Figure 1).
Figure 1. Prevalence rates (%, with 95% confidence intervals) for DSM–IV–TR eating disorders (Figure 1a) and DSM-5 eating disorders (Figure 1b) in males (n = 680) at ages 14, 17, and 20 years. EDNOS refers to Eating Disorder Not Otherwise Specified, and OSFED refers to Other Specified Feeding or Eating Disorder. Prevalence rates for DSM–IV–TR EDNOS disorders, and for DSM-5 bulimia nervosa, increased significantly from age 14 to age 20.
Rates for DSM-5 OSFED (“other” eating disorders) were significantly lower than rates for DSM–IV–TR EDNOS (“unspecified” eating disorders) at age 20 (McNemar χ2 = 6.67, p = .010), but not at ages 14 (McNemar χ2 = 0.50, p = .479) or 17 (McNemar χ2 = 0.10, p = .752). Conversely, rates of BN were significantly higher under DSM-5 than DSM–IV–TR at age 20 (McNemar χ2 = 7.11, p = .008), but not at ages 14 (McNemar χ2 = 0.50, p = .480) or 17 (McNemar χ2 = 2.25, p = .134).
The prevalence of DSM–IV–TR eating disorders in males increased significantly from age 14 to age 20, Wald χ2(2) = 7.54, p = .023). As no boys met DSM–IV–TR criteria for AN, and very few met criteria for BN, this was largely due to a significant increase in the prevalence of unspecified EDNOS cases, Wald χ2(2) = 7.02, p = .030 (see Figure 1a).
The prevalence of DSM-5 eating disorders did not change significantly between age 14 and age 20, overall, Wald χ2(2) = 5.42, p = .066), and there were also no significant changes in the prevalence of OSFED cases, Wald χ2(2) = 0.17, p = .919. The prevalence of BN did increase significantly from age 14 to age 20, Wald χ2(2) = 6.55, p = .038 (see Figure 1b). Group sizes were not sufficient to examine changes in DSM-5 AN, BED, or specific OSFED categories in boys. Within the DSM-5 OSFED category, there were three boys with PD at age 14 (0.4%), four with PD at age 17 (0.6%), and two with PD at age 20 (0.3%). At ages 14 and 20, two boys were classified with Atypical AN (0.3%), with no male participants receiving the diagnosis at age 17.
Female participants
Prevalence rates for DSM–IV–TR and DSM-5 eating disorders in females are summarized in Figures 2a and 2b, respectively.
Figure 2. Prevalence rates (%, with 95% confidence intervals) for DSM–IV–TR eating disorders (Figure 1a) and DSM-5 eating disorders (Figure 1b) in females (n = 703) at ages 14, 17, and 20 years. EDNOS refers to Eating Disorder Not Otherwise Specified, and OSFED refers to Other Specified Feeding or Eating Disorder. Prevalence rates for DSM–IV–TR EDNOS, and for DSM-5 bulimia nervosa and binge eating disorder, increased significantly from age 14 to age 20.
Total prevalence rates were significantly greater when applying DSM-5 than DSM–IV–TR criteria, at ages 14 (McNemar χ2 = 11.08, p < .001), 17 (McNemar χ2 = 14.06, p < .001), and 20 (McNemar χ2 = 17.05, p < .001). In large part, this was due to higher rates of BN when applying DSM-5 criteria, at ages 14 (McNemar χ2 = 10.08, p = .001), 17 (McNemar χ2 = 52.02, p < .001), and 20 (McNemar χ2 = 38.02, p < .001). Rates of “unspecified”/“other” eating disorders were significantly lower when applying DSM-5 than DSM–IV–TR criteria, at ages 14 (McNemar χ2 = 8.47, p = .004), 17 (McNemar χ2 = 37.96, p < .001), and 20 (McNemar χ2 = 38.25, p < .001).
The prevalence of DSM–IV–TR eating disorders in females increased significantly from age 14 to ages 17 and 20, Wald χ2(2) = 19.33, p < 001. This shift was largely accounted for by increases in the prevalence of unspecified EDNOS cases, Wald χ2(2) = 12.49, p = 002. The prevalence of AN was low and did not change significantly over time, Wald χ2(2) = 0.32, p = .571, nor did the prevalence of BN, Wald χ2(2) = 4.94, p = .085.
The prevalence of DSM-5 eating disorders in females also increased significantly from age 14 to ages 17 and 20, Wald χ2(2) = 19.66, p < .001. There were no significant changes in the prevalence of OSFED cases, Wald χ2(2) = 2.36, p = .307. Instead, the prevalence of BN increased significantly from age 14 to ages 17 and 20, Wald χ2(2) = 32.33, p < .001, and the prevalence of BED increased significantly from age 14 to age 20, Wald χ2(2) = 12.09, p = .002. There were no significant changes in the prevalence of AN, which remained low, Wald χ2(2) = 3.52, p = .172.
Within the DSM-5 OSFED category, the prevalence of PD was 2.7% (n = 19) at age 14, 2.1% (n = 15) at age 17, and 1.6% (n = 11) at age 20. These changes over time were not significant, Wald χ2(2) = 2.09, p = .352. The prevalence of atypical AN was low, at 0.9% (n = 6) at age 14, 0% at age 17, and 0.1% (n = 1) at age 20.
Hypothesis 3: Eating Disorder Stability and Diagnostic Cross-Over
Males
Given the small number of male eating disorder cases, diagnosis-specific stability, and rates of diagnostic cross-over, were not considered for male participants. At an overall level, there was evidence of eating disorder continuity from age 14 to age 17, and from age 17 to age 20, among males. A DSM–IV–TR eating disorder at 14 significantly predicted a disorder at age 17 (OR 47.00, 95% CIs [10.66, 207.20], p < .001), and a DSM–IV–TR disorder at age 17 significantly predicted a disorder at age 20 (OR 46.43, 95% CIs [10.53, 204.69], p < .001). However, 95% confidence intervals for odds ratios were extremely large, suggesting considerable uncertainty in the magnitude of associations. There was no significant association between 14-year eating disorder status and 20-year eating disorder status (p = .511).
The presence of a DSM-5 eating disorder at age 17 was also significant in predicting a DSM-5 disorder at age 20 (OR 39.45, 95% CIs [13.40, 116.12], p < .001), but there were no significant associations between a DSM-5 eating disorder at age 14 and a disorder at either 17 (p = .999) or 20 (p = .999).
Females
For females, it was possible to consider the stability of eating disorders overall and by diagnosis. Results are summarized in Table 2. Overall, a DSM–IV–TR eating disorder at age 14 significantly predicted a disorder at ages 17 and 20, and a DSM–IV–TR disorder at age 17 significantly predicted a disorder at age 20 (see Table 2). When considering specific diagnoses, BN at age 14 predicted EDNOS (but not BN) at age 17, and BN at age 17 predicted EDNOS disorder (but not BN) at age 20. There were no significant associations between BN at age 14 and DSM–IV disorders at age 20. An unspecified EDNOS diagnosis at age 14 predicted EDNOS and BN at age 17, and BN only at age 20. An unspecified EDNOS diagnosis at age 17 predicted EDNOS and BN at age 20.
Univariate Associations (Odds Ratios [With 95% CI]) Between DSM-I-TRV and DSM-5 Eating Disorder Diagnoses at Ages 14 and 17, and Diagnoses at Ages 17 and 20, For Female participants
Univariate Associations (Odds Ratios [With 95% CI]) Between DSM-I-TRV and DSM-5 Eating Disorder Diagnoses at Ages 14 and 17, and Diagnoses at Ages 17 and 20, For Female participants
For DSM-5, an eating disorder at age 14 significantly predicted a disorder at age 17, but not at age 20, and a disorder at age 17 significantly predicted a disorder at age 20 (see Table 2). The 3-year stability of BN was strong, with significant associations between BN at 14 and BN at 17, and between BN at 17 and BN at 20. A BN diagnosis at 14 did not predict any other eating disorder diagnosis at age 17, and it did not predict BN or other diagnoses at age 20. Although no diagnoses were significant in predicting BED over time, BED at age 14 was a significant predictor of BN at age 17, and BED at age 17 was a significant predictor of BN at age 20. Purging disorder at age 14 predicted PD at age 17 and BN at age 20, and PD at age 17 predicted PD and BN at age 20.
Patterns of diagnostic cross-over for female participants are summarized in Table 3. Statistical comparisons were not conducted for AN or atypical AN, due to small group sizes.
Diagnostic Cross-Over From Initial DSM-IV-TR or DSM-5 Eating Disorder Diagnosis to Later Diagnoses, for Female Participants (N [% of Initial Diagnosis])
For DSM–IV–TR, there were no significant differences in diagnostic cross-over rates for BN and those for unspecified disorders (Fisher’s exact test p = .610).
For DSM-5, cross-over rates were significantly lower for participants with an initial diagnosis of BN compared with participants with an initial diagnosis of BED (Fisher’s exact test p = .009) or PD (Fisher’s exact test p = .009). Cross-over was comparable for BED and PD (Fisher’s exact test p = .692), and the likelihood of progressing to BN did not differ significantly across those with an initial diagnosis of BED and those with an initial diagnosis of PD (Fisher’s exact test p = .086). Nonetheless, it is worth noting that the proportion of BED participants progressing to a BN diagnosis was double the proportion of PD participants making this transition (52% vs. 26%; see Table 3).
When comparing DSM–IV–TR and DSM-5 directly, cross-over was significantly more likely for DSM–IV–TR BN than for DSM-5 BN (Fisher’s exact test p = .015), and movement from BN to an “unspecified” or “other” disorder was also significantly more likely with DSM–IV–TR than with DSM-5 (Fisher’s exact test p < .001). Conversely, diagnostic cross-over was significantly less likely for DSM–IV EDNOS than for DSM-5 OSFED (Fisher’s exact test p = .009; see Table 3).
Hypothesis 4: Associations Between Eating Disorders, Depressive Symptoms, and Quality of Life
Males
Table 4 summarizes depressive symptom, quality of life scores, global eating disorder symptom, and BMI scores for male participants with and without an eating disorder, at ages 14, 17, and 20 and for DSM–IV–TR and DSM-5 disorders separately. Global eating disorder symptom scores were significantly higher for boys with an eating disorder, at all ages and regardless of the diagnostic system used, than for boys without an eating disorder. Depression scores were also significantly higher for boys with an eating disorder, regardless of the diagnostic system used, at ages 17 and 20 but not at age 14. At age 20, physical health quality of life did not differ significantly by eating disorder category, but mental health quality of life was significantly lower for males with a DSM–IV–TR eating disorder and for those with a DSM-5 eating disorder, compared with noneating disordered participants (see Table 4).
Depressive symptoms, Quality of life, Global Eating Disorder symptoms, and BMI in Males (M(SD)), by DSM-I-TRV and DSM-5 Diagnoses
Depressive symptoms, Quality of life, Global Eating Disorder symptoms, and BMI in Males (M(SD)), by DSM-I-TRV and DSM-5 Diagnoses
Females
Table 5 summarizes results for female participants by eating disorder diagnosis. Girls with DSM–IV–TR BN and EDNOS had significantly higher global eating disorder scores and depression scores at all time points than girls without a DSM–IV–TR disorder. At age 20, DSM–IV–TR BN and EDNOS were also associated with lower mental health quality of life, although not physical health quality of life (see Table 5). The only significant difference identified between DSM–IV–TR BN and EDNOS was at age 14, where participants with BN had higher global eating disorder scores than participants with EDNOS.
Depressive symptoms, Quality of life, Global Eating Disorder symptoms, and BMI in Females (M(SD)), by DSM-IV-TR and DSM-5 Diagnoses
Depressive symptoms, Quality of life, Global Eating Disorder symptoms, and BMI in Females (M(SD)), by DSM-IV-TR and DSM-5 Diagnoses
Girls with DSM-5 BN, BED, and PD also had significantly higher global eating disorder symptom scores and depression scores at all time points than girls without a DSM-5 disorder. Again, BN, BED, and PD were associated with lower mental health quality of life, but not physical health quality of life, at age 20. The only significant difference identified between DSM-5 BN, BED, and PD was at age 17, where participants with BN had higher global eating disorder scores than participants with BED and PD (see Table 5).
DiscussionThis study is unique in describing the prevalence, stability, and psychosocial correlates of DSM–IV–TR and DSM-5 eating disorders in male and female adolescents, using prospective data collected over 6 years. Consistent with Hypotheses 1 and 2, and with previous studies (Keel et al., 2011; Machado et al., 2013; Stice et al., 2013), eating disorder prevalence rates were significantly greater for female adolescents when applying DSM-5 than DSM–IV–TR criteria, at all time points assessed, and a greater proportion of female eating disorder cases received an AN, BN, or BED diagnosis rather than an “unspecified” or “other” diagnosis. For males, partial support was obtained for the first two hypotheses, with DSM-5 criteria associated with higher prevalence rates at age 17 but not ages 14 or 20, and lower rates of unspecified/other disorders at age 20 but not ages 14 or 17. Consistent with Hypothesis 3, there was evidence of moderate eating disorder stability and high rates of diagnostic cross-over. Consistent with Hypothesis 4, depressive symptoms and mental health quality of life were associated with DSM–IV–TR and DSM-5 eating disorder diagnoses in males and in females.
We observed high rates of eating disorders in female participants irrespective of the diagnostic system applied. Our DSM–IV–TR rates are comparable with those from previous studies that assessed for broadly defined EDNOS as well as AN and BN (e.g., Isomaa et al., 2009; Kjelsas et al., 2004). However, our rates for DSM-5 eating disorders are higher than those reported in Stice et al. (2013), the only prior study to undertake a longitudinal analysis of DSM-5 disorders across adolescence. When considering differences between the two studies, prevalence rates for AN, BED, Atypical AN, and PD were broadly similar across the current study and Stice et al. (2013). Prevalence rates for BN were higher in this sample, although rates for OSFED disorders were higher in Stice et al. (2013). The higher incidence of OSFED cases in Stice et al.’s study (2013) can be explained readily, as their research assessed for subthreshold BN and BED where we did not. This stemmed from our frequency assessments not being fine-grained enough to detect binge eating and compensatory behaviors that occurred less often than weekly. Our frequency assessments may also account for the higher incidence of BN in this study, as we assessed binge eating and compensatory behavior “weekly or a few times per month” over a 1-month period. Stice et al. (2013) assessment converges with the DSM-5 requirement of weekly episodes over a 3-month period, suggesting that our estimate of BN prevalence may be slightly inflated and/or capture some cases that would be better conceptualized as subthreshold BN.
This study did converge with Stice et al. (2013) in showing high rates of diagnostic cross-over from DSM-5 BED to DSM-5 BN in females, and high rates of disorder remission over 3 and 6 years. In our sample, diagnostic cross-over was lower for DSM-5 BN than DSM–IV–TR BN, suggesting that the reduced frequency requirements of DSM-5 may strengthen, rather than reduce, the continuity of BN over time. We also found DSM-5 BN to have lower rates of cross-over than DSM-5 BED or PD. Interestingly, BED and PD in early adolescence were significant in predicting BN in later adolescence, whereas BN in early adolescence did not predict other disorders at later time points. As with Stice et al. (2013), we observed depressive symptoms and impaired mental health quality of life for females with all forms of DSM-5 eating disorders (as well as all forms of DSM–IV–TR eating disorders). Thus, the new DSM-5 diagnoses appear to be capturing individuals with clinically significant difficulties.
For male participants, eating disorder prevalence rates were low irrespective of the diagnostic system applied and, contrary to predictions, DSM-5 was associated with higher prevalence rates at age 17 only. There was also a greater proportion of BN cases, relative to OSFED cases, when applying DSM-5 criteria at age 20. These results suggest that DSM-5 may not impact on the assessment of male eating disorders in early adolescence, but could have benefits in detecting and/or defining symptoms in middle to late adolescence. Depressive symptoms and impairments in mental health quality of life were also linked to eating disorders in middle and late adolescent boys, for DSM–IV–TR and DSM-5, although 14-year cases showed nonsignificant trends toward distress.
There are four main implications arising from this research. First, the study provides the most comprehensive data to date on differences between DSM–IV–TR and DSM-5 eating disorder rates, at different time points and for male and female participants separately. Results suggest that with DSM-5, marked increases in eating disorder prevalence may be expected for adolescent females in middle adolescence, particularly for BN, and that girls who develop BED or PD may be at risk for later BN. As with DSM–IV–TR, middle adolescence remains a potent period for prevention and intervention efforts with females. For males, DSM-5 prevalence rates only increased after age 17, suggesting that research on male eating pathology may best be targeted to middle adolescence and early adulthood. Second, “other” eating disorder cases (OSFED) still make up a modest proportion of individuals receiving an eating disorder diagnosis under DSM-5. In this sample, the proportion was greater at age 14 (approximately 50% in males and 40% in females) than ages 17 or 20 (15% to 30%). Ongoing attention to eating disorder classification systems is therefore important, and further revision or specification of diagnostic categories may be possible in future DSM revisions. Third, the stability of DSM–IV–TR and DSM-5 eating disorders in the community is low. For DSM-5, cross-over from BED to BN is common, and BED and PD in early adolescence predict BN in later adolescence. Fourth, depressive symptoms and poor mental health quality of life appear to be comparable across DSM–IV–TR and DSM-5 diagnoses. This provides further support for the clinical significance of previously “unspecified” cases and suggests that any increases in the prevalence of eating disorders in DSM-5, relative to DSM–IV–TR, may be warranted.
There are also limitations of this research that deserve note. First, we assessed for eating disorder symptoms over 1 month, rather than 3 months, and our frequency categories approximated rather than matched DSM requirements. Others have found good convergence between self-report assessment of a 1 month time frame and interview assessment of a 3-month time frame (Berg et al., 2012), but replication of our results with a 3-month assessment period, and with strictly defined frequency criteria, is important. As noted, differences between our frequency assessments and those of Stice et al. (2013) may have contributed to the prevalence differences between these studies. They also prevented us from assessing for subthreshold BED or BN. Second, we did not directly address criterion B for DSM-5 BED. Instead, we used overevaluation of weight or shape as a proxy marker for this criterion. This may be viewed as a conservative assessment approach, but means that our definition should be taken as an approximation, rather than strict assessment, of DSM-5 BED. It may also have contributed to the tendency for participants with BED in early adolescence to transition to BN (where overevaluation of weight and shape is a diagnostic criterion) with time. Research that directly assesses all BED criteria is clearly important. Third, group sizes for eating disorder diagnoses varied and at times were small, particularly for male participants. Our reported prevalence rates should be interpreted with this in mind, and in the context of 95% confidence intervals. Fourth, we did not assess night eating syndrome, which is included as an example of OSFED in DSM-5. Fifth, the loss of disadvantaged families is a well-replicated phenomenon in longitudinal cohort studies (Wolke et al., 2009) and was observed here. Notably, the Raine Study initially oversampled socially disadvantaged women, meaning that attrition has served to increase the representativeness of the cohort over time. Previous analyses have shown that participants who remained in the study to adolescence are broadly comparable with the Western Australian population on a range of sociodemographic indicators (Li et al., 2008). Despite this, replication of our results in other cohorts is important and would help to strengthen the findings observed here.
In summary, we have provided new data on the prevalence, stability, and psychosocial impact of DSM–IV–TR and DSM-5 eating disorders in male and female adolescents. Results confirm that eating disorder rates are likely to increase slightly, overall, with the release of DSM-5. Rates for BN may be expected to increase markedly in middle adolescent females. Binge eating or purging alone in initial presentation appear to predict binge eating and purging in combination with time. All DSM–IV–TR and DSM-5 eating disorder diagnoses were associated with psychological distress in this sample, confirming the clinical significance of BED and “other” (including PD) disorders.
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Submitted: January 18, 2013 Revised: July 5, 2013 Accepted: July 6, 2013
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Source: Journal of Abnormal Psychology. Vol. 122. (3), Aug, 2013 pp. 720-732)
Accession Number: 2013-30852-009
Digital Object Identifier: 10.1037/a0034004
Record: 59- Title:
- Effects of sexual assault on alcohol use and consequences among young adult sexual minority women.
- Authors:
- Rhew, Isaac C.. Department of Psychiatry and Behavioral Sciences, University of Washington, Seattle, WA, US, rhew@uw.edu
Stappenbeck, Cynthia A.. Department of Psychiatry and Behavioral Sciences, University of Washington, Seattle, WA, US
Bedard-Gilligan, Michele. Department of Psychiatry and Behavioral Sciences, University of Washington, Seattle, WA, US
Hughes, Tonda. College of Nursing, University of Illinois at Chicago, Chicago, IL, US
Kaysen, Debra. Department of Psychiatry and Behavioral Sciences, University of Washington, Seattle, WA, US - Address:
- Rhew, Isaac C., Department of Psychiatry and Behavioral Sciences, Center for the Study of Health and Risk Behaviors, University of Washington, 1100 North East 45th Street, 300, Seattle, WA, US, 98105, rhew@uw.edu
- Source:
- Journal of Consulting and Clinical Psychology, Vol 85(5), May, 2017. pp. 424-433.
- NLM Title Abbreviation:
- J Consult Clin Psychol
- Page Count:
- 10
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- Journal of Consulting Psychology
- Other Publishers:
- US : American Association for Applied Psychology
US : Dentan Printing Company
US : Science Press Printing Company - ISSN:
- 0022-006X (Print)
1939-2117 (Electronic) - Language:
- English
- Keywords:
- sexual assault, alcohol use, sexual minority women, marginal structural model
- Abstract (English):
- Objective: The purpose of this study was to examine effects of sexual assault victimization on later typical alcohol use and alcohol-related consequences among young sexual minority women (SMW). Method: Data were collected over 4 annual assessments from a national sample of 1,057 women who identified as lesbian or bisexual and were 18- to 25-years-old at baseline. Marginal structural modeling, an analytic approach that accounts for time-varying confounding through the use of inverse probability weighting, was used to examine effects of sexual assault and its severity (none, moderate, severe) on typical weekly number of drinks consumed and number of alcohol-related consequences 1-year later as well as 2-year cumulative sexual assault severity on alcohol outcomes at 36-month follow-up. Results: Findings showed that compared with not experiencing any sexual assault, severe sexual assault at the prior assessment was associated with a 71% higher number of typical weekly drinks (count ratio [CR] = 1.71; 95% confidence interval [CI] [1.27, 2.31]) and 63% higher number of alcohol-related consequences (CR = 1.63; 95% CI [1.21, 2.20]). Effects were attenuated when comparing moderate to no sexual assault; however, the linear trend across sexual assault categories was statistically significant for both outcomes. There were also effects of cumulative levels of sexual assault severity over 2 years on increased typical drinking and alcohol-related consequences at end of follow-up. Conclusions: Sexual assault may be an important cause of alcohol misuse among SMW. These findings further highlight the need for strategies to reduce the risk of sexual assault among SMW. (PsycINFO Database Record (c) 2017 APA, all rights reserved)
- Impact Statement:
- What is the public health significance of this article?—Sexual assault during young adulthood may be a cause of alcohol misuse among sexual minority women. Identification and implementation of effective strategies to prevent sexual assault in this population may reduce the disparity in alcohol misuse between sexual minority and heterosexual women. (PsycINFO Database Record (c) 2017 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Alcohol Abuse; *Alcohol Drinking Patterns; *Bisexuality; *Lesbianism; *Sex Offenses; Human Females; Minority Groups
- PsycINFO Classification:
- Psychological Disorders (3210)
- Population:
- Human
Female - Location:
- US
- Age Group:
- Adulthood (18 yrs & older)
Young Adulthood (18-29 yrs) - Tests & Measures:
- Daily Drinking Questionnaire
Sexual Experiences Survey--Revised
Drinking Norms Rating Form--Modified Version
Sexual Assault Severity Scale
Generalized Anxiety Disorder-7 Scale
Posttraumatic Stress Disorder Checklist
Center for Epidemiologic Studies Depression Scale
Traumatic Life Events Questionnaire DOI: 10.1037/t00545-000
Brief Young Adult Alcohol Consequences Questionnaire DOI: 10.1037/t03955-000 - Grant Sponsorship:
- Sponsor: National Institute on Alcohol Abuse and Alcoholism, US
Grant Number: R01AA018292
Recipients: Kaysen, Debra
Sponsor: National Institute on Alcohol Abuse and Alcoholism, US
Grant Number: K08AA021745
Recipients: Stappenbeck, Cynthia A.
Sponsor: National Institute on Alcohol Abuse and Alcoholism, US
Grant Number: R34AA022966
Recipients: Bedard-Gilligan, Michele - Methodology:
- Empirical Study; Longitudinal Study; Quantitative Study
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- First Posted: Mar 13, 2017; Accepted: Jan 28, 2017; Revised: Jan 23, 2017; First Submitted: Aug 22, 2016
- Release Date:
- 20170313
- Correction Date:
- 20170420
- Copyright:
- American Psychological Association. 2017
- Digital Object Identifier:
- http://dx.doi.org/10.1037/ccp0000202
- PMID:
- 28287804
- Accession Number:
- 2017-11101-001
- Persistent link to this record (Permalink):
- http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2017-11101-001&site=ehost-live
- Cut and Paste:
- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2017-11101-001&site=ehost-live">Effects of sexual assault on alcohol use and consequences among young adult sexual minority women.</A>
- Database:
- PsycINFO
Effects of Sexual Assault on Alcohol Use and Consequences Among Young Adult Sexual Minority Women
By: Isaac C. Rhew
Department of Psychiatry and Behavioral Sciences, University of Washington;
Cynthia A. Stappenbeck
Department of Psychiatry and Behavioral Sciences, University of Washington
Michele Bedard-Gilligan
Department of Psychiatry and Behavioral Sciences, University of Washington
Tonda Hughes
College of Nursing, University of Illinois at Chicago
Debra Kaysen
Department of Psychiatry and Behavioral Sciences, University of Washington
Acknowledgement: This work was supported by funding from the National Institute on Alcohol Abuse and Alcoholism (R01AA018292 awarded to Debra Kaysen, K08AA021745 awarded to Cynthia A. Stappenbeck, and R34AA022966 awarded to Michele Bedard-Gilligan).
Although reasons for the disparity are unclear, numerous studies have shown that sexual minority women (SMW) are more likely than heterosexual women to experience sexual assault in childhood and in adulthood. In a recent systematic review of the literature, prevalence of lifetime sexual assault ranged from 16% to 85% among SMW, with a median estimate of 43% of lesbian and bisexual women reporting adult or childhood sexual victimization across studies (Rothman, Exner, & Baughman, 2011). As noted by Rothman, Exner, and Baughman (2011), most studies on sexual victimization among SMW have focused predominantly on childhood sexual abuse, indicating the need for additional research that examines adult sexual assault in this population.
Despite recent advances in societal acceptance of sexual minorities in the U.S., negative social attitudes and behaviors toward SMW are still widespread (Herek, 1997). The minority stress model posits that the cumulative stressors associated with sexual minority status can lead to negative physical and mental health outcomes and help to explain health disparities (Hatzenbuehler, Phelan, & Link, 2013; Meyer, 2003). There is a growing body of evidence indicating that experiences of interpersonal and institutional discrimination increase problematic drinking among sexual minorities (Hatzenbuehler, Keyes, & Hasin, 2009; McCabe, Bostwick, Hughes, West, & Boyd, 2010; McLaughlin, Hatzenbuehler, & Keyes, 2010). Indeed, young adult SMW are at significantly elevated risk of heavy episodic drinking (consuming four or more drinks in 2 hours; National Institutes on Alcohol Abuse & Alcoholism, 2004) and of experiencing problems related to alcohol use compared with heterosexual women (Drabble, Midanik, & Trocki, 2005; McCabe, Hughes, Bostwick, West, & Boyd, 2009; Wilsnack et al., 2008). For example, lesbian and bisexual women aged 20 to 34 reported higher weekly alcohol consumption and less abstinence compared with older lesbian and bisexual women and to heterosexual women (Gruskin, Hart, Gordon, & Ackerson, 2001). In a study of college females, lesbian/bisexual women were 10.7 times more likely to consume alcohol than heterosexual women (Ridner, Frost, & Lajoie, 2006). Elevated risk of sexual assault has been generally included as an example of structural stigma or minority stress disproportionately affecting sexual minorities (Balsam, Lehavot, & Beadnell, 2011; Hughes, Johnson, & Wilsnack, 2001; Hughes et al., 2010). As such, one contributor to this elevated risk for heavy episodic drinking may be SMW’s greater exposure to sexual assault (Drabble, Trocki, Hughes, Korcha, & Lown, 2013). In both heterosexual women and SMW, those who experience sexual assault may be at greater risk for a variety of health consequences, including alcohol misuse. For example, a history of childhood sexual abuse is associated with later alcohol abuse and dependence (Afifi, Henriksen, Asmundson, & Sareen, 2012; Danielson et al., 2009; Gilmore et al., 2014). Although not entirely consistent, cross-sectional research has found that SMW who report sexual assault in adulthood are also more likely to engage in greater alcohol consumption and heavy episodic drinking than those who report no lifetime sexual assault (Hughes et al., 2010; Ullman, 2003; Wilsnack, Wilsnack, Kristjanson, Vogeltanz-Holm, & Windle, 2004). This may be explained at least in part by negative reinforcement models that suggest that alcohol use may reduce distress, which negatively reinforces continued and increased use of alcohol (Baker et al., 2004).
Longitudinal research is needed to establish temporal ordering and potential causation. Multiple studies in general female samples that did not explicitly consider sexual orientation have observed associations between sexual assault victimization and subsequent alcohol outcomes (e.g., Bryan et al., 2016; Parks, Hsieh, Taggart, & Bradizza, 2014; Testa, Hoffman, & Livingston, 2010; Ullman, 2016). To our knowledge, however, no prospective studies have examined these associations among SMW specifically. As longitudinal research moves forward on this topic, there are important methodologic issues that should be considered. First, the definition of sexual assault varies across studies and ranges from unwanted sexual contact to attempted or completed rape. Given that more severe forms of sexual assault tend to be more strongly associated with negative outcomes (Turchik, 2012), differing levels of assault severity may be associated with differing levels of drinking and alcohol-related problems. The tactics used by the perpetrator to obtain unwanted sex or sexual contact (i.e., verbal coercion, intoxication, and force or threats of force) may play a role in subsequent drinking by the victim and are not always considered (Littleton, Grills-Taquechel, & Axsom, 2009; Zinzow et al., 2012). Further, researchers often ignore the frequency of sexual assault experiences which is problematic given both high occurrence of repeated victimization and associations between the frequency of victimization and negative health consequences (Jozkowski & Sanders, 2012). Thus, a measure of sexual assault severity that incorporates both the severity of the assault experienced as well as the frequency of prior assaults may provide a more sensitive measure of “dose” of exposure and, thus, be a stronger predictor of health consequences.
Second, longitudinal studies with repeated measures of sexual assault exposure and alcohol-related outcomes provide opportunities to examine different aspects of how sexual assault could lead to alcohol misuse. For example, longitudinal studies can examine lagged longitudinal effects of sexual assault on alcohol outcomes at the following year across multiple study assessments. Further, the effects of cumulative exposure to sexual assault across multiple study waves on alcohol use and problems can be examined. SMW appear to be at elevated risk for not only any lifetime sexual assault victimization, but also revictimization (Hughes et al., 2010). Cross-sectional research suggests that individuals experiencing multiple victimization experiences may be at particularly elevated risk for substance use disorders (Hughes, McCabe, Wilsnack, West, & Boyd, 2010).
Finally, obtaining unbiased estimates of effects in longitudinal studies with repeated measures of the exposure can be challenging due to the additional possibility of time-varying confounding. For example, when examining effects of sexual assault on alcohol-related outcomes, it is possible that associations may be confounded by other time-varying factors such as prior levels of mental health symptoms (e.g., depression, anxiety, posttraumatic stress disorder), alcohol use, and prior trauma exposure. Approaches such as marginal structural modeling can be used to account for time-varying confounding and reduce potential bias through the use of inverse probability weights (IPWs) of exposure (Robins, Hernan, & Brumback, 2000). Marginal structural models use a counterfactual framework that can estimate an average causal effect comparing the potential outcome had a given individual been set to one covariate history versus a different covariate history, possibly contrary to the fact (VanderWeele, Hawkley, Thisted, & Cacioppo, 2011). Rather than include time-varying and other confounders in the statistical model, this approach weights subjects by the inverse probability of their own exposure (e.g., sexual assault) status according to covariates. Thus, when applying the IPWs, individuals who are underrepresented for their sexual assault status according to covariate history are given greater weight, whereas those who are overrepresented for a certain exposure level are given lower weight. This results in a “pseudo-population” with balanced distribution of time-varying and time-fixed covariates across levels of the exposure history, and application of IPWs should yield unconfounded estimates of the effects of sexual assault. Further introduction to marginal structural modeling and its applications are found elsewhere (e.g., Bodnar, Davidian, Siega-Riz, & Tsiatis, 2004; Thoemmes & Ong, 2016). To our knowledge, no longitudinal studies in general samples or in SMW-specific samples have utilized marginal structural modeling to investigate the potential causal relation between sexual assault and subsequent alcohol use and consequences.
In the current study we used marginal structural modeling to examine the effects of sexual assault severity assessed at one study wave on typical alcohol use and drinking-related consequences at the next annual study wave in a national sample of young adult SMW. In addition, we also examined whether cumulative exposure to sexual assault over 2 years was associated with alcohol use and consequences at the final study wave.
Method Participants and Procedures
Participants in this study were part of a longitudinal study of young adult (ages 18 to 25) SMW’s health and health behaviors. Women were recruited to participate via advertisements placed on the social networking website Facebook in such a way that only women who reported lesbian or bisexual identity on their profile and who were between the ages of 18–25 were shown the ad. Upon logging into Facebook, potential participants were shown the study advertisement (displayed in the side bar) with a link to the screening assessment. We used two types of advertisements: those that included sexual-minority-specific content (e.g., “LGB women needed for an online study on health behaviors”) and those that were non-LGB-specific (e.g., “We need you for an online study on partying”). Online advertisements were also placed on Craigslist in 12 metropolitan areas in the U.S.: Atlanta, Austin, Boston, Chicago, Houston, Los Angeles, New York, Philadelphia, San Francisco, Seattle, South Florida, and Washington, DC. Craigslist postings included a brief summary of the project and a URL link to the screening assessment.
Women who responded to the advertisements and accessed the study screening site were first shown information about the study. After agreeing to participate, potential participants were then routed to a 5-min screening assessment. A total of 4,119 women completed the online screening survey. Study eligibility criteria included: (a) residing in the U.S.; (b) having a valid e-mail address; (c) being between the ages of 18 and 25 years; and (d) reporting lesbian or bisexual identity at the time of the assessment. Eligible women (n = 1,877) were then invited to participate in the study. Of those eligible, 1,083 (57.7%) provided consent for participation in the larger study. Inconsistencies in the data that suggested a small number of participants were falsifying information (e.g., inconsistent birth dates over time) led us to omit 2.4% of the participants in the baseline sample; 1,057 were retained in the study. Compared with those who were retained in the study, those women who were eligible and did not consent were less likely to be White race (67.9% vs. 78.8%; χ2(1) = 30.5; p < .001) and more likely to be of Hispanic ethnicity (13.4% vs. 10.2%), χ2(1) = 4.6, p = .03. However, there were no statistically significant differences in age or sexual orientation.
Data were collected online at four annual assessments. Participants were paid $25 for completion of the baseline survey and $30 for completion of each of the three annual follow-up assessments. A Federal Certificate of Confidentiality was obtained for the study. The University’s Institutional Review Board reviewed and approved all study procedures.
Measures
Sexual assault
The revised Sexual Experiences Survey (SES) was used to assess sexual assault severity (Koss et al., 2007). This measure asks about experiences of different types of unwanted sexual behaviors (e.g., fondling, attempted or completed oral, vaginal or anal penetration) and tactics used to obtain each outcome (e.g., coercion, intoxication, and threat or use of physical force). Participants indicated how often (0 = never to 3 = 3 or more times) they experienced each unwanted sexual behavior by each tactic (e.g., how often they experienced attempted vaginal sex by coercion). At the baseline assessment, questions were asked in reference to “since age 18” and in follow-up assessments the reference period was “in the past year” (i.e., the time since the last assessment). An overall severity score was calculated as described by Davis and colleagues (2014). First, each sexual experience and tactic combination was assigned a severity rank from 0 (no history of sexual assault) to 6 (attempted or completed rape by threat or use of physical force). For each sexual experience and tactic combination, the severity rank was multiplied by the frequency of its occurrence and then summed for a total combined severity-frequency score with a possible range of 0–63. This severity score has shown strong convergent validity, especially among populations that report higher rates of assault (Davis et al., 2014).
Alcohol consumption
Typical weekly alcohol consumption during the previous 3 months was assessed using the Daily Drinking Questionnaire (DDQ; Collins, Parks, & Marlatt, 1985). Participants were asked “Consider a typical week during the last 3 months. How much alcohol (measured in number of standard drinks), on average, do you drink each day of a typical week?” Standard drinks were defined as 1.5 oz. of liquor, 5 oz. of wine, or 12 oz. of beer. Typical weekly drinking was the sum of the number of standard drinks for each day of the typical week.
Alcohol consequences
Alcohol consequences were measured using the Young Adult Alcohol Consequences Questionnaire (YAACQ; Read, Kahler, Strong, & Colder, 2006). The YAACQ obtains self-ratings of 48 possible drinking consequences. For each of the 48 consequences, participants indicate whether or not they experienced that consequence in the previous 30 days. The sum of the responses was calculated for a count of the number of past month alcohol consequences.
Covariates
A number of demographic and other measures were used for estimation of the predicted probability of past year sexual assault and level of severity. Demographic characteristics included age at baseline, race/ethnicity, sexual identity (lesbian, bisexual), and parent’s highest level of education. Additional psychosocial constructs were included because they tend to co-occur with sexual assault. Mental health problems were assessed using validated and commonly used measures including the Post traumatic stress disorder (PTSD) Checklist (PCL) to assess posttraumatic stress disorder symptoms (Ruggiero, Del Ben, Scotti, & Rabalais, 2003), the Center for Epidemiologic Studies Depression (CES-D) Scale to assess depression symptoms (Radloff, 1977), and the GAD-7 to assess generalized anxiety symptoms (Spitzer, Kroenke, Williams, & Lowe, 2006). The Daily Heterosexist Experiences Questionnaire (DHQ) was used to assess participants’ perceived minority stress due to their LGBT identity (Balsam, Beadnell, & Molina, 2013). Items ask about 38 stressors that LGBT individuals might experience such as difficulty finding a partner, pretending to be heterosexual, and being verbally harassed due to being LGBT. For each of the above psychosocial scales, the internal consistency was >.90 in this sample. Other traumatic events were assessed using the Traumatic Life Events Questionnaire (TLEQ; Kubany et al., 2000). At baseline, this measure asked about events in reference to one’s lifetime, but at annual follow-up visits the reference period was the past year. Further, the baseline TLEQ asked about sexual assaults that were experienced before age 13 and between age 13 and 18. Perceived drinking norms for sexual minority women were also assessed using a modified version of the Drinking Norms Rating Form (Baer, Stacy, & Larimer, 1991; Litt, Lewis, Rhew, Hodge, & Kaysen, 2015) that asked participants about the typical number of drinks consumed per week by a typical lesbian or bisexual woman.
Analytic Plan
We used marginal structural modeling to examine effects of sexual assault severity on alcohol use and drinking-related consequences. As the first step for these analyses, the inverse probability weights were calculated. To derive the IPWs for past year sexual assault, we first calculated the probability for being at one’s own level of sexual assault severity at each of the follow-up time points according to time-varying covariates measured at prior assessments as well as baseline covariates. Time-varying covariates included earlier levels of alcohol use and drinking-related consequences, past year sexual assault severity, other traumatic events, perceived heterosexism, perceived descriptive norms, PTSD symptoms, depression symptoms, and generalized anxiety symptoms. Baseline time-fixed covariates included age, race (White, Black, other race), Hispanic ethnicity, sexual identity (bisexual, lesbian), highest level of parents’ education, and sexual assault prior to age 18 (none, childhood = <13 years, adolescent = 13–18 years).
The distribution of the SES score at each of the follow-up visits was severely positively skewed with the vast majority of scores being 0 (>70%). Because of this, calculation of weights for the continuous SES score based on a probability density using a linear or log-linear model could lead to biased estimates (Naimi, Moodie, Auger, & Kaufman, 2014). The SES score was, thus, recategorized into three groups: (a) no sexual assault (score of 0); (b) moderate severity (score of 1 to 6); and (c) high severity (score of 7 or higher). These categories were selected in order to ensure that there were sufficient numbers within each category and that the size of the two highest bins was similar. Because of the categorical nature of this measure we used a cumulative probability (ordinal logistic) regression model to regress the sexual assault category at each relevant exposure time point (12- and 24-month assessment) on covariates reported prior to this time point. Model-predicted probabilities were used to derive the probability of one’s observed category of exposure (e.g., the predicted probability of being in the highest category of sexual assault severity for a woman who was actually in the highest category of severity) at each time point. These predicted probabilities served as the denominator of the IPWs. To improve precision of estimates, we used stabilized IPWs such that the numerator of the IPWs was the predicted probability of one’s own level of sexual assault severity according to time-fixed covariates only (e.g., race, baseline age). Thus, the stabilized IPW (SW) for subject i at follow-up visit j is defined as
where A is the category of sexual assault victimization, k is the level of exposure,
and
represent the exposure and covariate history up to time j, and V represents a vector of time-fixed covariates. Further, we truncated IPWs such that extreme low or high values were recoded to the first or 99th percentile in order to increase precision of estimates (Cole & Hernan, 2008).
A first set of models examined lagged effects of sexual assault severity at wave j − 1 on alcohol outcomes assessed at the following wave, j. For this set of models, the weights were applied to generalized estimating equations (GEE) models with robust standard errors and a working independence correlation in order to account for nesting of observations within individuals (Diggle, Heagerty, Liang, & Zeger, 2002). The alcohol outcome (typical drinks per week or alcohol-related consequences) at study time point j was regressed on sexual assault severity at the previous time point, j − 1. Because both alcohol outcomes were discrete counts that showed evidence of overdispersion, a negative binomial rather that Poisson form of the model was used. In negative binomial models, covariates are connected to the outcome via a log link. Coefficients for covariates are often exponentiated (eβ) to yield count ratios (CRs; also referred to as rate ratios) that describe the proportional change in the count associated with a one-unit increase in the covariate (Atkins & Gallop, 2007; Hilbe, 2014). Baseline time-fixed covariates were included in the GEE model to improve precision of parameter estimates (Cole & Hernan, 2008). The second set of models examined the effects of cumulative sexual assault severity over 2 years on the alcohol outcomes at the 36-month assessment. Cumulative severity was defined as the sum of SES categories at 12- and 24-month assessments (possible range: 0 to 4). Because only the 36-month outcomes were examined for these models, a single-level (non-GEE) negative binomial regression model was performed. The IPWs for this set of models was the product of the two wave-specific (12- and 24-month) IPWs. The same baseline time-fixed covariates were included in these models. For comparison, we also ran “traditional” nonweighted models that included the same time-fixed covariates for both the lagged and cumulative models.
As post hoc analyses, we also examined whether effects of sexual assault differed by baseline sexual orientation (lesbian vs. bisexual) by including sexual assault by sexual orientation interaction terms into the models.
There was 20%, 28%, and 30% of the sample missing data at 12-month 24-month and 36-month assessments, respectively. To account for missingness, we used multiple imputation where missing values were imputed according to various covariates including earlier and later measures of the variable (Graham, 2009). Assuming data are missing at random (MAR) such that missingness is not associated with unmeasured variables, parameter estimates using multiple imputation should be unbiased. At any given study visit, missingness of typical weekly drinking and alcohol-related consequences were not statistically significantly associated with those same measures from earlier or later assessments. Although not offering definitive proof, this is consistent with the MAR assumption. Imputation was performed using the multiple imputation chained equations (MICE) approach and 20 imputed data sets were created (Azur, Stuart, Frangakis, & Leaf, 2011; White, Royston, & Wood, 2011). Within each imputed dataset, the IPWs were calculated and then the weighted model was run. Parameter estimates were combined across the data sets and standard errors were calculated to account for the uncertainty of imputed values according to Rubin’s rules (Rubin, 2004). All analyses were performed using Stata 14 (StataCorp, College Station, TX).
ResultsTable 1 displays the distribution of selected demographics and other characteristics of the study sample. Nearly 60% of the sample identified as bisexual. The proportion of participants who had clinically elevated scores for depression, GAD, or PTSD was notably elevated compared with general population and primary care samples (Lowe et al., 2008; Mair et al., 2009; Stein, McQuaid, Pedrelli, Lenox, & McCahill, 2000; Walker, Newman, Dobie, Ciechanowski, & Katon, 2002). Consistent with extant literature, history of sexual assault was common with 39% of the sample reporting sexual assault during childhood (before age 13) and 35% reporting sexual assault during adolescence (between ages 13 and 18).
Distribution of Baseline Characteristics
Reports of sexual assault in adulthood were also common. As shown in Table 2, more than one half of the sample reported experiencing moderate or severe sexual assault between their 18th birthday and the baseline survey. Further, at each annual follow-up assessment more than 20% of the sample reported some form of sexual assault in the previous year. Based on the continuous version of the Sexual Assault Severity Scale (range: 0 to 30), the mean score was 2.0 at the 12- (SD = 5.2) and 24-month (SD = 5.4) follow-up visits and 1.6 (SD = 5.0) at the 36-month visit. Table 2 also shows levels of typical weekly drinking and drinking-related consequences at each of the study assessments.
Levels of Alcohol Use and Consequences and Sexual Assault Severity Across Study Assessments
Table 3 presents results from the first set of models that examined the effects of prior year sexual assault severity on typical level of drinking. According to findings from the marginal structural model, past year severe sexual assault was associated with a 71% higher count of typical drinks per week compared to no sexual assault (CR = 1.71; 95% CI [1.27, 2.31]). However, when comparing moderate to no sexual assault, there was no statistically significant association (CR = 1.13; 95% CI [0.85, 1.52]). The linear trend as assessed when modeling the SES as a single ordinal variable was statistically significant (p = .023). Notably, when using a “traditional” unweighted GEE model that adjusted only for the time-fixed covariates, the magnitude of associations was stronger compared to the weighted model. This suggests that effect sizes from the traditional model may be upwardly biased due to time-varying confounders not accounted for in the model.
Count Ratios for Typical Drinks per Week According to Sexual Assault Severity and Other Time-Fixed Covariates from Marginal Structural and Unweighted Models
Sexual assault severity also appeared to have effects on alcohol-related consequences (see Table 4). Similar to findings of typical drinking, past year severe sexual assault compared to no sexual assault was associated with a 63% higher level of alcohol consequences at the subsequent assessment (CR = 1.63; 95% CI [1.21, 2.20]). Although the association was not as strong, moderately severe compared to no sexual assault was also related to higher levels of alcohol-related consequences the following year (CR = 1.42; 95% CI [1.10, 1.84]). Further, the linear trend was statistically significant (p = .004). Again, nonweighted effect estimates were stronger than the weighted estimates.
Count Ratios for Alcohol Related Consequences According to Earlier Sexual Assault Severity and Other Time-Fixed Covariates from Marginal Structural and Unweighted Models
Effects of cumulative sexual assault severity over two years on alcohol outcomes at the 36-month follow-up assessment were also examined (see Table 5). Examining typical drinking as the outcome, findings from the marginal structural model showed that a one-unit increase in 2-year cumulative sexual assault severity categorical score was associated with a 27% increase in count of drinks per week at the final follow-up visit (CR = 1.27; 95% CI [1.14, 1.42]). There was also a strong association between 2-year cumulative sexual assault severity and drinking related consequences (CR = 1.27; 95% CI [1.12, 1.43]). Similar to analyses examining the 1-year lagged effects, findings showed stronger associations for both typical drinking and drinking consequences when using unweighted models. Parameters for other covariates (not shown) were similar to those from corresponding models shown in Tables 4 and 5. For the lagged and cumulative models, there was no evidence of moderation of sexual assault severity effects by sexual orientation for either alcohol outcome.
Count Ratiosa for Typical Drinks per Week and Alcohol-Related Consequences at 36-Month Follow-Up According to 2-Year Cumulative Sexual Assault Severity Based on Reports from 12- and 24-Month Follow-Up
DiscussionResults from marginal structural models indicated that there were deleterious effects of sexual assault on both typical drinking and alcohol-related consequences the following year. These effects were stronger with increasing levels of sexual assault severity. Further, there also appeared to be a cumulative effect of sexual assault on alcohol use where cumulative sexual assault severity over a 2-year period also predicted greater levels of drinking and more alcohol-related consequences. These findings point to the importance of prevention programming for SMW to decrease prevalence of sexual assault victimization and revictimization.
Consistent with other studies of prevalence of sexual assault exposure in SMW across the life span, we found high occurrence of exposure among young SMW (Balsam, Rothblum, & Beauchaine, 2005; D’Augelli, Pilkington, & Hershberger, 2002; Hughes et al., 2001; Hughes et al., 2010). In a review of 75 studies that included SMW, the median estimate of lifetime sexual assault was 43% for SMW (Rothman et al., 2011). In our sample, the prevalence of lifetime sexual assault was even higher—over one half experienced an assault as an adult and roughly one third experienced an assault in childhood or adolescence. This study highlights the important role of adult sexual victimization in understanding potential risk for alcohol use and consequences, even after accounting for multiple putative confounders. Sexual assault, as a particular risk for alcohol use among young SMW, has only relatively recently been an area of focus, while much of the earlier research has focused on child sexual victimization.
The minority stress model, where stress mediates relationships between a stigmatized sexual identity and, in this case, alcohol use and consequences (Meyer, 2003; Talley et al., 2016), has some limitations in that it does not incorporate potential psychological mediators between stress and health outcomes (Hatzenbeuhler, 2009). It also fails to incorporate moderators such as gender. The focus specifically on stressors that occur because of sexual orientation may fail to adequately consider issues of intersectionality, such as victimization that may be multiply determined by gender and by sexual orientation. It is also possible that there are cumulative effects of sexual victimization and discrimination among already marginalized and at risk populations where high intensity stressors such as moderate to severe repeated sexual victimization may have a particularly deleterious effect. More recent theoretical models address potential mechanisms of action for the effects of minority stress on health outcomes including coping and emotion regulation skills, social factors, and maladaptive cognitions (Hatzenbeuhler, 2009). Although this study was not situated to test these potential mechanisms, sexual victimization can lead to higher endorsement of coping motives for drinking, increased tension reduction alcohol expectancies, increased emotion dysregulation, and more negative cognitions about self and others, and higher drinking norms, all of which may help explain increased alcohol use and consequences (Bedard-Gilligan, Kaysen, Desai, & Lee, 2011; Gilmore et al., 2014; Stappenbeck, Bedard-Gilligan, Lee, & Kaysen, 2013; Ullman, Filipas, Townsend, & Starzynski, 2005). There may be an interaction between victimization and discrimination such that effects of these factors are stronger for women who have experienced both. Similarly, the appraisal of the intention of the assault (e.g., related to one’s sexual identity) may also amplify effects of the assault on alcohol outcomes. Future research should examine the explanatory and moderating roles of these factors among SMW.
The study findings have important public health and clinical implications. The widespread occurrence of sexual assault, both at baseline and across the three follow-up assessments, speaks to the vulnerability of this population and to the need for targeted services aimed at risk reduction. Findings highlight the need for targeted strategies to prevent sexual assault among SMW. Further, although sexual assault is a major public health problem in and of itself, implementation of effective sexual assault prevention strategies could also have substantial effects on reducing the burden of alcohol misuse in this population. As has been frequently noted, SMW are an important health disparities population and initiatives such as Healthy People 2020 (U.S. Department of Health & Human Services, 2016) have called for the reduction of sexual-orientation-related disparities across a range of health and behavioral outcomes. Based on the counterfactual framework and marginal structural model results, in this sample the average number of typical drinks per week among women who reported a severe sexual assault in the prior year would have declined from 10.3 to 6.0 and the average number of past-month alcohol-related consequences would have declined from 5.0 to 3.1 had these women actually not experienced any sexual assault. Further, although the difference would be more modest, had they not experienced any sexual assault, women experiencing a moderate sexual assault would have shown a decline from 4.4 to 3.1. In light of the magnitude of these potential reductions in drinking and alcohol-related consequences as well as the elevated prevalence of sexual assault among SMW, the prevention of sexual assault could yield important reductions in the disparity between SMW and heterosexual women in alcohol misuse.
Further, although the blame for victimization lies firmly on the perpetrator, there is clinical utility in implementing effective risk reduction programs to empower potential victims and decrease incidence of victimization among high-risk groups such as SMW. Future studies should seek to explore other factors that may play a role in understanding the relationship between sexual assault and drinking behavior, such as relationship with the perpetrator, the development of posttraumatic stress disorder, and adaptive and maladaptive coping strategies in the aftermath of an assault, to best understand how to improve prevention and intervention efforts for SMW. SMW are at unique increased risk for adverse societal and cultural experiences, such as discrimination and microaggressions, in addition to sexual assault. Thus, better understanding of the ways in which these factors interact and the strategies that can promote recovery and reduce risky behavior, such as drinking, for this population is crucial.
A major strength of this study is its use of marginal structural models to account for time-varying confounding. Using this approach, we were able to isolate and estimate the specific causal pathway from sexual assault to alcohol use adequately accounting for other time-varying factors that can often co-occur with both. The utility of this approach is highlighted when comparing results from the weighted and unweighted models. Results from the unweighted model were consistently stronger than those from the weighted model. This suggests that there are important confounders that were not adequately accounted for in the traditional model. Application of the marginal structural modeling approach in other longitudinal investigations of effects of risk factors on alcohol outcomes could prove beneficial in establishing more accurate effect sizes.
Despite these notable strengths there are limitations that should be considered when evaluating the study outcomes. First, this sample was recruited online and there were differences in racial composition between those who ultimately consented and those who declined participation. It is therefore possible that the sample may not be representative of the general population of young adult SMW in the United States. However, regarding the online sampling, prior studies have shown that online recruitment methods can be reflective of intended populations of interest (Harris, Loxton, Wigginton, & Lucke, 2015). Also, obtaining large samples of hard-to-reach minority populations using traditional sampling methods such as random digit dialing may not be feasible. Finally, as highlighted by recent discussions in epidemiology, although representativeness is necessary for descriptive epidemiologic studies that are intended to report on the health status of a population, it may not be as relevant for studies that are intended to understand causal mechanisms and that have appropriate adjustment for potential confounding (Rothman, Gallacher, & Hatch, 2013). Sexual orientation can change for women over time, yet sexual orientation analytically was treated as a time-fixed covariate. Indeed there was some evidence of sexual orientation change over follow-up. However, the prevalence of change at any follow-up wave was low (<8%). Thus, including this as a time-varying covariate in estimation of IPWs could lead to extreme weights for some participants. Descriptive statistics also suggest that change in orientation showed no significant association with sexual assault at the following wave. Thus, we would not expect changing sexual orientation to bias our findings in any appreciable manner. Another limitation is the use of self-report measures of alcohol use and consequences which may underestimate true levels of consumption and consequences. However, the DDQ and YAACQ, measures used for this study, have been validated against other criterion standards in multiple studies. Further, because of the longitudinal nature of the study, there is a lower likelihood of differential reporting of alcohol use and consequences due to sexual assault history.
To summarize, using a marginal structural modeling approach that accounts for both time-fixed and time-varying confounders, this study found effects of sexual assault on increasing levels of typical drinking and alcohol-related consequences one year later among SMW. Further, 2-year accumulation of sexual assault exposure also appeared to have effects on increasing alcohol use and alcohol-related consequences. This evidence highlights the health consequences of sexual assault and the need to identify effective prevention efforts to reduce risk of sexual assault and its long-term sequelae, especially among SMW. As research into this area progresses, understanding mechanisms through which sexual assault leads to increased drinking is necessary to provide clearer targets of intervention. Further, studies that include both sexual minority and heterosexual women may be informative to compare effects between the two groups. Overall, SMW are a highly vulnerable group for both sexual assault victimization and substance use and clearly there is a need for additional research to understand both shared and unique factors between SMW and heterosexual women that contribute to the increased risk.
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Submitted: August 22, 2016 Revised: January 23, 2017 Accepted: January 28, 2017
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Source: Journal of Consulting and Clinical Psychology. Vol. 85. (5), May, 2017 pp. 424-433)
Accession Number: 2017-11101-001
Digital Object Identifier: 10.1037/ccp0000202
Record: 60- Title:
- Efficacy of mindfulness-based addiction treatment (MBAT) for smoking cessation and lapse recovery: A randomized clinical trial.
- Authors:
- Vidrine, Jennifer Irvin. Stephenson Cancer Center, University of Oklahoma Health Sciences Center, Oklahoma City, OK, US, Jennifer-Vidrine@ouhsc.edu
Spears, Claire Adams. Department of Psychology, Catholic University of America, Washington, DC, US
Heppner, Whitney L.. Department of Psychological Science, Georgia College and State University, Milledgeville, GA, US
Reitzel, Lorraine R.. Department of Educational Psychology, University of Houston, Houston, TX, US
Marcus, Marianne T.. Center for Substance Abuse Prevention, Education and Research, UTHealth School of Nursing, TX, US
Cinciripini, Paul M.. Department of Behavioral Science, University of Texas MD Anderson Cancer Center, Houston, TX, US
Waters, Andrew J.. Department of Medical and Clinical Psychology, Uniformed Services University of the Health Sciences, Bethesda, MD, US
Li, Yisheng. Department of Biostatistics, University of Texas MD Anderson Cancer Center, Houston, TX, US
Nguyen, Nga Thi To. Department of Health Disparities Research, University of Texas MD Anderson Cancer Center, Houston, TX, US
Cao, Yumei. Michael E. DeBakey Veterans Affairs Medical Center Houston, Houston, TX, US
Tindle, Hilary A.. Division of General Internal Medicine and Public Health, Vanderbilt University, Nashville, TN, US
Fine, Micki. Mindful Living, Houston, TX, US
Safranek, Linda V.. Independent Practice, Kingwood, TX, US
Wetter, David W.. Department of Psychology, Rice University, Houston, TX, US - Address:
- Vidrine, Jennifer Irvin, Stephenson Cancer Center, University of Oklahoma Health Sciences Center, 655 Research Parkway, Suite 400, Office 454, Oklahoma City, OK, US, 73104, Jennifer-Vidrine@ouhsc.edu
- Source:
- Journal of Consulting and Clinical Psychology, Vol 84(9), Sep, 2016. pp. 824-838.
- NLM Title Abbreviation:
- J Consult Clin Psychol
- Page Count:
- 15
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- Journal of Consulting Psychology
- Other Publishers:
- US : American Association for Applied Psychology
US : Dentan Printing Company
US : Science Press Printing Company - ISSN:
- 0022-006X (Print)
1939-2117 (Electronic) - Language:
- English
- Keywords:
- mindfulness, tobacco treatment, group therapy, nicotine dependence
- Abstract (English):
- Objective: To compare the efficacy of Mindfulness-Based Addiction Treatment (MBAT) to a Cognitive Behavioral Treatment (CBT) that matched MBAT on treatment contact time, and a Usual Care (UC) condition that comprised brief individual counseling. Method: Participants (N = 412) were 48.2% African American, 41.5% non-Latino White, 5.4% Latino, and 4.9% other, and 57.6% reported a total annual household income < $30,000. The majority of participants were female (54.9%). Mean cigarettes per day was 19.9 (SD = 10.1). Following the baseline visit, participants were randomized to UC (n = 103), CBT (n = 155), or MBAT (n = 154). All participants were given self-help materials and nicotine patch therapy. CBT and MBAT groups received 8 2-hr in-person group counseling sessions. UC participants received 4 brief individual counseling sessions. Biochemically verified smoking abstinence was assessed 4 and 26 weeks after the quit date. Results: Logistic random effects model analyses over time indicated no overall significant treatment effects (completers only: F(2, 236) = 0.29, p = .749; intent-to-treat: F(2, 401) = 0.9, p = .407). Among participants classified as smoking at the last treatment session, analyses examining the recovery of abstinence revealed a significant overall treatment effect, F(2, 103) = 4.41, p = .015 (MBAT vs. CBT: OR = 4.94, 95% CI: 1.47 to 16.59, p = .010, Effect Size = .88; MBAT vs. UC: OR = 4.18, 95% CI: 1.04 to 16.75, p = .043, Effect Size = .79). Conclusion: Although there were no overall significant effects of treatment on abstinence, MBAT may be more effective than CBT or UC in promoting recovery from lapses. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
- Impact Statement:
- What is the public health significance of this article?—Although there were no significant differences in overall abstinence between Mindfulness-Based Addiction Treatment (MBAT) and traditional Guideline-based treatments within a diverse and relatively low SES sample of smokers, MBAT may be more efficacious than CBT or UC in facilitating lapse recovery. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Cognitive Behavior Therapy; *Group Counseling; *Nicotine; *Smoking Cessation; *Mindfulness; Drug Dependency; Recovery (Disorders); Relapse Prevention; Treatment Effectiveness Evaluation
- PsycINFO Classification:
- Health & Mental Health Treatment & Prevention (3300)
- Population:
- Human
Male
Female - Location:
- US
- Age Group:
- Adulthood (18 yrs & older)
- Tests & Measures:
- Mindfulness Technique Practice During Treatment Measure
Smoking Abstinence Measure
Mindfulness Attention Awareness Scale
Kentucky Inventory of Mindfulness
Heaviness of Smoking Index DOI: 10.1037/t04726-000 - Grant Sponsorship:
- Sponsor: National Institute on Drug Abuse, US
Recipients: No recipient indicated
Sponsor: Centers for Disease Control and Prevention, US
Recipients: No recipient indicated
Sponsor: National Cancer Institute, US
Recipients: No recipient indicated
Sponsor: National Center for Complementary and Integrative Health
Recipients: No recipient indicated
Sponsor: National Institute of General Medical Sciences, US
Recipients: No recipient indicated
Sponsor: Oklahoma Tobacco Settlement Endowment Trust, US
Recipients: No recipient indicated - Methodology:
- Clinical Trial; Empirical Study; Quantitative Study
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- First Posted: May 23, 2016; Accepted: Apr 8, 2016; Revised: Mar 8, 2016; First Submitted: Dec 31, 2014
- Release Date:
- 20160523
- Correction Date:
- 20160815
- Copyright:
- American Psychological Association. 2016
- Digital Object Identifier:
- http://dx.doi.org/10.1037/ccp0000117
- PMID:
- 27213492
- Accession Number:
- 2016-25468-001
- Number of Citations in Source:
- 85
- Persistent link to this record (Permalink):
- http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2016-25468-001&site=ehost-live
- Cut and Paste:
- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2016-25468-001&site=ehost-live">Efficacy of mindfulness-based addiction treatment (MBAT) for smoking cessation and lapse recovery: A randomized clinical trial.</A>
- Database:
- PsycINFO
Efficacy of Mindfulness-Based Addiction Treatment (MBAT) for Smoking Cessation and Lapse Recovery: A Randomized Clinical Trial
By: Jennifer Irvin Vidrine
Stephenson Cancer Center and Department of Family and Preventive Medicine, University of Oklahoma Health Sciences Center;
Claire Adams Spears
Department of Psychology, Catholic University of America
Whitney L. Heppner
Department of Psychological Science, Georgia College and State University
Lorraine R. Reitzel
Department of Educational Psychology, University of Houston
Marianne T. Marcus
Center for Substance Abuse Prevention, Education and Research, UTHealth School of Nursing
Paul M. Cinciripini
Department of Behavioral Science, University of Texas MD Anderson Cancer Center
Andrew J. Waters
Department of Medical and Clinical Psychology, Uniformed Services University of the Health Sciences
Yisheng Li
Department of Biostatistics, University of Texas MD Anderson Cancer Center
Nga Thi To Nguyen
Department of Health Disparities Research, University of Texas MD Anderson Cancer Center
Yumei Cao
Michael E. DeBakey Veterans Affairs Medical Center Houston, Texas
Hilary A. Tindle
Division of General Internal Medicine and Public Health, Vanderbilt University
Micki Fine
Mindful Living, Houston, Texas
Linda V. Safranek
Independent Practice, Kingwood, Texas
David W. Wetter
Department of Psychology, Rice University
Acknowledgement: This research and preparation of this article were supported by grants from the National Institute on Drug Abuse, the Centers for Disease Control and Prevention, the National Cancer Institute, the National Center for Complementary and Integrative Health, The National Institute of General Medical Sciences, and the Oklahoma Tobacco Settlement Endowment Trust. The content is solely the responsibility of the authors and does not necessarily represent the official views of the funding agencies.
The prevalence of smoking in the United States, although declining, remains high at 17.8% (Jamal et al., 2014). Most smokers want to quit, and nearly half of all smokers attempt to quit each year (CDC, 2009), but only about 5% of all smokers successfully quit each year (Cohen et al., 1989). These low quit rates are not surprising given that cessation is associated with increased levels of negative affect and stress that can persist for months, as assessed by both self-report and asymmetries in brain activity (Gilbert et al., 2002; Piasecki, Fiore, & Baker, 1998). This phenomenon is further complicated by a plethora of evidence indicating that stress, negative affect, and depression strongly predict and are setting events for relapse (Baker, Brandon, & Chassin, 2004; Borrelli, Bock, King, Pinto, & Marcus, 1996; Brandon, 1994; Correa-Fernandez et al., 2012; Glassman et al., 1990; Niaura et al., 1999; Shiffman, 2005; Welsch et al., 1999). Thus, an important goal for intervention development research is to carefully target these aversive emotional consequences of quitting smoking in an effort to enhance cessation rates and ultimately prevent relapse. One factor found to be broadly and consistently linked with enhanced emotional regulation is mindfulness, and mindfulness-based treatments may be particularly well suited for treating nicotine dependence and other substance use disorders.
Definition of MindfulnessMindfulness has been defined as “paying attention in a particular way: on purpose, in the present moment, and nonjudgmentally” (Kabat-Zinn, 1994) and as “bringing one’s complete attention to the present experience on a moment-to-moment basis” (Marlatt & Kristeller, 1999). All approaches that include mindfulness note that it should be practiced nonjudgmentally, meaning that to the extent possible, phenomena entering awareness should not be labeled as true or false, good or bad, and so forth (Kabat-Zinn, 1994; Linehan, 1994; Segal, Teasdale, Williams, & Gemar, 2002). A key characteristic of mindfulness is that by simply noticing emotions, cognitions, perceptions, and sensations in a nonjudgmental manner, individuals learn over time that these phenomena are transient and do not demand impulsive action (Heppner, Spears, Vidrine, & Wetter, 2015). Thus, flexible, adaptive responding is fostered when awareness is brought to the present moment (Roemer & Orsillo, 2003; Teasdale, 1997).
Evidence Broadly Supporting Mindfulness-Based TreatmentsThe two most prominent explicitly mindfulness-based treatments are Mindfulness-Based Stress Reduction (MBSR; Kabat-Zinn, 1990) and Mindfulness-Based Cognitive Therapy (MBCT; Segal, Vincent, & Levitt, 2002). MBSR was initially targeted at stress and pain-related disorders, and MBCT was developed to treat chronic or recurrent depressive disorders. Both of these approaches use meditation as the principal means of teaching mindfulness. Numerous meta-analyses have concluded that mindfulness-based treatments are effective across a wide range of conditions and disorders (e.g., stress, pain, anxiety-related disorders, eating disorders, depressive relapse, psychological and physiological outcomes for individuals with vascular disease, multiple sclerosis, fibromyalgia, somatization, and mental health disorders; Abbott et al., 2014; Aucoin, Lalonde-Parsi, & Cooley, 2014; Baer, 2003; Godfrey, Gallo, & Afari, 2015; Hatchard, Lepage, Hutton, Skidmore, & Poulin, 2014; Kabat-Zinn, 1982; Kabat-Zinn, Lipworth, & Burney, 1985; Kabat-Zinn, Lipworth, Burney, & Sellers, 1986; Khoury, Lecomte, Fortin et al., 2013; Khoury, Lecomte, Gaudiano, & Paquin, 2013; Kim et al., 2013; Kristeller & Hallett, 1999; Lakhan & Schofield, 2013; Lauche, Cramer, Dobos, Langhorst, & Schmidt, 2013; Speca, Carlson, Goodey, & Angen, 2000). In addition, multiple reviews of the meditation literature have concluded that MBSR and meditation were effective not only across numerous disorders and populations, but that in many cases, MBSR was effective when the individual treatment groups themselves were heterogeneous with respect to the condition/disorder being treated (Baer, 2003; Chiesa & Serretti, 2009, 2011, 2014; Hofmann, Sawyer, Witt, & Oh, 2010). Importantly, mindfulness-based treatments lead to improvements in both anxious and depressive mood states (Goyal et al., 2014), and mindfulness/metacognitive awareness appears to a key mechanism of action. Furthermore, MBCT has demonstrated strong efficacy in preventing relapse to depression compared to alternative approaches (Brown & Ryan, 2003; Teasdale et al., 2002).
There is a rapidly growing body of published studies that have evaluated the efficacy of mindfulness-based treatments for nicotine dependence and other substance use disorders. Outcomes evaluated have included tobacco and other substance use (Bowen et al., 2009; Bowen & Marlatt, 2009; Brewer et al., 2011; Brewer et al., 2009; Davis, Fleming, Bonus, & Baker, 2007; Davis, Goldberg, et al., 2014), psychological distress, craving, mindfulness (Davis et al., 2007; Davis, Manley, Goldberg, Smith, & Jorenby, 2014), and treatment dropout (Marcus et al., 2007). Although these studies have generally been small with varied outcomes, the results have been promising.
To date, at least six studies have evaluated the efficacy of mindfulness-based treatments for nicotine dependence. Four of the studies (Bowen & Marlatt, 2009; Brewer et al., 2011; Davis, Manley et al., 2014; Davis et al., 2013) found that the mindfulness-based intervention evaluated produced significantly higher smoking abstinence rates than the control treatment. The study conducted by Brewer and colleagues (2011) compared a Mindfulness Training (MT) intervention for smoking cessation to the American Lung Association’s Freedom From Smoking (FFS) treatment (N = 88). Both interventions were delivered in a group format over a 4-week period, twice per week. Smoking abstinence was assessed at the end of treatment and 13 weeks following the end of treatment. MT participants had slightly (although not significantly) higher abstinence rates at the end of treatment (i.e., 36% vs. 15%, p = .06), and significantly higher abstinence rates 13 weeks following the end of treatment (i.e., 31% vs. 6%, p = .01). One of these studies found that although mindfulness-based treatment was not associated with significantly higher abstinence rates compared to standard treatment (25.0% vs. 17.9%), mindfulness-based treatment participants reported significantly greater decreases in smoking urges, perceived stress, and experiential avoidance, and significantly greater increases in mindfulness (Davis, Manley et al., 2014). The remaining study was very small (n = 18) and uncontrolled, but found that an 8-week group mindfulness-based intervention yielded a 7-day biochemically confirmed point prevalence abstinence rate of 56% at 6 weeks following the quit date (Davis et al., 2007). Results further indicated that compliance with mindfulness meditation was positively associated with decreases in stress and affective distress. In addition, compliance with mindfulness meditation was also positively associated with smoking abstinence.
At least three studies have evaluated the efficacy of mindfulness-based treatments for other substance use. Two of these studies found that the mindfulness-based treatments evaluated were associated with significantly lower rates of substance use (Bowen et al., 2009, 2014), whereas the other study found no differences in substance use outcomes between a mindfulness-based treatment and a standard CBT-based control condition (Brewer et al., 2009). In addition, the Bowen and colleagues (2009) study described above found that mindfulness-based treatment was associated with significantly greater increases in acceptance and acting with awareness, and significantly greater decreases in craving.
Mindfulness-Based Addiction Treatment (MBAT)Given that many smokers have a history of failed quit attempts, high levels of nicotine dependence, and/or other comorbidities (Irvin & Brandon, 2000), there is a critical need for new behavioral treatments. Mindfulness-based treatments may add an innovative and important intervention option to the clinical end of the treatment continuum for nicotine dependence. Mindfulness-based treatments may be particularly appropriate given the efficacy of mindfulness-based interventions in reducing emotional distress across exceedingly diverse conditions and populations, and evidence that greater trait mindfulness is associated with higher smoking cessation rates, greater ability to recover from a smoking lapse, and a plethora of beneficial factors (Heppner et al., 2015, 2016; Vidrine et al., 2009; Waters et al., 2009). Furthermore, mindfulness researchers have noted that future rigorous tests of mindfulness-based interventions should include adequate control groups and sufficient power (Baer, 2003; Dimidjian & Linehan, 2003; Roemer & Orsillo, 2003; Teasdale, Segal, & Williams, 1995).
The current study was specifically designed to be responsive to these issues and to build upon the foundation of the studies described above, as well as other previous studies. To the best of our knowledge, the current study is the largest randomized clinical trial to evaluate a mindfulness-based treatment for nicotine dependence or other substance use disorder. This trial was adequately powered to support a rigorous evaluation of the efficacy of Mindfulness-Based Addiction Treatment (MBAT) compared to two control conditions, a Cognitive Behavioral Treatment (CBT) condition that matched MBAT on treatment contact time (i.e., number and length of counseling sessions) and a Usual Care (UC) condition comprised of brief individual counseling sessions based on the Treating Tobacco Use and Dependence Clinical Practice Guideline (Fiore et al., 2008). Although CBT and MBAT were matched on treatment contact time, MBAT included homework assignments whereas CBT did not include such assignments. Given our prior research suggesting that trait mindfulness is associated with significantly higher rates of abstinence and recovery of abstinence following a lapse (Heppner et al., 2016), we hypothesized that individuals randomized to MBAT (vs. CBT or UC) would be more likely to achieve abstinence and to recover abstinence following a smoking lapse.
Method Participants
Participants were recruited from the Houston metropolitan area via local print media. Inclusion criteria included: ≥18 years of age, current smoker with an average of at least 5 cigarettes per day for the past year, motivated to quit smoking within the next 30 days, had a viable home address and phone number, able to read and write in English, an expired air CO level of ≥8 ppm, and provided collateral contact information. Exclusion criteria included: contraindication for nicotine patch use, regular use of tobacco products other than cigarettes, use of bupropion or nicotine replacement products other than the study patches, pregnancy or lactation, another household member enrolled in the study, active substance dependence, current psychiatric disorder or use of psychotropic medications, and participation in a smoking cessation treatment program in the previous 90 days. Study advertisements asked if individuals wanted help with quitting smoking, indicated that counseling and nicotine patches would be provided, and stated that participants would be compensated for their time. All data were collected between January 2007 and February 2010. The study was approved by the institutional review board of The University of Texas MD Anderson Cancer Center and informed consent was obtained from all participants.
Procedures
Following the baseline visit, participants were randomized into UC (n = 103), CBT (n = 155), or MBAT (n = 154) using a form of adaptive randomization called minimization, see Table 3 for an overview of the treatment content and timeline for each of the three conditions. Randomization was based on age, education, race/ethnicity, depression history, and cigarettes per day. Participants and research personnel were not blinded to treatment condition following randomization. Fewer participants were randomized to UC (vs. MBAT and CBT) because we expected that there would be a larger difference in abstinence between MBAT and UC than between MBAT and CBT, and a power analysis revealed that a smaller sample size in the UC group yielded sufficient power. Participant flow through the study is detailed in Figure 1. All groups were given self-help materials and nicotine patch therapy. Patch therapy for participants who smoked >10 cigarettes per day consisted of 4 weeks of 21 mg patches, 1 week of 14 mg patches, and 1 week of 7 mg patches. Patch therapy for participants who smoked 5 to10 cigarettes per day consisted of 4 weeks of 14 mg patches and 2 weeks of 7 mg patches. Self-help materials consisted of the consumer products developed for the 2008 update of the Treating Tobacco Use and Dependence Clinical Practice Guideline (Fiore et al., 2008). The CBT and MBAT groups received eight 2-hr in-person group counseling sessions. UC participants received four 5- to 10-min Guideline-based individual counseling sessions (Fiore et al., 2008).
Treatment Content and Timeline
Figure 1. CONSORT flowchart for recruitment, enrollment, and follow-up assessments.
MBAT Intervention
MBCT represents an adaptation of MBSR that includes techniques from cognitive–behavioral therapy. Because MBAT integrates MBSR with a cognitive–behavioral/relapse prevention theory based approach to smoking cessation, MBAT is closely modeled on MBCT. The rationale and session-by-session instructions for MBCT have been published by Segal and colleagues (2002; Segal, Williams, & Teasdale, 2002). MBAT closely follows the MBCT treatment procedures, but replaces the depression-related material with nicotine dependence-related material. MBAT utilizes the same structure, within and across sessions, as does MBCT.
The core aims of MBAT are derived from MBCT. Those aims are to help individuals: (a) become more aware of thoughts, feelings, and sensations from moment to moment, (b) develop a different way of relating to thoughts, feelings, and sensations, and (c) increase the ability to disengage attention and choose skillful responses to any thoughts, feelings, or situations that arise. Therefore, Sessions 1–4 of MBAT concentrated on learning how to direct and focus attention. Participants were taught to become aware of how little attention is usually paid to what they are doing in their daily life (i.e., how much of their daily lives are spent on “automatic pilot”). In addition, they were taught to become aware of how rapidly the mind shifts between topics. Next, they learned how to not only notice that the mind is wandering, but to bring it back to a single focus on the breath. Furthermore, participants learned how a wandering mind can increase negative thoughts and feelings. For example, fantasies about smoking can lead to feelings of anger about being deprived of cigarettes. Engaging in these thoughts can easily escalate craving such that it becomes more difficult to enact a purposeful, adaptive response. By bringing attention back to the present moment, however, one can disengage from this cascade of thoughts and deal with the situation much more flexibly. For example, one could note that the craving is a sensation or mental event (as opposed to an imperative) and simply notice the sensations nonjudgmentally until they pass, choose to engage in a coping behavior, or bring one’s attention back to the breath, which is designed to refocus attention on the present moment.
It is important to note that MBAT is also similar to MBRP in many respects (Bowen et al., 2009; e.g., teaching mindfulness-based strategies for coping with cravings), but differs in key ways. First, MBRP has been evaluated primarily as an aftercare program, whereas MBAT is intended to serve as a primary treatment approach. Second, MBRP opens with at least 20 min of meditation, whereas MBAT incorporates meditation practice later within each treatment session. We created a manual of the MBAT program for use in this trial (Wetter et al., 2007), with content that paralleled that of CBT (described below) in addition to the mindfulness-based techniques.
The scheduled quit day was on Session 5 for participants randomized to MBAT. Sessions 5–8 focused on continuing to develop awareness of the present moment, along with an expansion of techniques for dealing with problematic thoughts, feelings, and situations. To provide an example, one technique is a “breathing space.” A breathing space involves three steps: (a) bringing attention into the present moment and becoming aware of one’s current experience (thoughts, feelings, and bodily sensations), (b) gathering one’s full attention so that it can be redirected to breathing and using the breath as a tool to anchor oneself in the present moment, (c) followed by expanding the field of awareness around breathing to the entire body. The breathing space occupies a central role in both MBCT and MBAT, can be used in virtually any situation, and is a technique for stepping out of automatic pilot by bringing attention to the present moment. Importantly, the breathing space is a method of generalizing the practice of mindfulness that is developed with formal meditation practices to one’s daily life. Participants were taught that they can utilize a breathing space whenever they become aware of urges, stressful situations, or other problematic phenomena.
Cognitive Behavioral Treatment (CBT)
CBT utilized a fairly standard problem-solving/coping skills training approach based on relapse prevention theory (Marlatt & Gordon, 1985) and the Guideline (Fiore et al., 2008). The treatment is manualized and the manual provides a detailed overview of each session including time estimates for each activity and notes to the therapist highlighting potential participant issues and possible responses/probes. All activities are geared toward promoting smoking cessation and the maintenance of abstinence. Each session has specific objectives, and each activity coincides with a minimum of at least one objective. Salient issues covered include nicotine replacement therapy, commitment to abstinence, social/peer pressure, health issues, motivation to change, commitment to change, and coping with stress. Major topics covered in the eight group sessions included: (a) planning to quit smoking; smoking patterns; tools to quit; (b) nicotine addiction; using the nicotine patch; health impact of smoking; triggers; (c) adjusting the stop smoking plan; (d) stress management tools; (e) nutrition and exercise; (f) coping skills; (g) social factors influencing smoking; costs/benefits of quitting; and (h) tapering off the patch; maintaining abstinence; and review of skills from the program. The scheduled quit day was on Session 5 for participants randomized to CBT in order to match MBAT.
We chose CBT as our control condition because it is an empirically supported and recommended treatment for smoking cessation (Fiore et al., 2000; Fiore et al., 2008). Treatment contact time and assessments were identical in CBT and MBAT, and therapists were completely crossed with treatment group (i.e., CBT and MBAT groups differed only with respect to counseling content). This study design was intended to allow us to carefully delineate MBAT mechanisms and effects from the effects of an empirically supported cessation treatment that was matched on treatment delivery modality (i.e., group), clinical contact (i.e., number of sessions and duration of each session), and therapists.
Usual Care (UC) Intervention
UC participants received four 5- to 10-min individual counseling sessions based on the Guideline (Fiore et al., 2008). UC was intended to be equivalent to the intervention a smoker might receive when asking a health care provider for help. The content of the sessions emphasized problem-solving and coping skills training. The scheduled quit day was on Session 3 for participants randomized to UC.
Treatment Delivery and Integrity
The CBT and MBAT groups were led by two masters-level therapists, both of whom were skilled in delivering MBSR and one of whom held a certification in MBSR awarded by the University of Massachusetts Medical Center. Both therapists had extensive personal mindfulness practices and completed approximately 15 hours of training on the components of treatment related to smoking cessation. All groups were led individually by a single therapist. To ensure that any potential treatment group differences would not be attributable to therapist effects, UC was also delivered by the same two therapists that delivered MBAT and CBT. Therapists were completely crossed with treatments such that each counselor delivered equal numbers of MBAT and CBT groups. Therefore, therapist effects were not controlled for in the analyses.
Overall, 37.7% of participants in MBAT completed all eight group counseling sessions, 53.2% completed between four and seven sessions, and 9.1% completed between one and three sessions. In CBT, 34.8% of participants completed all eight group counseling sessions, 52.9% completed between four and seven sessions, and 12.3% completed between one and three sessions. Of those in UC, 53.4% completed all four in-person individual counseling sessions, 30.1% completed three of the sessions, and 16.5% completed one or two sessions.
Measures
All questionnaires were administered and completed via computer. The measures and variables examined are described below.
Demographics
Demographic variables collected at baseline included age, gender, race/ethnicity, partner status, total annual household income, and educational level.
Nicotine dependence
Nicotine dependence was assessed at baseline using the Heaviness of Smoking Index (HSI) (Kozlowski, Porter, Orleans, Pope, & Heatherton, 1994). The HSI comprises the two items from the Fagerstrom Test for Nicotine Dependence (FTND) that most strongly predict smoking relapse, cigarettes per day (CPD) and minutes to the first cigarette after waking. Given that the HSI comprises only two items, and has demonstrated psychometric equivalence to the FTND in multiple studies (Borland, Yong, O’Connor, Hyland, & Thompson, 2010; Chabrol, Niezborala, Chastan, & de Leon, 2005; Hymowitz et al., 1997; Kozlowski et al., 1994; Schnoll, Goren, Annunziata, & Suaya, 2013), it was chosen to reduce participant burden.
Mindfulness technique practice during treatment
Among participants randomized to the MBAT condition, their practice of mindfulness techniques was assessed weekly during the course of treatment. Participants were asked to report (a) the average number of days spent engaging in any of the mindfulness techniques learned during the MBAT treatment during the previous week, and (b) the average number of days spent during the previous week engaging in specific mindfulness techniques taught during treatment (i.e., sitting meditation, body scan, walking meditation, yoga, and awareness of the breath). This measure was administered at seven time points (i.e., Weeks 2, 3, 4, 5, 6, 7, and 8), and self-reported days spent practicing were averaged across the weeks to generate composite variables that reflected average days per week spent practicing mindfulness techniques in general as well as for each of the specific mindfulness techniques.
Smoking abstinence
Seven-day point prevalence abstinence from smoking was assessed at two time points, 4 weeks following the quit day and 26 weeks following the quit day. Because participants were expected to be using the nicotine patch at the assessment that occurred 4 weeks following the quit day, self-reported abstinence was biochemically confirmed using a CO level <6 ppm. We elected to use a CO cutoff of <6 ppm rather than a cutoff of <10 ppm because some research has indicated that a cutoff of <10 ppm may be too high, and may ultimately result in the misclassification of a proportion of smokers as nonsmokers (Javors, Hatch, & Lamb, 2005). It is important to note that we analyzed our data using both cutoff points, and no statistically significant differences in outcomes were observed. Specifically, at the 4-week assessment, point-prevalence abstinence was defined as self-report of complete abstinence from smoking for the previous 7 days and an expired CO level <6 ppm. Continuous abstinence was defined as self-report of complete abstinence from smoking since the quit day, and an expired CO level <6 ppm.
At the 26-week assessment, point-prevalence abstinence was defined as self-report of complete abstinence from smoking for the previous 7 days and CO level <6 ppm. Continuous abstinence was defined as self-report of complete abstinence from smoking since the quit day, and a CO level <6 ppm. However, those participants who did not attend the in-person 26-week assessment visit were asked to provide a saliva cotinine sample via mail. For those participants who provided a saliva sample (n = 29), a saliva cotinine level cutoff of <20 ng/ml was utilized to biochemically confirm self-reported abstinence from smoking. Of the participants at the 26-week assessment who self-reported abstinence and lacked CO data, none reported use of any nicotine replacement products. Therefore, saliva cotinine levels should not have been affected by therapeutic nicotine use. This collection method has been validated in prior research (McBride et al., 1999).
Lapse recovery
Recovery from a lapse was assessed by examining biochemically confirmed (CO <6ppm), 7-day point prevalence abstinence rates at 26 weeks post quit day among participants who were classified as smoking at the end of treatment.
Data Analysis
Chi-square tests and one-way ANOVA tests were used to evaluate differences between MBAT, CBT, and UC at baseline on demographics, smoking rate, and levels of trait mindfulness. Biochemically confirmed 7-day point prevalence and continuous abstinence assessments were conducted 4 weeks and 26 weeks post quit day. Logistic random effects modeling examined 7-day point prevalence abstinence over time (i.e., at 4 and 26 weeks post quit day) and continuous ratio logit models examined continuous abstinence over time. Both unadjusted and adjusted analyses (controlling for age, education, gender, race/ethnicity, partner status, and HSI scores) were conducted. Because there were no differences between unadjusted and adjusted analyses, only adjusted analyses are reported. In addition, the time indicator (Week 4 and Week 26) was included as a covariate in the logistic random effects and continuous ratio logit models. Consistent with standard practice in smoking cessation trials, completers-only and intent-to-treat analyses (whereby participants lost to follow-up were coded as relapsed) were conducted. In addition, because single imputation methods may be more biased than other approaches to missing data, attrition analyses were conducted to ascertain whether there were any systematic differences between those with complete data versus those lost to follow-up. To further investigate this question, a sensitivity analysis was conducted to examine the effects of varying missing data assumptions.
Because behaviors within groups are often dependent (i.e., influenced by other members of the group), failure to take group effects into account in statistical analyses may lead to inaccurate inferences, particularly in the form of Type I errors (Herzog et al., 2002; Kapson, McDonald, & Haaga, 2012). Therefore, all models examined controlled for group effects. Group effects were controlled for by including a random intercept of treatment group membership to the model to account for the nested structure of the data (i.e., participants being nested in groups). The ICC was 0.005 for the group effect model. Due to the considerably reduced sample size of the lapse recovery group, this estimate of the covariance parameter was numerically unstable. Therefore, we did not calculate the ICC for this model.
Treatment effects of MBAT (vs. CBT and UC) in helping individuals to recover from lapses were also examined. Specifically, logistic random effect modeling was used to examine group differences in 7-day point prevalence abstinence rates over time among participants who were classified as smoking at the end of treatment (adjusting for age, education, gender, race/ethnicity, partner status, and HSI scores). Finally, associations of mindfulness practice with smoking cessation outcomes were examined among individuals in MBAT.
Results Participant Characteristics
Participants (N = 412) were racially/ethnically diverse (48.2% African American, 41.5% non-Latino White, 5.4% Latino, and 4.9% other) and most reported a total annual household income of less than $30,000 (57.6%). The majority of participants were female (54.9%) and were not married or living with a significant other (70.0%). Approximately one third of participants had less than or equal to a high school education or GED. Average smoking rate was 19.9 (SD = 10.1) cigarettes per day, and 38.6% of participants reported smoking their first cigarette within 5 min of waking (see Table 1).
Participant Baseline Demographics, Dependence and Mindfulness by Treatment Group
Baseline Differences
No significant baseline differences in demographics, nicotine dependence, or levels of trait mindfulness emerged among the three groups (see Table 1).
Overall Treatment Effects on Cessation Outcomes
Biochemically verified 7-day point prevalence abstinence rates based on a completers-only approach were 32.5% in UC, 39.1% in CBT, and 42.1% in MBAT at 4 weeks post quit day (1 week following the end of treatment) and 19.1% in UC, 23.8% in CBT, and 19.4% in MBAT 26 weeks post quit day. Using an intent-to-treat approach, 7-day point prevalence abstinence rates were 24.3% in UC, 32.3% in CBT, and 34.4% in MBAT 4 weeks post quit day and 11.7% UC, 15.5% in CBT, and 13.0% in MBAT 26 weeks post quit day (see Figure 2).
Figure 2. Seven-day point prevalence abstinence rates by treatment group at 4 and 26 weeks post quit day (intent to treat). N = 412. See the online article for the color version of this figure.
Logistic random effects model analyses that compared the efficacy of UC, CBT, and MBAT over time yielded no overall significant treatment effects, (F(2, 236) = 0.29, p = .749; intent-to-treat: F(2, 401) = 0.9, p = .407). Consistent with the point prevalence analyses, the main effect of treatment on continuous abstinence was not significant over time using an intent-to-treat or a completers-only approach.
MBAT versus CBT
To examine differences in 7-day point prevalence abstinence between MBAT and CBT, we conducted a separate set of analyses that included only these two treatment groups. A logistic random effects model indicated that there were no significant differences between these two conditions over time (completers only: OR = 1.09, 95% CI: .64 to 1.86, p = .750, Effect Size = .05; intent-to-treat: OR = 1.09, 95% CI: .64 to 1.85, p = .755, Effect Size = .05). Consistent with the point prevalence analyses, the main effect of treatment on continuous abstinence over time was not significant.
MBAT versus UC
To examine differences in 7-day point prevalence abstinence between MBAT and UC, we conducted a separate set of analyses that included only these two treatment groups. Over time analyses indicated that the difference between MBAT and UC was not significant (completers only: OR = 1.32, 95% CI: .67 to 2.60, p = .427, Effect Size = .15; intent-to-treat: OR = 1.58, 95% CI: .84 to 2.99, p = .159, Effect Size = .25). Consistent with the point prevalence analyses, the main effect of treatment on continuous abstinence was not significant over time.
Effects of MBAT in Facilitating Recovery From a Lapse
Among participants classified as smoking on the last treatment session (completers only; n = 145), 14.7% in UC,7.0% in CBT, and 27.8% in MBAT had recovered abstinence 1 week following the end of treatment. Twenty-six weeks following the quit day, 0% in UC, 5.0% in CBT and 10.3% in MBAT had recovered abstinence (completers only; n = 110). Logistic random effects model analyses that examined the effect of treatment on 7-day point prevalence abstinence over time revealed a significant overall treatment effect, F(2, 103) = 4.41, p = .015. Post hoc tests revealed significant differences between MBAT and CBT (MBAT vs. CBT: OR = 4.94, 95% CI: 1.47 to 16.59, p = .010, Effect Size = .88) and between MBAT and UC (MBAT vs. UC: OR = 4.18, 95% CI: 1.04 to 16.75, p = .043, Effect Size = .79).
Intent-to-treat analyses of recovery from a lapse revealed a similar pattern of results (n = 151). Among participants classified as smoking at the last treatment session, 13.2% in UC, 7.0% in CBT, and 26.8% in MBAT regained abstinence 1 week following the end of treatment, and 0% in UC, 3.5% in CBT, and 7.1% in MBAT regained abstinence by 26 weeks post quit day. Logistic random effect modeling analyses examining 7-day point prevalence abstinence over time indicated a significant overall treatment effect (F(2,146) = 4.57, p = .012) among participants classified as smoking at the last treatment session. Post hoc tests revealed a significant treatment effect between MBAT and CBT (MBAT vs. CBT: OR = 4.34, 95% CI: 1.35 to 13.99, p = .014, Effect Size = .81) and between MBAT and UC (MBAT vs. UC: OR = 4.82, 95% CI: 1.25 to 118.57, p = .023, Effect Size = .87; see Figure 3).
Figure 3. Seven-day point prevalence abstinence rates by treatment group at 4 and 26 weeks post quit day among individuals classified as smoking on the last treatment session (intent to treat). N=151. See the online article for the color version of this figure.
Associations of Mindfulness Practice Dosage During Treatment With Abstinence
Among individuals randomized to MBAT, self-reported mindfulness practice during treatment was examined (see Table 2). Specifically, six items assessed the average number of days over the previous week spent practicing the following activities: sitting meditation, body scan, walking meditation, yoga, awareness of the breath during the day, and the exercises in the workbook. These six items were administered at seven times points (i.e., at treatment Sessions 2 through 8). For each mindfulness practice technique at each time point, the association with abstinence was examined at the end of treatment, 4 weeks following the quit day, 26 weeks following the quit day, and over time (i.e., a total of 4 analyses for each of the 6 items). This resulted in 168 statistical comparisons (i.e., 6 practice technique items × 7 assessment time points × 4 abstinence measures = 168 tests of association). Analyses yielded six significant findings encompassing four different constructs out of 168 tests. Given that there are actually fewer significant results than would be expected by chance, and the fact that the significant results were not consistent with respect to identifying particular constructs or patterns that might be important, we do not report these results.
Average Self-Reported Days Spent Practicing MBAT Techniques Across the 8 Weeks of Treatment Among Individuals Randomized to MBAT
Association of Prior Meditation Experience With Abstinence
Associations between experience with meditation prior to study entry and abstinence were also examined. Results indicated that experience with meditation was not associated with smoking abstinence in the overall sample (ps > .184), and previous experience with meditation did not interact significantly with treatment condition to predict smoking abstinence (ps > .221). Among participants classified as smoking at the last treatment session, those who had (vs. did not have) previous experience with meditation were more likely to recover abstinence from smoking across the two follow-up assessment points (OR = 3.61, 95%: 1.21 to 10.74, p = .022, Effect Size = .71 for completers and OR = 3.34, 95%: 1.16 to 9.58, p = .025, Effect Size = .67 for intent-to-treat analyses). However, previous experience with meditation was not found to interact with treatment condition to predict abstinence recovery among individuals classified as smoking on the last treatment session (ps > .860). Finally, associations between total number of treatment sessions completed and smoking abstinence were examined. The analyses examining this association with smoking abstinence over time or in single time point analyses were not statistically significant (all ps ≥ .051).
Attrition and Sensitivity Analyses
Differences in abstinence rates at 4 weeks post quit day were examined between participants with complete data versus those lost to follow-up on demographics (age, gender, race/ethnicity, education, income, marital status, employment status), nicotine dependence, psychosocial factors (perceived stress, negative affect, positive affect, history of depression), and treatment group. No significant differences were found. Similarly, with regard to differences in attrition rates by treatment group, a chi-square analysis indicated that there were no significant differences, χ(2)2 = 2.738, p = .254, when examined 4 weeks following the quit day (MBAT = 6.8%; ST = 6.6%; UC = 6.3). However, participants with complete data and those with missing data at 26 weeks post quit day differed significantly in race/ethnicity (p = .004), marital status (p = .027), and positive affect at baseline (p = .037). Those lost to follow-up at 26 weeks post quit day were more likely to be non-Hispanic White (as opposed to African American), married or living with a partner, and have lower positive affect scores. Consistent with the examination of treatment group differences at 4 weeks post quit day, there were no significant differences in attrition rates by treatment group at the 26-week assessment (MBAT = 12.4%; ST = 13.1%; UC = 9.7%), χ(2)2 = 0.899, p = .638. Associations between perceived stress, negative affect, and positive affect at 4 weeks post quit day, and missingness at 26 weeks post quit day, were examined. No significant associations were found.
To address potential bias arising from missing data, sensitivity analyses were conducted to examine the effect of varying missing data assumptions using a multiple imputation approach for treatment effects on 7-day point prevalence abstinence outcomes. Pattern-mixture models were used to generate inferences for various scenarios under the MNAR assumption, with a shift parameter chosen as 0.5, 1, 5, −0.5, −1, or −5 to reflect different degrees of departure of the missing data mechanism from MAR (page 5100, chapter 63: The MI procedure, SAS 9.4 documentation). Results obtained from separate analyses of MBAT versus CBT, and MBAT versus UC, using the multiple imputation of treatment effects on 7-day point prevalence abstinence were similar to completers only or intent-to-treat analysis approaches (details of those nonsignificant results are not shown). The conclusions obtained under the missing not at random (MNAR) assumptions were similar to the ones under missing at random (MAR) in that the nonsignificant results remained the same. Therefore, we are confident that our study findings of treatment effects on 7-day point prevalence abstinence outcomes were robust.
Significant findings supporting the analyses examining the effect of MBAT in facilitating recovery from a lapse (MBAT vs. CBT) remained the same across all methods: multiple imputation, intent-to-treat, and completers only (OR = 3.22 to 5.01, p value = 0.010 to 0.020, see table below). However, the MBAT versus UC analysis result was slightly different in the multiple imputation approach (OR = 2.72, p = .123) compared with the other two methods (intent-to-treat OR = 4.82, p = .023, completers only: OR = 4.18, p = .043). Sensitivity analyses of the lapse recovery results (MBAT vs. UC) using multiple imputation with the MNAR assumption revealed similar results compared with the MAR assumption. Therefore, the significant finding based on the ITT analysis (missing = relapsed) in this case needs to be interpreted with caution.
DiscussionThis randomized clinical trial was designed to evaluate the efficacy of MBAT compared to CBT and UC with respect to both smoking cessation and recovery from a lapse. Results indicated that there were no significant overall differences in abstinence rates across the three treatments. The results were surprising for several reasons. First, several recent randomized controlled trials have indicated that mindfulness-based treatments for tobacco dependence improve abstinence outcomes compared to standard smoking cessation treatments (Brewer et al., 2011; Davis, Goldberg et al., 2014). Second, both MBAT and CBT were more intensive therapies than was UC, and treatment intensity has been strongly associated with greater efficacy (Fiore et al., 2008). However, MBAT did show benefits over and above CBT and UC in promoting recovery from a lapse, consistent with findings on the efficacy of MBRP for relapse prevention among individuals with substance use disorders (Bowen et al., 2014). Specifically, among participants who were not abstinent at the end of treatment, those randomized to MBAT appeared to be more likely to recover abstinence post treatment. Thus, although MBAT did not produce superior abstinence rates compared to UC or CBT, MBAT may be effective for preventing early lapses from transitioning to full-blown relapse.
There may be several potential reasons why we failed to find a significant effect of MBAT over the control conditions on abstinence. The 7-day point prevalence abstinence rate for our MBAT group 1 week posttreatment using an intent-to-treat approach was very similar to that found by both Brewer and colleagues (2011) and Davis, Goldberg et al. (2014) at the end of treatment (i.e., 38% in MBAT, 36% in the MT trial conducted by Brewer, and 25.7% in the MTS trial conducted by Davis). However, abstinence rates in our two control groups at the end of treatment were substantially higher than those observed in the Brewer and Davis trials (i.e., 38.1% in CBT and 27.2% in UC 1 week post treatment compared to 15% at the end of treatment in Brewer et al. (2011) and 17.6% at the end of treatment in Davis, Goldberg et al. (2014). Our comparison of MBAT to CBT was also extremely rigorous given that: (a) CBT represents the current state of the science approach, and (b) CBT and MBAT were matched on treatment duration, contact time, and therapists. Nevertheless, MBAT did not improve overall cessation rates as hypothesized. Another possibility is that the MBAT intervention may have unintentionally reduced nicotine patch use relative to the other two treatments. Such a scenario could potentially have led to an overall failure to find overall treatment group differences.
The finding that MBAT appeared to improve lapse recovery is consistent with theoretical and empirical work on mindfulness. Specifically, mindfulness is hypothesized to promote a “decentered perspective,” which reduces the tendency for automatic emotional reactions, and this enhanced emotional regulation is, in turn, thought to attenuate the likelihood of relapse. Mindfulness is also thought to moderate the association between negative affect and relapse such that in the face of negative affect, individuals with higher levels of mindfulness should have a lower likelihood of relapse compared to individuals with lower levels of mindfulness (i.e., the linkage between negative affect and relapse is weakened among individuals with higher levels of mindfulness; Roemer & Orsillo, 2003; Teasdale, 1997; Teasdale et al., 2002). Recent research has been supportive of both effects with respect to relations among mindfulness, negative affect, and alcohol problems (i.e., that mindfulness both reduces negative affect and reduces the strength of the association between negative affect and alcohol problems; Adams et al., 2014). Neurological studies also provide support that mindfulness training reduces both the severity of negative emotions and reactivity to those emotions (Brown, Goodman, & Inzlicht, 2013; Farb, Anderson, & Segal, 2012; Goldin & Gross, 2010; van den Hurk, Janssen, Giommi, Barendregt, & Gielen, 2010). Thus, MBAT may have improved recovery from a lapse by lessening the negative emotional response to a lapse, and/or by weakening the association between the negative emotional response to a lapse and the likelihood of future lapses.
The fact that MBAT may have some promise in helping smokers recover from early lapses has important implications given that existing treatments designed to prevent relapse and promote recovery from lapses have generally not demonstrated superior efficacy relative to other treatment approaches (Carroll, 1996; Lichtenstein & Glasgow, 1992). Our results suggest that incorporating mindfulness-based techniques into existing smoking cessation treatments could potentially improve the recovery of abstinence after lapses. For example, treatments that increase mindfulness might simply lessen the impact of a lapse when it does occur as noted above, and it also possible that mindfulness strategies could be strategically employed in response to lapses. In particular, briefer meditative practices and other “nonmeditation” mindfulness practices might be well-suited to acute lapse-recovery situations. Other possibilities are that mindfulness-based interventions might be particularly effective for more recalcitrant smokers who are likely to lapse early in a quit attempt, or that such interventions could improve cessation rates over a longer course of time in which smokers make multiple quit attempts, lapse, and attempt to regain abstinence. In addition, researchers have suggested that smokers with high anxiety sensitivity related to mental concerns (e.g., fear that having difficulty concentrating means that one is going crazy) might particularly benefit from mindfulness training (Guillot, Zvolensky, & Leventhal, 2015). Finally, MBAT may have important utility as a relapse-prevention intervention that is delivered after the achievement of initial abstinence from smoking. That is, mindfulness practice may have particular efficacy in mitigating the impact of lapses leading to full-blown relapse as opposed to facilitating initial cessation success. Further research evaluating the efficacy of mindfulness-based techniques in relapse prevention/recovery is warranted, as is research examining whether such approaches are particularly effective for certain people.
It is unclear why mindfulness practice was not related to overall abstinence in the current study. However, formal mindfulness practice rates were low, and the association of mindfulness practices with cessation could have been attenuated by a restriction in range in the practice variables. Along these lines, it may simply be that a greater amount of mindfulness practice that occurs outside treatment sessions is needed to meaningfully impact cessation outcomes. Another possibility is that our measures of mindfulness practice were crude and may not accurately capture the amount of practice, and they did not capture the quality of practice, which may be essential. In addition, more informal mindfulness practices that occur throughout the day (e.g., 3-min breathing space, mindful attention to thoughts or feelings) were not assessed, and these more in-the-moment practices may be important in influencing cessation outcomes.
A marked strength of the current study was the inclusion of two control groups representing different levels of treatment intensity. Our CBT treatment was delivered in a group format and matched the MBAT treatment on contact time and intensity. Our UC group was delivered individually and was comparable to standard Guideline-based treatment that might be delivered in the community. Our use of the same two therapists to deliver the three treatment conditions in the current study was an important study design consideration. We chose to use the same therapists to help ensure that any potential differences that emerged between the treatment conditions would be attributable to the treatment rather than to therapist characteristics.
Another considerable strength is our community-based sample. Participants were racially/ethnically diverse, relatively low-income, just over half were female, and more than two thirds were without a partner. The current findings indicate that MBAT yielded similar abstinence rates compared to more traditional Guideline-based treatments among a diverse and relatively low SES sample of smokers, suggesting that MBAT may be a viable treatment option for such individuals.
Some important limitations should also be acknowledged. Given that MBAT requires specialized and intensive training on the part of therapists and a high level of engagement on the part of individuals enrolled in the treatment, MBAT is not likely to be broadly disseminable in its current format. This is an important limitation from a public health perspective, and a critically important goal for future research should be to examine ways to enhance the disseminability of mindfulness-based strategies. Second, it is important to acknowledge that the use of the same two therapists to deliver all of the study treatments may have resulted in a phenomenon known as “treatment diffusion bias” (Kazdin, 1992). Treatment diffusion bias threatens internal validity, and this phenomenon may have contributed to the absence of significant differences between treatment conditions in the current study. One potential mechanism that may have contributed to treatment diffusion bias is the warmth and compassion expressed by therapists proficient in the delivery of mindfulness-based interventions. For example, modeling of self-compassion may have occurred in all three treatment conditions and may have served as a mechanism facilitating cessation. Such modeling has been suggested to be an active ingredient in mindfulness-based treatments (van der Velden et al., 2015).
A third important limitation is a lack of data to establish fidelity by study interventionists. The treatments were manualized and included specific checklists of topics and activities for each therapy approach and each session. MBAT included very specific activities that were major components of treatment with respect to both content and time spent in therapy that were clearly not part of the CBT or UC treatments. Thus descriptively the interventions differed in important and meaningful ways. However, interventionists were not rated for fidelity to each intervention. In addition, the assessment of treatment fidelity decreases the likelihood of treatment diffusion bias. Thus, the absence of treatment fidelity assessment is an important study limitation. Future research should incorporate ratings to establish that MBAT is conducted with fidelity. A fourth limitation is that rates of compliance with formal meditative practices were low in the current study. Thus, strategies that increase the acceptability of meditation-based practices, or the inclusion of more acceptable nonmeditation practices, are clearly needed when reaching out to the general population of smokers. A fifth limitation is that information on use of the nicotine patch was not collected during the study. Because patch use was not tracked, it was not possible to examine potential interactions between MBAT, patch use, and abstinence. A sixth limitation is that our definition of “lapse recovery” among individuals who were smoking at the end of treatment did not differentiate between individuals who never quit versus those individuals who achieved some period of abstinence during the treatment period. Finally, participant attrition is an important study limitation that should be acknowledged.
In summary, the results of this large RCT, at least with respect to comparison with other mindfulness-based treatment studies, indicate that MBAT yielded abstinence rates that were similar to two standard Guideline-based treatments of varying intensity among a diverse and relatively low SES sample of smokers. Furthermore, compared to the two control conditions, MBAT may have greater efficacy than CBT and UC in helping individuals recover from lapses. This finding has both clinical and theoretical implications, and future research should examine both replicability and the mechanisms underlying this effect. Future studies should also examine the efficacy of “booster” treatment sessions delivered during the follow-up period. Investigating the efficacy of mindfulness treatment approaches that do not utilize meditation as a primary technique is another important direction for future research. Finally, given that the population of remaining smokers appears to be becoming increasingly recalcitrant (Irvin & Brandon, 2000; Irvin, Hendricks, & Brandon, 2003), specialized, intensive treatments such as MBAT are likely to be needed for certain subgroups of smokers who may have particular difficulty quitting. As such, studies should examine individual differences as potential moderators of the efficacy of MBAT.
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Submitted: December 31, 2014 Revised: March 8, 2016 Accepted: April 8, 2016
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Source: Journal of Consulting and Clinical Psychology. Vol. 84. (9), Sep, 2016 pp. 824-838)
Accession Number: 2016-25468-001
Digital Object Identifier: 10.1037/ccp0000117
Record: 61- Title:
- Emotional responses to self-injury imagery among adults with borderline personality disorder.
- Authors:
- Welch, Stacy Shaw. Evidence Based Treatment Centers of Seattle, Seattle, WA, US, swelch@ebtseattle.com
Linehan, Marsha M.. Department of Psychology, University of Washington, Seattle, WA, US
Sylvers, Patrick. Department of Psychology, Emory University, Atlanta, GA, US
Chittams, Jesse. Biostatistics Analysis Center, University of Pennsylvania, Philadelphia, PA, US
Rizvi, Shireen L.. Department of Psychology, New School for Social Research, New York, NY, US - Address:
- Welch, Stacy Shaw, Evidence Based Treatment Centers of Seattle, Seattle, WA, US, 98101, swelch@ebtseattle.com
- Source:
- Journal of Consulting and Clinical Psychology, Vol 76(1), Feb, 2008. Suicide and Nonsuicidal Self-Injury. pp. 45-51.
- NLM Title Abbreviation:
- J Consult Clin Psychol
- Page Count:
- 7
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- Journal of Consulting Psychology
- Other Publishers:
- US : American Association for Applied Psychology
US : Dentan Printing Company
US : Science Press Printing Company - ISSN:
- 0022-006X (Print)
1939-2117 (Electronic) - Language:
- English
- Keywords:
- nonsuicidal self-injury, suicide attempts, suicide ideation, borderline personality disorder, emotional responses
- Abstract:
- Nonsuicidal self-injury (NSSI) and suicide attempts (SAs) are especially prevalent in borderline personality disorder. One proposed mechanism for the maintenance of NSSI and SAs is escape conditioning, whereby immediate reductions in aversive emotional states negatively reinforce the behaviors. Psychophysiological and subjective indicators of negative emotion associated with NSSI and SA imagery were examined in 42 individuals who met criteria for border personality disorder. Personally relevant imagery scripts that involved an NSSI and/or an SA incident were created, as were control scenes involving imagery of an accidental injury, an accidental death, or an emotionally neutral event. Results did not support the hypothesis that decreases in negative emotion would occur during NSSI imagery; however, decreases were found during imagery of the moments after NSSI, which suggests some support for escape conditioning. Support for the model was not found for SAs. Possible implications of patterns that demonstrate decreases in negative emotion during accidental death imagery are discussed. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Attempted Suicide; *Borderline Personality Disorder; *Emotional States; *Self-Inflicted Wounds; *Suicidal Ideation
- Medical Subject Headings (MeSH):
- Adult; Arousal; Borderline Personality Disorder; Comorbidity; Conditioning, Classical; Emotions; Escape Reaction; Female; Galvanic Skin Response; Heart Rate; Humans; Imagination; Male; Mental Disorders; Self-Injurious Behavior; Suicide, Attempted
- PsycINFO Classification:
- Personality Disorders (3217)
- Population:
- Human
- Tests & Measures:
- International Personality Disorder Examination-BPD section
Structured Clinical Interview for DSM-IV for Axis I Disorders--Patient Edition
Lifetime Parasuicide Count - Grant Sponsorship:
- Sponsor: National Institute of Mental Health
Grant Number: MH34486; MH01593
Recipients: No recipient indicated - Methodology:
- Empirical Study; Quantitative Study
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- Accepted: Nov 5, 2007; Revised: Nov 1, 2007; First Submitted: Feb 5, 2007
- Release Date:
- 20080128
- Copyright:
- American Psychological Association. 2008
- Digital Object Identifier:
- http://dx.doi.org/10.1037/0022-006X.76.1.45
- PMID:
- 18229982
- Accession Number:
- 2008-00950-007
- Number of Citations in Source:
- 16
- Persistent link to this record (Permalink):
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- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2008-00950-007&site=ehost-live">Emotional responses to self-injury imagery among adults with borderline personality disorder.</A>
- Database:
- PsycINFO
Emotional Responses to Self-Injury Imagery Among Adults With Borderline Personality Disorder
By: Stacy Shaw Welch
Evidence Based Treatment Centers of Seattle and Department of Psychiatry and Behavioral Sciences, University of Washington;
Marsha M. Linehan
Department of Psychology, University of Washington
Patrick Sylvers
Department of Psychology, Emory University
Jesse Chittams
Biostatistics Analysis Center, University of Pennsylvania
Shireen L. Rizvi
Department of Psychology, New School for Social Research
Acknowledgement: This study was supported by National Institute of Mental Health Grants MH34486 and MH01593. We thank David Atkins, Theodore Beauchaine, and Katherine A. Comtois for their significant help in the design and analysis of this study.
Nonsuicidal self-injury (NSSI) and suicide attempts (SAs; sometimes termed self-inflicted injury when referred to jointly) are especially prevalent in borderline personality disorder (BPD), and the associated public health costs are enormous (see Linehan, 1993). NSSI and SAs seem clearly linked; they often occur within the same individuals (Brown, Comtois, & Linehan, 2002), and both are risk factors for eventual suicide (Gunnell & Frankel, 1994). Most leading explanatory models of NSSI and SAs emphasize avoidance or escape from painful cognitions and emotions, which are often linked to difficult life events (Williams, 1997; see Linehan, 1993, for a discussion applied specifically to BPD). These ideas are consistent with the “escape conditioning” paradigm of behavioral learning theory, in which an operant response (NSSI or SA) leads to the removal of an aversive stimulus (negative emotions; Pierce & Epling, 1998).
More research on the escape conditioning model for NSSI and/or SAs is essential. First, important differences may exist between the behaviors. Distinct in intent, they are differentially correlated with other diagnoses and demographic characteristics (Nock & Kessler, 2006) and have possible differences in function (Brown et al., 2002). Furthermore, most data supporting an escape conditioning hypothesis in NSSI (e.g., Nock & Prinstein, 2004, 2005) and in SAs (Brown et al., 2002) rely on retrospective self-report. For such complex behaviors, studies are needed that (a) include multiple components of emotion (e.g., that add measures of physiological change in addition to subjective experience) and (b) use methodology that employs contextual cues to the emotional states surrounding the behaviors. A few studies with these characteristics have supported an escape conditioning model for NSSI. For example, Haines, Williams, Brain, and Wilson (1995) examined both physiological and subjective components of emotion with an innovative methodology, utilizing a paradigm that examined reactions in the moments before, immediately leading up to, during, and after the imagining of an act of past NSSI. Unfortunately, this study did not include a comparison of NSSI to SAs, and several of the psychophysiological measures (e.g., heart rate, finger pulse) can now be much more precisely measured with more sophisticated methods. For SAs, one study that used multiple components of emotional/contextual cues (Doron et al., 1998) found support for escape conditioning.
The current study was designed to test an escape conditioning model of NSSI and SAs in a population with BPD. Our methodological approach emphasized measurement of multiple components of emotion as well as relevant contextual cues by expanding on the novel methodology used by Haines et al. (1995). In addition to replicating this methodology to examine NSSI, we added a paradigm to examine SAs specifically, more sophisticated physiological measures, and greater methodological controls. Specifically, support for the escape conditioning model of self-inflicted injury was considered present if subjective and physiological arousal decreased at the time of injury and immediately following the injury. This pattern would be in contrast to the predicted pattern for accidental injury and accidental death imagery, in which subjective and physiological indicators of negative emotion would increase at the accident stage, with the “shock of injury” (Haines et al., 1995, p. 473). Thus, our primary hypotheses were as follows:
- During and immediately following NSSI imagery, both physiological and subjective indicators of negative emotion would decrease.
- In contrast, both physiological and subjective indicators of negative emotion would increase during accidental injury imagery.
- During and immediately following SA imagery, both physiological and subjective indicators of negative emotion would decrease.
- In contrast, both physiological and subjective indicators of negative emotion would increase during accidental death imagery.
- No changes in negative emotion would occur during or after neutral imagery.
Method Participants
Participants included 42 individuals who met criteria for BPD and who went through an informed consent process approved by the University of Washington Institutional Review Board. Participants were recruited from the general community, as well as from the university campus, university mental health clinics, and research centers, through flyers and newspaper advertisements. They were screened with the International Personality Disorder Examination, BPD section (Loranger, 1995), and the Structured Clinical Interview for DSM–IV for Axis I Disorders—Patient Edition (SCID–I/P; First, Spitzer, Gibbon, & Williams, 1995). Independent raters viewed tapes of 10% of interviews to check interrater reliability; the result was perfect agreement on the presence of BPD and kappas of .85–.97 on the SCID–I/P. Participants were primarily Caucasian (91%; the remainder of the sample was 5% Latino, 2% Alaskan/Eskimo, and 2% other), female (91%), and educated, with 71% having completed at least some college. Treatment status was not controlled. The mean age was 31 years (SD = 9.5).
As expected, the sample had high numbers of comorbid Axis I disorders (M = 3.74, SD = 1.91 for lifetime other disorders and M = 2.43, SD = 1.59 for current disorders). Depressive (74%) and anxiety (69%) disorders were most common, followed by posttraumatic stress disorder (38%), substance use disorders (14%), and eating disorders (12%). Participants were included if they had a history of SAs or NSSI, which was operationalized as at least two lifetime acts of an SA, NSSI, or both, with at least one of those acts having occurred in the past year. The majority of individuals had a history of both behaviors (75%), although some had a history of only NSSI (14%) or only SAs (11%). Median rates of both SAs (Md = 4.5) and NSSI (Md = 38) were high.
Imagery Procedure
Imagery scripts were created according to the procedure described in Haines et al. (1995). Briefly, information described by the participants was consolidated into scripts that contained moment-by-moment descriptions of five different real-life events. Scripts were recorded and played back to the participants during the psychophysiological testing, which occurred at a second appointment, while the participants imagined the events as if they were actually occurring. Events were divided into four stages: the setting (scene and behaviors in the moments before the event); the approach (behaviors and reactions in the moments immediately preceding the event); the incident (experience of the event itself); and the consequence (behaviors and reactions in the moments following the event).
The five script types were as follows: an episode of NSSI (e.g., cutting oneself with intent to harm but without suicidal intent); an accidental injury (AI; e.g., having an accident with a kitchen knife); an emotionally neutral event (N; e.g., making coffee); an SA (e.g., experiencing an overdose that did not result in death); and an accidental death (AD). The AD script was designed as a control for SA, in that it mimicked other typical aspects of the SA script, including the expectation of possible death, but manipulated the variable of intent to die.
To construct the AD script, we asked participants to describe a real-life past stressor (rated as an 8 or above on a scale of 1–10). They were then given a standardized script that depicted an impending AD involving taking medication for a headache, discovering that the wrong medication had been taken by mistake, and realizing that death was likely. This standardized incident was the only portion of any imagery script that had not actually occurred in real life. For instance, the consequence stage of the SA involved whatever had happened to participants in the moments following the attempt, as opposed to what they had intended or imagined would happen. In the AD script, no consequence stage was created to follow the imagination of the moments preceding the possibility of AD. Our decision to omit a consequence stage resulted from strong feedback from pilot testing, in which participants indicated that it was difficult to realistically imagine anything at all following this incident (iterations of drifting into unconsciousness or death were tried and rejected).
If a participant had a history of NSSI but not of SA, no SA script was included (likewise for NSSI), so only personally relevant events were used. The result was a total of 38 NSSI scripts and 22 SA scripts, with 18 individuals having both scripts. Scripts were presented in counterbalanced order.
Assessment
The primary self-report measures were the Lifetime Parasuicide Count (Comtois & Linehan, 1999), which assessed NSSI/SA history, and an amended Visual Analogue Scale used by Haines et al. (1995), which measured subjective emotions during the physiological recording session. SA/NSSI urges were added, as was level of dissociation. These variables were divided into two factors, subjective negative emotions (SNE) and urges for self-inflicted injury (U-SII), and were subjected to a confirmatory principal-components analysis. The psychophysiological measurements included respiratory sinus arrhythmia (RSA), an index of parasympathetic nervous system influence on heart period (see Beauchaine, 2001), and skin conductance response (SCR), a gross measure of autonomic arousal. A full list of other measures used, which were not relevant for the current study, is available from Stacy Shaw Welch.
Data Analysis
After careful evaluation, we determined that there was no significant impact of outliers, nonnormality, or baseline drift over time. We performed multivariate Script × Stage analyses to test the overall impact of imagery across the entire response matrix. Our analyses examined change from the approach to the incident stages for NSSI, AI, N, SA, and AD, as well as between the incident and consequence stages for NSSI, SA, AI, and N (there was no consequence stage for AD). Significant covariates were added to the final models. Bonferroni corrections were made for the nine omnibus tests. When there was a significant multivariate effect, we conducted univariate planned comparisons using a mixed model approach. We conducted confirmatory post hoc “between script” comparisons using a mixed model approach to test for slope differences between the approach/incident or incident/consequence stages on significant findings. All tests were two-tailed.
ResultsMeans and standard deviations for all relevant variables are listed by stage and script in Table 1. Multivariate analyses revealed that, on the NSSI script, there was no significant difference between the approach and incident stages. A significant difference was found between the incident/consequence stages across the matrix of negative emotion indicators, F(4, 29) = 7.1, p ≤ .001 (see Table 2). Here, follow-up univariate comparisons indicated significant decreases on three of the four measures (SCR, SNE, and U-SII; see Figures 1 and 2). The multivariate test comparing the approach and incident stages of the AI script was also significant, F(4, 31) = 5.92, p ≤ .01, driven by significant increases on SCR and SNE. The multivariate test between the approach and incident stages of the AI script was not significant. Confirmatory post hoc tests comparing the slopes between the approach and incident stages of the NSSI and AI scripts indicated significant differences on SNE and U-SII only (see Table 3). Slopes between these scripts from the incident to consequence stage differed only on U-SII, which decreased after NSSI imagery. No significant differences were found on tests within the N script between the approach/incident and incident/consequence stages. The confirmatory post hoc comparisons between the NSSI and N scripts were nonsignificant for the slopes from the approach to incident stages. The slope of U-SII was the only significant difference found on the incident/consequence slopes.
Script × Stage Means and Standard Deviations
Univariate Test Results for Significant Multivariate Script × Stage Comparisons
Figure 1. Psychophysiological indicators of emotion for nonsuicidal self-injury, accidental injury, and neutral scripts. The y-axis has been reversed for ease of viewing in all RSA graphs, such that increases on the graph indicate decreases in this indicator. Decreased RSA indicates parasympathetic withdrawal, an indicator of negative emotion. RSA = respiratory sinus arrhythmia; SCR = skin conductance response; Neutral = emotionally neutral event; Acc Inj = accidental injury; NSSI = nonsuicidal self-injury.
Figure 2. Subjective indicators of emotion for nonsuicidal self-injury, accidental injury, and neutral scripts. SNE = subjective negative emotions; U-SII = urges for self-inflicted injury; Neutral = emotionally neutral event; Acc Inj = accidental injury; NSSI = nonsuicidal self-injury.
Significant Between-Script Comparisons
Multivariate tests exploring whether indicators of negative emotion would decrease during and after SA imagery were not significant for comparisons between the approach/incident stages as well as the incident/consequence stages. The AD script, however, showed a significant decrease between the approach and incident stages, F(4, 25) = 5.09, p ≤ .05. Univariate tests indicated that negative emotions decreased at the incident stage of imagery on SCR, RSA, and SNE (see Table 2). Because of this unexpected result, exploratory post hoc slope comparisons with the AD script and the SA and N scripts were conducted. Slopes between the approach and incident stages differed between AD and SA scripts on SNE, whereas slopes between the AD and N scripts differed on SNE and U-SII (see Table 3; see Figures 3 and 4).
Figure 3. Psychophysiological indicators of emotion for accidental death, suicide attempt, and neutral scripts. RSA = respiratory sinus arrhythmia; SCR = skin conductance response; Neutral = emotionally neutral event; Acc Death = accidental death.
Figure 4. Subjective indicators of emotion for accidental death, suicide attempt, and neutral scripts. SNE = subjective negative emotions; U-SII = urges for self-inflicted injury; Neutral = emotionally neutral event; Acc Death = accidental death.
DiscussionTo our knowledge, our study is the first to use a multicomponent, contextual approach to evaluate both NSSI and SA. Results of this study provided partial support for an escape conditioning model of NSSI. Significant decreases in indicators of negative emotion were not found during NSSI imagery, as hypothesized. However, decreases were found immediately afterward on both subjective measures and one of the two physiological measures (SCR). A contrasting pattern was found in the control AI script, in which indicators of negative emotion increased during injury imagery, as predicted. Decreases in the AI script after injury imagery were not statistically significant. As expected, there were no significant changes in emotion during the incident or consequence stages of neutral imagery. These results are partially consistent with the results of Haines et al. (1995), in which changes were also found after NSSI imagery for subjective measures. However, Haines et al. found changes during NSSI imagery on physiological measures.
Results for the AI script and the N script were similar to those for the Haines (1995) study, as well. Differences between patterns for the NSSI and control scripts must be interpreted cautiously, however, as confirmatory comparisons between the slopes for the NSSI and N scripts did not reveal many significant differences, and support was found for slope differences between the NSSI and AI scripts during injury imagery on subjective measures only. Furthermore, although the decreases observed in negative emotion after the AI imagery were not statistically significant, the decreasing slopes differed on only one measure between NSSI and AI. The overall similarity in slopes could be interpreted as supportive of an escape conditioning model of AI. However, the increases in negative emotion in the AI script during the incident stage (during injury) are notably different and may suggest that escape from negative emotion that precedes the injury would be unlikely to reinforce accidental injury behavior. Pending replication, these patterns suggest that treatment of NSSI should emphasize assessment of the relationship between the behavior and patterns of emotion, with careful focus on other ways to tolerate or decrease negative emotions.
Our third major hypothesis in the study, that indicators of negative emotion would decrease during and after SA imagery, was not supported, which suggests that escape conditioning may not be an appropriate explanatory model. SAs may require more complex cognitions (Joiner, 2006). Alternatively, SAs could be intended to reduce negative emotions but might prove generally less effective than does NSSI because of more varied, negative, or uncontrollable outcomes. Interestingly, standard errors on all four indicators of negative emotion were notably highest for the SA script in these data, and content of the consequence stage varied widely from discovery/interpersonal support to more aversive consequences (e.g., negative hospital experiences, pain). It may have been that the depiction of the actual consequences of an SA, as opposed to the intended consequences, resulted in a lack of support for escape conditioning in these data. More work is needed. Results do underscore the importance of examining NSSI and SAs as distinct behaviors that may need to be assessed and treated differently in clinical contexts.
The finding that negative emotions decreased during moments of the AD imagery was contrary to our hypotheses. It is also noteworthy, particularly given the comments made by many participants that this scenario was strongly reminiscent of their experience of suicide ideation. It may be that this scenario at least partly replicated the experience of imagining or thinking about death/suicide. If so, iterations of this methodology could prove useful in testing an escape conditioning model of suicide ideation, which has not been well studied, or SAs. It could also be the case that the death imagery itself was not relevant and that any strong distractor or “escape” from the preceding stressful imagery would have produced similar results. The lack of a consequence stage prevented observation of what would have occurred following the AD imagery and thus limited interpretation of the results. Future studies should explore, refine, and challenge this methodology further.
Besides those mentioned above, limitations to the study include a sample homogenous in terms of race, gender, and diagnosis, which limited generalizability. There was a retrospective component to the study despite instructions to imagine events as if they were happening, which could have impacted results. The downward trend of some scripts could indicate regression to the mean, although there was no statistical evidence of drift. Despite these limitations, results of this study add weight to growing empirical evidence that NSSI may be negatively reinforced by reductions in negative emotion. Our results partially replicate those of Haines et al. (1995). They may also partially replicate recent results from Najmi, Wegner, and Nock (2007), who examined the relationship between thought suppression as an escape strategy and NSSI, SA, and suicide ideation. A tendency toward thought suppression partially mediated the relationship between emotional reactivity and both NSSI and suicide ideation but not between emotional reactivity and SA. The similarity of this pattern to those reported here, while tentative, suggests compelling future questions.
Footnotes 1 Sample size was determined on the basis of power analyses of pilot data with 6 pilot participants. On the basis of an effect size of 0.56, we determined that 41 participants were needed to power the study at .80 for main hypotheses. Power analyses were done for one-tailed tests, although two-tailed tests were used in the results presented here.
2 The point that the AD scenario might serve as a better test of an escape conditioning model for SAs than would the SA procedure used in this study, which emphasized the actual as opposed to the intended consequences, was noted by a reviewer of an earlier draft of this article.
References Beauchaine, T. P. (2001). Vagal tone, development, and Gray's motivational theory: Toward an integrated model of automatic nervous system functioning in psychopathology. Development and Psychopathology, 13, 183–214.
Brown, M. B., Comtois, K. A., & Linehan, M. M. (2002). Reasons for suicide attempts and non-suicidal self-injury in women with borderline personality disorder. Journal of Abnormal Psychology, 111, 198–202.
Comtois, K. A., & Linehan, M. M. (1999, April). Lifetime parasuicide count: Description and psychometrics. In D. M.Velting (Chair), Adolescent parasuicides. Symposium conducted at the annual conference of the American Association of Suicidology, Houston, TX.
Doron, A., Stein, D., Levine, Y., Abramovitch, Y., Eilat, E., & Neuman, M. (1998). Physiological reactions to a suicide film: Suicide attempters, suicide ideators, and nonsuicidal patients. Suicide and Life-Threatening Behavior, 28(3), 309–314.
First, M. B., Spitzer, R. L., Gibbon, M., & Williams, J. B. W. (1995). Structured clinical interview for Axis I DSM–IV Disorders—Patient Edition (SCID-I/P). New York: Biometrics Research Department, NY State Psychiatric Institute.
Gunnell, D., & Frankel, S. (1994). Prevention of suicide: Aspirations and evidence. British Medical Journal of Psychology, 39, 156–157.
Haines, J., Williams, C. L., Brain, K. L., & Wilson, G. V. (1995). The psychophysiology of self-mutilation. Journal of Abnormal Psychology, 104, 471–489.
Joiner, T. (2006). Why people die by suicide. Cambridge, MA: Harvard University Press.
Linehan, M. M. (1993). Cognitive–behavioral therapy of borderline personality disorder. New York: Guilford Press.
Loranger, A. W. (1995). Personality Disorder Examination (PDE) manual. White Plains, NY: Cornell Medical Center.
Najmi, S., Wegner, D. M., & Nock, M. K. (2007). Thought suppression and self-injurious thoughts and behaviors. Behaviour Research and Therapy, 45, 1957–1965.
Nock, M. K., & Kessler, R. C. (2006). Prevalence of and risk factors for suicide attempts versus suicide gestures: Analysis of the National Comorbidity Survey. Journal of Abnormal Psychology, 115, 616–623.
Nock, M. K., & Prinstein, M. J. (2004). A functional approach to the assessment of self-mutilative behavior. Journal of Consulting and Clinical Psychology, 72, 885–890.
Nock, M. K., & Prinstein, M. J. (2005). Contextual features and behavioral functions of self-mutilation among adolescents. Journal of Abnormal Psychology, 114, 140–146.
Pierce, W. D., & Epling, W. F. (1998). Behavior analysis and learning. New York: Prentice Hall.
Williams, M. (1997). Cry of pain: Understanding suicide and self-harm. New York: Penguin Books.
Submitted: February 5, 2007 Revised: November 1, 2007 Accepted: November 5, 2007
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Source: Journal of Consulting and Clinical Psychology. Vol. 76. (1), Feb, 2008 pp. 45-51)
Accession Number: 2008-00950-007
Digital Object Identifier: 10.1037/0022-006X.76.1.45
Record: 62- Title:
- Empirical correlates for the Minnesota Multiphasic Personality Inventory-2-Restructured Form in a German inpatient sample.
- Authors:
- Moultrie, Josefine K.. Department of Psychiatry and Psychotherapy, Ludwig-Maximilians-University Munich, Munich, Germany, josefine@moultrie.de
Engel, Rolf R.. Department of Psychiatry and Psychotherapy, Ludwig-Maximilians-University Munich, Munich, Germany - Address:
- Moultrie, Josefine K., Department of Psychiatry and Psychotherapy, Section of Psychological Testing, Ludwig-Maximilians-University Munich, Nussbaumstrasse 7, 80336, Munich, Germany, josefine@moultrie.de
- Source:
- Psychological Assessment, Vol 29(10), Oct, 2017. pp. 1273-1289.
- NLM Title Abbreviation:
- Psychol Assess
- Page Count:
- 17
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- Psychological Assessment: A Journal of Consulting and Clinical Psychology
- ISSN:
- 1040-3590 (Print)
1939-134X (Electronic) - Language:
- English
- Keywords:
- MMPI-2-RF, validity, international adaptation, psychiatric inpatients
- Abstract (English):
- We identified empirical correlates for the 42 substantive scales of the German language version of the Minnesota Multiphasic Personality Inventory (MMPI)-2-Restructured Form (MMPI-2-RF): Higher Order, Restructured Clinical, Specific Problem, Interest, and revised Personality Psychopathology Five scales. We collected external validity data by means of a 177-item chart review form in a sample of 488 psychiatric inpatients of a German university hospital. We structured our findings along the interpretational guidelines for the MMPI-2-RF and compared them with the validity data published in the tables of the MMPI-2-RF Technical Manual. Our results show significant correlations between MMPI-2-RF scales and conceptually relevant criteria. Most of the results were in line with U.S. validation studies. Some of the differences could be attributed to sample compositions. For most of the scales, construct validity coefficients were acceptable. Taken together, this study amplifies the enlarging body of research on empirical correlates of the MMPI-2-RF scales in a new sample. The study suggests that the interpretations given in the MMPI-2-RF manual may be generalizable to the German language MMPI-2-RF. (PsycINFO Database Record (c) 2017 APA, all rights reserved)
- Impact Statement:
- Public Significance Statement—This study displays significant correlations between scale scores of the German language version of the MMPI-2-Restructured Form (MMPI-2-RF; a clinical self-assessment instrument) and the diagnostic clinical findings of a German inpatient sample. Furthermore, it compares the results with U.S. validation studies und suggests that the interpretations given in the MMPI-2-RF manual are generalizable to the German language MMPI-2-RF. (PsycINFO Database Record (c) 2017 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Foreign Language Translation; *Minnesota Multiphasic Personality Inventory; *Personality Correlates; *Personality Measures; *Test Validity; Psychiatric Patients
- PsycINFO Classification:
- Personality Scales & Inventories (2223)
Psychological Disorders (3210) - Population:
- Human
Male
Female
Inpatient - Location:
- Germany
- Age Group:
- Adulthood (18 yrs & older)
- Tests & Measures:
- Minnesota Multiphasic Personality Inventory-2-Restructured Form-German Version
Minnesota Multiphasic Personality Inventory-2-German Version
Binary Review Form-German Version - Methodology:
- Empirical Study; Quantitative Study
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- First Posted: Dec 5, 2016; Accepted: Sep 29, 2016; Revised: Sep 27, 2016; First Submitted: Nov 30, 2015
- Release Date:
- 20161205
- Correction Date:
- 20171026
- Copyright:
- American Psychological Association. 2016
- Digital Object Identifier:
- http://dx.doi.org/10.1037/pas0000415
- PMID:
- 27918175
- Accession Number:
- 2016-58882-001
- Persistent link to this record (Permalink):
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- Database:
- PsycINFO
Empirical Correlates for the Minnesota Multiphasic Personality Inventory-2-Restructured Form in a German Inpatient Sample
By: Josefine K. Moultrie
Department of Psychiatry and Psychotherapy, Ludwig-Maximilians-University Munich;
Rolf R. Engel
Department of Psychiatry and Psychotherapy, Ludwig-Maximilians-University Munich
Acknowledgement: Rolf R. Engel is the Editor of the German MMPI-2 and MMPI-2-RF and earns royalties from its sale. We are indebted to the many psychiatrists who compiled the clinical charts and to the documentation unit of the Psychiatric University Hospital for making the data available for this research.
The MMPI-2-Restructured Form (MMPI-2-RF; Ben-Porath & Tellegen, 2008) is the most recent member of the Minnesota Multiphasic Personality Inventory (MMPI) family of clinical personality inventories. It is composed of 338 items of the MMPI-2 (MMPI-2; Butcher, Dahlstrom, Graham, Tellegen, & Kaemmer, 1989) that form nine validity scales and 42 substantive scales. Thirty-five of the substantive scales form a hierarchy: three higher-order (HO) scales measuring broadband clinical constructs stand at the top, nine restructured clinical (RC) scales derived from the classical clinical scales of the MMPI form the middle section, and 23 fine-grained specific problems (SP) scales and two interest scales build the base of the hierarchy. Five top-level scales measuring major dimensions of personality psychopathology (PSY-5) complete the set of scales of the MMPI-2-RF.
One of the main advantages of the MMPI instruments has always been its comprehensive empirical support, especially when contributed by means of external clinical criteria. Several studies have assessed the external validity of the RC scales of the MMPI-2-RF, often in comparison with the Clinical scales of the MMPI-2 in samples of psychiatric inpatients (Arbisi, Sellbom, & Ben-Porath, 2008; Handel & Archer, 2008), substance abuse patients (Forbey & Ben-Porath, 2007), independent practice therapy clients (Sellbom, Graham, & Schenk, 2006), students seeking help in a college counseling clinic (Sellbom, Ben-Porath, & Graham, 2006), military veterans (Simms, Casillas, Clark, Watson, & Doebbeling, 2005), child custody litigants (Archer, Hagan, Mason, Handel, & Archer, 2012), and college students (Forbey & Ben-Porath, 2008). The external validity has been evaluated using a large variety of measures, such as the Symptom Checklist 90-Revised (Derogatis, 1994), Brief Psychiatric Rating Scale (Overall & Gorham, 1988), Schedule for Nonadaptive and Adaptive Personality (Clark, 1993), Structured Clinical Interview for Diagnostic and Statistical Manual of Mental Disorders (4th ed., DSM–IV; First, Spitzer, Gibbon, & Williams, 1997), Aggression Questionnaire (Buss & Perry, 1992), Barratt Impulsivity Scale (Barratt, 1985), Beck Hopelessness Scale (Beck & Steer, 1988), a Client Description Form (Sellbom, Ben-Porath, & Graham, 2006), or a Chart Review Form (Arbisi et al., 2008). Most of the results demonstrated an improved discriminant and a comparable to enhanced convergent validity of the RC scales in comparison to the Clinical scales of the MMPI-2.
Similar results were found in two studies with non-English translations of the MMPI-2-RF. One study (van der Heijden, Egger, & Derksen, 2008) was conducted with the Dutch version of the MMPI-2-RF. In two large samples, the Dutch normative sample and a clinical outpatient sample, the authors found satisfactory reliability and promising internal validity for the RC scales in comparison to the Clinical scales of the MMPI-2. The authors concluded that the U.S. validation studies on the RC scales might be generalized to the Dutch version. The other study was conducted in Israel (Shkalim, 2015) with a clinical sample, focused again exclusively on the RC scales, and came to similar conclusions.
Published research on the substantive scales of the MMPI-2-RF has centered on the nine RC scales and the five PSY-5 scales (see Harkness et al., 2014). The other 28 substantive scales of the MMPI-2-RF have scarcely been investigated and discussed in published research. The main source of validity data are 136 tables presented in Appendix of the Technical Manual (TM; Tellegen & Ben-Porath, 2008). They stem from samples of community mental health outpatients (Graham, Ben-Porath, & McNulty, 1999), psychiatric inpatients (Arbisi, Ben-Porath, & McNulty, 2003), and samples of Department of Veterans Affairs mental health, medical, and substance abuse treatment patients, disability claimants, criminal defendants, and college students, not further referenced in the TM.
From the beginning, the MMPI instruments were translated into many languages and stimulated cross-national research (Butcher, 1996, 2004). There exist German language versions of the MMPI and the MMPI-2 and a German language version of the MMPI-2-RF is about to be published. Empirical research, especially external validity research, with the German language MMPI instruments has been lacking. Studies in patient samples with concurrently measured criterion variables could add validity data and evaluate the generalizability of the original validation studies toward other cultures and languages.
As a consequence, the two main aims of the current study were to broaden the empirical basis of the MMPI-2-RF and to investigate the applicability of the original validity studies to the interpretation of the German language MMPI-2-RF. We therefore collected empirical correlates for the HO scales, the RC scales, the SP scales, the Interest scales, and the revised PSY-5 scales by structured chart review in a German inpatient setting and compared the results to previous research.
Method Participants
Participants for this study were inpatients from the Ludwig-Maximilians-University Department of Psychiatry and Psychotherapy in Munich, Germany, who fulfilled the following inclusion criteria: (a) They were admitted between July 1997 and February 2005 (N = 12107). (b) They were assessed with the German version of the MMPI-2 (Engel, 2000) as part of routine psychological evaluation and provided protocols with fewer than 21 missing answers in the first 370 items (only protocols with these characteristics were stored in the database, n = 2956). (c) There were fewer than 18 (the criterion for the German form of the MMPI-2-RF, see below) missing answers to the items of the MMPI-2-RF (n = 2832). (d) MMPI-2-RF protocols derived from the MMPI-2 were valid according to the criteria (see below) for the German language MMPI-2-RF (n = 2734). In detail, the number of protocols beyond the accepted ranges were: 27 protocols with the German version of the Variable Response Inconsistency scale (VRIN-d) ≥ 80 T, 51 with True Response Inconsistency scale (TRIN-r) ≥ 80 T, 20 with Infrequent Responses scale (F-r) ≥ 110 T, and 7 with Infrequent Psychopathology Responses scale (Fp-r) ≥ 100 T. (e) Complete digital chart data was available in the clinical documentation system of the hospital, including the “Arbeitsgemeinschaft für Methodik und Dokumentation in der Psychiatrie” [Association for methodology and documentation in psychiatry] (AMDP) -system, a standardized psychiatric mental status rating (Guy & Ban, 1982), that we are at present analyzing in a companion paper that will be comparable in sample with the current study (n = 2014). (f) The MMPI-2 was administered within the first 7 days after admission and closer to admission than to discharge (N = 951). In detail, 29 patients took the MMPI-2 on the admission day, 182 on Day 1 after admission, 145 on Day 2, 151 on Day 3, 134 on Day 4, 110 on Day 5, 115 on Day 6 and 85 on Day 7. From this potential sample we randomly selected 488 patients (278 men and 210 women) for the study.
As part of the standard intake procedure, patients were diagnosed with the 10th revision of the International Classification of Diseases (ICD-10; World Health Organization, 1992). Table 1 shows the distribution of the more frequent ICD-10-diagnoses in the study sample, compared with all inpatients admitted during the study time period. Substance dependence, various depressive disorders, and schizophrenic disorders were the most frequent inpatient diagnoses. All diagnoses were represented in the study sample, although in varying degrees. The last column of Table 1 gives the sampling ratio for each ICD-10 code group, significant deviations from 1 are marked with an asterisk. Diagnoses typically associated with a low compliance in taking the questionnaire (e.g., manic disorders, paranoid schizophrenia) were undersampled. Diagnoses, for which a deeper knowledge of background personality is desirable (e.g., anxiety disorders, personality disorders), were oversampled. The distribution should be fairly representative for inpatients assessed with the MMPI-2.
Diagnostic Distribution of Study Sample Versus Hospital Inpatient Population
Measures
MMPI-2
We used the German language version (Engel, 2000) of the MMPI-2, a 567-item binary self-report personality inventory. The German version was developed following the guidelines existing at that time (Joint Committee, Joint Committee on Standards for Educational and Psychological Testing of the American Educational Research Association, the American Psychological Association, and the National Council on Measurement in Education, 1985). In short, the old items of the German MMPI were revised along the same lines as those of the original MMPI. Items new to the MMPI-2 were translated independently by two translators, a consensus-version back-translated into American English by a native speaker. Independently, the Language Department of the University of Minnesota developed German translations of all items of the MMPI-2. Differences resulting from the two independent translations and the back-translation were resolved and lead to a consensus version. The MMPI-2 protocols of the clients were used to derive answers to the items of the MMPI-2-RF, which are a subset of the MMPI-2-items.
MMPI-2-RF
A German language version of the MMPI-2-RF was developed according to standard guidelines and is due to be published soon (Engel, in press). Table 2 shows internal consistency coefficients (Cronbach’s Alpha) for all substantive scales of the MMPI-2-RF in the German standardization sample in comparison to the U.S.-American standardization sample and estimates of test-retest correlations. All data comes from the respective test manuals. Table 3 shows the mean and the range of intercorrelations within the scale groups of the MMPI-2-RF in comparison to the respective U.S.-American data. Generally the psychometric properties and the scale intercorrelations of the German version are largely comparable to the original version. MMPI-2-RF-protocols are considered valid in the original version, if they fulfill the following criteria: Cannot Say raw score <15, VRIN-r T < 80, TRIN-r T < 80, F-r T < 120, Fp-r T < 100. The criteria for the German language version deviate in three of the five points: (a) Missing answers are accepted until a Cannot Say score of 17 and estimated routinely from answers to similar items (the German language version is scored by a computer service). (b) VRIN-r is replaced in the German language MMPI-2-RF by a new scale (VRIN-d), constructed according to the same principles as VRIN-r, but based on a selection of item pairs according to their characteristics in German samples. In a simulation study comparable to that of Handel, Ben-Porath, Tellegen, and Archer (2010), VRIN-d was superior to VRIN-r and therefore included in the German version. The cut-off value was kept on T < 80. (c) F-r uses a lower cut off (T < 110) as the original version (T < 120). This is necessary to adapt to the higher standard deviation of the F raw scores in the German language standardization sample (SD = 3.43) of the MMPI-2-RF, compared to the US standardization (SD = 2.20). In German clinical samples F-r T values above 120 are extremely rare.
Psychometric Properties of the Substantive Scales
Summary Data of Substantive Scale Intercorrelations
Chart review form
To extract information from the clients’ medical records we developed a German 177-item binary review form (Appendix). Its construction was inspired by the Record Review Form (RRF) by Arbisi et al. (2003) and the Intake Form by Graham et al. (1999). The chart review form includes information concerning existing problems at time of admission, current stressors, admission medication, ICD-10 diagnoses at admission and discharge, and behavioral disorders in the past. Additionally, it contains items describing the results of the mental status examination (MSE) covering affect, cognition, mood, drive, interpersonal functioning and somatic complaints. The data was gathered by reviewing the patients’ chart, especially the intake clinician’s interview and the clinical course during the first seven days of hospitalization. The results of the psychiatrists’ mental status examinations are regularly reported independently from further specific examinations, including psychological tests.
All ratings were carried out by one of the authors (JM), a medical doctoral student. To assess the reliability of the chart review ratings 49 (10%) of the records were reviewed by a second rater (RRE), a clinical psychologist. During the rating period, the raters had no access to the MMPI-2 protocols and the AMDP data. The interrater reliability reached a mean Kappa coefficient (κn, Brennan & Prediger, 1981) of .89. Kappa coefficients for the individual items ranged from a low of .44 to a high of 1. Thirty-four items rated as present in less than five participants (1%) were excluded from the statistical analysis, thus reducing the chart review form to 143 items. The Appendix shows the items of the Chart Review Form, the frequency of occurrence in the sample, and the interrater reliability κn. Items excluded from correlation analysis are marked with an asterisk.
Data Analyses
To establish empirical correlates for the German MMPI-2-RF, Pearson product–moment correlations between the 42 MMPI-2-RF scales and the 143 chart review form items were calculated. With 42 * 143 = 6006 correlation coefficients a Bonferroni-correction of a familywise alpha level of .05 leads to an individual alpha level of .05/6006 = .000008325. With 488 subjects, correlation coefficients |r| ≥ .20 are statistically significant at this alpha level. Using this conservative approach a correlation coefficient of |r| ≥ .20 was determined as identifying an empirical correlate. A value |r| = .20 corresponds to an effect size d = .41, a value clearly beyond a “small” effect size.
To quantify the agreement of the empirical correlates of the German version with those of the U.S.-American original we calculated the construct validity (CV) coefficients ralerting-CV and rcontrast-CV (Westen & Rosenthal, 2003). Both are effect size correlations that describe the size of agreement between a pattern of correlations predicted (in our case provided by the external correlates of the U.S.-American version) with a pattern of correlations obtained. The first index, ralerting-CV, is the simple correlation between both correlation patterns, the second, rcontrast-CV, is a more complex measure that allows testing for significance. As our list of chart review items was not identical with the external correlates in the Technical Manual (which by themselves were not identical for inpatients and outpatients), we could only calculate the CV coefficients with a reduced set of correlates (64 items in the comparison with the TM inpatient data and 63 with the TM outpatient data). The items used in the comparisons are listed in the Appendix.
ResultsResults will be presented for all substantive scales of the MMPI-2-RF. The order of presentation will follow largely the framework of scale interpretation recommended by Ben-Porath and Tellegen (2008); Ben-Porath (2012) and Ben-Porath and Tellegen (2011).
Emotional Dysfunction
Table 4 shows the external correlates for the scales belonging to the interpretational section on emotional dysfunction. The HO scale Emotional/Internalizing Dysfunction (EID), the top level scale of this section, correlated with a large spectrum of chart review items marking internalizing disorders, namely rumination, suicidal ideation, anxiousness, depression, hopelessness, sleep disturbances, feelings of inadequacy, lack of drive, pessimism, and joylessness. There was also a positive relation to the use of antidepressants at admission. A negative correlation was found to alcohol dependence as an admission problem. The Demoralization scale (RCd) had a very high correlation to EID, in this sample the correlation coefficient was r = .92. The external correlations of RCd therefore corresponded to those of EID within random differences. The SP scales belonging to RCd showed a more differentiated picture: The Suicidal/Death Ideation scale (SUI) correlated .46 with the MSE item suicidal ideation. SUI also showed correlations to the MSE items hopelessness, rumination, feelings of guilt, and depression. The scale also correlated with a history of suicide attempts and with a suicide attempt before admission. The strongest correlates for the Helplessness/Hopelessness scale (HLP) were the items addressing rumination, suicidal ideation, hopelessness, depressed mood, and joylessness. The Self-Doubt scale (SFD) correlated with rumination, suicidal ideation, anxiety, feelings of inadequacy, and fear of failure. The Inefficacy scale (NFC) primarily demonstrated correlations with anxiety and suicidal ideation.
Correlates for Scales Indicating Emotional Dysfunction
The Low Positive Emotions scale (RC2) was related to largely the same variables as RCd, though with a slightly altered focus. In comparison to RCd, RC2 was related somewhat less to suicidal ideation, anxiety, and feelings of inadequacy and more to lack of drive. The scale Introversion/Low Positive Emotionality-Revised (INTR-r), having a strong correlation to RC2 (in this sample r = .84), followed this pattern.
The Dysfunctional Negative Emotions scale (RC7) showed correlations with items indicating suicidal ideation, anxiousness, rumination, history of bad relations with classmates, and feelings of guilt. The Stress/Worry scale (STW) was associated with anxiety and rumination. The Anxiety scale (AXY) was particularly related to items addressing anxiety, suicidal ideation, sleep disturbances, and rumination. The Anger Proneness scale (ANP) showed a significant correlation only to the item indicating a history of bad relations with classmates. The Behavior-Restricting Fears scale (BRF) correlated with anxiety. No correlates could be found for the Multiple Specific Fears scale (MSF). Finally, the Negative Emotionality/Neuroticism-Revised scale (NEGE-r) correlated considerably with anxiety and suicidal ideation, as well as with rumination, depression, feelings of inadequacy, and avoidant behavior.
Thought Dysfunction
Table 5 shows the external correlates of the scales belonging to the interpretational section on thought dysfunction. The MSE item auditory hallucinations correlated with all scales belonging to this section, most remarkably with the HO scale Thought Dysfunction (THD) and the Aberrant Experiences scale (RC8). Apart from the item auditory hallucinations, RC8 and RC6 (Ideas of Persecution scale) had different external correlates: RC6 was related to the history item bad relations with classmates, and RC8 to the MSE items derealization and depersonalization. RC6 showed a somewhat stronger relation to the MSE item paranoid than RC8 (r = .19 vs. r = .12), however, both correlations remain under the border of significance. External correlates for the PSY-5 scale Psychoticism-Revised (PSYC-r) resembled very much those for the Higher-Order scale THD. This could be expected because the intercorrelation of the two scales was r = .96.
Correlates for Scales Indicating Thought Dysfunction
Behavioral Dysfunction
External correlates of the scales belonging to the interpretational section on behavioral dysfunction are displayed in Table 6. The HO scale Behavioral/Externalizing Disorder (BXD) showed positive correlations to the items substance abuse at admission, history of substance abuse, history of polytoxicomania, alcohol dependence at admission, and the MSE item antisocial behavior. A negative correlation was found with the use of antidepressants at admission.
Correlates for Scales Indicating Behavioral Dysfunction
The scale Antisocial Behavior (RC4) had similar external correlates, with a stronger focus on alcohol dependence and somewhat less on antisocial behavior. The SP scale Juvenile Conduct Problems (JCP) correlated exclusively with history and admission items indicating substance abuse. The SP scale Substance Abuse (SUB) showed a very high correlation with alcohol dependence at admission (r = .62) and striking correlations with other substance abuse items. In addition, there were numerous negative correlations with MSE items typical for emotional/internalizing dysfunctions.
In contrast to RC4, RC9 (Hypomanic Activation) had almost no relation to the typical substance abuse items. The only MSE item with a positive correlation was aggression. The SP scale Aggression (AGG) was related to the MSE item aggression and the SP scale Activation (ACT) fell just short of showing a negative correlation to the MSE item psychomotor retardation (r = .19). Two of the PSY-5 scales belong to the interpretational section on behavioral dysfunctions. The PSY-5 scale Disconstraint-Revised (DISC-r) showed a very high correlation to BXD (r = .92 in this sample) and thus similar external correlates as BXD. The PSY-5 scale Aggressiveness-Revised (AGGR-r) correlated only modestly with AGG (r = .50) and much higher with the Interpersonal Passivity scale (IPP; r = −.90). Not unexpectedly, its strongest external correlate was a negative correlation to the MSE item anxious.
Somatic-Cognitive Dysfunction
The interpretational section on somatic-cognitive dysfunction (Table 7) consists of four scales with a focus on general (RC1, Somatic Complaints) or specific (GIC, Gastrointestinal Complaints; HPC, Head Pain Complaints and NUC, Neurological Complaints) somatic symptoms. These four scales had very limited external correlates: HPC correlated with the MSE item headache and RC1 correlated with the history item of being a victim of sexual abuse. The other two (SP) scales of this section describe poor health in a generalized sense (Malaise, MLS) and cognitive difficulties (Cognitive Complaints, COG). COG correlated to the MSE items rumination, attention disorder, depersonalization, and suicidal ideation. MLS correlated with many MSE items of the emotional/internalizing symptom spectrum such as rumination, lack of drive, depressed mood, and sleep disturbances. In addition, it correlated positively with the use of antidepressants at admission.
Correlates for Scales Indicating Somatic-Cognitive Dysfunction
Interpersonal Functioning and Interests
The interpretational section on interpersonal functioning (Table 8) is made up of the five SP scales Family Problems (FML), Interpersonal Passivity (IPP), Social Avoidance (SAV), Shyness (SHY), and Disaffiliativeness (DSF), as well as the RC scale Cynicism (RC3). External correlates of FML were the history item bad relations with classmates and the MSE item antisocial behavior. IPP was correlated with the MSE item anxious, SAV with the MSE item rumination, and SHY with the MSE items suicidal ideation and insecurity. The only external correlate of DSF was a history of no graduation, RC3 had no external correlate.
Correlates for Scales Indicating Interpersonal Functioning and Interests
Of the two interest scales, Aesthetic-Literary Interests (AES) and Mechanical-Physical Interests (MEC), AES had no external correlates and MEC showed correlations to driving under the influence of alcohol/drugs and alcohol dependence at admission (Table 8).
Construct Validity Summary
Table 9 shows the CV coefficients ralerting-CV and rcontrast-CV for the substantive scales of the MMPI-2-RF. The indices span a wide range from near zero to .75, with higher values for ralerting-CV than for rcontrast-CV. For most of the scales the CV coefficients were larger when comparing our (inpatient) correlation pattern with that obtained in the U.S.-American inpatient studies, than in those obtained with outpatients. This is especially true for scales measuring features of thought disorders (THD, RC6, PSYC-r), which are less present in typical outpatient populations. High validity correlations were obtained for scales measuring emotional/internalizing dysfunctions (EID, RCd, RC2, SUI, HLP, SFD, INTR-r). The content of these scales is adequately represented in the list of external correlates. For a number of scales very low validity coefficients were found (RC3, NUC, FML, AES).
Construct Validity Coefficients
DiscussionThe primary aim in this study was to provide information on the external validity of the MMPI-2-RF score interpretations in German language by identifying empirical correlates for the substantive scales in a sample of psychiatric inpatients. We will discuss these results together with the corresponding correlates of the U.S.-American original version as described in the literature (RC- and PSY-5-scales only) and the TM (Tellegen & Ben-Porath, 2008). We refer especially to TM Tables A-1 to A-8 and A-17 to A-24, based on psychiatric outpatients, and A-25 to A-48, based on psychiatric inpatients.
The interpretational section on emotional dysfunction is the largest section spanning 15 scales. The most comprehensive scale is the HO scale EID. In both the TM and our samples, EID showed medium high correlations with a large spectrum of chart review items marking internalizing disorders. Depression, hopelessness, sleep disturbances, suicidal ideation, and pessimism are examples of validity markers found in this study as well as those referenced in the TM. We also observed some differences: In our study we found negative correlations of EID to alcohol dependence at admission, whereas the tables of the TM report negative correlations of EID to psychosis-related items (e.g., delusions, psychosis at admission, bizarre) in three inpatient data sets (Tables A-25, A-29, A-33, A-37, A-41, A-45). Presumably, these correlations resulted from the heterogeneous composition of the samples which is known to produce spurious correlations (see, e.g., Hassler & Thadewald, 2003). Our sample included about 20% patients with alcohol dependence, most of them referred for detoxification. Many of them did not report symptoms of depression. In the two-dimensional distribution of EID versus alcohol dependence at admission, the group of patients with alcohol dependence forms a cluster in one corner of the distribution that is responsible for the negative correlation. In a sensitivity analysis we calculated the correlation coefficients between EID and alcohol dependence at admission separately for the subsample of patients diagnosed as alcohol dependent (n = 98, r = −.044) and for all other patients (n = 390, r = −.041), thus corroborating this interpretation. With a similar reasoning Arbisi et al. (2008) described the negative correlation between RCd and psychosis-related items (delusions, psychosis at admission) in a paper based on the same data as those used in the TM Tables A-25 to A-72. They saw a statistical artifact at work: The current criteria for inpatient hospitalization require either a risk of imminent self-harm or a florid psychosis. This may have led to a distinct group of psychotic patients lacking depressive symptoms as opposed to a large group of depressed patients, thus leading to an artifactual “administrative” negative correlation. In the end, clinical samples are always heterogeneous and statistical artifacts because of group clustering can only be inferred from the special characteristics of the sample.
SUI was the most specific of the four internalizing SP scales SUI, HLP, SFD, and NFC. The correlation with the chart item suicidal ideation was high (r = .46), similar to that in the TM samples (all rs between .32 and .55). HLP had a small, but recognizable focus on joylessness, hopelessness, and depression, whereas low self-esteem and anxiety belonged to the more specific validity indicators of SFD. All the internalizing SP scales, however, shared many validity markers and their intercorrelations were relatively high, considering their length of only 4 to 9 items. In the TM tables, some of the internalizing SP scales showed reasonable negative correlations with items such as “optimistic” and “energetic” (HLP), “copes well with stress” (SFD) and “energetic” and “self-reliant” (NFC). We could not find similar correlations with positively connoted adjectives, although they were initially included in the item list. Positive features were noted in such a low frequency (<1%) in the charts that we did not include them in the correlation statistics.
Empirical correlates for RC2 were similar to those for EID or RCd. Looking more closely, RC2 was somewhat less related to suicidal ideation, anxiety, and feelings of inadequacy and more to lack of drive. This shift in focus would be in line with Tellegen’s (1985) conceptualization of RC2 as a scale tapping low positive emotionality, that is, the lack of activity and power. INTR-r measured a very similar concept. The external correlates were more or less the same, although generally somewhat lower.
As expected, elevated scores on the RC7 scale went along with various negative emotions such as anxiety, rumination, and feelings of guilt. However, the strongest association with RC7 showed the item addressing suicidal ideation. The high correlation between this scale and suicidal ideation was also found by Arbisi et al. (2008) in an inpatient sample. In the TM tables sleep disturbance is a universal correlate of RC7, most prominent in the inpatient samples. The SP scale AXY correlated highest with the MSE item anxiety, but also with other internalizing symptoms. The latter relations were largely shared by STW. Both scales showed similar correlation patterns in the TM samples. Here the item “copes well with stress” was an additional external negative correlate to STW and AXY. In our sample, the only correlate for ANP was a history of bad relations with classmates. In the TM samples there were more specific correlates, but only in outpatients: has temper tantrums, overreactive, low frustration tolerance, excitable, and does not get along with coworkers are examples. BRF correlated with anxiety in all samples, other correlates were scarce. MSF had no positive correlates neither in our study nor in the TM samples. In the TM outpatient samples, however, there were negative correlations with items connected to stereotypic masculine behavior such as needs to achieve, work-oriented, competitive, energetic, and stereotypic masculine interests. The correlation coefficients were more prominent in the female sample and may have been a result of stereotypical, more than individual rating. NEGE-r was highly correlated with RC7 (r = .83 in our study, r between .86 and .88 in the five psychiatric TM samples) and had similar correlates as RC7.
For most of the internalizing SP scales the strongest correlates were rumination, suicidal ideation, and hopelessness or anxiety. This raises questions about the accuracy of the scales. In psychiatric samples one reason behind this finding is certainly the high comorbidity between depression, anxiety and suicidality (Lamers et al., 2011) which makes it difficult to separate concepts that may be more distinct in samples of healthy probands.
Thought dysfunction is addressed by the scales THD, RC6, RC8, and PSYC-r with PSYC-r being almost a replica of THD (r = .96 in this sample, r between .96 and .98 in the psychiatric TM samples) and sharing most of the items with it. Hallucinations, especially auditory hallucinations, were empirical correlates for all scales measuring thought dysfunction, in both our sample and the TM samples. In addition, RC6 showed an association with bad relations with classmates or coworkers across the samples, whereas RC8 was related to some forms of thought disturbance. This is consistent with Ben-Porath and Tellegen’s (2008) concept of these scales, as RC6 was designed to assess persecutory beliefs, whereas an increase in the RC8 score is supposed to indicate aberrant thought and perceptual experiences.
Nine scales make up the interpretational section on behavioral dysfunctions. At the top level of the scale hierarchy is BXD. In our sample external correlates of BXD were several items measuring substance abuse and the MSE item antisocial behavior. The same kind of items were correlates in the TM samples, although much larger in number. In the TM data most of the correlates came from outpatient samples (67 items with mean |r| ≥ .18), only 11 items from inpatients. In our sample, BXD correlated negatively with antidepressants at admission, an outcome not found in the TM samples.
External correlates of RC4 were similar to those of BXD, both in our sample and in the samples analyzed in the TM. Correlation coefficients for the substance abuse related items were a little higher for RC4. This leads to the assumption that RC4 is slightly more specific for these correlates, an observation which has also been described by others (Arbisi et al., 2008; Handel & Archer, 2008; Sellbom, Ben-Porath, & Graham, 2006). In our sample JCP had a relatively clear profile: All external correlates had to do with history or admission items. There were no correlations with actual mental state examination items. This result is the best you could expect for a scale describing historic juvenile conduct problems. In the TM samples, however, the JCP profile was not so specific: The correlation structure of JCP resembled that of BXD and RC4. The final scale in the RC4-related interpretational branch, SUB, showed clear profiles of external correlates in our sample as well as in those of the TM. High correlations were found with items relating to substance abuse in our sample (alcohol dependence at admission: r = .62) as well as in the TM samples (history of substance abuse: inpatients r = .52, .58, and .53, respectively for the three samples, intake diagnosis—substance abuse or dependence: outpatients r = .49 for males and .45 for females). In addition, in the TM samples many external correlates marking aggression, hostility, and sociopathic features as well as a history of suicide attempt were found, that did not occur in our sample. The many negative correlations of SUB with items marking emotional/internalizing dysfunctions in our sample were most probably a result of the special composition of the sample as discussed above. To give an example, the correlation of SUB with the MSE item depressed mood was r = −.28 in the total sample (N = 488), but r = −.06 in the alcohol dependence subsample (n = 98) and r = .02 in the rest of the sample (all other diagnoses; n = 390). This means that, as a group, the patients with alcohol dependence had higher scores on SUB and were described as less depressed. Within the groups, at the level of individual persons, no correlation could be found.
Patients with hypomanic activation, as measured by RC9, are usually underrepresented in typical samples of psychiatric patients assessed with the MMPI-2, as is the case in this study. This may have been one of the reasons why we, as well as the TM studies, did not find many external correlates for this scale. Aggression (in our sample), antisocial behavior, and substance abuse (in the TM samples) belong to the expected correlates. In a larger sample of patients in substance abuse treatment the correlation between RC9 and aggression was more pronounced (TM, Table A85). The SP scale AGG had the mental state item aggression as an external correlate in both our and the TM samples. In addition, AGG was connected to suicidal ideation in all of the TM samples. In the Department of Veterans Affairs (VA) inpatient sample (TM, Table A35) this scale was also correlated to a history of violent behavior. It is interesting that in the TM samples a history of cocaine abuse was positively correlated with all three scales RC9, AGG, and ACT. In our sample, the PSY-5 scale Aggressiveness-Revised (AGGR-r) fell short of having aggression as an external correlate (r = .17). The item anxious showed a negative correlation to this scale. In the TM, aggression and antisocial behavior are positive correlates, whereas passive in relationships and introversion are negative correlates. Thus, one may see AGGR-r as an inverted IPP scale, as described in Greene’s (2011) interpretive manual. DISC-r was so similar to BXD (r = .92) that it is not further discussed. DISC-r shares 15 of its 20 items with BXD.
Information on Somatic-Cognitive Dysfunction is provided by the scales RC1, MLS, GIC, HPC, NUC, and COG. The four scales focusing on general or specific somatic symptoms (RC1, GIC, HPC, NUC) showed very limited correlates in this study. The tables of the TM, though, reveal a comprehensive, but similar correlational pattern throughout the entire scales of this section, showing the strongest association to items that describe multiple somatic complaints and preoccupation with health problems, but also to items indicating depression, suicidal ideation, and concentration difficulties. In our study the scales MLS and COG displayed a more distinctive pattern. Both scales showed a correlation to rumination, but MLS had its focus on depression and associated somatic symptoms, for example, sleep disturbances, whereas COG correlated with the MSE items “attention disorder,” “suicidal ideation,” and “depersonalization.” The restricted number of empirical correlates for somatic scales in this study presumably goes back to the limited number of patients with somatic complaints (only 33 patients had a reported chronic medical or physical problem) or somatoform disorder in our sample.
Interpersonal Functioning is basically assessed by RC3 and the SP scales FML, IPP, SAV, SHY, and DSF. RC3 showed no significant correlation neither in our study, nor in the TM inpatient samples. In the TM outpatient samples, though, there were negative correlations with items indicating general motivation and ambition, for example, “has many interests”, “needs to achieve”, “creates good first impression”, and “work-orientated”. Reasons for the absence of correlates for RC3 in inpatient samples could have been the lack of items addressing cynicism in the Chart Review Form, as well as the lack of information on the patients’ cynicism in the records themselves. For FML we found positive correlations with a history of bad relations with classmates and antisocial behavior, whereas in the TM outpatient samples FML showed a stronger association with items pertaining to family problems, hopelessness, insecurity, and a history of having few or no friends. The three SP scales IPP, SAV, and SHY displayed a similar correlation pattern in both our study and the TM outpatient samples, showing the highest correlations with items addressing insecurity and anxiety. The only correlate for DSF in our study was a history of no graduation. In the tables of the TM, there are also nonspecific correlates for DSF, namely items addressing sleep disturbance, suicidal ideation, and depression. The correlation coefficients were much higher in the male sample. It is noticeable that all scales measuring interpersonal functioning showed no, or only few, correlates in the TM inpatient samples.
The interpretational section on interests includes the scales AES and MEC. The association of MEC with alcohol dependence at admission may be a result of the composition of the sample, as the majority of patients with alcohol dependence were male, and therefore more likely to achieve elevated scores on the MEC scale. Correlations were much lower in sensitivity analyses of subsamples by diagnosis (alcohol dependence) or gender. In the TM tables, the male outpatient sample displayed negative and positive correlations with items addressing stereotypic masculine behavior for AES and MEC, respectively.
Thus far, we have discussed only the most impressive correlates for each substantive scale in our comparison of German and U.S.-American data. An explicit identification of the peak correlations is essential for clinicians interpreting the MMPI-2-RF, but insufficient for giving an estimate of the congruence between the correlation pattern in the German data and those in the U.S.-American TM data. Weston and Rosenthal’s (2003) CV coefficients quantify the degree of association between a correlation pattern observed with a new measure (in our case an instrument adapted to another language) and a correlation pattern expected by theoretical reasoning or (in our case) by existing data with the original instrument. A good example using this approach was the work of Poythress et al. (2010) who assessed the validity of two self-report measures of psychopathy in comparison with an established observer checklist.
Comparing the validity pattern of our inpatients with the inpatients’ pattern in the TM, we found highly significant CV coefficients for the scales at the top of the MMPI-2-RF scales hierarchy (H-O and PSY-5-r scales) with ralerting-CV = .50 or higher. The patterns were calculated across 64 Chart Review Form items that had the same or a very similar item in the TM inpatient studies. Although this is a rather large amount of external variables, its content range is focused on psychiatric symptoms and does not extend to attributes typical for some of the smaller scales of the MMPI-2-RF. There were virtually no items that could measure concordant or discriminant constructs for scales like Cynicism or Neurological Complaints. The sampling of external measures is critical for the CV coefficients. Some differences between the CV coefficients for inpatients versus outpatients go back to sampling differences. To give an example: The scale Substance Abuse has a sufficient construct validity against TM inpatient data (ralerting-CV = .46) and an insufficient CV against TM outpatient data (ralerting-CV = .14). External criteria for the inpatient comparison included three relevant items (history of substance abuse, history of alcohol abuse, history of polysubstance abuse), but none of them is included in the outpatient criteria list. To sum up, Westen and Rosenthal’s (2003) proposal of CV coefficients amplifies the spectrum of meta-analytical statistical techniques with an interesting method. Not unexpectedly, its explanatory power depends on the sampling characteristics of the external measures.
In conclusion, this study amplifies the expanding body of research on empirical correlates for the MMPI-2-RF. Especially the correlates for the SP scales extend current literature, as studies published so far have only dealt with RC and PSY-5 scales. Altogether our findings are well consistent with the conceptual design of the MMPI-2-RF provided by Tellegen & Ben Porath (2008). Furthermore the results are very much in consistence with findings by Arbisi et al. (2008). Therefore this study presents a good indicator that the U.S. validity data may be applicable to the interpretation of the scores of the German language version of the MMPI-2-RF.
The current study holds limitations. From a formal point of view, one could argue that a translated version of an instrument validated in one language must demonstrate multiple levels of invariance compared with the “original” before the scale score elevations and subsequent interpretation can be reliably and validly applied. Measurement equivalency can be shown by various methods. Examples are the use of multigroup comparative confirmatory factor analysis (Bagby, Ayearst, Morariu, Watters, & Taylor, 2014; Dere et al., 2015) or tests of differential item functioning (Keefer, Taylor, Parker, Inslegers, & Michael Bagby, 2015). This should be done in future studies.
From a practical point of view, the sample used in this study is described solely by clinical information. We could only utilize the ICD-10 diagnoses attained in routine care and do not know how that would translate to Diagnostic and Statistical Manual of Mental Disorders (5th ed.; DSM–5; American Psychiatric Association, 2013) diagnoses obtained with structured clinical interviews.
Furthermore, the information that could be drawn from the charts was restricted. The quality of the documentation varied to a great extent between the records. Most of the charts lacked detailed information on interpersonal functioning. In addition, in this study we drew information from one sample only. This limits the generalizability of our findings to other settings such as outpatient treatment or forensic psychiatry.
Future research should confront these limitations by identifying empirical correlates in other samples. For example, a forensic psychiatry sample could provide further correlates for RC4 and DISC-r. A study with patients from a psychosomatic clinic would be of interest for the further evaluation for RC1 and the somatic scales. Furthermore, an outpatient setting could help to discriminate between the different anxiety scales. Another advance would be the use of standardized intake interviews that focus more on interpersonal behavior and nonpathologic factors of personality. This would result in a more homogeneous quality of external criteria and provide more in-depth information than the chart review. As the empirical body of the MMPI-2-RF scales grows—and past studies have shown the superiority of most of the RC scales over their Clinical scale counterparts (Handel & Archer, 2008; Sellbom, Ben-Porath, & Graham, 2006; Simms et al., 2005)—the German language MMPI-2-RF, once published, could be a valuable alternative to the German language MMPI-2.
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APPENDIX APPENDIX A: Chart Review Form Variables, Frequency of Occurrence in the Sample, κn, and Corresponding External Criteria in Studies With the U.S.-American Version
| Chart review form variable | %a | κnc | Corresponding external criterion in the Technical Manual tables A-1 to A-48b |
|---|
| Inpatients | Outpatients |
|---|
| a Percentage, in which item was rated as present. Chart review form variables rated present in less than 1 percent of cases are marked with an asterisk. They were dropped from correlation analysis; b Corresponding variables from the External Correlate Tables A-1 to A-48 of the MMPI-2-RF Technical Manual (MMPI-2-RF Technical Manual by Yossef S. Ben-Porath and Auke Tellegen. Copyright © 2008, 2011 by the Regents of the University of Minnesota. Reproduced by permission of the University of Minnesota Press. All rights reserved. “Minnesota Multiphasic Personality Inventory-2-RF” and “MMPI-2-RF” are trademarks owned by the Regents of the University of Minnesota.) are listed here; c κn according to Brennan and Prediger (1981); d The second rater missed one source for identifying drugs used at admission. This lead to the comparatively low values in these three items; DWI = driving while intoxicated; PTSD = posttraumatic stress disorder; MSE = Mental Status Exam variable; MS = Intake Mental Status variable. |
| Previous conviction | | | | |
| Delinquency | 4.9 | .96 | Contact with criminal justice system | |
| Penitentiary incarceration | 1.4 | .88 | Penitentiary incarceration | |
| Driving under influence of alcohol/drugs | 7.0 | 1 | DWI conviction | |
| Behavioral disorders in history | | | | |
| History of violent behavior | 1.0 | .84 | History of violent behavior | |
| Perpetrator of physical abuse | 2.5 | .88 | History of being a perpetrator of physical abuse | History of being physically abusive |
| Perpetrator of sexual abuse | .2* | 1 | | History of being sexually abusive |
| Victim of sexual abuse | 7.2 | .88 | History of being a victim of sexual abuse | History of being sexually abused |
| Victim of physical abuse | 4.7 | .96 | History of being a victim of physical abuse | History of being physically abused |
| Manic or hypomanic episodes in history | .0* | .96 | | |
| Eating disorder | 6.6 | .88 | | |
| History of substance abuse | 12.5 | .80 | History of substance abuse | |
| History of substance dependence | 1.6 | .92 | | |
| History of alcohol abuse | 4.3 | .76 | History of alcohol abuse | |
| History of alcohol dependence | 2.9 | .72 | | |
| History of polytoxicomania | 2.9 | 1 | History of polysubstance abuse | |
| Behavioral disorders in adolescence | | | | |
| School truancy/suspensions | 1.6 | .96 | | |
| History of bad relations with classmates | 9.8 | .76 | | |
| No graduation | 4.1 | .88 | | |
| Stealing | 2.7 | .92 | | |
| Running away | 1.6 | .96 | | |
| Violent behavior | 1.6 | .96 | | |
| History of family problems | 10.0 | .88 | | |
| Problems/stressors at admission | | | | |
| Intoxication at admission | 3.5 | .88 | Admit problem-intoxication | |
| Threatened assault at admission | .8* | 1 | Admit problem-threatened assault | |
| Assault at admission | .4* | 1 | Stressor-assault | History of committing domestic violence |
| Legal problems at admission | .8* | .96 | Admit problem-legal problems | |
| Marital/partnership conflicts at admission | 15.2 | .84 | Stressor-marital conflict | Marital problems |
| Loss of job at admission | 2.7 | .80 | Stressor-loss of job | Fired from past jobs |
| Family problems at admission | 2.1 | .84 | | Familial discord |
| Financial problems at admission | 13.3 | .64 | | |
| Work or school problem at admission | 13.5 | .84 | Stressor-work or school problem | Poor work performance |
| Unemployment at admission | 19.3 | .80 | | |
| Homelessness at admission | 1.6 | 1 | Stressor-homelessness | |
| Illness or death in family at admission | 4.1 | .92 | Stressor-illness or death in family | |
| Prescription drug abuse at admission | 8.2 | .84 | | |
| Alcohol abuse at admission | 8.2 | .84 | | |
| Alcohol dependence at admission | 23.2 | .96 | | |
| Substance abuse at admission | 6.2 | 1 | | |
| Substance dependence at admission | 1.8 | .96 | | |
| Chronic medical or physical problem at admission | 6.8 | .84 | Stressor-chronic medical or physical problem | |
| Medication at admission | | | | |
| Antidepressants at admission | 35.9 | .52d | Medication at intake-antidepressants | Current medications-antidepressant |
| Antipsychotics at admission | 18.0 | .72d | Medication at intake-antipsychotics | Current medications-antipsychotic |
| Anxiolytics at admission | 23.0 | .44d | Medication at intake-anxiolytics | Current medications-antianxiety |
| Antimanics at admission | 3.1 | .92 | Medication at intake-antimanics or impulse control | |
| Anticonvulsants at admission | 3.7 | .96 | | |
| Mental status exam | | | | |
| Disturbances of attention and memory | | | | |
| MSE: Attention disorder | 44.5 | .76 | MS: Poor concentration | Difficulty concentrating |
| MSE: Memorization problems | 14.1 | .84 | MS: Memory problems | Intake: Memory for distant events |
| MSE: Chronotaraxis | 1.6 | 1 | | |
| Disturbance of orientation | .2* | .96 | | Disoriented |
| Disorders of ego | | | | |
| MSE: Derealization | 4.7 | .96 | | |
| MSE: Depersonalization | 5.7 | 1 | | |
| Formal disorders of thought | | | | |
| MSE: Inhibited thinking | 18.4 | .92 | | |
| MSE: Circumstantial thinking | 8.2 | .96 | MS: Circumstantial | Intake: Circumstantiality |
| MSE: Tangential | 4.5 | .96 | MS: Tangential | |
| MSE: Rumination | 32.2 | .64 | | Ruminates |
| MSE: Incoherence | 3.7 | .92 | | |
| MSE: Flight of ideas | 2.7 | .96 | MS: Flight of ideas | |
| MSE: Loose associations | 7.0 | .92 | MS: Loose associations | Intake: Loose associations |
| MSE: Diffuse | 14.6 | .92 | | |
| MSE: Vague | 4.7 | .96 | | |
| MSE: Perplexity | 10.9 | .96 | | |
| Unrealistic thoughts | .4* | .92 | | Poor reality testing |
| Phobias and compulsion | | | | |
| MSE: Suspicious | 10.7 | .92 | MS: Guarded | Suspicious |
| MSE: Paranoid | 8.4 | .92 | MS: Paranoid/suspicious | Paranoid features |
| MSE: Hypochondriasis | 9.8 | .76 | | Hypochondriacal |
| MSE: Histrionic | 5.9 | .88 | | Histrionic |
| MSE: Compulsive disorder | 7.0 | .96 | | Compulsive |
| Delusions | | | | |
| MSE: Delusional ideas | 4.1 | .84 | MS: Delusions | Delusional thinking |
| MSE: Delusions of reference | 8.2 | .96 | MS: Ideas of reference | |
| MSE: Delusions of persecution | 3.3 | .96 | MS: Persecutory delusions | |
| MSE: Delusions of grandeur | 1.0 | 1 | MS: Grandiose delusions | |
| Religious delusions | .0* | 1 | MS: Religious delusions | |
| Disorders of perception | | | | |
| MSE: Hallucinations | 1.4 | .96 | MS: Hallucinations | Hallucinations |
| MSE: Auditory hallucinations | 3.7 | .92 | MS: Auditory hallucinations | |
| MSE: Visual hallucinations | 1.4 | .96 | MS: Visual hallucinations | |
| Extraordinary perceptions | .0* | .96 | | |
| Disturbances of affect | | | | |
| MSE: Blunted affect | 15.6 | .84 | MS: Affect-flat | Restricted affect |
| MSE: Affective lability | 17.4 | .88 | | Emotional lability |
| MSE: Depressed mood | 64.3 | .68 | MS: Mood-depressed | Depressed |
| MSE: Dysphoria | 8.2 | .80 | | |
| MSE: Pessimistic | 15.4 | .80 | | Pessimistic |
| MSE: Tearfulness | 21.9 | .72 | MS: Tearfulness | Tearful |
| MSE: Desperate | 14.3 | .72 | | |
| MSE: Feelings of guilt | 15.0 | .84 | MS: Guilt | Guilt-prone |
| MSE: Panic | 9.8 | .64 | MS: Panic | |
| MSE: Irritability | 9.6 | .88 | | Irritable |
| MSE: Angry | 2.3 | .96 | MS: Mood-angry | Angry |
| MSE: Euphoria | 3.1 | .92 | | |
| MSE: Poor impulse control | 5.5 | .96 | | Impulsive |
| MSE: Inner restlessness | 31.8 | .56 | | Restless |
| MSE: Joylessness | 18.2 | .68 | | |
| MSE: Loss of interest | 16.8 | .88 | MS: Loss of interest | |
| Easily bore d | .0* | .96 | | Bored |
| Suicidal tendencies. | | | | |
| MSE: Suicidal ideation | 25.2 | .84 | MS: Suicidal ideation | Suicidal ideation |
| MSE: Suicide attempt | 9.6 | .76 | MS: Suicide attempt | |
| MSE: History of suicide attempts | 15.6 | .80 | Admit problem-suicidal | History of suicide attempts |
| Helplessness/hopelessness | | | | |
| MSE: Hopelessness | 14.3 | .96 | MS: Helpless or hopeless | Feels hopeless |
| MSE: Helplessness | 3.3 | .92 | | |
| MSE: Feeling overwhelmed | 9.4 | .72 | | Feels overwhelmed |
| MSE: Feelings of inadequacy/ Fear of failure | 19.3 | .92 | | Feels like a failure |
| MSE: Believes cannot be helped | 2.1 | .96 | | Believes cannot be helped |
| Feels gets raw deal from life | .8* | 1 | | Feels gets raw deal from life |
| Low motivation to change one’s situation | .8* | .96 | | Difficult to motivate |
| Self-doubt | | | | |
| MSE: Low self-esteem | 9.4 | .80 | | Self-degrading |
| MSE: Insecure | 13.9 | .72 | | Insecure |
| MSE: Self-critical | 1.8 | 1 | | Self-doubting |
| Self-contempt | .0* | 1 | | |
| Feeling inferior | .2* | 1 | MS: Worthlessness | Feels inferior |
| Anxiety | | | | |
| MSE: Anxious | 44.3 | .72 | | Anxious |
| MSE: Avoidant | 4.1 | .92 | | |
| MSE: Nightmares | 2.1 | .92 | MS: Nightmares | Has many nightmares |
| Flashbacks | .2* | .96 | MS: Flashbacks | |
| Intrusive thoughts | .0* | .96 | MS: Intrusive thoughts | |
| Risk-averse | .0* | 1 | | |
| PTSD symptoms | .0* | .92 | | |
| Stress reaction/worries | | | | |
| MSE: Temper tantrums | 2.9 | .96 | | Has temper tantrums |
| MSE: Low frustration tolerance | 4.5 | 1 | | Low frustration tolerance |
| MSE: Worried | 6.4 | .84 | | Worrier |
| Copes poorly with stress | .8* | .92 | | Copes well with stress (inverted) |
| MSE: Physical symptoms in response to stress | 1.2 | .96 | | Physical symptoms in response to stress |
| MSE: Self-mutilation | 5.7 | .92 | | |
| Disorders of drive and psychomotility | | | | |
| MSE: Lack of drive | 52.1 | .68 | | |
| MSE: Decreased energy | 10.5 | .76 | MS: Decreased energy | |
| MSE: Increased drive | 5.5 | .80 | | |
| MSE: Psychomotor restlessness | 22.3 | .64 | MS: Psychomotor agitation | |
| MSE: Psychomotor retardation | 14.8 | .88 | MS: Psychomotor retardation | |
| MSE: Mannerisms | 2.1 | 1 | | |
| MSE: Logorrhea | 6.2 | .92 | | Accelerated speech |
| MSE: Tics | 1.8 | 1 | | |
| Somatic complaints | | | | |
| MSE: Sleep disturbances | 52.1 | .72 | MS: Sleep (decrease) | Complains of sleep disturbance |
| MSE: Increased appetite | 1.8 | .92 | MS: Appetite (increase) | |
| MSE: Decreased appetite | 24.4 | .84 | MS: Appetite (decrease) | |
| MSE: Increased weight | 2.5 | .96 | MS: Weight (increase) | |
| MSE: Weight loss | 12.9 | .92 | MS: Weight (decrease) | |
| MSE: Decreased sexual desire | 6.8 | 1 | MS: Decreased sex drive | Low sex drive |
| MSE: Multiple somatic complaints | 7.0 | .64 | | Multiple somatic complaints |
| MSE: Psychovegetative symptoms | 13.7 | .68 | | |
| MSE: Chronic pain | 4.7 | 1 | MS: Chronic pain | |
| MSE: Headache | 4.5 | .88 | MS: Headache | |
| MSE: Back pain | 1.4 | .96 | | |
| MSE: Gastrointestinal complaints | 5.1 | .92 | | |
| MSE: Dizziness | 3.9 | .88 | | |
| Coordination disturbances | .8* | 1 | | |
| MSE: Perception disturbances | 4.1 | .84 | | |
| Interpersonal behavior | | | | |
| Hostile | .0* | .96 | | Hostile |
| MSE: Frequent conflicts in relationships | 2.1 | 1 | | Stormy interpersonal relationships |
| MSE: Keeps others at distance | 1.4 | 1 | | Keeps others at a distance |
| MSE: Problems with authority figures | 2.1 | .80 | | Problems with authority figures |
| Argumentative | .4* | .96 | | Argumentative |
| MSE: Aggression | 8.4 | .88 | | Aggressive |
| MSE: Dominating | 1.2 | .96 | | |
| MSE: Antisocial behavior | 5.3 | .96 | | Antisocial behavior |
| Alienation | .4* | 1 | | |
| MSE: Social withdrawal | 27.1 | .68 | MS: Withdrawn | |
| MSE: Social isolation | 3.5 | .92 | | Lonely |
| Social integration | .8* | .88 | | Conforming |
| Sociable | .6* | .96 | | Empathetic |
| Extroverted | .6* | 1 | | Extroverted |
| MSE: Introverted | 3.9 | .88 | | Introverted |
| MSE: Sensitive | 3.9 | .92 | | Overly sensitive to criticism |
| MSE: Anxious in social situations | 4.1 | .96 | | |
| MSE: Submissive | 1.6 | .92 | | Submissive |
| Evasive/defensive | .8* | .96 | MS: Evasive or defensive | Evasive |
| MSE: Poor eye contact | 1.8 | .96 | MS: Poor eye contact | |
| MSE: Dependent | 7.6 | .88 | | |
| MSE: Poor cooperativeness | .8 | 1 | MS: Cooperativeness (inverted) | |
| Other disorders | | | | |
| MSE: Narcissistic | 11.5 | .80 | | Narcissistic |
| MSE: Lack of insight | 9.0 | .80 | MS: Insight (inverted) | Patient’s insight concerning presence of mental problems (inv.) |
| Homicidal ideation | .6* | 1 | MS: Homicidal ideation | |
| Blames others for difficulties | .6* | 1 | | |
| MSE: History of medical noncompliance | 4.9 | .96 | MS: History of medical noncompliance | |
| Unprincipled | .2* | 1 | | |
| Other personality traits/interests | | | | |
| Even-tempered | .2* | 1 | | |
| Optimistic | .0* | 1 | | Optimistic |
| Empathetic | .2* | 1 | | |
| MSE: Self-confident | 1.2 | 1 | | Assertive |
| Risk-taking | .0* | 1 | | |
Submitted: November 30, 2015 Revised: September 27, 2016 Accepted: September 29, 2016
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Source: Psychological Assessment. Vol. 29. (10), Oct, 2017 pp. 1273-1289)
Accession Number: 2016-58882-001
Digital Object Identifier: 10.1037/pas0000415
Record: 63- Title:
- Evaluating the validity of computerized content analysis programs for identification of emotional expression in cancer narratives.
- Authors:
- Bantum, Erin O'Carroll. Cancer Research Center of Hawaii, University of Hawaii at Manoa, Honolulu, HI, US, ebantum@crch.hawaii.edu
Owen, Jason E.. Department of Psychology, Loma Linda University, Loma Linda, CA, US - Address:
- Bantum, Erin O'Carroll, Cancer Research Center of Hawaii, Prevention and Control Program, 1960 East-West Road, Biomed C-105, Honolulu, HI, US, 96822, ebantum@crch.hawaii.edu
- Source:
- Psychological Assessment, Vol 21(1), Mar, 2009. pp. 79-88.
- NLM Title Abbreviation:
- Psychol Assess
- Page Count:
- 10
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- Psychological Assessment: A Journal of Consulting and Clinical Psychology
- ISSN:
- 1040-3590 (Print)
1939-134X (Electronic) - Language:
- English
- Keywords:
- linguistic analysis, emotion, cancer, validity, cancer narratives
- Abstract:
- Psychological interventions provide linguistic data that are particularly useful for testing mechanisms of action and improving intervention methodologies. For this study, emotional expression in an Internet-based intervention for women with breast cancer (n = 63) was analyzed via rater coding and 2 computerized coding methods (Linguistic Inquiry and Word Count [LIWC] and Psychiatric Content Analysis and Diagnosis [PCAD]). Although the computerized coding methods captured most of the emotion identified by raters (LIWC sensitivity = .88; PCAD sensitivity = .83), both over-identified emotional expression (LIWC positive predictive value = .31; PCAD positive predictive value = .19). Correlational analyses suggested better convergent and discriminant validity for LIWC. The results highlight previously unrecognized deficiencies in commonly used computerized content-analysis programs and suggest potential modifications to both programs that could improve overall accuracy of automated identification of emotional expression. Although the authors recognize these limitations, they conclude that LIWC is superior to PCAD for rapid identification of emotional expression in text. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Breast Neoplasms; *Emotions; *Linguistics; Narratives; Test Validity
- Medical Subject Headings (MeSH):
- Adaptation, Psychological; Anxiety; Automatic Data Processing; Breast Neoplasms; Depression; Discriminant Analysis; Expressed Emotion; Female; Humans; Middle Aged; Psychiatric Status Rating Scales; Psycholinguistics; Quality of Life; Reproducibility of Results; Self Disclosure; Sensitivity and Specificity; Signal Detection, Psychological; Social Support; Stress, Psychological
- PsycINFO Classification:
- Tests & Testing (2220)
Cancer (3293) - Population:
- Human
Female - Location:
- US
- Age Group:
- Adulthood (18 yrs & older)
Thirties (30-39 yrs)
Middle Age (40-64 yrs) - Tests & Measures:
- Functional Assessment of Cancer Therapy—Breast Cancer Form
Impact of Events Scale
Hospital Anxiety and Depression Scale DOI: 10.1037/t03589-000 - Methodology:
- Empirical Study; Quantitative Study
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- Accepted: Oct 22, 2008; Revised: Sep 30, 2008; First Submitted: Feb 21, 2008
- Release Date:
- 20090316
- Correction Date:
- 20130218
- Copyright:
- American Psychological Association. 2009
- Digital Object Identifier:
- http://dx.doi.org/10.1037/a0014643
- PMID:
- 19290768
- Accession Number:
- 2009-03401-011
- Number of Citations in Source:
- 57
- Persistent link to this record (Permalink):
- http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2009-03401-011&site=ehost-live
- Cut and Paste:
- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2009-03401-011&site=ehost-live">Evaluating the validity of computerized content analysis programs for identification of emotional expression in cancer narratives.</A>
- Database:
- PsycINFO
Evaluating the Validity of Computerized Content Analysis Programs for Identification of Emotional Expression in Cancer Narratives
By: Erin O'Carroll Bantum
Cancer Research Center of Hawaii, University of Hawaii at Manoa;
Jason E. Owen
Department of Psychology, Loma Linda University
Acknowledgement: Erin O'Carroll Bantum acknowledges her dissertation committee members for their help on this project: Jason E. Owen (chair), Michael Galbraith, Mark Haviland, Patricia Pothier, and Kendal Boyd.
The proliferation of expressive writing and web-based interventions, coupled with increasingly sophisticated speech-recognition technology, has added to the need for valid methods of rapidly analyzing text-based data for content relevant to therapeutic processes. These interventions provide researchers and clinicians with textual data that can be used objectively to measure emotional expression and supplement self-report measures. Extensive qualitative analysis has the potential to provide behavioral data relevant to understanding physical and psychological adjustment to a diagnosis and treatment for cancer. However, the time required to conduct a thorough and reliable qualitative analysis and to validate the results makes such analysis impractical for many potential applications. Computerized text-analysis programs exist, although they have not been well validated for the purpose of evaluating emotional expression in therapeutic discourse. The goal of the current study was to create a manual coding system that could be used to evaluate the validity of two widely used text-analysis programs for identification of emotional expression.
Emotional expression has been suggested as a target for psychological treatments, but the putative mechanism of action is poorly understood (Greenberg & Safran, 1989). Emotional expression may serve to help individuals acknowledge and synthesize emotions that were previously unavailable to conscious awareness, lead to habituation to intense emotions, access state-dependent core beliefs or modify maladaptive emotional responses (Greenberg & Safran, 1989). The idea of a cathartic release once emotion is expressed has long been theorized as a crucial aspect of beneficial therapy (Breuer & Freud, 1895/1966). Stanton and colleagues (e.g., Austenfeld & Stanton, 2004) suggested that coping through emotional expression is most useful when it is done in a social context that is receptive, when it helps frame goals that can then lead to action, and when it facilitates habituation to a stressor. Along similar lines, Kennedy-Moore and Watson (2001) proposed that emotional expression might produce benefits by alleviating anguish about distress and facilitating insight, which in turn leads to opportunities to respond to the environment.
Focused expressive writing was one of the first systematically evaluated types of emotionally expressive interventions (Pennebaker, 1997; Pennebaker & Beall, 1986). Meta-analyses have generally suggested beneficial outcomes associated with focused expressive writing intervention (Frisina, Borod, & Lepore, 2004; Sloan & Marx, 2004; Smyth, 1998). Emotional expression has also played a key role in intervention studies for cancer (Graves, Carter, Anderson, & Winett, 2003; Lieberman & Goldstein, 2006; Smith, Anderson-Hanley, Langrock, & Compas, 2005). Examples include unstructured journaling for women with newly diagnosed breast cancer (e.g., Smith et al., 2005), online support groups (e.g., Lieberman & Goldstein, 2006), face-to-face support groups (e.g., Graves et al., 2003), and supportive-expressive group therapy (Spiegel, Bloom, Kraemer, & Gottheil, 1989). Expressive writing and group-based intervention studies provide researchers with a wealth of textual data from both written essays and transcribed individual or group sessions. The resulting textual (or video) data often provide detailed accounts of the ways in which individuals cope with diagnosis and treatment of cancer. If valid methods for quantifying psychological processes in text were available, researchers and clinicians would be able to explore relationships between specific emotional factors (e.g., description of anger in text) and outcomes. Such methods would enhance our understanding of what types of emotional expression might be related to improved psychological adjustment.
There have been a few studies designed to evaluate the effect of emotional expression on both physical and psychological symptoms in breast cancer (Stanton, Danoff-Burg, Cameron, Snider, & Kirk, 1999, Stanton, Danoff-Burg, & Huggins, 2002; Walker, Nail, & Croyle, 1999; Zakowski, Ramati, Morton, Johnson, & Flanigan, 2004). Although in one study expressive writing was not associated with differences in positive affect, negative affect, intrusive thoughts, or avoidance (Walker et al., 1999), in the other three studies, fewer physical symptoms (Stanton et al., 1999; Stanton, Danoff-Burg, Sworowski, et al., 2002) and fewer social constraints after writing (Zakowski et al., 2004) were found in participants who were asked to write either about their feelings regarding breast cancer or benefits resulting from their experience with breast cancer. Supportive-expressive group psychotherapy for metastatic breast cancer patients has been related to a significant reduction in suppression of primary negative affect; improvement in restraint of inconsiderate, irresponsible, impulsive, aggressive behavior (Giese-Davis et al., 2002); and a decline in traumatic stress symptoms (Classen et al., 2001). Lieberman and Goldstein (2006) found that increased expression of anger was associated with improved quality of life (as identified by the Functional Assessment of Cancer Therapy—Breast Cancer Form [FACT–B]; Cella, 1994) outcomes and lower levels of depression (as identified by the Center for Epidemiological Studies—Depression scale; Radloff, 1977), although expression of fear and anxiety was associated with decreased quality of life and higher levels of depression. This complicates the picture and further brings into question the notion that emotional expression is a mechanism leading to psychological and physical betterments for cancer patients. Similar findings by Smith et al. (2005) revealed that the prevalence of negative emotion in journal entries of breast cancer patients was related to increased anxiety and depression.
Computational text analysis provides a set of tools for analyzing the large volume of text that is produced as a result of expressive writing studies or other types of interventions. In the psychological literature, only a few computational text-analysis programs have been described (see Pennebaker, Mehl, & Niederoffer, 2003, for review). Although the General Inquirer (Stone, 1966) is considered by some as the first computational text-analysis program, more recent programs, such as Mergenthaler's (1993) TAS/C and DICTION (North, Langerstrom, & Mitchell, 1984), also have applicability for coding emotional expression. Others have attempted to capture the multidimensional nature of emotionally relevant words. Lang, Bradley, and Cuthbert's (1999) Affective Norms for English Words provides an assessment of the degree of pleasure, arousal, and dominance evoked by each of over 3,000 words.
One of the more recently developed and used computational text-analysis programs that has been used to provide information regarding linguistic correlates of psychological and health outcomes after writing is Linguistic Inquiry and Word Count (LIWC; Pennebaker, Francis, & Booth, 2001). Pennebaker and Francis devised this tool to analyze text on a word-by-word basis. LIWC calculates a percentage of words falling into 74 different categories, ranging from emotion words to words about social context and religion. Pennebaker (1997) found four factors to correlate with the greatest health benefits. These include more positive emotion words used, a moderate number of negative emotion words used, and an increasing number of causal and insight words used over the course of writing (Pennebaker, 1997; Pennebaker & Chung, 2007).
LIWC was initially validated for content and construct validity by the creators of the program (Pennebaker & Francis, 1992; Pennebaker et al., 2001). Interrater reliability discrimination of category word elements has been found to range from 86% to 100%, depending on the dimension being assessed (Pennebaker et al., 2001), suggesting content validity. To assess construct validity, four judges rated 210 essays on several LIWC dimensions (Pennebaker, Mayne, & Francis, 1997). Moderate to strong correlations between LIWC and judges' global ratings of written essays were found for most emotion categories (0.22–0.75; Pennebaker et al., 1997). LIWC does not take context into consideration when identifying emotion words. It simply identifies words that are deemed by the developers of the program to appropriately fit into a number of different categories. It has been recognized that computer programs such as LIWC are inadequate for distinguishing between different meanings of the same word (Chung & Pennebaker, 2007). Very little work regarding psychometric properties has been done outside of the research conducted by those who developed LIWC. In one known study assessing psychometric properties of LIWC with breast cancer patients, researchers analyzed text for overall valence in a number of different categories (Alpers et al., 2005). Low to moderate correlations were found between rater codes and LIWC codes (Alpers et al., 2005), although the sample size was small (n = 9) and rater codes were assigned to each text file, rather than to individual words.
Another computer program that has been used to measure emotional expression in text and dialogue is Psychiatric Content Analysis and Diagnosis (PCAD). PCAD arose from the Gottschalk-Gleser scales (Gottschalk, Winget, & Gleser, 1969), in which verbal output is transferred into text and analyzed by extensively trained coders on a number of different facets, most of which are geared toward psychiatric diagnoses. Adequate construct validity and reliability have been established for all of the Gottschalk-Gleser scales (Gottschalk, 1995), but little is known about the psychometric properties of PCAD. One major difference between this program and LIWC is that PCAD takes context into consideration. Although this is an attractive aspect of PCAD, the types of scoring rules used by PCAD for coding emotion are unclear. This scoring ambiguity makes it difficult to critically evaluate the decisions and resulting scores PCAD provides for a number of different emotion domains.
In this study, we analyzed data collected by Owen et al. (2005) with the goal of comparing rater-coded emotional expression with emotion coded via LIWC and PCAD computerized text-analysis programs. We used signal-detection theory to guide the inclusion of signal-detection indices to help distinguish between signal and noise (Green & Swets, 1966). For our purposes here, a signal is emotional expression, and noise is the lack of emotional expression. The signal-detection indices used here were sensitivity, specificity, positive predictive value, and negative predictive value. In this study, sensitivity was the probability that a word that is actually representative of emotional expression would be characterized by LIWC as an emotion word (Portney & Watkins, 2000). Specificity was the probability that a word that is not indicative of emotional expression would be characterized by LIWC as a nonemotion word (Portney & Watkins, 2000). Positive and negative predictive values were used to assess the probability that a word characterized by LIWC as an emotion word is truly representative of an emotion word and the probability that a word characterized as not being indicative of emotional expression by LIWC is, in fact, absent of emotional expression, respectively (Portney & Watkins, 2000).
There were two primary aims of the study. The first aim was to assess the accuracy of LIWC and PCAD for the detection of emotional expression. To accomplish this aim, we created a reliable coding system to identify emotional expression. It was hypothesized that, relative to PCAD, which uses context to disambiguate meanings of words, LIWC would exhibit low sensitivity to nuanced emotional expression. Because of the text-independent coding rules employed by LIWC, it was also hypothesized that LIWC would systematically over-identify emotional expression (i.e., exhibit low positive predictive values). Thus, the goals of the study were to evaluate sensitivity and positive predictive power of LIWC and PCAD relative to human raters. Although we had no specific hypotheses about specificity and negative predictive value, these signal-detection indices are mathematically related to sensitivity and positive predictive power and provide useful information about the types of overlap between computerized text-analysis methods and human raters. For sake of completeness in reporting, we elected to present the data from specificity and negative predictive values alongside sensitivity and positive predictive values. The second aim was to explore the relationship between coding methods and self-report measures of emotional well-being. It was hypothesized that rater-coded emotional expression would be more closely associated with self-report measures of emotional functioning than would LIWC- or PCAD-identified emotional expression.
Method Participants
Participants in the initial study included 49 women with Stage 1 or 2 breast cancer, recruited from a hematology/oncology outpatient clinic at a large academic medical center in the southeastern United States. Participants were not excluded on the basis of time since diagnosis or medical treatment. They were recruited to participate in a randomized 12-week clinical trial of an Internet-based support group. Those who consented to participate completed a baseline assessment and were then randomized into a wait-list control group (n = 19) or an Internet-based support group (n = 30). Wait-listed participants were able to join a support group after completing a baseline questionnaire and waiting approximately 12 weeks for their group to begin. Additional details about the sample have been previously reported (Owen et al., 2005). For the current study, we included data from 14 additional participants. Thirteen women with Stage 3 or 4 breast cancer who were enrolled in a nonrandomized pilot of the online intervention were included in these analyses. Of those participants, 6 of 13 provided baseline data. In addition, text from 1 participant with Stage 2 breast cancer did not include any LIWC emotion words and was therefore not included in the findings displayed in the initial paper. For the analyses that included solely textual coding, 63 participants were included. For the analyses that included baseline assessment data, 55 participants were included. Participants had a mean age of 49.8 years (SD = 11.0), were largely college educated (M = 15.4 years; SD = 2.4), and were primarily Caucasian (93%).
Procedures
Those who were assigned to the online support group and those who later crossed over from the wait list into an online support group (n = 19) were encouraged to interact with one another remotely using a discussion board for general topics of conversation and a series of interactive coping-skills training exercises. All messages and responses to coping exercises were digitally stored in separate text files for each participant. Combined with the self-report survey data obtained at baseline and 12 weeks after beginning the support group (n = 55), the text files (n = 63) served as the primary source of data for the present study.
Rater coding of emotional expression
Developing a set of manual coding rules designed to identify emotional expression in text was a primary goal of the study. Definitions and coding rules for identification of emotional expression were developed iteratively. The initial coding rules were derived from a review of the literature on verbal and nonverbal behavioral indicators of emotional expression (Clore, Ortony, & Foss, 1987; Ekman, 1992; Mergenthaler, 1996; O'Rorke & Ortony, 1994; Ortony, Clore, & Foss, 1987; Ortony & Turner, 1996). Particularly helpful to this process were reports by Ortony and colleagues describing dictionaries of emotion words and decision rules for classifying words as indicators of emotional expression (Clore et al., 1987; O'Rorke & Ortony, 1994; Ortony et al., 1987). Their work on the affective lexicon provided a framework for considering different properties of emotion words and whether they should be included. An example of this was making the decision to code as emotion words words that referenced either internal feeling states or external feeling states (i.e., referencing emotion experienced by someone else; Clore et al., 1987).
After carefully reviewing the literature, we codified simple rules for identifying emotional expression and classifying various dimensions of positive and negative emotional expression in text. We then attempted to apply these rules to a subsample of approximately 33% of the available textual data, not to apply final coding decisions but, rather, to identify instances where the coding rules were unable to account for clear instances of emotional expression in the text. When such instances were identified, the coding rules were modified. After reviewing 33% of the text, we finalized coding rules and developed a brief training program used to teach blinded raters to apply the coding rules. Then, with the help of the blinded raters, we reevaluated all text data.
Brief description of coding rules
For raters, the initial decision to be made was regarding the presence of emotional expression. If it was determined that emotional expression was present, the decision was then made regarding the best fitting category of emotional expression: positive feelings, optimism, anxiety, anger, sadness, other positive emotion, or other negative emotion. Positive feeling was coded when it was deemed that the person was expressing a state of happiness, peacefulness, or gratitude. Optimism was coded when there was a demonstration in the text of the tendency to express the best possible outcome or the feeling that something would turn out well. The code of other positive emotion was given when a word and the adjoining phrase was representative of positively valenced emotion that was not better captured in the positive feelings or optimism categories (e.g., feelings of excitement). If a word or phrase represented uneasiness and apprehension, stress or tension, a feeling of being out of control, rumination, or a sense of hyperarousal, it would be coded in the anxiety category. Anger was coded when there was a demonstration of displeasure, hostility, frustration, or a dissatisfaction/discrepancy between an ideal and actual outcome. Sadness was coded when the word and/or phrase was characterized by sorrow or unhappiness. Sadness was also coded when the writer expressed a pessimistic sense of inadequacy, despondent lack of activity, or described behavior clearly associated with feelings of sadness (e.g., tears). The final emotion category coded was other negative emotion. If the word and/or phrase represented a negative emotion other than anxiety, anger, or sadness, it was placed in this category. An example of this instance is when there was a representation that was negative but ambiguous (e.g., “I felt really bad” when context made clear that the reference was not to a physical state). Categories mirrored dimensions of emotion identified in the literature (e.g., Clore et al., 1987) and emotion categories identified by LIWC, facilitating direct comparisons with LIWC emotional coding.
Rater-coding procedure
We replicated LIWC scoring rules using the Practical Extraction and Report Language (PERL) programming language and the default LIWC 2001 dictionaries. All text files were processed by our PERL code: Each word of each file was compared with the LIWC scoring dictionary, and words identified by the LIWC dictionary as emotion words were tagged (i.e., the word “sad” became “***sad***”). Tagged text files were then saved separately and made available for review by trained raters. Raters then reviewed each tagged text file and scored all tagged words using the coding rules described above. Raters were also instructed to score instances of emotional expression that had not been tagged. Words were coded into a specific emotion category (or coded as being absent of emotion). As with the trained raters, blinded raters were asked to review the text again for any missed emotion (words that were not tagged). Raters were blind to scoring decisions made by LIWC. The trained coders each independently coded all text files and then reconvened to assess congruency in codes. Discrepancies in coding were handled by describing the reasons for original coding decisions, and final codes were assigned by consensus. Interrater reliability between the two trained coders (EB, JO) was very good (κ = .80). Two additional raters who were blinded to the aims of the study underwent three training sessions, each lasting 2 hours, to learn the coding rules. Each of these blinded raters then independently reviewed 33% of the text, and interrater reliability was also established between two coders (κ = .69). The kappa statistic is very sensitive, and it is suggested that kappa scores of .61–.80 demonstrate “substantial” reliability, and scores of .81–1.0 demonstrate “almost perfect” reliability (Landis & Koch, 1977).
PERL text manipulation
PERL, which is an open-source programming language useful for evaluating patterns (i.e., pattern matching) with text-based data, was used to perform several key procedures in the present study. First, using the LIWC word library, we developed PERL code to reproduce LIWC scoring of emotion words, and this process allowed us to generate transcript files for manual coding in which all words coded by LIWC as emotion words were tagged by a common symbol. These tagged words were used to prompt coders to categorize the presence, absence, or type of emotional expression without indicating the specific code that LIWC would have assigned. Second, once rater coding was complete, we developed PERL code that matched each instance of emotional expression identified by rater coding with both LIWC-assigned codes and PCAD-assigned codes. Because PCAD assigns codes on the basis of clauses, rather than individual words, rater-assigned codes were matched with the clauses that contained the word(s) associated with the code. Our PERL code read each line of PCAD summary data for each text file, identified the clauses scored by PCAD, extracted the scoring decisions made by PCAD, and matched each clause with a Microsoft Excel spreadsheet containing the words and scoring decisions made by the human raters. We checked output from the PERL code for each subject to verify matching accuracy, and no errors were identified.
Materials
Self-report measures
Self-report measures for quality of life, cancer-related trauma, anxiety, and depression were administered to the treatment group (n = 30) at the beginning of the online support group and to the crossover control group (n = 32) 12 weeks after they entered the study (this is equivalent to the start date of their group).
Health-related quality of life
The Functional Assessment of Cancer Therapy—Breast Cancer Form (FACT–B) is a 27-item questionnaire that was used as a measure of perceived quality of life, with items of quality of life tapping into physical, social, emotional, and functional domains (Cella, 1994). This instrument has adequate internal consistency and good concurrent validity with the Eastern Cooperative Oncology Group performance status (Brady et al., 1997). The FACT–B has also been sensitive to change over time in persons with cancer. Participants were also asked to rate their overall health status on a 0–100 scale (using the EuroQuol 5–D; Brooks, 1996). On this scale, 0 = the least desirable state of health you can imagine and 100 = perfect health (Llach, Herdman, Schiaffino, & Dipstat, 1999).
Cancer-related trauma
We used the Impact of Events Scale (IES; Horowitz, Wilner, & Alvarez, 1979), which uses 22 items with a 5-point Likert-type scale to assess intrusiveness and avoidance of cancer-related thoughts and stimuli, to assess cancer-related anxiety. This instrument has been demonstrated to have good internal consistency and has been shown to be sensitive to effects of psychological intervention (Edgar, Rosberger, & Nowlis, 1992).
Anxiety and depression
We used the Hospital Anxiety and Depression Scale (HADS; Zigmond & Snaith, 1983), a 14-item measure that provides summary scores for depression and anxiety, to measure anxiety and depression. This is a self-report measure that was created to be used in medical populations, in that the scale does not overestimate for a mood disorder on the basis of somatic symptoms. It was found to have good interrater reliability and good construct validity for distinguishing between patients with and without mood disorder (Zigmond & Snaith, 1983). This measure is also sensitive to the effects of adjuvant psychotherapy treatment (Greer et al., 1992).
Self-reported positive and negative emotion
To compare coding methods with self-reported measures of both positive and negative emotion, we created two scales. We took individual items from the self-report measures previously described and used them in the initial study (Owen et al., 2005). The items were standardized, and Cronbach's alphas were assessed and found to be good for both self-reported positive emotion (Cronbach's α = .93) and negative emotion (Cronbach's α = .81). For the self-reported positive emotion scale, the following items were included from the FACT–B (Cella, 1994): “I feel close to my friends and family,” and “I feel close to my partner (or the person who is my main support).” The HADS items that were used in the scale were “I still enjoy the things I used to enjoy, I can laugh and see the funny side of things, I feel cheerful, I can sit at ease and feel relaxed, I look forward with enjoyment to things,” and “I can enjoy a good book or radio or TV program.” For the self-reported negative emotion scale, the following items were included from the FACT–B (Cella, 1994): “I feel sad, I am losing hope in the fight against my illness, I feel nervous, I worry about dying,” and “I worry that my condition will get worse.” The HADS items that were included in this scale were “I feel tense or 'wound up,' I get a sort of awful feeling as if something is about to happen, worrying thoughts go through my mind, I feel as if I am slowed down, I get a sort of frightened feeling like 'butterflies' in the stomach, I feel restless as if I have to be on the move,” and “I get sudden feelings of panic.” Four items from the IES (Horowitz, Wilner, & Alvarez, 1979) were included in the scale: “I had waves of strong feelings about cancer, I felt watchful and on guard, I felt jumpy and easily startled,” and “I felt irritable and angry.”
LIWC analysis
Text was analyzed across all participants in the website (n = 63). This included all of the text submitted by each participant. The LIWC computer program analyzes written text on a word-by-word basis and is available in Spanish, German, Dutch, Norwegian, Italian, and Portuguese. This tool was developed by a process in which groups of judges reviewed 2,000 words or word stems and decided how the reviewed words related to dozens of categories (e.g., word count, total first-person usage, negative emotion; Pennebaker & Francis, 1992). Every word of a text file is compared with “dictionaries” of 74 dimensions. The dimensions include (a) standard linguistic dimensions, such as words per sentence; (b) psychological constructs, such as positive emotions; (c) dimensions related to relativity, such as past-tense verbs; and (d) personal concern categories, such as the use of job- or work-related words. A word might fit and be placed into more than one category. The categories that were used in this study included all of the emotion categories. LIWC separates emotion into positive emotion and negative emotion. It then more specifically identifies the word as being either indicative of positive feelings or optimism in the positive emotion category and into anxiety, anger, or sadness in the negative emotion category. There are cases in which LIWC identifies a word as being positive or negative emotion but does not further identify the word as fitting into one of the more specific categories.
Psychiatric Content Analysis and Diagnosis
We also compared PCAD with the rater-coding system. PCAD was developed from the Gottschalk-Gleser scales (Gottschalk et al., 1969). The program was designed to transfer verbal output into text and analyze it on a number of different facets, most of which are geared toward psychiatric diagnoses (e.g., anxiety, dependency, and social alienation). In large part, the program was designed to assess transcripts from psychotherapy. Adequate construct validity and reliability have been reported for all of the scales (Gottschalk, 1995). PCAD is different from LIWC in that it evaluates the word in the context of the surrounding clause and scores it on a number of different dimensions. A clause that contained one of the rater-coded emotion words was identified, and scores given by both PCAD and rater codes were compared for agreement. To link PCAD scores with rater-defined emotion categories, we matched PCAD scores with the most appropriate rating category as follows: positive feelings (human relations codes A2 and D1), optimism (hope H1, H2, H3, and H4), anger (hostility outward, human relations D2, and frustrated dependency codes A and D), anxiety (death, mutilation, separation, guilt, shame, and diffuse anxiety), and sadness (hostility inward, any depression score, or any ambivalent hostility score).
ResultsThere were two primary aims of the study. The first aim was to assess the accuracy of LIWC and PCAD for the detection of emotional expression. The second aim was to explore the relationship between coding methods and self-report measures of emotional well-being. To accomplish these aims, we recruited 63 people to participate in the research website and engage in text-based interaction with other participants. The entire transcript available for analysis consisted of 165,754 words (278 pages of single-spaced text, 12-point font). The transcripts were spell checked, and the text that was used in the analyses included all of the available text for each participant. Writing samples averaged 2,631 words per subject, and there was considerable variation across subjects (SD = 2,868). On average, LIWC identified 1.43% (SD = 0.6) of total words as negative emotion and 3.2% (SD = 1.1) of total words as positive emotion. Raters identified less negative emotion (0.8%; SD = 0.5) and less positive emotion (0.8%; SD = 0.4) than did LIWC. PCAD identified an average of 2.5% (SD = 0.7) of total words as negative emotion and 2.9% (SD = 0.8) as positive emotion. For the more specific categories of emotion, the average LIWC codes were as follows: positive feeling (posfeel) = 1.0%, optimism = 0.8%, anxiety = 0.5%, anger = 0.2%, and sadness = 0.3%. For specific emotion categories, manually coded averages were as follows: posfeel = 0.8%, optimism = 0.03%, anxiety = 0.2%, anger = 0.1%, and sadness = 0.3%. Specific categories of emotion, as coded by PCAD, were as follows: posfeel = 0.2%, optimism = 0.8%, anxiety = 1.5%, anger = 0.6%, and sadness = 2.3%. Each instance of emotion was counted as one point, and frequency of a given emotion was divided by total words for that participant, resulting in a percentage of a given emotion for each participant. This was true for LIWC, manual codes, and PCAD.
Sensitivity
Sensitivity captures the proportion of total emotion words identified by raters as being indicative of emotional expression that were captured by either LIWC or PCAD. Sensitivity for overall emotional expression was good for both LIWC (0.88) and PCAD (0.83). LIWC sensitivity for both positive and negative emotion was also good, although LIWC did not perform as well in the subcategories of positive feelings, anger, and sadness (see Table 1). PCAD sensitivity for both total positive (0.77) and negative (0.78) emotion was good; however, there was considerable variability between specific types of emotion. PCAD sensitivity was poor for positive feelings (0.15) and anger (0.40) but was substantially higher for sadness (0.84). Sadness was the only category for which PCAD was significantly more sensitive than LIWC. LIWC performed significantly better in categories of overall emotional expression, positive emotion, positive feelings, and anger (see Table 1).
LIWC and PCAD Sensitivity and Specificity With 95% Confidence Intervals (CI; N = 63)
Specificity
Specificity measured the proportion of nonemotional words that were accurately coded by LIWC or PCAD (see Table 2). Specificity for LIWC was exceptional in all emotion categories (0.97–0.999). PCAD specificity was poor for overall emotional expression (0.58) but was slightly better for overall positive (0.74) and negative emotion (0.78). In all eight of the identified emotion categories, LIWC was significantly more specific than PCAD (see Table 1).
LIWC and PCAD Positive and Negative Predictive Power With 95% Confidence Intervals (CI; N = 63)
Positive Predictive Value
Positive predictive value measured the probability that a word identified by LIWC and PCAD as being indicative of emotional expression was in agreement with rater codings of emotional expression. For LIWC, only 32% of words identified as any type of emotion, 24% of words identified as positive emotional expression, and 43% of words identified as negative emotion were in agreement with rater codes (i.e., 69% of words identified by LIWC as indicators of emotional expression were not thought by raters to be instances of emotional expression). PCAD performed poorly in all emotion categories (0.005–0.26). LIWC's positive predictive value was significantly better than PCAD in all emotion categories (see Table 2).
Negative Predictive Value
Negative predictive value measured the probability that a word not identified as emotion by LIWC or PCAD agreed with raters' judgment that the word was not associated with emotional expression. Both LIWC and PCAD had excellent negative predictive value across all emotion categories. Negative predictive values for LIWC ranged from 0.995 for positive feelings to 0.999 for total positive emotion, optimism, anxiety, anger, and sadness. For PCAD, negative predictive values ranged from 0.96 for positive feelings to 0.998 for optimism and sadness. As with specificity and positive predictive value, LIWC performed significantly better than PCAD on each emotion category (see Table 2).
Relationship Between Coding Methods and Self-Report Measures
To assess the relationship between coding methods, we calculated Pearson product-moment correlations to compare rater codes with both LIWC and PCAD codes. Tables 3 and 4 display the results. All but one of the LIWC codes were significantly positively correlated with corresponding rater codes. Optimism was the only emotion category that was not correlated with rater codes of the construct (r = .07; p > .05). The strongest correlations were found for positive emotion (r = .75; p < .01) and anxiety (r = .69; p < .01). Among PCAD emotion codes, only anxiety was significantly correlated with its corresponding rater codes (r = .27; p < .05).
Correlations Between Rater-Coded Emotional Expression and Emotional Expression as Coded by LIWC (N = 63)
Correlations Between Manually Coded Emotional Expression and Emotional Expression as Coded by PCAD (N = 63)
It was anticipated that self-reported emotional well-being and psychological disturbance in women with breast cancer would be associated with some level of expression of those emotional experiences, particularly in the context of a structured coping-skills training program that encouraged active emotional coping efforts. To facilitate the examination of intercorrelations between emotional expression (positive and negative), as identified by different coding systems and self-report outcome measures, we used a multitrait, multimethod matrix to display these correlations. As previously stated, rater-coded positive and negative emotion and LIWC positive and negative emotion were highly correlated (for positive emotion, r = .77, p < .01; and for negative emotion, r = .78, p < .01). PCAD negative and positive emotion were not significantly correlated with corresponding categories in manual codes or LIWC. None of the coding systems was correlated with the created scales of self-reported positive and negative emotion, although the self-reported positive and negative scales were highly correlated (r = −.76; p < .01). Details of this analysis can be found in Table 5. In addition, we examined correlations between coding methods and the original scales. The correlations examined were as follows: Manual positive emotion and scores on FACT (r = −.04), manual positive emotion and scores on health status (r = .04), and manual positive emotion and scores on IES (r = .11) were not significant. Manual negative emotion and scores on FACT (r = .06), manual negative emotion and health status (r = −.01), and manual negative emotion and IES (r = −.07) were also not significant. It should be noted that the sample size is smaller in this table (n = 55) because not all participants returned the questionnaires that were included in this analysis. The text of the 55 participants who returned self-report data was included in these analyses.
Multitrait, Multimethod Matrix Correlating Coding Systems, Types of Emotion, and Outcome Measures (N = 55)
DiscussionOur hypothesis that high interrater reliability would be established between both trained and blinded coders was supported. Our second hypothesis, that PCAD would be more sensitive to emotional expression than LIWC, was not supported. LIWC's test characteristics were comparable to PCAD or significantly better than PCAD for all emotion categories. Both LIWC and PCAD exhibited poor positive predictive value, suggesting that they substantially over-identified emotional expression. However, PCAD's attempts to use context to disambiguate word meanings did not appear to be particularly effective. Although both LIWC and PCAD measure a number of domains other than emotional expression, our findings suggest that LIWC is superior to PCAD for identification of emotion in text.
Test characteristics for LIWC were more desirable for general emotion categories (i.e., overall emotion, negative emotion, positive emotion) than for specific types of emotion (i.e., positive feelings, optimism, anxiety, anger, and sadness), although this was not the case for PCAD. LIWC's stronger performance in general emotion categories indicated that, although it accurately captured that a given word was indicative of some kind of positive or negative emotion, it often did not adequately identify the specific types of emotional expression. LIWC uses a very simple strategy to place words into a given category on the basis of lists (“dictionaries”) of related words and does not take context into account. The simplicity of the strategy is appealing and works well in general, but accuracy of LIWC for identifying emotional expression could be improved using more sophisticated computational linguistic strategies, such as word disambiguation (Agirre & Edmonds, 2006) or key word in context (Weik, 1996). Revising simple programs, such as LIWC, to include other, more sophisticated computational linguistic strategies could be fruitful.
With the exception of optimism, there was good convergent and discriminant validity between LIWC codes and rater codes. Agreement between LIWC and trained coders was quite high, with large effects for the general emotion categories of positive and negative emotion and the subcategories of positive feelings and anxiety and small effects in the subcategories of anger and sadness. There was no correspondence between LIWC and rater codes for optimism, and raters noted that optimism was particularly difficult to examine. Raters did not code a given word as being indicative of optimism if it was thought to express desire (e.g., “I hope I get better” or “Hopefully, the weather will clear up”), rather than a feeling of desire (e.g., “His response was one of hope and caring”). Hope is an example of a word that LIWC automatically codes as optimism, but human coders identified a great deal of context-dependent variability for this word in particular.
A very different pattern of results was observed for the correlations between PCAD and rater-coded emotions. Across nearly all categories of emotion, there was little agreement between PCAD and trained coders on corresponding types of emotion. Agreement between PCAD and raters existed only for anxiety. Expected convergent and discriminant validities were not obtained. For example, PCAD codes of negative emotional expression and anger were significantly and unexpectedly positively correlated with rater codes of overall positive emotion and positive feelings. The original Gottschalk-Gleser content-analysis scales (Gottschalk et al., 1969) were developed with the goal of having a clinician use transcripts of therapy sessions to better understand psychological functioning, as well as to assist the clinician with making a diagnosis. Although the therapist was instructed to use clinical judgment to score specific aspects of psychological disturbance related to an emotion category for anxiety (e.g., death anxiety, mutilation anxiety, guilt anxiety, separation anxiety, shame anxiety, and diffuse anxiety), it is unclear how the PCAD computer program makes these decisions. This makes it difficult to identify potential improvements in the way PCAD assigns emotion-related scores.
Our hypothesis that rater-coded emotional expression would be more closely associated with self-report measures of positive and negative emotion than would LIWC was not supported. Self-reported positive and negative emotion were not highly correlated with rater, LIWC, or PCAD codes of positive or negative emotional expression. Other literature supports this finding (e.g., Owen et al., 2006). This finding suggests that behavioral linguistics and subjective self-report measures of emotional well-being provide different perspectives on the individual experience of emotion. Because so much of our understanding of how emotional experiences influence and are influenced by behavior rests on studies involving self-report measures of the emotional experience, it is imperative that we think more carefully about how objective measures of emotional expression could contribute to the measurement and conceptualization of emotion. We suggest that behavioral linguistics should be considered as a supplement to self-report data in subsequent studies.
LIWC appears to be a relatively useful instrument for the identification of emotional expression in text. However, LIWC over-identifies emotional terms, suggesting that LIWC's ability to disambiguate words that are often used to convey quite different meanings could be substantially improved. Specifically, there were approximately 6,000 unique instances where LIWC identified emotional expression that was at odds with human ratings of the word. LIWC failed to identify emotional expression in only approximately 500 instances in which raters had identified the presence of emotion. Thus, LIWC is 12 times more likely to make errors of over-identification than it is to make errors of under-identification. There were also a number of words that were frequently coded by LIWC as emotion that could be removed from the emotion dictionaries. For instance, the word “good” is coded as positive emotion by LIWC, yet in almost every instance (94% of cases), it was not deemed by raters to be representative of emotion. Other words that were frequently coded as emotion by LIWC but not coded as emotion by manual codes were “hope” (97% of cases found to demonstrate the absence of emotion), “like” (97% of cases found to demonstrate the absence of emotion), “beautiful” (100% of cases found to demonstrate the absence of emotion), and “best” (100% of cases found to demonstrate the absence of emotion). In general, there were a few consistent errors that, if addressed, could improve the ability of LIWC to accurately code emotional expression. Some of these errors can be corrected by making simple changes to the way LIWC codes words. To our knowledge, the ability to add additional guidelines or rules to LIWC, a feature built into the software, has not been tested. If the addition of simple qualifiers or guidelines to LIWC could improve the accuracy with which emotional expression is identified, the ability of researchers and clinicians to characterize associations between emotional expression and of physical and mental health benefits could be enhanced. Recently, an updated version of LIWC has become available (Pennebaker, Booth, & Francis, 2007). In this version, the emotion categories of positive feelings and optimism have been removed from the program. Additional studies are needed to independently evaluate the test characteristics of this new version of LIWC and perhaps to evaluate the contribution of other LIWC categories to the identification of emotional expression.
There were a few noteworthy limitations of the study. Because this study evaluated emotional expression in a population of women with breast cancer using an Internet-based group, it is unclear how these specific findings will generalize to other populations. Because women with breast cancer engage in more frequent emotional expression than do people with other cancer types (e.g., men with prostate cancer; Owen, Klapow, Roth, & Tucker, 2004), we believe breast cancer patients to be a particularly useful population for the study of emotional expression. In addition, findings might differ for face-to-face groups or groups that are instructed to engage in the prototypical expressive writing paradigm. It should also be noted that PCAD and LIWC emotion categories are not identical. Because the default PCAD scoring was used and scores were summed to create composite scores consistent with our taxonomy of emotions, our findings may somewhat underestimate PCAD's validity. Scoring rules used by PCAD were not readily available and so could not be used to tag our text files prior to qualitative analysis. Although the fairly small sample size can be noted as a limitation, the volume of text analyzed in the study provided a more than adequate sampling frame for characterizing the accuracy of LIWC and PCAD.
A final limitation concerns base rates. Base rates are known to affect signal-detection indices. It is worth noting that the base rate of emotional expression observed in this study (1.8% of words identified by raters) may not generalize to other types of writing or verbal expression. The base rate of emotional expression identified by LIWC was comparable in the present study (5.2% of overall affect) to that found across expressive writing samples (5.3%; Pennebaker et al., 2001). However, our results should be further evaluated in samples with different base rates of emotional expression.
Extensive qualitative analysis can be considered the gold standard for characterizing the emotional content of text-based data. In this study, we have successfully and reliably coded the emotional content of a large volume of text from an Internet-based psychosocial intervention for women with breast cancer. Although LIWC has notable limitations, it appears to have acceptable convergent and divergent validity for the identification of emotional expression in text. Given the importance of being able to analyze available behavioral data and the sheer volume of text-based communication in an increasingly digital world, additional efforts to improve the accuracy of automated identification of emotional expression and other behavioral markers are much needed.
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Submitted: February 21, 2008 Revised: September 30, 2008 Accepted: October 22, 2008
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Source: Psychological Assessment. Vol. 21. (1), Mar, 2009 pp. 79-88)
Accession Number: 2009-03401-011
Digital Object Identifier: 10.1037/a0014643
Record: 64- Title:
- Examining a dimensional representation of depression and anxiety disorders' comorbidity in psychiatric outpatients with item response modeling.
- Authors:
- McGlinchey, Joseph B.. Department of Psychiatry and Human Behavior, Brown University, Providence, RI, US, jmcglinchey@lifespan.org
Zimmerman, Mark. Department of Psychiatry and Human Behavior, Brown University, Providence, RI, US - Address:
- McGlinchey, Joseph B., Rhode Island Hospital-Bayside Medical Building, 235 Plain Street, Suite 501, Providence, RI, US, 02864, jmcglinchey@lifespan.org
- Source:
- Journal of Abnormal Psychology, Vol 116(3), Aug, 2007. pp. 464-474.
- NLM Title Abbreviation:
- J Abnorm Psychol
- Page Count:
- 11
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- The Journal of Abnormal and Social Psychology; The Journal of Abnormal Psychology; The Journal of Abnormal Psychology and Social Psychology
- ISSN:
- 0021-843X (Print)
1939-1846 (Electronic) - Language:
- English
- Keywords:
- comorbidity, item response models, DSM-V, dimensional versus categorical approaches, major depression
- Abstract:
- The current study replicated, in a sample of 2,300 outpatients seeking psychiatric treatment, a previous study (R. F. Krueger & M. S. Finger, 2001) that implemented an item response theory approach for modeling the comorbidity of common mood and anxiety disorders as indicators along the continuum of a shared latent factor (internalizing). The 5 disorders examined were major depressive disorder, social phobia, panic disorder/agoraphobia, specific phobia, and generalized anxiety disorder. The findings were consistent with the prior research. First, a confirmatory factor analysis yielded sufficient evidence for a nonspecific factor underlying the 5 diagnostic indicators. Second, a 2-parameter logistic item response model showed that the diagnoses were represented in the upper half of the internalizing continuum, and each was a strongly discriminating indicator of the factor. Third, the internalizing factor was significantly associated with 3 indexes of social burden: poorer social functioning, time missed from work, and lifetime hospitalizations. Rather than the categorical system of presumably discrete disorders presented in DSM-IV, these 5 mood and anxiety disorders may be alternatively viewed as higher end indicators of a common factor associated with social cost. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Anxiety Disorders; *Comorbidity; *Major Depression; Diagnostic and Statistical Manual; Item Response Theory; Outpatients
- Medical Subject Headings (MeSH):
- Adult; Anxiety Disorders; Comorbidity; Depressive Disorder, Major; Diagnostic and Statistical Manual of Mental Disorders; Female; Humans; Male; Mental Disorders
- PsycINFO Classification:
- Psychological Disorders (3210)
- Population:
- Human
Male
Female
Outpatient - Location:
- US
- Age Group:
- Adulthood (18 yrs & older)
Young Adulthood (18-29 yrs)
Thirties (30-39 yrs)
Middle Age (40-64 yrs) - Tests & Measures:
- University of Michigan's Composite International Diagnostic Interview
Structured Clinical Interview for DSM-IV - Methodology:
- Empirical Study; Quantitative Study
- Format Covered:
- Electronic
- Publication Type:
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Examining a Dimensional Representation of Depression and
Anxiety Disorders' Comorbidity in Psychiatric Outpatients
With Item Response Modeling
By: Joseph B. McGlinchey
Department of Psychiatry and Human Behavior,
Brown University;
Mark Zimmerman
Department of Psychiatry and Human Behavior,
Brown University
Acknowledgement:
The current system
of the Diagnostic and Statistical Manual of Mental
Disorders (4th ed.,
DSM–IV; American Psychiatric Association [APA],
1994) is based on a neo-Kraepelinian model
in which mental disorders are conceptualized and presented as
discrete entities. In the DSM–IV,
mental disorders are grouped together by rationally derived
classes on the basis of shared phenomenological features.
However, the current categorical DSM system,
which presumes distinct mental disorders with little overlap,
has been the subject of criticism, controversy, and debate
(Beutler &
Malik, 2002; Widiger & Clark,
2000). The potential benefits of
incorporating a dimensional system of measurement for assessing
mental pathology was the focal point of the November 2005 issue
of the Journal of Abnormal Psychology
(Krueger, Watson,
& Barlow, 2005). One of the main
limitations of the current categorical system of the
DSM concerns the issue of diagnostic
comorbidity, the co-occurrence of different disorders within the
same individual (Widiger & Samuel, 2005). The
observation of comorbidity rates greater than would be expected
by chance has remained a significant challenge to establishing a
sound nosology on which to base clinical assessment and
treatment. Such a large degree of diagnostic overlap suggests
the presence of shared core processes underlying supposedly
distinct disorders.
Substantive rates
of comorbidity have been especially well documented among mood
and anxiety disorders (Kessler et al., 1996;
Maser &
Cloninger, 1990; Mineka, Watson, & Clark,
1998; Zimmerman, Chelminski, & McDermut,
2002). As a result, explanatory models
attempting to account for mood and anxiety comorbidities have
gained prominence. Watson (2005) recently detailed the
history of such structural models. To briefly summarize, there
is evidence suggesting two dominant dimensions of affective
experience: a higher order dimension of negative emotion
representing subjective distress and dissatisfaction (negative
affect) as well as another dimension (positive affect)
reflecting co-occurrences among positive mood states
(Watson &
Clark, 1984; Watson & Tellegen,
1985). It was proposed that negative
affectivity represents a nonspecific factor common to both
depression and anxiety, whereas positive affectivity is a factor
specific to depression in that it exhibits more consistent
negative correlations with depression relative to anxiety. This
two-factor model was later expanded to a tripartite model
(Clark &
Watson, 1991) that included a third
factor of hyperarousability more specific to anxiety. Subsequent
modeling has suggested limitations of this third arousal factor
in accounting for the heterogeneity within anxiety disorders and
evidence that positive affectivity also exhibits consistent
negative associations with social phobia (Brown, Chorpita, & Barlow,
1998; Watson, Clark, & Carey,
1988). In light of these findings, an
integrative hierarchical model (Mineka et al., 1998) has been
advanced in which each individual mood and anxiety syndrome
contains a shared component (i.e., the common, higher order
factor of negative affectivity) and a unique component of
features that distinguish the disorder from others. In related
factor analytic work, Krueger (1999) determined that a
three-factor model best fit the data for describing the common
mental disorders of the
DSM–III–R
(American Psychiatric
Association, 1987). Two of these were
strongly correlated subfactors: anxious–misery,
consisting of major depression, dysthymia, and generalized
anxiety disorder (GAD); and fear, consisting of panic disorder,
agoraphobia, social phobia, and simple phobia. These two
subfactors composed a higher order internalizing factor that was
more modestly correlated with a higher order third factor,
externalizing, consisting of alcohol dependence, substance use
dependence, and antisocial personality disorder.
These structural
modeling studies challenge certain assumptions and
categorizations found in the current DSM. For
example, though the DSM rationally categorizes
GAD as an anxiety disorder, these studies suggest that the
diagnosis of GAD is actually more strongly correlated to
depression than it is with other anxiety disorders, with both
strongly loading onto a common factor of distress or misery.
Accordingly, a quantitative hierarchical system recently has
been advanced (Clark
& Watson, 2006;
Watson &
Clark, 2006), calling for a
reorganization of the current presentation of the
DSM. As an alternative to the
DSM system of grouping diagnoses on the basis
of perceived shared features, the hierarchical system supports
empirically based classification more accurately reflecting the
clusters of covariation among the disorders. In this system,
unipolar mood and anxiety disorders would be placed under a
general class of internalizing disorders, further subdivided
into two subclasses representing distress (i.e., major
depression, dysthymia, GAD, and posttraumatic stress disorder)
and fear (panic disorder, agoraphobia, and social and specific
phobias) disorders, respectively.
Contemporary
psychometric approaches have begun to play an important role in
informing alternative dimensional conceptualizations of
diagnostic co-occurrence. One such approach is the use of models
based on item response theory (IRT; Embretson & Reise,
2000). Item response modeling (IRM) refers
to a class of psychometric procedures that can be used to
quantitatively scale a set of observed indicators along a
dimensional continuum representing an underlying latent
construct. IRM conveys meaning in terms of trait level (i.e.,
the degree of the latent construct being measured) and in the
properties of the items used to represent the construct. The
dimensional continuum representing the latent construct is
scaled logistically or normally, expressed either in terms of
logit or standard deviation units ranging from –3.0 to
3.0. In applying IRM to the issue of mood and anxiety
comorbidity, if one administers a diagnostic interview to assess
depression and anxiety disorders and if substantive comorbidity
is observed linking these diagnoses together, then this should
be reflected by significant positive correlations among all of
the disorders. An appropriate model can then be applied to the
data so that one can assess how the diagnostic indicators
contribute to the measurement of the unobservable construct
underlying them.
To date,
Krueger and Finger
(2001) have performed the most extensive
study applying IRM to examine the comorbidity of unipolar
depression and anxiety disorders. Their data were drawn from the
National Comorbidity Survey (NCS; Kessler et al., 1994), one of
the largest national probabilistic studies undertaken to
ascertain the prevalence and comorbidity of psychiatric
disorders among the general U.S. population. Their study
examined a weighted subsample consisting of 251 NCS participants
who indicated that they had sought treatment for mental health
problems in the past year by affirmatively responding to the
question, “Are you currently seeing any professional
about your problems [with emotions, nerves, alcohol, or
drugs]?” (Krueger & Finger, 2001, p.
143). NCS participants were interviewed by nonclinicians trained
in administration of the University of Michigan's Composite
International Diagnostic Interview (UM–CIDI;
Wittchen &
Kessler, 1994). Krueger and Finger's
study consisted of three phases. In the first phase, they showed
that the observed comorbidity among lifetime diagnoses of seven
unipolar mood and anxiety disorders could be sufficiently
accounted for in terms of a common shared latent factor,
internalizing. The diagnoses they analyzed were major depressive
episode, GAD, social phobia, simple phobia, panic disorder,
agoraphobia, and dysthymia. In the second phase, having
established evidence for the tenability of a common
internalizing factor underlying the seven diagnoses, they used
IRM to examine how each of these diagnostic indicators mapped
along a continuum of internalizing. They found that, with the
possible exception of dysthymia, each of the seven diagnoses was
a strong indicator of internalizing and that they were
individually located at the upper half of the internalizing
continuum. When combined, the diagnoses measured the upper half
of the internalizing continuum with greater precision than did
the lower half. In the third phase of the study, Krueger and
Finger focused on validating the internalizing factor by
examining its association with two criteria of social burden:
the number of lifetime inpatient admissions that participants
had experienced because of a mental disorder and the number of
days in the past month that the participants' functioning had
been impaired because of a mental disorder. They found that
participants' trait estimates of internalizing were almost
perfectly correlated with a simple count of number of diagnoses
and that those participants who exhibited the greatest degree of
internalizing (i.e., meeting criteria for six or seven of the
disorders) had a significantly greater number of
hospitalizations and days of impaired functioning compared with
those with a lesser degree of internalizing (i.e., five or fewer
disorders).
In recognition of
the importance of replication in the ongoing debates concerning
the potential limitations of the categorical
DSM system and diagnostic comorbidity, we
attempted in the current study to reproduce the findings of
Krueger and Finger
(2001), this time applying their
analyses directly to a psychiatric outpatient population.
Specifically, the current study sample was 2,300 outpatients
seeking psychiatric treatment, corresponding with Krueger and
Finger's extraction of a treatment-seeking subsample from the
general community population of the NCS epidemiological data.
The use of a treatment-seeking outpatient sample is advantageous
for IRM, in that it provides for comparatively larger base rates
of psychiatric diagnoses relative to populations that include a
large percentage of individuals without psychiatric disorders.
In contrast to the NCS data, diagnoses for the current study
were established by trained and reliable clinician raters using
the semistructured Structured Clinical Interview for
DSM–IV (SCID; First, Spitzer, Gibbon, &
Williams, 1995).
We tested several
hypotheses based on the prior study. First, we hypothesized that
the comorbidity between unipolar depression and anxiety
disorders would result in significant positive correlations
among all of the diagnostic indicators and could be adequately
represented in terms of one shared higher order internalizing
factor. Second, we hypothesized that when mood
and anxiety disorders were fitted with the same unidimensional
item response model, these disorders each would be located
individually in the upper half of the internalizing continuum,
would be strong discriminators of internalizing, and when
combined would more accurately measure the upper end of the
internalizing continuum. Finally, we hypothesized that the
internalizing factor would show criterion validity as evidenced
by significant associations with three external criteria
indicative of social cost: patients' current social functioning,
missed days of work in the past 5 years, and number of lifetime
psychiatric inpatient admissions.
Method Sample
The current
data were taken from the Methods for Improving Diagnostic
Assessment and Services (MIDAS) project at the Department of
Psychiatry at Rhode Island Hospital. To the best of our
knowledge, the MIDAS project is the largest clinical
epidemiological study in which semistructured interviews are
used to assess a wide range of psychiatric disorders in a
general clinical outpatient practice (Zimmerman,
2003). Among the strengths of the
project are that diagnoses are based on reliable and valid
procedures used in research studies and that patients are
presenting to a community-based psychiatric outpatient
practice rather than to a specialized clinic focusing on the
research and treatment of one or few disorders. This private
practice group predominantly treats individuals with medical
insurance (including Medicare but not Medicaid) on a
fee-for-service basis.
To date, 2,300
psychiatric outpatients have been evaluated with the
semistructured diagnostic interview. Table 1 lists the sociodemographic
characteristics of the current sample in comparison with the
sample used by Krueger and Finger (2001). The
majority of the sample was female (60.5%), European American
(87.6%), and married or cohabiting (46.2%). The mean age of
the sample was 38.2 years (SD = 12.8).
Demographic Characteristics and Comparison With
Krueger and Finger's
(2001) Sample
Procedure and Assessment of Psychiatric Diagnoses
Greater detail
on the specific procedures and assessment of the MIDAS
project can be found elsewhere (Zimmerman,
2003). Briefly, patients were invited to
participate in a clinical study and provided written
informed consent. The research protocol was approved by the
institutional review board of the hospital. Patients were
then interviewed at their intake evaluation by a diagnostic
rater who administered the SCID. Diagnostic raters included
PhD-level psychologists and research assistants with college
degrees in the social or biological sciences. Research
assistants received 3–4 months of training in
which they observed at least 20 interviews and additionally
were observed and supervised in administering more than 20
evaluations. Psychologists observed 5 interviews and were
observed and supervised in administering 15–20
evaluations. During the course of the training, a senior
diagnostician with established reliability met with each
rater to review the interpretation and rating of every item
on the SCID. At the end of the training period, raters were
required to demonstrate exact, or nearly exact, agreement
with a senior diagnostician on 5 consecutive SCID
administrations. Throughout the MIDAS project, ongoing
supervision of raters consisted of weekly diagnostic case
conferences involving all members of the team, and item
ratings of every case were reviewed by the lead
diagnostician.
Data Analysis
In order to
maximize comparability, we analyzed the data of this study
adhering as closely as was possible to the methods of
Krueger and
Finger (2001), including use of the
same modeling, correlational indices, and estimation
procedures applied to the data. However, we should first
outline some important distinctions between the studies.
This study concerns analyses of current diagnoses, because
these are generally the reason for presentation in routine
clinical outpatient settings, whereas Krueger and Finger's
study examined lifetime diagnoses. As an epidemiologic
instrument geared toward the general population, the
UM–CIDI is structured to first assess for the
lifetime presence of disorders; initial probes for core
symptoms of disorders are placed together at the beginning
of the interview, and time of occurrence of a disorder is
established after diagnostic criteria have been confirmed.
For the MIDAS SCID, the initial probes are assessed for
current MDD and GAD but are assessed at the lifetime level
for the remaining anxiety disorders. Another difference
between instruments is that the MIDAS SCID applies criteria
from the DSM–IV for diagnoses,
whereas the UM–CIDI applies criteria from the
DSM–III–R. Both
instruments use comparable skipout placements for the
included disorders.
Krueger and
Finger's (2001) analysis of the
diagnoses did not take into account the hierarchical,
exclusionary rules outlined by the DSM for
making diagnoses (Zhao, Kessler, & Wittchen,
1994), whereas the MIDAS project's
assignment of the diagnoses did adhere to
DSM exclusionary rules. This difference
would be expected to decrease comorbidity rates in our data
relative to the NCS data. For example, a patient endorsing
diagnostic criteria for GAD occurring exclusively within MDD
would be assigned a diagnosis of modified GAD
(Zimmerman
& Chelminski, 2003) in the
MIDAS project, in keeping with the specifications of the
DSM, whereas the same patient would be
diagnosed as having GAD in Krueger and Finger's study in
which hierarchical rules were suspended. In the prior IRM
study, panic disorder and agoraphobia were captured under
two diagnostic indicators representing each of the
phenomena, whereas in the MIDAS project, the three
DSM categories are specified for these
phenomena: panic disorder with agoraphobia, panic disorder
without agoraphobia, and agoraphobia without panic disorder.
To accommodate these differences, we included diagnoses of
modified GAD together with GAD and also combined the three
DSM diagnoses for panic and agoraphobia
disorders into one diagnostic indicator. Finally, we chose
not to include dysthymic disorder in the current study. The
SCID provides a more stringent assessment of current
dysthymic disorder, which in the interview sequentially
follows the assessment of MDD. In the SCID's reflection of
the DSM's exclusionary rules, the attempt
is to maximally partition dysthymic disorder from MDD. In
the SCID, if a patient had already met criteria for current
MDD, as was frequent, then the presence of current dysthymia
was subsequently assessed by querying about depressed mood
for the 2-year period preceding the onset of the current
major depressive episode (i.e.,
“double-depression”). Relative to
Krueger and Finger's study in which DSM's
hierarchical rules were suspended, we expected this to
result in lower base rates of current dysthymic disorder and
a weaker degree of correlation between dysthymic disorder
and MDD. Supporting this, the rate of endorsement for
current dysthymia in our sample was 7.1% versus 22.1% in
Krueger and Finger's rate, and a measure of association for
observed counts of two dichotomous variables yielded
practically no association between MDD and dysthymic
disorder (φ = .03, p >
.05). Because of this assessment constraint, we judged that
the inclusion of dysthymic disorder would produce more noise
than signal and that MDD alone would serve as a sufficient
representation of unipolar depression.
In summary,
five dichotomous indicators were analyzed representing the
presence or absence of current MDD (corresponding to major
depressive episode), panic disorder/agoraphobia, specific
phobia (corresponding to simple phobia), social phobia, and
GAD. As an ongoing part of the MIDAS project,
joint-interview diagnostic reliability information was
collected on 48 participants. For disorders diagnosed in at
least 2 patients by at least one of the two raters, the
kappa coefficients were as follows: MDD, 0.91; panic
disorder, 1.0; social phobia, 0.84; specific phobia, 0.91;
and GAD, 0.93.
Like
Krueger and
Finger (2001), we applied the
two-parameter logistic (2PL) model using marginal maximum
likelihood (Bock
& Aitkin, 1981) estimation
in order to ascertain how the five diagnostic indicators
mapped onto the underlying internalizing factor. The 2PL is
a unidimensional item response model that assumes one,
common latent trait can account for the interrelationships
among dichotomous indicators. To test the assumption of
unidimensionality, we fit a one-factor confirmatory factor
analysis (CFA) to the data using Mplus 3.0
(Muthén & Muthén,
2004). To perform a CFA with dichotomous
indicators, we used weighted least squares mean- and
variance-adjusted estimation, in which a diagonal weight
matrix is applied to obtain parameter estimates and a full
weight matrix to obtain mean- and variance-adjusted standard
errors and chi-square test statistics. Weighted least
squares estimation requires the use of an asymptotic
correlation matrix, and therefore we used the tetrachoric
correlation as the appropriate index of association between
diagnostic indicators.
The two
parameters of the 2PL model are item difficulty and item
discrimination. Difficulty refers to the location
of each item (i.e., diagnosis) along the dimensional
continuum. When trait level (i.e., degree of internalizing)
is equivalent to the item difficulty parameter for the
diagnosis, there is a 50% likelihood of positive
endorsement. In this context, difficulty reflects the
likelihood of meeting the criteria for the diagnostic
indicator. A patient who would have a 50% chance of
positively endorsing a diagnosis with a large difficulty
parameter would have an even greater likelihood of endorsing
diagnoses of comparatively lower item difficulty. The
discrimination, or slope parameter, refers to how sharply
each diagnostic indicator differentiates individual
differences at all locations along the dimensional
continuum. The greater the discrimination parameter, the
more information the item contributes toward the latent
construct.
IRM also
provides a score, or latent trait estimate, that corresponds
to patients' estimated degree of internalizing. Because the
2PL model allows item discrimination parameters to vary
across indicators, a patient's estimated degree of
internalizing depends on his or her specific pattern of
endorsement for the set of disorders. Two different response
patterns can yield variable trait-level scores despite the
same number of diagnoses being endorsed, with disorders of
greater discrimination leading to higher scores. As in the
Krueger and Finger study, we used expected a posteriori
(EAP; Bock &
Aitkin, 1981) latent trait estimates
for the degree of internalizing for each patient,
corresponding to each of the 32 possible diagnostic patterns
for the five disorders. An advantage of EAPs is that they
accommodate perfect response vectors (i.e., those cases
where patients met criteria for all five disorders or for no
disorders).
We conducted
2PL analyses using MULTILOG (Version 7.0; Thissen, Chen, & Bock,
2002). In MULTILOG, log metric
scaling is used to fit the 2PL model (i.e., the latent trait
continuum ranges from –3.0 to 3.0 logit units). As
a result of this scaling, item parameters are approximately
1.7 times higher than they would be in a normal (i.e.,
z score) scaling metric. The latent
trait parametrization of IRM can be interpretable with the
common factor parametrization of factor analysis
(McDonald,
1999). For example, the item
discrimination parameter bears a relationship to
standardized factor loadings if the following formula is
used:
where bi represents the item discrimination parameter and
λi represents the item factor loading, respectively (see
Ackerman,
2005, p. 5).
Validating the Internalizing Factor
In the third
phase of Krueger
and Finger's (2001) study, they
assessed the criterion validity of the internalizing factor
common to mood and anxiety diagnoses by examining its
association with two indexes of social cost: number of
lifetime psychiatric hospitalizations and number of days in
the past month that patients were unable to work because of
psychiatric illness. In the current study, we chose three
ordinal variables representing social cost. Each was
negatively valenced, with a larger value having poorer
clinical implications. Two of these were taken from the
Schedule for Affective Disorders and Schizophrenia
(Endicott
& Spitzer, 1978): greatest
level of social functioning achieved in the past 5 years and
amount of missed work in the past 5 years because of
psychopathology. The third variable was number of lifetime
psychiatric hospitalizations, with an upper cutoff of five
or more. For analyses involving time missed from work in the
last 5 years, those patients for whom the item was not
applicable (n = 217) were excluded.
To determine
criterion validity, we examined correlations between EAPs
and the number of diagnoses endorsed and between EAPs and
the three social cost variables. We conducted separate
Kruskal–Wallis tests to examine differences
between groups comprising the number of diagnoses endorsed
on each of the three social cost variables. Patients who met
criteria for four or five of the diagnostic indicators were
collapsed into one category, yielding five total groups of
participants with zero, one, two, three, and four or five
diagnoses. For significant omnibus results, we conducted
post hoc analyses examining each pairwise comparison using
the Mann–Whitney test with Bonferroni adjustment
to control for the 10 comparisons (i.e., p
≤ .005) in each of the three variables.
Results Unidimensionality of the Diagnostic Indicators
Table
2
presents the tetrachoric correlation matrix between the five
diagnostic indicators. As expected, these were all
significantly positively correlated. Goodness-of-fit indices
suggested that the covariation among the five disorders can
be reasonably accounted for in terms of a shared higher
order factor (χ2 = 11.9, p = .04; comparative fit
index = .98; Tucker–Lewis index = .97;
root-mean-square error of approximation = .02). Although
there are no definitive standards for goodness of fit for
dichotomous indicators, under available conventions these
values suggest good fit between the observed data and the
fitted one-factor model (Hu & Bentler,
1999). Figure 1 presents the one-factor CFA model
loadings with parameter coefficients, standard errors (in
parentheses), and residual variances for the diagnoses.
Tetrachoric Correlations for Five Unipolar Mood and Anxiety
Diagnoses (N = 2,300)
Figure 1. One-factor confirmatory model for five unipolar mood and anxiety
disorders, showing parameter coefficients, standard errors (in
parentheses), and residual variances for the diagnoses.
IRT Analysis
Table
3
presents the 2PL parameter estimates for each
diagnosis. All diagnostic indicators were
located in the upper half of the internalizing continuum,
with all item difficulty parameter estimates greater than
0.0, the point representing the average degree of
internalizing for the outpatients. Difficulty parameters are
expected to be above zero when there is a less than 50%
overall endorsement of the diagnosis in the full sample, as
was true for all of the five disorders analyzed. MDD was the
most frequent disorder, with nearly half the sample meeting
criteria for the diagnosis; thus, its difficulty parameter
lies closest to 0.0. The anxiety disorders exhibited
increasingly greater levels of diagnostic difficulty in the
following order: generalized anxiety disorder, social
phobia, panic disorder/agoraphobia, and specific phobia.
Two-Parameter Logistic Item Response Model Parameter Estimates (N
= 2,300)
The five
diagnoses yielded item discrimination parameters greater
than 0.75, suggesting that each acts as a strong contributor
to the underlying internalizing factor. MDD and specific
phobia evidenced a similar degree of discrimination,
followed with increasing discrimination by panic
disorder/agoraphobia, social phobia, and generalized anxiety
disorder, respectively. Applying the previously given
formula to the data in Table 3 and Figure 1, with
1.7 in the numerator to convert between approximating
logistic and probit distributions, shows the relationship
between discrimination parameters and standardized factor
loadings. For MDD, the formula is as follows:
Figure
2
presents the test information function (TIF) and test
standard error of measurement (SEM), which
summarize the combined information of the five diagnoses as
a function of trait level. Information function represents
the squared precision of measurement of the five diagnostic
indicators taken together, whereas the standard error
represents imprecision. The standard error curve is
inversely related to the information function curve. As can
be seen, these vary along the latent trait continuum; IRM
contrasts with classical testing theory, in which standard
error is assumed to be constant regardless of a
participant's score. The diagnoses taken together measured
the higher end of the internalizing continuum with greater
precision than the lower end. The peak of the TIF is the
point at which the five diagnoses combined provides the most
precision in measuring internalizing. The TIF curve for the
diagnoses reached its summit above average internalizing for
the sample (peak = 1.2; SEM = .67). The
marginal reliability, representing the average reliability
across all estimated latent scores, was 0.43.
Figure 2. Test information function graph for internalizing factor. The
solid line represents the test information curve. The total test
information for a specific scale score is read from the left
vertical axis. The dotted line represents the standard error curve.
The standard error for a specific scale score is read from the right
vertical axis.
Validating the Internalizing Factor
Table
4
presents the sample frequency of the 32 possible diagnostic
profiles and their EAP latent trait estimates. EAPs were
nearly perfectly correlated with a count of the number of
diagnoses met (r = .98, p
< .001). EAPs were significantly correlated with
poorer social functioning and greater amount of missed work
(Spearman's ρ = .25 and .24, respectively;
p < .001 for each) and also with
lifetime hospitalizations (Spearman's ρ = .06,
p < .01).
Latent Trait Estimates of Internalizing for 32 Diagnostic
Response Patterns (N = 2,300)
Kruskal–Wallis tests revealed significant
differences overall between the five groups representing
number of diagnoses endorsed on social functioning
(χ2 = 146.6, p < .001), greater
amount of missed work (χ2 = 133.6, p
< .001), and number of lifetime hospitalizations
(χ2 = 12.8, p < .05). For
social functioning, post hoc analyses revealed significant
differences in the direction expected for every pairwise
comparison except two versus three diagnoses and three
versus four or five diagnoses. For missed work, there were
significant differences in the direction expected between
every pair except three versus four or five diagnoses. For
lifetime inpatient psychiatric hospitalizations, the only
significant difference among pairs was between zero versus
three diagnoses.
DiscussionThe current study
replicated the main findings of Krueger and Finger (2001),
with several noteworthy beneficial features. The sample size of
this study was nearly 10 times that of the prior study and also
consisted of a treatment-seeking sample. Our outpatient sample
was drawn from one clinical facility, raising questions of
generalizability. As can be seen from Table 1, despite not
being as nationally representative as the NCS, our sample showed
great comparability to the NCS subsample. In this study, trained
and reliable clinician raters administered the SCID, whereas in
the NCS data analyzed by Krueger and Finger, laypeople
administered a fully structured interview, and it was scored
wholly from participant responses. In the current study, we
maximized comparability with Krueger and Finger by suspending
DSM hierarchical rules wherever possible
and using the same correlational indices, estimation methods,
and model-fitting procedures.
Consistent
findings were obtained for all three phases between studies. As
in the first phase of Krueger and Finger (2001), the five
diagnoses assessed here all exhibited significant positive
correlations with one another, and a one-factor CFA reasonably
accounted for the observed covariation between the five
diagnoses. The 2PL model of this study also exhibited comparable
results to that of Krueger and Finger's second phase. The
ordinal relationship of item difficulty among the diagnoses was
similar: Major depression exhibited the lowest difficulty
estimate and was situated the closest to the average degree of
internalizing in the sample, whereas the remaining anxiety
disorders evidenced increasing thresholds of severity above an
average degree of internalizing. All diagnoses yielded
discrimination parameters greater than 0.75, suggesting that
each is individually responsive to changes among contiguous
levels of internalizing. This was also found for Krueger and
Finger's diagnoses, excepting dysthymic disorder, which was not
assessed here. The TIF curve, representing the combined
information of the diagnostic indicators, peaked at 1.2 units
above average internalizing in the entire sample
(SEM = .67), conforming with Krueger and
Finger's TIF (peak = 1.0, SEM = .57). Finally,
the third phase of the current study was consistent with Krueger
and Finger in showing the criterion validity of the
internalizing factor: Latent trait estimates were significantly
associated with poorer social functioning, greater amount of
missed work, and number of lifetime psychiatric
hospitalizations.
Nonetheless, there
were also differences observed between the studies. The two
largest diagnostic correlations in our data were between social
phobia and GAD (.36) and between MDD and GAD (0.31). In
Krueger and
Finger's (2001) study, these two
pairings were among the lower correlations. Although the
association between simple phobia and major depression fell in
the mid-range of correlations for their study, the corresponding
correlation in our study was clearly the lowest but still
statistically significant. The ordinal relationship of item
difficulty was reversed for three of the anxiety disorders. In
Krueger and Finger, the order in terms of increasing difficulty
was simple phobia, social phobia, and GAD, with simple and
social phobias exhibiting a similar degree of difficulty. In the
current study, the order was GAD, social phobia, and specific
phobia, with GAD and social phobia exhibiting a similar degree
of difficulty. GAD yielded the largest item discrimination in
the current study, whereas it had the lowest discrimination
among these disorders in Krueger and Finger's study.
Because the degree
of endorsement affects the difficulty parameter of a diagnosis
and the degree of noncomorbidity affects how well it coheres to
the common factor, and thus its discrimination, these different
findings serve to highlight considerations in sample and
methodology when IRM is used to model comorbidity. One important
issue concerns the composition of the treatment-seeking samples
in these studies. Our study concerned current psychiatric
diagnoses, whereas the prior study examined lifetime diagnoses.
Relative to current diagnoses, the analysis of lifetime
diagnoses would be expected to increase endorsement for the
disorders and the degree of positive correlation among them.
However, as shown in Table 1, despite lifetime analysis, the
percentage of noncomorbid cases for the NCS subsample was
actually greater across all disorders than in our study; for
social phobia and GAD, it was substantially greater. One
possible explanation for this is that the broad question used to
constitute the NCS subsample may have resulted in inclusion of a
greater number of treatment seekers who required a lower
threshold of mental health care (e.g., being treated by a
primary care physician for one disorder), whereas those
presenting for outpatient psychiatric treatment may have crossed
a higher threshold of mental health care, with greater potential
comorbidity.
The differences in
results may also reflect methodological differences between the
studies. One salient feature is that the modeling of both
studies was based on dichotomous variables that were outcomes of
algorithmic aggregations of symptom-level information from
DSM diagnostic classifications. Dichotomous
categorizations conducted at the diagnostic level have several
limitations (Brown
& Barlow, 2005;
Brown et al.,
1998; Watson, 2005). One limitation
is that the analysis is bound to the nosology that the
categorizations represent. Lifetime GAD endorsement was lower in
the NCS subsample than was endorsement of current GAD in our
sample. The UM–CIDI diagnoses were based on
DSM–III–R criteria,
whereas our SCID diagnoses were based on
DSM–IV criteria. The diagnostic
criteria for the disorders in these IRM studies remained largely
consistent between
DSM–III–R and
DSM–IV, except for GAD. In the
DSM–III–R criteria for
GAD, there were 18 accompanying symptom criteria for excessive
anxiety and worry, of which at least 6 had to be endorsed. The
DSM–IV revision was less
conservative, reducing the accompanying symptom criteria to at
least 3 of 6. This substantial revision in criteria between
DSM editions may have contributed to the
endorsement differences between the two studies, resulting
further in differences in comorbidity and IRM parameter
estimates. The correlation between GAD and MDD in our study is
more consistent with that of Brown et al. (1998), who used
structural modeling to examine dimensions of mood and anxiety
disorders based on DSM–IV criteria.
These dichotomized aggregates also reflect intraedition
DSM inconsistencies in criteria among
disorders (e.g., the 2-week time frame used to establish MDD
versus the 6-month time frame for GAD). A second limitation of
imposing diagnostic dichotomies is that by not reflecting the
dimensional nature of these phenomena, this may lead to the loss
of potentially important clinical information that may be
captured otherwise by continuous measurement. For example, one
criterion necessary in determining the full DSM
diagnosis for these disorders is whether the symptoms cause
significant impairment or distress. This criterion is
particularly pivotal for specific phobia, in which patients
often express fear toward specific stimuli but deny that they
experience significant distress or impairment from their fear.
As such, they do not meet full DSM criteria for
the disorder and would be considered
“absent” for the diagnosis in this study;
their information (features of the disorder) would be lost. A
third limitation of dichotomized diagnostic-level analyses is
that such analyses cannot address the significant heterogeneity
that may still exist within diagnostic labels. For anxiety
disorders, the concern of heterogeneity brought on by multiple
symptom dimensions within disorders is particularly salient for
PTSD, OCD, and specific phobias (Watson, 2005).
Differences in
instrumentation also could have contributed to differences in
findings between the studies. In Table 1, the presence of
specific phobia was only 10.4% in our study versus 28.1% in
Krueger and
Finger's (2001). In its initial probe
for the disorder, the SCID does not explicitly identify some
prominent fears for which the UM–CIDI explicitly
queries (e.g., fear of water and swimming, dentists, storms).
Also, as a structured interview, the UM–CIDI likely
allows a more liberal threshold for assigning the significant
distress or impairment criterion to simple phobias (i.e., merely
responding “Yes” to “Were you ever
very upset with yourself for having this fear?” or
“A lot” to “How much did this fear
ever interfere with your life or activities?”). MIDAS
evaluators using the SCID, in which clarification and further
qualitative assessment of impairment and distress are permitted,
may have a more stringent threshold in rating this criterion.
Despite these
differences, we suspect that reanalysis at the lifetime
diagnostic level would not yield unduly different results
contrasting with the present findings, given the chronic and
persistent nature of depression and anxiety disorders among
treatment seekers. Krueger and Finger (2001) noted in
reanalyzing their data on past-year diagnoses alone that their
results were “essentially identical” (p.
145) to lifetime analyses. Apart from GAD and specific phobia,
the diagnostic endorsement across the two studies in
Table
1 appears reasonably consistent. Still,
confirmation of this is a worthy target of future investigation.
What do the
current findings portend for the publication of
DSM-V? First (2005) argued that the addition
of new subtypes and disorders has increased the clinical utility
of the DSM–IV, although the number of
identified diagnoses increased by 300% between the
DSM–III–R and
DSM–IV (Zimmerman & Spitzer,
2005). Categorical labels hold appeal
because of their communicative efficiency for clinical decision
making (Zimmerman
& Spitzer, 2005); thus, this
trend toward diagnostic proliferation may continue. Given this
possibility, DSM diagnoses at least should be
coherently organized in a manner that optimally groups their
empirical overlap on shared underlying dimensions, lest the
continued arbitrary placement of these into rationally derived
classes leads to an increasing reception of the manual as
dissipated and inconsistent.
The exploration of
a hybrid DSM system in which dimensional
elements are conservatively and gradually integrated, whereas
existing categorical labels and criteria are retained,
represents the most realistic and least disruptive option for
DSM–V. The use of categorical and
dimensional approaches need not be mutually exclusive and can be
synergistic (Widiger
& Samuel, 2005). Toward this aim
of developing a hybrid system, diagnostic-level IRM studies may
be useful for addressing the reorganization of existing
disorders along a common latent dimension in an empirically
governed way. IRM may also be facilitative in light of recent
suggestions to dimensionalize existing DSM
categories (First,
2005) or to provide supplemental dimensional
severity ratings (Brown
& Barlow, 2005) on the basis of
information conveyed by a specific pattern of diagnoses via the
use of the EAP or other latent trait scores. However, clinical
considerations (e.g., the reasons patients present for
treatment; Zimmerman
& Mattia, 2000) must also be
weighed, and these may not be fully captured by a single
severity score on a shared dimension.
Our study lends
support to the reorganization proposed by the hierarchical
quantitative model of Watson and colleagues (e.g.,
Watson &
Clark, 2006): Rather than their current
placement into two separate categories (i.e., mood disorders and
anxiety disorders), the five disorders examined here are better
grouped under one overarching category representing a general
factor (i.e., internalizing), which better describes their
covariation and for which they each act as higher-end
indicators. Supporting prior work (Brown et al., 1998;
Watson,
2005), our data also uphold a particularly
strong association between GAD and MDD that is ignored by the
mood and anxiety distinction in the current
DSM. Because twin studies have found that GAD
and MDD are genetically indistinguishable (Kendler, 1996) and
because antidepressant medications have shown efficacy in the
treatment of GAD (Gorman, 2002), a reorganization
explicitly acknowledging this by placing GAD and MDD within the
same class may more optimally advance research on etiological
processes and treatment than the current
system. Further, our finding that GAD evidenced
the greatest discrimination for internalizing is consistent with
the findings of Brown et al. (1998), who also found that GAD
consistently exhibited the largest correlations with other
DSM–IV mood and anxiety disorders
and evidenced the largest association with a general dimension
of negative affect.
The use of IRM
conceivably may exert an influence on sculpting future editions
of clinical interviews based on DSM
criteria—which are often time consuming and difficult
to administer—to be more efficient. For example, our
data suggest that these mood and anxiety disorders, currently
assessed in separate modules with an intervening psychotic
disorders module separating them, should instead be combined.
Our findings suggest that assessment for specific phobia should
be placed after assessment for the other four disorders in such
a combined module. In the current SCID, specific phobia
sequentially precedes GAD, a disorder both more likely to be
endorsed and more informative to the internalizing dimension.
Though providing
evidence that a shared underlying factor of internalizing
sufficiently accounts for the covariation among these disorders,
both unidimensional IRM studies fail to address further possible
subspecifications within this general factor (i.e., the
placement of the disorders among distress versus fear
subclasses), and the modest tetrachoric correlations and factor
loadings of this study suggest the diagnoses contain large
specific components in addition to the common factor they share.
Future work in which IRM is applied to an internalizing spectrum
should include additional diagnostic indicators to further
inform the internalizing dimension. We purposefully did not
include in our study PTSD, OCD, and bipolar disorder to maximize
comparability with the study by Krueger and Finger (2001), who
also did not include these. Our psychiatric sample may overcome
the low base rates of OCD and bipolar disorder that have
hampered their inclusion for structural analyses with general
populations. We would expect that at the level of diagnostic
analysis, both PTSD and OCD would show significant, positive
correlations with the five disorders of this study, and each
would exhibit large discrimination parameters towards
internalizing. Determining the status of bipolar disorder within
an internalizing spectrum at a diagnostic level, however, is
likely to be more problematic. DSM rules render
the diagnosis mutually exclusive with MDD, creating difficulty
for correlational modeling. Also, within-disorder heterogeneity
may be even more pronounced for bipolar disorder, because the
diagnostic label could conceptually encompass a group ranging
from hypomanic patients who report increased efficiency in
functioning (i.e., minimal to no internalizing) to severely
depressed patients needing hospitalization (i.e., high
internalizing). In a recent factor analysis by
Watson
(2005), bipolar disorder had weak and
virtually identical loadings on the three factors identified by
Krueger
(1999). Finally, a comprehensive structural
scheme should model syndromes currently grouped in other
diagnostic classes outside mood and anxiety (Watson, 2005), for
example, the externalizing disorders spectrum to which the
internalizing disorders are more moderately correlated
(Krueger,
1999).
The recent debates
over the current categorical DSM system reflect
a momentous juncture in our psychiatric nosology. Like
Krueger and Finger
(2001), we view this study as
preliminary research in a fertile new area. We hope that further
IRM research will elucidate the relationship between currently
designated mood and anxiety disorders and their place in a
broader diagnostic system. Diagnostic covariation presented in a
more consistent manner may better guide prevention and
treatment.
Footnotes 1 To maintain
consistency with Krueger
and Finger (2001), we retained the label
internalizing to describe the higher order
factor. As they noted, this label is likely closely aligned with
others denoting the shared variance between depression and anxiety,
including negative affect (Watson & Clark, 1984), neuroticism
(Eysenck,
1994), and negative emotionality
(Waller, Tellegen,
McDonald, & Lykken, 1996).
2 One exception in which
skipouts were not used in the MIDAS project was MDD, for which
participants were queried for all symptom criteria for research
purposes not related to the current study. This suspension of the
skipout rule had no impact on the DSM–IV
diagnosis assigned.
3 Item difficulty and
item discrimination are the traditional labels given to the 2PL
model parameters. Although we retained their use here, it should be
acknowledged that these labels reflect origins in test construction
for maximum performance assessment of cognitive ability. In its
original context, item difficulty referred to the proportion of
individuals able to answer a test item correctly (i.e., how
difficult to pass an item is) and may be somewhat of a misnomer when
applied to diagnostic endorsement data that are collected by a
different process: retrospective, self-referenced recall assessment.
Aggen, Neale, and Kendler
(2004) have argued for the term
liability threshold as a more appropriate
descriptor.
4 In addition to
difficulty and discrimination parameters, a third statistic reported
in Krueger and Finger
(2001), the root-mean-square posterior
residual (RMSPR), was not available as part of the MULTILOG package
and is not reported here.
5 We evaluated the
justification for using the 2PL model over the simpler one-parameter
IRT model in which discrimination slopes are held constant across
all indicators. The difference between the chi-square values for the
two models was 12.1 (df = 4, p
< .05) in favor of the 2PL, indicating that item
discrimination should be allowed to vary across diagnoses.
6 Note that in the
reanalysis of the data upholding the exclusion rule of the
DSM–IV (i.e., excluding those cases
of modified GAD from GAD), the tetrachoric correlation between GAD
and MDD attenuated from .31 to .15 and became the second lowest
association among the five disorders overall.
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Submitted: July 21, 2006 Revised: January 16, 2007 Accepted: January 25, 2007
This publication is protected by US and international copyright laws and its content may not be copied without the copyright holders express written permission except for the print or download capabilities of the retrieval software used for access. This content is intended solely for the use of the individual user.
Source: Journal of Abnormal Psychology. Vol. 116. (3), Aug, 2007 pp. 464-474)
Accession Number: 2007-11737-003
Digital Object Identifier: 10.1037/0021-843X.116.3.464
Record: 65- Title:
- Examining the potential for gender bias in the prediction of symptom validity test failure by MMPI-2 symptom validity scale scores.
- Authors:
- Lee, Tayla T. C.. Department of Psychology, Kent State University, Kent, OH, US, tconnerl@gmail.com
Graham, John R.. Department of Psychology, Kent State University, Kent, OH, US
Sellbom, Martin. Department of Psychology, University of Alabama, AL, US
Gervais, Roger O.. Neurobehavioral Associates, Edmonton, AB, Canada - Address:
- Lee, Tayla T. C., Department of Psychology, Kent State University, Kent Hall, Kent, OH, US, 44242, tconnerl@gmail.com
- Source:
- Psychological Assessment, Vol 24(3), Sep, 2012. pp. 618-627.
- NLM Title Abbreviation:
- Psychol Assess
- Page Count:
- 10
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- Psychological Assessment: A Journal of Consulting and Clinical Psychology
- ISSN:
- 1040-3590 (Print)
1939-134X (Electronic) - Language:
- English
- Keywords:
- Minnesota Multiphasic Personality Inventory–2, Symptom Validity scale (FBS), gender bias, symptom validity tests
- Abstract:
- Using a sample of individuals undergoing medico-legal evaluations (690 men, 519 women), the present study extended past research on potential gender biases for scores of the Symptom Validity (FBS) scale of the Minnesota Multiphasic Personality Inventory–2 by examining score- and item-level differences between men and women and determining the extent to which FBS scores were able to correctly identify men and women who were divided into credible responders (n = 837) and noncredible responders (n = 372) on the basis of performance on symptom validity tests. Results indicated that women had slightly higher raw FBS scores than men (d = .29), and significant differences between men and women in item endorsement were demonstrated for 14 FBS items. Step-down hierarchical logistic regression procedures indicated predictive bias (χ2Δ = 23.72, p < .001). Follow-up analyses indicated intercept bias (χ2Δ = 23.51, p < .001) but not slope bias (χ2Δ = 0.22, p = .64). However, using the test publisher's recommended FBS cutoff scores (Ben-Porath, Graham, & Tellegen, 2009), classification accuracies were similar for women and men (T > 80, h = −.02; T > 100, h = −.22, respectively). On the basis of these results, we conclude there is no evidence of clinically meaningful bias in predictions of symptom validity test failure made using FBS scores for men and women. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Human Sex Differences; *Minnesota Multiphasic Personality Inventory; *Symptoms; *Test Bias; *Test Validity; Test Construction
- Medical Subject Headings (MeSH):
- Adult; Bias (Epidemiology); Female; Humans; MMPI; Male; Middle Aged; Neuropsychological Tests; Predictive Value of Tests; Psychiatric Status Rating Scales; Psychometrics; Sex Factors
- PsycINFO Classification:
- Personality Scales & Inventories (2223)
Personality Traits & Processes (3120) - Population:
- Human
Male
Female - Age Group:
- Adolescence (13-17 yrs)
Adulthood (18 yrs & older)
Young Adulthood (18-29 yrs)
Thirties (30-39 yrs)
Middle Age (40-64 yrs)
Aged (65 yrs & older) - Tests & Measures:
- Minnesota Multiphasic Personality Inventory-Restructured Form
Minnesota Multiphasic Personality Inventory-2 DOI: 10.1037/t15120-000 - Grant Sponsorship:
- Sponsor: University of Minnesota Press
Other Details: University of Minnesota Press is the publisher of the MMPI-2 and MMPI-2-Restructured Form and supported this research with a grant.
Recipients: No recipient indicated - Conference:
- Annual Meeting of the Society of Personality Assessment, 2010, San Jose, CA, US
- Conference Notes:
- Portions of this article were presented at the aforementioned conference.
- Methodology:
- Empirical Study; Quantitative Study
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- First Posted: Feb 6, 2012; Accepted: Sep 7, 2011; Revised: Sep 6, 2011; First Submitted: Sep 15, 2010
- Release Date:
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- Correction Date:
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- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2012-02771-001&site=ehost-live">Examining the potential for gender bias in the prediction of symptom validity test failure by MMPI-2 symptom validity scale scores.</A>
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Examining the Potential for Gender Bias in the Prediction of Symptom Validity Test Failure by MMPI-2 Symptom Validity Scale Scores
By: Tayla T. C. Lee
Department of Psychology, Kent State University;
John R. Graham
Department of Psychology, Kent State University
Martin Sellbom
Department of Psychology, The University of Alabama
Roger O. Gervais
Neurobehavioral Associates, Edmonton, Alberta, Canada;
Department of Educational Psychology, University of Alberta, Edmonton, Alberta, Canada
Acknowledgement: This research was supported by a grant from the University of Minnesota Press, publisher of the MMPI-2 and MMPI-2-Restructured Form. Portions of this article were presented at the 2010 Annual Meeting of the Society of Personality Assessment in San Jose, California.
The Symptom Validity scale (FBS) of the Minnesota Multiphasic Personality Inventory–2 (MMPI-2; Butcher et al., 2001) was developed by Lees-Haley, English, and Glenn (1991) to detect noncredible reporting of emotional difficulties by personal injury and other disability claimants. Created using a combination of rational, content-based item selection and endorsement rates for individuals thought to be malingering versus those believed to be responding honestly, the final scale consisted of 43 items, including statements assessing somatic and cognitive complaints as well as positive self-presentation (e.g., disavowal of a cynical world view, antisocial behavior, and substance use).
Although the original purpose of FBS was to identify exaggerated or feigned emotional distress and difficulties among personal injury claimants (Lees-Haley et al., 1991), subsequent research has suggested scores on this scale are not effective for this purpose (Ben-Porath, Graham, & Tellegen, 2009; Bury & Bagby, 2002; Efendov, Sellbom, & Bagby, 2008; Rogers, Sewell, Martin, & Vitacco, 2003). However, FBS scores have been demonstrated to be helpful in assessing noncredible reporting of cognitive and somatic difficulties (Ben-Porath, Graham, & Tellegen, 2009; Greiffenstein, Baker, Gola, Donders, & Miller, 2002). In fact, FBS scores have been demonstrated in large meta-analyses to best predict exaggerated or feigned cognitive and somatic symptoms, when compared with the prediction of these types of noncredible responding by other MMPI-2 Validity scale scores (e.g., Infrequency–Psychopatholgy; Nelson, Hoelzle, Sweet, Arbisi, & Demakis, 2010; Nelson, Sweet, & Demakis, 2006).
There have been many criticisms of FBS, including lack of construct validity, false identification of medical patients as noncredible responders, and overidentification of women as noncredible responders when compared with men (see Butcher, Gass, Cumella, Kally, & Williams, 2008, for a summary). Concerning gender bias, criticisms largely appear to originate from the scale developers' recommendations for the use of different raw cut scores as indicative of noncredible responding for men and women (Williams, Butcher, Gass, Cumella, & Kally, 2009). Several authors have examined scale score and item endorsement rates between men and women in settings where the MMPI-2 is regularly used (Butcher et al., 2008; Nichols, Greene, & Williams, 2009; Williams et al., 2009). Overall, results of these studies indicated women typically had slightly higher raw scores on the FBS scale when compared with men. Women were also more likely to endorse items with content related to the disavowal of cynicism, substance use, and antisocial practices as well as items affirming some somatic complaints. However, these studies were limited as the authors did not have external criterion measures that would have permitted comparison of classification accuracies for men versus women.
Score and item endorsement differences do not necessarily indicate differences in predictions made using a scale's scores (Greene, 1987; Prichard & Rosenblatt, 1980), and only one previous study has examined potential gender biases in predictions made with FBS scores. Presented in a reply to criticisms and using a known-groups design, Ben-Porath, Greve, Bianchini, and Kaufman (2009) examined the question of predictive differences for men and women in two large samples where FBS is frequently used. In their sample of individuals evaluated for difficulties related to mild traumatic brain injuries, the authors demonstrated significantly increased sensitivity of FBS scores in detecting cognitive/somatic overreporting for women compared with men, suggesting FBS scores were more accurate in correctly identifying noncredible responding in women. They demonstrated no differences in sensitivity of FBS scores in detection of cognitive/somatic malingering between genders in a sample of individuals undergoing evaluation for chronic pain. The authors suggested that, overall, these results did not support claims that FBS scores were biased against women.
The purpose of the present study was to extend research on potential gender differences in FBS scores into a non-neuropsychological disability evaluation setting. Using symptom validity tests (SVTs) to establish criterion groups of probable credible and noncredible responders, we first examined the effect of gender on FBS scores, comparing FBS scores and item endorsement rates. We then examined the accuracy of FBS scores in predicting SVT failure for men versus women. On the basis of previous research, we hypothesized that women would have higher raw scores on FBS than men. We also expected that women would endorse items related to cynicism, substance use, and antisocial practices with less frequency than men and that they would endorse items related to somatic complaints more frequently than men. Lastly, in congruence with previous research (Ben-Porath, Greve, et al., 2009), we hypothesized that gender would statistically moderate the relationship between FBS scores and credible versus noncredible response group membership (as defined by the absence or presence of SVT failure), but we did not expect this moderation to have a large effect size.
Method Participants
Participants were selected for inclusion in the current study from a large archival database of 3,219 consecutive disability claimants referred for medico-legal psychological assessment at an independent practice in Edmonton, Alberta, Canada. Each individual provided informed consent to the psychological assessment and for their information to be used for research purposes. In keeping with ethical guidelines for the use of archival data, the final data set contained no identifiable personal information, and the study was approved by the institutional review board at Kent State University. Subsets of this data set have been used in previous studies on SVT performance in chronic pain patients (Gervais, Green, Allen, & Iversen, 2001; Gervais, Rohling, Green, & Ford, 2004), development and validation of the Response Bias scale for the MMPI-2-Restructured Form (Gervais, Ben-Porath, Wygant, & Green, 2007, 2008; Gervais, Ben-Porath, Wygant, & Sellbom, 2010), validation of the MMPI-2-Restructured Form (Gervais, Ben-Porath, & Wygant, 2009; Gervais, Wygant, Sellbom, & Ben-Porath, 2011), and posttraumatic stress disorder (Demakis, Gervais, & Rohling, 2008). In addition, Greiffenstein, Baker, Axelrod, Peck, and Gervais (2004) used these data to examine the association between MMPI-2 Validity scales (including FBS) and SVT failure, and descriptive information on FBS scores in this sample was provided in the FBS monograph (Ben-Porath, Graham, & Tellegen, 2009).
From the larger data set, we extracted a subsample of individuals who had completed all of the instruments used in the current study, including the MMPI-2 and each of the three SVTs described later. This procedure resulted in the inclusion of 1,278 consecutive cases assessed between January 1999 and August 2009. In keeping with recommended MMPI-2 interpretive guidelines, participants were excluded on the basis of inconsistent responding as detected by MMPI-2 Validity scales (i.e., Cannot Say [CNS] ≥ 30, Variable Response Inconsistency [VRIN] T ≥ 80, and/or True Response Inconsistency [TRIN] T ≥ 80; Butcher et al., 2001). However, because of the nature of the current study, participants were not excluded for content-based response styles. Using these criteria, 69 (5.4%) of the participants were excluded. Subsequent analyses indicated there were no significant differences between excluded and included participants in terms of gender, age, or years of education.
The final sample consisted of 690 men and 519 women referred for worker's compensation (70.5%) and other medico-legal (29.5%) evaluations. None of these individuals were evaluated in the context of a criminal proceeding or to determine disability related to head injury. Included participants ranged in age from 17 to 73 years (M = 40.43, SD = 11.02) and reported having an average of 11.96 (SD = 2.54) years of education. Worker's compensation referrals were seen for psychological assessment to assist in determining eligibility for or maintenance of compensation or other disability benefits and services related to psychological conditions arising from workplace injuries (musculoskeletal and/or orthopedic injuries, motor vehicle collisions, and/or other incidents, such as robberies, assaults, and workplace conflicts). Fewer than 5% of female claimants were presenting with claims related to sexual harassment. Medico-legal referrals involved personal injury claims for psychological damages arising from motor vehicle collisions, with approximately 70% of assessments requested through plaintiff attorneys. Diagnoses were based on criteria from the Diagnostic and Statistical Manual of Mental Disorders (4th ed., American Psychiatric Association, 1994, and 4th ed., text revision, American Psychiatric Association, 2000), following an extensive clinical interview, review of psychological test data (which included the MMPI-2), and accompanying medical or other third-party documentation. The most frequently identified primary diagnoses were pain disorder (32.2%), anxiety-related disorders (e.g., adjustment disorder with anxiety or posttraumatic stress disorder, 39.4%), and mood disorders (e.g., major depressive disorder or adjustment disorder with depressed mood, 19.3%).
Measures
MMPI-2 (Butcher et al., 2001)
The MMPI-2 is a 567 item, true/false, self-report inventory assessing social, behavioral, and emotional functioning. The instrument is one of the most widely used measures of personality and psychopathology in clinical practice (Camara, Nathan, & Puente, 2000). The current study included only the FBS of the MMPI-2, which is discussed later.
FBS
The FBS was introduced in 1991 by Lees-Haley et al. and was adopted as part of the standard scoring of MMPI-2 scales in 2006. Previous research has demonstrated FBS scores are internally consistent and are a significant predictor of noncredible cognitive and physical symptom reports (Ben-Porath, Graham, & Tellegen, 2009; Nelson et al., 2006, 2010). In the current sample, internal consistency estimates for FBS scores were acceptable, with α = .76 (men) and α = .72 (women).
Word Memory Test (WMT; Green, 2003; Green, Allen, & Astner, 1996; Green & Astner, 1995)
The WMT is a computer-administered verbal memory SVT intended to assist an examiner in discriminating between individuals with genuine memory problems and those exhibiting incomplete effort or response bias associated with feigned memory deficits. The test has demonstrated high levels of sensitivity and specificity for response bias in simulator studies (e.g., Brockhaus & Merten, 2004; Tan, Slick, Strauss, & Hultsch, 2002). The administration and scoring software contains recommended scores for determining response bias (Green, 2003). These scores were used to evaluate the response validity of the respondents' WMT scores in the current study.
Computerized Assessment of Response Bias (CARB; Allen, Conder, Green, & Cox, 1997)
The CARB is an SVT that uses a forced choice digit recognition task to detect insufficient effort and response bias associated with exaggeration of memory problems. The test has good sensitivity (74%) and specificity (91%) in predicting WMT failure (Allen et al., 1997). The cutoff scores for biased responding specified in the test's manual were utilized in this study.
Test of Memory and Malingering (TOMM; Tombaugh, 1996)
The TOMM is an SVT using a forced choice visual recognition task designed to assist in discrimination between individuals with genuine and feigned memory impairment. We used the cutoff scores recommended in the test's manual to identify biased responding in the current study. Previous research has demonstrated that the TOMM is sensitive to insufficient effort and negative response bias in a variety of populations, including individuals with presenting problems related to traumatic brain injury, toxic exposure, chronic pain, and psychiatric difficulties in forensic settings (Gierok, Dickson, & Cole, 2005; Greve et al., 2006; Haber & Fichtenberg, 2006; Iverson, Le Page, Koehler, Shojania, & Badii, 2007).
Procedures
As part of a comprehensive 2-day evaluation, individuals were administered a psychological assessment battery consisting of the MMPI-2, several SVTs (including the WMT, CARB, and TOMM), and a variety of other cognitive, somatic, and psychopathological symptom self-report measures, depending on the focus of the assessment. Individuals referred for medical disability, workers' compensation, or medical–legal purposes completed a core psychological assessment battery as described in Gervais et al. (2009). Individuals referred for chronic pain assessments completed the same core battery of tests, though fewer cognitive tests were administered. Additionally, modifications were made to the test battery in cases of noncompliance, specific physical disabilities, and educational or linguistic limitations.
We determined that substantial external incentives were present for the entire sample. All individuals in this study were seen in the context of active disability claims or personal injury litigation and were either receiving or pursuing disability benefits or awards. The assessment was typically requested to address questions related to validity of symptom presentation, psychiatric diagnosis, and necessary work restrictions or modifications as well as their relationship to the reportedly disabling accident or event and the presence of pre-existing conditions. In cases already receiving temporary total disability benefits, a finding of fitness for return to either preaccident or modified duties could result in a change of claim status, including return to work protocols and a systematic reduction and termination of benefits. For individuals whose claim status had not yet been determined, results of the assessment could contribute to adjudicative decisions to deny benefits or services. In view of these contingencies, incentives for individuals to either maintain existing disability benefits or present in a manner that would support a favorable decision on pending claims could not be ruled out. Similar approaches to the definition of external incentives have been used by other researchers (e.g., Bianchini, Greve, & Glynn, 2005; Greve, Ord, Bianchini, & Curtis, 2009; Slick, Sherman, & Iverson, 1999).
For the purposes of this study, individuals were assigned to one of two response style groups, credible or noncredible responders. Specifically, individuals were assigned to the noncredible responder group if they scored below chance or below the recommended cut score or scores on one or more of the SVTs (e.g., WMT, CARB, or TOMM). Although SVT failure can occur in any context and may or may not be necessarily related to the noncredible report of purely psychiatric symptoms, this method of identification has been suggested for identifying exaggerated or feigned ability deficits (Bianchini, Greve, & Glynn, 2005; Heilbronner et al., 2009; Slick et al., 1999). Further, Wygant et al. (2007) demonstrated that SVT failure was associated with overreporting of somatic and cognitive symptoms but not psychiatric symptoms in personal injury and disability claimants. As such, this method of identifying noncredible responders, although having limited utility for detecting purely overreported psychological symptoms, is appropriate for use in the current study, as all the included individuals were claiming significant impairment in their abilities to perform daily routines and/or work-related duties. For example, individuals in this sample scored, on average, much higher on scales assessing both general and work-impairing memory problems on the Memory Complaints Inventory (Green, 2004) than individuals with medically verified, severe traumatic brain injuries.
Given that substantial external incentives were present for all individuals in the sample, using SVT performance as a criterion for noncredible response group assignment allowed the identification of definite (in the case of individuals who scored below chance levels on an SVT) or probable (in the case of individuals scoring below a particular recommended cut score on an SVT) response bias using the criteria for malingered neurocognitive dysfunction outlined by Slick et al. (1999). These criteria have also been suggested by Bianchini et al. (2005) for the identification of malingered pain-related disability as well as by the American Academy of Clinical Neuropsychology for screening of noncredible reporting of cognitive symptoms and ability deficits (e.g., difficulties with attention and memory) in individuals reporting depressive and/or anxiety related disabilities (Heilbronner et al., 2009). Further, this method of group assignment is congruent with recommendations issued within the consensus statement on SVT use issued by the National Association of Neuropsychology (Bush et al., 2005). Bush et al. (2005) asserted that SVTs were forensically and medically necessary in all evaluations of disability, because they assess not only cognitive effort and noncredible cognitive symptoms but also because poor performance on SVTs is associated with unreliable self-reports. As such, they concluded that the possibility of deliberate response distortion (i.e., a negative response bias) should be considered when an SVT score falls below recommended levels, no matter the presenting problem. Lastly, this method of group assignment is strongly supported by evidence of a nonspecific association between SVT failure and scores on MMPI-2 indices of overreported somatic complaints, depressive symptomatology, and general emotional distress in individuals undergoing evaluation for disability as an alleged result of neurological (e.g., traumatic brain injury), medical (e.g., pain), or psychological (e.g., depression or posttraumatic stress disorder) difficulties (Nelson, Sweet, Berry, Bryant, & Granacher, 2007; Thomas & Youngjohn, 2009).
This method of response group assignment resulted in identification of 372 probable noncredible responders and 837 credible responders. Of the claimants in the sample assigned to the noncredible responders group, 19 scored below chance on one or more the SVTs. Of the remaining 353 individuals in the noncredible responders group, 217 scored below recommended cut scores on one SVT, whereas 136 scored below the cutoffs on more than one SVT. The base rate of noncredible responding (30.8%) in this sample was comparable with previously published rates of noncredible responding in medico-legal evaluations (Mittenberg, Patton, Canyock, & Condit, 2002). Women were less likely to be identified as noncredible responders using this procedure, though the difference was small in effect size, χ2(1, N = 1209) = 11.29, p < .001, odds ratio [OR] = 0.65. Table 1 provides a breakdown of the credible and noncredible responder groups' demographic and diagnostic characteristics (which were assigned using all available test information, including SVT and MMPI-2 results) as well as results of analyses examining potential differences in these characteristics for the combined sample and by gender.
Demographic Characteristics of the Study Sample by Credible (0 SVT Failures) and Noncredible (>1 SVT Failures) Responders
Results Score- and Item-Level Differences
Score scale differences
To examine whether men and women differed on mean raw FBS scale scores, we conducted two t tests and then used Cohen's d (1988) to quantify the size of obtained effects (.3 = small effect, .5 = medium effect, .8 = large effect). Results of the first t test examining FBS raw score differences for men and women when both credible and noncredible responders were included in the sample indicated a small, statistically significant difference, t(1, 1207) = −5.26, p < .001, d = −.29. On average, men (M = 23.25, SD = 6.01) scored lower than women (M = 25.01, SD = 5.40). These raw scores correspond to T scores equal to 80 for men and 77 for women.
As potential differences could have been due to inflation of mean scores by the inclusion of noncredible responders in the analysis, we also examined potential gender differences in FBS raw scores for only the credible responders. Results were statistically significant with a small effect size, t(1, 835) = −5.98, p ≤ .001, d = −.41. When only credible responders were considered, men (M = 21.93, SD = 5.92) continued to score lower than women (M = 24.27, SD = 5.31) on average. These raw scores correspond to T scores of 77 and 75 for men and women, respectively.
Item-level differences
We next examined potential FBS item endorsement differences between men and women using a series of chi-square analyses, first calculating these analyses with the entire sample and then again with only the credible responder group. The practical significance of the chi-square results was interpreted by calculating ORs and by examining the 95% confidence intervals for the ORs. Because of the number of chi-square tests calculated, a Bonferroni correction was applied to correct for potential Type I error, and the required level for statistical significance was p < .001 (.05/43).
Results of the statistically significant chi-square analyses examining FBS item endorsement differences by gender are presented in Table 2. Overall, results indicated that men were more likely than women to endorse items in the keyed direction related to coughing blood, escapism, and suicidal thoughts, with ORs ranging from 0.57 to 1.74. Women were more likely to than men to endorse items in the keyed direction related to having many headaches, being easily tired, and having poor energy as well as items disavowing cynicism, antisocial beliefs, and alcohol use. ORs for these items ranged from 1.63 to 3.21. The pattern of results for men and women, with the exception of item 506 (suicidal ideation), was replicated when only honest responders were examined.
Statistically Significant Item Endorsement Frequencies/Differences for FBS by Gender for Entire Sample (Men = 690, Women = 518)
Prediction differences
We next conducted a series of modified step-down hierarchical regression analyses to determine if there were gender differences in the predictions made by FBS scores for the responder groups (credible vs. noncredible responder). In the current study, we modified the methodology proposed by Lautenschlager and Mendoza (1986) for use in a logistic regression context. Step-down hierarchical multiple regression analysis is a procedure consisting of a series of hierarchical regressions, which are conducted to examine whether the prediction of a criterion (i.e., response group membership) by a predictor (i.e., FBS scores) is moderated by a categorical variable (i.e., gender).
Results of the step-down hierarchical logistic regression analyses are presented in Table 3. The first step in the process consisted of testing for evidence of predictive differences. The test of prediction differences compared the prediction of group membership by FBS scores alone with the full model containing FBS scores, gender, and the interaction of FBS scores and gender. As seen in Table 3, comparison of the reduced model with the full model (i.e., the Prediction χ2Δ) was statistically significant. However, as indicated by the beta weights, only FBS scores and gender were significant individual predictors of credible/noncredible responder group membership. Higher FBS scores, as well as being female, were related to a higher probability of being classified as a noncredible responder compared with being classified as a credible responder, with ORs (i.e., Exp[B]) of 1.98 and 1.87 for FBS scores and gender, respectively.
Logistic Regression Analyses of Credible (0 SVT Failures) Versus Noncredible (>1 SVT Failures) Responder Group Status on Minnesota Multiphasic Personality Inventory–2 (MMPI-2) Symptom Validity (FBS) Scale, Gender, and FBS × Gender Interaction Term (N = 1,209)
Additional models were then tested to determine if the significant result was due to slope and/or intercept differences. The analysis examining slope differences, the second step in the process, compared a model with MMPI-2 FBS scores and gender with the model containing FBS scores, gender, and the interaction of FBS scores and gender in the prediction of response group membership. Statistically significant results supporting discrepant slope values would indicate FBS scores were differentially associated with prediction of response group membership for men and women. As seen in Table 3, in the prediction of credible/noncredible responder group membership results provided no support for statistically significant differences in slope values between men and women.
Because there was no evidence of slope differences, the last step of the process was an examination of potential intercept differences in which the prediction of response group membership by FBS scores alone was compared with results from the model containing FBS scores and gender. Results supporting discrepant intercept values would indicate FBS scores consistently under- or overpredicted group membership when applied to the different genders. As seen in Table 3, results indicated the addition of gender to the prediction of group membership to the model containing FBS scores was statistically significant and supported a discrepancy in intercept values for men and women. Inspection of logit weights (B) and associated ORs (Exp[B]) in this model suggested a greater likelihood of being classified as a noncredible responder compared with a credible responder as FBS scores increased (B = .66, p < .001, Exp[B] = 1.93) and suggested that the odds of being classified as a noncredible responder were significantly greater for women compared with men (B = .65, p < .001, Exp[B] = 1.91).
Classification accuracies and comparisons
The regression analyses described earlier allowed an examination of the prediction of group membership but did not allow for the quantification of the accuracy of those predictions. To determine the practical significance of predictive differences (i.e., the accuracy of the group membership predictions), using the formulas outlined by Meehl and Rosen (1955) we calculated classification accuracies, including overall correct classification, sensitivity, and specificity as well as negative and positive predictive powers, for the prediction of response group using the test publisher's recommended FBS score cutoffs of T > 80 and T > 100 (Ben-Porath, Graham, & Tellegen, 2009).
However, our interest in the current study was in contrasting classification accuracies between the genders to determine the impact of any predictive biases demonstrated in previous regression analyses, not in inspecting the classification accuracies themselves. As such, we compared the obtained accuracy of classification statistics for men and women using Cohen's h statistic (Cohen, 1988). Cohen's h is an effect size statistic reflecting the magnitude of the difference between two proportions, with values greater than .2/.3, .5, and .8 reflecting small, medium, and large differences, respectively.
Calculated classification accuracy statistics and comparisons of these proportions for men and women are presented in Table 4. Overall, results indicated that at T > 80, there were negligible-to-small differences in classification accuracies between men and women (h = −.05 to .26), with no meaningful differences in overall correction classification (h = −.02). Further, at a T > 100, results indicated negligible-to-small differences in the classification accuracies (h = −.29 to .32), with only small differences in overall correct classification (h = −.20). This result indicated more men than women were correctly classified into response style groups, although women who were, in fact, noncredible responders were more likely to have FBS scores below the recommended cut points (h for specificity at T ≥ 80 and T ≥ 100 was −.16 and −.29, respectively).
Classification Accuracies by Recommended FBS Scores for Prediction of Credible (0 SVT Failures) Versus Noncredible (>1 SVT Failures) Responder Group Status
DiscussionOverall, results of this study indicate FBS scores are not differentially accurate in identifying SVT failure in men and women. Women had slightly higher raw FBS scores than men, though these differences were negligible when converted to MMPI-2 T scores. Significant differences in item endorsement between men and women were found for 14 FBS items, indicating women were more likely to endorse items in the keyed direction related to headaches, problems with energy/motivation, and a disavowal of cynicism, antisociality, and alcohol use. These results are similar to those demonstrated in past research where differences were demonstrated in raw scale scores and item endorsements in various settings (Nichols et al., 2009; Williams et al., 2009).
As mentioned previously, scale score and item endorsement differences between men and women do not necessarily indicate that use of that scale's scores will lead to predictive differences. When predictive differences were examined using regression analyses, results of the current study indicated that, statistically, gender was a moderator in the prediction of SVT performance. However, although intercept differences were demonstrated, utilizing the test publisher's recommended FBS cutoff scores (T > 80 and T > 100; Ben-Porath, Graham, & Tellegen, 2009), results indicated classification accuracies were similar for women and men, as differences in classification were negligible-to-small in their effect. These results appear to parallel those demonstrated in previous research examining the effect of ethnicity on predictions made using MMPI-2 scales (e.g., Arbisi, Ben-Porath, & McNulty, 2002) where, although predictive differences were demonstrated, they had little to no practical meaning. This pattern of results is also in congruence with Ben-Porath, Greve, et al.'s (2009) demonstration of no practically meaningful differences in classification between men and women in a somatic pain sample. Lastly, a close examination of the pattern of classification differences in the current study would suggest some support for a finding similar to the traumatic brain injury group included in Ben-Porath, Greve, et al.'s (2009) study, as specificity was greater for women, indicating that more women than men who failed one or more SVT scored below T ≥ 80 and T ≥ 100, though differences between specificity statistics for men and women were of negligible-to-small effect sizes.
We believe it is important to note that in the current study, mean FBS T scores for credible responders (i.e., those not scoring below chance or recommended cut scores on any included SVT) were high (T = 77 and 75 for men and women, respectively), as by themselves these scores have an important implication. The high mean FBS T-score elevations for both men and women in the credible responders group are directly reflected in the demonstrated low sensitivity and specificity values in the classification results. However, the relatively high scores in honest responders in this sample are not completely unexpected, likely having been influenced by legitimately experienced symptoms as well as by the presence of external incentives. Previous research has demonstrated that FBS scores tend to be higher in individuals with medically related complaints (see, e.g., Tables 2–4 in Ben-Porath, Graham, & Tellegen, 2009) as well as directly and positively related to the presence of external incentives (Ben-Porath, Greve, et al., 2009). Further, though the relatively high FBS scores in the current study lowered the obtained classification values, these rates are not lower than would be expected given the base rate of noncredible responding defined in this sample. For example, these rates are comparable with those obtained in published, known-groups studies of individuals with pain-related symptoms (e.g., Bianchini, Etherton, Greve, Heinly, & Meyers, 2008). Nonetheless, the low sensitivity and specificity of FBS scores demonstrated in the current study directly support Ben-Porath, Graham, and Tellegen's (2009) cautions regarding over-interpreting FBS scores and against using scores on this scale in isolation to determine the credibility of somatic and cognitive symptom reports.
In addition to concerns with the version of FBS scored on the MMPI-2, Butcher et al. (2008) have asserted that the purported gender bias of FBS scores would carry over to the Symptom Validity Scale–Revised (FBS-r), which is scored on the Restructured Form of the MMPI-2 (Ben-Porath & Tellegen, 2008; Tellegen & Ben-Porath, 2008). Although not presented in the current article, we conducted the same analyses described for FBS using scores on FBS-r. Overall, results of these analyses conformed to the same pattern of results just discussed for FBS. There were raw score and item endorsement differences for men and women as well as statistical support for differences in predicting whether an individual had failed an SVT due to intercept bias. However, when classification accuracies were examined, the practical meaning of the demonstrated intercept difference was negligible to small, counter to the idea that scores on FBS-r are biased against women.
One limitation of the current study was that the sample used consisted of disability claimants who did not have head injuries. Using scores on FBS as a measure of noncredible cognitive symptom reports is based largely on the neuropsychological research with brain injury claims. In that context, FBS scores may well function differently. However, as reviewed by Ben-Porath, Graham, and Tellegen (2009) and supported by the recent meta-analysis conducted by Nelson et al. (2010), FBS scores are also sensitive to noncredible reporting of somatic symptoms in disability settings, which suggests that the results of the current study with claimants reporting disability not related to a head injury are likely generalizable as well.
Another consideration is the nature and number of SVTs used to assign response group membership. Other studies examining the validity of FBS scores have used alternative measures (e.g., Bianchini et al., 2008), which might correlate more strongly or be differentially predicted by FBS scores for the two genders. Because of sample size restrictions, we were only able to classify individuals as noncredible responders independently of the MMPI-2 using Slick et al.'s (1999)definite malingered neurocognitive classification for cases scoring below chance on any SVT and using probable response bias classification (Criterion B2) for individuals who failed one or more SVTs at established cutoffs and who, by definition, met the criteria for the presence of a substantial external incentive (Criterion A). It can be argued that different results might have been obtained if more stringent response group criteria had been used (e.g., failure of more than one SVT), as previous research has suggested use of multiple SVTs increases the identification of true positives while decreasing the identification of false positives (Greve, Ord, Curtis, Bianchini, & Brennan, 2008). However, it can also be argued that requiring failure on multiple SVTs is certainly appropriate when utilizing SVTs that have relatively low specificity but is unnecessarily restrictive when the SVTs in question already have an established high level of specificity in diverse clinical populations with significant cognitive impairment. Nonetheless, given the potential costs of misclassifying individuals as noncredible responders when they are, in reality, responding credibly, we suggest that future research continue to examine potential gender differences of FBS score predications in alternative samples and using various methods of defining noncredible group membership.
In summary, given that men and women have been demonstrated to have FBS raw score and item endorsement differences, some authors have called into question the utility of FBS (e.g., Williams et al., 2009). However, scale and item endorsement differences do not indicate whether a scale's scores are biased against some group of individuals in the prediction of external criteria (Prichard & Rosenblatt, 1980). On the basis of the results of the current and past studies examining predictive bias, we conclude there is no evidence for meaningful differences in the prediction of potential noncredible responders for men and women when using FBS scores as a predictor. Specifically, the current study demonstrated minimal differences in predictions of SVT failure in a sample of non-head-injury disability claimants and personal injury litigants between genders. Further, one previous study (Ben-Porath, Greve, et al., 2009) demonstrated minimal-to-small differences in the classification of men and women into malingerers using formal classification systems for malingered neurocognitive dysfunction and malingered pain-related disability. Further, when biased predictions have been demonstrated, results have suggested that FBS scores actually underpredict credible response group membership to a greater extent for men compared with women—countering claims that FBS scores are biased against women. Overall, it appears that use of FBS scores recommended in the FBS test monograph (T > 80 and T > 100; Ben-Porath, Graham, & Tellegen, 2009) does not lead to differential prediction of noncredible group membership in medico-legal settings for men and women.
Footnotes 1 As discussed by Rogers (2008), noncredible responding is not a unitary construct and can include exaggerated or false reports of any combination of emotional, somatic, and cognitive symptoms as well as feigned or exaggerated reports of distress and impairment. In the current study, our use of the term noncredible is limited to the noncredible report of cognitive and somatic complaints as well as negative response bias, as detected by SVTs. As such, our method of group assignment does not preclude that individuals assigned to the credible responders group could have been exaggerating or fabricating purely psychological symptoms during the evaluation.
2 It is unknown how many claimants declined consent for their data to be incorporated into the archival data set, as no records were retained for these cases.
3 We are not suggesting that individuals claiming disability because of psychiatric syndromes should be assessed solely with SVTs to screen for noncredible responding. Rather, we are arguing that SVTs provide one source of information regarding the noncredible report of ability deficits that should be obtained during a comprehensive assessment performed for purposes of a disability or personal injury evaluation.
4 These results are available from the first author.
5 These results are available from the first author.
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Submitted: September 15, 2010 Revised: September 6, 2011 Accepted: September 7, 2011
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Source: Psychological Assessment. Vol. 24. (3), Sep, 2012 pp. 618-627)
Accession Number: 2012-02771-001
Digital Object Identifier: 10.1037/a0026458
Record: 66- Title:
- Exposure to rapid succession disasters: A study of residents at the epicenter of the Chilean Bío Bío earthquake.
- Authors:
- Garfin, Dana Rose. Department of Psychology and Social Behavior, University of California, Irvine, CA, US
Silver, Roxane Cohen. Department of Psychology and Social Behavior, University of California, Irvine, CA, US, rsilver@uci.edu
Ugalde, Francisco Javier. Instituto de Salud Pública, Universidad Andrés Bello, Santiago, Chile
Linn, Heiko. Instituto de Salud Pública, Universidad Andrés Bello, Santiago, Chile
Inostroza, Manuel. Instituto de Salud Pública, Universidad Andrés Bello, Santiago, Chile - Address:
- Silver, Roxane Cohen, Department of Psychology & Social Behavior, University of California, 4201 Social & Behavioral Sciences Gateway, Irvine, CA, US, 92697-7085, rsilver@uci.edu
- Source:
- Journal of Abnormal Psychology, Vol 123(3), Aug, 2014. pp. 545-556.
- NLM Title Abbreviation:
- J Abnorm Psychol
- Page Count:
- 12
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- The Journal of Abnormal and Social Psychology; The Journal of Abnormal Psychology; The Journal of Abnormal Psychology and Social Psychology
- ISSN:
- 0021-843X (Print)
1939-1846 (Electronic) - Language:
- English
- Keywords:
- Latin America, disasters, earthquakes, posttraumatic stress symptoms, trauma
- Abstract:
- We examined cumulative and specific types of trauma exposure as predictors of distress and impairment following a multifaceted community disaster. Approximately 3 months after the 8.8 magnitude earthquake, tsunami, and subsequent looting in Bío Bío, Chile, face-to-face interviews were conducted in 5 provinces closest to the epicenter. Participants (N = 1,000) were randomly selected using military topographic records and census data. Demographics, exposure to discrete components of the disaster (earthquake, tsunami, looting), and exposure to secondary stressors (property loss, injury, death) were evaluated as predictors of posttraumatic stress (PTS) symptoms, global distress, and functional impairment. Prevalence of probable posttraumatic stress disorder was 18.95%. In adjusted models examining specificity of exposure to discrete disaster components and secondary stressors, PTS symptoms and global distress were associated with earthquake intensity, tsunami exposure, and injury to self/close other. Increased functional impairment correlated with earthquake intensity and injury to self/close other. In adjusted models, cumulative exposure to secondary stressors correlated with PTS symptoms, global distress, and functional impairment; cumulative count of exposure to discrete disaster components did not. Exploratory analyses indicated that, beyond direct exposure, appraising the tsunami and looting as the worst components of the disaster correlated with greater media exposure and higher socioeconomic status, respectively. Overall, threat to life indicators correlated with worse outcomes. As failure of government tsunami warnings resulted in many deaths, findings suggest disasters compounded by human errors may be particularly distressing. We advance theory regarding cumulative and specific trauma exposure as predictors of postdisaster distress and provide information for enhancing targeted postdisaster interventions. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Distress; *Natural Disasters; *Symptoms; *Trauma
- Medical Subject Headings (MeSH):
- Adolescent; Adult; Aged; Aged, 80 and over; Chile; Disasters; Earthquakes; Female; Humans; Life Change Events; Male; Middle Aged; Stress Disorders, Post-Traumatic; Surveys and Questionnaires; Young Adult
- PsycINFO Classification:
- Psychological Disorders (3210)
- Population:
- Human
Male
Female - Location:
- Chile
- Age Group:
- Adolescence (13-17 yrs)
Adulthood (18 yrs & older)
Young Adulthood (18-29 yrs)
Thirties (30-39 yrs)
Middle Age (40-64 yrs)
Aged (65 yrs & older)
Very Old (85 yrs & older) - Tests & Measures:
- Short Form 36 Health Survey
Brief Symptom Inventory DOI: 10.1037/t00789-000
PTSD Checklist - Methodology:
- Empirical Study; Interview; Quantitative Study
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- Accepted: Jun 3, 2014; Revised: Jun 2, 2014; First Submitted: Aug 14, 2013
- Release Date:
- 20140804
- Copyright:
- American Psychological Association. 2014
- Digital Object Identifier:
- http://dx.doi.org/10.1037/a0037374
- PMID:
- 25089656
- Accession Number:
- 2014-31174-001
- Number of Citations in Source:
- 52
- Persistent link to this record (Permalink):
- http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2014-31174-001&site=ehost-live
- Cut and Paste:
- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2014-31174-001&site=ehost-live">Exposure to rapid succession disasters: A study of residents at the epicenter of the Chilean Bío Bío earthquake.</A>
- Database:
- PsycINFO
Exposure to Rapid Succession Disasters: A Study of Residents at the Epicenter of the Chilean Bío Bío Earthquake
By: Dana Rose Garfin
Department of Psychology and Social Behavior, University of California, Irvine
Roxane Cohen Silver
Department of Psychology and Social Behavior, Department of Medicine, and Program in Public Health, University of California, Irvine;
Francisco Javier Ugalde
Instituto de Salud Pública, Universidad Andrés Bello, Santiago
Heiko Linn
Instituto de Salud Pública, Universidad Andrés Bello, Santiago
Manuel Inostroza
Instituto de Salud Pública, Universidad Andrés Bello, Santiago
Acknowledgement: We thank Pedro Uribe Jackson, MD (Universidad Andrés Bello), for his support of the project; the staff at Ipsos for their expertise with sampling design, weighting data, and survey administration; and JoAnn Prause, PhD (University of California, Irvine), for her statistical expertise. Project funding provided by Universidad Andrés Bello School of Medicine, Santiago, which played no other role in the research project or this article.
Superstorm Sandy of 2012, the 2011 Tôhoku Japanese earthquake, and Hurricane Katrina illustrate that natural disasters rarely occur in isolation. Frequently, one catastrophe begets a sequence of deleterious natural and man-made events, exacerbated by interrelated, associated disasters such as levee breakage, looting, or failure of governments to provide significant warnings or timely aid. Globally, natural disasters are increasing in number and severity; recent estimates indicate a 4.4%–7.5% lifetime prevalence of disaster exposure (Kessler, McLaughlin, Koenen, Petukhova, & Hill, 2012). The sixth largest recorded earthquake, an 8.8 magnitude temblor, struck off the coast of Concepción in Bío Bío, Chile, on February 27th, 2010. Millions of people were affected, 521 died, 12,000 were injured, and over 800,000 were displaced (American Red Cross Multi-Disciplinary Team, 2011). The Chilean earthquake typifies many multifaceted modern natural disasters. The earthquake (a primary precipitating event) was followed by two rapid-succession–associated disasters: a devastating tsunami and subsequent flooding that, through failure of the Hydrographic and Oceanographic Service of the Chilean Navy (SHOA), occurred without adequate warning, and several days of looting in the epicenter region.
Exposure to natural disasters is frequently associated with postdisaster mental health problems such as posttraumatic stress disorder (PTSD), global distress, and functional impairment (for reviews, see Garfin & Silver, in press; Norris et al., 2002), although many survivors will exhibit striking resilience (Bonanno, Brewin, Kaniasty, & La Greca, 2010). Much prior literature has nonetheless been limited by methodological weaknesses (e.g., nonrepresentative samples) and a narrow inclusion of predictors and outcomes (Bonanno et al., 2010; Garfin & Silver, in press). The present study addressed these limitations through a theoretically derived, multivariate inquiry into predictors of postdisaster distress and functioning using an epidemiological sample of adults directly exposed to the Bío Bío earthquake. Within a cross-cultural setting, we advance theories regarding responses to disasters specifically and trauma more generally by examining the influence of type and amount of trauma exposure and other key predictors, such as predisaster individual characteristics (Brewin, Andrews, & Valentine, 2000; Ozer, Best, Lipsey, & Weiss, 2003), on several postdisaster outcomes following the earthquake and associated disasters. Each will be considered in turn.
Type of DisasterMultifaceted disasters are common, yet surprisingly few studies have unpacked potential differences in how the type of disaster correlates with negative outcomes. Although the Diagnostic and Statistical Manual of Mental Disorders (5th ed.; DSM-5, American Psychiatric Association, 2013) groups all potentially traumatic events under “Criterion A” (stressor) for PTSD, research on risk perception has indicated differential associations between disaster types and hazard judgments (e.g., Ho, Shaw, Lin, & Chiu, 2008); such variability may also extend to other outcomes including postdisaster psychopathology. Decades ago, Brim (1980) theorized that the type of life event might differentially influence psychological processes. Baum (1987) posited community disasters with a man-made component might elicit greater distress compared to other events. In contrast, events involving social breakdown, such as looting, might more strongly influence distress by violating a world view that assumes community safety and trustworthy neighbors (Janoff-Bulman, 1992).
Yet recent research has largely ignored specificity in disaster type. Exceptions include Norris and colleagues’ (2002) literature review, which suggested violent disasters may be correlated with worse outcomes, and an empirical study comparing victims of political violence and earthquakes that found no differences between groups experiencing one event compared to another (Goenjian et al., 1994). Exploring how discrete disaster components (earthquake, tsunami, looting) correlate with psychological outcomes may address these theoretical questions and inform targeted allocation of limited postdisaster resources.
Exposure to Disaster-Related Secondary StressorsExposure to individual-level stressors occurring during or in the immediate aftermath of a disaster (e.g., property loss, injury, death) may influence mental health outcomes. Such occurrences have been conceptualized as “secondary stressors” in past postdisaster epidemiological studies (Galea et al., 2007; Kessler et al., 2012). After Hurricane Katrina, an event conceptually similar to the Chilean earthquake (i.e., a multifaceted natural disaster exacerbated by man-made failings), specificity in exposure to individual-level secondary stressors was associated with DSM–IV (American Psychiatric Association, 2000) anxiety disorders and PTSD (Galea et al., 2007); physical injury and adversity were particularly strong correlates of distress for those highly exposed. Clarifying how specific disaster-related stressors may be associated with postdisaster outcomes could further refine the design of interventions and answer theoretical questions regarding the role of specificity of traumatic stress exposure in negative outcomes (Brewin et al., 2000; Galea et al., 2007; Ozer et al., 2003).
Cumulative ExposureAlternatively, cumulative—rather than a specific type of—exposure to discrete disaster components and specific secondary stressors may predict postdisaster difficulties (Norris et al., 2002; Seery, Holman, & Silver, 2010; Turner & Lloyd, 1995). For example, after the 1988 Armenian earthquake, combined earthquake and political violence exposure predicted psychological distress; differential responses were not found between groups exposed to only one of those two events (Goenjian et al., 1994). More generally, number of traumatic events often predicts negative outcomes (Chapman et al., 2004), although not necessarily in a linear, “dose-response” relationship (Seery et al., 2010). Furthermore, exposure to negative events often co-occurs, particularly after large-scale disasters, yet few studies have considered additive effects of exposure to greater numbers of discrete disaster components or the secondary stressors that accompany such catastrophes.
Predisaster Individual CharacteristicsEmpirical evidence also indicates that preexisting individual-level characteristics can influence postdisaster mental health (Hobfoll, 1989; Norris et al., 2002). Demographic and socioeconomic indicators are frequently implicated, albeit at times inconsistently (e.g., Brewin et al., 2000; Norris et al., 2002). For example, females (Bonanno et al., 2010), individuals from disadvantaged backgrounds (Norris et al., 2002), and those with prior mental health problems (Norris et al., 2002; Silver, Holman, McIntosh, Poulin, & Gil-Rivas, 2002) are typically at greater risk for difficulties postdisaster. The roles of marital status and age in postdisaster outcomes have been inconsistent (Brewin et al., 2000; Norris et al., 2002), although married persons and younger individuals tend to exhibit different responses than comparison groups; age effects may vary based on event-type and outcome measure (Garfin & Silver, in press; Scott, Poulin, & Silver, 2013). Consequently, such individual-level characteristics should be considered in epidemiological assessments of postdisaster mental health and functioning.
The Present StudyIn sum, prior work suggests that type of traumatic event (both disaster component and secondary stressor) may differentially influence postdisaster psychological outcomes. Other evidence indicates that the aggregate number of traumatic events may also be an important indicator of negative outcomes. Little, if any, research has contrasted these predictors following a disaster where a series of catastrophic events (earthquake, tsunami, looting) and a variety of secondary stressors occur rapidly. Moreover, as noted in seminal meta-analyses (Brewin et al., 2000; Ozer et al., 2003), a key problem with examining theoretical predictors of posttraumatic responses is the heterogeneity of precipitating traumas. Studying reactions to an exogenous sequence of events such as the Chilean earthquake—with clearly demarcated categories of exposure—allows for a naturalistic “control” of factors that typically vary when comparing across disasters (e.g., Kessler et al., 2012) or other traumatic events (e.g., comparing child abuse to military combat; Seery et al., 2010).
The present study examined how exposure to different types of disaster component (earthquake, tsunami and subsequent flooding, looting) and secondary stressors (property loss, injury, death) differentially predicted deleterious outcomes following the Bío Bío Chilean earthquake. Specificity of exposure was also compared to cumulative exposure to these stressors. In addition, the role of predisaster individual characteristics was considered. We had several predictions. First, we expected that both specificity in exposure to secondary stressors and associated disasters, as well as cumulative counts of exposure, would be associated with negative outcomes. Second, similar to past epidemiological studies, we expected individual-level predictors (female gender, lower socioeconomic status [SES], mental health history) to correlate with postdisaster responses. We also explored which disaster components would be appraised as the worst. The tsunami, which had a man-made component, might be most distressing, yet the looting might be viewed as worse since it represented a breakdown in perceptions of community safety and/or benevolence of one’s neighbors.
Method Procedures
Shortly after the earthquake, Ipsos Public Affairs, an international policy and market research company, obtained a representative sample of 2,008 Chilean adults aged 15–90 who lived in provinces across Chile; the present study utilized a subsample of Chileans who lived in five regions closest to the earthquake’s epicenter (Concepción, Talcahuano, Tomé, Lota, and Talca). Data were collected via 35–40 minute face-to-face interviews from May 13 to June 7, 2010. Demographic quota sampling cells, constructed from Chilean National Statistics Institute census population estimates of region, gender, and age, determined participation eligibility. Geographic sampling maps were derived from these estimates along with topographic data from the Military Geographic Institute. Interviewers approached 4,221 homes and contacted a total of 1,711 eligible individuals; 1,004 participated in the interviews, resulting in a 59% participation rate. Demographic information (age, marital status, gender) was recorded by interviewers.
Homes were approached at least twice at different times of the day to account for varying work/activity schedules. If residents could not be reached, the interviewer would solicit information from neighbors to ensure vacancies were not systematic (e.g., due to property loss during the disaster or socioeconomic status). Interviewers attempted to find absent residents based on neighbor reports of work schedule, vacation plans, or relocation of the household to another property. Since the majority of people who lost their homes from the earthquake subsequently resided in tents on their own property (Jaime Vásquez, personal communication, 2013), earthquake-related vacancies were not a serious concern in interview solicitation.
Interviews were conducted in Spanish by professional staff trained by Ipsos in administering face-to-face interviews. Verbal consent was obtained from all participants. All measures were written in English and then translated and back-translated by Chilean bilingual psychologists (FJU, HL) and checked for linguistic and cultural accuracy.
Data from the interviews were entered manually into a database, with 5% of all responses reentered to check for data entry errors. The study was approved by the Institutional Review Boards at the University of California, Irvine, and Universidad Andrés Bello, Santiago.
Outcome Measures
Posttraumatic stress (PTS) symptoms
The PTSD Checklist (PCL; Weathers, Litz, Herman, Huska, & Keane, 1993), a well-validated 17-item self-report measure, was used to assess PTS symptoms. Individuals rated how distressed or bothered they were by symptoms related to the Chilean earthquake, tsunami, and their aftermath over the prior 7 days, with endpoints 1 (not at all) to 5 (extremely). Responses were summed to create a continuous measure of PTS symptoms (range 17–85); this continuous measure was utilized to account for variability in symptom severity in an inherently dimensional construct (cf. MacCallum, Zhang, Preacher, & Rucker, 2002). To estimate prevalence of probable PTSD, the PCL was scored according to a cutoff of 50, which is the most conservative estimate commonly used, as well as using DSM–IV scoring criteria (Ruggiero, Del Ben, Scotti, & Rabalais, 2003). Studies using confirmatory factor analysis have shown equivalence between Spanish and English versions of the PCL (Marshall, 2004).
Global distress
Distress was measured using the 18-item Brief Symptom Inventory (BSI-18; Derogatis, 2001). Respondents indicated their level of distress in the past 7 days (including the day of completion), with endpoints 1 (not at all) to 5 (extremely). The BSI-18 has been validated in community-based and medical samples and has demonstrated excellent reliability in field studies (Derogatis, 2001). Spanish versions have shown equivalence (Ruipérez, Ibáñez, Lorente, Moro, & Ortet, 2001). Internal consistency was excellent (range 18–90; α = .95).
Functional impairment
Four items modified from the Short Form 36 Health Survey (SF-36; Ware & Sherbourne, 1992) assessed impairment in work and social activities occurring as a consequence of physical or emotional health problems (range = 1–5, α = .93). A similar modification of the SF-36 was used in a prior epidemiological assessment of psychological outcomes following exposure to adverse events (Seery et al., 2010); Spanish versions of the SF-36 have indicated equivalence (Alonso, Prieto, & Antó, 1995).
Disaster-Related Characteristics
Earthquake intensity
The degree of destruction experienced during the earthquake was assessed using a version of the Modified Mercalli Intensity Scale (Wood & Neumann, 1931), commonly implemented to assess earthquake intensity for the nonscientist population (U.S. Geological Survey, 2013a). Participants reported their experience of the earthquake the night it occurred on an 8-point scale: 1 (not perceptible), 2 (felt slightly, no damage to objects), 3 (weakly felt, objects moved slightly), 4 (objects swayed, glass and windows rattled), 5 (strong shaking or rocking of entire building), 6 (objects broke, cracks in plaster), 7 (serious damage to surroundings), 8 (destructive, forcibly thrown to the ground, many objects broken, walls collapsed, location uninhabitable/unlivable). Four participants indicated that the earthquake was “not perceptible”; they were deleted from the final sample, resulting in N = 1,000.
Two additional measures of earthquake intensity were computed: residential region and kilometers from the geologic epicenter. The pattern of results was identical for all three measures; results using the Mercalli Intensity Scale are reported in the text and tables as this measure accounted for geographic variability in earthquake destruction and intensity and is more commonly used in research on earthquakes.
Additional disaster exposure
Participants also reported whether they were at the coast as the tsunami occurred, coded 0 (not at the coast), 1 (at the coast when tsunami hit). Looting exposure was assessed by asking participants whether they witnessed looting directly, participated in looting, lost property in looting, or knew someone close who lost property in the looting; endorsing any of these exposures was considered an affirmative exposure, coded 0 (no looting exposure), 1 (looting exposure).
A continuous variable of cumulative disaster exposure was also created via a count of exposure to the three disasters (earthquake, tsunami, looting; M = 1.57, SD = 0.55, range 1–3).
Exposure to secondary stressors
Participants reported experience with three possible secondary stressors to which they or a close other could have been exposed as a result of the earthquake and its aftermath. To remain consistent with DSM–IV (American Psychiatric Association, 2000) criterion A for exposure to potentially traumatic events, both experiences for self and close other were assessed. Items were modified from prior research on community disasters (Holman & Silver, 1998; Silver et al., 2002). Disaster-related property loss was assessed and categorized 0 (no property loss) or 1 (personally lost property in earthquake, tsunami, or looting and/or close other lost property). Participants reported experience with injury resulting from the earthquake, tsunami, or looting; responses were categorized 0 (no injury) or 1 (personally injured and/or close other injured). Disaster-related death was also assessed; responses were dichotomized 0 (no death experience) or 1 (personally knew someone who died in the earthquake or tsunami).
Exposures to potential secondary stressors (personally lost property, close other lost property, injury to self, injury to close other, knew someone who died) were counted and combined into a continuous measure of cumulative secondary stressors experienced (M = 1.25, SD = 1.01, range = 0–5).
Disaster appraisal
Participants were asked which of the three components of the disaster (earthquake, tsunami and associated flooding, or looting) they experienced as the worst; participants could select only one.
Individual-Level Characteristics
Socioeconomic status (SES)
A socioeconomic score (called the E&E Socioeconomic Classification in Chile) was calculated using type of employment and education level of head of household. This measure is commonly used in Chilean market and epidemiological research and correlates strongly with household income (Asociación Investigadores de Mercado [AIM] Chile, 2008; Ipsos, 2010). The E&E is computed by asking respondents the education level (seven possible choices range from “less than primary school” to “graduate degree obtained”) and type of work (six possible choices range from “occasional work/unemployed” to “organization director”) of the head of household. Households are then categorized via a matrix of possible responses and grouped into the greater than 90th, 70th, 45th, 10th, and lower than 10th percentiles (AIM Chile, 2008; Ipsos, 2010); lower percentiles indicate higher SES. This score was standardized and used as a continuous measure of SES in analyses (M = 3.27, SD = 1.00, range 1–5).
Physician-diagnosed mental health history
Participants reported any doctor or health care professional diagnosis of depression or anxiety disorder prior to February 2010 (before the earthquake). A continuous variable of physician-diagnosed mental health ailments was coded 0 (no history of depression or anxiety disorder), 1 (history of depression or anxiety disorder), or 2 (history of both depression and anxiety disorder). Similar categorizations have been used in past research (e.g., Holman, Garfin, & Silver, 2014; Silver et al., 2002).
Demographics
Gender was coded 0 (male), 1 (female). Marital status was coded as (a) single (never married), (b) married, or (c) widowed, divorced, or separated. Married persons comprised the reference group (coded “0” in analyses) as they often exhibit differential outcomes when compared to individuals who do not have a spouse present (Garfin & Silver, in press). Age was grouped into six categories (15–24, 25–34, 35–44, 45–54, 55–64, 65+). Individuals 15–24 years old comprised the reference group (coded “0” in analyses) since past research suggests younger individuals exhibit differential postdisaster distress responses when compared to older individuals (Garfin & Silver, in press; Norris et al., 2002).
Statistical Analyses
Statistical analyses were conducted using STATA 11.0 (Stata Corp, College Station, TX), a program well-suited for handling weighted survey data. Ipsos provided poststratification weights, calculated by multiplying individuals in a given demographic category (i.e., age, city population, gender) by a factor proportional to Census estimates of that particular demographic category and inversely proportional to the number obtained in our sample. Analyses were then weighted to adjust for differences in sample composition compared to Chilean census data, facilitating stronger population-based inferences.
First, we calculated descriptive statistics of exposure to the earthquake, tsunami and looting, PTS symptoms and probable rates of PTSD, and participants’ appraisal of which disaster component was the worst. Then, bivariate regression analyses examined independent associations between each of the three outcome variables (PTS symptoms, global distress, functional impairment) and individual and cumulative exposure to the disasters (earthquake, tsunami, looting) and individual and cumulative exposure to secondary stressors (property loss, injury, death).
Multivariate methods are recommended for postdisaster epidemiological studies to illustrate the independent contribution of covariates while controlling for the relative contribution of predictors (Bonanno et al., 2010). We conducted multivariate regression models that analyzed predictors of PTS symptoms, global distress, and functional impairment. For each of the three outcome variables, two sets of multivariate ordinary least squares (OLS) regression models were constructed using a hierarchical variable entry strategy. The first set examined the potential specific effects of exposure to the disasters and their secondary stressors. The second set examined the potential cumulative effects of exposure to the disasters (earthquake, tsunami, looting) and three types of secondary stressors (property loss, injury, death). On Step 1, disaster exposure variables (either dummy coded exposure variables to examine specific effects or counts of exposure to examine cumulative exposure) were entered. On Step 2, all other variables (physician-diagnosed mental health history, SES, demographics) were entered.
Interactions between specific and cumulative exposure to the three components of the disaster and specific and cumulative exposure to secondary stressors were examined. Interactions between SES and the three secondary stressors were tested according to theoretical significance (Galea et al., 2007).
Results Sample
Table 1 presents the demographic composition of the sample compared to Chilean census benchmarks. The sample was 46.10% married (unweighted n = 456); 12.74% widowed, divorced, or separated (unweighted n = 128); and 41.16% single (unweighted n = 415).
Demographic Composition of the Sample and Comparisons With Chilean Census Dataa (N = 1,000)
Table 2 presents weighted and unweighted percentages of participants’ exposure to the Chilean disaster, the component of the disaster participants appraised as the worst, the percentage with PTS symptoms, and rates of probable PTSD. Almost half of the sample reported intrusion/reexperiencing symptoms, and depending on scoring criteria, almost one fifth met DSM–IV diagnostic criteria for probable PTSD. Mean score on the PCL = 30.11 (95% confidence interval [CI], 29.18–31.05), M on the BSI-18 = 31.31 (95% CI, 30.39–32.22), and on the measure of functional impairment, M = 1.55 (95% CI, 1.49–1.60). All participants were directly exposed to the earthquake; approximately 46% of the sample (unweighted n = 454) did not have direct experience with an associated disaster (tsunami or looting). Direct exposure to the looting was reported by 49.56% (unweighted n = 498), 26 (2.63%) participants were exposed to the tsunami but not the looting, and 22 (2.08%) were directly exposed to all three disasters.
Disaster Exposure and Posttraumatic Stress Symptomatology (N = 1,000)
Correlates of Psychological Outcomes
Table 3 presents bivariate relationships between specific and cumulative disaster exposure variables and PTS symptoms, global distress, and functional impairment (not adjusting for covariates) to illustrate the independent effects of predictors included in the multivariate models. Earthquake intensity was positively associated with PTS symptoms, global distress, and functional impairment; exposure to the tsunami was associated with PTS symptoms and global distress; and exposure to the looting was negatively associated with functional impairment. Property loss (to self or close other) and injury (to self or close other) were positively associated with PTS symptoms, global distress, and functional impairment. Knowing someone who died in the earthquake or tsunami was not associated with any of the three outcome variables. Cumulative exposure to disasters and cumulative number of secondary stressors (property loss, injury, death) were positively associated with PTS symptoms, global distress, and functional impairment.
Bivariate Relationships Between Exposure Variables and Posttraumatic Stress Symptoms, Global Distress, and Functional Impairmenta
Specific Exposure to Disasters and Secondary Stressors
Table 4 presents standardized OLS regression coefficients for type of exposure to the disasters and secondary stressors, other key predictor variables, and PTS symptoms, global distress, and functional impairment. As depicted under Step 1 for each outcome variable, after controlling for the relative contribution of each exposure variable listed, earthquake intensity was positively associated with PTS symptoms, global distress, and functional impairment. Tsunami exposure was positively associated with PTS symptoms and global distress. Exposure to looting was negatively associated with functional impairment. Property loss and injury were associated with PTS symptoms, global distress, and functional impairment. The columns under Step 2 illustrate the correlation between exposure variables and each of the three outcome variables after controlling for the relative contribution of the other predictor variables. These results illustrate that earthquake intensity, injury, physician-diagnosed mental health history, lower SES, and female gender were positively associated with PTS symptoms, global distress, and functional impairment.
Multivariate Ordinary Least Squares Regression Analyses of Key Predictor Variables, Exposure to Specific Disaster-Related Events, and Posttraumatic Stress Symptoms (N = 974),a Global Distress (N = 968),a and Functional Impairment (N = 985)a
Cumulative Exposure to Disasters and Secondary Stressors
Table 5 presents standardized OLS regression coefficients for cumulative exposure to disaster-related events, other key predictor variables, and PTS symptoms, global distress, and functional impairment. While cumulative disaster exposure was not associated with any of the three outcomes in any of the multivariate analyses, cumulative secondary stressor exposure was associated with all three outcomes. In addition, earthquake intensity was positively associated with PTS symptoms, global distress, and functional impairment. Female gender, physician-diagnosed mental health history, and lower SES were correlated with PTS symptoms, global distress, and functional impairment (see Step 2 under each outcome variable).
Multivariate Ordinary Least Squares Regression Analyses of Key Predictor Variables, Cumulative Exposure to Disaster-Related Events, and Posttraumatic Stress Symptoms (N = 974),a Global Distress (N = 968),a and Functional Impairment (N = 985)a
Interactions
None of the interaction terms examined were significant predictors of any of the three outcomes.
Exploratory Analyses
Interestingly, the number of participants (n = 250, 24.74%) who endorsed the tsunami as the worst component of the disaster was substantially greater than the number who reported experiencing the disaster as it occurred (n = 48, 4.71%). We conducted post hoc analyses to examine what factors, including direct exposure to the event, might be associated with selection of the worst component. A multivariate multinomial logistic regression identified predictors of one’s appraisal of the worst aspect of the disaster; selecting the earthquake comprised the base category (i.e., served as the comparison group). Earthquake intensity, associated disaster exposure (tsunami and/or looting), secondary stressors (property loss, injury, death), SES, and postdisaster media exposure, were selected as potential correlates, consistent with recent epidemiological research on collective trauma (Holman et al., 2014). To assess media exposure, participants reported, on average, how many hours per day they spent (a) watching TV or listening to radio coverage of the earthquake, tsunami, and their aftermath; and (b) reading books, magazines or newspaper coverage of the earthquake, tsunami, and their aftermath. Responses were averaged (M = 1.77, SD = 2.52, range = 0–12.5) to obtain a mean media exposure score.
Results are reported as relative rate ratios (RRRs), which can be interpreted in a manner similar to odds ratios in logistic regression analyses. Endorsing the tsunami as the worst aspect of the disaster was positively associated with having been at the coast where the tsunami hit (RRR = 3.19, 95% CI, 1.61–6.32, p = .001) and with increased disaster-related media exposure (RRR = 1.04, 95% CI, 0.97–1.11, p = .003); it was negatively associated with directly experiencing the looting (RRR = 0.61, 95% CI, 0.44–0.84, p = .003). Endorsing the looting as the worst component of the disaster was associated with higher SES (RRR = 0.62, 95% CI, 0.53–0.72, p < .001).
DiscussionThe Bío Bío earthquake resulted in a series of traumatic events and mental health consequences for many residents near the epicenter. Logistical difficulties such as obtaining funding and rapid ethics approval typically preclude short-term postdisaster psychiatric epidemiological assessments (Norris, 2006). Nonetheless, early postdisaster assessments may help inform intervention efforts (Bryant & Litz, 2009) by helping to identify at-risk populations, important given the potential benefit of short-term interventions (Bonanno et al., 2010). By collecting data among a demographically representative sample of directly exposed residents shortly after the earthquake, we improve on methodological limitations of prior research and address the charge to use more sophisticated techniques in postdisaster assessments (Bonanno et al., 2010; Kessler et al., 2012). Moreover, our sample closely matched Chilean census benchmarks, strengthening population-based inferences and increasing the generalizability of our findings.
Rapid succession disaster sequences are common yet underexplored in the extant literature (Kessler et al., 2012); the present study addressed this absence and explored the relationship between rapid succession disaster exposure and subsequent responses. We found that specific, but not cumulative, exposure to the earthquake and associated disasters (tsunami, looting) was correlated with negative outcomes. Second, cumulative counts of and specificity in exposure to secondary stressors were both associated with adverse psychological outcomes. Lastly, several demographic predictors elucidated variability in postdisaster responses.
Type of Trauma Exposure
Results advance our understanding of differential effects of exposure to different types of traumatic events. Contrasting results from Tables 4 and 5 highlight the importance that disaster type has on distress responses. Distress was more strongly associated with the specific type (i.e., the tsunami; see Table 4)—rather than with the number (see Table 5)—of disaster components experienced. Although the prevalence of PTSD after natural disasters is typically lower than that occurring after man-made or technological disasters (Norris et al., 2002), prior research has not explored natural disasters compounded by human errors. Results indicated that exposure to the destructive tsunami, occurring despite assurances from the government that the coastal area was safe, had an independent contribution to deleterious outcomes. Negative psychological outcomes have been observed following traumatic events that were another person’s fault (Delahanty et al., 1997); disasters (such as the tsunami) that stem from or are exacerbated by large-scale failures of trusted authorities may be detrimental by a similar process. Our findings thus support theoretical models positing disasters caused or worsened by human failings may elicit greater distress (Baum, 1987). Interestingly, exposure to the looting was not correlated with increased PTS or global distress and was negatively correlated with functional impairment (see Table 4). Perhaps for those who either personally participated in the looting or who knew a friend or family member who did so, the looting instilled a sense of control in an otherwise uncontrollable situation; greater sense of control has been linked with more adaptive functioning (Folkman, 1984).
Results indicate that exposure to specific types of individual-level secondary stressors independently predicts distress (see Table 4). More specifically, experiencing injury after the Bío Bío disaster was more strongly associated with negative outcomes than was experiencing property loss or knowing someone who died. In a related vein, the majority of the disaster-related deaths were caused by the tsunami. Taken together, these findings bolster theories postulating that threat to life, perhaps even more so than loss, drives the emergence of PTS symptoms (Momartin, Silove, Manicavasagar, & Steel, 2004). The looting was also human-perpetrated, but it could not be blamed on a single organization, and the participation of many community members in the looting may have weakened the link between exposure to the looting and negative outcomes.
Cumulative Exposure to Traumatic Events
As illustrated in Table 5, the cumulative number of disaster exposures (earthquake, tsunami, looting) was not associated with negative outcomes. However, cumulative exposure to (i.e., experiencing greater numbers of) individual-level secondary stressors (property loss, injury, and death) was significantly associated with PTS, global distress and functional impairment. The latter finding supports a growing body of research demonstrating that increased exposure to negative life events tends to correlate with subsequent adverse physical and mental health outcomes (e.g., Chapman et al., 2004; Felitti et al., 1998). Postdisaster screenings, clinical intakes, and research endeavors should assess both type and amount of trauma exposure to help identify survivors who might be most at risk for problems.
Individual-Level Characteristics
Several person-level characteristics were linked with negative outcomes. Females were more at risk for psychological problems, as expected (Norris et al., 2002; van Griensven et al., 2006). In contrast to previous findings (Norris et al., 2002), however, middle age and older adults were more susceptible to negative outcomes following the earthquake and its aftermath, highlighting the benefit of nuanced conceptualizations of age effects that consider type of outcome measure (Scott et al., 2013). Lower SES was strongly related to negative outcomes, bolstering growing research linking SES and postdisaster mental health (Garfin & Silver, in press) and identifying an additional population segment to target for interventions. Findings were also consistent with substantial literature linking past mental health problems with postdisaster maladies (Garfin & Silver, in press). Outreach with this population may be particularly important: people with a history of poor mental health are at greater risk for postdisaster distress, yet are also more likely to stop psychological treatments, exacerbating existing problems (Wang et al., 2008).
Cultural Concerns
Short-term epidemiological postdisaster mental health assessments of representative samples, especially those in non-Western nations, are limited. South America’s Pacific Coast is particularly vulnerable to devastating earthquakes; six of the 12 strongest earthquakes have occurred in this region, with Chile experiencing some of the strongest (U.S. Geological Survey, 2013b). Yet few postdisaster studies are conducted in Pacific Latin America; to our knowledge, no prior studies have used epidemiological data to examine reactions to earthquakes there. Possible cross-cultural differences in response to traumatic events highlight the value of conducting international research to understand region-specific reactions, as North American and European models of trauma assessments and interventions do not necessarily translate directly to all cultures (Draguns & Tanaka-Matsumi, 2003). Indeed, rates of psychiatric disorders vary greatly among epidemiological studies in Latin America; for example, Chileans reported lower rates of both trauma exposure and PTSD compared to Mexicans (Zlotnick et al., 2006). Whereas reexperiencing and arousal symptoms of PTS appear to be biologically derived and thus universally experienced, even within the United States, Latinos tend to report more avoidance symptoms, perhaps due to cultural mores promoting individual subordination to group well-being (Zayfert, 2008). This emphasizes the need for culturally specific prevalence rates of postdisaster psychopathology, which are important for estimating postdisaster service needs in a community.
Our results inform the historical record in this highly seismically active region of Latin America by documenting prevalence rates and examining predictors of psychological distress. Findings suggest that factors that tend to correlate with distress in European contexts (e.g., demographics, prior mental health, threat to life) translate to Latin American contexts. Future research should seek to replicate and expand these results in Latin American and other cultures (e.g., Asian, African) to generate a basis for stronger culturally specific clinical outreach and public policy recommendations.
Appraisals in the Postdisaster Context
Although 5% of participants were at the coast when the tsunami hit, almost 25% reported the tsunami and its subsequent flooding as the worst component of the disaster. The tsunami was associated with the greatest number of deaths and the resulting flood water took several weeks to subside, resulting in severe—and long-lasting—structural damage to the community. Other than having been physically present at the coast, the strongest correlate of endorsing the tsunami as the worst component of the disaster was event-related media exposure, highlighting the importance of media exposure as a predictor of postdisaster distress and the importance of the appraisal process following traumatic events (Janoff-Bulman, 1992). Moreover, results support emerging theories and empirical evidence that starkly contrast traditional views of trauma exposure, suggesting that trauma can be experienced vicariously; media exposure, for example, can be a more powerful predictor of stress responses to collective traumas than direct exposure (Holman et al., 2014).
Over a quarter of participants endorsed looting as the worst component of the disaster, which was associated with higher SES. Past research suggests community members from more economically and socially disadvantaged groups are more likely to participate in crimes following natural disasters (Zaharan, Shelley, Peek, & Brody, 2009). Given this, perhaps wealthier participants (and their friends and family members) refrained from engaging in looting activities. Furthermore, the looting may have challenged participants’ former belief in the benevolence or trustworthiness of other community members, a particularly important world view for some (Janoff-Bulman, 1992).
Limitations
Several limitations must be acknowledged. While data were collected in a shorter timeframe than most postdisaster epidemiological research, no assessments occurred within the first month after the earthquake, precluding inferences regarding acute stress reactions. We were also unable to explore change over time. Although we collected data on a sample that was representative of the population from which it was drawn, a portion of those eligible refused the interviews. Nonetheless, our response rate was substantially higher than the 20% typical in face-to-face survey assessments in South America (Jaime Vásquez, personal communication, 2013) and rigorous surveying techniques helped ensure that nonresponse was not primarily a function of degree of exposure to the disaster or demographic characteristics. While the PCL has been validated for use in Spanish, it has not been previously used in epidemiological studies in Chile specifically. Given the link between disaster exposure, reaction to stressors, and physical health problems (Holman et al., 2008), future research should also include objective measures of physical health outcomes. Lastly, because all of our participants were highly exposed to the earthquake, we did not have a no- or low-exposure comparison group, which may have shown disparate patterns of responses (Palinkas, Downs, Petterson, & Russell, 1993).
ConclusionsFindings advance theoretical understandings of postdisaster traumatic stress responses by indicating that specificity in type—rather than only the amount—of trauma exposure predicts variability in distress responses. Assessments that incorporate specific exposures that occur in the context of a larger disaster may improve research, policy, and clinical interventions following community catastrophes. Models that consider cumulative effects of trauma provide gross estimates of how increased trauma exposure may correlate with increased susceptibility to psychiatric maladies (Asarnow et al., 1999). Yet our findings suggest that a more fine-grained approach that considers the type of trauma exposure should also be considered, particularly after natural disasters, where it might be advantageous—and feasible—to identify people based on exposure to different events. Policies could target specific neighborhoods or communities with increased psychosocial services according to the component of the disaster sequence most heavily experienced. For example, communities more heavily impacted by disasters with a man-made component or with greater death tolls could be targeted more aggressively with short-term outreach efforts such as Psychological First Aid (Ruzek et al., 2007), and psychiatric screenings could include questions regarding specificity of disaster exposure.
Future research should continue to explore questions relating to both amount and nature of exposures following negative events, as well as the mechanisms (e.g., subjective interpretations, physiological reactions) behind these responses. Mixed methods that incorporate qualitative interviews may be especially useful in future studies. For example, qualitative interviews that ask participants to report why they felt one component of the disaster was worse than another may help elucidate psychological processes.
Methodologically, our study provides a model for successfully executing population-based short-term psychological assessments in an international context. Important for traumatic stress theory, results illustrate that postdisaster distress is not merely a function of cumulative exposure to traumatic events and secondary stressors, but is likely to be event- and experience-specific. More broadly, findings indicate appraisal of disaster severity may be influenced by factors such as media exposure and individual-level characteristics such as SES. Finally, targeting population segments based on demographic considerations, disaster experiences, and secondary stressors exposure may facilitate effective distribution of postdisaster services with the hope of informing humanitarian outreach efforts following multifaceted, rapid succession community disasters.
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Submitted: August 14, 2013 Revised: June 2, 2014 Accepted: June 3, 2014
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Source: Journal of Abnormal Psychology. Vol. 123. (3), Aug, 2014 pp. 545-556)
Accession Number: 2014-31174-001
Digital Object Identifier: 10.1037/a0037374
Record: 67- Title:
- Facets of anger, childhood sexual victimization, and gender as predictors of suicide attempts by psychiatric patients after hospital discharge.
- Authors:
- Sadeh, Naomi, ORCID 0000-0002-8101-3190. Department of Psychiatry, University of California, San Francisco, San Francisco, CA, US, naomisadeh@gmail.com
McNiel, Dale E.. Department of Psychiatry, University of California, San Francisco, San Francisco, CA, US - Address:
- Sadeh, Naomi, Department of Psychiatry, University of California, San Francisco, Box 0984-CPT, 401 Parnassus Avenue, San Francisco, CA, US, 94143, naomisadeh@gmail.com
- Source:
- Journal of Abnormal Psychology, Vol 122(3), Aug, 2013. pp. 879-890.
- NLM Title Abbreviation:
- J Abnorm Psychol
- Page Count:
- 12
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- The Journal of Abnormal and Social Psychology; The Journal of Abnormal Psychology; The Journal of Abnormal Psychology and Social Psychology
- ISSN:
- 0021-843X (Print)
1939-1846 (Electronic) - Language:
- English
- Keywords:
- anger, gender, sexual victimization, suicide attempts, psychiatric patients, hospital discharge, risk factors, at-risk, abuse history
- Abstract:
- Models of suicidal behavior that assess the interplay of multiple risk factors are needed to better identify at-risk individuals during periods of elevated risk, including following psychiatric hospitalization. This study investigated contributions of facets of anger, gender, and sexual victimization to risk for suicide attempts after hospital discharge. Psychiatric patients (N = 748; ages 18–40; 44% female) recruited from 3 inpatient facilities were assessed during hospitalization and every 10 weeks during the year following discharge as part of the MacArthur Violence Risk Assessment Study. Multiple logistic regression models with facets of anger (disposition toward physiological arousal, hostile cognitions, and angry behavior) from the Novaco Anger Scale (Novaco, 1994), gender, and childhood sexual victimization history were used to predict suicide attempts in the year following hospital discharge. Facets of anger differentially predicted suicide attempts as a function of gender and sexual victimization history, over and above the variance accounted for by symptoms of depression, anxiety, and recent suicide attempts. In men, greater disposition toward angry behavior predicted an overall greater likelihood of a suicide attempt in the year following hospital discharge, particularly among men with childhood sexual victimization. In women with a history of childhood sexual victimization, physiological arousal predicted suicide attempts. Results indicate that facets of anger are relevant predictors of suicide attempts following hospital discharge for psychiatric patients with a history of childhood sexual victimization. Further, results suggest that incorporating gender and victimization history into models of risk for suicide can help clarify relationships between anger and self-directed violence. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Attempted Suicide; *Hospital Discharge; *Psychiatric Patients; *Risk Factors; Anger; At Risk Populations; Child Abuse; Human Sex Differences; Sexual Abuse; Victimization
- Medical Subject Headings (MeSH):
- Adolescent; Adult; Anger; Anxiety; Child Abuse, Sexual; Depression; Female; Hospitalization; Humans; Logistic Models; Longitudinal Studies; Male; Risk Factors; Sex Factors; Suicide, Attempted; Young Adult
- PsycINFO Classification:
- Psychological Disorders (3210)
- Population:
- Human
Male
Female - Location:
- US
- Age Group:
- Adulthood (18 yrs & older)
Young Adulthood (18-29 yrs)
Thirties (30-39 yrs)
Middle Age (40-64 yrs) - Tests & Measures:
- Diagnostic and Statistical Manual of Mental Disorder-3rd Edition-Revised Checklist
Structured Interview for Diagnostic and Statistical Manual of Mental Disorder-3rd Edition-Revised
Brief Psychiatric Rating Scale DOI: 10.1037/t01554-000
Novaco Anger Scale DOI: 10.1037/t02391-000 - Methodology:
- Empirical Study; Quantitative Study
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- First Posted: Jul 8, 2013; Accepted: Mar 22, 2013; Revised: Mar 22, 2013; First Submitted: Aug 12, 2012
- Release Date:
- 20130708
- Correction Date:
- 20130909
- Copyright:
- American Psychological Association. 2013
- Digital Object Identifier:
- http://dx.doi.org/10.1037/a0032769
- PMID:
- 23834063
- Accession Number:
- 2013-24297-001
- Number of Citations in Source:
- 86
- Persistent link to this record (Permalink):
- http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2013-24297-001&site=ehost-live
- Cut and Paste:
- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2013-24297-001&site=ehost-live">Facets of anger, childhood sexual victimization, and gender as predictors of suicide attempts by psychiatric patients after hospital discharge.</A>
- Database:
- PsycINFO
Facets of Anger, Childhood Sexual Victimization, and Gender as Predictors of Suicide Attempts by Psychiatric Patients After Hospital Discharge
By: Naomi Sadeh
Department of Psychiatry, University of California, San Francisco;
Dale E. McNiel
Department of Psychiatry, University of California, San Francisco
Acknowledgement:
Epidemiological surveys indicate that the prevalence of suicide is increasing in the United States (Crosby et al., 2011) and globally (World Health Organization, 2002, 2008). Approximately 1 million adults in the United States report attempting suicide within the last year (Crosby et al., 2011) and over half a million report visiting hospital emergency departments for suicide attempts and suicidal behavior (Centers for Disease Control, 2008). Psychiatric patients are at particularly elevated risk for suicide deaths and suicide-related behavior (e.g., Black, Winokur, & Nasrallah, 1987), and this risk increases following discharge from inpatient treatment (Bongar, 2002; Goldacre, Seagroatt, & Hawton, 1993). For instance, a national survey found that 24% (n = 519) of suicide deaths occurred within 3 months of discharge from a psychiatric hospital (Appleby et al., 1999). Similarly, a prospective study found that 3.3% of discharged inpatients had a suicide death, and over a third engaged in nonsuicidal self-injury or a suicide attempt within 6 months of hospital discharge (Links et al., 2012). These data highlight the need for additional research on factors that increase risk for suicide-related behavior following psychiatric inpatient treatment.
Suicide-related behavior is characterized by a range of thoughts and behaviors that increase the risk of suicide and vary in terms of lethal intent and physical injury (Silverman, Berman, Sanddal, O’Carroll, & Joiner, 2007). Examples include self-injury without the intent to kill oneself, thoughts of suicide, suicidal threats, and attempts to inflict lethal self-injury (G. K. Brown, Beck, Steer, & Grisham, 2000; Nock, Joiner, Gordon, Lloyd-Richardson, & Prinstein, 2006; Silverman et al., 2007). Research indicates that both nonsuicidal (without lethal intent) and suicidal (with lethal intent) ideation and behavior are important indicators for assessing risk for suicide (Fawcett et al., 1997).
Anger is potentially important to consider in models of suicide risk, because it is a symptom that cuts across multiple psychiatric diagnoses shown to be associated with elevated risk for suicide attempts, including posttraumatic stress disorder, borderline personality disorder, and antisocial personality disorder (M. Z. Brown, Comtois, & Linehan, 2002; Krysinska & Lester, 2010; Lieb, Zanarini, Schmahl, Linehan, & Bohus, 2004; Verona, Patrick, & Joiner, 2001; Wilcox, Storr, & Breslau, 2009). Thus, it has the potential to serve as a transdiagnostic indicator of risk for suicidal behavior across disorders with overlapping symptomatology (e.g., Nolen-Hoeksema & Watkins, 2011). Despite the potential relevance of anger as a risk factor for suicidal behavior, research to date has been relatively limited and produced mixed findings. On the one hand, there is a relatively large body of research that indicates anger and aggression are positively related to suicide attempts (Daniel, Goldston, Erkanli, Franklin, & Mayfield, 2009; Esposito, Spirito, Boergers, & Donaldson, 2003; Giegling et al., 2009; Swahn, Lubell, & Simon, 2004; Swogger, You, Cashman-Brown, & Conner, 2011) and suicidal behavior defined more broadly (e.g., nonsuicidal self-injury and suicide attempts; Gormley & McNiel, 2010; Horesh et al., 1997; Horesh, Gothelf, Ofek, Weizman, & Apter, 1999; Swogger, Walsh, Homaifar, Caine, & Conner, 2012). Yet, several studies have not found a reliable relationship between anger and suicidal behavior (Goldston et al., 1999; Horesh, Orbach, Gothelf, Efrati, & Apter, 2003; Kerr et al., 2007; Kingsbury, Hawton, Steinhardt, & James, 1999). Thus, more research is needed to delineate whether and under what circumstances anger confers risk for suicidal behavior. Accordingly, the present study examined whether (a) conceptualizing anger as a multidimensional construct and (b) including theoretically relevant moderators, specifically childhood sexual victimization and gender, into models of risk could further clarify anger relationships with suicide attempts.
Conceptualizing anger as a multidimensional rather than unitary construct may add specificity and clarity to models of anger as a risk factor for suicide-related behavior. Research suggests that anger can be reliably measured as distinct facets that each serve to activate and maintain anger, specifically dispositions toward physiological arousal, angry behavior, and hostile cognitions (Novaco, 1994). As described by Novaco (1994), the arousal facet characterizes the physiological activation and readiness for action component of anger. In contrast, the angry behavior facet describes behavioral manifestations of anger, and the hostile cognitions and attitudes facet indexes thought processes that initiate and maintain anger. Each of these facets may represent distinct anger-related risk factors for suicidal behavior. The physiological arousal facet may be particularly relevant for predicting suicide attempts among individuals with a tendency to experience intense somatic responses to anger, based on research suggesting that suicidal behavior can bring temporary relief from states of heightened arousal (Haines, Williams, Brain, & Wilson, 1995; Nock & Mendes, 2008). Conversely, the angry behavior facet may confer risk for suicide attempts via etiological mechanisms it shares with risk for other-directed violence, such as trait impulsivity, negative emotionality, and serotonin and dopamine dysfunction (Douglas et al., 2008; Seo, Patrick, & Kennealy, 2008; Verona et al., 2001). Additionally, high levels of hostile attributions and attitudes may increase risk for suicide-related behavior through a cognitive vulnerability to repetitive and ruminative thinking similar to that observed in depression. Thus, the anger facets may index heterogeneity in the mechanisms by which anger confers risk for suicide-related behavior, and examining their unique relationships with suicide attempts could provide insight into when a particular component of anger may be most relevant for assessing risk.
One factor that may moderate relationships between facets of anger and suicide-related behavior is trauma history. According to Van Orden, Joiner, and colleagues (Van Orden et al., 2010; Van Orden, Witte, Gordon, Bender, & Joiner, 2008), exposure to traumatic experiences increases risk for suicidal behavior by promoting habituation to fear and physical pain that is necessary for enacting lethal self-injury. Consistent with this theory, a meta-analysis of 37 studies found a mean Cohen’s d effect size of .44 between sexual abuse history and self-directed violence (broadly defined as recurrent suicidal ideation, plans, attempts, or nonsuicidal self-injury; Paolucci, Genuis, & Violato, 2001). Research also indicates that anger shows moderately strong relationships with exposure to traumatic events (Briere & Runtz, 1987; Neumann, Houskamp, Pollock, & Briere, 1996), suggesting that it is a potentially important predictor of self-directed violence in individuals with a trauma history. Novaco, Chemtob, and colleagues theorized that anger is activated as part of a “survival mode” of functioning related to the fight–flight–freeze response following exposure to a traumatic event (Chemtob, Novaco, Hamada, Gross, & Smith, 1997; Novaco & Chemtob, 1998). Events experienced as traumatic are theorized to promote anger by activating dispositions toward heightened physiological arousal, interpreting situations as threatening and engaging in angry behavior (Novaco & Chemtob, 1998). Among the anger facets, the physiological arousal facet shows the strongest relationship with symptoms of posttraumatic stress (Novaco & Chemtob, 2002) and is positively associated with retrospective reports of childhood abuse in community samples (Kendra, Bell, & Guimond, 2012).
Sexual victimization history, in particular, has been linked to heightened anger and suicide-related behavior (Briere & Runtz, 1987; Neumann, Houskamp, Pollock, & Briere, 1996; Paolucci et al., 2001). Further, it may be crucial to understanding risk for suicide attempts in psychiatric patients, given their heightened risk for childhood sexual victimization (Bryer, Nelson, Miller, & Krol, 1987). Research also suggests that sexual victimization may be a particularly salient risk factor for suicide attempts by females (Roy & Janal, 2006; Soloff, Lynch, & Kelly, 2002), partly as a consequence of the higher prevalence of sexual victimization reported by women and girls than men and boys (Beitchman et al., 1992; Martin, Bergen, Richardson, Roeger, & Allison, 2004). Strikingly, the prevalence of sexual victimization is estimated to be 1.5 to 3 times greater in female than male samples (Briere & Elliott, 2003; Finkelhor, 1994; Finkelhor, Hotaling, Lewis, & Smith, 1990). It is important to note that sexual abuse is not necessarily a stronger predictor of suicide attempts in women than men (Molnar, Berkman, & Buka, 2001; Paolucci et al., 2001). Rather, it is the higher prevalence of sexual victimization among females that makes it a particularly important context to consider in relation to suicide attempts in women.
Gender differences in the tendency for men and women to manifest anger inwardly or outwardly (e.g., Verona & Curtin, 2006) may also be relevant for understanding variability in anger relationships with suicide attempts. There is preliminary evidence to suggest that the expression of anger relates differentially to suicide attempts in men and women. A 13-year prospective study of 180 adolescents followed into young adulthood found that high levels of trait anger and outward expressions of anger (e.g., angry behavior) predicted suicide attempts above a diagnosis of major depressive disorder selectively in men, whereas there were no direct effects of anger on suicide attempts in women (Daniel et al., 2009). Proactive aggression was also associated with suicide attempts in male but not female patients in substance-dependence treatment (Conner, Swogger, & Houston, 2009). In contrast, trait anger was not associated with suicide attempts in male offenders when entered into a model with depressive symptoms (Sadeh, Javdani, Finy, & Verona, 2011). Hostile attitudes and attributions were, however, positively associated with suicide attempts in female offenders (Sadeh et al., 2011). Overall, these studies suggest that tendencies toward expressing anger behaviorally may be more predictive of suicide attempts in men, whereas tendencies toward internalized manifestations of anger (e.g., hostile cognitions and attitudes) may be more predictive of suicide attempts in women. Though few, these studies indicate that gender is a relevant moderator to consider when examining anger as a risk factor for suicide attempts.
On the basis of the literature reviewed, in our study we sought to clarify anger relationships with suicide risk by examining anger as a multidimensional construct (a disposition toward physiological arousal, hostile cognitions, and angry behavior) as well as examining childhood sexual victimization and gender as theoretically relevant moderating variables. We hypothesized that gender and sexual victimization would moderate associations of the anger facets with suicide attempts by psychiatric patients in the year following hospital discharge.
First, we expected childhood sexual victimization to strengthen relationships of the anger facets with suicide attempts, based on research indicating that a history of childhood sexual abuse increases anger symptoms (Neumann et al., 1996). We expected this moderation to be particularly strong in relation to the physiological arousal facet, given research linking trauma with physiological arousal and physiological arousal with suicidal behavior (Haines et al., 1995; Kendra et al., 2012; Novaco & Chemtob, 2002). In regard to gender differences, we predicted that suicide attempts in male psychiatric patients would be more strongly associated with the tendency to express anger behaviorally (Conner et al., 2009; Daniel et al., 2009), whereas suicide attempts in female psychiatric patients would be more strongly associated with the hostile cognition facet of anger (Sadeh et al., 2011). We did not make predictions about whether gender would moderate relationships of the physiological arousal facet with suicide attempts, given the lack of previous research on this topic. To assess whether facets of anger predicted above the variance already accounted for by well-established predictors of self-directed violence, we included a measure of depression and anxiety symptoms and recent suicide attempts in the 2 months prior to hospitalization as covariates in analyses.
Method Sample and Participant Selection
Participants were drawn from the MacArthur Violence Risk Assessment Study (MVRS), a longitudinal study of psychiatric patients recruited while hospitalized in one of three acute inpatient facilities (N = 1,136, see Monahan et al., 2001, for a more detailed description of the study methods). Participants were sampled based on age, gender, and ethnicity to ensure a consistent distribution of participants with these characteristics across the three recruitment sites. Individuals who spoke English and received a medical record diagnosis of one or more of the following diagnoses were eligible to participate: schizophrenia, schizoaffective disorder, schizophreniform disorder, dysthymia, mania, major depression, brief reactive psychosis, alcohol abuse, alcohol dependence, substance abuse, substance dependence, delusional disorder, or personality disorder (i.e., only when an Axis I diagnosis was not present). Chart diagnoses were verified by an interview with a research clinician using the Diagnostic and Statistical Manual of Mental Disorders (3rd ed., rev.; DSM–III–R; American Psychiatric Association, 1987) Checklist or Structured Interview for DSM–III–R (SCID) Personality and corresponded with the chart diagnosis in 85.7% of the cases. Discrepancies in diagnosis between the chart and research clinician were resolved by a consultant psychiatrist at each site. The prevalence rates for the diagnoses in the present sample are comparable to nationally representative samples of inpatient discharges (e.g., Banta, Belk, Newton, & Sherzai, 2010). Of the 1,695 eligible participants approached to participate, 71% agreed to participate in the study. Participants completed an initial assessment during the hospital stay and were reassessed every 10 weeks for the year following discharge. All participants were approached to participate in the study within 21 days of hospital admission, and the average amount of time between hospital admission and invitation to participate in the study was 4.5 days. The initial assessment was conducted at any point during the hospital stay, and the median length of hospitalization was 9 days.
This study involved analysis of data collected in the MVRS project, which has been made publically available by the MacArthur Research Network on Mental Health and the Law. Descriptions of how informed consent was obtained and institutional review board approvals for the MVRS are provided elsewhere (Monahan et al., 2001; Steadman et al., 1998). Additional institutional review board approval was not necessary for the present project, as it involved secondary analysis of a publically available, deidentified dataset.
During hospitalization, participants completed the Novaco Anger Scale (Novaco, 1994) questionnaire, and a structured clinical interview was conducted to assess history of childhood sexual abuse and each participant’s psychiatric symptoms on the Brief Psychiatric Rating Scale (BPRS; Overall & Gorham, 1962). Engagement in a suicide attempt was reassessed every 10 weeks in the year following discharge from the hospital. The number of participants with missing data at each follow-up assessment was as follows: 10-week assessment = 99 (11%); 20-week assessment = 113 (12%); 30-week assessment = 164 (18%); 40-week assessment = 185 (21%); 50-week assessment = 186 (21%). During the year posthospitalization, 533 (59%) participants completed all of the follow-up assessments, 159 (18%) participants missed one assessment, 81 (9%) participants missed two assessments, 70 (8%) participants missed three assessments, and 54 (6%) participants missed four assessments. To be included in the present study, participants must have (a) completed all of the measures administered during hospitalization, (b) completed at least three follow-up assessments in the year following hospital discharge, and (c) completed either the 40-week or 50-week follow-up assessment. These inclusion criteria resulted in 93.9% of the sample with data at the final 50-week follow-up assessment and 92.6% of the sample with at least four out of five follow-up assessments completed. Participants lost due to incomplete follow-up assessments were more likely to be male, score higher on dispositions toward angry behavior, score lower on symptoms of depression and anxiety, and were less likely to report childhood sexual victimization. Despite these differences, the effect of attrition on the present findings is likely minimal because the variables the groups differ on are included in the model as predictors and supplemental analyses indicate that results do not change when these individuals are included in analyses versus when they are removed.
The final sample consisted of 748 male (55.6%) and female (44.4%) psychiatric patients ages 18 to 40 (M = 30.0; SD = 6.23). The majority of participants self-identified as White (69.4%), followed by African-American (28.6%), and Hispanic (2%). Alcohol and substance use disorders were the most common diagnoses, n = 557, 74.5% (alcohol abuse: n = 117, 15.6%; alcohol dependence: n = 368, 49.2%; substance abuse: n = 277, 37.0%; substance dependence: n = 368, 49.2%), followed by mood disorders, n = 505, 67.5% (major depression: n = 428, 57.2%; dysthymia: n = 22, 2.9%; bipolar disorder: n = 104, 13.9%; mania: n = 65, 8.7%), and psychotic disorders, n = 145, 19.4% (schizophrenia: n = 115, 15.4%; schizoaffective disorder: n = 44, 5.9%; schizophreniform disorder: n = 2, 0.3%; brief reactive psychosis: n = 4, 0.5%, delusional disorder: n = 4, 0.5%). (Total exceeds 100% due to comorbidity.) Approximately 37% of the sample reported engaging in self-injurious behavior in the two months prior to hospital admission, and approximately 20% of participants reported inflicting self-injury with the intent to cause death (i.e., a suicide attempt).
Assessments and Measures
Facets of anger
The Novaco Anger Scale (NAS; Novaco, 1994) is a self-report questionnaire that was used to index facets of anger disposition related to physiological arousal, hostile cognitions, and angry behavior. The NAS shows good reliability and construct validity in psychiatric, forensic, and nonclinical samples (Hornsveld, Muris, & Kraaimaat, 2011; Jones, Thomas-Peter, & Trout, 1999; Mills, Kroner, & Forth, 1998; Novaco, 1994). Research indicates the NAS total and subscale scores show adequate 1-month test–retest reliability (e.g., rs > .78), concurrent validity with other anger measures (e.g., Aggression Questionnaire, Anger Expression Scale, State-Trait Anger Expression Inventory), and internal consistencies (e.g., alpha coefficients > .80 for the subscales) (Hornsveld et al., 2011; Jones et al., 1999; Mills et al., 1998; Novaco & Chemtob, 2002). Physiological arousal was measured using the 16-item NAS Arousal subscale that assesses physiological activation and readiness for action, including anger intensity, duration, somatic tension, and irritability (M = 37.2, SD = 6.4; Cronbach’s α = .89 for present sample). Disposition toward angry behavior was measured using the 16-item NAS Behavior subscale, which describes behavioral manifestations of anger including impulsive reaction, verbal aggression, physical confrontation, and indirect expression of aggression (M = 29.8, SD = 6.9; Cronbach’s α = .89 for present sample). Hostile cognitions and attitudes were assessed with the 16-item NAS Cognitive subscale, which indexes thought processes that initiate and maintain anger, including attentional focus, suspiciousness, rumination, and hostile attitudes (M = 31.7, SD = 5.2; Cronbach’s α = .79 for present sample). For each item, participants rated how true a statement was of their thoughts, feelings, and behavior on a scale from 1 (never true) to 3 (always true), and items were summed to create the three subscales. Participants completed the NAS during hospitalization, and the Arousal, Behavior, and Cognitive subscales were entered into analyses as hypothesized predictors of later suicide attempts in the year following hospital discharge.
Childhood sexual victimization
During hospitalization, participants were asked “Did anyone ever sexually abuse or assault you?” and participants who endorsed a history of sexual victimization were asked to provide the age at which the sexual abuse or assault first took place. A dichotomous sexual victimization variable was created for analysis based on the presence or absence of sexual abuse or assault before age 18 (1 = history of childhood sexual abuse or assault, 0 = absence of childhood sexual abuse or assault). Approximately 40% of the sample reported a history of childhood sexual victimization (n = 318). Participants were then asked to classify the type of sexual victimization that occurred, in categories that correspond to definitions of sexual violence and victimization used by the Centers for Disease Control (2002). The prevalence of sexual victimization reported in the final sample was 42.5%. The most commonly reported form of sexual victimization was forced sexual intercourse (28.3%), followed by inappropriate touching (21.3%), sodomy (19.1%), attempted intercourse (12.0%), and oral sex (10.6%). Approximately 40% of the participants who endorsed a history of experiencing sexual violence reported that abuse happened “frequently” or “too many times to count,” 12% reported it happened “sometimes,” and another 40% reported it happened “once” or “twice.” Childhood sexual victimization was measured during hospitalization and entered into analyses as a hypothesized predictor of later suicide attempts in the year following hospital discharge.
Depression & anxiety symptoms
Severity of depression and anxiety was assessed with the Brief Psychiatric Rating Scale (BPRS; Overall & Gorham, 1962), a widely used clinician rating scale of psychiatric symptoms. Clinicians rated participants on the severity of symptoms experienced in the previous week on a scale from 1 (none reported) to 7 (very severe) at the time of the assessment, which occurred during hospitalization. On the basis of factor analyses of the BPRS (Overall, Hollister, & Pichot, 1967; Shafer, 2005), a Depression and Anxiety symptom subscale was constructed by summing symptoms of depressive mood, guilt feelings, and anxiety (M = 10.7, SD = 4.6). The Depression and Anxiety BPRS subscale was entered as a covariate in analyses of suicide attempts after hospital discharge, based on research indicating symptoms of depression and anxiety are strong predictors of suicide attempts (Goldston, Reboussin, & Daniel, 2006; Kessler, Borges, & Walters, 1999; Sareen et al., 2005).
Recent suicide attempts (2-month history)
Given that a history of attempting suicide is one of the best predictors of future suicide attempts (Goldston et al., 1999; Joiner et al., 2005), we included a measure of suicide attempts in the 2 months prior to hospital admission as a covariate in analyses that predicted later suicide attempts during the year after hospital discharge. Consistent with the recommendations of Silverman et al., (2007), a suicide attempt was operationalized as a self-inflicted, potentially injurious behavior with a nonfatal outcome for which there is evidence of intent to die, which was assessed via self-report. Recent suicide attempts (2-month history) were assessed by asking each participant if he or she had attempted to hurt himself or herself with the intention of killing himself or herself in the 2 months prior to hospital admission. For purposes of analysis, suicide attempts were coded as a dichotomous variable (1 = recent suicide attempt present, 0 = recent suicide attempt absent). Approximately 20% of the sample reported a recent suicide attempt in the 2 months prior to hospitalization (n = 148). The recent suicide attempt (2-month history) variable was entered as a covariate in analyses of suicide attempts after discharge to the community.
Suicide attempts in the year following hospital discharge
At each 10-week assessment in the year following hospital discharge, participants were asked if they attempted to hurt themselves in the period since the previous assessment. Participants who reported an attempt to hurt themselves were then asked to specify the degree of harm sought by the act. A suicide attempt was operationalized as engaging in an act of self-injury with the intent to die, based on the nomenclature recommended by a panel of experts in suicidology (Silverman et al., 2007). Given that suicide attempts are low base-rate behaviors, suicide attempts over the course of the year following hospital discharge were aggregated into a single dependent variable that was coded dichotomously (1 = suicide attempt present; 0 = suicide attempt absent). The rates of suicide attempts at each follow-up assessment ranged from 3.7% to 5.3% of the final sample (10 week = 5.3%, 20 week = 4.0%, 30 week = 4.4%, 40 week = 3.6%, 50 week = 3.7%). Overall, approximately 17% of the sample reported engaging in a suicide attempt in the year following hospitalization (n = 124).
Data Analysis
Bivariate associations were assessed between (a) dichotomous variables with Phi coefficients and (b) a dichotomous and a continuous variable with point-biserial correlations, and (c) continuous variables with Pearson correlation coefficients. Univariate gender differences were assessed via independent samples t tests or chi-square analysis, depending on the whether the variable assessed was continuous or dichotomous. Hypothesized relationships among the study variables were examined in multiple logistic regression analyses. Gender, childhood sexual victimization, the NAS subscales (Arousal, Behavioral, Cognitive), and their interactions were entered as predictors of whether or not participants attempted suicide during the year following hospital discharge. To examine the influence of the hypothesized predictors above that of previously established predictors of suicide attempts, specifically symptoms of depression and anxiety, and recent suicide attempts (2-month history), these variables were included as covariates in the regression analyses in addition to age. The number of missing follow-up assessments posthospitalization and the period of time assessed posthospitalization were also included as covariates to account for variation in the posthospitalization assessment period across participants, but they were not hypothesized to predict future suicide attempts. Assessment of multicollinearity indicated that predictor intercorrelations (< .80; Leahy, 2000) and tolerance levels (> .20; Gaur & Gaur, 2006) were within acceptable ranges. Odds ratios were calculated to provide a measure of effect size. Predictors and covariates were z-scored before they were entered into the regression model. The figures were created by plotting the predicted probabilities of attempting suicide for each participant against his or her score on the relevant NAS subscale.
Results Descriptive Statistics
Bivariate correlations between the hypothesized covariates, predictors, and suicide attempts in the year following hospital discharge are provided in Table 1 as a reference for subsequent multivariate analyses. The NAS subscales showed moderate to strong positive interrelationships, which indicate they index related but distinguishable facets of anger. Among the anger facets, the tendency to experience physiological arousal and activation (NAS Arousal) correlated most strongly with the occurrence of suicide attempts in the year following hospital discharge and childhood sexual victimization. Recent suicide attempts (2-month history) and childhood sexual victimization also showed the expected positive relationships with the likelihood of suicide attempts in the year following hospital discharge.
Bivariate Relationships Among Covariates, Predictors, and Suicide Attempts in the Year Following Hospital Discharge
Table 2 includes descriptive statistics for the hypothesized covariates, predictors, and suicide attempts in the year following hospital discharge in the total sample and for women and men separately. Approximately 17% of the sample reported one or more suicide attempts in the year following discharge from the hospital. Proportionately more women reported suicide attempts in the 2 months prior to hospitalization than men, χ2(1) = 5.17, p = .027, but no gender differences emerged in the risk of suicide attempts during the year follow-up period. Clinicians rated women as having more severe symptoms of depression and anxiety on the BPRS, t(746) = − 4.10, p < .001, and women reported higher levels of physiological arousal on the NAS than men, t(746) = −2.94, p = .003. Women did not differ from men on the hostile cognitions or angry behavior NAS facets. As predicted, the genders differed by childhood sexual victimization history, with a substantially larger proportion of women than men reporting sexual abuse or assault as children (62.3%) than men (26.7%), χ2(1) = 96.1, p < .001.
Descriptive Statistics for the Final Sample and by Gender
Multiple Logistic Regressions
Results of regressing suicide attempts in the year following hospital discharge on gender, childhood sexual victimization, and the NAS subscales are presented in Table 3. A history of suicide attempts in the 2 months before hospital admission, Wald χ2 = 14.13, p < .001, odds ratio (OR) = 1.40, predicted the risk of attempting suicide after hospital discharge, whereas BPRS Depression and Anxiety Symptoms and age did not. Childhood sexual victimization also positively predicted attempting suicide in the year following hospital discharge, Wald χ2 = 7.84, p = .005, OR = 1.36. In the multivariate model, gender and the NAS subscales did not directly predict risk of attempting suicide above the variance accounted for by recent suicide attempts (2-month history), age, and symptoms of depression and anxiety during hospitalization.
Logistic Regression Models of Gender, Childhood Sexual Victimization, and Anger Facets Predicting Suicide Attempts in the Year Following Hospital Discharge
Gender and childhood sexual victimization moderated the relationships of the NAS subscales with risk of attempting suicide during the year follow-up period. First, a two-way Gender × NAS Behavior interaction emerged, Wald χ2 = 7.92, p = .005, OR = 0.61, that reflected a cross-over effect. Specifically, a disposition to engage in angry behavior positively predicted suicide attempts in men and negatively in women. These relationships were further qualified by a Gender × Childhood Sexual Victimization × NAS Behavior interaction, Wald χ2 = 6.87, p = .009, OR = .63, which is depicted in Figure 1. Follow-up analyses conducted within each gender as a function of childhood sexual victimization revealed that NAS Behavior significantly predicted suicide attempts in the year posthospital discharge for men, Wald χ2 = 5.50, p = .019, OR = 1.76, but not women (p > .15). In men with childhood sexual victimization, a tendency to act aggressively when angry positively predicted suicide attempts, Wald χ2 = 6.98, p = .008, OR = 3.26, whereas it did not in men without a history of sexual abuse or assault (p >.84).
Figure 1. NAS Behavior Predicting the Probability of Suicide Attempts in the Year Following Hospital Discharge. CSV = Childhood Sexual Victimization.
The regression analysis also produced a Gender × Childhood Sexual Victimization × NAS Arousal interaction, Wald χ2 = 4.03, p = .045, OR = 1.53. To disentangle the interaction, follow-up analyses were conducted within each gender. These analyses revealed a significant Childhood Sexual Victimization × NAS Arousal interaction for women, Wald χ2 = 4.29, p = .038, OR = 1.95, but not men (p > .46). As illustrated in Figure 2, physiological arousal positively predicted suicide attempts in women with childhood sexual victimization, Wald χ2 = 4.01, p = .045, OR = 2. 03, but did not in women without childhood sexual victimization (p > .34).
Figure 2. NAS Arousal Predicting the Probability of Suicide Attempts in the Year Following Hospital Discharge. CSV = Childhood Sexual Victimization.
In summary, the NAS facets differentially predicted suicide attempts in the year following hospital discharge as a function of gender and childhood sexual victimization above the variance accounted for by symptoms of depression and anxiety and recent suicide attempts. A disposition toward angry behavior predicted a greater likelihood of suicide attempts in men, particularly men with a history of childhood sexual victimization. In contrast, the arousal facet of anger was particularly important for predicting suicide attempts in women with a history of childhood sexual abuse or assault.
DiscussionThis study investigated whether facets of anger predict suicide attempts in the year following patients’ discharge from psychiatric hospitalization. As hypothesized, facets of anger predicted suicide attempts above the influence of other well-established risk factors, including symptoms of depression and anxiety, and a recent history of suicide attempts, when entered into a model that accounted for the moderating effects of gender and sexual victimization history. Physiological arousal predicted suicide attempts in women who experienced childhood sexual victimization. In contrast, a disposition toward angry behavior predicted suicide attempts in men, particularly those with childhood sexual victimization. In combination, the results suggest that facets of anger have distinct predictive relationships with risk for suicide attempts in the year following hospital discharge. Further, the results illustrate the importance of incorporating gender and sexual victimization into models of risk for self-directed violence.
The association of a disposition toward physiological arousal with increased risk for suicide attempts in the context of sexual victimization extends the growing literature that indicates heightened physiological arousal to stress is a risk factor for suicide and nonsuicidal self-injury (Nock, 2009; Nock & Mendes, 2008). Researchers have theorized that one function of engaging in suicidal behavior is to regulate aversive arousal and negative affect (M. Z. Brown et al., 2002; Nock & Mendes, 2008). However, few studies have directly tested the hypothesis that a disposition toward physiological arousal predicts future suicide-related behavior. Recent work has found that individuals with a history of nonsuicidal self-injury demonstrate increased skin conductance during a distressing task (Nock & Mendes, 2008), and arousal decreases in self-injurers when they imagine engaging in suicidal behavior (Haines et al., 1995). The present results extend these studies by showing that patients with a history of sexual victimization had a greater likelihood of suicide attempts following hospital discharge when they reported increased anger-related arousal, an effect that was particularly strong for women.
In contrast to the results for women, the findings for men indicate that a disposition toward angry behavior predicts risk for suicide attempts following hospital discharge. This finding replicates previous research showing that angry behavior predicts suicide attempts in young adult men but not women (Daniel et al., 2009) and proactive aggression predicts suicide attempts in male but not female patients in substance-dependence treatment (Conner et al., 2009). A disposition toward angry behavior may be more predictive of suicide attempts in men than women as a function of their tendency to have lower levels of behavioral constraint, a personality trait positively associated with angry behavior and suicide attempts (Douglas et al., 2008; Roberts, Caspi, & Moffitt, 2001; Verona et al., 2001).
We did not find that hostile cognitions are a stronger predictor of suicide attempts in women than men, which we expected based on previous work examining these relationships in an externalizing sample of adults (Sadeh et al., 2011). This failure to replicate may reflect differences in the sample composition (i.e., individuals involved in the criminal justice system vs. nonforensic psychiatric patients), though more research is needed to clarify relationships between gender, hostility, and risk for suicidal behavior. Although the present findings extend the growing literature on gender differences in risk factors for self-directed violence, more research is needed to better understand the mechanisms underlying the relationship between anger facets and risk for suicide attempts in men and women.
One contribution of this study that has implications for clinical practice is that it provides preliminary evidence that men and women with a history of sexual victimization may benefit from different treatment interventions. First, the risk conferred by physiological arousal for future suicide attempts in women supports the clinical relevance of teaching at-risk individuals ways to tolerate intense distress and aversive arousal, which is typically elevated among individuals with a history of trauma exposure (Kendra et al., 2012; Novaco & Chemtob, 2002). Thus, the present results support the potential value of the distress tolerance skills taught in dialectical behavior therapy and relaxation-based interventions for reducing risk for suicide attempts, particularly for women who report high levels of physiological arousal. Second, the finding that a disposition toward angry behavior is a predictor of suicide attempts in men with childhood sexual victimization suggests that anger management may reduce risk of suicide attempts in men with a history of sexual abuse. Indeed, meta-analyses conducted on the effectiveness of anger management interventions (e.g., cognitive restructuring, relaxation, skills training) indicate that they are effective at reducing anger and decreasing aggression (Beck & Fernandez, 1998; DiGiuseppe & Tafrate, 2003), though the potential of these interventions to reduce risk for suicide attempts has largely been neglected in the literature. Taken together, our findings suggest that teaching distress tolerance and relaxation skills may help reduce risk for suicide attempts in female psychiatric patients, whereas anger management techniques may help mitigate risk of suicide attempts in male psychiatric patients with a history of sexual victimization.
As with any investigation, this study has potential limitations. First, risk factors for suicide deaths were not studied, which may limit the generalizability of these results to nonfatal suicide attempts. Nonetheless, research indicates that nonfatal suicide attempts are among the strongest predictors of eventual suicide death (Borges et al., 2006), suggesting that the present results may still be relevant to understanding risk for suicide death. Second, our use of a retrospective self-report assessment of sexual victimization experiences is susceptible to recall and social-desirability bias, which may have increased measurement error and decreased our ability to detect certain relationships between sexual victimization and suicide attempts. Research examining the limitations associated with using retrospective measures of adverse events in childhood suggests that this methodological approach has measurement bias, but it can provide relevant information as long as the events assessed are adequately operationalized, do not rely on detailed accounts, and are serious enough to be recalled (Hardt & Rutter, 2004). Measurement of sexual victimization in this study meets these requirements, suggesting it is a useful measure despite the bias inherent in use of a retrospective measure of childhood adversity. Third, the longitudinal nature of this study may have impacted the likelihood that patients would attempt suicide, as there is some evidence that follow-up contacts with patients can influence the risk of suicide-related behavior (Kim et al., 2010; Motto & Bostom, 2001). Also, there is a possibility that attrition affected the findings, given that not all participants completed the follow-up assessments. Fourth, our measure of suicide attempts was based on self-report of the participants and data were not available to assess the reliability of this variable.
We also did not include psychiatric diagnosis as a moderating variable in this study and cannot speak to how diagnostic status may have affected our findings. It may be fruitful to explore whether the anger facets function differentially across psychiatric disorders in future research. For instance, anger may function differently in internalizing (e.g., mood and anxiety disorders) versus externalizing (e.g., substance use and antisocial) disorders in that the former may confer risk for an individual to direct anger inward, whereas the latter may increase the likelihood that an individual will direct anger toward others. However, research suggests that externalizing disorders are associated with risk for suicide attempts when comorbidity with internalizing disorders is accounted for (Verona, Sachs-Ericsson, & Joiner, 2004), which is not consistent with this model. Serotonin dysfunction and poor self-regulation are mechanisms by which anger may increase risk for suicide attempts in psychiatric patients across diagnoses (Douglas et al., 2008; Seo et al., 2008), as empirical evidence converges to suggest that low serotonin functioning and impaired executive functions are associated with both depression and impulsive aggression (Carver, Johnson, & Joormann, 2008). On a related note, it is possible that facets of anger could indirectly index factors that are not specific to anger, but encompass different risk processes than those typically captured by a unitary anger variable, such as trauma reexperiencing or other psychiatric diagnoses. Thus, the interpretation of the findings for the anger facets may not generalize to studies that have examined a more general anger variable. Further research is needed to explicate the external correlates of these facets in relation to anger-related psychiatric diagnoses and risk for suicide attempts.
This study has several strengths, including the use of a prospective design to evaluate risk factors for future suicide attempts, recruitment of a large and clinically relevant sample of psychiatric patients at elevated risk for suicide (Qin, Agerbo, & Mortensen, 2003), and examination of an integrative model of risk for suicide attempts. It extends the literature on risk for self-directed violence by examining anger as a multidimensional construct and adds to the burgeoning body of research that indicates gender and childhood victimization experiences are important moderators to consider when assessing risk for suicide attempts.
In summary, the results of our study support the conclusion that facets of anger and childhood sexual victimization increase the risk of suicide attempts by psychiatric patients after discharge to the community, and that assessment of these issues adds information over and above other well-established risk factors for suicidal behavior. The results suggest that dispositions toward physiological arousal and angry behavior differentially affect risk for attempted suicide in female versus male patients, respectively. Furthermore, the findings indicate that relationships between facets of anger and suicide attempts are strengthened in the context of childhood sexual victimization.
Footnotes 1 Swogger et al. (2012) also investigated self-directed violence (defined as any attempt to hurt oneself with or without suicidal intent) in relation to trait anger in the MacArthur Violence Risk Assessment Study. The present study expands on Swogger et al. by examining how gender and sexual victimization moderate relationships of the NAS anger facets with suicide attempts in the year following hospital discharge.
2 Supplemental analyses were conducted with the most prevalent diagnoses in the sample (i.e., major depression, alcohol abuse–dependence, substance abuse–dependence, and schizophrenia) to examine whether psychiatric diagnosis moderated the results. At the bivariate level, NAS Arousal correlated positively with major depressive disorder (r = .17, p = .001), alcohol abuse–dependence (r = .11, p = .002), and substance dependence (r = .11, p = .002), and negatively with schizophrenia (r = −.15, p = .001). NAS Behavior correlated positively with alcohol abuse–dependence (r = .18, p = .001) and substance dependence (r = .16, p = .001) and was unrelated to major depressive disorder and schizophrenia. NAS Cognitive correlated positively with alcohol abuse–dependence (r = .17, p = .001) and substance dependence (r = .13, p = .001) and was unrelated to major depressive disorder and schizophrenia. The unique and interactive effects of each diagnosis in the prediction of future suicide attempts were assessed in separate logistic regression analyses. A diagnosis of alcohol abuse or dependence predicted a greater likelihood of future suicide attempts, Wald χ2 = 4.26, p = .039, OR = 1.24, whereas a diagnosis of schizophrenia predicted a decreased likelihood of future suicide attempts, Wald χ2 = 12.1, p = .001, OR = 0.56. It is important to note that none of the diagnoses moderated the results reported in this study or produced new findings.
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Submitted: August 12, 2012 Revised: March 22, 2013 Accepted: March 22, 2013
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Source: Journal of Abnormal Psychology. Vol. 122. (3), Aug, 2013 pp. 879-890)
Accession Number: 2013-24297-001
Digital Object Identifier: 10.1037/a0032769
Record: 68- Title:
- Feasibility of text messaging for ecological momentary assessment of marijuana use in college students.
- Authors:
- Phillips, Michael M.. School of Psychological Sciences, University of Northern Colorado, Greeley, CO, US, Michael.Phillips@unco.edu
Phillips, Kristina T.. School of Psychological Sciences, University of Northern Colorado, Greeley, CO, US
Lalonde, Trent L.. Applied Statistics and Research Methods, University of Northern Colorado, Greeley, CO, US
Dykema, Kristy R.. School of Psychological Sciences, University of Northern Colorado, Greeley, CO, US - Address:
- Phillips, Michael M., School of Psychological Sciences, University of Northern Colorado, Campus Box 94, Greeley, CO, US, 80639, Michael.Phillips@unco.edu
- Source:
- Psychological Assessment, Vol 26(3), Sep, 2014. pp. 947-957.
- NLM Title Abbreviation:
- Psychol Assess
- Page Count:
- 11
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- Psychological Assessment: A Journal of Consulting and Clinical Psychology
- ISSN:
- 1040-3590 (Print)
1939-134X (Electronic) - Language:
- English
- Keywords:
- marijuana, cannabis, compliance, ecological momentary assessment, text messaging, self-reported substance use behavior
- Abstract:
- Measuring self-reported substance use behavior is challenging due to issues related to memory recall and patterns of bias in estimating behavior. Limited research has focused on the use of ecological momentary assessment (EMA) to evaluate marijuana use. This study assessed the feasibility of using short message service (SMS) texting as a method of EMA with college-age marijuana users. Our goals were to evaluate overall response/compliance rates and trends of data missingness, response time, baseline measures (e.g., problematic use) associated with compliance rates and response times, and differences between EMA responses of marijuana use compared to timeline followback (TLFB) recall. Nine questions were texted to participants on their personal cell phones 3 times a day over a 2-week period. Overall response rate was high (89%). When examining predictors of the probability of data missingness with a hierarchical logistic regression model, we found evidence of a higher propensity for missingness for Week 2 of the study compared to Week 1. Self-regulated learning was significantly associated with an increase in mean response time. A model fit at the participant level to explore response time found that more time spent smoking marijuana related to higher response times, while more time spent studying and greater 'in the moment' academic motivation and craving were associated with lower response times. Significant differences were found between the TLFB and EMA, with greater reports of marijuana use reported through EMA. Overall, results support the feasibility of using SMS text messaging as an EMA method for college-age marijuana users. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Ecological Psychology; *Electronic Communication; *Marijuana Usage; *Measurement; *Self-Report; Marijuana; Mobile Devices; Text Messaging
- Medical Subject Headings (MeSH):
- Adolescent; Adult; Data Collection; Feasibility Studies; Female; Humans; Logistic Models; Male; Marijuana Abuse; Marijuana Smoking; Mental Recall; Self Report; Students; Text Messaging; Universities; Young Adult
- PsycINFO Classification:
- Clinical Psychological Testing (2224)
Substance Abuse & Addiction (3233) - Population:
- Human
Male
Female - Location:
- US
- Age Group:
- Adulthood (18 yrs & older)
Young Adulthood (18-29 yrs)
Thirties (30-39 yrs) - Tests & Measures:
- Structured Clinical Interview for DSM–IV-Modified Version
Cannabis Use Disorder Identification Test Revised
Rutgers Marijuana Problem Index
Marijuana Use Measure
Timeline Followback
Brief Self-Control Scale
Ecological Momentary Assessment [Appended] DOI: 10.1037/t15771-000
Motivated Strategies for Learning Questionnaire DOI: 10.1037/t09161-000 - Methodology:
- Empirical Study; Quantitative Study
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- First Posted: Apr 21, 2014; Accepted: Mar 4, 2014; Revised: Feb 14, 2014; First Submitted: Jun 6, 2013
- Release Date:
- 20140421
- Correction Date:
- 20151207
- Copyright:
- American Psychological Association. 2014
- Digital Object Identifier:
- http://dx.doi.org/10.1037/a0036612
- PMID:
- 24749751
- Accession Number:
- 2014-14381-001
- Number of Citations in Source:
- 51
- Persistent link to this record (Permalink):
- http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2014-14381-001&site=ehost-live
- Cut and Paste:
- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2014-14381-001&site=ehost-live">Feasibility of text messaging for ecological momentary assessment of marijuana use in college students.</A>
- Database:
- PsycINFO
Feasibility of Text Messaging for Ecological Momentary Assessment of Marijuana Use in College Students
By: Michael M. Phillips
School of Psychological Sciences, University of Northern Colorado;
Kristina T. Phillips
School of Psychological Sciences, University of Northern Colorado
Trent L. Lalonde
Applied Statistics and Research Methods, University of Northern Colorado
Kristy R. Dykema
School of Psychological Sciences, University of Northern Colorado
Acknowledgement: We would like to thank our research assistants from the Motivation and Addiction Research Lab at the University of Northern Colorado for their time and diligence in data collection.
Traditionally, data collection in the area of substance use has relied heavily on biological measures, such as urine screens, and on self-report and recollection of substance use over specific time periods (e.g., 30+ days). Problems that arise in these latter types of assessments include low ecological validity, issues related to memory recall, and patterns of bias in estimating behavior. Alternatively, ecological momentary assessment (EMA) has been utilized for research purposes to gather data in the moment (Shiffman, 2009a; Lukasiewicz et al., 2007) within a participant’s actual environment, using multiple assessments over time (e.g., a several-week period) to provide more real-time information (Shiffman, Stone, & Hufford, 2008). By exploring naturally occurring settings and how these environments influence the behavior in question, a more thorough examination of trends in psychological phenomena and behavioral patterns can be attained (Ferguson & Shiffman, 2011).
One major benefit of EMA lies in the nature of “in the moment” data collection. Traditional retrospective self-report measures often rely on memory recall, which can be problematic, particularly in the area of substance use research. Time can degrade participant recall, leading to incomplete, inaccurate, or biased information (Ferguson & Shiffman, 2011). Global assessments of substance use behaviors can be limiting due to inaccurate reports of behaviors and other bias, such as the tendency for “heaping” or “digit bias,” where participants report estimates of their substance use around rounded values (Shiffman, 2009b). For example, when examining cigarette consumption behaviors among 232 smokers, Shiffman (2009b) found that both participant global self-reports (i.e., the average number of cigarettes smoked at baseline) and timeline followback (TLFB) calendar recall data included digit bias. Participant EMA data showed random distributions of smoking behavior, while the global self-reports and the TLFB data were clumped around rounded estimates much more often than would be expected. Though significant, the correlation between EMA and TLFB reports of cigarette consumption on a day-to-day basis was only 0.29. When comparing daily cigarette consumption reported through EMA and TLFB, participants reported smoking an average of 2.5 less cigarettes per day through EMA. However, participants reported more smoking through EMA on one third of the days monitored. Variations in carbon monoxide measures were associated with self-reported EMA cigarette consumption but not TLFB, demonstrating better validity for reports of momentary information with EMA. Using Shiffman’s (2009b) data, Griffith, Shiffman, and Heitjan (2009) compared EMA and TLFB reports of cigarette consumption using a Bland-Altman analysis (Altman & Bland, 1983). Similarly, cigarette counts were greater through TLFB compared to EMA. The authors concluded that EMA and TLFB measures are not really equivalent for assessing heavy cigarette smoking and that participants likely failed to report some cigarette use through both self-report methods.
Additional studies have examined the validity of EMA for measuring substance use and other behaviors and mood states, though few have compared EMA with established, valid, and reliable measures. A recent study by Serre and colleagues (2012) compared EMA reports of substance use, anxiety, and mood with baseline measures assessing substance use severity (Addiction Severity Index [ASI]; McLellan et al., 1992), anxiety (Beck Anxiety Inventory [BAI]; Beck, Epstein, Brown, & Steer, 1988), and depression (Beck Depression Inventory [BDI]; Beck, Ward, Mendelson, Mock, & Erbaugh, 1961) among treatment-seeking tobacco, marijuana, opiate, and alcohol users. The 2-week EMA data, assessed with personal digital assistant (PDA) devices, was generally consistent with the baseline measures and showed significant correlations between substance use and ASI scores, state anxiety and BAI scores, and state mood and BDI scores. Participants were given 20 min to respond to signal prompts. Overall response rate was good (83%), though the duration of EMA assessment completion (i.e., the time it took to respond to all of the EMA questions) decreased over the 2-week EMA monitoring period, possibly due to a practice or fatigue effect (Serre et al., 2012).
Substance use research utilizing EMA has focused almost exclusively on tobacco/cigarette and alcohol use. Illicit drugs have been examined less frequently, often due to concerns about compliance with time-consuming protocols and equipment breakage or loss (Shiffman, 2009a). A study with homeless crack cocaine users in treatment (Freedman, Lester, McNamara, Milby, & Schumacher, 2006) followed participants for 2 weeks and demonstrated good compliance (86%) with the extensive 2-week protocol, with only one cell phone out of 30 not returned. Epstein and colleagues (Epstein et al., 2010a, 2010b, 2009; Preston et al., 2009) followed cocaine and heroin users enrolled in methadone treatment for a lengthy period (up to 20 weeks) using PDAs with random and event-contingent prompts. Relatively good compliance was reported with random prompts (75%). Other studies have included polysubstance users, including those using ecstasy and other substances (Hopper et al., 2006), and treatment samples consisting of cocaine, opiate, marijuana, and alcohol users (Johnson et al., 2009). Across these studies, there appears to be minimal concerns about equipment loss or completion of the protocol.
Despite marijuana being the most commonly used illicit drug in the United States (Substance Abuse and Mental Health Services Administration [SAMHSA], 2012), limited research has been conducted using EMA with marijuana users. Out of the select studies that have been done, only one assessed feasibility (Serre et al., 2012), and it demonstrated an 80% compliance rate among marijuana users completing a 2-week EMA protocol. The majority of EMA marijuana studies have focused on patterns of marijuana use, predictors of use and craving, and the influence of in-the-moment affect (mood, anxiety) on marijuana use (Buckner, Crosby, Silgado, Wonderlich, & Schmidt 2012; Buckner et al., 2011; Johnson et al., 2009; Shrier, Walls, Kendall, & Blood 2012; Tournier, Sorbara, Gindre, Swendsen, & Verdoux, 2003).
Regardless of type of substance or other behavior assessed, EMA data collection ranges from low-tech (e.g., paper-and-pencil diaries) to high-tech (electronic diaries, PDAs, interactive voice response or IVR systems, and cell phones) options, with some studies combining methods. Each method introduces unique benefits and limitations as methods of assessment and data collection tools. Paper diaries often require participants to complete a number of written entries per day (Trull & Ebner-Priemer, 2009). Typically, participants are asked to follow a set schedule and fill out the provided assessments according to the schedule or complete the diary when the behavior of interest occurs (self- or event-initiated). Paper diaries have limitations due to lack of compliance with the schedule. Rather than completing entries per the research protocol when a specific phenomenon occurs, participants have been known to fill out the assessments at a different time using recall information (Ferguson & Shiffman, 2011; Stone, Shiffman, Schwartz, Broderick, & Hufford, 2003). In one study assessing chronic pain, researchers found that participants self-reported completing their paper diary entries 90% of the time, but upon examining the actual opening and closing of the diary binder through a fitted sensor, the researchers found that compliance was only 11% (Stone, Shiffman, Schwartz, Broderick, & Hufford, 2002; Stone et al., 2003). To improve compliance, some researchers have tried prompting or signaling participants to complete paper diary assessments (Broderick, Schwartz, Shiffman, Hufford, & Stone, 2003). Unfortunately, although this can increase compliance, compliance rates do not reach levels seen with electronic diaries (Broderick et al., 2003). Furthermore, participants may be dishonest about when they complete assessments. One study utilizing signal-contingent paper-based recordings with alcohol-dependent clients found that 70% of debriefed participants admitted to lying about the time and date they completed the assessment (Litt, Cooney, & Morse, 1998). This information is problematic given that EMA is designed to measure behavioral and affect variables in as close to the moment as possible.
Using electronic devices such as PDAs, Palm-Pilots, and smartphones for EMA can provide a host of benefits, including the use of signal prompting and an automatic date and time stamp when entries are made (Trull & Ebner-Priemer, 2009). Many of the features provided by such electronic devices can affect compliance with the EMA protocol, which impacts data quality and generalizability. Researchers using EMA with substance-using populations have generally reported adequate to high compliance with PDAs. Using PDAs with heavy drinkers, Collins et al. (1998) achieved close to 85% compliance with an 8-week protocol, while a 2-week EMA protocol by Hufford and colleagues (2002) yielded 86% compliance with college student drinkers. Smoking research (e.g., Shiffman, Paty, Gnys, Kassel, & Hickcox, 1996) has shown similar compliance rates. Piasecki et al. (2011) found that alcohol–tobacco co-users responded to 79% of random prompts over a 21-day EMA period. Use of PDA technology with cocaine users (Epstein & Preston, 2010b) yielded compliance rates between 77% and 81%, with higher participant compliance occurring during periods of abstinence. Research assessing marijuana use via PDAs has found lower compliance, with one study with college students yielding compliance rates around 62% (Buckner et al., 2011) for random prompts and another with adolescent/young adult community residents (Shrier et al., 2012) demonstrating a response rate of 70%. A study by Serre and colleagues (2012) in France found that treatment-seeking marijuana users responded to 80% of PDA prompts.
Cell phone technology has been utilized in more recent work. In one study with alcohol/tobacco users, researchers used cell phones paired with interactive voice response (IVR) systems and yielded an EMA compliance rate of 65% (Holt, Litt, & Cooney, 2012). With IVR, participants are typically called by the system at random times throughout the day to respond to questions by phone using a numeric keypad. Another EMA feasibility study (Freedman et al., 2006) utilized computer-automated cell phone interviews with homeless adults in treatment for crack cocaine misuse and found 86% compliance. Although not a substance use study, Courvoisier, Eid, and Lischetzke (2012) examined EMA compliance rates of mood reports utilizing a cell phone–based voice response system with Swiss university students and university graduates. A total response rate of 75% over the 7-day EMA period was calculated, with compliance rates varying throughout the course of the week.
Limited substance use studies have utilized short message service (SMS) texting as a method of EMA data collection with cell phones. Kuntsche and Robert (2009) used SMS texting and Internet assessment to examine drinking behavior among young Swiss adults. Though an overall response rate was not calculated, the researchers were able to retain 75% of participants across the study. Berkman, Dickenson, Falk, and Lieberman (2011) used SMS texting with participants’ personal cell phones to examine cigarette lapses, craving, and mood among 31 heavy smokers trying to quit. An overall response rate of 84% was calculated, with the majority of participant responses (80%) sent within 23 min of receiving the EMA text.
Although compliance rates are generally adequate when using electronic devices for EMA, other issues impacting data quality should be considered. Because EMA is time consuming, researchers should consider the burden threshold associated with specific protocols. Asking participants to respond to multiple prompts with numerous questions daily likely impacts their willingness to respond accurately or at all. Researchers must consider the length of expected responses so that participants are not overwhelmed. As discussed by Courvoisier et al. (2012), compliance might be related to individual differences in participants (e.g., gender, personality traits) or measurement times (day of week or time of day). Because EMA results in substantial amounts of data, it is inevitable that most participants will have some degree of missing data (Smyth & Stone, 2003). If certain patterns are noted for missing data, these need to be corrected for in the data analysis.
Issues of burden and data missingness might be better controlled with specific types of EMA and technology. Because substance misuse is common among young adults, there are considerable advantages to the use of cell phones, especially SMS texting, as a method of EMA. It is estimated that approximately 94% of Americans use a mobile phone, with almost two thirds owning a smart or multimedia phone, and 85% utilizing text messaging (Nielsen Mobile, 2013). At least 98% of college students report owning a cell phone, with mean usage of approximately 4 hr per day (Diamanduros, Jenkins, & Downs, 2007). When examining texting, Raacke and Bonds-Raacke (2011) found that over 88% of college students had a cell phone plan that contained SMS texting services, with the majority having a plan that contained unlimited texting. Participants reported sending an average of 40 texts and receiving an average of 44 texts on any given day. It is possible that using text messaging as a form of EMA with young adults may increase response rates and accuracy simply because the population is very familiar with this method of communication.
Certain limitations may be better addressed by utilizing SMS texting with participants’ personal cell phones. Purchasing electronic equipment (e.g., PDAs, pagers, cell phones) can be costly to researchers, and there is always the risk that participants may misplace, lose, or damage research equipment, which would require further funds dedicated to replacing equipment. If researchers must purchase equipment, this also limits the number of individuals who can participate in a study at any given time. Providing an electronic device to participants that they don’t normally carry runs the risk of them forgetting that device, which can lead to missing data. Using a participant’s personal cell phone could address replacement issues and possibly improve compliance and response times.
Past substance abuse studies have found a range of different response/compliance rates across EMA data collection methods, with rates ranging from roughly 62% to 86%. Few studies have examined the use of EMA with marijuana users, and none (to our knowledge) have utilized SMS texting with participants’ personal cell phones. The goal of the present study was to assess the feasibility of SMS texting as a means of EMA with college student marijuana users. We aimed to examine response/compliance rates, response times, any patterns of data missingness, baseline measures that could be used to predict compliance or response times, and the difference between EMA and TLFB when reporting marijuana use.
Method Participants
Forty-eight participants were recruited between March 2011 and April 2012 from a midsized western university. To be eligible to participate, students had to (1) be over the age of 18, (2) be enrolled at the university for a minimum of one prior semester, (3) report using marijuana at least 2 days per week, (4) report that their last marijuana use was within the last week, (5) test positive on a marijuana urine screen, and (6) own a cell phone with text messaging capabilities and understand that standard text messaging rates would apply if they did not have an unlimited texting plan. Data for this study were collected before the passing of Amendment 64 legalizing recreational use of marijuana in the state of Colorado.
Participation in the study was anonymous, in that participants were not asked for last name or university identification number. However, for the EMA protocol, researchers did have access to participants’ phone numbers, which were kept confidential. Upon conclusion of all EMA texts, participants’ cell phone numbers were deleted from the web-based text-messaging service and any paper documents used for contact purposes were destroyed.
Of the 48 participants, one participant was excluded from all data analyses. This participant stopped responding halfway through the study due to his cell phone becoming disconnected and did not present for the follow-up assessment. The overall study completion rate was 98%. The final sample consisted of 29 females and 18 males with an average age of 19.74 (SD = 2.22). Although age ranged from 18 to 33, all participants except one were between 18 and 22. The racial/ethnic breakdown of the sample was 81% Caucasian, 4% African American, 4% Latino/Hispanic, 4% Native American, 4% Other, and 2% Asian. Participants were on average sophomore status (range was freshmen to seniors) at the university, with 49% living on campus in the residence halls. There was a wide variation of majors represented (17% education; 15% science, nursing, or pre-health; 15% psychology; 15% other social science; 11% business/marketing; 6% undeclared; 21% other). Mean cumulative grade point average (GPA) was 2.85 (SD = 0.69), with a range from 0.80 to 4.00. Demographics from study participants were representative of the campus community where the data were collected.
Procedure
Participants were recruited through flyers posted around campus, announcements made in lower level undergraduate psychology and science courses, and an e-mail sent to all students living in the residence halls. A brief screening interview was used to determine prospective participants who were eligible for the study. All eligible participants were then scheduled for a baseline appointment at a separate time where they completed informed consent followed by a single-panel marijuana urine dip test (Redwood Toxicology Laboratory). Participants then met with a trained research assistant to complete a structured interview and a series of self-report measures. The initial baseline appointment lasted approximately 60 min. At the end of the baseline appointment, participants were trained on the EMA protocol and were informed that they would receive three text messages randomly throughout the day for the next 14 days. Prior to leaving the lab, participants were sent a practice text message to verify that they received it and to address any potential questions about the protocol.
Participants were asked to respond to a series of signal-contingent questions in the form of text messages (also known as short message service; SMS), with 42 prompts sent over a 2-week period. Participants were signaled on their personal cell phones three times per day randomly within three time blocks (8:00 a.m.–12:00 p.m., 12:30 p.m.–4:30 p.m., and 5:00 p.m.–10:00 p.m.) with the same nine questions, which were sent through two text messages (due to length). A texting schedule for the 14-day EMA period was developed for each participant by randomly selecting an EMA time (in 30-min increments) using a randomization chart. Texts were sent through a text messaging service (www.redoxygen.com). Using this service, participant responses were time-stamped and later downloaded by the researchers. Participants were not sent a reminder text if they did not respond to the initial text message. The next text message was sent at the scheduled time in the next time block based on the randomization schedule. To establish feasibility and get a sense of general responsiveness, all text message responses received from participants were counted as a response as long as they were received before the next prompt was sent.
EMA questions (see Appendix for questions and SMS shorthand) focused on participants’ current activity, academic motivation, craving for marijuana, marijuana use and frequency since last text message, social setting where marijuana was last used (i.e., alone or with others), and learning behaviors (e.g., time spent studying). The term marijuana was not used in any of the text messages, and participants were asked to use smoking as their reference to marijuana use when responding to the signals. Participants were given a small laminated card with the nine questions in their entirety along with the shortened SMS version that would be sent for each text message. Participants were asked to respond to all nine questions for each texting instance by numbering their responses to correspond with the questions.
All participants received their first text message for the EMA protocol at a random time the morning after their baseline appointment. A class schedule was collected from each participant to ensure that texts were not sent during class meeting times; however, text messages were sent with no regard to participants’ work schedule. Participants were instructed to respond to each text message immediately when possible and appropriate.
Following the 14-day EMA portion of the study, participants were scheduled for a follow-up appointment and sent a text message reminder the day before their appointment. Participants returned to the lab within 1 week of their last text message (average 5.96 days) and met with a research assistant to complete the 30-day TLFB assessment of their marijuana use. Though research assistants were not blind to overall study goals, they were not provided with details surrounding the researchers’ hypotheses on EMA–TLFB comparisons and did not have knowledge of how participants responded to marijuana use questions from their EMA responses. Once the TLFB was completed, participants received a $30 gift card as compensation for their participation. Compensation was not contingent upon type or rate of text-messaging responses.
Measures
Demographics
Participant gender, age, race/ethnicity, year at university, major, and living situation were self-reported through a questionnaire at the baseline appointment. Participants were asked to sign in to their unofficial university transcript so the interviewer could verify their cumulative and past semester GPA.
Substance use
Marijuana use was assessed using several validated measures and interview questions designed by our research group (discussed below). A single-panel marijuana urine dip test (Redwood Toxicology Laboratory) was used to confirm marijuana use for participant eligibility. In addition, DSM–IV cannabis abuse and dependence criteria according to the Diagnostic and Statistical Manual of Mental Disorders (4th ed.; DSM–IV;American Psychiatric Association, 1994) were assessed using a modified version of the Structured Clinical Interview for DSM–IV (SCID; First, Spitzer, Gibbon, & Williams, 2002).
Cannabis Use Disorder Identification Test Revised (CUDIT-R; Adamson et al., 2010)
The CUDIT-R is an eight-item measure shown to have high internal consistency (α = .91) and discriminant validity (Adamson et al., 2010), though we found a lower Cronbach’s alpha for the scores on this measure in the current study (α = .68). Items assess the frequency of cannabis use behaviors and consequences on a 0–4 scale. Scores range from 0–32, with a preliminary cutoff score above 13 indicating possible problematic use.
Rutgers Marijuana Problem Index (RMPI; White, Labouvie, & Papadaratsakis, 2005)
Adapted from the Rutgers Alcohol Problem Index, the RMPI contains 23 items that examine consequences associated with the use of marijuana (e.g., went to work high, neglected responsibilities) on a 0–3 scale. Data from a past study (Simons et al., 1998) indicates that the RMPI is internally consistent (α = .86). We found a similar Cronbach’s alpha for scores on this measure in the current study (α = .84).
Marijuana use measure
Created for use in this study, interview questions assessed marijuana use frequency (e.g., number of days used in last month, number of times used per day), primary method of ingestion (smoking via pipe, joint, etc., or oral use), history of marijuana use, and reasons for any medical prescription usage (legal in state of Colorado) during the baseline assessment.
Timeline followback (TLFB; Sobell & Sobell, 1996)
A TLFB calendar was completed at the 2-week follow-up to examine recall of daily number of marijuana use instances over the last 30 days. As a reliability check, we compared total daily marijuana use instances on this measure to EMA reports of marijuana instances for consistency.
Brief Self-Control Scale (BCS; Tangney, Baumeister, & Boone, 2004)
This 13-item measure examines individual differences in self-control and has been shown to be associated with higher GPA and better psychological adjustment (Tangney et al., 2004). The items for this measure (e.g., “I do certain things that are bad for me, if they are fun”) are endorsed on a 5-point scale ranging from 1 (Not like me at all) to 5 (Very much like me). Cronbach’s alpha was .73 for scores on this measure in the current study.
Motivated Strategies for Learning Questionnaire (MSLQ; Pintrich, Smith, Garcia, & McKeachie, 1991)
The MSLQ includes 15 subscales and is designed to examine student motivational orientation and learning strategies. For the purposes of this study, we examined the Metacognitive Self-Regulation subscale (MSLQ-SR; α = .79), which consists of 12 items, on a 7-point scale ranging from 1 (Not at all true of me) to 7 (Very true of me). Items focus on students’ planning, monitoring, and regulating of academic behaviors in a particular course. We adapted the questions to focus on all coursework instead of just one course (α = .75 for scores on this measure in the current study).
Data Analysis
Compliance focused on the proportion of participant responses to the 42 text messages sent over the 2-week protocol. For each participant, the total number of texts sent, the total number of texts received, and the time between text sent and text received were recorded overall, by time of day, by day of the week, by day of the EMA study (i.e., Day 1 through 14), and by week of the EMA study (i.e., Week 1 or 2). Time to respond focused on the length of time that it took participants to respond to all nine questions once they received the signal-contingent prompt, which were responded to in a block (i.e., not item by item). In order to establish general responsiveness, we counted all responses that were received before the next prompt was sent to participants. For all analyses, SAS Version 9.3 was used.
EMA response time
We report descriptive statistics (means, standard deviations, medians) to summarize response times. Mean response time was modeled using correlated log-linear regression with age, gender, MSLQ-SR, BCS, RMPI total, and CUDIT-R, while adjusting for week of participation in the study (i.e., Week 1 or 2) and day of the week. A Poisson relationship was applied to account for the skewness in response time, and generalized estimating equations (GEE) was used to estimate parameters. A second log-linear model was fit, using average response time (per participant) as the response, and aggregated EMA predictors (total time spent studying, total time spent smoking, average craving, and average academic motivation) to examine whether aggregated EMA data showed any association with response time at the participant level.
EMA compliance/response rate and data missingness
We report descriptive statistics (means, percentages) to summarize response rates. To model the probability of nonresponse, a hierarchical logistic regression model was fit to assess the significance of probability of missingness across age, gender, MSLQ-SR, BCS, RMPI total, and CUDIT-R, day of the week, and week of participation in the study, similar to the model of Courvoisier et al. (2012). An additional normal error term was included at the participant level to account for repeated observation of individuals. A second model was fit, using total number of responses (per participant) as the response, and aggregated EMA predictors: total time spent studying, total time spent smoking, average craving, and average academic motivation. For this model a Poisson distribution was applied to account for the skewness in number of responses.
EMA versus TLFB reports of marijuana use
A comparison of EMA and TLFB reports of marijuana use was made using descriptive statistics such as mean EMA total usage, mean TLFB total usage, and the mean difference (TLFB – EMA). Correlation between the totals was calculated, and the distribution of the difference in total reported values was investigated.
Results Substance Use
Based on baseline interview data, participants were active marijuana users, with 53% reporting use of marijuana at least once daily (34% of the total sample reported using more than once per day). At baseline, participants self-reported using marijuana an average of 25.43 days (SD = 5.42) out of the last 30 days, with an average use of two times daily (M = 2.22, SD = 1.22). Similarly, TLFB data collected at the 2-week follow-up indicated that the average number of marijuana use instances was 2.19 per day. Mean age of first marijuana use was 15.29 (SD = 0.25) years, with average age of weekly use at 17.42 (SD = 1.78). In terms of DSM–IV diagnoses, 30 individuals (64%) met criteria for cannabis dependence, and 10 (21%) met criteria for cannabis abuse.
All participants reported smoking marijuana, with most (62%) using a small pipe as their primary method to smoke. An additional 21% reported smoking primarily with a bong, water pipe, or bubbler, and 6% reported smoking marijuana as a joint or blunt. Two participants reported holding a current prescription for medical marijuana, which is legal in the state of Colorado. Both of these participants reported having a valid prescription due to pain but also noted that their use was recreational.
EMA Response Time
Due to the nature of EMA and the goal of gathering data “in the moment,” mean and median response times were calculated (see Tables 1 and 2). During their baseline appointment, participants were instructed to respond as quickly as possible to the text messages from the study. The overall mean response time was 44.41 min (Mdn = 11 min). Average response times by day ranged from 39.65 min for Saturday to 49.24 min for Tuesday (Mdns ranged from 7.5 min on Monday to 16.50 min on Saturday). In general, fastest median responses were received on Monday and Friday; slowest median responses were received on Saturday and Sunday. Saturday showed the lowest mean response time but the largest median response time, indicating fewer unusually long response times but a greater number of moderately long response times.
Response Rates and Response Times (in Minutes) by Day of Data Collection
Response Rates and Response Times (in Minutes) by Day of the Week
We gathered data on response time throughout the study to examine trends. Mean response time was 43.60 min (Mdn = 10.00 min) for the first week of the study and 45.24 min (Mdn = 14.00 min) for the second week. Mean response times across the 14 days of data collection ranged from 34.50 min for Day 2 to 60.91 min for Day 12 (Mdns from 7.00 min on Day 2 to 19.00 min on Day 12). Response time by time of day varied, with a mean time of 65.17 min for the morning texts (Mdn = 26.5 min), 33.51 min for the afternoon texts (Mdn = 8.00 min), and 34.27 min for the evening texts (Mdn = 7.00 min). The percentage of responses received within 5, 15, 30, 60, and 120 min of the signal-contingent prompt were calculated (38.1%, 53.9%, 64.7%, 76.8%, and 88.1%, respectively). A small number of participants (0.8%) responded after a 4-hr period of receiving the prompt.
A correlated log-linear regression model was fit using gender, age, CUDIT-R, BCS, RMPI total, and MSLQ-SR, adjusted for day of the week and week of participation in the study, to predict mean response time. Day of the week was included using dummy variables for all days except Monday, the reference day. GEE was applied using the exchangeable working correlation structure to account for the repeated observation of participants, and a Poisson mean-variance relationship was applied to account for the skewness in response times. In this model, Wald statistics using the empirical standard error showed that there was significance for the MSLQ-SR (p = .031, z = 2.08), with higher values of MSLQ-SR associated with higher response times, and for age (p = .012, z = −2.53), with older participants demonstrating lower response times. However, when the one participant over age 22 was removed from the analysis, age no longer showed significance (p = .128, z = −1.52), suggesting this participant represents an influential observation. No other variables showed significance. A second log-linear model was fit at the participant level, using average response time per participant as the response and including as predictors total time spent studying, total time spent smoking, average craving, and average academic motivation. All variables showed significance (p < .001), χ2(1) > 190. An increase in total minutes smoking was associated with higher response times, while increases in total minutes studying, average motivation, and average craving were all associated with lower response times. The estimated effect of craving leading to lower response times may be attributed to the collinearity that exists between craving, motivation, and minutes smoking. However, variance inflation factors for these independent variables all remained less than 1.5, suggesting the associations among independent variables are not affecting inferences in the model.
EMA Compliance/Response Rate and Data Missingness
Over the 14-day EMA period, a total of 1,942 texts were sent out to the 47 participants, and 1,725 responses were received, resulting in an overall response rate of 88.83%. Response rates were examined throughout the course of the EMA study. Close to one fifth of participants (19.15%) responded to every text message prompt sent, while almost half (48.94%) missed two or less. When examining response rate across the 14-day period, compliance ranged from 85.00% to 95.68% (see Table 1), with the highest response rate occurring on Day 2 of the 14-day EMA period and the lowest response rate occurring on Day 14 of the EMA period. Average response rate over the first week of the EMA trial was 90.32%, while over the second week it was 87.33%.
We calculated the response rate by day of the week (see Table 2) and time of day to investigate further trends in EMA response. Participants began the EMA protocol on different days of the week. Wednesday yielded the highest response rate, at 91.54%, and Friday the lowest, at 85.93%. The weekend response rate (Saturday and Sunday) was lower, at 87.57%, compared to the weekday response rate (89.34%). When examining participant responses by time of day, we found rates of 88.30% for the morning texts, 88.55% for the afternoon texts, and 89.67% for the evening texts.
In addition to the descriptive compliance rates reported by week, day of data collection, day of the week, and time of day, a hierarchical logistic regression model was fit to assess the significance of probability of missingness across gender, age, CUDIT-R, BCS, RMPI total, MSLQ-SR, day of the week, and week of the study, similar to the model of Courvoisier et al. (2012). An additional normal error term was included at the participant level to account for repeated observation of individuals. Day of the week was included using dummy variables for all days except Monday, the reference day. According to this model, none of the baseline measures were associated with a significant change in probability of missingness. The week of participation (i.e., first or second week) showed evidence of significance (p = .033), t(1507) = −2.14, with a higher probability of missingness during the second week of the study. Day of the week did not show significance.
A second model was fit, using total number of responses (per participant) as the response and including as predictors total time spent studying, total time spent smoking, average craving, and average academic motivation. A Poisson log-linear regression model was applied due to the skewed count. No variables showed significance at the .05 level. Thus, the data were determined to be missing at random.
EMA versus TLFB Reports of Marijuana Use
We compared EMA data with TLFB data to determine whether participants reported a similar number of marijuana use occurrences. The average number of marijuana use instances was calculated for each of the 14 days of EMA (totaled by day) and then compared to TLFB daily totals reported at the follow-up for the same 14-day period that overlapped with the EMA period. When examining whether participants reported uniformity across each day, only one participant reported the same number of marijuana instances every day on the TLFB. However, no participants reported uniform daily marijuana consumption based on the 14-day EMA data.
Several analyses were conducted to investigate trends in participant reports of the number of daily marijuana use occurrences. First, we examined the total marijuana use instances for the TLFB and EMA reported across the entire 14-day period. Total EMA values over the 14 days ranged from 7 to 127, with an overall average of 35.25 (SD = 22.59) occurrences across participants. Total TLFB values over the same 14 days ranged from 5 to 98, with an overall average of 30.61 (SD = 20.62) occurrences across participants. The Pearson correlation between the totals was significant (r = .851, p < .01, n = 39). Next we calculated the percentage agreement between the TLFB and EMA responses for the 630 direct daily comparisons. A total of 180 responses were exact matches (28.57%). Most of the inconsistencies (65.8%) showed that participants reported smoking less on the TLFB. Interestingly, 26% of the reported occurrences on the TLFB were different from those on the EMA by three or more smoking occurrences, while approximately 6% of the reported occurrences differed by five or more. We examined days that participants reported no marijuana use on the TLFB and found that these reports did not match with EMA reports of marijuana use 16% of the time.
We examined the difference between TLFB totals and EMA totals (TLFB – EMA) for each participant. These differences ranged from –53 (underestimated in the TLFB) to 22 (overestimated in the TLFB), with an average of –8.66 (SD = 12.30). This average suggests the mean difference is significantly different from zero (p < .01), t(39) = −4.39. The total values matched for only two participants; in 29 cases the TLFB total was smaller, while in eight cases the EMA total was smaller. Overall, our findings suggest that participants reported less marijuana use through the TLFB compared to the EMA.
We also examined the relationship between days elapsed between the end of the EMA and the completion of the TLFB at the follow-up, and the size of the difference between the TLFB and the EMA. The Pearson correlation showed no significance (r = .02, p = .88, n = 39), and the scatterplot showed little trend. This lack of association suggests that, as the elapsed days grow, the differences between EMA and TLFB values do not follow a discernible trend. Overall this suggests little association between time elapsed and the difference found between the TLFB and the EMA.
DiscussionBased on our review of the literature, this is the first study to use SMS texting as a form of EMA exclusively with heavy marijuana users. Our data suggest that text messaging with university marijuana users can result in high compliance, with few noted differences. Overall, we found that compliance averaged almost 89%, with a range of 85% to 96% across the 2-week EMA period. Prior EMA studies using PDAs with adolescent and young adult marijuana users have reported compliance rates from 62% to 80% (e.g., Buckner et al., 2011; Serre et al., 2012; Shrier et al., 2012). Compared to these studies, we had an overall higher compliance rate, but all three of these studies had more extensive signal prompting, and some limited the time period in which participants could respond. Shrier et al. (2012) and Serre et al. (2012) had four to six prompts daily for 2 weeks, and Buckner et al. (2011) used six daily semi-random prompts, end-of-day assessments, and event-contingent assessments (i.e., each time they were about to use marijuana during the 2-week period). For random prompts, Serre and colleagues allowed a 20-min window for participant responses, while Shrier et al. allowed up to 15 min. Buckner et al. requested that participants complete assessments within 1 hr, though it is unclear whether participants were able to respond after the 1-hr window. In comparison, we did not limit response time, and we counted all responses returned before the next prompt was sent (similar to Berkman et al., 2011), which is a limitation when considering overall response rate. We aimed to gain a better understanding of participants’ overall responsiveness to EMA text messaging and did not limit the cutoff time for a response. Over 75% of all responses were within 1 hr of the prompt, and the median response time over the 2-week assessment was 11 min.
Similar to Courvoisier et al. (2012), we collected 42 time points and found that our response rates dipped toward the end of the study, with the last 2 days being the lowest at an 85% rate. Unlike Courvoisier et al., the 42 time points we collected were over a 14-day time period (three times per day) instead of a 7-day period. Other studies have texted participants over longer time spans (e.g., 8 weeks; Collins et al., 1998), with as many as eight prompts per day (Berkman et al., 2011; Freedman et al., 2006). When examining predictors of the probability of data missingness, we found evidence of a higher propensity for missingness for Week 2 of the study compared to Week 1. Other measures assessing gender, age, marijuana-related consequences or problems, self-control, academic self-regulation, and day of the week did not impact the probability of missingness. There is likely a relationship between the number of prompts and compliance rates, and finding a good balance that doesn’t overwhelm participants is key. Further research is needed to explore the burden threshold for EMA data and the potential slide in compliance toward the end of EMA studies. It is possible that all EMA studies may have a dip in response rate toward the end of the study, but this needs further exploration.
Our findings on compliance were likely impacted by our sample and participants’ comfort with the technology. Most college students own a cell phone, carry their phone with them at all times, and text-message (Diamanduros, Jenkins, & Downs, 2007; Nielsen Mobile, 2013; Raacke & Bonds-Raacke, 2011). Using participants’ personal cell phones likely had some benefit in our study, as participants did not have to carry an additional device, making the protocol more convenient. Considering the expense to researchers associated with cell phones, PDAs, or other computer devices (e.g., tablets), this was a benefit that made it possible to complete the work at minimal cost. However, this does limit researchers to only those participants who own a cell phone with text-messaging capabilities, which impacts generalizability when considering broader community samples.
It is important to consider ethical implications associated with text messaging on sensitive topics (e.g., drug use), as participants may not be as careful about keeping their personal phone out of view from others. We asked participants to keep their messages private and informed them of the possible violation to confidentiality if they shared their personal study information with others. Although we did not receive any concerns about privacy from participants, this is an important issue that should be considered for future studies. For example, participants could be instructed on how to set up security protections (e.g., password lock, fingerprint reader) on their cell phones. When considering sensitive topics, such as illicit substance use, careful consideration should be placed on how to word text messages. We did not reference marijuana in our text prompts, and we asked participants to refrain from using the term or other slang references in their responses.
Due to the skewness of the mean data (based on several outliers), we also examined median response times over the 2-week period. Descriptive data showed that participants responded in a timely fashion and that the fastest median responses were received on Mondays and Fridays. Median values ranged from 7.0 to 19.0 min across all days of the study. Median response time was the longest on Saturdays even though it had the shortest mean response time, which indicated fewer extremes on this day of the week. It isn’t clear why participants responded more slowly on certain days compared to others. One might speculate that participants were busier on days when they responded less quickly, but the patterns we found don’t really indicate any specific trend. Participants responded more quickly at the beginning of the 2-week period compared to the end, but this difference was not found to be significant. Descriptively, we found that participants responded less quickly in the morning compared to the afternoon and evening time points.
When examining whether any baseline variables predicted overall response time, the MSLQ-SR and age were found to be significant predictors. However, when one outlier (i.e., the 33-year-old) was excluded from the analysis, age was no longer a significant predictor of response time. We expected that participants scoring high on a measure of self-regulation for learning (MSLQ-SR) might respond more quickly to EMA prompts. We found the opposite—participants who reported greater levels of regulating their learning tended to respond slower to the prompts. It is possible that these students were simply prioritizing other school activities (e.g., studying) over our study. Past research has found that self-regulated learners tend to have greater behavioral control when it comes to minimizing distractions for their learning when compared to their peers (Wolter & Taylor, 2012). Thus, participants with greater self-regulated learning might have been better at minimizing distractions in their environment (e.g., text messages) for the sake of their learning. This finding would need to be explored in greater depth.
When examining EMA predictors of response time at the participant level, an increase in the total minutes smoked was found to be associated with a higher response time. It is possible that greater time spent smoking marijuana may distract participants or contribute to less focus on the research study. Similarly, more time spent studying and greater academic motivation and craving were all associated with lower response time. The finding that greater craving was associated with lower response time did not make intuitive sense, as one would expect that craving would disrupt cognitive focus and lower response time. A potential explanation for this finding is that multicollinearity between these EMA variables contributed to this result. Multicollinearity doesn’t reduce the predictive power of the overall model but makes the interpretation of any specific variable uninterruptable due to statistical noise.
Our comparison between EMA and TLFB reports of daily marijuana use instances found that, although total reports of the number of times participants used marijuana were correlated, the daily occurrences were not well aligned. Only 29% of the instances matched exactly, and many instances were off by three or more occurrences. Our data suggest that TLFB reports tend to indicate less marijuana usage than do EMA reports. Our findings differ slightly from those of Shiffman (2009b) and Griffith and colleagues (2009), which compared TLFB counts of cigarette use with EMA reports among participants enrolled in smoking cessation treatment. In those studies (comparing EMA and TLFB data), cigarette counts were greater through TLFB compared to EMA. It is possible that cigarette recall is different from marijuana use recall. In addition, our participants were active marijuana users not enrolled in treatment who smoked primarily through a small pipe (vs. a joint). Treatment-seeking populations may have greater motivation to report less substance use when completing TLFB with a researcher or clinic staff member. Despite these differences, our study did similarly find that TLFB and EMA are not equivalent. Past research has shown that TLFB is reliable and valid (Fals-Stewart, O’Farrell, Freitas, McFarlin, & Rutigliano, 2000; Hjorthøj, Hjorthøj, & Nordentoft, 2012), but when resources allow, EMA may produce better data for examining daily occurrences (Shiffman, 2009b).
Limitations
This study has limitations. The time period of the EMA was limited to 2 weeks so as not to overburden participants, but longer periods (e.g., 3 weeks) may have provided more rich data. Participants were texted only during waking hours and not while in class, which could have impacted compliance. We did not send reminder prompts when participants did not respond to a text. We also did not limit the time considered for a response. Generally, median values suggested participants responded quickly, but future studies may want to consider a cutoff number of minutes (e.g., 30 or 60 min) for data to be considered “in the moment.”
The sample was small and focused exclusively on college student marijuana users who were compensated for their participation. University students as a whole are more highly educated and often have greater financial support. The campus we recruited from has 36% first generation students, with 22% of the student population identified as both first-generation and low income (i.e., based on Pell grant eligibility). We limited participation to heavy marijuana users, and our participants reported using marijuana almost daily.
Even though there are significant benefits to using SMS texting for momentary data, there are also several drawbacks. SMS text messages typically have a capacity of 160 characters, though this often depends on the cell phone carrier and changes in technological capacity. For our study, this meant that we had to use shorthand for our questions and limit the number of questions asked. Though we provided participants with a laminated business card with the full and shorthand version of the questions, it is possible that participants may not have carried the card with them consistently or could have forgotten what the shorthand meant. Second, because SMS texts have to be kept brief, it becomes more difficult to ask several questions to address a particular construct. With PDAs, there is increased space to ask a range of questions. Also, questions can be formatted with skip patterns when particular questions do not apply.
When considering comparisons between EMA and TLFB, we acknowledge the limitation of not being able to quantify marijuana use with a biological measure (e.g., a more sensitive urine screen) to compare degree of marijuana use to the EMA and the TLFB. However, Shiffman (2009b) used a biochemical measure to assess cigarette smoking and found that carbon monoxide readings were associated with EMA cigarette consumption but not TLFB. In the current study, both EMA and TLFB measures were based on self-report. We encourage the use of biological measures in future studies to better determine the validity of either method.
Conclusion
Overall, our findings support the feasibility of using SMS texting as a form of EMA with college-age marijuana users. We found little to predict the missingness of our data (i.e., similar to Courvoisier et al., 2012), which is encouraging for future EMA studies. Future research could benefit from utilizing EMA to gain a better understanding of various factors related to marijuana use.
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APPENDIX APPENDIX A: Ecological Momentary Assessment Questions in Full Format and Text-Message Abbreviation
1. What was the MAIN activity you were doing when we texted you?
Text message abbreviation: Doing now?
2. When was the last time you smoked? (day/time)
Text message abbreviation: Day/time last smokd?
3. How many times have you smoked since we last contacted you?
Text message abbreviation: # times smokd since last txt?
4. When you smoked LAST, were you ALONE or with OTHERS?
Text message abbreviation: Alone/others?
5. How many classes have you missed today? (answer “99” if no class today or if class is later)
Text message abbreviation: # classes missd?
6. Since the last time we texted you, estimate how much time (in minutes) you spent doing school work (e.g., reading, writing papers, or other homework assignments)?
Text message abbreviation: Mins spnt since last text on sch work?
7. Since the last time we texted you, estimate how much time (in minutes) you spent smoking?
Text message abbreviation: Mins spnt since last txt smokng?
8. Please rate your current craving or desire to smoke at this exact moment on a scale of 1-10, with 1 being “no cravings” and 10 being “extremely intense cravings.”
Text message abbreviation: Craving 1(none) - 10(high)
9. How motivated do you currently feel to focus on school work? Rate your motivation on a scale of 1-10, with 1 being “not at all” and 10 being “extremely motivated.”
Text message abbreviation: Motiv sch wrk 1(none) - 10(high)
Submitted: June 6, 2013 Revised: February 14, 2014 Accepted: March 4, 2014
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Source: Psychological Assessment. Vol. 26. (3), Sep, 2014 pp. 947-957)
Accession Number: 2014-14381-001
Digital Object Identifier: 10.1037/a0036612
Record: 69- Title:
- Field validity of Static-99/R scores in a statewide sample of 34,687 convicted sexual offenders.
- Authors:
- Boccaccini, Marcus T.. Department of Psychology and Philosophy, Sam Houston State University, Huntsville, TX, US, boccaccini@shsu.edu
Rice, Amanda K.. Department of Psychology and Philosophy, Sam Houston State University, Huntsville, TX, US
Helmus, L. Maaike. Global Institute of Forensic Research, LLC, Great Falls, VA, US
Murrie, Daniel C.. Institute of Law, Psychiatry, and Public Policy, University of Virginia, Charlottesville, VA, US
Harris, Paige B.. Department of Psychology and Philosophy, Sam Houston State University, Huntsville, TX, US - Address:
- Boccaccini, Marcus T., Department of Psychology and Philosophy, Sam Houston State University, Huntsville, TX, US, 77341, boccaccini@shsu.edu
- Source:
- Psychological Assessment, Vol 29(6), Jun, 2017. Special Issue: Field Reliability and Validity of Forensic Psychological Assessment Instruments and Procedures. pp. 611-623.
- NLM Title Abbreviation:
- Psychol Assess
- Page Count:
- 13
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- Psychological Assessment: A Journal of Consulting and Clinical Psychology
- ISSN:
- 1040-3590 (Print)
1939-134X (Electronic) - ISBN:
- 978-1-4338-9082-6
- Language:
- English
- Keywords:
- Static-99, Static-99R, calibration, field validity, sex offender, risk assessment, local norms
- Abstract:
- The Static-99 (and revision, the Static-99R) reflect the most researched and widely used approach to sex offender risk assessment. Because the measure is so widely applied in jurisdictions beyond those on which it was developed, it becomes crucial to examine its field validity and the degree to which published norms and recidivism rates apply to other jurisdictions. We present a new and greatly expanded field study of the predictive validity (M = 5.23 years follow-up) of the Static-99 as applied system-wide in Texas (N = 34,687). Results revealed stronger predictive validity than a prior Texas field study, especially among offenders scored after the release of an updated scoring manual in 2003 (AUC = .66 to .67, d = .65 to .69), when field reliability was also stronger. But calibration analyses revealed that the Static-99R routine sample norms led to a significant overestimation of risk in Texas, especially for offenders with scores ranging from 1 to 5. We used logistic regression to develop local Texas recidivism norms (with confidence intervals) for Static-99R scores. Overall, findings highlight the importance of revisiting and updating field study findings, and the potential benefits of using statewide data to develop local norms. (PsycINFO Database Record (c) 2017 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Criminals; *Sex Offenses; *Test Validity; *Risk Assessment; Statistical Validity
- PsycINFO Classification:
- Clinical Psychological Testing (2224)
Criminal Behavior & Juvenile Delinquency (3236) - Population:
- Human
Male - Location:
- US
- Age Group:
- Adulthood (18 yrs & older)
- Tests & Measures:
- Static-99/R
Static-99 DOI: 10.1037/t23469-000 - Methodology:
- Empirical Study; Field Study; Quantitative Study
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- Accepted: Jun 30, 2016; Revised: Jun 30, 2016; First Submitted: Mar 1, 2016
- Release Date:
- 20170608
- Copyright:
- American Psychological Association. 2017
- Digital Object Identifier:
- http://dx.doi.org/10.1037/pas0000377
- Accession Number:
- 2017-24382-002
- Persistent link to this record (Permalink):
- http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2017-24382-002&site=ehost-live
- Cut and Paste:
- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2017-24382-002&site=ehost-live">Field validity of Static-99/R scores in a statewide sample of 34,687 convicted sexual offenders.</A>
- Database:
- PsycINFO
Record: 70- Title:
- Further validation of the IDAS: Evidence of convergent, discriminant, criterion, and incremental validity.
- Authors:
- Watson, David. Department of Psychology, University of Iowa, Iowa City, IA, US, david-watson@uiowa.edu
O'Hara, Michael W.. Department of Psychology, University of Iowa, Iowa City, IA, US
Chmielewski, Michael. Department of Psychology, University of Iowa, Iowa City, IA, US
McDade-Montez, Elizabeth A.. Department of Psychology, University of Iowa, Iowa City, IA, US
Koffel, Erin. Department of Psychology, University of Iowa, Iowa City, IA, US
Naragon, Kristin. Department of Psychology, University of Iowa, Iowa City, IA, US
Stuart, Scott. Department of Psychiatry, University of Iowa, Iowa City, IA, US - Address:
- Watson, David, Department of Psychology, University of Iowa, E11 Seashore Hall, Iowa City, IA, US, 52242-1407, david-watson@uiowa.edu
- Source:
- Psychological Assessment, Vol 20(3), Sep, 2008. pp. 248-259.
- NLM Title Abbreviation:
- Psychol Assess
- Page Count:
- 12
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- Psychological Assessment: A Journal of Consulting and Clinical Psychology
- ISSN:
- 1040-3590 (Print)
1939-134X (Electronic) - Language:
- English
- Keywords:
- major depression, anxiety disorders, convergent validity, discriminant validity, criterion validity, Inventory of Depression and Anxiety Symptoms
- Abstract:
- The authors explicated the validity of the Inventory of Depression and Anxiety Symptoms (IDAS; D. Watson et al., 2007) in 2 samples (306 college students and 605 psychiatric patients). The IDAS scales showed strong convergent validity in relation to parallel interview-based scores on the Clinician Rating version of the IDAS; the mean convergent correlations were .51 and .62 in the student and patient samples, respectively. With the exception of the Well-Being Scale, the scales also consistently demonstrated significant discriminant validity. Furthermore, the scales displayed substantial criterion validity in relation to Diagnostic and Statistical Manual of Mental Disorders (4th ed.; DSM-IV; American Psychiatric Association, 1994) mood and anxiety disorder diagnoses in the patient sample. The authors identified particularly clear and strong associations between (a) major depression and the IDAS General Depression, Dysphoria and Well-Being scales, (b) panic disorder and IDAS Panic, (c) posttraumatic stress disorder and IDAS Traumatic Intrusions, and (d) social phobia and IDAS Social Anxiety. Finally, in logistic regression analyses, the IDAS scales showed significant incremental validity in predicting several DSM-IV diagnoses when compared against the Beck Depression Inventory-II (A. T. Beck, R. A. Steer, & G. K. Brown, 1996) and the Beck Anxiety Inventory (A. T. Beck & R. A. Steer, 1990). (PsycINFO Database Record (c) 2016 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Anxiety Disorders; *Inventories; *Major Depression; *Test Validity
- Medical Subject Headings (MeSH):
- Adolescent; Adult; Aged; Aged, 80 and over; Anxiety Disorders; Depressive Disorder, Major; Discriminant Analysis; Female; Humans; Male; Middle Aged; Psychiatric Status Rating Scales; Reproducibility of Results
- PsycINFO Classification:
- Clinical Psychological Testing (2224)
Psychological Disorders (3210) - Population:
- Human
Male
Female - Location:
- US
- Age Group:
- Adulthood (18 yrs & older)
Young Adulthood (18-29 yrs)
Thirties (30-39 yrs)
Middle Age (40-64 yrs)
Aged (65 yrs & older) - Tests & Measures:
- Inventory of Depression and Anxiety Symptoms-CR
Beck Depression Inventory–II DOI: 10.1037/t00742-000
Beck Anxiety Inventory DOI: 10.1037/t02025-000
Structured Clinical Interview for DSM-IV
Inventory of Depression and Anxiety Symptoms DOI: 10.1037/t00312-000 - Grant Sponsorship:
- Sponsor: National Institute of Mental Health
Grant Number: R01-MH068472
Recipients: Watson, David - Methodology:
- Empirical Study; Quantitative Study
- Supplemental Data:
- Tables and Figures Internet
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- Accepted: Mar 17, 2008; Revised: Mar 11, 2008; First Submitted: Sep 20, 2007
- Release Date:
- 20080908
- Correction Date:
- 20120827
- Copyright:
- American Psychological Association. 2008
- Digital Object Identifier:
- http://dx.doi.org/10.1037/a0012570; http://dx.doi.org/10.1037/a0012570.supp(Supplemental)
- PMID:
- 18778161
- Accession Number:
- 2008-12234-006
- Number of Citations in Source:
- 25
- Persistent link to this record (Permalink):
- http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2008-12234-006&site=ehost-live
- Cut and Paste:
- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2008-12234-006&site=ehost-live">Further validation of the IDAS: Evidence of convergent, discriminant, criterion, and incremental validity.</A>
- Database:
- PsycINFO
Further Validation of the IDAS: Evidence of Convergent, Discriminant, Criterion, and Incremental Validity
By: David Watson
Department of Psychology, University of Iowa;
Michael W. O'Hara
Department of Psychology, University of Iowa
Michael Chmielewski
Department of Psychology, University of Iowa
Elizabeth A. McDade-Montez
Department of Psychology, University of Iowa
Erin Koffel
Department of Psychology, University of Iowa
Kristin Naragon
Department of Psychology, University of Iowa
Scott Stuart
Department of Psychiatry, University of Iowa
Acknowledgement: This research was supported by National Institute of Mental Health Grant R01-MH068472 awarded to David Watson. We thank Lee Anna Clark, Wakiza Gamez, Roman Kotov, Jenny Gringer Richards, and Leonard J. Simms for their help in the preparation of this article.
Traditional self-report measures of depression—such as the Beck Depression Inventory–II (BDI-II; Beck, Steer, & Brown, 1996) and the Center for Epidemiological Studies Depression Scale (Radloff, 1977)—have been valuable clinical research tools for more than 40 years (for a recent review, see Joiner, Walker, Pettit, Perez, & Cukrowicz, 2005). At the same time, however, the accumulating research has also exposed some limitations of these instruments, thereby establishing the need for alternative measures (Joiner et al., 2005). Watson et al. (2007) created the Inventory of Depression and Anxiety Symptoms (IDAS) to complement these traditional measures and to address their limitations.
The IDAS differs from these older instruments in two basic ways. First, these traditional measures originally were created to yield a single overall index of symptom severity. These total scores are valuable in many contexts; nevertheless, this focus on global dysfunction ignores the heterogeneous and multidimensional nature of depressive symptoms, and it hampers the identification of meaningful subtypes (Ingram & Siegle, 2002; Joiner et al., 2005). In contrast, the IDAS was specifically designed to contain multiple scales assessing specific symptoms of depression (e.g., insomnia, suicidality, appetite loss).
Second, extensive evidence has established that these depression measures are very strongly associated with symptoms of anxiety (e.g., Clark & Watson, 1991; Mineka, Watson, & Clark, 1998; Watson, 2005). Consequently, the original IDAS item pool contained a broad range of anxiety-related symptoms. The inclusion of these items facilitated the development of depression scales with good discriminant validity and also eventually led to the creation of complementary anxiety scales (e.g., Social Anxiety, Panic).
Development and Preliminary Validation of the IDAS Development of the IDAS
An initial pool of 180 items was subjected to a series of analyses in a large undergraduate sample (see Watson et al., 2007, Study 1); this yielded a revised pool of 169 items. Next, this revised set of items was administered to large samples of college students, psychiatric patients, and community adults (Watson et al., 2007, Study 2). Data from these three samples were subjected to separate series of principal factor analyses. Ten specific content factors emerged in all three samples and were used to create corresponding scales. Five of these scales represent specific symptoms of major depression: Insomnia, Lassitude (which includes items reflecting fatigue, lack of energy, and hypersomnia), Suicidality, Appetite Loss, and Appetite Gain. In addition, three scales—Panic, Social Anxiety, and Traumatic Intrusions—assess specific types of anxiety symptoms. The other two content scales assess feelings of high energy and positive affect (Well-Being) and anger/hostility (Ill Temper).
A large, nonspecific factor also emerged in all three solutions; this dimension represents the core (and largely nonspecific) emotional and cognitive symptoms of depression and anxiety (see Watson et al., 2007, Table 1). The IDAS Dysphoria scale was created to capture the nature and scope of this diverse factor. It contains single items assessing depressed mood, anhedonia, worry, worthlessness, guilt, and hopelessness, as well as two items apiece tapping psychomotor disturbance (one reflecting retardation, the other agitation) and cognitive problems.
Prevalence and Interrater Reliability of SCID-IV Diagnoses in the Patient Sample
Although the Dysphoria scale is broad and nonspecific in its content, it is narrower in scope than most traditional measures of depression, such as the BDI-II. Watson et al. (2007) therefore created an expanded measure that more closely resembles these older measures and that includes a comprehensive range of depression-related content. Thus, the 20-item General Depression scale includes all 10 Dysphoria items, as well as two items apiece from Suicidality, Lassitude, Insomnia, Appetite Loss, and Well-Being (these items are reverse keyed).
Preliminary Validation
Reliability evidence
Watson et al. (2007) reported a variety of psychometric data in the three scale development samples and in five new samples (high school students, college students, young adults, postpartum women, and psychiatric patients; see Watson et al., Study 3). For instance, they presented evidence indicating that the scales are all internally consistent, with coefficient alphas typically exceeding .80 (see Watson et al., 2007, Table 3). In addition, they reported 1-week retest correlations in a sample of 250 psychiatric patients ranging from .72 (Ill Temper) to .84 (General Depression).
Correlations Between the IDAS and the IDAS-CR (Patient Sample)
Validity evidence
Watson et al. (2007) also presented various types of data to establish the validity of the scales. For instance, the 10 specific scales generally had low to moderate correlations with one another (typically in the |.20| to |.50| range; see Watson et al., 2007, Table 4), demonstrating that these symptom dimensions can be distinguished from one another. Moreover, item-level factor analyses established that the specific scales do an excellent job of capturing the underlying target dimensions.
Correlations Between the Self-Report Scales and SCID-IV Diagnoses in the Patient Sample
The Current Research Overview of the Research
The primary goal of this study is to explicate the convergent, discriminant, criterion and incremental validity of the IDAS scales. To do so, we present additional data from two of the Study 3 samples—college students and psychiatric patients—previously reported in Watson et al. (2007). In addition, we evaluate the comparative and incremental validity of the IDAS scales against the BDI-II and the Beck Anxiety Inventory (BAI; Beck & Steer, 1990). In the sections that follow, we briefly summarize relevant data reported earlier by Watson et al. (2007) and then describe how the current results augment and extend them.
Convergent and Discriminant Validity
Prior evidence
Watson et al. (2007) reported several types of evidence to establish convergent and discriminant validity. For example, they demonstrated that the IDAS General Depression and Dysphoria scales were very strongly correlated with the BDI-II (rs ranged from .81 to .83; see Watson et al., 2007, Table 6), whereas the IDAS Panic scale was highly related to the BAI (r = .79 and .78 in Studies 2 and 3, respectively).
Mean-Level Comparisons of Diagnosed Cases Versus Noncases (Expressed as Cohen's d) in the Patient Sample: Analyses Removing Comorbid Depression and Anxiety
Moreover, Watson et al. (2007) correlated eight of the IDAS scales with corresponding symptom composites from the Interview for Mood and Anxiety Symptoms (Kotov, Gamez, & Watson, 2005), a semistructured instrument that was administered approximately 6 weeks later. The mean convergent correlation was .50, which indicates good convergent validity (see Watson et al., 2007, Table 7). Next, a classic test of discriminant validity is that each of the convergent correlations should be higher than any of the other values in its row or column of the heteromethod block (Campbell & Fiske, 1959). Watson et al. conducted significance tests comparing the convergent correlations to each of the 14 discriminant correlations in the same row or column of the heteromethod block. Overall, 106 of these 112 comparisons (94.6%) were significant, which offers encouraging evidence of discriminant validity.
Odds Ratios (with 95% Confidence Intervals) from Logistic Regression Analyses of the IDAS Scales (Basic Analyses)
The current study
The current study extends this evidence by examining convergent and discriminant relations between self-report and interview-based measures of all 11 nonoverlapping IDAS scales (i.e., General Depression is excluded because it shares items with other scales). Because no suitable interview-based measure existed (i.e., one that assessed all of the IDAS dimensions), we created the Clinician Rating version of the IDAS (IDAS-CR), which consists of a single clinician rating for each of the 11 nonoverlapping IDAS scales. IDAS-CR data are available in both samples. Based on the earlier results reported by Watson et al. (2007), we expected the IDAS scales to show strong convergent validity and significant discriminant validity in relation to their IDAS-CR counterparts.
Criterion Validity
Prior evidence
Watson et al. (2007) examined the criterion validity of the IDAS scales by correlating them with clinicians' ratings on the widely used Hamilton Rating Scale for Depression (HRSD; Hamilton, 1960) in a postpartum sample (see Watson et al., 2007, Table 9); for comparison purposes, they also reported parallel results for the BDI-II and BAI. Three aspects of these data were noteworthy. First, consistent with previous research (Beck & Steer, 1993; Clark & Watson, 1991), the results demonstrated strong associations between self-rated and clinician-rated symptoms. Second, all of the IDAS scales were significantly correlated with the HRSD (rs ranged from |.30| to |.67|), which establishes some degree of criterion validity for each of them. Third, the two general IDAS scales—General Depression (r = .67) and Dysphoria (r = .64)—had correlations with the HRSD that were comparable to those of the BDI-II (r = .62) and BAI (r = .64), thereby demonstrating a similar level of criterion validity.
Odds Ratios (with 95% Confidence Intervals) from Logistic Regression Analyses of the IDAS and Beck Scales (Basic Analyses)
The current study
Although these HRSD results are encouraging, Watson et al. (2007) did not report any data relating the IDAS scales to formal Diagnostic and Statistical Manual of Mental Disorders (4th ed.; DSM–IV; American Psychiatric Association, 1994) diagnoses of major depression and the anxiety disorders. Accordingly, a basic goal of the current research was to relate the IDAS scales to DSM–IV diagnoses that were assessed using the Structured Clinical Interview for DSM-IV (SCID-IV; First, Spitzer, Gibbon, & Williams, 1997) in the patient sample.
Based on the data reported in Watson et al. (2007), we expected that the IDAS General Depression and Dysphoria scales would display correlates that are very similar to those of the BDI-II. All of these scales tap variance that is strongly related to the general distress/negative affectivity dimension that lies at the heart of the unipolar mood and anxiety disorders (see Clark & Watson, 1991; Mineka et al., 1998; Watson, 2005; Watson et al., 2007). All of these scales, therefore, should be significantly associated with a broad range of diagnoses but should display particularly strong links to distress-based disorders, such as major depression and generalized anxiety disorder (GAD; see Watson, 2005). We expected that the other IDAS scales would show weaker—but significant—associations with depression and GAD.
In addition, the three anxiety scales of the IDAS should show more specific associations with corresponding DSM–IV anxiety disorders. That is, one would expect to observe specific associations between (a) IDAS Panic and DSM–IV panic disorder, (b) IDAS Social Anxiety and social phobia, and (c) IDAS Traumatic Intrusions and Posttraumatic Stress Disorder (PTSD).
Multivariate analyses
We also report multivariate logistic regression analyses in which the self-report scales are examined together in relation to the DSM–IV diagnoses. These analyses have two basic goals. First, given that the IDAS scales are significantly correlated and, therefore, not completely independent, we were interested in identifying which of them showed unique, incremental power in relation to each DSM–IV disorder. Second, we used these analyses to examine the incremental validity of the IDAS scales in relation to the BDI-II and BAI.
Method Participants and Procedure
College student sample
The participants were 307 students enrolled in an introductory psychology course at the University of Iowa. They participated in partial fulfillment of a course research exposure requirement. They were assessed in small-group sessions. The sample consisted of 194 women and 112 men (the gender of one participant was unknown); it included 272 Whites (88.6%), 13 Asian Americans, (4.2%), 4 African Americans (1.3%), and 18 participants (5.9%) whose racial status was either unknown or from another category. Because of missing data, we subsequently report results based on data from 303 students.
Psychiatric patient sample
Watson et al. (2007, Study 3) reported findings from a sample of 337 psychiatric patients. We present data here on an expanded sample of 605 patients. The participants (age range = 18–83 years, M = 41.8 years) were recruited from the Community Mental Health Center of Mideastern Iowa, the Adult Psychiatry Clinic at the University of Iowa Hospital and Clinics, and the Seashore Psychology Clinic in the Department of Psychology at the University of Iowa. Patients at these sites were individually approached and asked whether they were interested in participating in a research study. They were assessed in small-group sessions and were paid for their participation. The sample consisted of 386 women and 217 men (the gender of two participants was unknown); it included 544 Whites (89.9%), 12 African Americans (2.0%), 10 Native Americans (1.7%), 9 Asian Americans (1.5%), 12 multiracial participants (2.0%), and 18 respondents (3.0%) whose racial status was either unknown or from another category. We subsequently report results based on data from 605 (IDAS-CR analyses) and 575 (SCID-IV analyses) patients.
Self-Report Measures
IDAS
All participants completed the final 64-item version of the IDAS; they indicated the extent to which they had experienced each symptom “during the past two weeks, including today” on a 5-point scale ranging from not at all to extremely. The IDAS contains the 10-item Dysphoria scale; 8-item measures of Well-Being and Panic; 6-item measures of Suicidality, Lassitude, and Insomnia; 5-item measures of Social Anxiety and Ill Temper; a 4-item Traumatic Intrusions scale; and 3-item measures of Appetite Loss and Appetite Gain. Finally, as discussed earlier, it also includes the 20-item General Depression scale, which contains all 10 Dysphoria items, as well as two items apiece from the Suicidality, Lassitude, Insomnia, Appetite Loss, and Well-Being (reverse-keyed) scales. Coefficient alphas for the scales in these samples are reported in Watson et al. (2007, Table 3).
BDI-II and BAI
Participants in the patient sample also completed the BDI-II (Beck et al., 1996) and BAI (Beck & Steer, 1990). The BDI-II is one of the most widely used and best validated self-report measures of depression (see Joiner et al., 2005). The BDI-II contains 21 items. Each item consists of four statements; respondents choose the statement that best characterizes how they have been feeling “during the past two weeks, including today.” The items are scored on 0–3 scales, so that total scores can range from 0 to 63. The BDI-II had a coefficient alpha of .93 in the patient sample.
The BAI assesses 21 affective and somatic symptoms of anxiety that are rated on a 4-point scale (ranging from 0 = not at all to 3 = severely/I could barely stand it). Respondents indicate to what extent they have been bothered by each symptom “during the past week, including today.” The BAI had a coefficient alpha of .94 in the patient sample.
Interview Measures
IDAS-CR
Participants in both samples were rated on the IDAS-CR. The interviewers were trained staff members who had masters' level training in clinical/counseling psychology or public health.
As noted earlier, the IDAS-CR consists of a series of ratings representing each of the 11 nonoverlapping IDAS scales (i.e., General Depression is not assessed). Each rating is made on a 3-point scale (absent, subthreshold, present). In order to make these ratings, the clinicians asked a standard initial probe question, as well as several standard follow up questions, for each symptom. In addition, the clinicians were free to ask additional questions to ensure the individual received a proper rating on the dimension. For the IDAS-CR Dysphoria rating, for example, the interviewers began with the standard probe question, “Did you feel sad, depressed, or down over the past two weeks?” They then asked a number of follow up questions, including “Have you felt inadequate?”, “Have you had trouble concentrating?”, and “Have you found yourself worrying much of the time?” The interviewers also clarified whether or not reported symptoms had been present “more days than not” over the past 2 weeks and whether they had (a) been noticed by others or (b) interfered with the patient's day-to-day activities.
To assess interrater reliability, the interviews were audiotaped; 56 (student sample) and 76 (patient sample) of them were scored independently by a second interviewer (due to audiotape problems, some intraclass correlations in the patient sample are based on an N of only 75). Intraclass correlations in the student sample ranged from .65 (Ill Temper) to .95 (Insomnia), with a mean value of .83 and a median value of .87. Intraclass correlations in the patient sample ranged from .74 (Well-Being) to .99 (Appetite Gain), with a mean value of .90 and a median value of .89. Intraclass correlations in this range indicate good to excellent interrater reliability (see Cicchetti, 1994).
SCID-IV
The patients were interviewed using the mood disorders, anxiety disorders, and substance use disorders modules of the SCID-IV. This last class was included primarily as a further test of discriminant validity (i.e., we expected that the IDAS scales would relate much more weakly to substance use disorders than they would to the mood and anxiety disorders). Because we had no differential predictions regarding individual substance use disorders, we collapsed them into a single overall category (i.e., “any substance use disorder”). This left 10 diagnoses for further consideration. Table 1 presents prevalence data for these disorders in this sample.
Prevalence and Interrater Reliability of SCID-IV Diagnoses in the Patient Sample
To assess interrater reliability, we audiotaped the SCID-IV interviews, and 76 of them were scored independently by a second interviewer (because of audiotape problems, the actual N ranged from 74 to 76 across various disorders). The resulting kappas are reported in Table 1. Nine diagnoses showed good to excellent interrater reliability; the kappas ranged from .70 (GAD) to .95 (major depression), with a median value of .86. Accordingly, these diagnoses were retained for subsequent analyses. In contrast, however, the interrater reliability for agoraphobia (κ = .46) was unacceptably low; it therefore was dropped from further consideration.
Results IDAS-CR Analyses
Student sample
We first evaluate the convergent and discriminant validity of the IDAS scales by examining correlations between them and parallel interview-based ratings on the IDAS-CR. Table 2 presents these correlations in the college student sample in the form of a heteromethod block (Campbell & Fiske, 1959). Looking first at convergent validity, all of the IDAS scales were significantly related to their IDAS-CR counterparts, with coefficients ranging from .30 (Well-Being) to .62 (Dysphoria). The mean convergent correlation was .51, which reflects a strong level of convergent validity; these results are particularly impressive given that the IDAS-CR consists of a series of single ratings. Thus, consistent with previous research (e.g., Beck, Steer, & Garbin, 1988; Clark & Watson, 1991; Watson et al., 2007), our results demonstrate substantial associations between self-report and interview-based symptom measures.
Correlations Between the IDAS and the IDAS-CR (Student Sample)
Having said that, however, we also must acknowledge that the convergent correlation for Well-Being (r = .30) is substantially lower than that for any other scale (the next lowest coefficient is r = .42 for Ill Temper). In this regard, some of our interviewers commented that they found it particularly challenging to rate well-being/positive affectivity (this rating also showed a relatively low level of interrater reliability in this sample, with an intraclass correlation of .69), given that they were much more used to evaluating dysfunction and psychopathology rather than healthy psychological functioning.
We turn now to discriminant validity. In discussing these data, it is important to emphasize that valid measures of depression and anxiety should not be completely independent of one another but rather should be significantly interrelated (Clark & Watson, 1991; Watson, 2005). Thus, the key issue here is the pattern of the correlations; that is, whether purported measures of the same construct (e.g., IDAS Lassitude vs. IDAS-CR Lassitude) correlate more highly than do purported measures of different constructs (e.g., IDAS Lassitude vs. IDAS-CR Insomnia).
As noted earlier, a classic test of discriminant validity is that each of the convergent correlations should be higher than any of the other values in its row or column of the heteromethod block (Campbell & Fiske, 1959). In part because of its low convergent correlation, Well-Being clearly failed this test. In contrast, however, the other 10 scales easily met this criterion. We further quantified these relations by conducting significance tests (using the Williams modification of the Hotelling test for two correlations involving a common variable; see Kenny, 1987) comparing these convergent correlations to each of the 20 discriminant coefficients in the same row or column of the block; this yields a total of 200 tests of discriminant validity across these 10 symptom dimensions. Overall, 199 of these 200 comparisons (99.5%) were significant (p < .05, one-tailed), which offers strong evidence of discriminant validity. The single exception was that the convergent coefficient for Ill Temper (r = .42) did not significantly exceed the .35 correlation between IDAS Ill Temper and IDAS-CR Dysphoria (z = 1.09, ns).
Patient sample
Table 3 presents parallel data from the psychiatric patient sample. It is interesting to note that the self-report and interview-based measures showed a stronger overall level of convergence in this sample. The convergent coefficients ranged from .52 (Well-Being) to .71 (Appetite Loss), with a mean value of .62; this reflects a very strong level of convergent validity, particularly when one considers the limitations of using single IDAS-CR ratings.
In part because of these stronger convergent correlations, the patient data also yielded clearer evidence of discriminant validity. As before, we conducted significance tests comparing each of the convergent correlations to all of the other values in its row or column of the heteromethod block. The results indicated that 219 of the 220 comparisons (99.5%) were significant (p < .05, one-tailed); the sole exception was that the convergent correlation for Well-Being (r = .52) was not significantly higher than the −.49 correlation between IDAS-CR Well-being and IDAS Dysphoria (z = 0.86, ns).
Summary
Overall, these data offer encouraging evidence of convergent and discriminant validity. All of the convergent correlations were significant and at least moderate in magnitude, with mean coefficients of .51 and .62 in the student and patient data, respectively. With the exception of Well-Being, the scales also consistently showed adequate discriminant validity; indeed, 399 of the 400 individual comparisons involving the other 10 scales were significant (p < .05, one-tailed) across the two samples. Thus, our data demonstrate that specific symptom dimensions—such as lassitude, insomnia, suicidality and panic—can be distinguished from one another across methods.
SCID-IV Analyses
Preliminary analyses
To examine the criterion validity of the scales, we first report point biserial correlations with the SCID-IV diagnoses; in these analyses, diagnoses are scored as 0 = absent, 1 = present, so that positive correlations indicate that higher scores on a scale are associated with an increased likelihood of receiving the diagnosis.
Preliminary analyses revealed that three diagnostic categories—specific phobia, dysthymic disorder, and any substance use disorder—had consistently weak associations with all of the self-report scales. In fact, the strongest correlations were only .16 (IDAS Panic and the BAI) for specific phobia, −.07 (IDAS Well-Being) for dysthymic disorder, and .16 (IDAS Ill Temper) for any substance use disorder. Moreover, mean-level comparisons (to be described in more detail subsequently) yielded 39 null or small effects, only 3 moderate effects (Panic and the BAI with specific phobia; Ill Temper with substance use), and no large effects. Consequently, these disorders will not be considered further.
Correlational analyses
Table 4 presents point biserial correlations with the six remaining disorders. Several aspects of these data are noteworthy. First, Table 4 displays many moderate to strong associations; thus, our results again demonstrate the substantial criterion validity of self-report symptom scales (see also Clark & Watson, 1991; Watson et al., 2007). Beyond this general finding of strong criterion validity, these data also clearly establish differential patterns of association within each disorder that were largely consistent with our predictions. Four disorders yielded particularly clear results that supported our hypotheses. As expected, General Depression, Dysphoria, and the BDI-II (all rs = .62) all had significantly stronger associations with depression (p < .01, one-tailed) than did any other scale. Next, the BAI and the IDAS Panic scales (r = .50 and .47, respectively) both had significantly stronger correlations with panic disorder (p < .01, one-tailed) than did all other scales. Similarly, the IDAS Traumatic Intrusions scale (r = .43) had a significantly stronger correlation with PTSD (p < .05, one-tailed) than did any other scale. Finally, the IDAS Social Anxiety scale (r = .39) had a significantly stronger correlation with social phobia (p < .01, one-tailed) than did all other scales. These data demonstrate clear differential relations that help to explicate the construct validity of these scales.
The other two disorders yielded more complex results. Our predictions regarding GAD received some support. The BAI, General Depression and Dysphoria scales (rs ranged from .37 to .38) had the strongest associations with GAD; moreover, follow-up tests indicated that these correlations were significantly stronger (p < .05, two-tailed) than were those for all other scales, with the single exception of the BDI-II (r = .34; zs ranged from 1.18 to 1.43, ns); thus, these findings demonstrate some limited evidence of specific, differential relations with GAD. In contrast, although many scales had relatively low but significant associations with obsessive-compulsive disorder (OCD), none of these correlations was high enough to establish a clear differential pattern.
Mean-level comparisons
Correlations offer an effective way of quantifying the differential magnitude of the relations between different self-report scales and a given diagnosis (e.g., establishing that major depression is more strongly related to Dysphoria than to Insomnia). However, they are more problematic in quantifying the differential strength of relations across diagnoses (e.g., examining how strongly Dysphoria is related to depression versus GAD). This is because correlations are influenced by the prevalence rates and variances of dichotomous diagnoses (i.e., relatively common disorders will tend to show stronger correlations than will rarer conditions). We therefore supplemented these correlational analyses with mean-level comparisons, which are expressed as Cohen's d (Cohen, 1988, 1992). These analyses were conducted separately for each diagnosis by first computing the mean self-report scale scores for those individuals with (“cases”) and without (“noncases”) the disorder and then dividing the difference between these means by the pooled standard deviation. The resulting d values are presented in Table 5. (The scale means and standard deviations for the cases and noncases in all of these analyses are available as online supplements, in Tables S1 through S6.)
Mean-Level Comparisons of Diagnosed Cases Versus Noncases (Expressed as Cohen's d): Basic Analyses
These analyses produced several interesting findings. First, it is important to note that the majority of these effect sizes are moderate to large in magnitude. Cohen (1992, Table 1) proposed that d values in the .20 to .49 range represent small effect sizes; those in the .50 to .79 range reflect moderate effect sizes; and values of .80 or greater indicate large effect sizes. Based on these criteria, 30 of the 84 effect sizes (35.7%) are large (9 for panic disorder, 8 for major depression, 7 for PTSD, 4 for GAD, and 1 apiece for social phobia and OCD), and 34 more (40.5%) are moderate (9 for OCD; 7 for GAD; 5 apiece for major depression, PTSD, and social phobia; and 3 for panic disorder).
Second, five scales show clear evidence of diagnostic specificity in these data. Corroborating key findings from the correlational analyses, (a) the BAI and IDAS Panic scales (d = 1.56 and 1.48, respectively) are most strongly related to panic disorder, (b) Traumatic Intrusions (d = 1.28) is most strongly linked to PTSD, and (c) the IDAS Social Anxiety scale (d = 1.13) has its strongest association with social phobia. In addition, the IDAS Well-Being scale had a stronger association with major depression (d = −0.89) than it did with any other disorder (ds ranged from −0.31 to −0.51). This last finding is consistent with previous research establishing that low positive affectivity is a relatively unique and specific component of depression (see Clark & Watson, 1991; Mineka et al., 1998; Watson, 2005).
In contrast, however, most of the scales showed relatively nonspecific associations with multiple disorders. Although the three general depression measures (i.e., the BDI-II and the IDAS General Depression and Dysphoria scales) all were strongly related to depression (all ds = 1.25)—and, in fact, had their strongest links to depression—they also had strong associations with panic disorder (ds ranged from 1.08 to 1.18), PTSD (ds ranged from 0.85 to 0.92) and GAD (ds ranged from 0.82 to 0.90). Five additional scales—Suicidality, Lassitude, Insomnia, Ill Temper, and Appetite Loss—had moderate to strong associations with three or more diagnoses and failed to exhibit a clear differential pattern. Finally, consistent with findings reported in Watson et al. (2007), Appetite Gain failed to show any moderate to strong effects.
These nonspecific effects are due, in part, to the substantial overlap among these disorders. In this sample, for instance, diagnoses of major depression had tetrachoric correlations of .48 with panic disorder, .45 with GAD and PTSD, .30 with OCD, and .21 with social phobia (see also Mineka et al., 1998; Watson, 2005). Consequently, we reconducted these analyses after eliminating the effects of comorbid depression and anxiety. Specifically, in the analyses involving major depression, we removed as cases any individuals who also met criteria for one of the five relevant anxiety disorders (i.e., GAD, PTSD, panic disorder, social phobia, OCD). Conversely, for each of these anxiety disorders, we eliminated as cases any individuals who also met criteria for major depression. As would be expected in light of the substantial comorbidity between depression and anxiety, this reduced the number of diagnosed cases considerably (the reduced numbers of cases were 88 for major depression, 41 for GAD, 21 for PTSD, 66 for panic disorder, 35 for social phobia, and 19 for OCD).
Table 6 reports the d values from these analyses (once again, the means and standard deviations for the cases and noncases are reported in the supplemental Tables S1 through S6). These results demonstrate that many of the Table 5 associations largely reflect overlapping variance between depression and the anxiety disorders. Indeed, after controlling for comorbid depression and anxiety, only 3 of the 84 effect sizes (3.6%) are large (2 for panic disorder, 1 for PTSD), and only 15 more (17.9%) are moderate (7 for panic disorder, 4 for PTSD, 2 for major depression, and 1 apiece for social phobia and OCD). The obvious advantage of eliminating these nonspecific effects is that it allows the few truly specific associations to emerge much more clearly. Thus, replicating the findings of Table 5, we again see that (a) the BAI (d = 1.31) and IDAS Panic (d = 1.17) are most strongly related to panic disorder, (b) Traumatic Intrusions (d = 1.05) is most strongly linked to PTSD, (c) Social Anxiety (d = 0.78) has its strongest association with social phobia, and (d) the IDAS Well-Being scale has its strongest association with major depression (d = −0.57). In contrast, the remaining scales had small to moderate associations with the diagnoses that failed to demonstrate a clear specific pattern.
Logistic regression analyses
Thus far, we have examined the criterion validity of individual self-report scales in a series of separate bivariate analyses. Although these analyses yield useful information, they do not take into account the significant correlations among the scales (for more information about these interscale correlations, see Watson et al., 2007). We therefore conducted three series of logistic regression analyses that were designed (a) to identify the unique, incremental power of the individual scales in relation to each DSM–IV disorder and (b) to examine the incremental validity of the IDAS scales in relation to the BDI-II and BAI. In all of these analyses, the scale scores were standardized to put them on the same metric. Each of the individual DSM–IV disorders served as a criterion in a separate analysis.
In the first series of regressions, the 11 nonoverlapping IDAS scales served as predictors (i.e., the IDAS General Depression scale was omitted because it shares items with the other scales) and the original SCID-IV diagnoses were used as the criteria (i.e., we did not control for comorbid depression and anxiety in these analyses). Table 7 presents the odds ratios and 95% confidence intervals from these logistic regressions. These results highlight the unique predictive power of several IDAS scales and they largely corroborate the bivariate results presented earlier. Thus, as would be expected from the bivariate analyses, (a) Dysphoria and (low) Well-Being made significant contributions to major depression, (b) Dysphoria also was significantly related to GAD and panic disorder, (c) Traumatic Intrusions made the strongest contribution to PTSD, (d) Panic was uniquely related to panic disorder, and (e) Social Anxiety was the only scale to have a significant association with social phobia. Two less obvious effects also emerged: Insomnia contributed to the prediction of GAD, whereas Social Anxiety was significantly linked to PTSD.
It is interesting to note that these analyses also produced one suppressor effect: Lower scores on Ill Temper were associated with an increased risk of major depression. Suppressor effects occur when the addition of a predictor either increases the effect size or changes the sign of another predictor in the regression equation (Paulhus, Robins, Trzesniewski, & Tracy, 2004). In this particular case, these results indicate that Ill Temper contains two different components that are oppositely related to this criterion, that is, it contains a nonspecific component (which overlaps with scales such as Dysphoria) that is positively related to major depression, as well as a unique, nonoverlapping component that is negatively linked to depression. Thus, after eliminating the influence of this nonspecific component, we see evidence that anger and hostility are associated with a reduced risk for major depression.
In the second set of logistic regressions, we reconducted these analyses using the recomputed diagnoses that controlled for comorbid depression and anxiety (these were discussed earlier in connection with the Table 6 analyses). These results are presented in Table 8. The most noteworthy aspect of these data is that we replicated five of the key findings from Table 7: Dysphoria and (low) Well-Being were significantly related to major depression, Traumatic Intrusions made a significant contribution to PTSD, Panic was a unique predictor of panic disorder, and Social Anxiety was significantly linked to social phobia.
Odds Ratios (with 95% Confidence Intervals) from Logistic Regression Analyses of the IDAS Scales (Comorbidity Analyses)
These analyses also yielded a much larger number of suppressor effects (e.g., low levels of Insomnia were associated with an increased risk for major depression, low levels of Dysphoria were linked to an elevated risk for PTSD). Most of these effects are at least partly artifactual and should be interpreted with considerable caution. That is, all of these participants were patients, and most of them reported substantial amounts of both depression and anxiety (see Watson et al., 2007). Moreover, previous research has shown that relatively “pure” cases of disorders (e.g., patients who meet criteria for PTSD but not major depression) tend to have less severe psychopathology than do comorbid cases (e.g., patients who meet criteria for both PTSD and major depression; see Clark, Watson, & Reynolds, 1995). Thus, it is hardly surprising that these relatively pure diagnostic cases actually would report somewhat lower levels of certain symptoms than the rest of the patients (most of whom met criteria for one or more disorders).
In the final series of logistic regressions, we examined the incremental validity of the IDAS scales vis-à-vis the BDI-II and BAI. The original SCID-IV diagnoses were used as the criteria in these analyses (i.e., we did not control for comorbid depression and anxiety). These results are presented in Table 9. The most noteworthy aspect of these data is that the IDAS scales made significant incremental contributions to five of the six disorders: Dysphoria to major depression, Dysphoria and Insomnia to GAD, Traumatic Intrusions and Social Anxiety to PTSD, Lassitude to panic disorder, and Social Anxiety to social phobia (there also are three suppressor effects that we will not consider further). In addition, the relation between (low) Well-Being and major depression approached significance (odds ratio = 0.75, p < .08). Overall, therefore, these data establish that the IDAS scales—particularly Traumatic Intrusions (in relation to PTSD) and Social Anxiety (in relation to social phobia)—have incremental validity and tap important variance that is not contained in the BDI-II and BAI.
Discussion Convergent and Discriminant Validity
We examined convergent and discriminant relations between self-report and interview-based measures of all 11 nonoverlapping IDAS scales in two large samples (303 college students and 605 psychiatric patients). Our data yielded consistent evidence of convergent validity. All of the convergent correlations were significant and at least moderate in magnitude (the convergent rs ranged from .30 to .71 across the two samples), with mean coefficients of .51 and .62 in the students and patients, respectively. This level of convergence is particularly impressive when one considers that each of these symptom dimensions was assessed using only a single 3-point rating on the IDAS-CR.
With the exception of Well-Being, the scales also showed clear discriminant validity. In fact, 399 of the 400 individual convergent-discriminant comparisons involving the other 10 IDAS scales were significant (p < .05, one-tailed) across the two samples. We note, moreover, that these scales showed distinctive patterns of clinical correlates with DSM–IV disorders and that many of them provided unique, incremental predictive power in our multivariate analyses. Thus, taken together with the earlier findings of Watson et al. (2007), these results demonstrate that specific symptom dimensions can be distinguished from one another across multiple methods; moreover, they demonstrate the clinical and heuristic value of assessing and analyzing these symptoms separately. We return to this latter issue subsequently.
Having said that, however, we also must acknowledge that the findings for Well-Being were more problematic: Although the scale performed well in the patient data, it clearly failed to show adequate discriminant validity in the student sample. Although these results are somewhat disappointing, we do not believe they indicate fundamental problems with the IDAS Well-Being scale for two related reasons. First, as suggested earlier, these mixed results primarily reflect a problem of convergence rather than of differentiation; that is, Well-Being had the lowest convergent correlations in both the student (r = .30) and patient (r = .52) samples. In this regard, it is noteworthy that Watson et al. (2007) reported that Well-Being had low to moderate correlations with all of the other IDAS scales; indeed, its strongest correlations were only −.47 and −.52 with Dysphoria in Studies 2 and 3, respectively (see Watson et al., 2007, Table 4). Moreover, Dysphoria actually had stronger associations with several other scales (e.g., Social Anxiety, Lassitude, Ill Temper, Panic). Thus, the Well-Being scale does not appear to present any particular problems related to discriminant validity.
Second, we strongly suspect that these mixed findings are due more to problems with the interview-based IDAS-CR than with the self-report Well-Being scale. As noted earlier, some of our interviewers found this dimension of euthymia/positive affectivity to be particularly challenging to rate; the challenging nature of this judgment was reflected in the relatively low interrater reliabilities for this rating (the intraclass correlations were .69 and .74 in the students and patients, respectively). Furthermore, the Well-Being scale showed substantial criterion validity in subsequent analyses and, in fact, made a significant incremental contribution to the prediction of major depression. Consequently, despite these mixed results, we believe that the bulk of the evidence supports the validity of the IDAS Well-Being scale.
Criterion Validity
Overall, our data offer strong support for the criterion validity of these self-report symptom scales (see Tables 4 and 5). We reported a total of 84 effect sizes in Table 5. Using the criteria provided by Cohen (1992), 30 of the 84 effect sizes (35.7%) were large and 34 more (40.5%) were moderate in magnitude. Overall, the scales showed the strongest links to panic disorder (9 large effects, 3 moderate effects), major depression (8 large effects, 5 moderate effects), PTSD (7 large effects, 5 moderate effects), and GAD (4 large effects, 7 moderate effects).
Clearly, however, most of these effects were nonspecific, reflecting both substantial correlations among the scales and significant comorbidity between these DSM–IV diagnoses. We controlled for these nonspecific effects in various ways, including eliminating comorbid depression and anxiety (see Table 6) and conducting two series of logistic regression analyses (see Tables 7 and 8). These analyses highlighted the distinctive qualities—as well as the unique predictive power—of several IDAS scales. Although the results varied somewhat across different analyses, we found robust evidence of five specific effects: (a) Dysphoria and (low) Well-Being both made significant contributions to major depression, (b) Traumatic Intrusions had the strongest association with PTSD, (c) Panic was uniquely related to panic disorder, and (d) Social Anxiety was significantly linked to social phobia.
In contrast, the remaining IDAS scales showed relatively limited evidence of specificity in relation to these DSM–IV diagnoses. Further research is needed to determine whether these findings reflect limitations in the scales (e.g., the IDAS Insomnia scale has unsatisfactory discriminant validity) or represent intrinsic features of these symptom dimensions (e.g., symptoms of insomnia are nonspecifically related to various mood and anxiety disorders).
Incremental Validity
One of the basic goals of this study was to examine the incremental validity of the IDAS scales against the BDI-II and BAI. We examined this issue in the final series of logistic regression analyses (see Table 9). These analyses established that the IDAS scales made significant incremental contributions to five of the six disorders: Dysphoria to major depression, Dysphoria and Insomnia to GAD, Traumatic Intrusions and Social Anxiety to PTSD, Lassitude to panic disorder, and Social Anxiety to social phobia. In addition, the effect of (low) Well-Being on major depression approached significance (odds ratio = 0.75, p < .08). We also should note that the IDAS Panic scale—which had strong, specific associations with panic disorder in all previous analyses (see Tables 4 through 8)—failed to emerge as an independent contributor here because of its very strong correlation with the BAI (r = .80) in this sample. In other words, although the IDAS Panic scale has a strong, specific link to panic disorder, this association is due to variance it shares with the BAI. Overall, however, these data establish that the IDAS scales—particularly Traumatic Intrusions (in relation to PTSD) and Social Anxiety (in relation to social phobia)—have incremental validity and tap important variance that is not contained in the BDI-II and BAI.
Limitations and Directions for Future Research
Need for replication
Our results generally were positive and consistent with expectations. Although these results are very encouraging, it clearly will be important to replicate these findings in other types of samples (e.g., adolescents, older adults). Moreover, given that our student (88.6%) and patient (89.9%) samples both were predominately White, it also will be important to examine the generalizability of these findings in more ethnically diverse samples.
Dysthymic disorder
We also obtained some weak and unexpected findings that merit discussion. Although the symptom scales showed substantial predictive power in relation to several DSM–IV mood and anxiety disorders—including major depression, GAD, panic disorder, PTSD, and social phobia—they displayed relatively weak associations with dysthymic disorder, specific phobia, and OCD. For instance, our analyses of dysthymic disorder yielded no moderate or large effect sizes; in fact, its strongest correlation was only −.07 with the IDAS Well-Being scale. These findings may be puzzling to some readers, given that (a) the self-report scales were substantially related to major depression and (b) several large epidemiological studies have reported strong comorbidity between major depression and dysthymic disorder (e.g., Krueger, 1999; Slade & Watson, 2006; Vollebergh, et al., 2001). For example, Krueger (1999) reported a tetrachoric correlation of .69 between major depression and dysthymia in the U.S. National Comorbidity Survey; Slade and Watson (2006) obtained a corresponding value of .72 in the Australian National Survey of Mental Health and Well-Being.
We emphasize that these surprising findings do not indicate any specific validity problems with the IDAS scales; indeed, the BDI-II (r = .06) and BAI (r = .04) also were unrelated to dysthymic disorder in this sample. Rather, the results reflect the specific manner with which our clinicians diagnosed this disorder. That is, our interviewers followed the DSM–IV guidelines for the differential diagnosis of these disorders very strictly and diagnosed dysthymic disorder only when they could identify a minimum 2-year period of dysphoria that clearly preceded the first onset of a major depressive episode. Because of this, diagnoses of major depression and dysthymic disorder actually showed a moderate negative association (tetrachoric r = −.42) in our data. Thus, these surprising findings basically reflect important differences in the diagnosis of dysthymic disorder across studies.
Further expansion of the IDAS
In contrast, our relatively weak findings for OCD and specific phobia highlight the fact that the final version of the IDAS contains no symptom content directly related to these disorders. In this regard, it is worth noting that the original IDAS item pool contained symptom content related to OCD and to agoraphobia but not to specific phobia. However, these OCD and agoraphobia items failed to define consistent factors in our structural analyses, and so these items were dropped from the final version of the instrument.
Overall, our validity data establish that the IDAS assesses a large number of distinctive symptom dimensions in a relatively efficient manner. Moreover, these data demonstrate the clinical and heuristic value of assessing and analyzing these symptoms separately. These encouraging results have indicated to us that it would be desirable to create an expanded instrument that provides even broader coverage and that assesses depression and anxiety symptoms more comprehensively. We therefore have begun the process of creating and testing new items related to other aspects of the mood (hypomania) and anxiety (OCD, agoraphobia, specific phobia) disorders. Our ultimate goal is to create an instrument that includes all of the important, distinctive symptom dimensions within this domain. In addition to its practical clinical utility, this expanded instrument could play an important role in clarifying the underlying symptom structure of this domain (see also Watson, 2005; Watson et al., 2007). This, in turn, potentially could have important taxonomic implications for the organization of these symptoms in subsequent editions of the DSM. Although we still have a long way to go, we already have made substantial progress in explicating—and assessing—the basic symptom dimensions within this domain.
Footnotes 1 The student sample reported here is the same as that presented in Watson et al. (2007, Study 3). However, as is described in more detail subsequently, the current patient sample is an expanded version of the sample reported previously in Watson et al. (2007, Study 3).
2 The participants in our student sample were also interviewed using the SCID-IV. As would be expected, however, the prevalence rates for most disorders were quite low in this unselected nonclinical sample. For example, only 7 students—or 2.3% of the sample—met formal diagnostic criteria for panic disorder. Consequently, these SCID-IV results are not presented here.
3 Analyses controlling for comorbid depression and anxiety generally yielded similar results, except for a larger number of suppressor effects.
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Submitted: September 20, 2007 Revised: March 11, 2008 Accepted: March 17, 2008
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Source: Psychological Assessment. Vol. 20. (3), Sep, 2008 pp. 248-259)
Accession Number: 2008-12234-006
Digital Object Identifier: 10.1037/a0012570
Record: 71- Title:
- Gambling-Related Cognition Scale (GRCS): Are skills-based games at a disadvantage?
- Authors:
- Lévesque, David. École de Psychologie, Université Laval, Quebec City, PQ, US, David.Levesque.6@ulaval.ca
Sévigny, Serge. Département des fondements et pratiques en éducation, Université Laval, Quebec City, PQ, US
Giroux, Isabelle. École de Psychologie, Université Laval, Quebec City, PQ, US
Jacques, Christian. École de Psychologie, Université Laval, Quebec City, PQ, US - Address:
- Lévesque, David, École de Psychologie, Université Laval, 2325 Rue des Bibliothèques, PAVFAS, Quebec City, PQ, US, G1V 0A6, David.Levesque.6@ulaval.ca
- Source:
- Psychology of Addictive Behaviors, Vol 31(6), Sep, 2017. pp. 647-654.
- NLM Title Abbreviation:
- Psychol Addict Behav
- Page Count:
- 8
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- Bulletin of the Society of Psychologists in Addictive Behaviors; Bulletin of the Society of Psychologists in Substance Abuse
- Other Publishers:
- US : Educational Publishing Foundation
Society of Psychologists in Addictive Behaviors - ISSN:
- 0893-164X (Print)
1939-1501 (Electronic) - Language:
- English
- Keywords:
- Gambling-Related Cognition Scale (GRCS), differential item functioning, cognitive distortions, item biases, type of gambling
- Abstract:
- The Gambling-Related Cognition Scale (GRCS; Raylu & Oei, 2004) was developed to evaluate gambling-related cognitive distortions for all types of gamblers, regardless of their gambling activities (poker, slot machine, etc.). It is therefore imperative to ascertain the validity of its interpretation across different types of gamblers; however, some skills-related items endorsed by players could be interpreted as a cognitive distortion despite the fact that they play skills-related games. Using an intergroup (168 poker players and 73 video lottery terminal [VLT] players) differential item functioning (DIF) analysis, this study examined the possible manifestation of item biases associated with the GRCS. DIF was analyzed with ordinal logistic regressions (OLRs) and Ramsay’s (1991) nonparametric kernel smoothing approach with TestGraf. Results show that half of the items display at least moderate DIF between groups and, depending on the type of analysis used, 3 to 7 items displayed large DIF. The 5 items with the most DIF were more significantly endorsed by poker players (uniform DIF) and were all related to skills, knowledge, learning, or probabilities. Poker players’ interpretations of some skills-related items may lead to an overestimation of their cognitive distortions due to their total score increased by measurement artifact. Findings indicate that the current structure of the GRCS contains potential biases to be considered when poker players are surveyed. The present study conveys new and important information on bias issues to ponder carefully before using and interpreting the GRCS and other similar wide-range instruments with poker players. (PsycINFO Database Record (c) 2017 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Cognition; *Gambling; *Pathological Gambling; Cognitive Impairment; Logistic Regression; Test Construction; Test Interpretation; Test Validity
- PsycINFO Classification:
- Clinical Psychological Testing (2224)
Behavior Disorders & Antisocial Behavior (3230) - Population:
- Human
Male - Location:
- Canada
- Age Group:
- Adulthood (18 yrs & older)
Young Adulthood (18-29 yrs)
Thirties (30-39 yrs)
Middle Age (40-64 yrs)
Aged (65 yrs & older) - Tests & Measures:
- Gambling-Related Cognition Scale [Appended]
Problem Gambling Severity Index
Gambling-Related Cognition Scale-French Version [Appended]
Problem Gambling Severity Index-French Version - Conference:
- European Conference on Gambling Studies and Policy Issues, 11th
- Conference Notes:
- The data and ideas in the manuscript were presented at the aforementioned conference.
- Methodology:
- Empirical Study; Quantitative Study
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- First Posted: Jul 17, 2017; Accepted: May 17, 2017; Revised: May 15, 2017; Dec 19, 2016
- Release Date:
- 20170717
- Correction Date:
- 20170911
- Copyright:
- American Psychological Association. 2017
- Digital Object Identifier:
- http://dx.doi.org/10.1037/adb0000297
- PMID:
- 28714724
- Accession Number:
- 2017-30690-001
- Persistent link to this record (Permalink):
- http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2017-30690-001&site=ehost-live
- Cut and Paste:
- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2017-30690-001&site=ehost-live">Gambling-Related Cognition Scale (GRCS): Are skills-based games at a disadvantage?</A>
- Database:
- PsycINFO
Gambling-Related Cognition Scale (GRCS): Are Skills-Based Games at a Disadvantage?
By: David Lévesque
École de Psychologie, Université Laval;
Serge Sévigny
Département des fondements et pratiques en éducation, Université Laval
Isabelle Giroux
École de Psychologie, Université Laval
Christian Jacques
École de Psychologie, Université Laval
Acknowledgement: The data and ideas in the manuscript were presented at the 11th European Conference on Gambling Studies and Policy Issues. David Lévesque was financially supported by the Fonds de recherche du Québec—Société et culture (FRQ-SC) and the Centre de réadaptation en dépendance de Montréal—Institut universitaire (CRDM-IU).
This study is based on the cross-sectional data that come from an unpublished doctoral thesis (Lévesque, 2017). Approval from the Université Laval Ethics Committee (Approval: 2013-038 A-2) was granted prior to commencement of the initial study in 2013.
Scientific research conducted over the last decades demonstrated the importance of the role of cognition in the conceptualization of gambling behavior. Cognition can be manifested as unique beliefs and attitudes toward control, luck, prediction, and chance (Ladouceur, & Walker, 1996; Langer, & Roth, 1975; Oei, Lin, & Raylu, 2008; Toneatto, 1999). Appropriate beliefs and attitudes are reflected in the perception that the game play and its outcomes are determined by chance; however, gamblers may develop the idea that results can be predicted and controlled (Barrault & Varescon, 2012). These cognitive distortions, also called irrational thoughts, appear to be related to the development and maintenance of problem gambling (Barrault & Varescon, 2012; Devynck, Giroux, & Jacques, 2012; Oei et al., 2008; Toneatto, 1999). Some studies suggest that they are more important for gamblers of skills-based games (Myrseth, Brunborg, & Eidem, 2010; Toneatto, Blitz-Miller, Calderwood, Dragonetti, & Tsanos, 1997).
Barrault (2012) used the GRCS (Raylu & Oei, 2004) to study cognitive distortions in 65 poker gamblers. She points out that some poker players could overestimate their skill level, which can be conceptualized as a cognitive distortion. However, she also observed that some cognitions may not be as erroneous as we think. The author reports Item 15 on the GRCS as an example (“Relating my losses to probability makes me continue gambling”) of a statement that may not be considered as a cognitive distortion among poker players in certain cases. When examining the GRCS items, one can try to identify which items could be an issue for strategic versus nonstrategic games (see Table 2 for a presentation of all items). At first sight, it seems possible that seven out of 23 items could be related to either skill (one’s skill or others’ skill: Items 5 and 22), probabilities (Items 10 and 15), or learning/experience (Items 9, 11, 14). Hence, these items could be more or less adapted to certain types of gamblers (e.g., poker players) and present measurement biases. Barrault (2012) emphasizes that at this time, no measure of cognitive distortions appear to be “totally adapted” to poker players. This view is shared by other researchers (Brochu, Sévigny, & Giroux, 2015; Devynck et al., 2012).
Item Means, Standard Deviations, and Results on Comparative Analyses on the Items According to Type of Game
Self-report measures of cognitive distortions usually play a role in screening all types of gamblers regardless of their gambling activities of choice. Accordingly, if the instruments used present biases according to the type of gambling game, there could be significant consequences. More notably, research findings using these instruments would be biased, and mislead clinicians who rely on these screening instruments. In short, for epidemiological, empirical, and clinical reasons, it is important to verify the potential cross-game bias of GRCS items.
This study’s main objective is to examine the DIF of the empirically validated French version of the GRCS (Raylu & Oei, 2004, French version: Grall-Bronnec et al., 2012) to shed light on one or more potential item biases relating to the type of gambling game. DIF is identified when a group responds differently to an item in comparison with a second group despite statistical control of the latent variable measured (e.g., total GRCS score). Accordingly, if a group endorses a GRCS item significantly more than the second group for a similar overall level of cognitive distortions (total GRCS score), the item is identified as presenting DIF. When DIF is detected, potential bias related to this item is suspected.
Essentially, the study seeks to answer the following research questions: (Q1) is DIF present among poker players and VLT players on the GRCS? (Q2) Do items presenting DIF have an impact on the measure? If the responses to some items vary between both groups, the presence of bias is assumed for these items. Items relating to the notion of skills may be more significantly endorsed by poker players as opposed to VLT players.
Method Participants
This study is based on the cross-sectional data of Lévesque’s unpublished doctoral thesis (Lévesque, 2017). The initial database included data from 272 gamblers. Participants had to meet the following inclusion criteria: (a) male, (b) at least 18 years of age, (c) gamble with money, (d) play poker at least twice a month or play VLTs at least once a month over the past 6 months, and (e) consider oneself as mainly a poker or VLT player. Participants who played both poker and VLT were excluded. Only participants who fully responded to the GRCS were retained for the current study. The final sample is comprised of 169 poker players and 73 VLT players. Participants are adults aged 19 to 82 years old (M = 35.29, Mdn = 29.00, SD = 15.57). Poker players are younger (M = 28.44, Mdn = 25.50, SD = 9.30) than VLT players (M = 51.07, Mdn = 54.00, SD = 15.66). Concerning poker players, 53.6% are students, 64.7% report having an annual income of $34,999 or less, 57.7% are single, and 82.2% have at least 11 years of education. As for VLT players, 43.8% work full-time, 54.8% report earning an annual income of $35,000 or more, 34.2% are single, and 68.5% have 11 years of education or less. The sociodemographic characteristics are similar to those found in studies conducted in the province of Québec, Canada (Dufour, Brunelle, & Roy, 2015; Sévigny et al., 2016) and in two populational studies (Young & Stevens, 2009; Svensson & Romild, 2014) recently conducted among similar groups of gamblers. The poker players (M = 2.07, SD = 2.57) had significantly lower scores for problem gambling than the VLT players (M = 6.61, SD = 6.28) (Welch’s t test = 5.99; p < .001).
Measurement Instrument
Cognitive distortions
The GRCS (Raylu & Oei, 2004) evaluates the presence, nature, and intensity of cognitive distortions among gamblers. This instrument includes 23 questions with 7-point Likert-type scale items (7 = strongly agree to 1 = strongly disagree). The higher the total score, the higher the number of gambling-related cognitions displayed. The measure has five subscales: (a) Perceived Inability to Stop Gambling (5 items; impaired control), (b) Interpretative Bias (4 items; reframing gambling outcomes), (c) Illusion of Control (4 items; ability to control gambling outcomes), (d) Gambling-Related Expectancies (4 items; expected effect of gambling) and (e) Predictive Control (6 items; ability to predict gambling outcomes). In the current study, the validated French version of the GRCS is used (Grall-Bronnec et al., 2012). The French version has good psychometric properties: acceptable indicators of the suitability of the factorial analysis (root-mean-square error of approximation = 0.07; confirmatory fit index = 0.93; normed fit index = 0.98; goodness of fit index = 0.88), good internal consistency of the subscales (Cronbach’s alpha > .7) and an adequate homogeneity coefficient (Loevinger’s H > 0.3). The five dimensions demonstrate good convergent and discriminant validity.
Problem gambling severity
The Problem Gambling Severity Index (PGSI; French version; Ferris & Wynne, 2001) is a validated self-report measure of problem gambling severity over the past 12 months. Nine items are evaluated with a Likert-type scale: never, sometimes, most of the time, and almost always. A score of 0 indicates nonproblem gambling while a score of 1 or 2 means low-risk gambling; a score of 3 to 7 designates moderate-risk gambling, and a score of 8 or more qualifies gambling as excessive (Ferris & Wynne, 2001). The PGSI presents good psychometric qualities (see Ferris & Wynne, 2001).
Procedure
Data for the original study was collected in two ways: online survey or telephone interview. The volunteer gamblers were recruited in the province of Quebec, Canada, via advertisements published in local and provincial newspapers, and e-mails sent through distribution lists at the Laval University. The advertisements were also published on social networks and discussion forums specifically for gambling-related issues. The recruitment campaign invited potential participants to complete the questionnaires on the LimeSurvey website or during a telephone interview. The telephone questions were presented in the same chronological order as the online version. Participants received compensation in the form of a gift certificate ($25 CAD) or participation in a drawing of five $100 CAD gift certificates. This procedure received approval from Laval University’s Research Ethics Committee (approval no.: 2013–038 A-2).
Statistical Analyses
The comparative analyses and verification of the undimensionality, and the OLRs were conducted using SPSS software (Version 21). The nonparametric kernel smoothing approach analyses were conducted using TestGraf software (Ramsay, 2000). In order to apply DIF analyses, unidimensionality of the measure was ascertained with Cronbach’s alpha, item-total correlations, and a principal components analysis (first and second factor ratio).
Comparative analyses
Preliminary comparative analyses (t tests) were conducted for each GRCS item to examine the relationship between the cognitive distortions and the preferred type of gambling game. Moreover, comparative analyses (analysis of variance [ANOVA]) were conducted between the two groups for total scores on the measure to verify if there was a significant difference between both groups.
DIF
Regarding DIF, the latent variable refers to the total GRCS score. Thus, pairing the participants of both gambling types with regard to their cognitive distortion level is necessary. When evaluating DIF, it is recommended to use several strategies and analyze the convergence of findings (Camilli & Shepard, 1994; Clauser & Mazor, 1998; Holland & Thayer, 1988). In this study, two strategies are used. The first method consists of using OLR (Zumbo, 1999) and the corresponding effect size measure. Within the scope of the present study, Zumbo’s (1999) threshold as well as Jodoin and Gierl’s (2001) more liberal threshold were used and compared.
The second method of DIF analysis consists of using nonparametric kernel smoothing analyses conducted with TestGraf (Ramsay, 2000). To detect DIF, the area between two item characteristic curves (intraclass correlation coefficient [ICC]) is calculated. If the β value of this area is superior to 0.30, it indicates the presence of a large DIF (Sachs, Law, & Chan, 2003; Santor, Ramsay, & Zuroff, 1994).
Examination of the impact of DIF on the measure
To verify the impact of problematic items and answer the second research question (Q2), comparative analyses using t tests for paired samples were conducted between the modified measure (created without the items presenting large DIF) and the original measure. Moreover, an intergroup comparative analysis (ANOVA) was conducted on participants’ total scores on the modified GRCS measure in order to verify whether results would be different from those obtained with the original measure.
ResultsThe results of factorial analyses conducted to verify the unidimensionality of the GRCS are presented in Table 1. All indicators suggest moderate support for the unidimensionality of the measure. Unidimensionality may be assumed when one dimension is clearly dominant (“general factor”) over other dimensions (Blais & Laurier, 1997).
Cronbach’s Alphas, Item-Total Correlations, and Results of the Principal Component Analysis on the GRCS
Preliminary comparative analyses provide evidence for significant differences for 11 of the 23 items of the GRCS. More precisely, poker players obtain a significantly greater mean score than VLT players for Items 1, 5, 9, 11, 15, and 22. On the contrary, VLT players present a significantly higher mean score than poker players for Items 7, 12, 17, 19, and 23. As for the instrument’s total score, poker players (M = 58.35, SD = 19.75) do not significantly differ from VLT players (M = 56.04, SD = 24.86; Welch’s F(1, 113.24) = 0.49; p = .484). Results of the comparative analyses are presented in Table 2.
Results of the DIF analyses are similar for OLR and the kernel smoothing estimation (TestGraf; Table 3). Items 5, 9, and 15 are identified as presenting DIF of high importance according to Zumbo’s (1999) criteria. When using Jodoin and Gierl’s (2001) criteria, the number of items presenting large DIF increases as they are less conservative than Zumbo’s (1999) criteria. Items 5, 7, 9, 13, 15, 22, and 23 present large DIFs, whereas Items 4, 11, 12, 17, 18, and 19 present moderate DIFs. As for the kernel smoothing estimation analyses (TestGraf), they identify five items with large DIFs (β > 0.30): Items 5, 9, 11, 15, and 22. Convergence of the results points to three items (5, 9, and 15) presenting large DIFs, all of uniform predominance and for which potential bias favors their endorsement by poker players. The ICC for Items 5, 9, and 15 for both subgroups are presented in Figure 1.
DIF Analyses of GRCS Items Between Poker and VLT Players
Figure 1. Plots showing differential item functioning (DIF) of Items 5, 9, and 15 for poker players (Line 1) and video lottery terminal (VLT) players (Line 2). Note: For each plot, the horizontal axis is the score or maximum likelihood estimates of the latent trait, and these scores are related to the total scores of the Gambling-Related Cognition Scale. The vertical axis is the degree of endorsement of the items. The dashed lines indicate the percentage of respondents that fell below various latent trait scores. The line numbered “1” is the mean score for poker players, whereas the line numbered “2” is the mean score for VLT players. DIF is inferred from the area formed between these lines, with larger areas indicating more DIF. The composite DIF is a weighted function of the difference between the two lines across the different trait levels. Composite DIF values >.30 are inferred as demonstrating large DIF.
To verify the impact of potentially biased items on the GRCS, the first comparative analysis reveals that the mean total score is significantly lower (M = 2.54, SD = 0.86 vs. M = 2.31, SD = 0.88) when Items 5, 9, and 15 are withdrawn for poker players: t(167) = 16.95, p < .001, and statistically higher (M = 2.44, SD = 1.08 vs. M = 2.49, SD = 1.09) for VLT players: t(72) = −3.46, p = .001. The ANOVA conducted on the total scores of the modified measure do not show any significant intergroup differences (p = .234), as was the case with the original measure. Two final post hoc comparative analyses were then conducted to explore the potential impact of the three items of interest on their subscales of origin (Interpretative Bias: Items 5 and 15; and Predictive Control: Item 9). In the original instrument, poker players presented a higher score on the Interpretative Bias subscale than VLT players (F(1, 238) = 24.45, p < .001). Yet, this difference disappears once Items 5 and 15 are removed from the subscale (F(1, 240) = 0.109, p = .741). No difference was found between the groups for the original instrument or the modified instrument with regard to the Predictive Control subscale.
Validity check
Invariance of the results was tested to avoid a potential confounding measurement bias with between-groups differences. Thus, DIF analyses (OLR) were conducted on three items (5, 9, and 15) with gamblers who have low problem gambling severity or no problem (PGSI <2) only. The results are similar (large DIF; ΔR2 > .108, p < .01.) to those obtained with the original sample. Correlation analyses were also conducted to examine the possible confounding effects of age, education, income, and gambling problem severity when interpreting the DIF results. As seen in Table 4, these variables cannot be considered as confounding variables in the present sample because of the absence of correlation between them and the three items of interest. Only one correlation was possibly problematic (.228 between education and Item 15 for medium GRCS scores: R2 = .05). All positive correlations between problem gambling severity and items (see Table 4) support our subsequent interpretation of the results (see the Discussion section). For example, the .330 correlation between problem gambling severity and Item 15 scores means that the more severe the poker player’s gambling problem, the more they will endorse Item 15. Considering that poker players in the total sample were less problematic than VLT players, Item 15 probably would have been more highly endorsed if the sample was comprised of more problematic poker gamblers. Thus, the difference between VLT and poker players on that item would have been greater than currently reported.
Bivariate Spearman Correlations Between Sociodemographic Variables; Problem Gambling; Items 5, 9, and 15 for Low, Medium, and High Total GRCS Scores Across Poker and VLT Players
DiscussionThe GRCS is an instrument created for all gamblers and validated among groups playing different types of games: games of pure chance and games involving some skill (Grall-Bronnec et al., 2012; Raylu & Oei, 2004). Given that it is intended for all types of gamblers, the administration and interpretation of the instrument should be uniform for all gamblers. This study aimed to assess GRCS items (French version) in order to shed light on potential biases relating to gambling activity type. The results show that several items present moderate or large DIF. The themes relating to Items 5, 9, and 15 (large DIF) are skills, probabilities, and abilities or learning related to gambling behavior. Poker players endorse many more of these items than VLT players, regardless of their total GRCS score. This could be explained in part by poker players’ perception of these items, which may differ due to the portion of skills inherent to poker regardless of the degree of their cognitive distortions and problem gambling severity. In the end, this group’s total score is “inflated” because of a methodological artifact, which would be interpreted as a greater intensity of cognitive distortion. Hence, a question that arises is whether these items are indeed referring to actual cognitive distortions. In this regard, Young and Stevens are emphatic: it would be false to believe that the notion of skill is the product of a simple erroneous belief (Stevens & Young, 2010; Young & Stevens, 2009). According to these authors, the notion of skill is a structural characteristic specific to a distinct category of gambling games and not a belief manifested independently of the facts (Stevens & Young, 2010; Young & Stevens, 2009). Considering the findings of this study as well as Young and Stevens’ arguments, one question arises: is it conceptually appropriate to include items pertaining to the notion of skill within a measure of cognitive distortions targeting players of skill-related gambling games? This question is not easy to answer as it inevitably refers to the definition and reliable evaluation of cognitive distortion. Langer (1975) defines cognitive distortion as “an expectancy of a personal success probability that is higher than the objective probability should warrant” (p. 313). However, in poker, the objective probability of success depends on several factors (cards dealt, game experience, etc.), rendering its calculation complex (Linnet et al., 2012; Linnet, Gebauer, Shaffer, Mouridsen, & Møller 2010, Siler, 2010). Because the inherent portion of skill in poker is real but the objective probability of success is immeasurable, self-evaluation of one’s skills is not exempt from errors, and the GRCS items as currently formulated do not make it possible to determine the true amount of cognitive distortion in a player’s evaluation. Therefore, more attention should be directed toward the structural composition of the type of gambling game to identify a player’s cognitive distortions, and toward the subsequent development of items to evaluate this concept.
This study also examined the impact of the GRCS’ potentially biased items by using the measure after having withdrawn the items identified as presenting large DIF. The modified measure did not change the absence of a significant difference in total scores between the two groups (poker vs. VLT); however, for one same group, a significant difference was observed between the means for the original measure and the modified measure. Moreover, post hoc comparative items revealed the larger impact on the Interpretative Bias subscale. This subscale seems more or less adapted to poker players. Studies evaluating cognitive distortions should consider the preferred type of gambling game when analyzing their data.
Now, what should be done with items presenting DIF in the instrument’s current form? According to Zumbo (1999), simply eliminating these items would limit the factor of interest or the concept measured. The author also points out that an item presenting DIF does not necessarily mean that it is problematic, just as an item without differential functioning may present an undetected bias. Further studies on the measure are therefore recommended. In the present study, items identified as potentially biased should be subject to critical analyses by experts and theoreticians of the cognitive approach to better understand the causes of this differential functioning. We focused on three largely biased items (to be conservative) to show that poker players are disadvantaged by some items related to skills. The main problem with biased items is that they lead to an invalid interpretation of the GRCS observed scores. If the GRCS were to measure gambling-related cognitions in order to compare two or more groups, these groups should not differ based on the type of game played (skills vs. no skill); otherwise, these differences may be due to item bias rather than real group differences on the construct being measured. Of course, the GRCS may possess other biases that were not examined here; these could be examined in other studies. It is necessary to continue research on how to correctly measure erroneous thinking among poker players and, on a larger scale, among players of gambling games involving skill.
Some limitations should be considered when interpreting the findings of this study. Sample size in DIF analyses have an effect on item detection (Acar, 2011). Small samples like those of the present study could underestimate the number of DIF items. In order to increase the findings’ generalizability, this study must be replicated with larger and more representative samples (e.g., samples comprising players of both genders). Second, even though the GRCS may resemble other measures that evaluate cognitive distortions, it is not identical to them. Thus, the findings may not be generalizable to other measures.
This study is the first to apply a DIF analysis to a measure of cognitive distortions in gambling. The findings suggest that a preferred type of gambling game may influence gamblers’ response patterns on the GRCS. The issue of DIF is important as it refers to the notion of equity between groups: a measure of cognitive distortions should not potentially advantage or disadvantage a group. Finally, this study leads to new research questions, namely, regarding the “cross-game” biases of “one size fits all” measures. In this context, the development of new scales specific to certain types of gambling games could also be an avenue for further studies. In conclusion, clinicians and researchers should be careful when using and interpreting the GRCS measure. Special attention should be paid to the items identified as manifesting DIF in this study.
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Submitted: December 19, 2016 Revised: May 15, 2017 Accepted: May 17, 2017
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Source: Psychology of Addictive Behaviors. Vol. 31. (6), Sep, 2017 pp. 647-654)
Accession Number: 2017-30690-001
Digital Object Identifier: 10.1037/adb0000297
Record: 72- Title:
- Gender differences in life events prior to onset of major depressive disorder: The moderating effect of age.
- Authors:
- Harkness, Kate L.. Department of Psychology, Queen’s University, Kingston, ON, Canada, harkness@queensu.ca
Alavi, Nazanin. Department of Psychology, Queen’s University, Kingston, ON, Canada
Monroe, Scott M.. Department of Psychology, University of Notre Dame, IN, US
Slavich, George M.. Department of Psychiatry, University of California, San Francisco, CA, US
Gotlib, Ian H.. Department of Psychology, Stanford University, Stanford, CA, US
Bagby, R. Michael. Department of Psychiatry, Centre for Addiction and Mental Health, University of Toronto, Toronto, ON, US - Address:
- Harkness, Kate L., Department of Psychology, Queen’s University, Kingston, ON, Canada, K7L 3N6, harkness@queensu.ca
- Source:
- Journal of Abnormal Psychology, Vol 119(4), Nov, 2010. pp. 791-803.
- NLM Title Abbreviation:
- J Abnorm Psychol
- Page Count:
- 13
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- The Journal of Abnormal and Social Psychology; The Journal of Abnormal Psychology; The Journal of Abnormal Psychology and Social Psychology
- ISSN:
- 0021-843X (Print)
1939-1846 (Electronic) - Language:
- English
- Keywords:
- adolescence, gender differences, major depression, stressful life events, onset
- Abstract:
- Theoretical models attempting to explain why approximately twice as many women as men suffer from depression often involve the role of stressful life events. However, detailed empirical evidence regarding gender differences in rates of life events that precede onset of depression is lacking, due in part to the common use of checklist assessments of stress that have been shown to possess poor validity. The present study reports on a combined sample of 375 individuals drawn from 4 studies in which all participants were diagnosed with major depressive disorder and assessed with the Life Events and Difficulties Schedule (Bifulco et al., 1989), a state-of-the-art contextual interview and life stress rating system. Women reported significantly more severe and nonsevere, independent and dependent, and other-focused and subject-focused life events prior to onset of depression than did men. Further, these relations were significantly moderated by age, such that gender differences in rates of most types of events were found primarily in young adulthood. These results are discussed in term of their implications for understanding the etiological role of stressful life events in depression. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Adolescent Development; *Human Sex Differences; *Major Depression; *Onset (Disorders); Age Differences; Experiences (Events); Stress
- Medical Subject Headings (MeSH):
- Adolescent; Adult; Age Factors; Aged; Analysis of Variance; Depressive Disorder, Major; Female; Humans; Interview, Psychological; Life Change Events; Logistic Models; Male; Middle Aged; Models, Psychological; Risk Factors; Severity of Illness Index; Sex Factors
- PsycINFO Classification:
- Affective Disorders (3211)
- Population:
- Human
Male
Female - Location:
- Canada
- Age Group:
- Adolescence (13-17 yrs)
Adulthood (18 yrs & older)
Young Adulthood (18-29 yrs)
Thirties (30-39 yrs)
Middle Age (40-64 yrs)
Aged (65 yrs & older) - Tests & Measures:
- Life Events and Difficulties Schedule
Beck Depression Inventory DOI: 10.1037/t00741-000
Schedule for Affective Disorders and Schizophrenia DOI: 10.1037/t07870-000 - Grant Sponsorship:
- Sponsor: Sick Kids Foundation
Other Details: New Investigator award
Recipients: Harkness, Kate L.
Sponsor: Ontario Mental Health Foundation, Canada
Recipients: Harkness, Kate L.; Bagby, R. Michael
Sponsor: National Institutes of Health
Grant Number: MH-60802
Recipients: Monroe, Scott M.; Gotlib, Ian H. - Methodology:
- Empirical Study; Quantitative Study
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- First Posted: Sep 20, 2010; Accepted: May 18, 2010; Revised: May 12, 2010; First Submitted: Sep 17, 2009
- Release Date:
- 20100920
- Correction Date:
- 20140519
- Copyright:
- American Psychological Association. 2010
- Digital Object Identifier:
- http://dx.doi.org/10.1037/a0020629
- PMID:
- 20853920
- Accession Number:
- 2010-19223-001
- Number of Citations in Source:
- 58
- Persistent link to this record (Permalink):
- http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2010-19223-001&site=ehost-live
- Cut and Paste:
- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2010-19223-001&site=ehost-live">Gender differences in life events prior to onset of major depressive disorder: The moderating effect of age.</A>
- Database:
- PsycINFO
Gender Differences in Life Events Prior to Onset of Major Depressive Disorder: The Moderating Effect of Age
By: Kate L. Harkness
Department of Psychology, Queen's University, Kingston, Ontario, Canada;
Nazanin Alavi
Department of Psychology, Queen's University, Kingston, Ontario, Canada
Scott M. Monroe
Department of Psychology, University of Notre Dame
George M. Slavich
Department of Psychiatry, University of California, San Francisco
Ian H. Gotlib
Department of Psychology, Stanford University
R. Michael Bagby
Department of Psychiatry, Centre for Addiction and Mental Health, University of Toronto, Toronto, Ontario, Canada
Acknowledgement: This study was supported by a New Investigator award from the Sick Kids Foundation to Kate L. Harkness; an operating grant from the Ontario Mental Health Foundation to Kate L. Harkness and R. Michael Bagby; and National Institutes of Health Grant MH-60802 to Scott M. Monroe and Ian H. Gotlib.
One of the most consistent and enduring findings in research on major depressive disorder (MDD) is a higher prevalence of MDD in women than in men. This gender difference appears in early adolescence, reaches a rate of approximately 2:1 by mid-adolescence, and persists at least through the end of midlife (Hankin & Abramson, 1999; Kessler, 2003). Gender differences in rates of MDD have been found cross culturally and cannot be accounted for by differences in treatment seeking (Nolen-Hoeksema, 1993).
Several theoretical explanations have been proposed for the emergence and persistence of gender differences in rates of MDD, most of which implicate stressful life events in their explanatory framework (e.g., Cyranowski, Frank, Young, & Shear, 2000; Hankin & Abramson, 2001; Nolen-Hoeksema, 1993). In particular, these models postulate that women possess biological and psychological vulnerabilities (e.g., a ruminative response style, higher levels of the affiliative hormone oxytocin) that both increase rates of stressful life events and increase women's likelihood of developing MDD in the face of stressful life events. That is, a key tenet of these theoretical models is that stressful life events play a stronger role in the etiology of MDD in women than in men.
Our goal in the present paper is to examine fine-grained differences in the stressful life events that precede the onset of MDD to determine whether stressful life events may, indeed, be more strongly associated with the etiology of MDD in women than in men. Stressful life events clearly precede the onset of MDD in women. In a seminal series of studies conducted by George Brown and his colleagues in the United Kingdom, women with MDD were up to three times more likely to have experienced a major (“severe”) life event in the 6 months prior to the onset of their depression than were nondepressed women in a comparable time period (Brown & Harris, 1978, 1989). Further, using a large sample of female twin pairs, Kendler and colleagues documented a causal relation of life events to MDD and found that life events were the strongest proximal predictor of onset of MDD in women (Kendler, Gardner, & Prescott, 2002; Kendler, Karkowski, & Prescott, 1999).
Very little work has been done, however, to carefully characterize the relation of stressful life events to MDD in men. Further, it is as yet unclear whether depressed women are, indeed, more likely than depressed men to experience stressful life events prior to onset. Proper assessment of life events is crucial to addressing the etiological role of life events in MDD. In particular, a sophisticated measure of life stress is required that (a) firmly dates events with respect to MDD to ensure that events temporally precede onset; (b) provides detailed contextual information that permits ratings on dimensions of stress that are most relevant to etiology, such as severity; and (c) limits the influence of preexisting psychological vulnerabilities that may be confounded with gender and may bias the reporting of life events (e.g., ruminative response style; see Monroe & Simons, 1991). Contextual life event interviews that employ anchored and objective rating systems, such as the Life Events and Difficulties Schedule (LEDS; Bifulco, Brown, & Harris, 1994), satisfy all three of these requirements (see Hammen, 2006; Monroe, 2008). However, this method is also time consuming and labor intensive. As a result, studies using the LEDS are often underpowered to examine fine-grained distinctions in terms of the life events that are likely to be most salient for MDD etiology.
Previous research has generally found no evidence for a gender difference in the overall number of life events occurring prior to onset of MDD (Hoffmann & Su, 1998; Kendler, Thornton, & Prescott, 2001; Maciejewski, Prigerson, & Mazure, 2001; Perris, 1984; Williamson, Birmaher, Anderson, Al-Shabbout, & Ryan, 1995; Zlotnick, Shea, Pilkonis, Elkin, & Ryan, 1996). Further, longitudinal studies have found only limited support for the hypothesis that stressful life events predict the onset of MDD more strongly in women than in men (Maciejewski et al., 2001; Nazroo, Edwards, & Brown, 1997; cf. Dalgard et al., 2006; Kendler, Kuhn, & Prescott, 2004; Kendler et al., 2001). Investigators have reported, however, that women may experience events that have particular relevance for the etiology of depression. In fact, this hypothesis has been explicitly integrated into theories of the gender differences in depression. For example, Cyranowski et al. (2000) posited that women's need for affiliation, mediated by hormonal changes at the pubertal transition, renders women particularly vulnerable to developing MDD in the face of interpersonal events. Consistent with this formulation, adult women report higher rates of life events involving their social network prior to the onset of MDD, whereas men report higher rates of events in the domains of work and crime (Dalgard et al., 2006; Kendler et al., 2001; Maciejewski et al., 2001). Again, the implication here is that the events that cluster in the period prior to MDD onset represent the stressors that were most central in triggering that onset.
There are two important limitations to the above research. First, previous studies have not examined the event dimensions that have been found in prior to research to have the most direct etiological relevance to MDD. This is important if one is examining a purported link between events and onset. Brown and Harris (1989) determined through their careful work using the LEDS that life events that occur in the 6 months prior to depression episode onset are the most central in precipitating that onset. Life events going further back in time than 6 months, in contrast, have substantially lower relevance to MDD onset. Further, severe events that are associated with at least a moderate degree of psychological threat are most strongly associated with MDD onset (e.g., job loss in a financially threatened context, spouse's unexpected request for separation after 20 years of marriage; Brown & Harris, 1989; Kendler et al., 1999). Nonsevere events, although still unpleasant, may not have the psychological impact required to trigger an episode of MDD. Similarly, events that are at least in part dependent on the individual's own behavior (e.g., breakup of a romantic relationship) more strongly predict MDD onset than do independent life events (e.g., job loss due to factory closure; see, e.g., Kendler et al., 1999; Williamson et al., 1995; cf. Shrout et al., 1989). Further, whereas in general life events that are focused directly on the participant (i.e., involve mostly the participant; e.g., job loss) are more impactful than are events that occur to others (e.g., close friend's job loss; Brown & Harris, 1989), research has determined that other-focused events are more common prior to MDD onset in women than in men (Dalgard et al., 2006; Kendler et al., 2001; Maciejewski et al., 2001). In the present study, examining gender differences in events occurring during the most etiologically central 6-month period prior to MDD onset stratified on these dimensions known to be most strongly associated with onset provided for a more complete understanding of the differential relation of stress to MDD in women versus men.
Second, previous studies examining gender differences in rates of stressful life events prior to MDD have not considered developmental changes in individuals' stressful life event context. Indirect evidence for the possibility that age might moderate gender differences in rates of life events prior to MDD comes from data suggesting that gender differences in life events may be more consistently supported in adolescent than in adult samples. Longitudinal studies of adolescents have found that negative life events predict MDD and general emotional maladjustment more strongly in girls than in boys (Bouma, Ormel, Verhulst, & Oldehinkel, 2008; Ge, Lorenz, Conger, Elder, & Simons, 1994; Rudolph & Hammen, 1999; Shih, Eberhart, Hammen, & Brennan, 2006; Silberg et al., 1999; Windle, 1992). It is important to note, though, that these were all community studies with low base rates of MDD, and most involved prediction of maladjustment (or symptoms) as opposed to the syndrome of MDD. Several also used self-report checklist measures of stress (for exceptions, see Rudolph & Hammen, 1999; Shih et al., 2006) that may have been biased by factors that are known to be stronger in girls than in boys (e.g., rumination, negative cognitive style; Abela & Hankin, 2008). Therefore, it is possible that findings in these studies may be driven by sex differences in these diathetic factors and not by differences in life events per se (see Monroe & Simons, 1991). One study using the LEDS interview and examining overall rates of events in the 6-month period prior to the onset of MDD in adolescence found no evidence of gender differences (Williamson et al., 1995).
To our knowledge, there are no studies that have examined how gender differences in rates of stressful life events prior to MDD are moderated by age across adulthood. Previous research has determined that rates of life events decrease with age across adulthood, both in the general population (Henderson, Byrne, & Duncan-Jones, 1981; Jordanova et al., 2007; Leskelä et al., 2004) and in individuals with MDD (Perris, 1984). Further, in a large epidemiological sample of 8,580 individuals ages 16–74 from the United Kingdom, researchers found that the decline in life events with age was stronger in women than in men (Jordanova et al., 2007). However, these results do not speak to whether gender differences in the experience of stress prior to MDD onset are moderated by age. This latter question is key to understanding gender differences in the role of life stress in the etiology and pathology of MDD across the life course.
In the present article we provide a detailed examination of gender differences in life events prior to MDD onset assessed for an amalgamated sample of 375 individuals, recruited from four study sites, who were diagnosed with MDD. Our full sample is diverse in terms of sex, age (range = 13–65 years), socioeconomic status, and geographic locale. Most important, life events for all individuals were assessed with the contextual LEDS system. As such, this sample allowed us to capitalize on the richness of the contextual method to address fine-grained questions that have previously been difficult to answer, given the necessity of a large sample. In particular, the present study was the first to examine gender and age differences in rates of life events experienced in the most etiologically central 6-month period prior to onset stratified on the basis of event dimensions that have the strongest relation to onset. Further, it is the first to examine whether age group moderates gender differences in rates of life events prior to MDD onset. In the present study we categorized our sample into four age groups that roughly map onto life span stages of development (Levinson, 1978): (a) adolescence (age 13–17); (b) young adulthood (age 18–29); (c) middle adulthood (age 30–49); and (d) upper middle adulthood (age 50–65). First, we predicted that women would report higher rates of (a) severe events, (b) dependent events, and (c) other-focused events than would men. Second, we predicted that rates of events prior to MDD onset would be higher in young adults than in adolescents and would decrease with age across middle and upper middle adulthood. Third, we predicted that age group would moderate gender differences in rates of events prior to onset. In particular, we predicted that gender differences in life events prior to MDD onset would emerge in the younger age groups (adolescence and young adulthood) but not in the older age groups.
Method Participants
Participants were 375 individuals who met criteria for a current episode of MDD and who took part in one of four larger studies investigating the relation of stress to MDD. Participants from Study 1 were 52 adolescent boys and girls (ages 13–17) recruited with advertisements and from community mental health centers in a suburban city in southeastern Ontario, Canada (see Harkness, Bruce, & Lumley, 2006). Participants in Study 2 were 76 adult women (ages 18–65) recruited with advertisements in a suburban city in Oregon (see Harkness & Monroe, 2006). Participants in Study 3 were 100 adult men and women (ages 18–58) recruited with advertisements in the San Francisco Bay area (see Monroe, Slavich, Torres, & Gotlib, 2007). Finally, participants in Study 4 were 147 adult men and women (ages 18–65) recruited via advertisements and doctor referrals in the Greater Toronto area (see Bulmash, Harkness, Stewart, & Bagby, 2009). These prior reports give full details regarding recruitment.
All participants were required to meet criteria of the Diagnostic and Statistical Manual of Mental Disorders (DSM–IV; American Psychiatric Association, 1994) for a current episode of nonbipolar MDD with a duration of less than two years. The duration criterion was included to maximize recall of the events that occurred in the 6 months prior to episode onset (Brown & Harris, 1978). Exclusion criteria consistent across all studies were the presence of a psychotic disorder, bipolar disorder, substance dependence, conduct disorder, or developmental disability (latter two diagnoses relevant to Study 1) and the presence of a medical disorder that could cause depression (by patient report; e.g., hypothyroidism). An additional inclusion criterion for Study 4 was a score of ≥16 on the 17-item Hamilton Rating Scale for Depression (Hamilton, 1960). All participants had a minimum Grade 8 education and were fluent in reading English. Table 1 presents descriptive data separately for each study.
Descriptive Characteristics by Study Site
Measures
Diagnosis
Participants in Study 1 were administered all sections of the child and adolescent version of the Schedule for Affective Disorders and Schizophrenia (K-SADS; Kaufman, Birmaher, Brent, Rao, & Ryan, 1996). Participants in Studies 2–4 were administered the Structured Clinical Interview for DSM–IV Axis I Disorders (SCID–I/P; First, Spitzer, Gibbon, & Williams, 2002). The K-SADS and the SCID–I/P are semistructured clinical interviews that derive DSM–IV Axis I diagnoses. Across studies, interviewers were advanced graduate students in clinical psychology who were trained to “gold standard” reliability status (see Grove, Andreasen, McDonald-Scott, Keller, & Shapiro, 1981).
Depression severity
The 21-item self-report Beck Depression Inventory (BDI; Beck & Steer, 1993) was administered to all participants to determine the presence and severity of depression symptoms. This measure is widely used in the study of depression in adolescents and adults and has internal consistency estimates ranging from .73 to .95 (Beck, Steer, & Garbin, 1988).
Stressful life events
The Life Events and Difficulties Schedule (LEDS–II; Bifulco et al., 1989) is a semistructured contextual interview and rating system that assesses recent stressful life events in 10 domains: education, occupation, housing, finances, role changes, legal, health, romantic relationships, other relationships, and deaths. The focus in the present study is on life events experienced in the 6-month period prior to the index MDD episode onset. All interviews were audiotaped. A research assistant then listened to the interviews and prepared vignettes of each event, deleting any information regarding the participant's depression and emotional reaction to the stressors. Rating teams at each site consisted of two to four raters who based their ratings on the LEDS manual, which includes explicit rules and criteria for rating life events, as well as over 5,000 case vignettes that are used to standardize the ratings. Raters had to justify each rating by appealing to specific vignettes. Studies have shown higher reliability and validity in the prediction of MDD with the LEDS than with checklist measures of stress (e.g., Brown & Harris, 1989; McQuaid, Monroe, Roberts, Kupfer, & Frank, 2000). Interrater reliability for event severity ratings averaged across the four studies reported here was k = .90.
Life events were rated for “focus,” which refers to the primary actor of the event, on a 3-point scale: 1 for subject (e.g., subject starts a job); 2 for joint (e.g., subject and boyfriend have a major argument); and 3 for other (e.g., subject's mother has a stroke). Subject- and joint-focused events were combined for analyses, as they predict vulnerability to MDD equally (Brown & Harris, 1989). Life events were rated for their level of contextual threat (i.e., severity) on a 5-point scale (1 = marked, 2a = high moderate, 2b = low moderate, 3 = some, 4 = little/none). Each rater provided his or her own threat rating for each event. Discrepancies among raters were discussed, and a consensus threat rating was achieved. This consensus rating was used in all analyses. Severe events were rated 1 (marked) or 2a (high moderate) on threat and 1 (subject) or 2 (joint) on focus (e.g., a woman learns that her husband of 10 years, on whom she is financially dependent, is having an affair). Nonsevere events were rated 2b (low moderate), 3 (some), or 4 (little/none) on threat and could be of any focus (e.g., participant has an argument with a close friend that is resolved within a week).
Events were also rated for independence. Independent life events were judged as totally or nearly totally independent of the actions or behavior of the individual (e.g., job loss due to plant closure, grandmother's death from cancer). Dependent life events were judged as at least partly dependent on the participant's actions or behavior (e.g., quit job, filed for divorce). Consensus decisions regarding independence were based on the context surrounding each event and adhered to the rules for making such distinctions found in the LEDS manual.
Event variables used in analyses were defined as event totals, except in the case of severe events and other-focused events. Because latter types of life events were too infrequent to permit parametric analyses, they were dichotomized as presence versus absence. Thus, the event variables used in analyses included the total number of subject/joint-focused events, the presence versus absence of an other-focused event, the total number of nonsevere events, the presence versus absence of a severe event, the total number of dependent events, and the total number of independent events. All events were reported for the most etiologically central 6-month time period prior to onset of the index episode (Brown & Harris, 1978).
Procedure
Ethical approval for each study was obtained by each institution's research ethics board. All participants and a parent or guardian for those under 18 provided written informed consent. Full details regarding each study procedure are provided in previous reports. Briefly, in Study 1, adolescents participated in two 2-hr assessments separated by one week. The K-SADS and questionnaires were administered during Session 1, and the LEDS was administered during Session 2. In Study 2, women participated in one 3-hr assessment. Again, the SCID–I/P interview and questionnaires were administered before the LEDS interview. In Study 3, participants took part in three interview sessions, each separated by approximately one week. The SCID–I/P was administered during Session 1, and the LEDS was administered in Session 3.
Study 4 was a treatment trial. Participants completed the SCID and questionnaires prior to beginning the trial. Participants were then randomized to receive 16 weeks of cognitive–behavioral therapy, interpersonal psychotherapy, or antidepressant medication according to a standard treatment algorithm. At the completion of the trial, participants were administered the LEDS interview, which covered the period from 6 months prior to onset of the index episode through the treatment trial (see Bulmash et al., 2009).
Data Analysis
We tested the hypotheses with a series of 2 (sex: female vs. male) × 4 (age group) analyses of variance (ANOVAs) using SPSS statistical software. The general linear model, which utilizes Type III sums of squares to account for unequal cell sizes, was employed. Estimated marginal means are reported in all figures. Four age groups were constructed that roughly correspond to life span stages of development: (a) adolescence (ages 13–17; n = 34 girls, 14 boys), (b) young adulthood (ages 18–29; n = 95 women, 19 men), (c) middle adulthood (ages 30–49; n = 123 women, 32 men), and (d) upper middle adulthood (ages 50–65; n = 34 women, 24 men). We chose not to examine age as a continuous variable, because we were not hypothesizing a linear relation of age and life events and because developmental discontinuities across age do not necessarily follow a linear trajectory. The dependent variables for the ANOVAs included the total number of (a) events regardless of focus, severity, or independence; (b) subject/joint-focused events; (c) dependent events; (d) independent events; and (e) nonsevere events, respectively. Skew for all variables was within acceptable limits (<|2|).
Other-focused and severe events were dichotomized (present/absent) as noted above, due to low frequencies, and were tested in logistic regression models. The main effects of gender and age group were entered in the first step, and the interaction was entered in the second step.
Results Site Differences and Descriptive Characteristics
Excluding Study 2, which included only women, sex was not differentially distributed across the study sites, χ2(2) = 1.29, p = .53 (see Table 1). Excluding Study 1, which included only adolescents, age did not differ significantly across studies, F(2, 320) = 2.12, p = .12. Occupation status differed across studies, χ2(3) = 17.35, p < .005, with Study 1 reporting the highest occupation status (in this case, of the parents). Study 1 and Study 4 had the highest proportion of individuals in their first episode of depression, χ2(3) = 51.96, p < .001. Study 4 and Study 2 had significantly higher BDI scores than did Study 1, F(3, 371) = 9.95, p < .001.
Table 2 presents the descriptive characteristics of the full sample of 375 participants stratified by our two variables of interest: sex and age group. In the full sample, male and female participants did not differ significantly in terms of age or BDI scores. However, male participants were significantly more likely than female participants to be employed (or have their parents employed) in a professional occupation, χ2(3) = 7.73, p < .005, and to be experiencing their first onset of depression, χ2(1) = 5.16, p < .05. In terms of age group, the 13- to 17-year-olds and the 50- to 70-year-olds had a higher proportion of males than did the other groups, χ2(3) = 14.72, p < .005. Further, the 13- to 17-year-olds had a significantly higher (parental) occupation status, χ2(3) = 11.66, p < .005, lower BDI scores, F(3, 371) = 4.40, p < .01, and a higher percentage of individuals on a first onset of depression, χ2(3) = 24.46, p < .005, than did the three adult groups.
Descriptive Statistics of Demographic and Clinical Variables Stratified by Sex and Age
Results of models that included depression history (first onset vs. recurrence), socioeconomic status, and depression severity (BDI scores) did not differ from those of uncontrolled models. Further, in exploratory analyses we failed to find evidence for two- or three-way interactions of depression history and either sex (ps > .64, η2 < .001) or age group (ps > .30, η2 < .003). Therefore, we present the uncontrolled models below for ease of interpretability.
Gender Differences in Life Events Prior to Onset of MDD
Descriptive statistics of life events stratified by sex and by age group are presented in Table 3. As generally predicted, the ANOVA model examining total number of events overall revealed a significant main effect of age group, F(3, 367) = 3.22, p < .05, η2 = .026, as well as a significant interaction of sex and age group, F(3, 367) = 3.52, p < .05, η2 = .03. The homogeneity of variance assumption was not upheld for this analysis, F(7, 367) = 4.55, p < .005. Therefore, we performed bootstrapping procedures on the interaction to establish the robustness of this effect (Howell, 2007). Sampling 5,000 times from our distribution resulted in a significant mean bootstrapped F of 4.23 (p = .01).
Descriptive Statistics of Event Variables Stratified by Sex and Age
Simple effects contrasts conducted on the interaction revealed that female participants experienced significantly more events than did male participants, F(1, 367) = 13.74, p < .001, η2 = .04, but only among those in the 18- to 29-year-old age group (see Figure 1). Further, among female participants, the 18- to 29-year-olds reported significantly more life events than did those in the 50+ age group, F(1, 367) = 18.60, p < .001, η2 = .05. The pattern was reversed for male participants, with 18- to 29-year-olds reporting fewer life events than did the 13- to 17-year-olds, F(1, 367) = 4.63, p < .05, η2 = .012.
Figure 1. Total number of life events reported in 6 months prior to onset by sex and age.
Event focus
The analysis of subject/joint-focused events yielded a significant main effect of age group, F(1, 367) = 3.57, p < .05, η2 = .03, and a significant Sex × Age Group interaction, F(3, 367) = 2.98, p < .05, η2 = .024. Again, the homogeneity of variance assumption was not upheld for this analysis, F(7, 367) = 6.13, p < .005. Bootstrapping procedures on the interaction that sampled 5,000 times from our distribution resulted in a mean bootstrapped F of 3.65 (p = .02).
As displayed in Figure 2a, female participants reported significantly more subject/joint-focused events than did male participants, F(1, 367) = 11.30, p < .005, η2 = .03, but only among those in the 18- to 29-year-old age group. Further, for female participants, 18- to 29-year-olds reported significantly more subject/joint-focused events than did the 13- to 17-year-olds, F(1, 367) = 5.61, p < .05, η2 = .02, and those in the 50+ age group, F(1, 367) = 22.26, p < .001, η2 = .06. Again, this pattern was reversed (although not statistically significantly so) in male participants.
Figure 2. Differences by sex and age in (a) subject-focused and (b) other-focused life events in 6 months prior to onset.
The first step of the logistic regression model examining the relation of sex and age group to the presence versus absence of an other-focused event was significant, χ2(4) = 14.20, p < .01, as was the model including the interaction of sex and age group, χ2(7) = 20.45, p < .005 (see Figure 2b). The nature of the interaction differed significantly between the 50+ group and the other three age groups, odds ratio (OR) = 5.18, Wald = 5.20, CI95 [1.26, 21.27]. In particular, female participants were more likely to have an other-focused event prior to onset than were male participants in all age groups except those over 50.
Event independence
For dependent events, there was a significant main effect of age group, F(3, 367) = 4.03, p < .01, η2 = .03, which was moderated by sex at a trend level, F(3, 367) = 2.12, p < .10, η2 = .02. Again, the homogeneity of variance assumption was not upheld for this analysis, F(7, 367) = 5.13, p < .005. Bootstrapping on the interaction sampling 5,000 times from our distribution confirmed the results of the general linear model, resulting in a mean bootstrapped F of 2.77 (p = .08). The pattern of means is presented in Figure 3a.
Figure 3. Differences by sex and age in (a) dependent and (b) independent life events in 6 months prior to onset.
For independent events, there was a significant main effect of sex, F(1, 367) = 4.90, p < .05, η2 = .013, and the age group effect approached significance, F(1, 367) = 2.38, p < .10, η2 = .02. The interaction of sex and age also approached significance, F(1, 367) = 2.45, p < .10, η2 = .02. Bootstrapping on the interaction sampling 5,000 times from our distribution confirmed the results of the general linear model, resulting in a mean bootstrapped F of 3.04 (p = .07). Of note, and in contrast to the above pattern for dependent events, female participants did not differ significantly in their rates of independent events across the four age groups, F(3, 282) = 1.05, p = .37. The pattern of means is presented in Figure 3b.
Event severity
For nonsevere events, there was a significant main effect of age, F(3, 367) = 5.59, p < .005, η2 = .04, which was significantly moderated by sex, F(3, 367) = 3.95, p < .01, η2 = .03. The homogeneity of variance assumption was not upheld for this analysis, F(7, 367) = 7.99, p < .005. Bootstrapping procedures on the interaction with 5,000 iterations resulted in a significant mean bootstrapped F of 4.80 (p = .003).
The pattern of this interaction is displayed in Figure 4a, such that female participants reported significantly more nonsevere events than did male participants, F(1, 367) = 14.67, p < .001, η2 = .04, in the 18-to 29-year-old group only. In addition, the 18- to 29-year-old women reported significantly more nonsevere events than did women in the 50+ age group, F(1, 367) = 21.80, p < .001, η2 = .06. In contrast, the 18- to 29-year-old men reported significantly fewer nonsevere events than did the male adolescents, F(1, 367) = 6.94, p < .01, η2 = .02.
Figure 4. Differences by sex and age in (a) nonsevere and (b) severe life events in 6 months prior to onset.
For severe events, as predicted, the logistic regression model containing the interaction of sex and age group was significant, χ2(7) = 14.69, p < .05, such that the nature of the gender difference between the adolescent and the adult groups differed significantly, OR = 5.99, Wald = 4.23, p < .05, CI95 [1.09, 33.33] (see Figure 4b). Adolescent girls and boys did not differ significantly in their likelihood of a severe event. Among the adults, women were significantly more likely than men to have had a severe event prior to onset across all three age groups.
DiscussionIn the present study, we provided a detailed examination of gender differences in life events using a rigorous contextual life event interview with a relatively large and well-diagnosed sample. Overall totals of life events as well as more specific categories of life events that have etiological relevance to MDD were examined. Further, this is the first study to consider gender differences in life events prior to MDD onset in the context of adolescent and adult development. Consistent with predictions, clear evidence was found for a female preponderance in rates of life events prior to MDD onset. Further, the gender difference for most life events was significant in early adulthood (age 18–29) but failed to reach significance in the mid-adult and upper middle adult groups. The present results are inconsistent with the previous literature, which has failed to find gender differences in rates of pre-onset life events. This discrepancy may be accounted for by the inclusion in these previous studies of a relatively older adult sample. Consistent with studies using the LEDS in adolescence, however, gender differences were not detected in our adolescent age group for any life event variable except other-focused events. Our results suggest, therefore, that the gender difference in rates of stressful life events prior to MDD onset is most pronounced in young adulthood.
Gender Differences in Life Events in Adulthood
Depressed women were significantly more likely to report a severe life event prior to MDD onset than were men across all three adult groups. Because it is severe events that are most strongly associated with the onset of MDD episodes, this finding may have significant implications for understanding the differential relation of stress to the etiology of MDD in women versus men (Brown & Harris, 1989; Hammen, 2006). It is important to note that our design is cross-sectional. Nevertheless, because these events occurred in close temporal proximity to MDD onset for women, this result suggests that severe stress may play a more prominent role in the etiology of MDD in women than in men.
It is unclear why rates of severe life events prior to MDD onset in men were so low. One possibility may be that men simply do not perceive these events as stressful and thus minimize their significance during the interview. This is unlikely to account for the present results, however, because the LEDS does not assess respondents' perceptions of stressfulness. It is the indication of event occurrence and relevant factual details that the raters use to objectively determine the event's contextual threat with reference to standardized case vignettes. Furthermore, severe events are high impact and unlikely to be forgotten over the short time period of the study. Alternatively, men may be preferentially sensitized to stress and, thus, could be more likely to succumb to depression in the face of nonsevere events than of severe events (Monroe & Harkness, 2005). This explanation is also unlikely, however, because greater sensitivity to life events should translate into higher rates of nonsevere events prior to onset in men, which was not the case in our sample.
A further potential explanation for our results may be that life stress is not as central to the etiology of MDD for men, as other factors (e.g., history of depression, biological or genetic dispositions) play a greater role. Consistent with this suggestion, Kendler, Gardner, and Prescott (2006) have reported that stressful life events in their sample of close to 3,000 male twin pairs have a weaker direct relation to depression onset in men versus women, with genetic risks, childhood loss, and low self-esteem having stronger and broader impacts in men.
Consistent with predictions, depressed women in all age groups except those over 50 were significantly more likely than were depressed men to report other-focused events prior to onset. Consistent with Cyranowski et al. (2000), it is possible that women are more sensitive to life events occurring to others (and thus would see these events clustered in close proximity to onset) due to their greater tendency to affiliate (e.g., Buss & Barnes, 1986) and to take on caregiving roles (Neal, Ingersoll-Dayton, & Starrels, 1997). Of note, only two of the 19 (10.5%) young adult men and only four of the 32 (12.5%) 30-to 45-year-old men reported an other-focused life event prior to onset of depression. Future prospective studies that predict the onset of MDD from other-focused events differentially in women versus men are necessary to clarify the exact processes mediating this gender difference.
The very high rates of life events prior to onset in the young adult women raise the possibility that these women may be in part creating their stressful environment (Hammen, 1991; Kendler & Karkowski-Shuman, 1997). Although the present study was not designed to test hypotheses related to stress generation, it is compelling that women experienced similar rates of independent events across age, but rates of dependent events were much higher in the young adulthood groups than the older adult groups (see Figures 3a–b). Indeed, the transition to young adulthood has been identified as a period of stress generation for women (e.g., Daley, Hammen, & Rao, 2000). A descriptive look at the life event profiles of some of these young women supports this assertion. For example, in just the 6 months prior to MDD onset, one woman started and then subsequently was fired from two different jobs, was served an eviction notice, and experienced the loss of two confiding relationships. Another young woman was caught in an extramarital affair after which her partner left her, was served an eviction notice, and fell out with her two best friends. These descriptions and those from other women in this age group point to turmoil in a number of domains that is at least in part caused by the women themselves. Future research is required to understand how the generation of life events plays a role in gender differences in the etiological relation of stress to MDD.
Gender Differences in Life Events in Adolescence
We generally failed to find evidence in adolescents for gender differences in rates of life events prior to MDD onset. Adolescents were more likely than those in the other age groups to be in a first episode of depression, raising the possibility that our results can be better accounted for by depression history. This is unlikely, however, because all of our models were robust when controlling for depression history. Further, we failed to find evidence for two-way interactions of depression history and either sex or age group (see Footnote 2). Therefore, individual differences across sex and age group in the frequency of life events prior to MDD onset was independent of differences due to the progression of the depression syndrome. Nevertheless, the potential role of depression history in further understanding gender differences in life events prior to MDD should be examined in future research with much larger samples.
Of particular note, adolescent boys and girls did not differ significantly in the percentage experiencing a severe event prior to onset, suggesting that stress may play a similar role in the etiology of depression in these initial onsets of depression during adolescence. A very important question for future research, then, is to understand what changes between adolescence and young adulthood that accounts for the significant increase in frequency of life events prior to onset in women and the significant decrease in life events prior to onset in men. As noted above, previous research has focused on young women's transition to adulthood and has documented high rates of stress generation in this group as a way of understanding the explosion of new cases of depression at this time. However, the present results suggest that it may be equally important to study changes in the etiological relation of events to onset during the transition to young adulthood in women and in men to fully understand the mechanism through which life events cause depression.
As expected, adolescents reported lower rates of most life events than did adults. Nevertheless, adolescents had the highest rates of other-focused and independent events, many of which were events that had happened to the adolescents' parents (e.g., father loses job, mother has a bout of pneumonia). The ratio of independent to dependent events in the adolescents was also higher (54%) than in the adults (only 29% for the young adults). Indeed, this lack of control over the environment just as adolescents are individuating may help to explain the potency of independent events in the onset of MDD in adolescence (Harkness et al., 2006).
Limitations
The present results should be interpreted in light of the following limitations. First, despite our large sample for this type of research, we were limited in the number of men in the present analyses. Men made up only about a quarter of the sample overall (89/275), and numbers of men were particularly small in the young adult group (n = 19/114; 17%) and mid-adult group (n = 32/155; 21%). In the present study this was likely due to the inclusion of a sample drawn from a study that included only women (Study 2). The small number of men limits the generalizability of our findings, and future research that oversamples for depressed men is required to confirm the results reported here. Nevertheless, it is important to note that the standard errors of the life event variables in these two cells with relatively smaller numbers were not notably larger than those observed in the remaining cells (see Figures 1–4). Further, we performed a number of measures to ensure the robustness of our findings, including bootstrapping analyses on all of our interaction terms.
Second, our sample did not include children (age < 13) or the old (age > 70). These are particularly important groups to examine because there is evidence that the gender difference in rates of MDD may not be as prominent in these groups as it is in adolescents and non-old adults (Bebbington et al., 1998; Bland, Newman, & Orn, 1988; Kessler, McGonagle, Swartz, Blazer, & Nelson, 1993). Third, our sample was constrained for looking at moderators (e.g., socioeconomic status, depression history, comorbidity). All results were robust when controlling for these variables, which suggests that our pattern was not confounded, for example, by an overrepresentation of first-onset, lower severity, and higher SES cases in the younger age groups. Nevertheless, future studies are required to more specifically examine the role of these factors in understanding the mechanism through which life events trigger MDD. Finally, because life event information was collected retrospectively, biases on the part of participants may have influenced the report of event occurrence and severity. The LEDS addresses the issue of respondent bias by employing rigorously trained raters who apply standardized rules and criteria when conducting rating of the events. Raters were also blind to the date of onset of MDD for respondents and to their subjective perceptions of the events (see McQuaid et al., 2000).
In summary, gender differences in life events prior to onset emerged for almost every type of event studied. It is particularly noteworthy, in adults, that depressed women maintained higher rates of severe life events throughout adulthood than did depressed men. This finding suggests that stress may play a different role in the triggering of episodes of depression for men and women and that other dimensions of life stress, as well as non-stress-related factors, may figure more prominently in the etiology of depression for men. Our results indicate a similar level of environmental disruption in adolescent boys and girls with MDD. These results have important clinical implications. Treatment interventions that emphasize stress coping may be effective in promoting remission and preventing relapse for boys and for girls in adolescence. However, in adulthood, such interventions may be useful only in the treatment of depression in women. The precision of definition in our measurement of life stress and our focus on life events that most reliably predict onsets of MDD sets this study apart from many previous studies of gender differences in stress. The present findings help us to gain a better understanding of individual differences in the etiological role of life stress over the lifetime course of depression.
Footnotes 1 Kate L. Harkness supervised the LEDS ratings in Studies 1, 2, and 4, and Scott M. Monroe supervised the LEDS ratings in Studies 1 and 3. In addition, the rating teams overlapped across Studies 1 and 4. The LEDS addresses the issue of rater drift by relying on anchoring of life event ratings to the manual. Prior to making a rating, each member of the rating team must appeal to an example in the LEDS manual upon which he or she is basing the rating. This feature enhances the validity and intersite reliability of the LEDS and ensures that all ratings are made according to the same criteria.
2 To increase our power to detect interactions with depression history, we also ran separate models testing only the two-way interactions of sex and depression history (both controlling for and not controlling for age group) and of age group and depression history (both controlling for and not controlling for sex). Again, none of these two-way interactions emerged as significant (ps > .47, all η2 < .008). Further, we collapsed age group into the two categories of most relevance to the episode number distinction: adolescents (13–18) versus adults (19+). Again, none of the two-way or three-way interactions of depression history with sex and/or the dichotomous age group emerged as significant (ps > .24, all η2 < .005). Results of the analyses including episode number, socioeconomic status, and depression severity are available from the authors by request.
3 The parametric critical value for F(3, 367) is 2.61, with the probability of F less than 1 (null hypothesis value).
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Submitted: September 17, 2009 Revised: May 12, 2010 Accepted: May 18, 2010
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Source: Journal of Abnormal Psychology. Vol. 119. (4), Nov, 2010 pp. 791-803)
Accession Number: 2010-19223-001
Digital Object Identifier: 10.1037/a0020629
Record: 73- Title:
- Generalizability of evidence-based assessment recommendations for pediatric bipolar disorder.
- Authors:
- Jenkins, Melissa M.. Department of Psychology, University of North Carolina at Chapel Hill, Chapel Hill, NC, US
Youngstrom, Eric A.. Department of Psychology, University of North Carolina at Chapel Hill, Chapel Hill, NC, US, eay@unc.edu
Youngstrom, Jennifer Kogos. Department of Psychology, University of North Carolina at Chapel Hill, Chapel Hill, NC, US
Feeny, Norah C.. Department of Psychology, Case Western Reserve University, Cleveland, OH, US
Findling, Robert L.. Department of Psychiatry, Case Western Reserve University, Cleveland, OH, US - Address:
- Youngstrom, Eric A., Departments of Psychology and Psychiatry, Center for Excellence in the Research and Treatment of Bipolar Disorder, University of North Carolina, CB #3270, Davie Hall, Chapel Hill, NC, US, 27599-3270, eay@unc.edu
- Source:
- Psychological Assessment, Vol 24(2), Jun, 2012. pp. 269-281.
- NLM Title Abbreviation:
- Psychol Assess
- Page Count:
- 13
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- Psychological Assessment: A Journal of Consulting and Clinical Psychology
- ISSN:
- 1040-3590 (Print)
1939-134X (Electronic) - Language:
- English
- Keywords:
- clinical judgment, decision making, diagnosis, evidence-based assessment, pediatric bipolar disorder
- Abstract:
- Bipolar disorder is frequently clinically diagnosed in youths who do not actually satisfy Diagnostic and Statistical Manual of Mental Disorders (4th ed., text revision; DSM–IV–TR; American Psychiatric Association, 1994) criteria, yet cases that would satisfy full DSM–IV–TR criteria are often undetected clinically. Evidence-based assessment methods that incorporate Bayesian reasoning have demonstrated improved diagnostic accuracy and consistency; however, their clinical utility is largely unexplored. The present study examines the effectiveness of promising evidence-based decision-making strategies compared with the clinical gold standard. Participants were 562 youths, ages 5 to 17 and predominantly African American, drawn from a community mental health clinic. Research diagnoses combined a semistructured interview with youths' psychiatric, developmental, and family mental health histories. Independent Bayesian estimates that relied on published risk estimates from other samples discriminated bipolar diagnoses (area under curve = .75, p < .00005). The Bayes and confidence ratings correlated at rs = .30. Agreement about an evidence-based assessment intervention threshold model (wait/assess/treat) was κ = .24, p < .05. No potential moderators of agreement between the Bayesian estimates and confidence ratings, including type of bipolar illness, were significant. Bayesian risk estimates were highly correlated with logistic regression estimates using optimal sample weights (r = .81, p < .0005). Clinical and Bayesian approaches agree in terms of overall concordance and deciding next clinical action, even when Bayesian predictions are based on published estimates from clinically and demographically different samples. Evidence-based assessment methods may be useful in settings in which gold standard assessments cannot be routinely used, and they may help decrease rates of overdiagnosis while promoting earlier identification of true cases. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Bipolar Disorder; *Child Psychopathology; *Evidence Based Practice; *Psychometrics; Decision Making; Medical Diagnosis; Pediatrics
- Medical Subject Headings (MeSH):
- Actuarial Analysis; Adolescent; Adult; African Americans; Bayes Theorem; Bipolar Disorder; Child; Child, Preschool; Community Mental Health Centers; Decision Support Techniques; Diagnostic Errors; Diagnostic and Statistical Manual of Mental Disorders; Evidence-Based Medicine; Female; Humans; Interview, Psychological; Judgment; Male; Nomograms; Risk Factors; Urban Population
- PsycINFO Classification:
- Clinical Psychological Testing (2224)
Affective Disorders (3211) - Population:
- Human
Male
Female - Age Group:
- Childhood (birth-12 yrs)
Preschool Age (2-5 yrs)
School Age (6-12 yrs)
Adolescence (13-17 yrs) - Tests & Measures:
- Parent General Behavior Inventory
Kiddie Schedule for Affective Disorders and Schizophrenia
Composite International Diagnostic Interview DOI: 10.1037/t02121-000
Mini International Neuropsychiatric Interview DOI: 10.1037/t18597-000
Structured Clinical Interview for DSM-IV - Grant Sponsorship:
- Sponsor: National Institutes of Health
Grant Number: R01 MH066647
Recipients: Youngstrom, Eric A. - Methodology:
- Empirical Study; Quantitative Study
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- First Posted: Oct 17, 2011; Accepted: Aug 18, 2011; Revised: Aug 17, 2011; First Submitted: Feb 8, 2011
- Release Date:
- 20111017
- Correction Date:
- 20160616
- Copyright:
- American Psychological Association. 2011
- Digital Object Identifier:
- http://dx.doi.org/10.1037/a0025775
- PMID:
- 22004538
- Accession Number:
- 2011-23750-001
- Number of Citations in Source:
- 101
- Persistent link to this record (Permalink):
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- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2011-23750-001&site=ehost-live">Generalizability of evidence-based assessment recommendations for pediatric bipolar disorder.</A>
- Database:
- PsycINFO
Generalizability of Evidence-Based Assessment Recommendations for Pediatric Bipolar Disorder
By: Melissa M. Jenkins
Department of Psychology, University of North Carolina at Chapel Hill
Eric A. Youngstrom
Department of Psychology and Department of Psychiatry, University of North Carolina at Chapel Hill;
Jennifer Kogos Youngstrom
Department of Psychology, University of North Carolina at Chapel Hill
Norah C. Feeny
Department of Psychology, Case Western Reserve University
Robert L. Findling
Department of Psychiatry, Case Western Reserve University
Acknowledgement: This work was supported in part by NIH R01 MH066647 (principal investigator: Eric A. Youngstrom). Robert L. Findling receives or has received research support, acted as a consultant, and/or served on a speaker's bureau for Abbott, Addrenex, Alexza, AstraZeneca, Biovail, Bristol-Myers Squibb, Forest, GlaxoSmithKline, Johnson & Johnson, KemPharm Lilly, Lundbeck, Merck, Neuropharm, Novartis, Noven, Organon, Otsuka, Pfizer, Rhodes Pharmaceuticals, Sanofi-Aventis, Schering-Plough, Seaside Therapeutics, Sepracore, Shire, Solvay, Sunovion, Supernus Pharmaceuticals, Transcept Pharmaceuticals, Validus, and Wyeth. Eric A. Youngstrom has received travel support from Bristol-Myers Squibb. The other authors have no conflicts of interest to disclose.
Bipolar disorder is a high-stakes diagnosis. Untreated, it often follows a recurrent and progressively worsening course, with increased risk of substance use, incarceration, and death by suicide or other causes (Goodwin & Jamison, 2007). If bipolar is misdiagnosed as depression or attention-deficit/hyperactivity disorder, there are delays initiating effective mood stabilizing treatments (Kowatch, Fristad, et al., 2005). There also are concerns that the options selected instead could worsen the course of the bipolar disorder (Akiskal et al., 2003; Altshuler et al., 1995; cf. Carlson, 2003; Hirschfeld, Bowden, et al., 2003; Joseph, Youngstrom, & Soares, 2009; Scheffer, Kowatch, Carmody, & Rush, 2005). Earlier identification may lead to better outcomes (DelBello, Adler, Whitsel, Stanford, & Strakowski, 2007; Findling et al., 2003; Fristad, Verducci, Walters, & Young, 2009), delay or prevent relapse (Findling et al., 2007), and afford more titrated or benign interventions (Miklowitz & Chang, 2008). The median lag exceeds 5 years between when mood symptoms begin to cause problems versus when a clinician arrives at a bipolar diagnosis in youths (Marchand, Wirth, & Simon, 2006) as well as adults (Hirschfeld, Lewis, & Vornik, 2003; Lish, Dime-Meenan, Whybrow, Price, & Hirschfeld, 1994). To close this gap, early identification has been promoted both in scholarly research (Miklowitz & Chang, 2008; Youngstrom, Findling, Youngstrom, & Calabrese, 2005) and in the popular media (Kluger & Song, 2002; Papolos & Papolos, 2002).
There is concern, however, that the pendulum may have swung too far in favor of early diagnosis (Healy, 2006; Parens, Johnston, & Carlson, 2010). Clinical diagnoses of pediatric bipolar disorder (PBD) have risen steeply in the United States (Blader & Carlson, 2007), with some estimates showing a 40-fold increase in diagnoses over the past decade (Moreno et al., 2007). Although evidence supports the validity of research diagnoses of bipolar disorder in terms of phenotypic, genetic, structural, functional, neurocognitive, and treatment response similarities to bipolar disorder in adults (Geller & Luby, 1997; Youngstrom, Birmaher, & Findling, 2008) and although longitudinal studies are demonstrating considerable developmental continuity (Birmaher et al., 2009; Geller, Tillman, Bolhofner, & Zimerman, 2008), the crucial issue from a public health and consumer point of view is whether the research diagnoses and clinical diagnoses identify the same cases.
Clinical diagnosis itself is a complex enterprise, fraught with potential biases and sources of error (Garb, 1998). Clinicians tend to rely on unstructured interviews and observations that have proven problematic (Neisworth & Bagnato, 2004). Clinicians also are prone to cognitive biases that often result in suboptimal diagnostic decisions (Croskerry, 2002; Galanter & Patel, 2005). More than 100 studies over the past 50 years have consistently demonstrated that clinical judgment is no better than simple actuarial algorithms and is often significantly worse (Ægisdóttir et al., 2006; Grove, Zald, Lebow, Snitz, & Nelson, 2000; Meehl, 1954).
The situation appears even worse with regard to clinical diagnoses of bipolar disorder. A recent meta-analysis comparing clinical diagnoses with those based on structured interviews found that bipolar disorder was an outlier even compared with the general trend of mediocrity in diagnostic reliability (with κ < .10; Rettew, Lynch, Achenbach, Dumenci, & Ivanova, 2009). Unfortunately, it is also very difficult to assess because of the phenomenology of the illness (Bowring & Kovacs, 1992; Youngstrom, Findling, et al., 2005) as there is substantial overlap in symptomatology (Leibenluft, Charney, Towbin, Bhangoo, & Pine, 2003; Weller, Danielyan, & Weller, 2004), making it hard to tease apart bipolar symptoms from symptoms of more prevalent diagnoses, such as attention-deficit/hyperactivity disorder. In addition, comorbidity complicates diagnosis, as youths frequently meet criteria for multiple psychiatric disorders (Axelson et al., 2006; Kowatch, Youngstrom, Danielyan, & Findling, 2005). Consequently, a typical presentation of bipolar in isolation is rare, and clinicians may focus on the comorbid condition and neglect PBD (Youngstrom, Findling, et al., 2005). Further, varied presentations of PBD threaten the reliability of diagnostic impressions. For example, classic bipolar I disorder can present as florid mania, severe depression, a mixed state, or normal functioning, depending on the mood state.
In addition to PBD being arguably one of the most difficult diagnoses to make correctly, broader practice issues of training, burden, and reimbursement can make research diagnostic instruments, such as structured and semistructured interviews, impractical for use in many clinical settings. As a result, many common assessment methods are not evidence based (EB). Thus, the difficulty in diagnosing PBD is compounded by complex presentation of illness, contextual constraints common to real-world clinical practice, and clinicians' cognitive vulnerabilities.
Clinical diagnoses of bipolar disorder in adults also have waxed and waned in popularity, without any evidence of a corresponding change in the base rate of the condition (Zuckerman, 1999). There is debate about whether the contemporary rise in clinical diagnoses is a correction from previous underdiagnosis—similar to historical changes in patterns of diagnosis for depression (Kovacs, 1989)—or is evidence of overdiagnosis, perhaps because of nonspecific use of the bipolar label to include a broad group of youths who have marked irritability and aggression but may not otherwise meet criteria for mania (Leibenluft et al., 2003). Overall, it appears that many youths diagnosed with bipolar do not actually have the disorder, whereas many true cases of bipolar go undiagnosed (Ghaemi, Sachs, Chiou, Pandurangi, & Goodwin, 1999).
PBD and EB AssessmentClinical decision making could benefit from EB assessment and decision-making strategies in diagnosing high-stakes conditions such as PBD. An alternative to traditional clinical assessment is taking an actuarial approach that quantifies the risk of illness using Bayesian reasoning. Moreover, Jaeschke, Guyatt, and Sackett (1994) recommended using a nomogram as a simple, practical method for combining information about risk with the diagnostic likelihood ratios associated with test results or other clinical findings. Nomograms are charts scaled in a way that eliminates the need for multiplication or division while working with probabilities. The nomogram can correctly combine information (i.e., base rate, familial risk, and test score) into consistent (less spread in opinion), unbiased (neither systematically over- nor underestimating risk), and efficient (using a parsimonious amount of information to arrive at the posterior probability) estimates (Jenkins, Youngstrom, Washburn, & Youngstrom, 2011). This estimate, the Bayesian posterior probability, can be used in the assessment of PBD to determine the likelihood that a youth has PBD and to guide next steps in clinical care (Youngstrom, Freeman, & Jenkins, 2009). An example of what a nomogram looks like and information about appropriate times for using this tool are provided in the Appendix (Jenkins et al., 2011; Youngstrom & Duax, 2005, provide worked examples).
Although the nomogram is central in EB medicine (Guyatt & Rennie, 1993; Straus, Richardson, Glasziou, & Haynes, 2005), it is not widely utilized in mental health. In two recent special journal issues devoted to EB assessment, the guest editors (Mash & Hunsley, 2005) and one of the invited commentaries (McFall, 2005) advocated for the adoption of this type of framework, but only three articles mentioned related approaches (such as receiver operating curves and diagnostic likelihood ratios), and only one described the nomogram approach. Recent evidence suggests that taking a Bayesian approach can significantly increase diagnostic accuracy and consensus among community clinicians and can significantly decrease overdiagnosis of PBD (Jenkins et al., 2011). Specifically, clinicians who learned how to use a nomogram (i.e., training took less than 30 min) and then participated in a clinical vignette exercise (i.e., testing proficiency in skill posttraining) were less likely to overestimate the risk of PBD and improved in consistency and accuracy (Jenkins et al., 2011). Although the actuarial strategies are not widely used in mental health settings, they have started gaining attention.
A second contribution of EB medicine is providing a framework for mapping the assessment results onto a decision-making threshold model. The threshold model maps the probability estimate onto a continuum with two major thresholds: the wait–test threshold and the test–treat threshold. Probabilities lower than the wait–test threshold are sufficiently small that the diagnosis is considered to be ruled out, and diagnostic assessment is discontinued. The clinician adopts the approach of waiting to see if anything changes. If the probability rises above the test–treat threshold, then the diagnosis is treated as if it is present, and treatment begins. In between the two thresholds is the zone where intensive assessment is indicated, until the additional information pushes the probability below the wait–test or above the test–treat threshold. Using actuarial methods, like the nomogram, in conjunction with an EB intervention threshold model allows clinicians to incorporate patient preferences into the decision-making process, by negotiating adjusted threshold points (Straus et al., 2005). The nomogram can also help anchor the likelihood of a bipolar diagnosis and inform next steps in clinical care (Youngstrom et al., 2009, provide a worked example). It is possible that taking this approach can help avoid unnecessary assessment batteries (i.e., cost–benefit analysis) as well as protect against prematurely ruling out bipolar disorder.
EB approaches to assessment could help considerably. Improving the accuracy of diagnoses can simultaneously reduce the number of false positives, averting the overdiagnosis of bipolar disorder, while also diminishing the delay between onset and correct identification. At present, several different measures had demonstrated diagnostic validity in research samples, but it is unknown how well the performance of these tools would generalize to new settings. Psychometric performance tends to shrink when cross-validated in new samples, and the amount of shrinkage can change depending on new sample characteristics. In addition, the Bayesian approach recommended by EB medicine involves some simplifications, such as using scoring thresholds, which may also change performance across samples.
Aims of the StudyAlthough the current research on actuarial strategies is encouraging (Jenkins et al., 2011; Youngstrom et al., 2009), to date no research has examined the degree to which the published actuarial estimates from one sample generalize to new samples. A crucial next step is to examine how well the EB assessment recommendations in the literature might extrapolate to new clinical settings. In the present study, we tested four main hypotheses to examine the effectiveness of actuarial methods for assessing real-world cases with potential PBD. First, we predicted a positive relationship between Bayesian estimates (actuarial estimates of having PBD based on the combination of family history and test score on the Parent General Behavior Inventory) and research diagnoses as well as best estimate clinical probabilities. Using the Bayesian estimates to predict diagnoses provides one means of estimating shrinkage, or reduced validity, when applying the weights in a new sample. Second, we predicted a positive relationship between Bayesian estimates and empirical estimates based on logistic regressions using the same predictors as the Bayesian model. The clinical ratings provide a second criterion against which to measure the extent to which a simple algorithm maps onto expert evaluation using additional sources of information, and the regression estimates provide a criterion for quantifying the shrinkage when using published weights compared with weights that are statistically optimal for the new sample.
Third, we anticipated that applying an EB assessment intervention threshold model (wait/assess/treat) to clinical ratings and Bayesian estimates would show clinically significant agreement between the two methods (e.g., Cicchetti et al., 2006). Fourth, we examined potential moderators of agreement between the clinical and actuarial approaches. We predicted that type of bipolar would statistically moderate agreement between the two approaches.
Method Participants
Youth participants (N = 562) were a consecutive case series recruited from an urban community mental health center with four urban sites (Youngstrom, Youngstrom, & Starr, 2005) as part of a larger project (R01 MH066647,principal investigator: Eric A. Youngstrom). The majority (80%) of youths were enrolled by their mothers to participate in the study. See Table 1 for youth participant demographics and diagnostic characteristics.
Participant Demographic and Diagnostic Characteristics (N = 562)
Youths were included if they were between 5 years 0 months and 17 years 11 months of age and if both the youth and the primary caregiver were available for the assessment. Youths were excluded if they (or their respective caregivers) could not communicate orally at a conversational level in English to complete the interview, had a pervasive developmental disorder, or had suspected moderate, severe, or profound mental retardation. The same assessment procedures were administered to all eligible participants.
Measures
Reference standard: Semistructured diagnostic interview using the Kiddie Schedule for Affective Disorders and Schizophrenia for School-Age Children (KSADS)
The KSADS–Present and Lifetime (Kaufman et al., 1997) and the mood disorders module from the Washington University KSADS (Geller, Zimerman, et al., 2001) were administered to all participants and their families. The KSADS is the most widely used semistructured diagnostic procedure for investigations of PBD (Nottelmann et al., 2001). Bipolar I, bipolar II, cyclothymic disorder, and bipolar not otherwise specified (NOS) diagnoses were made in accordance with diagnostic criteria from the Diagnostic and Statistical Manual of Mental Disorders (4th ed., text revision; DSM-IV–TR;American Psychiatric Association, 2000), including a strong emphasis on mood symptoms representing a clear change in functioning and following an episodic presentation. The most frequent reason for diagnosing BP-NOS was failure to meet strict DSM–IV–TR duration criteria, requiring 4 days for a hypomanic episode and 7 days or hospitalization for mania or mixed episodes (American Psychiatric Association, 2000; Leibenluft et al., 2003), consistent with emerging data about the duration of mood episodes in clinical and epidemiological samples in youths and adults (see Youngstrom, 2009, for review). Research assistants received extensive training prior to administering KSADS (κ > .85 at item level on 10 cases; Youngstrom, Meyers, et al., 2005).
Mini International Neuropsychiatric Interview (MINI; Sheehan et al., 1997)
The MINI is a brief (about 20 min) structured, reliable diagnostic interview covering common DSM–IV–TR and International Classification of Diseases psychiatric disorders. The MINI has been validated against the Structured Clinical Interview for DSM diagnoses (Lecrubier et al., 1997; Otsubo et al., 2005; Sheehan et al., 1998) and the Composite International Diagnostic Interview (Kadri, Agoub, Gnaoui, & Alami, 2005; Lecrubier et al., 1997) in several languages. It was consistently faster than both other interviews.
The primary caregivers completed the MINI first about themselves and then repeated the interview focusing on first degree relatives of the youth proband, using a modified family history–research diagnostic criteria format (Andreasen, Endicott, Spitzer, & Winokur, 1977).
Parent General Behavior Inventory (PGBI; Youngstrom, Findling, Danielson, & Calabrese, 2001)
The PGBI has parents of youths 5 to 18 years old rate the severity of depressive, manic, and mixed mood symptoms using a 0 to 3 Likert scale. The Hypomanic/Biphasic Scale has 28 items, alpha of .93 in this sample. Higher scores on the PGBI indicate a higher probability that a given youth has a bipolar disorder. The present study uses risk estimates (Straus et al., 2005) associated with PGBI scores based on a completely independent sample with higher average socioeconomic status and more mood disorders in the referral stream, published in Youngstrom et al. (2004).
Procedure
Clinical assessment
All study procedures were approved by the University Hospital of Cleveland, Case Western Reserve University, and Applewood Centers Institutional Review Boards. Parents or guardians provided written consent, and all youths gave written assent. Highly trained research assistants administered the KSADS (Kaufman et al., 1997) diagnostic interview to all participant families.
Parents completed a series of questionnaires while youths were interviewed, including the PGBI. Similarly, youths completed questionnaires while the parents completed the KSADS and family history interviews. KSADS interviewers were unaware of results from the rating scales.
After the client and his or her family completed the psychiatric evaluation, research diagnoses were reached through a Longitudinal Expert Evaluation of All Data (LEAD; Spitzer, 1983) conference. Specifically, all cases were reviewed by an expert consensus team, which always consisted of at least one licensed psychologist in addition to the rest of the members of the interview team for the given family. LEAD diagnoses were based on (a) results from the KSADS, (b) developmental history, (c) family history of mental illness, and (d) psychiatric history, including any current diagnoses. Clinical chart reviews often provided information regarding youths' developmental and psychiatric histories. For each diagnosis, the expert clinician assigned a confidence rating based on how likely he or she viewed the diagnosis given all of the available information. These diagnoses and corresponding confidence ratings (likelihood of illness from 0% to 100%) represent the closest criterion to a gold standard in clinical assessment that the field currently has; these ratings, the LEAD confidence ratings, were compared with the Bayesian estimates.
Actuarial assessment
Procedures for actuarial assessments involved four steps, which are described in more detail later: (a) determine the prevalence or starting base rate for the study population; (b) obtain family history of bipolar illness and test scores on the PGBI; (c) translate family history of bipolar illness from the MINI and test scores on the PGBI for these cases into diagnostic likelihood ratios (DLRs), using published estimates from prior samples and research (Youngstrom, Frazier, Demeter, Calabrese, & Findling, 2008; Youngstrom, Meyers, et al., 2005); (d) use Bayesian methods to combine the base rate of PBD and the DLRs for family history and PGBI scores to provide the probability of a bipolar diagnosis (i.e., the Bayesian estimate). The Bayesian estimates were probabilities that theoretically could range from 0 to 100 and in the present sample ranged from .004 to .746. These were the Bayesian estimates of the likelihood of a bipolar diagnoses based on combing the likelihood ratios for prevalence rate (6%), family history of mood disorder, and test score on the PGBI questionnaire.
Prevalence
The present study estimated prevalence rates for PDB on the basis of a literature review, as recommended in EB medicine books (Guyatt & Rennie, 2002; Straus et al., 2005), giving precedent to well-conducted research over local estimates. A recent meta-analysis of 12 epidemiological studies found an average rate of 2% for bipolar disorder in youths under age 19 (Van Meter, Moreira, & Youngstrom, 2011). In outpatient clinical populations, evidence suggests prevalence estimates between 0.6% and 15%, depending on the diagnostic instrument, clinic specialization, and referral source (Geller, Craney, et al., 2001; Lewinsohn, Klein, & Seeley, 1995; Strober et al., 1995). Because prevalence of bipolar disorder varies substantially by type of setting, it is important to consider the starting base rate in light of clinical context. To determine the starting base rate for the present study, we used benchmarks from the literature that approximated local conditions (e.g., Youngstrom, 2007; Youngstrom et al., 2009; Table 2)—similar to how clinicians could access base rate information if they were to take an actuarial approach in their clinical practice. Two published estimates indicated a base rate of 6% for bipolar spectrum disorders in an outpatient clinic, which by some standards is conservative (Pavuluri, Birmaher, & Naylor, 2005).
Regression Model Examining Whether Bipolar Subtype Moderates Agreement Between Bayesian Estimates and LEAD Ratings (N = 562)
DLRs
A DLR quantifies the extent to which new information, such as a test result or risk factor, changes the odds of a diagnosis. A DLR divides the percentage of cases with bipolar showing that result by the percentage of cases without bipolar that would also have similar test results. This is identical to dividing the diagnostic sensitivity by the false alarm rate (Pepe, 2003). Family history and PGBI test scores can be translated into DLRs and plotted on the middle column of a probability nomogram.
For the present study, we conducted a search using the terms pediatric bipolar disorder and sensitivity and specificity and diagnostic likelihood ratios to find the DLRs for PGBI test scores. We used the terms bipolar disorder and risk estimate and systematic review to find DLRs for family history of bipolar disorder. See Youngstrom et al. (2004) for the DLRs associated with test scores on the PGBI.
The present investigation concentrated on direct interview information about first degree relatives. For a biological parent (or other first degree relative) with bipolar disorder, the EB familial risk estimate is a diagnostic likelihood ratio of 5 (Hodgins, Faucher, Zarac, & Ellenbogen, 2002; Tsuchiya, Byrne, & Mortensen, 2003; Youngstrom & Duax, 2005).
ResultsLess than 2% of data points were missing. Given the small amount, missing data were excluded listwise. This approach provides less bias than pairwise deletion and is adequately suited for small amounts of missing data (Allison, 2002).
Agreement Between LEAD Diagnoses, Confidence, and the Bayesian Estimates
Each case received a Bayesian estimate based on DLRs for their age group on the PGBI as well as family history. Receiver operating characteristic analyses comparing Bayes estimates to the LEAD diagnoses of bipolar disorder found an area under the curve (AUC) of .75, p < .00005. This indicated a marginal degree of shrinkage from the diagnostic efficiency of the PGBI, with AUCs of .83 in the original report (Youngstrom et al., 2004) and .78 in the present sample. Bayesian estimates based on the combination of family history and test score on the PGBI using a base rate of 6% correlated rs = .30 with the LEAD confidence ratings, p < .00005, indicating a medium positive relationship (Cohen, 1988). This suggests that clinicians agree with the Bayesian estimate more often than is typically the case comparing clinical versus structured diagnoses of bipolar disorder (Rettew et al., 2009). On the other hand, these are not redundant or identical pieces of information, as the LEAD confidence rating also integrated other information not included in the Bayesian estimate.
Generalizability of Actuarial Approach
Pearson's correlation quantified how well Bayesian estimates using independent, published DLRs generalized to logistic regression estimates using optimal weights for the sample. The two estimates were highly correlated, r = .81, p < .0005, indicating a strong positive relationship (Cohen, 1988); see Figure 1.
Figure 1. Generalizability of the nomogram estimates based on published likelihood ratios compared with logistic regression estimates optimized for new sample (N = 562).
Agreement About Next Clinical Action
Cohen's kappa coefficient tested if applying an EB assessment intervention threshold model (wait/assess/treat) to LEAD confidence ratings and Bayesian estimates showed clinically significant agreement between the two assessment methodologies. Results indicate a κ = .24, p < .0005, when test–wait threshold and test–treat thresholds were set at 20% and 90%, respectively, indicating fair agreement (per Cicchetti, 2006).
Figure 2 superimposes the wait–test and test–treat thresholds on the scatter plot displaying Bayesian estimates and LEAD confidence ratings. The Bayesian estimates were generally more conservative, almost never crossing the treatment threshold and often falling in the low or indeterminate ranges for cases where the LEAD confidence ratings were high. This pattern suggests (a) the Bayesian approach by itself would not be sufficient to initiate treatment, at least not with the current set of inputs; (b) additional information captured in the LEAD process may be necessary to cross the treatment threshold; and (c) the Bayesian approach is unlikely to generate false positives that would be exposed to unwarranted treatments.
Figure 2. Agreement between clinician confidence ratings and Bayesian nomogram estimates based on published likelihood ratios. The data are skewed for the clinician confidence ratings. The distribution is zero-inflated, with 0 being the modal response because the clinicians assigned a low probability of bipolar disorder to the majority of the cases. LEAD = Longitudinal Expert Evaluation of All Data.
Potential Moderators of Agreement Between Nomogram and Clinical Confidence
Clinician confidence was significantly higher for cases with bipolar I or II (M = 84.4) versus the softer spectrum (M = 76.7), t(60.60) = 3.12, p = .003. An ordinary least squares regression approach tested whether type of bipolar moderated agreement between LEAD confidence ratings and Bayesian estimates. Specifically, the Bayesian estimate predicted the LEAD confidence rating, along with dummy codes for bipolar type and interaction terms for bipolar type with Bayesian estimates. None of the interaction terms were significant, indicating that type of bipolar disorder did not statistically moderate agreement between LEAD confidence ratings and Bayesian estimates; see Table 2.
Regression Model Examining Whether Bipolar Subtype Moderates Agreement Between Bayesian Estimates and LEAD Ratings (N = 562)
DiscussionThe overarching goal of the present study was to compare the current gold standard for clinical assessment of PBD—a LEAD diagnosis integrating a KSADS interview with collateral information and treatment history—with an innovative actuarial approach. We also examined agreement about next clinical action, using the threshold model developed in EB medicine (Straus et al., 2005). Additional analyses examined potential moderators of agreement between the Bayesian and clinical approaches, including whether agreement was higher for fully syndromal cases (i.e., bipolar I) versus other bipolar spectrum presentations.
Consistent with our hypotheses, Bayesian estimates derived from published risk estimates showed clinically meaningful diagnostic efficiency even when generalized to a new sample with different clinical and demographic characteristics. The AUC for the Bayesian estimates shrank compared with the original published estimates but still remained large (AUC = .75) and highly significant. Also as hypothesized, LEAD confidence ratings and Bayesian estimates—based on a much more circumscribed set of variables—showed medium-sized correlation. Clinician confidence integrated substantially more information through a LEAD process: Clinicians' LEAD confidence ratings reflected findings from the KSADS interview, detailed family history, and clinical chart information. The relationship between LEAD confidence ratings and Bayesian estimates would likely differ if clinicians did not have this additional information (see Jenkins et al., 2011). Given that LEAD confidence ratings tended to be higher than Bayesian estimates, clinicians may have been more confident in their bipolar diagnoses because of the additional supporting information from the KSADS.
As hypothesized, Bayesian estimates using published estimates were highly correlated with logistic regression estimates optimized for the present sample, indicating a high degree of generalizability. These findings are enhanced by the fact that the present sample is substantially different in terms of demography and socioeconomic status as well as clinical referral patterns from the prior research on the assessment of PBD (cf. Youngstrom et al., 2004). Whereas most prior work has relied on middle class, predominantly White participants with high rates of mood disorder seeking services in specialty clinics at academic centers (Hodgins et al., 2002), the present sample comprised low income, predominantly underserved ethnic minority families seeking services at a community mental health center, mostly for attention problems and disruptive behavior disorders. The high correlation between new regression estimates and Bayesian estimates using published weights provides strong confirmation that the EB assessment recommendations generalize across a wide range of demographic and clinical facets.
When a clinical action threshold model was applied to LEAD confidence ratings and Bayesian estimates, these different approaches evidenced fair agreement (Cicchetti et al., 2006; Landis & Koch, 1977). When they disagreed, the actuarial approach consistently recommended a more conservative approach—waiting or active assessment—rather than indicating active treatment. This finding has important clinical implications. Gold standard clinical assessments are often not feasible in real-world practice (Garb, 1998). For example, issues related to insurance reimbursement and staff training can make it difficult to implement a comprehensive assessment, such as the KSADS (Eisman et al., 2000). Moreover, many clinical settings lack the training and supervision resources to ensure acceptable administration of semistructured diagnostic interviews, which can result in unreliable diagnostic impressions and inappropriate treatment plans. Results suggest that the actuarial approach could be implemented using published likelihood ratios, rapidly and inexpensively identifying cases for further assessment without overdiagnosing or overtreating bipolar disorder. There also is precedent for third-party payors, such as Medicaid, reimbursing for KSADS or other follow-up procedures when they are clinically indicated rather than being administered to all comers.
Contrary to study predictions, the type of bipolar did not significantly moderate the level of agreement between LEAD confidence ratings and Bayesian estimates (i.e., the interaction terms were not significant). This finding is surprising given that clinician confidence was significantly higher about fully syndromal bipolar I or II versus softer spectrum cyclothymic or bipolar illness cases, consistent with findings from vignette studies (Dubicka, Carlson, Vail, & Harrington, 2008). Clinicians are less confident when diagnosing bipolar that does not fit into current categorical definitions of the disorder, consistent with current scientific debate about the diagnostic status of the NOS cases (e.g., Axelson et al., 2006; Dubicka et al., 2008; Findling, 2005; Leibenluft et al., 2003; Parens et al., 2010). Apparently, type of bipolar may affect clinicians' diagnostic confidence, but it does not influence the degree to which actuarial and clinical assessment methodologies agree.
It is noteworthy that the present sample was predominantly African American. Given the well-documented difficulties assessing bipolar using other methods in minority samples—especially African Americans (DelBello, Lopez-Larson, Soutullo, & Strakowski, 2001; Neighbors, Trierweiler, Ford, & Muroff, 2003)—the effectiveness of the actuarial approach is all the more impressive. Actuarial assessment methods may help decrease the rate at which clinicians misdiagnose African Americans and other minority groups by encouraging more systematic and thorough assessment as well as preventing or adjusting faulty clinical judgment or differences in initial description of the presenting problem (Jenkins et al., 2011). For example, if an individual presents with a family history of mental illness and/or receives a positive test score on a bipolar screening instrument, the actuarial approach generates the same probability of bipolar disorder regardless of race/ethnicity. If there were evidence that demographic characteristics moderated risk or test performance, then the actuarial approach could incorporate optimal weights to make the appropriate adjustments.
Limitations
A large number of the bipolar spectrum cases had diagnoses of cyclothymic disorder and bipolar NOS. By definition, these cases did not present with distinct episodes of week-long mania or sufficient severity to require hospitalization. However, the research diagnoses required an episodic presentation and required that symptoms be a clear change in functioning if they were counted toward a mood diagnosis. The majority of cases with NOS or cyclothymic diagnoses did not meet criteria for bipolar I or II because of insufficient duration of the activated mood states, but virtually all had multiple episodes and a mean episode duration well in excess of other research definitions (Birmaher et al., 2009). The emphasis on episodicity and differentiating mood from other possible sources of symptoms increases the specificity of the bipolar diagnoses. It also is important to understand how findings generalize across the bipolar spectrum, as the cyclothymic and NOS cases are proving to be more frequent in epidemiological studies (Merikangas et al., 2010; Van Meter et al., 2011) as well as clinical settings (Axelson et al., 2006) and continue to be associated with high degrees of impairment (Birmaher et al., 2009; Findling et al., 2005, 2010).
It is possible that the conservative approach used in defining risk due to family history of bipolar illness may have lowered estimates of agreement. Some research suggests that individuals with familial probands of bipolar disorder may be as much as 10 times more likely to manifest the illness (Smoller & Finn, 2003). Using a smaller likelihood ratio for family risk contributed to the Bayesian approach yielding lower risk estimates. This is not a general limitation of the EB assessment approach: In fact, a strength of EB assessment methods is that they can flexibly integrate new information. Also, clinicians have the option of performing sensitivity analyses, where they examine the effects of changing the inputs on the range of resulting probabilities. These what-if scenarios inform the clinician about how changes to assumptions, or the addition of new evidence, would change diagnostic probabilities.
Another caveat is that family history information sometimes was inaccurate or incomplete. Most family history information was gathered from participants' mothers, using a semistructured interview. Collateral report, or a family history method, is less accurate than direct interview of all relatives. The rate of bipolar disorder reported in the other family members was lower than the rates found in epidemiological studies in the general U.S. population (Merikangas & Pato, 2009), suggesting that the interview did not demonstrate high diagnostic sensitivity to bipolar conditions in fathers or second degree relatives. However, these same factors of low awareness of bipolar history in other family members, along with potential bias, are likely to apply to most clinical evaluations as well.
Some of the results are also dependent on starting values, such as the base rate of bipolar disorder or the choice of values for the wait–test and test–treat thresholds. This is intrinsic to using a Bayesian approach, but it is likely to be disconcerting at first because it seems both unfamiliar and potentially subjective (Gigerenzer & Goldstein, 1996). In fact, the Bayesian methods are often more consistent, efficient, and unbiased than clinical judgment in synthesizing information (Jenkins et al., 2011), and these advantages remain even when using inaccurate starting values. Additionally, EB assessment methods are self-correcting over time: Using the techniques changes diagnostic patterns to converge on the true base rate even when starting from inaccurate values (Meehl, 1954). The potential for subjectivity in choice of thresholds also is a potential strength, as it makes it possible to have meaningful discussions about costs and benefits with the patient and family (Kraemer, 1992; Straus et al., 2005; Swets, Dawes, & Monahan, 2000). Finally, clinician confidence is not completely equivalent to clinical judgment as operationalized in some other studies (cf. Meehl, 1954).
Future Directions and Clinical Implications
Dissemination efforts are more successful when procedures are acceptable, feasible, and likely to permit adherence (Arndorfer, Allen, & Aljazireh, 1999). Better understanding of clinician perspectives about actuarial assessment along with identifying potential practice barriers can enhance acceptance, feasibility, and adherence. Before attempting to create large scale change in current decision-making practice, researchers should first pilot EB tools with providers. It will be important to understand if clinicians on the front line are willing to adopt actuarial approaches in real-world practice. Recent research suggests that a majority of mental health professionals who received a brief nomogram training (<30 min) endorsed using it in practice (Jenkins et al., 2011). Qualitative methodologies may shed light on clinicians' attitudes and impressions that can inform future education and training of the nomogram.
More work needs to evaluate barriers related to more general policy considerations (e.g., supervision, clinical training models) as well as consumer preferences (Suppiger et al., 2009). Documenting and addressing these obstacles could accelerate uptake of EB assessment methods. Changes in packaging and interpretation software may also accelerate uptake. Innovative technology combined with EB decision-making tools might greatly appeal to audiences that find statistics intimidating. Using technology to expedite the delivery of EB mental health services is a rapidly growing niche (Bucholz et al., 1991; Erdman et al., 1992; Finfgeld, 1999; Kobak et al., 1997).
Another area of work will be studying the effects of varying the wait–test and test–treat thresholds, as well as incorporating patient preferences about risks and benefits. Some of the controversy around the bipolar diagnosis has focused on the consequences of erroneously prescribed pharmacological treatment (DSM5.org), rather than the underlying validity of the diagnosis, which has accumulated substantial evidence (Geller & Tillman, 2005; Youngstrom, Birmaher, & Findling, 2008). Psychopharmacological agents used to treat bipolar disorder carry a range of potential side effects extending from the unpleasant to the life threatening. The perceived adversity of any treatment's costs and side effects will naturally vary on an individual basis. More work is needed to understand how the interaction of clinicians' recommendations and patient preferences map on to the threshold model. Adjusting the test–treat threshold provides a mechanism for clinicians to directly address the potential for harm by literally setting the bar higher, requiring greater confirmatory evidence before initiating the riskiest or most expensive treatments (Straus et al., 2005).
EB assessment offers a fast and frugal approach to gathering information about bipolar disorder in youngsters. Combined with a two-threshold (wait–test, test–treat) model, the current generation of EB assessment tools can increase detection of bipolar without increasing the rate of false positive diagnoses, which cause individuals to be exposed to unnecessary treatments. Using brief, free/public domain tools can identify a limited number of cases for more intensive assessment and follow up. Medicaid and other payors have been found to reimburse this additional evaluation when medical necessity has been demonstrated. Upgrades in the tools and data available will further enhance the accuracy of this approach without requiring a change to the interpretive framework. Present findings also indicated that risk estimates derived from the published literature would generalize well, even to new samples with markedly different demographic and clinical characteristics. The EB approach could likely yield immediate improvements in many clinical settings, which would be further refined as new tools, risk factors, and moderators of assessment and treatment performance are identified.
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APPENDIX APPENDIX A: Nomogram for Combining Probability With Diagnostic Likelihood Ratios
Figure A1. A nomogram is a particularly helpful tool for quantifying the risk of bipolar disorder when certain warning signs are present (Youngstrom et al., 2009). Warning signs can include family history of bipolar disorder, a high score on a parent report questionnaire that is sensitive to manic symptoms, and/or a youth's clinical presentation of decreased need for sleep, elevated and expansive mood, and grandiosity or possible psychotic features.
Figure A1. A nomogram is a particularly helpful tool for quantifying the risk of bipolar disorder when certain warning signs are present (Youngstrom et al., 2009). Warning signs can include family history of bipolar disorder, a high score on a parent report questionnaire that is sensitive to manic symptoms, and/or a youth's clinical presentation of decreased need for sleep, elevated and expansive mood, and grandiosity or possible psychotic features.Submitted: February 8, 2011 Revised: August 17, 2011 Accepted: August 18, 2011
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Source: Psychological Assessment. Vol. 24. (2), Jun, 2012 pp. 269-281)
Accession Number: 2011-23750-001
Digital Object Identifier: 10.1037/a0025775
Record: 74- Title:
- Genetic and environmental bases of childhood antisocial behavior: A multi-informant twin study.
- Authors:
- Baker, Laura A.. Department of Psychology, University of Southern California, Los Angeles, CA, US, lbaker@usc.edu
Jacobson, Kristen C.. Department of Psychiatry, University of Chicago, Chicago, IL, US
Raine, Adrian. Department of Psychology, University of Southern California, Los Angeles, CA, US
Lozano, Dora Isabel. Department of Psychology, University of Southern California, Los Angeles, CA, US
Bezdjian, Serena. Department of Psychology, University of Southern California, Los Angeles, CA, US - Address:
- Baker, Laura A., Department of Psychology, University of Southern California, Los Angeles, CA, US, 90089-1061, lbaker@usc.edu
- Source:
- Journal of Abnormal Psychology, Vol 116(2), May, 2007. pp. 219-235.
- NLM Title Abbreviation:
- J Abnorm Psychol
- Page Count:
- 17
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- The Journal of Abnormal and Social Psychology; The Journal of Abnormal Psychology; The Journal of Abnormal Psychology and Social Psychology
- ISSN:
- 0021-843X (Print)
1939-1846 (Electronic) - Language:
- English
- Keywords:
- antisocial behavior, aggression, genes, environment
- Abstract:
- Genetic and environmental influences on childhood antisocial and aggressive behavior (ASB) during childhood were examined in 9- to 10-year-old twins, using a multi-informant approach. The sample (605 families of twins or triplets) was socioeconomically and ethnically diverse, representative of the culturally diverse urban population in Southern California. Measures of ASB included symptom counts for conduct disorder, ratings of aggression, delinquency, and psychopathic traits obtained through child self-reports, teacher, and caregiver ratings. Multivariate analysis revealed a common ASB factor across informants that was strongly heritable (heritability was .96), highlighting the importance of a broad, general measure obtained from multiple sources as a plausible construct for future investigations of specific genetic mechanisms in ASB. The best fitting multivariate model required informant-specific genetic, environmental, and rater effects for variation in observed ASB measures. The results suggest that parents, children, and teachers have only a partly 'shared view' and that the additional factors that influence the 'rater-specific' view of the child's antisocial behavior vary for different informants. This is the first study to demonstrate strong heritable effects on ASB in ethnically and economically diverse samples. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Aggressive Behavior; *Antisocial Behavior; *Environment; *Genetics; Twins
- Medical Subject Headings (MeSH):
- Aggression; Antisocial Personality Disorder; Child; Conduct Disorder; Diseases in Twins; Female; Humans; Juvenile Delinquency; Longitudinal Studies; Male; Personality Assessment; Risk Factors; Social Environment; South Carolina; Triplets; Twins, Dizygotic; Twins, Monozygotic
- PsycINFO Classification:
- Behavior Disorders & Antisocial Behavior (3230)
- Population:
- Human
Male
Female - Location:
- US
- Age Group:
- Childhood (birth-12 yrs)
School Age (6-12 yrs) - Tests & Measures:
- Diagnostic Interview Schedule for Children--Version IV
Child Psychopathy Scale
Child Behavior Checklist
Childhood Aggression Questionnaire DOI: 10.1037/t20800-000 - Grant Sponsorship:
- Sponsor: National Institute of Mental Health
Grant Number: R01 MH58354
Recipients: Baker, Laura A.
Sponsor: National Institute of Mental Health
Grant Number: K02 MH01114-08
Other Details: Independent Scientist Award
Recipients: Jacobson, Kristen C. - Methodology:
- Empirical Study; Quantitative Study; Twin Study
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- Accepted: Feb 9, 2007; Revised: Feb 6, 2007; First Submitted: Sep 1, 2005
- Release Date:
- 20070521
- Correction Date:
- 20130520
- Copyright:
- American Psychological Association. 2007
- Digital Object Identifier:
- http://dx.doi.org/10.1037/0021-843X.116.2.219
- PMID:
- 17516756
- Accession Number:
- 2007-06673-001
- Number of Citations in Source:
- 67
- Persistent link to this record (Permalink):
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- Cut and Paste:
- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2007-06673-001&site=ehost-live">Genetic and environmental bases of childhood antisocial behavior: A multi-informant twin study.</A>
- Database:
- PsycINFO
Genetic and Environmental Bases of Childhood Antisocial Behavior: A Multi-Informant Twin Study
By: Laura A. Baker
Department of Psychology, University of Southern California;
Kristen C. Jacobson
Department of Psychiatry, University of Chicago
Adrian Raine
Department of Psychology, University of Southern California
Dora Isabel Lozano
Department of Psychology, University of Southern California
Serena Bezdjian
Department of Psychology, University of Southern California
Acknowledgement: This study was supported by National Institute of Mental Health (NIMH) Grant NIMH R01 MH58354 to Laura A. Baker and NIMH Independent Scientist Award K02 MH01114-08 to Kristen Jacobson. We wish to thank the University of Southern California twin project staff, for assistance in data collection and scoring, and the twins and their families, for their participation in this research.
Why do some children grow up to be prosocial, law-abiding individuals, whereas others engage in patterns of disruptive, defiant, and delinquent behavior, even falling into the criminal justice system well before reaching adulthood? A plethora of studies have investigated the etiology of such individual differences, with abundant evidence demonstrating the importance of both social circumstances and biological risk factors in antisocial behavior across the life span (Baker, 1999; Raine, 1993, 2002; Raine, Brennan, Farrington, & Mednick, 1997; Stoff, Breiling, & Maser, 1997). Among these risk factors, genetic and environmental influences have been of considerable interest and are likely to play a key role in our understanding of aggression and other antisocial behaviors and, thus, our ability to avert them.
In fact, genetic and environmental influences in aggressive and antisocial behavior (ASB) have been studied extensively. Several early adoption studies in both Scandinavia and the United States have provided the intriguing finding that not only does the risk for adult criminal offending run in families but familial similarity is due primarily to shared genetic risk (Bohman, 1978; Cadoret, 1978; Hutchings & Mednick, 1971; Loehlin, Willerman, & Horn, 1985; Sigvardsson, Cloninger, Bohman, & Von Knorring, 1982). Genetic predispositions have also been shown to play a significant role in the normal variation in adult aggressive behavior, perhaps especially in more impulsive forms (Coccaro, Bergeman, Kavoussi, & Seroczynski, 1997). In contrast, studies that have included adolescents and younger children vary widely in their estimates of the relative importance of genes and environment, with heritability estimates (h2) indicating that genetic effects could explain as little as nil or upward of three fourths of the variance in ASB (see Rhee & Waldman, 2002, for the most recent review).
Using meta-analysis of key behavioral genetic studies in ASB, Rhee and Waldman (2002) found that, combining results across studies, there were significant effects of additive genetic influence (a2 = .32), of nonadditive genetic influences (d2 = .09), and of shared (e2s = .16) and nonshared environment (e2ns = .43). These genetic and environmental effects were found to differ, however, according to the definition and method of assessing ASB, as well as by the age at which ASB was studied. The nonadditive genetic effects appear most strongly for studies of criminal convictions compared with all other definitions of ASB. Shared environmental effects were stronger for parental reports of ASB compared with self-reports and with official records, and these shared environmental effects appear to diminish from childhood to adulthood.
It is also noteworthy, however, that age and method of assessment are confounded across studies—investigations of younger children tend to rely on parent or teacher reports, whereas studies of older adolescents and adults are more apt to use official records or self-report measures of ASB. Thus, the larger effect of shared environment during childhood may be due to greater reliance on parental or teacher ratings. Given these methodological confounds across studies, it is impossible to know the strength of genetic and environmental influences on individual differences in childhood ASB in particular. Additional studies are required to resolve the effects of genes and environment in ASB in children.
Defining Antisocial BehaviorDefinitions of ASB vary widely across studies and include violations of rules and social norms (e.g., lawbreaking), various forms of aggression (e.g., self-defense or other reactive forms and proactive behaviors such as bullying), and serious patterns of disruptive and aggressive behavior such as those observed in clinical disorders like conduct disorder and oppositional defiant disorder in children or antisocial personality disorder in adults. The variability found in the definitions of these key concepts is also found in the methods of measuring ASB; some studies are based on official records such as police arrests, court convictions, or school records, whereas others rely on behavioral ratings provided by parents or teachers or on self-reports about the participant’s own ASB. Each assessment method has its advantages and disadvantages with no one definition or method of assessment being clearly superior.
Nevertheless, in spite of the wide variations in definitions of ASB, as well as the possibility that the relative importance of genetic and environmental factors may vary for different measures (e.g., Eley, Lichtenstein, & Moffitt, 2003; Mednick, Gabrielli, & Hutchings, 1984; also see Rhee & Waldman, 2002, for review), there is also considerable evidence for a general externalizing dimension of problem behavior underlying these various behaviors and tendencies. Similar to the problem/behavior syndrome described earlier by Jessor and Jessor (1977), a broad latent factor has been purported to be a common link among antisocial behavior, substance dependence, and disinhibited personality traits (Krueger et al., 2002). The externalizing dimension has been found to be more continuous than categorical, with shades of gray describing a range of deviant behaviors across individuals (Markon & Krueger, 2005; Young, Stallings, Corley, Krauter, & Hewitt, 2000). Moreover, this common externalizing factor has been shown to have a strong heritability among adolescents (h2 = .80), accounting for much of the covariation among various aspects of antisocial behavior and disinhibition (Krueger et al., 2002). Among adults, there is also evidence for separate genetic factors for internalizing versus externalizing dimensions of psychopathology (Kendler, Prescott, Myers, & Neale, 2003). This general externalizing factor found across many studies may reflect an overall tendency to act in an unconstrained manner, a genetically based characteristic that manifests itself in various ways depending on the environment (Krueger, 2002). The higher heritability found for this externalizing factor compared with heritabilities obtained from studies that have focused on only one type of antisocial behavior suggests that using a composite measure based on different types of antisocial behavior may be a useful method in molecular genetic research.
Informant VariationAnother important aspect to consider when comparing results across studies is the source of the information about ASB. It is well-known that different informants produce different reports of a child’s behavior. Correlations between raters of the same child are typically about .60 between mother and father ratings, .28 between parent and teacher ratings, and .22 between the parent and child ratings (Achenbach, McConaughy, & Howell, 1987). Largely, each rater provides a unique perspective on the child’s behavior. Children would seem to be the most knowledgeable source to report on their own behavior (particularly covert actions) as well as their motivations, although their cognitive development, truthfulness, and social desirability factors may limit the accuracy of their reports. Parents may be more able to objectively report on a child’s externalizing behaviors, although they may be unaware of covert actions or unwilling to report them to researchers. Although teachers’ reports may also have the advantage of greater objectivity, teachers may have limited knowledge of the child’s antisocial behavior, particularly as it may occur outside of classroom or other school settings. Although researchers sometimes combine ratings across reporters in an attempt to increase scale reliability, different etiologies may exist for scales derived from different informants (Bartels et al., 2003, 2004; Saudino & Cherny, 2001). Thus, the best way to model information from multiple informants is to use a multivariate, factor-based approach that allows for both differences and correlations across informants simultaneously (Kraemer et al., 2003).
There are at least three advantages to using a factor-based approach when dealing with multiple informants in twin studies. First, such a model allows for the possibility that there may be different genetic and environmental etiologies depending upon the perspective of the rater. Second, it allows one to explicitly model and test for the significance of certain types of rater bias. Finally, because the underlying common factor will represent (by definition) a “shared view” of antisocial behavior across informants, the heritability of the common factor may be higher than the heritabilities obtained through any one informant. If this is the case, then combining information from different types of reporters may yield stronger genetic signals in molecular genetic studies. Previous studies of ASB in preadolescent children have relied heavily on either parent or teacher reports, although a few studies have obtained data from multiple reporters, most commonly from the mother and the father (e.g., Bartels et al., 2003, 2004; Neale & Stevenson, 1989; Hewitt, Silbert, Neale, Eaves, & Erickson, 1992) or from parent(s) and teachers (e.g., Hudziak et al., 2003; Martin, Scourfield, & McGuffin, 2002; Vierikko, Pulkkinen, Kaprio, & Rose, 2004) and occasionally from parents and children (e.g., Simonoff et al., 1995). We are unaware, however, of any published studies of externalizing disorder that have used reports from caregivers, teachers, and children simultaneously.
Sex DifferencesA final question to consider is whether there are sex differences in the relative importance of genetic and environmental factors for antisocial behavior. In spite of the fact that males are far more likely than females to engage in antisocial, aggressive, and criminal behavior, there are no apparent differences between the sexes in the relative importance of genetic factors (i.e., heritability) in explaining individual differences in antisocial behavior among adults. Heritability of liability toward nonviolent criminality appears equivalent for men and women, in studies of both twins (Cloninger & Gottesman, 1987) and adoptees (Baker, Mack, Moffitt, & Mednick, 1989), although the average genetic predispositions do appear greater for criminal women compared with criminal men (Baker et al., 1989; Sigvardsson et al., 1982). A few studies of childhood and adolescent ASB have examined sex differences in genetic and environmental etiology, although the results are not consistent. Some studies have found genetic effects to be of greater importance in boys and common environment more important in girls using parental ratings (Silberg et al., 1994), whereas others have found the opposite result using retrospective reports for adolescents (Jacobson, Prescott, & Kendler, 2002), and still others have not found sex-specific etiologies (Eley, Lichtenstein, & Stevenson, 1999). Aggregating across studies in their meta-analysis, Rhee and Waldman (2002) found that the relative importance of genetic and environmental factors in ASB does not differ for males and females, although it should be noted that their analyses did not investigate the extent to which sex differences in etiology might vary across development or method of assessment (i.e., rater). Overall, the question about different etiologies of ASB for males and females remains open.
The University of Southern California (USC) Twin Study of Risk Factors for ASBThis is one of the first prospective twin studies of preadolescent children to focus on aggressive and antisocial behavior using a multitrait, multi-informant approach. In this article we present results for the comprehensive phenotypic assessments of aggressive and antisocial behavior conducted during the first wave of the study, while the participants are at the brink of adolescence (ages 9 and 10 years old), and use multivariate genetic factor models to examine the extent to which genetic and environmental influences account for agreement and disagreement across raters. This study expands on previous research in the following important ways. First, it examined the relative influence of genetic and environmental factors on antisocial behavior using an ethnically and socioeconomically diverse sample. The ethnic and socioeconomic variability of the sample may allow for greater generalizability of results to the diverse populations in urban areas, where antisocial, aggressive, and violent behaviors present serious threats to the community at large. Second, it used multiple indices of antisocial behavior. Rather than relying on univariate comparisons of heritability estimates for various types and severities of antisocial behavior, the use of a composite measure based on all of the different indices may yield a stronger genetic signal than any one index of antisocial behavior alone. Third, the study relied on reports of antisocial behavior from multiple informants. This allowed us to (a) examine whether there are significant differences across raters; (b) test formally the extent to which rater bias may influence results; and (c) combine information from different raters in a multivariate model, allowing for the presence of a “shared” view of antisocial behavior that may be more reliable than any single viewpoint. Fourth, our sample consisted of both male and female twins, including opposite-sex pairs, allowing us to examine potential sex differences in the etiology of a shared view of ASB. Finally, it should be noted that although the present results are cross-sectional, they are part of a larger, ongoing longitudinal study. Therefore, in future analyses, we will be able to compare and contrast our results as participants move from the brink of adolescence into adolescence and young adulthood.
Method Overview of the USC Twin Study of Risk Factors for Antisocial Behavior
The USC Twin Study of Risk Factors for Antisocial Behavior is a longitudinal study of the interplay of genetic, environmental, social, and biological factors on the development of antisocial behavior across adolescence. The first wave of assessment occurred during 2001 to 2004, when the twins were 9 to 10 years old, with a 2-year follow-up assessment in the laboratory when twins were ages 11 to 12. Two additional follow-up assessments will be conducted when the twins are ages 14 to 15 (third wave) and 16 to 17 years old (fourth wave). The present analyses are based on data from the first wave. Comprehensive assessment of each child was made, including cognitive, behavioral, psychosocial, and psychophysiological measures based on individual testing and interviews of the child and primary caregiver during the laboratory visit, with additional teacher surveys completed and returned by mail. A detailed description of the study, including a summary of the measures, can be found in Baker, Barton, Lozano, Raine, and Fowler (2006).
Participant Recruitment
The twins and their families who are part of the USC Study of Risk Factors for Antisocial Behavior were recruited from the larger Southern California Twin Register, which contains over 1,400 total pairs of school-age twins born between 1990 and 1995. Participants in the Twin Register are volunteers, and families were ascertained primarily through local schools, both public and private, in Los Angeles and the surrounding communities—see Baker, Barton, and Raine (2002) for a detailed description of the recruitment process and Twin Register from which the twins were sampled. Families identified as having twins in the target age range were sent letters briefly describing the study and inviting them to participate.
Study participation required that the twins be (a) proficient in English and (b) 9 or 10 years old at first assessment (see Baker et al., 2006). In addition, either English or Spanish proficiency was required for the twins’ primary caregiver. Of the 1,400 families who joined the USC Twin Register and were in the target age range, approximately 860 families were contacted by phone to explain the study in greater detail and to schedule a testing session. The sample of 605 tested families thus constituted a 70% participation rate of those families whom we were able to contact. Approximately 30 families (3% of the total eligible sample) did not qualify because of limited English proficiency in the children. The remaining families were either never scheduled, cancelled, or did not show up for their testing session.
Procedure
Laboratory visit protocol
Testing and interviews of the child and caregiver were made during a 6- to 8-hr visit to the USC laboratories. The details of the protocol can be found in Baker et al. (2006). Briefly, the visit included behavioral interviews, neurocognitive testing, social risk factor assessment, and psychophysiological recording of the twins. Caregivers were also interviewed about their twins’ behavior, as well as their own behavior and relationship to each twin. Cheek swab samples were also collected from the participating families in order to extract DNA and test for zygosity.
Participating families were compensated for their visit to USC and provided with additional incentives for keeping scheduled appointments in a timely fashion (total payments were up to $125). Families were also provided with group summaries of study results and individual reports of their twins’ zygosity and each child’s cognitive testing results.
Given the sensitive nature of the information provided by the twins and their caregivers (including illegal behaviors), a Certificate of Confidentiality was obtained for this study from the National Institute of Mental Health to help protect the privacy of the participants. All participants were assured that the information they provided would be coded numerically and not linked to their names and that their individual information would not be shared with anyone outside the research team. The laboratory procedures and all aspects of the study were reviewed by the USC Institutional Review Board and were compliant with federal regulations at the time.
Assessments were conducted by rigorously trained examiners (see Baker et al., 2006, for details). All child interviews were conducted in English; caregiver interviews were conducted in either English (n = 492; 81.3%) or Spanish (n = 113; 18.7%), depending on the language preference of the participant. Less than half of the Hispanic caregivers (44.0%) preferred to be interviewed in Spanish. All caregiver surveys were translated into Spanish and back-translated into English by professional translators.
Teacher surveys
The twins’ teachers were asked to complete surveys about each child’s school behaviors and to return their survey packets to USC in prepaid, addressed envelopes. Teachers were not paid for their participation. Excluding pairs (n = 15) who were either homeschooled or for whom parents felt the teachers did not know their children well enough to rate their child, there was a 60% individual return rate for teacher surveys. Although we did not receive teacher surveys for all twins, we did have information on whether twins were in the same class at school for all but 18 twin pairs. Among the entire sample, 31.4% of twins were in the same classroom. Among the 269 pairs for whom both twins had teacher reports (see the Missing data section for details), 41.4% were in the same classroom at school and were therefore rated by the same teacher. This suggests that teachers were somewhat more willing to return surveys if both twins were in the same class at school. Female–female twin pairs were slightly more likely to be placed in the same classroom than male–male twin pairs (34.8% vs. 31.5%), and monozygotic (MZ) twins were slightly more likely than dizygotic (DZ) twins to be placed in the same class (36.0% vs. 29.0%). However, chi-square analysis revealed that neither of these effects was statistically significant (p = .46 and p = .14, respectively), indicating that our results for teacher reports are unlikely to be biased by differential response patterns.
Sample Characteristics
Participants in the present study consisted of 605 families of twins (n = 596 pairs) or triplets (n = 9 sets) and their primary caregivers who participated in the first wave of assessment in the USC Study of Risk Factors for ASB. To avoid problems of additional familial interdependency associated with the small number of triplet pairs, a single pair consisting of 2 of the 3 triplets was randomly selected for these analyses. The sample was composed of both male and female MZ and DZ pairs, including both same- and opposite-sex DZ twins. Among the 1,219 child participants, there was approximately equal gender distribution with 48.7% boys (n = 594) and 51.3% girls (n = 625); the 605 caregivers were primarily female (94.2%).
Caregiver participants were primarily biological mothers of the twins and triplets (91.4%; n = 553), although other relatives were also interviewed, including biological fathers (n = 35; 5.8%), stepparents (n = 2; 0.3%), adoptive parents (n = 4; 0.7%), grandparents (n = 7; 1.2%), or other relatives (n = 4; 0.6%). At the time of first-wave assessment, nearly two thirds of the children were living with both biological parents, who were either married or living together but unmarried (55.5% and 7.2% of total sample of families, respectively). Among the remaining families in which the biological parents were not living together (because of separation, divorce, death of the parent, or never having been married), the majority of these were not married or living with a partner at the time of first-wave testing—only 6.2% of the total sample was remarried to another partner. Thus, the majority of the children lived in two-parent households, although 114 twin or triplet pairs (18.8%) did live in a single-parent household with no other adult in the home. The remainder of the children (12.2%) resided with a single parent as well as one or more other adults (mostly grandparents).
The child’s ethnicity was determined by the ethnicity of their two biological parents as reported by the primary caregiver. As such, the twin–triplet sample was 26.6% Caucasian (n = 161 pairs), 14.3% Black (n = 86 pairs), 37.5% Hispanic (n = 227 pairs), 4.5% Asian (n = 27 pairs), 16.7% Mixed (n = 101 pairs), and 0.3% other ethnicities (n = 2 pairs). Among the Mixed group, most children (57.4%; n = 58 pairs) had one Hispanic parent, and thus nearly half of the sample (47.1%; n = 281 twin or triplet sets) was of at least partial Hispanic descent. This ethnic distribution is comparable to that in the general Los Angeles population (http://www.census.gov/main/www/cen2000.html) and therefore provides a diverse community sample representative of a large urban area.
Median family income was in the $40,000 to $54,000 range (the midpoint of which is $45,500), which is comparable to the median income in Southern California (including Los Angeles, Orange, Riverside, Ventura, and San Bernardino counties) between 2000 and 2002 (average Mdn = $43,042; http://www.census.gov/cgi-bin/saipe/saipe.cgi) and the state of California between 2001 and 2003 (average Mdn = $48,979; http://www.census.gov/hhes/income/income03/statemhi.html). Education levels, measured on a 6-point scale, ranged from 1 (less than high school) to 6 (post-graduate degree). Maternal and paternal education levels were significantly correlated (r = .61, p < .01), and significantly higher mean levels of education were reported for mothers (M = 3.70, SD = 1.58) than for fathers (M = 3.53, SD = 1.63), t(552) = 3.43, p < .001. A composite measure of both parents’ education levels, occupational status, and family income (Hollingshead, 1975) was used as an index of socioeconomic status (SES) in this study. The distribution of the SES factor was slightly skewed toward higher levels, although there was considerable range in SES in this study.
Zygosity of the same-sex twin pairs was determined for the majority of pairs (398/458 = 87%) through DNA microsatellite analysis (seven or more concordant and zero discordant markers = MZ; one or more discordant markers = DZ). A Twin Similarity Questionnaire (Lykken, 1978) was used to infer zygosity for the remaining 60 pairs for whom adequate DNA samples or results were not available. When both questionnaire and DNA results were available, there was 90% agreement between the two.
The frequencies of the five gender and zygosity groups are presented in Table 1, along with mean age and ethnic distribution. The mean ages during first-wave assessment were 9.60 years (SD = 0.60) for the total sample of children and 40.14 years (SD = 6.61) for their caregivers. Although zygosity groups did not differ in mean age of children at first-wave assessment, F(4, 604) = 0.70, p = .59, there were significant differences in current age of biological mother across groups, F(4, 594) = 3.64, p < .01—mothers of DZ pairs were significantly older compared with mothers of MZ pairs. There was also significant ethnic group variation across these five zygosity groups, χ2(16, N = 605) = 33.82, p < .01—Blacks and Caucasians appeared to be more frequently represented in the DZ groups, whereas a higher percentage of Asians and Hispanics were seen in the MZ groups, particularly among the male participants. These differences may stem from different twinning rates across ethnic groups, due in part to differences in maternal age and use of assisted reproduction methods when conceiving the twins. Although the overall zygosity distribution among same-sex pairs (60.2% MZ) was significantly greater than the expected 50% (p < .01), it was not as markedly high as in most other volunteer samples, in which two thirds of same-sex pairs are typically MZ (Lykken, McGue, & Tellegen, 1987). This sample of children and caregivers appears to be quite representative of both the multiple birth and general population in southern California.
Sample Characteristics
Measures
The present study used a total of 18 different measures of antisocial behavior taken from five different instruments from a total of three unique informants (caregivers, teachers, and children). Instruments varied in terms of their mode of assessment, with some being administered through semistructured interviews (i.e., the Diagnostic Interview Schedule for Children—Version IV [DISC–IV]) and others through questionnaires administered either in an interview format (i.e., the Childhood Aggression Questionnaire [CAQ] and the Child Psychopathy Scale [CPS]) or in paper-and-pencil format (i.e., the Child Behavior Checklist [CBCL]). Each instrument was given to at least two of the three possible informants. The following sections provide detailed information about each of the five instruments, including information about the instrument itself, mode of assessment, informant type, and use of any relevant subscales.
DISC–IV (Schaffer, Fisher, Lucas, Dulcan, & Schwab-Stone, 2000)
The DISC–IV is a highly structured interview designed to assess psychiatric disorders, adapted from the Diagnostic and Statistical Manual of Mental Disorders (4th ed.; DSM–IV; American Psychiatric Association, 1994), and symptoms in children and adolescents ages 6 to 17 years. The DISC was designed to be administered by well-trained lay interviewers for epidemiological research. It has a youth as well as a parallel parent version, both of which inquire about the child’s psychiatric symptoms. The Conduct Disorder module was administered using both youth and parent versions in the present study. Although not a focus of the current report, additional modules assessing oppositional defiant disorder, attention-deficit/hyperactivity disorder, major depression, and generalized anxiety in each child were also administered in the parent version. Both symptom counts and diagnoses were provided through computerized scoring of the DISC–IV Conduct Disorder module.
Conduct disorder diagnoses (for the past year) were made for 16 boys (2.7%) and 8 girls (1.3%) based on caregiver reports in the DISC–IV and for 9 boys (1.6%) and 2 girls (0.3%) based on child self-report. Although symptom counts for conduct disorder were significantly correlated between caregiver and youth reports (r = .31, p < .001), it is noteworthy that there was no overlap in conduct disorder diagnoses—that is, no single child reached conduct disorder criteria for both child and caregiver reports. Most likely, this pattern of results is due to the relatively young age of this sample and the fact that this is a population-based (nonclinical) sample. Although diagnosed cases according to one of the raters had elevated symptoms reported by the other rater, these individuals fell short of receiving a corresponding diagnosis from the other rater. In addition, the focus on conduct disorder behaviors during the past year (rather than lifetime prevalence used in most retrospective studies of twins) may have reduced both the prevalence of conduct disorder and the agreement among raters. Nevertheless, given the low prevalence of diagnosable conduct disorder at this age, number of conduct disorder symptoms was used rather than conduct disorder diagnosis. According to caregiver reports, 54.5% of boys and 39.2% of girls had at least one conduct disorder symptom. The corresponding figures for child reports were 47.8% of boys and 30.3% of girls.
The CAQ
This instrument was developed to assess overall, as well as various forms of, aggression. Three parallel forms of this questionnaire were used: (a) child self-report, (b) caregiver’s report of child’s behavior, and (c) teacher’s report of child’s behavior. The majority of the items were taken from Raine and Dodge’s Reactive and Proactive Aggression Questionnaire (Raine et al., 2006), including 11 reactive items (e.g., “I damage things when I am mad”; “I get mad or hit others when they tease me”) and 12 proactive items (e.g., “I threaten and bully other kids”; “I damage or break things for fun”). In addition, 5 items were added to yield relational aggression in the child and teacher versions (e.g., “I tell stories about people behind their back when I am mad at them”; “When I am mad at someone I tell my friends not to play with them”). Each of the items in the CAQ was rated on a 3-point scale (0 = never, 1 = sometimes, 2 = often), and responses were summed within each of the subtypes, for each of the 3 informants, separately. All three scales showed good internal consistency (Cronbach’s alpha ranged from .73 to .76 for child self-report, from .76 to .83 for mother ratings, and from .90 to .92 for teacher ratings).
The CPS (Lynam, 1997)
The CPS is composed of 14 subscales (based on 55 yes or no items), which consist of assessments of Glibness, Untruthfulness, Lack of Guilt, Callousness, Impulsiveness, Boredom Susceptibility, Manipulation, Poverty of Affect, Parasitic Lifestyle, Behavioral Dyscontrol, Lack of Planning, Unreliability, Failure to Accept Responsibility, and Grandiosity. Minor changes were made to the wording of some items for ease of understanding by 9- to 10-year-old children. Parallel versions of the CPS were administered to both the child and the caregiver in interview form. The two classic factors of psychopathy (Factor 1: Callous–Unemotional; Factor 2: Impulsive–Irresponsible) were derived in each of the caregiver and child self-reports, based on composites of the 14 subscales in the CPS within each rater. Both Factor 1 and Factor 2 showed reasonable internal consistency in caregiver ratings (α = .71 and .74, respectively), with somewhat lower values in child self-report (α = .63 and .61).
The CBCL (Achenbach, 1991)
The CBCL is a caregiver rating scale composed of 112 items concerning a child’s behavior within the past 12 months. Items are rated on a 3-point scale (0 = not true, 1 = sometimes or somewhat true, 2 = very true or often true) and are used to derive eight subscales: Withdrawn, Anxious/Depressed, Social Problems, Thought Problems, Attention Problems, Delinquent Behavior, Somatic Complaints, and Aggressive Behavior (Achenbach, 1991). For the purposes of the present article, however, only the Delinquent Behavior (13 items) and Aggressive Behavior (20 items) subscales were used in our analyses. The CBCL was administered during the laboratory visit to the caregivers in either survey (paper) or interview form. The CBCL was administered to the caregivers in interview form rather than in paper form if the subject’s reading comprehension skills were determined to be at or below a second-grade level as determined by the Woodcock–Johnson Reading Achievement Test (Woodcock & Johnson, 1989). Teachers were also given the parallel form of the CBCL (the Teacher Report Form) as part of the mail survey packet.
Short-Term Reliability
Thirty randomly selected families with complete data (both cotwins and their caregiver) completed the entire first-wave assessment a second time, approximately 6 months following their original laboratory visit. These retest families included exactly 50% of each gender (n = 30 boys; n = 30 girls) and were used to evaluate test–retest reliability for all measures used in this study. This sample was the basis for testing reliability of the measures across time. Test–retest correlations are presented in Table 2, separately for boys and girls, as well as for the combined sample. There was remarkable stability for these measures, although the correlations varied somewhat across rater and sex of child. Greatest stability was observed for caregiver reports, especially for ratings of boys. The lowest correlations in Table 2 are for caregiver ratings of conduct disorder symptoms (.57) and CBCL Delinquency (r = .47) in girls and for girls’ self-reported conduct disorder symptoms (r = .56). Inspecting graphical summaries of these correlations, however, revealed 1 outlier—a girl who received low ratings on these measures in the first testing and considerably higher ratings in the second. Written comments from the examiners for this family indicated that this girl had indeed experienced significant behavioral changes in the 6 months in between the two testing sessions (confirmed by both the caregiver and child examiners). Removing this case from this small sample of retest families resulted in higher retest correlations (r > .60 for all three instances). Thus, although there does appear to be considerable reliability in these measures, we must be cognizant of the fact that the potential for developmental change is possible at this age. Therefore, we suspect that these estimates of “short-term” reliability are actually conservative estimates (i.e., underestimates) of the true reliabilities.
Six-Month Test-Retest Correlations for Antisocial Behavior Measures
Statistical Analyses
General issues
Descriptive statistics, mean level comparisons, phenotypic correlations, and factor analyses were all conducted using the SPSS (Version 11.5) statistical package. Multivariate genetic analysis of the rater effects models was conducted using the structural equation modeling (SEM) program Mx (Neale, Boker, Xie, & Maes, 2003).
Missing data
Missing data for child- or caregiver reports of the different antisocial behavior measures were quite rare. For most measures, we had valid data for 1,210 to 1,219 of our total sample of 1,219 individual children. Missing data were somewhat greater for child and caregiver ratings of conduct disorder; still, we had complete child and caregiver data for more than 95% of the sample (see Table 3 for individual sample sizes for each measure). As detailed in the methods, the overall teacher response was approximately 60%; however, valid teacher-report data on the antisocial behavior measures were obtained for approximately 700 individual twins (57.4%). Of the 605 individual twin pairs, 269 pairs (44.5%) had teacher reports for both twins, and an additional 143 pairs (23.6%) had teacher reports of antisocial behavior for at least one of the two twins. Of the 269 pairs for whom we had valid teacher-report data for both twins, 111 pairs (41.4%) were in the same classroom at school and were thus rated by the same teacher informant.
Aggression, Delinquency, and Psychopathy: Means and Standard Deviations by Sex and Informant
Missing data were handled in a variety of different ways. For phenotypic analyses of mean level differences and correlations among individual subscales of ASB, a listwise deletion procedure was used, as these analyses are conducted for descriptive purposes only. For the creation of the factor-based composite scores, individuals with missing data on a given measure, within rater, were assigned a missing value for the composite scale. As missing data on individual measures were relatively rare among children and caregivers, we had valid factor scores for more than 96% of the sample (N = 1,175 for child-based factor scores, and N = 1,193 for caregiver-based factor scores). Among the 698 teachers who reported on the antisocial behavior of the children, we could create factor scores for more than 97% of them (N = 681; 55.9% of the total sample of 1,219 individuals).
One of the reasons for selecting Mx for the multivariate twin analyses is that it uses full information maximum-likelihood when fitting models to the raw data. Thus, all pairs in which at least 1 twin has nonmissing data on at least one measure can be included in the analyses, and fit functions are based on the calculation of twice the negative log-likelihood of all nonmissing observations (where an observation is defined by measure, not by individual). For the present analyses, only 1 of the 605 possible pairs did not have any usable data and was excluded from the twin analyses. Nearly all of the 605 pairs (N = 591 pairs, 97.8%) had valid composite scores for both twins based on the caregiver ratings. Over 90% (N = 559 pairs, 92.4%) of the pairs sample had both caregiver and child-report composite scores for both twins, and 42.0% of the sample (N = 254 pairs) had valid data for both twins from caregiver, child, and teacher reports. An additional 130 pairs (21.5% of the sample) had complete data from caregiver and child reports, and teacher report data for 1 member of the twin pair. Although these latter pairs could not contribute information regarding covariance across twins for teacher reports, they did provide information for the sample means and variance of the teacher reports, as well as for the within-person correlations across informants. Thus, including all 384 pairs (63.5%) for whom we had valid teacher reports for at least 1 of the 2 twins minimized potential sampling bias. Patterns of missingness did not vary significantly by sex or zygosity (results of the chi-square analyses are available upon request). For example, valid teacher report data for both twins were available from 40.8% to 48.8% of any given zygosity group. Complete pairwise data for caregiver and child reports were available for more than 92% of any given zygosity group.
Genetic models
The rater models used were based on extensions of the traditional ACE model that is typically used in behavioral genetic studies. These models use information from the observed twin variances and covariances (calculated from the raw data) to partition the overall variance into additive genetic (A), common (or shared) environmental (C), and nonshared environmental (E) influences (Neale & Cardon, 1992). In behavioral genetic models, additive genetic influences are correlated 1.0 among MZ twin pairs, as MZ twins have identical genotypes. In contrast, DZ twins share, on average, half of their segregating genes; thus, these models assume a correlation of .5 among DZ pairs. The proportion of variation that is due to genetic influences is called the heritability. Shared environmental factors include those environmental factors that serve to make individuals in a family similar to one another but that may differ across families. Thus, shared environmental influences can include such factors as SES, family structure, and shared peer influences, as well as broader contextual factors (e.g., school or neighborhood effects). In the ACE model, shared environmental influences are correlated 1.0 across twin pairs, regardless of zygosity. Nonshared environmental influences are any environmental influences that serve to make individuals dissimilar, including measurement errors (which are assumed to be random). By definition, nonshared environmental influences do not correlate across either MZ or DZ pairs.
By combining data from all three informants simultaneously in multivariate genetic models, we are able to differentiate genetic and environmental factors that influence a shared view of antisocial behavior from genetic and environmental factors that influence each informant’s own particular rating. Moreover, we can also investigate the extent to which rater effects may have biased estimates of heritability of ASB. Figure 1 shows the three multivariate models used to address this issue. All three models are variants of a common pathways model, which allowed for genetic and environmental influences on observed measures to operate through a single underlying phenotype (i.e., AC, CC, and EC; see Kendler, Heath, Martin, & Eaves, 1987; McArdle & Goldsmith, 1990, for details on common pathways models). In multiple-rater analyses, the underlying latent variable that allows for correlations across raters reflects a common, or shared, view of the child’s antisocial behavior. The genetic and environmental factors that influence this underlying shared view are further unbiased by either rater effects or measurement error, as these latter effects influence only the rater-specific views (this is discussed in more detail later). Each rater’s individual view loads on the underlying latent factor through the paths marked λ (with subscripts M, K, and T referring to caregiver [mother], child [kid], and teacher reports, respectively). Genetic and environmental influences that account for variation in the shared view of ASB are depicted through paths aC, cC, and eC (whereby the subscript C refers to influences that are common across raters). As described earlier, all additive genetic influences (A) correlate 1.0 across MZ twins and 0.5 across DZ twins, shared environmental (C) effects correlate 1.0 across twins, regardless of zygosity, and nonshared environmental influences (E) did not correlate across twins. All three models allow for informant-specific nonshared environmental influences (EM, EK, and ET), as any given measure is an imperfect estimate of the underlying “true score”; thus, informant-specific nonshared environmental effects in this model include errors of measure. In contrast, the nonshared environment that influences the common latent factor (EC) represents environmental factors that vary across twins in the same family, which are systematically associated with ASB (e.g., differential parental treatment or different peer groups).
Figure 1. (a) Rater effects model. (b) Measurement model. (c) Full common pathways model. Observed variables are represented by rectangles; latent variables are represented by circles. A = additive genetic effects; C = shared (common) environmental influences; E = nonshared environmental influences; R = rater effects; MZ = monozygotic; DZ = dizygotic; Cgvr = caregiver; Tchr = teacher. Path coefficients with a, c, e, and r correspond to the effects of these latent factors on the observed variables. Paths marked with λ represent the factor loadings on the shared view of antisocial behavior for each individual rater. Factors and corresponding path coefficients that reflect influences on the shared view of antisocial behavior are subscripted with C. The subscripts M, K, and T refer to factors and corresponding path coefficients that are specific to the caregiver (M), child (K), and teacher (T) reports, respectively. All latent A, C, E, and R factors have an assumed variance of 1.0; the variance in the factor representing the shared view has likewise been constrained to unity.
Figure 1a presents the rater effects model, which allows for additional within-informant correlation across twins for caregivers and teachers, due to the fact that the same rater is reporting on behavior for both twins. Individual twins only reported on their own behavior; therefore, it was not possible to estimate rater effects for child reports. As can be seen in Figure 1a, this model (also referred to as the correlated errors model; Simonoff et al., 1995) allows for latent variables representing rater effects to influence variation in caregiver and teacher reports (RM and RT, respectively). To the extent that ratings are influenced by the qualities of the informant, this would affect the ratings of both twins in a pair and may lead to overestimations of the twin correlations. As Figure 1a shows, the correlation for the rater effect among caregiver reports was 1.0, because all caregivers in our sample reported on the behavior of both twins. In contrast, the correlation of the rater effect for teachers could be either 1.0 or 0, depending on whether the same teacher rated both twins (a correlation of 1.0) or whether a different teacher rated each twin (a correlation of 0). By using a feature of Mx that allows for the use of definition variables as moderators of individual parameters (Neale et al., 2003), we were able to use a dummy code for each twin pair as a definition variable that represented whether the twins were in the same classroom (and thus were rated by the same teacher) to multiply the parameter for the teacher rater effect (rT) by either 1.0 (same class) or 0 (different class). If rT > 0, this would predict higher correlations among twins rated by the same teacher.
Figure 1b shows an alternative multivariate model known as the measurement model. This model, which is a restricted version of the model presented in Figure 1a, eliminates the rater effects for caregiver and teacher reports. The critical assumption of the measurement model is that the latent variable representing the shared view is the “true” representation of ASB and that all meaningful genetic and environmental influences on variation in reports of ASB are operating through the latent phenotype. Any residual variance on each rater’s individual perception of ASB that is not explained by the latent phenotype is assumed to be random measurement error that is not systematically related to characteristics of the rater and is, therefore, modeled as nonshared environment (E). Thus, the amount of variance accounted for by rater-specific E should be consistent with estimates of the reliability of each rating.
Figure 1c shows the third and final model, which is the full version of the common pathways model for multiple raters. In addition to allowing for the uncorrelated errors of measurement and rater effects (i.e., correlated errors of measurement) found in the aforementioned rater effects and measurement models, this model further allows for specific genetic and shared environmental factors to influence variation in each informant’s own ratings of the child’s ASB. For simplicity, the model is shown for 1 twin only; however, the specific A and C influences on each informant’s report of ASB correlate across twin pairs in the manner described earlier. As shown, the model allows for genetic influence on the specific viewpoints of each rater (AM, AK, and AT). The general assumption is that these genetic factors represent valid genetic variance that arises because each rater “sees” different aspects of ASB (but see the Discussion section for alternative explanations). Similarly, different rater perceptions of ASB can also be influenced by shared environmental factors (CM, CK, and CT). In this model, the potential effect of shared environmental influence on caregiver’s reports of ASB is confounded by potential rater effects, both of which would increase correlations of caregiver ratings across twins, regardless of zygosity (see Hewitt et al., 1992, for details). This is represented by the dashed lines for the RM and CM effects. Because of this confound, only one parameter can be estimated in the common pathways model, and this parameter may represent shared environment influences, a rater effect, or some combination of both. In contrast, because only some teachers rate only 1 twin per family, and others rate both twins, the shared environmental influences on teacher reports can be statistically differentiated from potential rater bias. As explained earlier, children reported only on their own behaviors; thus, the common pathways model cannot estimate rater effects for child reports.
The critical difference between this model and the models presented in Figure 1a and 1b is that this model treats differences in reports of ASB across raters as meaningful. In other words, this model assumes that there are systematic causes for disagreement among parents, teachers, and children that are not solely due to random errors of measurement and/or perceptual biases. This model would be consistent with the notion that parents, teachers, and children provide a unique perspective on the child’s behavior and that no single informant may necessarily be considered more valid or reliable than another.
Model comparisons
One of the advantages of using SEM to estimate genetic and environmental influences on variation and covariation among traits or behaviors is that SEM provides a framework for evaluating how well the theoretical model (or models) fits the observed behavior. Traditionally, two statistics have been used to compare the fit of two nested models: the likelihood-ratio test (LRT) statistic (Neale & Cardon, 1992) and the Akaike information criterion (AIC; Akaike, 1987; Medsker, Williams, & Holahan, 1994). The LRT is obtained by comparing the –2 log-likelihood (–2 LL) of a comparison model to the –2 LL of a nested (reduced) model. The LRT statistic is the difference in –2 LL between the two models, which is distributed as a chi-square statistic with degrees of freedom equal to the difference in degrees of freedom between the two models. The AIC is calculated as the LRT minus twice the difference in degrees of freedom; it indexes both goodness of fit and parsimony: The more negative the AIC, the better the balance between goodness of fit and parsimony. More recently, the Bayesian information criterion (BIC) is also being used to evaluate model fit. The BIC is similar to the AIC, except that it also adjusts for sample size (for details on the BIC and a comparison of fit statistics using simulated data, see Markon & Krueger, 2004). In this article, we present all three fit statistics, although when there is discrepancy, preference was given to the BIC (adjusted for sample size), based on the results of independent simulation studies (Markon & Krueger, 2004).
Evaluation of model fit for the multivariate analyses is done at two different levels. First, a model is fit to the data that perfectly recaptures the observed means, variances, and within- and cross-twin covariances from the three informants simultaneously. This “saturated” model provides a –2 LL statistic that can be used as the base likelihood from which the AIC and BIC statistics from each theoretical model are calculated, providing a standardized estimate of AIC and BIC values for comparison. Moreover, by comparing the fit of each of our ACE models to the fit of this saturated model using the LRT, we obtain an “absolute” estimate of how well each of our hypothesized models fits the observed data. Second, we can also calculate an LRT statistic by comparing ACE models that are “nested” within each other. We note that the measurement model (Figure 1b) is a nested submodel of the rater effects model (Figure 1a), which is itself a nested submodel of the full common pathways model (Figure 1c); therefore, LRT statistics can be calculated for each set of comparisons. Moreover, the significance of potential sex differences can also be calculated by obtaining LRT, AIC, and BIC values from a model where A, C, and E parameters are allowed to vary by sex with a model that constrains the parameters to be equal for boys and girls.
Results Sex and Informant Differences in Mean Level ASB
Descriptive statistics (means and standard deviations) for the various rating scales of aggression and delinquency are provided in Table 3, separately for caregiver, child, and teacher reports for boys and girls. These include proactive, reactive, and relational aggression, measured using the CAQ; psychopathy Factor 1 (Callous–Unemotional) and Factor 2 (Impulsive–Irresponsible) obtained on the CPS; the Aggression and Delinquency subscales from the CBCL; and conduct disorder symptom counts from the DISC–IV. Mean differences in ASB were examined between boys and girls, as well as among different informants. Significant sex differences (p < .01) emerged in the expected direction (boys > girls) for all scales except for teacher ratings of relational aggression, which showed no significant sex difference. Antisocial behavior was clearly more prevalent in boys than in girls at this age. These mean level differences were confirmed in the genetic analyses (results are available upon request); thus, means were estimated separately for males and females in all of the twin models. Although not shown in the table, it is noteworthy that diagnostic rates of disorders in this community sample are comparable to those reported in DSM–IV, for both conduct disorder (n = 25 boys, 4.2%; n = 10 girls, 1.6% received diagnoses from either youth or parent interviews) and oppositional defiant disorder (n = 70 boys, 11.9%; n = 49 girls, 8.1%; see Baker et al., 2006). Both the level and the range of ASB in this ethnically diverse community sample of twins thus appear to be comparable to those in other nontwin populations of children.
Several significant differences among informants also emerged (see Table 3). Caregivers provided significantly higher ratings than boys’ ratings of themselves for four of the five scales they had in common (reactive aggression, CPS Factors 1 and 2, and conduct disorders, but not proactive aggression). For girls, a similar pattern of higher ratings by caregivers than self-reports was also evident for several scales (proactive aggression, CPS Factor 1, and conduct disorder symptoms), although caregiver ratings of girls were lower for CPS Factor 2 and not significant for reactive aggression. Caregivers thus did not generally rate children higher or lower than children rated themselves across the board, although some rater differences were evident for both genders. Comparisons of teacher and caregiver ratings of boys also revealed significant differences for all scales except CBCL Aggression and Delinquency, although direction of difference again depended on the scale (i.e., teacher ratings lower for reactive aggression, but higher for proactive and relational aggression). The pattern of caregiver–teacher differences was similar in girls, whereby teachers again provided significantly lower ratings for reactive aggression, and all three CBCL scales, but higher ratings for proactive aggression. Teacher ratings were also significantly lower than child self-report for reactive aggression in both boys and girls, but higher for proactive aggression. Although not shown in the table, there were no differences in caregiver or child reports between children with teacher reports and children without teacher reports (results are available upon request).
The Unidimensionality of ASB in Childhood
Phenotypic correlations
We next tested whether each of the indices of antisocial behavior could be considered manifestations of a single higher order construct of externalizing behavior. We examined this through both correlational and principal-components analysis of the various ASB measures obtained through each rater. Table 4 presents the full correlation matrix (18 × 18) for boys (above the diagonal) and girls (below the diagonal). Moderate to high correlations were found among the scales of aggression and delinquency within each rater, with correlations ranging from .47 to .66 among child report measures, from .40 to .62 among caregiver report measures, and from .61 to .78 among the teacher report measures.
Phenotypic Intercorrelations Between Aggressive and Antisocial Behavior Measures for Boys (Above the Diagonal) and Girls (Below the Diagonal)
Additional comparisons of caregiver, teacher, and child reports of ASB were made by computing correlations between informants for the various scales (see Table 4). Informant agreement (indicated in boldface type in Table 4 for each measure common to two or more raters) was lowest between the child and either the caregiver or teacher (r = .17 to .29 for boys; r = .02 to .21 for girls). Agreement between caregiver and teacher ratings was somewhat higher (r = .26 to .43 for boys; r = .10 to .21 for girls) across the board. Although not shown in Table 4, correlations across raters for the composite measure of antisocial behavior (described in the next section) were also significant: r = .30 for caregiver–child agreement, r = .23 for child–teacher agreement, and r = .44 for caregiver–teacher agreement (sexes combined).
Principal-components analysis
Although all of the within-rater correlations were significant and were of moderate to high magnitude, they were not unity, which at first blush might indicate that heterogeneity of ASB may exist in these preadolescent children. The positive manifold of correlations within each rater, however, is suggestive of a single, general factor of antisocial behavior underlying the various measures. Principal-components analyses of the ASB scales within each rater confirmed that a single factor could account for much of the variance among these measures. Loadings on the first principal component within each rater are provided in Table 5, along with the percentage of variance explained among the scales in each case. All factor loadings were .70 or higher, and the general ASB factor accounted for 57.4% of the variance among the child report measures of ASB, 58.7% of variance among caregiver reports, and 77.4% of variance among teacher reports. Within each rater, scree plots clearly indicated a strong preference for a single principal component, such that only the first eigenvalue exceeded 1.0 (i.e., 3.44 for child report measures, 4.11 for caregiver ratings, and 3.87 for teacher ratings) with the second eigenvalue being clearly less than 1.0 in all three analyses (0.70, 0.72, and 0.41 for child, caregiver, and teacher ratings, respectively). It would thus appear that there is considerable overlap between the individual ASB scales, consistent with the notion of a general externalizing factor (Krueger, 2002). We therefore computed composite measures of ASB for each rater (using factor-weighted scores), and used these in the multivariate genetic models. It is noteworthy that the 6-month test–retest correlations were strong for the composite scores (r = .81 for child reports and .94 for caregiver reports) and that interrater agreement for the three composites (r = .30 between caregiver and child, r = .23 between child and teacher, and r = .44 between caregiver and teacher) was comparable to—and, in many instances, higher than—the values for each individual scale reported in Table 4. Although the parameter estimates obtained via maximum-likelihood estimation in Mx are largely robust to violations of nonnormality (Neale et al., 2003), given the slightly skewed nature of the ASB composite scores, we opted to use log-transformations to approximate normality; therefore, the biometrical model-fitting analyses were performed using the transformed scores. Parameter estimates for untransformed data were nearly identical to the results presented in this article (results are available upon request).
Factor Loadings for First Principal Component of Aggressive and Antisocial Behavior Measures Within Rater
Genetic Factor Models: Results From Multivariate Rater-Effects Models
The –2LL of the fully saturated comparison model was 7,835.34 (df = 2914). This model perfectly recaptured observed means and covariances and was therefore used to establish the adequacy of fit for each of the multivariate models shown in Figure 1. As previous analyses showed significant differences in mean level across gender (confirmed using Mx-based analyses of the composite ASB scores; results are available upon request), all subsequent multivariate models allowed for gender differences in mean levels for all three raters. The –2LL of the measurement model (see Figure 1b) was 8,161.42 (df = 3027). In comparison with the saturated model, this model fit the data very poorly by all three fit criteria (LRT = 326.08, df = 113, p < .001; AIC = 100.1, BIC = 169.8). Comparing the standard rater effects model (see Figure 1a) with the measurement model indicates that the addition of the parameters representing “correlated errors” among caregivers and teachers results in a highly significant improvement in fit (–2LL = 8,029.27, df = 3023; LRT = 132.2, df = 4, p < .001). Although the LRT statistic for the rater effects model based on a comparison with the saturated model was still highly significant (LRT = 193.93, df = 109, p < .001), both the AIC (–24.1) and the BIC (–868.2) statistics were less than zero, indicating that this model could adequately fit the observed patterns of means and variance–covariance. Nevertheless, the full common pathways model (see Figure 1c) further offered a significant improvement in fit relative to the rater effects model (–2LL = 7,965.21, df = 3013; LRT = 60.4, df = 4, p < .001). The AIC (–68.1) and the BIC (–884.1) statistics were the most negative for the common pathways model, indicating that a model that allowed for genetic and shared environmental influences on rater-specific reports of ASB in addition to the genetic and environmental influences operating through the latent variable was the best model to fit the data. In comparison with the saturated model, this model also showed a significant difference in fit by LRT criteria, indicating that the estimated variance and covariance from this model was significantly different from the observed variance and covariance, but at a much lower probability value than the other two models (LRT = 129.87, df = 99, p = .03). Finally, a model that constrained all of the parameter estimates from the common pathways model (except mean levels) to be the same for boys and girls yielded the lowest BIC statistic (–893.3), although the AIC statistic (–68.0) was nearly identical to the AIC statistic from the model that allowed these parameters to vary across gender. The –2LL for this model was 7,995.38 (df = 3028).
Standardized parameter estimates from the full common pathways model with equal effects across gender are provided in Figure 2. Estimates shown to be statistically significant at p < .05 are indicated with an asterisk (based on results of post hoc analyses; these analyses are available upon request). As shown, the common ASB factor underlying all three raters was primarily explained by genetic influences, with a heritability of .96 and no effect of shared twin environment. (In order to calculate estimates for proportions of variation, each standardized parameter estimate shown in Figure 2 is squared; i.e., h2 of shared view = .982.) Only a small proportion of variation in the underlying latent factor (.04) was explained by nonshared environmental influences (.192). Moreover, post hoc analyses indicated that these nonshared environmental influences were not statistically significant and that all variation in the latent ASB factor representing the shared view could be accounted for entirely by genetic influence (i.e., the h2 of the latent factor = 1.0). Figure 2 also demonstrates that the latent factor representing the shared viewpoint accounted for only 17.6% of the overall variation in child reports (.422) but explained approximately one third (.552 = .303) and nearly half (.672 = .449) of the variation in teacher and caregiver reports, respectively.
Figure 2. Standardized parameter estimates from full common pathways model. Paths marked with an asterisk are significantly different from zero. A = additive genetic effects; C = shared (common) environmental influences; E = nonshared environmental influences; R = rater effects. Factors influencing the underlying latent shared view of antisocial behavior are subscripted with C. The subscripts M, K, and T refer to factors that are specific to the caregiver (M), child (K), and teacher (T) reports, respectively. For Caregiver Report, rater effects (RM) and shared environmental effects (CM) cannot be statistically differentiated in this design. Thus, these influences are noted as a single path coefficient that may reflect either or both effects on variation in caregiver reports. All latent A, C, E, and R factors have an assumed variance of 1.0; the variance in the factor representing the shared view was likewise constrained to unity.
The aforementioned series of analyses indicate that models with nonrandom effects on rater-specific views of ASB provided a better fit to the data than the model, which assumed individual reports for each twin were influenced solely by random errors of measurement. As can be seen in Figure 2, correlated errors for caregiver reports, which could reflect rater effects, shared environmental influences, or both, accounted for 14.4% (.382) of the overall variation in caregiver reports and were significant. Rater effects accounted for more than one fourth of the variation in teacher reports (.532 = .281) and were significantly different from zero, as were shared environmental effects, which accounted for an additional 20.3% (.452) of the variation in teacher reports. Informant-specific shared environmental factors accounted for a nonsignificant amount of variation in child reports (.152 = .023). Finally, informant-specific genetic factors accounted for only a modest proportion of the overall variation in caregiver (.242 = .058) and teacher (.332 = .109) reports and were not significantly different from zero. In contrast, genetic factors accounted for nearly one third (.552 = .303) of the overall variation in child reports and were significant at p < .05.
Table 6 summarizes the proportions of variation in each informant’s report due to the various genetic, environmental, and rater-effects factors. In this table, we separated the influences that are common to each informant from the influences that are informant specific. A number of patterns are visible in the table. First, overall, genetic factors account for moderate amounts of variation in reports of ASB for all three raters, with heritabilities ranging from .397 (for teacher reports) to .495 (for caregiver reports). Nevertheless, an interesting pattern emerged with respect to the source of the genetic variance. For caregivers, the majority of the genetic variance (88.1%) came from the genetic influence operating through the shared view of ASB. In contrast, for child reports, only about one third of the overall genetic variation came from genetic influence operating on the shared view of ASB, and the majority of the genetic variation (64.4%) came from genetic influence on ASB that was specific to the child’s own self-rating. Teacher reports were somewhat in the middle but were more similar to caregiver ratings in that the majority of the genetic variance (72.8%) came from the genetic influence operating through the shared view of ASB. This is consistent both with the result that the child reports load less strongly on the underlying latent factor than caregiver or teacher reports and with the finding of significant informant-specific genetic influence only for child and not for caregiver or teacher reports.
Proportions of Variance Explained by Genetic and Environmental Influence: Summary of Results from the Full Common Pathways Model
The second notable pattern is that environmental influences, both shared and nonshared, influenced individual rater reports of ASB but did not play a large role in the shared view. For child reports, there was virtually no support for the effects of shared environmental factors on variation in ASB. Instead, nonshared environmental factors played a critical role in accounting for individual differences in reports of ASB and in fact accounted for a slight majority (50.6%) of the overall phenotypic variation. For caregiver reports, there were significant effects of either shared environmental influence or rater effects; however, these effects explained only a modest amount of the overall phenotypic variation (14.6%). Nonshared environmental influences accounted for roughly one third of variance in caregiver reports of ASB (36.0%). Unlike caregiver ratings, we did have the ability with teacher ratings to differentiate between shared environmental factors and rater effects. Rater effects accounted for approximately 28.1% of the variance in teacher reports, and shared environmental factors accounted for an additional 20.3% of the variance. Nonshared environmental factors showed only modest influence on teacher ratings (11.9%).
DiscussionThis article provides one of the first reports from a major longitudinal twin study of childhood aggression and antisocial behavior among a large ethnically diverse sample of twins. In this study, we focused on phenotypic and genetic analyses of antisocial behavior measures during a first wave of assessment at ages 9 to 10, when twins are on the cusp of adolescence. Instead of relying on information from one source (i.e., teacher or parent ratings of child behavior problems), we obtained ratings from 3 informants. The purpose of this article was to evaluate rater effects on the genetic and environmental influences on a shared view of antisocial behavior, using a composite measure based on a variety of types of aggressive and antisocial behavior.
In the present study we relied on composite measures of antisocial behavior created from 18 different subscales. Within each rater, subscales of reactive, proactive, and relational aggression; childhood psychopathy factors; and delinquent behavior measures (including conduct disorder symptoms) were all moderately to highly correlated with each other. These correlations (nearly uniform within each rater), as well as the results from our principal-components analyses, suggested the presence of a general antisocial or “deviance” factor underlying the various subscales provided by each rater. This general factor may be comparable to an overall externalizing factor that has been proposed by others (Achenbach & Edelbrock, 1981; Krueger et al., 2002) and reflects the wide range of behaviors exhibited by these preadolescent children. Although still somewhat negatively skewed, this general deviance factor well characterized the “shades of gray” in individual differences for antisocial behavior in this large sample and has proven useful in examining relationships with various biological (Jacobson, Zumberge, Lozano, Raine, & Baker, 2005) and social risk factors (Sanchez, Baker, & Raine, 2005) in this sample. Our continuous ASB factor may therefore reflect a wider spectrum of ASB than what is captured when relying on symptom counts (e.g., in Burt, McGue, Krueger, & Iacono, 2005) or on extreme forms of disruptive behavior, substance use, or criminal offending.
Our analyses revealed that although mean levels of ASB differed for boys and girls, the sources of individual differences in ASB were similar across gender. One of the most important findings from this study is that a shared view of antisocial behavior is strongly genetically influenced, with little or no effect of shared sibling environment. Although our analyses revealed a moderate genetic basis to individual views of antisocial behavior, with heritabilities ranging from .40 to .50 for individual composites from child, teacher, and caregiver, the estimated heritability of the underlying shared view of antisocial behavior from the common pathways model was nearly 1.0. This latent factor may reflect constellations of stable personality traits (e.g., disinhibition, lack of constraint) that may influence antisocial behavior across many contexts (Krueger et al., 2002). This highly heritable common factor representing the shared view across multiple informants could therefore prove especially useful in future investigations of specific genetic associations, or quantitative trait loci, in human aggression and antisocial behavior.
In this ethnically diverse sample of twins, heritability estimates within each rater are comparable to estimates from previous studies, which have been based primarily on Caucasian and European samples. Genetic influences for caregiver reports in our study are somewhat higher than those reported for young children in these reviews but are comparable to other recent twin studies of younger schoolchildren (Arsenault et al., 2003). The somewhat higher heritability in the present study may be due in part to our use of a general composite measure of antisocial behavior, which may be more reliable than individual subscales typically used. We have, in fact, found the pattern of genetic and environmental influences to be more variable when examining specific subscales (Raine, Baker, & Liu 2006, 2007; Ward, 2004).
Interrater agreement among caregiver, teacher, and child reports of aggression and antisocial behavior in the present study is comparable to that of other studies (Achenbach et al., 1987; Youngstrom, Loeber, & Stouthamer-Loeber, 2000); agreement is lowest between the child and either the caregiver or teacher and somewhat higher between caregiver and teacher ratings. This suggests that although there is clearly a significant degree of overlap among raters, each individual viewpoint is influenced by unique factors. Of particular importance was the identification of significant rater variance for both caregiver and teacher reports. Although we are considering these caregiver and teacher rater effects to be biases due to having the same rater report on both twins, other explanations for these “correlated errors” are possible. Specifically, among caregiver reports, it is not possible to disentangle rater effects from true effects of shared environmental influences (Hewitt et al., 1992). For example, family-level variables such as parental discipline may influence levels of antisocial behavior for twins in the same family. This shared environmental effect would also account for the correlated view in our model. Regardless of the specific source of the correlated view among caregivers, it is noteworthy that these effects accounted for a relatively modest (albeit statistically significant) proportion of variation (15%) in caregiver reports.
In contrast, for teacher reports, we were able to differentiate rater effects from true shared environmental effects, because although virtually all twins attended the same school, less than half of twin pairs were in the same classroom at school. This allowed us to disentangle shared environmental influences, which would affect the similarity of all twin pairs, regardless of classroom, from rater effects, which would only increase similarity among twins who were rated by the same teacher. In this study, rater effects accounted for more than one fourth (28.1%) of the overall variation in teacher reports. This indicates that the twins in the same classroom are rated more similarly than twins in different classrooms. Although we speculate that this is due to rater bias on the part of the teacher, it is theoretically possible that twins in the same classroom may in fact have a greater shared environment than those in separate classrooms (i.e., a direct classroom effect on behavior). To investigate this possibility, we examined post hoc whether caregiver or child ratings were also more similar if twins were in the same classroom at school, using the same dummy code for shared classroom that we used to evaluate the teacher rater effects (as described earlier). The results of these post hoc analyses indicate that being in the same classroom at school had virtually no effect on twin similarity of antisocial behavior as rated by either caregivers or the twins themselves. Being in the same classroom at school, therefore, does not lead to increased twin similarity in ASB based on either the caregiver’s or the child’s own view. Thus, our findings suggest that reports from teachers may be more heavily influenced by rater bias effects than are ratings from other reporters, leading to a spurious effect of shared environment when teacher reports are examined alone. However, in the absence of direct observational data, we cannot rule out the possibility that twins in the same classroom behave more similarly while at school. Nevertheless, if this is the case, it is important to note that these “classroom effects” are situational specific and do not affect similarity of behavior in other contexts.
In addition to these specific rater effects, shared environmental factors accounted for a significant 20% of the variation in teacher reports. These influences may reflect the effects of school context on ASB. For example, Rowe and colleagues conducted a behavioral genetic analysis within a hierarchical linear modeling framework and found that aggregate levels of parental warmth moderated both mean level of aggression as well as the overall impact of genetic and environmental influences on individual differences in aggression, with higher shared environmental influences on aggression found among twins and siblings from schools with lower average levels of parental warmth (Rowe, Almeida, & Jacobson, 1999). Their findings suggest that environmental context, measured at the school level, not only moderates mean levels of ASB but may also alter the sources of individual differences in ASB.
It is important to note that there was no indication that caregivers or teachers moderated their views of twin similarity on the basis of the twins’ zygosity. If caregivers or teachers were more likely to rate MZ twins more similarly than DZ twins, this would result in higher within-rater correlations for MZ twins than for DZ twins and would be interpreted in our model as specific genetic influences. The lack of specific genetic influence on either caregiver or teacher reports indicates that the rater effects we discovered did not upwardly bias estimates of heritability, nor can they be related to any characteristic of the child that is genetically influenced.
A different pattern emerged for the child’s own report of ASB, with at least two important findings. First, the child’s view contributes less weight to the shared view of ASB. Although the latent factor representing the shared view accounts for between 30% and 45% of the variation in caregiver reports and teacher reports, it accounts for only 17.6% of the variation in child reports. It is possible that children at this age are less reliable reporters of ASB. Such an interpretation is consistent with the fact that the 6-month test–retest correlations are somewhat lower for child reports than for caregiver reports (see Table 2) and with the higher estimate of specific nonshared environmental influence on child reports, as nonshared environmental effects include measurement error. On the other hand, the other notable finding is that child reports are the only reports that show evidence for statistically significant specific genetic influence, which may reflect genetic influence on ASB which occurs outside the radar of parent or teacher perception. If this is the case, our results may indicate that reports of ASB from children are more comprehensive and, therefore, more accurate than caregiver or teacher reports. Alternatively, the significant genetic influence on child reports may simply reflect some sort of response bias, which is correlated with genetically influenced personality traits, such as social desirability or overall honesty. Future analysis using cotwin reports of ASB may help untangle genetically influenced rater bias effects from “real” genetic influence on child reports of ASB.
Finally, our results are consistent with the idea that the greater shared environmental influence found among childhood ASB relative to adult ASB may actually be an artifact of rater bias, as studies of children often rely on caregiver or teacher reports. In this study, when young children are asked to report on their own ASB, there is no evidence for significant shared environmental influences, nor do shared environmental influences account for variation in the shared view of ASB. This is consistent with other studies that have found that much of the shared environmental variation in parent reports can be attributed to rater effects (e.g., Hewitt et al., 1992; although see Bartels et al., 2003, 2004, for contradictory results) and is consistent with the meta-analysis by Rhee and Waldman (2002), which found that shared environmental influences on ASB were higher for parental reports than for child self-reports.
The present study should be viewed in the context of several potential limitations. First, we have chosen to “lump” rather than “split” various types of ASB in these analyses. The magnitude and nature of genetic and environmental influences in ASB may very well vary across different types of ASB (e.g., Tackett, Krueger, Iacono, & McGue, 2005), a possibility we have examined in a separate article, which does suggest some distinction between aggressive–psychopathic behavior and nonaggressive delinquency, based on the underlying genetic and environmental architecture (Jacobson, Baker, & Raine, 2007). Second, the age of the sample resulted in relatively low average rates of ASB, which may limit the generalizability of these findings. We note, however, that these children and their families appeared to be a representative sample of this urban community, as their ethnic distribution and socioeconomic levels were comparable to those of the local population. Children also exhibited a wide range of behaviors, including some serious conduct problems. Third, results concerning the teacher reports should be viewed with some caution, given the somewhat modest teacher participation rate (60%). Still, teacher participation was not influenced by sex or zygosity of the twins, suggesting that our results are not an artifact of unequal participation of teachers. Moreover, we found no evidence for systematic bias due to this lower response, as caregiver and child-rated ASB did not differ for those whose teachers did and did not participate (results are available upon request). Finally, we have yet to identify the source of the strong genetic effect on the shared view of ASB found in this sample. Ongoing analyses are attempting to address this by examining the genetic covariation of the ASB measures with putative biological endophenotypes, including psychophysiological, neurocognitive, and personality measures (e.g., Baker, Isen, Bezdjian, & Raine, 2005; Jacobson et al., 2005). Additional waves of assessment are also ongoing (Wave 2, ages 11–12) with future waves planned and funded through age 17. The Wave 1 assessments of antisocial behavior described in the present article therefore provide an important basis for investigating genetic and environmental influences on the emergence of antisocial behavior in American youth throughout the course of adolescence.
Footnotes 1 Preliminary univariate analyses within each informant addressed whether models with nonadditive genetic variance fit better than models with common environmental variance (i.e., ACE vs. ADE models) and whether there was evidence for sibling interaction effects. Results indicated that the ACE model without sibling interaction effects was the best model for each informant (results are available upon request).
2 We also fit less stringent independent pathways and Cholesky models to our data. Based on Bayesian information criterion values, the common pathways model offered a better balance of goodness of fit and parsimony than either of these other less restrictive models.
3 Changing the order of the multivariate model comparisons does not change the fit statistics for the three models and therefore leads to the same conclusions. For ease of interpretation, we begin with the most restrictive model (the measurement model; see Figure 1b) and end with the least restrictive model (the common pathways model; see Figure 1c).
4 A model that further loosened the constraint that the genetic and environmental factors common to all three raters operate through a single underlying latent phenotype did not significantly improve fit relative to the more restricted common pathways model. The –2LL from this independent pathways model was 7,991.03 (df = 3024) with AIC = –64.3 and BIC = –889.0.
5 Estimates of the proportion of variance accounted for by the rater-specific effects were calculated by squaring the rater-specific paths shown in Figure 2. Estimates of genetic and environmental variance due to the common genetic and environmental factors were calculated by squaring the product of the factor loading that corresponds to the individual rater and the parameter estimate for the common genetic or environmental factor—that is, variance in child reports due to common nonshared influence is (.42 × .19)2 = .006. Estimates were calculated by Mx with parameters estimated to the fourth decimal place; thus, calculations based on paths shown in Figure 2 may vary slightly from those shown in Table 6 because of rounding error.
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Submitted: September 1, 2005 Revised: February 6, 2007 Accepted: February 9, 2007
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Source: Journal of Abnormal Psychology. Vol. 116. (2), May, 2007 pp. 219-235)
Accession Number: 2007-06673-001
Digital Object Identifier: 10.1037/0021-843X.116.2.219
Record: 75- Title:
- Higher stimulus control is associated with less cigarette intake in daily smokers.
- Authors:
- Ferguson, Stuart G., ORCID 0000-0001-7378-3497. School of Medicine, University of Tasmania, Hobart, TAS, Australia, stuart.ferguson@utas.edu.au
Shiffman, Saul. Department of Psychology, University of Pittsburgh, Pittsburgh, PA, US
Dunbar, Michael. Department of Psychology, University of Pittsburgh, Pittsburgh, PA, US
Schüz, Natalie. School of Health Sciences, University of Tasmania, Hobart, TAS, Australia - Address:
- Ferguson, Stuart G., School of Medicine, University of Tasmania, Private Bag 34, Hobart, TAS, Australia, 7001, stuart.ferguson@utas.edu.au
- Source:
- Psychology of Addictive Behaviors, Vol 30(2), Mar, 2016. pp. 229-237.
- NLM Title Abbreviation:
- Psychol Addict Behav
- Page Count:
- 9
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- Bulletin of the Society of Psychologists in Addictive Behaviors; Bulletin of the Society of Psychologists in Substance Abuse
- Other Publishers:
- US : Educational Publishing Foundation
Society of Psychologists in Addictive Behaviors - ISSN:
- 0893-164X (Print)
1939-1501 (Electronic) - Language:
- English
- Keywords:
- smoking, dependence, addiction, stimulus control
- Abstract:
- It is well established that environmental stimuli influence smoking in light, and to a lesser degree, heavy smokers. A 2-factor model of dependence suggests that the influence of stimulus control is masked among heavier smokers who primarily smoke for nicotine maintenance. The current study aimed to assess the influence of stimulus control across a range of moderate to heavy daily smokers. Furthermore, as local tobacco control policies may change the role of stimulus control, the study aimed to replicate previous U.S. findings on stimulus control in an Australian setting marked by strong tobacco control policies. In 2 Ecological Momentary Assessment studies, 420 participants monitored antecedents of smoking and nonsmoking situations. In a set of idiographic logistic regression analyses, situational antecedents were used to predict smoking occasions within each individual’s data. Linear regression analysis was used to test for the association between stimulus control and smoking rate, and to test for differences between the 2 samples. Daily smokers’ smoking was under considerable stimulus control, which was weaker at higher smoking rates. Overall, there was greater stimulus control in the Australian sample. Daily smokers also experience a degree of stimulus control, which is less influential in heavier smokers. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Addiction; *Daily Activities; *Drug Dependency; *Tobacco Smoking; Stimulus Control
- PsycINFO Classification:
- Substance Abuse & Addiction (3233)
- Population:
- Human
Male
Female - Location:
- US
- Age Group:
- Adulthood (18 yrs & older)
- Grant Sponsorship:
- Sponsor: Australian National Health and Medical Research Council, Australia
Grant Number: 1002874
Other Details: funded Study 1
Recipients: Ferguson, Stuart G.
Sponsor: National Institutes of Health, US
Grant Number: R01-DA020742
Other Details: funded Study 2
Recipients: Shiffman, Saul - Methodology:
- Empirical Study; Quantitative Study
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- First Posted: Jan 14, 2016; Accepted: Nov 23, 2015; Revised: Nov 16, 2015; First Submitted: Jun 4, 2015
- Release Date:
- 20160114
- Correction Date:
- 20160321
- Copyright:
- American Psychological Association. 2016
- Digital Object Identifier:
- http://dx.doi.org/10.1037/adb0000149
- PMID:
- 26766542
- Accession Number:
- 2016-02070-001
- Number of Citations in Source:
- 46
- Persistent link to this record (Permalink):
- http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2016-02070-001&site=ehost-live
- Cut and Paste:
- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2016-02070-001&site=ehost-live">Higher stimulus control is associated with less cigarette intake in daily smokers.</A>
- Database:
- PsycINFO
Record: 76- Title:
- Identifying indicators of harmful and problem gambling in a Canadian sample through receiver operating characteristic analysis.
- Authors:
- Quilty, Lena C.. Clinical Research Department, Centre for Addiction and Mental Health, Toronto, ON, Canada, lena.quilty@camh.ca
Avila Murati, Daniela. Clinical Research Department, Centre for Addiction and Mental Health, Toronto, ON, Canada
Bagby, R. Michael. Department of Psychology, University of Toronto, Toronto, ON, Canada - Address:
- Quilty, Lena C., 250 College Street, Suite 648, Toronto, ON, Canada, M5T 1R8, lena.quilty@camh.ca
- Source:
- Psychology of Addictive Behaviors, Vol 28(1), Mar, 2014. pp. 229-237.
- NLM Title Abbreviation:
- Psychol Addict Behav
- Page Count:
- 9
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- Bulletin of the Society of Psychologists in Addictive Behaviors; Bulletin of the Society of Psychologists in Substance Abuse
- Other Publishers:
- US : Educational Publishing Foundation
Society of Psychologists in Addictive Behaviors - ISSN:
- 0893-164X (Print)
1939-1501 (Electronic) - Language:
- English
- Keywords:
- abstinence, harmful gambling, problem gambling, receiver operating characteristic (ROC) analysis, self-control
- Abstract:
- Many gamblers would prefer to reduce gambling on their own rather than to adopt an abstinence approach within the context of a gambling treatment program. Yet responsible gambling guidelines lack quantifiable markers to guide gamblers in wagering safely. To address these issues, the current investigation implemented receiver operating characteristic (ROC) analysis to identify behavioral indicators of harmful and problem gambling. Gambling involvement was assessed in 503 participants (275 psychiatric outpatients and 228 community gamblers) with the Canadian Problem Gambling Index. Overall gambling frequency, duration, and expenditure were able to distinguish harmful and problematic gambling at a moderate level. Indicators of harmful gambling were generated for engagement in specific gambling activities: frequency of tickets and casino; duration of bingo, casino, and investments; and expenditures on bingo, casino, sports betting, games of skill, and investments. Indicators of problem gambling were similarly produced for frequency of tickets and casino, and expenditures on bingo, casino, games of skill, and investments. Logistic regression analyses revealed that overall gambling frequency uniquely predicted the presence of harmful and problem gambling. Furthermore, frequency indicators for tickets and casino uniquely predicted the presence of both harmful and problem gambling. Together, these findings contribute to the development of an empirically based method enabling the minimization of harmful or problem gambling through self-control rather than abstinence. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Pathological Gambling; *Statistical Analysis; Self-Control
- Medical Subject Headings (MeSH):
- Adolescent; Adult; Aged; Canada; Comorbidity; Gambling; Humans; Mental Disorders; Middle Aged; ROC Curve; Severity of Illness Index; Young Adult
- PsycINFO Classification:
- Behavior Disorders & Antisocial Behavior (3230)
- Population:
- Human
Male
Female
Outpatient - Location:
- Canada
- Age Group:
- Adulthood (18 yrs & older)
Young Adulthood (18-29 yrs)
Thirties (30-39 yrs)
Middle Age (40-64 yrs)
Aged (65 yrs & older) - Tests & Measures:
- Problem Gambling Severity Index
Structured Clinical Interview for DSM-IV
Canadian Problem Gambling Index DOI: 10.1037/t00772-000 - Grant Sponsorship:
- Sponsor: Ontario Problem Gambling Research Centre
Recipients: No recipient indicated - Methodology:
- Empirical Study; Interview; Quantitative Study
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- First Posted: May 6, 2013; Accepted: Mar 25, 2013; Revised: Mar 19, 2013; First Submitted: Aug 20, 2012
- Release Date:
- 20130506
- Correction Date:
- 20140414
- Copyright:
- American Psychological Association. 2013
- Digital Object Identifier:
- http://dx.doi.org/10.1037/a0032801
- PMID:
- 23647158
- Accession Number:
- 2013-15121-001
- Number of Citations in Source:
- 33
- Persistent link to this record (Permalink):
- http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2013-15121-001&site=ehost-live
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- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2013-15121-001&site=ehost-live">Identifying indicators of harmful and problem gambling in a Canadian sample through receiver operating characteristic analysis.</A>
- Database:
- PsycINFO
Identifying Indicators of Harmful and Problem Gambling in a Canadian Sample Through Receiver Operating Characteristic Analysis
By: Lena C. Quilty
Clinical Research Department, Centre for Addiction and Mental Health, Toronto, ON, Canada, and Department of Psychiatry, University of Toronto;
Daniela Avila Murati
Clinical Research Department, Centre for Addiction and Mental Health, Toronto, ON, Canada
R. Michael Bagby
Departments of Psychology and Psychiatry, University of Toronto, and Clinical Research Department, Centre for Addiction and Mental Health, Toronto, ON, Canada
Acknowledgement: This investigation was supported by an operating grant from the Ontario Problem Gambling Research Centre (OPGRC).
In 1985, an amendment to Canada’s Criminal Code granted Canadian provinces control over gambling and gaming devices (Korn, 2000). Since the 1990s, Canada has promoted its entertainment economy through legalized gambling, granting provincial governments increasing revenue without additional taxation (Campbell & Smith, 1998). Canada’s gambling industry expansion has provided opportunities for social and economic development, including job creation, tax reduction, debt repayment, and the funding of social programs (Azmier, Kelley, & Todosichuk, 2001). These broad societal benefits come at the cost of rising social and health consequences, however (Marshall & Wynne, 2003). The expansion of government-operated gambling venues, in conjunction with the development of new gaming technologies, has contributed to a significant increase in gambling participation among Canadians (Korn, 2000).
Researchers and policymakers strongly suggest that the proliferation of legalized gambling constitutes a public health issue due to the adverse effects that gambling participation can have on the prevalence of gambling pathology (Azmier et al., 2001; Volberg, 1994). Prior to Canada’s legalization of gambling, prevalence estimates of pathological gambling in Canada were less than 1%; rates increased to between 1.2% and 1.9% following this legislation (Ladouceur, 1996). Although some investigators have suggested that prevalence rates have remained consistent over time (Ladouceur, Chevalier, Sevigny, & Hamel, 2005; Stucki & Rihs-Middel, 2007), others have demonstrated an increase in pathological gambling (Shaffer & Hall, 2001). Further, a recent investigation taking measurement issues into careful consideration suggested that accessibility of gambling activities has been linked with gambling involvement and pathology (Sassen, Kraus, & Bühringer, 2011). Indeed, accessibility of gambling has been associated with gambling-related difficulties in several recent investigations (Moore, Thomas, Kyrios, Bates, & Meredyth, 2011; Thomas, Allen, Phillips, & Karantzas, 2011; Thomas, Bates, et al., 2011).
Despite current rates of gambling-related difficulties, pathological gambling treatment services remain underutilized (Toneatto et al., 2008). For many gamblers, such services—which often include abstinence as a therapeutic target—are the treatment of choice. According to Blaszczynski and Nower (2002), controlled gambling is a suboptimal treatment goal for some with pathological gambling. For example, pathological gamblers with co-occurring psychopathology may be less likely to benefit from moderation-focused treatments, as other conditions may negatively impact their gambling behavior (Blaszczynski & Nower, 2009). Further, empirical research suggests that gamblers presenting with more severe problems are more likely to eventually benefit from formal treatment and less likely to successfully initiate and achieve self-control (Hodgins & El-Guebaly, 2000).
Yet recovery from gambling pathology is possible through self-help methods rather than formal treatment (Hodgins & El-Guebaly, 2000). As Hodgins and El-Guebaly note, over 80% of gamblers endorsing difficulty report not wanting to seek formal treatment because they would rather try to cut down or stop gambling on their own. Indeed, gamblers with varied risk for, or severity of, pathology are likely to employ self-recovery strategies, such as limiting their frequency of play as well as the amount of money and time spent wagering (Wiebe, Mun, & Kauffman, 2005). Given the feasibility of the self-help approach among many gamblers, then, alternative pathways to recovery not necessitating professional assistance must be supported (Toneatto et al., 2008). Self-change or natural recovery from problematic gambling can be promoted by offering the general public information and education (Hodgins & El-Guebaly, 2000). It is critical that any such responsible gambling guidelines should be driven by empirical evidence (Blaszczynski, Ladouceur, & Shaffer, 2004). The derivation of quantitative behavioral indicators of harmful and problem gambling can support pathways to self-recovery and the maintenance of healthy gambling habits in this way.
Several researchers have undertaken this task, examining the relation between gambling involvement and gambling-related harm or pathology. Currie et al. (2006) evaluated the dose–response relation between gambling involvement and harm, that is, gambling-related problems of lesser severity than those associated with problem or pathological gambling. Using the national epidemiological investigation conducted by Statistics Canada, receiver operating characteristic (ROC) analyses generated optimal cutoffs including a gambling frequency of three times per month, and gambling expenditures of $1,000 CAN per year and of 1% of gross family income. Subsequently, Currie et al. (2008) conducted a replication study based on three provincial gambling surveys (i.e., Alberta, Ontario, British Columbia). Optimal cutoffs included a gambling frequency of once per week and expenditure of $85 CAN per month. Guidelines for gross income varied across provinces (1% in Ontario vs. 3% in Alberta). Based on the data collected in Alberta, the cutoff for gambling duration was 60 min per session. Finally, Currie, Miller, Hodgins, and Wang (2009) extended their previous work to include three different measures of harmful gambling, utilizing data culled from five gambling provincial surveys conducted in Alberta, British Columbia, Ontario, Manitoba, and Newfoundland. Optimal cutoffs included three times per month and 1% of gross income for gambling frequency and expenditures. Results were generally comparable regardless of the measure of harm employed. However, the cutoff for gambling expenditures varied from $153.50 CAN to $357 CAN per year from the most conservative measure of harm employed to the least conservative measure.
Independently, Weinstock, Ledgerwood, and Petry (2007) investigated the relation between posttreatment gambling behavior and harm in a sample of pathological gamblers. According to ROC analyses, the optimal indicators included a gambling frequency of once per month, duration of 1.5 hr per month, and expenditure of 1.9% of income per month. Subsequently, Weinstock, Whelan, and Meyers (2008) examined the behavioral indicators of problem gambling in a sample of college students. According to ROC analyses, problematic gambling cutoffs include a gambling frequency of 1.2 times per month, duration of 2.1 hr per month, and expenditure of 10.5% of monthly income.
Previous research thus suggests that quantitative indicators of harmful and problem gambling can be derived and may be comparable across distinct gambling populations. Such guidelines enable the development of evidence-based responsible gambling parameters to assist gamblers in the reduction of the intensity of their gambling behavior (Currie et al., 2008). The availability of this approach may encourage those experiencing gambling problems who do not wish to abstain from gambling to seek out problem-gambling treatment services (Ladouceur, 2005; Weinstock et al., 2007).
The Current InvestigationThe purpose of the current investigation study was to conduct a comprehensive examination of the relation between gambling involvement—as assessed by frequency of gambling behavior, amount of money spent on gambling activities, and time spent involved in games of chance—and harmful as well as problem gambling. We aimed to replicate the quantitative gambling cutoffs derived by Currie et al. (2006, 2008, 2009) and Weinstock et al. (2007, 2008). We further aimed to extend this work via the generation of cutoffs for overall gambling involvement as well as involvement in specific gambling activities (e.g., tickets, casino). We finally aimed to evaluate the unique contribution of related gambling activity indicators to predict harmful or problematic gambling, as such associations may highlight the most salient indicators in those engaging in multiple forms of gambling.
We utilized a combined clinical and community gambler sample to ensure a broad range of clinical and gambling features. We hypothesized that results for overall gambling involvement will replicate the quantitative behavioral indicators of harmful and problem gambling identified by Currie, Weinstock, and colleagues. Analyses of specific gambling activities were deemed exploratory; however, we hypothesized that the indicators associated with casino, bingo, and lottery play will be comparable with the gambling cutoffs derived for overall gambling involvement, as these forms of gaming exhibit the greatest prevalence in adult samples (Marshall & Wynne, 2003).
Method Participants
The community gambler sample consisted of 228 participants, including 115 men and 113 women ranging in age from 20 to 65 years (M = 40.47; SD = 13.12). Ninety participants met Diagnostic and Statistical Manual of Mental Disorders (DSM–IV; American Psychiatric Association, 1994) criteria for lifetime pathological gambling. Further, 38 participants exhibited other DSM–IV Axis I pathology (mood disorders, n = 16; anxiety disorders, n = 23; substance use disorders, n = 7; eating disorders, n = 1; n = 8 met criteria for more than one co-occurring disorder). The clinical sample consisted of 275 psychiatric outpatients, including 100 men and 175 women ranging in age from 18 to 65 years (M = 43.02 years; SD = 11.58). All participants met criteria for either a depressive disorder (n = 138; n = 119, major depressive disorder; n = 18, dysthymic disorder; n = 1, depressive disorder not otherwise specified) or a bipolar disorder (n = 137; n = 110, bipolar I disorder; n = 21, bipolar II disorder; n = 6, bipolar disorder not otherwise specified). Thirty participants met DSM–IV criteria for lifetime pathological gambling. Other co-occurring DSM–IV Axis I disorders were present in 137 participants (anxiety disorders, n = 108; substance use disorders, n = 39; eating disorders, n = 18; somatoform disorders, n = 10; adjustment disorders, n = 1; and impulse control disorders, n = 1; n = 56 met criteria for more than one co-occurring disorder).
Procedure
The community gambler sample was recruited via two advertising campaigns in local media for a study on personality, thinking patterns, and gambling behavior: one geared to those who have engaged in gambling activities (advertising text included, “Have you played the Slots or Bingo? Bought Scratch or Lottery Tickets? Have you bet on games of chance or skill?”) and one highlighting motivations for gambling and potential problematic gambling (advertising text included, “Do you gamble for fun? For money? Could you be gambling too much?”). Eligibility criteria included the presence of lifetime social or problem gambling, and being between 18 and 65 years of age, with at least 8 years of education and with proficiency in the English language. Exclusion criteria included having sought help for the treatment of gambling. A total of 443 individuals contacted the study and completed a telephone interview to determine eligibility. Of these, 309 people were deemed eligible for participation. Of these, 81 participants produced either invalid (n = 14) or incomplete (n = 67) assessment protocols. A final sample of 228 subjects completed the entire research protocol.
The clinical sample was recruited via local media advertisements for a study on “mood disorders and behavior.” Eligibility criteria included the presence a depressive, manic, hypomanic, and/or a mixed episode in the past 10 years, as assessed by the Structured Clinical Interview for DSM–IV (First, Spitzer, Gibbon, & Williams, 1995). Other criteria required for eligibility similarly included being between 18 and 65 years of age, having at least 8 years of education, and being fluent in English. Gambling involvement was not required for eligibility. Exclusion criteria included current severe mania, psychosis, and current substance intoxication, to ensure participants were able to tolerate the lengthy study protocol and/or to validly complete the study measures. A total of 610 individuals contacted and completed a telephone interview to determine eligibility. Of these, 300 people completed a 2-day assessment involving the administration of diagnostic interviews and self-report questionnaires to further determine eligibility. A final sample of 275 met all eligibility criteria and successfully complied with the research protocol.
Measures
The Structured Clinical Interview for DSM–IV (First et al., 1995) is a semistructured interview designed to assess Axis I disorders of the DSM–IV (American Psychiatric Association, 1994). This measure was utilized to confirm eligibility criteria for the clinical sample, and to provide a clinical characterization of both the clinical and community gambler samples.
The Canadian Problem Gambling Index (CPGI; Ferris & Wynne, 2001) is a multicomponent self-report questionnaire designed to assess gambling behavior as well as gambling-related harms in the general population. The CPGI has received rigorous psychometric testing prior to its incorporation in community surveys (Smith & Wynne, 2002). The CPGI enquires about frequency of play, duration of play per session, and monthly expenditures in the last year. Frequency is assessed with multiple-choice items, with the eight response alternatives ranging from “never” to “daily.” Duration is assessed in minutes, and expenditure is assessed in Canadian dollars; no ranges or limitations are placed on these two variables. The types of gambling activities assessed by the CPGI include tickets (i.e., lottery, daily lottery, sports lottery, instant win, scratch, raffle, fundraising), horse racing, bingo, casino (i.e., poker, blackjack, roulette, Keno, craps, Video Lottery Terminals (VLTs)), sports pools, cards or board games, games of skill (i.e., pool, bowling, darts), arcade or video games, Internet wagering, sports with a bookie, and investments (i.e., stocks, options, commodities market).
The CPGI generates the Problem Gambling Severity Index (PGSI), a nine-item index of gambling difficulties (Smith & Wynne, 2002). The PGSI measures harm through items that assess gambling behavioral problems (e.g., chasing losses) as well as gambling-related negative consequences (e.g., financial problems; see Currie et al., 2006). Moreover, each PGSI item is rated on a Likert-scale from 0 (never) to 3 (almost always).
Harmful and Problem Gambling
To assess the relation between level of gambling involvement and harm, several measures of harmful gambling were included. Although several instruments exist to assess problem and pathological gambling, no consensus exists regarding how to assess gambling-related harm. In order to be consistent with Currie et al. (2006), three measures of harm were calculated using items of the PGSI: (a) ≥2 PGSI items rated ≥1; (b) ≥2 PGSI items referring to negative consequences rated ≥1; and (c) ≥1 PGSI items referring to negative consequences rated ≥1. All analyses were conducted with each of these measures of harm, in turn, permitting the evaluation of the convergence between these closely related indices of gambling-related difficulty. Problem gambling was determined via a score of 8 or more on the PGSI (Smith & Wynne, 2002).
Statistical Analysis
ROC analysis was utilized to assess the classification performance of each gambling indicator and to select optimal cutoffs for harmful and problem gambling with respect to general gambling participation as well as specific gambling activities. ROC analysis determines the overall ability of a test to discriminate between two groups and the classification accuracy of every cut point associated with that test (Streiner & Cairney, 2007). More specifically, ROC analysis produces a graphical plot of the true positive rate (i.e., the number of participants correctly identified as gambling at a harmful or problematic level) against the false positive rate (i.e., the number of participants incorrectly identified as gambling at a harmful or problematic level) for each possible cutoff of a particular gambling indicator (i.e., frequency, duration, expenditure). This ROC curve effectively illustrates the inverse relation between sensitivity and specificity: Sensitivity measures the ability of a gambling indicator to accurately identify individuals with a condition, whereas specificity measures the ability of a gambling indicator to accurately identify individuals without a condition. The area under the curve (AUC) measures the overall classification performance of a gambling indicator: The further the curve from the 45-degree diagonal of the curve, the more accurate the test. AUC values thus range from 0 (100% misclassification) to 1 (100% correct classification), where .50 is representative of chance levels of correct classification. It is conventional for an AUC between .50 and .70 to be considered small, between .70 and .90 to be moderate, and over .90 to be high (Streiner & Cairney, 2007).
Consistent with previous research, cutoffs were identified by maximizing sensitivity and specificity. As participants reported engaging in more than one gambling activity and investing different amounts of time and money on diverse games of chance, a composite index was derived for overall gambling frequency, duration, and expenditures based on the highest value for each category. Due to the low rate of involvement in several specific gambling activities, analyses were restricted to those reported by a minimum of 50 participants.
A series of logistic regressions were undertaken to evaluate the unique contribution of indicators with groups of related indicators in the detection of the presence of harmful and problem gambling. Within these models, the χ2 value associated with the model provides information on the predictive utility as the group of indicators as a whole, whereas the χ2 value associated with each indicator provides information on the unique predictive utility of that variable. The odds ratio (OR) supplements the latter, providing information on the probabilities of risk corresponding to specific gambling indicators. Measures of harmful and problem gambling served as criterion variables in separate analyses. Indicators of harmful and problem gambling as identified in ROC analyses served as predictor variables.
Results Identifying Harmful and Problem Gambling Indicators
Overall gambling
AUC values, cutoff values, and associated sensitivity and specificity values for overall gambling and harmful and problem gambling are displayed in Table 1. An illustration of the curves associated with gambling frequency, duration, and expenditures and harmful wagering are displayed in Figure 1; ROC results, including sensitivity and specificity values associated with each cutoff value, are available upon request. All measures of harmful and problem gambling tested produced moderate prediction values. Moreover, all measures of harmful gambling generated similar cutoffs for gambling frequency, duration, and expenditure.
Results of Receiver Operating Characteristic Analyses Between Measures of Gambling Intensity and Indicators of Harmful and Problem Gambling
Figure 1. ROC curve indicating the performance of (a) gambling frequency, (b) gambling duration, and (c) gambling expenditures in classifying the presence of at least two negative consequences. The point on the curve farthest from the diagonal line is the cut point that maximizes sensitivity and 1-specificity.
AUC values for specific gambling activities and harmful and problem gambling are presented in Table 2, Table 3, and Table 4. Measures of harm and problem gambling tested generated small to moderate prediction values; only results at the moderate level are described in detail. Again, all measures of harmful gambling produced comparable cutoffs.
Results of Receiver Operating Characteristic Analyses Between Gambling Frequency and Indicators of Harmful and Problem Gambling for Specific Gambling Activities
Results of Receiver Operating Characteristic Analyses Between Gambling Duration Per Session and Indicators of Harmful and Problem Gambling for Specific Gambling Activities
Results of Receiver Operating Characteristic Analyses Between Gambling Expenditure per Month and Indicators of Harmful and Problem Gambling for Specific Gambling Activities
Specific gambling activities: frequency
For tickets, the gambling frequency cutoff associated with optimal sensitivity and specificity is once per month for harmful gambling (sensitivity = .67 to .69 and specificity = .73 to .76) and once per week for problem gambling (sensitivity = .51; specificity = .83). For wagering within a casino, the gambling frequency is “never” for harmful gambling (sensitivity = .68 to .72 and specificity = .76 to .78) and 5 times per year for problem gambling (sensitivity = .56; specificity = .87).
Specific gambling activities: duration
For bingo, the gambling duration cutoff is 2 hr 15 min per session for harmful gambling (sensitivity = .52; specificity = .82-.86). For casino play, the duration cutoff is 3 hr 10 min per session for harmful gambling (sensitivity = .47 and specificity = .84). For investments, the gambling duration cutoff is between 13 and 25 min per session for harmful gambling (sensitivity = .52-.54 and specificity = .86-.88).
Specific gambling activities: expenditures
For bingo, the gambling expenditure cutoff is $37.5 CAN per month for harmful gambling (sensitivity = .76; specificity = .62) and $95 CAN per month for problem gambling (sensitivity = .58; specificity = .83). For casino play, the gambling expenditure cutoff is $27.5 to $110 CAN per month for harmful gambling (sensitivity = .62 to .93; specificity = .48 to .74) and $180 CAN per month for problem gambling (sensitivity = .71; specificity = .65). For sports betting, the gambling expenditure cutoff is $42.5 to $65 CAN per month for harmful gambling (sensitivity = .52 to .66; specificity = .68 to .85). For games of skill, the gambling expenditure cutoff is $33.5 to $37.5 CAN per month for harmful gambling (sensitivity = .57 to .60; specificity = .80 to .82) and $33.5 CAN per month for problem gambling (sensitivity = .69; specificity = .64). For investments, the gambling expenditure cutoff is $7.5 CAN per month for harmful gambling (sensitivity = .55 to .61; specificity = .85 to .86) and $25 CAN per month for problem gambling (sensitivity = .63; specificity = .80).
Predicting Harmful and Problem Gambling
Logistic regressions results are displayed in Table 5. In Model 1A, gambling frequency, duration, and expenditures significantly predicted the presence versus absence of harmful gambling as a whole. Gambling frequency and duration were also significant, demonstrating that when considered together with frequency and duration, gambling expenditures do not contribute unique predictive value when predicting harmful gambling. ORs revealed that individuals gambling at a greater frequency are more likely to be classified as harmful gamblers; gambling duration may contribute little practical information in conjunction with gambling frequency. A similar pattern of results was found in Model 1B. Gambling frequency, duration, and expenditures predicted the presence versus absence of problem gambling as a whole. Of these indicators, gambling frequency and duration uniquely predicted problem gambling. Again, as individuals gamble more frequently, their likelihood of developing a gambling pathology increases. The OR associated with duration was again pragmatically close to zero.
Models Predicting Harmful and Problem Gambling Using General Gambling Involvement or Involvement in Specific Gambling Activities
In both Models 2A and 2B, the frequency of ticket and casino play predicted the presence versus absence of both harmful and problem gambling. Gambling frequency for both tickets and casino were significant, indicating that these gambling indicators predict harmful and problem gambling on their own. Although duration and expenditures associated with specific gambling activities predicted the presence versus absence of harmful and problem gambling as a whole, respectively, none of these predictor values contributed unique variance in the prediction of this outcome (Model 3A, Model 4B). On the other hand, the expenditures of (Model 4A) associated with specific gambling activities did not predict the presence versus absence of harmful gambling.
DiscussionThe current investigation evaluated the capacity of numerous indicators of gambling involvement to discriminate between the presence and absence of harmful and problem gambling. The application of ROC analysis to identify harmful gambling as well as problem gambling indicators revealed that numerous indices have moderate utility in this regard. The present study provides a replication and extension of previous work, including not only overall gambling involvement but also involvement in specific gambling activities. The results are reviewed next, with direct comparisons with previously supported cutoffs where possible.
Overall Gambling Involvement
For general gambling involvement, we derived a gambling frequency cutoff of once per month; the precise harmful gambling indicator supported in Weinstock et al. (2007). Currie et al. (2006, 2009) suggested a similar cutoff of two to three times per month. Currie et al. (2008) proposed a broader, but still comparable, cutoff of two to five times per month. For duration, results support a cutoff between 22.5 and 35 min. This cutoff diverges from the 60-min-per-session harmful indicator derived by Currie et al. (2008), and the 1 hr-30-min-per-session cutoff derived by Weinstock et al. (2007). For monthly gambling expenditures, results support a cutoff of $24.50 CAN. This cutoff again diverges from that of Currie et al. (2006, 2008). Currie et al. (2006) supported a monthly expenditure of $41.75 to $83.44 CAN, whereas Currie et al. (2008) introduced $33 to $85 CAN. Currie et al.’s (2009) subsequent investigation produced a gambling expenditure cutoff of $12.79 to $29.75 CAN per month, which is more comparable with the spending guideline proposed by this study.
The current study’s problem gambling indicators were comparable with several cutoffs supported by Weinstock et al. (2008). A discrepancy is evident between the present study’s frequency cutoff of once per week and Weinstock et al.’s corresponding cutoff of 1.2 times per month. It is possible that such contrast may be attributed to differences in sample: Weinstock et al. utilized a sample of college students, whereas the current study sample included both clinical outpatients and community gamblers. Yet this study’s problematic duration indicator of 1 hr 40 min per session is comparable with Weinstock et al.’s cutoff of 2 hr 6 min per month (or 2 hr 6 min per session in conjunction with frequency cutoffs).
Specific Gambling Activities
The implementation of ROC analysis to identify indicators of harmful and problem gambling behavior for specific gambling activities yielded a range of results. Guidelines were developed for games of chance with moderate levels of classification accuracy including: tickets, bingo, casino, sports betting, games of skill, and investments. The optimal cutoffs derived were particular to each gambling activity, and, for the most part, were not necessarily comparable with the harmful- or problem-gambling indicators produced in relation to overall gambling involvement or other specific gambling activities. The exception to this case was the harmful and problem-gambling frequency indicators for tickets, which were the precise frequency cutoffs presented by the current study with respect to overall gambling involvement. In addition, the problem-gambling expenditure indicator for bingo is also in agreement with the problem-gambling expenditure cutoff proposed by this study with respect to overall gambling involvement. It is notable that to be consistent with previous research, the cutoffs described herein are those associated with maximized sensitivity and specificity, which were often below preferred values. The choice of cutoff will be determined by the expected utility or consequences of true versus false test results (e.g., “misses” vs. “false positives”), and resulting emphasis on sensitivity versus specificity. Treatment contexts wishing to reduce the risk of relapse may be willing to tolerate a larger proportion of false positives, for example. These value judgments are important to consider in any application of such guidelines.
Logistic regression analyses revealed that although gambling frequency, duration, and expenditures, as a whole, effectively predict both the presence and absence of harmful and problem gambling, the unique prediction ability of overall gambling expenditures is not statistically significant, whereas that of overall gambling duration may be of questionable clinical significance. These results are striking in light of Currie et al.’s (2006) conclusion that what ultimately determines the impact of gambling is the gambler’s financial means. Logistic regression analyses further revealed that frequency indicators for tickets and casino individually predicted both the presence and absence of harmful and problem gambling. These results suggest that efforts to promote responsible gambling or curb problem gambling may focus upon activity-specific guidelines and upon some activities more than others.
The current study utilized the CPGI, an assessment instrument recognized for measuring gambling involvement indicators as well as problematic gambling. The CPGI not only inquires about gambling-related problems assessed by other well-known measures such as the South Oaks Gambling Screen (SOGS; Lesieur & Blume, 1987) but also evaluates gambling negative consequences (e.g., experiencing health problems; Stinchfield, 2002). One of the strengths of the CPGI lies in its capacity to ask for specific gambling expenditures or duration estimates, rather than providing ranges. The assessment of frequency is less exact, however. Duration and expenditures may provide useful or perhaps even more valid indicators. The cutoffs associated with frequency versus duration and expenditures for harmful casino play derived in the current investigation result in opposing recommendations, for example, and may be indicative of the need for more precise assays of these constructs. Further, confusion exists regarding how to rate activities that might fall in more than one category (e.g., online poker) in the CPGI, further emphasizing the need for measures sensitive to ever-evolving manifestation of gambling activities. Of note, cutoffs for each measure of harmful gambling derived from the PGSI were highly similar, suggesting that future work may be justified in utilizing only one such measure.
ConclusionsDeriving indicators of harmful and problem gambling through ROC analysis is a feasible approach. However, additional research of this nature is imperative. First, further research incorporating duration and expenditure is required, as the majority of the existing research has focused upon the frequency of gambling activities. Second, there is a dearth of research on the subject of identifying quantitative behavioral indicators of harmful or problem gambling with respect to specific gambling activities. Future research is crucial to replicate the results of this study and to further evaluate other activities that could not be fully evaluated by the current study, due to low involvement. Third, research incorporating alternate samples is necessary. Many of the optimal cutoffs generated by the current study were compared with the guidelines produced by other investigations based on clinical or college student samples. Research including representative community samples and adults across the life span (e.g., geriatric samples) would therefore be particularly beneficial. Indeed, the degree to which demographic and clinical characteristics moderate gambling guidelines requires empirical evaluation. The clinical significance of any differences in cutoffs across men versus women, for example, is of particular pragmatic use. Fourth, it is possible that some degree of underreporting might have taken place among certain participants. Although the conservative estimates that would result from an underreporting response still may be preferable, investigations incorporating measures of response bias would be of assistance.
The current study was limited by the few participants who engaged in the following activities: VLTs, arcade or video games, and Internet gambling. Due to an insufficient number of valid cases for these gambling activities, harmful- and problem-gambling indicators could not be produced for these activities. Furthermore, although the current investigation represents an important first step in this endeavor, it must be acknowledged that for some activity-specific ROC analyses, sample sizes were small. ROC results increase in accuracy with sample size, underscoring the need to conduct replications in large samples (Fluss, Faraggi, & Reiser, 2005).
Due to the absence of a conceptual rationale for selecting cutoffs, Currie et al. (2006, 2008, 2009) granted equal weighting to specificity and sensitivity, while maximizing the discrimination between the presence and absence of gambling harm. The current study employed the same approach. The confidence with which cutoffs can be applied is often evaluated via positive and negative predictive values (i.e., the probability of accurate classification), which are themselves influenced not only by sensitivity and specificity but also by the prevalence or baseline of gambling pathology in the sample evaluated. It is important to note, in this context, that positive and negative predictive values derived from the community gambler sample of the current investigation would thus be influenced by the elevated prevalence of gambling-related difficulties in the sample relative to the general population, due to our recruitment methodology and eligibility criteria. For example, in an at-risk sample such as ours—with a prevalence rate of pathological gambling of 39%—the probability that someone scoring above the cutoff actually experiencing gambling-related difficulties is .75. In contrast, in a representative community sample—with a prevalence rate of pathological gambling of 3% (Wiebe et al., 2005; Williams, Volberg, & Stevens, 2012)—the positive predictive value is only .13. False-positive rates clearly significantly impact the performance of a classification instrument within low base-rate samples. Thus, the sample in which these cutoffs will be used needs to be carefully considered in their utilization.
Generating safe gambling guidelines with respect to general gambling involvement and specific gambling activities may be not only feasible but also practical. Cutoffs of this nature could be integrated into diverse areas of work, including assessment, prevention, and treatment. Such indicators, for instance, could serve to enable the development of evidence-based responsible gambling guidelines. Advertising has widely promoted safe cutoffs for alcohol consumption among adult men and women; analogous campaigns highlighting excessively frequent casino visits, for example, would provide a clear message to the general population. Such parameters may be of considerable use to gamblers wishing to keep their gambling behavior under control. Treatment providers could employ these cutoffs to design treatments focused on self-control for gamblers whose addiction revolves around one or more than one gambling activity. Applications of such guidelines require rigorous empirical investigation prior to their widespread use in the prevention and treatment of gambling difficulties.
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Submitted: August 20, 2012 Revised: March 19, 2013 Accepted: March 25, 2013
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Source: Psychology of Addictive Behaviors. Vol. 28. (1), Mar, 2014 pp. 229-237)
Accession Number: 2013-15121-001
Digital Object Identifier: 10.1037/a0032801
Record: 77- Title:
- Identifying latent trajectories of personality disorder symptom change: Growth mixture modeling in the longitudinal study of personality disorders.
- Authors:
- Hallquist, Michael N.. Department of Psychology, State University of New York at Binghamton, Binghamton, NY, US, hallquistmn@upmc.edu
Lenzenweger, Mark F.. Department of Psychology, State University of New York at Binghamton, Binghamton, NY, US, mlenzen@binghamton.edu - Address:
- Hallquist, Michael N., Western Psychiatric Institute and Clinic, 3811 O’Hara Street, Pittsburgh, US, 15213, hallquistmn@upmc.edu
- Source:
- Journal of Abnormal Psychology, Vol 122(1), Feb, 2013. pp. 138-155.
- NLM Title Abbreviation:
- J Abnorm Psychol
- Page Count:
- 18
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- The Journal of Abnormal and Social Psychology; The Journal of Abnormal Psychology; The Journal of Abnormal Psychology and Social Psychology
- ISSN:
- 0021-843X (Print)
1939-1846 (Electronic) - Language:
- English
- Keywords:
- growth mixture modeling, longitudinal course, longitudinal study of personality disorders, personality disorder
- Abstract:
- Although previous reports have documented mean-level declines in personality disorder (PD) symptoms over time, little is known about whether personality pathology sometimes emerges among nonsymptomatic adults, or whether rates of change differ qualitatively among symptomatic persons. Our study sought to characterize heterogeneity in the longitudinal course of PD symptoms with the goal of testing for and describing latent trajectories. Participants were 250 young adults selected into two groups using a PD screening measure: those who met diagnostic criteria for a DSM–III–R PD (PPD, n = 129), and those with few PD symptoms (NoPD, n = 121). PD symptoms were assessed three times over a 4-year study using semistructured interviews. Total PD symptom counts and symptoms of each DSM–III–R PD were analyzed using growth mixture modeling. In the NoPD group, latent trajectories were characterized by stable, minor symptoms; the rapid or gradual remission of subclinical symptoms; or the emergence of symptoms of avoidant, obsessive-compulsive, or paranoid PD. In the PPD group, three latent trajectories were evident: rapid symptom remission, slow symptom decline, or a relative absence of symptoms. Rapid remission of PD symptoms was associated with fewer comorbid disorders, lower Negative Emotionality, and greater Positive Emotionality and Constraint, whereas emergent personality dysfunction was associated with comorbid PD symptoms and lower Positive Emotionality. In most cases, symptom change for one PD was associated with concomitant changes in other PDs, depressive symptoms, and anxiety. These results indicate that the longitudinal course of PD symptoms is heterogeneous, with distinct trajectories evident for both symptomatic and nonsymptomatic individuals. The prognosis of PD symptoms may be informed by an assessment of personality and comorbid psychopathology. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Disease Course; *Personality Disorders; *Symptoms
- Medical Subject Headings (MeSH):
- Adolescent; Diagnostic and Statistical Manual of Mental Disorders; Female; Humans; Longitudinal Studies; Male; Models, Psychological; Personality Development; Personality Disorders; Personality Inventory; Young Adult
- PsycINFO Classification:
- Personality Disorders (3217)
- Population:
- Human
Male
Female - Location:
- US
- Age Group:
- Adulthood (18 yrs & older)
- Tests & Measures:
- International Personality Disorder Examination DSM–III–R Screen
Structured Clinical Interview for DSM–III–R: Nonpatient Version
State Trait Anxiety Inventory
Beck Depression Inventory DOI: 10.1037/t00741-000
NEO Personality Inventory DOI: 10.1037/t07564-000 - Grant Sponsorship:
- Sponsor: National Institute of Mental Health
Grant Number: MH045448
Recipients: Lenzenweger, Mark F.
Sponsor: National Institute of Mental Health
Grant Number: F32 MH090629
Recipients: Hallquist, Michael N. - Methodology:
- Empirical Study; Quantitative Study
- Supplemental Data:
- Tables and Figures Internet
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- First Posted: Dec 10, 2012; Accepted: Jul 24, 2012; Revised: Jul 10, 2012; First Submitted: Sep 21, 2011
- Release Date:
- 20121210
- Correction Date:
- 20160616
- Copyright:
- American Psychological Association. 2012
- Digital Object Identifier:
- http://dx.doi.org/10.1037/a0030060; http://dx.doi.org/10.1037/a0030060.supp(Supplemental)
- PMID:
- 23231459
- Accession Number:
- 2012-32960-001
- Number of Citations in Source:
- 78
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- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2012-32960-001&site=ehost-live">Identifying latent trajectories of personality disorder symptom change: Growth mixture modeling in the longitudinal study of personality disorders.</A>
- Database:
- PsycINFO
Identifying Latent Trajectories of Personality Disorder Symptom Change: Growth Mixture Modeling in the Longitudinal Study of Personality Disorders
By: Michael N. Hallquist
Department of Psychology, State University of New York at Binghamton and Department of Psychiatry, University of Pittsburgh;
Mark F. Lenzenweger
Department of Psychology, State University of New York at Binghamton and Weill Cornell Medical College;
Acknowledgement: This research was funded in part by MH045448 from the National Institute of Mental Health to Mark F. Lenzenweger. Preparation of the manuscript was supported in part by NIMH Grant F32 MH090629 to Dr. Hallquist. We thank Armand W. Loranger for providing training and consultation on the use of the International Personality Disorder Examination (IPDE). We are grateful to Lauren Korfine for project coordination in the early phase of the study.
Although clinical thinking about personality pathology can be traced to the 19th century idea of “moral insanity” (Vaillant & Perry, 1985) and subsequent psychoanalytic studies of character pathology (Freud, 1959), the modern conception of personality disorders (PDs) originated with the introduction of the DSM–III in 1980. This nomenclature established explicit diagnostic criteria for 11 PDs putatively characterized by inflexible and maladaptive personality traits that are expressed pervasively across interpersonal situations (American Psychiatric Association, 1980). The notion that PDs are trait-like and enduring over time was largely untested at the time of the DSM–III, although contemporaneous personality research suggested a high degree of within-individual consistency over time (Costa, McCrae, & Arenberg, 1980).
To explore the stability of PD diagnoses and symptoms over time, several research groups undertook major longitudinal studies in the 1990s (Grilo, McGlashan, & Skodol, 2000; Lenzenweger, 1999; Paris, Brown, & Nowlis, 1987; Zanarini, Frankenburg, Hennen, Reich, & Silk, 2006). Accumulating evidence from these studies indicates that the mean number of symptoms for nearly all PDs declines over time and that these disorders are much less stable than previously thought (Lenzenweger, Johnson, & Willett, 2004; Skodol et al., 2005). For example, Zanarini, Frankenburg, Hennen, Reich, and Silk (2006) found that 88% of psychiatric patients with borderline PD no longer met the diagnostic threshold 10 years after diagnosis (and 39% of the sample remitted within 2 years). Furthermore, the stability of the diagnostic criteria that define certain PDs varies widely over relatively brief time intervals, suggesting that some criteria capture dysfunctional personality traits whereas others may be more sensitive to stress-related behaviors or state-dependent symptoms (McGlashan et al., 2005). Although reports from the Collaborative Longitudinal Personality Disorders Study (CLPS; Skodol et al., 2005) and the McLean Study of Adult Development (Zanarini, Frankenburg, Hennen, Reich, & Silk, 2005) have observed symptom remission for each of the PDs studied, they are potentially limited by the fact that participants were receiving psychiatric treatment at the initial study assessment and had high levels (above diagnostic threshold) of personality pathology, which raises a concern that PD symptom remission may partly reflect regression toward the mean (Campbell & Kenny, 1999).
A limitation of several longitudinal PD research reports to date (Gunderson et al., 2011; Johnson et al., 2000; Sanislow et al., 2009), including previous reports from the Longitudinal Study of Personality Disorders (LSPD; Lenzenweger, 1999), is that they have used statistical methods that characterize changes in the mean level of symptoms over time based either on group averages or individual growth curves. Such methods are insensitive to the possibility of latent subgroups mixed within the study sample whose symptoms change at different rates or who have qualitatively different symptom levels at baseline (Muthén, 2004). Thus, it remains unknown whether there are subgroups of individuals whose PD symptoms do not remit over time or affected persons whose symptoms remit especially rapidly. Even less is known about the potential development of PD symptoms among individuals who are initially asymptomatic (Cohen, Crawford, Johnson, & Kasen, 2005). Clarifying heterogeneity in the course of PDs is an important topic because persistent PD symptomatology is associated with poor treatment response (Newton-Howes, Tyrer, & Johnson, 2006) and psychosocial impairment (Gunderson et al., 2011). Thus, identifying the characteristics of individuals who experience chronic PD symptoms versus those whose symptoms remit rapidly over time may have direct implications for clinical assessment. Moreover, characterizing such heterogeneity may inform an understanding of the development and pathogenesis of personality pathology, which remains largely opaque to date.
Growth mixture modeling (GMM), a synthesis of latent growth curve modeling and finite mixture modeling, is a longitudinal data analytic approach that provides leverage on the question of whether change trajectories in a sample are homogeneous (with variation around mean parameters) or whether latent subgroups with distinct trajectories are commingled within the observed variation (Muthén & Shedden, 1999). This approach is ideally suited to parse heterogeneity in the longitudinal course of PD symptoms and has been used effectively to study the course of other forms of psychopathology (Lincoln & Takeuchi, 2010; Malone, Van Eck, Flory, & Lamis, 2010). In particular, GMM is an optimal technique for testing whether mean-level declines in PD symptoms occur universally or whether the longitudinal course of PDs is more heterogeneous than previously described.
The present study examined whether distinct latent trajectories of PD symptom change were evident in the LSPD, a multiwave prospective study designed to examine change in PD symptoms in early adulthood (Lenzenweger, 1999). Our study builds on the LSPD research corpus by focusing specifically on heterogeneity in the longitudinal trajectories of PD symptoms, whereas previous analyses of this dataset have addressed mean-level stability in the sample (Lenzenweger, 1999; Lenzenweger et al., 2004) and the associations among PDs and personality variables (e.g., Lenzenweger & Willett, 2007). Two groups of participants were observed in the LSPD: symptomatic individuals who met a diagnostic threshold for at least one DSM–III–R PD on a self-report screening instrument (PPD), and asymptomatic individuals who were drawn from a pool of subjects that did not the meet diagnostic threshold for any PD (NoPD). In the initial selection of LSPD subjects, no attempt was made to exclude participants with comorbid PDs. Thus, it is likely that symptom change at the disorder level represents a mixture of individuals with and without particular PD features, rather than a homogeneous, single-PD group. In addition, the NoPD group may have included persons at risk to develop a PD at baseline who developed personality pathology during the 4-year follow-up period.
Because the NoPD and PPD groups were sampled for the relative absence or presence, respectively, of any form of personality pathology, our primary analyses focused on identifying latent trajectories of growth in the total number of PD symptoms. This approach aligns with a large literature describing the core features of personality disorder that span diagnostic constructs (Livesley, 1998) and that are crucial in clinical decision making (Pilkonis, Hallquist, Morse, & Stepp, 2011). Furthermore, the notion of a general PD dimension is a primary component of current proposals for PD nomenclature in DSM-5 (Krueger, Skodol, Livesley, Shrout, & Huang, 2007). Separate GMMs were estimated for the NoPD and PPD groups (which varied considerably in their composition) so that the number and form of latent trajectories were not constrained by the study design. To explore heterogeneity in the course of specific PDs, we also conducted exploratory GMMs for each of the 11 DSM–III–R PDs in each group.
Personality disorders are often comorbid with each other and with Axis I psychopathology (Grilo et al., 2000; Zimmerman & Mattia, 1999), and greater comorbidity is associated with functional impairment and poor treatment response (Fournier et al., 2008; Newton-Howes et al., 2006). Moreover, personality traits may represent a common substrate that is related to many forms of psychopathology (Krueger, 2005; Krueger & Markon, 2006; Lahey, 2009). Thus, to characterize the covariation among PD symptoms, personality traits, and psychopathology, we compared PD latent trajectory classes in terms of person-specific estimates of the initial level and rate of change for all other PDs, depression, anxiety, and four major personality traits.
For the PPD group, we hypothesized that two latent trajectories would be evident for the total number of PD symptoms: (a) those whose symptoms were moderate to high at baseline and decreased little over time and (b) those with similar levels of baseline symptomatology who experienced significant remission. We further predicted that the persistent class would have greater Axis II comorbidity, anxiety, and depressive symptoms at baseline and that these comorbidities would remain higher over time than in the remitting class (Zanarini, Frankenburg, Hennen, Reich, & Silk, 2004; Zanarini, Frankenburg, Vujanovic, et al., 2004). For the NoPD group, we hypothesized that two trajectories would be observed for total PD symptoms: (a) those who exhibited minimal to subclinical symptomatology over time (consistent with the sampling strategy of the LSPD) and (b) those whose symptoms increased over time, suggesting the development of personality pathology in someone with low initial risk (Cohen et al., 2005). Analyses of individual PDs were undertaken within a context of discovery and, therefore, we did not have specific hypotheses about the number or form of latent trajectories at the disorder level.
Method Participants
Participants were 258 first-year undergraduate students from a pool of 2,000 first-year undergraduate students at Cornell University, Ithaca, NY. Subjects were drawn from all undergraduate units at Cornell, including the endowed (private) and the State University of New York units. Of the 2,000 persons randomly sampled from the incoming class, 1,658 completed the International Personality Disorder Examination DSM–III–R Screen (IPDE-S; Lenzenweger, Loranger, Korfine, & Neff, 1997). Extensive detail on the sampling procedure is given elsewhere (Lenzenweger, 2006; Lenzenweger et al., 1997). On the basis of responses to the IPDE-S, participants were divided into two groups: possible personality disorder (PPD) or no personality disorder (NoPD). Participants in the PPD group (n = 134) met the diagnostic threshold for at least one DSM–III–R PD, whereas NoPD participants (n = 124) had fewer than 10 PD features across Axis II disorders and did not meet criteria for any PD. Eight subjects did not complete the protocol because they transferred to other colleges (n = 6) or died in automobile accidents (n = 2). Thus, this study reports results from 250 subjects that completed all waves.
Complete demographic information has been reported elsewhere (Lenzenweger, 1999) and is omitted here to conserve space. The average age of the 129 participants in the PPD group was 18.85 years (SD = 0.58) and 64 were female (50%). Sixty-eight of the 121 NoPD participants were female (56%) and the average age was 18.90 (SD = 0.43). The groups did not significantly differ by age or sex composition. At study intake, 53 PPD participants met lifetime criteria for at least one Axis I disorder (41%), whereas 15 NoPD participants had at least one lifetime Axis I diagnosis (12%), and this difference was significant, χ2(1) = 24.52, p < .0001.
Participants gave voluntary informed consent and received payment of $50 at each wave. The protocol was approved by the institutional IRB of Cornell University and participants were treated in accordance with the “Ethical Principles of Psychologists and Code of Conduct” (American Psychological Association, 2010).
Measures
Personality disorder assessment
Participants completed personality disorder assessments at three time points: during the first, second, and fourth years of college. Skilled clinical interviewers administered the International Personality Disorder Examination for DSM–III–R (Loranger et al., 1994) at each measurement occasion and interrater agreement was high (Lenzenweger, 1999). Dimensional scores (i.e., number of criteria met) for the total number of DSM–III–R PD symptoms served as the primary dependent variables for the present analyses. We also explored latent trajectory models for each of the 11 DSM–III–R PDs using dimensional scores that represented sums of the individual PD criteria at each wave.
Personality assessment
At each assessment, participants completed the NEO Personality Inventory (Costa & McCrae, 1985), a well-known self-report measure of normal personality traits. Using algorithms derived from the factor analytic work of Church (1994) comparing the NEO-PI and Tellegen's constructs, we calculated scores for four major personality dimensions: Agentic Positive Emotionality, Communal Positive Emotionality, Negative Emotionality, and Constraint (for technical details, see Lenzenweger & Willett, 2007).
Proximal process assessment (early to middle childhood)
In 1991, when LSPD data collection commenced, there was no existing measure of a proximal process construct such as that hypothesized by Bronfenbrenner (Bronfenbrenner & Morris, 1998). Therefore, in consultation with Urie Bronfenbrenner, the senior investigator (MFL) developed a semistructured interview consisting of four focal questions designed to tap proximal processes in the child's relationships with important adults (e.g., parents; see Lenzenweger, 2010). Questions focused on the occurrence of regular and reciprocal involvement of an adult in facilitating the child's mastery of a task or skill, including exposure to progressively more complex information. Examples of proximal processes include teaching a child to play a musical instrument, regular reading with a child, or making plans with a child to pursue an activity of project. Assessment of these proximal process items relied upon subjects' retrospective recall, with a focus on the ages 5–12. The benefits of interviewer-based assessments for retrospective reports have been described (Brewin, Andrews, & Gotlib, 1993; Maughan & Rutter, 1997).
Axis I psychopathology assessment
Prior to the assessment of PDs at each wave, experienced clinical interviewers administered the Structured Clinical Interview for DSM–III–R: Nonpatient Version (Spitzer, Williams, & Gibbon, 1990). This well-validated semistructured interview was used to assess for DSM–III–R Axis I disorders. The presence of any lifetime Axis I disorder prior to or during the study period was the primary variable of interest. Participants were also asked whether they had sought mental health treatment at each wave, and lifetime use of treatment services was also analyzed.
In addition, participants completed the Beck Depression Inventory (BDI) and the State–Trait Anxiety Inventory—Trait Scale (STAI; Spielberger, 1983). The STAI is a well-validated 20-item self-report instrument of trait anxiety that has high internal consistency (Cronbach's alpha = .90; Ramanaiah, Franzen, & Schill, 1983). The BDI is an established 21-item self-report questionnaire that measures symptoms of depression experienced in the previous week (Beck & Steer, 1984).
Results Analytic Approach
GMMs were estimated for the total number of PD symptoms and symptoms of 11 individual PDs in each group (NoPD and PPD) using Mplus 6.12 software (Muthén & Muthén, 2010). Poisson-based models for the outcome variables were selected because PD symptoms represented counts of diagnostic criteria, which were not normally distributed, but aligned well with the Poisson distribution. Because participants varied somewhat in the timing of their follow-up assessments, individual times of observation were included in the GMMs such that growth parameters were sensitive to each person's assessment schedule. The number of latent trajectory classes was determined primarily by iteratively increasing the number of latent classes and comparing a k-class model against a model with k-1 classes using the bootstrapped likelihood ratio test (BLRT), which uses parametric bootstrap resampling to test an empirical distribution of likelihood ratio tests across bootstrapped samples. Relative to model selection criteria such as the Bayesian Information Criterion (BIC) and Akaike Information Criterion (AIC), the BLRT test is most sensitive to the number of latent classes in GMM (Nylund, Asparouhov, & Muthén, 2007) and is well-established in the finite mixture modeling literature (McLachlan & Peel, 2000). Following the recommendation of McLachlan and Peel (2000), 100 bootstrap samples were used for each BLRT computation, and the highest-class model with a significant BLRT (p ≤ .05) was selected. GMM parameter estimates describing each latent trajectory are presented in Table 1.
Initial Status and Rate of Change Estimates for Total PD Latent Trajectories Across LSPD Groups
An important point is that GMM does not inherently prefer multiclass solutions, and the empirical corroboration of a one-class GMM solution is consistent with the conclusion that a unitary mean trajectory (with normal variability around growth parameters) best characterizes the sample (cf. Bauer & Curran, 2003). Indeed, when a one-class GMM is preferred, the results are identical to the traditional latent growth curve model because the parameter estimates are no longer conditioned on latent class membership (Muthén, 2004).
In order to characterize the latent trajectories of PD symptom change, we compared classes in terms of comorbid PD symptoms and symptoms of depression and anxiety. We also compared trajectory classes on four major personality factors: Agentic Positive Emotionality, Communal Positive Emotionality, Negative Emotionality, and Constraint (Tellegen, 1985). These traits were selected because of prior research linking them to neurobehavioral systems underlying personality pathology (Depue, 2009; Depue & Lenzenweger, 2005). Mean-level differences across PD symptom latent trajectory classes were computed using the pseudoclass draw technique based on 20 pseudoclass draws from the posterior class distribution (Wang, Brown, & Bandeen-Roche, 2005). The statistical significance of mean differences in the conditional class means for each construct was evaluated using Wald tests.
To capture both initial standing and longitudinal rate of change in each of these constructs, which were measured at each wave, we conducted multilevel linear growth models using the lme4 package for R (Bates, Maechler, & Bolker, 2011; R Development Core Team, 2011). Growth models for each construct included fixed effects for group (PPD/NoPD), sex, age at study entry, and time of assessment, and random effects for subject and time. Multilevel models for the individual PDs were modeled using a Poisson distribution, whereas the other variables (personality, depression, and anxiety) were modeled as Gaussian. Individual-specific estimates of the initial level and rate of change for each construct (adjusting for group, entry age, and sex) were derived using the empirical best linear unbiased predictor (EBLUP) of the random effects (Frees & Kim, 2006). Thus, mean comparisons among classes were made both in terms of initial level and rate of change in each construct. Trajectory classes were also compared on sex, age at study entry, proximal processes, Axis I psychopathology (prior to or during the study), and mental health treatment use (prior to or during the study).
NoPD Group Results
Total PD
A three-class GMM best characterized total PD symptom change in the NoPD group according to the AICc, BIC, and BLRT (see Table 2), and there was a high degree of certainty about latent class membership, entropy = .87 (Celeux & Soromenho, 1996). The first latent class (n = 73) was characterized by low levels of PD symptoms at intake that increased slightly over time (Figure 1, left panel). The second latent class (n = 38) reported moderate to high initial levels of personality dysfunction that declined significantly over time. A third latent trajectory (n = 10) had mild to moderate PD symptomatology at intake that rapidly declined to zero by the first follow-up assessment.
Model Fit Statistics for Growth Mixture Models of PD Symptom Counts
Model Fit Statistics for Growth Mixture Models of PD Symptom Counts
Figure 1. Latent Trajectories of Total PD Symptoms. Note. Darkened circles represent the mean number of PD symptoms for individuals classified in a trajectory at that measurement occasion, where the three assessments are plotted at the median times of observation for the sample. Error bars represent the standard error of the mean.
At study baseline, symptoms of all 11 PDs, depression, and anxiety were higher in the moderate class than the low and rapid remission trajectory classes (Figure 2; Table 3). Axis I disorders were more prevalent in the moderate class (23.4%) at baseline than the low class (6.4%), χ2(1) = 4.98, p = .03, as was lifetime history of psychiatric treatment (17.2% vs. 4.3%; χ2[1] = 3.97, p = .05). The occurrence of new diagnoses or treatment utilization during the study did not differ significantly by latent class, however. Individuals in the moderate class were also approximately 4 months older, on average, than other NoPD participants (see Table 4). There was no significant difference in sex ratio across classes.
Figure 2. Mean Differences in the Initial Level and Growth of Personality Disorder Symptoms across NoPD Total PD Latent Trajectory Classes. Note. Symptom change is a rate ratio representing the expected change in the symptom count per year. Thus, a ratio of 1.0 corresponds to no average symptom change over time (shown by a horizontal black line above), ratios less than 1.0 correspond to symptom remission, and ratios greater than 1.0 indicate symptom growth. For example, a rate ratio of 1.5 would indicate that for each elapsed year, the expected number of symptoms is 1.5 times the level at the previous year.
Statistical Tests of Mean Differences in DSM-III-R PD Symptoms Across Total PD Latent Trajectory Classes
Mean Differences in Proximal Processes, Age, Personality, Depression, and Anxiety Across Total PD Latent Trajectories
Although the overall level of PD symptomatology was greater at baseline in the rapid remission class than the low-symptom class, mean comparisons for specific PDs were nonsignificant. Conversely, depressive symptoms were lowest and proximal processes were highest in the rapid remission group.
In terms of change over time, the moderate class experienced faster symptom remission for schizoid, histrionic, and obsessive-compulsive PD symptoms relative to the low class (of course, the low class had few symptoms to begin with), greater remission of anxiety symptoms, and slower symptom increases for antisocial and borderline PDs. Tempering these positive changes, dependent PD symptoms increased significantly in the moderate class over time, whereas they tended to decrease in the other two classes. Narcissistic PD symptoms remitted marginally more slowly in the moderate class than the low-symptom class, p = .07.
As detailed in Table 3, symptoms of schizotypal, narcissistic, avoidant, obsessive-compulsive, and passive-aggressive PDs declined significantly more quickly in the rapid remission class than the low-symptom class. Agentic Positive Emotionality at baseline was significantly lower in the moderate class than the low class, and Communal Positive Emotionality was marginally lower in the moderate class than the rapid remission class. Rates of change in personality variables were not associated with NoPD Total PD latent trajectory class.
Specific PDs
In the NoPD group, single-class GMMs were selected for antisocial, borderline, dependent, histrionic, narcissistic, passive-aggressive, schizoid, and schizotypal PDs. Among this set, antisocial PD symptoms increased significantly, albeit slightly, over time, whereas symptoms of histrionic, narcissistic, and schizotypal PD decreased significantly. Symptoms of borderline, dependent, passive-aggressive, and schizoid PDs were rather low in the NoPD group, on average, and exhibited no significant change over time (see Figure 3). Two-class GMMs were selected for avoidant, obsessive-compulsive, and paranoid PDs (see Table 2). Statistical tests, plots, and descriptive statistics for individual PD GMMs are included in an online supplement (e.g., Table S1), and descriptive summaries are provided here.
Figure 3. Mean Differences in the Initial Level and Growth of Personality Disorder Symptoms across PPD Total PD Latent Trajectory Classes. Note. Symptom change is a rate ratio representing the expected change in the symptom count per year.
Figure 4. Latent Growth Trajectories for Individuals in the NoPD Group. Note. Darkened circles represent the mean number of PD symptoms for individuals classified in a trajectory at that measurement occasion, where the three assessments are plotted at the median times of observation for the sample. Error bars represent the standard error of the mean.
Figure 5. Latent Growth Trajectories for Individuals in the Probable PD Group. Note. Darkened circles represent the mean number of PD symptoms for individuals classified in a trajectory at that measurement occasion, where the three assessments are plotted at the median times of observation for the sample. Error bars represent the standard error of the mean.
Avoidant PD
The majority of NoPD participants followed a low-symptom trajectory (n = 98) that had minimal symptomatology at baseline and zero avoidant symptoms at follow-up. The second latent trajectory (n = 23) had low avoidant PD symptoms at intake that increased significantly over time, approaching subclinical or clinical levels by the final follow-up. Individuals in the increasing class also had significantly higher baseline symptoms of dependent and schizoid PDs (Figure S1). In addition, individuals in the increasing avoidant PD trajectory class also experienced significantly increasing symptoms of dependent PD over time. Agentic Positive Emotionality was significantly lower in the increasing class (Table S2). Depressive symptoms, anxiety, and lifetime Axis I psychopathology did not differ across latent classes.
Obsessive-Compulsive PD
The first latent trajectory (n = 85) was characterized by few symptoms at intake and zero symptoms at the follow-up assessments. The second latent trajectory (n = 36) reported mild OCPD symptoms at intake that increased significantly over time, although average symptomatology was not high, on average, even at the final assessment. Individuals in the increasing class were more often male (64.5% vs. 43.1%; χ2[1] = 4.27, p = .04) and reported significantly higher initial levels of avoidant, paranoid, and schizoid PD symptoms relative to the low class (Figure S2). Also, dependent PD symptoms rose significantly over time in the increasing class, whereas increases in antisocial PD symptoms were greater in the low class. Remission of passive-aggressive PD symptoms was marginally slower in the increasing OCPD trajectory class, χ2(1) = 3.52, p = .06. Baseline levels of Communal Positive Emotionality were significantly lower in the increasing OCPD class (Table S2).
Paranoid PD
Whereas paranoid PD symptoms tended to decrease to zero in the majority of NoPD participants (n = 97), a second latent subgroup (n = 24) experienced significant increases in symptoms, although overall symptom levels remained subclinical throughout the study. Lifetime history of psychiatric treatment was greater in the increasing class (20.1% vs. 4.0%; χ2[1] = 4.39, p = .04), but Axis I diagnosis at baseline, depressive symptoms, and anxiety did not differ by latent class. Membership in the increasing latent class was marginally associated with greater symptoms of narcissistic PD at baseline, χ2(1) = 2.70, p = .10, but no other cross-PD associations approached statistical significance (Figure S3). Although the latent class differences for individual Cluster B PDs were nonsignificant, the total number of Cluster B symptoms at baseline was greater in the increasing class, χ2(1) = 3.93, p = .05. The classes were not significantly different on any personality variables.
PPD Group Results
Total PD
A three-class GMM best described the course of total PD symptoms in the PPD group according to AICc, BIC, and BLRT (see Table 2), entropy = 0.94. The majority of participants (n = 109) were classified into a trajectory characterized by moderate to high PD symptoms that declined significantly over the follow-up period, particularly between baseline and the 1-year follow-up assessment (see Figure 1, right panel). A second latent class (n = 11) had few PD symptoms at intake, but experienced mild symptom increases over the course of the study. The third trajectory class (n = 9) had high levels of PD symptoms at baseline, but experienced rapid symptom remission, approaching zero symptoms at the 1-year follow-up.
Individuals in the high-symptom class were more often male (54.4% vs. 21.6%, χ2[2] = 6.79, p = .03) and had a greater lifetime prevalence of Axis I disorders (46.0% vs. 12.5%, χ2[2] = 9.07, p = .01) relative to the other two groups. Relative to the rapid remission class, more high-symptom class members had received psychiatric treatment in the past (17.2% vs. .8%, χ2[1] = 8.37, p = .004) and there was also a greater incidence of new Axis I disorders in the high-symptom class (22.9% vs. .7%, χ2[1] = 16.28, p < .0001). Proximal processes were significantly higher in the low-symptom class than the high-symptom class (see Table 4). In terms of specific PD symptoms at intake, the high-symptom class had greater initial levels of all 11 PDs than the low-symptom class (see Figure 4). In addition, the high-symptom class reported significantly higher baseline levels of antisocial, borderline, dependent, and schizoid PD symptoms than the rapid remission class. Relative to the low-symptom class, the rapid remission class had higher levels of histrionic and narcissistic PD symptoms at baseline, and there were positive trends for avoidant, dependent, obsessive-compulsive, passive-aggressive, and schizotypal PDs.
Figure 4. Latent Growth Trajectories for Individuals in the NoPD Group. Note. Darkened circles represent the mean number of PD symptoms for individuals classified in a trajectory at that measurement occasion, where the three assessments are plotted at the median times of observation for the sample. Error bars represent the standard error of the mean.
In the rapid remission class, the rate of symptom decline exceeded the other trajectory classes for dependent, narcissistic, obsessive-compulsive, paranoid, passive-aggressive, and schizotypal PDs (see Table 3). Avoidant PD symptoms also decreased more quickly in the rapid remission class than the high-symptom class. Overall, the rates of change in PD symptoms were similar in the low-symptom and high-symptom classes (see Figure 4). However, antisocial PD symptoms increased more quickly and borderline PD symptoms decreased more slowly in the rapid remission and low-symptom classes relative to the high-symptom class. The slight symptom increases observed in the low-symptom class appear to have been driven by greater increases in Antisocial PD symptoms as well as relatively little change, on average, in borderline and paranoid PD symptoms.
Negative Emotionality at baseline was significantly higher in the high-symptom class than the rapid-remission class. Notably, however, Negative Emotionality decreased more rapidly over time in the high-symptom and low-symptom classes than the rapid-remission class. There was a statistical trend toward lower levels of Communal Positive Emotionality in the high-symptom class relative to the other classes. Also, anxiety and depression were highest in the high-symptom class at baseline, but anxiety also decreased most rapidly in this class.
Specific PDs
In the PPD group, a single-class GMM best described schizoid PD symptoms, but multiple latent trajectories were evident for the other 10 PDs (see Figure 5). Schizoid PD symptoms were low and stable in the PPD group.
Figure 5. Latent Growth Trajectories for Individuals in the Probable PD Group. Note. Darkened circles represent the mean number of PD symptoms for individuals classified in a trajectory at that measurement occasion, where the three assessments are plotted at the median times of observation for the sample. Error bars represent the standard error of the mean.
Antisocial PD
A latent trajectory class that included 79 PPD participants was characterized by subclinical-to-clinical levels of antisocial features that increased slightly, but significantly, over time. The second trajectory class (n = 50) was associated with few antisocial symptoms at intake and zero symptoms at follow-up. A greater proportion of males was represented in the increasing class (71.6% vs. 41.9%; χ2[1] = 7.24, p = .007). Baseline symptoms of borderline, paranoid, and schizotypal PDs were significantly higher in the increasing class (Figure S4). Whereas narcissistic PD symptoms declined somewhat more slowly in the increasing class, χ2[1] = 3.44, p = .06, borderline PD symptoms declined significantly faster in the increasing class. Constraint at baseline was significantly lower in the increasing antisocial PD class (Table S3).
Avoidant PD
Two latent trajectories for avoidant PD symptoms were evident in the PPD group. Seventy-six individuals had few, if any, symptoms at intake and zero symptoms at follow-up assessments. A second latent trajectory (n = 53) was characterized by subclinical-to-clinical avoidant PD symptoms (13 individuals had four or more symptoms, the threshold for diagnosis) that remained relatively stable over time. The subclinical group had a greater proportion of individuals with a lifetime Axis I disorder at baseline (51.6% vs. 27.7%, respectively; χ2(1) = 6.23, p = .01), but treatment utilization and incidence of Axis I disorders did not differ by class. Baseline levels of dependent, histrionic, passive-aggressive, schizoid, and schizotypal PDs were significantly higher in the subclinical trajectory than the low-symptom trajectory (Figure S5). Also, symptoms of dependent and obsessive-compulsive PDs remitted more slowly in the subclinical group. Negative Emotionality was significantly higher at baseline in the subclinical group, as were symptoms of depression.
Borderline PD
A four-class GMM best fit the longitudinal course of borderline PD symptoms in the PPD group. The first latent class (n = 57) had zero or one BPD symptoms throughout the study. The second latent class (n = 39) reported mild to subclinical symptoms that did not change over time. A third trajectory (n = 17) was characterized by clinical BPD symptoms at intake that remitted significantly, in most cases, to subclinical symptoms by the final follow-up. Finally, a fourth trajectory (n = 16) had subclinical-to-clinical symptoms at intake that remitted rapidly, with all individuals in this class having two or fewer symptoms at the final assessment.
Lifetime Axis I disorders at intake were significantly more prevalent in the high-symptom and rapid remission classes than the mild and minimal classes (72.7%, 62.9%, 37.7%, and 25.9%, respectively; χ2[3] = 14.37, p = .002). Interestingly, 40.3% of individuals in the mild BPD symptom trajectory developed new Axis I disorders during the study—significantly more than other trajectory classes (minimal = 15.0%, high = 16.5%, rapid remission = 7.2%; χ2[3] = 10.24, p = .02)—raising the possibility that BPD symptoms at baseline may have been precursors of subsequent psychopathology. Trajectory classes did not differ by sex, age, or treatment utilization.
In the high-symptom and rapid remission classes, baseline symptoms of antisocial, paranoid, and schizotypal PDs were significantly higher than the minimal symptom class. The high-symptom class was further distinguished by elevated symptoms of dependent, histrionic, narcissistic, and passive-aggressive PDs at baseline relative to the other three trajectories. The mild symptom class reported higher initial levels of antisocial PD than the minimal class (Figure S6).
In addition to remitting more quickly on borderline PD symptoms, individuals in the rapid remission class exceeded those in the high-symptom class in the rate of remission for avoidant, narcissistic, passive-aggressive, and schizotypal PD symptoms, and they also had slower growth in antisocial PD symptoms than the high-symptom class. Further, in the high-symptom class, symptoms of avoidant, dependent, narcissistic, and passive-aggressive PDs declined more slowly than in the mild and minimal trajectory classes. Symptoms of Paranoid PD declined more slowly in the mild class relative to the rapid remission class.
As detailed in Table S3, Constraint at baseline was significantly lower in the mild, rapid remission, and high symptom classes than the minimal symptom class. Negative Emotionality at baseline was highest in the high-symptom class, exceeding all other classes, whereas the rapid remission and mild symptom classes had higher levels of Negative Emotionality than the minimal class. Negative Emotionality decreased significantly more quickly over time in the rapid remission class than the mild and minimal symptom classes. Anxiety at baseline was highest in the high symptom class, followed by the mild symptom class, with the rapid remission and minimal classes having the lowest levels of anxiety. That said, anxiety also decreased more rapidly in the high symptom class than the minimal class. Baseline depression was highest in the high symptom class, followed by the rapid remission class, with the mild and minimal classes having the lowest levels. Depressive symptoms decreased significantly more quickly in the rapid remission class than the mild and minimal classes.
Dependent PD
Two latent trajectories were evident for Dependent PD symptoms: the first (n = 73) had few features at baseline and zero features at follow-up assessments, whereas the second trajectory (n = 56) had mild to moderate symptoms at baseline that decreased marginally over time (p = .06). Lifetime history of Axis I was significantly higher in the moderate class (52.7% vs. 24.8%; χ2[1] = 8.18, p = .004). Symptoms of avoidant, borderline, histrionic, narcissistic, and passive-aggressive PDs were significantly higher at baseline in the moderate class than in the minimal class (Figure S7). In addition to having more persistent dependent PD symptoms, the moderate trajectory was associated with slower declines in avoidant and obsessive-compulsive PD symptoms. Negative Emotionality, anxiety, and depressive symptoms were significantly higher in the moderate trajectory at baseline, but the rates of change in these constructs did not differ by trajectory class (Table S3).
Histrionic PD
Three latent trajectories characterized the level and rate of change in histrionic PD symptoms. The first class (n = 76) reported moderate symptoms of histrionic PD that decreased significantly over time. The second class (n = 45) experienced zero histrionic PD symptoms throughout the study. The third class (n = 8) reported moderate to severe histrionic PD symptoms at baseline but had zero symptoms at each follow-up.
Lifetime history of Axis I psychopathology was significantly higher at baseline in the moderate class than the zero class (51.7% vs. 19.0%, respectively; χ2[1] = 10.89, p = .001). Relative to the zero class, the moderate class also had significantly higher baseline levels of avoidant, borderline, dependent, narcissistic, obsessive-compulsive, paranoid, and passive-aggressive PDs (Figure S8). At baseline, the rapid remission class had higher levels of narcissistic PD than the zero class and lower levels of borderline PD than the moderate class. Symptoms of narcissistic and passive-aggressive PDs remitted more quickly in the rapid remission class than the moderate class. Symptoms of dependent PD were more persistent in the moderate class than the zero class. Negative Emotionality and depressive symptoms were significantly higher at baseline in the moderate class relative to the zero class.
Narcissistic PD
Two latent trajectory classes were evident for narcissistic PD: the first class (n = 71) reported few symptoms at baseline and zero symptoms at follow-up assessments. The second class (n = 58) reported subclinical to clinical levels of narcissistic PD, and these symptoms declined significantly over time. Symptoms of antisocial, borderline, histrionic, paranoid, and passive-aggressive PDs were significantly higher at baseline in the moderate class than the minimal class. Over the course of the study, symptoms of passive-aggressive and obsessive-compulsive PDs declined more slowly in the moderate class (Figure S9). There were no significant differences between trajectory classes in terms of personality, anxiety, or depressive symptoms.
Obsessive-compulsive PD
Three latent trajectories were identified that described change in OCPD symptoms over time. The first class (n = 54) reported moderate levels of OCPD at baseline that increased slightly, but significantly, over time. The second latent class (n = 52) had few OCPD symptoms at baseline and zero symptoms at follow-up. The third class (n = 23) reported clinical levels of OCPD at baseline that remitted rapidly approaching zero by the final assessment. The moderate class had significantly higher baseline levels of avoidant, dependent, narcissistic, paranoid, passive-aggressive, schizoid, and schizotypal PDs relative to the minimal class (Figure S10). Although baseline PD levels were often similar in the rapid remission and moderate classes, most of the statistical tests of class means were nonsignificant. Avoidant PD symptoms, however, were significantly higher in the rapid remission class than the minimal class.
Over time, slower declines were evident in the moderate class for dependent, narcissistic, paranoid, passive-aggressive, and schizotypal PD symptoms relative to the minimal class. In addition to remitting more quickly on OCPD symptoms, the rapid remission class also declined more quickly than the moderate class on symptoms of avoidant and narcissistic PDs. Communal Positive Emotionality was lower at baseline in the moderate class than in the minimal class, whereas anxiety and depression were highest in the moderate class (Table S3). Further, whereas constraint increased more quickly in the moderate class than the minimal class, growth in Communal Positive Emotionality over time was smallest in the moderate class.
Paranoid PD
A two-class GMM best fit paranoid PD symptoms in the PPD group. The first latent class (n = 74) reported few symptoms at baseline and zero symptoms at follow-up. The second latent class (n = 55) experienced mild to moderate symptoms at baseline that were stable over time. The moderate class had higher baseline symptoms of antisocial, avoidant, borderline, dependent, histrionic, narcissistic, and schizotypal PDs (Figure S11). Symptoms of avoidant, dependent, narcissistic, obsessive-compulsive, and passive-aggressive PDs declined more quickly in the minimal symptom class relative to the moderate class. Constraint was marginally lower in the moderate class at baseline, whereas initial anxiety and depression were significantly higher in this class.
Passive-aggressive PD
Three latent trajectories characterized symptoms of passive-aggressive PD. The first trajectory (n = 68) reported minimal symptomatology throughout the study. Individuals in the second trajectory (n = 35) reported subclinical levels of passive-aggressive PD at baseline that declined rapidly over time, reaching zero by the final follow-up. The third class (n = 26) reported subclinical to clinical levels of passive-aggressive PD at baseline that were stable over time. Relative to the minimal and rapid remission classes, the moderate class reported higher baseline levels of antisocial, borderline, histrionic, narcissistic, and paranoid PDs (Figure S12). Obsessive-compulsive PD features were also higher in the moderate class than the minimal class. Avoidant, dependent, and narcissistic symptoms remitted more slowly in the moderate class than the minimal class. Constraint and Communal Positive Emotionality were significantly lower at baseline in the moderate class than in the minimal and rapid remission classes, whereas baseline depression and anxiety were significantly higher in the moderate class.
Schizotypal PD
Two latent classes characterized schizotypal PD symptom trajectories. Seventy-four individuals experienced minimal schizotypal PD symptoms at intake that declined significantly over time, with all individuals reporting zero symptoms the two follow-up assessments. The second trajectory class (n = 55) reported subclinical to clinical levels of schizotypal PD at baseline and these symptoms declined significantly over time. There were marginally more females in the minimal symptom class than the moderate class (60.3% vs. 42.2%, respectively; χ2[1] = 3.08, p = .08). Symptoms of antisocial, avoidant, borderline, narcissistic, obsessive-compulsive, and schizoid PDs were significantly higher at baseline in the moderate class than the minimal class, but the rate of change in comorbid PD symptoms did not differ by latent class (Figure S13). Communal Positive Emotionality was significantly lower in the moderate schizotypal PD trajectory class, whereas anxiety was marginally higher (Table S3).
DiscussionRecent empirical findings from prospective longitudinal studies challenge the notion that PDs have a chronic course, with multiple studies demonstrating mean-level declines in PD symptoms over time (Johnson et al., 2000; Lenzenweger et al., 2004; Sanislow et al., 2009; Shea et al., 2002; Zanarini et al., 2006). Consistent with clinical observations (e.g., Stone, 1990), however, the expression of personality pathology over time differs across individuals, and there may be considerable variability in the longitudinal trajectories that people follow. In this study, we sought to characterize directly heterogeneity in the longitudinal course of PDs using growth mixture modeling, with the goal of identifying potentially distinctive trajectories over a 4-year observational longitudinal study of young adults. Our findings build upon previous longitudinal reports from the LSPD data (e.g., Lenzenweger et al., 2004) through the use of latent trajectory analyses and the richer characterization of longitudinal covariation among personality and comorbid Axis I and II symptoms. Our results corroborated the existence of multiple latent trajectories for the overall level of personality dysfunction, both for the symptomatic (PPD) and asymptomatic (NoPD) groups comprising the LSPD sample. This is the first study of personality disorders to characterize longitudinal heterogeneity in terms of qualitatively distinct symptom trajectories, and the results have important theoretical and clinical implications.
In the NoPD group, the majority of individuals followed trajectories characterized by minimal PD symptomatology at baseline (both in terms of the total number of PD symptoms and symptoms of specific disorders) that was relatively stable over the follow-up period. This result suggests that most individuals who have little or no personality pathology in early adulthood are unlikely to develop subsequent symptoms. This finding is novel insofar as previous research in this area has not probed specifically for the development of personality pathology in initially asymptomatic individuals. Approximately 30% of the NoPD group, however, experienced subclinical levels of overall personality dysfunction, which remitted significantly, but not completely, over the follow-up period. This finding is consistent with prior reports from the LSPD (Lenzenweger et al., 2004) and other longitudinal studies (Grilo et al., 2004) that initially symptomatic individuals often show symptom remission even over brief intervals. Finally, a small subset of NoPD participants experienced subclinical personality dysfunction at baseline that remitted entirely at the follow-up assessments. Given that study participants were college freshman at baseline, it is possible that the rapid remission of PD symptoms in some individuals may reflect initial turmoil upon entering college followed by adjustment and recovery.
NoPD individuals following the moderate PD symptom trajectory tended to have lower levels of Communal and Agentic Positive Emotionality at baseline, higher baseline anxiety and depressive symptoms, greater lifetime prevalence of Axis I psychopathology, and greater lifetime utilization of mental health treatment. By contrast, rapid remission of subclinical symptoms was associated with low levels of depression and higher proximal processes. The latter suggests that proximal processes may buffer the risk for persistent personality dysfunction and support the development of social affiliation (Lenzenweger, 2010).
The emergence of separate minimal- and moderate-symptom trajectories in the NoPD group is interesting because it suggests a potential dichotomy between individuals who have virtually no personality dysfunction and those whose symptoms, although not reaching the level of clinical diagnosis, are moderately persistent over time and are associated with Axis I psychopathology and low positive emotionality. NoPD individuals were sampled to have 10 or fewer PD symptoms at baseline, yet our results are inconsistent with the notion that PD symptomatology in a low-risk group varies dimensionally. This finding suggests the possibility that studies that have used a dimensional cutoff to identify individuals low in psychopathology (e.g., Bagge et al., 2004) may have included a mixture of individuals—some with subclinical psychopathology and some with minimal symptomatology. Also, the strong link between subclinical personality dysfunction and Axis I psychopathology, both lifetime and at study baseline, in the NoPD moderate-symptom trajectory raises questions about the boundaries between PDs and clinical syndromes (Krueger, 2005). For example, we found that the remission of PD symptoms in the NoPD moderate-symptom trajectory covaried with the remission of anxiety symptoms (cf. Tyrer, Seivewright, Ferguson, & Tyrer, 1992).
Although increasing symptoms of personality dysfunction were not evident in the NoPD group when symptoms were considered in aggregate, we identified latent trajectories for avoidant, obsessive-compulsive, and paranoid PDs that were characterized by greater symptomatology over time, consistent with our hypothesis that personality dysfunction develops in some young adults who were previously nonsymptomatic. In most cases, symptom severity remained below diagnostic thresholds, but six individuals (5% of the NoPD sample) exhibited increasing symptoms over the follow-up period that resulted in new PD diagnoses at the final assessment (four avoidant PD, one OCPD, and one paranoid PD). NoPD participants characterized by increasing symptom trajectories tended to have greater Axis II comorbidity at baseline (especially avoidant, dependent, and schizoid PDs) and to exhibit increasing symptoms of dependent PD over time. Communal and Agentic Positive Emotionality were also lower for those in the increasing avoidant and obsessive-compulsive symptom trajectories. These findings are novel and illustrate the importance of studying low-risk individuals using methods such as GMM to detect meaningful symptom increases over time. Furthermore, the finding of de novo personality dysfunction in young adults raises questions about the developmental psychopathology and etiology of PDs. Developmental research has previously implicated adolescence as a key risk period for the onset of serious personality dysfunction (Johnson et al., 2000), yet our findings suggest that risk for PDs continues into early adulthood in some cases.
In the PPD group, three latent trajectories for total PD symptomatology were identified: Many individuals experienced considerable remission of moderate to severe symptomatology, some individuals experienced rapid remission, and a small subset reported few symptoms throughout the study. Symptom remission in the moderate trajectory was especially rapid between the baseline and 1-year follow-up assessments, which is consistent with previous reports on the LSPD sample (Lenzenweger, 1999; Lenzenweger et al., 2004), as well as a growing literature on mean-level declines in personality pathology in adulthood, particularly among symptomatic individuals and psychiatric patients (McGlashan et al., 2005; Sanislow et al., 2009; Zanarini et al., 2006). GMMs for individual PD symptoms in the PPD group often identified a latent trajectory characterized by moderate symptoms that declined somewhat or were persistent over time. Incidence and lifetime prevalence and of Axis I psychopathology were higher in the moderate trajectory classes, as was lifetime mental health treatment utilization. A key finding from our study was that slower remission of PD symptoms, whether for total symptom counts or for individual PDs, was closely linked with comorbid Axis II psychopathology. More specifically, baseline levels of PD symptoms were highly overlapping, consistent with previous reports on the poor discriminant validity of PDs (Sanislow et al., 2009; Zanarini et al., 2004; Zimmerman, Rothschild, & Chelminski, 2005). Furthermore, the rates of remission across PDs were often coupled such that slower declines for symptoms of one PD were accompanied by slow declines in comorbid PDs.
A fraction of PPD participants experienced rapid remission of total PD symptoms, dropping 15 or more symptoms within a single year. Exploratory GMMs of individuals PDs corroborated the existence of rapid remission trajectories for borderline, histrionic, obsessive-compulsive, and passive-aggressive PDs. Rapid remission of specific PD symptoms was associated with concomitant declines in comorbid PD symptomatology, higher proximal processes in childhood, lower Negative Emotionality at baseline, higher Positive Emotionality, and higher Constraint. For borderline PD symptoms, rapid remission was also linked with decreasing Negative Emotionality over time, suggesting meaningful temporal links between personality and PDs. This topic has explored by Warner et al. (2004) in the CLPS dataset, who found that changes in personality traits often preceded declines in PD symptoms. A previous report from our group (Lenzenweger & Willett, 2007) also described links between personality and PDs, finding that the initial level of Negative Emotionality, Positive Emotionality, and Constraint were often predictive of PD symptom trajectories over time.
Despite reporting a number of PD symptoms on the self-report screening questionnaire, a fraction of PPD participants were best classified by a latent trajectory with few symptoms upon clinical interview at each assessment. This latent trajectory was unexpected but illustrates the potential for false positives when self-report screening measures are used, and it reinforces the compelling literature describing discrepancies among sources of information about personality dysfunction (e.g., Oltmanns & Turkheimer, 2006).
The consistent identification of remission trajectory classes across NoPD and PPD groups suggests that transient personality pathology probably occurs in a subset of the population and deserves further study. As articulated above, numerous features distinguished rapid remission trajectories from trajectories characterized by slower symptom declines, including fewer comorbid PD symptoms, higher Communal Positive Emotionality, higher Constraint, lower Negative Emotionality, and lower rates of Axis I psychopathology. Nevertheless, because transient personality dysfunction was evident at the baseline assessment in our data, it is difficult to know the precursors of such trajectories. Our findings suggest that a more salubrious configuration of baseline personality traits was associated with a rapid remission latent trajectory for specific PD symptoms, which comports with previous studies (Lenzenweger & Willett, 2007; Warner et al., 2004).
The approach and findings of this report extend beyond previous analyses of the LSPD and other longitudinal studies of PDs in two major ways. First, we have used GMM to test for heterogeneity in the longitudinal course of PDs, and our results corroborated the existence of distinct symptom trajectories for overall personality dysfunction and for many specific PDs. Initial findings from the LSPD (Lenzenweger et al., 2004) and other major studies of PDs (e.g., Gunderson et al., 2011) have used methods that assume that the longitudinal course of PDs can be adequately summarized by a single mean trajectory (allowing for normal variation around the mean). Such methods may have averaged over clinically meaningful variability. In the PPD group, for example, a traditional growth curve model would have averaged together the low-symptom/false positive and high-symptom trajectories, potentially providing an overly optimistic view of symptom remission. Furthermore, the rapid remission trajectory, which was markedly different on various measures of Axis I psychopathology, PD symptomatology, and personality traits, would have been missed altogether, resulting in the combination of two groups with different prognoses.
Second, we have analyzed PD symptom data using Poisson-based GMMs, rather than treating PD symptom data as Gaussian. Although this is a technical innovation, it has important practical significance. Poisson-based growth models represent change over time in terms of the natural logarithm of PD symptomatology, such that nonlinear growth curves can be accommodated. As is evident in Figures 1, 3, and 5 the longitudinal course of PDs is linear in some cases and quite nonlinear in others. Thus, the assumption of linear change implicit in previous reports, including those from our group (Lenzenweger et al., 2004), may not be supported by the data, and substantive conclusions about the course of PD symptoms may be considerably different if nonlinear models of change are considered. For example, PD symptoms in the LSPD tended to change most between the first and second assessments (cf. Lenzenweger, 1999), whereas changes at the final follow-up assessment were subtler. The Poisson-based growth model captures this meaningful nonlinearity (as might be evident in a more traditional ANOVA approach) and retains the strengths of a growth modeling framework (cf. Lenzenweger et al., 2004). Poisson models are also better suited to count data that have low means and/or many zero values, as is common with PD symptom data, and Gaussian models of such data may fail to capture the relationships among PDs, personality traits, and other forms of psychopathology (Wright & Lenzenweger, in press).
Altogether, the present study revealed that there is considerable heterogeneity in the longitudinal course of PD symptoms, both for asymptomatic and symptomatic individuals. This work provides an initial demonstration that traditional growth modeling techniques may tend to overemphasize commonalities in the course of PDs (i.e., the mean growth trajectory) while missing important latent trajectories mixed within the data. That said, even among symptomatic individuals, only antisocial, obsessive-compulsive, and passive-aggressive PDs included a latent trajectory with stable, persistent symptoms, suggesting that previous research on mean-level declines in PD symptoms presents a reasonably accurate picture of the modal course of personality pathology. Clinically, our findings underscore the importance of assessing for comorbid Axis I and II disorders when diagnosing PDs (Grilo et al., 2000; Loranger et al., 1991; Morey et al., 2010; Zanarini, Frankenburg, Vujanovic, et al., 2004; Zimmerman et al., 2005) and also point to the incremental utility of considering personality dimensions when formulating treatment plans (Harkness & Lilienfeld, 1997).
Our study had several limitations. First, because the LSPD sampled for overall personality dysfunction, individual PD GMM results should be interpreted with caution because symptoms of some clinical disorders (e.g., schizoid) were low. Thus, our finding that the course of some PDs was best characterized by a single trajectory should not be interpreted as evidence that some PDs are relatively homogeneous over time, whereas others show marked discontinuities. Neither should the number or form of latent trajectories in our study be seen as an authoritative description of change in PD symptoms. GMM is sensitive to the composition of the sample and is potentially vulnerable to overextraction of latent trajectories when model assumptions are violated (Bauer & Curran, 2003). We also note that our characterization of the links between personality and PD symptomatology focused only on major traits, and a finer analysis of traits may reveal incremental information about covariation among these constructs (Widiger & Simonsen, 2005).
Prior research has also documented that PD diagnostic criteria have different levels of stability over time, with some criteria likely reflecting trait-like characteristics, whereas others may potentially reflect stress-related behaviors (Gunderson et al., 2003; McGlashan et al., 2005). Thus, the use of summed symptom counts in the present study limited our ability to test for criterion-level differences in the longitudinal course of PD symptoms. Growth mixture modeling can accommodate more complex measurement models that would be sensitive to differential criterion stability, but a much larger sample would be needed to estimate the high number of parameters required for such models. There is a possibility that the remission of PD symptoms in some individuals might reflect a retest artifact whereby study participants are more likely to deny symptomatology at follow-up, perhaps because of a desire to shorten the interview or due to boredom. Although this issue has not been studied closely in the PDs literature, there is little evidence that retest artifacts are likely to account for the remission of PD symptomatology, particularly over longer retest intervals (Loranger et al., 1991; Samuel et al., 2011; Zimmerman, 1994).
The LSPD subjects are now approaching age 40 and will be assessed again in the fourth wave of this ongoing project, which will allow for a 20-year follow-up assessment that could be studied using the GMM approach articulated here. Future research should investigate more closely the emergence of personality pathology, especially avoidant, obsessive-compulsive, and paranoid PDs, in adulthood. Our findings suggest that lower levels of Positive Emotionality, existing subclinical symptoms of PDs, and increasing symptoms of dependent PD may be associated with risk for the development of clinically significant personality dysfunction in early adulthood, but this novel finding needs to be validated in an independent sample. Also intriguing is that personality dysfunction may be transient in some individuals, and prospective longitudinal studies of low-symptom individuals may help to identify the precursors of individuals whose PD symptoms are rather brief. A possibility suggested by our data is that an adaptive configuration of personality traits (e.g., low Negative Emotionality and high Constraint) may help to guard against the long-term persistence of PD symptoms. Consistent with the growing literature on the associations between normative and abnormal personality (Widiger & Simonsen, 2005) and the common neurobehavioral systems that may give rise to personality and PDs (Depue & Lenzenweger, 2005), we hope that future research may help to uncover the links between transient personality pathology and normative personality traits. Altogether, our results demonstrate the power of growth mixture modeling to uncover qualitatively distinct longitudinal trajectories of PD symptoms that are differentially associated with Axis I psychopathology, comorbid PD symptomatology, and personality traits. We hope that our study stimulates further research on the longitudinal heterogeneity of PDs and that theories of personality and psychopathology explore more specifically the pathogenesis of transient, emergent, and persistent personality dysfunction, as well as the mediators of PD symptom remission.
Footnotes 1 Although Total PD symptoms increased slightly in the low-symptom trajectory, symptom levels remained low, and the increase may be reflective of regression toward the mean. We thank a reviewer for suggesting this interpretation.
2 More specifically, Poisson models are linear with respect to the link function, which is the natural logarithm of the response variable.
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Submitted: September 21, 2011 Revised: July 10, 2012 Accepted: July 24, 2012
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Source: Journal of Abnormal Psychology. Vol. 122. (1), Feb, 2013 pp. 138-155)
Accession Number: 2012-32960-001
Digital Object Identifier: 10.1037/a0030060
Record: 78- Title:
- Identifying risky drinking patterns over the course of Saturday evenings: An event-level study.
- Authors:
- Kuntsche, Emmanuel. Addiction Switzerland, Research Institute, Lausanne, Switzerland, ekuntsche@addictionsuisse.ch
Otten, Roy. Behavioural Science Institute, Radboud University, Netherlands
Labhart, Florian, ORCID 0000-0002-6646-9544. Addiction Switzerland, Research Institute, Lausanne, Switzerland - Address:
- Kuntsche, Emmanuel, Addiction Switzerland, Research Institute, P.O. Box 870, CH 1001, Lausanne, Switzerland, ekuntsche@addictionsuisse.ch
- Source:
- Psychology of Addictive Behaviors, Vol 29(3), Sep, 2015. pp. 744-752.
- NLM Title Abbreviation:
- Psychol Addict Behav
- Page Count:
- 9
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- Bulletin of the Society of Psychologists in Addictive Behaviors; Bulletin of the Society of Psychologists in Substance Abuse
- Other Publishers:
- US : Educational Publishing Foundation
Society of Psychologists in Addictive Behaviors - ISSN:
- 0893-164X (Print)
1939-1501 (Electronic) - Language:
- English
- Keywords:
- heavy weekend drinking, young adults, event-level study, growth mixture modeling, drinking motives
- Abstract:
- Gaining a better understanding of young adults’ excessive drinking on nights out is crucial to ensure prevention efforts are effectively targeted. This study aims to identify Saturdays with similar evening drinking patterns and corresponding situation-specific and person-specific determinants. Growth mixture modeling and multilevel logistic regressions were based on 3,084 questionnaires completed by 164 young adults on 514 evenings via the Internet-based cell phone optimized assessment technique (ICAT). The results showed that the 2-group solution best fitted the data with a 'stable low' drinking pattern (64.0% of all evenings, 0.2 drinks per hour on average, 1.5 drinks in total) and an 'accelerated' drinking pattern (36.0%, increased drinking pace from about 1 drink per hour before 8 p.m. to about 2 drinks per hour after 10 p.m.; 11.5 drinks in total). The presence of more same-sex friends (ORwomen = 1.29, 95% CI [1.09–1.53]; ORmen = 1.35, 95% CI [1.15–1.58], engaging in predrinking (ORwomen = 2.80, 95% CI [1.35–5.81]; ORmen = 3.78, 95% CI [1.67–8.55] and more time spent in drinking establishments among men (ORmen = 1.46, 95% CI [1.12–1.90] predicted accelerated drinking evenings. Accelerated drinking was also likely among women scoring high on coping motives at baseline (ORwomen = 2.40, 95% CI [1.43–4.03] and among men scoring high on enhancement motives (ORmen = 2.36, 95% CI [1.46–3.80]. To conclude, with a total evening consumption that is almost twice the threshold for binge drinking, the identified accelerated drinking pattern signifies a burden for individual and public health. Promoting personal goal setting and commitment, and reinforcing self-efficacy and resistance skills training appear to be promising strategies to impede the acceleration of drinking pace on Saturday evenings. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Alcohol Drinking Patterns; *Motivation; *Risk Taking; Binge Drinking
- Medical Subject Headings (MeSH):
- Adaptation, Psychological; Adult; Alcohol Drinking; Binge Drinking; Female; Humans; Internet; Logistic Models; Male; Motivation; Multilevel Analysis; Risk-Taking; Surveys and Questionnaires; Young Adult
- PsycINFO Classification:
- Substance Abuse & Addiction (3233)
- Population:
- Human
Male
Female - Location:
- Switzerland
- Age Group:
- Adulthood (18 yrs & older)
- Tests & Measures:
- Young Adult Alcohol Consequences Questionnaire-Brief Version
Drinking Motive Questionnaire Revised
Young Adult Alcohol Consequences Questionnaire DOI: 10.1037/t03947-000 - Grant Sponsorship:
- Sponsor: Swiss National Science Foundation, Switzerland
Grant Number: 100014_124568/1
Recipients: No recipient indicated - Methodology:
- Empirical Study; Quantitative Study
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- First Posted: Apr 6, 2015; Accepted: Dec 14, 2014; Revised: Dec 12, 2014; First Submitted: Jul 23, 2014
- Release Date:
- 20150406
- Correction Date:
- 20150928
- Copyright:
- American Psychological Association. 2015
- Digital Object Identifier:
- http://dx.doi.org/10.1037/adb0000057
- PMID:
- 25844829
- Accession Number:
- 2015-14525-001
- Number of Citations in Source:
- 55
- Persistent link to this record (Permalink):
- http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2015-14525-001&site=ehost-live
- Cut and Paste:
- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2015-14525-001&site=ehost-live">Identifying risky drinking patterns over the course of Saturday evenings: An event-level study.</A>
- Database:
- PsycINFO
Identifying Risky Drinking Patterns Over the Course of Saturday Evenings: An Event-Level Study
By: Emmanuel Kuntsche
Addiction Switzerland, Research Institute, Lausanne, Switzerland, and Behavioural Science Institute, Radboud University;
Roy Otten
Behavioural Science Institute, Radboud University
Florian Labhart
Addiction Switzerland, Research Institute, Lausanne, Switzerland
Acknowledgement: The study was supported by the Swiss National Science Foundation, Grant 100014_124568/1. We thank Valentin Vago for the development of the web-based application and Gemma Brown for English copyediting of this article.
The high prevalence of drinking in young adults is a serious public health concern, and risky drinking in young people is a primary cause of mortality and morbidity (Rehm, Gmel, Room, & Frick, 2001). However, it is not simply the fact that young people are drinking but the specific way in which they drink that puts them at such high risk of alcohol-related problems. Research consistently shows that people tend to drink the heaviest in their late teens and early to midtwenties (Gmel, Kuntsche, & Rehm, 2011; Kuntsche & Gmel, 2013).
Most young people drink heavily on weekends with a peak on Saturday evenings when people go out and do not have any work or study responsibilities the following day. Drinking at weekends tends to occur to such a frequent and excessive degree that authors in several countries have spoken of a “heavy drinking weekend culture” (Heeb, Gmel, Rehm, & Mohler-Kuo, 2008; Kuntsche & Cooper, 2010; Parker & Williams, 2003; van Wersch & Walker, 2009). This is worrisome since heavy drinking on evenings out is likely to result in a number of particularly detrimental consequences, such as accidents, injuries, victimization, and aggression (Graham & Wells, 2003; Harford, Wechsler, & Muthén, 2003; Nyaronga, Greenfield, & McDaniel, 2009; Rossow & Hauge, 2004).
In order to effectively target prevention efforts, it is crucial to better understand the patterns of young people’s drinking throughout the evening, particularly on Saturdays, which eventually leads to these consequences (Sunderland, Chalmers, McKetin, & Bright, 2014). However, despite extensive research in recent decades, little is known about what actually happens when young people are out, consuming alcohol in their “natural” environments. Using data collected by means of participants’ cell phones 6 times per evening, this study aims to identify evenings with similar drinking patterns (i.e., groups of evenings in which drinking from before 8 p.m. until after midnight happened in a similar way) and associated risk factors, which could contribute to more effective prevention strategies.
As little is known about what actually happens in practice, research about drinking patterns over the course of Saturday evenings is also scarce. A previous study showed that the average consumption at the beginning of the evening was about the same for Thursdays, Fridays, and Saturdays (Kuntsche & Labhart, 2012). However, over the course of the evening, consumption tended to decrease on Thursdays, remained stable over the course of the evening on Fridays, but increased on Saturdays. In other words, while participants drank on average less and less per hour on Thursdays, consumption remained stable at about 0.4 drinks per hour on average for women and 0.7 drinks per hour for men on Fridays. On Saturdays, however, consumption increased among men on average from fewer than 0.6 drinks per hour before 8 p.m. to more than 1.0 drink per hour after 10 p.m. (from 0.4 to 0.6 drinks for women). The authors therefore argued that it is important to focus prevention efforts on curbing the acceleration of young people’s drinking that is likely to occur on Saturday evenings (Kuntsche & Labhart, 2012).
It is still unknown whether this average increase in drinks per hour on Saturday evenings represents a specific evening drinking pattern. This is important, however, because in order to target prevention efforts more effectively, it is crucial to (a) identify evenings in which such an accelerated drinking pattern occurs and (b) to identify characteristics of evenings and individuals that predict whether or not accelerated drinking is likely to occur on a given evening. Previous studies have shown that heavy weekend drinking is likely to occur on evenings when friends are present, when people drink in a private location before going out (called predrinking, pregaming, or preloading), or when spending time in bars and among individuals that are male and drink for enhancement motives.
In this study, we collected data 6 times per evening covering the timeframe from 5 p.m. until 11 a.m. the next morning on 5 subsequent weekends using the recently developed Internet-based cell phone-optimized assessment technique (ICAT: Kuntsche & Labhart, 2013b). Using Growth Mixture Modeling (GMM), the first objective was to identify homogenous groups of Saturday evenings in which participants showed a similar progression of consumed drinks from 1 hr to the next throughout the course of the evening. We expect to find at least one homogenous group of evenings that is characterized by accelerated drinking, that is, in which an increasing number of drinks per hour are consumed.
The second objective was to use situation-specific and person-specific variables to predict the occurrence of accelerated drinking on a given evening. At situational level, we expect accelerated drinking when spending time in bars or with friends (Clapp & Shillington, 2001; Harford, Wechsler, & Seibring, 2002; Hartzler & Fromme, 2003; Wilks & Callan, 1990). At the individual level, we expect accelerated drinking to be more likely among men than among women and among those who score high on enhancement motives.
Method Study Design and Sample
Using ICAT (Kuntsche & Labhart, 2013b), data were collected between April and July 2010 by means of a baseline questionnaire to be completed online directly after registration and a series of Internet-based questionnaires that participants completed on their personal cell phones. In Lausanne and Geneva, the two major cities in French-speaking Switzerland, participants were recruited from three higher education institutions. At each institution, an e-mail containing information about the study with a hyperlink to the study’s homepage was submitted to all students. Both the e-mail and the study homepage provided information on the aim of the study, explained that any answers were voluntary and would be treated as confidential, and outlined the participation incentives (i.e., those who returned at least 80% of the cell phone questionnaires would receive a randomly drawn voucher worth 40 to 80 USD). The homepage also provided a hyperlink, which allowed participants to test whether their cell phone enabled mobile Internet access. To register, participants had to indicate their cell phone number at the bottom of the homepage. The study was approved by the Ethical Committee of Lausanne University (protocol no. 223/08).
On Thursdays, Fridays, and Saturdays on 5 subsequent weekends, text messages (SMS) containing unique hyperlinks were sent to the participants’ cell phones at 8 p.m., 9 p.m., 10 p.m., 11 p.m., midnight, and the next morning at 11 a.m. Clicking on the hyperlink automatically opened a blank questionnaire in the cell phone browser. Different time frames were used to be able to cover the entire evening with a reasonable response burden, that is, the questionnaire at 8 p.m. referred to the events between 5 p.m. and 8 p.m.; the following questionnaires (i.e., at 9 p.m., 10 p.m., 11 p.m., and midnight) referred to the preceding hour; the questionnaire at 11 a.m. referred to the events since midnight. To minimize recall bias, questionnaires could only be accessed once and only within the 12-hr period following dispatch of the SMS.
The overall analytic sample consisted of 7,828 assessments on 1,441 evenings provided by 183 participants. Since excessive drinking is particularly likely to occur on Saturdays (Kuntsche & Labhart, 2012), only the 514 Saturday evenings, based on 3,084 single questionnaires reported by 164 participants (54.3% females), were included in this study.
Measures
Individual-level characteristics included in the baseline Internet questionnaire
Participants were asked to indicate whether they were female (coded as 0) or male (1) and their year of birth. The 20-item Drinking Motive Questionnaire Revised (DMQ-R; Cooper, 1994) was used to assess four conceptually and empirically distinct dimensions of drinking motives: enhancement motives (e.g., drinking to have fun and to get drunk), social motives (e.g., to improve parties and to celebrate a special occasion with friends), coping motives (e.g., to forget about worries and problems), and conformity motives (e.g., to fit in with a group and not to feel left out). Participants were asked to consider all the times they had drunk alcohol in the last 12 months and to indicate, on a relative frequency scale ranging from never/almost never (coded as 1) to almost always (coded as 5), on how many occasions they had drunk for each given motive. Internal consistencies were αsocial = .80, αenhancement = .72, αcoping = .74, and αconformity = .56. For each of the four dimensions, the five items were averaged.
Evening-level characteristics included in the cell phone questionnaires
To assess alcohol use in the evenings, the question was “How many of the following alcoholic drinks did you have between . . .?” At baseline, participants were shown pictograms of “standard drinks,” corresponding to 10 g of pure alcohol (Kuntsche & Labhart, 2012). The time frames of the 6 evening assessments were 5–8 p.m., 8–9 p.m., 9–10 p.m., 10–11 p.m., 11 p.m.–midnight, and since midnight (assessed at 11 a.m. the next morning). With separate questions, participants could indicate how many beer, wine, champagne, aperitifs (e.g., port), liqueur at 20°, (straight) spirits, self-mixed drinks (e.g., whisky-coke), cocktails, and alcopops they had consumed in the given time frame. Answer categories were 0, 1, 2, 3, 4, and 5 or more (coded as 5.5). Since the first and the last assessment referred to an extended time period (i.e., 5–8 p.m. and after midnight, respectively), as recommended by Kuntsche and Labhart (2012), two thirds of the indicated consumption was taken to approximate the consumption shortly before 8 p.m. and shortly after midnight.
To assess the number of male and female friends present, the question was “How many people were you with between . . .?” The time frames were the same as the first 5 for alcohol use mentioned above. Two questions asked participants to indicate how many male and female friends (including romantic partner) were present in the given time frame. Answer categories were 0, 1, 2–4, 5–20, and 20 or more. Midpoints of categories were used, and 23.5 for the upper category (20 persons plus half range to midpoint of adjacent category, i.e., 3.75 = 20 – ((12.5 + 20)/2)), to create a linear measure that represents the actual number of friends present (Labhart, Wells, Graham, & Kuntsche, 2014). For each evening, the 5 assessments were averaged.
Participants were also asked how much time they spent at different locations (at home, traveling, etc.) at 5–8 p.m., 8–9 p.m., 9–10 p.m., 10–11 p.m., and 11 p.m.–midnight. One question asked about the time spent in bars and other drinking establishments (restaurants, pubs, nightclubs, etc.). Answer categories were given in half-hour increments (0, 30, 60, up to 180 min) for the 5–8 p.m. assessment and in quarter-hour increments (0, 15, up to 60 min) for the 4 subsequent 1-hr assessments.
Predrinking was defined as the consumption of at least 1 drink off-premise (e.g., at home or at a friend’s home, while traveling, outdoors) before spending time in an on-premise establishment (e.g., restaurants, pubs or nightclubs, cultural or sporting venues; Labhart, Graham, Wells, & Kuntsche, 2013). The variable was coded 1 if this happened at a given evening and 0 otherwise.
Alcohol-related consequences were mostly taken from the Brief Version of the Young Adult Alcohol Consequences Questionnaire (B-YAACQ; Kahler, Strong, & Read, 2005) and included in the 11 a.m. questionnaire. Participants were asked: “Did any of the following occur last night as a result of your drinking?” Response categories were: hangover, drunk driving, fight or quarrel, injured self or someone else, blackout (not remembering what happened even for a short period of time), unplanned use of other substances, unintended or unprotected sexual intercourse, and property damage or vandalism. Because of severe space constraints in the cell phone questionnaire, we selected only concrete outcomes that were likely to be experienced (Labhart et al., 2013). The 8 consequences (coded as 0 = no; 1 = yes) were added up on a scale ranging from 0 to 8.
Statistical Analyses
Analyses in this study were conducted in three steps. In the first step, we used GMM, a semiparametric clustering technique (Muthén & Muthén, 1998–2012), to identify homogenous groups of evenings based on similarities in alcohol consumption at the beginning of the evening (intercept) and the progression of drinking pace (number of drinks consumed per hour) throughout the course of the evening (slopes). To determine the number of groups that would best fit the data, a series of models with an increasing number of groups was fitted starting with a one-group model, moving to a six-group model. For simplicity and robustness reasons, this was first done based on a linear slope (assuming a strictly linear increase in the number of drinks consumed per hour). However, since a previous publication (Kuntsche & Labhart, 2012) indicated that the increase in drinking pace stabilized later in the evening, we subsequently included an additional quadratic slope in the two best-fitting models.
Evaluation of the best-fitting model was accomplished using the Bayesian Information Criterion (BIC), Akaike Information Criteria (AIC), the entropy, and the Lo-Mendell-Rubin Adjusted LRT Test (LMR). The BIC and the AIC are commonly used fit indices where lower values indicate a more parsimonious model (Akaike, 1987; Raftery, 1995). Entropy is a measure of classification accuracy where values closer to one indicate greater precision (McLachlan & Peel, 2000). Finally, the LMR test compares the improvement in fit between neighboring models. (i.e., comparing k - and the k-g -class models, where g < k) and provides a p value that can be used to determine if there is a statistically significant improvement in fit for the inclusion of one more class (Lo, Mendell, & Rubin, 2001). Because we assessed the number of drinks throughout the evening, we treated the dependent variable as count data. Count data can take only nonnegative integer values that arise from counting (e.g., Cameron & Trivedi, 1998). Obviously, in this study, the counts represent the number of drinks within a certain period of time.
In a second step, after determining the number of groups that best fitted the data, posterior probabilities were used to assign the evenings to one of the groups that were subsequently statistically compared in terms of group size, individuals’ mean age, gender proportion, total drinks in the evening, and resulting consequences. Because of the clustering of evening observations within individuals, differences between the groups of evenings were tested in the software STATA SE 12.0 (StataCorp, 2011) by using design-adjusted proportion and mean tests.
In a third and final step, a multilevel logistic regression model was estimated to predict membership in the identified groups of evening drinking patterns. The group with the lowest overall consumption was taken as reference group and membership in the other emerging groups was regressed (a) at evening level, on the number of male and female friends present, whether predrinking happened that evening or not, and the time spent in bars and other drinking establishments and, (b) at individual level, on age, and the four drinking motive dimensions. Because of known gender differences in weekend evening drinking, the models were estimated for males and females separately using the software Mplus 7 (Muthén & Muthén, 1998–2012).
Results Sample Description
On average, three male and two female friends were present during the five Saturday evenings included in the study (see Table 1). On one in six evenings, participants drank before going out (predrinking). On more than one third of the Saturday evenings, participants spent time in a bar or another drinking establishment and if they did, they stayed there for 2 hr and 20 min on an average evening. Over the course of the evening, males consumed more than two drinks more than females (6.3 vs. 4.0) on average. Males also reported experiencing more consequences (on average on 2 of the 5 Saturdays that were included in the study, whereas for women this was only 1). There was no age difference between males and females. Males scored higher on social motives than females, but no differences were found for the other motive dimensions.
Sample Description Means and (Standard Deviations)
Identifying and Describing Evening Drinking Patterns
The results of the GMM procedure were in favor of either a two-group or a three-group solution (upper part of Table 2). The entropy was superior for a two-group solution (0.872), indicating a particularly accurate classification. Moreover, the LMR p value of 0.000 illustrated that a two-group model would better fit the data than a one-group model. Solutions with more than two groups were less accurate, as demonstrated by lower entropy values. However, the three-group solution showed a significant p value for the LMR, together with lower AIC, BIC, and χ2 values indicating a slightly better fit to the data. Similar results were found when performing the GMM separately by gender.
Model Fit of the GMM Estimated
For the two-group and the three-group solutions, models with an additional quadratic slope were estimated (lower part of Table 2). The results showed that the entropy was identical for the two-group solution with or without quadratic trend. However, the slightly lower AIC, BIC, and χ2 values for the former than for the latter indicated a slightly better fit of the two-group model with a quadratic trend. For the three-group solution, the entropy was basically the same, the AIC and BIC values were slightly lower, but the χ2 value was higher in the model with quadratic slope than in the one without. Moreover, among the models with quadratic slope, the three-group model was not better fitted to the data than the two-group model as expressed by a nonsignificant LMR value. Taking the results together and also considering the parsimonious principle, it appears that the two-group model with a linear and a quadratic trend provided the best representation of the data.
Almost two thirds of the evenings (64.0%, Table 3) were characterized by a stable low drinking pattern (see Figure 1), that is, the slight decrease from 8 p.m. until midnight was not statistically significant, Blinear slope = −0.133, SE = 0.149, p > .05. There was also no change in the kind of progression throughout the evening in terms of a quadratic trend, Bquadratic slope = 0.004, SE = 0.034, p > .05. More than one third of the evenings (36.0%) were characterized by accelerated drinking, BSlope = 0.354, SE = 0.063, p < .001, with a consumption of almost 1 drink per hour on average at the beginning of the evening increasing to a consumption of almost 2 drinks an hour on average. From 10 p.m. on, acceleration ceased (expressed by a significant quadratic trend: Bquadratic slope = −0.046, SE = 0.011, p < .001), that is, drinking pace was more constant with an average of 2 drinks per hour.
Statistical Description of the Two Drinking Trajectories Emerging From the GMM
Figure 1. Graphical representation of the two drinking trajectories emerging from the growth mixture model based on the raw data, split by gender. Since the first and the last assessment referred to an extended time period (i.e., 5–8 p.m. and later than midnight, respectively), two thirds of the indicated consumption was taken to approximate the consumption shortly before 8 p.m. and shortly after midnight (Kuntsche & Labhart, 2012).
Figure 1 also shows that the two evening drinking patterns were similar for males and females. Adding gender as a predictor to the GMM revealed no gender differences in the shape of the evening drinking pattern in both the stable low (Blinear slope difference for men = 0.166, SE = 0.315, p > .05; Bquadratic slope difference for men = 0.002, SE = 0.066, p > .05) and the accelerated pattern (Blinear slope difference for men = 0.003, SE = 0.129, p > .05; Bquadratic slope difference for men = −0.001, SE = 0.002, p > .05). The proportion of males in the accelerated drinking pattern was higher than in the stable low pattern (see Table 3), however. There were no differences in mean age. During accelerated drinking evenings, participants consumed an average of around 8 times more alcoholic drinks and reported more than 17 times more adverse alcohol-related consequences as on evenings classified as stable low.
Predicting Evening Drinking Patterns
The higher the number of same-sex friends present, engaging in predrinking, and the more time spent in bars and other drinking establishments (significant for men only), the higher the likelihood that accelerated drinking occured on a given evening (see Table 4). However, a higher number of female friends present decreased the likelihood of accelerated drinking among males. Females who scored high on coping motives and low on conformity motives at baseline had a higher likelihood of engaging in accelerated drinking over the five Saturday evenings included in the study. The same was true for males in respect to enhancement motives.
ORs (95% CI in Brackets) of the Multilevel Logistic Regression Model Predicting Occurrence of the Accelerated Evening Drinking Pattern
DiscussionThis study aimed to identify Saturday evenings with similar patterns of alcohol consumption. GMM resulted in two groups characterized by a stable low and an accelerated evening drinking pattern. It is known that excessive drinking is likely to occur on Saturday evenings when people go out and do not have any work or study responsibilities the following day (Gmel, Gaume, Faouzi, Kulling, & Daeppen, 2008; Heeb et al., 2008; Kauer, Reid, Sanci, & Patton, 2009; Kuntsche & Labhart, 2012; Parker & Williams, 2003). The present study adds two important issues to this literature. First, excessive drinking did not occur in the majority of Saturday evenings. In 65% of the evenings, consumption remained low with one and a half drinks on average consumed during the entire evening. Second, if excessive drinking occurred, the consumption was characterized by accelerated drinking pace with an increasing number of drinks consumed per hour until around 10 p.m. and a stabilization at around two drinks per hour afterward. This was the case in about one in three Saturday evenings, and the total consumption was more than 11 drinks on average, which is actually more than twice the threshold for binge drinking (i.e., four or more drinks for women and five or more drinks for men; Kuntsche & Labhart, 2013a; Wechsler & Nelson, 2001). Losing self-control with increasing inebriation, the personal intention to get drunk, social pressure, or drinking norms have been shown to fuel excessive drinking and may be among the driving factors for consuming more and more drinks per hour.
This is worrisome since heavy drinking on evenings out is likely to result in a number of particularly detrimental consequences, such as accidents, injuries, victimization, and aggression (Graham & Wells, 2003; Harford et al., 2003; Nyaronga et al., 2009; Rossow & Hauge, 2004). In this study, we found 17 times more alcohol-related consequences following evenings with accelerated drinking than after stable low drinking evenings (see Table 3).
To inform and better target prevention strategies, this study revealed evening-specific and individual-specific factors that discriminated between stable low and accelerated drinking evenings. The latter, drinking at an accelerated pace, was more likely when same-sex friends were present during the evening. Among males, masculine drinking norms, a lack of self-regulation, and peer pressure appear likely explanations (Borsari & Carey, 2001; Iwamoto, Cheng, Lee, Takamatsu, & Gordon, 2011). In females, drinking is usually more affected by personal anticipation related to the consequences of drinking (Suls & Green, 2003), and females specifically report drinking in the company of close friends who they trust to safeguard their well-being as an important factor when getting drunk (Sheard, 2011). For males, the presence of women decreased the likelihood of drinking at an accelerated pace. To avoid behaving badly, the desire to present oneself as responsible, or romantic intentions might cause men to refrain from increasing drinking pace when females are present.
For both genders, engaging in predrinking made drinking at an accelerated pace about three times more likely than when not predrinking. This is surprising since the higher prices of alcoholic beverages on-premise (Forsyth, 2010) and having consumed sufficiently large amounts of alcohol during predrinking are reasons not to drink large amounts after predrinking (Wells, Graham, & Purcell, 2009). On the contrary, the present findings are not only consistent with previous evidence showing that predrinking adds to the total amount consumed on a given evening (Hughes, Anderson, Morleo, & Bellis, 2008; Labhart et al., 2013), they also indicate that even nonexcessive predrinking can be a risk factor of drinking at a fast pace later on. This study therefore suggests that even small amounts of alcohol consumed earlier in the evening while predrinking may instigate drinking at an accelerated pace afterward, leading to excessive drinking and related consequences later in the evening.
In addition, that is, irrespective of whether predrinking took place, the more time men spent in bars and other drinking establishments, the higher their likelihood of accelerated drinking pace. The constant alcohol availability in bars may be partly responsible for this finding, but also the idea that persistent exposure to drinking cues (which is usually the case in bars and drinking establishments) encourages constant drinking (Koordeman, Kuntsche, Anschutz, van Baaren, & Engels, 2011; Larsen, Lichtwarck-Aschoff, Kuntsche, Granic, & Engels, 2013). Moreover, it is known that a lack of self-regulation in people who have self-imposed drinking limits leads to more drinking, which is more likely to occur when people spend more time in a bar.
Concerning individual-specific factors, this study extends current evidence by showing that males are more likely than females to drink at an accelerated pace throughout the course of the evening. Moreover, among men who indicated a high level of enhancement motives at baseline, accelerated drinking was particularly likely.
Increased drinking pace was also observed among females who scored high on coping motives at baseline. There are different explanations for this result. On the one hand, one would expect those who usually drink to forget about their problems to do the same on Saturday evenings. On the other, females were found to use alcohol to feel more self-confident and to alleviate social anxiety and stress, for example, when meeting new people on a night out. This is consistent with the result that the presence of males did not decrease the likelihood of accelerated drinking for females in the same way as the presence of females had for males. In contrast, women who scored high on conformity motives at baseline were less likely to have evenings with increased drinking pace. This is consistent with both conceptual consideration and empirical evidence on alcohol consumption in general. Conceptually, having one or two drinks is often sufficient to fit in with a group one likes or not to feel rejected because of nondrinking. Empirically, conformity motives have been consistently found to be negatively related to heavy episodic drinking in a number of studies (Cooper, 1994; Kuntsche et al., 2014; Kuntsche & Labhart, 2013a; Kuntsche, Stewart, & Cooper, 2008).
Limitations and Strengths
Although participants were recruited from several institutions in the two major cities, the nonrandom sample of cell phone users, which may not be representative of young adults in Switzerland, is a limitation of the study. While our decision to select a two-group solution was supported by a combination of statistical as well as theoretical arguments, this does not rule out the possibility that a different number of trajectories would arise with larger samples or in other cultures. The fact that the entire study was based on self-reports is another limitation. While the very short timeframes (mostly 60 min) minimized recall bias due to memory deficits (Kuntsche & Labhart, 2012) and average response times were generally low (Kuntsche & Labhart, 2013b), we cannot guarantee the precision of the participants’ responses, particularly later at night and bearing in mind their increasing state of intoxication. Despite the fact that we have included the number of male and female friends present and the time spent in bars as evening-level predictors, there are many other factors potentially explaining accelerated drinking on the given evening such as having something to celebrate, being at a party or festival, or meeting particular people (best friends, colleagues, sport team members, etc.). These issues clearly demonstrate that further studies with even larger samples, possibly also from other cultures and using further evening-level predictors and complementary data collection methods such as qualitative interviews, are needed to confirm and to provide further insights into the reported evening drinking patterns and associated determinants.
One of the study’s strengths is its complex, and to our knowledge unique, design, that is, using participants’ cell phones to collect event-level data six times per evening over five subsequent Saturdays. Participants appreciated the conciseness of the Internet-stored questionnaires and the fact that they could be easily answered anywhere in real time (Kuntsche & Labhart, 2013b). The fact that cell phones could be used independently of the specific operating system, which is not usually the case with cell phone applications, is another advantage (Kuntsche & Labhart, 2013b).
Implications for Preventive Action
Within these limitations, the results have important implications to prevent accelerated drinking pace (a) in general, (b) by targeting situational determinants, and (c) by taking account of individual characteristics. In general, it appears important to make people aware that there are evenings when they increase the number of drinks consumed from one hour to the next. Since this is clearly not the norm (i.e., almost two thirds of Saturday evenings were characterized by stable low drinking in this sample of young adult drinkers), providing personalized normative feedback in brief motivational interventions (Carey, Scott-Sheldon, Carey, & DeMartini, 2007; Grossberg et al., 2010; Walters, Vader, Harris, Field, & Jouriles, 2009) appears useful to impede acceleration of drinking pace. Goal setting (e.g., aiming not to drink more than 1 drink per hour), goal commitment, and reinforcing self-efficacy for goal achievement (Lozano & Stephens, 2010; Voogt, Poelen, Kleinjan, Lemmers, & Engels, 2014) are also promising strategies in this respect. For this purpose, it is important that people self-monitor and keep track of their drinking behavior throughout the course of the evening (Grossberg et al., 2010; Otten et al., 2014).
Since an accelerated drinking pace was found when same-sex friends were present, resistance skills training designed to impede the modeling of alcohol use and to reinforce drinking refusal self-efficacy and resistance to offers of alcohol by peers (Botvin, Griffin, & Murphy, 2011; Griffin & Botvin, 2010; Voogt et al., 2014; Witkiewitz, Donovan, & Hartzler, 2012) can be a promising strategy in impeding the acceleration of drinking pace on Saturday evenings. Since such a drinking pattern was also found when spending a great deal of time in bars and other drinking establishments, the implementation of structural measures such as banning happy hours, making nonalcoholic beverages available at a low price, and training staff not to serve alcoholic beverages to inebriated patrons, appear important to promote safer drinking environments (Homel, Carvolth, Hauritz, McIlwain, & Teague, 2004; Measham, 2006).
ConclusionsBased on an innovative design, that is, using participants’ cell phones to collect event-level data six times per evening over five subsequent Saturdays, this study identified a group of evenings (35.2%) during which participants consumed an increasing number of drinks each hour from before 8 p.m. until after midnight, resulting in a total consumption of more than 11 drinks per evening on average. This is a concern in terms of individual and public health due to a number of particularly detrimental consequences, which were confirmed in this study. Concerning evening predictors, drinking at an accelerated pace was more likely when same-sex friends were present (presence of women was protective for men) and when a great deal of time was spent in bars and other drinking establishments. Concerning individual predictors, men were more likely than women to accelerate the pace of their drinking over the course of the evening, as were those who indicated a high level of enhancement motives at baseline. Women scoring high on coping motives were also more likely to engage in accelerated drinking. Providing personalized normative feedback about Saturday evening drinking patterns, goal setting, goal commitment, reinforcing self-efficacy for goal achievement, resistance skills training, and promotion of safer drinking environments are likely to impede acceleration of drinking pace on Saturday evenings.
Footnotes 1 Results not shown but available from the authors on request.
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Submitted: July 23, 2014 Revised: December 12, 2014 Accepted: December 14, 2014
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Source: Psychology of Addictive Behaviors. Vol. 29. (3), Sep, 2015 pp. 744-752)
Accession Number: 2015-14525-001
Digital Object Identifier: 10.1037/adb0000057
Record: 79- Title:
- Individual and situational factors that influence the efficacy of personalized feedback substance use interventions for mandated college students.
- Authors:
- Mun, Eun Young. Center of Alcohol Studies, Rutgers, The State University of New Jersey, Piscataway, NJ, US, eymun@rci.rutgers.edu
White, Helene R.. Center of Alcohol Studies, Rutgers, The State University of New Jersey, Piscataway, NJ, US
Morgan, Thomas J.. Center of Alcohol Studies, Rutgers, The State University of New Jersey, Piscataway, NJ, US - Address:
- Mun, Eun Young, Center of Alcohol Studies, Rutgers, The State University of New Jersey, 607 Allison Road, Piscataway, NJ, US, 08854, eymun@rci.rutgers.edu
- Source:
- Journal of Consulting and Clinical Psychology, Vol 77(1), Feb, 2009. pp. 88-102.
- NLM Title Abbreviation:
- J Consult Clin Psychol
- Page Count:
- 15
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- Journal of Consulting Psychology
- Other Publishers:
- US : American Association for Applied Psychology
US : Dentan Printing Company
US : Science Press Printing Company - ISSN:
- 0022-006X (Print)
1939-2117 (Electronic) - Language:
- English
- Keywords:
- alcohol, college students, brief intervention, personalized feedback intervention, evidence-based treatment
- Abstract:
- Little is known about individual and situational factors that moderate the efficacy of personalized feedback interventions (PFIs). Mandated college students (N = 348) were randomly assigned either to a PFI delivered in the context of a brief motivational interview (BMI; n = 180) or to a written PFI only (WF) condition and were followed up at 4 months and 15 months postintervention. The authors empirically identified heterogeneous subgroups utilizing mixture modeling analysis based on heavy episodic drinking and alcohol-related problems. The 4 identified groups were dichotomized into an improved group (53.4%) and a nonimproved group (46.6%). Logistic regression results indicated that the BMI was no more efficacious than the WF across all mandated students. However, mandated students who experienced a serious incident requiring medical or police attention and those with higher levels of alcohol-related problems at baseline benefited more from the BMI than from the WF. It may be an efficacious and cost-effective approach to provide a written PFI for low-risk mandated students and an enhanced PFI with a BMI for those students who experience a serious incident or have higher baseline alcohol-related problems. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Alcohol Drinking Attitudes; *Alcohol Drinking Patterns; *Evidence Based Practice; *Feedback; *Intervention; College Students
- Medical Subject Headings (MeSH):
- Alcohol-Related Disorders; Feedback; Humans; Life Change Events; Mandatory Programs; Psychotherapy, Brief; Social Environment; Students; Substance-Related Disorders; Treatment Outcome; Universities
- PsycINFO Classification:
- Health & Mental Health Treatment & Prevention (3300)
- Population:
- Human
Male
Female - Location:
- US
- Age Group:
- Adulthood (18 yrs & older)
Young Adulthood (18-29 yrs) - Tests & Measures:
- Readiness to Change Questionnaire DOI: 10.1037/t00434-000
Beck Depression Inventory DOI: 10.1037/t00741-000
Comprehensive Effects of Alcohol Questionnaire DOI: 10.1037/t00697-000
Rutgers Alcohol Problem Index DOI: 10.1037/t00517-000 - Grant Sponsorship:
- Sponsor: National Institute on Drug Abuse
Grant Number: DA 17552; DA 17552-05S1
Recipients: No recipient indicated
Sponsor: Rutgers Transdisciplinary Prevention Research Center
Recipients: Pandina, Robert J. (Prin Inv) - Conference:
- Annual Scientific Meeting of the Research Society on Alcoholism in Washington, 31st, Jul, 2008, Washington, DC, US
- Conference Notes:
- An earlier version of this article was presented at the aforementioned conference.
- Methodology:
- Empirical Study; Quantitative Study
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- Accepted: Nov 17, 2008; Revised: Nov 11, 2008; First Submitted: Jan 15, 2008
- Release Date:
- 20090126
- Correction Date:
- 20130114
- Copyright:
- American Psychological Association. 2009
- Digital Object Identifier:
- http://dx.doi.org/10.1037/a0014679
- PMID:
- 19170456
- Accession Number:
- 2009-00563-017
- Number of Citations in Source:
- 88
- Persistent link to this record (Permalink):
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- Database:
- PsycINFO
Individual and Situational Factors That Influence the Efficacy of Personalized Feedback Substance Use Interventions for Mandated College Students
By: Eun Young Mun
Center of Alcohol Studies, Rutgers, The State University of New Jersey;
Helene R. White
Center of Alcohol Studies, Rutgers, The State University of New Jersey
Thomas J. Morgan
Center of Alcohol Studies, Rutgers, The State University of New Jersey
Acknowledgement: An earlier version of this article was presented at the 31st Annual Scientific Meeting of the Research Society on Alcoholism in Washington, DC, July 2008. This study was supported by National Institute on Drug Abuse Grants DA 17552 and DA 17552-05S1 as part of the Rutgers Transdisciplinary Prevention Research Center (Robert J. Pandina, principal investigator). We thank Katarzyna Celinska, Sarah Fink, Corey Grassl, Barbara Kachur, Brian Kaye, Lisa Laitman, Polly McLaughlin, Lisa Pugh, Kelly Pugh, and Malina Spirito for their help with the data collection and their commitment to the research project and Adam Thacker for his help with database management.
Over 40% of college students report having engaged in heavy episodic drinking (HED) at least once in the past 2 weeks, and over 20% of college students report having engaged in HED three or more times in the past 2 weeks (Wechsler et al., 2002). Consequences of excessive drinking among college students include injuries, motor vehicle accidents, unprotected sex, sexual victimization, academic problems, health problems, suicide attempts, destructive behavior, and police involvement (Engs, Diebold, & Hanson, 1994; Hingson, Heeren, Zakocs, Kopstein, & Wechsler, 2002; Presley, Meilman, & Cashin, 1996; Wechsler, Lee, Nelson, & Lee, 2001). In 2001, more than 1,700 U.S. college student deaths and over 500,000 unintentional injuries were alcohol related (Hingson, Heeren, Winter, & Wechsler, 2005). In response, a number of preventive interventions have been implemented to help college students move safely through this risky transitional developmental period between adolescence and young adulthood (i.e., emerging adulthood; Arnett, 2000, 2007; Dimeff, Baer, Kivlahan, & Marlatt, 1999). The massive growth in college prevention programs seen over the last decade (Anderson & Milgram, 1996, 2001; Wechsler et al., 2002) reflects efforts to provide universal and selective preventive interventions to college students.
Personalized Feedback InterventionsThe available evidence suggests that individually oriented, multicomponent interventions that enhance cognitive–behavioral skills, enhance motivation to change, provide accurate peer norms for alcohol use and drug use on campus, and challenge any inaccurate alcohol expectancies are efficacious for college students (Larimer & Cronce, 2002; National Institute on Alcohol Abuse and Alcoholism, 2002). In particular, personalized feedback interventions (PFIs), which are often delivered within the context of a brief motivational interview (BMI), have been shown to be efficacious with heavy drinking volunteer students (e.g., Baer, Kivlahan, Blume, McKnight, & Marlatt, 2001; Baer et al., 1992; Borsari & Carey, 2000; Carey, Carey, Misto, & Henson, 2006; Larimer et al., 2001; Marlatt et al., 1998; Murphy et al., 2001) and with mandated students (Borsari & Carey, 2005; White, Mun, Pugh, & Morgan, 2007). The theoretical rationale behind PFIs is that personalized feedback will increase the readiness of a student to change his or her drinking behaviors (Miller & Rollnick, 2002). Also, students will alter their perceptions about risk and peer use norms, as well as their alcohol/drug expectancies (Dimeff et al., 1999). These changes will lead to reduced drinking, and this reduction should reduce negative consequences of alcohol use. Therefore, when PFIs are presented within the context of a BMI, in which the counselor provides feedback in an empathetic, nonthreatening, and nonjudgmental manner, it is expected that they will increase students' readiness to change and help guide students through the change process.
Recent reviews of individual-focused interventions have found that in-person interventions that include motivational interviewing and personalized normative feedback are more efficacious than other types, such as education-focused programs (see Carey, Scott-Sheldon, Carey, & DeMartini, 2007; Larimer & Cronce, 2007). White et al. (2007) found that, over a long-term follow-up, a PFI delivered with a BMI proved to be more efficacious in reducing risky drinking and related problems for mandated college students than did a written PFI without a BMI. Few studies, however, have empirically examined moderating factors of PFIs on drinking outcomes. Thus, it is not well understood under which conditions or for whom PFIs work best (for reviews, see Carey, Scott-Sheldon, et al., 2007;Larimer & Cronce, 2007; Neighbors, Larimer, Lostutter, & Woods, 2006; Walters & Neighbors, 2005; White, 2006). It is critical that we begin to determine for whom PFIs work best and for whom we need different types of interventions. The present study is an attempt to fill this gap. It assessed whether there are individual and situational factors that moderate the efficacy of brief PFIs for mandated students over a long term.
Individual and Situational Factors That Influence PFI Efficacy Preintervention Drinking Levels
It has been suggested that PFIs may have a greater effect for heavier drinkers than for lighter drinkers because feedback for the former group is more extreme (Walters & Neighbors, 2005). However, studies have been inconsistent in their findings among nonmandated students (Larimer et al., 2007; Murphy et al., 2001). Murphy et al. compared the efficacy of a PFI within the context of a BMI and an educational intervention with that of an assessment-only control on weekly alcohol consumption and binge drinking among 84 volunteer high-risk students. They found that the PFI contributed to greater reductions in alcohol use and heavy drinking at the 3-month and 9-month follow-ups among those students who were heavier drinkers at baseline. This finding should be interpreted with caution, because Murphy et al. did not formally test the interaction between baseline drinking and PFI conditions; also, due to a small sample size, they used α = .15 as the Type I error rate. In contrast, in a large sample of volunteer students, Larimer et al. did not find that severity of baseline drinking moderated the efficacy of a mailed PFI at the 1-year follow-up. It is interesting that Larimer et al. reported that abstainers benefited more from the feedback than did drinkers. A recent meta-analysis of 62 studies (Carey, Scott-Sheldon, et al., 2007) reported that individual-level interventions were less successful when heavy drinkers or members of other at-risk groups (e.g., Greeks, athletes, first-year students) were targeted.
This apparent inconsistency regarding whether preintervention alcohol use levels play a role in PFI efficacy may be attributed to several methodological issues. First, some of the previous studies may have lacked the power necessary to detect moderation effects due to their insufficient overall sample size. Whereas many clinical trial studies are designed to have enough power to detect treatment effects (i.e., main effects), few have enough power to detect differential efficacy across subgroups (i.e., moderation effects; see Pocock, Assmann, Enos, & Kasten, 2002, for a review). The power to detect moderation is also affected by subgroup sample sizes, restriction in predictor variable range, magnitude of the moderating effect (Aguinis & Stone-Romero, 1997), and measurement error (Sackett, Harris, & Orr, 1986). Therefore, some existing studies may have inadvertently restricted the range of observations by screening out those individuals whose baseline drinking levels were at lower ends of the spectrum; this restriction may have resulted in lowered power.
Second, previously reported findings are often based on univariate/bivariate analysis, although preintervention drinking levels are generally known to be confounded with other individual and situational factors (e.g., gender). Treatment groups are typically balanced through random assignment on measured and unmeasured variables. However, any covariates that are strongly related to outcomes should be adjusted when examining treatment effects (Pocock et al., 2002). This recommendation is also applicable when examining moderation effects. Thus, adjusting for individual and situational factors related to treatment outcomes may help clarify whether preintervention drinking levels affect the efficacy of a PFI above and beyond the influences of these confounding factors.
Third, some of the existing studies categorized students on the basis of an a priori definition (e.g., those with five or more drinks in a row in the past 2 weeks, or those in the upper half of a sample on the basis of number of drinks). However, this heuristic dichotomization approach may be arbitrary. In recent studies of natural trajectories of alcohol use among adolescents and college students, heterogeneous subgroups have been empirically identified on the basis of their trajectories over time (e.g., Sher, Gotham, & Watson, 2004). The same methodology may be adopted for evaluation studies when documenting subgroups with distinctive profiles of change over time postintervention and when examining predictors and moderators of change.
Incident Seriousness
Existing studies on mandated students have not addressed the possibility that mandated students may initiate the self-regulatory self-recovery process due to their having been caught and sanctioned and that PFIs may facilitate rather than cause this self-recovery process. A few recent studies of mandated students suggest that the alcohol-related violation itself prior to any intervention contributes to reductions in alcohol use (Morgan, White, & Mun, 2008) and that perceived aversiveness of the incident is positively related to the motivation of students to change their drinking (Barnett, Goldstein, Murphy, Colby, & Monti, 2006). Barnett et al. hypothesized that salient alcohol-related events (e.g., hospitalization or medical problems) would bring about self-evaluation and greater motivation to change, especially among those with less prior experience with alcohol and fewer prior alcohol problems. Barnett et al. found, as expected, that prior alcohol use was negatively linked to incident aversiveness and that prior alcohol-related problems (AP) were negatively associated with personal attribution of the incident. In addition, greater perceived incident aversiveness was linked with greater motivation to change alcohol use. Morgan et al. (2008) provided some empirical evidence that mandated students, who had been involved in an incident that required medical or police attention, actually reduced their drinking prior to the intervention more than did those students who had been involved in a nonserious incident. Therefore, to better understand changes among mandated students, it is critical that we look at the nature of the incident when we examine the efficacy and moderated efficacy of PFIs.
Readiness to Change
The findings that an incident itself (or self-regulation following it) has an effect on behavior change (Morgan et al., 2008) underscore the need to examine students' readiness to change or motivation to change following the incident as a potential explanation for differential intervention efficacy across different individuals. The existing literature is inconclusive regarding whether readiness to change moderates the efficacy of PFIs. Although Carey, Henson, Carey, and Maisto (2007) did not find a significant moderation effect between BMI and readiness to change among volunteer students, there is limited evidence that such an effect exists. For example, Fromme and Corbin (2004) found that, at baseline, mandated participants reported higher levels of readiness to change than did volunteer students. When they tested readiness to change as a potential moderator of intervention efficacy, results showed a trend toward greater reductions in heavy alcohol consumption following the intervention, compared with the control condition, among the volunteer but not the mandated students with greater readiness to change at baseline.
Positive Alcohol Expectancies
Alcohol expectancies are defined as “structures in long-term memory that have impact on cognitive processes governing current and future consumption” (Jones, Corbin, & Fromme, 2001, p. 59). It is hypothesized that a PFI can alter one's positive alcohol expectancies and can thus reduce motivations to use and advance one's movement across the stages of change (Dimeff et al., 1999). Limited evidence exists that those who drink to enhance their social functions (positive alcohol expectancies) may benefit more from a PFI, at least in a college volunteer sample, because those individuals may be more sensitive to peer norms (Neighbors, Larimer, & Lewis, 2004). However, little is known as to whether positive alcohol expectancies are related to differential efficacy of PFIs among mandated students.
Gender
A number of studies have looked at gender as a potential moderator of PFI efficacy among volunteer college samples, and the results have been equivocal. Murphy et al. (2004) found that women in both PFI conditions with and without a motivational interview lowered their weekly drinking at the 6-month follow-up, whereas men did not reduce their drinking in either condition. Similarly, Chiauzzi, Green, Lord, Thum, and Goldstein (2005) reported that, although volunteer students who received a PFI were not statistically different from students in the control group overall, a subset of heavy drinking women in the PFI condition reduced their total drinks and HED during special occasions more than did their heavy drinking counterparts in the educational control condition. In contrast, there were no such group differences among men. However, several other studies have found no gender differences in response to PFIs (e.g., Carey, Henson, et al., 2007; Marlatt et al., 1998). For example, Marlatt et al. reported that, although women overall reported significantly more declines in AP than did men, men and women responded similarly to a PFI. Thus, it is generally unclear whether a relative advantage for women exists following a PFI.
First-Year Student in College and Other Drug Use
First-year students in college are generally considered to be at risk for excessive alcohol use. Although evidence of the efficacy of PFIs exists for first-year college students (see Larimer & Cronce, 2007), PFIs may be less beneficial for first-year students, according to Carey, Scott-Sheldon, et al. (2007). However, it is unclear whether the efficacy of PFIs works differently for first-year students, compared with non-first-year students, when their different patterns of alcohol use are controlled. In addition, many mandated students are caught for drug use. The current study investigated whether other drug use at baseline moderates PFI efficacy among mandated students.
The Current StudyThe current study sought to examine (a) whether some mandated students reduce alcohol use more following a PFI than do other students and (b) whether some students respond better to a PFI delivered in the context of a BMI than to a written PFI only. In this study, we aimed to extend an earlier study with the same sample (White et al., 2007) by empirically identifying heterogeneous subgroups of mandated students that differentially respond to a PFI. To achieve this goal, we analyzed HED and AP on the basis of their change patterns, as well as their overall levels. We did so using the latent change score approach proposed in a recent study (Mun, von Eye, & White, in press) and an extension of mixture modeling analysis. HED and AP were chosen because reductions in these alcohol use behaviors reflect self-regulated harm reduction better than do other alcohol use measures. We used empirically identified groups as the outcome variable in subsequent logistic regression analyses. We formally tested the following six individual and situational factors as predictors of change in the context of a PFI: incident seriousness, readiness to change, positive alcohol expectancies, gender, first-year student, and other drug use. We then examined whether the efficacy of a PFI delivered in the context of a BMI and the efficacy of a written PFI differed depending on individual and situational factors, as well as baseline HED and AP (i.e., differential efficacy of the PFI types by individual and situational factors). Thus, we tested for moderation effects by examining the interaction between PFI condition and each of the predictors.
MethodParticipants
Participants were students mandated to a university Alcohol and Other Drug Assistance Program due to their infractions of university rules about alcohol and drug use in residence halls. The sample was recruited during the fall semester 2003 and spring and fall semesters 2004. Of the 390 mandated students, 24 (6.2%) were ineligible for the study on the basis of the following exclusion criteria: prior substance abuse treatment, a score greater than 13 on the Beck Depression Inventory (Beck & Steer, 1984), a .24% blood alcohol concentration (BAC) or higher in a typical week, more than 10 occasions of HED (five or more drinks on one occasion for men and four or more for women) in the past month, nine or more alcohol/drug-related negative consequences, near-daily marijuana use, or abstinence from alcohol and drugs (i.e., they were caught in a room with alcohol or drugs but had never used them themselves).
Because this was a randomized study and there was no prior research to support the efficacy of written feedback alone for mandated students, the highest risk students were excluded for ethical and clinical reasons. All of these high-risk students received an in-person intervention. In addition, only first offenders were eligible for the study. Another 18 students (4.9%) declined to participate in the research study, which left a final sample of 348 students (see Figure 1 for participant flow). The resulting sample was 60.1% male, and most students were in their first (61.6%) or second (29.9%) year of college. The sample was 79% Caucasian, 15.5% Asian American, 2.2% African American, and 3.4% other or mixed ethnicity. Over 90% of participants were caught violating residence life rules while in a group, and 88.6% were referred for alcohol-related violations (for greater detail on sample characteristics, see White et al., 2006, 2007).
Figure 1. A flowchart of recruitment, participation, and follow-up rates.
Procedures and Interventions
All students referred to the Alcohol and Other Drug Assistance Program completed a baseline assessment questionnaire. Using data from the initial assessment, we determined eligibility and created an individualized profile for each eligible student. The personal profile included information on peer norms for alcohol and drug use, typical peak BAC, alcohol- and drug-related problems, alcohol expectancies, high-risk behaviors (e.g., driving under the influence, unplanned sex after using alcohol or drugs), and personal risk factors (e.g., depression, family history of alcoholism). In addition, the profile contained general information about the effects of various BAC levels and tolerance to alcohol. Students returned approximately 1 week later and were randomly assigned (by a flip of a coin) either to a BMI condition (n = 180, 51.7%) or to a written feedback only (WF) condition (n = 168, 48.3%).
Students in the BMI condition met individually with a counselor and discussed their written personal profile, which they were given to take home. The counselor provided feedback in an empathic, nonconfrontational, and nonjudgmental style based on the principles of motivational interviewing (Miller & Rollnick, 2002). Students in the WF condition were handed their written profile, and they left without discussing it with their counselor. Intervention fidelity was assured in several ways. First, counselors were trained specifically in motivational interviewing techniques and received weekly supervision from Thomas J. Morgan, a clinical psychologist with expertise in motivational interviewing techniques. Second, five BMI and two WF sessions for each counselor were audiotaped. The supervising clinical psychologist listened to the audiotapes and provided feedback to the counselor. Third, the counselors completed a therapist checklist after each BMI session. The checklist consisted of the therapeutic tasks during the session, as well as a self-evaluation for the counselor that focused on being empathic and nonjudgmental and providing support to the student. The clinical supervisor reviewed the checklists to ensure that the counselors adhered to the protocol.
Students were followed up approximately 4 months after the second session (n = 319, 91.7%) and again 15 months postbaseline (n = 220, 63.2%). There were no significant differences between those who were followed up and those who dropped out on demographic or baseline alcohol use characteristics (see White et al., 2007, for means and standard deviations).
Measures
Alcohol use variables
Students reported the number of HED occasions they had experienced in the past month (defined as five or more standard drinks for men and four or more for women; Wechsler et al., 2002). The number of AP was obtained from the 18-item short version of the Rutgers Alcohol Problem Index (RAPI; White & Labouvie, 1989, 2000). The RAPI has demonstrated reliability and discriminant construct validity in both general population and clinical samples of adolescents and young adults (White, Filstead, Labouvie, Conlin, & Pandina, 1988; White & Labouvie, 1989, 2000), and the 18-item version correlates above .9 with the 23-item version (White & Labouvie, 2000). Students reported on the total number of AP experienced in the last 3 months (αs = .73–.80 across the three assessments). The distributions of the HED and AP were positively skewed and leptokurtic. They were subsequently log-transformed after a constant of 1 was added to normalize skewed distributions.
The self-report alcohol use measures we used in the current study are widely used in the literature on college drinking and have been found to be reliable when corroborated by collateral reports (Borsari & Carey, 2005; Marlatt et al., 1998). Other studies of college student drinking and related problems have shown that use of collateral reports does not improve validity of the data (Carey et al., 2006; Marlatt et al., 1998).
Incident seriousness and demographic variables
The incident for which the student was mandated was coded as “serious” (coded 1) if the referral was made by emergency medical services/hospital (15.3%) or law enforcement personnel (2.3%) and as “nonserious” (coded 0) if the student was referred by a residence hall advisor (83.4%). Gender was coded 1 for men and 0 for women. First-year students were coded 1, and all others were coded 0. Other drug use (cigarettes, marijuana, and other substances) at baseline was coded 1 for those with any use of any substance and 0 for those without any use in the past month. Existing studies have found that students provide valid self-report drug use data (e.g., Johnston, O'Malley, Bachman, & Schulenberg, 2007).
Readiness to change
Readiness to change was measured at baseline by the Readiness to Change Questionnaire (RCQ; Heather, Rollnick, & Bell, 1993). The RCQ is a 12-item self-report measure designed to provide a single stage of change assignment (precontemplation, contemplation, or action) as well as a continuous score for each of the three stages of change. Items (e.g., “I am trying to drink less than I used to,” “I enjoy my drinking, but sometimes I drink too much”) were presented on a 5-point Likert scale that ranged from strongly disagree to strongly agree. In the present study, four items capturing the precontemplation stage were reverse coded and were averaged with the other items to create a continuous scale score (α = .88 at baseline). Higher scores reflect the greater readiness of a person to start to change or to actually be changing his or her drinking habits.
Positive alcohol expectancies
Alcohol expectancies were measured at baseline by the Comprehensive Effects of Alcohol Questionnaire (CEOA; Fromme, Stroot, & Kaplan, 1993). The CEOA consists of 20 positive and 18 negative expectancy items. Positive alcohol expectancies included items related to tension reduction, sexuality, liquid courage, and sociability factors. Example items from each factor, respectively, are “I would feel calm,” “I would be a better lover,” “I would be courageous,” and “I would act sociable.” Students responded on a 4-point Likert-type scale ranging from disagree to agree. We administered only 8 positive expectancy items out of the original 20 positive items in order to lessen the burden of students of filling out a lengthy questionnaire (we used the 2 items with the highest factor loadings from each of the four factors; Fromme et al., 1993). The 8 items were averaged to create a positive alcohol expectancy score. Higher positive expectancy scores reflect stronger beliefs that consuming alcohol would result in positive effects for the participant (α = .73 at baseline).
Social desirability
We included a 13-item shortened version (Reynolds, 1982) of the original Marlowe–Crowne Social Desirability Scale (Crowne & Marlowe, 1960) that assesses the tendency of a person to present himself or herself in a socially desirable way. This short version has been found to discriminate criminal and noncriminal groups and been known to have acceptable test–retest reliability and internal consistency (Andrews & Meyer, 2003). We included this social desirability scale in the baseline assessment to control for potential demand characteristics among mandated students in reporting substance use. Example items are “I'm always willing to admit it when I make a mistake,” “I have never deliberately said something that hurt someone's feelings,” and “I have never been annoyed when people expressed ideas very different from my own.” Responses were coded 1 for “true” and 0 for “false” responses. The scale score was created by summing responses (α = .66 at baseline). High scores indicate higher levels of social desirability. Mandated students may be more motivated to underreport alcohol use levels than are volunteer students.
In a previous study, we reported from a different sample that mandated students with high demand characteristics tended to report lower levels of alcohol and drug use (White et al., 2008). Therefore, although there was no difference in social desirability between two PFI conditions at baseline with the present sample (White et al., 2007), we controlled for social desirability mean levels (and variances) by constraining them to be equal across classes in mixture analysis.
Missing Data
We used the expectation maximization algorithm for maximum likelihood estimation to impute missing data with SAS, after the Little's chi-square test of missing completely at random (Little, 1988) had a nonsignificant result, χ2(8020) = 8,078.96, p > .05. This result indicated that missing values were a random subset of the complete data. Thus, we deemed that the imputed data were unbiased (Little & Rubin, 1987; Schafer, 1997).
ResultsWe utilized a latent change score approach based on latent curve models. The previous study (White et al., 2007) showed that overall substance use decreased between baseline and the 4-month follow-up assessment and increased between the 4-month and 15-month follow-up assessments. Instead of analyzing this change pattern with typical nonlinear latent curve models, we examined latent changes between baseline and the first follow-up at 4 months (i.e., latent change variable; see Figure 2) and between 4 months and 15 months postintervention (i.e., latent change variable). We specified the outcome levels at 15 months postintervention as the intercept level (i.e., latent variable) because participants in the present study were randomly assigned to a treatment condition and there were no group differences between the BMI and WF groups at baseline. Thus, we focused on the long-term outcome levels rather than baseline levels. This intercept selection approach is equivalent to centering a time metric variable at 15 months in latent curve models (see the Appendix). All latent variable analyses were conducted with Mplus Version 5.0 (Muthén & Muthén, 1998–2007), and subsequent logistic regressions were conducted with SPSS Version 16.
Figure 2. Analyzed mixture models using latent change variables. Solid lines indicate directly estimated parameters, and dotted lines indicate either fixed parameters (i.e., factor loadings) or a mixture part of the analyzed model (i.e., class to latent variables). Social desirability was constrained to be equal in mean, variance, and paths across classes. Level = outcome levels at 15 months postintervention; IC = initial change from baseline to 4 months postintervention; SC = subsequent change from 4 months to 15 months postintervention.
Latent Change Score Analysis With Mixture Modeling Analysis and Outcome Groups
We analyzed the number both of HED and of AP over time simultaneously using mixture analysis. We added social desirability as a covariate to ensure that derived groups were equivalent in social desirability. Results indicated that, on the basis of the Bayesian information criterion (BIC), the model with four latent classes was the best fitting, most parsimonious model (BIC2 = 5,654.72, BIC3 = 5,604.54, BIC4 = 5,432.33, and BIC5 = 5,465.26 for two-, three-, four-, and five-latent class models, respectively). The four latent classes were very well separated (entropy = .99), and the average posterior probability for the most likely class exceeded .98. Entropy values approaching 1 are considered to indicate well-separated classes (Celeux & Soromenho, 1996). Figure 3 clearly illustrates that a considerable number of students (Classes 1 through 3) continued to engage in HED (see Figure 3A) and to report AP (see Figure 3B) throughout the observed period. In the present study, we decided to focus on those who improved versus those who did not, primarily to increase power and improve accuracy of parameter estimates in detecting predictors and moderators in subsequent logistic regression analyses. For instance, cross-tabulating incident seriousness with the four classes resulted in a few cells with a limited number of observations, especially for the smallest class (Class 1). Previous studies in the literature have combined empirically identified groups into a smaller number of groups on the basis of other practical and conceptual considerations (e.g., Bongers, Koot, van der Ende, & Verhulst, 2004) or have rejected alternate solutions from considerations based on additional criteria (e.g., minimum cluster size [5% or more], distinctively shaped trajectories, or large bivariate residuals). Therefore, in all subsequent analyses, Classes 1 through 3 were combined into a single nonimproved group (n = 162, 46.6%). The remaining group was labeled as an improved group (n = 186, 53.4%).
Figure 3. Estimated mean growth trajectories of heavy episodic drinking (HED; Figure 3A) and alcohol-related problems (AP; Figure 3B) for the four-class models specified as shown in Figure 2. In all subsequent analysis, Classes 1 through 3 were combined into a single nonimproved group, and Class 4 was classified as an improved group. pp = the average posterior probability for the most likely class; T1 = baseline; T2 = 4 months postintervention; T3 = 15 months postintervention.
Table 1 shows the means and standard deviations of the alcohol use outcome variables at the three time points for these two groups. The improved group reported significantly lower levels of HED and AP at baseline as well as at the two follow-up assessments. Table 2 shows the within-person changes (mean changes, t values, and effect sizes) from baseline to 4 months, from 4 months to 15 months, and from baseline to 15 months postintervention from paired t tests. The improved group, compared with the nonimproved group, showed reductions in the range of medium-to-large effect sizes (see Table 2; d = 0.68–0.77; d = 0.2, 0.5, and 0.8 for small, medium, and large effects; Cohen, 1988) from baseline to 4 months, followed by significant upward swings in the range of small-to-moderate effect sizes (d = 0.41–0.61). The initial reduction in AP was maintained over time for the improved group; however, the initial reduction in HED was not maintained over the long term. When followed up at 15 months postintervention, the improved group reported lower levels of AP, but not of HED, compared with its baseline levels. The nonimproved group did not improve in HED or AP over the long term. It had higher levels of HED and AP than did the improved group at all times, and the only positive outcome for this group was the initial reduction in AP from baseline to the 4-month follow-up.
Means and Standard Deviations of Heavy Episodic Drinking (HED) and Alcohol-Related Problems (AP) for the Improved and Nonimproved Groups
Within-Individual Change in Heavy Episodic Drinking (HED) and Alcohol-Related Problems (AP) Following Brief Interventions (N = 348)
Predictors of Change in the Context of the PFI
First, we used univariate logistic regression without controlling for any covariates to investigate whether each of the individual and situational factors, as well as intervention condition and baseline HED and AP, significantly predicted improved group membership. As expected, all individual and situational factors, with the exception of readiness to change, significantly predicted improved group membership when we examined these factors separately in univariate logistic regression analysis without adjusting for any other individual and situational variables or baseline HED and AP (see the first column in Table 3). Also, as expected, baseline HED and AP levels were significantly associated with improved group membership.
Individual and Situational Factors as Predictors of Change in the Context of the PFI
Next, we added baseline HED and AP levels to each model to statistically control for their effects, in order to examine whether the individual and situational factors uniquely contributed to improved group membership above and beyond the influences of baseline HED and AP. Thus, including these covariates enabled us to infer predictors of change that were not confounded with preintervention drinking levels. In other words, we examined, given the same levels of HED and AP at baseline, whether the individual and situational factors predicted improved group membership. When we adjusted for baseline HED and AP (see the middle column in Table 3), we found that experiencing a serious incident, reporting greater readiness to change, being female or a non-first-year student, and reporting no other drug use significantly predicted improved group membership. Baseline AP and positive alcohol expectancies no longer significantly predicted improved group membership. Positive alcohol expectancies and AP at baseline may largely be accounted for by baseline HED. It is interesting that greater readiness to change was a significant predictor of improved group membership only after baseline HED and AP levels were taken into consideration. This result suggests that, given the same levels of HED and AP, those with greater readiness to change were more likely to be in the improved group.
When all variables were examined simultaneously in a single multivariate model (see the last column in Table 3), results indicated that reporting lower levels of HED at baseline, experiencing a serious incident, and being female were the only significant predictors of improved group membership. Other individual and situational factors (e.g., reporting greater readiness to change and positive alcohol expectancies, being a first-year student, and reporting other drug use) did not uniquely predict improved group membership when statistical adjustment was made to remove confounding influences. These findings indicate that because many individual and situational factors are somewhat related, their unique contributions to intervention outcomes cannot be comprehended fully through use of univariate analysis alone. For example, greater readiness to change no longer significantly predicted the outcome, in part because it was related to incident seriousness (r = .32, p < .01) and its effects were confounded with incident seriousness. In contrast, the advantage for female students and for those who experienced a serious incident persisted above and beyond their other co-occurring individual and situational factors and different preintervention drinking levels. Note that, in all three analyses, we found that having received the BMI did not predict improved group membership.
Moderators of the PFI Efficacy Across the BMI and WF Conditions
We then examined whether PFI efficacy was different across the BMI and WF conditions depending on individual and situational factors. In other words, we tested for moderation effects (i.e., differential efficacy of the PFI types by individual and situational factors). In addition to the six individual and situational factors, we examined baseline levels of HED and AP as potential moderators. All continuous variables were centered in order to avoid potential multicollinearity problems, and interaction terms were created with centered variables. We added each interaction term one at a time to the final multivariate model shown in Table 3.
Incident seriousness and AP at baseline were linked to the differential PFI efficacy across the BMI and WF conditions. Of the mandated students who experienced a serious incident, those who were assigned to the BMI were more likely to be in the improved than the nonimproved group, log odds ratio (logit) = 1.56, odds ratio (OR) = 4.76, 95% confidence interval (CI) = 1.21–18.66, p < .05 (see Figure 4 for simple slopes). In addition, of the mandated students who reported higher levels of AP at baseline, those who were assigned to the BMI were more likely than were those in the WF to be in the improved group, logit = 1.43, OR = 4.18, 95% CI = 2.02–8.64, p < .01 (see Figure 5 for simple slopes). All other individual and situational factors were not statistically significant moderators.
Figure 4. The interaction of PFI condition with incident seriousness. The BMI is more efficacious than the WF for mandated students who were referred after a serious incident. PFI = personalized feedback intervention; BMI = brief motivational interview; WF = written feedback only.
Figure 5. The interaction of PFI condition with baseline level of alcohol-related problems (AP). The BMI is more efficacious than the WF for mandated students with higher levels of AP at baseline. BMI = brief motivational interview; WF = written feedback only.
DiscussionThis study examined whether subgroups exist in the response of mandated students to a PFI and whether different PFIs are differentially efficacious for mandated college students. We found heterogeneous subgroups with distinctively different outcome trajectories. Overall, we found that the majority of the mandated students (53.4%) improved in both HED and AP after the PFI regardless of whether they were assigned to the BMI or the WF condition. The nonimproved group consisted of 46.6% of the mandated students who improved neither in HED nor in AP over the long term. This group may represent individuals who have chronic drinking problems and resist changes in their drinking. However, it is noteworthy that the mandated students in the current study were relatively low-risk individuals compared with participants in other studies that screened and selected high-risk volunteer students (e.g., Chiauzzi et al., 2005; Murphy et al., 2001; Walters, Roudsari, Vader, & Harris, 2007) or other studies of mandated samples (e.g., Barnett et al., 2006) because of our study's clinical exclusion criteria. For example, many of the students in Barnett et al. had been mandated for more serious infractions than ours (e.g., 82% were referred for acute intoxication or an alcohol-related injury; only 15% of our students were referred for an incident that required police or medical attention).
Predictors of Change, Moderators of the PFI Efficacy, and Clinical Implications
The findings from the present study may provide some answers to the inconsistent findings in the literature. Our findings indicated that it was lighter drinking individuals who improved more following the PFI over a long-term follow-up. This finding is consistent with a recent conclusion from a large meta-analysis that PFIs are more beneficial for lighter drinking individuals (Carey, Scott-Sheldon, et al., 2007). We also found that there was no overall difference in the efficacy between the BMI and WF groups. Therefore, for mandated students whose baseline levels of HED or AP are low, written or Web-based personalized feedback may be a cost-effective way to deliver a PFI as a selective intervention. The present study also demonstrated that the advantage for those who are female, who experienced a serious incident, or who engaged in less frequent HED at baseline was maintained even after we took into account other individual and situational factors as well as preintervention AP levels. In previous studies, it has been difficult to assess to what extent ensuing reductions are due to unique effects of individual and situational factors, above and beyond other confounding variables. In the present study, findings for the effects of being a female, experiencing a serious incident, or being a less heavy drinker cannot be considered an artifact of omitted baseline confounding factors because these confounded effects were statistically adjusted. There may be other factors that were not considered in the present study. However, the individual and situational factors considered in the present study, as well as the alcohol use controls, represent most of the factors that have been discussed in the literature.
The present study also suggests that it may still be a valuable goal for interventionists to improve readiness to change, as well as to reduce other drug use. In particular, greater readiness to change, being a non-first-year student, and no other drug use at baseline predicted a better intervention outcome when baseline HED and AP levels were statistically controlled but not when other individual and situational factors were examined simultaneously. Although this finding indicates that there were no unique effects of these variables above and beyond other co-existing individual and situational factors, targeting these co-existing factors might improve intervention outcomes. Whereas experiencing a serious incident and, certainly, being a first-year student cannot be subject to change by interventions, early preventive interventions with incoming students might help them reduce problematic behaviors that may lead to serious incidents.
In our analysis of the moderated PFI efficacy, we found that experiencing a serious incident prior to the PFI and reporting high levels of AP at baseline were statistically significant moderators of the PFI efficacy favoring the BMI over the WF. These findings indicate that there is an additional benefit of an in-person, face-to-face motivational interview for students who have experienced a serious incident or have reported higher levels of AP at baseline. Other individual and situational factors—gender, first year in college, other drug use, readiness to change, and positive alcohol expectancies—did not moderate the efficacy of the BMI. In addition, the BMI efficacy did not differ across different levels of HED (i.e., no interaction effect was found between BMI and baseline HED).
In a previous study, we reported that there were no group differences in intermediate-term (4 months postintervention) alcohol reductions across the BMI and WF conditions and discussed that, given the cost of administering an in-person motivational interview in terms of time and staffing, provision of a written feedback alone may be a cost-effective way of reducing alcohol use among mandated college students (White et al., 2006). However, the more long-term follow-up study from the same sample demonstrated that additive benefits of providing an in-person BMI exist for AP above and beyond the benefit from a normative written feedback alone for the mandated students (White et al., 2007). On the basis of findings from the present study, we conclude that not all mandated students benefit additionally from an in-person BMI. Mandated students with lower levels of AP and HED may benefit just as much from a written personalized feedback alone as from an in-person BMI.
The current finding that the BMI was no more efficacious than the WF appears to differ from the previous finding (White et al., 2007). The difference in the findings may be understood in the context of differences in our approaches. First, in the present study, the PFI efficacy was assessed in terms of whether mandated students could be considered as an improved case. In contrast, in the previous study we measured the PFI efficacy using quantitative increments in each outcome variable unit. Therefore, statistically significant treatment group differences from the previous study may not sufficiently translate into a case of improvement (i.e., qualitative distinction), as defined in the current study. Second, in the present study we analyzed HED and AP simultaneously when we identified heterogeneous subgroups. In the previous study, we had examined each behavioral outcome separately. Perhaps the best way to understand the findings from these two studies is that, incrementally, the BMI was more efficacious than the WF, especially for AP. However, there was no clear advantage of the BMI over the WF across all individuals when we defined the efficacy outcome as a qualitatively distinct, categorical improvement. It is important to highlight that the BMI was more efficacious than the WF selectively for certain mandated students in the current study. That is, for those mandated students who had experienced a serious incident or whose levels of AP at baseline were high, an in-person brief motivational interview was more efficacious than was written feedback alone. These findings underscore the importance of better understanding the goodness of fit between necessary components of evidence-based treatments and different groups of students with different needs.
Limitations and Contributions
The current findings must be interpreted with caution in light of several limitations. First, we studied mandated students and did not have a true no-treatment control group (because of ethical considerations and program requirements). This restriction most likely decreased our power to detect stronger intervention effects, although our effect sizes were comparable to those of other studies on mandated and volunteer students (see Carey, Scott-Sheldon, et al., 2007; Larimer & Cronce, 2007; see also Barnett & Read, 2005, for likely reasons). In addition, the absence of a true control group prohibited us from attributing change to a PFI. Thus, we interpreted individual and situational factors as the predictors of change in the context of a PFI.
Second, findings from studies of mandated students, including the current study, may need to be understood in the context of being mandated. Two recent studies from a different sample that compared a WF with a delayed treatment control reported that mandated students reduced alcohol use on their own prior to the PFI (Morgan et al., 2008), and there were no differences between students who received the WF and those who did not at 2 months postbaseline (White et al., 2008). Therefore, reductions in alcohol use among mandated students postintervention may be attributed in part to cognitive and affective reactions to the incident for which they were mandated and subsequent self-regulation. In the present study, we did not have sufficient data on students' alcohol use following the incident but prior to the PFI. Thus, it is unclear to what extent that students had self-regulated their drinking behaviors on their own prior to the PFI. Nonetheless, the findings from the present study suggest that, following a serious incident, mandated students tended to reduce their HED and AP over the long term, especially if they received the BMI. In addition, mandated students with high levels of AP before the incident were more likely to be among the improved students if they were assigned to the BMI.
Third, on a related issue, we did not measure how intoxicated students were when caught or how aversely or seriously students perceived the incident for which they were mandated. More detailed information regarding the nature and subjective evaluation of the incident would facilitate better understanding of the efficacy of PFIs among mandated students. Fourth, the sample consisted of primarily white and Asian American students, and the findings may not generalize to other ethnic/racial groups. In addition, the findings may not be generalized to other mandated student populations with different university policies on alcohol and other drugs and policy enforcement practices (see Barnett et al., 2008).
Fifth, we found from mixture analysis that heterogeneity existed even among the nonimproved group. Factors that differentiate the three subgroups and their long-term trajectories may be of interest for the development of more intensive treatment models for these high-risk groups. The present study did not have sufficient sample size for us to conduct comparative analysis on these groups. A larger scale study of mandated students will allow researchers to examine whether the three distinctive groups, identified on the basis of statistical considerations such as the BIC and entropy statistics, can be useful in practice (Everitt, Landau, & Leese, 2001; Muthén & Muthén, 2000).
Finally, we examined the potential moderators one by one in the current study because little is known about predictors and moderators of the PFI efficacy in the literature. In addition, power to detect moderation effects is well known to be low (McClelland & Judd, 1993; Sackett et al., 1986). Given that identifying different subgroups is critical for screening, triaging, and implementing cost-effective interventions for those students in need, we adopted an exploratory approach in the current study. In a larger scale study designed to test moderation effects, it would be preferable to examine potential moderators simultaneously to understand their unique contributions.
Despite these limitations, the present study contributes to prevention research for alcohol use and AP among emerging adults and, more broadly, to evidence-based treatment research. First, the present study sheds new light on predictors of change in the context of a PFI and the efficacy of an in-person PFI delivered within a BMI among mandated students. On the basis of these findings, we suggest that it may be more cost-effective to deliver a written or Web-based PFI for low-risk mandated students and to provide an enhanced PFI with an in-person BMI for those students who have experienced a serious incident or have higher levels of AP at baseline. A two-session intervention utilizing an in-person motivational interview with personalized normative feedback presents a relatively low-cost psychological intervention. However, findings from the current study suggest that, even at low cost, an in-person BMI does not provide an additional benefit over a written PFI for many low-risk mandated students. Therefore, more research that could further identify other important moderators of PFIs among mandated and volunteer students is sorely needed to identify which students require which types of interventions.
Second, this study draws attention to the utility of a person-oriented approach (Bergman & Magnusson, 1997; von Eye & Bergman, 2003) for evaluation research and of the integrative strategy between person-oriented and variable-oriented approaches (Bates, 2000) to clinical research more broadly. As Foster, Dodge, and Jones (2003) discussed, many prevention and treatment studies are conducted from a variable-oriented perspective. Foster et al. illustrated that although studies that utilize a variable-oriented approach allow one to measure cost-effectiveness per one unit improvement in a single outcome measure, it is difficult to answer whether the cost of interventions outweighs benefits when the emphasis lies not on persons but on variables. It is especially challenging when outcomes co-occur. Foster et al. therefore suggested that a person-oriented outcome may be used as a global measure of cost-effectiveness for prevention research.
Use of two related outcome measures (e.g., AP and HED) to identify heterogeneous subgroups may be more insightful than use of an isolated single outcome when one is assessing either clinical significance at the individual level or global cost-effectiveness. Recent advances in longitudinal research methodology (see Foster & Kalil, 2008) provide attractive analytic options for evidence-based intervention research. In future, the refined focus on subgroup analysis utilized in the present study may be beneficial in the tailoring of necessary intervention components to those who need them the most.
Footnotes 1 The data reported in Morgan et al. (2008) are based on a later study (White, Mun, & Morgan, 2008) that compared a WF with a no treatment wait list control. The questions regarding students' alcohol use 30 days prior to the incident were asked very late for this study. Therefore, unfortunately, only about one third of the sample provided responses. Given the added requirement of nonoverlapped time referents, we did not have sufficient data to report on the role of the incident on alcohol use reductions. However, on the basis of evidence reported in Morgan et al. (2008), it is likely that the mandated students as a group reduced alcohol use on their own prior to the PFI, especially if they were mandated following an incident requiring medical/police attention.
2 In comparison with repeated-measures analysis of variance (ANOVA), latent curve models are a better use of the available data for evaluation studies because latent curve models tend to be more powerful and flexible, and they do not require unreasonable assumptions (see Curran & Muthén, 1999; Muthén & Curran, 1997). Simulation studies have demonstrated that latent curve models are more powerful in detecting change than are ANOVAs for a one-outcome series (e.g., Fan, 2003; Muthén & Curran, 1997), and a latent variable modeling approach has been noted as a flexible integrative analytic frame in which both fixed and random effects for linear, as well as nonlinear, outcomes are easily analyzed (Raykov, 2007; Skrondal & Rabe-Hesketh, 2004). Mun et al. (in press) demonstrated that latent curve models that use latent change scores can be specified to yield overidentified, testable models that are tailored to examine posttreatment effects or long-term follow-up effects for the analysis of data collected using pre–post–post designs. In addition, Mun et al. observed that mixture models would be a nice extension with which to examine heterogeneous subgroups that respond to a treatment in distinctively different manners in evaluation studies. The present study includes two related repeated-measures outcomes within a mixture analysis application.
3 We did not have the data on recidivism because individuals with a prior history of being mandated were not eligible to participate in the study. Note that Barnett, Murphy, Colby, and Monti (2007) reported that 15.8% of their mandated students were caught again. However, it is difficult to extrapolate the recidivism rate of this sample from other studies, due to differences in sample characteristics, university policies, and enforcement practices.
4 The skewness/kurtosis coefficients across the three assessments were 1.87/4.14, 2.87/10.39, and 2.58/8.63 for HED and 1.37/1.83, 2.93/11.08, and 2.00/4.35 for AP. After the log-transformation, the distributions were normalized. The resulting skewness/kurtosis coefficients from the transformed data across the three assessments were 0.54/−0.82, 1.06/0.20, and 0.68/−0.55 for HED and 0.19/−1.17, 1.27/0.78, and 0.70/−0.65 for AP.
5 Budd and Rollnick (1996) showed that the RCQ items can be rescored to create a continuous measure of readiness to change that has adequate reliability and predictive validity. In addition, a critical review by Carey, Purnine, Maisto, and Carey (1999) suggested that readiness to change may be more appropriately conceptualized as a continuous construct rather than as a discrete stage of change. A number of studies have utilized a continuous overall score (e.g., Carey, Henson, et al., 2007; Fromme & Corbin, 2004). In addition, the stages of change approach resulted in an inadequate number of observations for logistic regression due to a seriously unbalanced number of observations across the three stages in the current study. The majority of the students were in the precontemplation stage (67%) or action stage (29%). Only 4% of the students were in the contemplation stage. Furthermore, the correlations between the three stage scores and the continuous scale scores for readiness to change were very high and were in the expected direction. For the precontemplation, contemplation, and action stages, respectively, they were −.73, .84, and .88 (p < .05).
6 Note that the measured social desirability in this study reflects dispositional styles. The situation or context of the intervention program for mandated students may draw additional demand characteristics and may elicit socially desirable responses that are quite different from the individual dispositional tendency.
7 With three assessments, we could examine postintervention changes only linearly between the intervention and the 4-month follow-up assessment and between the 4-month and 15-month assessments postintervention. For the change process during the 15-month period following a PFI, a quadratic trajectory could be an alternative, in principle, to the latent change score approach shown in this study. However, polynomial nonlinear trajectories are unbounded with respect to time and do not reach an asymptote (see Curran & Willoughby, 2003). In addition, polynomial interpolation between assessments is necessary with a quadratic trajectory model. We concluded that with three assessments, potential extrapolation and interpolation errors could not be detected by the data and that discrete linear latent changes would be more appropriate for analysis than would continuous nonlinear trajectories. A study with more intensive assessments pre- and postintervention would be necessary to truly answer this interesting question.
8 We also analyzed HED and AP separately. The analysis of HED alone resulted in the same classification as that in the analysis that included both of the alcohol use measures. The analysis of AP resulted in similar patterns of changes but with more students who could be classified into improved cases. When the improved and nonimproved groups from each analysis were cross-tabulated into four groups and all subsequent analyses were carried out, almost all of the major findings reported in this study, including the two statistically significant moderators, were observed. Although the two approaches resulted in the same overall conclusion, we decided to report the findings from the analysis of two outcome measures conducted at the same time because doing so is statistically more parsimonious and simpler in interpretation. In addition, from the interventionist's perspective, clinical significance exists in empirically detecting subgroups on the basis of two harmful alcohol-use behaviors, rather than a single behavior isolated from the other. In all analyses, we used increased random sets of starting values of up to 100 for the initial stage and of up to 10 for the final stage optimization to avoid convergence of the final solution on local maxima. For the selected model, we increased these numbers to 1,000 and 50, respectively.
9 The Mplus program produces two separate plots for each repeated-measures outcome. The two panels in Figure 3 were from the analysis based on the simultaneous analysis shown in Figure 2.
10 Note that mixture modeling analysis is generally exploratory. It is increasingly clear that groups from mixture analysis do not necessarily provide evidence of a taxonic structure from a confirmatory analytic perspective but rather provide evidence of potentially useful, exploratory groups (Bauer, 2007; Bauer & Curran, 2003, 2004; Mun, Windle, & Schainker, 2008; Muthén, 2003; Muthén & Muthén, 2000; Sampson & Laub, 2005; von Eye & Bergman, 2003).
11 In the present study, the family-wise Type I error rate was not protected using an overly conservative procedure, such as the Bonferroni adjustment procedure, because the present study had a moderate sample size and because a trade-off exists between Type I and Type II error rates. The Bonferroni procedure is well known to be extremely conservative, and it thus has very little power for detecting true relations. Given that little is known about predictors and moderators of the PFI efficacy in the literature and that Type II error rates to detect moderation effects are high (McClelland & Judd, 1993; Sackett et al., 1986), we reasoned that the practical importance of an effect can be distinctively different from its statistical significance (or statistically defined small, medium, and large effect sizes; for a more detailed discussion, see McCartney & Rosenthal, 2000) and that it is important to balance between these two important considerations.
12 The ORs (95% CIs) for the other nonsignificant interaction terms were 1.79 (0.83–3.89), 1.27 (0.88–1.85), 0.68 (0.41–1.11), 1.00 (0.36–2.74), 1.45 (0.53–3.98), and 0.65 (0.24–1.78), respectively, for BMI × HED at Baseline, BMI × Readiness to Change, BMI × Positive Alcohol Expectancies, BMI × Female, BMI × First-Year Student, and BMI × Other Drug Use. When all main effects and interaction effects were simultaneously tested in a single model, the BMI × Incident Seriousness interaction effect was no longer significant (p > .05). The BMI × AP interaction effect remained significant (p < .05).
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APPENDIX APPENDIX A: Specification of Factor Loading Matrix
With three latent variables, the long-term follow-up outcome level (Level), initial change (IC) from baseline to 4 months postintervention, and subsequent change (SC) from 4 months to 15 months postintervention, the factor-loading matrix for each repeated-measures outcome shown in Figure 2 was specified
Thus, the observation y for individual i at Time 1, Time 2, and Time 3 can be expressed as
Therefore, the expected average at Time 3 indicates the long-term follow-up outcome level. The expected average changes from baseline to the 4-month assesssment and from the 4-month to the 15-month assessment are indicated by IC and SC, respectively (for greater detail, see Mun et al., in press).
Submitted: January 15, 2008 Revised: November 11, 2008 Accepted: November 17, 2008
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Source: Journal of Consulting and Clinical Psychology. Vol. 77. (1), Feb, 2009 pp. 88-102)
Accession Number: 2009-00563-017
Digital Object Identifier: 10.1037/a0014679
Record: 80- Title:
- Informant discrepancies in adult social anxiety disorder assessments: Links with contextual variations in observed behavior.
- Authors:
- De Los Reyes, Andres. Comprehensive Assessment and Intervention Program, Department of Psychology, University of Maryland at College Park, College Park, MD, US, adlr@umd.edu
Bunnell, Brian E., ORCID 0000-0002-4964-0688. Anxiety Disorders Clinic, Department of Psychology, University of Central Florida, FL, US
Beidel, Deborah C.. Anxiety Disorders Clinic, Department of Psychology, University of Central Florida, FL, US - Address:
- De Los Reyes, Andres, Comprehensive Assessment and Intervention Program, Department of Psychology, University of Maryland at College Park, Biology/Psychology Building, Room 3123H, College Park, MD, US, 20742
- Source:
- Journal of Abnormal Psychology, Vol 122(2), May, 2013. pp. 376-386.
- NLM Title Abbreviation:
- J Abnorm Psychol
- Page Count:
- 11
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- The Journal of Abnormal and Social Psychology; The Journal of Abnormal Psychology; The Journal of Abnormal Psychology and Social Psychology
- ISSN:
- 0021-843X (Print)
1939-1846 (Electronic) - Language:
- English
- Keywords:
- Operations Triad Model, correspondence, informant discrepancies, multiple informants, social anxiety disorder, patients, clinicians, symptoms
- Abstract:
- Multi-informant assessments of adult psychopathology often result in discrepancies among informants’ reports. Among 157 adults meeting criteria for either the generalized (n = 106) or nongeneralized (n = 51) social anxiety disorder (SAD) subtype, we examined whether discrepancies between patients’ and clinicians’ reports of patients’ symptoms related to variations in both SAD subtype and expressions of social skills deficits across multiple social interaction tasks. Latent class analyses revealed two behavioral patterns: (a) context-specific social skills deficits and (b) cross-context social skills deficits. Similarly, patients’ symptom reports could be characterized by concordance or discordance with clinicians’ reports. Patient–clinician concordance on relatively high levels of patients’ symptoms related to an increased likelihood of the patient meeting criteria for the generalized relative to nongeneralized subtype. Further, patient–clinician concordance on relatively high levels of patients’ symptoms related to an increased likelihood of consistently exhibiting social skills deficits across social interaction tasks (relative to context-specific social skills deficits). These relations were robust in accounting for patient age, clinical severity, and Axis I and II comorbidity. Further, clinical severity did not completely explain variability in patients’ behavior on laboratory tasks or discrepancies between patient and clinician reports. Findings provide the first laboratory-based support for the ability of informant discrepancies to indicate cross-contextual variability in clinical adult assessments, and the first of any developmental period to indicate this for SAD assessments. These findings have important implications for clinical assessment and developmental psychopathology research. (PsycINFO Database Record (c) 2018 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Anxiety Disorders; *Informants; *Social Anxiety; *Subtypes (Disorders); *Symptoms; Clinicians; Patients
- Medical Subject Headings (MeSH):
- Adult; Anxiety Disorders; Female; Humans; Logistic Models; Male; Models, Statistical; Research Design; Self Report; Severity of Illness Index; Social Behavior
- PsycINFO Classification:
- Neuroses & Anxiety Disorders (3215)
- Population:
- Human
Male
Female - Age Group:
- Adulthood (18 yrs & older)
Thirties (30-39 yrs)
Middle Age (40-64 yrs)
Aged (65 yrs & older) - Tests & Measures:
- Hamilton Rating Scale for Anxiety
Clinical Global Impressions Severity Scale
Simulated Social Interaction Test
Unstructured Conversation Tasks
Impromptu Speech Task
Social Phobia and Anxiety Inventory
Hamilton Rating Scale for Depression DOI: 10.1037/t04100-000 - Grant Sponsorship:
- Sponsor: National Institute of Mental Health
Grant Number: R01MH062547
Recipients: Beidel, Deborah C. - Methodology:
- Empirical Study; Quantitative Study
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- First Posted: Feb 18, 2013; Accepted: Nov 5, 2012; Revised: Oct 31, 2012; First Submitted: Mar 24, 2012
- Release Date:
- 20130218
- Correction Date:
- 20180614
- Copyright:
- American Psychological Association. 2013
- Digital Object Identifier:
- http://dx.doi.org/10.1037/a0031150
- PMID:
- 23421526
- Accession Number:
- 2013-04861-001
- Number of Citations in Source:
- 56
- Persistent link to this record (Permalink):
- http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2013-04861-001&site=ehost-live
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- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2013-04861-001&site=ehost-live">Informant discrepancies in adult social anxiety disorder assessments: Links with contextual variations in observed behavior.</A>
- Database:
- PsycINFO
Informant Discrepancies in Adult Social Anxiety Disorder Assessments: Links With Contextual Variations in Observed Behavior
By: Andres De Los Reyes
Comprehensive Assessment and Intervention Program, Department of Psychology, University of Maryland at College Park;
Brian E. Bunnell
Anxiety Disorders Clinic, Department of Psychology, University of Central Florida
Deborah C. Beidel
Anxiety Disorders Clinic, Department of Psychology, University of Central Florida
Acknowledgement: This research was supported by NIMH Grant R01MH062547 to the third author and Samuel M. Turner, PhD.
Deborah C. Beidel receives royalties on sales of the Social Phobia and Anxiety Inventory, an instrument administered as part of this research.
Best practices in clinical assessments involve taking and incorporating multiple informants’ reports (Hunsley & Mash, 2007). A key assumption underlying this practice is that there are no “definitive” measures of psychopathology (e.g., anxiety and aggression; Richters, 1992). The informants used vary widely, depending on developmental level, psychopathology domain, and purpose (e.g., diagnosis or treatment response). For instance, informants completing reports for children and adolescents (hereafter referred to collectively as “youth”) include significant others (e.g., parents, teachers, and peers), self-report, and/or trained observers (e.g., clinical interviewers and behavioral coders; De Los Reyes & Kazdin, 2005).
In youth assessments, informants’ clinical reports exhibit low-to-moderate correspondence (i.e., rs in the 0.20s to 0.30s; Achenbach, McConaughy, & Howell, 1987). Low correspondence levels translate into inconsistent findings in research and practice settings concerning gauging treatment response (De Los Reyes & Kazdin, 2006), treatment planning (Hawley & Weisz, 2003), and identifying efficacious treatments (De Los Reyes, Kundey, & Wang, 2011); and thus, introduce uncertainty into clinical decision-making (De Los Reyes, Alfano, & Beidel, 2011). Furthermore, historically informant discrepancies have largely been interpreted as measurement error or informant bias (De Los Reyes, in press). Yet, recent work demonstrates that informant discrepancies reflect the idea that (a) informants systematically vary in the contexts within which they observe youth behavior and (b) youth systematically vary in the contexts within which they express behaviors measured in clinical assessments (De Los Reyes & Kazdin, 2005; Kraemer et al., 2003). Thus, rather than measurement error, informant discrepancies convey meaningful information about how assessed behaviors vary across contexts (e.g., home vs. school; De Los Reyes, 2011). In turn, informant discrepancies may inform interpretations of diagnostic status, treatment response, and context-specific symptom expressions (Comer & Kendall, 2004; De Los Reyes, Henry, Tolan, & Wakschlag, 2009; Dirks, De Los Reyes, Briggs-Gowan, Cella, & Wakschlag, 2012). In this study, we extended research on informant discrepancies in clinical youth assessments to clinical adult assessments.
Multi-Informant Clinical Adult AssessmentsMulti-informant clinical adult assessments yield low-to-moderate correspondence levels that are only slightly higher than those observed for youth assessments (i.e., rs in the 0.30s to 0.40s; Achenbach, Krukowski, Dumenci, & Ivanova, 2005). Relative to youth assessments, multi-informant clinical adult assessments typically rely on a constrained subset of clinician reports, self-reports, collateral reports (e.g., spouses), and in limited circumstances, behavioral coders of patients’ performance on laboratory tasks (e.g., van der Ende, Verhulst, & Tiemier, 2012). In fact, a recent quantitative review (Achenbach et al., 2005) identified only 108 out of 51,000 articles published in a 10-year span that provided sufficient information to assess cross-informant correspondence (i.e., 0.2% of all studies). Consequently, whereas informant discrepancies research in youth assessments has evolved to viewing discrepancies as reflections of contextual variation (De Los Reyes, 2011, in press), with few exceptions (Mosterman & Hendriks, 2011; Oltmanns & Turkheimer, 2009), discrepancies research in clinical adult assessments remains descriptive in scope.
The Operations Triad Model of Multi-Informant AssessmentInterestingly, recent theoretical work indicates that researchers conducting clinical adult assessments may also benefit from interpreting informant discrepancies as markers of contextual variability in symptom expression. Specifically, the Operations Triad Model (OTM; De Los Reyes, Thomas, Goodman, & Kundey, 2013) conceptualizes circumstances in which multiple informants’ reports may be compared and interpreted. In one circumstance, Diverging Operations, informants’ reports yield different outcomes, and the differences reflect patients’ symptom expressions in some contexts and not others. In an alternative circumstance, Compensating Operations, informants’ reports yield different outcomes, and these differences arise for methodological reasons (e.g., measurement error in one, some, or both reports).
Importantly, Diverging and Compensating Operations may inform interpretations of discrepancies between patients’ self-reports and clinicians’ reports. One possibility is that these discrepancies reflect Compensating Operations (e.g., patient reports less reliable or valid than clinician reports or vice versa). However, an alternative possibility is that the discrepancies reflect Diverging Operations. Specifically, discordance versus concordance may signal true inconsistencies versus consistencies in contextual expressions of patients’ behaviors. Similar to child self-reports (e.g., Kraemer et al., 2003), adult self-reports ought to reflect observations of their own behavior within and across contexts (e.g., home and work). Conversely, in addition to accounting for patients’ self-reports, clinicians are trained to incorporate into their reports observations of patients in the clinic setting (Groth-Marnat, 2009). Thus, clinicians’ own observations of patients in a single context can be viewed as a key factor for meaningfully differentiating clinicians’ reports from patients’ self-reports. In fact, discrepancies may reflect behavior occurring within specific contexts, whereas concordance may indicate cross-context consistencies in behavior (Achenbach et al., 2005).
Multi-Informant Clinical Assessments of Adulthood Social Anxiety DisorderExamining reporting discrepancies may be particularly beneficial to interpreting adulthood social anxiety disorder (SAD) assessments. Indeed, in multi-informant assessments of adolescent social anxiety, patient self-reports often disagree with other informants’ reports (e.g., parents; De Los Reyes, Alfano, et al., 2011), and objective measures (e.g., psychophysiology; Thomas, Aldao, & De Los Reyes, 2012). Yet, even when reports disagree they all nevertheless yield valid data about social anxiety (De Los Reyes et al., 2012). Presumably, each report captures social anxiety in different ways (Silverman & Ollendick, 2005).
Multi-informant SAD assessments yield valid and contextually sensitive information, and in this respect informant discrepancies hold promise for interpreting assessment outcomes. Yet, phenomenological challenges arise with regard to how the construct SAD ought to be conceptualized and assessed. For example, informants’ reports of youth qualitatively vary; some self-reports disagree with other informants’ reports, and some self-reports correspond to a considerable extent with other informants’ reports (e.g., De Los Reyes et al., 2011a, 2011b). These findings suggest that discrepancies ought to be modeled categorically. However, recent work indicates that SAD symptoms tend to be expressed along a continuum of severity (e.g., Aderka, Nickerson, & Hoffman, 2012; El-Gabalawy, Cox, Clara, & Mackenzie, 2010; Ruscio, 2010). Alternatively, diagnostic manuals categorize SAD subtypes typified across contexts (i.e., generalized) or within specific contexts (i.e., nongeneralized; American Psychiatric Association [APA], 2001; Beidel, Rao, Scharfstein, Wong, & Alfano, 2010). Further, recommendations for the latest edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-V) involve maintaining the subtypes to identify patients who experience anxiety specifically within performance contexts (e.g., public speaking) based, in part, on research indicating that, relative to patients experiencing generalized SAD, patients experiencing SAD specific to performance situations express greater heart rate responses to laboratory speech tasks (Bögels et al., 2010).
In sum, SAD subtypes, to some extent, reflect cross-context consistencies (or inconsistencies). Therefore, subtypes can be used to test the ability of informant discrepancies to inform assessment outcomes. Specifically, when patient and clinician reports on questionnaire measures correspond on high symptom levels, this may indicate a generalized SAD subtype endorsed on a structured interview. Conversely, low correspondence may indicate a nongeneralized subtype. A second way to examine the meaning of informant discrepancies involves examining patients’ variations in associated features of SAD, such as social skills deficits (APA, 2001; Turner, Beidel, Dancu, & Keys, 1986). Importantly, the availability of multiple observational tasks allows for a cross-context assessment of patients’ social skills (e.g., structured or unstructured social interactions and performance situations; Beidel et al., 2010). Thus, assessing social skills deficits within multiple tasks may characterize patients on how consistently they express social skills deficits (i.e., cross-context social skills deficits vs. context-specific social skills deficits). If discrepancies between patient self-reports and clinician reports meaningfully correspond to contextual variations in patients’ behavior, then these discrepancies should be able to distinguish patients who consistently express social skills deficits from those who express context-specific deficits.
Purpose and HypothesesThis study extended the literature on informant discrepancies in clinical adult assessments. In a sample of adult patients who met diagnostic criteria for SAD, we examined whether patient–clinician reporting discrepancies related to two forms of behavioral variations of SAD: (a) SAD subtype and (b) laboratory observations of social skills.
We tested four hypotheses. First, as with adolescent SAD (De Los Reyes et al., 2012; De Los Reyes, Alfano et al., 2011; Thomas et al., 2012), we hypothesized low-to-moderate correspondence between patients’ self-reports and clinician reports. Second, as with preschool disruptive behavior (De Los Reyes et al., 2009), we expected to identify subgroups of patients who varied in whether they expressed social skills deficits across social skills tasks or not. Third, based on prior work with youth (De Los Reyes et al., 2011a, 2011b) we expected to identify patient–clinician subgroups that varied in correspondence on high symptom levels.
Fourth, we expected patients and clinicians whose reports corresponded on high symptom levels to be more likely than patients and clinicians whose reports did not correspond on high symptom levels to meet criteria for the generalized subtype. Similarly, we hypothesized that patients and clinicians whose reports corresponded on high symptom levels would be more likely than patients and clinicians whose reports did not correspond to relate to patients’ cross-context expressions of social skills deficits. Consistent with the OTM (De Los Reyes, Thomas et al., 2013), observations supporting these hypotheses would reflect Diverging Operations, with null effects reflecting Compensating Operations.
We considered two factors that might relate to informant discrepancies and contextual variations. First, greater contextually consistent expressions may reflect greater clinical severity. Importantly, prior work is equivocal (e.g., assessments of attention/hyperactivity, antisocial and disruptive behavior, and social anxiety): Some studies find greater impairment for patients expressing symptoms across contexts versus specific contexts and other studies find no such differences (cf. Bögels et al., 2010; De Los Reyes et al., 2009; Dirks et al., 2012). Nonetheless, it was important to account for clinical severity when examining contextual variations in behavior. Second, clinical severity co-occurs with other patient characteristics, namely comorbid mood and personality disorder diagnoses (e.g., Hunsley & Lee, 2010). Impaired social skills also co-occur with depressive symptoms (e.g., Beidel et al., 2010). Thus, we accounted for clinical characteristics representing patients’ clinical severity and diagnostic comorbidity.
Method Participants and Procedure
Participants were drawn from a larger study of 464 adults responding to recruitment efforts for adults meeting criteria for SAD and adults meeting criteria for no psychological disorder. A full description of the total sample, recruitment methods, and procedures has been reported elsewhere (Beidel et al., 2010). We focused on the 179 patients who met primary diagnostic criteria via the fourth edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM–IV; APA, 2001) for either the generalized (n = 119) or nongeneralized (n = 60) SAD subtype. Diagnostic assessments were carried out using the Structured Clinical Interview for DSM–IV (First, Spitzer, Gibbon, & Williams, 1997), the Structured Clinical Interview for DSM–IV Axis II (First, Gibbon, Spitzer, & Williams, 1997), and the Anxiety Disorders Interview Schedule (ADIS) for DSM–IV (Di Nardo, Brown, & Barlow, 1995).
Of the 179 generalized and nongeneralized patients, 157 patients provided complete data. We conducted exploratory analyses to examine whether the 157 participants differed from the 22 excluded participants as a function of demographics (age, gender, race, marital status, and number of children), and clinical characteristics (global illness severity, clinical severity rating of primary diagnosis, SAD subtype, comorbid Axis I diagnoses [i.e., any diagnosis, major depression, generalized anxiety], and comorbid Axis II diagnoses [i.e., any diagnosis, avoidant personality disorder, obsessive–compulsive personality disorder]). We conducted a large number of tests (n = 14) and did not have a priori hypotheses. Thus, we set a predefined bonferroni-corrected p value threshold of 0.003 (i.e., 0.05/14). No factor evidenced a significant relation to study inclusion/exclusion. The 157 participants we examined ranged in age from 18 to 78 years (M = 38.39, SD = 13.80). Sixty-five participants (41.4%) were male and 92 (58.6%) were female. Eighty-three participants were single (52.9%), 62 (39.5%) were married, and 12 (7.7%) were divorced or widowed. There were 111 (70.7%) Caucasians, 26 (16.6%) African Americans, eight (5.1%) Asians, four (2.5%) Latinas/Latinos, three (1.9%) from the Indian subcontinent, one (0.6%) Middle Easterner, three (1.9%) Pacific Islanders, and one (0.6%) adult of unknown race/ethnicity.
Prior work in this sample indicates that generalized and nongeneralized subtypes differ on demographic and clinical characteristics (Beidel et al., 2010). Thus, we conducted exploratory comparisons among the 157 participants, and in particular between the 106 generalized and 51 nongeneralized patients. The groups were compared on demographics (age, gender, race, marital status, and number of children), and clinical characteristics (global illness severity, clinical severity rating of the primary diagnosis, comorbid Axis I diagnoses [i.e., any diagnosis, major depression, generalized anxiety], and comorbid Axis II diagnoses [i.e., any diagnosis, avoidant personality disorder, obsessive–compulsive personality disorder]). Due to the number of tests (n = 13), we set a predefined bonferroni-corrected p value threshold of 0.004 (i.e., 0.05/13). Six factors evidenced a significant relation to subtype: Axis I comorbidity (generalized patients had higher rates of comorbidity [36.8%] relative to nongeneralized patients [9.8%]; χ2 = 12.43, p < 0.001); comorbid major depression (generalized patients had higher rates of comorbidity [15.1%] relative to nongeneralized patients [0%]; χ2 = 8.57, p < 0.004); Axis II comorbidity (generalized patients had higher rates of comorbidity [63.2%] relative to nongeneralized patients [7.8%]; χ2 = 42.60, p < 0.001); comorbid avoidant personality disorder diagnosis (generalized patients had higher rates of this diagnosis [58.5%] relative to nongeneralized patients [2%]; χ2 = 45.80, p < 0.001); patient age (generalized patients were younger [M = 35.95] relative to nongeneralized patients [M = 43.45]; F(1, 155) = 10.80, p < 0.002); global illness severity (generalized patients had a higher illness severity [M = 5.24] relative to nongeneralized patients [M = 4.67]; F(1, 155) = 15.11, p < 0.001); and clinical severity rating of the primary diagnosis (generalized patients had higher clinical severity ratings [M = 6.11] relative to nongeneralized patients [M = 5.47]; F(1, 155) = 11.76, p < 0.002). Thus, we statistically controlled for these six variables in tests of our main hypothesis (i.e., Hypothesis 4 below).
Measures
Patient self-report instruments of patients’ symptoms
We collected two self-report measures. First, the Social Phobia and Anxiety Inventory (SPAI; Turner, Beidel, Dancu, & Stanley, 1989) assessed severity of SAD symptoms. The SPAI has high test–retest reliability, differentiates SAD patients from normal controls and other anxiety patients (Turner et al., 1989), has good concurrent and external validity (Beidel, Borden, Turner, & Jacob, 1989; Beidel, Turner, Jacob, & Cooley, 1989), and reflects both statistically reliable and clinically significant change following treatment (Beidel, Turner, & Cooley, 1993). We used the SPAI difference score. Second, participants completed the Fear Questionnaire (Marks & Matthews, 1979), which has a 5-item Social Phobia subscale that assesses avoidance of performance or observation situations. Extensive evidence supports the reliability and validity of the Social Phobia subscale (e.g., Connor et al., 2000; Cox, Parker, & Swinson, 1996; Herbert, Bellack, & Hope, 1991).
Clinician report instruments of patients’ symptoms
Clinician reports were based on the Hamilton Rating Scale for Anxiety (Hamilton, 1959) and Hamilton Rating Scale for Depression (Hamilton, 1960). We used total summary scores for both of these scales.
Indices of clinical severity
Doctoral-level clinicians who completed the diagnostic interviews also completed the 7-point Clinical Global Impressions Severity Scale (CGI; Guy, 1976). Additionally, we assessed severity of the patient’s primary diagnosis using the clinical severity rating of the ADIS for DSM–IV (Di Nardo et al., 1995). We used these scores as covariates in tests of Hypothesis 4 to control for both patients’ overall clinical severity, as well as clinical severity specific to the patient’s anxiety.
Behavioral tasks used to assess patients’ social skills
Three tasks were used to assess social skill. In the conversation tasks, participants interacted with a confederate (or confederates) trained to respond in a friendly but neutral fashion (e.g., interacting with, but not leading, the conversation). In the speech task, three confederates sat silently, looking polite but not being overly encouraging. Each task was introduced by the experimenter in an adjacent room, directing the assessment over an intercom. The three tasks included different types of social discourse and together, allowed for assessment of social skill across contexts. Beidel and colleagues (2010) provided complete psychometric information on all tasks.
First, the Simulated Social Interaction Test (SSIT; Curran, 1982) is a structured task that requires the participant to interact with a confederate in eight role-play scenarios. Each role-play lasted approximately 3 min. For each scene, the confederate had two standardized responses that were delivered (one at a time). Thus, the examiner read the scene, the confederate delivered a prompt, the participant responded, the confederate delivered a second prompt, and the participant responded. The participant interacted with a male confederate in four scenes, and interacted with a female confederate in another four scenes.
Second, there were two Unstructured Conversation Tasks (UCT; Turner, Beidel, Cooley, Woody, & Messer, 1994), one involving interaction with an opposite sex confederate (e.g., “pretend you are at a dinner party and get to know the person next to you”) and one with a same sex confederate (e.g., “you just moved into a new house and see your neighbor in the back yard”). Each scenario was 3 min long (6 min total), and counterbalanced on task type (i.e., dinner party or neighbor interaction) and sex of confederate (i.e., same sex or opposite sex confederate). Because the UCTs involved a general scenario, there were no specific confederate prompts. Confederates responded to the participant, but did not assume the burden of the conversation.
Third was the Impromptu Speech Task (IST). Participants delivered a 10 min impromptu speech using up to three topics (provided by the experimenter). The audience consisted of three confederates. Participants were given 3 min to prepare their speech and allowed to terminate the speech after 3 min, by holding up a stop card, if they felt the stress of speaking was too great.
Independent observers’ reports of social skills
We used the independent observers’ ratings of social skills that were published previously (for rating and psychometric information, see Beidel et al., 2010). Specifically, assessments were videotaped and rated by independent raters unaware of diagnostic status. Raters were undergraduate students who were trained to criterion by a doctoral graduate student. Each SSIT interaction was rated for participant’s degree of social skill using a 5-point Likert scale. Higher ratings reflected better skill. Ratings for social skills in positive interactions were examined separately from ratings in negative interactions. A similar rating strategy was used to take overall social skill ratings for the UCT and IST. As in prior research (Beidel et al., 2010; Jacobson & Truax, 1991), the four social skill ratings were dichotomized. Each participant was classified as to whether their social skills were two standard deviations below the sample mean (in this study, coded “1”) versus not (in this study, coded “0”). As described below, we used these four dichotomous variables to construct analytic models of social skills performance across the behavioral tasks. The frequencies of participants coded as exhibiting social skills deficits are presented in Table 1.
Descriptive Statistics of Main Study Variables and Clinical Covariates (n = 157)
Data-Analytic Plan
We first conducted preliminary analyses to detect deviations from normality. To test our first hypothesis, we computed within- and cross-informant correlations. We tested our second hypothesis by conducting exploratory latent class analyses (LCA; McCutcheon, 1987) on the four dichotomous observer reports of patients’ social skills. Like cluster analysis, LCA identifies groups of cases based on similar patterns of indicator variables. Like confirmatory factor analysis, LCA tests the absolute and relative fit of models yielding indices such as the Bayesian Information Criterion (BIC) to examine whether a given model is a parsimonious solution to the data (relative to other model solutions), with lower scores indicating greater parsimony (Raftery, 1986, 1995). Latent class analysis uses categorical or ordinal variables to produce classes within which there is local independence of indicators (i.e., indicator variables are statistically independent within levels of each latent class). Thus, LCA is a person-centered approach that allowed us to identify classes of patients varying in expressions of social skills deficits across tasks. Probabilities provided by an LCA solution may be used to assess the confidence with which cases are assigned (McCutcheon, 1987). We tested one- through three-class solutions, and assessed model fit using each solution’s BIC index, as well as the probabilities used to assign participants to classes.
We tested our third hypothesis by conducting exploratory latent profile analyses (LPA) on patient and clinician reports (Bartholomew, Steele, Moustaki, & Galbraith, 2002). Latent profile analysis focuses on continuous indicators; these procedures are a generalization of the LCA procedure used to model observer reports of patients’ social skills, which uses categorical or ordinal variables (McCutcheon, 1987). We tested one- through five-class solutions, and assessed model fit using both BIC indices of the solutions and profile assignment probabilities.
We tested our fourth hypothesis by conducting two separate hierarchical logistic regression analyses. First, we entered SAD subtype as a nominal dependent variable. Independent variables included patient age, the CGI Severity of Illness score, the clinical severity rating of the primary diagnosis, Axis I comorbidity, presence of a major depression diagnosis, Axis II comorbidity, presence of an avoidant personality disorder diagnosis, and a variable representing latent profile assignments of patient–clinician reporting discrepancies. Second, we entered a variable representing latent class assignments of observers’ reports of patients’ social skills as a nominal dependent variable, and we entered as independent variables patient age, the CGI Severity of Illness score, the clinical severity rating of the primary diagnosis, Axis I comorbidity, presence of a major depression diagnosis, Axis II comorbidity, presence of an avoidant personality disorder diagnosis, and a variable representing latent profile assignments of patient–clinician reporting discrepancies. For both tests, we centered all continuous variables (patient age, CGI Severity of Illness score, and clinical severity rating of the primary diagnosis), and all nominal independent variables were coded either “0” or “1.”
Results Preliminary Analyses
Frequency distributions for all continuous variables did not reveal any deviations from normality (see Table 1).
Hypothesis 1: Cross-Informant Correlations for Patient and Clinician Reports
There were moderate correlations between patient and clinician reports (mean r = 0.43; Cohen, 1988; see Table 2). In contrast, we observed large correlations within patient self-reports, as well as within clinician reports of patients (mean r = 0.72; Cohen, 1988). Correlations between reports completed by the same informant were larger than between-informant correlations, making these reports amendable to latent profile modeling of patient–clinician reporting discrepancies. In Table 2, we also report correlations between the patient and clinician reports and the indices of clinical severity used as covariates. Importantly, indices of clinical severity significantly correlated with both patient self-reports and clinician reports, providing further support for using these indices as covariates.
Correlations Among Patient Self-Reports and Clinician Reports About Patients (n = 157)
Hypothesis 2: Latent Classifications of Behavioral Observations of Patient Social Skills
Latent class analyses of observers’ reports of patients’ social skills revealed superior model fit for a two-class solution, BIC(based on L2) = −19.53. This BIC was lower (i.e., more parsimonious a fit) than the BIC indices of the one- (BIC = 63.76) and three-class solutions (BIC = −2.2). We present the descriptive statistics of this model solution in Table 3. Broadly, patients in both classes exhibited a nonzero probability of exhibiting social skills deficits to some extent (i.e., at minimum, within specific tasks). Importantly, one class exhibited social skills deficits consistently across tasks, whereas the other class exhibited these deficits within some tasks and not others. Thus, our two-class solution consisted of participants who evidenced context-specific social skills deficits across tasks (n = 127; Context-Specific Social Skills Deficits), or social skills deficits across tasks (n = 30; Cross-Context Social Skills Deficits). Importantly, the mean assignment probabilities for both classes were well above the 0.70 threshold recommended by Nagin (2006).
Latent Class Solution of Independent Observer Ratings of Patients’ Expressions of Social Skills Deficits (n = 157)
Hypothesis 3: Latent Profiles of Patient–Clinician Reporting Discrepancies
Latent profile analyses of patient–clinician reporting discrepancies revealed superior model fit for a four-class solution, BIC(based on LL) = 4484.63. This BIC was lower (i.e., more parsimonious a fit) than the BIC indices of the three- (BIC = 4498.54) and five-class solutions (BIC = 4498.45). We present the descriptive statistics of this model solution in Table 4. The four-class solution consisted of patient–clinician dyads that varied in whether their reports concurred on the level of the patients’ symptoms. Specifically, three groups could be characterized by concordance on relatively high, (n = 51), relatively moderate (n = 48), or relatively low (n = 28) symptom levels across patient and clinician reports. A fourth group (n = 30) could be characterized by discordance between patient and clinician reports, in that patients reported relatively moderate symptom levels whereas clinicians reported relatively low symptom levels. In light of the similarities in both concordance between reports and relative symptom levels, we grouped the concordant-high and concordant-moderate profiles into a single group, and the concordant-low and discordant profiles into a second group. We refer to this variable below as “Latent Profiles of Patient–Clinician Reporting Discrepancies.” This variable consisted of a “Concordant on Relatively High Reports between Patient and Clinician” group (n = 99) and a “Not Concordant on Relatively High Reports between Patient and Clinician” group (n = 58). This was the key independent variable used in tests of Hypothesis 4 (see Tables 5 and 6).
Latent Profile Solution of Patient-Clinician Reporting Discrepancies of Patients’ Symptoms (n = 157)
Nominal Logistic Regression Predicting Patients’ Diagnostic Status (Generalized Versus Nongeneralized Social Anxiety Disorder) as a Function of Latent Profiles of Patient-Clinician Informant Discrepancies (n = 157)
Nominal Logistic Regression Predicting Independent Observers’ Ratings of Patients’ Social Skills (Context-Specific Social Skills Deficits Versus Cross-Context Social Skills Deficits) as a Function of Latent Profiles of Patient-Clinician Informant Discrepancies (n = 157)
Hypothesis 4a: Patient–Clinician Reporting Discrepancies and Diagnostic Status
Nominal logistic regression analyses of the relation between patient–clinician reporting discrepancies and diagnostic status are presented in Table 5. Of the control variables, only patient age (greater age related to decreased likelihood of a generalized subtype diagnosis) and the presence of an avoidant personality disorder diagnosis (presence of a diagnosis related to increased likelihood of a generalized subtype diagnosis) related to subtype. Consistent with our hypotheses, concordance between patient and clinician reports of patients’ high symptom levels related to an increased likelihood of the patient receiving a generalized subtype diagnosis, relative to a nongeneralized subtype diagnosis.
Hypothesis 4b: Patient–Clinician Reporting Discrepancies and Behavioral Observations
Nominal logistic regression analyses of the relation between patient–clinician reporting discrepancies and behavioral observations of patients’ social skills (i.e., context-specific social skills deficits vs. cross-context social skills deficits) are presented in Table 6. Of the control variables, none related to behavioral observations of patients’ social skills. Consistent with our hypotheses, concordance between patient and clinician reports of patients’ high symptom levels related to an increased likelihood of the patient consistently expressing social skills deficits across behavioral tasks, relative to expressing context-specific social skills deficits.
In tests of Hypotheses 4a and 4b, we did not observe significant effects of patients’ clinical severity in relation to diagnostic status nor behavioral observations (Tables 5 and 6). Nevertheless, it was important to conduct an additional analysis to examine whether patients’ clinical severity levels explained variance in patient–clinician discrepancies. Specifically, we entered the Latent Profiles of Patient–Clinician Reporting Discrepancies as a nominal dependent variable, and entered as independent variables patient age, the CGI Severity of Illness score, the clinical severity rating of the primary diagnosis, Axis I comorbidity, presence of a major depression diagnosis, Axis II comorbidity, and presence of an avoidant personality disorder diagnosis. Importantly, patient–clinician discrepancies evidenced nonsignificant relations with the CGI Severity of Illness score (p > 0.45) and the clinical severity rating of the primary diagnosis (p > 0.35).
Discussion Main Findings
In a clinical assessment battery of adults that included multiple informants’ reports, structured diagnostic interviews, and cross-contextual behavioral assessments, there were four main findings. First, we observed moderate correlations between patient and clinician reports of patients’ symptoms. In fact, the mean correlation was nearly identical to the mean cross-informant correlation of internalizing psychopathology reported in a recent quantitative review (Achenbach et al., 2005). Second, latent class analyses identified two patterns of patients’ performance on social skills tasks: (a) context-specific social skills deficits and (b) cross-context social skills deficits. Third, we could similarly characterize patients’ self-reports by concordance versus discordance with clinicians’ reports.
Fourth, we observed the ability of patient–clinician discrepancies to inform interpretations of SAD assessments. Specifically, patient–clinician concordance on relatively high levels of patients’ symptoms increased the likelihood of the patient being diagnosed with generalized rather than nongeneralized SAD. Additionally, patient–clinician concordance on relatively high levels of patients’ symptoms increased the likelihood of the patient expressing social skills deficits across social interaction tasks rather than within specific tasks. These findings are consistent with the OTM (De Los Reyes et al., 2013). Rather than signaling measurement error, discordance versus concordance between patient and clinician reports signaled inconsistent expressions of patients’ social skills deficits across contexts. For some patients, laboratory measures indicated social skills deficits in some but not all settings, and patient and clinician reports reflected these variations. These observations met the expectations of findings as interpreted using the OTM’s Diverging Operations concept (De Los Reyes et al., 2013).
We observed our main findings although accounting for other factors that might have explained the relations. First, relative to inconsistent symptom expressions across situations, consistent expressions may indicate greater clinical severity, although prior work across multiple symptom domains (e.g., attention and hyperactivity, antisocial and disruptive behavior, and social anxiety) does not consistently support this idea (cf. Bögels et al., 2010; De Los Reyes et al., 2009; Dirks et al., 2012). Importantly, our findings add to a growing body of research that indicates that cross-contextual variability in patients’ behavior is more than simply a marker of clinical severity. Specifically, in tests of the main hypotheses (Tables 5 and 6), indices of clinical severity did not relate to contextual variability in patients’ behavior. Second, we previously reported that indices of clinical severity also did not significantly relate to measurements of patient–clinician reporting discrepancies. This is not to say that clinical severity plays no role in cross-contextual variability in patients’ behavior, and no role in discrepancies between patient and clinician reports of patients’ behavior. Instead, our data, and data from other research teams, indicates that clinical severity does not completely explain these behaviors. In sum, patterns of concordance and discrepancies between patient and clinician reports reflect contextual variations in patients’ behavior.
Significance of Main Findings
This study expands the landscape within which researchers may find value in meaningfully interpreting discrepant outcomes in multi-informant assessments. Indeed, to our knowledge, this is the first investigation to demonstrate the ability of informant discrepancies to indicate cross-contextual variability in adult patients’ behavior. Further, no previous study of patients of any developmental period has demonstrated the ability of informant discrepancies in SAD assessments to indicate cross-contextual behavioral variations. To date, such data exist only for reports of youth externalizing psychopathology (for a review see Dirks et al., 2012).
Limitations
There are limitations to the present study. First, we examined patient–clinician reporting discrepancies in relation to SAD subtypes. In addition to examining social skills, examining subtypes allowed us to test whether informant discrepancies could increase understanding of multi-informant adult assessments. Recent work indicates that SAD symptoms exist along a continuum (e.g., Aderka et al., 2012; El-Gabalawy et al., 2010; Ruscio, 2010). Importantly, we accounted for clinical severity both in overall clinical presentation and with regard to severity of the primary diagnosis. Thus, severity does not appear to account for our effects (Tables 5 and 6). Nevertheless, future research seeking to replicate and extend our findings should examine other clinical indices of SAD such as psychophysiological arousal during social tasks.
Second, we examined informant discrepancies in adult SAD assessments with a focus on patient and clinician reports, consistent with the majority of research in this area (Achenbach et al., 2005). These assessments render the report of one informant (the clinician) to systematically reflect, in part, the verbal report of patients’ perspectives. As a result, we may have observed inflated levels of informant concordance, relative to reports between informants who may exhibit fundamentally different perspectives about patient functioning (e.g., self-report vs. parent, teacher, and partner reports; van der Ende et al., 2012). If this is true, it may be that one might gain a richer understanding of contextual variations by examining discrepancies among other informants’ reports. Thus, future research should incorporate multiple informants’ reports beyond those of patients and clinicians.
Third, our findings reflect informant discrepancies in adult SAD assessments. Historically, informant discrepancies have been interpreted as measurement error or informant bias. Our findings are in keeping with the idea that error or bias accounts for some, but not all of the variance in informant discrepancies (De Los Reyes, in press). That is, we observed a statistically significant link between patient–clinician reporting discrepancies and variations in both SAD subtype and social skills deficits (Tables 5 and 6). Yet, although roughly one third of the sample presented with the nongeneralized subtype, a much larger percentage exhibited context-specific social skills deficits (see Table 3). Stated differently, patient–clinician reporting discrepancies are not completely accurate signals of contextual variations. To this end, discrepancies between informants’ reports will not always offer valuable behavioral information.
Research and Theoretical Implications
Consistent with research in youth (De Los Reyes, 2011), this study suggests that informant discrepancies can reflect contextual variability in manifestations of adult psychopathology. In line with these findings, future research should examine whether informant discrepancies provide meaningful insight into situational specificity in adult symptom expressions. Future work should examine whether informant discrepancies reveal symptom variations attributable to the specific demands of social situations. For example, consider a study in which patient and clinician reports disagree as to whether they evidence high symptom levels; that is, only patient reports reveal high symptom levels. Interpreting these discrepant assessment outcomes as yielding meaningful information might result in concluding that these findings indicate true differences in the extent of symptom expression within and across various contexts. For example, perhaps patient–clinician disagreement suggests that patients engage in social contexts that vary in contingencies that influence symptom expression (e.g., one-on-one social interactions but not group-based social interactions). Such insight might lead to improvements in the capacity for diagnostic assessments to inform treatment planning. That is, improved characterizations of patients’ behavior may translate in increased likelihoods that researchers and practitioners formulate treatment plans that specifically target behavioral variations in symptom expression (i.e., specific contexts for which symptom reduction may be particularly effective). Additionally, cross-contextual behavioral assessment paradigms as used in prior work (Beidel et al., 2010) might be used in future research as independent measures by which to examine whether informant-based discrepancies in reports reflect meaningful context-specific symptom expressions. The investigation of the clinical meaning of informant discrepancies within diagnostic formulation and treatment planning contexts may be particularly fruitful avenues for future research.
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Submitted: March 24, 2012 Revised: October 31, 2012 Accepted: November 5, 2012
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Source: Journal of Abnormal Psychology. Vol. 122. (2), May, 2013 pp. 376-386)
Accession Number: 2013-04861-001
Digital Object Identifier: 10.1037/a0031150
Record: 81- Title:
- Intimate partner violence and specific substance use disorders: Findings from the National Epidemiologic Survey on Alcohol and Related Conditions.
- Authors:
- Smith, Philip H.. Department of Community Health and Health Behavior, School of Public Health and Health Professions, University at Buffalo, State University of New York, Buffalo, NY, US, psmith3@buffalo.edu
Homish, Gregory G.. Department of Community Health and Health Behavior, School of Public Health and Health Professions, Research Institute on Addictions, University at Buffalo, State University of New York, Buffalo, NY, US
Leonard, Kenneth E.. Research Institute on Addictions, Department of Psychiatry, University at Buffalo, State University of New York, Buffalo, NY, US
Cornelius, Jack R.. Department of Psychiatry, University of Pittsburgh, PA, US - Address:
- Smith, Philip H., Department of Community Health and Health Behavior, School of Public Health and Health Professions, University at Buffalo, State University of New York, 329 Kimball Tower, 3435 Main Street, Buffalo, NY, US, 14214, psmith3@buffalo.edu
- Source:
- Psychology of Addictive Behaviors, Vol 26(2), Jun, 2012. pp. 236-245.
- NLM Title Abbreviation:
- Psychol Addict Behav
- Page Count:
- 10
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- Bulletin of the Society of Psychologists in Addictive Behaviors; Bulletin of the Society of Psychologists in Substance Abuse
- Other Publishers:
- US : Educational Publishing Foundation
Society of Psychologists in Addictive Behaviors - ISSN:
- 0893-164X (Print)
1939-1501 (Electronic) - Language:
- English
- Keywords:
- alcohol use, illicit drug use, intimate partner violence, mental health, substance use, cannabis, cocaine, opioid
- Abstract:
- [Correction Notice: An Erratum for this article was reported in Vol 26(2) of Psychology of Addictive Behaviors (see record 2012-09243-001). There is an error in the last sentence in the first paragraph of the Results section. The corrected sentence is presented in the erratum.] The association between substance use and intimate partner violence (IPV) is robust. It is less clear how the use of specific substances relates to relationship violence. This study examined IPV perpetration and victimization related to the following specific substance use disorders: alcohol, cannabis, cocaine, and opioid. The poly substance use of alcohol and cocaine, as well as alcohol and marijuana, were also examined. Data were analyzed from wave two of the National Epidemiologic Survey on Alcohol and Related Conditions (2004–2005). Associations between substance use disorders and IPV were tested using logistic regression models while controlling for important covariates and accounting for the complex survey design. Alcohol use disorders and cocaine use disorders were most strongly associated with IPV perpetration, while cannabis use disorders and opioid use disorders were most strongly associated with IPV victimization. A diagnosis of both an alcohol use disorder and cannabis use disorder decreased the likelihood of IPV perpetration compared to each individual substance use disorder. A diagnosis of both an alcohol use disorder and cocaine use disorder increased likelihood of reporting IPV perpetration compared with alcohol use disorders alone but decreased likelihood of perpetration compared with a cocaine use disorder diagnosis alone. Overall, substance use disorders were consistently related to intimate partner violence after controlling for important covariates. These results provide further evidence for the important link between substance use disorders and IPV and add to our knowledge of which specific substances may be related to relationship violence. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Drug Abuse; *Intimate Partner Violence; *Mental Health; Alcohols; Cannabis; Cocaine; Mental Disorders; Opiates; Substance Use Disorder
- Medical Subject Headings (MeSH):
- Adult; Alcohol-Related Disorders; Alcoholic Intoxication; Comorbidity; Crime Victims; Female; Health Surveys; Humans; Interview, Psychological; Logistic Models; Male; Middle Aged; Prevalence; Risk Factors; Sex Distribution; Spouse Abuse; Substance-Related Disorders; United States; Violence; Young Adult
- PsycINFO Classification:
- Behavior Disorders & Antisocial Behavior (3230)
- Population:
- Human
Male
Female - Location:
- US
- Age Group:
- Adulthood (18 yrs & older)
- Tests & Measures:
- Alcohol Use Disorder and Associated Disabilities Interview Schedule-DSM–IV version
Intimate partner violence measure - Grant Sponsorship:
- Sponsor: ABMRF/The Foundation for Alcohol Research
Recipients: Homish, Gregory G. - Methodology:
- Empirical Study; Quantitative Study
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- First Posted: Aug 8, 2011; Accepted: Jun 17, 2011; Revised: Jun 8, 2011; First Submitted: Oct 13, 2010
- Release Date:
- 20110808
- Correction Date:
- 20151207
- Copyright:
- American Psychological Association. 2011
- Digital Object Identifier:
- http://dx.doi.org/10.1037/a0024855
- PMID:
- 21823768
- Accession Number:
- 2011-16753-001
- Number of Citations in Source:
- 37
- Persistent link to this record (Permalink):
- http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2011-16753-001&site=ehost-live
- Cut and Paste:
- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2011-16753-001&site=ehost-live">Intimate partner violence and specific substance use disorders: Findings from the National Epidemiologic Survey on Alcohol and Related Conditions.</A>
- Database:
- PsycINFO
Intimate Partner Violence and Specific Substance Use Disorders: Findings From the National Epidemiologic Survey on Alcohol and Related Conditions
By: Philip H. Smith
Department of Community Health and Health Behavior, School of Public Health and Health Professions, University at Buffalo, The State University of New York;
Gregory G. Homish
Department of Community Health and Health Behavior, School of Public Health and Health Professions, and Research Institute on Addictions, University at Buffalo, The State University of New York
Kenneth E. Leonard
Research Institute on Addictions and Department of Psychiatry, University at Buffalo, The State University of New York
Jack R. Cornelius
Department of Psychiatry, University of Pittsburgh
Acknowledgement: The research and manuscript were supported by a grant from the ABMRF/The Foundation for Alcohol Research, awarded to Gregory G. Homish.
See page 254 for a correction to this article.
The likelihood that an individual will experience intimate partner violence (IPV) during their lifetime is high (Breiding, Black, & Ryan, 2008b; Coker et al., 2002). Based on the National Violence Against Women Survey, Coker and colleagues (2002) estimated that the lifetime prevalence of psychological, physical, or sexual IPV was 28.9% for women and 22.9% for men. Using data from the Behavioral Risk Factor Surveillance System, Breiding et al. (2008b) found that 29.4% of women and 15.9% of men reported at least one lifetime occurrence of physical or sexual IPV. IPV victimization is associated with numerous adverse health outcomes, such as current poor health, depressive symptoms, chronic disease, chronic mental illness, injury, posttraumatic stress disorder, and HIV risk (Breiding, Black, & Ryan, 2008a; Campbell, 2002; Coker et al., 2002; Hill, Schroeder, Bradley, Kaplan, & Angel, 2009; Johnson & Leone, 2005). While there is strong evidence that substance use is both a risk factor and outcome associated with IPV (Caetano, McGrath, Ramisetty-Mikler, & Field, 2005; El-Bassel, Gilbert, Wu, Go, & Hill, 2005; Leonard, 1993; Stuart, Temple, & Moore, 2007), our understanding of the association between specific substances and IPV is limited. A greater understanding of these associations will potentially allow intervention and prevention efforts to focus more specifically on the substances most closely associated with relationship violence.
Specific Substance Use and Intimate Partner Violence Perpetration
Experimental research has tested a direct psychopharmacologic link between specific substance use and IPV, but the strength of the findings varies by drug class. For example, there is robust evidence that alcohol intoxication increases aggression (Chermack & Giancola, 1997), but findings for marijuana and cocaine are equivocal (Hoaken & Stewart, 2003). These experimental studies are limited in that they only test a direct pathway between acute substance use and aggressive behavior. Thus, insignificant findings for a specific drug type do not necessarily imply that use of this drug does not contribute to relationship violence. There are other pathways through which use of specific substances may lead to relationship violence perpetration (Leonard, 1993; T. M. Moore et al., 2008), and observational research may be better equipped to tap into these mechanisms.
There is a large body of observational research examining the association between specific substances and IPV perpetration (Foran & O'Leary, 2008; Leonard, 1993; T. M. Moore et al., 2008); however, the consistency of the findings varies with regard to specific substances. There is strong and consistent evidence that alcohol use is associated with intimate partner violence (Foran & O'Leary, 2008; Leonard, 1993), notwithstanding the few studies that do not find evidence of this effect (Feingold, Kerr, & Capaldi, 2008). Findings on the relation between specific illicit drugs and IPV perpetration are less consistent (Feingold et al., 2008; Murphy, O'Farrell, Fals-Stewart, & Feehan, 2001; Stuart et al., 2008). The majority of these studies use treatment- or community-based samples of convenience, each of which has important limitations. Treatment samples tend to be relatively homogenous and are not necessarily representative of the population of individuals with substance use disorders; thus, findings may lack generalizability. Findings from community samples tend to be more broadly applicable, but the prevalence of use disorders with respect to specific substances is often too uncommon in these samples to be studied in any meaningful way. As a solution, most community samples have collapsed drug use into a single variable or measured general drug use rather than substance use disorders.
Population-based samples may be particularly advantageous for addressing the relation between specific substances and intimate partner violence. These samples may be large enough to assess the problematic use of individual substances, while at the same time providing a high degree of external validity. A few previous studies have examined substance use and partner violence perpetration using population based samples (Anderson, 2002; Caetano et al., 2005; Cunradi, Caetano, & Schafer, 2002; Kantor & Straus, 1987; Stalans & Ritchie, 2008); however, some only examined alcohol (Caetano et al., 2005; Kantor & Straus, 1987), while others collapsed drug use into a single category (Anderson, 2002; Cunradi et al., 2002). Stalans and Ritchie (2008) examined associations between specific substance use types and relationship violence using data from the National Household Survey on Drug Abuse. Their findings indicated that in the overall sample marijuana abuse or dependence was not associated with IPV perpetration, while a significant association was found for past year stimulant use. These findings provide insight into the relation between specific substance use and IPV perpetration; however, the authors were unable to test associations between the abuse of specific stimulant drugs and IPV perpetration, and the IPV outcome measure only included hitting, leaving out other types of physically violent behavior. In an attempt to summarize previous findings across a diverse range of studies, Moore and colleagues (2008) conducted a comprehensive meta-analysis on the relation between specific substance use and IPV perpetration. With regard to physical IPV perpetration, significant effects were found for cocaine and opiates, while the effects for sedatives, marijuana, stimulants, and hallucinogens were not statistically significant.
Moore and colleagues (2008) also called attention to the need for research on how poly substance use contributes to relationship violence. Substance users often use combinations of substances or substances in sequence (S. C. Moore, 2010), and comorbid substance use may differentially impact relationship violence compared to the use of individual substances. Because of the relatively common comorbid use of these substances, it may be important to assess the impact on relationship aggression. Moore and Stuart (2004) examined the interaction between alcohol use and other drug use among men in a batterer intervention program and found a significant effect. However, Murphy et al. (2001) conducted a similar analysis among men in alcohol treatment and did not find evidence of an interaction. These studies did not assess the interaction between alcohol and individual substances and are subject to the previously noted limitations of treatment samples, which could account for the conflicting results. Research is needed to assess poly substance use in more heterogeneous populations.
Specific Substance Use and IPV Victimization
The relation between specific substance use and IPV victimization is also complex, and multiple pathways have been hypothesized (El-Bassel et al., 2005; Kilpatrick, Acierno, Resnick, Saunders, & Best, 1997). For one, outcomes associated with substance use may generally increase risk for conflict in relationships, leading to violent behavior. Substance use issues can lead to increased stress in the relationship and disputes with regard to, for example, spending money or where and with whom a couple spends time, which may manifest as violent conflict in some couples (Goldstein, 1985). Women may be particularly vulnerable to IPV victimization while they are under the influence of substance use. Also, it has been suggested that women may use drugs like marijuana and tranquilizers to self-medicate the physical and emotional pains of victimization, a factor that may operate in cross-sectional studies (Gilbert, El-Bassel, Rajah, et al., 2000; Gilbert, El-Bassel, Schilling, Wada, & Bennet, 2000; Kilpatrick et al., 1997).
As with IPV perpetration, research generally supports an association between victimization and substance use. Empirical evidence for an association between alcohol use and IPV victimization is conflicting (Breiding et al., 2008a; Coker et al., 2002; El-Bassel et al., 2005; Testa, Livingston, & Leonard, 2003; Walton et al., 2009). Findings from the small number of studies on IPV victimization and individual illicit drug categories are mixed, making it difficult to draw conclusions about these relations (Coker et al., 2002; El-Bassel et al., 2005; Kilpatrick et al., 1997; Testa et al., 2003; Walton et al., 2009). In a population-based study of U.S. couples, Coker et al. (2002) found that female IPV victimization was associated with heavy alcohol use and painkiller use, but not with other illicit drug use (the majority of which was likely marijuana). Male IPV victimization was associated with painkiller use and other drug use, but not heavy alcohol use. Although this study was unique in its assessment of specific substances, problematic use was not assessed, which may have resulted in weakened or null findings.
There is reason to speculate that multiple substance use may interact synergistically when associated with victimization. If the theory that individuals self-medicate with substance use to cope with the emotional and physical pains of IPV victimization is accurate, there is also reason to believe individuals may tap into the synergistic effects of concurrent substance use for this purpose. Thus, it is expected that multiple substance use may be associated with IPV victimization beyond the additive effects of individual substances. Research is needed to examine these effects.
The Present Study
The primary objective of this study was to examine the association between relationship violence and the problematic use of alcohol, cocaine, cannabis, and opiates. Secondary objectives were to test for gender differences in these associations, as well as to explore the effect of poly substance use on relationship violence. Data were examined from the second wave of the National Epidemiologic Survey on Alcohol and Related Conditions. This relatively large dataset, acquired from a nationally representative sample of the U.S. noninstitutionalized adult population, contains information on substance use and use disorder diagnoses. The data also contain information on relationship violence perpetration and victimization, as well as several relevant social and mental health variables, making it suitable for addressing this research question. There is little theory to give guidance to which specific substances aside from alcohol may be related to relationship violence, and so hypotheses were driven by findings from previous research. It was hypothesized that alcohol use disorders would be associated with both IPV perpetration and victimization, based on the robust evidence that alcohol is related to relationship violence. It was also hypothesized that cocaine abuse would be associated with IPV perpetration given the existing evidence for this relation. The evidence for an association between marijuana and violence perpetration is highly conflictual. The comprehensive meta-analysis conducted by Moore and colleagues (2008) found that marijuana related to psychological but not physical relationship aggression. This study examined physical relationship violence, thus it was hypothesized that marijuana abuse would not be associated with IPV perpetration. A priori hypotheses were not generated for the association between opiate abuse and IPV perpetration, for the association between specific substances and IPV victimization, for the assessment of gender differences, or for the assessment of drug interactions because of the small number of previous studies examining these topics.
Method Study Sample
A detailed account of the NESARC methodology can be found elsewhere (Grant & Kaplan, 2005; Grant, Kaplan, Shepard, & Moore, 2003). Briefly, the first wave of NESARC data was collected during 2001 and 2002, and the second during 2004 and 2005. The response rate for the first wave was 81%, and the sample of 43,093 represented the civilian, noninstitutionalized adult population in the United States. The second wave included 34,653 of the original respondents. For both waves, surveys were administered face-to-face, using computer-assisted personal interviews. Blacks, Hispanics, and young adults were oversampled, and the data were weighted to adjust for nonresponse at the household and personal levels. Based on the 2000 Decennial Census, the data were adjusted on sociodemographic variables to ensure an accurate representation of the U.S. population. This study was based on the 25,778 wave two respondents who reported being married, dating, or being in a relationship during the past year.
Measures
Substance use disorders
The NESARC survey contained the National Institute on Alcohol Abuse and Alcoholism's Alcohol Use Disorder and Associated Disabilities Interview Schedule-DSM–IV version (AUDADIS-IV) to measure substance use disorders (Grant & Dawson, 2000; Grant, Hasin, Chou, Stinson, & Dawson, 2004). This tool assesses both abuse and dependence diagnoses for alcohol and other specific substances. For a full description of these measures, see Grant et al. (2004). In total, nine different drugs/drug categories were assessed in the NESARC data set: cannabis, cocaine, heroin, opioids, tranquilizers, sedatives, hallucinogens, amphetamines, and inhalants. Questions about drug use were prefaced by requesting that respondents only reported on use not prescribed by a physician. Thus, respondents reported on the illicit use of prescription drugs, such as sedatives, and not their medical use. For this study, respondents were considered problematic substance users if they received either an abuse or dependence diagnosis. Of the drug categories assessed in NESARC, only alcohol, cocaine, cannabis, and opioids were examined for this study; other substances were not examined because of low endorsement of their use. Binge drinking was also included in this study to test for its impact relative to the AUD measure. Binge drinking was defined as consuming five or more drinks for men and four or more drinks for women in a single day, and participants reported on how frequently they drank this much during the past 12 months. Responses ranged from 1 to 11 (1 = every day, 11 = never in the last year), and were reverse coded to aid interpretability (0 = never in the last year, 10 = every day).
Two specific poly substance use combinations were chosen for analysis in this study: diagnosis of both alcohol and cocaine use disorders, and diagnosis of both alcohol and marijuana use disorders. These particular combinations were chosen because of the frequency of their use (S. C. Moore, 2010). Interaction terms were added to regression models to create groupings for these poly substance use combinations.
Intimate partner violence
Five items were used to assess physical IPV perpetration. Respondents were asked how frequently they had engaged in the violent behaviors during the past year (from 0 to 4, never to more than once per month), and then were asked how frequently they had experienced the violent behaviors from their partner. For example, participants were asked, “In the last 12 months, how often did you push, grab, or shove your spouse or partner?” and then, “How often did your spouse or partner do this to you?” For this study, responses to the five perpetration questions were combined into a single binary variable representing whether the respondent perpetrated IPV during the past year. The same was done for IPV victimization. There were several reasons for this decision. First, the response options for these questions could not be easily combined. The options included “never,” “once,” and “twice,” but then jumped to “monthly” and “more than monthly,” which precluded summing them for a frequency of violence measure. In addition, two items assessed physical aggressive acts, “pushing, grabbing, or shoving” and “slap, kick, bite, or hit,” and two items were directed at injuries attributable to violence, which may have occurred because of one of the specific actions listed above, but may have occurred because of other aggressive acts that are not captured by a limited number of items (e.g., pinched, twisted arm). As a result, it was necessary to include the injury items because they might capture additional aggressive acts, but they could not simply be added to the frequency of the aggressive acts because it would constitute double counting for some individuals. For example, a hit that left a bruise might be counted as two items. As a consequence, the more valid approach was to simply indicate whether any IPV had occurred.
Covariates
There are a number of demographic, socioeconomic, and mental health variables that could potentially account for the association between specific drug types and mental health disorders. Age, gender, race/ethnicity, education, and household income were examined as sociodemographic covariates, and antisocial symptoms and depression symptoms as mental health covariates. The NESARC survey asked a number of questions regarding depression and antisocial symptoms, to which respondents answered yes or no for whether or not they occurred during the past year for depression and during their lifetime for antisocial behavior. Affirmative responses were summed for each of these variables, creating a count variable for antisocial (0–30) and depression (0–19) symptoms.
As pointed out by Anderson (2002) and others (Johnson & Leone, 2005), a large proportion of violence in community-based samples tends to be mutual, and so victims are often perpetrators and perpetrators are often also victims. In light of evidence that both perpetration and victimization are related to substance use, it is important to consider the potential confounding effect of victimization when examining perpetration, and vice versa. Thus, victimization was examined as a covariate in perpetration models, and perpetration was examined as a covariate in the victimization models.
Statistical Analyses
All statistical analyses for this study were conducted using Stata/SE version 10.0 (StataCorp, 2007), taking into account NESARC's complex survey design (sampling, weighting scheme, etc.). Chi-square tests of independence and t tests were used to examine differences in frequencies and means of covariates and substance use variables between those reporting IPV and the remainder of the sample. Logistic regression analyses were conducted to examine associations while adjusting for potentially confounding covariates. Models were created in a stepwise fashion, and separate models were calculated for IPV perpetration and victimization. Given the correlations between perpetration and victimization, one would ideally want to assess the influence of substance use on perpetration after controlling for victimization and vice versa. However, many episodes of couple violence involve aggressive actions by both members (Anderson, 2002; Johnson, 2006). As a result, statistically controlling for victimization when examining perpetration can have the effect of removing predictable variance that is attributable to perpetration among mutually violent couples. A similar problem arises in examining victimization. Consequently, we present analyses of perpetration with and without victimization in the model. Similarly, our analyses of victimization were conducted with and without perpetration in the model. Understanding whether a specific substance is related to perpetration or victimization depends upon the full set of analyses.
For our base models, initially a main effects model was created that controlled for sociodemographic characteristics, symptoms of depression, and antisocial symptoms. Second, several interactions were tested for significance: gender by each substance use variable, alcohol use disorder by cocaine use disorder, and alcohol use disorder by cannabis use disorder. If a significant interaction was detected, simple slope analyses were conducted to generate separate odds ratios for individual groups. These same procedures were then repeated with the inclusion of victimization as a covariate in the analysis of perpetration and the inclusion of perpetration as a covariate in the analysis of victimization.
Because of listwise deletion of respondents with missing data, the full logistic model for IPV perpetration included 25,633 of the 25,778 respondents in a relationship (99.4%), and the full model for IPV victimization included 25,631 of the respondents in a relationship (99.4%). Those with missing data were older (M = 50.70 compared with 46.43) and more likely to be black, Hispanic/Latino, or other race/ethnicity. There was also a slightly lower prevalence of IPV among those with missing data (3.0% compared to 5.3% for victimization, and 3.7% compared to 5.4% for perpetration). However, it was not expected that these differences would impact the results in any meaningful way, given that less than 1% of the sample had any missing data.
Results Intimate Partner Violence
Females were slightly more likely to report past year IPV perpetration than males (6.9% and 4.0%, respectively). The reverse was true for victimization, with 5.6% of men experiencing IPV victimization compared to 5.0% of women. Of those reporting IPV perpetration, 74.9% of men and 54.3% of women also reported IPV victimization. Table 1 displays a comparison between those reporting IPV perpetration, those reporting IPV victimization, and the remainder of the sample on sociodemographic and mental health variables. Perpetrators were more likely to be female, were younger, were more likely to be of non-White race/ethnicity, had lower levels of education and household income, and displayed greater numbers of depression and antisocial symptoms (all p values <0.001). IPV victims were more likely to be female, were younger, were more likely to be of non-White race/ethnicity, had lower levels of education and household income, and displayed greater numbers of depression and antisocial symptoms (all p values <0.001).
Description of Sample by Perpetrator and Victim Status: Socio-Demographic Characteristics and Mental Health Illness Symptoms
Table 2 displays the prevalence of substance use disorders by IPV perpetrator and victim status. All substance use disorders examined (alcohol, cocaine, cannabis, opioids) were more prevalent among both IPV perpetrators and IPV victims than the remainder of the sample (all p values <0.001). Alcohol use disorders were the most prevalent use disorders among IPV perpetrators (21.7%), followed by cannabis use disorders (5.8%). Cocaine use disorders and opioid use disorders were less prevalent (2.1% and 1.5%, respectively). Alcohol use disorders were the most prevalent use disorders among IPV victims (24.6%), followed by cannabis use disorders (7.4%). Cocaine use disorders and opioid use disorders were less prevalent (2.0% and 2.4%, respectively).
Frequencies of Specific Substance Use Disorders by Perpetrator and Victim Status
Intimate Partner Violence perpetration: Logistic Regression Results
Table 3 displays the odds ratios for substance use disorder associations with IPV perpetration, calculated using logistic regression and adjusted for demographics. There were notable differences between models unadjusted for and adjusted for victimization. For the main effects models alcohol use disorders and cocaine use disorders were significantly associated with IPV perpetration both before and after controlling for victimization. Binge drinking frequency and cannabis use disorders were significantly associated with perpetration only when estimates were unadjusted for victimization. Opioid use disorders became significantly inversely associated with perpetration when estimates accounted for victimization.
Intimate Partner Violence Perpetration Results From Logistic Regression (n = 25,633)
Significant gender by substance use interactions were found for alcohol use disorders, binge drinking frequency, and cannabis use disorders both before and after including victimization in model estimates. No significant gender by substance use interactions were found for cocaine or opioid use disorders. Without controlling for victimization, the association with alcohol use disorders was significant for both males and females, although the association was stronger for females than males. When victimization was accounted for in estimates, the association was no longer significant for males. Binge drinking frequency was associated with IPV perpetration for females but not males regardless of whether victimization was included in the model. For males, the association with cannabis use disorders was nonsignificant without controlling for victimization, and inversely significant when victimization was accounted for. For females, the association was no longer significant when estimates accounted for victimization.
Intimate Partner Violence Victimization: Logistic Regression Results
Table 4 displays the odds ratios for substance use disorder associations with IPV victimization, calculated using logistic regression and adjusted for demographics. There were notable differences between models unadjusted for and adjusted for perpetration. For the main effects models, alcohol use disorders, frequency of binge drinking, and cannabis use disorders were significantly associated with IPV victimization both before and after controlling for perpetration. Opioid use disorders became significantly associated with victimization after controlling for perpetration. Cocaine use disorders became significantly inversely associated with victimization after controlling for perpetration.
Intimate Partner Violence Victimization Results From Logistic Regression (n = 25,631)
In models that did not account for perpetration, significant gender by use disorder interactions were found for all substances except cocaine. The associations with alcohol use disorders and cannabis use disorders were significant for both males and females, with odds ratios that were slightly lower for males than females. The associations with binge drinking and opioid use disorders were significant only for females. There were no significant gender by use disorder interactions when victimization was statistically controlled. However, the interaction odds ratio for opioid use disorders was close to significant (p = .052), with a significant, positive association for females and no association for males.
Poly Substance Use Interactions
Results from the logistic regression analysis of poly substance use interactions are shown in Table 5. These odds ratios were adjusted for demographic covariates. With regards to IPV perpetration, a significant interaction was found for both alcohol use disorder by cocaine use disorder and alcohol use disorder by cannabis use disorder. Alcohol use disorder without cocaine use disorder and cocaine use disorder without alcohol use disorder were both significantly associated with IPV perpetration, regardless of whether models adjusted for victimization. Evidence was found that those with both a cocaine use disorder and alcohol use disorder had greater odds of reporting IPV perpetration than those with an alcohol use disorder only, although the association was statistically significant only in the model adjusting for victimization. Conversely, having both use disorders was associated with decreased odds of reporting IPV perpetration compared to those with a cocaine use disorder only. Again, this was only significant in the model adjusting for victimization. With regards to cannabis, the general finding was that alcohol use disorders alone and cannabis use disorders alone were associated with increased odds of IPV perpetration; however, having comorbid use disorders decreased the odds of IPV perpetration relative to each individual use disorder.
Poly-Substance Use and Relationship violence: Testing Interactions Between Multiple Substance Use disorders
With regard to IPV victimization, no evidence was found for an interaction between alcohol and cocaine. In the model controlling for perpetration, the interaction between cannabis use disorder and alcohol use disorder was statistically significant. Having combined alcohol and cannabis use disorders was associated with increased odds of IPV victimization relative to each individual substance.
DiscussionThis study examined the associations between specific substance use disorders and intimate partner violence, using data from wave two of NESARC. The findings substantiated our understanding of the role illicit drug use plays in relationship violence. The NESARC dataset was large enough to examine these relations and produce nationally generalizable results. Consistent patterns emerged for the individual substances examined in this study, some of which differed by gender.
One of the key aspects of the results is to understand the interdependent nature of perpetration and victimization in the context of partner violence. It is often the case that aggressing against one's partner and receiving aggression from one's partner are linked at the level of the incident. As a result, if a drug were to have an acute effect only on the perpetration of violence, it would necessarily have a relationship both with perpetration directly and influence the relationship with victimization indirectly through the impact of perpetration on the other person's defensive or retaliatory response. If nearly every perpetration were followed by an aggressive response by the other person, controlling for victimization in the analysis would remove a substantial portion of the variance in perpetration. Consequently, understanding the role of substance use in partner violence requires that we examine both perpetration and victimization, both independently as well as controlling for each other.
Cocaine Use Disorders
Perhaps the clearest picture emerges for the association between cocaine use disorders and partner violence. Our hypothesis that cocaine use disorders would be associated with relationship violence perpetration was confirmed, and the result was consistent for both males and females, with and without controlling for victimization. Moreover, this relationship is actually strengthened after controlling for victimization. This provides robust evidence that problematic cocaine use is associated with relationship violence perpetration. In a recent meta-analysis of the association between illicit drug use and IPV perpetration, Moore et al. (2008) found that cocaine use had the largest effect size compared with other drug use, and our results support this finding.
Conversely, cocaine use disorders were not related to victimization in the unadjusted model but were related to a reduced likelihood of IPV victimization for both men and women in the model adjusted for perpetration. There is little previous research with which to compare this finding. El-Bassel et al. (2005) found some evidence that women using cocaine were more likely to be victims of relationship violence; however, their study used a treatment sample of women seeking methadone, and it is difficult to draw comparisons across these highly different study samples. One can speculate that the positive association with perpetration and inverse association with victimization are attributable to the psychopharmacologic effects of cocaine use, but alternative explanations cannot be ruled out because of this study's design.
Opioid Use Disorders
The results relative to opiate use and partner violence also provide a fairly consistent pattern. Opioid use disorders were not associated with violence in the unadjusted models. However, they were associated with a decreased likelihood of violence perpetration for both men and women when victimization was added to the model. Conversely, opioid use disorders were positively associated with victimization. However, the interaction of opioid disorders and gender, which was significant in the unadjusted model and marginal (p = .052) when perpetration was controlled, indicated that opioid disorders were associated with an increased risk of victimization for women. This may indicate that opioid disorders increase the likelihood of victimization for women, or that victimization leads to opioid use for women. Female victims of relationship violence tend to experience more injury and psychological distress than men (Stets & Straus, 1990), which might account for the significant association of opioid use disorders and victimization among women but not among men.
Marijuana
In their meta-analysis, Moore et al. (2008) found that marijuana use was associated with psychological but not physical IPV perpetration. Physical violence was the outcome of interest in this study; thus, we hypothesized a null association between marijuana and IPV perpetration. Our findings were mixed, based on gender differences and whether models accounted for variance associated with IPV victimization. The interaction between cannabis use disorders and gender was significant in both the unadjusted and the models adjusting for victimization. For women, marijuana was associated with increased perpetration, although the association when controlling for victimization did not reach statistical significance (p = .08). This may suggest that marijuana use is more strongly implicated in mutual relationship violence than independently perpetrated violence for women. The association was inverse and significant for men when victimization was entered into the model, suggesting that heavy, problematic marijuana use may decrease the likelihood of nonreciprocated violence. Marijuana use disorders were robustly associated with IPV victimization, for both men and women. Previous researchers have speculated that IPV victims may self-medicate with substance use to cope with the effects of violence (El-Bassel et al., 2005; Testa et al., 2003); therefore, it is possible these associations with marijuana are attributable to the analgesic effects of acute use.
Alcohol Use Disorders
There is a large body of literature implicating problematic alcohol use as a risk factor for relationship violence (Foran & O'Leary, 2008; Leonard, 1993). Thus, we hypothesized that alcohol use disorders would be associated with both violence perpetration and victimization. The results were generally consistent with this expectation. Alcohol use disorders were robustly associated with IPV perpetration and victimization. However, there was also an interaction between AUD and gender, both for perpetration and victimization, suggesting that the effect of AUD, although significant for women and men, was stronger for women than for men. This interaction was not significant for victimization when perpetration was statistically controlled, suggesting that it might be applicable primarily for mutual violence. The interaction remained significant for perpetration while controlling for victimization and indicated that the association between AUD and perpetration was significant for women, but not for men. Again, inasmuch as there is a very strong relationship between perpetration and victimization, these findings suggest that AUD is related to mutual IPV among men (the relationship held when victimization was controlled) but may be less relevant for male-only violence.
The findings with respect to the relationship between frequency of binge drinking and IPV were somewhat different. These analyses suggested that binge drinking was associated with women's perpetration and that this relationship was not affected by controlling for victimization. For men, binge drinking was not related to perpetration. For both men and women, binge drinking was related to victimization, irrespective of perpetration. The pattern of findings suggests that binge drinking was specifically related to women's perpetration and associated with men's and women's victimization.
Poly Substance Use
The exploratory analyses for specific drug interactions uncovered some interesting patterns. For the alcohol by cocaine interaction, findings suggest that having both an alcohol and cocaine use disorder increased risk for violence perpetration relative to having only an alcohol use disorder, but decreased risk for violence perpetration relative to having only a cocaine use disorder. It seems plausible that this reflects a patterning of use in which alcohol is used, either simultaneously or subsequent to cocaine use to modulate the effect of the cocaine. This pattern has been reported to be fairly common (S.C. Moore, 2010). Similarly, the combination of cannabis and alcohol use disorders decreased risk relative to both individual use disorders, possibly suggesting that the synergistic effects of simultaneous cannabis and alcohol consumption decrease risk for aggressive behavior. However, it is important to note that explanations based on simultaneous use are purely speculative, as NESARC did not assess whether these substances were used simultaneously. Clearly, more research is needed to understand the joint impact of these substance use disorders with regards to violent behavior.
Limitations
This study was subject to some limitations. As previously noted, the study design for the current report was cross-sectional, which does not allow analyses to establish temporal relationships between variables. Although NESARC is a two-wave longitudinal study, intimate partner violence was assessed differently at waves one and two, making a comparison between waves methodologically unsound. The wave two data were chosen for this study because the assessment of IPV was more comprehensive than the first wave. Past research has focused on substance use as a risk factor for IPV perpetration and both predictors and outcomes related to victimization, but this directionality cannot be supported or refuted by this study. Taken in context with previous research, the findings from this study provide valuable information on the relationship between specific substance use and IPV. Also, intimate partner violence was only measured at the individual and not the couple level, thus there was no way to verify the respondents' reports of either IPV victimization or perpetration. This may be important given that relationship violence tends to be underreported in survey research (Dutton & Hemphill, 1992). It would have been difficult for the NESARC study to evaluate couple-level variables and still obtain such a large sample size, and it is the large sample size that allowed this study to examine specific substance use disorders for both men and women, and for both IPV perpetration and victimization. Future longitudinal research that assesses these variables at the couple level can build on this study's findings. Lastly, those with a predisposition to act aggressively may be less likely to be in a relationship at any one time point than others, thus a limitation of this sample is that aggressive individuals may be underrepresented in the study sample.
Conclusions
All substance use disorders examined in this study were related to intimate partner violence after controlling for important covariates. There were several differences between results when models were adjusted or unadjusted for the interdependence between IPV perpetration and victimization, and the comparison of these findings provides insight into possible differences between mutually violent couples and couples where only one partner is physically violent. The findings from this study, especially when adjusting for the correlation between victimization and perpetration, were largely consistent with what might be expected when considering the psychopharmacological effects of the drugs. Alcohol and cocaine were most strongly associated with intimate partner violence, while cannabis and opioid analgesics were most strongly associated with victimization. Conversely, associations with victimization were weak or inverse for cocaine, while associations with perpetration were weak or inverse for marijuana and opioids. It is impossible to conclusively determine the mechanisms underlying the findings of this study, but this consistency is certainly worth noting and may indicate that the psychopharmacologic effects of drugs are more strongly implicated when only one partner is violent, while other mechanisms such as conflict surrounding drug use may be more strongly related to mutual IPV. When significant gender effects were detected, associations tended to be stronger for women than for men, especially with regard to victimization. Poly substance use effects for specific combined use disorders were also detected, highlighting the importance of the further exploration of these findings. Overall, this study supported the continued exploration of possible mechanisms underlying the association between substance use and relationship violence, as well as the simultaneous treatment of these problematic behaviors (Stuart et al., 2007).
Footnotes 1 We thank an anonymous reviewer for suggesting the presentation of models with and without accounting for the correlation between IPV perpetration and victimization.
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Submitted: October 13, 2010 Revised: June 8, 2011 Accepted: June 17, 2011
This publication is protected by US and international copyright laws and its content may not be copied without the copyright holders express written permission except for the print or download capabilities of the retrieval software used for access. This content is intended solely for the use of the individual user.
Source: Psychology of Addictive Behaviors. Vol. 26. (2), Jun, 2012 pp. 236-245)
Accession Number: 2011-16753-001
Digital Object Identifier: 10.1037/a0024855
Record: 82- Title:
- 'Intimate partner violence and specific substance use disorders: Findings from the national epidemiologic survey on alcohol and related conditions': Correction to Smith et al. (2011).
- Authors:
- Smith, Philip H.. Department of Community Health and Health Behavior, School of Public Health and Health Professions, University at Buffalo, The State University of New York, Buffalo, NY, US
Homish, Gregory G.. Department of Community Health and Health Behavior, School of Public Health and Health Professions, University at Buffalo, The State University of New York, Buffalo, NY, US
Leonard, Kenneth E.. Research Institute on Addictions, University at Buffalo, The State University of New York, Buffalo, NY, US
Cornelius, Jack R.. Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, US - Source:
- Psychology of Addictive Behaviors, Vol 26(2), Jun, 2012. pp. 254.
- NLM Title Abbreviation:
- Psychol Addict Behav
- Page Count:
- 1
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- Bulletin of the Society of Psychologists in Addictive Behaviors; Bulletin of the Society of Psychologists in Substance Abuse
- Other Publishers:
- US : Educational Publishing Foundation
Society of Psychologists in Addictive Behaviors - ISSN:
- 0893-164X (Print)
1939-1501 (Electronic) - Language:
- English
- Keywords:
- intimate partner violence, substance use, mental health, alcohol use, illicit drug use, cocaine, marijuana, opioid
- Abstract:
- Reports an error in 'Intimate partner violence and specific substance use disorders: Findings from the national epidemiologic survey on alcohol and related conditions' by Philip H. Smith, Gregory G. Homish, Kenneth E. Leonard and Jack R. Cornelius (Psychology of Addictive Behaviors, Advanced Online Publication, Aug 8, 2011, np). There is an error in the last sentence in the first paragraph of the Results section. The corrected sentence is presented in the erratum. (The following abstract of the original article appeared in record 2011-16753-001.) The association between substance use and intimate partner violence (IPV) is robust. It is less clear how the use of specific substances relates to relationship violence. This study examined IPV perpetration and victimization related to the following specific substance use disorders: alcohol, cannabis, cocaine, and opioid. The poly substance use of alcohol and cocaine, as well as alcohol and marijuana, were also examined. Data were analyzed from wave two of the National Epidemiologic Survey on Alcohol and Related Conditions (2004–2005). Associations between substance use disorders and IPV were tested using logistic regression models while controlling for important covariates and accounting for the complex survey design. Alcohol use disorders and cocaine use disorders were most strongly associated with IPV perpetration, while cannabis use disorders and opioid use disorders were most strongly associated with IPV victimization. A diagnosis of both an alcohol use disorder and cannabis use disorder decreased the likelihood of IPV perpetration compared to each individual substance use disorder. A diagnosis of both an alcohol use disorder and cocaine use disorder increased likelihood of reporting IPV perpetration compared with alcohol use disorders alone but decreased likelihood of perpetration compared with a cocaine use disorder diagnosis alone. Overall, substance use disorders were consistently related to intimate partner violence after controlling for important covariates. These results provide further evidence for the important link between substance use disorders and IPV and add to our knowledge of which specific substances may be related to relationship violence. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
- Document Type:
- Erratum/Correction
- Subjects:
- *Drug Abuse; *Intimate Partner Violence; Alcohol Abuse; Cannabis; Cocaine; Marijuana; Mental Disorders; Mental Health; Opiates
- PsycINFO Classification:
- Behavior Disorders & Antisocial Behavior (3230)
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- First Posted: Apr 9, 2012
- Release Date:
- 20120409
- Correction Date:
- 20160307
- Digital Object Identifier:
- http://dx.doi.org/10.1037/a0028217
- PMID:
- 22486332
- Accession Number:
- 2012-09243-001
- Persistent link to this record (Permalink):
- http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2012-09243-001&site=ehost-live
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- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2012-09243-001&site=ehost-live">'Intimate partner violence and specific substance use disorders: Findings from the national epidemiologic survey on alcohol and related conditions': Correction to Smith et al. (2011).</A>
- Database:
- PsycINFO
Correction to Smith et al. (2011)
In the article “Intimate Partner Violence and Specific Substance Use Disorders: Findings From the National Epidemiologic Survey on Alcohol and Related Conditions” by Philip H. Smith, Gregory G. Homish, Kenneth E. Leonard, and Jack R. Cornelius (Psychology of Addictive Behaviors, Advance online publication, August 8, 2011. doi: 10.1037/a0024855), there is an error in the first paragraph of the Results section. The last sentence of the first paragraph reads: “IPV victims were more likely to be female…”, and should have read, “IPV victims were more likely to be male…”.
This publication is protected by US and international copyright laws and its content may not be copied without the copyright holders express written permission except for the print or download capabilities of the retrieval software used for access. This content is intended solely for the use of the individual user.
Source: Psychology of Addictive Behaviors. Vol. 26. (2), Jun, 2012 pp. 254)
Accession Number: 2012-09243-001
Digital Object Identifier: 10.1037/a0028217
Record: 83- Title:
- Intrapersonal positive future thinking predicts repeat suicide attempts in hospital-treated suicide attempters.

- Authors:
- O'Connor, Rory C.. Suicidal Behavior Research Laboratory, Institute of Health & Wellbeing, University of Glasgow, Glasgow, United Kingdom, rory.oconnor@glasgow.ac.uk
Smyth, Roger. Department of Psychological Medicine, Royal Infirmary of Edinburgh, Edinburgh, Scotland
Williams, J. Mark G.. Department of Psychiatry, University of Oxford, Oxford, United Kingdom - Address:
- O'Connor, Rory C., Suicidal Behavior Research Laboratory, Institute of Health & Wellbeing, University of Glasgow, Glasgow, United Kingdom, G12 0XH, rory.oconnor@glasgow.ac.uk
- Source:
- Journal of Consulting and Clinical Psychology, Vol 83(1), Feb, 2015. pp. 169-176.
- NLM Title Abbreviation:
- J Consult Clin Psychol
- Page Count:
- 8
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- Journal of Consulting Psychology
- Other Publishers:
- US : American Association for Applied Psychology
US : Dentan Printing Company
US : Science Press Printing Company - ISSN:
- 0022-006X (Print)
1939-2117 (Electronic) - Language:
- English
- Keywords:
- suicidal, psychology, prospective, cognitive
- Abstract (English):
- Objective: Although there is clear evidence that low levels of positive future thinking (anticipation of positive experiences in the future) and hopelessness are associated with suicide risk, the relationship between the content of positive future thinking and suicidal behavior has yet to be investigated. This is the first study to determine whether the positive future thinking–suicide attempt relationship varies as a function of the content of the thoughts and whether positive future thinking predicts suicide attempts over time. Method: A total of 388 patients hospitalized following a suicide attempt completed a range of clinical and psychological measures (depression, hopelessness, suicidal ideation, suicidal intent and positive future thinking). Fifteen months later, a nationally linked database was used to determine who had been hospitalized again after a suicide attempt. Results: During follow-up, 25.6% of linked participants were readmitted to hospital following a suicide attempt. In univariate logistic regression analyses, previous suicide attempts, suicidal ideation, hopelessness, and depression—as well as low levels of achievement, low levels of financial positive future thoughts, and high levels of intrapersonal (thoughts about the individual and no one else) positive future thoughts predicted repeat suicide attempts. However, only previous suicide attempts, suicidal ideation, and high levels of intrapersonal positive future thinking were significant predictors in multivariate analyses. Discussion: Positive future thinking has predictive utility over time; however, the content of the thinking affects the direction and strength of the positive future thinking–suicidal behavior relationship. Future research is required to understand the mechanisms that link high levels of intrapersonal positive future thinking to suicide risk and how intrapersonal thinking should be targeted in treatment interventions. (PsycINFO Database Record (c) 2017 APA, all rights reserved)
- Impact Statement:
- What is the public health significance of this article?—This study highlights the importance of positive future thinking as a predictor of future suicidal behavior. Clinicians ought to consider the content of positive future thinking, as not all types of positive future thinking are protective over time. (PsycINFO Database Record (c) 2017 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Attempted Suicide; *Hospital Admission; *Thinking; Future; Hopelessness; Hospitalized Patients; Major Depression; Suicidal Ideation
- Medical Subject Headings (MeSH):
- Adolescent; Adult; Aged; Depressive Disorder; Female; Follow-Up Studies; Hope; Hospitalization; Humans; Male; Middle Aged; Risk; Self Concept; Self-Injurious Behavior; Suicidal Ideation; Suicide, Attempted; Thinking; Young Adult
- PsycINFO Classification:
- Behavior Disorders & Antisocial Behavior (3230)
- Population:
- Human
Male
Female
Inpatient - Location:
- Scotland
- Age Group:
- Adolescence (13-17 yrs)
Adulthood (18 yrs & older)
Young Adulthood (18-29 yrs)
Thirties (30-39 yrs)
Middle Age (40-64 yrs)
Aged (65 yrs & older) - Tests & Measures:
- Positive Future Thinking Measure
Beck Depression Inventory DOI: 10.1037/t00741-000
Beck Hopelessness Scale
Scale for Suicide Ideation DOI: 10.1037/t01299-000 - Grant Sponsorship:
- Sponsor: Scottish Government, Chief Scientist Office, Scotland
Grant Number: CZH/4/449
Recipients: No recipient indicated
Sponsor: Wellcome Trust
Grant Number: GRO67797
Recipients: Williams, J. Mark G. - Methodology:
- Empirical Study; Quantitative Study
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- First Posted: Sep 1, 2014; Accepted: Jun 30, 2014; Revised: Jun 26, 2014; First Submitted: Nov 8, 2013
- Release Date:
- 20140901
- Correction Date:
- 20170223
- Copyright:
- The Author(s). 2014
- Digital Object Identifier:
- http://dx.doi.org/10.1037/a0037846
- PMID:
- 25181026
- Accession Number:
- 2014-36319-001
- Number of Citations in Source:
- 34
- Persistent link to this record (Permalink):
- http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2014-36319-001&site=ehost-live
- Cut and Paste:
- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2014-36319-001&site=ehost-live">Intrapersonal positive future thinking predicts repeat suicide attempts in hospital-treated suicide attempters.</A>
- Database:
- PsycINFO
Intrapersonal Positive Future Thinking Predicts Repeat Suicide Attempts in Hospital-Treated Suicide Attempters
By: Rory C. O’Connor
Suicidal Behavior Research Laboratory, Institute of Health & Wellbeing, University of Glasgow;
Roger Smyth
Department of Psychological Medicine, Royal Infirmary of Edinburgh, Edinburgh, Scotland
J. Mark G. Williams
Department of Psychiatry, University of Oxford
Acknowledgement: We would like to thank information analyst Andrew Duffy, NHS National Services Scotland, for conducting the data extraction for the linkage component of the study. This research was supported by funding from the Chief Scientist Office, Scottish Government (CZH/4/449). Many thanks to Caoimhe Ryan who collected the data. J. Mark G. Williams is supported by the Wellcome Trust (Grant GRO67797).
Suicide and attempted suicide are major public health concerns, with approximately one million people dying by suicide annually across the globe (World Health Organization, n.d.). Indeed, as previous suicidal behavior is one of the strongest predictors of suicide (Hawton & van Heeringen, 2009), considerable research effort has been directed at understanding the etiology and course of suicide attempts. In recent years, there has also been increased recognition that we need to move beyond psychiatric categories and epidemiological risk factors to identify more specific markers of suicide risk (O’Connor & Nock, 2014; O’Connor, Smyth, Ferguson, Ryan, & Williams, 2013; van Heeringen, 2001). This has led to a concerted focus on basic science approaches to advance understanding of the psychological mechanisms that lead to suicidal behavior (e.g., Joiner, 2005; Nock et al., 2010; O’Connor, 2011; Van Orden et al., 2010; Williams, Barnhofer, Crane, & Beck, 2005).
One of the key advances has been the establishment of the link between hopelessness, defined as pessimism for the future, and suicide risk (O’Connor, Connery, & Cheyne, 2000; Petrie, Chamberlain, & Clarke, 1988; Beck, Steer, Kovacs, & Garrison, 1985). Hopelessness consistently predicts suicidal ideation and behavior (e.g., Brezo, Paris, & Turecki, 2006; Hawton, Saunders, & O’Connor, 2012). Although this bivariate relationship is robust, the work of MacLeod and others has demonstrated that hopelessness characterized by low levels of positive future thinking, rather than the preponderance of negative future thinking, is particularly important in the suicidal process (Hunter & O’Connor, 2003; MacLeod, Pankhania, Lee, & Mitchell, 1997; MacLeod et al., 1998; O’Connor, Fraser, Whyte, MacHale, & Masterton, 2008). Positive future thinking, defined as anticipation of positive experiences in the future, is usually assessed via the future thinking task (MacLeod et al., 1997), during which participants are asked to generate as many future events or experiences as possible that they are looking forward to.
Evidence from both clinical and nonclinical populations and from different research groups is consistent: Low levels of positive future thinking (i.e., few positive future thoughts) are associated with suicidality independent of depression, verbal fluency, and negative attributional style (Hunter & O’Connor, 2003; MacLeod et al., 1997; O’Connor, Connery, & Cheyne, 2000; Williams, Van der Does, Barnhofer, Crane, & Segal, 2008). This finding is clinically important, as positive future thinking provides targets for treatment intervention; theoretically, it is noteworthy as future plans and goals are key components of predominant models of suicidal behavior (O’Connor, 2011; Williams, 2001) as well as self-regulatory theories of wellbeing (Carver & Scheier, 1998).
Despite the accumulation of evidence in support of the positive future thinking–suicidality relationship, there are a number of key questions about the nature of this relationship that have yet to be addressed. First, does positive future thinking predict suicide-related outcomes over the medium to long term? To date, there is no evidence that low levels of positive future thinking have predictive validity beyond the first 2 to 3 months following an index suicide attempt. In the only clinical study of its kind, O’Connor et al. (2008) found that low levels of positive future thinking were better predictors of suicidal ideation than global hopelessness 2 to 3 months following a suicide attempt. To our knowledge, no other longer term studies of suicidal individuals have been conducted, and no previous study has investigated whether positive future thinking predicts actual suicidal behavior over time.
Second, it is unclear whether all types of positive future thinking are protective against suicidal behavior. The studies thus far have focused on establishing the presence of a relationship between the frequency of positive future thinking or the likelihood of these future events occurring and suicidality. None of the previous studies had been set up to investigate whether the content of positive future thinking affects the relationship between positive future thinking and suicidality. It is reasonable to posit, for example, that positive future thinking focused on changing a personal attribute (for the better) may not be protective if it is not possible to realize this change over time. Arguably, trait-like intrapersonal characteristics (e.g., being more confident, optimistic) may fall into this category. It may be, therefore, that high levels of such thinking are problematic in some circumstances. According to the integrated motivational–volitional model of suicidal behavior (IMV; O’Connor, 2011), such thinking, if experienced contemporaneously with feelings of entrapment (defined as the inability to escape from defeating or stressful circumstances, Gilbert & Allan, 1998; Williams, 2001), would increase the likelihood of suicidal thoughts developing. It is the thwarted motivation to escape that distinguishes entrapment from hopelessness, and it is posited that as entrapment increases (and no solutions are found) the likelihood that suicide will be considered as an escape strategy also increases (Gilbert & Allan, 1998; O’Connor et al., 2013; Taylor, Gooding, Wood, & Tarrier, 2011).
To address the former question directly, we modified an existing coding frame for positive future thinking (Godley, Tchanturia, MacLeod, & Schmidt, 2001) and classified the content of positive future thinking from a large sample of suicide attempters into seven categories. Using linkage methodology, we were able to investigate (a) whether positive future thinking predicts repeat suicidal behavior up to 15 months following an index suicide attempt (beyond the effects of traditional clinical risk factors), and (b) whether the content of positive future thinking affects the relationship between positive future thinking and repeat suicidal behavior.
Method Participants and Procedure
We recruited 388 patients who were seen by the liaison psychiatry service the morning after presenting at a single general hospital in Edinburgh, Scotland, following a suicide attempt between January 2008 and September 2009. The hospital provides a full range of acute medical and surgical services, including an accident and emergency service. The vast majority of patients had presented with overdose (93%, n = 361). Exclusions were limited to participants who were unfit to participate (e.g., actively psychotic), who were unable to give informed consent (e.g., medically unfit to give informed consent), who were participating in one of the other studies being conducted in the hospital, or who were unable to understand English. Approximately 10% of participants who were approached declined to take part (10.2%, N = 44). There were 220 females and 168 males, with an overall mean age of 35.3 years (SD = 13.91, range = 16–71 years). The men (M = 38.40, SD = 14.04) and women (M = 32.92, SD = 13.36) did not differ significantly in age, t(386) = 3.92, ns. Ethnicity was not recorded.
Baseline data were collected in hospital, usually within 24 hr of admission. The Information Services Division of the National Health Service Scotland maintains a national database of hospital records and mortality data. This nationally linked database is a powerful resource, as it allows us to determine whether a patient is readmitted to hospital in Scotland with self-harm at any time since their index episode. We asked the Information Services Division to extract hospital admissions for self-harm in the period between the index self-harm episode and 15 months later for each patient. We also reviewed the electronic medical records of those patients who were hospitalized again following self-harm during the follow-up period to determine whether the repeat self-harm episode was a suicide attempt or not.
Participants completed the following measures in hospital.
Baseline Measures
Positive future thinking
Positive future thinking was recorded via the future thinking task (MacLeod et al., 1997). This requires participants to think of potential future experiences that they are looking forward to across three time periods: the next week (including today), the next year, and the next 5 to 10 years. On each occasion, participants have 1 min to think of future experiences for a given time period; this is repeated until all three periods are assessed. Before administration of the future thinking task, all participants complete the standard verbal fluency task (to control for general cognitive fluency) in which they have to generate as many words as possible to three letters (F, A, S), with 1 min allowed per letter. Consistent with previous research (MacLeod et al., 1997), the time periods are aggregated to yield total positive future thinking scores (i.e., the total number of positive future thoughts per participant). The contents of positive future thinking were coded according to a modified version of Godley et al.’s (2001) coding frame for positive future thinking to yield a total number of positive future thoughts per category (see Table 1). There were seven categories of positive future thinking. Social/interpersonal relates to positive future thinking that involves at least one other person (e.g., marriage). Achievement relates to the anticipation of any achievement-related event (e.g., new job). Intrapersonal thinking is any thought that concerns the individual and no-one else (e.g., being happy). Leisure/pleasure refers to any event or activity that is undertaken for leisure or pleasure (e.g., going on holiday). Health of others (e.g., mother getting better) and financial and home (e.g., decorating the house) describe thinking that concerns improvement in the health of family or friends and any aspect of finance or home, respectively. The final category, other, describes any thinking that does not fit into the preceding categories. Three raters independently rated 15% of the responses and agreement was good (κ = .83, .90, .85 for raters 1 + 2, 1 + 3, and 2 + 3, respectively). All of the responses were then categorized by the first coder.
Coding System for the Content of Positive Future Thinking and Mean Number of Thoughts as a Function of a Suicide Attempt or Suicide During Follow-Up
Depression
The Beck Depression Inventory (Beck, Steer, & Brown, 1996) is a well-established measure of depressive symptomatology. It consists of 21 groups of statements that assess the presence of depressive symptoms in the past 2 weeks with good reliability and validity. Cronbach’s α was .91.
Hopelessness
Hopelessness was measured using the 20-item Beck Hopelessness Scale. This is reliable and valid and has been shown to predict eventual suicide (Beck, Schuyler, & Herman, 1974; Beck, Steer, Kovacs, & Garrison, 1985). In the present study, internal consistency was very good (Kuder-Richardson–20 = .92).
Suicidal ideation
Participants’ thoughts of suicide over the past week were assessed via the 21-item Scale for Suicide Ideation (SSI; Beck, Steer, & Ranieri, 1988; Beck, Steer, & Brown, 1996). Cronbach’s α was .94.
Suicide intent
Suicide intent was assessed via the SSI (Beck et al., 1974). The SSI consists of 15 items that assess the objective circumstances related to a suicide attempt (eight items) and self-reported beliefs about one’s intention (seven items). Cronbach’s α was .72.
Outcome Measure
Readmission to hospital with a suicide attempt
An episode of self-harm was recorded if a patient was admitted to any hospital in Scotland with self-harm in the 15 months following the index episode. For this data set, the Information Services Division successfully linked 96.4% of the sample (n = 374/388). Where a patient was readmitted to hospital with self-harm during the study period, we reviewed their medical records to ascertain whether this episode was a suicide attempt or not. We were able to determine the presence/absence of suicidal intent in 93.1% (94/101) of those who were admitted to hospital with self-harm again during the study period. Therefore, all analyses are based on the 367 participants who were linked and for whom we have suicide intent data if they were readmitted to hospital with self-harm (which represents 95% of the original sample). Two trained coders independently rated the extracts from the medical records and agreed on all cases. Coders were unaware of any of the baseline measures.
Statistical analyses
We conducted a series of univariate logistic regression analyses for each predictor of a future suicide attempt. The total number of positive future thoughts per category is entered into the regression analyses. Although we are interested specifically in the positive future thinking logistic regression analyses, we present the findings for other established predictors of suicidal behavior (i.e., depression, hopelessness, suicide ideation, past suicide attempts). To test the two hypotheses, we also conducted multivariate logistic regression analyses including all significant univariate predictors, as appropriate.
Results Linked Sample
There were 208 women and 159 men with an overall mean age of 35 years (SD = 13.7, range: 16–71 years) in the linked sample. At baseline, 39.0% of participants (n = 143) reported no previous suicide attempts, 24.0% of participants reported one previous attempt (n = 88), 8.7% reported two previous attempts (n = 32) and 28.3% reported three or more previous episodes (n = 104). As anticipated, all indices of psychological distress were positively correlated (see Table 2). For the most part, the different categories of positive future thinking were negatively correlated with depression, hopelessness, and suicidal ideation. Suicidal intent was negatively correlated with two of the positive future thinking categories (interpersonal and achievement positive future thinking), as well as positively correlated with the psychological distress indicators. Finally, more previous suicide attempts were associated with increased distress and less interpersonal, achievement and leisure/pleasure positive future thinking.
Correlations, Means, and Standard Deviations for All of the Study Variables for All Participants
Individual and Multivariate Predictors of Repeat Suicide Attempts
Between Time 1 and Time 2 (15 months after the index episode), 25.6% (n = 94) of the linked participants either were readmitted to hospital with a suicide attempt or died by suicide (5/94). We conducted a series of logistic regression analyses to determine the variables for entry into the multivariate analyses. Established correlates of suicidal behavior (e.g., depression, suicidal ideation) were included in the analyses to ensure a robust test of the positive future thinking–repeat suicide attempt relationship. None of the demographic variables were significant univariate predictors of repeat suicide attempts (see Table 3). However, among the clinical predictors, the number of previous suicide attempts, suicidal ideation, hopelessness, and depression emerged as significant predictors. In respect to positive future thinking, low levels of achievement and financial positive future thinking were associated with suicide attempts between Time 1 and Time 2, whereas high levels of intrapersonal positive future thinking was also significant (see Table 3).
Univariate Associations Between Predictors and Suicide Attempts or Suicide Between Time 1 and Time 2
To investigate whether positive future thinking has utility in predicting repeat suicidal behavior up to 15 months following an index suicide attempt (beyond the effects of traditional clinical risk factors) and whether the content of positive future thinking affects the relationship between positive future thinking and repeat suicidal behavior, the significant univariate predictors were entered into the multivariate logistic regression in two stages. The traditional clinical risk factors were entered at Step 1, followed by the positive future thinking variables at Step 2. As is evident in Table 4, intrapersonal positive future thinking is a significant predictor of repeat suicide attempts in the final model (OR = 1.25, 95% CI [1.07, 1.44]), and its inclusion adds incremental predictive validity over previous suicide attempts and suicidal ideation (χ2 = 11.34, p < .01).
Multivariate Logistic Regression Analysis to Predict Suicide Attempts or Suicide Between Time 1 and Time 2
DiscussionThe present study extends understanding of the relationship between positive future thinking and suicide attempts. First, the findings demonstrate that some intrapersonal positive future thoughts predict repeat suicidal behavior up to 15 months following an index suicide attempt. Second, they also show that the relationship between positive future thinking and suicidality varies as a function of the content of such thinking. Specifically, in the univariate analyses, high levels of intrapersonal positive future thinking were associated with the risk of repetition, whereas low levels of achievement and financial positive future thinking were associated with repeat suicidal behavior. What is more, the multivariate analyses suggest that intrapersonal positive future thinking is most pernicious of all, as the effects of achievement and financial future thinking were rendered nonsignificant when considered alongside past suicidal behavior, suicidal ideation, hopelessness, and depression.
The findings are also noteworthy because they highlight not only that the types of positive future thinking have differential predictive validity but crucially because they show that high levels of positive future thinking are not always protective. On the face of it, this may seem counterintuitive, given the generally accepted view that high levels of positive thinking buffer against distress in the face of life stress (e.g., O’Connor, O’Connor, O’Connor, Smallwood, & Miles, 2004). Moreover, closer inspection of the baseline correlations shows that the degree of protection also changes as a function of the individual’s current context. When participants are in crisis, in the hours following a suicide attempt, high levels of intrapersonal positive future thinking appear to be protective, as illustrated by the negative correlations between intrapersonal future thinking, hopelessness, and suicidal ideation. These baseline findings are consonant with the extant literature on positive future thinking, which has consistently demonstrated that suicidal individuals generate lower levels of positive future thinking than controls (e.g., Hunter & O’Connor, 2003; MacLeod et al., 1997).
However, over the subsequent 15 months, the reverse relationship is apparent. The likelihood of another suicide attempt was elevated among those who reported more intrapersonal positive future thinking at baseline (when in crisis). As noted in the introduction, one possible explanation for the latter relationship may be that, over time, participants develop beliefs that their intrapersonal future thoughts are not attainable as they have not been able to achieve what they had expected within the intrapersonal domain over the duration of the study. It may be that these beliefs exacerbate their sense of entrapment, thereby increasing their risk of repeat suicidal behavior. Alternatively, it may simply be that the generation of positive future thinking is confounded by contemporaneous mood effects. The latter is unlikely, however, as baseline mood was controlled for in the multivariate analyses. Nonetheless, the unachievability hypothesis requires closer scrutiny in future research, as entrapment was not assessed in the present study, and assessing the impact of mood on intrapersonal versus external positive future thinking requires a specific test in which mood is experimentally manipulated. A further competing hypothesis is that frequent swings in self-image from high to low and vice versa that characterize some clients’ cognitions (e.g., clients with borderline personality or bipolar disorder) may account for the present findings. As we only assessed positive future thinking at one time point (and we also did not assess clinical disorder), it was not possible to test this hypothesis directly. Therefore, future research should investigate whether this instability in cognition has explanatory power in the present context.
Two other methodological points also merit comment. The first point relates to the test–retest reliability of the positive future thinking task. To our knowledge, this has not been formally tested; however, evidence from a recent experimental study in which positive future thinking was assessed twice within a single testing session suggests that responses are stable in the very short term (O’Connor & Williams, 2014). However, it is important to investigate this issue further to tease out whether, for example, intrapersonal positive future thinking is highly unstable when assessed over a period of days and weeks rather than hours.
Another issue relates to the extent to which the positive future thinking task is useful outside of the 24 hr following a suicide attempt. Although most studies have administered it within this time frame, other studies have employed it within 7 days of a suicidal episode (MacLeod et al., 2005), and others still have employed it in healthy populations (O’Connor & Williams, 2014; Williams et al., 2008) and found the expected relationships with hopelessness and suicidal ideation. Given this evidence, we do not think that the findings reported here are circumscribed to the immediate post-suicide-attempt period. Indeed, it is likely that the pattern of positive future thinking found in the perisuicidal period is similar to that found in the post-suicide-attempt period, but this is an empirical question. Indeed, it is critical that future research explore the trajectory of positive future thinking over time, to better understand the dynamic relationship between the levels of positive future thinking and suicide risk before, during, and after crisis.
Implications
Irrespective of the mechanism(s) of effect, the preeminence of intrapersonal rather than other dimensions of positive future thinking, including interpersonal thoughts, is clear from the present findings, as the former was the only category of positive future thinking to emerge from the multivariate analyses. This pattern of findings is also consistent with the integrated motivational–volitional model of suicidal behavior (O’Connor, 2011), which argues that positive future thinking may increase the likelihood that suicidality emerges from entrapment beliefs. Furthermore, the present findings have implications for how to intervene effectively with those who have attempted suicide to reduce risk of repetition. They clearly suggest that the content of positive future thinking requires careful consideration as part of the formulation process. Indeed, it may be helpful to monitor the achievability or otherwise of intrapersonal positive future thinking and to develop strategies to maximize the likelihood that the intrapersonal expectations are attainable. Patients may also benefit from help with problem solving when the expectations are not realized. Alternatively, in situations where the expectations are unrealistic or unattainable (e.g., O’Connor, O’Carroll, Ryan, & Smyth, 2012; Wrosch, 2010), working collaboratively with the individual to disengage from such future expectations in a safe manner and engage with new, more realistic positive future thinking may bear fruit.
There are also a number of research implications. First, positive future thinking is treated as a continuous variable in the present study. It would be useful, therefore, to investigate whether there is a critical threshold at which positive future thinking becomes especially deleterious, but this is likely best achieved by also assessing the perceived achievability of the positive future thinking. It would also be useful to investigate an individual’s certainty about a positive event occurring in the future and how this relates to risk (Sargalska, Miranda, & Marroquin, 2011). Second, for pragmatic reasons we employed cognitive assessment to record current psychological state rather than conducting a formal clinical assessment. It may be helpful in the future, therefore, to investigate whether the relationship between positive future thinking and suicide risk varies as a function of clinical diagnostic category. Third, whereas we coded the contents of individuals’ thinking post hoc, it would be interesting to ask participants to generate specific types of positive future thinking to determine whether there are different ways in which individuals rate their own thinking. More generally, the findings highlight the utility of focusing on psychological processes to identify more specific markers of suicide risk (O’Connor & Nock, 2014).
Although the longitudinal design and the use of an objective outcome measure are notable strengths of the present study, there are a number of potential limitations that merit comment. First, as we concentrated on hospital admissions and mortality, the present study was not designed to capture less medically serious suicide attempts that did not come to the attention of clinical services. In addition, although unlikely, we also would have missed any hospitalizations or deaths that occurred outside Scotland. Also, the national linkage methodology did not record those individuals who presented to the emergency department but were subsequently discharged without hospitalization. Consequently, future research is required to determine whether a similar pattern of findings would hold for non-medically serious suicide attempts. It would also be useful to look at nonsuicidal self-injury as another self-destructive outcome variable. Finally, as all of the participants had attempted suicide at baseline, it is unclear whether high levels of intrapersonal positive future thinking predict a first episode suicide attempt.
ConclusionsThis is the first study to investigate whether the content of positive future thinking predicts repeat suicidal behavior over the medium term. The findings demonstrate clearly that the content of the thoughts affects the direction and strength of the positive future thinking–suicidal behavior relationship. Whereas previous research had shown that low levels of positive future thinking are associated with suicidal behavior, the present study found that high levels of intrapersonal positive future thoughts predict repeat suicide attempts over time. Future research is required to understand the mechanisms that link intrapersonal positive future thinking to suicide risk and how intrapersonal positive future thinking should be targeted in treatment interventions.
Footnotes 1 As general verbal fluency did not predict repeat suicide attempts (OR = .98, 95% CI [0.94, 1.01]), it was not considered any further in the main analyses.
2 It is worth highlighting that none of the earlier studies analyzed positive future thinking as a function of thought content.
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Submitted: November 8, 2013 Revised: June 26, 2014 Accepted: June 30, 2014
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Source: Journal of Consulting and Clinical Psychology. Vol. 83. (1), Feb, 2015 pp. 169-176)
Accession Number: 2014-36319-001
Digital Object Identifier: 10.1037/a0037846
Record: 84- Title:
- Intrusive memories in perpetrators of violent crime: Emotions and cognitions.
- Authors:
- Evans, Ceri. Department of Psychological Medicine, St. George's Hospital Medical School, London, England, ceri.evans@cdhb.govt.nz
Ehlers, Anke, ORCID 0000-0002-8742-0192. Department of Psychology, Institute of Psychiatry, King's College, London, England
Mezey, Gillian. Department of Psychological Medicine, St. George's Hospital Medical School, London, England
Clark, David M., ORCID 0000-0002-8173-6022. Department of Psychology, Institute of Psychiatry, King's College, London, England - Address:
- Evans, Ceri, Medlicott Academic Unit of Forensic Psychiatry, Forensic Psychiatry Services, Hillmorton Hospital, Private Bag 4733, Christchurch, New Zealand, ceri.evans@cdhb.govt.nz
- Source:
- Journal of Consulting and Clinical Psychology, Vol 75(1), Feb, 2007. pp. 134-144.
- NLM Title Abbreviation:
- J Consult Clin Psychol
- Page Count:
- 11
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- Journal of Consulting Psychology
- Other Publishers:
- US : American Association for Applied Psychology
US : Dentan Printing Company
US : Science Press Printing Company - ISSN:
- 0022-006X (Print)
1939-2117 (Electronic) - Language:
- English
- Keywords:
- perpetrators, violent crime, intrusive memories, posttraumatic stress disorder, dissociation, emotions, cognitions
- Abstract:
- The authors investigated factors that may determine whether perpetrators of violent crime develop intrusive memories of their offense. Of 105 young offenders who were convicted of killing or seriously harming others, 46% reported distressing intrusive memories, and 6% had posttraumatic stress disorder. Intrusions were associated with lower antisocial beliefs before the assault, greater helplessness, fear, dissociation, data-driven processing and lack of self-referent processing during the assault, more disorganized assault narratives, and greater negative view of the self, negative interpretations of intrusive memories, perceived permanent change, and self-blame. In a logistic regression analysis, the cognitive and emotional variables explained substantial variance over and above demographic factors. The results suggest that cognitive factors that predict reexperiencing symptoms in victims of crime generalize to perpetrators. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Dissociation; *Memory; *Perpetrators; *Posttraumatic Stress Disorder; *Violent Crime; Cognitive Processes; Emotional States
- Medical Subject Headings (MeSH):
- Adult; Affect; Cognition; Crime; Dissociative Disorders; Humans; Memory; Prevalence; Stress Disorders, Post-Traumatic; Surveys and Questionnaires; Violence
- PsycINFO Classification:
- Criminal Behavior & Juvenile Delinquency (3236)
- Population:
- Human
Male - Location:
- United Kingdom
- Age Group:
- Adulthood (18 yrs & older)
- Tests & Measures:
- Index Offence Interview
Intrusion Interview
Posttraumatic Stress Scale-Interview version
Perceived Physical Threat Scale
Emotions During the Assault Scale
Negative View of the Self Scale
Self-Blame Scale
Data-Driven Processing scale
Posttraumatic Diagnostic Scale DOI: 10.1037/t02485-000
Antisocial Beliefs Scale DOI: 10.1037/t08054-000
Lack of Self-Referent Processing Scale DOI: 10.1037/t08235-000
Perceived Social Image Damage Scale DOI: 10.1037/t08261-000
Peritraumatic Dissociative Experiences Questionnaire—Rater Version DOI: 10.1037/t02464-000 - Grant Sponsorship:
- Sponsor: Wellcome Trust
Other Details: Principal Research Fellowship
Recipients: Ehlers, Anke - Methodology:
- Empirical Study; Quantitative Study
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- Accepted: Jul 20, 2006; Revised: Jul 18, 2006; First Submitted: Nov 28, 2005
- Release Date:
- 20070212
- Correction Date:
- 20120827
- Copyright:
- American Psychological Association. 2007
- Digital Object Identifier:
- http://dx.doi.org/10.1037/0022-006X.75.1.134
- PMID:
- 17295572
- Accession Number:
- 2007-00916-014
- Number of Citations in Source:
- 49
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- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2007-00916-014&site=ehost-live">Intrusive memories in perpetrators of violent crime: Emotions and cognitions.</A>
- Database:
- PsycINFO
Intrusive Memories in Perpetrators of Violent Crime: Emotions and Cognitions
By: Ceri Evans
Department of Psychological Medicine, St. George's Hospital Medical School, London, England;
Anke Ehlers
Department of Psychology, Institute of Psychiatry, King's College, London, England
Gillian Mezey
Department of Psychological Medicine, St. George's Hospital Medical School, London, England
David M. Clark
Department of Psychology, Institute of Psychiatry, King's College, London, England
Acknowledgement: The present study was supported by a Wellcome Trust Principal Research Fellowship, awarded to Anke Ehlers.
Recent studies have suggested that a minority of perpetrators of violent crime may develop posttraumatic stress disorder (PTSD; Kruppa, Hickey, & Hubbard, 1995; Spitzer et al., 2001), but little is known about the conditions that may turn an intentional violent act into a trauma for the perpetrator. Clinical examples include exposure to the gruesome consequences of violence (e.g., victim's body covered in blood), unintended seriousness of the consequences of the violence (e.g., victim died, although the perpetrator did not intend to kill him or her), or greater violence than intended under social pressure (e.g., as part of gang violence; Evans, Ehlers, Mezey, & Clark, in press).
The present article was designed to systematically investigate factors that may lead to perpetrators' intrusive memories of violent crime. As reexperiencing symptoms are the hallmark symptom of PTSD (Horowitz, 1976), studying the factors that lead to intrusive memories is a crucial step in understanding how PTSD may develop in perpetrators. Previous theoretical and empirical work identified the following factors in the etiology of intrusive memories after trauma: (a) cognitive schemas (beliefs, appraisals) before and after the assault, (b) perceived threat to life, (c) overwhelming negative emotions, and (d) disrupted cognitive processing, leading to problems with the autobiographical memory for the trauma (Brewin, Dalgleish, & Joseph, 1996; Ehlers & Clark, 2000; Horowitz, 1976; van der Kolk & Fisler, 1995). In the present study, we investigated whether these factors apply to perpetrators of violent crime.
Cognitive Schemas Before the TraumaThe development of intrusive memories in survivors of trauma has been attributed to a shattering of their pretrauma beliefs about safety, personal vulnerability, and the predictability of the future (Foa & Riggs, 1993; Janoff-Bulman, 1992; Resick & Schnicke, 1993). One would thus expect that perpetrators with antisocial personality disorder—who hold beliefs such as “I am entitled to break rules to look after myself” or “Force or cunning is the best way to get things done” (Beck, Freeman, & Associates, 1990)—to be at low risk of developing intrusions of their crimes.
Perceived Threat and Negative Emotions During TraumaThe exceptionally threatening character of traumatic events has been highlighted in the diagnostic criteria for PTSD (American Psychiatric Association, 1980; World Health Organization, 1992). Perceived threat to life during trauma showed consistent correlations with PTSD severity in a recent meta-analysis (Ozer, Best, Lipsey, & Weiss, 2003), with an average weighted correlation of .26. For perpetrators of violence, the perceived threat to their social status may be an important additional source of threat (Beck, 1999). It was therefore included as a possible predictor in the present study.
Emotional reactions during trauma are also highlighted in the diagnostic criteria for PTSD, in particular fear, helplessness, or horror (American Psychiatric Association, 1994). In Ozer et al.'s (2003) meta-analysis, the intensity of such negative emotions showed an average weighted correlation of .26 with PTSD severity. Other negative emotions that have been shown to predict PTSD include anger and shame (Andrews, Brewin, Rose, & Kirk, 2000).
Cognitive Processing and Disorganized Trauma MemoriesTheories of PTSD suggest that information processing is compromised during trauma and that compromised information processing explains PTSD symptom severity over and above what is explained by high arousal and negative emotions (e.g., Brewin et al., 1996; Ehlers & Clark, 2000). The most widely investigated indicator of such compromised processing is dissociation, which was the best predictor of PTSD in Ozer et al.'s (2003) meta-analysis, with an average weighted correlation of .35.
Dissociation is a complex concept, and it is unclear how it relates to other forms of cognitive processing that have been shown to influence memory (Roediger, 1990; Wheeler, 1997, 2000). Ehlers and Clark (2000) suggested that two further cognitive processing dimensions, data-driven processing (i.e., the predominant processing of sensory as opposed to conceptual information) and lack of self-referent processing (i.e., failure to encode new information as related to the self and other autobiographical information), predict whether people develop reexperiencing symptoms after trauma. These processes are thought to overlap in part with aspects of dissociation. Preliminary empirical support for a role of data-driven processing and lack of self-referent processing in intrusive trauma memories was found in studies of trauma survivors and volunteers exposed to distressing films (Murray, Ehlers, & Mayou, 2002; Rosario, Williams, & Ehlers, 2006).
Compromised cognitive processing is thought to lead to deficits in the autobiographical memory for the traumatic event. There are different hypotheses about the nature of this deficit, including a deficit in memory representations that facilitate intentional recall (Brewin et al., 1996), highly fragmented memories (e.g., Foa & Riggs, 1993; Herman, 1992), and poorly elaborated memories that are inadequately incorporated into their context of other autobiographical memories (e.g., Ehlers & Clark, 2000). Poor elaboration is thought to lead to poor inhibition of unintentional triggering of aspects of the trauma memory by matching cues. Ehlers, Hackmann, and Michael (2004) further suggested that the poor elaboration should be most pronounced for those parts of the trauma that are later reexperienced.
The mechanisms involved with the formation of trauma memories and deficits in recall specified in the different PTSD models are difficult to measure (Ehlers et al., 2004; McNally, 2003). One way is to code narratives of the traumatic event for indicators of the hypothesized mechanism. Common to the fragmentation and poor elaboration models is the hypothesis that intentional recall of trauma memories should be disorganized. Several studies have shown preliminary support for more disorganized trauma narratives in patients with PTSD versus those without PTSD (Foa, Molnar, & Cashman, 1995; Halligan, Michael, Clark, & Ehlers, 2003; Murray et al., 2002) and in volunteers exposed to a highly unpleasant film who developed intrusive memories than those without subsequent intrusions (Halligan, Clark, & Ehlers, 2002).
Appraisals of the Trauma and Its AftermathPTSD has been found to be associated with excessively negative appraisals of traumatic events (Ehlers & Clark, 2000; Foa & Riggs, 1993; Resick & Schnicke, 1993). For example, trauma survivors who blame themselves for the event or those who appraise a traumatic event as a sign of a negative (e.g., incompetent, unworthy, inadequate) self have more persistent PTSD symptoms than those who do not (Andrews et al., 2000; Dunmore, Clark, & Ehlers, 1997, 1999, 2001; Ehlers, Maercker, & Boos, 2000; Foa, Tolin, Ehlers, Clark, & Orsillo, 1999).
Although it is common for people to experience temporary unwanted memories following trauma, only a subgroup suffer from persisting intrusive memories (e.g., Baum & Hall, 1993). Ehlers and Steil (1995) suggested that negative interpretations of intrusions and other PTSD symptoms contribute to the maintenance of intrusive memories because they motivate the survivor to engage in behaviors that prevent processing of the trauma and may even increase intrusion frequency (e.g., rumination, thought suppression, use of alcohol and drugs). Several studies have supported the role of negative interpretations of intrusions in maintaining intrusions and PTSD (e.g., Dunmore et al., 1999, 2001; Ehlers, Mayou, & Bryant, 1998). Other trauma sequelae may also be interpreted in a negative way, contributing to the maintenance of PTSD (Ehlers & Clark, 2000). A common example is that trauma survivors interpret the trauma and its consequences as meaning that they have permanently changed for the worse as a person. Perceived permanent change has been shown to predict chronic PTSD (Dunmore et al., 1999, 2001; Ehlers et al., 2000).
Study Aims and HypothesesWe investigated the relationship between emotional and cognitive factors and intrusive memories in perpetrators of violent crime. On the basis of prior research and theories of PTSD, we expected that intrusive memories would be associated with (a) low prior antisocial beliefs; (b) threat perception during the assault; (c) negative emotions during the assault; (d) dissociative, data-driven, and lack of self-referential cognitive processing during the assault, (e) disorganization of the assault narrative; and (f) negative appraisals of the assault and/or its aftermath. We also expected these variables to be associated with PTSD symptom severity. In addition, we explored Ehlers et al.'s (2004) hypothesis that problems in intentional recall in PTSD are greatest for the moments of the trauma that are reexperienced.
Method Participants
Participants were 105 male prisoners, all of whom had been convicted of grievous bodily harm (GBH), attempted murder, manslaughter, or murder. All participants were imprisoned at two young offenders institutions (YOIs) within the United Kingdom during the 20-month study period. The exclusion criteria were (a) unable to speak English fluently, (b) severe learning disability, (c) active psychosis, (d) actively suicidal, (e) denied being present at the scene of the offense, and (f) unacceptably high security risk (e.g., a history of hostage taking). Of the 149 prisoners who met the legally defined entry criteria during the study period, 113 were suitable for inclusion in the study. All suitable prisoners were invited to take part. Of these, 6 (5%) declined to participate without stating a reason, and 2 (2%) refused because they experienced distressing flashbacks during the consenting process, giving an overall compliance rate of 105 out of 113 participants approached (93%). All participants completed the study measures.
Measures
Demographic characteristics were assessed using a semistructured interview, adapted for perpetrators from Dunmore et al. (1999, 2001). It included questions relating to demographic information, history of treatment for a psychiatric disorder, and history of a previous violent offense. Previous traumatic experiences were assessed with the trauma checklist from the first part of the Posttraumatic Diagnostic Scale (Foa, Cashman, Jaycox, & Perry, 1997).
Characteristics of the offense were assessed using The Index Offence Interview, a semistructured interview adapted for perpetrators from Dunmore et al. (1999, 2001). It included questions related to (a) legal aspects (e.g., conviction, plea, initial charge, sentence), (b) descriptive aspects (e.g., victim[s], location, timing, duration, use of weapons), (c) medical aspects (e.g., victim and perpetrator injuries), and (d) situational aspects (e.g., drug or alcohol intoxication, background stress, perceived provocation, planning and preparation, motivation for the assault, including intent to kill the victim).
Measures of Intrusions and PTSD Symptoms
Intrusion interview
The presence or absence of intrusive memories for the index offense was assessed using the Intrusion Interview (Michael, Ehlers, Halligan, & Clark, 2005), a semistructured 30-min interview that covers occurrence, content, frequency, modalities, and qualities of intrusive memories. Intrusive memories were defined as memories that (a) were part of what actually happened at the time and (b) were recurrent, distressing, and involuntarily triggered. The interviewer first asked a generic screening question designed to elicit reports of unwanted memories of the assault of an intrusive nature:
People who have committed a violent offence [sic] can remember the event in different ways. Some people have memories of parts of the assault that just pop into their mind when they do not want them to. These are usually from particular moments from before, during or after the incident that somehow “got stuck” in memory and keep coming back. These memories consist of part of what actually happened at the time, rather than your thoughts about what has happened since, such as being in prison because of the assault. Do you sometimes get such unwanted recollections of the assault?
If endorsed, then participants were asked to describe all such intrusive memories in detail. If more than one intrusive memory was identified, then the participant was asked to identify the one that was most upsetting or distressing and to describe this intrusion in greater detail. Examples of the intrusions included images of the wounded victim (e.g., “I get the picture of his face in my head… I can see blood coming out the back of his head… I thought he was dead”), or intrusions of the sensations accompanying the weapon causing damage to the victim (e.g., “The knife goes in and I see… blood squirt out… you know, you get that smell of blood… and the squirt… its just like the smell of blood. A lot of blood… a kind of 'iron-ey' kind of smell… I hear the squirt of the blood.”).
Interviews were transcribed verbatim. Two raters independently rated the transcripts of the intrusion interviews to determine whether intrusive memories reported by the participant met criteria for an intrusive memory. The interrater reliability was high (κ = 0.90, p < .001, N = 105). Discussion between the two raters led to resolution of all five cases involving disagreement. A previous study showed that the 1-week test–retest reliability of the interview scales ranged between r = .61 and r = .72 (Speckens, Ehlers, Hackmann, Ruths, & Clark, 2006).
The Posttraumatic Stress Scale–Interview version (PSS-I; Foa, Riggs, Dancu, & Rothbaum, 1993)
The PSS-I is a 17-item structured interview that assessed current symptoms of PTSD in relation to the index offense as defined by the Diagnostic and Statistical Manual of Mental Disorders, 4th edition (DSM–IV;American Psychiatric Association, 1994). The interviewer rates each symptom on a scale ranging from 0 (not at all) to 3 (five or more times per week/very much). The total PSS-I score is the sum of the ratings for the 17 items. The scale has high internal consistency (α = .85), moderate to high correlations with other measures of psychopathology, high test–retest reliability (r = .80), high interrater reliability (κ = 0.91), and good diagnostic agreement with the Structured Clinical Interview for DSM (Foa et al., 1993) and the Clinician-Administered PTSD Scale (Foa & Tolin, 2000). In order to qualify for a diagnosis of PTSD, participants had to have the minimum number of symptoms specified in the DSM–IV, scored with at least 1 (once per week or less/a little).
Measures of Predictor Variables
If not mentioned otherwise, participants rated their agreement with each item of the following questionnaires on a 7-point scale ranging from 1 (strongly disagree) to 7 (strongly agree).
Antisocial Beliefs Scale
This questionnaire was developed for the purposes of the present study to assess antisocial beliefs prior to the offense, using typical antisocial beliefs listed in Beck et al. (1990; six items, e.g., “force or cunning is the best way to get things done”; α = .85). Participants were instructed to answer the scale items with respect to their beliefs before the index offense.
The Perceived Physical Threat Scale (Dunmore et al., 1997)
This measure was used to ask participants about the extent to which he believed he would be seriously injured at the time of the assault (two items such as “During the assault I believed that I would be seriously injured”; α = .77).
The Perceived Social Image Damage Scale
This measure was developed for the purposes of the present study and assessed the extent to which the participant felt diminished as a result of the victim's actions immediately before the assault, particularly with respect to undermining the image that he perceived that others held of him. The items were based on Beck's (1999) categories of social transgressions, which can lead to perceived psychological injury or damaged personal self-esteem (12 items such as “The victim's actions caused me to lose face”; α = 85).
Emotions During the Assault Scale (Dunmore et al., 1999)
Participants rated the extent to which they experienced each of a list of 23 emotions during the assault on a 5-point scale ranging from 0 (not at all) to 4 (very strongly). A principal-axis factor analysis with oblimin rotation extracted six factors with eigenvalues greater than 1.00. The four scales reflecting negative emotions were interpreted as Helpless (four items: helpless, sad, betrayed, inferior; α = .73), Anger (five items: angry, furious, frustrated, hatred, insulted; α = .83), Shame (two items: ashamed, embarrassed; α = .85), and Fear (two items: terrified, afraid; α = .90).
The Negative View of the Self Scale
This measure assessed the extent to which the participant held a general negative view of himself, and items were derived from the Negative Thoughts About the Self subscale of the Posttraumatic Cognitions Inventory (Foa et al., 1999; five items such as “I am worthless”; α = .91).
The Self-Blame Scale
This measure assessed the degree to which participants continued to reproach themselves for their violent actions (four items such as “I am constantly troubled by my conscience for the crime I committed”; α = .90). The items were derived from the 18-item Guilt Attribution subscale of the Revised Gudjonsson Blame Attribution Inventory (Gudjonsson & Singh, 1989), a scale developed to assess remorse in offenders that has good reliability and transcultural validity (Gudjonsson & Petursson, 1991).
Interpretation of Posttraumatic Symptoms Inventory and Permanent Change scales (Dunmore et al., 1999, 2001)
These scales assessed the extent to which participants interpreted symptoms arising from the assault in a negative way (11 items such as “My reactions since the event show I must be losing my mind”; α = .90) and the extent to which participants perceived that the assault had irreversibly affected them as a person in a negative way (nine items such as “I have permanently changed for the worse”; α = .89). Both measures have been shown to have good reliability and predictive validity in assault survivors (Dunmore et al., 1999, 2001).
The Peritraumatic Dissociative Experiences Questionnaire-rater version (Marmar, Weiss, & Meltzer, 1997)
This 10-item structured interview assesses the degree of dissociation experienced during and immediately after a traumatic event. Each dissociative experience (e.g., derealization, out-of-body experiences) reported by the participant was rated by the interviewer on a 3-point scale ranging from 1 (no) to 3 (threshold). The scale has been shown to have good internal consistency and satisfactory convergent and discriminative validity (Marmar et al., 1997). Internal consistency in the present sample was α = .84.
The Lack of Self-Referent Processing and Data-Driven Processing scales (Halligan et al., 2003)
These eight-item scales assess (a) the extent to which participants failed to process the assault as happening to themselves and to incorporate the experience with other autobiographical information relating to the self (lack of self-referent processing; e.g., “I felt as if it was happening to someone else”; “I felt cut off from my past”) and (b) the extent to which participants primarily engaged in the processing of sensory as opposed to meaning information during the assault (data-driven processing, e.g., “It was just like a stream of unconnected impressions following each other”). Both scales have been shown to have good internal consistency and to predict memory disorganization and the development of PTSD symptoms in trauma survivors (e.g., Halligan et al., 2003). Internal consistencies in the present sample were α = .83 and α = .84, respectively.
Assault narrative task
Participants were asked to give a detailed narrative of the assault by recalling it as vividly, clearly, and in as much detail as possible, while describing events in the order in which they occurred without interruption. All narratives were tape-recorded and transcribed verbatim. Scoring for disorganization followed Foa et al. (1995), in the adaptation by Halligan et al. (2003). Narratives were divided into “chunks” or clauses containing “only one thought, action, or speech utterance.” Three indices of memory disorganization were assessed: (a) repetitions: clauses consisting of repetitions; (b) disorganized thoughts: clear expressions of uncertainty with regard to memory, confusion, or nonconsecutive chunks (e.g., “I know something didn't… at least… they were broken”); and (c) organized thoughts: clauses indicating understanding of what was happening, as a reverse indicator of disorganization. Each score was z transformed in order to control for the variable narrative length, and the composite memory disorganization score was calculated as z(1) + z(2) – z(3) (Halligan et al., 2003). In addition, the rater gave a global disorganization rating, ranging from 1 (not at all disorganized; temporally sequential with high amounts of detail) to 10 (extremely disorganized), after reading each narrative. Interrater reliability (two raters, 20 narratives) showed high agreement for the composite memory disorganization score (r = .92, p < .001) and for the global memory disorganization rating (r = .96, p < .001).
To compare sections of the narrative that corresponded to the main intrusion with other parts of the narrative, global disorganization ratings were done separately for (a) a five-chunk section of the narrative corresponding to the time of the stated intrusion, (b) a randomly selected five-segment section beginning at least 10 chunks after the intrusion in the assault narrative, and (c) a randomly selected five-chunk narrative segment global memory disorganization finishing at least 10 chunks prior to the intrusion. Examination of the assault narratives showed that 11 participants (22.9%) in the intrusion group did not describe their intrusive memory within the narrative. To ensure conservative testing of the hypothesis, these cases were excluded even if the intrusion was from the time period covered in the narrative.
Procedure
The Prison Health Research Ethics Committee (PHREC) approved the study, and the investigators obtained prior written approval of the governors and the lead clinician of the two participating YOIs. The heads of security and operations at the YOIs approved the use of recording equipment. Participant responses were kept confidential, including from the institutional authorities. Participants were not reimbursed.
After the participant had given written informed consent, the semistructured interviews assessing demographic and offense characteristics were administered. Participants then gave a narrative account of the event and filled in the questionnaires. The Intrusion Interview and the PSS-I followed. The session took between 1.5 and 2 hr. All interviews were conducted individually by Ceri Evans. Participants also completed short interviews on rumination and amnesia, which will be presented elsewhere. Where relevant, participants were provided with enlarged rating scales for each questionnaire or interview to consider while the researcher read questions or statements out loud to minimize any potential confounding effect of reading ability.
Statistical Analyses
Data were analyzed with the SPSS for Windows, Version 11.5. Chi-square tests (categorical data, or Fisher's exact test if the chi-square was invalid) or t tests (continuous data, or, when indicated by Levene's equality of variance test, t tests based on unequal variances) were used to compare demographic and assault characteristics of participants with and without intrusions. The cognitive and emotional factors under investigation were analyzed using a hierarchical approach. First, participants with and without intrusions were compared on groups of variables by using multivariate analyses of variance (MANOVAs). If the multivariate test was significant, then univariate comparisons followed. Logistic regression analysis was used to examine whether the cognitive and emotional factors explain the presence of intrusions over and above what can be predicted from demographic factors. Stepwise discriminant function analysis was used to cross-validate the best predictors from the logistic regression with another method. In addition, correlations of the predictors with PTSD symptom severity, as measured by the PSS-I, are reported. The following variables were log transformed to normalize distributions: PSS-I scores, helplessness, self-referent processing, permanent change, interpretation of symptoms, and global narrative disorganization rating. No outliers had to be removed (alpha level was set at p < .05), and all tests are two-tailed.
Results Prevalence of Intrusions
Forty-eight participants (45.7%) reported current intrusive memories of their violent offense. Two additional participants reported having had intrusions in the first few months after the assault that had ceased by the time of the interview (these were included in the no-intrusion group). Six participants (5.7%) met diagnostic criteria for PTSD.
Table 1 shows that the intrusion and no-intrusion groups were comparable for nearly all demographic and assault characteristics, including a history of previous trauma. Participants with intrusions were more likely than those without intrusions to report a history of psychiatric disorders (48% vs. 23%) and a history of previous violent offenses (58% vs. 33%). As to be expected, they also scored higher on the PSS-I.
Demographic and Assault Characteristics
Comparison of Participants With and Without Intrusive Memories
Table 2 compares the intrusion and no-intrusion groups on the cognitive and emotional variables under investigation. The table also shows the correlation of the variables with PTSD symptom severity, as measured by the PSS-I.
Cognitive Variables and Emotions Differences Between Perpetrators With and Without Intrusions and Correlations With Posttraumatic Stress Symptom Severity
Participants with intrusions reported lower antisocial beliefs for the time before the assault. For the measures of perceived threat (perceived physical threat, social image damage), the multivariate analysis of variance (MANOVA) failed to show a significant group difference. The MANOVA of negative emotions showed a significant group difference (p = .049). The intrusion group reported greater intensity of negative emotions during the trauma than the no-intrusion group. The univariate comparisons showed that this was because of greater helplessness and fear in the intrusion group. The intrusion groups did not differ in the extent to which they felt angry or ashamed during the assault, although greater shame correlated with PTSD symptom severity.
The MANOVAs for cognitive processing and memory disorganization also showed significant group differences (ps = .001). Participants with intrusions reported greater dissociation, lack of self-referent processing, and data-driven processing during the assault than those without intrusions, and showed greater disorganization of the assault narrative as indexed by both the composite score and the global rating.
The MANOVA of appraisals of the assault and its aftermath also showed a highly significant group difference (p < .001). The intrusion group scored higher on negative view of self, negative interpretation of symptoms, permanent change, and self-blame than the no-intrusion group.
Further Analyses of the Cognitive Processing and Memory Measures
In the intrusion group, the mean global memory disorganization rating scores for the five-chunk section of the narrative corresponding to the time of the stated intrusion (M = 3.44, SD = 1.14) was significantly greater than a randomly selected five-segment section beginning at least 10 chunks after the intrusion (M = 0.24, SD = 0.58), t(40) = 17.54, p < .001. However, there was no significant difference between the five-chunk narrative segment global memory disorganization ratings at the time of the intrusion and a randomly elected segment finishing at least 10 chunks prior to the intrusion, t(43) = 0.620, p = .54.
Dissociation, data-driven processing and lack of self-referent processing were moderately correlated (rs between .50 and .56, all ps < .001). The two measures of memory disorganization correlated with r = .28 (p = .004).
Regression Analyses
We used a hierarchical logistic regression analysis to test whether the emotional and cognitive factors explained the presence of intrusions over and above what can be explained by demographic factors. Groups of variables were entered in blocks of theoretically linked concepts (Ehlers & Clark, 2000) in approximate temporal order (i.e., antisocial beliefs were entered in Block 2, followed by emotions during the assault in Block 3, cognitive processing and trauma memory measures in Block 4, and appraisals of the event and its aftermath in Block 5). Only variables that had shown significant group differences were entered in the equation. To reduce the risk of multicollinearity, for cognitive processing and memory disorganization only, one measure was entered, and perceived permanent change was dropped from the appraisal block. We expected that each block would add significantly to the explanation of intrusive memories.
Table 3 shows the means and intercorrelations between the predictors. Table 4 shows that, as expected, all blocks of variables significantly increased the amount of variance explained. Demographic variables (past psychiatric history and previous criminal offense) explained 18% of the variance of the presence of intrusive memories of the offense. In Block 2, antisocial beliefs prior to the offense significantly added to the prediction and explained a further 5.4% of the variance (24% explained in total). In Block 3, emotions at the time of the offense (helplessness and fear) explained an additional 10% (34% in total). In Block 4, the measures of cognitive processing and memory disorganization predicted an additional 10% of the variance over and above that explained by the previous measures (44% in total). In Block 5, appraisals of the assault and its aftermath measures contributed a further 16% of the predicted variance (60% in total), and 85% of the participants were correctly identified. In the final model, a history of psychiatric disorders and self-blame explained unique variance at p < .05, and there were trends for dissociation and the composite memory disorganization score at p < .10.
Means, Standard Deviations, and Intercorrelations of Predictors of Intrusive Memories (N = 105)
Logistic Regression Analysis Predicting the Presence and Absence of Intrusive Memories
Discriminant function analysis was used to replicate the result with a different regression method, using variables that discriminated most strongly between the groups. In this analysis, the variables self-blame, history of psychiatric disorders, dissociation, and composite memory disorganization score were selected. These variables had a canonical correlation with intrusive memories of r = .66 (Wilks's λ = .564), χ2(4, 103) = 56.77, p < .001. The standardized discriminant function coefficients for the selected variables were .78, .46, .33, and .32, respectively.
DiscussionIn line with preliminary reports (Kruppa et al., 1995; Spitzer et al., 2001), a substantial proportion (46%) of violent offenders reported intrusive memories of the crimes they committed, and a minority (6%) met diagnostic criteria for PTSD. Given that participants had intentionally harmed other people, it is not surprising that the PTSD rate in this sample was much lower than the rates observed in victims of assault (Andrews et al., 2000; Halligan et al., 2003). Nevertheless, the results indicated that for some perpetrators, their violent crime turns into a traumatic experience. Their distressing intrusive memories resembled those observed in assault survivors (Ehlers et al., 2004).
If the conditions that lead perpetrators to involuntarily reexperience parts of the crimes they committed are better understood, then this will provide an important stepping stone in explaining how PTSD develops in this population. The present study was designed to address this question. Drawing on theoretical models of PTSD and previous research with assault survivors, we chose a range of potential emotional and cognitive predictors of intrusive memories. With the exception of perceived threat, the results supported the hypothesis that the theoretical models and findings on intrusive memories in assault victims generalize to perpetrators. In line with previous research (e.g., Foa et al., 1999; Halligan et al., 2003; Ozer et al., 2003) and theoretical models of PTSD (Brewin et al., 1996; Ehlers & Clark, 2000; Foa & Riggs, 1993; Janoff-Bulman, 1992; Resick & Schnicke, 1993), low antisocial beliefs, negative emotions and problematic information processing during the assault, disorganized trauma memories, and negative appraisals of the trauma and its aftermath, were related to intrusive memories and the severity of PTSD symptoms. The logistic regression analysis further showed that the cognitive and emotional factors under investigation improved the prediction of intrusive memories considerably over and above what can be explained by demographic factors. A history of psychiatric disorders and previous violent offenses explained 18% of the variance. Emotional and cognitive predictors predicted a further 42% of the variance.
Cognitive Schemas Before the Trauma
The data supported the hypothesis that antisocial beliefs would be protective against the development of intrusive memories. This finding is in line with “discrepancy theories” of trauma reactions, in which it is argued that intrusive memories arise from an incompatibility between deeply held beliefs and actual behavior (see Brewin & Holmes, 2003, for a review). Individuals with antisocial beliefs may be less likely to perceive a discrepancy with their values when they behave violently and, hence, less likely to develop intrusive memories. It would be interesting to include a measure of psychopathy in future studies to explore these findings further.
Perceived Threat and Negative Emotions During Trauma
There may be a number of reasons why perceived threat during the assault was not significantly related to intrusive memories. First, whereas perceived threat to life is predictive in victims of assault, it may be less relevant for perpetrators who inflict harm. Second, we may not have assessed other important aspects of threat that are important for perpetrators. One interesting dimension for future studies may be perceived moral breach during the assault. A qualitative analysis (Evans et al., in press) included the suggestion that in some cases, a sense of having acted unacceptably or in a way that the community would not condone seemed to be linked to the development of intrusive memories. In support of this argument, self-blame after the assault showed a strong association with intrusions in the present study.
Our finding that participants with intrusive memories reported to have felt greater helplessness and fear during the assault than those without intrusions corresponds well to Criterion A2 of the DSM–IV diagnostic criteria for PTSD (American Psychiatric Association, 1994). It is interesting that the emphasis on helplessness and fear replicated in the present sample of perpetrators, as one may have assumed that other emotions may be more relevant in this population. The helplessness factor may, however, have somewhat different connotations in perpetrators than in victims of violence in that this scale may have reflected feelings of degradation rather than helplessness in defending oneself. The findings that anger and shame were not significantly related to intrusions is in line with Brewin et al.'s (1996) hypothesis that emotions such as shame are secondary emotions that only develop after the trauma.
Cognitive Processing and Memory Disorganization
As in previous research (Ozer et al., 2003), dissociation during the trauma was associated with intrusive memories and PTSD symptoms. In line with Ehlers and Clark's (2000) model, data-driven processing and lack of self-referent processing were also related to reexperiencing symptoms and correlated moderately with dissociation. As in Halligan et al.'s (2003) study, memory disorganization was related to intrusive memories and PTSD symptoms. The two measures of memory disorganization only showed a small correlation with each other. This is consistent with reviews suggesting that different measures assess different components of problematic trauma memory retrieval (Ehlers et al., 2004; McNally, 2003). For example, gaps in memory increase the global disorganization rating but not the composite memory disorganization score. Furthermore, not all parts of the trauma memory may show deficits, especially if the trauma is a prolonged event. In the present study, we found some preliminary support for Ehlers et al.'s (2004) suggestion that the deficits in intentional recall should be most marked for those parts of the trauma that are reexperienced. The section of the assault narrative corresponding to the intrusive memory was rated as more disorganized than a subsequent section of assault memory transcript. However, no significant difference was found when comparing the intrusion segment with a narrative segment before the intrusion. This negative finding may have been influenced by the fact that we excluded 23% of the intrusion group who did not mention the part corresponding to the intrusion in their narratives. This procedure may have been overly conservative, as one may argue that omissions in the narrative may indicate difficulties with intentional retrieval or even a gap in memory.
Appraisals of the Trauma and Its Aftermath
In support of theories that emphasize the role of negative appraisals of the trauma and its aftermath in PTSD (Ehlers & Clark, 2000; Foa & Riggs, 1993; Resick & Schnicke, 1993), we found that such appraisals related to intrusive memories and PTSD symptom severity in perpetrators of violent crime. The appraisal factors explained an additional 16% of the variance over and above the other variables included in the logistic regression analysis. The findings parallel those obtained in victims of assault and torture (Dunmore et al., 1999, 2001; Ehlers et al., 2000; Foa et al., 1999; Halligan et al., 2003).
Limitations
The present study had several limitations. First, the study was cross-sectional, and the results remain correlational. It is therefore not possible to establish causal relationships between the cognitive and emotional factors under investigation and intrusive memories. Second, participants were interviewed after being convicted for the crime, which meant that cognitive processing and emotions were assessed many months after the event. It is therefore possible that recall was imprecise and may have been affected by subsequent events such as interrogations and court proceedings. It is unlikely, though, that these events would have created a systematic bias in favor of the hypotheses under investigation. Most likely, they may have contributed to the error variance. Moreover, time since the assault was not related to intrusions. It is possible, however, that experiencing intrusive memories may have led the participants to reevaluate the perceived threat during the assault. Third, the findings rely on self-report, and we cannot rule out that participants did not always give valid answers. However, there was no incentive to distort the answers because participants had already been convicted, the results of the interviews were confidential and did not have any influence on their sentence and conditions in prison, and there was no financial incentive. Furthermore, the main dependent variable—presence of intrusive memories—was not based on simple participant endorsement but on detailed descriptions, which were rated by experts on the phenomenology of intrusive memories in patients with PTSD. Similarly, interviewer ratings were used to measure dissociation. Fourth, our assessment of memory disorganization rests upon the assumption that disorganization in a narrative reflects disorganization in an underlying memory representation. However, disorganization in the narrative may result from other processes, such as problems with expressing the contents of memory or censoring. Fifth, we used 16 cognitive processes and emotions as predictor variables in a study with 105 participants. Even though we used a hierarchical approach to data analysis, the possibility of chance findings cannot be ruled out. However, all positive findings, with the exception of the role of antisocial beliefs, replicate findings of other studies with assault victims, which supports the validity of these findings. Sixth, some of the items of the Permanent Change scale may have been affected by the experience of being in prison and may have somewhat different meanings for perpetrators and victims. Seventh, the present findings were obtained with a group of young, predominantly male perpetrators of violent crime. Masculine confrontations, which are essentially “honor” contests in public settings and involve alcohol, were overrepresented in the present sample, whereas sexual or domestic homicides were less frequent than might be expected in studies involving older prisoners (Daly & Wilson, 1988; Polk, 1994). It is unclear whether such differences would affect the generalizability of these findings to other offender populations. Finally, the study focused on intrusive memories rather than on PTSD, and it remains to be tested whether the factors highlighted in the present article also predict PTSD in this population. The correlations of the predictor variables with the PSS-I suggest that this is likely to be the case. Future studies will need to investigate what factors determine whether perpetrators who have intrusive memories of their crimes develop the full syndrome of PTSD.
Conclusion
In summary, the results support the hypothesis that similar mechanisms explain intrusive memories in victims and perpetrators of violence. They may also have clinical implications for the treatment of violent offenders, as there are effective cognitive–behavioral treatment programs for distressing, intrusive traumatic memories and PTSD (e.g., Ehlers, Clark, Hackmann, McManus, & Fennell, 2005; Foa & Rothbaum, 1998; Resick & Schnicke, 1993). However, the issue of whether distressing intrusive memories of the offense in perpetrators should be treated is not straightforward. From a clinical perspective, it can be argued that individuals deserve treatment for their mental distress, regardless of their perceived responsibility for their distress. Furthermore, it could be argued that, without treatment, the offender's risk of future violent behavior may be increased because of general symptoms, such as increased irritability, or by specific triggering of intrusive memories and flashbacks. A counterargument would be that intrusive memories, and the distress associated with these memories, provide regular, uncomfortable reminders of the crime and help to reduce the risk of violent reoffending. Whether treatment of intrusive memories in violent offenders has an impact on subsequent offenses will need to be tested empirically.
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Submitted: November 28, 2005 Revised: July 18, 2006 Accepted: July 20, 2006
This publication is protected by US and international copyright laws and its content may not be copied without the copyright holders express written permission except for the print or download capabilities of the retrieval software used for access. This content is intended solely for the use of the individual user.
Source: Journal of Consulting and Clinical Psychology. Vol. 75. (1), Feb, 2007 pp. 134-144)
Accession Number: 2007-00916-014
Digital Object Identifier: 10.1037/0022-006X.75.1.134
Record: 85- Title:
- 'Is it beneficial to have an Alcoholics Anonymous sponsor': Correction to Tonigan and Rice (2010).
- Authors:
- Tonigan, J. Scott. Center on Alcoholism, Substance Abuse, and Addictions, Department of Psychology, University of New Mexico, Albuquerque, NM, US, jtonigan@unm.edu
Rice, Samara L.. Center on Alcoholism, Substance Abuse, and Addictions, Department of Psychology, University of New Mexico, Albuquerque, NM, US - Address:
- Tonigan, J. Scott, Center on Alcoholism, Substance Abuse, and Addictions (CASAA), 2650 Alamo S.E., University of New Mexico, Albuquerque, NM, US, 87106, jtonigan@unm.edu
- Source:
- Psychology of Addictive Behaviors, Vol 27(2), Jun, 2013. Special Issue: Neuroimaging Mechanisms of Change in Psychotherapy for Addictive Behaviors. pp. 465.
- NLM Title Abbreviation:
- Psychol Addict Behav
- Page Count:
- 1
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- Bulletin of the Society of Psychologists in Addictive Behaviors; Bulletin of the Society of Psychologists in Substance Abuse
- Other Publishers:
- US : Educational Publishing Foundation
Society of Psychologists in Addictive Behaviors - ISSN:
- 0893-164X (Print)
1939-1501 (Electronic) - ISBN:
- 1-4338-1635-0
- Language:
- English
- Keywords:
- AA sponsors, Alcoholics Anonymous, mutual help, self-help, problem drinkers
- Abstract:
- Reports an error in 'Is it beneficial to have an alcoholics anonymous sponsor' by J. Scott Tonigan and Samara L. Rice (Psychology of Addictive Behaviors, 2010[Sep], Vol 24[3], 397-403). There was an error in the Method section, under the Participants paragraph. The sentence 'The parent study recruited 253 alcohol dependent adults from community-based AA ( n 68) and as they presented for outpatient substance abuse treatment ( n = 185).' is incorrect. This sentence should have read 'The parent study recruited 253 alcohol dependent adults from community-based AA ( n = 68), as they presented for outpatient substance abuse treatment ( n = 87), and through word of mouth and advertisements ( n = 98).' (The following abstract of the original article appeared in record 2010-19026-004.) Alcoholics Anonymous (AA) attendance is predictive of increased abstinence for many problem drinkers and treatment referral to AA is common. Strong encouragement to acquire an AA sponsor is likewise typical, and findings about the benefits associated with social support for abstinence in AA support this practice, at least indirectly. Despite this widespread practice, however, prospective tests of the unique contribution of having an AA sponsor are lacking. This prospective study investigated the contribution of acquiring an AA sponsor using a methodologically rigorous design that isolated the specific effects of AA sponsorship. Participants were recruited from AA and outpatient treatment. Intake and follow-up assessments included questionnaires, semi-structured interviews, and urine toxicology screens. Eligibility criteria limited prior treatment and AA histories to clarify the relationship of interest while, for generalizability purposes, broad substance abuse criteria were used. Of the 253 participants, 182 (72%) provided complete data on measures central to the aims of this study. Overall reductions in alcohol, marijuana, and cocaine use were found over 12-months and lagged analyses indicated that AA attendance significantly predicted increased abstinence. During early AA affiliation but not later logistic regressions showed that having an AA sponsor predicted increased alcohol-abstinence and abstinence from marijuana and cocaine after first controlling for a host of AA-related, treatment, and motivational measures that are associated with AA exposure or are generally prognostic of outcome. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
- Document Type:
- Erratum/Correction
- Subjects:
- *Alcohol Abuse; *Alcoholics Anonymous; *Self-Help Techniques; *Social Support
- PsycINFO Classification:
- Self Help Groups (3353)
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Release Date:
- 20130701
- Correction Date:
- 20160307
- Digital Object Identifier:
- http://dx.doi.org/10.1037/a0031580
- Accession Number:
- 2013-21666-003
- Persistent link to this record (Permalink):
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- Database:
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Correction to Tonigan and Rice (2010)
In the article “Is it beneficial to have an Alcoholics Anonymous sponsor?” by J. Scott Tonigan and Samara L. Rice (Psychology of Addictive Behaviors, Vol. 24, No. 3, pp. 397–403), there was an error in the Method section, under the Participants paragraph. The sentence “The parent study recruited 253 alcohol dependent adults from community-based AA (n = 68) and as they presented for outpatient substance abuse treatment (n = 185).” is incorrect. This sentence should have read “The parent study recruited 253 alcohol dependent adults from community-based AA (n = 68), as they presented for outpatient substance abuse treatment (n = 87), and through word of mouth and advertisements (n = 98).”
This publication is protected by US and international copyright laws and its content may not be copied without the copyright holders express written permission except for the print or download capabilities of the retrieval software used for access. This content is intended solely for the use of the individual user.
Source: Psychology of Addictive Behaviors. Vol. 27. (2), Jun, 2013 pp. 465)
Accession Number: 2013-21666-003
Digital Object Identifier: 10.1037/a0031580
Record: 86- Title:
- Is it beneficial to have an Alcoholics Anonymous sponsor?
- Authors:
- Tonigan, J. Scott. Center on Alcoholism, Substance Abuse, and Addictions, Department of Psychology, University of New Mexico, Albuquerque, NM, US, jtonigan@unm.edu
Rice, Samara L.. Center on Alcoholism, Substance Abuse, and Addictions, Department of Psychology, University of New Mexico, Albuquerque, NM, US - Address:
- Tonigan, J. Scott, Center on Alcoholism, Substance Abuse, and Addictions (CASAA), 2650 Alamo S.E., University of New Mexico, Albuquerque, NM, US, 87106, jtonigan@unm.edu
- Source:
- Psychology of Addictive Behaviors, Vol 24(3), Sep, 2010. pp. 397-403.
- NLM Title Abbreviation:
- Psychol Addict Behav
- Page Count:
- 7
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- Bulletin of the Society of Psychologists in Addictive Behaviors; Bulletin of the Society of Psychologists in Substance Abuse
- Other Publishers:
- US : Educational Publishing Foundation
Society of Psychologists in Addictive Behaviors - ISSN:
- 0893-164X (Print)
1939-1501 (Electronic) - Language:
- English
- Keywords:
- AA sponsors, Alcoholics Anonymous, mutual help, self-help, problem drinkers
- Abstract:
- [Correction Notice: An Erratum for this article was reported in Vol 27(2) of Psychology of Addictive Behaviors (see record 2013-21666-003). There was an error in the Method section, under the Participants paragraph. The sentence 'The parent study recruited 253 alcohol dependent adults from community-based AA ( n 68) and as they presented for outpatient substance abuse treatment ( n = 185).' is incorrect. This sentence should have read 'The parent study recruited 253 alcohol dependent adults from community-based AA ( n = 68), as they presented for outpatient substance abuse treatment ( n = 87), and through word of mouth and advertisements ( n = 98).'] Alcoholics Anonymous (AA) attendance is predictive of increased abstinence for many problem drinkers and treatment referral to AA is common. Strong encouragement to acquire an AA sponsor is likewise typical, and findings about the benefits associated with social support for abstinence in AA support this practice, at least indirectly. Despite this widespread practice, however, prospective tests of the unique contribution of having an AA sponsor are lacking. This prospective study investigated the contribution of acquiring an AA sponsor using a methodologically rigorous design that isolated the specific effects of AA sponsorship. Participants were recruited from AA and outpatient treatment. Intake and follow-up assessments included questionnaires, semi-structured interviews, and urine toxicology screens. Eligibility criteria limited prior treatment and AA histories to clarify the relationship of interest while, for generalizability purposes, broad substance abuse criteria were used. Of the 253 participants, 182 (72%) provided complete data on measures central to the aims of this study. Overall reductions in alcohol, marijuana, and cocaine use were found over 12-months and lagged analyses indicated that AA attendance significantly predicted increased abstinence. During early AA affiliation but not later logistic regressions showed that having an AA sponsor predicted increased alcohol-abstinence and abstinence from marijuana and cocaine after first controlling for a host of AA-related, treatment, and motivational measures that are associated with AA exposure or are generally prognostic of outcome. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Alcohol Abuse; *Alcoholics Anonymous; *Self-Help Techniques; *Social Support
- Medical Subject Headings (MeSH):
- Adult; Alcoholics Anonymous; Alcoholism; Behavior Therapy; Female; Humans; Logistic Models; Male; Middle Aged; Prospective Studies; Social Support; Surveys and Questionnaires; Temperance; Treatment Outcome
- PsycINFO Classification:
- Self Help Groups (3353)
- Population:
- Human
Male
Female
Outpatient - Location:
- US
- Age Group:
- Childhood (birth-12 yrs)
School Age (6-12 yrs)
Adolescence (13-17 yrs)
Adulthood (18 yrs & older) - Tests & Measures:
- General Alcoholics Anonymous Tools of Recovery
Alcoholics Anonymous Involvement questionnaire
Form 90 DOI: 10.1037/t03952-000
Stages of Change Readiness and Treatment Eagerness Scale DOI: 10.1037/t00536-000 - Grant Sponsorship:
- Sponsor: National Institute on Alcohol Abuse and Alcoholism
Grant Number: K02–AA00326; R01AA014197
Recipients: No recipient indicated - Methodology:
- Empirical Study; Longitudinal Study; Prospective Study; Interview; Quantitative Study
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- Accepted: Jan 17, 2010; Revised: Jan 13, 2010; First Submitted: Oct 19, 2009
- Release Date:
- 20100920
- Correction Date:
- 20140217
- Copyright:
- American Psychological Association. 2010
- Digital Object Identifier:
- http://dx.doi.org/10.1037/a0019013
- PMID:
- 20853924
- Accession Number:
- 2010-19026-004
- Number of Citations in Source:
- 40
- Persistent link to this record (Permalink):
- http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2010-19026-004&site=ehost-live
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- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2010-19026-004&site=ehost-live">Is it beneficial to have an Alcoholics Anonymous sponsor?</A>
- Database:
- PsycINFO
Is It Beneficial to Have an Alcoholics Anonymous Sponsor?
By: J. Scott Tonigan
Center on Alcoholism, Substance Abuse, and Addictions, Department of Psychology, University of New Mexico;
Samara L. Rice
Center on Alcoholism, Substance Abuse, and Addictions, Department of Psychology, University of New Mexico
Acknowledgement: This research was supported by Grants K02–AA00326 and R01AA014197 from the National Institute on Alcohol Abuse and Alcoholism (NIAAA). The views expressed are those of the authors and do not necessarily represent the views of the NIAAA.
Twelve-step (TS) therapy, based on Alcoholics Anonymous (AA) doctrine and practice, is the prevailing alcohol treatment model in the United States, and a primary objective of TS therapy is to facilitate community-based AA affiliation. Four meta-analytic reviews have provided relatively consistent estimates of the magnitude of AA-related benefit in terms of frequency of AA attendance and increased abstinence, for example, rw = .31 ( Emrick, Tonigan, Montgomery, & Little, 1993; Forcehimes & Tonigan, 2008; Tonigan, 2001; Tonigan, Toscova, & Miller, 1995), and prospective studies indicate that AA-related benefit includes both reduced drinking intensity (e.g., Kelly, Stout, Zywiak, & Schneider, 2006) and increased abstinence (e.g., Fiorentine & Hillhouse, 2000; Moos & Moos, 2006). Given these findings, attention has focused on the investigation of the mechanisms that account for AA-related benefit.
Social support for abstinence in AA is an important factor accounting for AA-related benefit (see Groh, Jason, & Keys, 2008, for a review). Humphreys & Noke (1997), for example, first reported that TS involvement in a Veterans Administration (VA) sample was associated with enlarged social networks supportive of abstinence at 1-year follow-up, a finding that has been replicated with a broader based sample of adult substance abusers seeking outpatient treatment (Kaskutas, Bond, & Humphreys, 2002). Work also has shown that social support for abstinence statistically mediated the positive and significant relationship between AA involvement and substance use reductions (e.g., Humphreys, Huebsch, Finney, & Moos, 1999; Laudet, Cleland, Magura, Vogel, & Knight, 2004). Here, evidence suggests that both structural aspects of AA social networks differentially influence and mediate increased abstinence among TS participants (Bond, Kaskutas, & Weisner, 2003), and that AA social networks may be more beneficial than non-AA social networks during early efforts to change behavior (Kaskutas et al., 2002).
AA sponsorship represents the intersection between the social network supportive of abstinence and the purported active ingredients of AA, working the TS ( AA World Services, 2001). Defined, the primary role of an AA sponsor is to guide a junior member through the prescribed TS of AA; a role identified in approved AA literature and recently confirmed in a qualitative study of the perceived roles of 38 AA sponsors (Whelan, Marshall, Ball, & Humphreys, 2009). In this endeavor, two AA members may have frequent social contact outside of AA meetings, and it is commonly recommended that AA members contact a sponsor when abstinence is at risk. Given the importance of general abstinence social support in AA it seems on face value that the sponsor/sponsee dyad would be important for mobilizing and sustaining increased abstinence.
What is known about the prevalence, practice, and benefit associated with AA sponsorship? Regarding prevalence, Caldwell and Cutter (1998) reported that 75% of the adults in TS treatment had a sponsor in the first 3-months after treatment. This figure is consistent with the 2007 Triennial AA survey that reported that 73% of new AA members acquire an AA sponsor within a 90-day period (AA General Service Office, 2007). It seems, however, that although a majority of AA exposed adults may initially acquire an AA sponsor this percentage decays over the course of 12 months. In Project MATCH, for example, about one in five participants (17.5%) reported having an AA sponsor at 9-month follow-up (Tonigan, Connors, & Miller, 2003), and Kaskutas et al. (2002) found in a naturalistic study of the 10 largest treatment centers in northern California that about 26% of the participants reported having an AA sponsor at 1-year follow-up. Consistent with these estimates, Mankowski, Humphreys, and Moos (2001) reported that 19.7% of a large VA sample indicated talking with an AA sponsor once or several times per week whereas 73.5% indicated that they never talked with an AA sponsor.
Predictably, AA sponsorship is positively associated with other AA-related prescribed behaviors and practices. Kelly and Moos (2003), for example, reported that acquiring an AA sponsor during treatment was a significant predictor of continued AA attendance at 1-year follow-up. Likewise, Morgenstern, Kahler, Frey, and Labouvie (1996) found that talking with a sponsor was significantly associated with working the TS, seeking the advice of other AA members, doing AA-prescribed service work, and prayer. Expanding this list, Thomassen (2002) found significant and positive associations between having a sponsor, reading aloud at AA meetings, and using the phone to talk with other AA members, and Pagano, Friend, Tonigan, and Stout (2004) reported that having an AA sponsor was predictive of later helping behaviors, with such behaviors defined as working with other alcoholics. Finally, at a more global level in investigating social support for abstinence in AA among female members in general (Rush, 2002) and residents of an Oxford House residential program in particular (Majer, Jason, Ferrari, Venable, & Olson, 2002) it has been reported that having a sponsor was significantly predictive of increased perceptions of social and personal social support.
Despite identifying 18 studies that, to some degree, investigated AA sponsorship, the actual benefits specific to having a TS sponsor are not clear. In a retrospective cross-sectional and community-based study of AA members, for example, Sheeren (1988) reported that relapse was significantly more likely when AA members did not have a sponsor and/or when they reported accessing their sponsor less often. Bond et al. (2003) provided a more rigorous test of this question by examining the bivariate associations between having a sponsor and abstinence in the 90-day period before a 1- and 3-year follow-up interview. Having a sponsor significantly and positively covaried with abstinence at both 1- and 3-year follow-ups (1 year: 42% abstinent with sponsor, 13% abstinent without sponsor; 3 year: 36% abstinent with sponsor, 12% abstinent without sponsor). Witbrodt and Kaskutas (2005) improved on this covariation strategy by statistically controlling for a host of correlated AA-related behaviors when examining associations between sponsorship and abstinence, for example, reading AA literature. Here, they reported that across several substance dependence categories, having an AA sponsor significantly increased the odds of abstinence at 6- and 12-month follow-up interviews. In contrast to these positive findings, in a large longitudinal health services study Zemore and Kaskutas (2008) found that having a sponsor at 2, 4, and 8 weeks after presentation for treatment did not increase the odds of complete abstinence at 6-month follow-up, with abstinence defined as the 30-day period before the 6-month interview. Consistent with this negative finding, in a 1-year naturalistic single-group design of inner-city drug injection users, Crape, Latkin, Laris, and Knowlton (2002) reported that although TS attendance was strongly predictive of complete abstinence at 1-year follow-up (24.2% versus 48.7% abstinent), having a TS sponsor was unrelated to abstinence.
This paper aims to investigate the direct and specific effects of AA sponsorship on later substance use. In so doing, this study will address many of the methodological ambiguities associated in prior work on AA sponsorship. First, variables of interest will be temporally and logically sequenced to both minimize issues of measurement covariation as well as clarify the impact, if any, of AA sponsorship on later substance use. Second, to isolate the relationship of interest a comprehensive set of variables reflecting several dimensions of AA participation, formal help seeking, and motivation will be used as covariates. Third, continuous daily drinking and illicit drug use data will be used to define abstinence, hence avoiding the need to infer that a subset of days adjacent to the interview represents behavior throughout the entire assessment window. Fourth, and related, four measures of substance use outcome will be considered to adequately and sensitively detect potential effects.
Method Participants
This study was conducted in the context of a larger prospective study investigating AA-related behavior change. The parent study recruited 253 alcohol dependent adults from community-based AA (n = 68) and as they presented for outpatient substance abuse treatment (n = 185). Eligibility criteria were narrow in terms of lifetime and recent treatment and AA experiences to investigate how substance abusers mobilize and sustain behavior change in AA, unconfounded by prior change histories. Thus, prospective participants were excluded if they reported more than 16 weeks of lifetime AA exposure and/or if they reported having successfully achieved abstinence for 12 months or longer after they had first determined their substance use to have become a problem. To be included, participants had to meet current Diagnostic and Statistical Manual of Mental Disorders (4th ed., DSM–IV; American Psychiatric Association) criteria for at least alcohol abuse, consumed alcohol in the prior 90 days, and attended at least one AA meeting in the prior 3 months. Illicit drug abuse and dependence were not exclusion criteria.
Procedures
Breathalyzers were used to ensure that a participant's blood alcohol concentration (BAC) did not exceed .05 prior to the consent process or at any of the subsequent interviews. Once consented, participants were administered a baseline interview that included 15 self-report questionnaires, three semi-structured interviews, and urine toxicology screens for five classes of illicit drugs. Follow-up interviews were conducted in 3-month increments for the first year (e.g., 3, 6, 9, and 12). No intervention was offered in this assessment-only study although clinical referral could be made when requested by the participant or when deemed warranted by Center on Alcoholism, Substance Abuse, and Addictions (CASAA) clinical staff. Follow-up rates for the 3-, 6-, 9-, and 12-month interviews were 93.7, 94.1, 93.7, and 91.7%, respectively. Participants were compensated $50 for each completed interview. All procedures and assessments were approved by the institutional review board at the University of New Mexico (UNM Protocol No. 24028).
Assessments
Substance use
The Form 90 ( Miller, 1996) was used to gather calendar-based alcohol use and other drug use data, ideally collected in 90-day intervals. One reliability study (Tonigan, Miller, & Brown, 1997) indicated satisfactory self-report reliability on abstinent days from alcohol (r = .79 for outpatients and r = .97 for aftercare patients), heavy drinking use days (r = .96, and .97), and number of drinks per drinking day (r = .94, and .95), and a second test–retest study (Westerberg, Tonigan, & Miller, 1998) with polysubstance abusers, reported that the calendar-based procedure had good reliability on frequency of cocaine use days (r = .77) and marijuana use days (r = .80). Urine toxicology screens for five classes of illicit drugs were collected at intake and at the 3- and 24-month follow-up interview. The Syva Rapid Test is manufactured by Siemens Healthcare Diagnostics in Deerfield, IL. It employs a one-step solid phase immunoassay technology to rapidly and qualitatively detect the presence of THC, opiates, cocaine, PCP, and amphetamine. Cut-off ng/ml concentrations for the five drugs were: THC (50), opiates (300), cocaine (300), PCP (25), and amphetamine (1,000).
Four outcome measures of substance use were computed using the Form 90. Complete abstinence from alcohol was defined as no reported alcohol use between the 3- and 6-month interview (6-month outcome analyses: M number of days = 93.68, SD = 24.26) and no alcohol use between the 9- and 12-month interviews for the 12-month outcome analyses (M number of days = 102.35, SD = 41.86). A parallel definition was used for the 6- and 12-month analyses when determining complete abstinence from alcohol, marijuana, and cocaine. Proportion of days abstinent (PDA), the third outcome measure, was defined as the number of alcohol abstinent days in a period divided by the total number of days in the assessment period. Last, drinks per drinking day (DPDD) was defined as number of drinks consumed per drinking day divided by the number of drinking days in a period (abstinent days not included in the denominator).
Help-seeking behaviors
The Alcoholics Anonymous Involvement (AAI) questionnaire ( Tonigan, Connors, & Miller, 1996) was developed to assess AA program and fellowship behaviors and practices. Normative data have been published on the AAI, and test–retest psychometric analyses indicate that the AAI scales and items are reliable and valid. A single item from the AAI was used at each interview to identify which respondents currently had an AA sponsor (yes/no). The General Alcoholics Anonymous Tools of Recovery (GAATOR; Montgomery, Miller, & Tonigan, 1995) is a 24-item 4-point Likert scaled self-report survey that was developed to assess commitment to, and practice of, the TS of AA. Sample items include: “I have shared my personal inventory with someone I trust,” “I have made a list of my resentments,” and “I have prayed and meditated.” Items were evaluated on a scale from 1 to 4, with 1 indicating strong disagreement and 4 indicating strong agreement. Psychometric work suggests that the GAATOR has three scales that can be used separately or summed to yield a total score representing the practice of prescribed AA-related behaviors and beliefs (Tonigan, Miller, & Vick, 2000). Readiness for behavior change was measured using the Stages of Change Readiness and Treatment Eagerness Scale (SOCRATES; Miller & Tonigan, 1996), a self-report tool with 19 items that yields three scales: Ambivalence (four items), Problem Recognition (seven items), and Taking Steps (eight items). Problem Recognition and Taking Steps scales have demonstrated prognostic value in representing the positive role motivation serves in predicting later substance use (e.g., Miller & Tonigan, 2001), and these two scales were selected as covariates in an effort to further isolate the impact of AA sponsorship.
In addition to calendar-based drinking information, the Form 90 also collects frequency data on formal and informal health care utilization. Nonoverlapping number of day's treatment for alcohol, drug, and emotional problems were summed and divided by the total number of days (outpatient and inpatient) in the assessment period to derive a proportion of day's formal psychosocial treatment in an assessment period. Likewise, the number of days AA was attended in an assessment period was divided by the total number of days in an assessment window to compute proportion of days that AA was attended. This strategy is an effective and psychometrically sound method to use Form 90 frequency counts when the actual number of days in an assessment window varies across individuals ( Tonigan et al., 1997; Westerberg et al., 1998).
Statistical analysis
Four dependent measures were separately evaluated at both 6- and 12-month follow-up (two binary and two interval scaled). For the two binary outcomes (1) alcohol abstinence and (2) abstinence from alcohol, marijuana, and cocaine, hierarchical logistic regression analyses were conducted to assess the unique contributions of sponsorship (yes/no) on the binary outcome, with classification of sponsorship group determined by self-report (3 month for the 6-month analyses, and 9 month for the 12-month analyses). Prior to entering the dummy coded sponsorship variable in step two, in step one we entered total GAATOR score, two scales from the SOCRATES, proportion of days attending AA, proportion of days receiving treatment, and baseline PDA and DPDD. Important, the total GAATOR score, the two scales of the SOCRATES, and the proportion of days attending AA and/or treatment variables were for these time periods: the 3 months prior to beginning the study, from intake to 3 months, and from 4 to 6 months. The same strategy using hierarchical linear regression analysis was used for the two interval scaled outcomes, PDA and DPDD (6 and 12 months). Prior to analyses, PDA and DPDD were subjected to data transformations, arcsine and square root, respectively. As before, 17 covariates were entered in Block 1 and the sponsorship variable was entered in Block 2.
ResultsData from five different assessment periods were used to isolate the specific effect of sponsorship on four different substance abuse outcome measures. Seventy-two percent (n = 182) of the recruited sample had complete data. Table 1 provides key demographic and drinking variables for included and excluded participants. No significant differences were found between the two groups on any of the demographic or drinking variables. We find it interesting that men and women in the included group did not differ on any demographic variables except for unemployment. Men were significantly more likely to be unemployed, Pearson's χ2(1, N = 181) = 6.73, p < .01, κ = .193. Regarding substance use measures, the only significant gender difference was that men were higher in Problem Recognition relative to women, t(179) = 2.06, p < .05.
Characteristics of Included and Excluded Study Participants
Corroboration of Self-Report
High agreement was obtained between self-report and urine toxicology (UA) screens, with the frequently observed pattern that self-reported use identified a larger percentage of participants as illicit drug users. Specifically, at intake UAs indicated that one participant had used marijuana although the participant denied such use, and at 3 months, there were no contradictory reports of marijuana use. Noteworthy, UAs failed to identify 51 participants who reported marijuana use at intake and 21 participants who reported marijuana use at the 3-month interview. For cocaine, five participants were positive via the UA, although denying it at intake, and at 3 months, four participants provided positive UAs, although denying such use. In contrast, at intake 47 participants reported cocaine use who did not provide a positive UA, and at 3 months, 19 participants had negative UAs, although they reported cocaine use.
Substance Use and AA
Table 2 shows the substance use and AA participation of the sample for 12 months. Large reductions in the intensity of drinking were observed over the 12 months (d = −.98) and frequency of abstinent days increased about 25%. Slightly less than half of the participants reported complete abstinence from alcohol for Months 10 to 12 (41%). Forty-six percent of participants had an AA sponsor at intake, and approximately 40% reported having a sponsor at each follow-up assessment. Secondary analyses indicated that having a sponsor at 3 months was unrelated to participant gender (p < .42) and problem severity as measured by the Alcohol Dependence Scale, p < .46 (ADS; Skinner & Allen, 1982). Readiness for change measured by the Problem Recognition and Taking Steps scales of the SOCRATES at intake, was significantly related to having a sponsor at 3 months, r = .22, p < .01 and r = .25, p < .01. Seventy-two percent of those reporting having a sponsor at 3-months also had a sponsor at 9-months, and again, participant gender was unrelated with sponsorship at 3 and 6 months. A majority of the participants reported attending AA at each interview and, on average, they attended an AA meeting about once every 7 days throughout the course of the study, except for the first 3 months when AA was attended about twice a week.
Sample Drinking and AA Measures: Intake Through 12 Months
Proportion of AA attendance days (Months 0 to 3) was significantly predictive of each of the four substance use outcome measures (Months 4 to 6). Specifically, bivariate correlations between proportion AA days and (1) alcohol abstinence was r = .36, p < .001; (2) combined abstinence from alcohol, marijuana, and cocaine, r = .29, p < .001; (3) proportion of days abstinent, r = .40, p < .001; and (4) drinks per drinking day, r = −.18, p < .05. Use of the Q statistic indicated that the absolute difference in the magnitude of these four bivariate correlations did not exceed sampling error, Q(3) = 1.77, p < .62. Thus, the mean weighted bivariate correlation of rw = .28, 95% CI [.14. .41] is conceptually the most stable estimate of the magnitude of AA-related benefit at early follow-up in this study, an estimate that compares favorably with a prior meta-analytic estimate, for example, rw = .31 ( Emrick et al., 1993).
AA Sponsorship
Two hierarchical logistic regression and two hierarchical linear regression analyses were conducted to investigate the effect of sponsorship from Months 0 to 3 on substance use from Months 4 to 6. The first and second steps of the regression analyses were the same in all four analyses. To isolate the effect of sponsorship, known correlates of reduced substance use were added in the first step. The measures entered in Step 1 were intake PDA and DPDD and five additional covariates, each of which contributed three measures (collected at intake, 3-, and 6-month interviews). The covariates were proportion days AA attended, total GAATOR score, two SOCRATES scales, and proportion days any type of treatment. Sponsorship from Months 0 to 3 was entered in the second step for each regression analysis in predicting 4- to 6-month substance use.
Early AA Sponsorship
The first logistic regression investigated the effect of sponsorship (Months 0 to 3) on self-reported abstinence from alcohol (Months 4 to 6). Controlling for variables entered in Step 1, having a sponsor was significantly predictive of abstinence, β = 1.30, p < .01, odds ratio (OR) = 3.67, 95% CI [1.48, 9.13]. A second logistic regression employed a binary measure of self-reported abstinence from alcohol, marijuana, and cocaine. Again, sponsorship was a significant predictor after first controlling for a host of AA-related and substance use-related variables (Months 0 to 6), β = 1.16, p < .05, OR = 3.19, 95% CI [1.30, 7.82]. Summarized, having an AA sponsor (Months 0 to 3) increased the probability of complete abstinence at Months 4 to 6 nearly three-fold after first controlling for past and concurrent: AA, treatment, readiness for change, and intake drinking. Two hierarchical linear regressions were then conducted to assess the effect of sponsorship (Months 0 to 3) on the continuous measures of PDA and DPDD (Months 4 to 6). An arcsine transformation was applied to PDA and a square root transformation was used with DPDD. Having a sponsor made a significant and independent contribution to the prediction of PDA, standardized β = 0.19, p < .01, and DPDD, standardized β = −0.17, p < .05. With a Bonferroni correction to control for inflated Type I error (.05/4 = .0125), the three abstinence-based outcomes for Months 4 to 6 retained significance although the measure of drinking intensity, DPDD, failed to achieve statistical significance.
Later AA Sponsorship
An identical analytical strategy was used to assess if having a sponsor (Months 7 to 9) predicted substance use outcomes at Months 10 to 12. The same covariates were used, this time collected at Months 3 to 12. Intake PDA and DPDD were, as before, entered to control for prestudy drinking. Sponsorship at Months 7 to 9 was not predictive of any of the four substance use measures at the 12-month follow-up (smallest p < .12).
Post Hoc Analyses
Sponsorship involves encouragement to work the TS and the social support for achieving this objective. Independent t tests were done to see if sponsored and nonsponsored adults differed in the mean number of steps completed, with the completion of steps grouped as Surrender steps (1 to 3, score range 0 to 3), Action steps (4 to 9, score range 0 to 6), and Maintenance steps (10 to 12, score range 0 to 3). No mean differences in the number of steps completed in any of the three step categories were found at 6 months contingent on AA sponsor status at 3 months, smallest p value < .17. In contrast, AA sponsor status at 9 months was generally significantly related to step completion at 12-month follow-up. Here, adults with sponsors reported, on average, significantly higher rates of completing Surrender steps, t(96.76) = 2.91, p < .01, Maintenance steps, t(85.92) = 1.91, p < .06, and Action steps, t(107.62) = 2.02, p < .05.
DiscussionFindings offer strong support for the importance and benefits of acquiring an AA sponsor during early AA affiliation. Specifically, having an AA sponsor during early AA affiliation was significantly and positively predictive of later abstinence, regardless of whether abstinence did or did not consider the use of illicit drugs. Illustrating the advantage of having an AA sponsor, for instance, participants with sponsors at 3 months were almost three times as likely to be abstinent from alcohol at 6 months as AA-exposed adults who had not acquired an AA sponsor. Continuing, participants with sponsors at 3 months reported 21% more abstinent days (in a 90-day window) at the 6-month interview and, when drinking did occur, they reported drinking two drinks less than AA-exposed adults without a sponsor. Noteworthy, the benefits associated with sponsorship were found after first statistically controlling for a host of prior and concurrent variables that are associated with AA participation and that are also reported to be prognostic of outcome.
In contrast, no support was found for the unique value of AA sponsorship at 9 months in predicting 1-year abstinence on any of the outcome measures. How can we reconcile the pattern of our findings with the extant literature? In particular, studies have offered mixed conclusions about the benefit, if any, associated with AA sponsorship in the first 6 months of AA affiliation (e.g., Witbrodt & Kaskutas, 2005; Zemore & Kaskutas, 2008) but most studies have reported that, at 1 year and later, AA sponsorship was significantly associated with increased abstinence (e.g., Bond et al., 2003). Our findings suggested exactly the opposite. In part, we believe that different analytic strategies may account for this disparity in findings. Using a covariation approach, for example, in our study 52.1% of the adults with sponsors (at 12 months) also reported complete alcohol-abstinence at 12 months while only 32.7% of the adults without a sponsor reported abstinence, an important cross-sectional association. The lagged-based findings in this study, however, suggest that this association is simply that; adults in AA who remain abstinent at 12 months also tend to have sponsors more often than AA-exposed adults who do not remain abstinent. In this regard, AA sponsorship appears to be best considered an active ingredient with highest potency during initial efforts to engage in AA.
How can we explain the changing benefit of AA sponsorship? Speculating, it seems reasonable that AA affiliates with sponsors were more likely to have more social support for abstinence than were people without a sponsor, especially during early AA affiliation when group- and member-based social relationships were still developing. At 12 months, however, this relative advantage in social support may have become diluted as people without an AA sponsor became increasingly integrated within the AA social context. Encouragement to work the TS, the second role of an AA sponsor, is a less plausible explanation for study findings. Specifically, when sponsorship was associated with increased step work (later follow-up) this did not become manifest in differential improvement. Oppositely, when no difference was found in completed step work between sponsored and nonsponsored groups (early follow-up) the sponsored group reported higher rates of abstinence.
Several points deserve attention. First, overall pre–post reductions in substance use in this sample were substantial and they were observed across three commonly abused drugs; alcohol, marijuana, and cocaine. Illustrating these improvements, pre–post effect size estimates for increased days abstinence (d = .77) and reduced drinks per drinking episode (d = −.98) were large by most standards, for example, Cohen (1988). Second, at 6 months, 37% of the participants reported alcohol abstinence and, of these adults, 30% reported abstinence from alcohol, marijuana, and cocaine. This discrepancy in overall sample abstinence rates with and without consideration of illicit drugs was found at all follow-up interviews. Although this study did not assess whether illicit drug use exceeded clinical thresholds of abuse and/or dependence, findings do suggest that alcohol abstinence does not necessarily imply abstinence from marijuana and cocaine. In this regard, we recommend that AA-focused researchers cast a broad net when assessing TS-related benefit. Third, contrary to our expectation the percentage of participants in this study with an AA sponsor remained relatively stable over 12 months. Unlike prior work that reported rapid decay in rates of AA sponsorship over a 12-month interval, we found that 41% of the sample reported having a sponsor at both the 3- and 9-month interviews. The reasons why the rate of AA sponsorship remained high in this study are unclear, but may relate to study inclusion criteria. In particular, study participants had, at most, only limited prior histories in AA and with formal treatment. In addition, at intake none of the participants reported prior success in achieving 1 or more years of abstinence once alcohol had been self-identified to be a problem. The sustaining of a sponsor may be less likely among adults with extensive prior AA histories. And, fourth, a majority of the participants at 9 months with a sponsor (72%, n = 48) also reported having a sponsor at 3 months. It therefore seems unlikely that the absence of an AA sponsor effect at 9 months was the result of examining the relationship of interest using participants that systematically differed from those with sponsors at the earlier follow-up. Also surprising was the relative stability in reported practice of the TS as measured by the GAATOR over time, a finding that may be associated with the relatively stable use of AA sponsors.
Some study limitations should be noted. Foremost, although the role of the AA sponsor is relatively clear there is wide variability in how sponsorship is actually structured and experienced. This study did not assess the frequency of sponsor contact and/or sponsee progress through the TS, for example, nor did we assess the extent that individuals perceived how and why sponsors were helpful (or not). We suspect that the direct effect identified between those who did and did not have a sponsor in this study may be influenced by the nature and practice of the sponsor–sponsee relationship. Second, the four outcome measures used in this study were correlated, sometimes highly (e.g., two binary abstinence measures, r = .84 at 6 months). This situation exacerbates the multiple-comparison problem, for example, inflated Type I error. Noteworthy, however, with Bonferroni adjustment to control for inflated Type I error three of the four inferential tests conducted at the 6-month period retained statistical significance. Third, excluded participants did not appreciably differ from those who provided sufficient information to be included in this study. At intake, the two largest observed differences centered on frequency of marijuana use and one scale on the readiness for change measure. On average, included participants reported lower values on both of these measures although the absolute magnitude of these differences was deemed modest and not statistically significant. Nevertheless, the possibility remains that unintended biases were introduced through study selection criteria.
In sum, acquiring an AA sponsor is highly encouraged within AA, and TS therapy frequently uses evidence-based strategies to facilitate the acquisition of an AA sponsor while clients are still in treatment. Findings suggest that these recommendations and practices are justified, especially immediately after treatment when relapse rates are highest. A stronger case can be made that abstinence-related benefit associated with having a sponsor in early AA affiliation is the result of focused social support, but this inference lacks empirical support at this time.
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Submitted: October 19, 2009 Revised: January 13, 2010 Accepted: January 17, 2010
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Source: Psychology of Addictive Behaviors. Vol. 24. (3), Sep, 2010 pp. 397-403)
Accession Number: 2010-19026-004
Digital Object Identifier: 10.1037/a0019013
Record: 87- Title:
- Is the Short Form of the Mini-Mental State Examination (MMSE) a better screening instrument for dementia in older primary care patients than the original MMSE? Results of the German study on ageing, cognition, and dementia in primary care patients (AgeCoDe).
- Authors:
- Stein, Janine. Institute of Social Medicine, Occupational Health and Public Health, University of Leipzig, Leipzig, Germany, Janine.Stein@medizin.uni-leipzig.de
Luppa, Melanie. Institute of Social Medicine, Occupational Health and Public Health, University of Leipzig, Leipzig, Germany
Kaduszkiewicz, Hanna. Institute of Primary Medical Care, University Medical Center Hamburg-Eppendorf, Germany
Eisele, Marion. Institute of Primary Medical Care, University Medical Center Hamburg-Eppendorf, Germany
Weyerer, Siegfried. Central Institute of Mental Health, Medical Faculty, Mannheim/ Heidelberg University, Germany
Werle, Jochen. Central Institute of Mental Health, Medical Faculty, Mannheim/ Heidelberg University, Germany
Bickel, Horst. Department of Psychiatry, Technical University of Munich, Germany
Mösch, Edelgard. Department of Psychiatry, Technical University of Munich, Germany
Wiese, Birgitt. Institute for General Practice, Working Group Medical Statistics and IT-Infrastructure, Hannover Medical School, Germany
Prokein, Jana. Institute for General Practice, Working Group Medical Statistics and IT-Infrastructure, Hannover Medical School, Germany
Pentzek, Michael. Institute of General Practice, Medical Faculty, University of Düsseldorf, Germany
Fuchs, Angela. Institute of General Practice, Medical Faculty, University of Düsseldorf, Germany
König, Hans-Helmut. Department of Health Economics and Health Services Research, Hamburg Center for Health Economics, University Medical Center Hamburg-Eppendorf, University of Hamburg, Germany
Brettschneider, Christian, ORCID 0000-0002-5280-1075. Department of Health Economics and Health Services Research, Hamburg Center for Health Economics, University Medical Center Hamburg-Eppendorf, University of Hamburg, Germany
Heser, Kathrin. Department of Psychiatry, University of Bonn, Germany
Wagner, Michael, ORCID 0000-0003-2589-6440. Department of Psychiatry, University of Bonn, Germany
Riedel-Heller, Steffi G.. Institute of Social Medicine, Occupational Health and Public Health, University of Leipzig, Leipzig, Germany
Scherer, Martin. Institute of Primary Medical Care, University Medical Center Hamburg-Eppendorf, Germany
Maier, Wolfgang. Department of Psychiatry, University of Bonn, Germany - Address:
- Stein, Janine, Institute of Social Medicine, Occupational Health and Public Health, University of Leipzig, Philipp-Rosenthal-Straße 55, 04103, Leipzig, Germany, Janine.Stein@medizin.uni-leipzig.de
- Source:
- Psychological Assessment, Vol 27(3), Sep, 2015. pp. 895-904.
- NLM Title Abbreviation:
- Psychol Assess
- Page Count:
- 10
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- Psychological Assessment: A Journal of Consulting and Clinical Psychology
- ISSN:
- 1040-3590 (Print)
1939-134X (Electronic) - Language:
- English
- Keywords:
- screening, dementia, Mini-Mental State Examination, Short Form of the Mini-Mental State Examination, psychometric properties
- Abstract:
- The aim of the study was to investigate the psychometric properties of a Short Form of the Mini-Mental State Examination (SMMSE) for the screening of dementia in older primary care patients. Data were obtained from a large longitudinal cohort study of initially nondemented individuals recruited via primary care chart registries and followed at 18-month intervals. Item and scale parameters for MMSE and SMMSE scores were analyzed and cross-validated for 2 follow-up assessments (n1 = 2,657 and n2 = 2,274). Binary logistic regression and receiver-operating-characteristic (ROC) curve analyses were conducted in order to assess diagnostic accuracy parameters for MMSE and SMMSE scores. Cross-sectional differentiation between dementia-free and dementia patients yielded moderate to good results for MMSE and SMMSE scores. With regard to most diagnostic accuracy parameters, SMMSE scores did not outperform the MMSE scores. The current study provides first evidence regarding the psychometric properties of the SMMSE score in a sample of older primary care patients. However, our findings do not confirm previous findings that the SMMSE is a more accurate screening instrument for dementia than the original MMSE. Further studies are needed in order to assess and to develop short, reliable and valid instruments for routine cognitive screening in clinical practice and primary care settings. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Dementia; *Mini Mental State Examination; *Psychometrics; *Screening; Test Reliability; Test Validity
- PsycINFO Classification:
- Clinical Psychological Testing (2224)
Neurological Disorders & Brain Damage (3297) - Population:
- Human
Male
Female - Location:
- Germany
- Age Group:
- Adulthood (18 yrs & older)
Aged (65 yrs & older) - Tests & Measures:
- Structured Interview for the Diagnosis of Dementia of the Alzheimer Type, Multiinfarct Dementia and Dementia of other Etiology-German Version
Hachinski-Rosen-Scale
Short Form of the Mini-Mental State Examination
Mini Mental State Examination
Geriatric Depression Scale DOI: 10.1037/t00930-000 - Grant Sponsorship:
- Sponsor: German Federal Ministry of Education and Research, KND, Germany
Grant Number: 01GI0102, 01GI0420, 01GI0422, 01GI0423, 01GI0429, 01GI0431, 01GI0433, 01GI0434
Recipients: No recipient indicated
Sponsor: German Federal Ministry of Education and Research, KNDD, Germany
Grant Number: 01GI0710, 01GI0711, 01GI0712, 01GI0713,01GI0714, 01GI0715, 01GI0716, 01ET1006B
Recipients: No recipient indicated - Methodology:
- Empirical Study; Quantitative Study
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- First Posted: Mar 30, 2015; Accepted: Nov 18, 2014; Revised: Oct 1, 2014; First Submitted: Jan 17, 2014
- Release Date:
- 20150330
- Correction Date:
- 20150824
- Copyright:
- American Psychological Association. 2015
- Digital Object Identifier:
- http://dx.doi.org/10.1037/pas0000076
- PMID:
- 25822830
- Accession Number:
- 2015-13973-001
- Number of Citations in Source:
- 53
- Persistent link to this record (Permalink):
- http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2015-13973-001&site=ehost-live
- Cut and Paste:
- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2015-13973-001&site=ehost-live">Is the Short Form of the Mini-Mental State Examination (MMSE) a better screening instrument for dementia in older primary care patients than the original MMSE? Results of the German study on ageing, cognition, and dementia in primary care patients (AgeCoDe).</A>
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Is the Short Form of the Mini-Mental State Examination (MMSE) a Better Screening Instrument for Dementia in Older Primary Care Patients Than the Original MMSE? Results of the German Study on Ageing, Cognition, and Dementia in Primary Care Patients (AgeCoDe)
By: Janine Stein
Institute of Social Medicine, Occupational Health and Public Health, University of Leipzig;
Melanie Luppa
Institute of Social Medicine, Occupational Health and Public Health, University of Leipzig
Hanna Kaduszkiewicz
Institute of Primary Medical Care, University Medical Center Hamburg-Eppendorf, and Institute of General Practice, Medical Faculty, Kiel University
Marion Eisele
Institute of Primary Medical Care, University Medical Center Hamburg-Eppendorf
Siegfried Weyerer
Central Institute of Mental Health, Medical Faculty, Mannheim/Heidelberg University
Jochen Werle
Central Institute of Mental Health, Medical Faculty, Mannheim/Heidelberg University
Horst Bickel
Department of Psychiatry, Technical University of Munich
Edelgard Mösch
Department of Psychiatry, Technical University of Munich
Birgitt Wiese
Institute for General Practice, Working Group Medical Statistics and IT-Infrastructure, Hannover Medical School
Jana Prokein
Institute for General Practice, Working Group Medical Statistics and IT-Infrastructure, Hannover Medical School
Michael Pentzek
Institute of General Practice, Medical Faculty, University of Düsseldorf
Angela Fuchs
Institute of General Practice, Medical Faculty, University of Düsseldorf
Hans-Helmut König
Department of Health Economics and Health Services Research, Hamburg Center for Health Economics, University Medical Center Hamburg-Eppendorf, University of Hamburg
Christian Brettschneider
Department of Health Economics and Health Services Research, Hamburg Center for Health Economics, University Medical Center Hamburg-Eppendorf, University of Hamburg
Kathrin Heser
Department of Psychiatry, University of Bonn and German Center for Neurodegenerative Diseases within the Helmholtz Association
Michael Wagner
Department of Psychiatry, University of Bonn and German Center for Neurodegenerative Diseases within the Helmholtz Association
Steffi G. Riedel-Heller
Institute of Social Medicine, Occupational Health and Public Health, University of Leipzig
Martin Scherer
Institute of Primary Medical Care, University Medical Center Hamburg-Eppendorf
Wolfgang Maier
Department of Psychiatry, University of Bonn and German Center for Neurodegenerative Diseases within the Helmholtz Association
Acknowledgement: The authors performed this work on behalf of the AgeCoDe Study Group. They also wish to thank the other members of AgeCoDe Study Group: Heinz-Harald Abholz, Cadja Bachmann, Wolfgang Blank, Sandra Eifflaender-Gorfer, Annette Ernst, Frank Jessen, Teresa Kaufeler, Mirjam Köhler, Alexander Koppara, Carolin Lange, Hanna Leicht, Tobias Luck, Manfred Mayer, Julia Olbrich, Anna Schumacher, Susanne Steinmann, Franziska Tebarth, Klaus Weckbecker, Dagmar Weeg, Steffen Wolfsgruber, Thomas Zimmermann. Principal Investigators: Wolfgang Maier, Martin Scherer, Hendrik van den Bussche (2002-2011). This study/publication is part of the German Research Network on Dementia (KND) and the German Research Network on Degenerative Dementia (KNDD) and was funded by the German Federal Ministry of Education and Research (Grants KND: 01GI0102, 01GI0420, 01GI0422, 01GI0423, 01GI0429, 01GI0431, 01GI0433, 01GI0434; Grants KNDD: 01GI0710, 01GI0711, 01GI0712, 01GI0713, 01GI0714, 01GI0715, 01GI0716, 01ET1006B).
The diagnostic criteria for dementia syndromes require evidence of multiple cognitive deficits including the loss of memory functions and cognitive impairment in other cognitive domains (aphasia, apraxia, agnosia or executive functions; American Psychiatric Association, 2000). Recent research confirmed that dementia patients, in comparison with cognitively healthy individuals, show a diminished ability to learn and recall learned words or word lists immediately or with a delay. Tasks that involve a delayed recall of learned words or word lists have, in particular, been shown to best differentiate between patients with and without dementia (Beck, Gagneux-Zurbriggen, Berres, Taylor, & Monsch, 2012; Beeri et al., 2006).
General practitioners (GPs) play a key role in the early detection and diagnosis of cognitive impairment and dementia as the majority of the elderly visit their GP on a frequent and regular basis (Pentzek et al., 2009; Linden, Gilberg, Horgas, & Steinhagen-Thiessen, 1996). In the GP practice setting, cognitive screening tests are used to detect cognitive impairment and first symptoms of dementia. Currently, there are only few instruments for routine use in general practice: for example, the Mini-Cog (Borson, Scanlan, Chen, & Ganguli, 2003), Memory Impairment Screen (MIS; Buschke et al., 1999), and Clock Drawing Test (CDT; Shulman, 2000; Sunderland et al., 1989). Currently available instruments are usually lacking in psychometric quality and feasibility (length and test duration). There is a rising need for instruments that fulfill these requirements. Thus, it is increasingly important to provide and implement short, reliable, and valid cognitive screening tests for the early detection of cognitive impairment and dementia in the primary care sector (Brodaty, Low, Gibson, & Burns, 2006; Ismail, Rajji, & Shulman, 2010; Lorentz, Scanlan, & Borson, 2002; Milne, Culverwell, Guss, Tuppen, & Whelton, 2008).
The Mini-Mental State Examination (MMSE; Folstein, Folstein, & McHugh, 1975) is one of the most commonly used cognitive screening tests worldwide. It was initially developed as an inpatient screening tool for general cognitive impairment and is often used as a standard cognitive screening test for cognitive impairment and dementia in outpatient settings (Ismail et al., 2010). The MMSE consists of 30 tasks. It has been translated into many languages and modified in many ways since its publication almost 40 years ago (Steis & Schrauf, 2009). Overall, psychometric properties of the MMSE score have been empirically tested and criticized many times. Mitchell showed in a meta-analysis of 39 studies that its value as a screening measure for dementia is limited in many settings including primary care. This study also showed that the case-finding ability of the MMSE was best when confirming a suspected dementia diagnosis in specialist settings (Mitchell, 2009). In addition, the diagnostic and psychometric quality of MMSE score seems restricted due to several shortcomings including its length, ceiling or floor effects, and that several items lacked the power to differentiate between individuals with and without cognitive impairment (Bossers, van der Woude, Boersma, Scherder, & van Heuvelen, 2012; Mitchell, 2009; Tombaugh & Mcintyre, 1992). Recently, numerous studies were conducted to assess the diagnostic accuracy of abbreviated versions of the MMSE as a screening tool for dementia. These studies demonstrated that several items or specific combinations of items (i.e., short forms of the MMSE) achieved good results for the psychometric properties in detecting cognitive impairment and dementia (Fayers et al., 2005; Fong et al., 2011; Schultz-Larsen, Kreiner, & Lomholt, 2007a; Schultz-Larsen, Lomholt, & Kreiner, 2007b; Haubois et al., 2011; Haubois et al., 2012; Callahan, Unverzagt, Hui, Perkins, & Hendrie, 2002; Kaufer et al., 2008). Moreover, research has shown an increased diagnostic power when several MMSE items were combined with other instruments or methods (Belmin et al., 2007; Chen, Leung, & Chen, 2011; Rapp, Rieckmann, Gutzmann, & Folstein, 2002). Haubois and colleagues (Haubois et al., 2011; Haubois et al., 2012) suggested and assessed the Short Form of the Mini-Mental State Examination (SMMSE). Focusing on deficits in memory functions as a key symptom of dementia, the SMMSE was constructed from the six memory items of the MMSE (immediate recall of three words and delayed recall of three words). The authors showed that the SMMSE score performed as well as, or even outperformed, the MMSE score with regard to the psychometric and diagnostic quality as a screening instrument for dementia. However, the study was based on a rather small sample of community-dwelling older patients of a memory clinic.
Following the studies of Haubois and colleagues (Haubois et al., 2011; Haubois et al., 2012), the aim of this study was to investigate the psychometric characteristics of the SMMSE score and to find further evidence for its diagnostic quality for screening of dementia. The authors emphasized the confirmation of its predictive value in older primary care patients. Based on a large cohort of older primary care patients participating in our study, this study offers an excellent opportunity to investigate the psychometric quality of the SMMSE as screening tool for dementia in a sample of older primary care patients.
Method Study Design and Sample
The present study is based on a sample of older individuals participating in a large general medical practice registry-based prospective longitudinal study on the early detection of mild cognitive impairment (MCI) and dementia. The study has been in progress since 2003 in six study centers in Germany. Figure 1 shows the sample selection process of the study. In the first step, 22,701 patients were recruited via general practitioners (GPs). Initial criteria for participation in the study were being 75 years of age or older, absent of dementia (according to the GP’s judgment), and having had at least one contact with the GP within the last 12 months. Exclusion criteria were having had GP consultations only by home visits, residing in a nursing home, having a severe illness which the GP deemed would be fatal within 3 months, an insufficient knowledge of the German language, deafness, blindness, the inability to provide informed consent and the status as having been only an occasional patient of the participating GP. From the sample of eligible patients (N = 10,850), a randomly selected sample of 6,619 patients was drawn. These patients were informed about the project and invited to participate in the study. Of the total sample of 6,619 patients, 1,517 (22.9%) could not be contacted and 1,775 (26.8%) refused to participate in the study. Finally, 3,327 patients (50.3%) agreed to participate in the study. The entire study protocol was approved by the local ethics committee and all participants gave written informed consent. A detailed description of the study design can also be found elsewhere (Stein et al., 2012).
Figure 1. Sample selection flowchart.
As shown in Figure 1, a total of 3,327 patients were successfully contacted and assessed at baseline via structured clinical interviews in their homes. The structured clinical interviews were conducted by trained study staff which consisted largely of physicians and psychologists. In order to assure standardization, all interviewers were trained to conduct interviews by members of the study team. The interviewer training included a theoretically grounded instruction, coaching, supervised practice and ongoing supervision during the course of the study. The use of structured interviews including a set of standardized questions, tests, and scoring systems with guidelines for rating also contributed to a greater standardization of the interview process and consistency across interviewers. The structured clinical interviews included a comprehensive neuropsychological and cognitive assessment including dementia diagnostics. Follow-up measurements with face-to-face interviews and neuropsychological assessment were performed at 18-month intervals after baseline assessment. In this study, complete data from personal interviews at Follow-Up I (n = 2,657/79.8% of the 3,327 patients initially included at baseline) and Follow-Up II (n = 2,274/68.3% of the 3,327 patients initially included at baseline) were obtained. For our study, further criteria for inclusion and exclusion in the study samples were considered. Criteria for inclusion into the study samples were (a) complete MMSE data and face-to-face cognitive assessment, (b) age of 75 years and older at baseline, and (c) German as a first language. At Follow-Up I, we excluded 670 (25.2%) patients from the study sample due to (a) having incomplete MMSE or missing cognitive assessment, (b) being younger than 75 years, and (c) having a language other than German as their first language. At Follow-Up II, we excluded 1,053 (46.3%) patients from the study sample for the same reasons (see Figure 1).
Procedure and Instruments
The clinical and neuropsychological evaluation via structured clinical interviews at each assessment of this study included the validated German version of the Structured Interview for the Diagnosis of Dementia of the Alzheimer Type, Multiinfarct Dementia and Dementia of other Etiology according to DSM–III–R, DSM–IV, and ICD-10 (SIDAM; Zaudig & Hiller, 1996). The SIDAM was specifically developed for diagnosis of dementia according to the established criteria and consists of (a) a neuropsychological test battery; (b) a 14-item scale for the assessment of activities of daily living (SIDAM-ADL); and (c) the Hachinski-Rosen-Scale (Rosen, Terry, Fuld, Katzman, & Peck, 1980). The neuropsychological test part of the SIDAM consists of 55 items forming the SIDAM-cognitive-score (SISCO). In this study, dementia diagnoses were based on consensus conferences with the interviewer and an experienced geriatrician or geriatric psychiatrist according to DSM–IV criteria, which were implemented as a diagnostic algorithm in the SIDAM. The algorithm includes cognitive impairment as defined by the SISCO and impairment of activities of daily living as defined by a score of at least two points on the SIDAM-ADL scale. The SIDAM includes all 30 items of the MMSE. In this study, the MMSE contained the three word recall using the words “apple,” “table,” and “penny.” Within this task, patients were asked to name and repeat the three words after the interviewer has said them. After a few minutes delay, patients were asked to remember and repeat the three learned words. The same MMSE version was used at every visit; for more details on items, please see Table 2. The Short Form of the Mini-Mental State Examination (SMMSE) was constructed according to the version suggested by Haubois and colleagues (Haubois et al., 2011; Haubois et al., 2012), that is, summing up the items 6 a, b, c (immediate recall of the three words “apple,” “table,” “penny”) and 16 a, b, c (delayed recall of the three words). The SMMSE has a maximum score of 6 indicating best cognitive performance.
Item and Scale Characteristics of the Mini-Mental State Examination (MMSE) and the Short Form of the MMSE (SMMSE) at Follow-Up I (n = 2,657)
Furthermore, the 15-item version of the Geriatric Depression Scale (GDS; Sheik & Yesavage, 1986) was used to determine possible depressive symptoms. The critical cut-off value of 6 points was found to most accurately discriminate between depressive and nondepressive patients (Gauggel & Birkner, 1999). Data on sociodemographic variables as well as other possible risk factors for dementia were also collected. Education level (low, medium, and high) was classified according to the new CASMIN classification system (Brauns & Steinmann, 1999).
Statistics
Statistical analyses were performed using PASW Statistics 20 for Windows and Dag-stat a spreadsheet for the calculation of comprehensive statistics for the assessment of diagnostic tests (Mackinnon, 2000). If not otherwise stated, the level of α error was set to 0.05 in all computations. Analyses were based on two follow-up assessments. Differences in sociodemographic characteristics and in mean test scores between dementia-free and dementia patients were examined via t tests or chi-square tests, as appropriate. Item and scale characteristics were calculated. The reliability of the MMSE and SMMSE scores was determined in terms of Cronbach’s alpha. Binary logistic regression analyses were performed with diagnostic status (“no dementia vs. dementia” as the dependent variable [criterion]). Continuous age and SMMSE score (maximum score of 6 indicating best performance) were used as independent variables (predictors). The ordinal variables level of education (new CASMIN), gender, and GDS score (using the cut-off score = 6 for best differentiation between depressed and nondepressed individuals) were coded as dummy-variables and also implemented in the model as predictors. In the next step, ROC analyses were performed to evaluate the diagnostic accuracy of MMSE and SMMSE scores for cross-sectional discrimination between dementia-free and dementia patients. The area under the curve (AUC) values of the respective ROC curves (reported with 95% confidence intervals) served as a measure of overall diagnostic accuracy. Based on the optimal cut-off values determined by the ROC analyses of sensitivity and specificity, further parameters describing diagnostic accuracy, including correct classification rate (EFF), positive/negative predictive values (PVP, PVN), likelihood ratios of positive/negative test (LR+, LR−) and odds ratios for dementia, were computed.
Results Sample Characteristics
Table 1 provides an overview of sociodemographic characteristics and neuropsychological data of the study samples at Follow-Up I and Follow-Up II (right column). Of the 2,657 patients included in the analysis at Follow-Up I, 86 patients received a diagnosis of dementia. At Follow-Up II, 2,274 patients were included in the analyses and 105 patients were diagnosed with dementia. Dementia patients were somewhat older than patients without dementia (Follow-Up I: t = −4.259, p < .001; Follow-Up II: t = −4.396, p < .001), but did not significantly differ in terms of educational level (Follow-Up I: chi-square = 3.853, p = .146; Follow-Up II: chi-square = 4.950, p = .084). Also, there were no gender differences observed at Follow-Up I (chi-square = 0.168, p = .682) and Follow-Up II (chi-square = 3.532, p = .060). Altogether, dementia patients demonstrated significantly lower test performance than patients without dementia on all test scores at both follow-up assessments (p < .001).
Characteristics of the Study Samples at Follow-Ups I and II
Item and Scale Characteristics of the MMSE
In Table 2, the results of the item and scale analyses of the MMSE and SMMSE scores at Follow-Up I are displayed. Item discriminability ranged from 0.037 to 0.428 for MMSE items and from 0.065 to 0.398 for SMMSE items. Item difficulty was rather low for all items of the MMSE and SMMSE ranging from 45.6% to 99.8%. The items of the SMMSE (see Table 2) showed moderate to good item discriminability and item difficulty, especially for the items of the recall task. The item and scale analyses at Follow-Up II (not displayed in Table 2) yielded even slightly better results with item discriminability ranging from 0.076 to 0.512 for MMSE items and from 0.134 to 0.424 for SMMSE items. Item difficulty at Follow-Up II ranged from 47.8% to 99.9%. In order to determine the reliability of the MMSE and SMMSE scores, internal consistency by means of Cronbach’s alpha was calculated. At Follow-Up I, internal consistency was α = .69 for the MMSE score and α = .45 for the SMMSE score. At Follow-Up II, internal consistency was α = .78 for the MMSE score and α = .49 for the SMMSE score.
Results of the Logistic Regression Analysis
In Table 3, the results of the binary logistic regression analyses at Follow-Ups I and II are displayed. Overall, MMSE score and SMMSE score were found to be significant predictors of dementia in both follow-up assessments. In particular, a 1-point increase in MMSE score was significantly associated with a 65.2% decrease of the chance of dementia diagnosis, and a 1-point increase in SMMSE score was associated with an 86.1% decrease of the chance of dementia diagnosis (Follow-Up I). In Model II at Follow-Up I (including SMMSE score as one predictor), a higher age was significantly associated with an increase in the risk for dementia diagnosis. Analyses at Follow-Up II yielded similar results (see Table 3). However, GDS score was significantly associated with dementia diagnosis in Models I and II at Follow-Up II. Furthermore, gender turned out to be a significant predictor of dementia in Model II at Follow-Up II.
Results of the Logistic Regression Analysis for Cross-Sectional Prediction of Dementia at Follow-Ups I and II
ROC Curves and Diagnostic Accuracy
The results of the ROC curve analyses are shown in Figure 2 and Table 4. At Follow-Up I, the ROC curves showed an area under the curve (AUC) of 0.97 for the MMSE score and 0.90 for the SMMSE score. The cross-sectional classification at Follow-Up II showed similar results.
Figure 2. Receiver operating characteristic (ROC) curves for the Mini-Mental State Examination (MMSE) and the Short Form of the MMSE (SMMSE) at Follow-Up I and Follow-Up II.
Results of the Receiver Operating Characteristic (ROC) Analyses and Diagnostic Accuracy of MMSE and SMMSE Scores
Based on sensitivity and specificity values, the optimal cut-off points for the diagnosis of dementia were defined as the point with maximal Youden’s-Index-Value (sensitivity + specificity − 1). For the MMSE, the optimal cut-off point was ≤ 24 in both samples (Follow-Up I: Youden’s-Index = 0.889; Follow-Up II: Youden’s-Index = 0.902). In both study samples, the SMMSE showed an optimal cut-off value of ≤ 4 (Follow-Up I: Youden’s-Index = 0.688; Follow-Up II: Youden’s-Index = 0.677). Additionally, the diagnostic accuracy parameters were analyzed at both assessments; the results are summarized in Table 4. Overall, the diagnostic accuracy was higher for MMSE than for SMMSE. Although the sensitivity was slightly higher for SMMSE scores in both assessments, the specificity was substantially lower for SMMSE compared with MMSE scores. Correct classification rate (EFF) was highest for MMSE and lowest for the SMMSE scores. Predictive values of negative test (PVN) were exceptionally high for both forms of the MMSE while predictive values of positive test (PVP) showed rather low values. The similar pattern of results was found for the likelihood ratios of positive/negative test. In summary, the MMSE score demonstrated better parameters for diagnostic accuracy than the SMMSE score in the cross-sectional classification of dementia in both study samples.
DiscussionThis study aimed at evaluating the psychometric quality of the MMSE score and the Short Form of the MMSE (SMMSE) consisting of the six memory items of the MMSE for screening of dementia. Analyses were based on two follow-up assessments of large samples of primary care patients aged 75 years and older. To our knowledge, this is one of the first studies evaluating the psychometric properties of the SMMSE score in older primary care patients.
Worldwide, the MMSE is one of the most popular, widespread, and best studied screening instruments for cognitive impairment and dementia. In the past, numerous studies evaluating the psychometric characteristics of the MMSE score identified several shortcomings of the MMSE, including its length, ceiling effects in very mild disease, and a floor effect in advanced dementia and in patients with less education (Mitchell, 2009; Schultz-Larsen et al., 2007a). Furthermore, it has been shown that MMSE scores are affected by several factors such as age and educational level. Since the development of the MMSE, efforts have been made to adjust for the performance biases introduced by these factors (Ismail et al., 2010; Lorentz et al., 2002; Mitchell & Malladi, 2010). Moreover, a recent meta-analysis showed moderate diagnostic accuracy for the MMSE score as a screening instrument for dementia and limited value for the diagnostic distinction between patients with mild cognitive impairment (MCI) and cognitively healthy individuals (Mitchell, 2009). In accordance with these findings, the results of the current study indicate that the overall psychometric quality of the MMSE score is satisfactory but also limited with respect to the overall psychometric quality of the scale.
In consideration of its limitations, numerous studies were conducted to explore the MMSE score and its psychometric properties as a screening test for dementia by identifying the items with best discriminative power and by examining the effectiveness of the various items of the MMSE for the screening of dementia. Braekhus and colleagues found that a 12-item version of the MMSE outperformed the full MMSE version regarding sensitivity, specificity, and reliability of test scores (Braekhus, Laake, & Engedal, 1992). Schultz-Larsen et al. examined a variety of subsets of the MMSE by Item Response Theory (IRT) analyses and found that a nine-item version of the MMSE was as accurate as the original MMSE in screening for dementia (Schultz-Larsen et al., 2007a; Schultz-Larsen et al., 2007b). A study by Chen, Leung, and Chen, (2011) revealed that an eight-item version of the MMSE (three item recall, attention and calculation) showed the best performance among other tests examined. Fayers et al. (2005) demonstrated that a set of four items performed as well as the full MMSE version in screening for delirium and cognitive impairment in palliative care patients. Lou, Dai, Huang, and Yu (2007) also showed in a sample of older Taiwanese patients that the MMSE could be shortened without substantial loss of sensitivity and specificity. They identified the most efficient 16 items of the MMSE (orientation, recall, attention, and calculation) for screening of cognitive function. Fong et al. (2011) also proposed a 16-item version of the MMSE that achieved an equivalent or superior diagnostic accuracy compared with the MMSE score. Beside conceptual differences, most of the studies suggest that a short, valid form of the MMSE could perform at least as accurately as the original MMSE in screening for dementia. Furthermore, several studies examined whether the combination of the MMSE (or selected items from the MMSE) with other dementia specific instruments or tools could improve diagnostic accuracy. It is generally known that a measure’s psychometric properties improve as test length increases (Kaplan & Saccuzzo, 2013). For example, Schultz-Larsen, Lomholt, and Kreiner (2007b) suggested combining the nine-item short version of the MMSE with other neuropsychological tests or informant reports to enhance its screening performance. Jacqmin-Gadda and colleagues proposed a screening battery for dementia that combined the items with best discriminative power of seven neuropsychological tests including selected items of the MMSE (orientation to time and recall of three objects). This battery showed enhanced sensitivity and specificity compared to single measures (Jacqmin-Gadda, Fabrigoule, Commenges, Letenneur, & Dartigues, 2000). Belmin et al. (2007) developed and validated a 3-min cognitive disorders examination (Codex) for the detection of dementia that includes items of the MMSE and a simplified clock drawing test. Codex test scores showed high sensitivity and specificity compared to the original MMSE scores. Wouters et al. (2012) concluded that measurement precision and diagnostic accuracy in dementia consistently improves after adding neuropsychological tests to the MMSE. Overall, studies showed that psychometric quality might substantially increase when combining short forms of the MMSE with other neuropsychological instruments or diagnostic tools. However, this strategy may be of limited value for users who have no access to more accurate tests or methods. Moreover, one should bear in mind that the MMSE alone is already perceived to be too long by many in primary care (Mitchell, 2009).
Haubois and colleagues showed that the six-item short form of the MMSE (SMMSE) performed as well as, or even outperformed, the original MMSE in screening for dementia in a sample of community-dwelling older adults with memory complaints (Haubois et al., 2011; Haubois et al., 2012). Consistent with these and other findings of population-based studies (Artero, Tierney, Touchon, & Ritchie, 2003; Fleisher et al., 2007; Elias et al., 2000; Jungwirth et al., 2009; Small, Mobly, Laukka, Jones, & Backman, 2003), our results indicate that, apart from age, memory impairment, deficits in delayed recall of verbal information, and deficits in episodic memory are all significant predictors of dementia. Accordingly, the results of our study showed that both the MMSE and SMMSE score identified dementia with sufficient sensitivity and specificity. In line with previous findings of Haubois and colleagues (Haubois et al., 2011; Haubois et al., 2012), the SMMSE score showed slightly higher sensitivity values than the MMSE score in our study. In accordance with their results, the optimal cut-off score for the SMMSE in our study was ≤ 4. However, our data also show, in contrast to the findings of Haubois and colleagues, that the shorter version of the MMSE (SMMSE) does not outperform the full version of the MMSE with regard to specificity, correct classification rate (EFF), positive and negative predictive values and likelihood ratios (PVP, PVN, LR+, LR−).
The strengths of the current study include the cohort design, the large sample sizes, and that comprehensive clinical and neurological examination procedures to assess mental status and cognition of participants were conducted. Furthermore, individuals with mild cognitive impairment (MCI) were also included in our study sample because MCI is a common presentation in older primary care patients even when the detection rate is low (Jessen et al., 2011; Mitchell, Meader, & Pentzek, 2011). However, the present study has some limitations. First, the six-item version of the MMSE could not be used independently as a single instrument in the study. Therefore, the SMMSE was constructed and applied as a part of the full MMSE version. Second, the reported diagnostic accuracy parameters of MMSE and SMMSE scores will be overestimations of the actual accuracy because incorporation bias is likely to have influenced the results (Ransohoff & Feinstein, 1978; Worster & Carpenter, 2008). Furthermore, the diagnosis of dementia could not be confirmed by the examination of biological markers, imaging or autopsy. Thus, it cannot be ruled out that the validity and diagnostic accuracy of the dementia diagnoses may be diminished in comparison with those in specialized clinical settings. On the other hand, repeated standardized neuropsychological examination conducted by trained interviewers and thorough monitoring over the years of the study, in combination with expert consensus conferences and validation of dementia diagnoses, contributed to a relatively high data quality. Moreover, a variety of procedures to improve data quality and to detect inaccurate or incomplete data including automatic plausibility and integrity checks, as well as data error reports to the collaborating centers, was conducted in order to ensure the data quality of the study.
In clinical practice, especially in primary care, there is a need for reliable and valid neuropsychological instruments that are shorter and no less accurate than the MMSE in screening for dementia (Mitchell & Malladi, 2010). In the future, dementia diseases are expected to become a more common phenomenon in our health care system, especially in primary care settings. Thus, GPs play a central role in diagnosing and treating dementia in its initial stages. GPs may be the first to detect and take care of beginning dementia diseases. Results of the Berlin Aging Study (BASE) showed that over 93% of the elderly population aged 70 years and over visit their GP on a regular basis (Linden et al., 1996). Furthermore, there is evidence that the majority of patients consult their GP first when symptoms of cognitive decline appear (Wilkinson, Stave, Keohane, & Vincenzino, 2004). In contrast, present research indicates that identification of persons with dementia in the primary care setting is problematic, and GPs fail to diagnose dementia at early stages, leading to an underdiagnosis of the disease (Harvan & Cotter, 2006). Thus, routine screening for dementia in primary care settings using short and easy-to-administer instruments should be optimized and conducted to a greater extent. Consequently, the early detection of first symptoms such as memory impairment requires not only short but also high-quality screening tests. The MMSE has a long history and its clinical utility in measuring cognitive impairment has been investigated and cited in many studies (Mitchell, 2009). Although many authors have criticized the psychometric performance of MMSE items, the MMSE is still one of the most popular and commonly used screening measures for cognitive impairment and dementia in community and primary care. Many validated alternatives to the MMSE currently exist and clinicians have the option to choose from many potentially briefer alternatives to the MMSE. Our results highlight that the exclusive use of selected items from the MMSE may be rather inappropriate for the development of a brief screening test for dementia. In other words, the short form of the MMSE failed to appear suitable as a screening measure for dementia in primary care. However, the combination of selected MMSE items with other instruments in order to enhance diagnostic accuracy is worth further exploration. Altogether, further research is needed on the development and utilization of short and valid neuropsychological tests for the screening of cognitive impairment and dementia, especially in the primary care sector.
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Submitted: January 17, 2014 Revised: October 1, 2014 Accepted: November 18, 2014
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Source: Psychological Assessment. Vol. 27. (3), Sep, 2015 pp. 895-904)
Accession Number: 2015-13973-001
Digital Object Identifier: 10.1037/pas0000076
Record: 88- Title:
- Item analysis of the Leeds Dependence Questionnaire in community treatment centers.
- Authors:
- Galecki, Jeffrey M., ORCID 0000-0002-5707-2561. Department of Psychology, Loyola University Maryland, Baltimore, MD, US
Sherman, Martin F.. Department of Psychology, Loyola University Maryland, Baltimore, MD, US, msherman@loyola.edu
Prenoveau, Jason M.. Department of Psychology, Loyola University Maryland, Baltimore, MD, US
Chan, Kitty S.. Department of Health Policy and Management, Johns Hopkins University Bloom School of Public Health, Baltimore, MD, US - Address:
- Sherman, Martin F., Department of Psychology, Loyola University Maryland, Beatty Hall, Room 22B, 4501 North Charles Street, Baltimore, MD, US, 21210, msherman@loyola.edu
- Source:
- Psychological Assessment, Vol 28(9), Sep, 2016. Special Issue: Assessment in Health Psychology. pp. 1061-1073.
- NLM Title Abbreviation:
- Psychol Assess
- Page Count:
- 13
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- Psychological Assessment: A Journal of Consulting and Clinical Psychology
- ISSN:
- 1040-3590 (Print)
1939-134X (Electronic) - ISBN:
- 1-4338-9006-2
978-1-4338-9006-2 - Language:
- English
- Keywords:
- item response theory, differential item functioning, model-data fit, psychological dependence, substance dependence
- Abstract:
- The present study extends the item-level psychometric information of the Leeds Dependence Questionnaire (LDQ; Raistrick et al., 1994) that has been purported to measure psychological dependence and the International Statistical Classification of Diseases and Related Health Problems–10th edition substance dependence criteria. Prior research on the LDQ has not established item-level properties or the degree of differential item functioning (DIF) by gender and substance type. Principal component and Mokken scale analyses were used to assess unidimensionality and monotonicity of the responses to the scale items, respectively. Graphical and statistical methods examined the model-data fit of the graded response model and two-parameter logistic model of LDQ responses (n = 1,681) obtained from 2 community treatment centers. DIF analysis was performed on gender (men = 1,313, women = 353) and substance (alcohol = 821, opiates = 528) groups. The 2PL achieved the best model-data fit. Three items provided little information about standing on the underlying construct, indicating that they are likely not good indicators of the 'pure' psychological construct the LDQ aims to measure. Overall, the LDQ offers the greatest precision in quantifying psychological dependence in a clinical sample along the lower to mid ranges of this construct. Uniform DIF was present in Item 7 of the dichotomized responses by substance (alcohol vs. opiates). DIF by gender was not found in any of the LDQ items. Recommendations include revising the scaling and discussing the need to obtain LDQ data from different levels of care and primary identified substance. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Community Mental Health Centers; *Drug Dependency; *Item Analysis (Test); *Item Response Theory; *Questionnaires; Dependency (Personality)
- PsycINFO Classification:
- Tests & Testing (2220)
Substance Abuse & Addiction (3233) - Population:
- Human
Male
Female - Location:
- United Kingdom
- Age Group:
- Adulthood (18 yrs & older)
- Tests & Measures:
- Leeds Dependence Questionnaire
- Grant Sponsorship:
- Sponsor: Applied Psychological Measurement
Other Details: in the form of a grant that paid for some of the software used
Recipients: No recipient indicated - Methodology:
- Empirical Study; Quantitative Study
- Supplemental Data:
- Tables and Figures Internet
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- Accepted: Feb 10, 2016; Revised: Feb 5, 2016; First Submitted: Apr 13, 2015
- Release Date:
- 20160818
- Correction Date:
- 20161020
- Copyright:
- American Psychological Association. 2016
- Digital Object Identifier:
- http://dx.doi.org/10.1037/pas0000306; http://dx.doi.org/10.1037/pas0000306.supp(Supplemental)
- PMID:
- 27537001
- Accession Number:
- 2016-40116-004
- Number of Citations in Source:
- 71
- Persistent link to this record (Permalink):
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- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2016-40116-004&site=ehost-live">Item analysis of the Leeds Dependence Questionnaire in community treatment centers.</A>
- Database:
- PsycINFO
Item Analysis of the Leeds Dependence Questionnaire in Community Treatment Centers
By: Jeffrey M. Galecki
Department of Psychology, Loyola University Maryland
Martin F. Sherman
Department of Psychology, Loyola University Maryland;
Jason M. Prenoveau
Department of Psychology, Loyola University Maryland
Kitty S. Chan
Department of Health Policy and Management, Johns Hopkins University Bloom School of Public Health
Acknowledgement: This research was funded by Applied Psychological Measurement in the form of a grant that paid for some of the software used. This project was previously submitted as a thesis.
Substance dependence at various times has been conceptualized as primarily a physiological, biopsychosocial, or psychological process (Tober, 2003). Presently, The Diagnostic and Statistical Manual of Mental Disorders (5th ed.; DSM–5; American Psychiatric Association, 2013) has stated that substance use disorders are associated with cognitive, behavioral, and physiological symptoms. Severity of substance use is coded based on the number of these symptoms. The DSM–5 also has substance withdrawal as an associated diagnosis that includes psychological features (e.g., anxiety). Under the DSM–5 diagnostic system, substance dependence or substance withdrawal may be diagnosed with the respective substance listed for each dependence or withdrawal diagnosis made.
Early research conducted in substance dependence hypothesized that substance dependence was purely physiological in nature (Wise, 2000). Physiological dependence has been seen as the development of physical withdrawal symptoms upon cessation of a particular substance (Cloninger, Bohman, & Sigvardsson, 1981; Edwards, Arif, & Hadgson, 1981; Kleber, 2008). From this perspective, if a patient were treated for substance dependence via detoxification, the symptoms of physiological dependence would cease along with the substance dependence disorder (Widiger & Smith, 1994). Over time, treatment of substance dependence has not been as straightforward as simply reducing the physiological dependence upon the substance alone. But must also address dealing with psychological urges and compulsions to use the substance (Morgan, Morgenstern, Blanchard, Labouvie, & Bux, 2004), and the rituals and routines involved in obtaining and using the substance (Friedman, Dar, & Shilony, 2000). In addition, treatment should also address family, legal, and employment issues (Kleber, 2008). A contrasting approach to the purely physiological model of substance dependence has relied upon social learning theory as its foundation (Bandura, 1977; Brandon, Herzog, Irvin, & Gwaltney, 2004). From a social learning theory perspective, substance dependence is viewed as being maintained through classical and operant conditioning (Kadden, 2002). In classical conditioning neutral stimuli, urges, or cravings may become triggers for substance use (Kadden, 2002). In operant conditioning, substance abuse behaviors tend to be reinforced by the outcomes from using substances, such as reductions in stress or attaining a euphoric state (Brandon et al., 2004; Kadden, 2002).
Tober (1992) identified the overall lack of agreement on the nature of substance dependence in the field and affirmed that substance dependence is addressed best from the psychological sense as being the result of a reinforced behavior (Azrin, 1976; Heather & Robertson, 1989). This resultant view has psychological dependence distinct from substance dependence, with the former as a purely psychological process that involves reinforced behaviors and impulses and the latter being defined as being largely the physiological dependence on a chemical substance. Grouping substance and behavioral addictions together is important as it further marks the move away from the emphasis prior substance dependence theories placed on physical tolerance.
Understanding the genesis of substance dependence is critical because substance use has a negative impact on society and the negative impact it has on individuals is profound. Increases in job losses, hospital emergency room uses, traffic accidents and fatalities, and criminal activities are a few of the consequences related to substance use (Hill, 2009). In many applications, both clinical and research, a self-report measure that captures a general psychological construct underlying abuse of different substances may be more desirable than a measure that assesses a variety of substances with separate items for each substance, particularly if trained personnel must administer the interview (Kelly, Magill, Slaymaker, & Kahler, 2010).
In health care, a growing trend toward evidence-based practices prioritizes the need for accurate assessment of disorders, treatment modalities, and the ability to assess uniform outcomes of those treatment entities that receive public funds (Barlow, 2005). Furthermore, one of the National Institute on Drug Abuse’s (2012) “Principles of Effective Treatment” outlined the need for pairing an individual’s problem with specific treatment and interventions. In these respects, accurate and fair measurement of psychological dependence has key implications diagnostically, for treatment planning, developing and assessing client change along with assessing overall clinic effectiveness, and in performing substance dependence related research. Consequently, a measure that can enhance the efficiency and accuracy of adequately assessing psychological substance dependence is of value to clinicians, clients, and society (Kelly et al., 2010).
The Leeds Dependence QuestionnaireThe Leeds Dependence Questionnaire (LDQ; Raistrick et al., 1994) is the only measure that attempts to quantify a pure psychological dependence construct. Previous efforts at quantifying substance dependence usually relied on classical test theory (CTT; González-Saiz et al., 2008) and covered multiple aspects of substance dependence (e.g., compulsive use, tolerance and withdrawal) rather than a core psychological dependence construct (Heather, Raistrick, Tober, Godfrey, & Parrott, 2001; Kelly et al., 2010; Raistrick et al., 1994). Current measurement instruments for substance dependence require special training and or copious amounts of time to administer (Mäkelä, 2004). For instance, the Addiction Severity Index (McLellan, Luborsky, O’Brien, & Woody, 1980), which is one of the most widely used assessment tools in America and Europe, is a composite scale that has been used to assess substance dependence (Schippers, Broekman, Koeter, & Van Den Berg, 2004). It requires a clinical interview and takes almost 90 min to administer and score (Alterman et al., 1998; McLellan et al., 1980). It is not always desirable or feasible to administer a full clinical interview in clinical settings where there are time constraints (Kelly et al., 2010) and where a full clinical interview may be a greater burden for the participant. In many applications, a self-report measure that captures overall psychological dependence may be more desirable than a measure that assesses a variety of substances with separate items for each substance, especially if trained personnel must administer a full clinical interview (Kelly et al., 2010). Partially in an attempt to address issues in length and content in the Addiction Severity Index, Fairhurst et al. (2014) examined the LDQ within the context of a package of measures that assess an overall construct labeled psychological distress, of which the LDQ measures the psychological condition facet. They concluded that this package of measures could be beneficial in clinical samples and might be more flexible because the separate measures that comprise the package could be readministered during treatment at separate intervals as warranted.
The LDQ (Raistrick et al., 1994) was constructed with substance dependence being purely psychological in nature where physical symptoms are considered repercussions of continued excess use separate from the substance dependence. The LDQ is a 10-item self-report measure designed to be sensitive to changes over time and that measures a broad range of psychological dependence from mild to severe (Heather et al., 2001). Each of the 10 items measures one element of the International Statistical Classification of Diseases and Related Health Problems–10th edition (ICD-10) substance dependence criteria that are preoccupation with substance use, salience of substance use, compulsion to start substance use, planning around substance use, maximize effect from substance use, a narrowing of repertoire, compulsion to continue, primacy of effect, constant state, and cognitive set (World Health Organization, 1992). Maximize effect, primacy of effect, and constant state correspond to the effects the substance has on the user. For instance, on the LDQ the item “Do you want to take more drink or drugs when the effect starts to wear off?” measures constant state. Other elements such as preoccupation, salience of substance use, and cognitive set are concerned with cognitions regarding substance use. These are assessed on the LDQ by items asking the user to rate the thinking about his or her next use and the difficulty of that the user might have if the substance is not used.
Raistrick et al. (1994) reported content, concurrent, and discriminant validity for the responses to the LDQ. The sample used to gather initial validity and for the initial creation of the LDQ was based on a total sample of 174, composed of those in the Leeds Addiction Unit (n = 96), college students (n = 64), and a random sample of those attending a physician’s general practice (n = 14). All participants had reported the consumption of an alcoholic drink in the prior week. The sample mean age was 29.15 years (SD = 7.81) with 103 male participants and 71 female participants. Concurrent validity was established by comparing responses from the LDQ to those from the Severity of Opiate Questionnaire (Sutherland et al., 1986) and to the Severity of Alcohol Dependence Questionnaire responses (Stockwell, Hodgson, Edwards, Taylor, & Rankin, 1979). Furthermore, known group validity was found by examining LDQ scores in alcohol users under treatment, students, and general patients in a health practice with respective cross-sectional declines in scores from alcohol users as the highest, student scores in the middle, and general patients being the lowest. Raistrick et al. (1994) reported discriminant validity data by examining variables potentially connected to opiate and alcohol use (gender, age, method of use). Findings revealed no statistically significant relations with these variables, and thus, the authors concluded that the LDQ scores were independent of the abovementioned variables. A Cronbach’s alpha of .94 was reported and accepted as a satisfactory measure of internal consistency. Raistrick et al. (1994) concluded that the responses to the LDQ demonstrate adequate content, concurrent, and discriminant validity as well as internal consistency. Several follow-up studies with adults in community treatment programs (Heather et al., 2001), young adults (Kelly et al., 2010), psychiatric inpatients (Ford, 2003), and several cultural populations within New Zealand (Paton-Simpson & MacKinnon, 1999) reached similar conclusions regarding responses to the LDQ.
Bias in Measurement InstrumentsConsistent with the Standards for Educational and Psychological Testing (American Educational Research Association, American Psychological Association, & National Council on Measurement in Education, 2014), it has been recommended that any measure of psychological dependence should assess whether there exists any gender bias across the items of the scale. Additionally, given that that underlying construct being measured by the LDQ is hypothesized to be psychological dependence that is substance neutral (Raistrick et al., 1994), potential bias by substance type should also be explored. To date, research investigating the LDQ has not adequately examined item bias by gender or substance and this is a key step toward using the LDQ for clinical diagnostic purposes (American Educational Research Association et al., 2014). One statistical technique that can be used to detect response bias across items is differential item functioning (DIF). DIF occurs when respondents from different groups with equal amounts of the ability or construct being measured do not have the same probability of endorsing an item (Hambleton, Swaminathan, & Rogers, 1991). Individuals with the same amount of the ability or construct being assessed are differing based upon their respective group membership. If two individuals have the same amount of psychological dependence, they should score similarly on an item regardless of their gender. Likewise, if psychological dependence is a substance invariant construct, then an individual’s identification of primary substance should not be relevant. The LDQ should be measuring the strength of psychological dependence and not the strength of the physical effects. Lack of DIF by substance would indicate the LDQ is likely tapping a common construct applicable to all substance users regardless of primary substance of abuse. DIF is only one of a host of item-level metrics that was examined in the present study. The overall framework for item-level analysis is item response theory (IRT).
Item Response TheoryThe key concept underlying IRT posits the existence of a relation between observable phenomenon, such as responses to items, and an underlying trait that the items are assumed to be related to (Hambleton, 1985). IRT provides for a set of mathematical models to explain the relation between a response to an item and the unobservable latent trait (de Ayala, 2009; Embretson & Reise, 2000). The latent variable being assessed by the test items is denoted by the Greek symbol theta (θ), representing the trait or characteristic, that the measure seeks to quantify, with a group mean of 0 and a standard deviation of 1 (Hambleton, 1985). Lower values of theta indicate lower levels of the latent trait being measured. When assumptions for IRT have been met and satisfactory model-data fit established, then IRT provides information about item quality, that may be used to shorten scales by removing poorer performing items and for selecting items with best properties for a specific application (de Ayala, 2009). Model-data fit is the ability of a particular mathematical model to accurately fit or model the observed response data with the mathematical model’s predictions (Hambleton et al., 1991). IRT can be used to create new scales or be applied to existing scales. It enhances the scales utility by exploring whether items function differently by groups and provides a more comprehensive understanding of the psychometric properties of the responses to the scale items. If the assumptions are met and model-data fit achieved, IRT can provide item and individual level information regarding the construct being measured that can be far superior to CTT (Embretson & Reise, 2000).
The LDQ has been theorized to measure a construct labeled psychological dependence that is alleged to be substance insensitive (Heather et al., 2001; Kelly et al., 2010; Raistrick et al., 1994). Additionally, there has been no satisfactory examination of the potential bias by gender which would be in line with current fairness in testing standards (American Educational Research Association et al., 2014). A brief and accurate measure of psychological dependence has clear utility for matching clients to treatment modalities, assessing clients’ progress in treatment, and for examining the overall effectiveness of various treatment modalities and the agencies that use them. The present study aimed to examine the LDQ using IRT and DIF analysis. IRT allowed for item and individual level information that was not possible under CTT techniques. In addition, DIF was used for examining gender and substance type differences. The expectation was that adequate IRT model-data fit would be achieved and that DIF by gender (men and women) and substance (opiate and alcohol) would not be found. Such results would be consistent with the original aims of the LDQ.
Method Participants
Participants were from a study conducted by Heather et al. (2001) who obtained participants from the Leeds Addiction Unit (n = 956) and the Northern Regional Alcohol and Drug Service (n = 725), which are substance abuse treatment centers located in the United Kingdom. Data was collected from January 1994 through July 1996. Clerical errors resulted in 43 of the total 1,681 respondents not having complete sex and or age information in the dataset used in the present study. The mean age of those 1,638 respondents with age recorded was 33.96 years (SD = 10.76) and was composed of 1,312 men (78%) and 353 women (22%). Participants had responded to the LDQ with main problem substance for heroin (n = 435), methadone (n = 55), other opioids (n = 38), barbiturates (n = 1), benzodiazepines (n = 52), amphetamines (n = 71), cocaine (n = 14), hallucinogens (n = 1), cannabis (n = 32), solvents (n = 13), alcohol (n = 821), and any other substance (n = 24). Of the total sample, 124 participants did not provide a main problem substance.
Materials
The LDQ is a 10-item self-report questionnaire of substance dependence (Raistrick et al., 1994). Each of these 10 questions on the LDQ accounts for the 10 facets of substance dependence defined in the ICD-10 (Heather et al., 2001; Raistrick et al., 1994; World Health Organization, 1992). Participants respond using a 4-point Likert-type scales with choices of never, sometimes, often, and nearly always. Typical scoring of the LDQ assigns 0–3 for each item (0 = never and 3 = nearly always) and summing all of the total items for a total possible score out of 30. As discussed below in detail, the responses were ultimately dichotomized due to model-data fit lacking for the ordinal data. The LDQ response data were dichotomized with the two lowest categories and the two highest categories being collapsed so that each item’s response would be in binary format. Several studies have found a one-factor solution to the LDQ with at least 53.9% of the variance accounted for by this factor (Heather et al., 2001; Kelly et al., 2010; Raistrick et al., 1994).
Procedure
Each participant was given a standardized evaluation packet that included a form asking about basic demographic information and the LDQ. The participants completed this evaluation packet while they waited for a caseworker or a nurse to conduct basic health assessments at each site. Informed consent was obtained verbally from all participants. Furthermore, participants were informed that no “tricks or wrong answers” were in the enclosed evaluations and that honesty in answering responses was desired because they could help in formulating a treatment plan for them.
Data Analysis
Unidimensionality
MicroFACT 2.1 (Waller, 2001) was used to conduct exploratory factor analysis with principle component analysis on polychoric and tetrachoric correlations to assess unidimensionality in polytomous and dichotomous responses respectively. It has been suggested that the first factor should explain at least 20% of the total variance for the items to be considered to have meet the assumption of unidimensionality (Reckase, 1979; Reeve et al., 2007). Another proposed criterion for assessing unidimensionality is a ratio of the first to second eigenvalue being greater than 3 (Morizot, Ainsworth, & Reise, 2007). In the present study, both of these criteria were used to assess if the LDQ response data satisfied the assumption of unidimensionality.
Monotonicity
The assumption of monotonicity in LDQ data was examined using an R package (R Development Core Team, 2011), called Mokken (van der Ark, 2007) based on Mokken scale analysis (Mokken, 1971) originally developed for dichotomous response data and extended to polytomous response data by Molenaar (1991, 1997). In the Mokken package, the default for minsize and minvi of 0.03 were used in both the dichotomous and polytomous monotonicity analyses. With samples greater than 500, as in the present study, minsize is suggested to be N/10 or 168. Only significant violations of 0.03, p < .95 were considered along with a visual inspection of monotonicity plots to assess if the items were monotonically increasing. Significant violations along with graphs depicting nonmonotonically increasing functions would indicate that the assumption of monotonicity had been violated in an item (Molenaar & Sijtsma, 2000). This technique for examining the assumption of monotonicity has been used in numerous studies on response data (Koster, Timmerman, Nakken, Pikl, & van Houten, 2009; Murray, McKenzie, Murray, & Richelieu, 2014; Stochl, Jones, & Croudace, 2012).
Model-data fit
MODFIT (Stark, Chemyshenko, Chuah, Lee, & Wad, 2001), an Excel spreadsheet that examines model-data fit as detailed by Drasgow, Levine, Tsien, Williams, and Mead (1995), was used to assess fit between IRT models and the data. MODFIT produced graphical fit plots along with χ2/df fit statistics adjusted from the observed responses to a sample size of 3,000. The cross-validation sample in MODFIT was calculated using a sample disjoint from the sample used for observed responses. Detailed formulas and rationale have been discussed by Drasgow et al. (1995) and LaHuis, Clark, and O’Brien (2011). For the graphical fit plots, observed and expected response functions along with 95% confidence intervals for the expected response function were graphed. If the fit plot showed the expected response functions deviating outside of the 95% confidence interval for the observed response data functions it would therefore suggest that a good model-data fit had not been achieved.
In addition to the graphical fit plots, adjusted chi-square fit-statistics were used to assess model-data fit. To make the model-data fit results more manageable the test items were divided into n/3 sets of three items, and for each set a χ2 was calculated for each item (singlet), each pairs (doublets), and each set of triples (triplets). Single item χ2/df fit statistics were used to determine if the model inferred response probabilities deviated from the expected response probabilities observed in each respective LDQ item. The doublets and triplets comparisons are sensitive to the assumption of local independence. The items with corresponding misfit have larger χ2/df fit statistics than those items without misfit. In accord with the recommendations of Drasgow et al. (1995) and Chernyshenko, Stark, Chan, Drasgow, and Williams (2001), adjusted χ2/df fit statistics of 3 or less were considered to be evidence of good model-data fit.
Parameter estimation
Item parameters are estimated when satisfactory model-data fit has been obtained. PARSCALE 4 (Muraki & Bock, 2003) was used for item parameter estimation of the LDQ dichotomously coded item response data. In the present study, this type of scoring represents a true/false dichotomy for each LDQ item. Parameters for quantifying the level of each respondent, on each respective item, were estimated along the theoretical construct (θ) of psychological dependence (Hambleton, 1985). Difficulty (b) item parameters indicate the point along the psychological dependence continuum where a respondent has a 0.5 probability of endorsing that item. Discrimination (a) item parameters avail how well that item is at differentiating respondents with different levels of the latent trait with discriminations of 1.0 or less being considered low (Baker, 2001; Hambleton, 1985). Along with the item parameters, estimated graphical plots were also obtained. Item characteristic curves (ICCs) were plotted for each LDQ item to model the overall probability of endorsing an item along the psychological dependence continuum. In IRT, item information is defined as the precision of measurement across the entire continuum of psychological dependence and is acquired for each item. Item information functions (IIFs) are plotted to represent item information graphically and test information functions (TIF) are the summing of the individual TIFs.
Differential item function
An ordinal logistic regression (OLR) technique put forth by Zumbo (1999) was used to assess uniform and nonuniform DIF by examining response patterns between gender (men vs. women) and substance (alcohol vs. opiate) on LDQ items. Uniform DIF occurs when there is no interaction between θ level and group membership; nonuniform DIF occurs when there is an interaction present between θ and group membership. These techniques are separate from IRT and both polytomous and dichotomous versions of the response data were assessed. Of the 1,681 participants with complete response for all 10 LDQ items, two DIF analyses were conducted. The first DIF analysis was conducted between those indicating alcohol (n = 821) and opiates (n = 528). whereas a second DIF analysis was undertaken between women’s (n = 353) and men’s (n = 1,321) responses to the LDQ. The OLR approach for detecting DIF was performed using SPSS 19.0 and was conducted for each of the 10 LDQ items (SPSS, 2010). In this OLR approach, three OLR models per DIF analysis were conducted and compared to determine the presence and nature of any DIF. The order of variables entered into the subsequent OLRs were (a) total LDQ score, (b) group membership (men vs. women and primary substance opiates vs. alcohol), and, finally, (c) interaction term (Total LDQ Score × Group Membership). In the present study, and consistent with the recommendations by Zumbo (1999), DIF was considered to be operating when the 2-df chi-square obtained by subtracting the Step C chi-square from the Step A chi-square and the Zumbo-Thomas effect measure had an R2 of 0.13. The chi-square test value for the logistic regression was obtained for Step C and subtracted from the chi-square test for the logistic regression acquired in Step A and was an indication DIF is present. To determine if the DIF was uniform, the chi-square value obtained from Step B was subtracted from Step A. Nonuniform DIF was assessed by subtracting the chi-square from Step C with the chi-square obtained in Step B. When an item is identified as having DIF, then the process of purifying the matching criterion was used. In this process, items identified with DIF were then excluded from the total score and then each remaining item was run through a second subsequent DIF analysis (Zumbo, 1999).
ResultsTable 1 depicts the polytomous LDQ item endorsements for each of the 10 items for the full sample, by gender (men and women), and by primary substance (opiate and alcohol). The dichotomously coded categories are listed in Table 2 with frequencies for the true/false coding.
Means (and Standard Deviations) of the LDQ Responses as a Function of Gender and Primary Substance
Frequencies of False (True) Categories of the LDQ Responses as a Function of Gender and Primary Substance
In order to examine the assumptions of unidimensionality, a principal component analysis (PCA) was applied to a matrix of polychoric correlations (see Table 3) and tetrachoric correlations (see Table 4) that were calculated from the respective raw response data using MicroFACT 2.1 (Waller, 2001). In the polychoric PCA, one component accounted for 62.14% of the total variance with no other component accounting for more than 10% of the total variance with the first three eigenvalues having values of 6.21, 0.90, and 0.58. In the tetrachoric PCA, one component accounted for 64.53% of the total variance with the first three eigenvalues of 6.45, 0.88, and 0.60. The ratio of the first to second eigenvalue was above 3. Both of the PCA analyses had total variance accounted for by the first component well beyond the 20% threshold recommended by Reckase (1979) and Reeve et al. (2007). Additionally, both PCAs had a first to second eigenvalue ratio above 3 that had been previously suggested to be an indication of unidimensionality (Morizot et al., 2007).
Polychoric Correlation Matrix
Tetrachoric Correlation Matrix
The polytomous LDQ response data showed no violations for any item and visual inspection of the monotonicity plots evidenced that the polytomous data for all 10 of the LDQ items were monotonically increasing. One violation of monotonicity was not significant for the dichotomously coded Item 5 (“Do you drink or take drugs in a particular way in order to increase the effect it gives you?”). Visual inspection of the monotonicity graph for Item 5 indicated, along with the other nine dichotomous LDQ coded items, that all were increasing monotonically and that the assumption of monotonicity in both polytomous and dichotomous LDQ data was not violated.
Model-Data Fit
Both statistical and graphical models for assessing model-data fit were employed for the graded response model (GRM) and two-parameter logistic (2PL) models. An examination of the fit plots for the GRM model indicated that good model-data fit was not achieved in the LDQ response data because the observed response functions 95% confidence intervals departed substantially from the expected line for most of the items. The fit plots for the GRM model are available online as supplementary material. Fit plots for the 2PL LDQ response (see Figure 1) did not substantially deviate from the 95% confidence interval bands around the expected lines. For the 2PL response data this was considered evidence that model-data fit was good based on visual inspection of the graphical fit plots.
Figure 1. Expected fit plot lines overlaid by observed fit lines with 95% confidence intervals.
The second approach for assessing model-data fit involved examining χ2/df ratios and provided further evidence for the conclusions regarding model-data fit being reached in the graphical method. Frequencies of χ2/df ratios for both the GRM and 2PL model are presented in Table 5. For the GRM response data, the average ratio of χ2 to df for singlets, doublets, and triplets was χsinglets2/df = 52.21, χdoublets2/df = 43.58, and χtriplets2/df = 26.63, and in the 2PL response data χ2 average adjusted ratios, χsinglets2/df = 0.00, χdoublets2/df = 1.18, and χtriplets2/df = 2.25. The frequencies of the χ2/df values for the GRM exceeded 3 whereas those for the 2PL model were well below the threshold of 3. The graphical and statistical methods used in the present study provide evidence for only the 2PL achieving satisfactory model-data fit with the dichotomized LDQ response data.
Frequencies of the Adjusted Chi-Squared to df Ratios for GRM and 2PL Model Data-Fit
2PL Parameter Estimation
Discrimination (a) and location (b) parameters for 2PL parameter estimation for each of the 10 LDQ items are reported in Table 6. The respective discrimination parameters exceeded one for all except three of the LDQ items with 5, 7, and 8, having the lowest discrimination. Two of these three lowest discriminating items deal with the effect of the substance whereas the third relates to the compulsion to continue using the substance. Item characteristic curves for each item were plotted (see Figure 2) and illustrate the “flatness” of Items 5, 7, and 8 and their poorer ability to discriminate across various levels of psychological dependence. Location parameters on Table 6 ranged from −0.83 to −0.01 and indicate that LDQ items typically were endorsed at the lower range of psychological dependence. Figure 2 graphically represents the location parameter as the point along θ where the inflection point of each curve occurs. Items 2, 4, and 5 required greater amounts of psychological dependence to endorse compared to 1, 7, and 9, respectively.
2PL Discrimination and Location Parameters
Figure 2. Two-parameter logistic item characteristic curves for each item.
Figure 3 shows the IIFs for each LDQ item. Items 1–4 provided the highest amount of information, while Items 5, 7, and 8 provided the least amount of information. Summing the 10 individual IIFs formed a TIF shown in Figure 4. The TIF graphically represents the amount of precision along θ with standard errors. Additionally, Figure 4 illustrates that overall the LDQ is best at differentiating individuals who fall in the lower to midrange for psychological dependence among those seeking substance abuse treatment. The range of psychological dependence measured by the LDQ across all items and participants in this sample was −0.9 and 0.9 standard deviations from the mean.
Figure 3. Two-parameter logistic item information functions for each item.
Figure 4. Total information function (solid line) and standard error (dashed line) for two-parameter logistic model. See the online article for the color version of this figure.
Differential Item Function
Table 7 presents the results of the DIF analysis for polytomous LDQ data (e.g., Likert-type with four response options) listing the χ2 and R2 increase in explained item variance when group membership was included in the OLR. According to Zumbo (1999), DIF is occurring when an item has a Δχ2 that is significant at p < .01 and the ΔR2 is at least 0.13. None of the items in either the polytomous substance or gender DIF analysis met this criterion. It is noteworthy that Item 7 (“Do you feel you have to carry on drinking or taking drugs once you have started?”) was significant at p < .01 and had a ΔR2 that was near the 0.13 threshold for substance.
Differential Item Function Analysis of Polytomous Data
A separate DIF analysis was performed on the dichotomized LDQ response data. The resultant DIF analysis between opiate and alcohol users showed that for Item 7 meaningful DIF was occurring (see Table 8). The difference between R2 for Step B (0.56) and R2 Step C (0.56) of the DIF analysis was negligible, indicating that uniform DIF was operating for Item 7. This means that the ICCs by substance (alcohol vs. opiate) had significantly distinct locations along θ. If DIF was not occurring in Item 7, the ICCs for both of these groups would be along the same location and indistinguishable from each other. Table 9 shows the results of the matching purification strategy that was used when DIF in an item was detected. The total score was recalculated with Item 7 removed and the remaining nine items were reanalyzed in a subsequent DIF analysis. As seen in Table 9, none of the remaining nine LDQ items showed DIF.
Differential Function Analysis Dichotomous Data
Differential Item Function Analysis Using the Matching Purification Strategy
Next, a 2PL model was conducted for each group and the resultant ICCs layered to compare the uniform DIF visually by substance detected for Item 7. Figure 5 depicts the ICCs for the alcohol group (a = 1.20, b = −0.87) in red, and the opiate group (a = 0.90, b = −0.45) in black for Item 7. Thus, in the dichotomized version of the LDQ response data, individuals reporting alcohol were less likely to endorse Item 7 compared to those respondents that did report opiates as primary substance. The presence of uniform DIF suggests Item 7 is not an equivalent measure of psychological dependence across those identifying alcohol and opiate as primary substance on the LDQ.
Figure 5. Item Characteristic Curves of alcohol (dashed line) and opiate (solid line) respondents on dichotomized item 7 responses.
DiscussionThis study was undertaken to examine the model-data fit between LDQ item responses obtained from a sample of 1,631 clients referred to two substance treatment programs in the United Kingdom. Results indicated model-data fit was not achieved for the GRM using the original polytomous coding of item responses, but was achieved for the 2PL model when the original item responses were dichotomized. Additionally, the assumptions for unidimensionality and monotonicity were examined and indicated the LDQ was measuring a unidimensional construct and monotonicity was not an issue in the polytomous data. Differential item function was not found in any items by gender; however, DIF by substance was operating in the dichotomously coded Item 7 (“Do you feel you have to carry on drinking or taking drugs once you have started?”). Overall, the LDQ was found to provide the most accurate information and measurement precision at the mid to lower range of psychological dependence in those seeking outpatient substance use treatment.
In the original polytomous response coding, the respondent was asked to rate each LDQ item as either never, sometimes, often, and nearly always. However, this coding did not have sufficient model-data fit, whereas dichotomously coded responses with options collapsed into two categories did achieve sufficient model-data fit. Despite research suggesting a reduction in information when categories are collapsed (Donoghue, 1994; Grassi et al., 2007; Tay-lim & Zhang, 2015), questions have been raised regarding the ability of respondents to distinguish between item response options. Elliott et al. (2006) cautions that it is false to assume that respondents to a particular scale are able to distinguish between the different response options given and our findings suggest the existing response categories do not provide useful information. It may be that there is not a discernable difference between the various response option alternatives for the LDQ respondents. As an example, they may not perceive a difference between often and nearly always for any given LDQ question.
For the LDQ, a dichotomized item response format would enhance the clinical utility of the measure. The LDQ was constructed with each question concordant to an ICD-10 substance dependence criteria, thus clinicians would be able to quickly score and obtain a count of ICD-10 criteria endorsed (Raistrick et al., 1994). Other measures of clinical symptomatology have been similarly dichotomized from their original Likert response format to a coding representing the presence or absence of symptoms (Paulus, McCain, & Cox, 1978; Sanchez-Garcia et al., 2015), with Kahler, Strong, Stuart, Moore, and Ramsey (2003) noting an increase in assessment efficiency with dichotomous responses. Additionally, a dichotomous format as a method of capturing symptom presence as yes/no has found support as a standard (American Educational Research Association et al., 2014).
The technique the authors chose for dichotomizing the original polytomous responses during the search for model-data fit is not the only solution to the issue where respondents are unable to adequately discern the differences to the response options provided (as discussed above). Other techniques apart from transforming the response data are to have mixed hybrid models where some items are polytomous and some are dichotomous (Purpura, Wilson, & Lonigan, 2010). Similarly, one could have hybrid polytomous models for certain sets of items on the measure such as GRM items and generalized partial credit model items (Liu & Maydeu-Olivares, 2014). Another technique offered by Tay-lim and Zhang (2015) is to drop items that are not fitting but the authors conclude collapsing categories is a more conservative approach than removing an item.
The item, “Do you drink or take drugs in a particular way in order to increase the effect it gives you?” had low information and was approaching a violation of the monotonicity assumption. Although this does not indicate monotonicity was violated, it could be that in this particular item respondents had more difficulty discerning among the response options more so than in the other LDQ items. This is problematic when scales are constructed and modeled according to monotonically increasing response options such with the GRM.
The other three LDQ items providing the lowest amount of information (“Do you drink or take drugs in a particular way in order to increase the effect it gives you?” “Do you feel you have to carry on drinking or taking drugs once you have started?” and “Is it getting the effect you want more important than the particular drink or drug you use?”) all concern the effect the substance has and the compulsion to continuing using the substance. It is difficult to discern the extent that the low information in these items is an artifact of dichotomizing the original responses into a dichotomous format (due to not having a polytomous model to compare information values against), a nonoptimal fit of response categories, or that these items in general should be removed altogether from the LDQ as they do not provide as much information regarding psychological dependence compared to the other LDQ items with greater information functions. By revising the number or labels for response options available to LDQ respondents, model-data fit with a polytomous model may be achieved producing greater information or fit with an IRT model that allows for nonmonotonic responses functions.
The 2PL item parameters indicate that seven of the LDQ items discriminate well at and just below the midrange of psychological dependence with the three items related to effect of substances having the weakest discrimination and, as previously discussed, providing the lowest amounts of information. Prior research has indicated that post hoc collapsing of response category data does not fundamentally alter the underlying construct and in some cases may even be optimal (Jansen & Roskam, 1986; Samejima, 1976). The sample used in this study was from individuals seeking outpatient treatment for substance use, and the items of the LDQ tend to discriminate around the midrange of psychological dependence; one may conclude the appropriateness to outpatient substance abuse treatment for those with similar levels of psychological dependence.
Differential item function analysis offered evidence that uniform DIF was occurring in the dichotomous response data for Item 7 (“Do you feel you have to carry on drinking or taking drugs once you have started?”) by substance. This item purports to measure compulsion to continue using the substance. For this item, those indicating alcohol as their primary substance endorsed lower amounts of psychological dependence compared with higher levels of psychological dependence typically being endorsed by those that indicated opiates as their respective primary substance. This DIF finding demonstrates that alcohol users required less of the construct (e.g., psychological dependence) to endorse the compulsion to continue item versus opiate users. This particular item appears sensitive to the difference in neuropsychopharmacologic effects between alcohol and opiates. If Item 7 were solely measuring psychological dependence, it would display no DIF by substance. Overall, it is reassuring that DIF was found in only one item and in one group comparison for that item. This is the first time DIF has been assessed for the LDQ and this DIF finding may be used to revise, remove, or account for this finding in future studies and clinical work with the LDQ.
The present study examined data obtained from clients being referred to community substance abuse treatment clinics in the United Kingdom. More data on the LDQ should be obtained from additional countries to provide information on how the LDQ performs in different areas that are composed of distinct ethnic and socioeconomic groups. This would also capture more wide-ranging substances of primary abuse that often differ geographically (e.g., methamphetamine being consumed more in rural vs. urban environments; see Gfroerer, Larson, & Colliver, 2007) and that were underrepresented in the United Kingdom LDQ data set analyzed in the present study. The overall clinical utility of the LDQ would be enhanced if it could confidently be deployed among heterogeneous populations such as in inner cities. It would be of further benefit to have lower and higher psychological dependence score profiles with the aim of using the LDQ to support clinical diagnosis, to inform treatment planning, or for use as a metric in program effectiveness. Substance abuse treatment can range from early psychoeducation to medically supervised inpatient detoxification with a broad range of outpatient treatment in between (American Society of Addiction Medicine, 2013). Future studies should examine the LDQ in context of these different treatment levels to establish LDQ item properties across a broader range of psychological dependence (e.g., those in least restrictive environments up to and including medically supervised detoxification). With additional LDQ information and ratings from clients across these different levels of care, the aims to use the LDQ for treatment and program evaluation would be closer to being achieved.
Identification of DIF by substance in one of the LDQ items is a clear indication that all potential primary substances of abuse should be examined for DIF to determine the effect, if any, a particular substance has on LDQ item response. Prior evidence of gender differences in the LDQ have been reported in Kelly et al. (2010)’s study and direct comparisons are tenuous for two reasons. Kelly et al. (2010) identified their gender differences as preliminary due to not having a large enough sample size and given the population sample differences between Kelly et al. (2010)’s study and the present one, it would be inappropriate to compare gender differences between young adult and adults.
A limitation of the present study was the lack of sufficient sample sizes of all other primary substances clients identified. DIF analysis for substances other then alcohol and opiates could not be conducted as a result. The need to increase the sample of clients identifying these primary substances was underscored by the DIF finding by substance in one LDQ item.
The present study has established the first item-level analysis of the LDQ, a measure that purports to assess psychological dependence. With further study and refinement of the identified weaknesses of a few of the LDQ items, a more robust measure of this construct may be obtained. Quantifying the psychological dependence construct of the LDQ by using IRT, and examining DIF by substance and gender are critical first steps toward using the LDQ to inform diagnostic, treatment planning, and program evaluation.
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Submitted: April 13, 2015 Revised: February 5, 2016 Accepted: February 10, 2016
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Source: Psychological Assessment. Vol. 28. (9), Sep, 2016 pp. 1061-1073)
Accession Number: 2016-40116-004
Digital Object Identifier: 10.1037/pas0000306
Record: 89- Title:
- Just showing up is not enough: Homework adherence and outcome in cognitive–behavioral therapy for cocaine dependence.
- Authors:
- Decker, Suzanne E.. New England Mental Illness Research Education and Clinical Center, West Haven, CT, US, suzanne.decker@yale.edu
Kiluk, Brian D.. Department of Psychiatry, Yale University School of Medicine, CT, US
Frankforter, Tami. Department of Psychiatry, Yale University School of Medicine, CT, US
Babuscio, Theresa. Department of Psychiatry, Yale University School of Medicine, CT, US
Nich, Charla. Department of Psychiatry, Yale University School of Medicine, CT, US
Carroll, Kathleen M.. Department of Psychiatry, Yale University School of Medicine, CT, US - Address:
- Decker, Suzanne E., VA Connecticut Health Care System, 950 Campbell Avenue (151D), West Haven, CT, US, 06516, suzanne.decker@yale.edu
- Source:
- Journal of Consulting and Clinical Psychology, Vol 84(10), Oct, 2016. pp. 907-912.
- NLM Title Abbreviation:
- J Consult Clin Psychol
- Page Count:
- 6
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- Journal of Consulting Psychology
- Other Publishers:
- US : American Association for Applied Psychology
US : Dentan Printing Company
US : Science Press Printing Company - ISSN:
- 0022-006X (Print)
1939-2117 (Electronic) - Language:
- English
- Keywords:
- cocaine, homework, psychotherapy, cognitive–behavioral therapy, treatment outcome
- Abstract (English):
- Objective: Homework in cognitive–behavioral therapy (CBT) provides opportunities to practice skills. In prior studies, homework adherence was associated with improved outcome across a variety of disorders. Few studies have examined whether the relationship between homework adherence and outcome is maintained after treatment end or is independent of treatment attendance. Method: This study combined data from 4 randomized clinical trials of CBT for cocaine dependence to examine relationships among homework adherence, participant variables, and cocaine use outcomes during treatment and at follow-up. The data set included only participants who attended at least 2 CBT sessions to allow for assignment and return of homework (N = 158). Results: Participants returned slightly less than half (41.1%) of assigned homework. Longitudinal random effects regression suggested a greater reduction in cocaine use during treatment and through 12-month follow-up for participants who completed half or more of assigned homework (3-way interaction), F(2, 910.69) = 4.28, p = .01. In multiple linear regression, the percentage of homework adherence was associated with greater number of cocaine-negative urine toxicology screens during treatment, even when accounting for baseline cocaine use frequency and treatment attendance; at 3 months follow-up, multiple logistic regression indicated homework adherence was associated with cocaine-negative urine toxicology screen, controlling for baseline cocaine use and treatment attendance. Conclusions: These results extend findings from prior studies regarding the importance of homework adherence by demonstrating associations among homework and cocaine use outcomes during treatment and up to 12 months after, independent of treatment attendance and baseline cocaine use severity. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
- Impact Statement:
- What is the public health significance of this article?—This examination of data from 4 randomized trials suggests that homework adherence in cognitive–behavioral therapy for cocaine dependence is associated with better cocaine outcomes during treatment and through 12 months follow-up, independent of the effects of treatment attendance or baseline cocaine severity. This study joins others in demonstrating an association between homework adherence and symptom change during CBT, and suggests homework assignment and adherence warrant continued study as key ingredients in CBT. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Cognitive Behavior Therapy; *Drug Dependency; *Homework; *Treatment Compliance; *Treatment Outcomes; Cocaine
- PsycINFO Classification:
- Cognitive Therapy (3311)
- Population:
- Human
Male
Female - Age Group:
- Adulthood (18 yrs & older)
- Tests & Measures:
- Timeline Followback
Substance Use Calendar - Grant Sponsorship:
- Sponsor: VA Connecticut Health Care System, US
Recipients: Decker, Suzanne E.
Sponsor: VISN 1 MIRECC
Recipients: Decker, Suzanne E.
Sponsor: National Institute on Drug Abuse, US
Grant Number: R01DA015969-09S1 and P50DA09241
Recipients: No recipient indicated - Methodology:
- Empirical Study; Quantitative Study; Treatment Outcome
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- First Posted: Jul 25, 2016; Accepted: May 10, 2016; Revised: Feb 1, 2016; First Submitted: May 29, 2015
- Release Date:
- 20160725
- Correction Date:
- 20160926
- Digital Object Identifier:
- http://dx.doi.org/10.1037/ccp0000126
- PMID:
- 27454780
- Accession Number:
- 2016-36128-001
- Persistent link to this record (Permalink):
- http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2016-36128-001&site=ehost-live
- Cut and Paste:
- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2016-36128-001&site=ehost-live">Just showing up is not enough: Homework adherence and outcome in cognitive–behavioral therapy for cocaine dependence.</A>
- Database:
- PsycINFO
Just Showing Up Is Not Enough: Homework Adherence and Outcome in Cognitive–Behavioral Therapy for Cocaine Dependence / BRIEF REPORT
By: Suzanne E. Decker
New England Mental Illness Research Education and Clinical Center, West Haven, Connecticut, and Department of Psychiatry, Yale University School of Medicine;
Brian D. Kiluk
Department of Psychiatry, Yale University School of Medicine
Tami Frankforter
Department of Psychiatry, Yale University School of Medicine
Theresa Babuscio
Department of Psychiatry, Yale University School of Medicine
Charla Nich
Department of Psychiatry, Yale University School of Medicine
Kathleen M. Carroll
Department of Psychiatry, Yale University School of Medicine
Acknowledgement: Suzanne E. Decker is supported by VA Connecticut Health Care System and VISN 1 MIRECC. Other authors’ work for this study was supported by National Institute on Drug Abuse (NIDA) grants: R01DA015969-09S1 and P50DA09241. Kathleen M. Carroll is a Member in Trust of CBT4CBT LLC. All authors were involved in the planning, interpretation, and writing of this article. Kathleen M. Carroll was the lead investigator on studies from which these data are drawn. Suzanne E. Decker developed the initial plan for data analysis and wrote the first manuscript draft; Charla Nich, Suzanne E. Decker, and Theresa Babuscio conducted statistical analyses; Theresa Babuscio and Tami Frankforter conducted data management; Charla Nich provided statistical and interpretive consultation; Brian D. Kiluk and Kathleen M. Carroll provided editorial assistance. The NIDA, Department of Veterans Affairs, Veterans Affairs Connecticut Healthcare System, and MIRECC had no further role in study design; in the collection, analysis and interpretation of data; in the writing of the report; or in the decision to submit the manuscript for publication. Views expressed in this article are those of the authors.
A central component of cognitive–behavioral therapy (CBT) is emphasis on between-session practice assignments, or “homework.” Homework provides opportunities to practice new skills, test new ideas, and generalize learning outside of session (Kazantzis, Whittington, & Dattilio, 2010). Meta-analyses suggest homework adherence (partial or full completion of homework) has been associated with improved CBT outcome across a variety of disorders (Kazantzis et al., 2010; Mausbach, Moore, Roesch, Cardenas, & Patterson, 2010). Other dimensions of homework include its quality (Detweiler & Whisman, 1999). In meta-analyses comparing outcomes of treatments with and without homework, a small-to-medium mean effect size (d = 0.48) favored treatments with homework (Kazantzis et al., 2010). Homework adherence and improved symptoms have been found to be associated in several studies (e.g., Bryant, Simons, & Thase, 1999; Burns & Spangler, 2000; although see also Weck, Richtberg, Esch, Höfling, & Stangier, 2013). In the addictions literature, homework adherence has been associated with improved symptoms in three studies (meta-analysis, r = .27; Mausbach et al., 2010) and associated with reduced cocaine use, as indicated by both self-report and cocaine-negative urine toxicology screens (Carroll, Nich, & Ball, 2005; Carroll et al., 2008).
The relationship between homework adherence and symptom change may take several forms, including a direct impact of homework on symptoms or reflecting a third variable, such as client motivation (Burns & Spangler, 2000; Gonzalez, Schmitz, & DeLaune, 2006). Homework adherence has been associated with participant variables indicating clinical severity (e.g., previous depressive episodes; Bryant et al., 1999), although direct correlations between initial symptom severity and homework adherence have not been consistently found (e.g., Bryant et al., 1999; Weck et al., 2013). Other potential correlates of homework adherence include therapist, working alliance, task characteristics (Detweiler & Whisman, 1999), and therapist competence (Bryant et al., 1999; Kazantzis, Ronan, & Deane, 2001; Weck et al., 2013). Although treatment attendance has been associated with homework adherence (Burns & Spangler, 2000), it has not been examined in all studies (e.g., Weck et al., 2013). Client ratings of homework’s helpfulness have been correlated with treatment attendance in cocaine dependence treatment (Siqueland et al., 2004), suggesting that attendance and client opinions on homework are related but distinct. As prior studies have not consistently included attendance, less is known about whether homework adherence is associated with symptom change when accounting for treatment attendance.
The present trial extends earlier findings that showed an association between homework adherence and cocaine outcomes (Carroll et al., 2005) by using data pooled across four independent outpatient CBT trials, resulting in a larger and more diverse sample, and including data through 12 months after treatment’s end. To avoid overlap (Carroll et al., 2005), analyses were conducted with and without data from this study.
MethodData for these analyses were drawn from four randomized clinical trials (A: Carroll et al., 1998; B: Carroll et al., 2016; C: Carroll et al., 2004; D: Carroll et al., 2008) evaluating CBT for cocaine dependence. Trials were conducted by the same research group, using similar assessment batteries and procedures. Participants were included in this analysis if they were assigned to receive CBT and attended at least two CBT sessions, thus having the opportunity to be assigned and to return homework at least once. Three studies (A, B, C) used a 12-week manualized CBT protocol, with follow-up assessments at 1, 3, 6, and 12 months posttreatment. Therapist training included a didactic seminar and completion of at least one closely supervised training case; all sessions were recorded for fidelity monitoring. The remaining study (D) evaluated an 8-week computerized CBT protocol, with follow-up assessments at 1, 3, and 6 months posttreatment. As Study A, B, C’s fidelity measures were similar but not identical, and Study D represented a different modality of treatment, therapist fidelity data were not included in these analyses. All studies were reviewed and approved by the institutional review board; participants provided written informed consent.
Measures
Cocaine use
We evaluated cocaine use with self-report (percent days abstinent) and a biological measure (percentage of urine specimens that were negative for cocaine metabolites). Self-reported cocaine use in the 28 days prior to randomization, during the treatment period, and at each follow-up period was assessed using the Substance Use Calendar (Carroll et al., 2004), a calendar-format interview based on the Timeline Followback (Sobell & Sobell, 1992). Urine samples were collected weekly or more during treatment and at each follow-up interview, tested for cocaine metabolites, and compared with standard cutoff values (benzoylecgonine level <300 ng/mL considered cocaine-negative). The percentage of cocaine-negative urine samples during treatment was calculated by dividing the number of cocaine-negative urine samples by the number of urine samples obtained. Further detail on original trials and operationalization of outcome variables is available in Carroll et al. (2014).
Homework assignment and adherence
Homework was assigned at most sessions starting with Session 1 (Studies A, B, C) or at the completion of each computerized CBT module (Study D). A dichotomous report of homework assignment at each session was generated from therapist report (Studies A, C) or computer (Study D); in Study B, the report of homework assignment was generated at the subsequent session (i.e., was homework completed, not completed, or not assigned?). Study therapists recorded whether participants had partially or fully completed the previous week’s homework assignment at each weekly session in Studies A, B, and C. In Study D, the computer program asked each participant if they had completed homework at the start of each session. Homework adherence was calculated by dividing the number of homework assignments reported as partially or fully completed by number of homework assignments given. For participants who terminated treatment early, the calculation was based on the data from available sessions (number of homework assignments reported as partially or fully completed divided by the number of sessions in which homework was checked) to avoid artificial deflation because of missing homework adherence data for the last session.
Analyses
Descriptive analyses (mean, percent) were used to examine homework adherence; t tests, ANOVA, and Pearson’s correlation were used to evaluate relationships among homework adherence, participant variables, and treatment attendance. We hypothesized that greater homework adherence would be associated with lower levels of self-reported cocaine use and more cocaine-negative urine toxicology screens. Random effects regression models were used to evaluate relationships of homework adherence to self-reported cocaine use outcomes across time (from baseline to treatment end or to 12-month follow-up). For the random regression models, homework adherence was categorized as an ordinal variable with three levels: (1) no homework adherence; (2) some homework adherence, but no more than 50%; or (3) more than 50% of homework assignments completed. For models using data from baseline to treatment end, time was log transformed to account for the high rate of change in the first weeks of treatment. Piecewise models (Singer & Willet, 2003) were used to evaluate cocaine use from baseline to 12-month follow-up, with both treatment month and treatment phase (Weeks 1–12 vs. follow-up) as independent variables. These analyses were replicated in a subsample excluding the previously examined Study C (n = 110). To separate homework adherence from treatment attendance, analyses were replicated in treatment completers only (n = 81); a third model included treatment completion as an independent variable. For cocaine-negative urine toxicology screens, longitudinal models were precluded by having only one urine result at follow-up points. The relationship between homework adherence and the percentage of cocaine-negative urine toxicology screens from baseline to treatment end was examined using multiple linear regression; multiple logistic regression was used for urine toxicology screen result at each follow-up point. Models included baseline frequency of cocaine use (self-reported cocaine use in 28 days prior to study), percentage of sessions attended, and study protocol. As the sample size did not permit multiple regression without Study C, we examined partial correlations among homework and percentage of cocaine-negative urine toxicology screens for each study, controlling for baseline cocaine use and attendance.
Results Sample Characteristics
Across the four studies, 243 participants were assigned to CBT. Of these, 158 (65.0%) who attended at least two CBT sessions were included in this report. Participant demographic information across the four studies is presented in Table 1. The sample was largely male (n = 115, 72.8%), and African American (n = 68, 43.0%) or Caucasian (n = 73, 46.2%). Although there were no significant gender or educational differences across studies, participants in Trial D were more likely to be employed, married, referred by the criminal justice system, or on public assistance (see Table 1). Participants reported they used cocaine a mean of 13.8 of the 28 days prior to randomization (SD = 8.51). Across studies, participants attended more than 50% of CBT sessions offered; post hoc testing indicated higher levels of attendance in Study C than Study B (mean difference = 19.0, SD = 5.8, p = .01, 95% confidence interval [CI] [4.4, 35.5]). There were no significant main effects of study on self-reported cocaine abstinence (see Table 1), suggesting outcomes were similar across CBT protocols and combining data was appropriate.
Descriptive Statistics for Sample
Homework Adherence
The mean number of homework assignments given and reported as partially or fully completed were 5.7 (SD = 3.3) and 2.6 (SD = 2.6), respectively, such that participants returned 41.1% (SD = 32.5; range = 0% to 100%) of assigned homework. Percentage of homework assignments completed did not differ by gender, race, education, referral by the criminal justice system, previous outpatient mental health treatment, lifetime diagnoses of depression, alcohol use disorder, or anxiety disorder, or current antisocial personality diagnosis (results available on request). Percentage of homework assignments completed was not significantly correlated with percentage of sessions attended (r = .14, p = .08), nor with baseline cocaine use frequency (r = .03, p = .71).
Homework Adherence and Self-Reported Cocaine Use Over Time
Random effects regression indicated a significant reduction in frequency of cocaine use across time. In the model using data from baseline to treatment end (Table 2, Model 1), an interaction between percent of homework adherence and time indicated greater cocaine use reduction in those who completed more than 50% of homework assignments compared with those with 50% or less homework adherence, or those who completed no homework (Homework × Time, F[2, 390.24] = 6.77, p = .00). A second model included data from baseline through 12-month follow-up (Table 2, Model 2). A three-way interaction between homework group, time, and phase indicated that although the change in cocaine use was greatest during active treatment for those with homework adherence more than 50% of the time compared with those with 50% or less homework adherence, the rate of change in cocaine use during follow-up was less than that during treatment, F(2, 910.69) = 4.28, p = .01, but the effect of homework group remained significant through follow-up. When these models were repeated in the subsamples excluding Study C (n = 110), or in treatment completers only (n = 81), power was limited. However, the patterns of results did not change direction. To examine whether the relationship of homework to cocaine use was because of treatment attendance, an additional model included a dichotomous indicator of treatment attendance (completed treatment vs. dropped out). The three-way interaction between homework, time, and phase remained statistically significant, indicating support for the finding that completion of greater than 50% of homework assigned was associated with less cocaine use during treatment and through follow-up, and suggesting that the relationship of homework to reduced cocaine use was independent of treatment attendance.
Longitudinal Models on Self-Reported Cocaine Use Over Time
Homework Adherence and Cocaine-Negative Urine Toxicology Screens
The multiple linear regression model indicated that greater homework adherence was associated with more cocaine-negative urine toxicology screens during treatment, even with treatment attendance in the model (Table 3; β = 0.17, t = 2.59, p = .01, sr2 = 0.17). Partial Pearson’s correlations on homework and percentage of cocaine-negative urine toxicology screens during treatment, controlling for baseline cocaine frequency and attendance, had small samples, and only that of Study C reached statistical significance. At 1, 3, and 6 months follow-up, logistic regression models for cocaine-negative urine toxicology screen result were significant (see Table 4). Homework adherence was associated with cocaine-negative urine toxicology screen at 3-month follow-up (β = 1.02, 95% CI [1.00, 1.04], p = .01). The model was not significant at 12-month follow-up. Small sample size did not permit meaningful comparison of cocaine-negative urine toxicology screen results for each study at each time point.
Multiple Linear Regression Analysis and Partial Correlations on Percentage of Cocaine-Negative Urine Toxicology Screens
Logistic Regression on Cocaine-Negative Urine Toxicology Screen at Follow-Up
DiscussionThis examination of pooled data from four randomized controlled trials evaluating clinician- and computer-delivered CBT indicated that homework adherence was associated with significantly less cocaine use from baseline to treatment end on two indicators (self-report and cocaine-negative urine toxicology screen). Longitudinal models suggested that participants with greater than 50% homework adherence had a greater reduction in cocaine use than those with less homework adherence during treatment and up to 12 months after, even when accounting for treatment attendance. Greater homework adherence was associated with cocaine-negative urine toxicology screens during treatment and at 3 months follow-up.
Correlations and bivariate analyses indicated homework adherence was not associated with baseline cocaine use or other participant variables. This was consistent with other studies showing no direct correlation between homework adherence and initial symptom severity (Bryant et al., 1999; Burns & Spangler, 2000) or other participant variables (Weck et al., 2013).
Why might homework adherence be associated with improved outcomes? Although homework may be related to participant motivation (Detweiler & Whisman, 1999; Gonzalez et al., 2006), its association with outcomes during and after treatment was independent of treatment attendance. Homework adherence may be associated with acquisition of new skills (Kazantzis et al., 2010), or increases in coping skill quality and quantity (Carroll et al., 2005); skill quality has been shown to mediate the relationship between CBT and substance use treatment outcomes (Kiluk, Nich, Babuscio, & Carroll, 2010). The persistence of homework’s association with reduced cocaine use at up to 12 months after treatment also suggests homework may have been associated with learning generalization, although these analyses could not evaluate relationships between homework adherence and skills acquisition or generalization across these four studies.
Despite the emphasis on homework in most CBT protocols and manuals, data on the association of homework and outcomes are still relatively sparse. To date, this is the first report evaluating the role of homework in substance use disorder treatment using combined samples from multiple studies and using longitudinal models to evaluate cocaine use through 12-month follow-up. Other strengths include drawing data from well-controlled randomized controlled trials based on the same CBT manual; evaluation of cocaine use via both self-report and biological samples; and use of weekly reports of homework adherence rather than retrospective reports (Bryant et al., 1999). Limitations of the current study include the limited range of indicators potentially associated with homework across trials, such as acquisition of coping skills, motivation, or therapist competence, as these were not collected uniformly across all studies. Other limitations include the absence of data on homework quality or a continuous measure of homework adherence; missing data, particularly at follow-up points; and varying data collection methods on homework completion across studies (Mausbach et al., 2010). These analyses were conducted without correction for multiple analyses. Nevertheless, this report adds to the accumulating evidence that homework is associated with improved outcome in CBT, that its positive effects remain even after treatment ends, and that it may be a factor associated with the durability of CBT in many samples.
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Submitted: May 29, 2015 Revised: February 1, 2016 Accepted: May 10, 2016
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Source: Journal of Consulting and Clinical Psychology. Vol. 84. (10), Oct, 2016 pp. 907-912)
Accession Number: 2016-36128-001
Digital Object Identifier: 10.1037/ccp0000126
Record: 90- Title:
- Language-based measures of mindfulness: Initial validity and clinical utility.
- Authors:
- Collins, Susan E.. University of Washington, Seattle, WA, US, susan.collins@gmx.net
Chawla, Neharika. University of Washington, Seattle, WA, US
Hsu, Sharon H.. University of Washington, Seattle, WA, US
Grow, Joel. University of Washington, Seattle, WA, US
Otto, Jacqueline M.. University of Washington, Seattle, WA, US
Marlatt, G. Alan. University of Washington, Seattle, WA, US - Address:
- Collins, Susan E., Addictive Behaviors Research Center, University of Washington, Box 351629, Seattle, WA, US, 98195, susan.collins@gmx.net
- Source:
- Psychology of Addictive Behaviors, Vol 23(4), Dec, 2009. pp. 743-749.
- NLM Title Abbreviation:
- Psychol Addict Behav
- Page Count:
- 7
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- Bulletin of the Society of Psychologists in Addictive Behaviors; Bulletin of the Society of Psychologists in Substance Abuse
- Other Publishers:
- US : Educational Publishing Foundation
Society of Psychologists in Addictive Behaviors - ISSN:
- 0893-164X (Print)
1939-1501 (Electronic) - Language:
- English
- Keywords:
- mindfulness, validity, substance use, linguistic marker, language, measurement, clinical utility, relapse prevention, treatment outcomes, alcohol & other drug use disorders
- Abstract:
- This study examined relationships among language use, mindfulness, and substance-use treatment outcomes in the context of an efficacy trial of mindfulness-based relapse prevention (MBRP) for adults with alcohol and other drug use (AOD) disorders. An expert panel generated two categories of mindfulness language (ML) describing the mindfulness state and the more encompassing 'mindfulness journey,' which included words describing challenges of developing a mindfulness practice. MBRP participants (n = 48) completed baseline sociodemographic and AOD measures, and participated in the 8-week MBRP program. AOD data were collected during the 4-month follow-up. A word count program assessed the frequency of ML and other linguistic markers in participants’ responses to open-ended questions about their postintervention impressions of mindfulness practice and MBRP. Findings supported concurrent validity of ML categories: ML words appeared more frequently in the MBRP manual compared to the 12-step Big Book. Further, ML categories correlated with other linguistic variables related to the mindfulness construct. Finally, predictive validity was supported: greater use of ML predicted fewer AOD use days during the 4-month follow-up. This study provided initial support for ML as a valid, clinically useful mindfulness measure. If future studies replicate these findings, ML could be used in conjunction with self-report to provide a more complete picture of the mindfulness experience. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Drug Rehabilitation; *Language; *Relapse Prevention; *Treatment Outcomes; *Mindfulness; Alcohol Abuse; Drug Abuse; Psycholinguistics; Test Validity
- Medical Subject Headings (MeSH):
- Adaptation, Psychological; Adult; Attention; Female; Humans; Language; Male; Middle Aged; Mind-Body Relations, Metaphysical; Reproducibility of Results; Secondary Prevention; Substance-Related Disorders; Surveys and Questionnaires; Treatment Outcome
- PsycINFO Classification:
- Clinical Psychological Testing (2224)
Drug & Alcohol Rehabilitation (3383) - Population:
- Human
Male
Female - Age Group:
- Adulthood (18 yrs & older)
- Tests & Measures:
- Timeline Followback
- Grant Sponsorship:
- Sponsor: National Institute on Drug Abuse
Grant Number: R21 DA010562
Recipients: Marlatt, G. Alan
Sponsor: National Institute on Alcohol Abuse and Alcoholism
Grant Number: T32AA007455
Other Details: Institutional Training Grant awarded to Mary E. Larimer at the University of Washington
Recipients: Collins, Susan E. - Methodology:
- Empirical Study; Quantitative Study
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- Accepted: Aug 4, 2009; Revised: Jul 31, 2009; First Submitted: Mar 25, 2009
- Release Date:
- 20091221
- Correction Date:
- 20100308
- Copyright:
- American Psychological Association. 2009
- Digital Object Identifier:
- http://dx.doi.org/10.1037/a0017579
- PMID:
- 20025383
- Accession Number:
- 2009-24023-023
- Number of Citations in Source:
- 38
- Persistent link to this record (Permalink):
- http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2009-24023-023&site=ehost-live
- Cut and Paste:
- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2009-24023-023&site=ehost-live">Language-based measures of mindfulness: Initial validity and clinical utility.</A>
- Database:
- PsycINFO
Language-Based Measures of Mindfulness: Initial Validity and Clinical Utility
By: Susan E. Collins
University of Washington;
Neharika Chawla
University of Washington
Sharon H. Hsu
University of Washington
Joel Grow
University of Washington
Jacqueline M. Otto
University of Washington
G. Alan Marlatt
University of Washington
Acknowledgement: This research was supported by National Institute on Drug Abuse Grant R21 DA010562 to G. Alan Marlatt. Susan E. Collins’s time was supported by an NIAAA Institutional Training Grant (T32AA007455) awarded to Mary E. Larimer at the University of Washington.
We would like to thank our research assistants, Benjamin Ady and Scott Henry, for their help entering and cleaning the data for this study. Thanks also to Anne Douglass, Katie Witkiewitz, Ph.D., Sarah Bowen, Ph.D., and Michelle Garner, Ph.D., for their input during the mindfulness language discussion round.
Mindfulness-based relapse prevention (MBRP; Witkiewitz et al., 2005) is an 8-week substance-use aftercare program that integrates mindfulness practice with cognitive-behavioral relapse prevention (Daley & Marlatt, 2006; Marlatt & Gordon, 1985). In a recent randomized trial comparing the efficacy of MBRP to treatment as usual (TAU), MBRP significantly reduced rates of substance use and craving during the 4-month follow-up (Bowen, Chawla, Collins, et al., in press). Relative to TAU participants, MBRP participants exhibited significant increases in acceptance and ability to act with awareness. Additionally, high participant satisfaction and treatment compliance demonstrated the feasibility of the MBRP program. Thus, findings supported MBRP as an efficacious and feasible aftercare treatment for substance use disorders.
A logical next-step is to evaluate potential correlates of the observed MBRP treatment effects. According to Shapiro, Carlson, Astin, and Freedman (2006), self-report measures may help determine whether posttreatment increases in mindfulness are correlated with better health outcomes. However, the language individuals use to describe their experience of mindfulness may also serve as a behavioral indicator of the development of mindfulness. In fact, the ability to describe observed phenomena by applying words comprises one component of mindfulness as it is currently defined (Baer, Smith, Hopkins, Krietemeyer, & Toney, 2004). This ability may also be indicative of a “de-centered” or “metacognitive” perspective, which has been proposed to be an important mechanism by which mindfulness affects outcomes (Teasdale et al., 2002). Additionally, facilitators of mindfulness-based interventions are trained to model qualities such as acceptance and being in the present moment, which is aided by the careful and intentional use of language.
On the other hand, the compatibility of literal language and mindfulness has been disputed. It has been asserted that verbal processing undermines acceptance and attention to the present moment because it activates a concrete and learned “relational network” of meanings and labels (Hayes & Shenk, 2004; Hayes & Wilson, 2003). Despite these differing assertions, no studies to date have empirically examined the relationship between language use and mindfulness, which indicates a gap in the literature that should be addressed.
One strategy for examining language and assessing its role in treatment outcomes is the use of word count programs, such as the Linguistic Inquiry and Word Count program (LIWC2007; Pennebaker, Booth, & Francis, 2007). The LIWC locates and counts the occurrence of words contained in writing samples that represent linguistic and psychological categories. The LIWC issued from research exploring the impact of expressive writing on physical and psychological health (Pennebaker, 1997; Pennebaker, Kiecolt-Glaser, & Glaser, 1988), but it has been more recently expanded to other psychological research areas, including how linguistic markers reflect psychological states (Pennebaker & Chung, 2007), social behavior (Mehl & Pennebaker, 2003; Pennebaker & Graybeal, 2001), and psychopathology (e.g., depression, suicidality; Rude, Gortner, & Pennebaker, 2004; Stirman & Pennebaker, 2001).
Although the LIWC has been used with adult substance-use populations (Pennebaker & King, 1999; Vano, 2002), only one study to date has applied this method to predict substance-use outcomes following an intervention (Collins, Carey, & Smyth, 2005). Specifically, college drinkers receiving personalized normative feedback versus an alcohol education brochure used more first person singular and school-related words and fewer discrepancy, second person and body-related words when describing their responses to the intervention. Use of first- and second-person pronouns, which typically reflects level of personal “ownership” and self versus other focus, mediated the intervention effects. Personalized normative feedback recipients appeared to connect with and internalize the intervention, and in turn, decreased their drinking more than participants who received the alcohol education brochure. This study showed that word-count analyses can be used to learn more about relevant language correlates and corresponding cognitive processes potentially underlying substance-use intervention effects.
Current Study: Aims and HypothesesThis study examined relationships among language use, mindfulness, and substance-use outcomes in the context of an efficacy trial of MBRP for adults with substance-use disorders. An expert panel of mindfulness and substance-use researchers generated two categories of mindfulness language (ML) words believed to reflect the mindfulness experience. The first category describes the actual state of mindfulness; whereas the second category describes the more encompassing “mindfulness journey,” which includes words describing the mindfulness state as well as the challenges involved in developing a mindfulness practice. Next, the LIWC word count program assessed the frequency of ML encountered in participants’ responses to open-ended questions about their postintervention impressions of mindfulness practice and the MBRP program. Finally, concurrent, discriminant and predictive validity of these ML categories was tested by assessing their relationship to other linguistic categories, related text-based sources, and alcohol and other drug (AOD) use days (i.e., frequency of AOD use) during the 4-month follow-up.
It was hypothesized that the ML categories would demonstrate convergent validity by corresponding to substantive high-frequency words in the MBRP treatment manual. ML categories would also demonstrate discriminant validity by showing little overlap with the twelve-step, self-help manual, the “Big Book” (Alcoholics Anonymous, 2006), which comes from a different theoretical and philosophical tradition. It was also hypothesized that ML would correlate with related linguistic variables. Specifically, the focus of mindfulness practice on experiences in the present moment (e.g., Baer et al., 2006; Kabat-Zinn, 1994; Marlatt & Kristeller, 1999) led us to hypothesize that ML would positively correlate with use of the present tense and would inversely correlate with past and future tense. Further, considering the focus of mindfulness on examining one’s own internal states, we hypothesized that ML would positively and negatively correlate with use of personal versus impersonal pronouns, respectively. Given the focus of mindfulness practices on increasing awareness and perception of states and sensations in the body (Bowen, Chawla, & Marlatt, in press), we hypothesized a correlation with perception and body-related words. Because mindfulness teaches clients to accept negative emotions and engage in skillful rather than reactive behavior, we hypothesized relationships between ML, affect, and anger words. It was also hypothesized that ML would evince clinical relevance and predictive validity by inversely predicting number of AOD use days during the 4-month follow-up.
Method
Participants
Participants (n = 48) were drawn from the MBRP treatment group (n = 93) that was part of a larger randomized treatment trial (n = 168) conducted in a nonprofit public service treatment agency (for details, see Bowen, Chawla, Collins et al., in press). The final subsample was reduced to 48 participants because only 52% of MBRP participants attended the final treatment session during which ML was assessed. Participants’ (27% women; n = 13) average age was 40.89 years (SD = 10.61). Most participants self-identified as White (69%); whereas 13% self-identified as Black, 8% as Hispanic/Latino, 8% as Multiracial, 6% as Native American, and 2% as Asian/Pacific Islander. Employment status varied: 33% were unemployed, 29% received public assistance or social security, 19% were employed part-time, and the remaining 17% were employed full-time. A high school degree or equivalent was the highest level of education for 31% of the sample; 4% did not complete high school, 42% completed community college or some 4-year college, and 21% obtained a 4-year college degree or higher.
Measures
A Sociodemographics Questionnaire assessed age, gender, race or ethnicity, employment status, and educational level. The Timeline Followback (TLFB; Sobell & Sobell, 1992) assessed daily AOD use and was used to create AOD use days (frequency) variables for the 2 months before baseline and during the 4-month follow-up. The TLFB has shown good reliability and validity for AOD use assessment (Carney et al., 1998; Fals-Stewart et al., 2000). The Participant Feedback Form included four open-ended questions that addressed participants’ experiences in the MBRP program and was administered at the final treatment session [e.g., “What did you get out of coming (to the MBRP group), if anything?”]. Responses to these items provided the writing samples used to assess ML use.
Materials
Individual text files were analyzed using the LIWC (Pennebaker, Booth et al., 2007), a computer program that categorizes words into 80 hierarchical dimensions, including linguistic, psychological, relativity-related, and other content categories. The LIWC created proportions of the number of words in each writing sample representing linguistic categories hypothesized to be significantly correlated with mindfulness (i.e., verb tense, pronoun use, affect, anger, insight, body, and perception). The linguistic categories are built into the default dictionary (i.e., LIWC 2007 English dictionary; Pennebaker, Booth et al., 2007) and have been researched and validated in various writing samples since the development of the program (Pennebaker, Chung et al., 2007). ML dictionary words were brainstormed and discussed by mindfulness experts, entered into two LIWC dictionaries, and counted by the LIWC program (see Table 1). Word frequency counts for the MBRP treatment manual (Bowen, Chawla, & Marlatt, in press), and the 12-step Big Book (Alcoholics Anonymous, 2006), were generated using custom software written in the Perl computer programming language. This program scanned the manuals and sorted words from most to least frequent. A separate table held “filtered” words (i.e., articles, pronouns, and prepositions) to be ignored when inspecting manual overlap.
Mindfulness Language Categories
Procedure
Near the end of their inpatient or intensive outpatient treatment, participants were recruited, screened and randomized into the larger treatment trial (see Bowen, Chawla, Collins, et al., in press). Participants presented at their treatment site to complete the baseline data assessment, including assessment of AOD and sociodemographic variables. Surveys were self-administered using computers set up to access the Web-based survey. Trained, undergraduate research assistants provided instruction and assistance during assessments as needed. Next, participants underwent either TAU or the 8-week, group-based MBRP treatment. The Participant Feedback Form was completed by MBRP participants who attended the final treatment week. Participants provided AOD use days data at the posttest, 2- and 4-month follow-ups, which were completed at the treatment facility. If participants did not complete scheduled follow-ups, substance use data were obtained via telephone. Participants received gift cards for completion of assessments.
Data Preparation
Trained undergraduate research assistants double entered data from the Participant Feedback Form into separate text files for each participant. Text files were cleaned by the first author and were run through the LIWC program using the LIWC 2007 default dictionary and the two ML dictionaries created for the current study. The program yielded the proportion of participants’ writing samples reflecting words from the chosen linguistic and ML categories. These data were entered into STATA 10 (StataCorp, 2007) for the analyses.
ResultsPreliminary analyses showed a significant association between race and use of mindfulness journey words (U = 68, p = .03); thus, race was entered as a covariate in later AOD use analyses. There were no other associations between baseline demographic variables and AOD use or ML (ps > .12). As predicted, ML words largely correlated with hypothesized linguistic markers (see Table 2). Convergent validity was further supported in that a word count conducted on the MBRP manual (nwords = 28,989) indicated that all ML words appeared at least twice (M = 62.98, SD = 74.40), and collectively made up 13.5% of the total manual words. Word frequency analyses established the 100 most frequently occurring words in the MBRP manual and 12-step Big Book. Aside from articles, pronouns and prepositions, there was only a 1-word overlap between ML and the 100 most frequent words in the Big Book (see Figure 1). A proportional z test indicated that overlap between ML and the MBRP treatment manual was significantly higher than the overlap between ML and the Big Book (z = 4.70, p < .001).
Bivariate Spearman’s Rho Correlations Between Mindfulness and Other Linguistic Categories
Figure 1. Mindfulness language (ML) words are in the center column. Words shaded in black represent overlap between ML and the 100 most frequent words in the MBRP manual (Bowen, Chawla, & Marlatt, in press), which account for 55.60% (n = 16,119) of the total words in the MBRP manual. Words shaded in gray represent overlap between ML and the 100 most frequent words in the Big Book (Alcoholics Anonymous, 2006), which account for 56.76% (n = 16428) of the total words in the Big Book.
Separate zero-inflated negative binomial models (ZINB; Cameron & Trivedi, 1998; Hardin & Hilbe, 2007) tested the association of ML and total number of AOD use days during the 4-month follow-up. The two, separate mindfulness state, χ2(3, n = 41) = 18.22, p = .0004, and journey, χ2(3, n = 41) = 8.15, p = .04, models both predicted AOD use days. After controlling for race and baseline AOD use days, there were inverse effects for mindfulness state (IRR = .03, SE = .05, p = .02) and journey (IRR = .55, SE = .13, p = .01) language. Thus, for each 1% increase in the use of ML in a given writing sample, the rate of AOD use during the 4-month follow-up was reduced by 97% and 45%, respectively. Neither mindfulness state nor journey words predicted the zero-inflation process (ps > .19).
DiscussionThis study examined the relationships among mindfulness language, linguistic markers, and substance-use treatment outcomes. Findings largely supported the convergent validity of the ML categories by confirming the hypothesized associations between ML and linguistic variables. ML was inversely related to participants’ use of past tense words, which reflects the focus of mindfulness practices on present moment experience (e.g., Baer et al., 2006; Kabat-Zinn, 1994; Marlatt & Kristeller, 1999). Greater use of ML was also associated with decreased use of impersonal pronouns, which may reflect the MBRP focus on examining one’s own internal states and supports the notion that mindfulness practice fosters a greater sense of agency and personal choice (Kabat-Zinn, 1990; Segal et al., 2002). Mindfulness state words were positively associated with use of affect and body-related words, which is consistent with the focus of mindfulness practices on increasing awareness of affective states and associated bodily sensations (Bowen, Chawla, & Marlatt, in press), and with neurobiological research showing that mindfulness meditation is associated with changes in areas of the brain involved in interoceptive and visceral awareness (Critchley et al., 2004; Holzel et al., 2008). The association of mindfulness journey words with fewer anger and more insight words also fits the focus of mindfulness practice on interrupting automatic and reactive behavior and helping participants develop skillful responses when confronted by triggering situations (Segal et al., 2002).
Convergent validity was further supported in the text-based analyses: high-frequency words in the MBRP manual resembled the ML categories in that they comprised more active, present tense verbs as well as tactile and sensory experience words. Further, 38% of the ML words were contained within the 100 most frequent words in the MBRP manual. The percent of content overlap between ML and the MBRP manual was significantly higher than the overlap between ML and the Big Book.
In support of the ML categories’ discriminant validity, only one word (“time”) in the mindfulness journey category appeared in the 100 most frequent words in the Big Book. The words were also qualitatively different: the Big Book list comprised less experiential and more concrete words (e.g., “had,” “not,” “drink,” “God,” “alcoholic”) than the ML and MBRP lists. This finding conformed to hypotheses and is not surprising given the philosophical differences between MBRP and 12-step approaches. Although there are several points of overlap between the two models, including emphasis on acceptance and the value of meditation (Hsu, Grow, & Marlatt, 2008), the philosophical underpinnings of 12-step approaches are based largely on the disease and spiritual models of addiction (Spicer, 1993). Affected individuals are encouraged to accept the label of an “addict” or “alcoholic,” and to enlist the support of a Higher Power to aid them in their recovery, which may explain the emphasis in the Big Book on words such as “alcoholic” and “God.” In contrast, MBRP discourages use of and identification with labels and encourages ongoing observation and acceptance of all thoughts, sensations, and emotional states (Bowen, Chawla, & Marlatt, in press; Marlatt, Bowen, Chawla, & Witkiewitz, 2008). This may explain the greater emphasis of the MBRP manual on tactile, sensory, and present tense verbs and its considerable overlap with ML.
ML use predicted AOD frequency during the 4-month follow-up period, which supported the predictive validity of ML. This finding also provided support for the hypothesized underlying process: that MBRP should increase mindfulness, which should in turn help participants decrease their AOD use. Unfortunately, because there was no baseline assessment of ML and because the writing samples were not available for participants in the control condition, it is impossible to ascribe causality to the relationship between mindfulness and later substance use. This finding did, however, provide evidence that level of mindfulness language is a valid and clinically relevant construct. Future experimental studies may explore whether change in ML over the course of the MBRP treatment mediates substance use behavior change.
Limitations of this study deserve mention. First, there was a relatively low rate of attendance at the final session, during which participants completed the writing samples used to assess ML. It is also important to note that the mindfulness state words are a subset of the mindfulness journey category; thus, the two categories are overlapping and highly correlated. Although it was deemed important to capture the subtle distinctions between the actual state of mindfulness versus the challenges inherent in mindfulness practice, the predictive models of AOD should not be interpreted independently without acknowledgment of this overlap. Finally, follow-up attrition was relatively high over the 4-month follow-up period. The resulting data missingness may have introduced bias into the dataset and reduced power to find significant differences (Kazdin, 1998). These flaws limit the conclusions that can be drawn; however, the robustness of the findings that ML is a valid and clinically relevant behavioral measure of mindfulness is encouraging.
Despite the limitations, ML showed potential as a valid and clinically relevant representation of mindfulness. ML was associated with other linguistic variables believed to represent key aspects of mindfulness, showed appropriate content overlap with relevant text-based sources, and predicted subsequent substance use outcomes. Future studies may use larger samples and experimental designs to further investigate ML as a valid and clinically useful way to assess mindfulness and as a potential mechanism underlying mindfulness-based treatment effects on substance use outcomes.
Footnotes 1 Zero-inflated negative binomial (ZINB) regression is a type of generalized linear models designed for count outcomes that are positively skewed, overdispersed (i.e., the variance is greater than the mean), and have a preponderance of zeroes (i.e., zero responses are more frequently observed than would be expected given the distribution). ZINB models two processes for each participant. The first process is a Bernoulli trial, which much like a logistic regression, determines the probability that an observation is consistently zero or is a feasible count response predicted by the negative binomial distribution. For example, if participants are abstinent from AOD use before treatment begin and remain so, they may never enter the count process because they are considered to be “always-zero” responses (Hardin & Hilbe, 2007). If the observation may be predicted by the negative binomial process, it enters this count estimation.Negative binomial regression provides output much like a multiple (OLS) regression, but the interpretation of the regression coefficients is different. Instead of standardized regression coefficients or betas, exponentiated coefficients or incident rate ratios (IRR) may be interpreted as the rate of change in the outcome variable for each one-point increase in the predictors. IRRs ranging from 0 to 1 indicate an inverse relationship between the predictor and outcome; whereas IRRs greater than 1 indicate a positive relationship between the predictor and outcome. There is no widely accepted statistic that provides a percentage of variance accounted for (R2). There are various pseudo-R2 statistics for generalized linear models more generally, but they may neither be interpreted as percent variance accounted for nor are they widely agreed upon (Hardin & Hilbe, 2007).
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Submitted: March 25, 2009 Revised: July 31, 2009 Accepted: August 4, 2009
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Source: Psychology of Addictive Behaviors. Vol. 23. (4), Dec, 2009 pp. 743-749)
Accession Number: 2009-24023-023
Digital Object Identifier: 10.1037/a0017579
Record: 91- Title:
- Linear and nonlinear growth models: Describing a Bayesian perspective.
- Authors:
- Depaoli, Sarah. Psychological Sciences, School of Social Sciences, Humanities, and Arts, University of California, Merced, CA, US, sdepaoli@ucmerced.edu
Boyajian, Jonathan. Psychological Sciences, School of Social Sciences, Humanities, and Arts, University of California, Merced, CA, US - Address:
- Depaoli, Sarah, Psychological Sciences, School of Social Sciences, Humanities, and Arts, University of California, 5200 N. Lake Road, Merced, CA, US, 95343, sdepaoli@ucmerced.edu
- Source:
- Journal of Consulting and Clinical Psychology, Vol 82(5), Oct, 2014. Special Issue: Advances in Data Analytic Methods. pp. 784-802.
- NLM Title Abbreviation:
- J Consult Clin Psychol
- Page Count:
- 19
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- Journal of Consulting Psychology
- Other Publishers:
- US : American Association for Applied Psychology
US : Dentan Printing Company
US : Science Press Printing Company - ISSN:
- 0022-006X (Print)
1939-2117 (Electronic) - ISBN:
- 1-4338-1955-4
- Language:
- English
- Keywords:
- Bayesian estimation, growth curve modeling, growth mixture modeling, nonlinear growth models, Markov chain Monte Carlo
- Abstract:
- Objective: Conventional estimation of longitudinal growth models can produce inaccurate parameter estimates under certain research scenarios (e.g., smaller sample sizes and nonlinear growth patterns) and thus lead to potentially misleading interpretations of results (i.e., interpreting growth patterns that do not reflect the population patterns). The current article used patterns of change in cigarette and alcohol abuse prevalence and depression levels to demonstrate an alternative method for estimating growth models more accurately under these conditions, namely, via the Bayesian estimation framework. This article acts as an introduction and tutorial for implementing Bayesian methods when examining growth or change over time, particularly nonlinear growth. Method: The National Longitudinal Survey of Youth 1997 database was used to highlight different linear and nonlinear (quadratic and logistic) growth models via growth curve modeling (GCM) and growth mixture modeling (GMM). The specific focus was on changes in cigarette/alcohol consumption and depression throughout adolescence and young adulthood. Specifically, a nationally representative group of individuals between the ages of 12 and 16 years were assessed at 4 time-points for levels of cigarette consumption, alcohol use, and depression. Results: The results for each example illustrated different patterns of linear and nonlinear growth via GCM and GMM through the versatile Bayesian estimation framework. Conclusions: Growth models may benefit from the Bayesian perspective by incorporating prior information or knowledge into the model, especially when sample sizes are small or growth is nonlinear. A step-by-step tutorial for assessing various growth models via the Bayesian perspective is provided as online supplemental material. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Statistical Estimation; *Statistics; Models
- Medical Subject Headings (MeSH):
- Adolescent; Bayes Theorem; Female; Humans; Linear Models; Longitudinal Studies; Male; Models, Statistical
- PsycINFO Classification:
- Statistics & Mathematics (2240)
- Population:
- Human
- Location:
- US
- Age Group:
- Childhood (birth-12 yrs)
School Age (6-12 yrs)
Adolescence (13-17 yrs) - Supplemental Data:
- Other Internet
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- First Posted: Dec 23, 2013; Accepted: Oct 7, 2013; Revised: Sep 26, 2013; First Submitted: Oct 31, 2012
- Release Date:
- 20131223
- Correction Date:
- 20140922
- Copyright:
- American Psychological Association. 2013
- Digital Object Identifier:
- http://dx.doi.org/10.1037/a0035147; http://dx.doi.org/10.1037/a0035147.supp(Supplemental)
- PMID:
- 24364797
- Accession Number:
- 2013-44747-001
- Number of Citations in Source:
- 62
- Persistent link to this record (Permalink):
- http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2013-44747-001&site=ehost-live
- Cut and Paste:
- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2013-44747-001&site=ehost-live">Linear and nonlinear growth models: Describing a Bayesian perspective.</A>
- Database:
- PsycINFO
Record: 92- Title:
- Low social rhythm regularity predicts first onset of bipolar spectrum disorders among at-risk individuals with reward hypersensitivity.
- Authors:
- Alloy, Lauren B.. Department of Psychology, Temple University, Philadelphia, PA, US, lalloy@temple.edu
Boland, Elaine M.. Corporal Michael J. Crescenz VA Medical Center, Philadelphia, PA, US
Ng, Tommy H.. Department of Psychology, Temple University, Philadelphia, PA, US
Whitehouse, Wayne G.. Department of Psychology, Temple University, Philadelphia, PA, US
Abramson, Lyn Y.. Department of Psychology, University of Wisconsin, WI, US - Address:
- Alloy, Lauren B., Department of Psychology, Temple University, 1701 North 13th Street, Philadelphia, PA, US, 19122, lalloy@temple.edu
- Source:
- Journal of Abnormal Psychology, Vol 124(4), Nov, 2015. pp. 944-952.
- NLM Title Abbreviation:
- J Abnorm Psychol
- Page Count:
- 9
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- The Journal of Abnormal and Social Psychology; The Journal of Abnormal Psychology; The Journal of Abnormal Psychology and Social Psychology
- ISSN:
- 0021-843X (Print)
1939-1846 (Electronic) - Language:
- English
- Keywords:
- social rhythm regularity, social zeitgebers, reward sensitivity, bipolar spectrum disorders
- Abstract (English):
- The social zeitgeber model (Ehlers, Frank, & Kupfer, 1988) suggests that irregular daily schedules or social rhythms provide vulnerability to bipolar spectrum disorders. This study tested whether social rhythm regularity prospectively predicted first lifetime onset of bipolar spectrum disorders in adolescents already at risk for bipolar disorder based on exhibiting reward hypersensitivity. Adolescents (ages 14–19 years) previously screened to have high (n = 138) or moderate (n = 95) reward sensitivity, but no lifetime history of bipolar spectrum disorder, completed measures of depressive and manic symptoms, family history of bipolar disorder, and the Social Rhythm Metric. They were followed prospectively with semistructured diagnostic interviews every 6 months for an average of 31.7 (SD = 20.1) months. Hierarchical logistic regression indicated that low social rhythm regularity at baseline predicted greater likelihood of first onset of bipolar spectrum disorder over follow-up among high-reward-sensitivity adolescents but not moderate-reward-sensitivity adolescents, controlling for follow-up time, gender, age, family history of bipolar disorder, and initial manic and depressive symptoms (β = −.150, Wald = 4.365, p = .037, odds ratio = .861, 95% confidence interval [.748, .991]). Consistent with the social zeitgeber theory, low social rhythm regularity provides vulnerability to first onset of bipolar spectrum disorder among at-risk adolescents. It may be possible to identify adolescents at risk for developing a bipolar spectrum disorder based on exhibiting both reward hypersensitivity and social rhythm irregularity before onset occurs. (PsycINFO Database Record (c) 2018 APA, all rights reserved)
- Impact Statement:
- General Scientific Summary—The tendency to maintain irregular daily activity schedules predicts first onset of bipolar spectrum disorder in adolescents with high sensitivity to rewards. (PsycINFO Database Record (c) 2018 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Bipolar Disorder; *Onset (Disorders); *Social Processes; Rewards
- Medical Subject Headings (MeSH):
- Adolescent; Bipolar Disorder; Female; Humans; Male; Prospective Studies; Reward; Risk Factors; Social Behavior; Young Adult
- PsycINFO Classification:
- Affective Disorders (3211)
- Population:
- Human
- Location:
- US
- Age Group:
- Adolescence (13-17 yrs)
Adulthood (18 yrs & older)
Young Adulthood (18-29 yrs) - Tests & Measures:
- Behavioral Inhibition System/Behavioral Activation System Scales
Sensitivity to Punishment Sensitivity to Reward Questionnaire
Schedule for Affective Disorders and Schizophrenia–Lifetime
Altman Self-Rating Mania Scale DOI: 10.1037/t64284-000
Beck Depression Inventory DOI: 10.1037/t00741-000 - Grant Sponsorship:
- Sponsor: National Institute of Mental Health, US
Grant Number: MH77908 and MH102310
Recipients: Alloy, Lauren B.
Sponsor: Office of Academic Affiliations, Department of Veterans Affairs, US
Other Details: Advanced Fellowship Program in Mental Illness Research and Treatment
Recipients: No recipient indicated - Methodology:
- Empirical Study; Longitudinal Study; Prospective Study; Interview; Quantitative Study
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- Accepted: Aug 12, 2015; Revised: Aug 9, 2015; First Submitted: Jan 23, 2015
- Release Date:
- 20151123
- Correction Date:
- 20180215
- Copyright:
- American Psychological Association. 2015
- Digital Object Identifier:
- http://dx.doi.org/10.1037/abn0000107
- PMID:
- 26595474
- Accession Number:
- 2015-52362-009
- Number of Citations in Source:
- 54
- Persistent link to this record (Permalink):
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- Cut and Paste:
- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2015-52362-009&site=ehost-live">Low social rhythm regularity predicts first onset of bipolar spectrum disorders among at-risk individuals with reward hypersensitivity.</A>
- Database:
- PsycINFO
Record: 93- Title:
- Mandated college students’ response to sequentially administered alcohol interventions in a randomized clinical trial using stepped care.
- Authors:
- Borsari, Brian. Mental Health and Behavioral Sciences Service, Department of Veterans Affairs Medical Center, Providence, RI, US, Brian.Borsari@va.gov
Magill, Molly. Center for Alcohol and Addiction Studies, Brown University School of Public Health, RI, US
Mastroleo, Nadine R.. College of Community and Public Affairs, Binghamton University, NY, US
Hustad, John T. P.. Department of Medicine and Public Health Sciences, Pennsylvania State College of Medicine, PA, US
Tevyaw, Tracy O'Leary. Mental Health and Behavioral Sciences Service, Department of Veterans Affairs Medical Center, Providence, RI, US
Barnett, Nancy P.. Center for Alcohol and Addiction Studies, Brown University School of Public Health, RI, US
Kahler, Christopher W.. Center for Alcohol and Addiction Studies, Brown University School of Public Health, RI, US
Eaton, Erica. Center for Alcohol and Addiction Studies, Brown University School of Public Health, RI, US
Monti, Peter M.. Center for Alcohol and Addiction Studies, Brown University School of Public Health, RI, US - Address:
- Borsari, Brian, San Francisco VA Medical Center, (116B) 4150 Clement Street, San Francisco, CA, US, 94121, Brian.Borsari@va.gov
- Source:
- Journal of Consulting and Clinical Psychology, Vol 84(2), Feb, 2016. pp. 103-112.
- NLM Title Abbreviation:
- J Consult Clin Psychol
- Page Count:
- 10
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- Journal of Consulting Psychology
- Other Publishers:
- US : American Association for Applied Psychology
US : Dentan Printing Company
US : Science Press Printing Company - ISSN:
- 0022-006X (Print)
1939-2117 (Electronic) - Language:
- English
- Keywords:
- alcohol, peers, brief advice, brief motivational intervention
- Abstract (English):
- Objective: Students referred to school administration for alcohol policies violations currently receive a wide variety of interventions. This study examined predictors of response to 2 interventions delivered to mandated college students (N = 598) using a stepped care approach incorporating a peer-delivered 15-min brief advice (BA) session (Step 1) and a 60- to 90-min brief motivational intervention (BMI) delivered by trained interventionists (Step 2). Method: Analyses were completed in 2 stages. First, 3 types of variables (screening variables, alcohol-related cognitions, mandated student profile) were examined in a logistic regression model as putative predictors of lower risk drinking (defined as 3 or fewer heavy episodic drinking [HED] episodes and/or 4 or fewer alcohol-related consequences in the past month) 6 weeks following the BA session. Second, we used generalized estimating equations to examine putative moderators of BMI effects on HED and peak blood alcohol content compared with assessment only (AO) control over the 3-, 6-, and 9-month follow-ups. Results: Participants reporting lower scores on the Alcohol Use Disorders Identification Test, more benefits to changing alcohol use, and those who fit the 'Bad Incident' profile at baseline were more likely to report lower risk drinking 6 weeks after the BA session. Moderation analyses revealed that Bad Incident students who received the BMI reported more HED at 9-month follow-up than those who received AO. Conclusion: Current alcohol use as well as personal reaction to the referral event may have clinical utility in identifying which mandated students benefit from treatments of varying content and intensity. (PsycINFO Database Record (c) 2017 APA, all rights reserved)
- Impact Statement:
- What is the public health significance of this article?—This study indicates that for mandated college students, the personal reaction to the referral event may have clinical utility in identifying which individuals benefit from treatments of varying content and intensity. In the context of stepped care, the findings provide support for the sequential delivery of 2 efficacious yet relatively low-intensity approaches that can be widely implemented with this at-risk population. (PsycINFO Database Record (c) 2017 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Alcohol Drinking Patterns; *College Students; *Intervention; *Motivational Interviewing; *Peers
- Medical Subject Headings (MeSH):
- Adolescent; Adult; Alcohol Drinking in College; Alcohol-Related Disorders; Female; Humans; Male; Mandatory Programs; Outcome Assessment (Health Care); Peer Group; Psychotherapy, Brief; Young Adult
- PsycINFO Classification:
- Drug & Alcohol Rehabilitation (3383)
- Population:
- Human
Male
Female - Location:
- US
- Age Group:
- Adulthood (18 yrs & older)
- Tests & Measures:
- Alcohol and Drug Use Measure
Reasons for Limited Drinking Scale
Alcohol Use Disorders Identification Test DOI: 10.1037/t01528-000
Brief Sensation-Seeking Scale-4 DOI: 10.1037/t17549-000
Alcohol and Drug Consequences Questionnaire DOI: 10.1037/t04155-000
Young Adult Alcohol Consequences Questionnaire DOI: 10.1037/t03947-000
Brief Comprehensive Effects of Alcohol Scale DOI: 10.1037/t05132-000 - Grant Sponsorship:
- Sponsor: National Institute on Alcohol Abuse and Alcoholism, US
Grant Number: R01-AA015518, R01-AA017874
Recipients: Borsari, Brian
Sponsor: National Institute on Alcohol Abuse and Alcoholism, US
Grant Number: K23 AA018126
Recipients: Magill, Molly
Sponsor: National Institute on Alcohol Abuse and Alcoholism, US
Grant Number: T32 AA07459.
Recipients: Mastroleo, Nadine R.
Sponsor: National Center for Research Resources, US
Recipients: Hustad, John T. P.
Sponsor: National Institutes of Health, National Center for Advancing Translational Sciences, US
Grant Number: UL1RR033184, KL2RR033180
Recipients: Hustad, John T. P. - Methodology:
- Clinical Trial; Empirical Study; Quantitative Study
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- First Posted: Oct 12, 2015; Accepted: Aug 19, 2015; Revised: Jun 9, 2015; First Submitted: Oct 9, 2013
- Release Date:
- 20151012
- Correction Date:
- 20170306
- Copyright:
- American Psychological Association. 2015
- Digital Object Identifier:
- http://dx.doi.org/10.1037/a0039800
- PMID:
- 26460571
- Accession Number:
- 2015-46455-001
- Number of Citations in Source:
- 79
- Persistent link to this record (Permalink):
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- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2015-46455-001&site=ehost-live">Mandated college students’ response to sequentially administered alcohol interventions in a randomized clinical trial using stepped care.</A>
- Database:
- PsycINFO
Record: 94- Title:
- Measurement error and outcome distributions: Methodological issues in regression analyses of behavioral coding data.
- Authors:
- Holsclaw, Tracy. Department of Statistics, University of California, Irvine, Irvine, CA, US
Hallgren, Kevin A.. Department of Psychiatry and Behavioral Sciences, University of Washington, Seattle, WA, US, khallgre@u.washington.edu
Steyvers, Mark. Department of Cognitive Sciences, University of California, Irvine, Irvine, CA, US
Smyth, Padhraic. Department of Computer Science, University of California, Irvine, Irvine, CA, US
Atkins, David C.. Department of Psychiatry and Behavioral Sciences, University of Washington, Seattle, WA, US - Address:
- Hallgren, Kevin A., Department of Psychiatry and Behavioral Sciences, University of Washington, Box 354944, Seattle, WA, US, 98195, khallgre@u.washington.edu
- Source:
- Psychology of Addictive Behaviors, Vol 29(4), Dec, 2015. pp. 1031-1040.
- NLM Title Abbreviation:
- Psychol Addict Behav
- Page Count:
- 10
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- Bulletin of the Society of Psychologists in Addictive Behaviors; Bulletin of the Society of Psychologists in Substance Abuse
- Other Publishers:
- US : Educational Publishing Foundation
Society of Psychologists in Addictive Behaviors - ISSN:
- 0893-164X (Print)
1939-1501 (Electronic) - Language:
- English
- Keywords:
- behavioral coding data, motivational interviewing, psychotherapy coding, statistical modeling, substance use disorder treatment
- Abstract:
- Behavioral coding is increasingly used for studying mechanisms of change in psychosocial treatments for substance use disorders (SUDs). However, behavioral coding data typically include features that can be problematic in regression analyses, including measurement error in independent variables, non normal distributions of count outcome variables, and conflation of predictor and outcome variables with third variables, such as session length. Methodological research in econometrics has shown that these issues can lead to biased parameter estimates, inaccurate standard errors, and increased Type I and Type II error rates, yet these statistical issues are not widely known within SUD treatment research, or more generally, within psychotherapy coding research. Using minimally technical language intended for a broad audience of SUD treatment researchers, the present paper illustrates the nature in which these data issues are problematic. We draw on real-world data and simulation-based examples to illustrate how these data features can bias estimation of parameters and interpretation of models. A weighted negative binomial regression is introduced as an alternative to ordinary linear regression that appropriately addresses the data characteristics common to SUD treatment behavioral coding data. We conclude by demonstrating how to use and interpret these models with data from a study of motivational interviewing. SPSS and R syntax for weighted negative binomial regression models is included in online supplemental materials. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Motivational Interviewing; *Treatment; *Substance Use Disorder; Statistical Data; Statistical Regression
- PsycINFO Classification:
- Drug & Alcohol Rehabilitation (3383)
- Population:
- Human
- Grant Sponsorship:
- Sponsor: National Institute on Alcohol Abuse and Alcoholism, US
Grant Number: R01AA018673
Recipients: Holsclaw, Tracy; Steyvers, Mark; Smyth, Padhraic; Atkins, David C.
Sponsor: National Institute on Alcohol Abuse and Alcoholism, US
Grant Number: T32AA007455
Recipients: Hallgren, Kevin A.
Sponsor: National Institute on Alcohol Abuse and Alcoholism, US
Grant Number: R01AA13696
Other Details: Theresa Moyers’s behavioral coding data was obtained with support
Recipients: No recipient indicated - Supplemental Data:
- Data Sets Internet
Text Internet - Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- First Posted: Jun 22, 2015; Accepted: Apr 1, 2015; Revised: Mar 31, 2015; Oct 6, 2014
- Release Date:
- 20150622
- Correction Date:
- 20160104
- Copyright:
- American Psychological Association. 2015
- Digital Object Identifier:
- http://dx.doi.org/10.1037/adb0000091; http://dx.doi.org/10.1037/adb0000091.supp(Supplemental)
- Accession Number:
- 2015-27691-001
- Number of Citations in Source:
- 36
- Persistent link to this record (Permalink):
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- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2015-27691-001&site=ehost-live">Measurement error and outcome distributions: Methodological issues in regression analyses of behavioral coding data.</A>
- Database:
- PsycINFO
Record: 95- Title:
- Measurement of DSM-5 section II personality disorder constructs using the MMPI-2-RF in clinical and forensic samples.
- Authors:
- Anderson, Jaime L.. Department of Psychology, University of Alabama, AL, US
Sellbom, Martin. Research School of Psychology, Australian National University, Canberra, ACT, Australia, martin.sellbom@anu.edu.au
Pymont, Carly. Research School of Psychology, Australian National University, Canberra, ACT, Australia
Smid, Wineke. Forensic Care Specialists (De Forensische Zorgspecialisten), Utrecht, Netherlands
De Saeger, Hilde. De Viersprong, Halsteren, Netherlands
Kamphuis, Jan H.. Department of Psychology, University of Amsterdam, Amsterdam, Netherlands - Address:
- Sellbom, Martin, Research School of Psychology, Australian National University, Building 39, Canberra, ACT, Australia, 0200, martin.sellbom@anu.edu.au
- Source:
- Psychological Assessment, Vol 27(3), Sep, 2015. pp. 786-800.
- NLM Title Abbreviation:
- Psychol Assess
- Page Count:
- 15
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- Psychological Assessment: A Journal of Consulting and Clinical Psychology
- ISSN:
- 1040-3590 (Print)
1939-134X (Electronic) - Language:
- English
- Keywords:
- MMPI–2–RF, DSM–5, personality disorders
- Abstract:
- In the current study, we evaluated the associations between the Minnesota Multiphasic Personality Inventory-2 Restructured Form (MMPI-2-RF; Ben-Porath & Tellegen, 2008) scale scores and the Diagnostic and Statistical Manual of Mental Disorders (5th ed.; DSM–5; American Psychiatric Association, 2013) Section II personality disorder (PD) criterion counts in inpatient and forensic psychiatric samples from The Netherlands using structured clinical interviews to operationalize PDs. The inpatient psychiatric sample included 190 male and female patients and the forensic sample included 162 male psychiatric patients. We conducted correlation and count regression analyses to evaluate the utility of relevant MMPI–2–RF scales in predicting PD criterion count scores. Generally, results from these analyses emerged as conceptually expected and provided evidence that MMPI–2–RF scales can be useful in assessing PDs. At the zero-order level, most hypothesized associations between Section II disorders and MMPI–2–RF scales were supported. Similarly, in the regression analyses, a unique set of predictors emerged for each PD that was generally in line with conceptual expectations. Additionally, the results provided general evidence that PDs can be captured by dimensional psychopathology constructs, which has implications for both DSM–5 Section III specifically and the personality psychopathology literature more broadly. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Diagnostic and Statistical Manual; *Minnesota Multiphasic Personality Inventory; *Personality Disorders; Psychometrics
- PsycINFO Classification:
- Personality Scales & Inventories (2223)
Personality Disorders (3217) - Population:
- Human
- Location:
- Netherlands
- Age Group:
- Adulthood (18 yrs & older)
Young Adulthood (18-29 yrs)
Thirties (30-39 yrs)
Middle Age (40-64 yrs) - Tests & Measures:
- Minnesota Multiphasic Personality Inventory-2 Restructured Form
Structured Interview for DSM–IV Personality
Structured Clinical Interview for DSM-IV Axis II Personality Disorders - Grant Sponsorship:
- Sponsor: University of Minnesota Press, US
Recipients: Sellbom, Martin; Kamphuis, Jan H. - Methodology:
- Empirical Study; Quantitative Study
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- First Posted: Mar 23, 2015; Accepted: Jan 21, 2015; Revised: Jan 16, 2015; First Submitted: Jul 28, 2014
- Release Date:
- 20150323
- Correction Date:
- 20150824
- Copyright:
- American Psychological Association. 2015
- Digital Object Identifier:
- http://dx.doi.org/10.1037/pas0000103
- PMID:
- 25799092
- Accession Number:
- 2015-12652-001
- Number of Citations in Source:
- 61
- Persistent link to this record (Permalink):
- http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2015-12652-001&site=ehost-live
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- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2015-12652-001&site=ehost-live">Measurement of DSM-5 section II personality disorder constructs using the MMPI-2-RF in clinical and forensic samples.</A>
- Database:
- PsycINFO
Record: 96- Title:
- Mental disorders among undocumented Mexican immigrants in high-risk neighborhoods: Prevalence, comorbidity, and vulnerabilities.
- Authors:
- Garcini, Luz M.. Department of Psychology, Rice University, Houston, TX, US, lmg7@rice.edu
Peña, Juan M.. Department of Psychology, University of New Mexico, Albuquerque, NM, US
Galvan, Thania. Department of Psychology, University of Denver, Denver, CO, US
Fagundes, Christopher P.. Department of Psychology, Rice University, Houston, TX, US
Malcarne, Vanessa. Joint Doctoral Program in Clinical Psychology, San Diego State University, San Diego, CA, US
Klonoff, Elizabeth A.. Office of Graduate Studies, University of Central Florida, Orlando, FL, US - Address:
- Garcini, Luz M., Department of Psychology, Rice University, 6100 Main Street, Houston, TX, US, 77005, lmg7@rice.edu
- Source:
- Journal of Consulting and Clinical Psychology, Vol 85(10), Oct, 2017. pp. 927-936.
- NLM Title Abbreviation:
- J Consult Clin Psychol
- Page Count:
- 10
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- Journal of Consulting Psychology
- Other Publishers:
- US : American Association for Applied Psychology
US : Dentan Printing Company
US : Science Press Printing Company - ISSN:
- 0022-006X (Print)
1939-2117 (Electronic) - Language:
- English
- Keywords:
- undocumented, Latinx, Mexican, mental illness, mental disorders
- Abstract (English):
- Objective: This study aimed to: (a) provide population-based estimates for the prevalence of mental disorders, including substance use, among undocumented Mexican immigrants; (b) assess for relevant comorbidities; and (c) identify sociodemographic, immigration and contextual vulnerabilities associated with meeting criteria for a disorder. Method: This cross-sectional study used Respondent Driven Sampling (RDS) to collect and analyze data from clinical interviews with 248 undocumented Mexican immigrants residing near the California–Mexico border. The M.I.N.I. Mini International Neuropsychiatric Interview was used as the primary outcome of interest. For all analyses, inferential statistics accounted for design effects and sample weights to produce weighted estimates. Logistic regression was used in multivariate analyses. Results: Overall, 23% of participants met criteria for a disorder (95% CI = 17.1; 29.0). The most prevalent disorders were Major Depressive Disorder (14%, 95% CI = 10.2; 18.6), Panic Disorder (8%, 95% CI = 5.0; 11.9) and Generalized Anxiety Disorder (7%, 95% CI = 3.4; 9.8). Approximately 4% of participants met criteria for a substance use disorder (95% CI = 1.2; 6.1). After controlling for covariates, being 18 to 25 years and experiencing distress from postmigration living difficulties were significantly associated with meeting criteria for a disorder. Conclusion: Undocumented Mexican immigrants are an at-risk population for mental disorders, particularly depression and anxiety disorders. Given that distress from postmigration living difficulties is associated with meeting criteria for a disorder, revisiting policies and developing new alternatives to facilitate access and provision of context-sensitive mental health services for this population is necessary to protect the human rights of these immigrants and that of their U.S. families. (PsycINFO Database Record (c) 2017 APA, all rights reserved)
- Impact Statement:
- What is the public health significance of this article?—To our knowledge, this is the first study to provide population-based estimates for the prevalence of current mental and substance use disorders among undocumented Mexican immigrants residing in high-risk neighborhoods. This information is essential to inform advocacy efforts, break down existing stereotypes, and inform the development and provision of contextually and culturally sensitive mental health interventions for this at-risk immigrant population. (PsycINFO Database Record (c) 2017 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Comorbidity; *Immigration; *Mental Disorders; *Mexican Americans; Demographic Characteristics; Drug Abuse; Epidemiology; Generalized Anxiety Disorder; Major Depression; Panic Disorder; Posttraumatic Stress Disorder; Susceptibility (Disorders)
- PsycINFO Classification:
- Psychological Disorders (3210)
- Population:
- Human
Male
Female - Location:
- US
- Age Group:
- Adulthood (18 yrs & older)
Young Adulthood (18-29 yrs)
Thirties (30-39 yrs)
Middle Age (40-64 yrs) - Tests & Measures:
- San Diego Labor Trafficking Survey Questionnaire
Harvard Trauma Questionnaire-Adapted--Spanish Version
Structured Clinical Interview for DSM
Composite International Diagnostic Interview DOI: 10.1037/t02121-000
Mini International Neuropsychiatric Interview DOI: 10.1037/t18597-000 - Grant Sponsorship:
- Sponsor: Ford Fellowship
Recipients: Garcini, Luz M.
Sponsor: UC
Other Details: MEXUS Award
Recipients: Garcini, Luz M.
Sponsor: Sponsor name not included
Grant Number: 5R25GM058906-16
Other Details: Minority Biomedical Research Support Initiative for Maximizing Student Development
Recipients: Peña, Juan M.
Sponsor: Institute for Behavioral and Community Health, Training and Mentoring Program
Grant Number: 5R25MD006853-05
Recipients: Peña, Juan M. - Methodology:
- Empirical Study; Quantitative Study
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- Accepted: Jun 22, 2017; Revised: Jun 19, 2017; First Submitted: Mar 14, 2017
- Release Date:
- 20170928
- Copyright:
- American Psychological Association. 2017
- Digital Object Identifier:
- http://dx.doi.org/10.1037/ccp0000237
- Accession Number:
- 2017-42717-001
- Persistent link to this record (Permalink):
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- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2017-42717-001&site=ehost-live">Mental disorders among undocumented Mexican immigrants in high-risk neighborhoods: Prevalence, comorbidity, and vulnerabilities.</A>
- Database:
- PsycINFO
Record: 97- Title:
- Moderators of informant agreement in the assessment of adolescent psychopathology: Extension to a forensic sample.
- Authors:
- Penney, Stephanie R.. Law and Mental Health Program, Centre for Addiction and Mental Health, Toronto, ON, Canada, stephanie_penney@camh.net
Skilling, Tracey A.. Child, Youth, and Family Program, Centre for Addiction and Mental Health, Toronto, ON, Canada - Address:
- Penney, Stephanie R., Law and Mental Health Program, Centre for Addiction and Mental Health, 1001 Queen Street West, Toronto, ON, Canada, M6J 1H4, stephanie_penney@camh.net
- Source:
- Psychological Assessment, Vol 24(2), Jun, 2012. pp. 386-401.
- NLM Title Abbreviation:
- Psychol Assess
- Page Count:
- 16
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- Psychological Assessment: A Journal of Consulting and Clinical Psychology
- ISSN:
- 1040-3590 (Print)
1939-134X (Electronic) - Language:
- English
- Keywords:
- adolescent psychopathology, antisocial behavior, forensic assessment, informant discrepancy, sex differences
- Abstract:
- A well-documented finding in developmental psychopathology research is that different informants often provide discrepant ratings of a youth's internalizing and externalizing problems. The current study examines youth- and parent-based moderators (i.e., youth age, gender, and IQ; type of psychopathology; offense category; psychopathic traits; parental education, income, and stress) of informant discrepancies in a sample of young offenders and compares the utility of youth and caregiver reports against relevant clinical outcomes. Results indicate that gender moderated the discrepancy between informant reports of somatic complaints, while parenting stress moderated the discrepancies across reports of internalizing and externalizing psychopathology. Variables unique to the forensic context (e.g., offense category) were found to moderate cross-informant discrepancies in reports of internalizing and externalizing psychopathology. Further, youth self-reports of internalizing symptoms predicted a clinician-generated diagnosis of a mood disorder, while caregiver reports of aggressive behaviors predicted the presence of an externalizing diagnosis. Results highlight the importance of assessing informant agreement in the context of forensic assessment and raise questions surrounding the optimal use of informant data in this setting. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Adolescent Psychopathology; *Informants; *Interrater Reliability; *Juvenile Delinquency; *Mentally Ill Offenders
- Medical Subject Headings (MeSH):
- Adolescent; Adolescent Psychiatry; Adult; Child; Child, Preschool; Female; Forensic Psychiatry; Humans; Interview, Psychological; Juvenile Delinquency; Logistic Models; Male; Mental Disorders; Observer Variation; Parenting; Parents; Proportional Hazards Models; Psychiatric Status Rating Scales; Psychological Tests; Reproducibility of Results; Self Report; Sex Factors; Young Adult
- PsycINFO Classification:
- Clinical Psychological Testing (2224)
Criminal Law & Adjudication (4230) - Population:
- Human
Male
Female - Location:
- Canada
- Age Group:
- Childhood (birth-12 yrs)
School Age (6-12 yrs)
Adolescence (13-17 yrs)
Adulthood (18 yrs & older)
Young Adulthood (18-29 yrs) - Tests & Measures:
- Wechsler Adult Intelligence Scale-III
Wechsler Adult Intelligence Scale-IV
Wechsler Intelligence Scale for Children-IV
Psychopathy Checklist: Youth Version
Child Behavior Checklist
Stress Index for Parents of Adolescents
Youth Self-Report
Paulhus Deception Scales DOI: 10.1037/t05053-000 - Methodology:
- Empirical Study; Quantitative Study
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- First Posted: Oct 3, 2011; Accepted: Aug 24, 2011; Revised: Aug 22, 2011; First Submitted: Dec 29, 2010
- Release Date:
- 20111003
- Correction Date:
- 20120604
- Copyright:
- American Psychological Association. 2011
- Digital Object Identifier:
- http://dx.doi.org/10.1037/a0025693
- PMID:
- 21966931
- Accession Number:
- 2011-22370-001
- Number of Citations in Source:
- 78
- Persistent link to this record (Permalink):
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- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2011-22370-001&site=ehost-live">Moderators of informant agreement in the assessment of adolescent psychopathology: Extension to a forensic sample.</A>
- Database:
- PsycINFO
Record: 98- Title:
- Movement abnormalities predict conversion to Axis I psychosis among prodromal adolescents.
- Authors:
- Mittal, Vijay A.. Department of Psychology, Emory University, Atlanta, GA, US, vmittal@emory.edu
Walker, Elaine F.. Department of Psychology, Emory University, Atlanta, GA, US - Address:
- Mittal, Vijay A., Department of Psychology, Emory University, Psychological Center, 235 Dental Building, 1462 Clifton Road, Atlanta, GA, US, 30322, vmittal@emory.edu
- Source:
- Journal of Abnormal Psychology, Vol 116(4), Nov, 2007. pp. 796-803.
- NLM Title Abbreviation:
- J Abnorm Psychol
- Page Count:
- 8
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- The Journal of Abnormal and Social Psychology; The Journal of Abnormal Psychology; The Journal of Abnormal Psychology and Social Psychology
- ISSN:
- 0021-843X (Print)
1939-1846 (Electronic) - Language:
- English
- Keywords:
- schizophrenia, prodromal adolescents, movement abnormality, conversion, psychosis
- Abstract:
- Evidence suggests that movement abnormalities are a precursor of psychosis. The link between movement abnormalities and psychotic disorders is presumed to reflect common neural mechanisms that influence both motor functions and vulnerability to psychosis. The authors coded movement abnormalities from videotapes of 40 adolescents at risk for psychosis (designated prodromal on the Structured Interview for Prodromal Symptoms; T. J. Miller et al., 2002). Following initial assessment, participants were evaluated for diagnostic status at 4 times annually. Ten participants converted to an Axis I psychosis (e.g., schizophrenia) over the 4-year period. Comparisons of converted and nonconverted participants at baseline indicated that the groups did not differ on demographic characteristics or levels of prodromal symptomatology, but those who converted exhibited significantly more movement abnormalities. Movement abnormalities and prodromal symptoms were strongly associated and logistic regression analyses indicated that abnormalities in the face and upper body regions were most predictive of conversion. Findings suggest that individuals with elevated movement abnormalities may represent a subgroup of prodromal adolescents who are at the highest risk for conversion. The implications for neural mechanisms and for identifying candidates for preventive intervention are discussed. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Conversion Disorder; *Movement Disorders; *Prodrome; *Psychosis; Schizophrenia
- Medical Subject Headings (MeSH):
- Adolescent; Child; Diagnostic and Statistical Manual of Mental Disorders; Female; Humans; Male; Movement Disorders; Predictive Value of Tests; Prospective Studies; Psychotic Disorders
- PsycINFO Classification:
- Schizophrenia & Psychotic States (3213)
- Population:
- Human
Male
Female - Location:
- US
- Age Group:
- Childhood (birth-12 yrs)
School Age (6-12 yrs)
Adolescence (13-17 yrs)
Adulthood (18 yrs & older)
Young Adulthood (18-29 yrs) - Tests & Measures:
- Dyskinesia Identification System: Condensed User Scale
Structured Clinical Interview for DSM-IV Axis I Disorders - Grant Sponsorship:
- Sponsor: National Institute of Mental Health
Grant Number: RO1-MH4062066
Recipients: Walker, Elaine F. - Methodology:
- Empirical Study; Quantitative Study
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- Accepted: Mar 12, 2007; Revised: Mar 12, 2007; First Submitted: Dec 11, 2006
- Release Date:
- 20071119
- Correction Date:
- 20120312
- Copyright:
- American Psychological Association. 2007
- Digital Object Identifier:
- http://dx.doi.org/10.1037/0021-843X.116.4.796
- PMID:
- 18020725
- Accession Number:
- 2007-17062-012
- Number of Citations in Source:
- 41
- Persistent link to this record (Permalink):
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- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2007-17062-012&site=ehost-live">Movement abnormalities predict conversion to Axis I psychosis among prodromal adolescents.</A>
- Database:
- PsycINFO
Record: 99- Title:
- Multisystemic Therapy for high-risk African American adolescents with asthma: A randomized clinical trial.
- Authors:
- Naar-King, Sylvie. Department of Pediatrics, Wayne State University, Detroit, MI, US, snaarkin@med.wayne.edu
Ellis, Deborah. Department of Pediatrics, Wayne State University, Detroit, MI, US
King, Pamela S.. Department of Pediatrics, Wayne State University, Detroit, MI, US
Lam, Phebe. Department of Pediatrics, Wayne State University, Detroit, MI, US
Cunningham, Phillippe. Department of Psychiatry, Medical University of South Carolina, SC, US
Secord, Elizabeth. Department of Pediatrics, Wayne State University, Detroit, MI, US
Bruzzese, Jean-Marie, ORCID 0000-0002-1866-488X. Department of Child and Adolescent Psychiatry, New York University, NY, US
Templin, Thomas. Department of Pediatrics, Wayne State University, Detroit, MI, US - Address:
- Naar-King, Sylvie, Department of Pediatrics, Wayne State University School of Medicine, Detroit, MI, US, 48201, snaarkin@med.wayne.edu
- Source:
- Journal of Consulting and Clinical Psychology, Vol 82(3), Jun, 2014. pp. 536-545.
- NLM Title Abbreviation:
- J Consult Clin Psychol
- Page Count:
- 10
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- Journal of Consulting Psychology
- Other Publishers:
- US : American Association for Applied Psychology
US : Dentan Printing Company
US : Science Press Printing Company - ISSN:
- 0022-006X (Print)
1939-2117 (Electronic) - Language:
- English
- Keywords:
- adolescents, asthma, health disparities, multisystemic therapy, health outcomes, high risk African Americans
- Abstract:
- Objective: The primary purpose of the study was to determine whether Multisystemic Therapy adapted for health care settings (MST-HC) improved asthma management and health outcomes in high-risk African American adolescents with asthma. Method: Eligibility included self-reported African American ethnicity, ages 12 to 16, moderate to severe asthma, and an inpatient hospitalization or at least 2 emergency department visits for asthma in the last 12 months. Adolescents and their families (N = 170) were randomized to MST-HC or in-home family support. Data were collected at baseline and posttreatment (7 months) based on an asthma management interview, medication adherence phone diary, and lung function biomarker (forced expiratory volume in 1 s [FEV1]). Analyses were conducted using linear mixed modeling for continuous outcomes and generalized linear mixed modeling for binary outcomes. Results: In intent-to-treat analyses, adolescents randomized to MST-HC were more likely to improve on 2 of the measures of medication adherence and FEV1. Per-protocol analysis demonstrated that MST-HC had a medium effect on adherence measures and had a small to medium effect on lung function and the adolescent’s response to asthma exacerbations. Conclusion: There are few interventions that have been shown to successfully improve asthma management in minority youth at highest risk for poor morbidity and mortality. MST, a home-based psychotherapy originally developed to target behavior problems in youth, improved asthma management and lung function compared to a strong comparison condition. Further follow-up is necessary to determine whether MST-HC reduces health care utilization accounting for seasonal variability. A limitation to the study is that a greater number of participants in the control group came from single-parent families than in the MST group. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Asthma; *At Risk Populations; *Blacks; *Treatment Outcomes; *Multisystemic Therapy; Health Disparities
- Medical Subject Headings (MeSH):
- Adolescent; African Americans; Asthma; Ethnic Groups; Female; Hospitalization; Humans; Male; Medication Adherence; Psychotherapy; Young Adult
- PsycINFO Classification:
- Health & Mental Health Treatment & Prevention (3300)
- Population:
- Human
Male
Female - Age Group:
- Childhood (birth-12 yrs)
School Age (6-12 yrs)
Adolescence (13-17 yrs) - Tests & Measures:
- Daily Phone Diary DOI: 10.1037/t05273-000
Family Asthma Management System Scale DOI: 10.1037/t05269-000 - Grant Sponsorship:
- Sponsor: National Institutes of Health
Grant Number: 1R01AA022891-01
Recipients: No recipient indicated - Methodology:
- Empirical Study; Quantitative Study; Treatment Outcome
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- First Posted: Mar 3, 2014; Accepted: Dec 16, 2013; Revised: Dec 11, 2013; First Submitted: Jul 23, 2013
- Release Date:
- 20140303
- Correction Date:
- 20141124
- Copyright:
- American Psychological Association. 2014
- Digital Object Identifier:
- http://dx.doi.org/10.1037/a0036092
- PMID:
- 24588407
- Accession Number:
- 2014-07547-001
- Number of Citations in Source:
- 56
- Persistent link to this record (Permalink):
- http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2014-07547-001&site=ehost-live
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- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2014-07547-001&site=ehost-live">Multisystemic Therapy for high-risk African American adolescents with asthma: A randomized clinical trial.</A>
- Database:
- PsycINFO
Record: 100- Title:
- New approaches for examining associations with latent categorical variables: Applications to substance abuse and aggression.
- Authors:
- Feingold, Alan. Oregon Social Learning Center, Eugene, OR, US, alanf@oslc.org
Tiberio, Stacey S.. Oregon Social Learning Center, Eugene, OR, US
Capaldi, Deborah M.. Oregon Social Learning Center, Eugene, OR, US - Address:
- Feingold, Alan, Oregon Social Learning Center, 10 Shelton McMurphey Boulevard, Eugene, OR, US, 97401-4928, alanf@oslc.org
- Source:
- Psychology of Addictive Behaviors, Vol 28(1), Mar, 2014. pp. 257-267.
- NLM Title Abbreviation:
- Psychol Addict Behav
- Page Count:
- 11
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- Bulletin of the Society of Psychologists in Addictive Behaviors; Bulletin of the Society of Psychologists in Substance Abuse
- Other Publishers:
- US : Educational Publishing Foundation
Society of Psychologists in Addictive Behaviors - ISSN:
- 0893-164X (Print)
1939-1501 (Electronic) - Language:
- English
- Keywords:
- categorical analysis, intimate partner violence, latent class analysis, structural equation modeling, substance abuse
- Abstract:
- Assessments of substance use behaviors often include categorical variables that are frequently related to other measures using logistic regression or chi-square analysis. When the categorical variable is latent (e.g., extracted from a latent class analysis [LCA]), classification of observations is often used to create an observed nominal variable from the latent one for use in a subsequent analysis. However, recent simulation studies have found that this classical 3-step analysis championed by the pioneers of LCA produces underestimates of the associations of latent classes with other variables. Two preferable but underused alternatives for examining such linkages—each of which is most appropriate under certain conditions—are (a) 3-step analysis, which corrects the underestimation bias of the classical approach, and (b) 1-step analysis. The purpose of this article is to dissuade researchers from conducting classical 3-step analysis and to promote the use of the 2 newer approaches that are described and compared. In addition, the applications of these newer models—for use when the independent, the dependent, or both categorical variables are latent—are illustrated through substantive analyses relating classes of substance abusers to classes of intimate partner aggressors. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Analysis; *Drug Abuse; *Intimate Partner Violence; *Structural Equation Modeling
- Medical Subject Headings (MeSH):
- Adult; Aggression; Data Interpretation, Statistical; Humans; Male; Spouse Abuse; Substance-Related Disorders
- PsycINFO Classification:
- Statistics & Mathematics (2240)
Psychological Disorders (3210) - Population:
- Human
Male - Location:
- US
- Age Group:
- Adulthood (18 yrs & older)
Young Adulthood (18-29 yrs) - Tests & Measures:
- Composite International Diagnostic Interview-Version 2.0
Conflict Tactics Scales DOI: 10.1037/t02125-000 - Grant Sponsorship:
- Sponsor: National Institutes of Health
Grant Number: RC1DA028344
Recipients: No recipient indicated
Sponsor: National Institute of Drug Abuse
Grant Number: R01AA018669
Recipients: No recipient indicated
Sponsor: National Institute of Alcoholism and Alcohol Abuse
Recipients: No recipient indicated
Sponsor: National Institute of Child Health and Human Development
Grant Number: R01HD46364
Recipients: No recipient indicated - Methodology:
- Empirical Study; Quantitative Study
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- First Posted: Jun 17, 2013; Accepted: Dec 4, 2012; Revised: Dec 3, 2012; First Submitted: Jul 30, 2012
- Release Date:
- 20130617
- Correction Date:
- 20140414
- Copyright:
- American Psychological Association. 2013
- Digital Object Identifier:
- http://dx.doi.org/10.1037/a0031487
- PMID:
- 23772759
- Accession Number:
- 2013-20765-001
- Number of Citations in Source:
- 50
- Persistent link to this record (Permalink):
- http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2013-20765-001&site=ehost-live
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- Database:
- PsycINFO
Record: 101- Title:
- Noncredible cognitive performance at clinical evaluation of adult ADHD: An embedded validity indicator in a visuospatial working memory test.
- Authors:
- Fuermaier, Anselm B. M.. Department of Clinical and Developmental Neuropsychology, University of Groningen, Groningen, Netherlands, a.b.m.fuermaier@rug.nl
Tucha, Oliver. Department of Clinical and Developmental Neuropsychology, University of Groningen, Groningen, Netherlands
Koerts, Janneke. Department of Clinical and Developmental Neuropsychology, University of Groningen, Groningen, Netherlands
Lange, Klaus W.. Department of Experimental Psychology, University of Regensburg, Regensburg, Germany
Weisbrod, Matthias. Department of Psychiatry and Psychotherapy, SRH Clinic Karlsbad-Langensteinbach, Karlsbad, Germany
Aschenbrenner, Steffen. Department of Clinical Psychology and Neuropsychology, SRH Clinic Karlsbad-Langensteinbach, Karlsbad, Germany
Tucha, Lara. Department of Clinical and Developmental Neuropsychology, University of Groningen, Groningen, Netherlands - Address:
- Fuermaier, Anselm B. M., Department of Clinical and Developmental Neuropsychology, University of Groningen, Groningen, Grote Kruisstraat 2/1, 9712 TS, Groningen, Netherlands, a.b.m.fuermaier@rug.nl
- Source:
- Psychological Assessment, Vol 29(12), Dec, 2017. Assessment of Noncredible Presentation in Attention Deficit/Hyperactivity Disorder (ADHD). pp. 1466-1479.
- NLM Title Abbreviation:
- Psychol Assess
- Page Count:
- 14
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- Psychological Assessment: A Journal of Consulting and Clinical Psychology
- ISSN:
- 1040-3590 (Print)
1939-134X (Electronic) - Language:
- English
- Keywords:
- noncredible, feigning, faking, adult ADHD, performance validity
- Abstract (English):
- The assessment of performance validity is an essential part of the neuropsychological evaluation of adults with attention-deficit/hyperactivity disorder (ADHD). Most available tools, however, are inaccurate regarding the identification of noncredible performance. This study describes the development of a visuospatial working memory test, including a validity indicator for noncredible cognitive performance of adults with ADHD. Visuospatial working memory of adults with ADHD (n = 48) was first compared to the test performance of healthy individuals (n = 48). Furthermore, a simulation design was performed including 252 individuals who were randomly assigned to either a control group (n = 48) or to 1 of 3 simulation groups who were requested to feign ADHD (n = 204). Additional samples of 27 adults with ADHD and 69 instructed simulators were included to cross-validate findings from the first samples. Adults with ADHD showed impaired visuospatial working memory performance of medium size as compared to healthy individuals. Simulation groups committed significantly more errors and had shorter response times as compared to patients with ADHD. Moreover, binary logistic regression analysis was carried out to derive a validity index that optimally differentiates between true and feigned ADHD. ROC analysis demonstrated high classification rates of the validity index, as shown in excellent specificity (95.8%) and adequate sensitivity (60.3%). The visuospatial working memory test as presented in this study therefore appears sensitive in indicating cognitive impairment of adults with ADHD. Furthermore, the embedded validity index revealed promising results concerning the detection of noncredible cognitive performance of adults with ADHD. (PsycINFO Database Record (c) 2017 APA, all rights reserved)
- Impact Statement:
- Public Significance Statement—The clinical evaluation of adults with attention-deficit/hyperactivity disorder (ADHD) is complicated by the fact that a sizable number of individuals deliberately feign symptoms and impairments. This study contributes to solving this issue by the development of a test that is helpful in the assessment of both, function and effort of individuals. (PsycINFO Database Record (c) 2017 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Attention Deficit Disorder with Hyperactivity; *Cognitive Ability; *Evaluation; *Visuospatial Memory; Faking; Test Validity; Test Performance
- PsycINFO Classification:
- Tests & Testing (2220)
Developmental Disorders & Autism (3250) - Population:
- Human
Male
Female - Location:
- Germany
- Age Group:
- Adulthood (18 yrs & older)
- Tests & Measures:
- Visuospatial Working Memory Test
Dot Counting Test
VIGIL Test of the Vienna Test System VTS
Wender Utah Rating Scale--Short Version
ADHD Self-Report Scale
Test of Memory Malingering DOI: 10.1037/t05074-000
Wender Utah Rating Scale DOI: 10.1037/t16514-000
Multiple Choice Vocabulary Test DOI: 10.1037/t11749-000 - Methodology:
- Empirical Study; Quantitative Study
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- Accepted: Aug 15, 2017; Revised: Aug 4, 2017; First Submitted: Feb 6, 2017
- Release Date:
- 20171211
- Copyright:
- American Psychological Association. 2017
- Digital Object Identifier:
- http://dx.doi.org/10.1037/pas0000534
- Accession Number:
- 2017-54244-006
- Persistent link to this record (Permalink):
- http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2017-54244-006&site=ehost-live
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- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2017-54244-006&site=ehost-live">Noncredible cognitive performance at clinical evaluation of adult ADHD: An embedded validity indicator in a visuospatial working memory test.</A>
- Database:
- PsycINFO
Noncredible Cognitive Performance at Clinical Evaluation of Adult ADHD: An Embedded Validity Indicator in a Visuospatial Working Memory Test
By: Anselm B. M. Fuermaier
Department of Clinical and Developmental Neuropsychology, University of Groningen;
Oliver Tucha
Department of Clinical and Developmental Neuropsychology, University of Groningen
Janneke Koerts
Department of Clinical and Developmental Neuropsychology, University of Groningen
Klaus W. Lange
Department of Experimental Psychology, University of Regensburg
Matthias Weisbrod
Department of Psychiatry and Psychotherapy, SRH Clinic Karlsbad-Langensteinbach, Karlsbad, Germany, and Department of General Psychiatry, Center of Psychosocial Medicine, University of Heidelberg
Steffen Aschenbrenner
Department of Clinical Psychology and Neuropsychology, SRH Clinic Karlsbad-Langensteinbach
Lara Tucha
Department of Clinical and Developmental Neuropsychology, University of Groningen
Acknowledgement: Matthias Weisbrod and Steffen Aschenbrenner have contracts for the development of neuropsychological diagnostic and training tools with Schuhfried GmbH.
In the last decade, a growing body of literature has emphasized that the diagnostic assessment of attention-deficit/hyperactivity disorder (ADHD) in adulthood is complicated by a considerable number of individuals showing noncredible effort and symptom reporting at clinical evaluation (Fuermaier, Tucha, Koerts, Weisbrod, et al., 2016; Harrison & Edwards, 2010; Harrison, Edwards, & Parker, 2007; L. Tucha, Sontag, Walitza, & Lange, 2009; Pella, Hill, Shelton, Elliott, & Gouvier, 2012; Suhr, Hammers, Dobbins-Buckland, Zimak, & Hughes, 2008; Sullivan, May, & Galbally, 2007). A proportion of these individuals may exaggerate or deliberately feign symptoms and impairments to benefit from being diagnosed with ADHD. Possible benefits of being diagnosed with this disorder are having access to stimulant medication, either as a cognitive enhancer or for recreational purposes, or to receive special accommodations in the academic context, such as being awarded extra time for assignments and exams (Lensing, Zeiner, Sandvik, & Opjordsmoen, 2013; Rabiner, 2013). Thus, there is broad agreement that the assessment of symptom and performance validity should become an essential part of the clinical evaluation of adults with ADHD (Musso & Gouvier, 2014; L. Tucha, Fuermaier, Koerts, Groen, & Thome, 2015). In this respect, several tools have been examined for their utility to detect noncredible performance and symptom reporting of individuals evaluated for ADHD, comprising personality inventories, self-report rating scales of ADHD symptom severity, neuropsychological tests routinely used in clinical practice for the assessment of cognition, as well as tests that were specifically designed for the detection of feigned cognitive dysfunctions (including SVTs; for comprehensive reviews, see Musso & Gouvier, 2014; L. Tucha et al., 2015). Even though some of these tools showed promising classification rates in terms of sensitivity and specificity, there is no consensus yet on a standardized procedure for the assessment of noncredible performance and symptom reporting of individuals at clinical evaluation of ADHD.
With regard to neuropsychological functioning of adults with ADHD, a large number of studies described ADHD as a disorder of attention and executive control (Boonstra, Oosterlaan, Sergeant, & Buitelaar, 2005; Boonstra, Kooij, Oosterlaan, Sergeant, & Buitelaar, 2010; Fuermaier et al., 2015; Lange et al., 2014; Mostert et al., 2015; Schoechlin & Engel, 2005; L. Tucha et al., 2008; O. Tucha et al., 2006). In this context, findings indicating working memory deficits are very robust among both children (Willcutt, Doyle, Nigg, Faraone, & Pennington, 2005) and adults with ADHD (Alderson, Kasper, Hudec, & Patros, 2013), so that working memory deficits consequently have been proposed as a core feature of, and potential endophenotype for, ADHD (Alderson et al., 2013; Castellanos & Tannock, 2002; Rapport, VanVoorhis, Tzelepis, & Friedman, 2001; van Ewijk et al., 2014). Given the common use of neuropsychological test batteries, including working memory tests, in the assessment of adults with ADHD, several studies examined the utility of standard measures of cognition for detecting noncredible cognitive performance at clinical evaluation (Musso & Gouvier, 2014; L. Tucha et al., 2015). Among these studies, three studies also included working memory paradigms (Booksh, Pella, Singh, & Gouvier, 2010; Harrison, Rosenblum, & Currie, 2010; Suhr et al., 2008), such as subtests of the Wechsler Adult Intelligence Scale (e.g., the Digit Span as a measure of verbal working memory; Wechsler, 1997). Although Suhr and colleagues (2008) found significant differences between a noncredible and a credible group of patients with ADHD, Harrison et al. (2010) as well as Booksh et al. (2010) failed to adequately differentiate these groups from one other on the basis of these measures. Harrison et al. (2010) showed that Digit Span performance was helpful in identifying credible cognitive performance of individuals with ADHD with high specificity, however, sensitivity toward noncredible performance was low. Conclusive findings on the usefulness of working memory paradigms for the detection of noncredible performance of adults with ADHD could therefore not be drawn from these studies.
Although previous studies using working memory tests for the detection of noncredible cognitive performance of adults with ADHD included paradigms of verbal working memory, the present study aimed to explore the potential value of a test measuring visuospatial working memory (VSWM). There are reasons to assume that a test for VSWM may represent a promising candidate for the simultaneous assessment of both function and noncredible cognitive performance at clinical evaluation of ADHD. First, VSWM deficits were suggested as one of the most consistently impaired executive functions in patients with ADHD (Alderson et al., 2013; Martinussen, Hayden, Hogg-Johnson, & Tannock, 2005; van Ewijk et al., 2014). Second, we assume that such a test might also be helpful for the assessment of credibility of clients, as most cognitive tasks in daily life somehow involve the processing of verbal information. Thus, people appear to be very experienced with regard to the level of verbal functions, which may facilitate them in feigning cognitive dysfunctions on tests using verbal material (such as verbal working memory). However, as people have less experience in dealing with nonverbal (e.g., spatial) information in daily life, it can be assumed that individuals feigning ADHD may have more difficulties in mimicking the level of performance that one would expect from genuine ADHD on tests using visuospatial information.
For the purpose of the present study, a VSWM test (VSWMT) was developed that followed the principles of the task, as devised by Brooks (1968), in requiring participants to develop, inspect, and navigate through mental images (e.g., a geometric figure shaping the letter “F”). Healthy individuals were allocated to either a control condition which requires participants to perform the VSWMT to the best of their abilities, or to one of three simulation conditions asking participants to perform the VSWMT as if they suffered from ADHD. VSWMT performance of the simulation groups were compared to VSWMT performance of a group of adults with ADHD as well as a healthy control group to explore the utility of the VSWMT in detecting noncredible cognitive performance.
When compared to a healthy comparison group, patients with ADHD were expected to show impairments in VSWM. Further, participants of the simulation conditions were expected to show overly poor performance on the VSWMT as compared to both healthy comparison participants and patients with ADHD. A valid indicator of noncredible cognitive performance of adults with ADHD should achieve specificity of at least 90% (Boone, 2007; Marshall et al., 2010). Additional analyses were conducted to identify cut-off scores for the VSWMT that would result in the optimal combination of sensitivity and specificity rates (Fuermaier, Tucha, Koerts, Grabski, et al., 2016).
Method Design and Procedure
The present study employed a simulation design in which the test performance of adults with ADHD was compared to test performances of several groups of healthy individuals. Although some of the healthy individuals (i.e., the healthy comparison group [HCG] and the control group [CG]) were instructed to show normal behavior, others (i.e., the naïve simulation group [NSG], symptom-coached simulation group [SSG], test-coached simulation group [TSG], and symptom- and test-coached simulation group [STSG]) were instructed to assume the role of someone who aims to feign ADHD. Table 1 presents an overview of instructions given per group, including the type of information participants received, characteristics of groups are presented in Table 2. The utility of the newly developed VSWMT for the detection of feigned ADHD will be determined by comparing the performances of patients with ADHD with the performances of a collapsed group of all individuals instructed to feign ADHD. An effort index (VSWMT index) will be calculated via a weighted sum of all variables of the VSWMT. The weights will be determined via regression coefficients of a logistic regression model for the prediction of feigned ADHD relative to true ADHD. In addition, the utility of the effort index will be cross-validated on two additional and independent samples, that is, an additional sample of patients with ADHD as well as another group of instructed simulators.
Type of Information and Instruction Given per Group
Characteristics of Participants
Assessment of patients with ADHD
All patients with ADHD were tested individually and received no reward for their participation. Written informed consent was sought from all participants prior to the assessment. The first sample of patients with ADHD (ADHD1, n = 48) was assessed with a comprehensive battery consisting of self-report questionnaires, interviews, the VSWMT, and routine measures of cognition (i.e., tests for vigilance and verbal working memory). The second and independent sample of patients with ADHD (ADHD2, n = 27) was assessed by requesting them to perform the VSWMT in addition to three established effort tests, in this case the TOMM, the DCT, and the b Test. Patients were only included in the present study if they ‘passed’ all three effort tests to have sufficient evidence that all patients of this sample showed credible effort at neuropsychological assessment. This additional sample of patients with ADHD was recruited to examine whether VSWMT performance, as obtained from the patients of ADHD1, can be replicated on an independent sample of patients. The total duration of the assessment of patients with ADHD was about two hours. The study was conducted in compliance with ethical standards of the Helsinki Declaration and was approved by the local institutional ethical committee (Medical Faculty of the University of Heidelberg, Germany).
Assessment of healthy participants
All healthy participants were tested individually in a quiet laboratory. At the beginning of the experiment, descriptive information was obtained including age, sex, vocabulary skills (intellectual functions), and self-reported ADHD symptom severity. Further, participants were asked for any history of psychiatric or neurological diseases as well as pharmacological treatment. The assessment of healthy individuals was approved by the Ethical Committee Psychology affiliated with the University of Groningen, the Netherlands. All participants gave written informed consent prior to participation and were debriefed at the end of the assessment.
Healthy comparison group (HCG) and control group (CG)
Participants of the HCG and CG were asked to perform all tests applied to the best of their abilities. Participants received a notification by email on the day before the assessment in which the clinical significance of the study was outlined, but which did not contain information on the aim of the study. The duration of the assessment of these groups was about 50 min.
Simulation groups
Participants of the simulation groups (NSG, SSG, TSG, and STSG) were asked to perform the VSWMT while pretending to be affected with ADHD (feigning ADHD). For this purpose, participants were presented with a vignette, describing reasons why someone would be motivated to feign ADHD. Several benefits that may be associated with a diagnosis of ADHD were introduced in the vignette; for example, financial accommodations, more flexibility and freedom concerning working hours and deadlines, and the prescription of stimulant medication. Participants were further explicitly instructed to make their feigning of ADHD seem realistic, that is, by avoiding a very obvious exaggeration. To encourage participants to feign ADHD in a believable and realistic manner, participants were informed that the participant who feigns the condition best would be awarded with a top of the range tablet PC. However, due to ethical reasons, the tablet PC was in fact assigned randomly to one of the participants across all conditions, independently of the participants’ test performance. The NSG received no further information and no suggestions of how to fake ADHD. The SSG received a description of the diagnostic criteria for ADHD as outlined in the Diagnostic and Statistical Manual of Mental Disorders (4th ed.; DSM–IV; American Psychiatric Association, 1994). Participants of the TSG were informed about how a diagnostic clinical examination of ADHD is commonly performed. Participants of the TSG were also informed about typical behaviors of patients with ADHD when they are being tested. Participants of the STSG received both a description of the diagnostic criteria for ADHD and information on how a diagnostic clinical examination of ADHD is usually performed. Participants of all simulation groups received the respective information by the experimenter on two occasions; that is, by email on the day before the assessment and at the beginning of the assessment. Prior to assessments, participants of the SSG, TSG, and STSG were requested to respond to a number of questions on the content of information they were provided with, to ascertain that they indeed read but also understood the information. Finally, participants were requested to start feigning ADHD and to perform the assessment as if they would seek a diagnosis of ADHD. At the end of the assessment, all participants of simulation groups indicated that they followed instructions, including instructions to feign ADHD. The assessment of participants of the simulation groups took about 70 min.
Participants
Patients with ADHD
The sample of ADHD1 included 48 adults diagnosed with ADHD. Current treatment with ADHD medication (i.e., stimulant medication) was an exclusion criterion as individuals of simulation groups were trying to simulate test performance of patients with ADHD not being treated with such medication. This seems relevant considering that those attempting to feign ADHD at clinical evaluation do so to get access to certain incentives, such as stimulant medication. Thus, the level of performance that individuals feigning ADHD try to mimic at clinical evaluation is the performance of (still untreated) genuine patients with ADHD. Because stimulant medication is known to affect cognitive performance, we only included patients with ADHD if they were not treated with stimulants at the time of assessments. Patients were referred from local psychiatrists or neurologists to the Department of Psychiatry and Psychotherapy of the SRH (Stiftung Rehabilitation Heidelberg) Clinic Karlsbad-Langensteinbach, Germany. A diagnostic assessment of adult ADHD as well as participation in the research study was offered to all participants. Diagnostic assessments were performed by experienced clinicians associated to the Department of Psychiatry and Psychotherapy and involved a clinical psychiatric interview based on DSM–IV criteria for ADHD (American Psychiatric Association, 1994; Barkley & Murphy, 1998), including both the retrospective assessment of childhood symptoms as well as current symptoms. All diagnoses were made by mutual agreement between at least two clinicians who were part of a diagnostic team and experienced in the assessment and treatment of adults with ADHD. The diagnostic assessment also included identifying objective impairments supporting the diagnosis of ADHD (e.g., evidence derived from school reports, failure in academic and/or occupational achievement) and comprised multiple informants, such as employer evaluation and partner- or parent-reports. Moreover, all participants completed two standardized self-report rating scales designed to quantify current and retrospective ADHD symptoms (Wender Utah Rating Scale (German: WURS-K) and ADHD Self-Report Scale (ASR); Rösler, Retz-Junginger, Retz, & Stieglitz, 2008; Ward, Wender, & Reimherr, 1993). After completion of the recruitment and assessment of the patients of ADHD1 as described above, an independent second sample of 27 adults with ADHD (ADHD2) was recruited and assessed.
Healthy individuals
The HCG (n = 48) was recruited via public announcements and word-of-mouth. Healthy individuals were selected according to age, gender and vocabulary skills (intellectual functions), resulting in comparable characteristics to the patients of ADHD1.
Furthermore, 252 first-year psychology students of the University of Groningen, the Netherlands, took part in the study in exchange for course credits. Prior to assessment, these participants were randomly assigned to one of four conditions; the CG, the NSG, the SSG, or the TSG. All three simulation groups were collapsed and formed Simulation Group 1 (SG1).
After completion of the recruitment and assessment of participants described above, an independent sample of first-year psychology students of the University of Groningen, the Netherlands, were recruited and formed Simulation Group 2 (SG2, n = 69). Participants of the SG2 were randomly assigned to one of three conditions, that is, a naïve simulation condition (n = 24), a symptom-coached simulation condition (n = 19), or a symptom- and test-coached simulation condition (n = 26).
Materials
Vocabulary skills (intellectual functions)
Vocabulary skills (intellectual functions) were measured using the Multiple Choice Vocabulary Test (MWT-B; Lehrl, 1995). This test consists of 37 lines, each comprising one authentic word and four fictitious words. The participants were required to find the authentic word by underlining it. The MWT-B was suggested to provide a useful measure for premorbid IQ that is relatively insensitive to cerebral dysfunction of individuals (Lehrl, 1995; Lehrl, Triebig, & Fischer, 1995). In studies on typically developing individuals, the MWT-B was shown to correlate fairly well with global IQ, yielding a median correlation of r = .72 in 22 samples (Lehrl et al., 1995).
Clinical assessment of ADHD symptom severity
Childhood ADHD symptoms were self-rated on the short version of the Wender Utah Rating Scale (WURS-K), which includes 25 items rated on a five-point scale (Ward et al., 1993). Severity of current ADHD symptoms was self-rated with the ADHD Self-Report Scale (ASR; Rösler et al., 2008) consisting of 18 items rated on a four-point scale corresponding to the diagnostic criteria of DSM–IV (American Psychiatric Association, 1994; Rösler et al., 2008). A sum score was calculated for each rating scale.
Visuospatial working memory test (VSWMT)
A VSWMT was developed that required participants to navigate through a mental image and to count the number of corners this image contained from memory (based on the principles of the task as devised by Brooks, 1968). The VSWMT comprised 60 trials, divided into three blocks of 20 trials each. A fixation cross appeared in the middle of a computer screen at the beginning of each trial, followed by the presentation of a geometric figure (for 2,000 ms). Subsequently, a visual mask (unstructured black and white dots) was displayed for 500 ms to prevent aftereffects. Directly after the presentation of the visual mask, participants were requested to navigate through the mental image from memory and to count all corners the geometric figure contained. The response had to be given verbally and was noted by the experimenter. Furthermore, the response time was registered by measuring the time from the moment the visual mask disappeared to the participant’s response. The subsequent trial was initiated by the experimenter. Block 1 contained 8 trials presenting letters, and 12 trails presenting nonletters (straight lines forming geometric figures, see Figure 1 for examples). Block 2 contained the same figures as Block 1, but presented the figures in a view rotated by either 90 or 180 degrees. Block 3 was a repetition of Block 1, containing the same figures in the same spatial orientation. Presentation order of trials was randomized in each block. Two example trials were administered before testing started, to allow participants to familiarize themselves with the task. The VSWMT was conducted on a computer of 15.4 in. screen size, using the presentation software E-Prime 2.0. The number of errors on letters and nonletters were counted for each block. Furthermore, the mean response times for correct responses were measured for letters and nonletters per block. The response times for incorrect responses were not considered for analysis, as no clear instruction was given to participants on how to respond if they failed to keep the geometric figure as a mental image; for example giving no response, giving a best guess as quickly as possible, or giving a best guess after careful consideration. The response time of incorrect responses would therefore not provide a useful measure of task performance. As the VSWMT was specifically designed for the present study and has not been used before, its psychometric properties, such as reliability of scores, are not known yet. In the present study, internal consistency of scores of the VSWMT (Cronbach’s alpha) ranged from 0.81 to 0.84 for patients with ADHD, healthy control groups, and instructed simulators.
Figure 1. Example items of the visuospatial working memory test. Left: Example item of a letter (top: normal view as used for Blocks 1 and 3; bottom: rotated view as used for Block 2); Right: Example item of a nonletter (top: normal view as used for Blocks 1 and 3; bottom: rotated view as used for Block 2).
Routine measures of cognition
Vigilance was measured with the computerized VIGIL test of the Vienna Test System VTS (Schuhfried, 2013). In this test, a white dot moved along a circular path (resembling the second hand of an analog clock) in small regular jumps. Occasionally (1 out of 10 jumps), the white dot made a double jump (critical stimulus). The participants were requested to react to this infrequent event of a double jump as quickly as possible with a button press on a response panel. The number of correct responses (to critical stimuli), the number of incorrect responses (to noncritical stimuli, i.e., to events without a double jump), and the mean reaction time (RT; ms) for correct responses were measured.
The N-Back Verbal (NBV) of the VTS (Schuhfried, 2013) was applied as a test for verbal working memory. In this test, letter sequences are presented on a computer screen and participants are required to respond by pressing a button when the letter displayed at that time is identical to the second to last letter. The number of errors, as well as the mean response time to correct and incorrect responses were registered.
Tests for performance validity
Three established effort tests were applied, that is the Test of Memory Malingering (TOMM; Tombaugh, 1996), the Dot Counting Test (DCT; Boone, Lu, & Herzberg, 2002b), and the b Test (Boone, Lu, & Herzberg, 2002a). The TOMM is a visual recognition test consisting of two learning trials and a retention trial. The DCT is a short test requesting participants to visually perceive patterns of dots and use elementary counting and multiplication skills. The b Test is a letter-recognition and discrimination task that asks participants to circle, as quickly as possible, all the b’s that appear on several pages of a stimulus booklet. All three tests measure skills that are assumed to be preserved in most individuals with cognitive dysfunction. By employing recommended cutoffs of the TOMM (suspect effort if Trial 2 or Retention score <45), the DCT (suspect effort if E-Score >13), and the b Test (suspect effort if E-Score >119) as specified in the test manuals (Boone et al., 2002a, 2002b; Tombaugh, 1996), these tests can be applied to distinguish between credible and suspect effort among the validation sample of patients with ADHD.
Statistical Analysis
Neuropsychological test performance on vigilance and verbal working memory of the patients of ADHD1 was analyzed using descriptive statistics. A neuropsychological impairment was determined on the basis of a commonly accepted categorization of ability levels using age-based test norms as provided by the test publisher (percentile ≤10; Lezak, Howieson, & Loring, 2004). VSWMT scores (total number of errors and total mean response time of correct responses) of the patients of ADHD1 and the HCG were compared using multivariate analysis of variance (MANOVA). Effect sizes of univariate group differences were indicated by Cohen’s d and were classified, based on Cohen’s classification, into negligible effects (d < 0.20), small effects (0.20 ≤ d < 0.50), medium effects (0.50 ≤ d < 0.80), and large effects (d ≥ 0.80; Cohen, 1988). The association between visuospatial working memory performance (VSWMT) and (a) verbal working memory (NBV) and (b) vigilance (VIGIL) was explored by Pearson correlations. It is expected that visuospatial working memory is significantly associated with verbal working memory in both error variables and RT measures. However, visuospatial working memory is expected not to be significantly associated with vigilance performance, that is, neither in accuracy nor RTs. Correlation coefficients were interpreted as negligible (r < .1), small (0.1 ≤ r < .3), medium (0.3 ≤ r < .5), and large (r > .5; Cohen, 1988).
Furthermore, MANOVA was applied to compare VSWMT performance between the patients of ADHD1, the CG, and all simulation groups. Statistical significance was calculated for the multivariate comparison as well as for univariate comparisons of each variable of the VSWMT. Pairwise group comparisons were performed by using Tukey’s Honest Significant Difference (HSD) post hoc tests. A rigorous alpha level was set at .01 to control for the problem of multiple comparisons. However, interpretations were largely based on effect sizes, as effect sizes indicate the magnitude of an effect independently from the significance and are also considered to be more informative in malingering research compared to statistical significance (Rogers, 2008). Effect sizes were indicated by Cohen’s d and η2. The index η2 was used as an effect size estimator for (M)ANOVAs, providing information about the proportion of variance which is accounted for by the factor group membership. According to Cohen (1988), a small effect size corresponds to an η2 = .0099, a medium effect size to an η2 = .0588, and a large effect size to an η2 = .1379. The effect size Cohen’s d was computed for pairwise comparisons between simulation groups and patients with ADHD. As Cohen’s d (and its categorization) was designed to consider relatively small effects as relevant to research (Cohen, 1988), it was stressed that more rigorous standards are needed for the assessment of malingering (Rogers, 2008). Rogers (2008), therefore, introduced a categorization of effects sizes (Cohen’s d) suitable for malingering research and distinguished between moderate effects (0.75 ≤ d < 1.25), large effects (1.25 ≤ d < 1.50), and very large effects (d ≥ 1.50).
In addition, binary logistic regression analysis was carried out to determine the utility of the VSWMT in predicting feigned ADHD relative to true ADHD (ADHD1). All simulation conditions (NSG, SSG, and TSG) were collapsed for the purpose of this analysis into one feigning group (i.e., SG1) as it likely remains unknown in any given assessment context if and how individuals had prepared themselves prior to the diagnostic evaluation. The VSWMT index was calculated for each participant by summating the VSWMT scores that were individually weighted with their logistic regression coefficients, to achieve the best combination of VSWMT scores to predict feigned ADHD. The accuracy of the VSWMT index in detecting individuals feigning ADHD (i.e., SG1, n = 204) relative to patients with ADHD (ADHD1, n = 48) was explored in receiver operating characteristics (ROC). A ROC curve plots the sensitivity against ‘1—specificity’ at each level of the VSWMT index to predict the criterion (feigned ADHD). ROC analysis allows for determination of diagnostic accuracy as measured by the area under the curve (AUC), indicating the probability that a score drawn at random from the first sample is higher than a score drawn at random from the second sample (Rice & Harris, 2005). For further examination of the internal validity of the interpretations of scores from the prediction model, a bootstrap resampling procedure was applied. A bootstrap resampling procedure allows assigning measures of accuracy (e.g., confidence intervals) to AUCs as derived from ROC analyses. From the original dataset of 252 participants (204 instructed simulators of SG1 and 48 patients with ADHD), 3000 random samples were drawn with replacement for bootstrap analysis. Bootstrap AUC and its associated 90% confidence interval were estimated for the prediction model using Skalská & Freylich’s bootstrap application (Skalská & Freylich, 2006).
Finally, results as derived from the analyses described above were cross-validated on independent samples of patients with ADHD (i.e., ADHD2, n = 27) and instructed simulators (SG2, n = 69). VSWMT performance of the independent group of patients with ADHD (validation sample, n = 27) was compared to VSWMT performance of the patients of ADHD1 (n = 48) by means of multivariate and univariate ANOVA. In addition, cutoff values of the VSWMT, as derived from the patients of ADHD1, were also applied to the validation sample of patients. A comparable performance and similar classification rates (i.e., specificity) of both groups would support the credibility of test performance of the patients of ADHD1. Moreover, for a complete cross-validation, ROC analysis was also performed using SG2. Furthermore, cutoff values of the VSWMT as derived from SG1 were applied on SG2 to determine sensitivity rates on a new and independent sample of individuals instructed to feign ADHD.
Results Characteristics of Participants
Characteristics of participants are presented in Table 2. An official diagnosis of ADHD in childhood had been reported by some of the patients with ADHD, however, diagnostic veracity of ADHD in childhood could not be confirmed in the majority of cases due to incomplete medical records. Therefore, presence of childhood ADHD was assessed retrospectively in all cases. Patients with ADHD suffering from comorbid psychiatric disorders were not excluded because comorbidity is very prevalent among patients with ADHD and is therefore representative for the clinical picture of this condition (Biederman et al., 1993). Statistics as presented in Table 3 reveal that participants of ADHD1 and the HCG did not differ significantly with regard to age, gender, and vocabulary skills. However, as expected, patients with ADHD scored significantly higher on self-report scales for both current ADHD symptoms and retrospective ADHD symptoms.
Statistical Comparisons of Characteristics Between Groups
Healthy participants of SG1 and CG (175 female, 77 male) had a mean age of 21.1 years (SD = 2.2 years) and a mean score on vocabulary skills of 100.3 (SD = 9.4). After group allocation (CG, NSG, SSG, TSG), descriptive variables did not differ significantly between groups, neither with regard to age, gender, vocabulary skills, nor ADHD symptom severity (see Table 3). None of the healthy individuals reported to have a history of neurological or psychiatric diseases and none were taking any medication known to affect the central nervous system.
Furthermore, characteristics of SG2 were comparable to characteristics of other simulation groups, although it must be noted that the test for vocabulary skills was not applied in this group (see Table 2). However, given the similar process of recruitment among the same population (first-year psychology students), it can be assumed that educational level and vocabulary skills of participants of SG2 were comparable to participants of SG1.
Neuropsychological Functions of Patients With ADHD
Neuropsychological test performance of patients with ADHD (ADHD1, n = 48) as assessed with routine measures of cognition are presented in Table 4. Cognitive impairments of patients with ADHD were most prominent in vigilance, with more than half of the patients with ADHD exhibiting impairments. In contrast, only a small number of patients with ADHD displayed impairments in verbal working memory (% of patients with impairment <10).
Routine Measures of Cognition as Performed by Patients With ADHD (ADHD1; n = 48)
A correlation analysis between patients’ visuospatial working memory (as assessed by the VSWMT) and verbal working memory (as assessed by the NVB) revealed a significant association, of medium size, between the error variables of both tests (see Table 5). Nonsignificant correlations of negligible to small size were found between the pairs of variables representing response times. Moreover, nonsignificant associations of small size were found between the VSWMT and vigilance test (VIGIL), both with regard to accuracy of responses and RTs (see Table 5).
Pearson Correlations Among Patients With ADHD (ADHD1; n = 48) Between Visuospatial Working Memory Performance (VSWM) and Verbal Working Memory Performance (NBV), as Well as Between VSWM Performance and Vigilance (VIGIL) Performance
The statistical comparison of visuospatial working memory performance of patients with ADHD (ADHD1, n = 48) with that of the healthy comparison group revealed a significantly lower performance of large size among patients with ADHD, Wilk’s λ = 0.825, F(2, 93) = 9.847, p < .001, η2 = .175 (see Table 6). Univariate comparisons demonstrated medium and significant effects regarding errors, F(1, 94) = 8.170, p = .005, d = 0.74, and total mean response time of correct responses, F(1, 94) = 12.617, p = .001, d = 0.72, indicating a poorer visuospatial working memory performance in patients with ADHD.
VSWMT Performance of Patients With ADHD, Control Participants, and Simulation Groups
Utility of the VSWMT for the Detection of Feigned Adult ADHD
Group comparisons between patients with ADHD and instructed simulators
Table 6 presents VSWMT performances of all given variables per group. A multivariate comparison (MANOVA) of VSWMT performance between patients of ADHD1, the control group, and all simulation groups revealed a large significant effect (Wilk’s λ = .420, F(65, 1369.1) = 5.088, p < .001, η2 = .159). Univariate comparisons indicated significant differences between groups on each of the variables of the VSWMT, including B1-L-Errors (F(5, 358) = 14.132, p < .001, η2 = .165), B1-L-RT (F(5, 358) = 9.336, p < .001, η2 = .115), B1-NL-Errors (F(5, 358) = 14.144, p < .001, η2 = .165), B1-NL-RT (F(5, 358) = 13.442, p < .001, η2 = .158), B2-L-Errors (F(5, 358) = 18.361, p < .001, η2 = .204), B2-L-RT (F(5, 358) = 9.673, p < .001, η2 = .119), B2-NL-Errors (F(5, 358) = 21.222, p < .001, η2 = .229), B2-NL-RT (F(5, 358) = 11.480, p < .001, η2 = .138), B3-L-Errors (F(5, 358) = 18.967, p < .001, η2 = .209), B3-L-RT (F(5, 358) = 6.464, p < .001, η2 = .083), B3-NL-Errors (F(5, 358) = 19.927, p < .001, η2 = .218), B3-NL-RT (F(5, 358) = 13.192, p < .001, η2 = .156), VSWMT Errors (F(5, 358) = 30.883, p < .001, η2 = .301), VSWMT RT Correct (F(5, 358) = 16.845, p < .001, η2 = .190), and VSWMT index (F(5, 358) = 38.449, p < .001, η2 = .349). Effect sizes ranged from medium to large. Post hoc pairwise comparisons (Tukey’s HSD) denoted that simulation groups committed significantly more errors and had shorter response times on the VSWMT compared to patients with ADHD. When compared to control participants, simulation groups committed significantly more errors, but did not differ in response times. As a consequence, participants of simulation groups had significantly larger scores on the VSWMT index than patients with ADHD and the control group.
Logistic regression analysis for the prediction of feigned ADHD
The utility of the VSWMT to predict feigned ADHD relative to true ADHD was examined by means of a binary logistic regression model. Based on all variables of the VSWMT as predictors, a model was derived that significantly predicts feigned ADHD (collapsed group of SG1, n = 204) relative to true ADHD (ADHD1, n = 48), χ2(12, N = 252) = 107.174, p < .001, explaining 34.6% of the variance (Cox & Snell R2).
Parameters of the logistic regression model are presented in Table 7. To determine the best combination of VSWMT scores to predict feigned ADHD for each individual, unstandardized regression coefficients as derived from the model were used to weight VSWMT scores of each individual. A VSWMT index was calculated by summing up these weighted VSWMT scores with a greater total value indicating a greater likelihood for feigning ADHD.
Summary Statistics of Binary Logistic Regression Model That Predicts Feigned ADHD (SG1; n = 204) Relative to ADHD (ADHD1; n = 48) Based on the VSWMT
Classification accuracies
A ROC analysis was performed to determine the accuracy of the VSWMT index in the identification of feigned ADHD (SG1, n = 204) relative to ADHD (ADHD1, n = 48). Data analysis supported the utility of the VSWMT index in predicting feigned ADHD, AUC = 0.902, SE = 0.024, CI = 0.855;0.949, p < .001. Internal validity of test score interpretation derived from the prediction model was supported by the bootstrap resampling procedure, which yielded high diagnostic accuracy close to the original AUC estimation, bootstrap AUC = 0.893, 90%-CI = 0.851;0.932. An inspection of classification statistics for various cutoffs of the VSWMT index as presented in Table 8 reveals adequate diagnostic accuracy at a cutoff of 2.17, reaching excellent specificity toward true ADHD (95.8%) and adequate sensitivity toward feigned ADHD (60.3%). When giving equal weight to false positives and false negatives, similar levels of sensitivity (80.4%) and specificity (81.2%) are obtained at a cutoff of 1.04. Figure 2 shows a graphical plot of the ROC curve, representing a visualization of the diagnostic accuracy of the VSWMT index for the identification of individuals feigning ADHD relative to patients with ADHD (see Figure 2).
Classification Statistics for the Identification of Instructed Simulators Relative to Patients With ADHD for Various Cutoffs of the VSWMT Index
Figure 2. Receiver operating characteristics (ROC) curve indicating diagnostic accuracy of the visuospatial working memory test (VSWMT) in identifying feigned ADHD (Simulation Group 1, n = 204) relative to ADHD (ADHD1, n = 48).
Cross-validation of the VSWMT for the prediction of feigned ADHD
To replicate VSWMT performance of the patients of ADHD1, a validation sample of patients with ADHD was recruited, including patients who all ‘passed’ three established effort tests for performance validity. A multivariate statistical comparison (MANOVA) between both groups revealed a nonsignificant difference in the VSWMT performance between both samples of patients with ADHD, Wilk’s λ = .821, F(12, 62) = 1.128, p = .355, η2 = .179 (see Table 6). When the cutoff values of the VSWMT as derived on the patients of ADHD1 are applied on the validation sample of patients, similar rates of specificity were reached, such as 77.8% on a cutoff of 1.04, and 96.3% on a cutoff of 2.17 (see Table 8).
Furthermore, sensitivity to detect feigned ADHD was compared between the original sample of instructed simulators (SG1) and SG2. A comparison of sensitivity rates as presented in Table 8 reveals that the VSWMT index still achieved satisfactory accuracy rates in detecting noncredible cognitive performance of SG2, although sensitivity rates were lower compared to the sensitivity rates of the original sample (SG1). Similarly, a ROC analysis showed that the VSWMT index was also successful in the identification of instructed simulators of SG2 relative to ADHD1, AUC = 0.867, SE = 0.033, CI = 0.802;0.932, p < .001 (see Figure 3). As compared to diagnostic accuracy of SG1 (90.2%), diagnostic accuracy of SG2 was lower, but still satisfactory.
Figure 3. Receiver operating characteristics (ROC) curve indicating diagnostic accuracy of the visuospatial working memory test (VSWMT) in identifying feigned ADHD (Simulation Group 2, n = 69) relative to ADHD (ADHD1, n = 48).
Discussion Cognitive Functions of Patients With ADHD
Neuropsychological measures of patients with ADHD largely confirm previous research in showing that the majority of patients with ADHD demonstrate impairments in some, but not all aspects of attention and/or executive control (Mostert et al., 2015; Thome et al., 2012). With regard to working memory performance of patients with ADHD, impaired performance was found in the newly developed visuospatial working memory test, whereas intact abilities were observed in verbal working memory. This appears surprising in the light of the relatively robust findings indicating both verbal (phonological) and visuospatial working memory deficits in adults with ADHD, for example as revealed in the meta-analyses of Alderson and colleagues (2013) who demonstrated working memory deficits of medium size in adults with ADHD. However, even though working memory deficits seem to be very frequent among adults with ADHD, it has been noted that these deficits are not universal (Alderson et al., 2013) and are thus neither necessary nor sufficient for a diagnosis of ADHD (Willcutt et al., 2005). Support for the construct validity (convergent validity) of test score interpretation obtained from the newly developed visuospatial working memory test is given by the medium sized correlations between the error variables of both working memory tasks in patients with ADHD. The lack of association between variables representing speed of responses of both working memory tasks could be explained by the fact that the NBV requires fast responses from participants by a sequential stimuli presentation at a fixed pace that is independent from the response of the participants. The VSWMT, in contrast, does not require or instruct participants to respond quickly. Also conform our expectations, no significant association was found between the accuracy of responses as well as RT measures of the visuospatial working memory test and vigilance test, which gives further support to the construct validity (divergent validity). Finally, content validity has been established as the visuospatial working memory test represents all facets of a typical working memory task by requiring to develop, inspect, and navigate through a mental image, thus tapping a cardinal function of visuospatial working memory (Brooks, 1968).
VSWMT to Detect Feigned Adult ADHD
There is a good indication that group manipulations in the context of the simulation design were successful as all participants of simulation groups were able to respond to questions regarding simulation instructions and also indicated after completion of the study that they followed instructions. Further evidence is given by group statistics demonstrating significantly elevated error rates of all simulation groups compared to the CG.
A comparison of VSWMT performance between the different simulation groups revealed that neither information about the criteria for ADHD (symptom-coaching) as per the DSM–IV (American Psychiatric Association, 1994) nor knowledge about the procedure of a diagnostic clinical examination (test-coaching) benefitted instructed malingerers to feign ADHD more realistically. Similar results have been reported by Dunn, Shear, Howe, and Ris (2003), who found little evidence for the utility of symptom information and coaching in feigning cognitive dysfunctions after brain injury. To examine the robustness of tools against the preparation of individuals attempting to feign ADHD, it is suggested that future research includes simulation conditions that inform individuals about the specific tool that will be used, and present the conduct of this tool in short demonstrations to individuals instructed to feign prior to the assessment.
The utility of the VSWMT in detecting feigning was advocated by moderate to very large group differences (Roger’s categorization) between individuals instructed to feign ADHD and patients of ADHD1. The utility of the VSWMT was further highlighted in the ROC analysis by high diagnostic accuracy (AUC = 90.2%), with excellent specificity (95.8%), and adequate sensitivity (60.3%). Results of internal validation (bootstrap resampling procedure) may be seen as an approximation to external validity (i.e., generalizability) and supports the utility of the derived prediction model on new samples (Steyerberg et al., 2001). Furthermore, cross-validation obtained stable specificity rates on the independent sample of patients with ADHD, whereas sensitivity dropped from the first to the second group of instructed simulators, though still reaching adequate diagnostic accuracy.
Previous research on patients with brain injury already considered embedded validity measures in working memory tests for the identification of noncredible cognitive performance, and suggested that the Digit Span test of the Wechsler Adult Intelligence Scale could provide such information. In this respect, cutoffs of the scaled Digit Span score and the Reliable Digit Span have been introduced as valid indicators of noncredible cognitive performance in patients with brain injury (Etherton, Bianchini, Greve, & Heinly, 2005; Greve et al., 2007; Iverson & Franzen, 1994; Mathias, Greve, Bianchini, Houston, & Crouch, 2002). These scores have also been examined for their utility to detect noncredible cognitive performance in the clinical evaluation of adults with ADHD (Booksh et al., 2010; Harrison et al., 2010; Suhr et al., 2008), but were shown to be less successful as they have been initially proposed for brain injured patients (Booksh et al., 2010; Harrison et al., 2010; Suhr et al., 2008). Whereas specificity rates toward credible performance of adults with ADHD were high, sensitivity rates toward noncredible cognitive performance of individuals with ADHD were low (Harrison et al., 2010). Based on the previous literature using working memory tasks as described above, as well as findings of the present study, it may be concluded that visuospatial working memory measures, in contrast to verbal working memory measures, are more useful for the development of validity indicators of neuropsychological test performance of adults with ADHD. Although it remains speculation, one could explain this discrepancy by the rather complex material as used in visuospatial working memory tasks that may be difficult to judge and evaluate by individuals attempting to feign ADHD, in comparison to digits as used in verbal working memory tasks that most people are more familiar with. Individuals are naturally more acquainted with verbal information from many tasks of daily living and might find it relatively easy to judge and evaluate verbal information in cognitive tasks when attempting to feign ADHD.
The present study demonstrates that effort testing can be embedded in routine measures of cognition (here in a working memory test). This is a meaningful finding as effort testing is considered as an essential and routine part of clinical neuropsychological assessment (Bush et al., 2005). Because effort testing takes time, leading to costs that can largely be avoided when cleverly combining test principles of regular measures of cognition (i.e., measuring a particular cognitive function) with principles of effort testing (i.e., detecting feigning), allowing the clinician to make conclusions about both the integrity of function and effort during assessment. Because clinical services have to be prepared to face increasing numbers of requests for neuropsychological assessments due to the demographical changes of society with an increasing number of older people developing conditions, saving time while maintaining quality of assessments becomes pivotal.
Limitations and Future Directions
As a limitation of this analysis, visuospatial working memory of patients was compared to a matched group of healthy individuals, whereas verbal working memory of patients was evaluated based on test norms. More valid conclusions on verbal and visuospatial working memory performance of adults with ADHD can be drawn if test performances of patients with ADHD in both types of working memory measures are evaluated based on the same reference group; a healthy comparison group that performed the verbal and visuospatial working memory test. To establish construct validity of the VSWMT, it would be relevant to correlate verbal and visuospatial working memory performance not only among patients with ADHD, but also among a group of healthy individuals.
As another limitation of the present study, it must be noted that the patients of ADHD1 did not perform any established measures of performance validity. Thus, it cannot be excluded that some of the patients might have exaggerated or even feigned symptoms, which would have distorted the present analysis and results. To control for this problem, we were very careful in the selection of patients with ADHD for the present study and considered collateral information and objective evidence of impairment to support diagnostic status. Additional support for the credibility of the patients of ADHD1 was given by comparing their test performance to the validation sample of patients with ADHD that comprised patients who all ‘passed’ three established measures of performance validity.
To further explore the utility of the validity indicator of the VSWMT for the identification of noncredible cognitive performance, more research is needed that examines diagnostic accuracies of the VSWMT in comparison to other measures that were previously suggested for the identification of noncredible symptom reporting and performance, including self-report rating scales, personality inventories, or effort tests that were specifically designed to detect feigning (Musso & Gouvier, 2014; L. Tucha et al., 2015). Such research should make use of different methodology, including known-groups comparisons, considering that simulation designs are inherently prone to external validity problems. The implications of the present study would be strengthened if the VSWMT also shows high accuracy in identifying individuals who indeed seek clinical evaluation for ADHD, but who are believed to actually feign symptoms.
Finally, to improve diagnostic accuracy of noncredible cognitive performance at clinical evaluation of adult ADHD, it appears promising to explore the utility of the VSWMT in combination with other routine measures of cognition that are commonly performed within a comprehensive neuropsychological assessment of adults with ADHD. Some evidence is given by previous research suggesting measures of attention and processing speed, such as the Continuous Performance Test or the Stroop Test, representing the most promising means among routine measures of cognition as validity indicators of neuropsychological test performance of adults with ADHD (Booksh et al., 2010; Marshall et al., 2010; Quinn, 2003; Sollman, Ranseen, & Berry, 2010; Suhr et al., 2008). A major advantage of embedded measures of performance validity within routine neuropsychological assessment is low face validity, as also well-prepared individuals attempting to feign ADHD are presumably not able to recognize implemented validity indicators and, thus, cannot adapt their behavior accordingly. VSWMT performance indicating noncredible performance, in combination with other behavioral characteristics that are uncommon for genuine ADHD (e.g., excessive error rates or contradicting performance on cognitive tests measuring similar constructs), might increase sensitivity toward exaggerated or feigned cognitive impairment at clinical evaluation of adults with ADHD.
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Submitted: February 6, 2017 Revised: August 4, 2017 Accepted: August 15, 2017
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Source: Psychological Assessment. Vol. 29. (12), Dec, 2017 pp. 1466-1479)
Accession Number: 2017-54244-006
Digital Object Identifier: 10.1037/pas0000534
Record: 102- Title:
- Nonjudging facet of mindfulness predicts enhanced smoking cessation in Hispanics.
- Authors:
- Spears, Claire Adams. Department of Psychology, The Catholic University of America, Washington, DC, US, spears@cua.edu
Houchins, Sean C.. Department of Psychology, The Catholic University of America, Washington, DC, US
Stewart, Diana W.. Department of Health Disparities Research, The University of Texas MD Anderson Cancer Center, TX, US
Chen, Minxing. Department of Biostatistics, The University of Texas MD Anderson Cancer Center, TX, US
Correa-Fernández, Virmarie. Department of Health Disparities Research, The University of Texas MD Anderson Cancer Center, TX, US
Cano, Miguel Ángel. Department of Epidemiology, Florida International University, FL, US
Heppner, Whitney L.. Department of Psychology, Georgia College & State University, GA, US
Vidrine, Jennifer I.. Department of Health Disparities Research, The University of Texas MD Anderson Cancer Center, TX, US
Wetter, David W.. Department of Psychology, Rice University, TX, US - Address:
- Spears, Claire Adams, Department of Psychology, The Catholic University of America, 620 Michigan Avenue, NE, Washington, DC, US, 20064, spears@cua.edu
- Source:
- Psychology of Addictive Behaviors, Vol 29(4), Dec, 2015. pp. 918-923.
- NLM Title Abbreviation:
- Psychol Addict Behav
- Page Count:
- 6
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- Bulletin of the Society of Psychologists in Addictive Behaviors; Bulletin of the Society of Psychologists in Substance Abuse
- Other Publishers:
- US : Educational Publishing Foundation
Society of Psychologists in Addictive Behaviors - ISSN:
- 0893-164X (Print)
1939-1501 (Electronic) - Language:
- English
- Keywords:
- mindfulness, smoking cessation, Hispanic smokers
- Abstract:
- Although most smokers express interest in quitting, actual quit rates are low. Identifying strategies to enhance smoking cessation is critical, particularly among underserved populations, including Hispanics, for whom many of the leading causes of death are related to smoking. Mindfulness (purposeful, nonjudgmental attention to the present moment) has been linked to increased likelihood of cessation. Given that mindfulness is multifaceted, determining which aspects of mindfulness predict cessation could help to inform interventions. This study examined whether facets of mindfulness predict cessation in 199 Spanish-speaking smokers of Mexican heritage (63.3% male, mean age of 39 years, 77.9% with a high school education or less) receiving smoking cessation treatment. Primary outcomes were 7-day abstinence at weeks 3 and 26 postquit (biochemically confirmed and determined using an intent-to-treat approach). Logistic random coefficient regression models were utilized to examine the relationship between mindfulness facets and abstinence over time. Independent variables were subscales of the Five Facet Mindfulness Questionnaire (Observing, Describing, Acting With Awareness, Nonjudging, and Nonreactivity). The Nonjudging subscale (i.e., accepting thoughts and feelings without evaluating them) uniquely predicted better odds of abstinence up to 26 weeks postquit. This is the first known study to examine whether specific facets of mindfulness predict smoking cessation. The ability to experience thoughts, emotions, and withdrawal symptoms without judging them may be critical in the process of quitting smoking. Results indicate potential benefits of mindfulness among smokers of Mexican heritage and suggest that smoking cessation interventions might be enhanced by central focus on the Nonjudging aspect of mindfulness. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Intervention; *Smoking Cessation; *Tobacco Smoking; *Mindfulness; *Latinos/Latinas
- PsycINFO Classification:
- Drug & Alcohol Rehabilitation (3383)
- Population:
- Human
Male
Female - Location:
- US
- Age Group:
- Adulthood (18 yrs & older)
- Tests & Measures:
- Center of Epidemiological Studies Depression Scale
Heaviness of Smoking Index DOI: 10.1037/t04726-000
Five Facet Mindfulness Questionnaire DOI: 10.1037/t05514-000 - Grant Sponsorship:
- Sponsor: National Center on Minority Health and Health Disparities, US
Grant Number: P60MD000503
Recipients: No recipient indicated
Sponsor: National Cancer Institute, University of Texas MD Anderson Cancer Center, US
Grant Number: CA016672
Other Details: Support Grant
Recipients: No recipient indicated
Sponsor: Latinos Contra el Cancer Community Networks
Grant Number: U54CA153505
Other Details: Program Center Grant
Recipients: No recipient indicated - Methodology:
- Empirical Study; Longitudinal Study; Quantitative Study
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- First Posted: May 11, 2015; Accepted: Mar 19, 2015; Revised: Mar 19, 2015; First Submitted: Nov 14, 2014
- Release Date:
- 20150511
- Correction Date:
- 20160104
- Copyright:
- American Psychological Association. 2015
- Digital Object Identifier:
- http://dx.doi.org/10.1037/adb0000087
- Accession Number:
- 2015-20853-001
- Number of Citations in Source:
- 39
- Persistent link to this record (Permalink):
- http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2015-20853-001&site=ehost-live
- Cut and Paste:
- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2015-20853-001&site=ehost-live">Nonjudging facet of mindfulness predicts enhanced smoking cessation in Hispanics.</A>
- Database:
- PsycINFO
Nonjudging Facet of Mindfulness Predicts Enhanced Smoking Cessation in Hispanics / BRIEF REPORT
By: Claire Adams Spears
Department of Psychology, The Catholic University of America;
Sean C. Houchins
Department of Psychology, The Catholic University of America
Diana W. Stewart
Department of Health Disparities Research, The University of Texas MD Anderson Cancer Center
Minxing Chen
Department of Biostatistics, The University of Texas MD Anderson Cancer Center
Virmarie Correa-Fernández
Department of Health Disparities Research, The University of Texas MD Anderson Cancer Center
Miguel Ángel Cano
Department of Epidemiology, Florida International University
Whitney L. Heppner
Department of Psychology, Georgia College & State University
Jennifer I. Vidrine
Department of Health Disparities Research, The University of Texas MD Anderson Cancer Center
David W. Wetter
Department of Psychology, Rice University, and Department of Health Disparities Research, The University of Texas MD Anderson Cancer Center
Acknowledgement: This work was supported by the National Center on Minority Health and Health Disparities through Grant P60MD000503 and by the National Cancer Institute through The University of Texas MD Anderson Cancer Center’s Support Grant CA016672 and the Latinos Contra el Cancer Community Networks Program Center Grant U54CA153505. This work was also supported by the National Center for Complementary and Integrative Health under Award Number K23AT008442 and a faculty fellowship from The University of Texas MD Anderson Cancer Center Duncan Family Institute for Cancer Prevention and Risk Assessment, both awarded to Claire Adams Spears. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
Given that tobacco use is the leading cause of preventable morbidity and mortality in the United States (Mokdad, Marks, Stroup, & Gerberding, 2004) and that most smokers who attempt to quit are unsuccessful (Centers for Disease Control and Prevention [CDC], 2011), identifying strategies to enhance smoking cessation is critical. This is particularly important among Hispanics, who represent the largest ethnic minority group in the United States (U.S. Census Bureau, 2014) and experience profound health disparities (Myers, 2009). Although the prevalence of smoking is lower among Hispanics than in the general U.S. population (12.5% vs. 18.1%; Agaku, King, & Dube, 2014), three of the four leading causes of death in Hispanics are related to smoking (Kochanek, Xu, Murphy, Miniño, & Kung, 2011). Hispanics living in the United States experience culturally related stressors (including discrimination and acculturative stress) that have harmful consequences for mental health and smoking cessation (Kendzor et al., 2014; Torres, Driscoll, & Voell, 2012). Identifying strategies to enhance cessation despite high levels of stress in this understudied and underserved population is critical. The current study sought to investigate whether specific aspects of mindfulness predict smoking cessation among smokers of Mexican heritage.
Mindfulness has been defined as “paying attention in a particular way: on purpose, in the present moment, and nonjudgmentally” (Kabat-Zinn, 1994, p. 4). This form of nonjudgmental attention should foster self-acceptance in the midst of difficult life experiences and lessen the likelihood of impulsive reactions to stress. Indeed, research suggests that mindfulness-based training reduces emotional reactivity in the face of stressors (Arch & Craske, 2010; Britton, Shahar, Szepsenwol, & Jacobs, 2012). Dispositional mindfulness, the tendency for mindful responding in daily life, is associated with lower perceived stress, depressive symptoms, and neuroticism (Brown & Ryan, 2003). There is also growing evidence that mindfulness-based interventions, aimed at increasing dispositional mindfulness, enhance smoking cessation (Brewer et al., 2011; Davis, Fleming, Bonus, & Baker, 2007; Davis, Goldberg, et al., 2014; Davis, Manley, Goldberg, Smith, & Jorenby, 2014). These trials included 10.3%, 0.0%, 1.5%, and 1.7% Hispanics, respectively, highlighting the need for research on mindfulness and smoking cessation in this population.
Vidrine, Businelle et al. (2009) reported that among smokers interested in quitting (10% of whom were Hispanic), those with greater mindfulness indicated lower nicotine dependence, lower withdrawal severity, higher self-efficacy for avoiding smoking in high-risk situations, and greater expectancies that they could control emotions without smoking. Moreover, Heppner et al. (2015) found that among African American smokers receiving cessation treatment, those with greater mindfulness were more likely to be abstinent up to 26 weeks postquit. Thus, initial research suggests that mindfulness is linked to improved cessation outcomes; however, more work is needed to determine whether this association exists among Hispanic populations.
In addition, research is needed to clarify which aspects of mindfulness might promote smoking cessation. Although mindfulness has been conceptualized as multifaceted (Baer, Smith, Hopkins, Krietemeyer, & Toney, 2006), the aforementioned studies of dispositional mindfulness and cessation used unidimensional measures of mindfulness. Baer et al. (2006) conducted an influential factor analysis of mindfulness questionnaires that revealed five facets: (a) Observing (paying attention to present sensations), (b) Describing (labeling thoughts and feelings), (c) Acting With Awareness (staying focused on the present moment and acting deliberately), (d) Nonjudging (accepting thoughts and feelings without judging them), and (e) Nonreactivity (perceiving thoughts and feelings without reacting to them). Although these facets are related, they can be distinguished conceptually. For example, a person may be highly attuned to emotions (i.e., observing anxiety) but not be able to describe them in words or refrain from judging them as negative. The work by Baer et al. resulted in the Five Facet Mindfulness Questionnaire (FFMQ).
Research has begun to examine differential associations between FFMQ facets and psychosocial functioning. Cebolla et al. (2012) found that in an adult Spanish sample, Nonjudging, Acting With Awareness, Describing, and Nonreactivity were each related to lower psychiatric symptoms, but Observing was not. Among the subscales, the Nonjudging facet showed the strongest associations with lower psychiatric symptoms. Although no known research has examined associations between FFMQ subscales and smoking cessation, Roberts and Danoff-Burg (2010) found that Acting With Awareness was associated with smoking fewer cigarettes per day among college students. Additional research suggests that Nonjudging, Describing, and Acting With Awareness are related to lower eating pathology and alcohol use (Adams et al., 2012; Fernandez, Wood, Stein, & Rossi, 2010). The Observing facet may only predict better psychosocial functioning and healthier behaviors among experienced meditators, who have practiced observing sensations with a nonjudgmental, nonreactive stance (Baer et al., 2008). In fact, in nonmeditating samples, greater observation of experiences may be maladaptive if individuals are prone to focusing on unpleasant thoughts and emotions with a judgmental attitude. Thus, we did not expect Observing to predict smoking cessation in the current sample of nonmeditators.
Notably, none of the above studies on mindfulness and health risk behaviors focused on Hispanics living in the United States. The current study is the first known to examine associations between dispositional mindfulness and smoking cessation in a Hispanic population. In a sample of Spanish-speaking smokers of Mexican heritage, we sought to examine whether specific facets of mindfulness predict smoking cessation over time. Hispanics living in the United States frequently experience stress related to social disadvantage, discrimination, and acculturation, and these stressors can impede efforts to quit smoking and contribute to health disparities (Kendzor et al., 2014; Myers, 2009; Torres et al., 2012). Mindfulness appears to promote enhanced emotion regulation in stressful situations (Arch & Craske, 2010; Britton et al., 2012), and Hispanic smokers who are able to notice uncomfortable experiences nonjudgmentally and without automatically reacting to them might be less likely to smoke in an attempt to relieve distress. Thus, we hypothesized that the Nonjudging and Nonreactivity FFMQ subscales (which focus on how participants respond to distressing thoughts, emotions, and situations) would predict abstinence. Determining which specific aspects of mindfulness are linked to cessation could be critical to inform mindfulness-based smoking cessation treatments for Hispanic populations.
MethodData were collected as part of a longitudinal study examining predictors of smoking cessation among Spanish-speaking adults of Mexican heritage. As part of this clinical research study, participants received smoking cessation treatment, including nicotine patch therapy, self-help materials, and six brief in-person and telephone counseling sessions based on an empirically validated intervention for Spanish-speaking smokers (Wetter et al., 2007). All participants received the same treatment, which was based on the Treating Tobacco Use and Dependence clinical practice guideline (Fiore et al., 2000) and motivational interviewing (Miller & Rollnick, 2002) and did not specifically teach mindfulness. Questionnaire data (including trait mindfulness) were collected at baseline (1 week before the quit date), and biochemically confirmed smoking cessation was assessed at 3 and 26 weeks postquit. Procedures were approved by the institutional review board of The University of Texas MD Anderson Cancer Center, and all participants completed the informed consent process.
Participants
In total, 199 participants were recruited through media advertising (n = 165) in the Houston area or through the population-based Mexican American Cohort Study (n = 34), a longitudinal study of health risk factors among individuals of Mexican heritage (Barcenas et al., 2007). Individuals were eligible if they (a) were of Mexican heritage, (b) preferred to speak Spanish, (c) were 18–65 years old, (d) were a current smoker having smoked ≥5 cigarettes/day in the past year, (e) had an expired carbon monoxide (CO) level of ≥8 ppm (Benowitz et al., 2002), (f) were motivated to quit smoking in the next month, and (g) possessed a valid home address and home telephone number. Exclusion criteria were (a) contraindication for use of the nicotine patch, (b) active substance use disorder, (c) regular use of tobacco products other than cigarettes, (d) use of bupropion or nicotine replacement products other than patches supplied by the study, (e) pregnancy or lactation, (f) another household member enrolled in the study, or (g) participation in another smoking cessation program or research study within the past 90 days.
Measures
Demographic information
Participants indicated their age, gender, partner status (married/living with partner vs. single/divorced/separated/widowed), and educational attainment.
Nicotine dependence
The Heaviness of Smoking Index (HSI; Heatherton, Kozlowski, Frecker, Rickert, & Robinson, 1989) is a two-item self-report measure that is supported as a reliable and valid indicator of nicotine dependence (Borland, Yong, O’Connor, Hyland, & Thompson, 2010) and has been used in Hispanic smokers (Vidrine, Vidrine, et al., 2009). The two items (administered at baseline) are the following: “How many cigarettes a day do you smoke on average?” and “How soon after you wake up do you smoke your first cigarette?” (“time to first cigarette”).
Mindfulness
The FFMQ (Baer et al., 2006) is a 39-item self-report measure of dispositional mindfulness. Participants rate how often each item describes them from 1 (never or rarely true) to 5 (very often or always true). The FFMQ yields five subscales: (a) Observing (e.g., “I pay attention to sensations, such as the wind in my hair or the sun on my face”), (b) Describing (e.g., “I’m good at finding the words to describe my feelings”), (c) Acting With Awareness (e.g., “I rush through activities without really being attentive to them [reverse-scored]), (d) Nonjudging (e.g., “I think some of my emotions are bad or inappropriate and I shouldn’t feel them” [reverse-coded]), and (e) Nonreactivity (e.g., “I perceive my feelings and emotions without having to react to them”). For the current study, the FFMQ was translated into Spanish using a back-translation procedure by two bilingual individuals of Hispanic origin and reviewed by personnel of the institution’s International Department of Medical Translation. The translated version was then reviewed by Mexican American individuals reflecting diverse levels of acculturation so that consensus on wording was reached. The resulting Spanish FFMQ was administered at baseline. In the current sample, all subscales showed adequate internal consistency (α = 0.71–0.83).
Smoking abstinence
Seven-day point prevalence abstinence at 3 and 26 weeks postquit was defined as self-reported complete abstinence from smoking for the previous 7 days, verified by either CO <8 ppm or salivary cotinine <20 ng/ml. At each time point, participants who reported a lapse and/or produced CO or cotinine levels inconsistent with abstinence were considered not abstinent. An intent-to-treat (ITT) approach was used, such that when abstinence status could not be determined due to missing data, participants were considered not abstinent.
Depressive symptoms
Given that mindfulness is associated with lower depressive symptoms (Brown & Ryan, 2003) and that depressive symptoms often predict worse cessation outcomes (Leventhal, Ramsey, Brown, LaChance, & Kahler, 2008), ancillary analyses controlled for depression. The Center of Epidemiological Studies Depression Scale (CES-D; Radloff, 1977), a psychometrically sound 20-item measure of past-week depressive symptoms, was administered at baseline.
Statistical Analyses
To examine the relationship between baseline mindfulness and abstinence over time, we utilized logistic random coefficients regression models. Models specified an unstructured covariance matrix for the vector of random intercept and slope of time for each participant. Primary outcomes were biochemically confirmed 7-day abstinence at weeks 3 and 26 postquit. First, models were created to predict repeated-measures abstinence from all FFMQ subscales entered simultaneously. Next, separate models were created to predict abstinence from each FFMQ facet. Analyses were conducted with and without controlling for demographic variables (gender, education, age, and partner status, chosen based on past research; e.g., Businelle et al., 2010) and nicotine dependence (HSI). Finally, models were created to examine whether any associations remained significant after controlling for baseline depressive symptoms.
ResultsOf 199 participants, 63.3% were male, 69.3% were married or living with a partner, and 77.9% reported less than or equal to high school education. Mean age was 38.73 years (SD = 10.14). Mean scores on FFMQ subscales were as follows: Observing, M = 24.19 (SD = 6.37); Describing, M = 27.24 (SD = 5.17); Acting With Awareness, M = 30.42 (SD = 5.99); Nonjudging, M = 27.30 (SD = 6.31); and Nonreactivity, M = 19.16 (SD = 4.97).
The Nonjudging facet predicted greater odds of abstinence, both with (OR = 1.06, p = .02) and without (OR = 1.08, p = .01) controlling for demographic covariates and dependence. None of the other subscales were significant predictors in separate models (ps > .15). When subscales were entered simultaneously (rather than in separate models), the same pattern emerged: Nonjudging predicted greater odds of abstinence (OR = 1.09, p = .03), over and above other facets of mindfulness. None of the other facets emerged as significant predictors (ps > .30). After controlling for demographic covariates and dependence, Nonjudging remained a significant predictor of greater odds of abstinence (OR = 1.09, p = .03). Analyses were also conducted using completers only (rather than ITT), and the pattern of results was identical. Finally, given that Nonjudging was associated with lower depressive symptoms, r = −.39, p < .001, baseline CES-D score was entered as a covariate. In separate models, Nonjudging remained a significant predictor after controlling for depression and demographics (OR = 1.06, p = .04); this association approached significance when also controlling for dependence (OR = 1.06, p = .057). In simultaneous models, Nonjudging remained significant after controlling for depression and demographics (OR = 1.09, p = .048) and approached significance after also controlling for dependence (OR = 1.07, p = .07). Although Nonjudging was not significant at the .05 level once all covariates were included, it was still a significant predictor when both depression and demographics were controlled. Thus, the relationship between Nonjudging and abstinence does not appear to be fully explained by lower concurrent depressive symptoms.
In effort to enhance our understanding of the clinical significance of the findings, abstinence rates were examined for participants low versus high on Nonjudging. At week 3 postquit, 22.7% of participants in the lowest quartile of Nonjudging were abstinent, versus 53.8% of those in the highest quartile. At week 26 postquit, only 4.5% of those low in Nonjudging were abstinent, compared to 23.1% of those high in Nonjudging.
DiscussionNonjudgment may be a key aspect of mindfulness, contributing to enhanced cessation outcomes among Spanish-speaking smokers of Mexican heritage. Notably, Bishop et al. (2004) highlighted a nonjudgmental, accepting orientation to experience as one of two core aspects of mindfulness. Learning to experience unpleasant thoughts, feelings, and physical sensations associated with smoking cessation without judging them may lessen distress and increase likelihood of abstinence. Example FFMQ Nonjudging items (reverse-coded) are the following: “I tell myself I shouldn’t be feeling the way I’m feeling” and “When I have distressing thoughts or images, I judge myself as good or bad, depending what the thought/image is about” (Baer et al., 2006). If a person tells himself that he should not be feeling irritable and judges himself as a “bad” person because he is having strong cravings, these judgmental self-statements may further escalate negative emotions, increasing the likelihood of smoking in an attempt to relieve distress (Marlatt & Witkiewitz, 2005). Alternatively, if a person recognizes that unpleasant sensations are a natural part of the quit process (i.e., accepting instead of judging them as “bad” sensations that need to be escaped), he might be more likely to abstain from smoking in the context of these sensations.
Accepting thoughts, feelings, and physical sensations without judgment may be particularly helpful for Hispanic smokers in the United States, for whom discrimination and stress associated with acculturation can increase psychological distress and interfere with cessation (Kendzor et al., 2014; Torres et al., 2012). If individuals are able to notice uncomfortable experiences without judgment, they may be less likely to smoke in an attempt to escape distress. Importantly, mindfulness does not involve ignoring/suppressing or denying thoughts or emotions related to difficult situations. Rather, mindfulness skills encourage individuals to notice distressing experiences (including any associated thoughts and emotions) and then bring their attention back to other features of the present moment so that their responses are flexible, adaptive, and nonimpulsive. It is unclear why the Nonreactivity facet did not predict cessation in this sample; research should continue to examine the relevance of this facet for cessation.
Although no other known research has examined facets of mindfulness with regard to smoking cessation, at least three studies support the unique importance of Nonjudgment in relation to alcohol use. Ostafin, Kassman, and Wessel (2013) found that Nonjudging moderated the association between automatic responses to alcohol and alcohol preoccupation (i.e., Nonjudging weakened the link between automatic emotional reactions to alcohol and difficulty disengaging from alcohol-related thoughts). Ostafin and Marlatt (2008) reported that “Accepting Without Judgment” weakened the link between automatic motivation to drink and problematic alcohol use. Fernandez et al. (2010) found that Nonjudging was uniquely associated with lower alcohol-related consequences. Given that smoking and problematic drinking are often fueled by self-criticism and negative emotions, mindfully experiencing uncomfortable thoughts and feelings without judging them may reduce the likelihood that they will trigger unhealthy behaviors.
The current study is limited by an exclusive focus on Spanish-speaking smokers of Mexican heritage who were motivated to quit smoking, and results may not generalize to smokers who would be ineligible for this study (e.g., smokers who are not motivated to quit, have a substance use disorder, are pregnant, or for whom nicotine patches are contraindicated). The majority (63%) of participants were male, and the results could be more applicable to men than women. In addition, mindfulness was only measured at baseline, and future research should examine how changes in mindfulness over time relate to abstinence outcomes. This study is strengthened by its focus on an underserved ethnic group, examination of multiple facets of mindfulness, use of longitudinal data, and biochemical confirmation of smoking status.
Results highlight the importance of the Nonjudging facet of mindfulness in predicting enhanced smoking cessation outcomes in smokers of Mexican heritage. Notably, this study examined dispositional mindfulness (i.e., naturally occurring individual differences) rather than mindfulness-based treatment. Research should examine whether mindfulness-based treatment enhances certain aspects of mindfulness and whether increases in mindfulness facets lead to higher abstinence rates. Smoking cessation interventions that encourage mindful experience of thoughts, emotions, and physical sensations without judgment might be effective for enhancing smoking cessation among Hispanics and potentially for other populations of smokers as well.
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Submitted: November 14, 2014 Revised: March 19, 2015 Accepted: March 19, 2015
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Source: Psychology of Addictive Behaviors. Vol. 29. (4), Dec, 2015 pp. 918-923)
Accession Number: 2015-20853-001
Digital Object Identifier: 10.1037/adb0000087
Record: 103- Title:
- Nonsuicidal self-injury among 'privileged' youths: Longitudinal and cross-sectional approaches to developmental process.
- Authors:
- Yates, Tuppett M.. University of California, Department of Psychology, Riverside, CA, US, Tuppett.Yates@ucr.edu
Tracy, Allison J.. Centers for Research on Women, Wellesley College, Wellesley, MA, US
Luthar, Suniya S.. Developmental and Clinical Psychology Programs, Teachers College, Columbia University, New York, NY, US - Address:
- Yates, Tuppett M., University of California, Department of Psychology, 2320 Olmsted Hall, Riverside, CA, US, 92521, Tuppett.Yates@ucr.edu
- Source:
- Journal of Consulting and Clinical Psychology, Vol 76(1), Feb, 2008. Suicide and Nonsuicidal Self-Injury. pp. 52-62.
- NLM Title Abbreviation:
- J Consult Clin Psychol
- Page Count:
- 11
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- Journal of Consulting Psychology
- Other Publishers:
- US : American Association for Applied Psychology
US : Dentan Printing Company
US : Science Press Printing Company - ISSN:
- 0022-006X (Print)
1939-2117 (Electronic) - Language:
- English
- Keywords:
- nonsuicidal self-injury, privileged youths, developmental psychopathology, delinquency, zero-inflated Poisson regression models
- Abstract:
- This investigation examined process-level pathways to nonsuicidal self-injury (NSSI; e.g., self-cutting, -burning, -hitting) in 2 cohorts of suburban, upper-middle-class youths: a cross-sectional sample of 9th-12th graders (n = 1,036, 51.9% girls) on the West Coast and a longitudinal sample followed annually from the 6th through 12th grades (n = 245, 53.1% girls) on the East Coast. High rates of NSSI were found in both the cross-sectional (37.2%) and the longitudinal (26.1%) samples. Zero-inflated Poisson regression models estimated process-level pathways from perceived parental criticism to NSSI via youth-reported alienation toward parents. Pathways toward the initiation of NSSI were distinct from those accounting for its frequency. Parental criticism was associated with increased NSSI, and youth alienation toward parents emerged as a relevant process underlying this pathway, particularly for boys. The specificity of these pathways was explored by examining separate trajectories toward delinquent outcomes. The findings illustrate the prominence of NSSI among 'privileged' youths, the salience of the caregiving environment in NSSI, the importance of parental alienation in explaining these relations, and the value of incorporating multiple systems in treatment approaches for adolescents who self-injure. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Human Development; *Juvenile Delinquency; *Psychopathology; *Self-Inflicted Wounds; *Upper Class
- Medical Subject Headings (MeSH):
- Adolescent; Cohort Studies; Conflict (Psychology); Cross-Sectional Studies; Female; Humans; Juvenile Delinquency; Longitudinal Studies; Male; Parent-Child Relations; Personality Inventory; Poisson Distribution; Probability; Risk Factors; Self-Injurious Behavior; Sex Factors; Social Alienation; Social Class; Substance-Related Disorders; Suicide, Attempted
- PsycINFO Classification:
- Behavior Disorders & Antisocial Behavior (3230)
- Population:
- Human
Male
Female - Location:
- US
- Age Group:
- Childhood (birth-12 yrs)
School Age (6-12 yrs)
Adolescence (13-17 yrs) - Tests & Measures:
- Inventory of Parent and Peer Attachment-Alienation subscale
Functional Assessment of Self-Mutilation
Child Behavior Checklist Youth Self-Report (YSR) form--Rule-Breaking subscale
Multidimensional Perfectionism Scale - Grant Sponsorship:
- Sponsor: National Institute of Mental Health
Grant Number: R01-DA14385
Recipients: No recipient indicated
Sponsor: William T. Grant Foundation
Recipients: No recipient indicated - Methodology:
- Empirical Study; Longitudinal Study; Quantitative Study
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- Accepted: Aug 20, 2007; Revised: Aug 10, 2007; First Submitted: Feb 9, 2007
- Release Date:
- 20080128
- Correction Date:
- 20120827
- Copyright:
- American Psychological Association. 2008
- Digital Object Identifier:
- http://dx.doi.org/10.1037/0022-006X.76.1.52
- PMID:
- 18229983
- Accession Number:
- 2008-00950-008
- Number of Citations in Source:
- 53
- Persistent link to this record (Permalink):
- http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2008-00950-008&site=ehost-live
- Cut and Paste:
- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2008-00950-008&site=ehost-live">Nonsuicidal self-injury among 'privileged' youths: Longitudinal and cross-sectional approaches to developmental process.</A>
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Nonsuicidal Self-Injury Among “Privileged” Youths: Longitudinal and Cross-Sectional Approaches to Developmental Process
By: Tuppett M. Yates
Department of Psychology, University of California, Riverside;
Allison J. Tracy
Centers for Research on Women, Wellesley College
Suniya S. Luthar
Developmental and Clinical Psychology Programs, Teachers College, Columbia University
Acknowledgement: Preparation of this article was funded in part by National Institute of Mental Health Grant R01-DA14385 and by the William T. Grant Foundation. We thank Monica Ghailian and Chandra Reynolds for their assistance and comments.
In recent years, nonsuicidal self-injury (NSSI; e.g., self-cutting, -burning, -hitting) has transcended the bounds of clinical wards and medical journals to reveal itself as a prominent and burgeoning health concern among community youths (Gratz, Conrad, & Roemer, 2002; Laye-Gindhu & Schonert-Reichl, 2005; Muehlenkamp & Guttierez, 2004; Ross & Heath, 2002; Whitlock, Eckenrode, & Silverman, 2006). However, the extant literature on NSSI, particularly in community settings, has focused on descriptive studies to the relative neglect of theoretically informed, process-oriented investigations that recognize NSSI as both a developmental and clinical phenomenon. Addressing this gap in the literature, the present study examined putative developmental processes underlying self-injurious pathways in two cohorts of suburban, upper-middle-class youths: a cross-sectional sample of 9th–12th graders on the West Coast and a longitudinal sample that was followed annually from the 6th through 12th grades on the East Coast.
The Phenomenology of NSSIBuilding on previous definitions of NSSI (see Simeon & Favazza, 2001, for review), this study examined self-inflicted, direct, socially unacceptable destruction or alteration of body tissue that occurred in the absence of conscious suicidal intent or pervasive developmental disorder. Recent community studies point to striking rates of NSSI, as defined here, among adolescents. For example, Gratz et al. (2002) found that 38% of a college student sample endorsed a history of NSSI, whereas Ross and Heath (2002) found that 14% of a high school sample reported NSSI (see also Laye-Gindhu & Schonert-Reichl, 2005). Drawing on a large, multisite study of more than 3,000 college students, Whitlock et al. (2006) found that 17% of college students reported NSSI and that 75% of these self-injurers endorsed more than one episode.
The prevalence and phenomenology of NSSI across different gender, ethnic, and economic groups remain unclear. Although some studies have suggested that girls are 1.5–3 times more likely to self-injure than are boys (Clery, 2000; Favazza, 1999), others have suggested that gender differences are less pronounced (Garrison et al., 1993; Gratz et al., 2002; Tyler, Whitbeck, Hoyt, & Johnson, 2003). In contrast to gender differences, socioeconomic and ethnic differences have rarely been examined in studies of NSSI. A recent survey of college students found no relation between social class (as indicated by parental education level) and NSSI (Whitlock et al., 2006), but other findings have suggested that rates of self-injury may be elevated among low-income individuals (Nada-Raja, Skegg, Langley, Morrison, & Sowerby, 2004). Similarly, although a few studies have reported elevated rates of NSSI among Caucasian individuals (e.g., Ross & Heath, 2002), others have revealed significant rates among minority youths (Gratz, 2006; Lipschitz et al., 1999; Marshall & Yazdani, 1999; Nada-Raja et al., 2004). Building on this literature, the present study examined the phenomenology and sociodemographic patterning of NSSI among 1,300 high school students who were attending suburban coeducational schools that primarily cater to children of highly educated, white-collar professionals.
Developmental Pathways to NSSIRelative to the descriptive literature on NSSI, less is known about developmental pathways toward self-injurious outcomes. Retrospective findings strongly implicate the quality of the caregiving environment in the etiology of NSSI, with up to 79% of adult self-injurers reporting a childhood history of abuse or neglect (Gratz et al., 2002; Low, Jones, MacLeod, Power, & Duggan, 2000; van der Kolk, Perry, & Herman, 1991; Wiederman, Sansone, & Sansone, 1999). However, little is known about etiologic and developmental processes underlying NSSI in adolescence, despite evidence that this is the period during which self-injurious pathways are typically initiated (Favazza, 1999). Moreover, researchers have rarely examined the potential contribution of less extreme forms of negative parent–child interactions (e.g., critical parenting) to NSSI (see Wedig & Nock, 2007). Building on a recent application of a developmental psychopathology perspective on NSSI (Yates, 2004), this study examined developmental pathways and mechanisms by which parental criticism may contribute to NSSI in adolescence.
Grounded in an understanding of normative development and informed by core tenets of attachment and organizational theories of development (Sroufe, 1990), Yates (2004) identified several process-level pathways toward NSSI that may follow from the deleterious impact of adverse caregiving on development. In this view, harsh or critical parenting may contribute to NSSI by undermining emerging representations of relationships as reliable and rewarding (i.e., motivational processes); complementary views of the self as worthy of care (i.e., attitudinal processes); capacities to integrate experience across multiple levels of thinking and feeling (i.e., integrative processes); abilities to modulate emotion and arousal (i.e., emotional processes); and/or resources to form reciprocal and empathic relationships (i.e., relational processes). This investigation tested a motivational pathway toward NSSI, wherein we hypothesized that parental criticism would undermine adolescents' representations of others, thereby prompting them to turn toward the self and the body, rather than to others, in times of challenge or distress. This motivational hypothesis is consistent with evidence that parental criticism is associated with invalidating and rejecting caregiving environments (McCarty, Lau, Valeri, & Weisz, 2004), which may instill a sense of alienation from caregivers and a broader mistrust of others (Fonagy, Target, & Gergely, 2000; Sroufe, 1990), as well as with the overwhelming evidence that NSSI subserves self- and affect-regulatory functions (Brain, Haines, & Williams, 1998; Nock & Prinstein, 2004, 2005).
Developmental Specificity of Self-Injurious PathwaysAlthough recent studies have considered self-injurious pathways and relevant developmental processes theoretically (Yates, 2004) and empirically (Ross & Heath, 2003; Yates, Carlson, & Egeland, in press), there remains a pressing need to ascertain whether identified risks and processes provide explanatory power that is unique to self-injurious outcomes or whether they are merely characteristic of global psychopathology. Contrary to the hypothesis that a sense of alienation from others will prompt individuals to turn in and against the self in times of duress or need, an alternative model predicts that adolescents may turn out and against others as a consequence of negative relational representations (Egeland, Yates, Appleyard, & van Dulmen, 2002; Sankey & Huon, 1999). Thus, our final aim in this investigation was to explore whether the motivational vulnerabilities that follow from critical parenting (i.e., youth alienation toward parents) contributed to delinquent outcomes in adolescence (i.e., rule-breaking behavior) and whether these paths differ between girls and boys and/or from those toward NSSI.
SummaryThis study evaluated theoretically informed, process-level pathways between perceived parental criticism and NSSI among “privileged” youths in a cross-sectional sample of 9th–12th graders and a longitudinal sample that was followed from the 6th through 12th grades. Our first aim in this study was to describe the phenomenology of NSSI among children of highly educated, white-collar professionals, a population that has been largely overlooked in previous studies of psychopathology (see Luthar, 2003, for discussion). Second, we sought to evaluate a motivational pathway to NSSI, in which we predicted that critical parenting would contribute to NSSI via its negative impact on parental representations, as reflected by increased feelings of alienation toward parents. Given prior evidence of meaningful gender differences in NSSI, these processes were estimated independently for girls and for boys. Our final goal was to explore the specificity of the proposed motivational pathway toward NSSI by examining a parallel model using delinquent behavior as the outcome. Together, these goals draw on the complementary strengths of cross-sectional and longitudinal research designs to enable the description and preliminary temporal specification of self-injurious pathways among suburban, upper-middle-class youths.
Method Participants
West Coast cross-sectional sample
Participants in this sample were drawn from a single high school in a West Coast suburban community. As of the 2000 census, the median household income in this community was $91,904 (equivalent to ~$111,116 in 2006); 69.1% of adults had at least a college degree, and only 1.9% of families lived at or below the poverty line. Of the original 1,185 participants, 1,036 (538 girls, 498 boys) provided complete data on NSSI. The current sample was evenly distributed across the 9th, 10th, 11th, and 12th grades. The ethnic composition of the sample was 70.7% Caucasian, 18.1% Asian, 2.4% Hispanic, 1.5% Black, 1% other minority (e.g., Native American), and 6.3% multiracial. Students who provided complete data on NSSI did not differ from the larger sample with respect to salient demographics, including ethnicity, gender, and grade membership. Participants who provided complete data on NSSI but not on other relevant variables (e.g., parental criticism) were not included in the path analyses (n = 57, 5.5%). The ethnic, gender, and grade distribution of the sample in the path analyses was comparable to that for the broader sample.
Students in the West Coast sample were assessed at the request of the local community and school. Following a series of incidents involving substance use and suicide attempts, community representatives invited Suniya S. Luthar to present available data on youths in such communities and to discuss possibilities for the assessment of students to ascertain intervention needs. Prior to data collection, the entire student body in both schools saw a videotaped presentation by Suniya S. Luthar that introduced the study, briefly explained that little was known about the lives of children of well-educated professionals, requested participation while clarifying that it was in no way required, and assured the anonymity of responses. Parents were sent letters that explained the study and gave them the opportunity to refuse consent for their child to participate. All 1,185 students who were in school (243 students were absent) and were eligible to participate (8 students were in special education) on the day of data collection completed the questionnaires, yielding an 82.9% response rate. Data collection occurred in the classrooms via paper-and-pen survey; there was no collection of personally identifying information. The administration of measures was performed by community personnel and teachers, who were instructed simply to maintain order (i.e., not to walk around the room and potentially glimpse students' responses). Upon completing the questionnaire, students sealed their response packets in an envelope and received a gift certificate in appreciation for their participation. All procedures were reviewed and approved by the Institutional Review Board for the Protection of Human Subjects, Teachers College, Columbia University.
East Coast longitudinal sample
Participants in this sample were drawn from the New England Study of Suburban Youth (NESSY), which is a longitudinal study of development and adaptation among a cohort of high-income, suburban schoolchildren first recruited in the 6th grade and followed annually thereafter through the 12th grade (Luthar & Goldstein, in press; Luthar & Latendresse, 2005; Luthar, Shoum, & Brown, 2006). The original NESSY sample consisted of 314 sixth graders (150 girls, 164 boys) from the two schools in this upper-middle-class community of highly educated, white-collar professionals. As of the 2000 census, the median household income in this community was $125,381; 32.8% of the adults had earned a graduate degree, and only 3% of the students received free or reduced-price lunches (Luthar & Sexton, 2004). At the time of the 12th-grade assessment, when NSSI was assessed, all 245 students (130 girls, 115 boys) who were in school (48 students were absent) and were eligible to participate (17 students did not have parental consent) completed the questionnaires, yielding a 79.5% response rate. The sample was 89% Caucasian and 5% Hispanic; the remaining 6% of the sample was evenly distributed across Asian, African American, and other racial groups, including multiracial identifications. Relative to the original sample, there were no significant differences in the ethnic or gender makeup of the 12th-grade sample, though the current sample was slightly more diverse than the original sample, which was 93% Caucasian. Participants who provided complete data in Grade 12 but who were not assessed at earlier time points were not included in the path analyses, because they were missing data on key predictor variables (e.g., parental criticism: n = 34, 13.9%). The ethnic and gender distribution of the sample in the path analyses was comparable with that for the broader sample.
As in the West Coast sample, the NESSY grew out of community concern about the welfare of children, which precipitated a school-based initiative to understand and encourage positive youth development. Student recruitment was based on passive consent procedures. Administrators sent letters to parents that described the study, emphasized that data would be presented only in aggregate form, and requested notification from parents who did not wish their child to participate. A few days prior to data collection, the parents were again informed about the study and given the opportunity to request that their child not participate. The children themselves were given the opportunity to decline to participate in the study. Data were collected in the classrooms. Test items were administered both visually and orally to prevent bias due to variability in reading abilities. Upon completion of each data collection, gift certificates were provided to all participating students. All procedures were reviewed and approved by the Institutional Review Board for the Protection of Human Subjects.
Measures
Parental criticism
Parental criticism was measured with the Multidimensional Perfectionism Scale (MPS; Frost, Marten, Lahart, & Rosenblate, 1990). The MPS consists of 35 statements that describe a range of perfectionistic beliefs, which are rated with a 5-point Likert scale from 1 (strongly disagree) to 5 (strongly agree). The Parental Criticism subscale consists of 4 items, including “I am punished for doing things less than perfectly,” “My parents never try to understand my mistakes,” “I never feel like I can meet my parents' expectations,” and “I never feel like I can meet my parents' standards.” Parental criticism was assessed cross-sectionally in the West Coast sample (αs = .77–.85) and was averaged across Grades 6, 7, and 8 in the East Coast sample (αs = .76–.86).
Parental alienation
Adolescents' feelings of alienation toward their parents were assessed with the Alienation subscale of the Inventory of Parent and Peer Attachment (IPPA; Armsden & Greenberg, 1987). The IPPA consists of 50 items (25 pertaining to each parent), which are rated on a 5-point Likert scale from 1 (almost never or never true) to 5 (almost always or always true). The Alienation scale consists of 12 items (6 for each parent) that assess the youth's feelings of anger, isolation, and mistrust in relating to each parent (e.g., “Talking over my problems with my mother/father makes me feel ashamed or foolish,” “I feel angry with my mother/father”). Due to the high correlations between maternal and paternal alienation (rs = .67–.71), we averaged these scales to create a global alienation score. Parental alienation was assessed cross-sectionally in the West Coast sample (αs = .86–.88) and was averaged across Grades 9, 10, and 11 in the East Coast sample (αs = .76–.85).
NSSI
We used the Functional Assessment of Self-Mutilation (FASM; Lloyd, Kelley, & Hope, 1997) to assess rates and methods of NSSI during the 12 months preceding the time of data collection. The utility of the FASM has been established across several studies (Guertin, Lloyd-Richardson, Spirito, Donaldson, & Boergers, 2001; Nock & Prinstein, 2004, 2005). Respondents indicated whether and how often they had engaged in 11 different forms of NSSI, including cutting or carving skin, picking at a wound, self-hitting, scraping skin to bleed, self-biting, picking areas of body to bleed, inserting objects under skin or nails, self-tattooing, burning skin, pulling out hair, or erasing skin to bleed. Frequency was rated using a 5-point scale that ranged across 1 (0 times), 2 (1 time), 3 (2–5 times), 4 (6–10 times), and 5 (≥ 11 times). NSSI was assessed cross-sectionally in the West Coast sample (αs = .84–.91) and in the 12th grade in the East Coast sample (αs = .67–.85).
Delinquent behavior
Delinquent behavior was assessed with the Rule-Breaking subscale of the Youth Self-Report (YSR) form of the Child Behavior Checklist (Achenbach, 1991b). This measure consists of 118 behavioral items rated by the adolescent on a 3-point scale as 0 (not true), 1 (somewhat or sometimes true), or 2 (very true or often true). T scores on the YSR stem from extensive normative data, evidence short-term test–retest reliability, and discriminate between clinic-referred and nonreferred youths (Achenbach, 1991a). The Rule-Breaking subscale includes items that capture a range of delinquent behaviors, such as associating with deviant peers, lying, and stealing. Delinquent behavior was assessed cross-sectionally in the West Coast sample (αs = .71–.76) and in the 12th grade in the East Coast sample (αs = .83).
Statistical Analyses
As is often observed in community-based studies of psychopathology, NSSI was not normally distributed across participants in this investigation. In both samples, the distribution of NSSI was positively skewed with a precipitous drop, such that even a transformed distribution would substantially violate the assumptions of normality required for parametric analytic approaches (Papoulis & Pillai, 2002). This characteristic inherent in the data requires a special case of regression analysis called zero-inflated Poisson (ZIP) regression. ZIP models are well suited to the analysis of count data with excess zeros (Lambert, 1992). The present analyses employed ZIP path models to permit the simultaneous prediction of two variables that, together, describe the obtained distribution of NSSI: namely, the occurrence of NSSI (i.e., “0” representing noninjurers, “1” representing all NSSI values greater than zero) and the frequency of NSSI once initiated (i.e., the specific value of NSSI greater than zero).
While ZIP regression models appropriately account for the distinct nonnormality of NSSI, several characteristics of this analytic paradigm warrant consideration. First, the statistical power needed for detection of a given effect size is greater than in the standard linear regression paradigm (Dufour & Zung, 2005). Second, standardized model fit indices and estimates of effect sizes (e.g., R2, standardized regression weights) developed for linear regression analysis are not available (Muthén & Muthén, 1998–2006). Third, the estimation technique required for appropriate handling of missing data in a Poisson-distributed dependent variable requires Monte Carlo numerical integration, which precludes the estimation of the statistical significance of indirect pathways (Muthén & Muthén, 1998–2006). Therefore, we reported unstandardized parameter estimates and their standard errors and constructed 95% confidence intervals to compare parameters across groups.
The path models were conducted in a general latent-variable modeling framework with multiple groups, which allowed the simultaneous estimation of hypothesized pathways across gender. Initial models specified only the direct relation between perceived parental criticism and NSSI. Next, the hypothesized mediating pathway from parental criticism through alienation to NSSI was introduced. As discussed previously, we also estimated these pathways in the prediction of delinquent behavior to test the specificity of the predicted motivational path for NSSI. In all models, the data from the West and East Coast samples were fit separately. Because the sample size was considerably smaller in the East Coast sample, resulting in relatively low statistical power, we have presented the results of the East Coast models as preliminary evidence of the directionality of the hypothesized processes.
Results Descriptive Analyses
Table 1 details the frequency of NSSI methods in each sample for girls and for boys during the preceding year. The FASM item corresponding to “pick at a wound” was not included in these analyses, because disproportionately high rates of endorsement suggested that this item captured a largely normative adolescent behavior. Across the remaining forms of NSSI, West Coast participants endorsed higher levels of NSSI (7.7% reported one incident, 29.5% reported more than one incident) than did East Coast respondents (10.2% reported one incident, 15.9% reported more than one incident), χ2(2, N = 1,281) = 18.68, p < .001. Across samples, girls reported significantly higher rates of NSSI (8.8% reported one incident, 30.5% reported more than one incident) than did boys (7.5% reported one incident, 22.8% reported more than one incident), χ2(2, N = 1,281) = 11.76, p < .01. Chi-square analyses did not reveal developmental differences in rates of NSSI among the West Coast respondents across the 9th–12th grades. However, significant differences in NSSI rates were apparent across the ethnic groups in the West Coast sample with respect to all forms of injury, χ2(5, N = 1,026) = 15.57–51.41 (all ps < .01), except for self-biting. Students who endorsed “Black” or “Other” ethnic identities (most of whom were Native American) reported higher rates of NSSI than did White, Hispanic, Asian, and multiracial respondents.
Frequencies of Nonsuicidal Self-Injury Among Female and Male Participants in the West Coast and East Coast Samples
The means and standard deviations for perceived parental criticism, parental alienation, and delinquent behavior are presented separately by gender and sample in Table 2. A two-way multivariate analysis of variance (ANOVA; Sample × Gender) revealed significant main effects for sample source, Wilks's λ = 0.96, F(3, 1160) = 17.85, p < .001; gender, Wilks's λ = 0.96, F(3, 1160) = 17.30, p < .001; and their interaction, Wilks's λ = 0.96, F(3, 1160) = 16.36, p < .001. Follow-up univariate ANOVAs revealed that participants in the West Coast sample reported higher levels of parental criticism, F(1, 1166) = 6.71, p < .01, and of alienation, F(1, 1166) = 17.63, p < .001, than did participants in the East Coast sample. Girls endorsed higher levels of parental criticism, F(1, 1166) = 4.63, p < .05, and of alienation, F(1, 1166) = 38.41, p < .001, than did boys. One significant Sample × Gender interaction emerged, with girls in the West Coast sample reporting higher levels of parental alienation than did boys, whereas rates of alienation were lower among girls than among boys in the East Coast sample, F(1, 1166) = 24.22, p < .001.
Descriptive Data for Independent and Dependent Variables by Sample and Gender
Zero-Inflated Poisson Path Analyses
NSSI
We used procedures within the Mplus program (Version 4.1; Muthén & Muthén, 1998–2006) to determine if and how parental criticism contributed to the occurrence of NSSI (i.e., “0” representing noninjurers, “1” representing all NSSI values greater than zero) and to the frequency of NSSI once initiated (i.e., the specific value of NSSI greater than zero). The presence of a mediated pathway through parental alienation was examined in models, which showed a significant effect of criticism prior to the inclusion of the mediating pathway. The presented figures include tests of mediating paths through parental alienation.
Among girls in the West Coast sample, perceived parental criticism was associated with an increased probability of engaging in NSSI (BP(NSSI) = 0.11, SEB = 0.02, p < .05, 95% CI = 0.07, 0.16) but was not related to the frequency of NSSI once initiated (BFrequency = 0.02, SEB = 0.01, ns). When parental alienation was added to the baseline model (see Figure 1, top), the direct relation between parental criticism and the probability of NSSI was no longer significant (BP(NSSI) = 0.02, SEB = 0.03, ns, 95% CI = −0.03, 0.07). In this model, the indirect path through parental alienation (BAlienation = 0.69, SEB = 0.04, p < .001; BAlienation →P(NSSI) = 0.15, SEB = 0.02, p < .001) accounted for much of the direct relation between parental criticism and an increased probability of NSSI.
Figure 1. Zero-inflated Poisson path analysis predicting the impact of parental criticism on the probability and frequency of NSSI via alienation for female participants (n = 514; top) and for male participants (n = 465; bottom) in the West Coast sample. Coefficients reflect unstandardized point estimates, with standard errors of the estimate in parentheses. Coefficients prior to mediation are in brackets. NSSI = nonsuicidal self-injury. *p < .05. **p < .01. ***p < .001.
Among boys in the West Coast sample, perceived parental criticism was associated both with an increased probability of NSSI (BP(NSSI) = 0.08, SEB = 0.03, p < .05, 95% CI = 0.02, 0.13) and with greater repetition of NSSI once initiated (BFrequency = 0.07, SEB = 0.02, p < .01, 95% CI = 0.04, 0.11). In the mediated model (see Figure 1, bottom), neither the direct path from parental criticism to the probability of any NSSI (BP(NSSI) = 0.00, SEB = 0.03, ns, 95% CI = −0.06, 0.07) nor the direct path from parental criticism to the frequency of NSSI (BFrequency = 0.04, SEB = 0.02, ns, 95% CI = −0.01, 0.08) was significantly different from zero. As among girls, the indirect path between parental criticism and an increased probability of NSSI through parental alienation (BAlienation = 0.61, SEB = 0.05, p < .001; BAlienation →P(NSSI) = 0.12, SEB = 0.03, p < .001) accounted for much of the direct relation between parental criticism and the probability of NSSI obtained in the initial model. Similarly, the indirect path between parental alienation and the frequency of NSSI (BAlienation = 0.61, SEB = 0.05, p < .001; BAlienation →Frequency = 0.07, SEB = 0.03, p < .05) accounted for a proportion of the direct relation between parental criticism and the repetition of NSSI found in the initial model.
Similar to results for the cross-sectional models obtained in the West Coast sample, perceived parental criticism in Grades 6–8 increased the likelihood of being a self-injurer 6 years later among girls in the East Coast sample (BP(NSSI)= 0.13, SEB = 0.07, p < .05, 95% CI = 0.01, 0.26) but was not related to the frequency of girls' NSSI once initiated (BFrequency = 0.04, SEB = 0.05, ns). When parental alienation in Grades 9–11 was added to the baseline model (see Figure 2), the direct path between perceived parental criticism in middle school and the probability of NSSI in 12th grade dropped to nonsignificance (BP(NSSI) = 0.08, SEB = 0.08, 95% CI = −0.08, 0.25).
Figure 2. Zero-inflated Poisson path analysis predicting the impact of parental criticism on the probability and frequency of NSSI via alienation for female participants (n = 111) in the East Coast sample. Coefficients reflect unstandardized point estimates, with standard errors of the estimate in parentheses. Coefficients prior to mediation are in brackets. NSSI = nonsuicidal self-injury. *p < .05. ***p < .001.
Among boys, perceived parental criticism in middle school increased the likelihood of being an injurer 6 years later, though only marginally (BP(NSSI) = 0.14, SEB = 0.08, p < .10), and was not related to the frequency of boys' NSSI once initiated (BFrequency = 0.04, SEB = 0.04, ns). Because these initial effects did not reach standard levels of statistical significance, mediated models were not examined among boys in the East Coast sample.
Delinquent behavior
Among girls in the West Coast sample, the level of perceived parental criticism was significantly related to increased rule-breaking behavior (BRule = 0.29, SEB = 0.05, p < .001, 95% CI = 0.19, 0.37). When parental alienation was added to the baseline model (see Figure 3, top), the direct relation between parental criticism and rule-breaking behavior dropped to nonsignificance (BRule = 0.07, SEB = 0.06, 95% CI = −0.05, .019) as a result of the indirect path through parental alienation (BAlienation = 0.68, SEB = 0.04, p < .001; BAlienation→Rule = 0.32, SEB = 0.06, p < .001).
Figure 3. Path analysis predicting the impact of parental criticism on rule-breaking behavior via alienation for female participants (n = 514; top) and for male participants (n = 464; bottom) in the West Coast sample. Coefficients reflect unstandardized point estimates, with standard errors of the estimate in parentheses. Coefficients prior to mediation are in brackets. ***p < .001.
Among boys in the West Coast sample, the level of perceived parental criticism was significantly related to increased rule-breaking behavior (BRule = 0.24, SEB = 0.06, p < .001, 95% CI = 0.12, 0.36). When parental alienation was added to the baseline model (see Figure 3, bottom), the direct relation between parental criticism and rule-breaking behavior dropped to nonsignificance (BRule = 0.02, SEB = 0.07, 95% CI = −0.12, 0.15). The indirect path through parental alienation (BAlienation = 0.60, SEB = 0.05, p < .001; BAlienation→Rule = 0.36, SEB = 0.05, p < .001) accounted for much of the direct relation between parental criticism and increased rule-breaking behavior among boys.
As in the cross-sectional models obtained in the West Coast sample, perceived parental criticism in Grades 6–8 contributed to increased rule-breaking behavior 6 years later among girls in the East Coast sample (BRule = 0.23, SEB = 0.07, p < .001, 95% CI = 0.09, 0.38). Unlike in the West Coast sample, however, when parental alienation in Grades 9–11 was added to the baseline model (see Figure 4, top), the direct path between parental criticism in middle school and rule-breaking behavior in 12th grade remained significant (BRule = 0.23, SEB = 0.07, p < .01, 95% CI = 0.09, 0.38). The pathways making up the indirect effect through parental alienation were not significant (BAlienation = 0.07, SEB = 0.18; BAlienation →Rule = −0.03, SEB = 0.03).
Figure 4. Path analysis predicting the impact of parental criticism on rule-breaking behavior via alienation for female participants (n = 123; top) and for male participants (n = 111; bottom) in the East Coast sample. Coefficients reflect unstandardized point estimates, with standard errors of the estimate in parentheses. Coefficients prior to mediation are in brackets. *p < .05. **p < .01.
A similar pattern was found among boys in the East Coast sample, with perceived parental criticism in middle school contributing to increased rule-breaking behavior 6 years later (BRule = 0.20, SEB = 0.08, p < .01, 95% CI = 0.05, 0.35). When parental alienation in Grades 9–11 was added to the baseline model (see Figure 4, bottom), the direct path between perceived parental criticism in middle school and rule-breaking behavior in 12th grade remained significant (BRule = 0.21, SEB = 0.08, p < .01, 95% CI = 0.06, 0.36). This result follows from the pathways making up the indirect effect through parental alienation being weak or nonsignificant (BAlienation = .42, SEB = 0.18, p < .05; BAlienation→Rule = –0.02, SEB = 0.04, ns).
Discussion The Phenomenology of NSSI Among “Privileged” Youths
NSSI emerged as a prominent and recurrent phenomenon among the 1,300 children of highly educated, white-collar professionals examined here. Nearly a third of these adolescents reported engaging in NSSI during the previous year, with approximately three quarters of injurers endorsing recurrent episodes of NSSI. These rates are higher than those observed in most other school settings (Laye-Gindhu & Schonert-Reichl, 2005; Muehlenkamp & Guttierez, 2004; Ross & Heath, 2002) and may reflect one or more factors. First, heightened media attention to NSSI in recent years may have contributed to increased rates of NSSI and/or to youths' comfort with reporting it. Second, the FASM, which was used to measure NSSI in this study, captures a wider range of self-injury methods than do other measures of NSSI (e.g., body picking, skin scraping, and self-biting), which renders it highly sensitive but perhaps overly inclusive. Finally, rates of NSSI may, in fact, be elevated among upper-middle-class, suburban youths, perhaps as a function of increased pressure to contain their emotions and achieve at superior levels (Luthar & Becker, 2002).
Rates of NSSI were especially pronounced among the West Coast participants, which may qualify the generalizability of these findings. As mentioned previously, the current study was invited by school leaders in this suburban community following a series of self-destructive behaviors among local adolescents during the preceding year. It is impossible to ascertain if or how these community events may have influenced adolescents' NSSI as reported here, but they certainly warrant cautious interpretation of these high endorsement rates. Beyond community experience effects, however, much of the observed difference in NSSI rates between the West and East Coast samples may follow from the unique design features of these studies. The West Coast students were assured that their survey responses would remain anonymous, whereas the East Coast students were advised that their responses were connected with their identity and that the research team was required to report instances of significant concern for a student's safety. Thus, youths in the East Coast sample may have been more reluctant to disclose NSSI than were their West Coast counterparts. The comparable rates of delinquent behavior reported in the West and East Coast samples suggest that student reports of NSSI may be especially sensitive to data collection procedures. Despite concerns about the generalizability of these findings, the data clearly suggest that all is not well among these purportedly “privileged” and protected youths.
Beyond sample effects, gender emerged as a salient influence on rates and methods of NSSI. Although reports of NSSI were elevated among girls, the boys in these samples endorsed significant levels of NSSI. These findings replicate data from other community samples, which suggest that gender differences in rates of NSSI are more modest than previously thought (Garrison et al., 1993; Gratz et al., 2002; Tyler et al., 2003). These data point to the need for increased research and clinical attention to NSSI among boys, particularly given current evidence that gender may moderate self-injurious pathways. Similarly, there is a need for greater consideration of ethnic differentials in NSSI, given the suggestion here and elsewhere that some groups may be at disproportionately high or low risk for NSSI (Gratz, 2006; Lipschitz et al., 1999; Marshall & Yazdani, 1999; Nada-Raja et al., 2004).
Parental Criticism, Alienation, and NSSI
Beyond the descriptive level, the present findings generally support the proposed motivational pathway from parental criticism to NSSI via negative relationship representations (i.e., parental alienation). Perceived parental criticism statistically predicted NSSI in both the cross-sectional and the longitudinal samples. Moreover, adolescents' reported sense of alienation toward parents emerged as a salient process explaining these relations. In the West Coast sample, parental alienation accounted for much of the relation between perceived parental criticism and the initiation of NSSI among both girls and boys, as well as for the frequency of NSSI among boys. Longitudinal patterns in the East Coast sample provided preliminary support for the directionality of this motivational pathway. Discrepant patterns in the West and East Coast samples may reflect regional variations, distinct developmental patterns and processes, and/or unstable parameter estimates due to the small size of the East Coast sample. Although there is a need for replication studies to confirm these directional interpretations, the data support the assertion that critical parenting may contribute to negative representations of others, thereby decreasing youths' motivation to turn to others in times of duress and increasing the likelihood of NSSI as a self- and body-based coping strategy.
However, the specificity of this motivational pathway to NSSI was not supported in this study. Significant paths from perceived parental criticism to delinquent behavior via parental alienation revealed that these are important processes for understanding both self- and other-directed distress and aggression. Perceived parental criticism was related to rule-breaking behavior among girls and boys, and parental alienation played a mediating role in these relations in the West Coast sample. As with NSSI, these patterns were less consistent in the longitudinal East Coast sample, but there was preliminary support for their directionality.
Overall, the present findings are consistent with the extant literature on the role of expressed emotion, particularly parental criticism, on rates and patterns of clinical dysfunction among youths (Asarnow, Tompson, Woo, & Cantwell, 2001; McCarty et al., 2004; Wedig & Nock, 2007), as well as with recent work demonstrating the contribution of alienation to youth maladaptation (O'Donnell, Schwab-Stone, & Ruchkin, 2006; Sankey & Huon, 1999). However, this study examined a single developmental pathway, and its limited statistical power precluded the consideration of protective and/or vulnerability processes that may moderate (or mediate) these relations. For example, many of the youths who reported parental criticism in this study may have experienced overt forms of maltreatment as well. Future research must investigate other processes that influence pathways from adverse caregiving experiences to specific forms of psychopathology. Moreover, issues of specificity remain to be clarified with respect to factors that influence pathways toward different kinds of outcomes (e.g., NSSI vs. delinquency), as well as to those factors that may underlie a specific outcome in various developmental contexts (e.g., NSSI in adolescence vs. adulthood).
Strengths and Limitations
Notwithstanding the unique and complementary strengths of these cross-sectional and longitudinal, process-oriented analyses, these findings should be considered in the context of the unique features of this investigation. As noted above, this study evaluated only one developmental pathway from critical parenting to NSSI. Furthermore, although the use of youth self-reports in this study was informed by a wealth of literature pointing to the value of adolescent self-reports in studies of parent–adolescent interaction quality (De Ross, Marrinan, Schattner, & Gullone, 1999), such data have limitations, particularly when self-reports are used as the sole method of data collection (Schwartz, 1999). The monomethod, single-informant research design in this investigation may introduce concerns about shared method variance, despite the removal of shared variance across predictors in these multivariate analyses. These findings await replication in future studies using multiple methods (e.g., family observation) and informants (e.g., parents, teachers).
Our data offer a valuable view into the lives of upper-middle-class, suburban youths, but the unique features of the communities may constrain the generalizability of the present findings to other settings. For example, the measure of perceived parental criticism used here is closely connected to broader constructs related to perfectionistic tendencies and parental expectations. Thus, the present findings may reflect the undue influence of parental pressure in a context of high-achievement orientation, rather than (or in addition to) critical parenting per se. Alternatively, as with most school-based samples, these findings may be biased toward health, as more maladaptive adolescents may have refused to participate, dropped out of high school, or been enrolled in an alternative educational milieu at the time of data collection.
As discussed previously, the present findings may reflect features unique to the measure of NSSI in this study. Although the FASM has been employed in several empirical studies to date (Guertin et al., 2001; Nock & Prinstein, 2004, 2005), it is in the early stages of psychometric evaluation and validation. Moreover, this study did not include the functional portion of the FASM, which may have compromised its reliability and validity. In addition to being unable to examine functional aspects of NSSI in these samples, it is important to note, we were not able to verify that the self-injurers in this sample met the full criteria for NSSI, because we did not ask about suicidal intent.
Finally, the limited statistical power of the longitudinal analyses in this investigation constrained our ability to issue firm statements about the temporal patterning of the obtained results. Similarly, we were not able to ascertain whether patterns of NSSI differed as a function of maternal versus paternal criticism and/or of a youth's perceived alienation from mother, father, or both parents (Luthar & Latendresse, 2005). The limited size of the East Coast sample in combination with the sophistication and computational demands of the current analyses required to account for the distributional properties of the NSSI outcome may have occluded meaningful patterns in the data. Nevertheless, we believe that ZIP regression models offer an important analytic option in future studies of NSSI.
The pattern of NSSI observed here is typical of that seen in other community settings in which the preponderance of participants deny NSSI, yielding scores of zero, and a subset of respondents endorse various rates of NSSI. Researchers have long struggled to work with these kinds of nonnormal distributions; typically, they impose categorical cutoffs to dichotomize NSSI as absent or present or to trichotomize it as absent, present, or recurrent (Low et al., 2000; Whitlock et al., 2006; Yates et al., in press). However, categorical approaches may obscure meaningful distinctions in levels of NSSI, and they often rely on arbitrary frequency cutoffs. ZIP regression models offer a computationally demanding yet appropriate alternative to traditional analytic approaches. With this modeling paradigm, it is possible to hypothesize different precursors, mechanisms, and consequences regarding the initiation of NSSI versus its maintenance, escalation, or desistance over time. Thus, ZIP modeling provides a powerful tool to inform intervention efforts, as it can identify personal, social, ecological, and/or physiological forces that increase the relative resilience or fragility of individuals with regard to the initiation and/or maintenance of specific behaviors, such as a NSSI.
Clinical Implications
Clinical guidelines for practice related to NSSI have emerged over the past 5–10 years (Evans, 2000; Muehlenkamp, 2006). Building on the cognitive–behavioral work of Linehan and others (e.g., Linehan, Armstrong, Suarez, Allmon, & Heard, 1991), these approaches tend to emphasize the individual as the clinical focus. However, this investigation highlights the relevance of subtle family dynamics as salient influences on development and as promising targets for therapeutic intervention. These data suggest that incorporating the broader family system into the treatment of adolescent injurers through family therapy or concurrent parent education may provide incremental utility to more traditional treatments.
Beyond attending to the parent–adolescent relationship, the present findings suggest that treatments that adopt a critical- or shame-based approach to practice may inadvertently reinforce a heretofore unrecognized force underlying NSSI. Parents, teachers, and clinicians often localize NSSI within the adolescent, as they fail to recognize that NSSI follows from multivariate transactions between the adolescent and her or his environments. Thus, applied work with self-injuring youths must incorporate psychoeducation to help parents and other stakeholders recognize the multifaceted psychosocial systems, including NSSI, that influence adolescent behavior. Moreover, strength-based approaches to treatment will empower caregivers to effect positive changes in their families and communities to support youths. Just as the family or community environment may instantiate vulnerabilities to NSSI, so too might these systems buffer or prevent such pathways. Research that clarifies processes that promote resistance to, or desistance from, pathological pathways toward NSSI will inform efforts to develop strength- and competence-based approaches to practice (Yates & Masten, 2004).
Still, even the best services will do little to help self-injurers if they are not utilized. It is rare for those who self-injure to seek psychological services (Whitlock et al., 2006), and this is likely to be especially true in adolescence, when youths have few resources to seek services independently. This reticence to seek services, coupled with the pernicious and pervasive tendency for clinicians, school administrators, policymakers, and parents to overlook signs of distress among high-achieving, high-income youths, is a recipe for disaster (Luthar, 2003). The present findings join a broader cadre of evidence that distress and pathology are thriving within seemingly pristine and protected communities. Moreover, the driving forces underlying adolescent NSSI among upper-middle-class, suburban youths (and likely other youths) extend beyond the individual to include the family system and, perhaps, broader systems of influence (e.g., peers, media). In closing, we echo prior calls to offer multifaceted services targeting these “privileged” yet pained youths, their families, and their communities (Luthar, 2003).
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Submitted: February 9, 2007 Revised: August 10, 2007 Accepted: August 20, 2007
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Source: Journal of Consulting and Clinical Psychology. Vol. 76. (1), Feb, 2008 pp. 52-62)
Accession Number: 2008-00950-008
Digital Object Identifier: 10.1037/0022-006X.76.1.52
Record: 104- Title:
- Nonsuicidal self-injury as a time-invariant predictor of adolescent suicide ideation and attempts in a diverse community sample.
- Authors:
- Guan, Karen. University of North Carolina at Chapel Hill, Department of Psychology, Chapel Hill, NC, US
Fox, Kathryn R.. University of North Carolina at Chapel Hill, Department of Psychology, Chapel Hill, NC, US
Prinstein, Mitchell J.. University of North Carolina at Chapel Hill, Department of Psychology, Chapel Hill, NC, US, mitch.prinstein@unc.edu - Address:
- Prinstein, Mitchell J., University of North Carolina at Chapel Hill, Department of Psychology, Davie Hall, Campus Box 3270, Chapel Hill, NC, US, 27599-3270, mitch.prinstein@unc.edu
- Source:
- Journal of Consulting and Clinical Psychology, Vol 80(5), Oct, 2012. pp. 842-849.
- NLM Title Abbreviation:
- J Consult Clin Psychol
- Page Count:
- 8
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- Journal of Consulting Psychology
- Other Publishers:
- US : American Association for Applied Psychology
US : Dentan Printing Company
US : Science Press Printing Company - ISSN:
- 0022-006X (Print)
1939-2117 (Electronic) - Language:
- English
- Keywords:
- adolescents, nonsuicidal self-injury, suicide attempts, suicide ideation, depression
- Abstract:
- Objective: Longitudinal data on adolescent self-injury are rare. Little is known regarding the associations between various forms of self-injurious thoughts and behaviors over time, particularly within community samples that are most relevant for prevention efforts. This study examined nonsuicidal self-injury (NSSI) as a time-invariant, prospective predictor of adolescent suicide ideation, threats or gestures, and attempts over a 2.5-year interval. Method: A diverse (55% female; 51% non-White) adolescent community sample (n = 399) reported depressive symptoms, frequency of NSSI, suicide ideation, threats or gestures, and attempts in 9th grade (i.e., baseline) and at 4 subsequent time points. Generalized estimating equations and logistic regressions were conducted to reveal the associations between baseline NSSI and the likelihood of each suicidal self-injury outcome postbaseline while controlling for depressive symptoms and related indices of suicidal self-injury as competing predictors. Results: Baseline NSSI was significantly, prospectively associated with elevated levels of suicide ideation and suicide attempts, but not threats or gestures. Neither gender nor ethnicity moderated results. Conclusions: Above and beyond established risk factors such as depressive symptoms and previous suicidality, adolescent NSSI may be an especially important factor to assess when determining risk for later suicidality. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Adolescent Psychology; *Attempted Suicide; *Self-Injurious Behavior; *Suicidal Ideation; *Suicide; Major Depression; Risk Factors
- Medical Subject Headings (MeSH):
- Adolescent; Depression; Female; Humans; Male; Predictive Value of Tests; Prospective Studies; Risk Factors; Self-Injurious Behavior; Suicidal Ideation; Suicide, Attempted
- PsycINFO Classification:
- Behavior Disorders & Antisocial Behavior (3230)
- Population:
- Human
Male
Female - Location:
- US
- Age Group:
- Adolescence (13-17 yrs)
- Tests & Measures:
- Mood and Feelings Questionnaire
- Grant Sponsorship:
- Sponsor: National Institutes of Health
Grant Number: R01-MH85505; R01-HD055342
Recipients: Prinstein, Mitchell J. - Methodology:
- Empirical Study; Longitudinal Study; Quantitative Study
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- First Posted: Jul 30, 2012; Accepted: Jun 18, 2012; Revised: Jun 11, 2012; First Submitted: Nov 1, 2011
- Release Date:
- 20120730
- Correction Date:
- 20120924
- Copyright:
- American Psychological Association. 2012
- Digital Object Identifier:
- http://dx.doi.org/10.1037/a0029429
- PMID:
- 22845782
- Accession Number:
- 2012-20362-001
- Number of Citations in Source:
- 40
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- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2012-20362-001&site=ehost-live">Nonsuicidal self-injury as a time-invariant predictor of adolescent suicide ideation and attempts in a diverse community sample.</A>
- Database:
- PsycINFO
Nonsuicidal Self-Injury as a Time-Invariant Predictor of Adolescent Suicide Ideation and Attempts in a Diverse Community Sample
By: Karen Guan
Department of Psychology, University of North Carolina at Chapel Hill
Kathryn R. Fox
Department of Psychology, University of North Carolina at Chapel Hill
Mitchell J. Prinstein
Department of Psychology, University of North Carolina at Chapel Hill;
Acknowledgement: This work was supported in part by National Institutes of Health Grants R01-MH85505 and R01-HD055342 awarded to Mitchell J. Prinstein.
Self-injurious thoughts and behaviors are remarkably dangerous yet relatively understudied phenomena (Prinstein, 2008). In particular, relatively few longitudinal data are available to understand prospective predictors of self-injury. Moreover, research studies rarely discretely identify and examine associations among the multiple types of self-injurious thoughts and behaviors that have been identified in the literature (e.g., nonsuicidal self-injury [NSSI], suicide ideation, threats, gestures, attempts, etc.; Nock & Kessler, 2006; Silverman, Berman, Sanddal, O'Carroll, & Joiner, 2007). Consequently, relatively little is known regarding the predictors of self-injury beyond broad, distal factors (e.g., depressive symptoms, prior self-injury, substance use), particularly in adolescence (Nock, 2009; Prinstein, 2008).
However, a substantial body of research recently has emerged on at least one form of self-injury: NSSI. Defined as behavior that is direct, deliberate, and not socially sanctioned, NSSI causes damage to one's body tissue and is enacted without the intent to die (Nock, 2010). NSSI is remarkably prevalent, especially among adolescents. Lifetime prevalence rates in community samples range from 15.9% to 21.2% (e.g., Muehlenkamp & Gutierrez, 2004; Ross & Heath, 2002). Studies of NSSI have proliferated and gained added clinical importance for at least three reasons. First, NSSI is a proposed diagnostic category in the draft DSM-V (Selby, Bender, Gordon, Nock, & Joiner, 2012). Second, NSSI is associated concurrently with suicidal thoughts and behaviors (Andover & Gibb, 2010; Klonsky & Olino, 2008; Lloyd-Richardson, Perrine, Dierker, & Kelley, 2007; Nock, Joiner, Gordon, Lloyd-Richardson, & Prinstein, 2006). Third, recent theories suggest that NSSI may be a risk factor for later suicidal thoughts and behaviors (Brent, 2011; Joiner, 2005). The latter hypothesis has been examined infrequently, however.
The present study examines NSSI as a long-term, time-invariant longitudinal risk factor for suicide ideation, threats or gestures, and attempts during an adolescent transition period associated with increased risk for suicidality. Epidemiological data suggest that suicide ideation is remarkably prevalent during the high school aged adolescent period (13.8%; Centers for Disease Control and Prevention [CDC], 2010). The prevalence of suicide attempts is also quite high (6.3%; CDC, 2010). Between the age groups of 10–14 to 15–19 years, the rate of completed suicide increases almost sevenfold (from 1.1 to 7.4 per 100,000; CDC, 2008). In addition, suicide is the third leading cause of death for children and adolescents aged 10–19 years (CDC, 2008). Less is known about the prevalence of suicide threats or gestures, sometimes defined as “self-injury in which there is no intent to die, but instead an intent to give the appearance of a suicide attempt in order to communicate with others” (Nock & Kessler, 2006, p. 616), or as “any interpersonal action, verbal or nonverbal, without a direct self-injurious component, that a reasonable person would interpret as communicating or suggesting that suicidal behavior might occur in the near future” (Silverman et al., 2007, p. 268). Self-injurious behaviors such as threats or gestures and uncompleted suicide attempts are crucial outcomes for investigation, as they place an enormous burden on emergency health care systems, cause significant distress among family and friends, and are the strongest predictors of eventual suicide (Cvinar, 2005; Joiner et al., 2005; Olfson, Gameroff, Marcus, Greenberg, & Shaffer, 2005).
Recent theoretical and empirical work suggests that NSSI may be associated with increased suicidal capability (Franklin, Hessel, & Prinstein, 2011; Joiner, 2005). Joiner (2005) suggested that repeated episodes of painful and provocative experiences, such as cutting or burning, may habituate those who engage in NSSI to higher levels of pain. This habituation, or acquired capability for suicide, may act as a vulnerability that, when combined with desire for suicide, has been found to significantly predict suicidal behaviors, including attempts and completed suicides (Anestis & Joiner, 2011; Joiner et al., 2009; Nademin et al., 2008; Van Orden, Witte, Gordon, Bender, & Joiner, 2008).
Preliminary empirical findings offer some initial support for NSSI as a concurrent correlate of suicidal thoughts and behavior. Higher frequencies of NSSI concurrently are associated with higher levels of suicide ideation and a history of suicide attempts (Andover & Gibb, 2010; Klonsky & Olino, 2008; Lloyd-Richardson et al., 2007; Nock et al., 2006).
Few studies have examined the longitudinal association among various forms of self-injury (e.g., Asarnow et al., 2011; Cooper et al., 2005; Owens, Horrocks, & House, 2002; Wilkinson, Kelvin, Roberts, Dubicka, & Goodyer, 2011). In some cases, different forms of self-injury (i.e., suicidal vs. nonsuicidal) have not been explicitly differentiated. For example, Cooper and colleagues (2005) revealed a prospective relationship between any self-harm episode resulting in a hospitalization (including NSSI or suicidal self-injury) and later completed suicide. Similarly, Owens and colleagues (2002) reviewed multiple studies examining the relationship between various forms of self-harm and later nonfatal or fatal self-harm; findings suggested elevated risk for suicide among self-harm patients as compared to the general population.
Some short-term longitudinal data examining associations specifically between NSSI and suicidal thoughts or behaviors in clinically referred populations also have been reported recently. For example, NSSI has been associated with slower decreases in suicide ideation in the 9 months following hospital discharge (Prinstein et al., 2008). Asarnow and colleagues (2011) and Wilkinson and colleagues (2011) revealed NSSI frequencies to be a stronger predictor of suicide attempts than were previous suicide attempts over a 24- and 28-week period, respectively.
This study aims to offer at least five unique contributions to the literature examining putative risk associated with NSSI. First, this study uses a definition of NSSI consistent with contemporary theory; thus, it has been possible to examine NSSI (specifically without suicidal intent) as a predictor of suicidal thoughts and behaviors. Second, this study focuses on a diverse community sample, offering findings that are most relevant for prevention efforts. Third, this study has been designed to examine the long-term longitudinal prediction of suicidal thoughts and behaviors from NSSI. A 2.5-year longitudinal interval has been used to examine hypotheses. Fourth, multiple outcomes reflecting discrete suicidal thoughts and behaviors (i.e., suicide ideation, threats or gestures, and attempts) are examined. Last, and perhaps most importantly, this study examines NSSI as a predictor of suicidal thoughts and behaviors while controlling for depressive symptoms and related suicidality as competing predictors. This stringent examination offers a robust test of NSSI as a predictor of later suicidal thoughts and behaviors.
It was hypothesized that NSSI would be associated with a higher likelihood of suicidal thoughts over time. Although prior longitudinal research on the developmental course of suicidal ideation is rare, empirical data suggest that ideation may occur episodically, perhaps in conjunction with major depressive episodes (Prinstein et al., 2008; Williams, Crane, Barnhofer, Van der Does, & Segal, 2006). A primary aim of this study was to examine NSSI as a longitudinal predictor of clinically significant levels of suicide ideation. Thus, data were coded to reflect severe levels of suicide ideation, and an analytic approach allowing for the examination of repeated occurrences nested within individuals was employed. It also was hypothesized that NSSI would be associated with higher occurrences of suicide threats or gestures and suicide attempts over time.
Past research has suggested adolescent girls and Latino Americans are more likely than adolescent boys and non-Latino Americans to report suicide ideation, attempts, and depressive symptoms (CDC, 2010). However, there are few extant theories suggesting that the magnitude of the association between NSSI and suicidal thoughts and behaviors may vary with respect to gender or ethnicity. Therefore, each of these demographic variables was explored as a moderator of hypothesized associations for descriptive purposes.
Method Participants
A total of 399 ninth-grade adolescents (54.8% girls) participated in the study. The ethnic distribution of the sample was 49.2% Caucasian, 22.7% African American, 19.3% Latino American (of which 64% were from Mexico, and 8% each were from Puerto Rico, Honduras, and El Salvador), and 8.8% other/mixed ethnicity within a city of lower-class socioeconomic status. According to school records, approximately 67% of students in this district were eligible for free or reduced-price lunch. Approximately 19% of adolescents reported that their parents were never married; 32% reported that their parents had separated or divorced. The majority of adolescents reported that they lived in a household with two adults (47% with two biological parents, 30% with a parent and a stepparent, grandparent, or other relative); 23% reported living in a single-parent household.
Procedure
All students in ninth grade from three rural high schools in a single county were recruited for participation (N = 712), with the exception of students in self-contained special education classes. A letter of consent initially was distributed to each adolescent's family followed by a series of reminders and additional letters distributed by school and research personnel. Response forms included an option for parents to grant or deny consent; adolescents were asked to return their signed response forms regardless of their parents' decision. Numerous adolescent-, teacher-, and school-based incentives were used to ensure the return of these consent forms (i.e., candy for each returned consent form, $30 gift card raffles during each week of recruitment, one $300 gift card grand prize raffle). Consent forms were returned by 75% of families (n = 533); of these, 80% of parents gave consent for their child's participation (n = 423). Data were unavailable for 24 participants due to changing schools (n = 18), student absenteeism on the days of testing (n = 2), or declining to participate (n = 4), yielding a Time 1 sample of 399 (56% of total population). Adolescent assent was requested at the start of data collection, following written and verbal descriptions of the study procedures. All procedures were approved by the university human subjects committee.
Measures were administered in the spring of ninth grade and then every 6 months for a total of five time points (until the spring of 11th grade). Retention varied between 90% and 99% between adjacent time points. Retention between Times 1 and 5 was 77%; 67% of attrition was due to students withdrawing from school. Attrition analyses revealed no significant differences on any study variable between adolescents who participated at one versus all time points, with one exception: Latino American adolescents were less likely to have complete data than non-Latino American adolescents, χ2(1) = 6.06, p = .01. All 399 cases were used in analyses; maximum-likelihood methods were used to account for missing data. Generalized estimating equations (GEEs) used all available data from the full sample (n = 399). Analyses conducted with only available data revealed an identical pattern of findings.
Measures
All measures were administered in adolescents' classrooms or school auditoriums. Measures were completed in a group setting, and researchers ensured that participants had sufficient privacy to complete questionnaires confidentially (e.g., participants were placed several seats and rows apart). Measures of suicide ideation, threats or gestures, and attempts were administered at all five time points. Measures of depressive symptoms and NSSI were administered at Time 1.
Nonsuicidal self-injury (NSSI)
Adolescents reported the frequency that they engaged in six types of nonsuicidal self-injurious behaviors (i.e., cut/carved skin, hit self, burned skin, inserted objects under skin, scraped/picked skin, bit self) without intending to die in the past year (Prinstein et al., 2008). The anchors for this scale were adapted from the aforementioned study to allow more accurate reporting (1 = Never, 2 = 1–2 times, 3 = 3–5 times, 4 = 6–9 times, 5 = 10 or more times). Prior research supports the concurrent validity of this assessment through significant associations with other measures of NSSI (Prinstein et al., 2008).
Suicide ideation, threats or gestures, and attempts
Suicide ideation was assessed using an adaptation of a 15-item measure (Heilbron & Prinstein, 2010). The version of the measure employed in the present study included the same eight items assessing thoughts about suicide (e.g., “I thought about death,” “I thought about how I would kill myself,” “I thought that killing myself would solve my problems”) interspersed with seven filler items from the Reasons for Living scale (Linehan, Goodstein, Nielsen, & Chiles, 1983). Suicide ideation within the past year was assessed at baseline, and ideation within the past 6 months was assessed at each follow-up time point. This composite measure included suicide ideation items drawn from the Suicidal Ideation Questionnaire (Reynolds, 1988), and the National Institute of Mental Health Diagnostic Interview Schedule for Children Version IV (Shaffer, Fisher, Lucas, Dulcan, & Schwab-Stone, 2000). Each item is scored on a 5-point scale ranging from 1 (Never) to 5 (Almost every day); higher scores are indicative of higher frequencies of suicide ideation. Internal consistency (α) across all time points ranged between .83 and .94.
Suicide threats or gestures were measured with a single item added to the above instrument (“I tried to make someone believe that I might end my life, but I didn't do it”). Adolescents responded to this item using the same 5-point scale.
Suicide attempts also were assessed with a binary item asking whether adolescents “have tried to kill themselves” in the past year (at baseline) and the past 6 months (at each follow-up time point). Two indices were computed, representing the presence of a recent suicide attempt at baseline and the presence of at least one suicide attempt between Times 2 through 5.
Depressive symptoms
Depressive symptoms were assessed using the Mood and Feelings Questionnaire (MFQ), a 33-item self-report measure designed to assess criteria for depression in children and adolescents ages 8–18 (Costello & Angold, 1988). MFQ items include statements such as “I felt miserable or unhappy,” “I cried a lot,” and “I thought bad things would happen to me.” Each item is scored on a 3-point scale: mostly true, sometimes true, or not true for the individual over the past 2 weeks. Higher scores are indicative of higher levels of depressive symptoms. In the present study, internal consistency was excellent (α =. 93).
Data Analysis
Three sets of analyses were conducted to examine study hypotheses. Descriptive statistics first were conducted to examine frequencies, gender and ethnic differences, and correlations among continuous primary variables across all five time points.
Second, GEEs were used to account for clustered within-person observations in analyses predicting the occurrence of suicide ideation and threats or gestures. As may be expected based on previous research, inspection of data for suicide ideation and threats or gestures revealed that neither construct was best characterized by linear growth over time (Prinstein et al., 2008; Williams et al., 2006). In other words, it was rare for individuals to report gradual, linear increases (or decreases) in the frequency of suicide ideation or threats or gestures across the five time points of the study. Rather, suicide ideation and threats or gestures each occurred in a sporadic manner, with intermittent peaks usually surrounded by periods of low or absent suicidality. Thus, a binary logistic outcome was modeled with an autoregressive correlation matrix structure. This procedure allowed for multiple occurrences (i.e., elevated ideation, threats/gestures) within individuals. Analyses revealed no association between time and elevated levels of ideation or threats/gestures, suggesting no change in the hazard as a function of elapsed time.
Because the literature currently offers no consistent data to suggest a meaningful cutoff score indicating elevated suicide ideation, occurrences of elevated ideation were computed in two ways. First, based on clinical judgment, it was determined that any adolescent reporting a frequency of suicide ideation “at least once per week” or “almost every day” would be of strong clinical concern. Thus, within each time point, adolescents who reported a score of 4 or 5 on any suicide ideation item were defined as having elevated suicide ideation. This yielded a total of approximately 8% of the sample with elevated ideation at each time point. Notably, recent data suggest that 13.8% of high school aged adolescents report “seriously considered attempting suicide” within a 1-year interval (CDC, 2010). Thus, our estimate was conservative. A second computation for determining elevated suicide ideation occurrences was statistically based. A single grand mean and standard deviation of suicide ideation scores across person and time were computed. Scores one standard deviation above this grand mean were coded to reflect elevated suicide ideation. Between 5% and 9% at each time point exceeded this cutoff score. All analyses below were conducted twice, using each cutoff score, respectively. The pattern of findings was identical. To offer more utility for clinical purposes, analyses using the former approach for establishing elevated suicide ideation scores are presented below.
A similar procedure was used to dichotomize suicide threat or gesture scores. Any suicide threat or gesture is of clinical concern. Thus, any response indicating that adolescents engaged in a suicide threat or gesture more often than “never” was included as an occurrence. A range of 2% to 7% of adolescents reported an occurrence of suicide threat or gesture at each time point.
In both GEE models prospectively predicting ideation and threats or gestures, respectively, analyses included occurrences postbaseline (i.e., at Time Points 2, 3, 4, and 5) as a dependent measure. All measures of related baseline suicidality (i.e., ideation, threats or gestures, and suicide attempts) were included as independent variables, as were dummy codes for gender (female), African American, and Latino American adolescents. In addition, main effects of baseline depressive symptoms were entered, followed by a test of baseline NSSI as an independent variable. Interactions examining gender and ethnicity as moderators initially were examined (i.e., Gender × NSSI, African American [dummy coded] × NSSI, Latino American [dummy coded] × NSSI [dummy coded]); however, none reached significance, and thus, they are not reported below. The final model for suicidal ideation is shown below; the model for threats or gestures was identical in structure.
Variables with the prefix BL represent baseline measures, i indicates the individual, and t indicates the time point (e.g., Postbaseline Assessments 2, 3, 4, or 5). For each outcome variable, suicidal ideation and threats or gestures, an autoregressive working correlation structure was specified for the residuals to account for the dependence in the repeated measures.
Third, a logistic regression analysis was conducted to examine the prediction of suicide attempts. Because suicide attempts were low in frequency (i.e., between one and nine at each time point), a single outcome variable was computed identifying adolescents who did or did not report a suicide attempt at any point between Times 2 and 5. A logistic regression analysis, using all of the same predictors described above, was conducted to examine prospective prediction of suicide attempts. Because a single measure of attempts was taken for each individual, this analysis did not require the use of GEE (or a working correlation structure) and was fitted to the data using the typical maximum-likelihood estimator under the assumption of independent observations.
Results Preliminary Analyses
Table 1 presents descriptive statistics for all study variables, as well as the results of t tests and chi-square tests examining gender differences. Results indicated that a range of 7.1% to 8.9% of adolescents reported elevated levels of suicide ideation and 1.5% to 6.9% reported engaging in suicide threats or gestures at each time point. At baseline, 29.5% reported that they had engaged in NSSI; 3.3% of adolescents reported that they had attempted suicide in the past year, and 5.2% of adolescents reported having attempted suicide across the follow-up period. Females reported significantly higher levels of baseline depressive symptoms than males as well as higher levels of baseline NSSI. No significant gender differences were found for suicide ideation, threats or gestures, or attempts at any time point. Tests of ethnic differences revealed that African Americans and Latino Americans reported significantly higher levels of baseline depressive symptoms than Whites/Caucasians, t(398) = 2.51, p = .01, and t(398) = 2.71, p < .01, respectively. In addition, at Time 2, significantly more Latino Americans reported engaging in a suicide threat or gesture as compared to other ethnic groups, χ2(1) = 5.69, p < .05; at Time 5, African Americans reported significantly higher levels of suicide ideation as compared to other ethnic groups, χ2(1) = 8.74, p < .01. No significant ethnic group differences were found for NSSI, suicide ideation, threats or gestures, or attempts at all other time points.
Descriptive Statistics and Tests of Gender Differences for All Primary Variables
Table 2 presents correlations among continuous primary variables of depressive symptoms, NSSI, and suicide ideation. As expected, all variables were significantly positively associated across time.
Bivariate Associations Among Continuous Primary Variables
Longitudinal Prediction of Suicide Ideation
Table 3 displays parameter estimates from two GEEs predicting suicide ideation and threats or gestures, respectively. Results for suicide ideation revealed that after controlling for baseline self-injurious thoughts and behaviors and depressive symptoms, African American adolescents were about a third as likely to report an occurrence of elevated suicide ideation over the follow-up period. Consistent with hypotheses, results suggested that after accounting for these other effects, each additional point in reported NSSI at baseline was associated with a more than fivefold increase in the odds of a future occurrence of elevated suicide ideation. Interestingly, with this association between NSSI and later suicide ideation included, there was no significant association between baseline ideation and ideation occurrences at follow-up. In addition, no significant effects were revealed for other demographic predictors (i.e., gender, Latino American ethnicity), other measures of self-injury (i.e., baseline suicide threats or gestures, baseline suicide attempts), or baseline depressive symptoms.
Generalized Estimating Equation Results Predicting Suicide Ideation and Threats or Gestures During Follow-Up From Demographic Variables, Baseline Suicidal Thoughts and Behaviors, Depressive Symptoms, and Nonsuicidal Self-Injury
Longitudinal Prediction of Suicide Threats or Gestures
Results for the prediction of suicide threats or gestures revealed several main effects. Females were about half as likely as male adolescents to report the occurrence of a suicide threat or gesture during the follow-up interval. Additionally, African American adolescents were approximately a third as likely to report a suicide threat or gesture during follow-up. No other significant effects were revealed.
Longitudinal Prediction of Suicide Attempts
Consistent with past research, analyses revealed that prior suicide attempts were associated with future suicide attempts. Results suggested that a suicide attempt at baseline was associated with a nearly ninefold increase in the likelihood of a suicide attempt over the follow-up period. After controlling for this effect, and also consistent with prior work, results suggested that being female was associated with a nearly twofold increase in the likelihood of later suicide attempts, and each 1- point increase in depressive symptoms was associated with a fourfold increase in the likelihood of future suicide attempts. Interestingly, results suggested that after accounting for each of these effects, each additional-unit increase in reported NSSI was associated with a sevenfold increase in the likelihood of future suicide attempts. No other significant effects were revealed (see Table 4). Importantly, suicide attempts were reported at each separate time point. The vast majority occurred in the first year of follow-up (of 19 attempts during the follow-up period, eight were at Time 2, nine at Time 3, one at Time 4, and one at Time 5). Thus, it should be noted that each of these predictors was mostly associated with the likelihood of attempts occurring within 1 year of the baseline assessment.
Logistic Regression Results Predicting Suicide Attempts During Follow-Up From Demographic Variables, Baseline Attempts, Depressive Symptoms, and Nonsuicidal Self-Injury
DiscussionNSSI is an important behavior to understand and prevent in adolescence in its own right. Researchers have suggested that NSSI also may be an important predictor of later suicidal behavior; however, this hypothesis has not received substantial empirical attention. The current study offers compelling evidence from a diverse community-based sample suggesting that higher frequencies of NSSI are indeed associated with significantly increased risks of suicide ideation and attempts, but not threats or gestures. These results are particularly notable given the strong theoretical overlap and moderate correlations among NSSI, depressive symptoms, and suicide thoughts and behaviors. Results offer a useful evidence-based tool for clinicians attempting to assess the risk of suicidal behavior among adolescent clients; specifically, a past history of NSSI offers an important contribution to risk assessment above and beyond the role of prior suicidality and depressive symptoms as risk factors.
Results may be interpreted in light of several theoretical perspectives. First, as hypothesized by Joiner (2005), NSSI may be an experience that promotes adolescents' acquired capability for suicide. In other words, adolescents who engage in NSSI may develop an increased tolerance for pain and a decreased fear of death. NSSI also may promote a habituation to self-injurious behaviors, the development of more positive attributions regarding self-injury, or behavioral reinforcement through perceived social or internal rewards for self-injury. Any of these factors may mediate the association between NSSI and later suicidality (e.g., Franklin et al., 2011; Hooley, Ho, Slater, & Lockshin, 2010). Theories regarding acquired capability for suicidality do not specify the precise mechanism that is responsible for the link between early painful/provocative experiences and later suicidality, nor was this a focus of the present study. However, this would be an important area for further exploration.
An alternate explanation for observed results pertains to possible third-variable factors that may be responsible for both NSSI and suicidal self-injurious thoughts/behaviors. For instance, more frequent stressful experiences or a deterioration of adaptive coping skills may be responsible for both the occurrence of NSSI and later suicidal behavior. Neither of these factors may be fully accounted for by the presence of depressive symptoms and prior suicidality in our models.
Interestingly, NSSI was not associated prospectively with the occurrence of suicide threats or gestures. Prior research has suggested that suicide ideation, threats or gestures, and attempts are discrete constructs with unique correlates (e.g., Nock & Kazdin, 2002; Nock & Kessler, 2006). The results from this study partially support this idea, perhaps suggesting that suicide threats or gestures are motivated by different processes than are suicide ideation and attempts. Among adolescents, suicide ideation and attempts do not always reflect a true desire to die (Silverman et al., 2007). However, suicide threats or gestures, by their definition, may be even less motivated by suicidal intent. As they have been defined in the literature, suicide threats or gestures seem to be interpersonally directed, and may be more closely associated with NSSI serving social functions rather than NSSI primarily addressing automatic, internal functions (Nock & Prinstein, 2004). As the present study measured this construct with only a single item, it also is possible that the lack of significant findings is simply due to poor validity in our measurement of threats or gestures. More comprehensive assessments of self-injurious thoughts and behaviors are needed in future work.
Results regarding gender and ethnicity as main effects or moderators of findings have been presented. As demonstrated in prior research, being female was related to an increased risk of future suicide attempts (CDC, 2010). Also consistent with prior research, our results suggest that African American adolescents were at less risk of future suicide ideation and threats or gestures (e.g., Joe, Baser, Breeden, Neighbors, & Jackson, 2006). No findings suggest that demographic factors moderated the association between NSSI and future self-injurious thoughts and behavior. Combined with emerging evidence suggesting consistency in the frequency of NSSI across gender, ethnicity, and multiple cultures (Giletta, Scholte, Engles, Ciairano, & Prinstein, 2012), results suggest that NSSI is a phenomenon that may present similar risks across multiple populations of youth. However, as these analyses were exploratory, replication is needed to better explain the role of gender and ethnicity as moderators of suicidal behaviors over time.
In our analyses, baseline ideation was not a significant predictor of later ideation in the presence of all other predictors (e.g., NSSI, depressive symptoms, gender). One interpretation of this counterintuitive result is that suicidal thoughts may be episodic in nature (Prinstein et al., 2008; Williams et al., 2006); thus, stability in these constructs may not be expected across time across all possible time points. This finding should be replicated before drawing concrete conclusions.
Overall, results offer empirical evidence for the importance of NSSI as a construct that has predictive value in assessing risk for adolescent suicide ideation and attempts. Future research on this topic would benefit from addressing some of the limitations in this study. First, the generality of results may have been compromised by the relatively low participation rate recruited and retained in this sample. Although the sample compares quite favorably to other low-income, ethnically diverse longitudinal samples, the overall rate of participation nevertheless limits confidence in applying these results to all populations. Second, we assessed all self-injurious thoughts and behaviors using adolescent self-report, and two constructs were measured using single-item indices. While this method allowed us to assess a large number of adolescents, future studies also could include parent reports of adolescents' self-injury, as well as more thorough instruments examining multiple self-injury constructs. Too often in the literature, self-injury is assessed in a brief, cursory manner that does not allow for a careful delineation of the different forms of self-injury that have been identified as discrete constructs in past research (e.g., Nock & Kazdin, 2002; Nock & Kessler, 2006). Third, results regarding significant associations over time do not imply causal links between NSSI and these other self-injurious outcomes. This is a common limitation for studies of this type.
In a relatively brief period of time, NSSI has become a widely prevalent behavior, particularly among adolescents (Nock, 2010). Accordingly, the study of NSSI has become a burgeoning research area. Results from this study suggest that NSSI may be a notable risk factor for future suicide ideation and attempts during a developmental period known to be associated with heightened risk for self-injury. Understanding why some adolescents who engage in NSSI are at risk for suicidal self-injury, while others are not, will be an important direction for research and clinical efforts.
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Submitted: November 1, 2011 Revised: June 11, 2012 Accepted: June 18, 2012
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Source: Journal of Consulting and Clinical Psychology. Vol. 80. (5), Oct, 2012 pp. 842-849)
Accession Number: 2012-20362-001
Digital Object Identifier: 10.1037/a0029429
Record: 105- Title:
- Older maternal age is associated with depression, anxiety, and stress symptoms in young adult female offspring.
- Authors:
- Tearne, Jessica E.. Telethon Kids Institute, The University of Western Australia, Crawley, WAU, Australia, jessica.tearne@research.uwa.edu.au
Robinson, Monique. Telethon Kids Institute, The University of Western Australia, Crawley, WAU, Australia
Jacoby, Peter. Telethon Kids Institute, The University of Western Australia, Crawley, WAU, Australia
Allen, Karina L.. School of Psychology, The University of Western Australia, Crawley, WAU, Australia
Cunningham, Nadia K.. School of Psychology, The University of Western Australia, Crawley, WAU, Australia
Li, Jianghong. WZB Berlin Social Research Center, Germany
McLean, Neil J.. School of Psychology, The University of Western Australia, Crawley, WAU, Australia - Address:
- Tearne, Jessica E., School of Psychology, The University of Western Australia, M304, 35 Stirling Highway, Crawley, WAU, Australia, 6009, jessica.tearne@research.uwa.edu.au
- Source:
- Journal of Abnormal Psychology, Vol 125(1), Jan, 2016. pp. 1-10.
- NLM Title Abbreviation:
- J Abnorm Psychol
- Page Count:
- 10
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- The Journal of Abnormal and Social Psychology; The Journal of Abnormal Psychology; The Journal of Abnormal Psychology and Social Psychology
- ISSN:
- 0021-843X (Print)
1939-1846 (Electronic) - Language:
- English
- Keywords:
- DASS, Raine Study, cohort study, maternal age, paternal age
- Abstract (English):
- The evidence regarding older parental age and incidence of mood disorder symptoms in offspring is limited, and that which exists is mixed. We sought to clarify these relationships by using data from the Western Australian Pregnancy Cohort (Raine) Study. The Raine Study provided comprehensive data from 2,900 pregnancies, resulting in 2,868 live born children. A total of 1,220 participants completed the short form of the Depression Anxiety Stress Scale (DASS-21) at the 20-year cohort follow-up. We used negative binomial regression analyses with log link and with adjustment for known perinatal risk factors to examine the extent to which maternal and paternal age at childbirth predicted continuous DASS-21 index scores. In the final multivariate models, a maternal age of 30–34 years was associated with significant increases in stress DASS-21 scores in female offspring relative to female offspring of 25- to 29-year-old mothers. A maternal age of 35 years and over was associated with increased scores on all DASS-21 scales in female offspring. Our results indicate that older maternal age is associated with depression, anxiety, and stress symptoms in young adult females. Further research into the mechanisms underpinning this relationship is needed. (PsycINFO Database Record (c) 2018 APA, all rights reserved)
- Impact Statement:
- This study suggests that older maternal age is associated with adverse symptoms of depression, anxiety, and distress in young adult females. Paternal age was not found to be associated with mental health outcomes for either males or females in this sample. (PsycINFO Database Record (c) 2018 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Age Differences; *Fathers; *Major Depression; *Mothers; *Stress; Adult Offspring; Anxiety; Risk Factors; Symptoms
- Medical Subject Headings (MeSH):
- Adult; Adult Children; Anxiety; Australia; Depression; Female; Humans; Maternal Age; Mothers; Pregnancy; Psychiatric Status Rating Scales; Risk Factors; Stress, Psychological; Surveys and Questionnaires; Young Adult
- PsycINFO Classification:
- Affective Disorders (3211)
- Population:
- Human
Male
Female - Location:
- Australia
- Age Group:
- Adulthood (18 yrs & older)
Young Adulthood (18-29 yrs)
Thirties (30-39 yrs)
Middle Age (40-64 yrs) - Tests & Measures:
- Depression Anxiety Stress Scales
Beck Anxiety Inventory DOI: 10.1037/t02025-000
Beck Depression Inventory DOI: 10.1037/t00741-000 - Grant Sponsorship:
- Sponsor: University of Western Australia, Australia
Other Details: Completion Scholarship
Recipients: Tearne, Jessica E.
Sponsor: National Health and Medical Research Council, Australia
Other Details: Early Career Fellowship
Recipients: Robinson, Monique
Sponsor: Raine Medical Research Foundation
Recipients: No recipient indicated
Sponsor: The University of Western Australia, Australia
Recipients: No recipient indicated
Sponsor: Telethon Kids Institute, Australia
Recipients: No recipient indicated
Sponsor: University of Western Australia, Faculty of Medicine, Dentistry and Health Sciences, Australia
Recipients: No recipient indicated
Sponsor: Women and Infants Research Foundation
Recipients: No recipient indicated
Sponsor: Curtin University of Technology, Australia
Recipients: No recipient indicated - Methodology:
- Empirical Study; Followup Study; Quantitative Study
- Supplemental Data:
- Tables and Figures Internet
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- First Posted: Nov 16, 2015; Accepted: Sep 10, 2015; Revised: Sep 9, 2015; First Submitted: Jan 27, 2015
- Release Date:
- 20151116
- Correction Date:
- 20180625
- Copyright:
- American Psychological Association. 2015
- Digital Object Identifier:
- http://dx.doi.org/10.1037/abn0000119; http://dx.doi.org/10.1037/abn0000119.supp(Supplemental)
- PMID:
- 26569038
- Accession Number:
- 2015-51755-001
- Number of Citations in Source:
- 39
- Persistent link to this record (Permalink):
- http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2015-51755-001&site=ehost-live
- Cut and Paste:
- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2015-51755-001&site=ehost-live">Older maternal age is associated with depression, anxiety, and stress symptoms in young adult female offspring.</A>
- Database:
- PsycINFO
Older Maternal Age Is Associated With Depression, Anxiety, and Stress Symptoms in Young Adult Female Offspring
By: Jessica E. Tearne
Telethon Kids Institute and School of Psychology, The University of Western Australia;
Monique Robinson
Telethon Kids Institute, The University of Western Australia
Peter Jacoby
Telethon Kids Institute, The University of Western Australia
Karina L. Allen
School of Psychology, The University of Western Australia
Nadia K. Cunningham
School of Psychology, The University of Western Australia
Jianghong Li
WZB Berlin Social Research Center, Germany, and Centre for Population Health Research, Faculty of Health Sciences, Curtin University
Neil J. McLean
School of Psychology, The University of Western Australia
Acknowledgement: Jessica E. Tearne was supported by a University of Western Australia Completion Scholarship. Monique Robinson is supported by a NHMRC Early Career Fellowship. The authors acknowledge the funding and support of the Raine Medical Research Foundation, The University of Western Australia (UWA), the Telethon Kids Institute, the UWA Faculty of Medicine, Dentistry and Health Sciences, the Women and Infants Research Foundation, and Curtin University of Technology. We acknowledge the long term support and funding from the National Health and Medical Research Council (NHMRC) of Australia and the Raine Medical Research Foundation. The authors are extremely grateful to all the Raine Study participants and their families who took part in this study, as well as the Raine Study team for their cohort coordination and data collection.
Both younger and older parental age has been linked to mental health problems in offspring. There is a substantial literature relating young parenthood, particularly teenaged motherhood, to adverse mental health outcomes in young children (Do et al., 1998; Fergusson & Woodward, 1999; Harden et al., 2007; McGrath et al., 2014). Relative to offspring of older aged parents, offspring of teenaged mothers are at increased risk of mood disorders, internalizing problems (e.g., withdrawal, depression/anxiety, somatic symptoms), substance misuse, and juvenile crime (Fergusson & Woodward, 1999; Harden et al., 2007). In terms of psychiatric diagnoses, offspring of teenaged mothers have been found to have a 51% increased risk of having any psychiatric diagnosis, and offspring of teenaged fathers a 28% increased risk (McGrath et al., 2014).
In the case of older parental age and offspring mental health problems, the research has focused overwhelmingly on psychiatric diagnoses. There is now strong evidence that the children of older fathers are at heightened risk of schizophrenia and autism spectrum disorders (Hultman, Sandin, Levine, Lichtenstein, & Reichenberg, 2011; Miller et al., 2011), while the evidence for increased risk for bipolar disorder diagnosis is mixed with some studies suggesting a relationship (Frans et al., 2008; Menezes et al., 2010) and others finding no association (Buizer-Voskamp et al., 2011; McGrath et al., 2014). It has also been suggested that the effect of paternal age may be sexually dimorphic (Byrne, Agerbo, Ewald, Eaton, & Mortensen, 2003; Miller et al., 2011). In the case of maternal age, advanced maternal age has been linked to increased risk for autism spectrum disorders (Sandin et al., 2012). Another study found that older maternal age increases risk for bipolar disorder diagnosis in offspring (Menezes et al., 2010), whereas other studies do not support this relationship (Frans et al., 2008; McGrath et al., 2014).
There is less information on the relationship between parental age and other mood disorders such as depression and anxiety. One large scale study using data from a Dutch population registry found adult offspring of both younger (<20) and older (≥40) fathers had significantly increased odds of a major depressive disorder diagnosis (Buizer-Voskamp et al., 2011). Similarly, relative to offspring of 25- to 29-year-old parents, the adult offspring of teenaged mothers and fathers, as well as older fathers, have been found to have increased incidence of mood disorders (McGrath et al., 2014). Conversely, Fergusson and Woodward (1999) found a significant linear association between increasing maternal age and decreasing rates of anxious and depressive disorders (as per Diagnostic and Statistical Manual of Mental Disorders, 4th ed. [DSM–IV] criteria) in 18-year-old offspring. Other studies using data from the Western Australian Pregnancy Cohort (Raine) Study have indicated that maternal age is a significant prenatal predictor of risk for child behavior outcomes from age 2 to 14 (Tearne et al., 2014) and that there is a significant linear association between maternal age and risk for problem internalizing and externalizing behaviors in children from ages 2–17, whereas older maternal age is associated with decreased risk for child behavior problems (Tearne et al., 2015).
To our knowledge, there are no studies that examine the incidence of symptoms of depression and anxiety (as opposed to diagnosis with a major depressive or anxious disorder) as a function of parental age in young adults. Furthermore, parental age has not been examined in relation to stress in offspring as far as we are aware. Investigation of these issues is important because it is recognized that mental health issues may not always be limited to those who meet criteria for a psychiatric diagnosis. By limiting the focus of study to those who meet diagnostic criteria, the broader spectrum of psychological adjustment and distress is ignored. This study sought to examine the relationship between maternal and paternal age and depression, anxiety and stress symptoms, measured by the short form of the Depression Anxiety Stress Scales (DASS-21) in offspring in a population-based cohort of Western Australian young adults, and to build upon previous studies from the Raine cohort examining parental age and mental health outcomes in offspring by considering outcomes in young adults. Given findings that there may be a sexually dimorphic effect of parental age on offspring outcomes in terms of severe mental health outcomes, we also sought to determine whether sex modifies the relationship between parental age and a broader spectrum of mental health outcomes in offspring. In line with previous literature, it was hypothesized that offspring of teenaged mothers and fathers would be at increased risk of elevated DASS-21 scores. The existing findings relating to mood symptoms in offspring and older parents are mixed, and as such we sought to clarify what relationships, if any, existed between older parental age and depression, anxiety and stress in offspring.
Method Study Population
The Raine Study is a population-based prospective pregnancy cohort study. The methodology for the study has been described in detail elsewhere (Newnham, Evans, Michael, Stanley, & Landau, 1993). Briefly, 2900 pregnant women were recruited to the study between 16 and 20 weeks’ gestation through the public antenatal clinic at King Edward Memorial Hospital (KEMH) in Perth, Western Australia, or surrounding private practices between May 1989 and November 1991. The criteria for enrolment into the study were English language proficiency sufficient to understand the implications of participating in the study, an expectation that they would deliver at KEMH, and an intention to remain in Western Australia to facilitate follow-up of their child(ren). Ninety percent of eligible women agreed to take part. Participants provided data on psychosocial and sociodemographic characteristics at enrolment and again at 34 weeks’ gestation. A total of 2,868 live infants and their families have since undergone assessment at ages 1, 2, 3, 5, 8, 10, 14, 17, 20, and 23 years. Written parental and adolescent/young adult consent (14, 17, 20, and 23) was provided at each follow-up. It has been previously reported that the initial Raine sample overrepresented socially disadvantaged families, and that selective attrition of the sample over time led to a closer representation of those in the sample to the Western Australian population (Robinson et al., 2010). The protocols for the study were approved by the Human Research Ethics Committees at KEMH and the Princess Margaret Hospital for Children in Perth, Western Australia. Ethics approval for the 20-year follow-up was obtained from the University of Western Australia Human Research Ethics Committee.
Loss to Follow-up
Data collection for the 20-year follow-up took place between March 2010 and April 2012. There were 2,125 young adults eligible for follow-up at 20 years. Of the 1,565 (74%) who participated, 78% (n = 1,220) completed the DASS-21. Characteristics of those who completed the DASS-21 at follow-up compared with those from the original cohort who did not are presented in Table 1.
Characteristics of Participants and Nonparticipants From the Original Cohort
Mental Health Data
Anxiety, depression, and stress were assessed by using the short form of the Depression Anxiety Stress Scales (DASS-21; S. H. Lovibond & P. F. Lovibond, 1995b). The DASS-21 is a short form of the 42-item DASS (S. H. Lovibond & P. F. Lovibond, 1995b), with both scales found to have good reliability and validity in clinical and nonclinical samples (Antony, Bieling, Cox, Enns, & Swinson, 1998; Crawford & Henry, 2003; Henry & Crawford, 2005). The DASS comprises three 7-item scales measuring depression, anxiety, and stress. The depression scale assesses dysphoria, hopelessness, devaluation of life, self-depreciation, lack of interest/involvement, anhedonia, and inertia; the anxiety scale measures autonomic arousal, skeletal musculature effects, situational anxiety, and subjective experience of anxious affect; and the stress scale assesses difficulty relaxing, nervous arousal, being easily upset/agitated, irritable/overreactive, and impatient (S. H. Lovibond & P. F. Lovibond, 1995b). Participants were asked to rate the severity of each symptom during the past week on a 4-point scale ranging from 0 (“did not apply to me at all”) to 3 (“applied to me very much, or most of the time”). Scores were doubled as per the scoring instructions.
The depression and anxiety scales of the 42-item DASS show good convergent validity with the Beck Anxiety and Depression Inventories (Lovibond & Lovibond, 1995a). Several studies have suggested temporal stability of the DASS across time (Brown, Chorpita, Korotitsch, & Barlow, 1997; Cunningham, Brown, Brooks, & Page, 2013; Page, Hooke, & Morrison, 2007; Willemsen, Markey, Declercq, & Vanheule, 2011). A large-scale study has shown stability of symptoms as measured by the DASS over 3 to 8 years (Lovibond, 1998). Analyses specific to the DASS-21 have shown a quadripartite structure, which consisted of a general factor that the authors suggested reflected general psychological distress and orthogonal factors suggested to represent depression, anxiety, and stress (Henry & Crawford, 2005). While there is evidence for a common factor representing shared variance underlying the DASS scales, there is also strong evidence for specific factors underlying the depression, anxiety, and stress subscales. Furthermore, there is extensive evidence in the literature that anxiety and depression are not independent constructs (Clark & Watson, 1991), and thus evidence for shared variance underlying the DASS subscales provides support for the construct validity of the DASS. As a result, the three subscales were included as the outcome measures in the present study.
Predictor Variables
Parental age and date of birth were recorded at initial recruitment. Both maternal and paternal age in years at birth of the study child were calculated and modeled as continuous and categorical variables. In the case of parental age as a categorical variable, age was stratified into 5-year age groups (<20, 20–24, 25–29, 30–34, 35–39, ≥40 years of age), but for mothers the older two age categories were collapsed to form one (≥35) because of small numbers of women aged 40 and over in the sample. This categorization is often used in classification of population fertility data and broader epidemiological investigations (Australian Bureau of Statistics, 2010; Buizer-Voskamp et al., 2011). A maternal and paternal age of 25–29 years was set as the reference group in all analyses because the peak fertility rate for Australian women was in this age group at the time of recruitment to the Raine Study (Australian Bureau of Statistics, 2010).
Control Variables
We adjusted for several prenatal variables previously established as key predictors of mental health outcomes in the Raine cohort (Tearne et al., 2014). These variables included maternal education (12 or more years of education compared with 11 or fewer), maternal smoking during the first 18 weeks of pregnancy (no smoking compared with any smoking), maternal experience of stressful life events in the first 18 weeks of pregnancy (two or fewer compared with three or more), total family income as at 18 weeks of pregnancy (<24,000 AUD compared with ≥24,000 AUD, in accordance with the poverty line at the time of collection), and maternal diagnosis of gestational hypertension (no hypertension compared with any hypertension).
Statistical Analyses
We compared characteristics of participants who completed the DASS-21 at the 20-year follow-up with nonparticipants from the original cohort based on gender, race, maternal education, total family income at 18 weeks’ gestation, maternal smoking in the first 18 weeks of pregnancy, maternal experience of stressful life events in the first 18 weeks of pregnancy, gestational hypertension, and maternal and paternal age at birth of study child using χ2 tests. We examined the skewness of the DASS-21 scales and set skewness >1 as an indicator of suitability for nonparametric analysis. All subscales were found to be skewed.
Given the skewness of the DASS-21 subscale scores, we performed negative binomial regression analyses with a log link to investigate the association between maternal and paternal age and DASS-21 scores (depression, anxiety and stress subscale scores). These elicited a rate ratio (RR) which we interpreted as the proportional increase in DASS-21 scores compared with the reference category for the categorical models. Several studies report gender based differences in DASS scores (Crawford & Henry, 2003; Gomez, Summers, Summers, Wolf, & Summers, 2014). In particular, one study using a nonclinical sample found gender differences in depression and anxiety subscale scores and on total DASS scores (female scores significantly higher than male; Crawford & Henry, 2003). As such, preliminary analyses tested whether sex moderated the association between parental age and mental health outcomes. We initially ran models adjusting only for the age of the other parent, with the subsequent models adjusting for age and known confounding variables as detailed previously. Analyses were performed using IBM SPSS Statistics Version 22.
ResultsCharacteristics of participants and nonparticipants from the original cohort are presented in Table 1. Compared with those from the original cohort who did not take part in the current study (n = 1,648), those participants who took part in the current study (n = 1,220) were more likely to have older mothers, older fathers, and to have come from families with an income above the poverty line during pregnancy with the study child. Their mothers were more likely to have finished high school and were less likely to have smoked and experienced stressful life events during pregnancy with the study child. Boys from the original cohort were less likely than girls to participate at age 20. There were no differences based on race or gestational hypertension between participants and nonparticipants from the original cohort. Given the significant differences between the initial Raine population and those that completed the DASS at the 20-year follow-up, inverse probability weighting was used to standardize the sample and adjust for bias that may result from nonrandom attrition. Weights were created using the previously mentioned pregnancy variables, from which a probability of participation and the inverse of this probability were created. Applying these weights created a sample with approximately similar distribution to that of the original Raine cohort.
The internal consistency of the DASS-21 scales was moderate to high (Cronbach’s alpha; Total scale = .93; Depression scale = .89; Anxiety scale = .76; Stress scale = .86). Median and mean DASS-21 total and subscale scores are presented in Table 2. Mean and median scores in this sample were slightly higher than those reported in another nonclinical sample (Henry & Crawford, 2005). There were significant differences between male and female scores on all subscales of the DASS-21, with females scoring higher. Maternal and paternal age were moderately correlated with each other (r = .47, p > .01).
Median, Mean (SD) Depression Anxiety Stress Scale (DASS) Total and Subscales Scores for Females and Males
We initially tested whether there was an interaction between maternal age and offspring gender, and paternal age and offspring gender, and offspring outcomes. There was a significant interaction between maternal age and offspring gender, and paternal age and offspring gender for total DASS scores and all symptom scale scores. As such, all analyses were stratified based on gender (see supplementary information available online).
In the final multivariate models, where we adjusted for age of other parent and known confounders, a maternal age of 30–34 years was associated with significantly increased stress (RR = 1.27, p = .031) subscale scores in female offspring relative to the reference group (Table 3). A maternal age of 35 years and over was associated with increases in all subscale scores in female offspring (depression: RR = 1.51, p = .026; anxiety: RR = 1.51, p = .029; stress: RR = 1.36, p = .033). There was some evidence of an association between a paternal age of 30–34 years and decreased stress subscale scores in female offspring (RR = .80, p = .045). No other paternal ages were associated with significantly different risk for DASS subscale scores in female offspring. The relationships between maternal and paternal age and DASS subscale scores are presented in Figures 1–3. There was some evidence that young maternal age was associated with decreased stress scale scores in male offspring (RR = .63, p = .04). No other maternal nor paternal age groups were associated with significantly different DASS subscale scores (Table 4).
Adjusted Analyses Estimating the Effect of Maternal and Paternal Age on Total Depression Anxiety Stress Scale (DASS) Scores and Depression, Anxiety, and Stress Subscale Scores in Girls
Figure 1. Depression subscale. See the online article for the color version of this figure.
Figure 2. Anxiety subscale. See the online article for the color version of this figure.
Figure 3. Stress subscale. See the online article for the color version of this figure.
Adjusted Analyses Estimating the Effect of Maternal and Paternal Age on Total Depression Anxiety Stress Scale (DASS) Scores and Depression, Anxiety, and Stress Subscale Scores in Boys
Figure 1. Depression subscale. See the online article for the color version of this figure.
Figure 2. Anxiety subscale. See the online article for the color version of this figure.
Figure 3. Stress subscale. See the online article for the color version of this figure.
DiscussionOur results suggest that older maternal age is related to an increased risk of depression, anxiety, and stress symptoms in young adult females. Paternal age was not found to be related to risk in females, and neither maternal nor paternal age predicted risk of these symptoms in young adult males. These relationships persisted after adjustment for a number of factors known to influence mental health in offspring.
Our results differ from other studies suggesting that older paternal age is linked to increased incidence of mood disorders (Buizer-Voskamp et al., 2011; McGrath et al., 2014) and with a larger body of literature suggesting older paternal age is associated with a range of other adverse psychiatric outcomes in offspring (Hultman et al., 2011; McGrath et al., 2014; Miller et al., 2011). A key difference is that our study examined self-reported symptoms of depression, anxiety, and stress rather than clinical diagnoses. It is plausible that the risk factors for psychological adjustment and distress differ from those risk factors identified for more severe psychiatric outcomes. The results of our study suggest that when moving beyond diagnosis to consider a broader spectrum of psychological distress and adjustment in offspring, paternal age is not an important factor of influence, at least in this sample when using the DASS-21 as an outcome variable. This is an important finding when placed in the context of the existing literature, because it suggests that father’s age may have a differential impact on different types of psychiatric distress/illness and may not be relevant for all outcomes. It is plausible that at the level of distress, rather than disorder, associations with parental age may stem from environmental factors, such as interactions with the parent, rather than biology. It may be the case that the significance of maternal and not paternal age as predictors of offspring outcomes may reflect an imbalance in key relationships in the caregiving of the child, such that maternal age exerts a greater influence because mothers may have played a greater caregiving role. In the few existing studies examining maternal age and mood disorders in offspring, older maternal age has been found to have no significant association with offspring outcome in two studies (Buizer-Voskamp et al., 2011; McGrath et al., 2014) and was associated with decreased risk for depressive and anxious disorders in 18-year-old offspring (Fergusson & Woodward, 1999), and decreased risk for internalizing disorders across childhood (Tearne et al., 2015). Our study suggests that maternal age is implicated in the subsequent experience of symptoms of depression, anxiety, and stress in female young adult offspring. This is somewhat different from the results presented in the aforementioned studies, although our findings are consistent with a study finding older maternal age may be associated with increased risk for bipolar affective disorder (Menezes et al., 2010). This finding is also broadly consistent with a number of studies suggesting advanced maternal age is associated with increased risk for autism spectrum disorders in offspring (Croen, Najjar, Fireman, & Grether, 2007; Durkin et al., 2008; Grether, Anderson, Croen, Smith, & Windham, 2009; King, Fountain, Dakhlallah, & Bearman, 2009; Parner et al., 2012; Sandin et al., 2012)
Future research should attend to uncovering potential mechanisms underlying the relationship between maternal age and depression, anxiety and stress symptoms in female offspring. It is possible that it is not so much age at pregnancy that underpins the relationship between maternal age and symptoms in female offspring, but age of the mother at follow-up assessment (which is an indirect effect of age at pregnancy). One possible hypothesis is difficulties in the mother–daughter relationship because of a large age difference between the two parties. The “older mothers” in our sample were 50–54 and 55 and over when their offspring were 20 years of age. It may be that a 30 or more year age difference between mother and daughter leads to a significant difference in the value systems held by each, as well as generational differences that may cause tension in the relationship, particularly during the transition period of young adulthood, leading to stress, worry, and sadness in the child. The increased incidence of depression, anxiety, and stress symptoms may reflect a stressful period in the lives of both mother and daughter. Another example of possible age-related differences in mother–daughter relationships is the impact of age-related health changes and problems in mothers. The median age at which women in Australia go through menopause is around 51 years of age (Do et al., 1998). Statistics from the Centers for Disease Control and Prevention suggest that once women enter their fifties, the leading causes of mortality are various cancers, heart disease, and chronic respiratory conditions (Centers for Disease Control and Prevention, 2010). It has been found that levels of emotional distress and behavioral problems escalate in adolescents and young adults with an immediate family member with a cancer diagnosis (Sahler et al., 1994), and another study suggested that adolescent female offspring are most negatively affected by a parent’s diagnosis with serious illness (Osborn, 2007). Thus, the higher risk of depression, anxiety, and stress in offspring of women in their fifties may be because of health-related stress and concern within the family. It may be that significant life changes are occurring in parallel in mothers and daughters, which may influence emotion dysregulation in offspring.
Another possible explanation for our results is that the relationship between advancing maternal age and offspring mental health outcomes observed in this study may be because of unmeasured confounding. Examining the relationship between maternal age and offspring mental health outcomes is complex, owing to the great number of variables associated with older motherhood that may also exert an influence on offspring outcomes. The statistical position taken in this study was that variables measured at the same time as the key outcome variables (i.e., prenatal variables) were considered as potential confounders, and our large sample size allowed for an exhaustive list of control variables to eliminate, as far as possible, confounding. However, there are myriad other factors that may influence the mental health of offspring. Recent studies in the area using quasi-experimental designs to control for environmental and genetic influences that vary within families using sibling-comparison analyses have yielded interesting findings. One study indicated that environmental factors associated with maternal age at childbirth which also vary within families are implicated in the incidence of delinquent behaviors in offspring (D’Onofrio et al., 2009), while another indicated that controlling for variables shared within families strengthened the association between advanced paternal age and various indices of psychopathology, consistent with a causal hypothesis (D’Onofrio et al., 2014). Although beyond this scope of this study, future research designs controlling for factors shared within families may leave researchers better placed to identify the specific factors, be they genetic, environmental, or both, that influence offspring behavior. This would allow us to better specify how maternal age may influence depression, anxiety and stress symptoms in young adult offspring, and why this relationship may be specific to female offspring.
There are a number of strengths associated with this study. Our prospectively collected data are drawn from a large cohort study, allowing us the opportunity for a comprehensive assessment of the impact of parental age on anxiety, stress, and depression symptoms in offspring in a nonclinical population. However, our findings must be interpreted in the context of a number of limitations. First, a limitation is our use of self-report data. Self-report measures have been validated as a valid means of assessing depression, anxiety, and stress (Antony et al., 1998). While we did not set out to measure clinical levels of distress, but rather more general symptoms of distress in our sample, we cannot rule out the possibility of over- or underreporting. A second limitation is the relatively small numbers of parents in the oldest (aged 40 and over) and youngest (19 and under) age groups at childbirth in our sample (2.3% and 9.7%, respectively). This may have impacted upon the strength of the influence of parental age upon offspring in these categories. Furthermore, the DASS-21 data measure symptoms over the past week. While a study using the longer version of the DASS scale has shown stability of each of the syndromes over substantial periods of time (3 to 8 years), future research could look to investigate the stability of symptoms over time in the Raine and similar cohorts. Another consideration was that it was not possible to differentiate between parental age at birth of first child versus birth of the study child. It has been suggested it may be parental age at birth of first child, not birth of the individual child, which predicts mental health outcomes in offspring (Petersen, 2011). An investigation of this type was beyond the scope of this study but is a worthy focus of future research. Finally, we controlled for a comprehensive range of other prenatal variables known to impact upon mental health in offspring, but this list is not exhaustive and does not take into account the myriad other influences on mental health across the life span. For example, family structure was not accounted for in this study. Many variables of interest, such as maternal mental health at follow-up, were not available to us. These variables may impact upon the relationships observed in the data, and further research is necessary to evaluate their impact. Despite these limitations, our data provide new insights into the impact of parental age on general symptoms of anxiety, depression, and stress in young adult offspring.
ConclusionsWe found that a maternal age of 30–34 years was associated with significant increases in total DASS-21 scores in female offspring, and a maternal age of 35 years and over was associated with significant increases in total and subscale DASS-21 scores. Paternal age was not found to be associated with offspring depression, anxiety, and stress. It appears that when examining a broad spectrum of psychological adjustment, the relationships between parental age and offspring symptomatology differ from those in the literature on parental age and severe psychiatric outcomes. We suggest that maternal age when the young adult is assessed may be as important as considering age at pregnancy.
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Submitted: January 27, 2015 Revised: September 9, 2015 Accepted: September 10, 2015
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Source: Journal of Abnormal Psychology. Vol. 125. (1), Jan, 2016 pp. 1-10)
Accession Number: 2015-51755-001
Digital Object Identifier: 10.1037/abn0000119
Record: 106- Title:
- Onset of alcohol or substance use disorders following treatment for adolescent depression.
- Authors:
- Curry, John. Department of Psychiatry and Behavioral Sciences, Duke University Medical Center, Durham, NC, US, john.curry@duke.edu
Silva, Susan. Department of Psychiatry and Behavioral Sciences, Duke University Medical Center, Durham, NC, US
Rohde, Paul. Oregon Research Institute, Eugene, OR, US
Ginsburg, Golda. Department of Psychiatry and Behavioral Sciences, The Johns Hopkins University School of Medicine, Baltimore, MD, US
Kennard, Betsy. Department of Psychiatry, University of Texas Southwestern Medical Center, Dallas, TX, US
Kratochvil, Christopher. Department of Psychiatry, University of Nebraska Medical Center, NE, US
Simons, Anne. Department of Psychology, University of Oregon, Eugene, OR, US
Kirchner, Jerry. Duke Clinical Research Institute, NC, US
May, Diane. Department of Psychiatry, University of Nebraska Medical Center, NE, US
Mayes, Taryn. Department of Psychiatry, University of Texas Southwestern Medical Center, Dallas, TX, US
Feeny, Norah. Department of Psychology, Case Western Reserve University, Cleveland, OH, US
Albano, Anne Marie. Department of Psychiatry, Columbia University Medical Center, New York, NY, US
Lavanier, Sarah. Division of Psychiatry, Cincinnati Children’s Medical Center, Cincinnati, OH, US
Reinecke, Mark. Department of Psychiatry and Behavioral Sciences, Northwestern University Feinberg School of Medicine, IL, US
Jacobs, Rachel. Department of Psychiatry and Behavioral Sciences, Northwestern University Feinberg School of Medicine, IL, US
Becker-Weidman, Emily. Department of Psychiatry and Behavioral Sciences, Northwestern University Feinberg School of Medicine, IL, US
Weller, Elizabeth. Department of Child and Adolescent Psychiatry and Behavioral Science, Children’s Hospital of Philadelphia, Philadelphia, PA, US
Emslie, Graham. Department of Psychiatry, University of Texas Southwestern Medical Center, Dallas, TX, US
Walkup, John. Department of Psychiatry and Behavioral Sciences, The Johns Hopkins University School of Medicine, Baltimore, MD, US
Kastelic, Elizabeth. Department of Psychiatry and Behavioral Sciences, The Johns Hopkins University School of Medicine, Baltimore, MD, US
Burns, Barbara. Department of Psychiatry and Behavioral Sciences, Duke University Medical Center, Durham, NC, US
Wells, Karen. Department of Psychiatry and Behavioral Sciences, Duke University Medical Center, Durham, NC, US
March, John. Department of Psychiatry and Behavioral Sciences, Duke University Medical Center, Durham, NC, US - Address:
- Curry, John, Duke Child and Family Study Center, 2608 Erwin Road, Suite 300, Durham, NC, US, 27705, john.curry@duke.edu
- Source:
- Journal of Consulting and Clinical Psychology, Vol 80(2), Apr, 2012. pp. 299-312.
- NLM Title Abbreviation:
- J Consult Clin Psychol
- Page Count:
- 14
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- Journal of Consulting Psychology
- Other Publishers:
- US : American Association for Applied Psychology
US : Dentan Printing Company
US : Science Press Printing Company - ISSN:
- 0022-006X (Print)
1939-2117 (Electronic) - Language:
- English
- Keywords:
- adolescents, alcohol use disorders, major depression, substance use disorders
- Abstract:
- Objective: This study tested whether positive response to short-term treatment for adolescent major depressive disorder (MDD) would have the secondary benefit of preventing subsequent alcohol use disorders (AUD) or substance use disorders (SUD). Method: For 5 years, we followed 192 adolescents (56.2% female; 20.8% minority) who had participated in the Treatment for Adolescents with Depression Study (TADS; TADS Team, 2004) and who had no prior diagnoses of AUD or SUD. TADS initial treatments were cognitive behavior therapy (CBT), fluoxetine alone (FLX), the combination of CBT and FLX (COMB), or clinical management with pill placebo (PBO). We used both the original TADS treatment response rating and a more restrictive symptom count rating. During follow-up, diagnostic interviews were completed at 6- or 12-month intervals to assess onset of AUD or SUD as well as MDD recovery and recurrence. Results: Achieving a positive response to MDD treatment was unrelated to subsequent AUD but predicted a lower rate of subsequent SUD, regardless of the measure of positive response (11.65% vs. 24.72%, or 10.0% vs. 24.5%, respectively). Type of initial MDD treatment was not related to either outcome. Prior to depression treatment, greater involvement with alcohol or drugs predicted later AUD or SUD, as did older age (for AUD) and more comorbid disorders (for SUD). Among those with recurrent MDD and AUD, AUD preceded MDD recurrence in 24 of 25 cases. Conclusion: Effective short-term adolescent depression treatment significantly reduces the rate of subsequent SUD but not AUD. Alcohol or drug use should be assessed prior to adolescent MDD treatment and monitored even after MDD recovery. (PsycINFO Database Record (c) 2017 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Adolescent Psychopathology; *Alcoholism; *Drug Abuse; *Major Depression; *Onset (Disorders); Substance Use Disorder
- Medical Subject Headings (MeSH):
- Adolescent; Antidepressive Agents; Cognitive Therapy; Combined Modality Therapy; Depressive Disorder, Major; Female; Fluoxetine; Follow-Up Studies; Humans; Male; Substance-Related Disorders; Treatment Outcome
- PsycINFO Classification:
- Psychological & Physical Disorders (3200)
- Population:
- Human
Male
Female - Age Group:
- Adolescence (13-17 yrs)
- Tests & Measures:
- Survey of Outcomes Following Treatment for Adolescent Depression
Clinical Global Impressions–Improvement scale
Suicide Ideation Questionnaire–Junior High Version
Children's Depression Rating Scale--Revised DOI: 10.1037/t55280-000
Personal Experience Screening Questionnaire DOI: 10.1037/t15632-000
Reynolds Adolescent Depression Scale
Children's Global Assessment Scale
Schedule for Affective Disorders and Schizophrenia for School-Age Children-Present and Lifetime Version DOI: 10.1037/t03988-000 - Grant Sponsorship:
- Sponsor: National Institute of Mental Health
Grant Number: MH70494
Recipients: Curry, John - Methodology:
- Empirical Study; Followup Study; Longitudinal Study
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- First Posted: Jan 16, 2012; Accepted: Nov 16, 2011; Revised: Nov 1, 2011; First Submitted: Jan 20, 2011
- Release Date:
- 20120116
- Correction Date:
- 20170413
- Copyright:
- American Psychological Association. 2012
- Digital Object Identifier:
- http://dx.doi.org/10.1037/a0026929
- PMID:
- 22250853
- Accession Number:
- 2012-00540-001
- Number of Citations in Source:
- 62
- Persistent link to this record (Permalink):
- http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2012-00540-001&site=ehost-live
- Cut and Paste:
- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2012-00540-001&site=ehost-live">Onset of alcohol or substance use disorders following treatment for adolescent depression.</A>
- Database:
- PsycINFO
Onset of Alcohol or Substance Use Disorders Following Treatment for Adolescent Depression
By: John Curry
Department of Psychiatry and Behavioral Sciences, Duke University Medical Center, and Duke Clinical Research Institute, Durham, North Carolina;
Susan Silva
Department of Psychiatry and Behavioral Sciences, Duke University Medical Center, and Duke Clinical Research Institute, Durham, North Carolina
Paul Rohde
Oregon Research Institute, Eugene, Oregon;
Department of Psychology, University of Oregon
Golda Ginsburg
Department of Psychiatry and Behavioral Sciences, The Johns Hopkins University School of Medicine
Betsy Kennard
Department of Psychiatry, University of Texas Southwestern Medical Center at Dallas
Christopher Kratochvil
Department of Psychiatry, University of Nebraska Medical Center
Anne Simons
Department of Psychology, University of Oregon
Jerry Kirchner
Duke Clinical Research Institute
Diane May
Department of Psychiatry, University of Nebraska Medical Center
Taryn Mayes
Department of Psychiatry, University of Texas Southwestern Medical Center at Dallas
Norah Feeny
Department of Psychology, Case Western Reserve University
Anne Marie Albano
Department of Psychiatry, Columbia University Medical Center
Sarah Lavanier
Division of Psychiatry, Cincinnati Children's Medical Center, Cincinnati, Ohio
Mark Reinecke
Department of Psychiatry and Behavioral Sciences, Northwestern University Feinberg School of Medicine
Rachel Jacobs
Department of Psychiatry and Behavioral Sciences, Northwestern University Feinberg School of Medicine
Emily Becker-Weidman
Department of Psychiatry and Behavioral Sciences, Northwestern University Feinberg School of Medicine
Elizabeth Weller
Department of Child and Adolescent Psychiatry and Behavioral Science, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania
Graham Emslie
Department of Psychiatry, University of Texas Southwestern Medical Center at Dallas
John Walkup
Department of Psychiatry and Behavioral Sciences, The Johns Hopkins University School of Medicine
Elizabeth Kastelic
Department of Psychiatry and Behavioral Sciences, The Johns Hopkins University School of Medicine
Barbara Burns
Department of Psychiatry and Behavioral Sciences, Duke University Medical Center
Karen Wells
Department of Psychiatry and Behavioral Sciences, Duke University Medical Center
John March
Department of Psychiatry and Behavioral Sciences, Duke University Medical Center, and Duke Clinical Research Institute
Acknowledgement: Elizabeth Weller is now deceased. We gratefully acknowledge her many contributions to the treatment of depressed adolescents.
Susan Silva is now at the Duke School of Nursing. Anne Simons is now at the Department of Psychology, University of Notre Dame. Sarah Lavanier is now at the Lindner Center for Hope, Mason, Ohio. Rachel Jacobs is now at the Department of Psychiatry, Columbia University Medical Center. Emily Becker-Weidman is now at the New York University Child Study Center. John Walkup is now at the Department of Psychiatry, Weill Cornell Medical Center.
Christopher Kratochvil, Graham Emslie, and John March have been consultants to—and Christopher Kratochvil and Graham Emslie have had research support from—Eli Lilly, which manufactures fluoxetine.
This research was supported by National Institute of Mental Health (NIMH) Grant MH70494 to John Curry. We gratefully acknowledge the contributions of Benedetto Vitiello, who coordinated administration of this project at the NIMH. We also thank the study participants, the site recruitment staff (including Margaret Price, Stephanie Frank, and Sue Babb), and the site management coordinator (Kathleen Girardin).
Alcohol or other substance use disorders (AOSUDs), including psychoactive substance abuse or dependence, are among the most common adolescent psychiatric disorders. Point prevalence rates are approximately 2%–3%, with lifetime prevalence rates reaching 12.2% by 16 years of age (Costello, Mustillo, Erkanli, Keeler, & Angold, 2003; Lewinsohn, Hops, Roberts, Seeley, & Andrews, 1993). AOSUDs increase over the adolescent age range (Costello et al., 2003), frequently follow a chronic or relapsing course (Kaminer, Burleson, & Burke, 2008), and are associated with multiple negative correlates or outcomes, including criminal justice involvement, high-risk sexual behavior, and suicide attempts (Tims et al., 2002; Wu et al., 2004). Because of their prevalence and negative functional impact, it is critical to prevent development of these disorders in vulnerable adolescents.
Adolescents with AOSUDs frequently have other disorders, such as conduct disorder or depression. Such disorders may develop earlier than, and constitute risk factors for, subsequent alcohol or drug disorders (Armstrong & Costello, 2002). Therefore, to the extent that treatments for these earlier disorders are effective, they might also mitigate the risk for development of subsequent AOSUDs (Kendall & Kessler, 2002). In the present study, we investigate whether effective treatment for adolescent major depressive disorder (MDD) exerts such a secondary benefit (Glantz et al., 2009) by investigating subsequent onset of AOSUDs among participants in the multisite Treatment for Adolescents with Depression Study (TADS; TADS Team, 2004).
Depressive symptoms in adolescence have been associated with subsequent increases in alcohol or drug use or related problems in several studies. However, studies vary in whether their focus is on alcohol or other substances, and findings are not entirely consistent, appearing to vary by gender and age. In longitudinal studies including both genders, Stice, Barrera, and Chassin (1998) found that depressed and anxious symptoms during adolescence predicted alcohol-related problems 1 year later, and Chen, Anthony, and Crum (1999) found that childhood or early adolescent depressive symptoms predicted early- to mid-adolescent alcohol-related problems. In the Dunedin Longitudinal Study, Henry et al. (1993) found that early adolescent depressive symptoms predicted mid-adolescent drug problems (multiple substance use), but only for boys. Similarly, in the Great Smokey Mountains Study, the effects of depression on substance use were stronger for boys than for girls: Boys with depressive symptoms reported them prior to the onset of cannabis use, abuse, or dependence (Costello, Erkanli, Federman, & Angold, 1999). Finally, Marmorstein (2009) found that depressive symptoms in early male adolescents, but not in female adolescents, predicted faster growth in alcohol-related problems through adolescence.
Two studies that failed to find a link between adolescent depressive symptoms and subsequent alcohol or drug problems measured late adolescent or early adult outcomes. Chassin, Pitts, DeLucia, and Todd (1999) found no effect of internalizing symptoms during adolescence on young adult alcohol use disorders (AUD) in a high-risk sample; and in the Dunedin Longitudinal Study, depressive symptoms at 15 years of age did not predict increased cannabis use at 18 years of age (McGee, Williams, Poulton, & Moffitt, 2000).
Even in single gender longitudinal studies, there is variability in the link between depression and subsequent alcohol versus drug outcomes. Two recent reports from a study of adolescent girls (Measelle, Stice, & Hogansen, 2006; Measelle, Stice, & Springer, 2006) indicated that negative emotionality predicted onset of alcohol or substance abuse, whereas depressive symptoms predicted worsening in substance abuse. Taken together, these findings suggest that the link between depressive symptoms and subsequent alcohol or substance use, problems, or disorders may vary by age, gender, and whether alcohol or other substance-related outcomes are assessed.
Compared to depressive symptoms, fewer studies have investigated the potential link between diagnosed adolescent depressive disorders and subsequent alcohol or other substance-related problems. A large prospective study of Finnish twins concluded that depressive disorders at 14 years of age predicted more frequent drug and alcohol use and recurrent intoxication by 17.5 years of age (Sihvola et al., 2008). Rohde, Lewinsohn, and Seeley (1996) found that among adolescents with both depressive and alcohol disorders, there was not a consistent temporal pattern of onset, but depression occurred first in a substantial number of cases (58%). On the other hand, some studies have found that adolescent depressive disorders do not predict subsequent AOSUDs, or that earlier drug or alcohol use predicts depressive disorders (D. W. Brook, Brook, Zhang, Cohen, & Whiteman, 2002; J. S. Brook, Cohen, & Brook, 1998).
Two considerations may clarify the nature of the link between adolescent depressive disorders and AOSUDs. First, any linkage may be bidirectional (Costello et al., 1999; Swendsen & Merikangas, 2000). If so, a test of the potential secondary preventive benefits of effective depression treatment on subsequent AOSUDs should be conducted with a depressed sample free of preexisting AOSUDs. Second, the link between depressive disorders and subsequent AOSUDs may be indirect, that is, attributable to other factors. Fergusson and Woodward (2002) found that adolescents who developed MDD between 14 and 16 years of age were significantly more likely than those who did not to develop both recurrent MDD and an AUD by 21 years of age. However, whereas the link between initial and subsequent MDD episodes was direct, the link between adolescent MDD and subsequent AUD was attributable to other factors, including early drinking and peer influence. Thus, any elevated risk of AOSUDs associated with adolescent MDD may be small or non-significant when assessed in the context of other factors (Measelle, Stice, & Springer, 2006). Therefore, when testing whether effective treatment of adolescent MDD has a secondary benefit of preventing subsequent AOSUDs, it is important to include additional predictors of AOSUDs that were present before treatment.
Taking these two considerations into account, in this study we investigated the preventive effects of successful depression treatment on subsequent AUD or other substance use disorders (SUD) in a sample with no preexisting AUD or SUD. We investigated AUD and SUD separately for several reasons. First, as noted above, previous studies have found variable results, depending on whether alcohol or other substance-related problems were assessed. Second, the trajectories of AUD and SUD differ across the age range under investigation. AUD is slightly more prevalent than SUD in early adolescence (Chassin, Ritter, Trim, & King, 2003), but it becomes much more prevalent by 20 years of age (Cohen et al., 1993) and has a substantially higher lifetime prevalence among adults (Kessler et al., 2005). Third, among adolescent psychiatric patients, the correlates of alcohol abuse and of other substance abuse are not identical (Becker & Guilo, 2006), and among college students, alcohol abuse is associated with major depression, but other substance abuse is associated both with major depression and with other comorbid diagnoses (Deykin, Levy, & Wells, 1987).
We took into account several possible additional predictors of AUD and SUD evident before depression treatment to ensure that any secondary benefit of successful depression treatment on subsequent AUD or SUD could not be accounted for by these other predictors. The potential predictors included demographic variables (age, gender, and ethnicity), comorbid disorders, and pretreatment use of alcohol or drugs. Demographic variables are important to consider, not only because previous studies have found age and gender differences in the linkage between depression and substance abuse but also because alcohol and drug use are more prevalent in older adolescents, are more prevalent in male adolescents, and vary by ethnic group (Substance Abuse and Mental Health Services Administration [SAMHSA], 2010). We included comorbid disorders because a large percentage of adolescents with MDD present with additional comorbid disorders (Kovacs, 1996), and the comorbid disorders, such as anxiety or disruptive behavior disorders, may be the source of risk for subsequent AUD or SUD (Armstrong & Costello, 2002). Lastly, use of alcohol or drugs prior to treatment for MDD must be considered. Costello et al. (1999) found that first alcohol use preceded diagnosed AUD by approximately 6 years, with a comparable period of about 3 years between first cannabis use and a diagnosable SUD. It may be that depressed adolescents who are already involved in alcohol or drug use at the time of depression treatment have greater risk for subsequent AUD or SUD than those who are not.
Finally, we took into account the course of MDD following treatment, because this may influence development of AUD or SUD. The great majority of treated adolescents recover from their index MDD episode within 1–2 years, but rates of recurrent MDD across community and clinical samples range from 40% to 70% (Birmaher et al., 2000). In a previous report, we found that 88.3% of TADS adolescents recovered within 2 years (96.4% within 5 years) but that 46.6% of recovered adolescents experienced recurrent MDD within 5 years (Curry et al., 2011). Chronic or recurrent depression may increase the risk of AUD or SUD. Among adults treated for alcohol or drug dependence, an earlier lifetime history of MDD lowered the likelihood of successful drug or alcohol treatment, and MDD during a period of sustained alcohol or drug abstinence increased the risk of relapse (Hasin et al., 2002). In adolescents, depression is associated with more severe SUD and higher risk for SUD relapse (McCarthy, Tomlinson, Anderson, Marlatt, & Brown, 2005; Riggs, Baker, Mikulich, Young, & Crowley, 1995); in turn, alcohol or substance abuse is associated with longer episodes of depression in girls (King et al., 1996). None of these findings directly demonstrate that chronic or recurrent MDD raises the risk of AUD or SUD onset, but they suggest that persistent/ongoing MDD complicates efforts to avoid or achieve sustained remission from AUD or SUD. In the present study, we explored whether chronic or recurrent MDD among treated adolescents increased the risk of AUD or SUD onset.
In summary, we tested the hypothesis that, among depressed adolescents with no history of AUD or SUD, effective depression treatment would have the secondary benefit of preventing subsequent AUD or SUD. As noted by Kendall and Kessler (2002), it is not possible to compare treated versus untreated depressed adolescents, because withholding treatment would be unethical. However, it is possible to compare more effective versus less effective treatment of MDD. Indeed, Kendall, Safford, Flannery-Schroeder, and Webb (2004) showed that effective treatment of youth anxiety disorders lowered risk of subsequent substance use problems. Thus, we compared onset of AUD and SUD among TADS adolescents who successfully responded to acute depression treatment compared to non-responders.
We supplemented our primary analyses with secondary analyses to investigate whether the receipt of a specific type of acute depression treatment or the achievement of response to a specific acute treatment was associated with lower risk for subsequent AUD or SUD. In TADS, fluoxetine alone (FLX) led to a greater rate of short-term response than did cognitive behavior therapy (CBT), and the combination of CBT and FLX (COMB) led to the highest rate of positive short-term treatment response (TADS Team, 2004). On the other hand, CBT, alone or as part of COMB, was a skills-based intervention and included some skills that are also embedded in effective substance abuse prevention programs (e.g., goal-setting, problem-solving, social skills; Lochman & Wells, 2002). We had an insufficient basis in prior research to justify a priori hypotheses for these analyses, but it was possible that faster response (through COMB or FLX) or skills acquisition (through COMB or CBT) might be associated with more favorable subsequent AUD or SUD outcomes.
Method Relation of TADS to the Present Study
TADS compared CBT, FLX, and COMB to one another over the course of short-term (12 weeks), continuation (6 weeks), and maintenance (18 weeks) stages of treatment. During the first stage, the three active treatments were also compared to clinical management with a pill placebo (PBO). At Week 12, the medication blind was broken, and PBO non-responders were offered their TADS treatment of choice. After all three treatment stages (Week 36), adolescents were followed openly for 1 year (TADS Team, 2009).
The present study, Survey of Outcomes Following Treatment for Adolescent Depression (SOFTAD), was an open follow-up extending an additional 3.5 years. The total TADS–SOFTAD time period spanned 63 months (21 months of TADS and 42 months of SOFTAD), with diagnostic interviews administered at baseline and then at the following months after baseline: 3, 9, 15, 21 (end of TADS), 27, 33, 39, 51, 63.
The design, sample characteristics, and outcomes of TADS have been described in previous publications (TADS Team, 2004, 2007, 2009). TADS participants were 439 adolescents from 13 sites with moderate-to-severe, non-psychotic MDD. At the end of short-term treatment, positive response was defined as an independent evaluator rating of 1 (very much improved) or 2 (much improved) on the 7-point Clinical Global Impressions–Improvement scale (CGI-I; Guy, 1976). Adolescents rated with a score of 3 (minimally improved) or higher (no change or worsening) were categorized as non-responders.
Participants in SOFTAD were recruited from all 439 adolescents in TADS, regardless of compliance with treatment or assessments, treatment response, or time since TADS baseline, provided this was no greater than 63 months. TADS recruitment began in Spring 2000 and ended in Summer 2003. SOFTAD recruitment and assessments began in Spring 2004 and concluded in Winter 2008. Recruitment involved (a) recontacting TADS early completers and dropouts, and (b) after Spring 2004, asking adolescents and parents completing TADS to participate in SOFTAD. Written informed consent and, for minors, assent were obtained. The Duke University Health System Institutional Review Board (IRB) and each site IRB approved this study.
The initial SOFTAD assessment optimally occurred 27 months after TADS baseline. SOFTAD participants who were enrolled at that juncture could complete seven assessments at 6-month intervals, of which five included the diagnostic interviews that were used in the present analyses. Some participants, however, were not recruited until after 27 months, and their SOFTAD enrollment visit was the assessment that corresponded to their point of entry.
Sample Description
The total SOFTAD sample included 196 adolescents, recruited at 12 of the 13 TADS sites. Four SOFTAD participants were excluded from the present study because of AUD or SUD diagnosed at or before the end of TADS short-term treatment (Week 12). Thus, the sample for the present study consisted of 192 adolescents (84 male adolescents and 108 female adolescents; 43.7% of the 439 youths randomized to TADS treatments), with an average age at entry into TADS of 14.9 years (SD = 1.5 years). Their age at the end of SOFTAD ranged from 17 to 23 years, with a mean of 20.1 years (SD = 1.5 years). Table 1 includes demographic and clinical characteristics of the present sample at the time they entered TADS. The sample was 79% Caucasian, 9% Latino, 8% African American, and 4% other ethnicity. Ninety percent of the sample had been in their first episode of MDD at entry into TADS. At intake into TADS, they had been moderately to severely depressed, as indicated on the Children's Depression Rating Scale–Revised (CDRS-R; Poznanski & Mokros, 1996; sample raw score M = 59.4, SD = 10.3). Functional impairment was also in the moderate range on the 100-point Children's Global Assessment Scale (CGAS; Shaffer et al., 1983; sample M = 50.3, SD = 7.8). Forty-one of these adolescents (21%) had a comorbid disruptive behavior disorder, and 44 (23%) had a comorbid anxiety disorder.
TADS Baseline Characteristics of Current Study Participants and Non-Participants
The participants' point of entry into SOFTAD, in months since TADS baseline, was as follows: Month 27 (33%), Month 33 (22%), Month 39 (14%), Month 45 (11%), Month 51 (10%), Month 57 (8%), and Month 63 (2%). Of seven possible SOFTAD assessments, the modal number of completed assessments was 5, with a mean of 3.5 (SD = 1.5).
Criterion Measures
Diagnoses
To establish diagnoses, including those of AUD and SUD, the Schedule for Affective Disorders and Schizophrenia for School-Age Children–Present and Lifetime Version (K-SADS-PL; Kaufman et al., 1997) was administered at five of the seven SOFTAD assessment points. (The Month 45 and Month 57 assessment points included only self-report scales.) The K-SADS-PL had been used in TADS and, thus, was familiar to all participants. It was used to assess mood, anxiety, disruptive behavior, eating, substance use, psychotic, and tic disorders using Diagnostic and Statistical Manual of Mental Disorders (4th ed., text rev.; DSM–IV–TR; American Psychiatric Association, 2000) criteria. This interview has high concurrent validity, interrater, and test–retest reliability (Kaufman et al., 1997; TADS Team, 2004). At each K-SADS-PL administration, inquiry was made about symptoms and episodes of any disorder since the last TADS or SOFTAD assessment that the participant had completed and also about current symptoms of MDD, AUD, or SUD. The K-SADS-PL is typically administered to both the adolescent and a parent. Interview administration was adapted for SOFTAD, as participants were transitioning into young adulthood: The participant was always interviewed; the parent was interviewed if the participant was still living at home. This modification is consistent with other adaptations of the K-SADS for circumstances in which parental involvement is not feasible (Lewinsohn et al., 1993)
The K-SADS-PL includes an initial screen interview, with supplements for each disorder. The supplements are administered only if the screen indicates the possibility of the disorder. For AUD, the screen interview includes questions about quantity (three or more drinks in a day) and frequency of drinking (three or more days a week), and about whether significant others have expressed concern about the participant's drinking. If any item is answered positively, the supplement is administered. For SUD, the screen includes a list of possible drugs of abuse (cannabis, stimulants, anxiolytics, sedatives, cocaine, opioids, phencyclidine, hallucinogens, solvents, inhalants, ecstasy, and prescription drugs), and the participant is asked whether he or she has used any of these in the past 6 months. If non-prescribed use has occurred more than once a month, the supplement is administered. Supplement questions are anchored to DSM–IV–TR symptoms of abuse or dependence.
Episodes of MDD, AUD, or SUD
When the K-SADS-PL indicated that the participant met criteria for MDD, AUD, or SUD (other than nicotine), at any point since the last interview, the interviewer inquired about onset and, if relevant, offset of the episode. Onset was estimated as the month when the adolescent met all criteria for a disorder episode. Offset was estimated as the month when the adolescent had no remaining clinically significant symptoms of the disorder.
In a previous report (Curry et al., 2011), we focused on recovery from the TADS index episode of MDD and on recurrent MDD. Recurrent MDD was defined as a new episode following at least 8 weeks of no MDD symptoms. In this study, we focused on the emergence of episodes of AUD or SUD after short-term depression treatment, defined as those diagnosed after the TADS Week 12 interview. We also investigated the association between recurrent episodes of MDD and the onset of AUD or SUD.
Interviewer training and monitoring
SOFTAD evaluators met the same criteria as those of TADS evaluators (master's or doctoral degree in a mental health profession with previous experience administering research diagnostic interviews). Evaluators completed these steps for certification: (1) a videoconference training session; (2) a knowledge test passed with 80% correct answers; (3) rating a videotaped standard patient interview provided by the coordinating center, with 80% agreement on the full MDD, AUD, and SUD DSM–IV–TR criterion sets, agreement on these diagnoses, and agreement on other classes of disorders (e.g., anxiety disorder); and (4) completion and rating of an audiotaped site-based interview with an adolescent, subsequently rated at the coordinating center with acceptable reliability using the same criteria as in Step 3.
Following certification, evaluators participated in monthly conference calls to review interviews. Each evaluator was required during their 2nd and 3rd year in the project to reliably rate a patient interview provided by the coordinating center. On these recertification interviews (n = 24), there was complete agreement between evaluators and coordinating center raters on diagnosis of MDD “since the last interview” (k = 1.00) and there was 96% agreement on “current” MDD (k = .92); 91% of evaluator ratings for each time frame exceeded the 80% agreement level on DSM–IV–TR diagnostic criteria sets. For AUD, there was complete agreement for the diagnosis both “since the last interview” and “current episode” (k = 1.00). On the DSM–IV–TR criteria sets, 92% of ratings for “since last interview” and 90% for “current episode” exceeded 80% agreement. For SUD, there was 92% agreement on the diagnosis at each time frame (k = .82). For each time frame, 83% of evaluator ratings exceeded the 80% agreement level on the DSM–IV–TR criteria sets.
TADS Baseline Measures
For purposes of sample description or as potential predictors of subsequent AUD or SUD, the following variables were assessed at TADS baseline:
Age, race/ethnicity, gender, family income, and referral source
Age in years, gender, and race/ethnicity (Caucasian, African American, Latino, Asian, or other) were reported by participants at TADS study entry. Race/ethnicity was dichotomized as majority (non-Latino White) or minority because of limited sample sizes. Parents reported annual family income and whether they had been referred from a clinic or were responding to a study advertisement.
Duration of index major depressive episode
An independent evaluator completed a K-SADS-PL interview and estimated the date of onset and duration in weeks of the index episode of MDD at the point of entry into TADS.
CDRS-R
The CDRS-R is a 17-item symptom interview completed by the independent evaluator with reference to the past week, which yields an overall severity score. It has high internal consistency (α = .85) as well as high test–retest (Poznanski & Mokros, 1996) and interrater reliability (intraclass correlation coefficient = .95; TADS Team, 2004).
Reynolds Adolescent Depression Scale (RADS; Reynolds, 1987b)
Adolescents completed the RADS, a 30-item scale pertaining to the past week, to assess self-reported depression severity. The RADS has high internal consistency (α = .92) and test–retest reliability (r = .80; Reynolds, 1987b).
Suicidal Ideation Questionnaire–Junior High Version (SIQ-Jr; Reynolds, 1987a)
The 15-item SIQ-Jr was completed by adolescents to assess severity of suicidal ideation. The SIQ-Jr has high internal consistency (α = .91) and test–retest reliability (r = .89; Reynolds, 1987a).
CGAS
The independent evaluator assigned a rating of general functioning for the past week on the 100-point CGAS. This scale has good reliability and validity (Shaffer et al., 1983).
Comorbidity
In addition to MDD, the K-SADS-PL yielded baseline diagnoses of current dysthymia, any anxiety, disruptive behavior, alcohol or substance use, eating, or tic disorder, and total number of comorbid disorders. The disruptive behavior disorders included conduct disorder, oppositional defiant disorder, and attention-deficit/hyperactivity disorder. The anxiety disorders included general anxiety disorder, separation anxiety disorder, social phobia, posttraumatic stress disorder, panic disorder, and agoraphobia.
Personal Experience Screening Questionnaire (PESQ; Winters, 1991)
Adolescents completed the PESQ, which includes a well standardized 18-item Problem Severity score that measures the extent to which the adolescent is psychologically and behaviorally involved with alcohol or other drugs. Scores range from 18 to 72. Internal consistency reliability (.90–.95) and validity have been established with normal, delinquent, and substance abusing adolescents (Winters, 1991).
TADS Short-Term Depression Treatment Response
In the TADS project, positive short-term treatment response at Week 12 was defined as a rating by an independent evaluator of 1 (very much improved) or 2 (much improved) on the 7-point CGI-I. Non-response was defined as ratings of three (minimally improved) or higher. We compared TADS responders to non-responders using this definition.
To facilitate comparison with other depression treatment studies, we supplemented the above definition of short-term treatment response with a second, more stringent definition used in similar studies, for example, the Treatment of Resistant Depression in Adolescents study (TORDIA; Brent et al., 2008). This second definition of response required both a CGI-I of 1 or 2 and a 50% reduction in CDRS-R raw score. We designated those adolescents who met this definition as symptom count responders.
Course of MDD
MDD recovery, recurrence, and persistence
Recovery from the index episode of MDD was defined as absence of any MDD symptoms for a period of at least 8 weeks. Recurrence of MDD was defined as a new episode following recovery. Chronic or persistent MDD was defined as an index episode from which the adolescent never recovered over the entire TADS–SOFTAD period.
Frequency of Alcohol or Marijuana Use
To determine whether participants with diagnoses of AUD or SUD during SOFTAD were using alcohol or drugs more frequently than other participants, all participants completed 7-point frequency ratings at each SOFTAD assessment point. Each rating indicated frequency of use of alcohol, marijuana, or hard drugs over the past month, with the following intervals: none, 1–2 times, 3–5 times, 6–9 times, 10–19 times, 20–39 times, or over 40 times.
Statistical Analysis
Non-directional hypotheses were tested, and the level of significance was set a .05 for each two-tailed test. Due to the exploratory nature of the study, the alpha was not adjusted for multiple tests.
First, we compared the demographic and clinical characteristics of the TADS participants who were included in the present study (N = 192) to those who were not (N = 247) using general linear models for continuous measures and chi-square tests for binary outcomes. Alternatively, a Wilcoxon Two-Sample Test or Fisher's Exact Test was used when the assumptions of the corresponding parametric test were not met.
As a check on the validity of AUD and SUD diagnoses, we compared participants with AUD to those without AUD on maximum reported frequency of past month alcohol use, using a non-parametric median test. Similarly, we compared SUD to non-SUD participants on highest reported past month frequency of their drug of abuse (cannabis or hard drugs).
The primary outcomes were rates of AUD and SUD for the 192 participants in the present study. Potential predictors of AUD or SUD were grouped in two clusters: (1) short-term treatment response variables and (2) prerandomization baseline variables. Within the first cluster were the two definitions of treatment response: TADS response and symptom count response. Individual bivariate logistic regressions were conducted on each of these separately. In the second cluster, individual bivariate logistic regressions were conducted on each candidate predictor, and measures that were significant at the .10 level were included in a subsequent multivariable logistic regression. We selected this inclusion criterion because it is sometimes possible for an explanatory variable that had a tendency to influence the outcome in bivariate models (p < .10) to become a statistically significant predictor of the outcome (p < .05) when evaluated in the multivariable context. Thus, we selected a liberal .10 significance level as inclusion criterion for the multivariable model to avoid premature elimination of potentially significant predictors (Jaccard, Guilamo-Ramos, Johansson, & Bouris, 2006). Next, multivariable logistic regression analysis was conducted, and a stepwise variable selection approach was applied to derive the most parsimonious baseline variables prediction model. For the stepwise procedure, an entry criterion of .10 and a retention criterion of .05 were specified. The resulting multivariable model only included those variables that were significant at the .05 level after taking into account the relative contribution of the other predictor variables. Each step of multivariable regression analysis was checked for multicollinearity and violation of model assumptions.
Following these analyses, each of the two Week 12 treatment response measures, if individually significant, was added in separate final multivariable models to evaluate the effects of acute treatment response after controlling for other (baseline) predictors in the model.
Secondary exploratory analyses were conducted to determine whether assignment to, or response to, any of the four initial TADS treatments were predictive of subsequent AUD or SUD. We compared rates of subsequent AUD or SUD across the four treatment conditions using chi-square tests. Exploratory logistic regression analyses were then conducted with potential predictors of AUD or SUD that included initial treatment assignment, treatment response, and the interactions of treatment assignment with response. Separate analyses were conducted using each of the two definitions of response.
Finally, logistic regression was employed to examine the association between MDD course (ordered as 0 = recovery with no recurrence, 1 = recovery with one or more recurrences, 2 = persistent depression) and the development of AUD or SUD. Among those who experienced MDD recurrence following recovery, we then described the relation between timing of the recurrence and onset of the AUD or SUD.
Results Preliminary Analyses
Comparing TADS participants who did not participate in SOFTAD and the four SOFTAD participants excluded from the present study because of prior AUD or SUD to the current study participants, we found that participants and non-participants did not differ on percentages randomized to the four TADS treatment conditions, χ2(3, N = 439) = 1.70, p = .64. The percentage of current study participants who had been randomized to each short-term treatment condition was COMB = 25%, FLX = 24%, CBT = 28%, and PBO = 23%.
Table 1 includes comparisons of participants and non-participants on variables related to our hypotheses and on demographic and clinical variables at TADS baseline. There were no differences in percentage of TADS treatment responders (53.6% vs. 51.0%), χ2(1, N = 439) = 0.30, p = .58, or percentages of symptom count responders (47% vs. 44.5%), χ2(1, N = 439) = 0.24, p = .62. The only significant demographic differences were that study participants were somewhat younger than non-participants (M = 14. 3, SD = 1.5 vs. M = 14.8, SD = 1.6), F(1, 437) = 9.22, p = .0025, and included a smaller percentage of minority adolescents (21.4% vs. 30.0%), χ2(1, N = 439) = 4.14, p = .04. The significant baseline clinical differences were that study participants were more likely than non-participants to have entered TADS during their initial episode of MDD (90.5% vs. 82.5%), χ2(1, N = 429) = 5.59, p = .02, and had fewer total comorbid disorders (Mdn = 0 vs. 1; z = –2.51, p = .012). Participants' involvement with alcohol or drugs at baseline was also significantly lower than that of non-participants (PESQ Problem Severity M = 21.2, SD = 6.0 vs. M = 23.0, SD = 7.7), F(1, 422) = 6.81, p = .009.
Rates of Subsequent AUD and SUD
Of the 192 participants, 49 (25.5%) developed an AUD or SUD during the 60 months following short-term depression treatment. As shown in Table 2, 37 (19.3%) developed an AUD, and 34 (17.7%) developed an SUD. These rates are not significantly different from each other (McNemar test p = .70). Twenty-two adolescents (11.5%) developed both disorders. The mean onset age of AUD was 18.0 years (SD = 1.7), and for SUD, the mean onset age was 17.4 years (SD = 1.7). As indicated in Table 2, one third of those with initial SUD-only went on to develop AUD as well, whereas none of those with initial AUD-only proceeded to also develop SUD during the follow-up period. Perhaps related to the slightly older age of onset of AUD compared to SUD in this sample, initial diagnoses of AUD were about equally likely to be made in K-SADS interviews with only the adolescent (20 of 37 or 54.0%) or with the adolescent and a parent (17 of 37 or 45.9%), whereas initial diagnoses of SUD were more likely to be made in K-SADS interviews with the adolescent and a parent (22 of 34 or 64.7%) than in interviews with the adolescent alone (12 of 34 or 35.3%). Those who developed AUD did not differ from those who did not, on their average month of initial SOFTAD assessment, t(190) = 1.35, p = .178. Similarly, those who developed SUD did not differ from those who did not on this measure, t(190) = 1.15, p = .251.
Onset of AUD and/or SUD in 192 Adolescents Over 5 Years Following Treatment for MDD
Among the illicit drugs of abuse, marijuana was the most prevalent drug of abuse, accounting for 26 of the 34 SUD diagnoses. Cocaine, opiates, hallucinogens, other drugs, or polydrug use accounted for the other diagnoses. As a verification of diagnoses, the median peak score for participants with an AUD on past month drinking frequency was 6–9 times versus a median of 1–2 times for those without AUD (z = 4.64, p < .0001). The median peak score for those with an SUD on past month drug use frequency was 10–19 times per month versus a median of no use for those without SUD (z = 5.35, p < .0001).
Treatment Response Analysis
Using logistic regression analysis, we tested whether response to short-term depression treatment reduced the probability of developing either AUD or SUD, using both the TADS response and the symptom count response measures. For AUD, the hypothesis was not confirmed using either definition of response. Among 103 TADS treatment responders, 18 (17.5%) developed AUD; among 89 non-responders, 19 (21.4%) developed AUD, χ2(1, N = 192) = 0.46, odds ratio (OR) = 1.28, 95% CI [0.62, 2.63], p = .498. Among 90 symptom count responders, 17 (18.9%) developed AUD; among 102 non-symptom count responders, 20 (19.6%) developed AUD, χ2(1, N = 192) = 0.02, OR = 1.05, 95% CI [0.51, 2.15], p = .899.
We explored whether randomized treatment assignment, or response to a specific treatment, reduced the probability of developing AUD. Across the four randomized treatment arms, rates of subsequent AUD were 20.8% (COMB), 14.9% (FLX), 20.8% (CBT), and 20.5% (PBO), χ2(3, N = 192) = 0.76, p = .86. Neither this overall comparison nor a post hoc comparison of FLX (which had the lowest rate) to the other three treatments indicated significant differences between treatments in reducing the probability of developing AUD. For the comparison of FLX to other treatments, the percentages developing subsequent AUD were 14.9% and 20.7%, respectively, χ2(1, N = 192) = 0.76, p = .384. An exploratory logistic regression analysis including treatment assignment, treatment response and the interactions of treatment assignment and treatment response as predictors of AUD was not significant, regardless of whether the more global TADS measure of response, or the more restrictive symptom count response, was used in the analysis. For the full model using the TADS response measure, χ2(7, N = 192) = 2.92, p = .892. With the symptom count response measure, χ2(7, N = 192) = 3.065, p = .879.
For SUD, the hypothesis was confirmed: Response to MDD treatment reduced the probability of subsequent SUD. This finding occurred with both measures of response. Twelve of 103 TADS treatment responders (11.6%) developed an SUD versus 22 of 89 non-responders (24.7%), χ2(1, N = 192) = 5.38, OR = 2.49, 95% CI [1.15, 5.38], p = .02. Nine of 90 symptom count responders (10%) versus 25 of 102 non-symptom count responders (24.5%) developed an SUD, χ2(1, N = 192) = 6.52, OR = 2.92, 95% CI [1.28, 6.66], p = .011.
Exploratory analyses showed no significant differences in rates of subsequent SUD across the four TADS treatment conditions, with SUD rates of 14.6% (COMB), 17.0% (FLX), 20.8% (CBT), and 18.2% (PBO), χ2(3, N = 192) = 0.68, p = .88. Neither this overall comparison nor a post hoc comparison of COMB (which had the lowest rate) to the other three treatments indicated significant differences between treatments in reducing the probability of developing SUD. For the comparison of COMB to other treatments, the percentages developing subsequent SUD were 14.6% and 18.8%, respectively, χ2(1, N = 192) = 0.43, p = .514. When the four assigned treatments, treatment response, and the interactions between assigned treatments and response were entered into exploratory logistic regression analyses, the predictive models showed trends toward statistical significance, using either measure of response: With TADS response, χ2(7, N = 192) = 12.11, p = .097; with symptom count response, χ2(7, N = 192) = 12.23, p = .093. However, because neither model attained statistical significance, further analyses were not warranted.
Baseline Predictors Analysis
To evaluate the effects of MDD treatment on AUD and SUD in the context of possible significant baseline predictors, we next tested whether TADS baseline demographic and clinical variables predicted subsequent AUD or SUD. Because of skewed distributions, index episode duration and number of comorbid disorders were natural log transformed. Results are depicted in Table 3.
Individual Logistic Regression Analyses: TADS Baseline Predictors of Subsequent Alcohol or Substance Use Disorder
For subsequent AUD, older age, χ2(1, N = 192) = 8.81, OR = 1.49, 95% CI [1.14, 1.93], p = .003, and higher alcohol or drug involvement, χ2(1, N = 185) = 11.93, OR = 1.11, 95% CI [1.05, 1.18], p < .001, were significant individual predictors. Those who developed AUD averaged 15.0 years of age at baseline (SD = 1.4 years) versus 14.1 years (SD = 1.5 years) for other participants. Adolescents who developed AUD had mean baseline PESQ scores of 24.8 (SD = 8.2), whereas those who did not averaged 20.3 (SD = 4.9).
There was a trend for male adolescents to have lower risk for subsequent AUD than female adolescents. Among 84 male adolescents, 11 (13.1%) developed AUD, whereas 26 of 108 female adolescents (24.1%) did so, χ2(1, N = 192) = 3.56, OR = 0.48, 95% CI [0.22, 1.03], p = .059. There was also a trend for youths with longer episodes of MDD prior to TADS treatment to be more likely to develop later AUD, χ2(1, N = 192) = 3.05, OR = 1.37, 95% CI [0.96, 1.95], p = .081.
When baseline PESQ score, MDD episode duration, age, and gender were entered into a stepwise model, older age, χ2(1, N = 185) = 5.13, OR = 1.37, 95% CI [1.04, 1.81], p = .024, and higher PESQ score, χ2(1, N = 185) = 9.16, OR = 1.10, 95% CI [1.03, 1.16], p = .002, were retained as significant predictors of subsequent AUD. No further multivariable model was tested because MDD treatment response had not proven to be a significant predictor.
For subsequent SUD, significant individual baseline predictors included the total number of comorbid disorders, χ2(1, N = 192) = 5.78, OR = 2.39, 95% CI [1.17, 4.85], p = .016, and the PESQ Problem Severity score, χ2(1, N = 185) = 7.13, OR = 1.08, 95% CI [1.02, 1.14], p = .008. Depressed adolescents who later developed SUD had a mean of 1.1 comorbid disorders (SD = 1.3) compared to a mean of 0.6 comorbid disorders (SD = 0.9) for those who did not. They also had higher PESQ scores at baseline (M = 24.1, SD = 8.4) than other adolescents (M = 20.6, SD = 5.2).
When these two predictors were entered into a stepwise multivariable model, both were retained as significant predictors: PESQ, χ2(1, N = 185) = 7.25, OR = 1.08, 95% CI [1.02, 1.14], p = .007; number of comorbid disorders, χ2(1, N = 185) = 4.17, OR = 2.23, 95% CI [1.03, 4.79], p = .04.
We then tested whether poor treatment response predicted subsequent SUD when the two significant baseline predictors were included in overall models using the two definitions of treatment response. Results indicated that it did. With TADS treatment response in the final model, all three predictors were significant: TADS treatment response, χ2(1, N = 185) = 3.84, OR = 2.30, 95% CI [0.999, 5.28], p = .050; PESQ, χ2(1, N = 185) = 6.48, OR = 1.07, 95% CI [1.02, 1.14], p = .011; number of comorbid disorders, χ2(1, N = 185) = 4.12, OR = 2.23, 95% CI [1.03, 4.86], p = .042. Similarly, with symptom count response in the model, all three predictors remained significant: symptom count response, χ2(1, N = 185) = 4.65, OR = 2.61, 95% CI [1.09, 6.24], p = .031; PESQ, χ2(1, N = 185) = 6.95, OR = 1.07, 95% CI [1.02, 1.14], p = .008; number of comorbid disorders, χ2(1, N = 185) = 4.28, OR = 2.29, 95% CI [1.04, 5.02], p = .038. Characteristics of adolescents who developed AUD, SUD, or neither are described in Table 4.
Characteristics of Adolescents Who Developed AUD, SUD, or Neither, Following Treatment for MDD
MDD Course Analysis
The course of MDD for study participants through the end of SOFTAD was as follows: 98 (51.0%) recovered from their index episode with no recurrence, 87 (45.3%) recovered but had at least one recurrence, and 7 (3.7%) experienced chronic MDD. AUD was diagnosed in 10 of the 98 recovery cases (10.2%), in 25 of the 87 recovery and recurrence cases (28.7%), and in 2 of the 7 persistent depression cases (28.6%). Comparing the 98 recovery cases to the 94 cases with either chronic or recurrent MDD, a logistic regression indicated that depression recovery was negatively associated with onset of AUD, χ2(1, N = 192) = 9.8, OR = 0.28, 95% CI [0.13, 0.62], p = .002.
SUD was diagnosed in 13 of the 98 recovery cases (13.3%), in 19 of the 87 recovery and recurrence cases (21.8%), and in 2 of the 7 chronic depression cases (28.6%). Logistic regression indicated a trend for the depression recovery group to have fewer cases of SUD onset, χ2(1, N = 192) = 2.77, OR = 0.53, 95% CI [0.24, 1.14], p = .103.
Lastly, we explored the timing of MDD recurrence in relation to AUD or SUD onset. Among the 87 participants with recurrent MDD, 62 (71.3%) did not develop AUD, one (1.1%) had MDD recurrence before AUD onset, and 24 (27.6%) had MDD recurrence after AUD onset. In this latter group, the onset of first MDD recurrence was, on average, 22.7 months (SD = 11.8) after the AUD onset. A similar pattern was observed for MDD recurrence and SUD: 68 participants with recurrent MDD (78.2%) did not develop SUD, two (2.3%) had MDD recurrence before SUD onset, and 17 (19.5%) had MDD recurrence after SUD onset. Onset of the first recurrence was, on average, 19 months (SD = 9.9) after SUD onset.
DiscussionWe followed the largest sample to date of adolescents who had been treated for MDD, and we restricted the focus of the present study to those with no preexisting AUD or SUD to determine whether effective MDD treatment reduced the likelihood of developing either AUD or SUD. Five years after the end of short-term depression treatment, a quarter of the sample (25.5%) had developed either AUD or SUD. Positive response to short-term depression treatment was not related to later onset of AUD but lowered the likelihood of future SUD, even when baseline predictors of SUD were taken into account. Significant baseline predictors of AUD were older age and greater involvement with alcohol or drugs at entry into treatment. Significant baseline predictors of SUD were comorbid disorders and greater involvement with alcohol or drugs at entry into treatment.
The prevalence of AOSUDs in this sample of adolescents and young adults can be put in perspective by comparison with community or epidemiological studies. At the end of the follow-up period, the mean age of our participants was 20.1 years (range = 17–23). The most recent National Survey on Drug Use and Health (NSDUH; SAMHSA, 2010) indicated that the age group of 18–25 had the highest rate of past year diagnoses of AUD or SUD (20.8%) among three broad age groups surveyed (12- to 17-year-olds had a rate of 7.6%; those 26 years of age or older had a rate of 7.0%). Two other studies of younger adolescents yielded lifetime diagnoses for AOSUDs that were lower than those in our sample (12.2% by 16 years of age and 10.8% by 18 years of age; Costello et al., 2003; and Lewinsohn et al., 1993, respectively). The National Comorbidity Study replication (NCS-2; Kessler et al., 2005) did not include participants under 18 years of age but reported an AOSUD rate of 16.7% for those 18–29 years of age. Based on comparison with these studies, the rate of AOSUDs in our sample of treated, formerly depressed adolescents (25.5%) is most similar to, but exceeds that of, the NSDUH for 18- to 25-year-olds. Given methodological differences, and lacking a direct comparison with matched non-depressed adolescents, our study cannot conclusively state that the rate of AOSUDs in formerly depressed adolescents is elevated, but the rate is high enough to warrant concern and further study. In addition, as discussed below, the overall AOSUDs rate may have been even higher if all TADS adolescents had participated in the follow-up study. Finally, we did not follow a group of untreated depressed adolescents to determine whether the overall rate of subsequent AOSUDs would have been even more elevated in the absence of treatment.
Also of note is the relative frequency of AUD (19.3%) and SUD (17.7%) in our sample. Most epidemiological studies indicate that AUD occurs at a higher frequency than SUD, whereas in our study the two rates were not significantly different. A New York study (Cohen et al., 1993) found alcohol abuse far more prevalent in the 17- to 20-year-old age group than marijuana abuse (14.6% vs. 2.9%). AUD was also more prevalent than SUD among 18- to 29-year-olds in the NCS-2 (Kessler et al., 2005) and about 2.5 times more prevalent in the most recent NSDUH, affecting 7.3% of the U.S. population ages 12 through adulthood compared to 2.8% for SUD. An exception to this pattern was an Oregon study (Lewinsohn et al., 1993) that found SUD somewhat more prevalent at 18 years of age than AUD (8.2% vs. 6.2%), suggesting that although AUD is typically more prevalent than SUD, relative rates can be affected by geographic or temporal factors.
We found that SUD was predicted by comorbid psychopathology at baseline and by failure to respond to short-term depression treatment, whereas AUD was predicted by older age, a normal developmental factor, and not by depression treatment response. Considering these findings in the context of the relatively high prevalence rate of SUD in our sample, there may be a stronger link among depressed adolescents between adolescent psychopathology and subsequent SUD than there is for AUD. Reduction in overall psychopathology through successful depression treatment may have had more impact in preventing SUD than in preventing AUD because of such a link. By contrast, AUD tends to become elevated in the age range we studied, as alcohol use becomes more normative and part of social interactions. These possibilities are, of course, speculative, but they are consistent with an earlier cross-sectional study of college students in which MDD was associated with both AUD and SUD, but only SUD was also associated with comorbid diagnoses (Deykin et al., 1987).
Exploratory analyses indicated that no specific TADS MDD treatment proved more effective than others in reducing risk of subsequent AUD or SUD. This finding should not be interpreted to indicate that failure to actively treat adolescent MDD would be as effective as the TADS treatments in reducing risk for subsequent AUD or SUD. Three of the four TADS conditions involved an active treatment, and the acute phase PBO condition included regular clinical contact, support, and symptom reviews during the first 12 weeks, generally followed by open treatment after the blind was broken (Kennard et al., 2009). Moreover, both the present study and an earlier report on this sample indicate that attaining a full response to acute depression treatment is important. In the previous study (Curry et al., 2011), a positive short-term treatment response predicted greater likelihood of full recovery from MDD within 2 years, whereas the present study indicated that full response to treatment lowered the risk of subsequent SUD.
Our findings indicated that positive response to depression treatment, rather than engagement in a specific treatment, reduced risk of subsequent SUD. This is consistent with studies indicating that depressive symptoms are a risk factor for later substance abuse in older adolescents (e.g., Lewinsohn, Gotlib, & Seeley, 1995). The mechanisms through which effective depression treatment reduces risk for later SUD require further research and may vary by treatment. It is possible that problem-solving and coping skills learned in the CBT and COMB conditions, which parallel effective components of substance abuse treatment (Waldron & Turner, 2008), contributed to this positive outcome. Similarly, improved mood regulation due to medication effects, or shared elements common to all four interventions (support, psychoeducation about depression), may have been effective mechanisms. Alternatively, because adolescent depression has a negative impact on peer, family, and academic functioning (Jaycox et al., 2009), it is possible that TADS treatment responders' improved functioning, which was accounted for by reduced depression (Vitiello et al., 2006), reduced their risk for subsequent involvement with substances.
We found a trend (p = .059) for female participants to have higher rates of AUD than male participants. This stands in contrast to the general finding that adult men have higher rates of AUD than adult women. However, the gender difference in prevalence of AUD begins to emerge only around 18 years of age and is less significant among adults who have both depression and AUD (Schulte, Ramo, & Brown, 2009). In our sample of formerly depressed adolescents, female gender was not a protective factor against development of AUD.
A more negative course of MDD after acute treatment was significantly associated with AUD onset in the present sample, with a comparable trend result for SUD. Recurrent or chronic MDD was linked to higher probability of an AUD. This finding is consistent with previously noted associations between more prolonged depression and alcohol or substance abuse (King et al., 1996) in adolescent girls. When participants in the present study developed both recurrent depression and AUD, the AUD most often occurred prior to the recurrent episode of MDD. The present findings suggest that AUD raised the risk of MDD recurrence, rather than recurrence increasing the probability of AUD.
Clinical Implications
The importance of attaining a full response to MDD treatment, regardless of type, is reinforced by the present findings. The significance of attaining a full response to short-term treatment in reducing risk for SUD was evident even when considering other significant risk factors for SUD. Thus, augmenting or changing partially effective MDD treatments after a relatively brief acute intervention period is recommended for achieving the secondary benefit of reduced SUD risk. For depressed adolescent non-responders to selective serotonin reuptake inhibitors, augmenting medication treatment with CBT significantly improved outcome (Brent et al., 2008). No parallel study has been completed to investigate the augmenting effect of medication among non-responders to CBT, but clinical guidelines advocate augmenting or changing ineffective psychotherapy after a reasonable period of time (Hughes et al., 2007).
Depressed adolescents who later develop AUD or SUD are more likely than those who do not to already be using alcohol or drugs at the time they enter depression treatment. Indeed, a single score indicative of such involvement predicted both AUD and SUD. When combined with older age, alcohol or drug involvement at entry into depression treatment predicted AUD, and when combined with comorbid disorders, it predicted SUD. Thus, it is important to assess all levels of alcohol and drug use before starting treatment for adolescent depression to monitor depressed adolescents who are using alcohol or drugs and to intervene quickly if AUD or SUD develops.
After recovery from adolescent MDD, AUD significantly increased the likelihood of depression recurrence, with a similar trend for SUD. Thus, our findings are more consistent with a “drinking consequences” model than with a “self-medication” model of the relation between negative mood and drinking (Hussong, Gould, & Hersh, 2008), at least among adolescents with a history of MDD. Adolescents who have experienced successful treatment for MDD and their parents should be advised of the risk for recurrence that is associated with significant alcohol misuse. Formerly depressed adolescents who then develop AUD or SUD should be monitored for a return of depressive symptoms and should be offered interventions to reduce risk of a recurrent depressive episode.
Limitations
Although this is the largest sample of treated depressed adolescents with long-term follow-up data, power to detect a significant difference when testing the main hypothesis was limited. For example, with 103 responders and 89 non-responders on the TADS acute treatment response measure, power to detect a significant difference between the rates of subsequent SUD in these two groups (11.6% vs. 24.7%, respectively) was only .64. Moreover, the number of participants who developed AUD or SUD was relatively small. Therefore, conclusions based on rates of AUD or SUD must be viewed with caution, pending replication.
Another significant study limitation is that the SOFTAD sample consisted of slightly under half of the initial TADS sample. Previous follow-up studies of treated depressed adolescents have retained higher rates of participants than did SOFTAD, ranging from 97% (Birmaher et al., 2000) to approximately 60% of originally randomized and treated adolescents (Clarke, Rohde, Lewinsohn, Hops, & Seeley, 1999). However, these studies were conducted in one or two sites over 2 years, compared to the present multisite, 5-year project, in which both retention of later TADS completers and recontacting of early TADS completers were required. As has been reported by others (Badawi, Eaton, Myllyluoma, Weimer, & Gallo, 1999; Cotter, Burke, Loeber, & Navratil, 2002), our retention was more challenging with older adolescents and with minority participants. On most indices, the SOFTAD sample was representative of the full TADS sample, but our sample was somewhat younger, had fewer comorbid disorders, and had less involvement with alcohol or drugs at TADS baseline than non-participants who had been in the TADS sample. All three of these factors were associated in our follow-up sample with lower likelihood of developing AUD or SUD. Therefore, it is very possible that the rates of these later disorders might have been higher if the entire TADS sample had participated in the extended follow-up.
Also, because the SOFTAD sample was derived entirely from the TADS sample, it was limited to adolescents who passed TADS exclusion criteria. No adolescents with bipolar disorder, severe (violent or assaultive) conduct disorder, pervasive developmental disorder, or thought disorder were included. The first two exclusions, in particular, may also have led to lower rates of subsequent AUD or SUD than might be found in a depressed adolescent outpatient sample not similarly restricted.
Our study is also limited by the lack of a non-depressed comparison group. Without such a direct comparison, we cannot be certain that the rates of AUD or SUD among formerly depressed adolescents exceed those of similar but non-depressed adolescents. Finally, for obvious ethical reasons, we did not include a group of untreated adolescents with MDD. Therefore, we do not know the rates of subsequent AUD or SUD among depressed young people who are untreated.
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Submitted: January 20, 2011 Revised: November 1, 2011 Accepted: November 16, 2011
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Source: Journal of Consulting and Clinical Psychology. Vol. 80. (2), Apr, 2012 pp. 299-312)
Accession Number: 2012-00540-001
Digital Object Identifier: 10.1037/a0026929
Record: 107- Title:
- Parallel demand–withdraw processes in family therapy for adolescent drug abuse.
- Authors:
- Rynes, Kristina N.. Center on Alcoholism, Substance Abuse, and Addictions (CASAA), University of New Mexico, Albuquerque, NM, US, krynes@unm.edu
Rohrbaugh, Michael J.. Department of Psychology, University of Arizona, AZ, US
Lebensohn-Chialvo, Florencia. Department of Psychology, University of Arizona, AZ, US
Shoham, Varda. Department of Psychology, University of Arizona, AZ, US - Address:
- Rynes, Kristina N., Center on Alcoholism, Substance Abuse, and Addictions, University of New Mexico, 2650 Yale Boulevard, SE, MSC11-6280, Albuquerque, NM, US, 87106
- Source:
- Psychology of Addictive Behaviors, Vol 28(2), Jun, 2014. pp. 420-430.
- NLM Title Abbreviation:
- Psychol Addict Behav
- Page Count:
- 11
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- Bulletin of the Society of Psychologists in Addictive Behaviors; Bulletin of the Society of Psychologists in Substance Abuse
- Other Publishers:
- US : Educational Publishing Foundation
Society of Psychologists in Addictive Behaviors - ISSN:
- 0893-164X (Print)
1939-1501 (Electronic) - Language:
- English
- Keywords:
- BSFT, adolescent substance abuse, demand–withdraw interaction, family therapy, process–outcome research, brief strategic family therapy
- Abstract:
- Isomorphism, or parallel process, occurs in family therapy when patterns of therapist–client interaction replicate problematic interaction patterns within the family. This study investigated parallel demand–withdraw processes in brief strategic family therapy (BSFT) for adolescent drug abuse, hypothesizing that therapist-demand/adolescent-withdraw interaction (TD/AW) cycles observed early in treatment would predict poor adolescent outcomes at follow-up for families who exhibited entrenched parent-demand/adolescent-withdraw interaction (PD/AW) before treatment began. Participants were 91 families who received at least four sessions of BSFT in a multisite clinical trial on adolescent drug abuse (Robbins et al., 2011). Prior to receiving therapy, families completed videotaped family interaction tasks from which trained observers coded PD/AW. Another team of raters coded TD/AW during two early BSFT sessions. The main dependent variable was the number of drug-use days that adolescents reported in timeline follow-back interviews 7 to 12 months after family therapy began. Zero-inflated Poisson regression analyses supported the main hypothesis, showing that PD/AW and TD/AW interacted to predict adolescent drug use at follow-up. For adolescents in high PD/AW families, higher levels of TD/AW predicted significant increases in drug use at follow-up, whereas for low PD/AW families, TD/AW and follow-up drug use were unrelated. Results suggest that attending to parallel demand–withdraw processes in parent–adolescent and therapist–adolescent dyads may be useful in family therapy for substance-using adolescents. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Brief Psychotherapy; *Drug Abuse; *Psychotherapeutic Processes; *Strategic Family Therapy
- Medical Subject Headings (MeSH):
- Adolescent; Behavior Therapy; Family Therapy; Female; Humans; Male; Outcome and Process Assessment (Health Care); Parent-Child Relations; Parents; Professional-Patient Relations; Psychotherapeutic Processes; Psychotherapy, Brief; Regression Analysis; Substance-Related Disorders; Treatment Outcome
- PsycINFO Classification:
- Drug & Alcohol Rehabilitation (3383)
- Population:
- Human
Male
Female - Location:
- US
- Age Group:
- Adolescence (13-17 yrs)
- Tests & Measures:
- Timeline Follow-Back Interview
Parenting Practices Questionnaire DOI: 10.1037/t08384-000 - Grant Sponsorship:
- Sponsor: National Institute on Drug Abuse
Grant Number: R01-DA17539-01, U10-DA15815, and U10-DA13720
Recipients: No recipient indicated
Sponsor: National Institute on Alcohol Abuse and Alcoholism
Grant Number: T32-AA0018108-01A1
Recipients: No recipient indicated - Methodology:
- Empirical Study; Quantitative Study; Treatment Outcome
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- First Posted: Feb 25, 2013; Accepted: Dec 18, 2012; Revised: Dec 17, 2012; First Submitted: Jul 23, 2012
- Release Date:
- 20130225
- Correction Date:
- 20140623
- Copyright:
- American Psychological Association. 2013
- Digital Object Identifier:
- http://dx.doi.org/10.1037/a0031812
- PMID:
- 23438248
- Accession Number:
- 2013-06062-001
- Number of Citations in Source:
- 58
- Persistent link to this record (Permalink):
- http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2013-06062-001&site=ehost-live
- Cut and Paste:
- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2013-06062-001&site=ehost-live">Parallel demand–withdraw processes in family therapy for adolescent drug abuse.</A>
- Database:
- PsycINFO
Parallel Demand–Withdraw Processes in Family Therapy for Adolescent Drug Abuse
By: Kristina N. Rynes
Center on Alcoholism, Substance Abuse, and Addictions (CASAA), University of New Mexico;
Michael J. Rohrbaugh
Department of Psychology, University of Arizona
Florencia Lebensohn-Chialvo
Department of Psychology, University of Arizona
Varda Shoham
Department of Psychology, University of Arizona and the National Institute of Mental Health, Bethesda, Maryland
Acknowledgement: This project was supported by National Institute on Drug Abuse Grants R01-DA17539-01, U10-DA15815, and U10-DA13720 and National Institute on Alcohol Abuse and Alcoholism Grant T32-AA0018108-01A1. We extend appreciation to Brant Hasler, Katherine Calkins, Carlos Figueroa, Katie Gallardo, and Brittany Martell, whose work made the successful completion of this project possible. We also thank David A. Sbarra, Emily A. Butler, and Barbara S. McCrady for their helpful comments on this article.
The efficacy of family therapy for adolescent substance abuse is well documented. Many studies have demonstrated that, compared with individual therapy and treatment as usual, generic family therapy is associated with greater reductions in adolescent substance use and other types of positive behavior change (cf. Rowe, 2012; Stanton & Shadish, 1997; Waldron & Turner, 2008 for reviews). Specific models, such as multidimensional family therapy (Liddle, 2002) and functional family therapy (Sexton & Alexander, 2002), have achieved the status of “well-established treatments” in this arena (cf. Waldron & Turner, 2008). Brief strategic family therapy (BSFT)—a relatively pure form of systemic therapy, and the focus of this report—has demonstrated clear promise as well (Robbins et al., 2011; Santisteban et al., 2003; Szapocznik et al., 1989; Williams & Chang, 2000).
As outcome evidence accumulates, researchers are recognizing that process studies are needed to determine how and for whom family interventions work (Rowe, 2012). By understanding the mediators and moderators of family therapy, researchers will be able to refine family interventions by emphasizing the aspects that are most highly associated with positive outcomes and eliminating the aspects that are inert. Such refinement efforts will help make family therapy models more efficient and cost-effective (Kazdin & Nock, 2003; La Greca, Silverman, & Lochman, 2009).
In the current study, we investigate a process hypothesis based on the systemic notion of isomorphism, or parallel process—namely, that poor outcomes are likely when patterns of therapist–client interaction replicate, or resemble in form, problematic patterns of interaction within the family itself. In the context of family therapy, isomorphism occurs when relational roles or patterns in one part of the therapy system correspond closely to the roles or interaction patterns in another subsystem (Liddle & Saba, 1983, 1985; Schwartz, Liddle, & Breunlin, 1988). For example, Liddle and Saba (1983) described the supervisory system and the therapy system as isomorphic: the supervisor’s role as “teacher” to the therapist is similar to the therapist’s role as an agent of learning and change to the family. Liddle and Saba (1983) also described isomorphism that develops within the therapy system, where therapists form relationships with family members that are isomorphic to existing relationships within the family. For instance, if a therapist tries to change the behavior of an adolescent in the same way that a parent tries to change the adolescent, the therapist–adolescent relationship can become isomorphic to the parent–adolescent relationship.
The current study focuses on a communication pattern called demand–withdraw interaction (DW) and investigates whether isomorphic DW processes in the therapist-adolescent and parent-adolescent subsystems of BSFT predict adolescent drug use outcomes. DW interaction is a dyadic pattern of communication in which one family member nags, criticizes, or pressures another family member to change, while the other ignores, distances, or refuses to discuss the topic (Eldridge & Christensen, 2002; Shoham, Rohrbaugh, & Cleary, 2008). Researchers have focused mainly on DW in couples, where this pattern correlates strongly with relationship dissatisfaction (Christensen, 1987; Christensen & Heavey, 1990; Heavey, Layne, & Christensen, 1993; Noller & White, 1990), domestic violence (Babcock, Waltz, Jacobson, & Gottman, 1993; Holtzworth-Munroe, Smutzler, & Stuart, 1998), and alcohol abuse (Kelly, Halford, & Young, 2002). Two studies have also investigated DW in parent–child dyads, finding that it correlated with relationship dissatisfaction of both parents and adolescent children as well as increased adolescent substance use (Caughlin & Malis, 2004a, 2004b).
From a family-systems perspective, DW is a problem-maintaining process that is fueled by both the demanding partner’s wish to change the other and the withdrawn partner’s desire to ignore the demanding partner in an attempt to preserve the status quo (Rohrbaugh & Shoham, 2001; Watzlawick, Bavelas, & Jackson, 1967). An example of a DW interaction that might occur in the family of a drug-abusing adolescent could involve a mother pressuring her son to quit using drugs while the son withdraws from her. The more the mother demands, the more the son ignores her; and the less likely it becomes that he will follow her advice—an ironic outcome given the mother’s wish for her son to stop using drugs.
The observation that DW often reflects one family member’s attempts to change another receives support from studies of couples, showing that demand and withdraw roles change according to the importance of the discussion topic for each partner (Heavey et al., 1993; Klinetob & Smith, 1996). For example, Klinetob and Smith (1996) found that when a discussion topic centered on something the wife wanted to change about her husband, wife-demand/husband-withdraw was more likely to occur than husband-demand/wife-withdraw. If the couple was discussing something the husband wanted to change about his wife, however, a husband-demand/wife-withdraw pattern was more likely to occur.
In view of this, social contexts like couple or family therapy, where people at different levels of an interactional system often attempt to induce change in another person’s behavior, provide fertile ground for parallel processes to develop. Thus, in family therapy for adolescent drug abuse, a therapist’s ultimate goal may parallel the goal of a parent—namely, to change the drug-use behavior of the adolescent identified patient (IP). If a central dynamic in the family is a parent-demand/adolescent-withdraw (PD/AW) dynamic, a therapist who replicates the parental stance by putting direct pressure on the adolescent IP to change risks provoking the IP to withdraw from the therapist in the same way he or she withdraws from the demanding parent(s). If the pattern repeats, and a therapist-demand/adolescent-withdraw (TD/AW) cycle develops, it is not difficult to imagine how such an escalating pattern could undermine successful family therapy and contribute to poor IP drug-use outcomes.
Empirical support for these ideas about parallel DW processes in couple and family therapy comes from a study comparing cognitive–behavioral therapy (CBT) and family-systems therapy (FST) for couples in which the male partner was alcoholic (Shoham, Rohrbaugh, Stickle, & Jacob, 1998). The CBT approach placed a high demand on the drinker to abstain from alcohol use, whereas FST therapists remained neutral about abstinence until the couple decided they were ready to change. Prior to therapy, all couples participated in a video-recorded interaction task in which they discussed a conflict as well as the husband’s drinking. From these recordings, investigators later derived observational measures of DW interaction. Strikingly, couples high on wife-demand/husband-withdraw were much more likely than couples low on this pattern to drop out of the high-demand CBT therapy, whereas DW had little bearing on continuance in low-demand FST. The authors interpreted this result to suggest that drinkers responded to a demanding therapy (or therapist) in ways that paralleled their response to a demanding partner or spouse.
The current study investigates parallel DW processes in family therapy for adolescent drug abuse, hypothesizing that TD/AW interaction cycles observed early in treatment would predict poor adolescent outcomes at follow-up for families who exhibited entrenched PD/AW interaction before treatment began. The main participant and outcome data are from a multisite effectiveness study of BSFT for adolescent drug abuse (Robbins et al., 2011; Szapocznik, Hervis, & Schwartz, 2003) administered from the University of Miami as protocol CTN-0014 in the National Institute on Drug Abuse (NIDA) Clinical Trials Network. The observational ratings of parent–adolescent and therapist–family interaction, on the other hand, come from a NIDA platform study conducted at the University of Arizona.
Because BSFT is a “structural” variant of family therapy, related to the work of Minuchin, Haley, and others (Haley, 1976; Madanes, 1981; Minuchin, 1974; Minuchin & Fishman, 1981; Nichols, 2010), the place of DW in this framework deserves comment. For one thing, BSFT focuses less on DW and other dyadic interaction patterns than it does on structural patterns like enmeshment, disengagement, cross-generation coalitions, and hierarchical anomalies in the broader family system (cf. Szapocznik et al., 2003). However, despite its central concern with relationship structure, the actual practice of BSFT focuses largely on interrupting specific patterns of interaction (behavioral sequences) that define this structure. For example, when working with a family in which some members are emotionally disengaged from one another, a BSFT therapist would want to change the interaction patterns that maintain this disengagement—and patterns of DW are likely to figure prominently in this. Another consideration is that good BSFT therapists are active, direct, and even to some extent demanding, as their prescribed role is to orchestrate change in the family system by actively restructuring relational patterns associated with the adolescent’s drug abuse (Szapocznik et al., 2003). Equally and perhaps more important, however, is for the therapist to remain decentralized and work through the family hierarchy to help parents more effectively nurture and control their children. This implies directing interventions (including therapeutic demands for change) toward parental figures more than children. In other words, a central goal of BSFT is to reorganize the family so that the parent figures are in a leadership position, which in practice involves placing more responsibility for change on parents than on children. Thus, therapist demand on adolescents and TD/AW interaction is not consistent with the BSFT model.
This study tested two hypotheses. First, consistent with previous research (Caughlin & Malis, 2004b), we expected that PD/AW would be associated with greater IP drug use at both baseline and follow-up. Second, we expected that TD/AW would moderate the association between PD/AW and IP drug use at follow-up, such that TD/AW would predict increased drug use for IPs with high baseline levels of PD/AW but not for those with low baseline PD/AW.
Method Participants
Participating adolescent IPs and families met two sets of inclusion criteria—one for the parent study and another for the more fine-grained observational analyses reported here. The parent study recruited 13- to 17-year-old clients from eight community treatment programs (CTPs), including one site each in Arizona, California, Colorado, North Carolina, Ohio, and Puerto Rico, and two sites in Florida. Adolescents were included if they reported using illicit drugs other than alcohol or tobacco in the 30-day period preceding their baseline assessment or had been referred from an institution (e.g., detention or residential treatment) for the treatment of a substance-use disorder. They were excluded if they did not reside in the same home as a parent figure, if they reported suicidal or homicidal ideation, or if they had current or pending severe criminal charges.
A narrower set of criteria was necessary to ensure sufficiently complete data for examining parallel DW processes. Families needed to have participated in at least four therapy sessions for which there were at least two adequate (ratable) video recordings. Treatment as usual (TAU) was not videotaped; thus, only BSFT cases were included. Families also needed to have completed a baseline family interaction task (FIAT) with good enough video and audio quality for the observational ratings and to have completed timeline follow-back (TLFB) interviews assessing IP substance use through at least 8 months of the 12-month follow-up period. Of the 245 cases randomized to BSFT, 91 met these criteria.
Table 1 shows demographic characteristics of adolescents and families in the study sample, and Table 2 contains information regarding therapists’ characteristics. As Table 1 shows, about 80% of the adolescents were male and 34% were white. Almost half of the adolescents had a previous history of incarceration. Chi-square analyses and t-tests showed no differences with regard to sex, ethnicity, blended family status, household income, family size, or history of incarceration (all ps ≥ .05) between the 91 IPs included in the current study sample and the other 389 parent-study participants who were not included. Adolescents in the study sample did, however, use drugs on more days in the month prior to baseline than did adolescents not included in the study, t(473) = −2.27, p = .02. For therapist participants, there were no differences in age, sex, ethnicity, degree earned, or years of counseling experience between the 18 included therapists and the 31 who were not included, all ps ≥ .12.
Adolescent and Family Demographic Characteristics
Therapist Demographic Characteristics
Procedure
In the BSFT clinical trial, investigators at the University of Miami recruited adolescents and families from participating CTPs, randomized them to treatment condition, and collected self-reported drug-use data from adolescents. The Miami team also developed a BSFT manual (Szapocznik et al., 2003), recruited therapists from participating CTPs, randomized them to treatment condition, trained therapists in how to implement BSFT, and continuously monitored the progress of the BSFT therapists via supervision and adherence ratings of videotaped therapy sessions (cf. Robbins et al., 2011 for details about these procedures). Investigators at the University of Arizona coordinated the administration of videotaped structured FIATs at baseline, from which a team of research assistants (RAs) later rated PD/AW. A second, independent group of Arizona RAs rated videos of selected BSFT sessions to code the therapist’s level of demand for IP change and the IP’s response to these demands (generating the measure of TD/AW). The institutional review boards of the University of Miami, the University of Arizona, and each participating research site approved the study procedures.
Descriptive statistics indicated that BSFT families in the clinical trial received an average of 8.1 (SD = 5.2) therapy sessions over 5.7 (SD = 3.2) months. In contrast, families in the present subsample received an average of 11.3 (SD = 4.5) sessions over 6.6 (SD = 2.7) months. Most participants (58.2%) had completed BSFT by the 6-month assessment. Therapy sessions lasted about 1 hour and took place in families’ homes (63%) or in clinic settings (32%).
Observational measures of PD/AW came from video-recorded FIATs administered by RAs at each site prior to the initiation of treatment. RAs asked that all family household members over the age of 6 gather in a place that was comfortable and convenient for them, such as their home or the CTP facility. The RAs then administered three sequential FIAT tasks based on earlier work by Minuchin, Montalvo, Guerney, Rosman, and Schumer (1967) and the Miami group (e.g., Santisteban et al., 2003). The tasks were (a) plan a dinner menu, (b) discuss likes and dislikes about each family member, and (c) discuss a recent family argument. To ensure that all families received the same task information, RAs used audiotaped instructions to initiate each task. On average, FIATs included 3.7 (SD = 1.4) family members and in 48.4% of FIATs, two parental figures were present. It took families an average of 2.4 (SD = 1.2) minutes to complete the first FIAT task, 4.9 (SD = 3.6) minutes to complete the second task, and 4.2 (SD = 2.7) minutes to complete the third task. Total FIAT time averaged 11.5 minutes (SD = 6.3).
To capture TD/AW, the first author and a team of four trained RAs coded levels of therapist demand and the adolescent IP’s response to that demand. The team rated three 5-min segments of two BSFT sessions, the first occurring early in the process of therapy (Sessions 1–4) and the second occurring about midway through therapy (Sessions 5–7). As a rule, we selected the first usable (ratable) session in each of these blocks, and the rationale for sampling from two blocks was to estimate the consistency of therapist-demand behavior across sessions and to include a session likely to involve active intervention (e.g., restructuring). Within each session, the team observed and rated the first 5 min, another 5 min occurring 40% of the way into the session, and a final 5 min occurring 80% of the way into the session.
Measures
Adolescent drug use
Each month for 12 months, parent-study RAs blind to treatment condition administered an adolescent-specific version of the TLFB interview (Bry, Conboy, & Bisgay, 1986; Bry & Krinsley, 1992; Sobell & Sobell, 1992) to all adolescent IPs. The RAs also conducted monthly urine drug screens using the SureStep Drug Screen Card 10A (Orlando, FL) to encourage accurate reporting of drug use.
The main outcome variable in this process study was the number of days that adolescent IPs reported using drugs in the 7–12 month follow-up period. The number of IP drug-use days in the month prior to baseline was included as a covariate in each study analysis. Past research has shown that responses on the TLFB interview are reliable, yielding consistently high test–retest correlations (Mason, Cauce, Gonzales, Hiraga, & Grove, 1994) and month-to-month stability coefficients in the present study were high as well (all rs ≥ .78, all ps ≤ .0001).
Parent-Demand/Adolescent-Withdraw (PD/AW)
Observational ratings of FIATs assessed the amount of PD/AW in each family at baseline. At least 2 raters independently coded all FIATs using Global Structural Family Systems Ratings (GSFSR), a coding system that includes scales for rating both family structure and specific family-interaction patterns, including PD/AW (Rohrbaugh, Hasler, Lebensohn-Chialvo, & Shoham, 2007). The GSFSR defines PD/AW as a pattern in which the parent(s) request, demand, nag, blame, criticize or try to discuss a problem with the IP while the IP becomes silent or disengaged, refuses to discuss the issue, or diverts attention away from the issue. Raters assessed the level of PD/AW in each task of the baseline FIATs using two rating scales, one measuring mother-demand/adolescent-withdraw (MD/AW) and another measuring father-demand/adolescent-withdraw (FD/AW). A score of 1 indicated no evidence of DW occurring at any time during the task, and a score of 5 indicated pervasive evidence of DW occurring throughout most of the task. Interrater reliability was continually monitored while ratings were taking place, and ICCs were consistently greater than .60. When ratings differed by more than 1.5 scale points, coders rereviewed the FIAT to arrive at a consensus rating.
We performed a series of repeated-measures ANOVAs to understand specific patterns of PD/AW across tasks and roles. There was a main effect of task, F(1, 90) = 30.09, p < .001 with the highest PD/AW scores occurring in Task 3 (family argument) and each successive task generating significantly higher scores than the previous task, ps < .01. Somewhat surprisingly, given the emphasis on gender roles in the DW literature, there was no significant main effect of parental role (MD/AW vs. FD/AW) and no statistical interaction between parental role and task (ps > .80), suggesting that the balance of MD/AW and FD/AW was similar in each task. Nor did the average amount of PD/AW in families that had two parent figures present for the FIAT (M = 1.31, SD = .37) differ from the average amount of PD/AW in families with only one parent present (M = 1.49, SD = .70), F(1, 89) = 2.30, p = .13.
In tests of the study hypotheses, the measure of PD/AW at baseline reflects the highest level of PD/AW recorded in interactions between the adolescent and either parent in any of the three FIAT tasks. In other words, the family-level score could represent either MD/AW or FD/AW (depending which was greater) and could come from discussions of menus (Task 1), likes and dislikes (Task 2), or family arguments (Task 3). We chose to measure PD/AW using this maximum score to ensure that we captured PD/AW whenever it occurred. In some FIAT tasks, the maximum rating of PD/AW was “1,” meaning that PD/AW never occurred. Specifically, in Task 1, 86% of families never engaged in PD/AW, in Task 2, 58.2% of families never engaged in PD/AW, and in Task 3, 48.4% did not engage in PD/AW. Using the highest rating of PD/AW from each family’s baseline FIAT maximized the probability that our measure captured instances of PD/AW. Nevertheless, families’ scores on this maximum PD/AW variable were relatively low, with a mean of 1.9 (SD = 1.0) on the 1–5 rating scale.
Parental monitoring
To assess the differential construct validity of the PD/AW ratings, we compared them to self-reports of parental monitoring, a set of parenting practices that have been shown to predict decreased adolescent drug use and behavior problems (cf. Dishion, Li, Spracklen, Brown, & Haas, 1998; Patterson & Stouthamer-Loeber, 1984). The parent study assessed parental monitoring at baseline using the parental monitoring subscale from the Parenting Practices Questionnaire (Thornberry, Huizinga, & Loeber, 1995). This subscale consists of 13 items on the adolescent version and 12 items on the parent version of the Parenting Practices Questionnaire. Representative items include: “When was the last time that you discussed with your child his or her plans for the coming day?” and “When your child is out, do you know what time he or she will be home?” Participants rated these items on 1–5-point scales where 1 indicated don’t know and 5 indicated almost every day. Parent study investigators found that the parental monitoring subscale had good internal consistency (Cronbach’s αs ≥ .72) for both adolescents and parents (Feaster et al., 2010).
Therapist-Demand/Adolescent-Withdraw (TD/AW)
The research team developed original observational coding scales to rate therapist demand on the adolescent IP and the IP’s response to this demand. We conceptualized therapist demand as involving requests that the adolescent change some behavior, or accept some viewpoint or definition of a behavioral reality (e.g., that drug use is “dangerous,” talking back “disrespectful,” and so on). Two 5-point scales captured, respectively, the extent and negative valence of therapist demand. On the extent scale, a rating of 1 indicated that the therapist never requested the IP to change either his or her behavior or perceptions of something, and 5 indicated that the therapist either did this very frequently or made more than one particularly salient demand for change. On the negative valence scale, raters coded the degree to which the therapist’s demands were critical, hostile, judgmental, or accusatory, with 1 indicating no negative valence and 5 very high negative valence (with one or two very salient examples justifying a score of 5). Interrater reliability for both of these observational scales was very good, with all ICCs ≥ .81.
On the adolescent IP side, two 5-point scales measured IP response to therapist demands. First, an accept–reject scale indicated whether IPs accepted or refused to comply with the therapist’s demands. Here a rating of 1 indicated consistent acceptance of the therapist’s demands and 5 indicated consistent refusal. Second, an active rejection response scale captured how passively or actively the IP rejected the therapist’s demands by attempting to change the subject, becoming defensive, or justifying him or herself. Here a rating of 1 indicated that all IP rejections were passive and 5 indicated that all were active. Scores on these IP-response scales had good interrater reliability, ICCs ≥ .72.
The measure of TD/AW interaction consisted of an aggregation of the ratings of therapist demand and IP response across the two rated sessions. Ratings of the extent and negative valence of therapist demand were positively correlated (r = .42, p < .0001). Thus, we averaged these ratings to form an aggregate measure of therapist demand. Ratings on the accept–reject and active rejection scales measuring IP withdrawal were also positively correlated (r = .33, p = .001). Thus, we averaged these scores to form an aggregate measure of IP withdrawal. The aggregate therapist-demand measure from the early session correlated with the aggregate therapist-demand score from the middle session (r = .54, p < .0001) and the aggregate IP-withdrawal score from the early session correlated with the aggregate IP-withdrawal score from the middle session, r = .33, p < .002, indicating that therapist demand and IP withdrawal were consistent over time. Therefore, we averaged therapist-demand ratings across sessions and the IP withdrawal across sessions to create final measures of each of these constructs. A final analysis showed that the average amount of therapist demand across sessions significantly correlated with the average amount of IP withdrawal across sessions, r = .47, p < .0001. Thus, we created a final measure of TD/AW by adding these two aggregate scores and dividing the sum by 2.
Analytic Strategy
To test the study hypotheses, we used zero-inflated Poisson (ZIP) regression analyses and executed these analyses using the “pscl” package (Zeileis, Kleiber, & Jackman, 2008) in R (R Development Core Team, 2009). ZIP regression is appropriate for analyzing nonnormally distributed dependent variables that consist of a number of discrete events (e.g., drug-use days) when the most common frequency count is zero and the frequency of the remaining counts have a Poisson distribution. The distribution of drug-use days in treatment studies is often nonnormal and well characterized by such ZIP distributions (cf. Hildebrandt, McCrady, Epstein, Cook, & Jensen, 2010). This was true in the present study: 13.5% of adolescents never used drugs during follow-up and the frequencies of the remaining counts of drug-use days were much lower (≤5.5%) and followed a Poisson distribution.
ZIP models contain two components, namely a binomial logistic regression that estimates the odds of being in the zero class (e.g., the odds of achieving complete abstinence from drugs) and a Poisson regression that estimates the Poisson mean of all values of the dependent variable, e.g., the number of drug-use days (Zeileis et al., 2008). Thus, ZIP analyses provide two sets of parameter estimates, one that indicates the extent to which each independent variable predicts the probability of achieving complete abstinence and another that indicates the extent to which each independent variable is associated with the number of drug-use days in the follow-up period.
Results Descriptive Statistics and Preliminary Analyses
Table 3 presents statistics describing the variables included in the study’s main analyses, namely, adolescents’ drug use, PD/AW interaction at baseline, and TD/AW interaction. This table shows that the percentage of IPs who were abstinent from drugs significantly decreased from baseline (30%) to follow-up (15%), χ2(1, N = 91) = 5.98, p = .01. This decrease appeared unrelated to whether IPs were recruited from restricted environments at baseline. Indeed, a logistic regression that controlled for baseline abstinence showed that the number of days in the month prior to baseline that IPs spent in restricted environments was not associated with abstinence at follow-up (OR = .08, SE = 2.43, p = .29). There were no significant correlations between PD/AW, TD/AW, baseline drug-use days, and therapists’ years of counseling experience (all ps ≥ .11). Thus, drug-use days at baseline were not related to either TD/AW (r = −.03, p = .79) or PD/AW (r = −.09, p = .40). In addition, PD/AW was not associated with having a history of previous incarceration, t(88) = .88, p = .38 or multiple arrests, t(88) = .05, p = .96. TD/AW, however, was more pronounced for adolescents with a previous history of multiple arrests, t(88) = −2.51, p = .01 but did not differ according to whether adolescents had a history of incarceration, t(88) = .20, p = .84. PD/AW was unassociated with both adolescents’ and parents’ reports of parental monitoring, r = −.06, p = .57 and r = .00, p = 1.00, respectively.
Descriptive Statistics: Adolescent, Family and Therapist Variables
Associations Between PD/AW and IP Drug Use at Baseline and Follow-Up
A ZIP analysis that regressed baseline IP drug-use days on baseline PD/AW, controlling for days that IPs were in restricted environments at baseline, showed that PD/AW was not related to baseline drug-use days (B = −.06, SE = .04, p = .16), but was marginally associated with a greater likelihood of abstinence at baseline, B = .46, SE = .25, p = .06. Number of days spent in a restricted environment before baseline was not related to baseline drug-use days (B = −.03, SE = .02, p = .11) but was associated with a greater likelihood of abstinence at baseline, B = .21, SE = .09, p = .01. A second ZIP analysis that regressed follow-up drug-use days on baseline PD/AW, controlling for baseline drug-use days and days spent in restricted environments at baseline and follow-up, showed that PD/AW was associated with increased drug-use days during the follow-up period (B = .06, SE = .02, p < .001) but was unrelated to abstinence at follow-up, B = .23, SE = .35, p = .52. Not surprisingly, baseline drug use was associated with increased follow-up drug-use days (B = .06, SE = .00, p < .001) and a marginal decrease in likelihood of abstinence at follow-up (B = −.49, SE = .26, p = .06). Days in restricted environments during follow-up were unrelated to abstinence at follow-up (B = .02, SE = .08, p = .78) but were marginally associated with increased follow-up drug-use days, B = .01, SE = .01, p = .06.
TD/AW as a Moderator of the Association Between PD/AW and IP Drug-Use Days at Follow-Up
A ZIP analysis that controlled for baseline drug-use days, days spent in restricted environments at baseline and follow-up, and therapists’ years of counseling experience showed that PD/AW and TD/AW interacted to predict drug-use days at follow-up (B = .35, SE = .05, p < .001). Figure 1 illustrates the pattern of this interaction and Table 4 contains the full set of results. As shown in Figure 1, for IPs with relatively high levels of PD/AW at baseline (e.g., IPs whose level of PD/AW was at least 1/2 SD above the mean), as TD/AW increased, the IPs’ follow-up drug-use days increased, B = .34, SE = .05, p < .001. In contrast, for IPs who engaged in low levels of PD/AW (e.g., ≤1/2 SD below the average level of PD/AW), TD/AW was not associated with drug-use days during follow-up, B = −.01, SE = .05, p = .87.
Figure 1. Adolescents’ predicted number of drug-use days in the 7- to 12-month follow-up period as a function of baseline level of parent-demand/adolescent-withdraw interaction and level of therapist-demand/adolescent-withdraw in BSFT sessions.
Results of Zero-Inflated Poisson Regression Examining Predictors of Drug-Use Days and Complete Abstinence From T7 Through T12
DiscussionThis study attempted to evaluate a parallel-process hypothesis about DW interaction in family therapy for adolescent drug use. Specifically, we hypothesized that therapists’ demands for change, when linked with concomitant adolescent withdrawal, would predict poor substance-use outcomes in the context of established PD/AW family interaction. Results supported this hypothesis. TD/AW interacted with PD/AW such that, for families that engaged in a relatively high amount of PD/AW at baseline, TD/AW predicted increased IP drug-use days at follow-up, whereas for families low in PD/AW, TD/AW was unrelated to IP drug use. A separate analysis also showed that baseline PD/AW was associated with increases in IP drug use at follow-up. Interestingly, however, baseline PD/AW was marginally associated with increased likelihood of IP abstinence at baseline.
Our parallel DW process hypothesis was based on the assumption that PD/AW would act as a problem-maintaining interactional process associated with increases in IP drug use. One study has shown PD/AW to be associated with increases in adolescent drug use (Caughlin & Malis, 2004b) and several studies have found that DW interaction is associated with relationship dissatisfaction in couples (cf. Eldridge & Christensen, 2002). The current study findings agree with and build upon these findings. Whereas Caughlin and Malis (2004a, 2004b) found that PD/AW was cross-sectionally associated with increased adolescent drug use, we found that PD/AW predicted future increases in adolescent drug use. In addition, previous research that our group conducted using the same sample as the one the current study used showed that the PD/AW pattern was positively associated with observations of disengagement in the parent–adolescent dyads at baseline (Rohrbaugh et al., 2007). Disengagement is characterized by a lack of connectedness, empathy, and emotional support between the parent and adolescent, as well as impermeable boundaries, emotional distance, and absence of communication. Studies have shown that disengagement is associated with increases in adolescent drug use as well as internalizing behavior problems (Baumrind, 1991; Brook, Brook, Gordon, & Whiteman, 1990). The association between PD/AW and parent–adolescent disengagement and the fact that both PD/AW and family disengagement are associated with increased adolescent drug use supports the notion that PD/AW is a problematic interaction pattern in families of adolescent drug abusers.
Despite the fact that PD/AW predicted increases in follow-up drug use and increased parent–adolescent disengagement, PD/AW was marginally associated with increased abstinence at baseline. There may be a couple of explanations for this result. First, the level of PD/AW in this sample was low. In fact, the average rating of PD/AW was 1.86 (SD = .99) on a 1–5 scale, and only 10% of families had more than moderate (e.g., ratings higher than 3) levels of PD/AW. These statistics suggest that, at baseline, pronounced, repetitive PD/AW cycles were rare in the study families. Thus, baseline PD/AW may not have been at a level where it was a problem-maintenance process for most study families. The severity of the drug use of adolescents in the current study may also help explain why PD/AW was not associated with increases in adolescent drug use at baseline. Only one study to date has found an association between PD/AW and increased adolescent drug use and it used a sample of normal (nonclinical) adolescents who had low levels of drug use (Caughlin & Malis, 2004b). Indeed, on a 4-point scale with 1 indicating no recent alcohol or drug use and 4 indicating at least 10 episodes of use, adolescents’ average score was 1.21 (SD = .45). In comparison, adolescents in the current study used drugs on an average of eight days per month at baseline. Given these differences, it may be possible that PD/AW plays a different role in the drug use of adolescents with diagnosed substance abuse than it does in adolescents who infrequently use drugs. For adolescents who do not often use drugs, the amount of increase in PD/AW that is associated with increases in drug use may not serve to predict significant increases in drug use of adolescents with more severe drug-use problems.
The finding that PD/AW was marginally associated with increased abstinence at baseline might lead one to think that PD/AW was somehow associated with positive parenting practices that might have helped some adolescents stay abstinent from drugs. However, study findings suggest that this was unlikely. Indeed, we found that PD/AW was not associated with the positive discipline practices used in parental monitoring. Parental monitoring consists of paying attention to and tracking a child’s whereabouts, activities, and adaptations (Dishion & McMahon, 1998). Studies have shown that it is negatively associated with the development of both substance use (Dishion et al., 1998; Dishion & Loeber, 1985; Steinberg, 1986) and antisocial behavior (Patterson & Stouthamer-Loeber, 1984) in adolescents referred to treatment for these behaviors. The lack of association between parental monitoring and PD/AW suggests that PD/AW is a communication pattern that operates independently of the positive disciplinary practices that are involved in parental monitoring.
Interestingly, even though PD/AW was not significantly associated with adolescent drug use at baseline, it did interact with TD/AW to predict greater drug use at follow-up. Viewing DW interaction from a family-systems perspective may help explain this finding. In family systems theory (Rohrbaugh & Shoham, 2001; Watzlawick, Bavelas, & Jackson, 1967), DW interaction is conceptualized as a cyclical process in which demand and withdraw behaviors reinforce one another via positive feedback. Increased demands lead to increased withdrawal, which in turn feeds more demand. In the current study, the parallel DW processes in the parent–adolescent and therapist–adolescent dyads may have reciprocally reinforced one another. Increased therapist demands (e.g., telling the adolescent that drug use is dangerous, or that talking back is disrespectful) may have been associated with adolescent withdrawal, which in turn may have increased demands of the parent on the adolescent (e.g., “don’t be disrespectful”). In this way, TD/AW may have escalated the overall amount of DW in the family therapeutic system to a level where it became a problematic cycle associated with increases in the adolescent’s presenting drug-use problems. This may indicate that mirroring PD/AW by engaging in TD/AW interaction, even when the PD/AW cycle is not yet entrenched and problematic, could have the effect of contributing to poor adolescent drug-use outcomes. Alternatively, individual difference factors may have contributed to the study results. For example, an earlier finding from our group showed that, in this study sample, BSFT therapists tended to respond to adolescents who had relatively high baseline levels of substance abuse and conduct problems (e.g., histories of multiple arrests) with off-model behaviors such as didactic and prescriptive (e.g., “demanding”) interventions (Lebensohn-Chialvo, Hasler, Rohrbaugh, & Shoham, 2010). This result seemed to suggest that adolescents with more severe drug-use and behavior problems at baseline “pulled” off-model demanding and directive therapist behaviors. These behaviors in turn were correlated with poor overall fidelity to BSFT. These findings may help explain how parallel DW cycles formed in the current study. That is, as the drug use of adolescents involved in PD/AW worsened, this increased drug-use severity may have made it more likely that therapists would shift off-model to using a didactic, demanding stance in relation to the adolescent.
Other studies conducted from an individual-differences perspective may also help explain the study results. For example, it may be possible that IPs who engaged in PD/AW were particularly sensitive to therapist demands and unwilling to comply with them. This sensitivity, or reactance, may have lessened the effectiveness of BSFT for these IPs. Previous research has shown that client resistance in family therapy is associated with low therapist ratings of outcome (Chamberlain, Patterson, Reid, Kavanagh, & Forgatch, 1984), and in alcohol-related interventions, increased alcohol use one year following treatment (Miller, Benefield, & Tonigan, 1993). In addition, studies have shown that interactions between therapist directiveness and client reactance predict negative outcomes of alcohol-related interventions (Karno, Beutler, & Harwood, 2002; Karno & Longabaugh, 2005). These studies indicated that, for highly reactant clients, therapist directiveness was associated with increased posttreatment alcohol use, whereas low-reactance clients either had a better drinking outcome after following therapists’ directives (Karno et al., 2002) or their alcohol use was not affected by therapist directiveness (Karno & Longabaugh, 2005). From the perspective of this research, the individual traits of IP reactance and therapist directiveness, rather than isomorphic DW interaction dynamics, may explain why PD/AW interacted with TD/AW to predict IP drug use.
The current study had some limitations. First, the study sample consisted of a subset of participants from the parent study that were not selected at random. Although there were no demographic differences between this group and the adolescents that were excluded, the included participants attended significantly more BSFT sessions than did the excluded participants. Thus, our findings may not generalize to samples that receive lower levels of treatment or drop out early from treatment. A second limitation pertains to the correlational nature of the study. Therapists were not randomized to “low-demand” and “high-demand” conditions. Thus, third variables such as therapist behaviors correlated with TD/AW may have contributed to our results. A third factor that may limit the generalizability of findings concerns the trainee status of the study therapists. The demand behaviors we observed in the study therapists may be specific to their developmental stage as new BSFT trainees. It is quite possible that more experienced BSFT therapists would interact differently with family clients and that our results would not replicate with these therapists. Lastly, the study results occurred in a sample of adolescents whose number of drug-use days did not change significantly from baseline to follow-up, and whose abstinence rates decreased from baseline to follow-up. Thus, the effects of PD/AW and TD/AW may differ in samples that decrease their drug use over time.
In sum, results suggest that parallel DW processes in family therapy for adolescent drug abuse can compromise treatment outcome. Findings highlight two key BSFT principles—remaining decentralized and placing more demand for change on parents than on adolescents—and suggest that these principles are particularly important to observe when therapists work with families that exhibit PD/AW. More research is needed to discern the role that systemic DW processes versus individual traits such as therapist directiveness and adolescent reactance play in predicting outcomes of family therapy. For example, future research could evaluate whether instances of TD/AW interaction reliably predict subsequent PD/AW, which then predict increased TD/AW (e.g., a therapist tells the adolescent that drug use is dangerous, the adolescent withdraws, and the parent then tells the adolescent to listen to the therapist). Coding such sequences of DW communication may provide a more purely systemic measure of parallel DW interactions than the one used in the current study. Using such a measure, researchers could compare coded DW sequences with observations of therapist directiveness and adolescent reactance to test the degree to which each measure predicts adolescent drug-use outcomes. Such research would provide concrete information to family therapists regarding the degree to which demand–withdraw processes versus adolescent reactance and therapist directiveness traits predict poor outcome. Information provided by this type of research would likely be particularly beneficial to family therapy trainees and supervisors seeking to avoid getting involved in maladaptive interaction processes with the family. It would also help improve family interventions by clarifying processes or traits that may contribute to poor therapy outcomes.
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Submitted: July 23, 2012 Revised: December 17, 2012 Accepted: December 18, 2012
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Source: Psychology of Addictive Behaviors. Vol. 28. (2), Jun, 2014 pp. 420-430)
Accession Number: 2013-06062-001
Digital Object Identifier: 10.1037/a0031812
Record: 108- Title:
- Parental alcohol involvement and adolescent alcohol expectancies predict alcohol involvement in male adolescents.
- Authors:
- Cranford, James A.. Addiction Research Center, Department of Psychiatry, University of Michigan, Ann Arbor, MI, US, jcranfor@med.umich.edu
Zucker, Robert A.. Addiction Research Center, Department of Psychiatry, University of Michigan, Ann Arbor, MI, US
Jester, Jennifer M.. Addiction Research Center, Department of Psychiatry, University of Michigan, Ann Arbor, MI, US
Puttler, Leon I.. Addiction Research Center, Department of Psychiatry, University of Michigan, Ann Arbor, MI, US
Fitzgerald, Hiram E.. University Outreach and Engagement, Kellogg Center, Michigan State University, Ann Arbor, MI, US - Address:
- Cranford, James A., Addiction Research Center (UMARC), Department of Psychiatry, University of Michigan, Rachel Upjohn Building, 4250 Plymouth Rd., Ann Arbor, MI, US, 48109-2700, jcranfor@med.umich.edu
- Source:
- Psychology of Addictive Behaviors, Vol 24(3), Sep, 2010. pp. 386-396.
- NLM Title Abbreviation:
- Psychol Addict Behav
- Page Count:
- 11
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- Bulletin of the Society of Psychologists in Addictive Behaviors; Bulletin of the Society of Psychologists in Substance Abuse
- Other Publishers:
- US : Educational Publishing Foundation
Society of Psychologists in Addictive Behaviors - ISSN:
- 0893-164X (Print)
1939-1501 (Electronic) - Language:
- English
- Keywords:
- adolescent alcohol expectancies, adolescent drinking behavior, parental drinking, parental influence, male adolescents
- Abstract:
- Current models of adolescent drinking behavior hypothesize that alcohol expectancies mediate the effects of other proximal and distal risk factors. This longitudinal study tested the hypothesis that the effects of parental alcohol involvement on their children's drinking behavior in mid-adolescence are mediated by the children's alcohol expectancies in early adolescence. A sample of 148 initially 9–11 year old boys and their parents from a high-risk population and a contrast group of community families completed measures of drinking behavior and alcohol expectancies over a 6-year interval. We analyzed data from middle childhood (M age = 10.4 years), early adolescence (M age = 13.5 years), and mid-adolescence (M age = 16.5 years). The sample was restricted only to adolescents who had begun to drink by mid-adolescence. Results from zero-inflated Poisson regression analyses showed that 1) maternal drinking during their children's middle childhood predicted number of drinking days in middle adolescence; 2) negative and positive alcohol expectancies in early adolescence predicted odds of any intoxication in middle adolescence; and 3) paternal alcoholism during their children's middle childhood and adolescents' alcohol expectancies in early adolescence predicted frequency of intoxication in middle adolescence. Contrary to predictions, child alcohol expectancies did not mediate the effects of parental alcohol involvement in this high-risk sample. Different aspects of parental alcohol involvement, along with early adolescent alcohol expectancies, independently predicted adolescent drinking behavior in middle adolescence. Alternative pathways for the influence of maternal and paternal alcohol involvement and implications for expectancy models of adolescent drinking behavior were discussed. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Adolescent Development; *Alcohol Drinking Attitudes; *Child Attitudes; *Drinking Behavior; *Parent Child Relations; Expectations; Parental Involvement; Parents
- Medical Subject Headings (MeSH):
- Adolescent; Adolescent Behavior; Alcohol Drinking; Alcoholic Intoxication; Humans; Male; Parent-Child Relations; Parents; Regression Analysis; Social Environment
- PsycINFO Classification:
- Psychosocial & Personality Development (2840)
Childrearing & Child Care (2956) - Population:
- Human
Male - Location:
- US
- Age Group:
- Childhood (birth-12 yrs)
School Age (6-12 yrs) - Tests & Measures:
- Short Michigan Alcoholism Screening Test
Drinking and Drug History Questionnaire
Diagnostic Interview Schedule Version IV
Beverage Opinion Questionnaire
Alcohol Expectancy Questionnaire DOI: 10.1037/t00696-000 - Grant Sponsorship:
- Sponsor: National Institute on Alcohol Abuse and Alcoholism
Grant Number: T32 AA07477; R37 AA07065
Recipients: Zucker, Robert A. - Methodology:
- Empirical Study; Longitudinal Study; Prospective Study; Quantitative Study
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- Accepted: Apr 5, 2010; Revised: Mar 26, 2010; First Submitted: Sep 21, 2009
- Release Date:
- 20100920
- Correction Date:
- 20120827
- Copyright:
- American Psychological Association. 2010
- Digital Object Identifier:
- http://dx.doi.org/10.1037/a0019801
- PMID:
- 20853923
- Accession Number:
- 2010-19026-003
- Number of Citations in Source:
- 103
- Persistent link to this record (Permalink):
- http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2010-19026-003&site=ehost-live
- Cut and Paste:
- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2010-19026-003&site=ehost-live">Parental alcohol involvement and adolescent alcohol expectancies predict alcohol involvement in male adolescents.</A>
- Database:
- PsycINFO
Parental Alcohol Involvement and Adolescent Alcohol Expectancies Predict Alcohol Involvement in Male Adolescents
By: James A. Cranford
Addiction Research Center, Department of Psychiatry, University of Michigan;
Robert A. Zucker
Addiction Research Center, Department of Psychiatry, University of Michigan
Jennifer M. Jester
Addiction Research Center, Department of Psychiatry, University of Michigan
Leon I. Puttler
Addiction Research Center, Department of Psychiatry, University of Michigan
Hiram E. Fitzgerald
University Outreach and Engagement, Kellogg Center, Michigan State University
Acknowledgement: This work was supported by Grants T32 AA07477 and Grant R37 AA07065 from the National Institute on Alcohol Abuse and Alcoholism to Robert A. Zucker, PhD. We thank the families who participate in the Michigan Longitudinal Study and Susan K. Refior, whose sustained work with the families in this study has been a major contributor to the study's continuation. We also thank the editor and the anonymous reviewers for helpful comments on previous drafts of this manuscript.
Does exposure to parental drinking influence the drinking behavior of their children once drinking onset has occurred? If so, are the effects mediated by childhood alcohol expectancies (AEs) preceding the child's drinking? A probabilistic-developmental model of risk ( Zucker, 1994; Zucker, Fitzgerald, & Moses, 1995; Zucker, Donovan, Masten, Mattson, & Moss, 2008) emphasizes the cumulation of risk factors over time for the developmental course of problem drinking, and the specification of how various risk factors work together to produce negative outcomes has been identified as a priority topic (Kraemer, Stice, Kazdin, Offord, & Kupfer, 2001). In this paper, we examine the longitudinal effects of parental alcohol involvement and adolescent AEs on subsequent alcohol involvement among adolescents who had begun drinking by mid-adolescence. Below, we review the literature on parental alcohol involvement and adolescent AEs on later drinking behavior.
Effects of Parental Alcohol Involvement on Adolescent Drinking BehaviorAn extensive body of evidence showed that parental substance use and family history of substance use are predictive of adolescent substance use (for reviews, see Ellis, Zucker, & Fitzgerald, 1997; Hawkins, Catalano, & Miller, 1992; Scheier, 2001; Sher, Grekin, & Williams, 2005; Wills & Yaeger, 2003; Windle, 1996). For example, Sher, Walitzer, Wood, and Brent (1991) examined the concurrent effects of parental alcoholism on adolescent alcohol involvement in a large sample of college freshmen (M age = 18.2 years). They found that children of alcoholics (COAs) reported higher levels of alcohol involvement (e.g., quantity-frequency of consumption, heavy drinking, and alcohol dependence symptoms) compared to non-COAs, although the extent to which alcoholic parents were actually drinking during the child's earlier years was not evaluated. Similar findings have been obtained with samples of younger adolescents. For example, working with a sample of alcoholic and nonalcoholic families from the community, Chassin, Rogosch, and Barrera (1991) examined the concurrent effects of parental alcoholism on the alcohol involvement of the adolescent offspring (M age = 12.7). Results showed that paternal but not maternal alcoholism predicted greater adolescent alcohol involvement, and this effect was stronger among the older adolescents.
Other evidence in addition to the Chassin et al. (1991) study indicated that the association between parental and offspring alcohol involvement differs depending on the gender of the parent and/or the child. However, the nature of the relationship has not been consistently replicated. Thus, Zhang, Welte, and Wieczorek (1999) found that paternal but not maternal drinking had a direct concurrent effect on adolescent boys' drinking (age range 16–19). By contrast, Ohannessian et al. (2004) found that maternal substance use consequences were more consistently related to concurrent adolescent psychopathology (including alcohol dependence, depression, and conduct disorder) than paternal substance use consequences. Furthermore, parental effects are not always observed; in another cross-sectional study, Cooper, Peirce, and Tidwell (1995) found no consistent associations between maternal or paternal drinking and adolescent substance use. Similarly, Yu (2003) showed that parental alcohol use was related to lifetime but not current alcohol use of their adolescent children (age range 15–18).
Although smaller in number, longitudinal studies have yielded similar results. White, Johnson, and Buyske (2000) followed adolescents across four waves from ages 15 to 28. Paternal and maternal drinking were predictive of a heavy drinking trajectory among sons and daughters. Wills, Sandy, Yaeger, and Shinar (2001) assessed 1,269 adolescents in 6th, 7th, and 8th grade and found that parental substance use (alcohol and tobacco use as reported by the child) predicted higher adolescent substance use (a latent variable comprised of alcohol, tobacco, and marijuana use) in grade 6, but did not predict increases in adolescent use over time. In one of the few longitudinal studies utilizing a high-risk sample, Chassin, Curran, Hussong, and Colder (1996) found that paternal and maternal alcoholism predicted initial levels of substance use (alcohol and illicit drug use) among girls and boys, but only paternal alcoholism and adolescent male gender predicted increases in substance use over a 3-year interval. Although the effects of paternal alcoholism were partially mediated by fathers' monitoring and adolescents' stress, negative affect, and associations with substance-using peers, these hypothesized mediators did not fully account for the paternal alcoholism effect.
Effects of Child and Adolescent AEs on Adolescent Drinking BehaviorAEs are another potent risk factor for adolescent alcohol involvement. The available evidence indicates that alcohol expectancies are “among the strongest predictors of drinking, even after other variables are controlled” ( Goldman, Del Boca, & Darkes, 1999, p. 219). Previous research using school-based samples showed that AEs predicted drinking onset among adolescents (Bauman, Fisher, Bryan, & Chenoweth, 1985; Killen et al., 1996), and results from several longitudinal studies found that adolescent AEs predicted increases in alcohol consumption over time (e.g., Aas, Leigh, Anderssen, & Jakobsen, 1998; Bauman et al., 1985; Christiansen, Smith, Roehling, & Goldman, 1989; Newcomb, Chou, Bentler, & Huba, 1988; Smith, Goldman, Greenbaum, & Christiansen, 1995). Similar findings have been obtained from longitudinal studies of college student samples (Darkes, Greenbaum, & Goldman, 2004; Goldman, Greenbaum, & Darkes, 1997; Kidorf, Sherman, Johnson, & Bigelow, 1995; Sher, Wood, Wood, & Raskin, 1996). In addition, there is longitudinal evidence that AEs predict the transition from nonproblem to problem drinking (Christiansen et al., 1989), and a recent study of high school students (M age = 16.2) found that tension-reduction AEs were concurrently associated with frequency of drunkenness (Catanzaro & Laurent, 2004).
Child and Adolescent AEs as Mediators of the Effects of Parental Drinking on Adolescent DrinkingTo this point, our review has indicated some linkages between a) parental and adolescent drinking, and b) adolescent AEs and adolescent drinking. Models of AEs emphasize their role as mediators of the effects of more distal risk factors ( Goldman et al., 1999; Petraitis, Flay, & Miller, 1995). Tests of social learning theory explanations of adolescent alcohol use have shown that exposure to parents who use alcohol has a direct relationship to AEs (Zucker, Kincaid, Fitzgerald, & Bingham, 1995), which in turn predicts alcohol involvement (Petraitis et al). The hypothesized mediational effects of AEs have been elaborated by a number of alcohol researchers (Goldman et al., 1999; Scheier & Botvin, 1997; Sher et al., 1991).
However, direct tests of the mediational role of AEs, based on the assumption that AEs are among the most proximal correlates of drinking behavior, have yielded conflicting findings. For example, the cross-sectional study by Sher et al. (1991) found that the effect of family history of paternal alcoholism on college students' alcohol involvement was mediated by the students' own level of behavioral under-control as well as by their (positive) AE level. Ouellette, Gerrard, Gibbons, and Reis-Bergan (1999) followed parents and their offspring over 4 years and showed that the effect of parental drinking (average of maternal and paternal consumption) on adolescent alcohol consumption was mediated by adolescent AEs. By contrast, a longitudinal study by Colder, Chassin, Stice, and Curran (1997) found that parental alcoholism had a direct effect on increases in adolescent heavy drinking that was not mediated by the adolescents' AEs.
The Present StudyTaken together, the available evidence remains unclear with respect to the hypothesis that adolescent AEs mediate the effects of their parents' alcohol involvement on their own drinking during adolescence. Although evidence suggests linkages between a) maternal and paternal alcohol involvement and their adolescent children's drinking and b) the adolescent's AEs on their own drinking, longitudinal studies have not produced consistent support for the mediational hypothesis. A major challenge to understanding these relationships is that both drinking and nondrinking adolescents have typically been included in the same analyses even though evidence has indicated that AEs change substantially as a function of the transition from nondrinking to drinking (see Christiansen, Goldman, & Inn, 1982; Schell, Martino, Ellickson, Collins, & McCaffrey, 2005). Such changes have been linked to the concrete experience of drinking and the exposure to alcohol's pharmacodynamic effects (Aas et al., 1998). Thus, inclusion of both groups creates a confounded predictor (the expectancies). In this paper, we examine the possible mediational role of childhood AEs in explaining the association between parental alcohol involvement and adolescent drinking behavior once drinking has begun (i.e., after the transition has occurred). Using longitudinal data, we tested the hypothesis that the association between parental drinking and adolescent drinking is mediated by adolescents' AEs.
Method Participants
The present work is from the ongoing Michigan Longitudinal Study (MLS; Zucker et al., 2000), a prospective study that is following a community sample of initially intact families with high levels of substance use/abuse, along with a community contrast sample of families drawn from the same neighborhoods, but without the high substance abuse profile. The long term focus of the project is the emergence and development of substance abuse and problems in the children, and the patterns of stability and change in drug involvement among the parents.
A community-based, but high alcohol-involved, sample of initially intact families was recruited by identifying fathers on the basis of a drunk driving conviction with a high blood alcohol level (0.15 percent if a first conviction, 0.12 percent if not the first). Families were required to have at least one son in the 3–5 year age range, and daughters in the 3–11 year age range were recruited when present. Presence of fetal alcohol syndrome was ruled out by study exclusionary criteria. Both biological parents were required to be living with the child at the time of recruitment, and mothers' substance use status was free to vary. A contrast/control group of families who resided in the same neighborhoods as the drunk driver families but had no substance abuse history for either parent was also recruited. A second subset of families with a father who also had an alcohol use disorder was uncovered and recruited during the community canvass for controls ( Zucker et al., 2000). After initial recruitment and assessment, individuals participated in multi-session assessments every 3 years. Data collection was completed by professional staff, graduate students, and carefully trained and supervised undergraduates.
Participants for the present study were biological fathers, mothers, and sons who completed relevant measures at child ages 9–11 (middle childhood), 12–14 (early adolescence), and 15–17 (mid-adolescence). For ease of presentation, we refer to these time points as baseline (T1), T2, and T3 (although the MLS designation is T3, T4, and T5). A total of 259 MLS families completed the study protocol at T3. This total included a subset of girls potentially available for the study. However, the sample of girls available for this study (n = 21) was not large enough to conduct meaningful analyses by gender, and because substantial sex differences are known to exist for many drinking indicators, we limited our analyses to boys. Also, because the present work is focused on individual differences in later adolescence, when drinking has to a large extent been initiated, we analyzed data only from families where the boys had begun drinking by T3 (when they were an average of 16.5 years old). Of the 259 boys who completed the T3 protocol, 57.1% (N = 148) had begun drinking. We analyzed data from these 148 boys and their parents at T1, T2, and T3. Based on responses to the question “How old were you the first time you ever took a drink? Do not count the times you were given a 'sip' by an adult,” the mean (SD) of drinking onset was 13.5 (2.5) years old (range = 5 to 17 years old). Although the sample of 148 boys included 5 male siblings of the male target children (MTCs), all participants were treated as independent based on an intraclass correlation of 0. At baseline, T2, and T3, average ages for children (with standard deviations in parentheses) were 10.4 (.9), 13.5 (.9), and 16.5 (1.0) years.
Parents at T1 were 148 mothers and fathers whose mean (SD) ages were 36.6 (4.0), and 39.2 (5.0) years, respectively. Couples had been married for an average of 11.4 years. Both couple members had completed about 2 years of education beyond high school: mean total education years for mothers, M (SD) = 14.2 (1.9); for fathers, M = 14.9 (2.3), and median family income was $40,000. All families were Caucasian because less than 4% of the population we sampled from was non-Caucasian. Given our sample size, this precluded effective analyses of race and ethnic differences.
Measures
Parental lifetime alcohol use disorder (AUD) and alcohol involvement at T1
DSM-IV alcoholism diagnosis for both parents was assessed using several measures, including the Short Michigan Alcoholism Screening Test (SMAST; Selzer, Vinokur, & van Rooijen, 1975), the Drinking and Drug History Questionnaire (DDH; Zucker, 1991), and the Diagnostic Interview Schedule—Version IV (DIS-IV; Robins, Helzer, Croughan, & Ratcliff, 1981). The SMAST is a 13-item self-report screening inventory that assesses alcohol problems. The DDH contains a series of questions asking about alcohol and other drug use and alcohol-related consequences over the past 6 months. The DIS-IV is a structured diagnostic interview that collects extensive information about physical, alcohol- and drug-related symptoms, and other psychiatric symptoms. Trained clinicians used data from all three sources of data to create a best-estimate diagnosis (Leckman, Sholomskas, Thompson, Belanger, & Weisman, 1982) of a lifetime alcohol use disorder (abuse or dependence) for both parents. DIS data were used as the base supplemented by the DDH and SMAST data, guided by the principle that when a symptom was admitted, even from only one source, it probably was present. To evaluate the reliability of this pooled diagnosis, two raters independently diagnosed a series of 26 protocols. Agreement as evaluated by kappa was .81, indicating acceptable reliability. In this subsample of N = 148 drinking boys, 33.1% (n = 49) of the mothers and 76.4% (n = 113) of the fathers had a lifetime AUD. In terms of family risk status, 23.7% (n = 35) of the families were control families; 18.9% (n = 28) were families from the community in which the father had an AUD; and 57.4% (n = 85) were families in which the father had a drunk driving conviction.
Less severe forms of parental alcohol involvement (e.g., frequency of alcohol consumption; Ary, Tildesley, Hops, & Andrews, 1993) have also shown longitudinal associations with adolescent drinking. Accordingly, we included a measure of average number of drinking days per month in the last 6 months from the DDH (Zucker, 1991). The average number of drinking days per month was lower for mothers (M = 4.1, SD = 5.6) than fathers (M = 8.6, SD = 8.5), paired t(135) = −6.2, p < .01.
Alcohol expectancies at T2
AEs were assessed with the Beverage Opinion Questionnaire (BOQ; Fitzgerald, Zucker, & Noll, 1990) which was administered to participants starting when they were between the ages of 6 and 8 years and then again at T1, T2, and T3. This 25-item questionnaire assesses negative (5 items) and positive (20 items) expectancies for alcohol and, as a buffer, also includes 30 expectancy questions about soft drinks. The BOQ is based on the adolescent version of the Alcohol Expectancy Questionnaire (AEQ-A; Christiansen et al., 1982; Brown, Christiansen, & Goldman, 1987) and the adult version of the Alcohol Expectancy Questionnaire (AEQ; Brown, Goldman, Inn, & Anderson, 1980). The original version of the AEQ-A was developed for use with adolescents ages 12–19 (Christiansen et al., 1982) and consists of 90 items. To reduce participant burden we sought to reduce the number of items for inclusion in the BOQ. Further, because we began asking about AEs when participants were between the ages of 6 and 8 years old, we selected those items that seemed likely to be most comprehensible when read to children by the interviewers. With these concerns in mind, we selected 23 items from the AEQ-A. In addition, we selected two items from the adult AEQ that focused on sleep. Each statement concerning alcohol is in the format “Drinking beer or wine would…”, followed by a phrase indicating an expectancy for alcohol, e.g., “Drinking beer or wine would make me feel good” (positive expectancy), “Drinking beer or wine would make me feel angry” (negative expectancy). Adolescents were asked to respond to each item on a four-point scale (1 = agree completely, 2 = somewhat agree, 3 = somewhat disagree, 4 = completely disagree). Inspection of the item distributions showed that few participants selected the “somewhat agree” or “somewhat disagree” response options. Thus, we collapsed response options to create binary versions of each item, where 0 = disagree completely or somewhat disagree and 1 = agree completely or somewhat agree. Items were summed to create negative and positive expectancies scores for each participant. At T2, the positive AEs scale had an alpha of .88 (M = 2.4, SD = 3.6), the negative AEs scale had an alpha of .74 (M = 1.9, SD = 1.4), and the two scales were moderately correlated, r = .44, p < .01.
Adolescent alcohol involvement at T3
For the adolescent version of the DDH, participants were asked about average drinking days per month (drinking days) over the past 6 months at T3 (M = 2.8, SD = 3.8). In other words, adolescents reported drinking on approximately 18 days during the past 6 months. Adolescent participants at T3 were also asked to indicate how many times during the past 6 months they had gotten drunk or very high from drinking alcohol. Scores on this variable ranged from 0 (not at all) to 52 (about twice a week) with a mean (SD) of 9.8 (20.4) episodes of drunkenness during the past 6 months.
Analytic Plan
Correlation and regression analyses were used to test the study hypotheses. Our two dependent variables (number of drinking days and number of drunken episodes in the past 6 months) were count variables. Count variables can sometimes be modeled as Poisson variables, but the Poisson distribution is restricted to a single parameter for the mean and the variance, and alcohol-related count variables often exceed this restriction by having larger variances than means (i.e., overdispersion; Horton, Kim, & Saitz, 2007). Although the present sample consisted only of those who had started drinking, a substantial percentage of participants reported that they did not drink or experience any drunken episodes in the past 6 months (29.1% and 34.5% of the sample, respectively). Count variables with large numbers of zeroes are referred to as “zero-inflated” (Karaszia & van Dulmen, 2008). Accordingly, zero-inflated Poisson (ZIP) regression analysis (Lambert, 1992) was used to examine predictors of drinking behaviors. For ZIP models, two regression equations are estimated simultaneously: 1) a logistic regression model is used to predict whether or not a given behavior occurs (i.e. membership in an “always zero” versus a “not always zero” latent group; Karazsia & van Dulmen, 2008), and 2) a Poisson regression model is used to predict the number of times a given behavior occurs (Atkins & Gallop, 2007; Muthen & Muthen, 2007). Estimating ZIP models thus allows for the possibility that different variables may predict whether or not someone drinks and how much or how often someone drinks (Atkins & Gallop, 2007). All ZIP models were estimated with the Mplus software package using maximum likelihood estimation with robust standard errors (Muthen & Muthen, 2007). For ease of interpretation of the logistic regression results, we report the reciprocal odds ratio for each predictor so that they represent the odds of being in the nonzero class, i.e. the odds of the occurrence of each drinking behavior.
Missing data
Because the pairwise sample sizes for the variables in our models ranged from n = 87 to n = 148, we used multiple imputation (MI; Rubin, 1987; Sinharay, Stern, & Russell, 2001) to impute missing data. In MI, each missing value is replaced by m > 1 simulated values, resulting in m complete data sets (Schafer, 1997; Schafer & Graham, 2002). These m data sets are analyzed using standard analytic methods, and the results are combined to obtain parameter estimates and standard errors that take into account missing data uncertainty (Sinharay et al., 2001). MI is based on the assumption that the data are missing at random (MAR; Sinharay et al., 2001). Since this assumption is not testable, we included several variables in the imputation model that could potentially be linked to the missingness of the imputed variables (Schafer, 1997; Sinharay et al., 2001).
The pattern of missing data appeared to be arbitrary, and so we used the Markov Chain Monte Carlo (MCMC) imputation method ( Schafer, 1997). For the variables in the models we tested, the rate of missing information (λ) ranged from a low of .15 to a high of .48. Results from a simulation study (Graham, Olchowski, & Gilreath, 2007) showed with m = 10 imputations: 1) the power to detect a small effect size when λ = .50 showed a decrease of only about 3%, compared to simulations with m = 100 imputations; and 2) the relative efficiency of a given parameter estimate when λ = .50 is .96 (compared to simulations with m = 100 imputations). Accordingly, we used SAS PROC MI (SAS, 2004) to create m = 10 imputed data sets. We then used PROC CORR to conduct correlational analyses on the m = 10 imputed data sets, and PROC MIANALYZE to combine the results from analyses of the m = 10 data sets. As noted earlier, for the ZIP models, we used the Mplus program with multiple imputation and maximum likelihood estimation with robust standard errors (Muthen & Muthen, 2007).
Attrition analyses
For mothers and fathers, we compared T1 responders and nonresponders on the measure of drinking frequency at T3, and no significant differences between responders and nonresponders were observed. For adolescents, we compared T2 responders and nonresponders on the measures of positive and negative AEs at T3, and no significant differences between responders and nonresponders were observed. These results indicate that any nonresponse bias was minimal.
ResultsCorrelations between all study variables are presented in Table 1. Maternal and paternal measures of AUD and drinking frequency were moderately correlated (rs ranged from .22 to .33). Interestingly, there was no significant association between paternal AUD and maternal drinking frequency, or between maternal AUD and paternal drinking frequency. No statistically significant correlations between parental alcohol involvement and adolescents' positive AEs were observed, but there was a weak direct association between paternal AUD and adolescents' negative AEs 3 years later at T2. Paternal AUD and maternal drinking frequency were significantly associated with both measures of adolescent alcohol involvement. Maternal AUD was also positively associated with adolescents' drinking frequency (but not frequency of drunkenness), and paternal drinking frequency was positively associated with adolescents' frequency of drunkenness (but not drinking frequency). Adolescents' positive AEs at T2 were positively related to frequency of drunkenness (but not drinking frequency) at T3. Adolescents' negative AEs at T2 were not significantly associated with either measure of adolescent alcohol involvement.
Zero-Order Correlations Between Study Variables
Baron and Kenny (1986) outline methods for testing mediational hypotheses. These steps include 1) establishing an association between the predictor and the outcome variable; 2) establishing an association between the predictor and the putative mediator variable; 3) establishing an association between mediator and the outcome variable when the predictor is statistically controlled; and 4) showing that the association between the predictor and the outcome is reduced in magnitude when the mediator variable is entered into the regression equation. Although demonstration of a relationship between the predictor and the outcome is not always required (Shrout & Bolger, 2002), an association between the predictor and the putative mediator variable is necessary to establish mediation. However, as seen in Table 1, only one of the measures of parental alcohol involvement was associated with adolescent AEs: paternal AUD showed a direct association with negative AEs. Further, negative AEs were not associated with either measure of adolescent alcohol involvement. These findings do not support the hypothesis that adolescent AEs mediate the effects of parental alcohol involvement on adolescent drinking.
We then examined the effects of T1 parental alcohol involvement and T2 adolescent AEs as independent predictors of adolescent drinking at T3. ZIP regression analyses were conducted using the Mplus statistical software program ( Muthen & Muthen, 2007). Results are presented in Table 2. We first tested the effects of lifetime paternal and maternal AUD, T1 paternal and maternal alcohol involvement, and T2 adolescent AEs as predictors of average drinking days in the past 6 months at T3. Because there was variation in age within waves, we controlled for adolescents' age at T3 in all analyses. For the logistic regression of the binary part of the dependent variable, age was the only significant predictor, and every 1 unit increase in age resulted in a 2.05 increase in the odds of drinking on any days in the past 6 months. For the Poisson regression of the count part of the dependent variable, the only significant predictor was T1 maternal drinking. To gain perspective on the meaning of the Poisson coefficients, we used procedures outlined by Long (1997, p. 229). For each 1-unit increase in mothers' average drinking days per month, adolescents' drinking days per month increased by a factor of 1.033, an increase of 3.3%, when all other predictors were statistically controlled. Although significant, this appears to be a relatively small effect. For example, at the average level of mothers' average drinking days per month (4.3), the expected number of adolescents' drinking days is 3.2. At one standard deviation above the average level of mothers' average drinking days per month (10.7), the expected number of adolescents' drinking days is 4.0.
Zero-Inflated Poisson Regression Analysis of Longitudinal Predictors of Adolescents' Average Number of Drinking Days per Month and Number of Times Intoxicated in Past 6 Months
Next, we tested the effects of lifetime paternal and maternal AUD, T1 paternal and maternal alcohol involvement, and T2 adolescent AEs as predictors of frequency of intoxication in the past 6 months at T3. As seen in Table 2, for the logistic regression of the binary part of the dependent variable, age and negative and positive AEs were significant predictors of any intoxication in the past 6 months. Increases in age and positive expectancies resulted in higher odds of any intoxication, and increases in negative expectancies resulted in lower odds of any intoxication. None of the maternal or paternal alcohol involvement variables were associated with any intoxication in the past 6 months. For the Poisson regression of the count part of the dependent variable, lifetime paternal AUD and T2 positive expectancies were significantly associated with the frequency of intoxication in the past 6 months.
We again used procedures outlined by Long (1997) to gain perspective on the meaning of the Poisson coefficients. For each 1-unit increase in positive AEs at T2, adolescents' number of times intoxicated increased by a factor of exp(.069) = 1.07, an increase of 7.0%, when all other predictors were statistically controlled. Although significant, this appears to be a relatively small effect. For example, at the average level of positive AEs at T2 (2.4), the expected number of times intoxicated is 8.3. At one unit above the average level of positive AEs at T2 (3.4), the expected number of times intoxicated is 8.9. We also used procedures outlined by Long (1997) to calculate the additive change in number of times intoxicated for adolescents as a function of father's lifetime AUD. For adolescents with a non-AUD father, the expected number of times intoxicated in the past 6 months is 3.3. By contrast, for adolescents with an AUD father, the expected number of times intoxicated in the past 6 months is 11.0, holding all other variables constant. Thus, while lifetime paternal AUD and T2 positive expectancies were independently associated with increases in the number of times intoxicated in the past 6 months at T3, the effects of paternal lifetime AUD were particularly strong in magnitude.
DiscussionThis study tested the hypothesis that AEs mediate the effects of parental alcohol involvement on adolescent drinking behavior. In partial support of our hypotheses, we found that two aspects of parental alcohol involvement (i.e., paternal lifetime AUD and maternal average drinking days per month) during middle childhood (T1) predicted some dimensions of mid-adolescent drinking (T3). Contrary to our hypothesis, results showed that the effects of parental alcohol involvement were not mediated by adolescent AEs. Rather, parental drinking and positive and negative adolescent AEs had independent longitudinal associations with adolescent drinking behavior.
Parental Alcohol Involvement and Adolescents' Drinking Behaviors
Our findings with respect to the effects of parental alcohol involvement on adolescents' drinking behaviors are consistent with a long line of work indicating that parents have profound effects on the drinking behaviors of their children (e.g., Fitzgerald, Davies, & Zucker, 2002; Jacob & Johnson, 1997; Wills & Yaeger, 2003; Windle, 1996; Zucker et al., 2000, 2008). Our findings are unique, however, in showing that different aspects of paternal and maternal alcohol involvement are longitudinally associated with different aspects of their sons' alcohol involvement 6 years later. The finding that mothers' but not fathers' drinking behavior was predictive of subsequent alcohol involvement in their sons is consistent with the work of Brook and her colleagues (Brook, Whiteman, Gordon, & Cohen, 1986), who found that aspects of the mother-child relationship were stronger protective factors for adolescent drug use than were similar aspects of the father-child relationship. Brook et al. speculated that mothers have more influence on child-rearing practices than do fathers. Our findings indicate that this influence extends to the domain of alcohol involvement, at least in terms of adolescents' average drinking days per month (also see Christiansen & Goldman, 1983). Related to this point, the greater amount of time spent with mothers versus fathers may lead adolescents to more closely model their own drinking behavior after that of their mothers. This may in part explain why maternal drinking behavior, but not maternal AUD, predicted their son's drinking behavior (Ohannessian & Hesselbrock, 2004).
By contrast, paternal alcoholism (but not paternal drinking behavior) was predictive of sons' alcohol involvement, and this effect was limited to frequency of intoxication. Paternal alcoholism is associated with a wide range of parenting variables, including less parental discipline ( King & Chassin, 2004), lower levels of parental monitoring (Chassin, Pillow, Curran, Molina, & Barrera, 1993; Chassin et al., 1996), and higher levels of child abuse and neglect (Richter & Richter, 2001); all of these variables are separately associated with earlier and heavier drinking among offspring. Our findings are thus consistent with recent work showing that COAs continue to show elevated levels of heavy drinking even when their fathers' alcoholism has remitted (DeLucia, Belz, & Chassin, 2001). In addition, parental alcoholism confers heightened genetic risk among some COAs (Zucker et al., 2008). The combination of socialization and genetic risk may explain the relatively large magnitude of the effect of paternal alcoholism on adolescents' intoxication.
With respect to the null findings for paternal drinking behavior, we note that paternal as compared to maternal drinking is more likely to occur on a sporadic basis for antisocial alcoholics ( Jacob & Leonard, 1988), a group that has a substantial representation in the current sample of alcoholics. Thus, exposure to father drinking, especially during late preadolescence, would have been less available to the children than exposure to mother drinking. Last, to some degree the alcoholic fathers' drinking behavior was dampened during the earlier years of the study as a result of conviction for drunk driving. Such convictions sometimes required attendance at alcohol education classes and produced a dampening effect on fathers' consumption which would distort the relationship between their own undampened drinking and their children's alcohol involvement. This process was not in operation for the mothers.
The observation that paternal alcoholism—but not paternal drinking behavior—was predictive of sons' alcohol involvement is not readily attributable to a drinking-modeling explanation (see Sher et al., 2005). However, exposure to parental modeling was not directly measured in this study. Brown, Tate, Vik, Haas, & Aarons (1999) showed that degree of exposure to an alcohol-abusing family member mediated the association between parental alcoholism and positive AEs, and noted that variation in exposure to alcohol-abusing family members, even within families characterized by a biological history of alcoholism, might be considerable. In the absence of a direct measure of parental modeling, a drinking-modeling explanation for the present findings related to paternal AUD cannot be ruled out.
Adolescent AEs and Drinking Behaviors
Our results also showed that adolescents' negative and positive AEs were longitudinally associated with higher odds of any intoxication 3 years later, and positive AEs further predicted frequency of drunkenness, independently of parental alcohol involvement. These findings replicate previous work in showing that AEs in early adolescence are longitudinally associated with drinking later in adolescence ( Reese, Chassin, & Molina, 1994; Smith et al., 1995), and more generally with previous results showing that positive and negative expectancies are predictive of alcohol involvement (Goldman & Darkes, 2004). Further, the differential effects of negative and positive AEs on the occurrence and frequency of intoxication are consistent with evidence reported by Leigh and Stacy (2004), who suggested that “negative expectancy predicts abstention while positive expectancy predicts amount of drinking among those who drink” (p. 224) (also see Chen, Grube, & Madden, 1994).
Results are also consistent with the hypothesis advanced by Sher and Gotham (1999) that AEs are developmentally specific risk factors for alcohol involvement. Alcohol schemas emerge as early as age 3 (Zucker et al., 1995), and evidence indicated a shift in AEs from more negative to more positive during the period from middle childhood to early adolescence (grades 6 to 9; Dunn & Goldman, 1998, 2000; cf. Spiegler, 1983). Positive expectancies may better predict alcohol involvement than negative expectancies among younger participants, but the effects of negative AEs become stronger as a function of age (Leigh & Stacy, 2004). The current results add to this literature by showing that positive expectancies in middle adolescence have a stronger association to risky drinking than to overall frequency of drinking behavior.
Adolescent AEs as Mediators of the Effects of Parental Alcohol Involvement
Our findings did not support the hypothesis that the effects of parental alcohol involvement are mediated by AEs ( Chassin et al., 1996). However, it is important to consider some aspects of the current study that limited our ability to draw firm conclusions about the mediation hypothesis. Our sample was by design limited to Caucasian males, many of whom were living in high-risk families, and this limits the generality of the findings. Also, our relatively small sample size was underpowered to detect mediation when the associations between a) the independent variable and the dependent variable, and b) the mediator and the dependent variable are in the small to moderate range (Fritz & MacKinnon, 2007).
Our own, as well as other, work suggests a complex relationship between parental drinking behavior and AEs in children and adolescents. As noted earlier, several studies have found evidence for linkages between paternal alcohol involvement and their children's AEs (e.g., Brown et al., 1999). By contrast, Kraus, Smith, and Ratner (1994) found no cross-sectional associations between AEs among 268 children in grades 2 through 4 and maternal and paternal drinking attitudes, parental problem drinking, and family history of AUD (also see Brown, Creamer, & Stetson, 1987; Henderson, Goldman, Coovert, & Carnevalla, 1994; Miller, Smith, & Goldman, 1990). Reasons for variability in the association between parental alcohol involvement and adolescent AEs include sample heterogeneity and use of different measures of alcohol involvement and expectancies (e.g., Sher et al., 1991). Another important difference relates to the time lag between longitudinal assessments. Collins and colleagues (Collins & Graham, 2002) noted that the association between two variables can change dramatically across different measurement intervals and highlighted the importance of temporal design—defined as “the timing, frequency, and spacing of observations in a longitudinal study” (Collins, 2006, p.508)—for longitudinal studies of developmental processes (see Handley & Chassin, 2009). Greater attention to temporal design will clarify the status of mediational hypotheses about parental alcohol involvement and adolescent AEs (also see Sher et al., 1996).
Adolescent AEs and Parental Alcohol Involvement as Independent Risk Factors for Adolescent Alcohol Involvement
The current findings are most consistent with the hypothesis that parental alcohol involvement and AEs represent independent risk factors for subsequent alcohol involvement among adolescents (e.g., Mann, Chassin, & Sher, 1987). This pattern of results represents a conceptual replication of previous work showing unique longitudinal associations between parental alcohol involvement and AEs and subsequent alcohol involvement in adolescents (e.g., Reese et al., 1994). However, to our knowledge, ours is the first study to find unique longitudinal effects of maternal drinking and paternal AUD (assessed in middle childhood) and positive and negative AEs (assessed in early adolescence) on different dimensions of alcohol involvement (assessed in middle adolescence). Thus, at least in this sample, the evidence suggests that a) cognitive factors may be of greater importance than parental factors in terms of whether or not an adolescent decides to engage in risk drinking; and b) paternal AUD may be of greater importance than cognitive factors in terms of the frequency of engaging in risk drinking.
Limitations
The present study has several limitations. First, the sample was limited to adolescent boys, and there is some evidence that the effects of parental alcohol involvement and AEs may be different for adolescent girls (see Pastor & Evans, 2003). Second, the sample was limited to Caucasians, and evidence showed that ethnicity moderates some associations between expectancies and alcohol involvement (Chartier, Hesselbrock, & Hesselbrock, 2009). Third, we relied on adolescents' self-reports of alcohol use. Although self-report measures of alcohol involvement seem to have adequate reliability and validity (Babor, Steinberg, Anton, & Del Boca, 2000), there are numerous factors that influence the validity of self-reports, including age and forgetting (Brener, Billy, & Grady, 2003; Del Boca & Darkes, 2003), and these factors may have a stronger effect on self-report in younger adolescents. Fourth, the restricted range/class of families in the “low risk” group may have contributed to the nonsignificant correlations we observed between parental AUD and adolescent AEs. In addition, our use of 3-year assessment intervals, combined with the relatively small sample size, might have reduced the likelihood of detecting some of the hypothesized mediational processes. Further research using designs that combine shorter (e.g., daily) and longer time lags will clarify the status of the expectancy mediation hypothesis.
Conclusions and Implications
Despite these limitations, the present study has several important strengths. Results build on our earlier work showing that alcohol schemas form as early as age 3 ( Zucker et al., 1995), and our design allowed us to follow children and their parents from middle childhood to middle adolescence, which covers the critical period during which adolescents first begin experimenting with alcohol and other drugs (Zucker, 2006). Also, by tracking children who transitioned from nondrinkers in middle childhood to drinkers in middle adolescence, we were able to confirm that AEs precede the development of risky drinking. Intervention studies have shown that AEs are amenable to experimental manipulation, and challenges to expectancies predict reductions in alcohol consumption among males (e.g., Dunn, Lau, & Cruz, 2000). The present results highlight the potential utility of challenges targeting positive AEs for reducing risky drinking behaviors.
Our findings are particularly important in light of recent evidence that family influences on adolescent substance use are more pervasive than peer and neighborhood influences ( Ennett et al., 2008) and persist through late adolescence (Wood, Read, Mitchell, & Brand, 2004). An important avenue for further work is identification of how different risk and protective factors at different levels influence one another (Buu et al., 2009). Also, while expectancies are clearly important precursors of alcohol involvement, other cognitive constructs (e.g., substance use intentions; Anderson, Smith, & Fischer, 2003) and personality constructs (e.g., resilience; Lee & Cranford, 2008) should also be considered.
It is important to note that these results were obtained across a particularly crucial period in adolescent development. Parents and adolescents were assessed when offspring were in middle childhood (ages 9–11, M age = 10.6 years), early adolescence (ages 12–14, M age = 13.5 years), and mid-adolescence (ages 15–17, M age = 16.5 years). Evidence showed that positive AEs increase over this period ( Dunn & Goldman, 1998), particularly among adolescents exposed to peer and parental drinking (Cumsille, Sayer, & Graham, 2000). Yet, in a recent review, Windle et al. (2008, p. S285) asserted that “Unfortunately, we do not yet have longitudinal data mapping the progression of expectancy endorsement and its prediction of subsequent drinking among children 10 to 12 years of age. This gap in the literature is an important one that needs to be rectified.” Windle et al. noted that this period usually involves the transition to middle school and adolescence; as such, “this transition may become a turning point for some children, and their developmental trajectories may become characterized by maladaptive features.” The current study addresses this gap in the literature and demonstrates the unique longitudinal effects of maternal and paternal alcohol involvement and adolescent AEs for specific dimensions of underage drinking.
Footnotes 1 Brown et al. (1987) noted that, compared to the adult version of the AEQ, for the AEQ-A “statements are worded more generally to accommodate adolescents who have had little or no experience with alcohol” (p. 485). Specifically, “the Adult AEQ involves statements regarding the effects of alcohol on the respondent, whereas the Adolescent AEQ focuses on the effect of alcohol on people in general” (p. 488). For example, the adult AEQ item “Drinking makes me feel good” was modified for the AEQ-A to “Drinking alcohol makes a person feel good and happy.” We agree with Brown et al. that such modifications likely make the AEQ-A items more applicable to the entire adolescent population, including “adolescents who have not yet had direct or personal experience with alcohol” (p. 489). Because the MLS by design recruited a sample of families that was likely to have extensive experience with alcohol, we decided to retain the original wording of the items such that they referred to the adolescent. Recognizing that even in a high-risk sample not all adolescents will have consumed alcohol, each item was framed as a pure expectancy, rather than as a putative effect of alcohol involvement. Returning to the earlier example, the AEQ item “Drinking makes me feel good” was modified for the AEQ-A to “Drinking alcohol makes a person feel good and happy,” and we in turn modified this item for the BOQ to “Drinking alcohol would make me feel good.”
2 Three cases were identified as outliers (i.e., more than 3 standard deviations above the mean; Stevens, 1998) on this variable. All analyses were conducted with and without these three outlier cases. The results did not differ for the two sets of analyses. Because descriptive statistics were unduly influenced by these outlier cases, all descriptive statistics are reported with the outlier cases excluded.
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Submitted: September 21, 2009 Revised: March 26, 2010 Accepted: April 5, 2010
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Source: Psychology of Addictive Behaviors. Vol. 24. (3), Sep, 2010 pp. 386-396)
Accession Number: 2010-19026-003
Digital Object Identifier: 10.1037/a0019801
Record: 109- Title:
- Participant, rater, and computer measures of coherence in posttraumatic stress disorder.
- Authors:
- Rubin, David C.. Department of Psychology and Neuroscience, Duke University, Durham, NC, US, david.rubin@duke.edu
Deffler, Samantha A.. Department of Psychology and Neuroscience, Duke University, Durham, NC, US
Ogle, Christin M.. Department of Psychology and Neuroscience, Duke University, Durham, NC, US
Dowell, Nia M.. Department of Psychology, University of Memphis, Memphis, TN, US
Graesser, Arthur C.. Department of Psychology, University of Memphis, Memphis, TN, US
Beckham, Jean C.. Department of Psychiatry and Behavioral Sciences, Duke University Medical Center, Durham, NC, US - Address:
- Rubin, David C., Department of Psychology and Neuroscience, Duke University, Box 90086, Durham, NC, US, 27708, david.rubin@duke.edu
- Source:
- Journal of Abnormal Psychology, Vol 125(1), Jan, 2016. pp. 11-25.
- NLM Title Abbreviation:
- J Abnorm Psychol
- Page Count:
- 15
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- The Journal of Abnormal and Social Psychology; The Journal of Abnormal Psychology; The Journal of Abnormal Psychology and Social Psychology
- ISSN:
- 0021-843X (Print)
1939-1846 (Electronic) - Language:
- English
- Keywords:
- narrative, coherence, posttraumatic stress disorder, acute stress disorder
- Abstract (English):
- We examined the coherence of trauma memories in a trauma-exposed community sample of 30 adults with and 30 without posttraumatic stress disorder. The groups had similar categories of traumas and were matched on multiple factors that could affect the coherence of memories. We compared the transcribed oral trauma memories of participants with their most important and most positive memories. A comprehensive set of 28 measures of coherence including 3 ratings by the participants, 7 ratings by outside raters, and 18 computer-scored measures, provided a variety of approaches to defining and measuring coherence. A multivariate analysis of variance indicated differences in coherence among the trauma, important, and positive memories, but not between the diagnostic groups or their interaction with these memory types. Most differences were small in magnitude; in some cases, the trauma memories were more, rather than less, coherent than the control memories. Where differences existed, the results agreed with the existing literature, suggesting that factors other than the incoherence of trauma memories are most likely to be central to the maintenance of posttraumatic stress disorder and thus its treatment. (PsycINFO Database Record (c) 2018 APA, all rights reserved)
- Impact Statement:
- We used a battery of 28 different measures of narrative coherence drawn from educational research, developmental psychology, autobiographical memory research, and clinical psychology to investigate the coherence of trauma memories in posttraumatic stress disorder (PTSD). We found that for most measures trauma memories were as coherent as very important and very positive memories, and we found no evidence that people with PTSD differ on how coherent their memories were when compared to people without PTSD. Thus, counter to some views, incoherent trauma memories do not seem to be a common property of PTSD. (PsycINFO Database Record (c) 2018 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Acute Stress Disorder; *Memory; *Posttraumatic Stress Disorder; *Trauma; *Sense of Coherence; Anxiety; Stress; Exposure
- Medical Subject Headings (MeSH):
- Adaptation, Psychological; Adult; Female; Humans; Life Change Events; Male; Memory; Middle Aged; Sense of Coherence; Stress Disorders, Post-Traumatic
- PsycINFO Classification:
- Neuroses & Anxiety Disorders (3215)
- Population:
- Human
Male
Female - Location:
- US
- Age Group:
- Adulthood (18 yrs & older)
- Tests & Measures:
- Structured Clinical Interview for DSM–IV Diagnosis
Dissociative Experience Scale
Hollingshead Index of Socioeconomic Status
Narrative Coherence Coding Scheme
Global Coherence Measures
Coh-Metrix Principal Component Measures
Linguistic Inquiry Word Count
Autobiographical Memory Questionnaire DOI: 10.1037/t27204-000
Beck Depression Inventory DOI: 10.1037/t00741-000
Clinician-Administered PTSD Scale DOI: 10.1037/t00072-000
PTSD Checklist
Traumatic Life Events Questionnaire DOI: 10.1037/t00545-000 - Grant Sponsorship:
- Sponsor: National Institute of Mental Health, US
Grant Number: R01-MH066079
Recipients: No recipient indicated
Sponsor: Institute of Education Sciences
Grant Number: R305G020018, R305A080589
Other Details: Coh-Metrix measures
Recipients: No recipient indicated
Sponsor: Department of Defense, Air Force Office of Scientific Research, US
Grant Number: FA9550-14-0308
Other Details: Minerva Initiative
Recipients: No recipient indicated
Sponsor: Danish National Research Foundation, Denmark
Grant Number: DNRF93
Recipients: No recipient indicated
Sponsor: VA Clinical Sciences Research Development, US
Other Details: Career Scientist Award
Recipients: Beckham, Jean C. - Methodology:
- Empirical Study; Quantitative Study
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- First Posted: Nov 2, 2015; Accepted: Sep 24, 2015; Revised: Sep 22, 2015; First Submitted: Mar 16, 2015
- Release Date:
- 20151102
- Correction Date:
- 20180625
- Copyright:
- American Psychological Association. 2015
- Digital Object Identifier:
- http://dx.doi.org/10.1037/abn0000126
- PMID:
- 26523945
- Accession Number:
- 2015-49420-001
- Number of Citations in Source:
- 76
- Persistent link to this record (Permalink):
- http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2015-49420-001&site=ehost-live
- Cut and Paste:
- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2015-49420-001&site=ehost-live">Participant, rater, and computer measures of coherence in posttraumatic stress disorder.</A>
- Database:
- PsycINFO
Participant, Rater, and Computer Measures of Coherence in Posttraumatic Stress Disorder
By: David C. Rubin
Department of Psychology and Neuroscience, Duke University, and Center on Autobiographical Memory Research, Aarhus University;
Samantha A. Deffler
Department of Psychology and Neuroscience, Duke University
Christin M. Ogle
Department of Psychology and Neuroscience, Duke University
Nia M. Dowell
Department of Psychology, University of Memphis
Arthur C. Graesser
Department of Psychology, University of Memphis
Jean C. Beckham
Department of Psychiatry and Behavioral Sciences, Duke University Medical Center, and the Mid-Atlantic Research Education and Clinical Center, Durham Veterans Affairs Medical Center, Durham, North Carolina
Acknowledgement: This study was funded by the National Institute of Mental Health (R01-MH066079); the Coh-Metrix measures by the Institute of Education Sciences (R305G020018, R305A080589) and the Minerva Initiative, Air Force Office of Scientific Research, Department of Defense (FA9550-14-0308); the Danish National Research Foundation (DNRF93); and partial support to Jean C. Beckham by a VA Clinical Sciences Research and Development through a Career Scientist Award. This material is the result of work supported in part with resources and the use of facilities at the Durham, North Carolina, Veterans Affairs Medical Center and National Institutes of Health (NIH). The contents do not represent the views of NIH, the U.S. Department of Veterans Affairs, or the United States Government.
Since the formulation of the posttraumatic stress disorder (PTSD) diagnosis, researchers have differed on whether memories of traumas in people with PTSD are especially incoherent, incomplete, and fragmented. This incoherence issue remains unresolved. The claim that trauma memories are especially incoherent in people with PTSD justifies incoherence as a symptom in the diagnosis of PTSD and as a mechanism that produces and maintains PTSD. Mechanisms that have been suggested specifically to operate more strongly after a trauma in people who develop PTSD include impoverished encoding, active repression, dissociation of trauma memories, and the reduction of conceptual and verbal processes combined with increases of sensory or perceptual processes that produce sensory details that are not well integrated conceptually (e.g., Brewin, Dalgleish, & Joseph, 1996; Ehlers & Clark, 2000; Horowitz, 1976; for reviews from various perspectives, see Brewin & Holmes, 2003; Dalgleish, 2004; McNally, 2003a, b; Porter & Birt, 2001; and Shobe & Kihlstrom, 1997).
According to an alternative view, incoherence can be understood in terms of cognitive and affective processes that have been developed to account for memory in general. These processes hold equally for all memories of stressful events regardless of the person remembering them and for all people regardless of the type of memory they are recalling. For example, trauma memories may be less coherent in all people regardless of diagnostic status because traumatic events are actually less coherent. Alternatively they could be more coherent due to the increased continuing effort expended in trying to understand the trauma. In either case, the effect should be similar in people with and without PTSD. Similarly, a wide range of memories may be less coherent in people with PTSD because PTSD affects cognitive abilities. The combination of these memory-specific and individual-differences processes is assumed to be additive and large enough to account for any observed incoherence in the memories of traumas in people with PTSD (Rubin, 2011; Rubin, Boals, & Berntsen, 2008; Rubin, Dennis, & Beckham, 2011). If these memory-specific and person-specific processes did not interact in a way that was especially powerful for trauma memories in people with PTSD, there would be no reason to consider the incoherence of trauma memories especially important for PTSD. Rather, negatively emotional events would have similar properties in all people and people with PTSD would have general changes in cognitive processes.
To investigate these alternative views, the concept of coherence needs to be examined in more detail. Coherence is not a concept that is restricted to or is well defined in the PTSD literature. Diagnostic manuals give little guidance on how to measure incoherence, so PTSD researchers have developed a multitude of measures. However, these measures have not been compared to one another or calibrated outside of PTSD (or even within studies in which they are dependent measures). This can be a serious problem because memories can be incoherent in some ways and not others. Thus, we cast a wide net in our selection of measures to include in the present study in order to have the greatest chance of finding any way in which trauma memories in people with PTSD are incoherent. We include measures from PTSD research, and we add measures that have been used in other contexts and so have known properties outside any role in PTSD.
In particular, we include measures from autobiographical memory research, such as participant ratings of whether their memory is a coherent story, or comes in pieces with parts missing, or has a known setting, measures which have also been used in PTSD research (Rubin et al., 2011). We include a system from developmental psychology where the ability to narrate a coherent telling of an event increases dramatically until adulthood, allowing a direct way to calibrate the system (Reese et al., 2011). We adapt questions given to patients with neuropsychological damage (Greenberg & Rubin, 2003) by asking the same questions of raters provided with narratives. These include knowing the goals and emotions of the narrator, which are basic to showing that the narrative is coherent at a more abstract level than has been used in the PTSD literature. We borrow measures from educational research that are used to calibrate texts on how easy they are to understand and to grade student essays (McNamara, Graesser, McCarthy, & Cai, 2014). We use counts of the frequencies of various classes of words developed to measure how people are coping with stressful events in the expressive writing method (Pennebaker, Booth, & Francis, 2007). All of these measures address different aspects of coherence and all have been used extensively so that much is known about their relations to coherence as measured outside of PTSD research.
In addition to comparing a wide variety of coherence measures in a trauma-exposed sample, we ask two different questions about the incoherence of trauma memories in people with PTSD. First, we ask the standard question of whether we can show differences in the incoherence of trauma memories between people with and without PTSD. Second, we ask whether these differences in incoherence are substantial enough to be part of a causal mechanism that produces and maintains PTSD. Providing strict conceptual criteria for this second question is more difficult; however, we can define incoherence operationally as being out of the range of normal behavior to the extent that it would be classified as a symptom by a well-trained clinician. At this level, incoherence could be the basis of either the inability-to-recall-an-important-aspect-of-the trauma symptom of PTSD or one of the dissociation symptoms of acute stress disorder (ASD; American Psychiatric Association, 2000), or it can be a cause of other symptoms that can be measured independently of coherence as is claimed by some theoretical approaches (e.g., Horowitz, 1976).
Considerable data already exist on the incoherence issue from three sources: the inclusion of the incoherence of trauma memories in the PTSD diagnosis, the overall level of the incoherence of trauma memories in PTSD, and comparisons of the incoherence of trauma memories with nontrauma memories in populations with and without PTSD.
The first source of information for the incoherence issue is the diagnosis itself. The symptoms of PTSD have been translated in a fairly literal fashion into many PTSD diagnostic and screening instruments for which the psychometric properties of the individual symptoms have been analyzed. The Diagnostic and Statistical Manual of Mental Disorders, fourth edition, revised (DSM–IV–TR), which was used in the current study, includes the “inability to recall an important aspect of the trauma” as symptom C3 (American Psychiatric Association, 2000, pp. 467–468). The DSM–5 changed this wording slightly; “trauma” became “traumatic event(s)” and “typically due to dissociative amnesia and not to other factors such as head injury, alcohol, or drugs” was added (American Psychiatric Association, 2013, p. 271). The DSM–5 also moved this item from an avoidance, or C, symptom to a newly added category of negative alterations in cognition and mood, which are termed D symptoms. Using these guidelines, a clinician must determine whether a person cannot recall an important aspect of an event to a degree beyond the normal range. If so, the memory of that event can be considered incoherent. Invoking dissociative amnesia as a cause also indicates incoherence (Berntsen & Rubin, 2014). However, two meta-analyses demonstrated that the psychometric properties of the C3, inability-to-recall-an-important-aspect-of-the-trauma, symptom did not support the view that memories of traumas in PTSD are highly correlated with the other PTSD symptoms.
Rubin, Berntsen, and Bohni (2008, Table 4) reviewed studies containing 35 separate analyses that investigated the underlying factor structure of the 17 symptoms of the DSM–IV–TR diagnosis. The studies involved a wide variety of subject populations, including populations both with and without a clinical diagnosis of PTSD. Different types of measures were used for the 17 symptoms, including both self-report measures and structured clinical interviews. The factor analyses varied and included exploratory and confirmatory analyses and two-, three-, and four-factor solutions. Across these studies, the results for the C3 symptom were similar: The magnitude of the loading of the C3 symptom in the majority of analyses had a rank of 15, 16, or 17 among the 17 symptoms. The C3 often had the lowest loading, often much lower than and out of the range of, the rest of the items. A study by Foa, Riggs, and Gershuny (1995) could not be included because the C3 loaded on its own factor and, thus, the study removed it from their factor analysis. In contrast, a study by Stewart, Conrod, Pihl, and Dongier (1999) reported that the C3 ranked highest among the 17 symptoms. However, participants for this study were selected because of their substance abuse, not their PTSD. The changes in awareness and memory that often accompany substance abuse provide an alternative explanation for the correlation of the C3 with other PTSD symptoms, one explicitly excluded from the DSM–5.
Yufik and Simms (2010) obtained, combined, and reanalyzed original data from 40 studies, which included a total of 14,827 participants. They produced two preferred models, one of which had four factors that approximated the DSM–5 symptom categories. For both models, the C3 loaded at .53, whereas the remaining 16 symptoms loaded between .71 and .87, indicating that the C3 accounted for 37% to 56% as much variance as the other 16 items. Similar results exist for the DSM–5 (Gentes et al., 2014). Thus, the psychometric properties of the symptom most directly related to the incoherence issue are outliers, with lower loadings than other symptoms.
It is possible for an item that does not correlate with other items on a test to still be useful in diagnosis. Assume that dissociation was a co-occurring disorder or tendency. This assumption would allow the inability-to-recall-an-important-aspect-of-the-trauma symptom to have low loadings in factor analyses and for its explicit association with dissociation in the DSM–5 to be maintained. This assumption would also be consistent with the claims being made here, according to which many forms of dissociation would lead to a lack of coherence, but that such forms of dissociation are separate mechanisms that need not be an integral part of the diagnosis of PTSD because they are not present in the vast majority of recalls of trauma memories. In addition, this assumption would be consistent with the inability-to-recall-an-important-aspect-of-the-trauma symptom not being required for a PTSD diagnosis, but for that same diagnosis to be specified as with dissociative symptoms if one of the two dissociation symptoms that are not part of the PTSD diagnosis itself but that are listed in the with dissociative symptoms section of the DSM–5 PTSD diagnosis is present.
The second source of information on the incoherence issue is the overall level of the incoherence of trauma memories in PTSD. As incoherence is included in the diagnosis of PTSD and is considered important in the production and maintenance of PTSD, it should be fairly easy to observe in experimental studies. We searched for studies of groups of individuals in which the trauma memories of participants with PTSD were incoherent to the degree that would be needed to argue for incoherence as an explanatory mechanism. Because the theoretical concepts of coherence, incoherence, and fragmentation are hard to define and their operational definition is often not well justified (O’Kearney & Perrott, 2006), we searched for studies that had measures with clear face validity and interpretations, such as scales of incoherence defined by the anchors of 0 as extremely coherent and 10 as extremely incoherent. We also searched for studies without such easy-to-interpret measures but that included a comparison to memories that should not be especially incoherent, such as nontrauma memories in participants with PTSD or trauma memories in participants without PTSD. We could find no experimental study that met these criteria that had clearly incoherent memories.
As examples of studies that had measures with clear face validity and interpretations, we start with results from two research groups that consider the incoherence of trauma memories as an important factor in PTSD. Halligan, Michael, Clark, and Ehlers (2003) had assault victims rate their own trauma memory on a 0-to-4 scale of disorganization. In Study 1, three groups of participants, those with current PTSD, those who no longer had PTSD, and those who never had PTSD had ratings of 1.2 (SD = 1.2), 0.9 (SD = 0.8), and 0.4 (SD = 0.6) on their scale, respectively. In Study 2, trauma and nontrauma memories were compared and had means of 0.69 (SD = 0.84) and 0.42 (SD = 0.56), respectively. Thus, although the effects were in the direction that would be expected, on average, the participants in both studies rated their trauma memories as quite coherent. Jones, Harvey, and Brewin (2007) found differences between people with and without PTSD for their trauma memory of a road traffic accident on an experimenter-rated measure of global (in)coherence. The scale ranged from 0 (extremely coherent) to 10 (extremely incoherent). The mean ratings averaged over three testing points for the trauma memories of participants with PTSD were 2.09 if participants did not have traumatic brain injury and 1.69 if they did. For participants without PTSD these values were 0.75 and 0.93, respectively. The average standard deviation calculated from the square root of the average variances were 2.18, 1.00, 0.66, and 0.82, respectively. Because all of the participants were victims of road traffic accidents, some incoherence might be due to the reported or subclinical brain injury. However, none of the values indicated incoherence on the 11-point scale. In both of these papers, each of which used scales starting at zero, the standard deviations of each measure were roughly equivalent to the means of the measure.
The third source of information on the incoherence issue compares the trauma memories of people with PTSD both to their nontrauma memories and to the trauma and nontrauma memories of people without PTSD. Many studies find that for some conditions and measures of coherence, trauma memories can be more or less coherent than nontrauma memories, or that people with PTSD can have more or less coherent trauma memories. The issue here, however, is whether people with PTSD have especially incoherent memories for their trauma(s); this requires both a comparison across memory types and across participants with and without PTSD. Few studies report data that allows this comparison and none seem to show that participants with PTSD have especially incoherent trauma memories.
The Halligan et al. (2003) Study 2, just reviewed, found an interaction in which participants who had PTSD at any of four testing points compared to participants who did not reported more self-rated incoherence for trauma versus nontrauma memories, but as noted earlier, none of the values indicated incoherence. Jelinek, Randjbar, Seifert, Kellner, and Moritz (2009) found a similar interaction for one rater-coded measure but not for another rater-coded measure or a self-reported coherence questionnaire. Römisch, Leban, Habermas, and Döll-Hentschker (2014) found more fragmentation in their analysis of distressing, trauma-like memories than for memories involving anger or happiness, but these effects did not interact with whether participants had PTSD or not.
In one study from our group, trauma exposed participants with and without PTSD wrote narratives of their trauma and of their most important and most positive events that occurred within a year of their trauma (Rubin, 2011). There were three rater measures of general coherence, which are used in modified form here. In addition, the participants provided a measure of coherence which was the average of their ratings on six scales, three of which are included as individual measures here. For these measures, there was one significant effect: a main effect in which participants with PTSD rated their own narratives as more disorganized than participants without PTSD, with means of 1.80 (SD = .32) and 1.51 (SD = .28), respectively, on a scale of 1 (none) to 7 (almost all). There were no effects of memory type or of a memory type by PTSD interaction. The study also included computer-scored measures of the frequency of cognitive mechanisms, insight, and causal words from the Linguistic Inquiry Word Count (LIWC; Pennebaker et al., 2007) that measure coherence. These measures varied with memory type but not with PTSD or the interaction of diagnostic group and memory type.
In another study from our group, 75 participants with and 42 without PTSD each rated their three most stressful, three most positive, seven most important, and 15 word-cued memories on a number of scales (Rubin et al., 2011). Among 18 rating scales were two measures of coherence: story and pieces. The story measure asked whether the memory was recalled as a coherent story and pieces asked whether the memory was recalled in pieces with missing bits. For all four types of memories, the PTSD group had numerically higher scores than the control group on both the story and the pieces scale, even though the scales have different conceptual directions in terms of coherence. It is possible that the more one thinks about one’s memories, the more coherent they become, but also the more often gaps are found. For stressful memories, pieces was significantly higher for the PTSD than the control group, with means of 3.68 (SD = 1.95) versus 2.93 (SD = 1.82), on a scale of 1 (not at all) to 7 (completely). They were not significantly higher for positive memories, which had means of 3.18 (SD = 2.09) versus 2.89 (SD = 1.93), leading to the one significant interaction between group and stressful versus each of the three types of control memories. However, there was no indication of high levels of incoherence for trauma memories in the PTSD group.
Participants also recorded involuntary memories as they occurred for 2 weeks following the experiment just described, and then recorded a voluntary memory from approximately the same time period as the involuntary memory. Again the PTSD group rated story higher, but here there was no difference for pieces. There were no differences for voluntary versus involuntary memories and no interactions for the story and pieces scales. When memories related and unrelated to their traumas were compared, the only significant effect in the story and pieces scales was that events related to the trauma were rated higher on story. There were no interactions with PTSD group, involuntary versus voluntary retrieval, or the combined three-way interactions.
A similar experiment was conducted with 115 undergraduates who were high or low on PTSD symptom severity (Rubin, Boals, & Berntsen, 2008) and produced results similar to Rubin et al. (2011) though the effects were often smaller, most likely because the range of PTSD symptom severity was smaller. In both studies, there were significant effects of other self-ratings, especially of emotional intensity and emotional reactions, and retrospective reports of the frequency of voluntary and involuntary rehearsal, and of the event’s centrality to the life story. Thus, overall, the results are replicable, but they offer little support for large differences on coherence in either voluntary or involuntary memories, and the differences that exist are in some cases in the direction of more coherence for trauma memories.
A recent review of research on memory disorganization and fragmentation in ASD and PTSD also discusses empirical findings relevant to the issue of incoherence (Brewin, 2014). Synthesizing data from across nine studies (reported in eight articles), the author concludes, “A considerable amount of evidence now strongly favors the claims that in samples suffering from ASD or PTSD trauma memories” include “fragmentation or disorganization accompanying voluntary recall,” (Brewin, 2014, p .78). However, in our own review of the studies included in Brewin (2014), we found little support for the claim that incoherence is especially severe in the trauma memories of people with PTSD. We discussed the findings of three of the eight articles reviewed by Brewin (2014) previously in this paper. To summarize in brief here, Jelinek et al., (2009) reported a memory type by PTSD diagnosis interaction for one of three measures of incoherence, but no interactions were found for the other two incoherence measures. Halligan et al. (2003) and Jones et al. (2007) reported main effects for PTSD, but the mean values on the incoherence scales used in these studies were all in the coherent as opposed to incoherent range. Halligan et al. (2003) also report a memory type by PTSD diagnosis interaction for disorganization again with all mean values in the coherent range. Moreover, in this study participants were classified into the PTSD versus the non-PTSD group based on having PTSD at any of four time periods, not necessarily at the time period when the disorganization ratings were obtained.
Of the five remaining studies, two (Berntsen, Willert, & Rubin, 2003; Rubin, Feldman, & Beckham, 2004) showed no significant differences. Two other studies showed significant differences using an ASD diagnosis (Harvey & Bryant, 1999; Salmond et al., 2011). However, the diagnosis of ASD cannot be used as empirical evidence for incoherence because the ASD diagnosis requires dissociation which includes symptoms likely to implicate incoherence. In particular, in adults, ASD requires at least three of the following five dissociation symptoms for a diagnosis: (a) numbing, which unlike the other four symptoms, does not implicate incoherence; (b) reduced awareness; (c) depersonalization; (d) derealization; and (e) amnesia. Thus, having an incoherent trauma narrative is required and cannot be considered an empirical finding; the incoherence issue we are arguing against in PTSD is assumed to be true for ASD. Simply put, the claim that people with ASD have incoherent memories of their stressful event is true by definition and so presenting it as an empirical finding is circular. One of these studies (Salmond et al., 2011) was for children where the diagnosis does not require the dissociation symptoms but it used supplemental dissociative questions to facilitate diagnosis.
The remaining study included “an exploratory investigation” in its title and concluded that “results provided only weak evidence of an association between dissociative trauma narrative themes and PTSD symptoms.” (Kenardy et al., 2007, p.456). Thus, a review of the same basic issue by a distinguished senior researcher with a different theoretical perspective, in our view, uncovered little support for the claim that incoherence is especially severe in people with PTSD. Both the differences in interpretation and the small number of studies found that could possibly be seen as supporting incoherence are striking given that the issue of incoherence has existed since the beginning of the PTSD diagnosis.
The Present StudyThe ideal study to provide evidence that might help to resolve the incoherence issue would have (a) a variety of measures of incoherence to ensure it is not missing measures that would produce different results, a full 2 × 2 design with both (b) memories for traumatic and nontraumatic events, and (c) clinically diagnosed participants and matched control participants who (d) have experienced similar traumas and (e) do not vary on clinical diagnoses other than PTSD that could produce incoherence. The present study includes all five of these components. The coherence of narratives is affected by factors such as education and socioeconomic status and by many disorders including those that involve dissociation, though the only aspect of dissociation that would be of importance here is incoherence (Giesbrecht, Lynn, Lilienfeld, & Merckelbach, 2008). We therefore balanced these characteristics across groups so that they would not affect the results. To ensure a variety of measures of incoherence, we measured the coherence of memories from (a) the perspective of the person who experienced and recalled the event and thus had full access to the memory beyond what they reported, (b) the perspective of trained undergraduate raters who had only the transcribed memory, and (c) the Coh-Metrix and LIWC computer programs, which also had only the transcribed memory.
Method Participants
Adults from the community were screened as part of a larger study by clinicians who were trained and worked regularly in a research setting. Participants were recruited via advertising for a study on memory for stressful or traumatic events and how they differ from more everyday memories. The Clinician Administered PTSD Scale (CAPS, Blake et al., 1995) was used to determine PTSD diagnostic status. Current diagnoses were determined by a 1-month time frame for PTSD. Exclusion criteria included current alcohol or other substance dependence/abuse measured by self-report and urine drug screen, neurological damage (including head trauma or disease), and current psychotic disorder or bipolar disorder with active manic symptoms based on the Structured Clinical Interview for DSM–IV Diagnosis (SCID; First, Spitzer, Gibbon, & Williams, 1996). Participants were also excluded if they were medically unstable or if they could not complete the study procedures. Nicotine dependence was allowed. To be included in the study, all participants were required to have met the DSM–IV–TR PTSD diagnostic A-criterion of having experienced, witnessed, or confronted “an event or events that involved actual or threatened death or injury, or a threat to the physical integrity of the self or others” (American Psychiatric Association, 2000, p. 467). From a pool of 101 eligible individuals who met our inclusion and exclusion criteria, of which 41 had current PTSD and 60 had no history of current or lifetime PTSD, 30 participants with PTSD and 30 control participants were randomly selected. The two groups were then compared on their average Hollingshead score, Beck Depression Inventory (BDI) score, and education level, as well as the number of females, minorities, veterans, and percentage with major depressive disorder (MDD), other psychiatric disorders (including anxiety disorders), and histories of substance dependence/abuse diagnoses. When differences between the two groups were found, in an iterative fashion, participants with high (or low) scores on a given measure were removed and quasirandomly replaced with another individual from the pool of eligible participants until no participants remained who could further reduce the differences between groups on these measures. Information on the PTSD group and control group is presented in Table 1.
Participant Demographics
Procedure
All administration of the protocol was done individually with each participant by trained employees in the Durham Veterans Affairs Medical Center. This staff attended regular sessions to ensure they were conforming to criteria of the study and administration of standard instruments. Participants in our sample were enrolled in a larger study for which they were compensated $500 for completing six total sessions. During the first session, participants were first screened for any current illicit drug use. Then, they were administered the Traumatic Life Events Questionnaire (Kubany et al., 2000), CAPS, and Structured Clinical Interview for DSM Disorders, and were queried about current medications and treatment history. During the second session, the Autobiographical Memory Questionnaire (AMQ; Rubin, Schrauf, & Greenberg, 2003, 2004) was administered for 15 cue words, and the BDI-II (Beck, Steer, & Brown, 1996), Dissociative Experience Scale (DES; Bernstein & Putnam, 1986), and other instruments were administered. The third session is key to our current investigation. During that session, participants orally narrated their three most negative, stressful, or traumatic life events followed by their three most positive and finally their three most important life events that were not among the most traumatic or positive. After each event they completed the AMQ, Centrality of Event Scale (Berntsen & Rubin, 2006, 2007), and PTSD Checklist (PCL; Weathers, Litz, Huska, & Keane, 1994) for that event. The order of narration was chosen to obtain data on the trauma memories first as these were the most crucial. The positive memories followed both to reduce any lingering mood effects of recalling the trauma memories and to prevent any of the memories of the three most positive events from being selected as among the three most important memories. We considered the positive and important memories as reasonable comparisons to the trauma memories due to their high emotional intensity but opposite valence and their similar impact on the participants’ lives, respectively. They were not of interest on their own and so we accepted the loss of any advantages of counterbalancing in order to obtain narratives of the trauma memories that were not influenced by the later memories. After later sessions, participants were then debriefed and referred for support if necessary. The narratives were transcribed prior to coding.
Initial Data Processing
All links to individual participants were replaced by arbitrary participant numbers to ensure confidentiality. The oral narratives were audiotaped, provided with participant numbers and then transcribed verbatim into computer files. Oral nonfluencies and fillers were then recoded for computer scoring. The transcriptions were then examined to remove any remaining information that could possibly allow the participant to be identified. Such information was changed to similar words with different referents so that the coherence coding would produce reasonable results. For the neutral observer ratings, the individual memories of each type were given new random numbers so that the raters would not know which narratives came from the same participant. The neutral observer ratings of the narrative coherence coding scheme (NaCC) and the global coherence measures were rated by undergraduate research assistants. We used α alpha to estimate how well our raters would agree with a new set of an equal number of raters. The raters were trained on narratives that were not part of this study. Once they understood the instructions to their satisfaction, they rated the entire set of narratives from this study independently of each other. We averaged the ratings of the three narratives of each memory type from each participant for all analyses including reporting reliabilities.
Instruments Used to Select and Describe Our Sample
Beck Depression Inventory (BDI-II)
The BDI-II (Beck et al., 1996) is the sum of 21 items rated on a 0-to-3 scale. The measure has good internal consistency (α = .91 to .93) and .93 1-week test–retest reliability (Beck et al., 1996). Arnau, Meagher, Norris, and Bramson (2001) demonstrated discriminate validity for the BDI-II in a primary care setting with significant differences in mean BDI-II scores between participants with and without MDD diagnoses.
Clinician-Administered PTSD Scale (CAPS)
The CAPS (Blake et al., 1995; Weathers, Keane, & Davidson, 2001) is a clinical structured interview that assesses the frequency and intensity of the 17 DSM–IV PTSD diagnostic symptoms. The CAPS has strong psychometric properties including high internal consistency (α = .94) and good convergent validity with other PTSD scales. The 0-to-4 severity and intensity scores for all 17 symptoms are summed to provide an overall severity score, with higher scores indicating a more severe PTSD diagnosis. Presence of each symptom was determined using the frequency ≥1 and intensity ≥2 rule (Weathers et al., 2001), which requires a symptom to be endorsed at a frequency of at least once per month and intensity of at least moderate impairment or distress to be counted as present.
The Dissociative Experience Scale (DES)
The DES (Bernstein & Putnam, 1986; Carlson & Putnam, 1993) is the average of 28-items that measure normal to pathological dissociative experiences, each rated on a 0-to-100 scale. Test–retest reliabilities are above .79 (Carlson & Putnam, 1993). The DES correlates highly with the severity of dissociative symptoms assessed in structured clinical interviews (i.e., SCID, rs = .58–.78; Draijer & Boon, 1993) and by alternative measures of dissociation (e.g., Perceptual Alteration Scale, r = .82; Nadon, Hoyt, Register, & Kihlstrom, 1991).
Hollingshead Index of Socioeconomic Status (Hollingshead SES)
On the Hollingshead SES (Hollingshead, 2011/1975), lower scores indicate higher socioeconomic status; scores range from 11 (upper class) to 77 (lower class).
PTSD symptom severity (PCL)
The PTSD Check List–Stressor-Specific Version (Weathers et al., 1994) is a 17-item measure of PTSD symptoms in reference to a specific event. Using 5-point scales (1 = not at all, 5 = extremely), respondents indicate the extent to which a specific event produced each of the B, C, and D DSM–IV–TR symptoms of PTSD during the previous month. The PCL has strong psychometric properties (Blanchard, Jones-Alexander, Buckley, & Forneris, 1996) and has been shown to have high diagnostic agreement with the CAPS (r = .93; Blake et al., 1995). Respondents completed the PCL-S for each of their three trauma memories.
Measures of Coherence
Autobiographical Memory Questionnaire (AMQ)
The AMQ (Rubin et al., 2003, 2004) includes a series of questions concerning processes involved in remembering an event. Participants completed the AMQ for each of their nine memories. We analyzed three items from the AMQ relevant to narrative coherence. Setting, “While remembering the event, I know the setting where it occurred” was rated on a scale from 1 (not at all) to 7 (as if it were happening now). Story (“While remembering the event, it comes to me in words or in pictures as a coherent story or episode and not as an isolated fact, observation, or scene”) and pieces (“My memory comes in pieces with missing bits”) were rated on a scale from 1 (not at all) to 7 (completely).
Narrative Coherence Coding Scheme (NaCCs)
The NaCCs (Morris, Baker-Ward, & Bauer, 2010; Reese et al., 2011) is comprised of three coherence dimensions: context, chronology, and theme rated on a 4-point scale from 0 to 3. Context measures the degree to which the narrator provides information needed to locate the event in space and time. Chronology concerns the degree to which the narrator provides sufficient information to place the actions in the event on a time line. Theme measures the degree to which the narrator substantially develops the narrative using causal linkages, interpretations, and elaborations; describes a resolution that relates the event to other autobiographical experiences, self-concept, or identity; and describes a sense of closure. The NaCCs has been used to assess the development of these three components of narrative coherence across the life span for autobiographical memories (Chen, McAnally, Wang, & Reese, 2012; Larkina & Bauer, 2012; Reese et al., 2011) and laboratory events (Bauer et al., 2012). Relevant to the current study, the NaCCs has also been used to quantify coherence in intensely positive and negative narratives in healthy undergraduates (Waters, Bohanek, Marin, & Fivush, 2013) and in narratives of stressful events in participants with and without a history of abuse (Follmer Greenhoot, Sun, Bunnel, & Lindboe, 2013). This rating scheme moves beyond components at the word or sentence level, and instead examines the coherence of the entire narrative. Five undergraduate raters independently provided values for these measures; αs for context, chronology, and theme were .90, .72, and .70, respectively.
Global Coherence measures
These four were similar to measures used in Greenberg and Rubin (2003) and are intended to test a more abstract level of narrative skills than the measures of the NaCCs. The first two assess narrative abilities tested in neuropsychological assessments. Narrator asks, “Do you understand more about who the narrator is, how they deal with the world, and how they must have felt?” Emotion asks, “Does the text evoke an emotional reaction in you and/or your empathy for the narrator?” Both were rated on a scale of 1 (not at all), 2 (not really), 3 (a little bit), 4 (a moderate amount), 5 (yes, quite a bit), 6 (yes, very much so), and 7 (yes, as much as any text of about this length could). Percent irrelevant asks, “How much of the writing does not add to the development of the narrative?” and was rated on a scale of 0% to 100% with markers at 10% intervals. Disorganization is based on Halligan et al. (2003). It asks, “Rather than concentrating on the narrator, your emotions, or amount of irrelevant content rated in questions 1, 2, and 3, step back and look at the narrative as a story that is meant to communicate the main ideas of what happened at an event.” and was rated on a scale from 0 (not at all disorganized) to 10 (extremely disorganized) with the numbers 0 through 10 listed as markers. Four coders rated the measures; αs for narrator, emotion, percent irrelevant, and disorganization were .76, .87, .85, and .87, respectively.
Coh-Metrix
Coh-Metrix is an automated linguistics facility that analyzes higher-level features of language and discourse, which has been widely validated (http://cohmetrix.com; Graesser, McNamara, Louwerse, & Cai, 2004; McNamara et al., 2014; McNamara, Louwerse, McCarthy, & Graesser, 2010). Coh-Metrix includes sophisticated methods of natural language processing, such as syntactic parsing and cohesion computation, to capture deeper language characteristics. It provides measures at multiple levels, including genre, cohesion, syntax, and words, as well as other characteristics of language and discourse. Here, we provide only a brief description of the measures of cohesion, principal component scores, and composite formality used in the current study.
Coh-Metrix principal component measures
These measures were derived from a Varimax (orthogonal) principal component analysis conducted during the development of the measures prior to this study. The orthogonal solution was preferred because it yielded a very small percentage of cross-loadings across components that exceeded |.30| and because oblique solutions added minimal increments. Eight orthogonal dimensions accounted for 67% of the variance in text variability in a large corpus of over 37,000 texts (Graesser, McNamara, & Kulikowich, 2011). Of these, the first five components, which accounted for 54% of the variance, were used and are listed below. These Coh-Metrix dimensions align with multilevel theoretical frameworks of language and discourse (Graesser & McNamara, 2011; Kintsch, 1998; Snow, 2002), which distinguish representations of meaning, structures, strategies, and cognitive processes at different levels of discourse.
Narrativity
Higher scores indicate high word familiarity, oral language, and greater ease of comprehension affiliated with everyday oral conversation.
Syntactic ease
Higher scores indicate that the sentences have fewer words and simpler, more familiar syntactic structures, which are easier to process and understand.
Word concreteness
Higher scores indicate more concrete language, which evokes mental images and thus should be more meaningful to the reader.
Referential cohesion
A low referential cohesion score indicates that a text has few overlapping content words and ideas.
Deep cohesion
The extent to which the ideas in a text are connected with causal, intentional, or temporal connectives at the situation model level. A high deep cohesion score indicates that the narrator has a more complex and coherent representation of a mental model of the story.
Other Coh-Metrix measures
Content word overlap
This measure considers the proportion of content words that overlap between pairs of sentences. It is calculated by dividing the number of content words in each pair of sentences that overlap by the total number of content words.
Latent semantic analysis
This measure considers the conceptual similarity between adjacent text constituents.
Connectives
These are sets of individual words that have the special function of connecting clauses and other constituents in the text base. The categories of connectives in Coh-Metrix that are most related to coherence are temporal, extended temporal, causal, adversative, logical, and additive. The incidence of each word class is computed as the number of occurrences per 1,000 words. A higher incidence of connectives increases cohesion with the narrative and also indicates that the narrator’s representation of events is more coherent.
Linguistic Inquiry Word Count (LIWC)
The LIWC (Pennebaker et al., 2007) analyzes written or transcribed text and calculates the percentage of different types of words used. We focused on categories related to coherence (cognitive mechanisms, insight, cause, nonfluencies, filler words; Pennebaker, Mehl, & Niederhoffer, 2003; Rubin, 2011; Rude, Gortner, & Pennebaker, 2004).
Relations among the measures of coherence
In the introduction, we argued that coherence is a broad concept defined in many ways in different literatures and that there has been little agreement among PTSD researchers as to how it should be measured, in part because the PTSD diagnosis offers little guidance at a detailed level. To examine whether our measures are related empirically, we report on three correlation matrices, one for each memory type, among our 28 measures of coherence. The percent of correlations whose magnitude was greater than .255, and thus were significant at the p = .05 level, for the trauma, positive, and important memories were 21%, 18%, and 21%. To examine how these correlations are distributed across the five categories of coherence measures shown in Tables 2 and 3, we examined the correlations of the individual measures within each category versus the correlation between the measures in each category and the measures in the other four categories. Because the results were similar we report the average percentage of significant correlations across all three memory types. The correlations among the AMQ measures were significant 33% of the time versus being significant with the measures in the other four categories 6% of the time. For the NaCCs, Global Coherence, Coh-Metrix, and LIWC these values were 11% versus 16%, 39% versus 18%, 35% versus 17%, and 20% versus 18%. Thus, correlations exist both within categories and between categories at substantial rates, with the exception of the self-rated AMQ measures when compared to the other ratings which were all rated by observers.
Means (SDs) and ANOVAs for Measures of Narrative Coherence for Trauma, Positive, and Important Memories
Post Hoc Power and Required N for Univariate ANOVAs
Results Group and Trauma Characteristics
As shown in Table 1, participants in the PTSD and control groups were matched on minority status, gender, combat service, MDD, past substance abuse, age, and years of education. Compared to the control group, participants in the PTSD group had lower socioeconomic status (as indicated by higher Hollingshead’s SES scores) and higher scores on the BDI-II and DES. We excluded participants with clinical disorders related to extreme scores on these scales, but not to extreme values on the scales themselves, which allowed this difference to occur. The frequencies of the wide-range of criterion A-qualifying traumas participants reported during the diagnosis are also shown in Table 1. A chi-square analysis revealed no differences in the type of trauma experienced between the two groups. Thus, the groups were matched on, or due to the exclusion criteria were lacking, many factors that could cause incoherence outside of PTSD itself.
The Narrative Coherence of Trauma, Positive, and Important Memories
Table 2 presents the main findings of our study. There are 28 measures of coherence: three self-report measures, seven neutral observer measures done by raters, 13 measures from Coh-Metrix, and five measures from the LIWC. The multivariate analysis of variance (MANOVA) used to reduce the risk of Type I and Type II errors among these 28 measures had a main effect of memory type, F(53, 3) = 30.93, p = .008, but not a main effect of group, F(28, 31) = 1.46, p = .154, or their interaction, F(56, 3) = 4.50, p = .120. The significant main effects of memory type are indicated in Table 2. Even if we relaxed the experiment-wise risk of Type I and Type II errors by not using a MANOVA, with one exception, all the univariate effects that would be significant at p < .01 level or lower would be effects of memory type, and all the effects that would survive a Bonferroni correction would be main effects of memory type.
Within the memory type effects, the comparisons of trauma memories with the two kinds of control memories are the main interest. The comparison of trauma with positive but not important memories was significant for one of the 12 main effects of memory type: AMQ pieces, t(59) = 2.40, p < .05. The comparison of trauma memories with important but not positive memories was significant for two of the 12 main effects of memory type: NaCCs theme, t(59) = 3.54, p < .001, and LIWC cause, t(59) = 3.15, p < .01. For the remaining nine significant main effects, trauma memories were significantly different from both positive and important memories: NaCCs chronology, t(59) = 7.05, p < .0001 and t(59) = 5.49, p < .0001, respectively; Global Coherence narrator, t(59) = 3.20, p < .01 and t(59) = 2.30, p < .05; emotion, t(59) = 14.03, p < .0001 and t(59) = 12.69, p < .0001, and percent irrelevant, t(59) = 3.42, p < .01 and t(59) = 5.01, p < .0001; Coh-Metrix word concreteness, t(59) = 2.63, p < .05 and t(59) = 3.37, p < .001, deep cohesion, t(59) = 3.98, p < .001 and t(59) = 4.75, p < .0001, extended temporal connectives, t(59) = 5.27, p < .0001 and t(59) = 3.30, p < .01, causal connectives, t(59) = 4.49, p < .0001 and t(59) = 4.38, p < .0001, and logical connectives, t(59) = 3.36, p < .001 and t(59) = 3.16, p < .01.
Of these 12 measures, the significant differences were split evenly with trauma memories being less coherent in six measures and more coherent in six measures. Trauma memories are less coherent than the control memories for AMQ pieces; NaCCs theme; Coh-Metrix deep cohesion, external temporal connectives, causal connectives, and logical connectives; and more coherent for NaCCs chronology; Global Coherence narrator, emotion, and percent irrelevant; Coh-Metrix concreteness; and LIWC cause. The mixed direction of these effects does not offer strong support for trauma memories being generally more incoherent than memories of positive and important events. Because these results are about the kind of event being narrated they can be used to understand which aspects of coherence vary more for different kinds of events. However, none of the significant results pertain to the effects of PTSD or how it interacts with different measures of coherence.
There are five empirical issues that need to be addressed to ensure our conclusions are valid. The first issue is to examine the post hoc power of our results. This is especially important because based on our review of the literature, the effect we expected to find is close to a null hypothesis finding. Therefore we need to explore what genuine statistical effects we might have missed due to limited power and whether the effects we did find were chance occurrences. The second issue is to examine the relationship among the coherence measures we used to see if they were related empirically. Although our use of the concept of coherence and the MANOVA can be justified on conceptual grounds, empirical support can be evaluated based on similarities among the measures. The third issue is that narratives of the different memory types varied in length and this could affect our measures. The fourth is that either one, two, or three of the three trauma memories from each participant were of traumatic events as defined by the DSM–IV–TR PTSD diagnostic A-criterion (American Psychiatric Association, 2000) that was in use when the study was conducted. An analysis restricted to trauma memories that meet the diagnostic criteria is needed to ensure that our results are not caused by mixing events that are traumatic as defined by the diagnosis with other negative, stressful events. Similarly, an analysis that included only the trauma on which the PTSD diagnosis was made would ensure that the trauma on which we base our groups produces results that do not differ from what we have just reported. The fifth issue is that although we tried to equate our PTSD and control group on all conventional demographic and relevant clinical characteristics except for the PTSD diagnosis and symptom severity, there were some residual differences as shown in Table 1. We therefore include these characteristics as covariates in an additional analysis. We address each of these five issues in turn.
Table 3 includes the post hoc power for all measures along with the number of participants that would be needed to observe a p < .05 level effect with a probability of .80. Excluding the 12 significant main effects of memory type noted in Table 2, the number of participants required to observe significant effects 80% of the time is substantial, with only one main effect of group (i.e., for LIWC cognitive mechanisms) needing less than 100 participants and only three other effects (i.e., interaction effect for AMQ story, main effect of memory type for content word overlap, main effect of group for additive connectives) needing less than 200 participants. Thus, based on our sample and measures there seems to be little else that would emerge without a much larger sample. Of the 12 significant main effects of memory type, six have a post hoc power of .80 or above. Of these, three neutral observer measures are in the direction of trauma memories being more coherent than the comparison memories and three computer-scored Coh-Metrix measures are in the direction of trauma memories being less coherent than comparison memories, so the basic conclusions are not affected by this result. The remaining six measures have post hoc power between .38 and .73 and offer a similarly mixed picture with respect to the direction of the effect.
The number of words per narrative varied across memory type, but not with diagnosis or their interaction, F(2, 116) = 4.19, p < .05, with means of 436, 331, and 348 for the positive, important, and trauma memories. Theoretically, the length of the narratives should have their biggest effect on the neutral observer NaCCs and Global Coherence ratings because the only information the raters could base their judgments on were the transcribed narratives. In contrast, the Participant AMQ ratings were not based on the narratives the participants produced, and the computer-scoring methods have a correction for the number of words in each narrative. Empirically, when word count was correlated with the 28 measures shown in Tables 2 and 3 for the positive, important, and trauma memories separately, 12 correlations were significant at the .05 level, and all but one of these were in the neutral observer ratings. We therefore used word count as a covariate for the neutral observer ratings.
The MANCOVA for the seven neutral observer ratings with word count as a covariate had a main effect of memory type, F(14, 220) = 11.33 p < .0001, but no main effect of group, F(7, 51) = 1.00, p = .441, or their interaction, F(14, 220) = .95, p = .501. The significant covariate effects were theme, t(58) = 2.17, p = .034, narrator, t(58) = 4.07 p < .001, percent irrelevant, t(58) = 9.01, p < .0001, and disorganization, t(58) = 3.42, p = .001. The results of the univariate ANCOVAs with word count as a covariate had only minor changes compared to the previously reported results without word count covaried. The corrected Fs(2, 115) for the significant neutral observer ratings are: chronology, 20.64, p < .0001; theme, 10.83, p < .0001; narrator, 3.53, p = .033; emotion, 110.05, p < .0001; and percent irrelevant, 9.68 p < .0001. Thus, there was no change in the results that would affect our overall conclusions.
We examine whether the occurrence of the traumas as indexed by the PTSD diagnosis was having a serious effect on our results. To do this we first report on how many of the three trauma memories were A-traumas for each participant and then repeat our main analysis for only A-traumas. In the PTSD group, there were 15 people with three A-traumas, 11 with two, and four with one A-trauma (M = 2.37, SD = .72) compared to the non-PTSD group which had 12 with three A-traumas, seven with two, and 11 with one (M = 2.03, SD = .89), χ2(2) = 4.49, p = .106; t(58) = 1.60, p = .115. Constraining the analysis to only the A-traumas reduces the reliability of our measures because there are fewer memories being considered. Similar to the analysis using all trauma memories, the MANOVA has a significant main effect of memory type, F(56, 3) = 17.13, p = .019, but not of group or their interaction, F(28, 31) = 1.37, p = .193, and F(56, 3) = .59, p = .823, respectively. The individual significant univariate effects of memory type have minor changes compared to those shown in Table 2 for all memories with some F values increasing slightly (emotion, word concreteness, extended temporal connectives, causal connectives, and cause) while others decreased slightly (pieces, chronology, theme, narrator, percent irrelevant, deep cohesion, and logical connectives) and two new measures becoming significant at the .05 level, disorganization: F(2, 116) = 3.30 p = .040; content word overlap: F(2, 116) = 4.52, p = .01.
When we examine only the index traumas, none of the MANOVA effects are significant, main effect of memory type: F(56, 3) = 2.77, p = .218; main effect of group: F(28, 31) = 1.76, p = .329; interaction: F(56, 3) = 4.33, p = .126. If we nonetheless examine the univariate main effects of memory type, there are minor changes that do not affect our overall results. All but four effects that were significant in the original analyses remain significant (chronology, theme, emotion, percent irrelevant, deep cohesion, extended temporal connectives, causal connectives, and cause) and there are three new significant effects, disorganization: F(2, 116) = 3.09, p = .049; insight: F(2, 116) = 4.10, p = .019; and content word overlap: F(2, 116) = 3.62, p = .030.
Finally, we examined whether our results were affected by differences in the three measures in Table 1 that we did not completely balance across the PTSD and the control groups: the Hollingshead SES, BDI-II, and DES. Given that dysphoria and dissociation as measured by the BDI-II and DES have been associated with PTSD and that in the DSM–5 (American Psychiatric Association, 2013) dysphoria is an official symptom cluster and dissociation is implicated in one symptom (i.e., the inability to remember important parts of the trauma), correcting for them can be seen as problematic in that they are inherent parts of the disorder. Nonetheless, for completeness we examine dysphoria and dissociation as covariates to examine their effects on the results. To achieve this, we added the Hollingshead SES, BDI-II, and DES as covariates to a MANOVA identical to the one described in our initial analysis. As in the initial analyses without covariates, the main effect of memory type was significant, F(56, 3) = 30.93, p = .008, but the main effect of group, F(28, 28) = .98, p = .524, and their interaction was not, F(56, 3) = 4.50, p = .120. However, because the three covariates are individual differences that are the same for all memory types, they do not have an effect on our main findings. For the Hollingshead SES, the significant covariate effects were theme, t(58) = −2.07, p = .043; narrativity, t(58) = 2.84, p = .006; referential cohesion, t(58) = 2.45, p = .017; adversative connectives, t(58) = −3.31, p = .002; logical connectives, t(58) = −2.60, p = .012; and cognitive mechanism, t(58) = −2.08, p = .042. For the DES, they were referential cohesion, t(58) = −2.84 p = .006 and content word overlap, t(58) = −2.42, p = .019. For the BDI-II, they were deep cohesion, t(58) = 2.03, p = .047; causal connectives, t(58) = 2.03, p = .047; and filler, t(58) = 2.22, p = .031.
DiscussionWe examined the coherence of trauma memories in individuals with and without PTSD. We measured the coherence of memories for events in as many ways as we could find that have been used in the literature on coherence and added newer computer-scoring methods developed in educational research. To provide baselines for these measures, we compared trauma memories to the participants’ most important and most positive memories. We chose this approach because these memories share many properties with traumas, including importance to the participants’ lives and intense emotions. To examine the effects of PTSD, and ideally only PTSD, we compared two samples that we matched as closely as we could on factors other than PTSD. The results, in simplest terms, were that no consistent differences were observed as a function of diagnosis or the interaction of diagnosis and memory type. Several differences in coherence were observed as a function of memory type; most were small in magnitude and they were as likely to be more coherent as less coherent in trauma memories.
Given the overall abundance of mixed and null findings, we need to ensure that our results are valid. A power analysis indicated that we had sufficient power to observe differences in coherence between trauma and comparison memories and that the differences caused by diagnosis or its interaction with memory type were small enough that a much larger sample would be needed to see effects. Moreover, the differences in memory type were as likely to show that trauma memories were more coherent as less coherent. Most importantly, our results do not reflect a failure to replicate. Our findings are consistent with results from previous studies, so increasing power is not likely to produce results pointing to incoherence in the trauma memories of people with PTSD. In particular, the data presented here parallel the literature as a whole, which as reviewed in the introduction, is characterized by the absence of empirical demonstrations of high levels of incoherence in the trauma memories of groups with PTSD and more broadly in reviews of studies of narrative in PTSD, which use a variety of measures and sample a variety of populations (Crespo & Fernández-Lansac, 2015; O’Kearney & Perrott, 2006). Although claims about incoherence depend on interpretations of the anchors of the scales used (Blanton & Jaccard, 2006), it is hard to see the reported mean levels of incoherence being at high levels. On a more detailed level, the individual results reported here are consistent with recent studies of narrative that compare differences in just population or memory type (e.g., Fivush, McDermott Sales, & Bohanek, 2008; Waters, Bohanek, Marin, & Fivush, 2013) and, in our view, with studies in a review which argued that voluntarily recalled trauma memories are more incoherent in PTSD (Brewin, 2014).
Our ability to draw conclusions from these mixed and mostly null findings is further strengthened by our finding that the pattern of results was not substantively affected by variations in depressive and dissociative symptomology or SES. Although efforts were made to select the PTSD and control groups so that the groups were similar on all demographic and relevant clinical characteristics except for a PTSD diagnosis and the level of PTSD symptom severity, residual group differences emerged for these three variables. However, results from an analysis with these three variables added as covariates were substantively unchanged compared to those from the primary analysis: the main effect of memory type was significant but the main effect of group and their interaction was not. The consistency of the results across analyses with and without covariates suggests that our findings were not due to the effects of depressive symptoms, dissociative symptoms, or SES. Similarly, analyses restricted to trauma memories that met the diagnostic criteria and to the trauma on which the PTSD diagnosis was made were conducted to ensure that our results were not caused by mixing events that are traumatic as defined by the diagnosis with other negative, stressful events. These analyses revealed only minor differences that do not affect our conclusions, suggesting that our results were not explained by variations in severity of the trauma memory as defined by DSM–IV diagnostic criteria.
Another strength of our study was our use of a broad range of coherence measures drawn from multiple disciplines and areas of research including autobiographical memory studies, developmental psychology, neuropsychological assessment research, education research, and expressive writing. Sampling measures and methods of measuring coherence from multiple literatures was necessary because the concept of incoherence is poorly defined and operationalized in the PTSD literature and diagnosis. Despite the wide variety of measures used, analyses concerning the relations among these varied measures of coherence indicated a substantial convergence both within and between categories of measures, with the exception of the self-rated AMQ ratings. The low percentage of covariance between the AMQ ratings and the other categories of ratings was not unexpected, however, given that the AMQ ratings were subjective ratings of the memory narratives and the other categories of measures were computer-scored measures of coherence or ratings made by neutral observers. Thus these results provide evidence of convergent validity both with and between the categories of coherence measures tested in the present study.
We need to note that our results and theoretical claims do not speak to increases in coherence that occur during psychotherapy. Consider an early classic study by Foa, Molnar, and Cashman (1995). Patients with PTSD who had a trauma entered exposure therapy. The therapy included seven sessions with a trained clinician in which, for 45 to 60 min, with eyes closed, they repeatedly imagined and narrated a trauma as vividly and in as much detail as possible in the present tense as if it were happening again. The patients were asked not only to describe what was happening as they imagined the event but also how they felt and what they were thinking, which are all tasks that should increase coherence. Patients decreased their symptoms of PTSD, anxiety, and depression, and their narratives became more coherent on a number of measures, though such increases in coherence are not always observed. Although not measured, it is also likely that, due to such exposure therapy, the patients over time perceived their traumas as less fear-provoking, less damaging to them, and perhaps less likely to occur in the future. It is also clear that such a procedure, even in a milder form and in less clinically skilled hands, can change the content and evaluation of memories and can even create memories for new events (e.g., Goff & Roediger, 1998; Hyman & Pentland, 1996). But it is not clear from any studies we can find whether increases in coherence alone cause the change, are a partial contributor along with the decreases in the negative effects of the trauma memory caused by exposure therapy, or are just an index that the trauma memory has changed in the desired direction. Under any of these possibilities, increases in the coherence of the trauma memory with therapy would correlate with decreases in PTSD symptoms.
This study has limitations. First, we tested only one sample of moderate size. Second, we did not equate the PTSD and control groups completely on factors that might influence coherence. In some cases, but not all, this is justified. For instance, dysphoria is involved in symptoms of PTSD and having PTSD and control groups matched on dysphoria may leave results that are not representative of PTSD (e.g., Meehl, 1971). Third, coherence is not an easy concept to operationalize and measure (McAdams, 2006). Although we tried to include as many measures of coherence as we could, it could still be the case that there is a particular kind of incoherence central to PTSD and trauma memories that we, and the literature we surveyed to assemble our measures, have not yet articulated.
We can only speculate as to why some clinicians find the trauma memories of people with PTSD to be generally more incoherent when more controlled studies do not. One possibility is the nature of the observations on which these generalizations are based. PTSD patients in therapy spend more time trying to understand their traumas than positive events. If they were asked to explore in depth a happy event from their past with the same expectations and social situation that occurs in therapy, they might find they also cannot recall important parts of the event and the organization of the event that is not set by convention may seem fragmented. It is also possible that even a small proportion of the patients with PTSD who have a dissociative disorder could suggest a general effect of incoherence related to PTSD. Finally, over the course of therapy, the repeated attempt to narrate the traumatic event in a way that makes it easier to understand and cope with should make its narration more coherent (e.g., Foa et al., 1995). In contrast, studies like those used here that compare trauma to control memories, often exclude or match people who are likely to dissociate, and contrast the results of people with PTSD to people without the diagnosis. All of these potential factors may eliminate possible sources of incoherence.
There is a clear clinical implication to our work. In order to have PTSD, an individual must exhibit specific symptoms that result in “clinically significant distress or impairment in social, occupational, or other important areas of functioning” (American Psychiatric Association, 2013, p. 272). Here, we argued from the existing literature and demonstrated from our data that the incoherence of the trauma memory has little support as a component of PTSD, in that incoherence is not substantially greater in trauma memories than it is in control memories nor is it substantially greater in PTSD participants than non-PTSD participants. We argued that there is no real evidence to include this symptom, but this is a null hypothesis claim. Even if some effects can be claimed to exist, they are small and thus the incoherence symptom may be less likely to cause distress that affects important areas of functioning compared to the other symptoms of the disorder. If, as we demonstrated, the trauma memory is not especially incoherent in PTSD, then during treatment more effort could be spent on changing other aspects of the memory that may lead to greater distress and impairment.
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Submitted: March 16, 2015 Revised: September 22, 2015 Accepted: September 24, 2015
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Source: Journal of Abnormal Psychology. Vol. 125. (1), Jan, 2016 pp. 11-25)
Accession Number: 2015-49420-001
Digital Object Identifier: 10.1037/abn0000126
Record: 110- Title:
- Pathways from childhood abuse and neglect to HIV-risk sexual behavior in middle adulthood.
- Authors:
- Wilson, Helen W.. Department of Psychology, Rosalind Franklin University of Medicine and Science, North Chicago, IL, US, helen.wilson@rosalindfranklin.edu
Widom, Cathy Spatz. John Jay College of Criminal Justice, City University of New York, NY, US - Address:
- Wilson, Helen W., Department of Psychology, Rosalind Franklin University of Medicine and Science, 3333 Green Bay Road, North Chicago, IL, US, 60064, helen.wilson@rosalindfranklin.edu
- Source:
- Journal of Consulting and Clinical Psychology, Vol 79(2), Apr, 2011. pp. 236-246.
- NLM Title Abbreviation:
- J Consult Clin Psychol
- Page Count:
- 11
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- Journal of Consulting Psychology
- Other Publishers:
- US : American Association for Applied Psychology
US : Dentan Printing Company
US : Science Press Printing Company - ISSN:
- 0022-006X (Print)
1939-2117 (Electronic) - Language:
- English
- Keywords:
- HIV risk, child abuse and neglect, sexual behavior, psychosocial factors, sexual risk taking
- Abstract:
- Objective: This study examines the relationship between childhood abuse and neglect and sexual risk behavior in middle adulthood and whether psychosocial factors (risky romantic relationships, affective symptoms, drug and alcohol use, and delinquent and criminal behavior) mediate this relationship. Method: Children with documented cases of physical abuse, sexual abuse, and neglect (ages 0–11) processed during 1967–1971 were matched with nonmaltreated children and followed into middle adulthood (approximate age 41). Mediators were assessed in young adulthood (approximate age 29) through in-person interviews between 1989 and 1995 and official arrest records through 1994 (N = 1,196). Past year HIV-risk sexual behavior was assessed via self-reports during 2003–2004 (N = 800). Logistic regression was used to examine differences in sexual risk behavior between the abuse and neglect and control groups, and latent variable structural equation modeling was used to test mediator models. Results: Child abuse and neglect was associated with increased likelihood of risky sexual behavior in middle adulthood, odds ratio = 2.84, 95% CI [1.74, 4.64], p ≤ .001, and this relationship was mediated by risky romantic relationships in young adulthood. Conclusions: Results of this study draw attention to the potential long-term consequences of child abuse and neglect for physical health, in particular sexual risk, and point to romantic relationships as an important focus of intervention and prevention efforts. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Child Abuse; *Child Neglect; *HIV; *Psychosocial Factors; *Sexual Risk Taking
- Medical Subject Headings (MeSH):
- Adult; Adult Survivors of Child Abuse; Child; Child, Preschool; Female; HIV Infections; HIV Seropositivity; Humans; Infant; Interpersonal Relations; Male; Middle Aged; Odds Ratio; Regression Analysis; Risk Factors; Risk-Taking; Sexual Behavior; Substance-Related Disorders
- PsycINFO Classification:
- Behavior Disorders & Antisocial Behavior (3230)
- Population:
- Human
Male
Female - Location:
- US
- Age Group:
- Childhood (birth-12 yrs)
Neonatal (birth-1 mo)
Infancy (2-23 mo)
Preschool Age (2-5 yrs)
School Age (6-12 yrs)
Adulthood (18 yrs & older)
Young Adulthood (18-29 yrs)
Thirties (30-39 yrs)
Middle Age (40-64 yrs) - Tests & Measures:
- National Institute of Mental Health Diagnostic Interview Schedule—Revised
HIV-Risk Sexual Behavior measure - Grant Sponsorship:
- Sponsor: Eunice Kennedy Shriver National Institute of Child Health and Human Development
Grant Number: HD40774
Recipients: No recipient indicated
Sponsor: National Institute of Mental Health
Grant Number: MH49467; MH58386
Recipients: No recipient indicated
Sponsor: National Institute of Justice
Grant Number: 86-IJ-CX-0033; 89-IJ-CX-0007; 93-IJ-CX-0031
Recipients: No recipient indicated
Sponsor: National Institute on Drug Abuse
Grant Number: DA17842; DA10060
Recipients: No recipient indicated
Sponsor: National Institute on Alcohol Abuse and Alcoholism
Grant Number: AA09238; AA11108
Recipients: No recipient indicated
Sponsor: Doris Duke Charitable Foundation
Recipients: No recipient indicated - Methodology:
- Empirical Study; Longitudinal Study; Quantitative Study
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- First Posted: Feb 28, 2011; Accepted: Dec 20, 2010; Revised: Nov 17, 2010; First Submitted: Jun 22, 2010
- Release Date:
- 20110228
- Correction Date:
- 20110328
- Copyright:
- American Psychological Association. 2011
- Digital Object Identifier:
- http://dx.doi.org/10.1037/a0022915
- PMID:
- 21355638
- Accession Number:
- 2011-04114-001
- Number of Citations in Source:
- 51
- Persistent link to this record (Permalink):
- http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2011-04114-001&site=ehost-live
- Cut and Paste:
- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2011-04114-001&site=ehost-live">Pathways from childhood abuse and neglect to HIV-risk sexual behavior in middle adulthood.</A>
- Database:
- PsycINFO
Pathways From Childhood Abuse and Neglect to HIV-Risk Sexual Behavior in Middle Adulthood
By: Helen W. Wilson
Department of Psychology, Rosalind Franklin University of Medicine and Science;
Cathy Spatz Widom
John Jay College of Criminal Justice, City University of New York
Acknowledgement: This research was supported in part by National Institute of Child Health and Human Development Grant HD40774; National Institute of Mental Health Grants MH49467 and MH58386; National Institute of Justice Grants 86-IJ-CX-0033, 89-IJ-CX-0007, and 93-IJ-CX-0031; National Institute on Drug Abuse Grants DA17842 and DA10060; National Institute on Alcohol Abuse and Alcoholism Grants AA09238 and AA11108; and by the Doris Duke Charitable Foundation. Points of view are our own and do not necessarily represent the position of the U.S. government. We thank Sally Czaja for her consultation regarding statistical analyses.
Numerous studies have linked childhood maltreatment to risky sexual behavior later in life (e.g., Bensley, Van Eenwyk, & Simmons, 2000; Berenson, Wiemann, & McCombs, 2001; Cunningham, Stiffman, Dore, & Earls, 1994; Dube, Felitti, Dong, Giles, & Anda, 2003; Koenig & Clark, 2004; Mullings, Marquart, & Brewer, 2000; National Institute of Mental Health Multisite HIV Prevention Trial Group, 2001; Noll, Trickett, & Putnam, 2003; Paolucci, Genuis, & Violato, 2001; Purcell, Malow, Dolezal, & Carballo-Diéguez, 2004; Rodgers et al., 2004; Stiffman, Dore, Cunningham, & Earls, 1995). However, the majority of studies have focused only on sexual abuse (Senn, Carey, & Vanable, 2008), and most have relied on retrospective reports of childhood maltreatment. As an exception, findings from a prospective cohort design study revealed that individuals with documented cases of childhood physical abuse, sexual abuse, and neglect, compared to matched controls, were at increased risk for prostitution (Widom & Kuhns, 1996; Wilson & Widom, 2008a) and early sexual initiation (Wilson & Widom, 2008a) assessed in young adulthood. This study also found that victims of child abuse and neglect were more likely than nonmaltreated controls to be HIV positive and to report having had other sexually transmitted diseases in middle adulthood (Wilson & Widom, 2008a, 2009b). The current study expands upon this work to examine links from childhood abuse and neglect to risky sexual behavior in the same sample followed up in middle adulthood, at approximate age 41.
Recent research implicates childhood maltreatment as a risk factor for long-term physical health problems and health risk behaviors (Dube et al., 2003; Rodgers et al., 2004; Walker et al., 1999). Childhood abuse and neglect can result in a cascade of negative effects across multiple domains of physiological, social, psychological, and behavioral development, which may explain increased propensity for risky sexual behavior in adulthood. The self-trauma model (Briere, 1996) suggests that childhood maltreatment can lead to problematic outcomes in adulthood through multiple developmental pathways involving attachment problems; symptoms of posttraumatic stress disorder (PTSD); maladaptive coping; and negatively distorted appraisals of oneself, others, and the future. According to this model, disruption of basic developmental processes can result in chaotic and conflictual relationships, use of poor coping strategies such as substance use and aggression, affective dysregulation, and psychological distress. For example, disruption of the early attachment relationship appears to undermine the development of intimate relationships and emotional regulation. Childhood maltreatment also appears to affect the development of neurological and physiological processes related to stress response, affect regulation, social and emotional development, and cognition (De Bellis, 2001; Glaser, 2000). In an integrative model, Repetti, Taylor, and Seeman (2002) suggest that maladaptive family environments, characterized by anger and aggression, low warmth and support, and neglect, increase risk for health-compromising behaviors through deficits in children's emotional control and expression, social competence, and physiological regulation. Thus, deficits in multiple domains of psychosocial functioning may mediate the relationship between childhood maltreatment and risky sexual behavior. However, very little research has directly examined social, emotional, and behavioral mechanisms that may explain the relationship between childhood maltreatment and risky sexual behavior (Senn et al., 2008).
The present study extends and expands on earlier work (Widom & Kuhns, 1996; Wilson & Widom, 2008a, 2009b, 2010b) by examining data collected during middle adulthood with a sample of individuals with documented histories of child abuse and neglect and of matched controls. We had two primary hypotheses. First, we predicted that victims of childhood abuse and neglect would be more likely than controls to report HIV-risk sexual behavior in middle adulthood. We expected this relationship would apply for three forms of child maltreatment: sexual abuse, physical abuse, and neglect. Second, we predicted that a set of psychosocial mediators (risky relationships, affective symptoms, drug and alcohol use, and delinquent and criminal behavior) would explain the relationship between childhood maltreatment and risky sexual behavior. We compared these potential mediating pathways to determine which specific risk factors were most likely to explain increased sexual risk in victims of childhood abuse and neglect.
Method Design and Participants
Data were collected as part of a large prospective cohort design study in which abused and/or neglected children were matched with nonabused, nonneglected children and followed into adulthood. Because of the matching procedure, participants are assumed to differ only in the risk factor (i.e., whether they have experienced childhood sexual or physical abuse or neglect). Because it is not possible to assign participants randomly to groups, the assumption of equivalency for the groups is an approximation. The control group may also differ from the abused and neglected individuals on other variables nested with abuse or neglect. For complete details of the study design and participant selection criteria, see Widom (1989a).
The original sample of abused and neglected children (N = 908) was made up of substantiated cases of childhood physical and sexual abuse and neglect processed from 1967 to 1971 in the county juvenile (family) or adult criminal courts of a midwestern metropolitan area. Cases of abuse and neglect were restricted to children 11 years of age or younger at the time of the incident and, therefore, represent childhood maltreatment. A control group of children without documented histories of childhood abuse and/or neglect (N = 667) was matched with the abuse and/or neglect group on age, sex, race or ethnicity, and approximate family social class during the time that the abuse and neglect records were processed. The abuse and neglect and control groups were identified approximately twenty years after cases of abuse and neglect occurred.
The control group represents a critical component of the design of the study. Children who were under school age at the time of the abuse and/or neglect were matched with children of the same sex, race, date of birth (±1 week), and hospital of birth through the use of county birth record information. For children of school age, records of more than 100 elementary schools for the same time period were used to find matches with children of the same sex, race, date of birth (±6 months), class in elementary school during the years 1967 to 1971, and home address, preferably within a five-block radius of the abused/neglected child. Overall, matches were found for 74% of the abused and neglected children. Nonmatches occurred for a number of reasons. For birth records, nonmatches occurred in situations when the abused and neglected child was born outside the county or state or when date of birth information was missing. For school records, nonmatches occurred because of lack of adequate identifying information for the abused and neglected children or because the elementary school had closed over the last 20 years and class registers were unavailable. Reanalyses of findings on criminal behavior were conducted only with matched pairs (i.e., excluding abused and neglected participants without matches), and the results did not change with the smaller sample size (Widom, 1989b). Court records were searched for individuals identified for the control group, and those found to have cases of abuse or neglect were dropped (N = 11).
The initial phase of the larger longitudinal study compared the abused and/or neglected children to the matched comparison group (total N = 1,575) on juvenile and adult criminal arrest records (Widom, 1989a). A second phase involved tracking, locating, and interviewing the abused and/or neglected and comparison groups during 1989–1995, approximately twenty years after the incidents of abuse and neglect (N = 1,196). This interview consisted of a series of structured and semistructured questionnaires and rating scales, including the National Institute of Mental Health Diagnostic Interview Schedule—Revised (DIS–III–R; Robins, Helzer, Cottler, & Goldring, 1989), a standardized psychiatric assessment that yields Diagnostic and Statistical Manual of Mental Disorders (3rd ed., rev.; DSM–III–R; American Psychiatric Association, 1987) diagnoses. Subsequent follow-up interviews were conducted in 2000–2002 and in 2003–2004. The research presented in this paper uses self-reports and criminal records gathered in the 1989–1995 interviews and information on risky sexual behavior gathered as part of a medical status examination in 2003–2004.
Although there was attrition associated with death, refusals, and our inability to locate individuals over the various waves of the study, the composition of the sample at the four time points has remained about the same. The abuse and neglect group represented 56–58% at each time period; White, non-Hispanics were 62–66% and men made up 48–51% of the samples. There were no significant differences across the samples on these variables or in mean age across the four phases of the study.
Interview, 1989–1995
Of the original sample, 1,307 participants (83%) were located and 1,196 (76%) participated in the first interview. Of those not interviewed, 43 were deceased, eight were unable to be interviewed, 268 were not found, and 60 refused to participate. At this wave of the study, the sample was an average of 29.2 years old (range = 19.0–40.7 years, SD = 3.8) and included 582 women (49%). Based on self-reports of race or ethnicity, 61% of participants were White, non-Hispanic, 33% were Black, 4% were Hispanic, 1.5% were American Indian, and less than 1% were Pacific Islander or other. The median occupational level (Hollingshead, 1975) for the group was semiskilled workers, and only 13% held professional jobs. On average, participants had completed 11.5 years of education (SD = 2.14). Thus, the sample was skewed toward the lower end of the socioeconomic spectrum. Of the 1,196 participants, 520 were in the control group and 676 were in the abuse and/or neglect group (543 cases of neglect, 110 cases of physical abuse, and 96 cases of sexual abuse). These numbers add up to more than 676 because some individuals experienced more than one type of abuse or neglect.
Interview, 2003–2004
A total of 808 individuals completed the third interview, and 800 participants provided information about risky sexual behavior. This sample included 454 cases of abuse and neglect (367 neglect, 78 physical abuse, and 60 sexual abuse) and 346 matched controls. They were on average 41.2 years of age (range 32.0–49.0 years), and 52.9% were women. Based on self-reports of race/ethnic background, 59.0% were White, non-Hispanic, 34.4% were Black, non-Hispanic, 4.0% were Hispanic, and 2.6% were of other racial/ethnic backgrounds. Women from minority backgrounds composed 22% of the sample.
Procedures
Participants completed the interview and comprehensive medical examination in their homes or another place appropriate for the interview, as they preferred. The interviewers were blind to the purpose of the study and to the inclusion of an abused and/or neglected group. Participants were also blind to the purpose of the study and were told that they had been selected to participate as part of a large group of individuals who grew up in the late 1960s and early 1970s. Institutional review board approval was obtained for the procedures involved in this study, and participants who participated gave written, informed consent. For individuals with limited reading ability, the consent form was presented and explained verbally.
Measures
Child abuse and neglect
Childhood physical and sexual abuse and neglect were assessed through review of official records processed during the years 1967 to 1971. Physical abuse cases included injuries such as bruises, welts, burns, abrasions, lacerations, wounds, cuts, bone and skull fractures, and other evidence of physical injury. Sexual abuse cases had charges ranging from relatively nonspecific charges of “assault and battery with intent to gratify sexual desires” to more specific charges, such as “fondling or touching in an obscene manner,” sodomy, incest, or rape. Neglect cases reflected a judgment that the parents' deficiencies in child care were beyond those found acceptable by community and professional standards at the time and represented extreme failure to provide adequate food, clothing, shelter, and medical attention to children.
HIV-risk sexual behavior
As part of a medical history interview in 2003–2004, participants reported whether they had in the past year (a) been treated for a sexually transmitted disease; (b) given or received money or drugs in exchange for sex; (c) had anal sex without a condom; or (d) used intravenous drugs. Participants who endorsed any of the four items were then asked, if comfortable, to identify which situations applied to them. Participants also reported how many sexual partners they had in the past year and whether they used a condom at the last sexual intercourse. A composite score was created to reflect any risky sexual behavior in the past year, and separate items indicated (a) sexually transmitted disease (STD); (b) trading sex; (c) unprotected anal sex; and (d) multiple partners and inconsistent condom use. This study focused only on HIV-risk sexual behavior, because relationships between childhood maltreatment and drug use in this sample are not straightforward (Widom, Marmorstein, & White, 2006).
Risky relationships
Participants were asked a series of questions about intimate relationship functioning in young adulthood as part of the Antisocial Personality Disorder (APD) module of the DIS–III–R. These items asked about lifetime and current involvement in intimate relationships, including whether the participant had ever walked out on a partner for several weeks or longer, been sexually faithful for at least a year, or had sexual relations outside of marriage with at least three people (Colman & Widom, 2004). Additional interview questions asked about intimate relationship history, including marriage and cohabitation. Separate dichotomous variables (1 = yes; 0 = no) were created to reflect (a) walking out on a partner; (b) never sexually faithful; (c) sexual relations outside of marriage; (d) temporary separation from a partner; and (e) multiple marriages.
Affective symptoms
Lifetime symptoms of depression, dysthymia, and PTSD were assessed in young adulthood with the DIS–III–R and therefore correspond to DSM–III criteria for these disorders. Continuous variables reflecting the number of symptoms reported are used in analyses.
Drug and alcohol use
Drug and alcohol use were assessed through self-reports on the DIS–III–R substance use module completed in young adulthood. Three variables were created to reflect (a) number of problems associated with alcohol use; (b) number of problems associated with drug use; and (c) number of illicit drugs used more than five times.
Delinquent and criminal behavior
Delinquency and crime were measured with two variables self-reported in young adulthood and collection of arrest records through 1994: (a) number of officially documented arrests for crimes other than prostitution; (b) self-report on the DIS–III–R APD module of having been arrested (0 = never; 1 = yes); and (c) number of delinquent and criminal behaviors (e.g., property damage, theft, sexual and physical assault, carrying and/or using weapons) reported on a measure adapted from Wolfgang and Weiner (1989).
Control variables
Age in middle adulthood, gender, and race or ethnicity were examined as potential control variables, given differences in rates of HIV risk behavior reported in the literature (Centers for Disease Control and Prevention, 2005; Leigh, Temple, & Trocki, 1993). Gender was coded 1 for men and 0 for women. Race or ethnicity was coded as non-Hispanic Black (1), non-Hispanic White (2), or Hispanic (3). Other racial or ethnic groups were not included in this variable because their proportion of the sample was too small for meaningful comparison (2.3%).
Analyses
Differences between the abuse and/or neglect and control groups in overall HIV risk and each indicator of HIV risk were assessed with logistic regression. Odds ratios (OR) were generated by exponentiation of the regression coefficients. Each regression included all participants with complete data on the variables included in that regression model (i.e., pairwise deletion). Therefore, the sample size differed somewhat depending on the outcome (see Table 1), due to a small number of missing responses (e.g., participant refused to answer a particular question).
Variable Descriptive Statistics and Factor Loadings in Confirmatory Factor Analysis
Latent variable structural equation modeling (SEM) with Mplus Version 5.1 was used to examine mediator models. SEM proceeded in three stages. First, we conducted confirmatory factor analysis to assess the measurement model describing relationships between the observed indicators and latent constructs. Table 1 lists the observed variables that loaded onto each latent factor. Residual correlations among the observed indicators for each latent factor were included in the model. HIV risk behavior was indicated by the single binary composite score, due to the low prevalence of any individual risk behavior, and childhood abuse and neglect was also indicated by a single binary variable. All bivariate correlations between the latent variables, HIV risk behavior, and childhood abuse and neglect were included in the model. Thus, in addition to assessing the fit of the measurement model and significance of factor loadings, confirmatory factor analysis provided a test of the criteria for mediation that (a) the predictor is related to the outcome; (b) the predictor is related to the mediator; and (c) the mediator is related to the outcome (Kenny, Kashy, & Bolger, 1998). Second, we examined separate path models with each mediator, which evaluated the two additional criteria for mediation by each latent factor: (d) the relationship between the mediator and the outcome remains significant when controlling for the predictor and (e) the direct relationship between the predictor and the outcome is significantly reduced when the mediator is included. Third, we examined a multiple-mediator model that included all mediators supported by analysis of the separate path models considering traditional criteria (Kenny et al., 1998) and current recommendations of MacKinnon (2008), as well as the moderated mediation approach recommended by Kraemer, Kiernan, Essex, and Kupfer (2008). Although the Kenny et al. approach to mediation has been criticized as overly conservative, and newer approaches suggest that Criteria 1 and 5 are not essential (MacKinnon, 2008), we considered all criteria in evaluating the potential mediators.
For measurement and structural models, we evaluated multiple indices of overall model fit. A chi-square statistic (χ2) reflects the difference between the observed model relationships and estimated relationships based on the specified model. A low chi-square and nonsignificance (p < .05) are desirable, and a chi-square to degrees of freedom (df) ratio of less than 5 is considered adequate (Bollen, 1989). A comparative fit index (CFI) and Tucker–Lewis index (TLI) of .90 or higher indicate good fit. Root-mean-square error of approximation (RMSEA) of less than .05 is considered a close fit, and weighted root-mean-square residual (WRMR) of less than 1 indicates a good fit. Current recommendations support consideration of both the chi-square test and other indices of model fit (Barrett, 2007); chi-square and WRMR can be overly sensitive to discrepancies between observed and expected relationships with a large sample.
Individual factor loadings (measurement models) and path estimates (structural models) were standardized linear regression coefficients for continuous factor indicators or dependent variables, including latent constructs, and standardized probit regression coefficients for binary (0–1) factor indicators or dependent variables, including HIV risk behavior. Probit coefficients represent change in the cumulative normal probability of the dependent variable associated with a one-unit increase in the predictor. Thus, the magnitudes of coefficients corresponding to continuous and binary outcomes (or factor indicators) are not directly comparable. Statistical significance was assessed with z scores, and R2 provided a measure of effect size, indicating the amount of variance explained in the HIV risk behavior by each model. Strength of mediational relationships was evaluated with tests of indirect effects (MacKinnon, Lockwood, Hoffman, West, & Sheets, 2002) and bias-corrected bootstrapped confidence intervals (MacKinnon, Lockwood, & Williams, 2004).
Full information maximum likelihood estimation was used to handle missing data. This method uses all data available for each case and thus avoids biases and loss of power associated with traditional approaches to missing data (Allison, 2003; Schlomer, Bauman, & Card, 2010). Full information maximum likelihood calculates weighted least square parameter estimates using a diagonal weight matrix with standard errors and mean- and variance adjusted chi-square test statistics that use a full weight matrix, and this estimator is robust to deviations from normality. Analysis of missing data using Mplus revealed 33 specific patterns. The most common pattern reflected participants who had complete data on the young adulthood variables but who were lost to attrition at middle adulthood or did not respond to questions about sexual risk (17%); an additional 217 (18%) were missing sexual risk data as well as at least one young adulthood variable. The next most common pattern (11%) reflected individuals who were missing data on the variables of “temporary separation from a partner” and “never sexually faithful” because they had never had a marital, live-in, or exclusive partner (these individuals were coded 0 for “walking out on a partner,” “multiple marriages,” and “sexual relations outside of marriage”). Other patterns were associated with less than 10% of the sample. SEM analyses including only cases with available data on sexual risk in middle adulthood (N = 778) yielded findings consistent with those reported below.
Results Prevalence of Risky Sexual Behavior in the Sample
Overall, 12.7% of the sample reported at least one type of risky sexual behavior in middle adulthood. In particular, 2.1% reported treatment for an STD, 1.6% reported trading sex, 5.5% reported unprotected anal sex, and 7.2% reported multiple partners with no condom use at last sexual intercourse. Rates of risky sexual behavior were not associated with age, OR = 0.98, 95% CI [0.92, 1.04], p > .10, and did not differ significantly for women and men, OR = 1.09, 95% CI [0.71, 1.66], p > .10, or for different racial or ethnic groups, F(2, 754) = 0.65, p > .10. Therefore, these variables were not controlled in subsequent analyses.
Relationships Between Childhood Abuse and Neglect and Risky Sexual Behavior
As shown in Table 2, child abuse and neglect overall was significantly associated with increased likelihood of any risky sexual behavior, OR = 2.84, 95% CI [1.74, 4.64], p ≤ .001, in the past year and specifically for treatment for an STD, OR = 3.65, 95% CI [1.04, 12.79], p ≤ .05; unprotected anal sex, OR = 2.73, 95% CI [1.33, 5.60], p ≤ .01; and multiple partners with inconsistent condom use, OR = 2.68, 95% CI [1.42, 5.07], p ≤ .01. The association with any risky sexual behavior was evident for all three types of maltreatment but was strongest for childhood neglect, OR = 2.88, 95% CI [1.74, 4.77], p ≤ .001, and weakest for sexual abuse, OR = 2.46, 95% CI [1.08, 5.62], p ≤ .05. In addition, relationships with specific types of sexual risk taking varied for different types of childhood abuse or neglect. Sexual abuse was associated with unprotected anal sex, OR = 3.11, 95% CI [1.02, 9.45], p ≤ .05. Physical abuse was associated with treatment for an STD, OR = 7.83, 95% CI [1.83, 33.50], p ≤ .01, and multiple partners with inconsistent condom use, OR = 3.35, 95% CI [1.38, 8.15], p ≤ .01. Neglect was associated with unprotected anal sex, OR = 2.88, 95% CI [1.38, 6.01], p ≤ .01, and multiple partners with inconsistent condom use, OR = 2.70, 95% CI [1.40, 5.20], p ≤ .01. It should also be noted that the ORs reflecting the magnitude of relationships between childhood abuse and neglect and trading sex were substantial (3.83–6.04), although they reached only marginal statistical significance, possibly because of reduced power to detect a significant effect due to the small number of individuals who reported this behavior.
Past Year HIV Risk Behavior Among Abused and Neglected Children and Matched Controls Followed Up at Approximate Age 41
Structural Equation Modeling
The measurement model provided an acceptable fit, χ2(49) = 191.12, p < .05, CFI = 0.90, TLI = 0.93, RMSEA = .05, WRMR = 1.14. Factor loadings on the latent constructs were all statistically significant and ranged from .32 to .84 (see Table 1). Bivariate correlations indicated significant direct relationships between childhood abuse and neglect, HIV risk behavior, and each of the four proposed mediators (see Table 3), thereby supporting the initial criteria for mediation. However, the correlation between child abuse and neglect and drug and alcohol use was very small.
Bivariate Correlations Between Variables in Confirmatory Factor Analysis
The results from separate path models with each potential mediator are depicted in Figure 1. Only the risky relationships construct was clearly supported as a mediator. This model explained 13% of the variance in HIV risk behavior, and indirect effects were significant, β = .07, 95% CI [.01, .13]. The other mediators were not strongly supported, based on consideration of statistical significance and magnitude of indirect effects. The model with delinquent and criminal behavior explained 3% of the variance in risky sexual behavior, and indirect effects were small, β = .02, 95% CI [.001, .03]. In this model, the coefficient for the path from delinquent and criminal behavior was small, despite being statistically significant. In the path model with affective symptoms, childhood abuse and neglect predicted increased affective symptoms, but affective symptoms did not significantly predict HIV risk behavior. The indirect effects in this model were small, β = .03, 95% CI [−.003, .06]. In the model with drug and alcohol use, on the other hand, childhood abuse and neglect did not significantly predict drug and alcohol use, although drug and alcohol use did increase the likelihood of HIV risk behavior. In all four models, the direct path between childhood abuse and neglect and HIV risk behavior remained significant, suggesting that risky relationships only partially mediate this relationship.
Figure 1. Separate mediator models predicting HIV risk behavior from childhood abuse and neglect. Numbers in parentheses are bias-corrected bootstrapped 95% confidence intervals around the indirect effect. χ2 = chi-square; df = degrees of freedom; CFI = comparative fit index; TLI = Tucker–Lewis index; RMSEA = root-mean-square error of approximation; WRMR = weighted root-mean-square residual. * p < .05. ** p < .01. *** p < .001.
As recommended by Kraemer et al. (2008), moderated mediation models were examined for affective symptoms and for delinquent and criminal behavior, which were associated with abuse and neglect but not HIV risk behavior in the multivariate models. Inclusion of the interaction between affective symptoms and child abuse and/or neglect in a model with random slopes using a robust maximum likelihood estimator (Klein & Moosbrugger, 2000) did not yield a significant moderation effect for affective symptoms (z = 0.78). Similarly, the interaction of child abuse and neglect with delinquent and criminal behavior was not significant (z = 1.49).
Although risky relationships alone emerged as a significant mediator, we subjected this mediator to a more strenuous test by examining two separate multiple-mediator models, one including delinquent and criminal relationships and the second including affective symptoms. Results from the model with both risky relationships and delinquent and criminal behavior as mediators are depicted in Figure 2. The model provided a good fit, χ2(17) = 53.05, p < .05, CFI = 0.96, TLI = 0.93, RMSEA = .04, WRMR = 0.99, and explained 14% of the variance in HIV risk behavior. Risky relationships remained a significant mediator, when delinquent and criminal behavior was included. The indirect effect through risky relationships was only marginally significant, although the magnitude remained the same as in the single-mediator model, β = .07, 95% CI [−.002, .14]. Delinquent and criminal behavior did not contribute substantially to variance in HIV risk behavior, although risky relationships and delinquent and criminal behavior were moderately correlated with each other.
Figure 2. Path model predicting HIV risk behavior from child abuse and neglect through risky relationships and delinquent and criminal behavior. Numbers in parentheses are bias-corrected bootstrapped 95% confidence intervals around the indirect effect. χ2 = chi-square; df = degrees of freedom; CFI = comparative fit index; TLI = Tucker–Lewis index; RMSEA = root-mean-square error of approximation; WRMR = weighted root-mean-square residual. * p < .05. ** p < .01. *** p < .001.
Findings for the model including both risky relationships and affective symptoms as mediators (see Figure 3) were similar. The model provided a strong fit, χ2(14) = 12.20, p > .05, CFI = 1.00, TLI = 1.00, RMSEA = .00, WRMR = 0.46, and explained 12% of the variance in HIV risk behavior. In this model, the path from risky relationships to HIV risk behavior was only marginally significant, although the magnitude of the relationship was consistent, and the indirect effect decreased. However, the relationship between affective symptoms and HIV risk behavior, as well as associated indirect effects, was close to zero. In both multiple-mediator models, the direct relationship between child abuse and neglect and HIV risk behavior remained significant.
Figure 3. Path model predicting HIV risk behavior from child abuse and neglect through risky relationships and affective symptoms. Numbers in parentheses are bias-corrected bootstrapped 95% confidence intervals around the indirect effect. χ2 = chi-square; df = degrees of freedom; CFI = comparative fit index; TLI = Tucker–Lewis index; RMSEA = root-mean-square error of approximation; WRMR = weighted root-mean-square residual. ** p < .01. *** p < .001.
DiscussionThe first notable finding from this study is that individuals with documented cases of childhood abuse and neglect reported increased HIV risk behavior in middle adulthood, 30 years after these childhood experiences. Documentation of increased sexual risk taking in these abused and neglected individuals followed up in middle adulthood extends findings with the same sample in young adulthood and adds validity to the larger body of research indicating correlations between sexual risk behavior and adult retrospective reports of childhood maltreatment. Victims of childhood abuse and neglect appear to be at risk for a long-term pattern of health-compromising sexual behaviors that extends into middle adulthood, when risky sexual behavior decreases for most individuals. In our sample overall, 13% reported sexual risk taking in middle adulthood, but 17% of individuals with histories of childhood abuse or neglect reported sexual risk, and this rate was a nearly threefold increase over that of the controls. Our findings add to increasing recognition that the long-term consequences of childhood abuse and neglect extend to physical health risk, and they underscore the importance of clinical interventions to reduce sexual risk taking among victims of childhood abuse and neglect.
In addition to documenting increased sexual risk behavior, findings from this study shed light on mechanisms that may explain the link from childhood abuse and neglect to sexual risk. Of the set of potential mediators assessed (risky relationships, affective symptoms, drug and alcohol use, and delinquent and criminal behavior), risky relationships emerged as the most powerful factor linking child abuse and neglect to risky sexual behavior. Thus, risky sexual behavior appears to take place in the context of generally chaotic, unstable relationships characterized by disruptions, sexual infidelity, and lack of monogamy. These problematic relationship patterns may develop as a result of disrupted early attachment (Main, 1996) as well as the neurobiological effects of abuse and neglect (De Bellis, 2001; Glaser, 2000). It is important to note that the data available in this study reflect participant reports about their own behavior rather than their partners' behavior or level of risk. Other research suggests that involvement with risky partners may largely explain increased risk for STDs among women with histories of sexual abuse (Testa, VanZile-Tamsen, & Livingston, 2005). In addition, the indicators of relationship risk included in this study may not necessarily reflect abnormal or maladaptive relationships (e.g., multiple marriages). Taken together, however, the construct reflects a pattern of romantic relationships lacking stability or commitment. Moreover, risky relationships were associated with delinquent and criminal behavior, suggesting that these relationship characteristics are associated with a general pattern of risky, deviant behavior.
The relationship between delinquent and criminal behavior in young adulthood and HIV risk behavior in middle adulthood was tenuous, and this pathway was no longer significant when risky relationships were included in the model. As noted above, however, risky relationships were associated with delinquent and criminal behavior. Thus, it may be that these relationship characteristics develop as part of a larger pattern of antisocial behavior among victims of child abuse and neglect. Other analyses with this sample, which did not include relationship risk, have emphasized the role of antisocial behavior as a mediator in the link to risky sexual behavior (Wilson & Widom, 2008b, 2010a). Indeed, several of the items used to assess relationship risk were drawn from a measure of antisocial personality disorder. Nonetheless, it appears that deviance in the context of romantic relationships may provide the direct link to risky sexual behavior and may even mediate the pathway from general antisocial behavior to sexual risk taking. Because both general delinquency and relationship risk were assessed at the same point in time in this study, we could not directly test the more complex mediational pathway, but evidence of a connection between the two constructs provides some support for this hypothesis.
In this study, neither affective symptoms nor drug and alcohol use was supported as a mediator of the relationship between childhood maltreatment and risky sexual behavior. Lack of a strong connection between child abuse and neglect and substance use in young adulthood is consistent with other findings from this sample (Widom, Weiler, & Cottler, 1999). In this sample, relationships between child maltreatment and substance use do not emerge until middle adulthood and exist primarily for women (Widom et al., 2006). Nonetheless, drug and alcohol use did appear to increase HIV risk sexual behavior for the sample overall. Moreover, it is possible that greater drug use in middle adulthood among women who experienced abuse and neglect (Wilson & Widom, 2009a) contributes to sexual risk taking at this time point.
An opposite pattern was revealed for affective symptoms, which were associated with childhood abuse and neglect but were not strongly linked to sexual risk. Although symptoms of depression and PTSD have been linked to sexual risk in other studies (Mazzaferro et al., 2006; Swanholm, Vosvick, & Chng, 2009), these problems in young adulthood did not predict later sexual risk behavior in our sample. As with delinquent and criminal behavior, affective symptoms were associated with risky relationships and may contribute to risk for involvement in unstable, chaotic relationships. However, affective symptoms in young adulthood may not necessarily persist into middle adulthood or directly influence behavior in middle adulthood. Thus, correlations found between affective symptoms and sexual risk behavior at a single point in time may not generalize to the longitudinal relationship assessed in this study. Furthermore, findings from another recent study suggest that although there are correlations between these phenomena, depression may not be involved in the relationship between child abuse and sexual risk behavior (Morokoff et al., 2009).
This study had several advantages. First, the prospective longitudinal design allowed for determination of the correct temporal sequence in the variables of interest. Second, unlike most studies, which end at adolescence or young adulthood, this study traced development into middle adulthood. Third, the sample is large, includes men and women, and is ethnically diverse. Fourth, documented cases of childhood maltreatment minimize potential problems with reliance on retrospective self-reports and provide a nonambiguous definition of childhood abuse and neglect. Fifth, we examined mediating mechanisms to uncover specific processes that may explain this relationship.
Despite the strengths of this study, a number of important limitations must be noted. First, although the use of documented cases of child abuse and neglect is an advantage, this means that only cases that came to the attention of authorities and met the threshold for a legal definition of abuse and neglect were included. Less severe or unreported cases were not reflected. Second, our measure of risky relationships reflects participants' own behavior, rather than characteristics of participants' partners. Third, we examined only a subset of the possible mediators that may explain this relationship. Fourth, the base rate of HIV risk behavior in middle adulthood was fairly low, and this may have reduced power. Finally, cases of abuse and neglect occurred in the late 1960s and early 1970s in a midwestern metropolitan area in the United States, and therefore our results may not generalize to all cases of child maltreatment.
Results of this study draw attention to the potential long-term consequences of child abuse and neglect for physical health, particularly sexual risk. Findings also point to romantic relationships as an important focus of intervention and prevention efforts for reducing HIV risk behavior among victims of childhood abuse and neglect. Helping victims of abuse and neglect to form healthy romantic relationships early in life may reduce risky sexual behavior that persists into middle adulthood.
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Submitted: June 22, 2010 Revised: November 17, 2010 Accepted: December 20, 2010
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Source: Journal of Consulting and Clinical Psychology. Vol. 79. (2), Apr, 2011 pp. 236-246)
Accession Number: 2011-04114-001
Digital Object Identifier: 10.1037/a0022915
Record: 111- Title:
- Patterns of pregnancy and postpartum depressive symptoms: Latent class trajectories and predictors.
- Authors:
- Fredriksen, Eivor, ORCID 0000-0002-3442-4480. Department of Psychology, University of Oslo, Oslo, Norway, eivor.fredriksen@psykologi.uio.no
von Soest, Tilmann. Department of Psychology, University of Oslo, Oslo, Norway
Smith, Lars. Department of Psychology, University of Oslo, Oslo, Norway
Moe, Vibeke. Department of Psychology, University of Oslo, Oslo, Norway - Address:
- Fredriksen, Eivor, Department of Psychology, University of Oslo, P.O. Box 1094 Blindern, 0317, Oslo, Norway, eivor.fredriksen@psykologi.uio.no
- Source:
- Journal of Abnormal Psychology, Vol 126(2), Feb, 2017. pp. 173-183.
- NLM Title Abbreviation:
- J Abnorm Psychol
- Page Count:
- 11
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- The Journal of Abnormal and Social Psychology; The Journal of Abnormal Psychology; The Journal of Abnormal Psychology and Social Psychology
- ISSN:
- 0021-843X (Print)
1939-1846 (Electronic) - Language:
- English
- Keywords:
- postpartum depression, perinatal depression, maternal dysphoria, growth mixture modeling
- Abstract (English):
- Depressive symptoms among pregnant and postpartum women are common. However, recent studies indicate that depressive symptoms in the perinatal period do not follow a uniform course, and investigations of the heterogeneity of time courses and associated factors are needed. The aim of this study was to explore whether depressive symptoms in the perinatal period could be categorized into several distinct trajectories of symptom development among subgroups of perinatal women, and to identify predictors of these trajectory groups. The study used data from 1,036 Norwegian women participating in a community-based prospective study from midpregnancy until 12-months postpartum. Depressive symptoms were assessed with the Edinburgh Postnatal Depression Scale at 7 time points (4 during pregnancy). Partner-related attachment, stress, childhood adversities, pregnancy-related anxiety, previous psychopathology, and socioeconomic conditions were assessed at enrollment. By means of growth mixture modeling based on piecewise growth curves, 4 classes of depressive symptom trajectories were identified, including (a) pregnancy only (4.4%); (b) postpartum only (2.2%); (c) moderate-persistent (10.5%); and (d) minimum symptoms (82.9%) classes. Multinomial logistic regression analyses showed that membership in the pregnancy only and postpartum only classes primarily was associated with pregnancy-related anxiety and previous psychopathology, respectively, whereas the moderate-persistent class was associated with diverse psychosocial adversity factors. Findings suggest heterogeneity in temporal patterns of elevated depressive mood, relating specific trajectories of time courses with distinct adversity factors. Researchers and clinicians should be aware of possible multiple courses of elevated perinatal depressive mood, and inquire about possible diverse adversity factors, aberrant pathways, and prognoses. (PsycINFO Database Record (c) 2017 APA, all rights reserved)
- Impact Statement:
- General Scientific Summary—This study suggests that depressive symptoms during pregnancy and the postpartum period do not follow a uniform course, but rather supports a model of several distinct time courses of depressed mood associated with diverse psychosocial adversity factors. (PsycINFO Database Record (c) 2017 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Major Depression; *Perinatal Period; *Postpartum Depression; *Pregnancy; Mothers
- PsycINFO Classification:
- Affective Disorders (3211)
- Population:
- Human
Female - Location:
- Norway; US
- Age Group:
- Adolescence (13-17 yrs)
Adulthood (18 yrs & older)
Young Adulthood (18-29 yrs)
Thirties (30-39 yrs)
Middle Age (40-64 yrs) - Tests & Measures:
- Experiences in Close Relationships Scale
Parenting Stress Index--Norwegian Version
Adverse Childhood Experiences Scale
Pregnancy-Related Anxiety Questionnaire--Revised DOI: 10.1037/t57856-000
Edinburgh Postnatal Depression Scale DOI: 10.1037/t01756-000
Parenting Stress Index DOI: 10.1037/t02445-000 - Grant Sponsorship:
- Sponsor: Research Council of Norway, Norway
Grant Number: 196156
Recipients: No recipient indicated - Methodology:
- Empirical Study; Longitudinal Study; Quantitative Study
- Supplemental Data:
- Tables and Figures Internet
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- First Posted: Dec 1, 2016; Accepted: Nov 4, 2016; Revised: Nov 4, 2016; First Submitted: May 6, 2016
- Release Date:
- 20161201
- Correction Date:
- 20170323
- Copyright:
- American Psychological Association. 2016
- Digital Object Identifier:
- http://dx.doi.org/10.1037/abn0000246; http://dx.doi.org/10.1037/abn0000246.supp(Supplemental)
- PMID:
- 27935730
- Accession Number:
- 2016-58118-001
- Persistent link to this record (Permalink):
- http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2016-58118-001&site=ehost-live
- Cut and Paste:
- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2016-58118-001&site=ehost-live">Patterns of pregnancy and postpartum depressive symptoms: Latent class trajectories and predictors.</A>
- Database:
- PsycINFO
Patterns of Pregnancy and Postpartum Depressive Symptoms: Latent Class Trajectories and Predictors
By: Eivor Fredriksen
Department of Psychology, University of Oslo and National Network for Infant Mental Health in Norway, Centre for Child and Adolescent Mental Health, Eastern and Southern Norway, Oslo, Norway;
Tilmann von Soest
Department of Psychology, University of Oslo
Lars Smith
Department of Psychology, University of Oslo
Vibeke Moe
Department of Psychology, University of Oslo and National Network for Infant Mental Health in Norway, Centre for Child and Adolescent Mental Health, Eastern and Southern Norway
Acknowledgement: This research was supported by grant 196156 from the Research Council of Norway. There has been no prior dissemination of the data or ideas appearing in this article.
Postpartum depression (PPD) is one of the most common concomitants of childbirth, and with the accompanying risk of adverse consequences on maternal mental health, child development, and family functioning (Goodman et al., 2011; Meltzer-Brody & Stuebe, 2014), it has been of interest to both clinicians and researchers for decades. More recently, findings indicate that timing and duration of depressive symptoms in the perinatal period do not follow a uniform course, suggesting considerable heterogeneity in symptom trajectories as well as associated antecedents (Cents et al., 2013; Mora et al., 2009; PACT Consortium, 2015; Sutter-Dallay, Cosnefroy, Glatigny-Dallay, Verdoux, & Rascle, 2012; Wisner et al., 2013). Investigating differential patterns of depressive symptomatology may enable efforts to develop more personalized approaches to treatment and prevention (Cuijpers et al., 2012). Exploring differential time courses and associated predictors may further provide a basis for investigating possible diverse etiologies, outcomes, and long-term prognoses.
This study employed a large, multisite community-based sample (N = 1,036) with seven data collection waves to examine trajectories of depressive symptoms from early pregnancy to 1-year postpartum with a dimensional approach. We examined whether subgroups of women following distinct trajectories of depressive symptoms can be identified, and whether a range of psychosocial adversity factors supposed to be risk factors for PPD can predict class membership.
Time Course of Depressive Symptoms Across Pregnancy and the Postpartum PeriodThere is an ongoing debate about the temporal definition of symptom onset in PPD (PACT Consortium, 2015; Wisner, Moses-Kolko, & Sit, 2010). Some emphasize the elevated incident rates during the first few weeks after birth, suggesting a narrowly defined period for symptom onset. This early elevated risk has been connected with physiological and psychological changes in the first postpartum weeks and suggested to constitute a specific phenotype (Forty et al., 2006; Munk-Olsen, Laursen, Pedersen, Mors, & Mortensen, 2006). Others have expanded the time frame of PPD up to 1-year postpartum (O’Hara & McCabe, 2013). With a broadened time frame, PPD has been understood as a continuation of earlier mental health problems (Patton et al., 2015). Further, there is a growing number of reports highlighting the importance of investigating onset of depression during pregnancy, as well as depression limited to the pregnancy period (Pearson et al., 2013). For example, a study addressing the heterogeneity of PPD found that among women with the most severe subtype of PPD, the majority had a pregnancy onset (67%). In less severe subtypes of PPD, pregnancy onset was rarer (11% and 34%; PACT Consortium, 2015). Moreover, in a study screening 10,000 women, Wisner et al. (2013) found that among screen-positive cases only 40% of women’s depressive episodes began postpartum, while 33% had a pregnancy onset. There is also considerable variation regarding the duration of PPD; for most women diagnosed with PPD it seems to be a time-limited condition, whereas for a substantial subgroup (38%) depression develops into a persistent disorder (Vliegen, Casalin, & Luyten, 2014).
Only two studies have investigated heterogeneous time courses of depressive symptoms in a time frame limited to pregnancy and the postpartum period (Mora et al., 2009; Sutter-Dallay et al., 2012). Mora et al. (2009) found three groups with transient courses with high level symptoms predominantly (a) during pregnancy, (b) early postpartum, and (c) late postpartum, respectively. Additionally, they identified stable classes with (d) low symptom levels and (e) a chronic high trajectory. With a somewhat smaller sample Sutter-Dallay, Cosnefroy, Glatigny-Dallay, Verdoux, and Rascle (2012) described similar classes; however, they did not find specific postpartum classes reaching clinical levels. Several studies have investigated the heterogeneity of maternal depressive symptom trajectories from the perinatal period into childhood years (Campbell, Matestic, von Stauffenberg, Mohan, & Kirchner, 2007; Cents et al., 2013; Luoma, Korhonen, Salmelin, Helminen, & Tamminen, 2015; Matijasevich et al., 2015; van der Waerden et al., 2015). All studies report several classes, ranging from four to six, with distinct trajectories, suggesting that a singular model of symptom onset and course is unwarranted. A robust finding across studies is that most women follow trajectories of minimal or mild symptoms. Further, all studies found a small class with a chronic high symptom burden. Stable trajectories (at different levels of severity) were common, whereas various transient trajectories tended to comprise relatively smaller class proportions. The findings are in accordance with research on heterogeneous courses in the transition to parenthood in concepts such as life satisfaction, where most participants report stable levels, although small subgroups show increasing or decreasing trajectories (Galatzer-Levy, Mazursky, Mancini, & Bonanno, 2011).
However, of studies spanning the perinatal period into childhood years, only one found a pregnancy-only class (van der Waerden et al., 2015), and none reported specific postpartum classes. None of these studies had more than one measurement point during pregnancy, and several had none. In most of the studies, the majority of measurement points was after the postpartum period had passed. By including measurement points outside the postpartum period, there is a danger of missing mood changes specific for this period, because trends more typical of maternal mood at later stages may disguise fine grained developmental trends that may be found particularly in this period. To be able to capture the specific mood changes of the pregnancy and postpartum period, it is useful to apply a limited time frame with enough measurement points (Ram & Grimm, 2007). The present work extends extant studies by including several measurement points during pregnancy, by limiting the time period to pregnancy and 1-year postpartum, and building a statistical model suited to detect shorter-term changes in symptom levels in close proximity to childbirth.
Risk Factors of Depressive Symptoms in Pregnancy and the Postpartum PeriodReviews of risk factors for perinatal depression include previous psychopathology, domestic violence, history of abuse, life stress, lack of social or partner support, migration status, and anxiety during pregnancy as robust risk factors across studies. Pregnancy complications, neuroticism, family history of psychiatric illness, low socioeconomic status, substance misuse, and chronic illness are listed as risk factors with slightly less systematic evidence (Biaggi, Conroy, Pawlby, & Pariante, 2016; Howard et al., 2014; O’Hara & McCabe, 2013). The extent to which the same risk factors predict various trajectories of depressive symptoms in the perinatal period has received less attention. In studies investigating differential courses of depressive symptoms in this period sociodemographic variables, anxiety, stress, previous psychopathology, lack of social support, poor relationship quality, and minority status predicted class membership in subgroups with increased symptom burden relative to subgroups with minimal symptoms (Cents et al., 2013; Luoma et al., 2015; Mora et al., 2009; Sutter-Dallay et al., 2012; van der Waerden et al., 2015). Moreover, a review investigating differences between chronic and transient courses of PPD found that poor partner relationship, life stress, contextual risk, personality factors, and to some extent childhood abuse and low maternal care were associated with chronic time courses of PPD, relative to remitting time courses (Vliegen et al., 2014).
This study builds on and extends these findings in several ways. Measures of previous psychopathology, partner-related attachment patterns, life stress, pregnancy-related anxiety, childhood trauma, and sociodemographic variables are included as predictor variables. These constitute important risk factors across several contexts; however, less is known of how these specific factors are related to various depressive symptom trajectories in the perinatal period. Specifically, this study extends earlier research on partner relations by including a measure of partner-related attachment patterns. Partner-related attachment has received little attention in research on perinatal depression; however, insecure attachment styles have been related to a diagnosis of PDD (Ikeda, Hayashi, & Kamibeppu, 2014). Further, ambivalent attachment styles predicted increases in depressive symptoms from pregnancy to the postnatal period (Simpson, Rholes, Campbell, Tran, & Wilson, 2003). Moreover, instead of applying a general measure of anxiety; this study assessed pregnancy-related anxiety, because including features of the perinatal period is central to the idea of the study. Pregnancy-related anxiety is considered to constitute a distinct clinical entity with the capacity of predicting birth outcome independently of more generalized symptom measures, as well as explaining unique variance in postnatal mood disturbance (Blackmore, Gustafsson, Gilchrist, Wyman, & O’Connor, 2016; Huizink, Mulder, Robles de Medina, Visser, & Buitelaar, 2004). Finally, by including childhood trauma as a predictor, this study relates to research showing an increased risk of PPD among women with a history of abuse (Howard et al., 2014), and extends this literature by including a broad range of childhood adversities.
Study Aims and HypothesesIn this study, we investigated maternal depressive symptoms with a dimensional approach within a large multisite community-based sample of women at seven time points from pregnancy through 12-months postpartum. The first aim was to explore whether maternal depressive symptoms throughout this period could be categorized into several distinct, empirically defined trajectories. Based on extant literature we expected (a) one trajectory characterizing women with elevated symptoms limited to the pregnancy period (Mora et al., 2009; Pearson et al., 2013); (b) one trajectory of early postpartum onset and a gradual recovery, based on findings of increased incidence early postpartum (Munk-Olsen et al., 2006) and studies of heterogeneous trajectories (Mora et al., 2009); (c) a stable trajectory at a moderate level with pregnancy onset in which symptoms continue into the postpartum period (PACT Consortium, 2015; Wisner et al., 2013); (d) a small group of women with a very high symptom level throughout the period of study, as this has been a consistent finding in studies of heterogeneous courses (Cents et al., 2013; Mora et al., 2009; van der Waerden et al., 2015); and (e) a majority of women presenting minimum symptoms (Cents et al., 2013; Mora et al., 2009; van der Waerden et al., 2015).
Second, we aimed to investigate whether potential psychosocial adversity factors, such as sociodemographic factors, previous psychopathology, stress, partner-related attachment patterns, pregnancy-related anxiety, and childhood trauma were differentially associated with the hypothesized trajectories. More specifically, we expected that higher levels of adversity predicted membership in trajectory classes with elevated symptom burden, relative to trajectories with low symptoms. Further, we expected stable courses with elevated symptoms to be predicted by more adversity factors than transient courses, as it has been shown that persistent courses of PPD are characterized by higher levels of adversities than time-limited courses (Vliegen et al., 2014).
Method Procedure and Participants
This study is based on data from 1,036 women participating in the prospective multisite Little in Norway study (Moe & Smith, 2010). From September 2011 until October 2012, all pregnant women receiving routine prenatal care at nine public well-baby clinics in Norway were invited to participate in the study. Initially 1,041 women consented to participate; five women later withdrew their consent, leaving 1,036 (99.5%) women as participants. There were no exclusion criteria. At five clinics, the staff did not establish reliable routines to monitor rates of participation. At the remaining four clinics 50.7% of all women attending the clinic consented to participate. Participation rates were probably similar at the other five sites because recruitment strategies and resources allocated to the data collection were similar at all well-baby clinics. Comparisons of educational level of this sample with official national statistics of Norwegian women of similar age and residential area showed that participants in the study had a significantly higher educational level (Statistics Norway, 2014). This study uses data from seven time points: at average gestational Week 21 (range: weeks 8–34, T1); Week 28 (T2); Week 32 (T3); and Week 36 (T4); 6-weeks postpartum (T5); 6-months postpartum (T6); and 12-months postpartum (T7). Participants were recruited at their first prenatal care examination at the well-baby clinics. There is considerable variation in local and individual practices as to when pregnant women first receive prenatal care at a well-baby clinic (many choose to receive initial checkups at their general practitioner). As a result, the time frame for enrollment was rather large (i.e., between gestational week 8 and 34), and a comprehensive number of participants missed the early data collection points. Thus, the recruited numbers of participants at T1 and T2 were n = 659 and 579, respectively. Response rates at T2 were considerably lower than at other time points due to shortage of staff members to collect data. Response rates at T7 were also lower, reflecting the fact that paid parental leave ends one year after birth in Norway, and parents are returning to work. Information about recruitment and response rates is depicted in Figure 1.
Figure 1. Recruitment and response rates (N = 1,036). There is considerable variation in local and individual practices as to when pregnant women first receive prenatal care at a well-baby clinic and, consequently, were recruited to participate. As a result, the time frame for enrollment is wide (varying from gestational week 8 to 34), and a considerably number of participants missed the early data collection points. This resulted in reduced participant numbers at T1 (n = 659) and T2 (n = 579).
Data were collected digitally by means of web-based questionnaires at all time points. Primarily, responses were submitted at designated computers at the well-baby clinics. However, at T3 and T4, respondents were asked to complete the questionnaire at their private computers at home. The nine well-baby clinics were located at geographically diverse sites across Norway.
Attrition analyses were conducted by means of univariate logistic regression analyses and showed that lower education (OR = 0.93, 95% CI [0.87, 0.99], p = .02); parity (OR = 0.75, 95% CI [0.57, 0.99], p = .04); and childhood trauma (OR = 1.20, 95% CI [1.06, 1.37], p < .01) predicted dropout at T7. Age, previous psychopathology, partner-related attachment, life stress, and pregnancy-related anxiety did not show any significant associations with missing status (p > .05). Further, high levels of depressive symptoms T1 to T5 significantly predicted dropout (ORs = 1.06–1.10, p < .05), whereas depressive symptoms at T6 were not predictive (OR = 1.05, 95% CI [1.00, 1.10], p = .06).
At enrollment the mean age of the participants was 30.3 years (range: 17–43, SD = 4.8), 54.9% of the women were nulliparous. Most women were married (36.2%) or cohabiting (59.7%), with only a small fraction being single/divorced/separated (2.7%), or not specifying their marital status (1.4%). A large proportion of participants was educated at university level (77.1%), while the highest completed education of the remaining participants was high school level (19.8%) or lower (3.1%). At enrollment, 77.3% of the participants were full-time employed, 5.8% full-time students, 13.6% part-time students/part-time employed, while 3.0% reported being unemployed/on benefits/homemakers. Median annual personal income ranged from the equivalent of $36,000–$55,000 (44.4%), while 31.1% had lower and 24.3% higher income. The ethnic majority was Norwegian (93.9%), with a few reporting a diversity of other ethnic backgrounds (6.1%).
Measures
With the exception of measures of depressive symptoms, which were assessed at all seven data collection points, all measures described below were collected at enrollment.
Depressive symptoms
Maternal depressive symptoms were assessed using the Edinburgh Postnatal Depression Scale (EPDS), originally developed to screen for depressive symptoms in women in the postpartum period (Cox, Holden, & Sagovsky, 1987), and later validated for antepartum use (Murray & Cox, 1990). The EPDS is a 10-item self-report questionnaire asking respondents to consider various depressive symptoms during the last 7 days on a 4-point scale (range: 0–30). Although developed with cut-off scores indicating probable depression, the EPDS composite score has also been used as a continuous variable for research purposes (Matijasevich et al., 2015), with the benefit of yielding a more detailed range of depressive symptomatology at both clinical and subclinical levels. The EPDS composite score was used as a continuous variable in this study. Cronbach’s alphas were high at each assessment (ranging from .80 to .85), indicating good internal consistency.
Sociodemographic factors
Education was stipulated in years of education. Parity was assessed by asking participants to state number of previous children, and was coded as a dichotomous variable (nulliparous/multiparous).
Previous psychopathology
Participants were asked the following question: “Have you ever experienced mental health problems earlier in life? (yes/no).” Similar single question measures have been shown to serve as acceptable screeners for mental health problems (Veldhuizen, Rush, & Urbanoski, 2014), and have previously been used extensively in research (van der Waerden et al., 2015).
Partner relationship
Characteristics of partner relationship were assessed by the Experiences in Close Relationships Scale (ECR), which is a 36-item self-report measure of adult romantic attachment styles rated on a 7-point scale. ECR yields two subscales of underlying attachment: anxiety (fear of interpersonal rejection or abandonment, an excessive need for approval from others, and distress when one’s partner is unavailable or unresponsive), and avoidance (fear of dependence and interpersonal intimacy, an excessive need for self-reliance, and reluctance to self-disclose; Brennan, Clark, & Shaver, 1998). Higher scores reflect greater levels of insecure attachment within each relationships domain (range 18–126 on each subscale). In this study Cronbach’s alphas were .88 and .89 for anxiety and avoidance subscales, respectively, in accordance with the high level of internal consistency reported in other studies (Brennan et al., 1998).
Stressful life events
Stress was measured by the life stress subscale, which is part of The Parenting Stress Index (PSI; Abidin, 1995). The Norwegian version of the subscale lists 22 major life events (Kaaresen, Ronning, Ulvund, & Dahl, 2006), such as serious illness in the family, changing school or work place (range 0–91). The respondents are asked to indicate whether the family had experienced each of the life events during the last 12 months. Items were weighted according to the Professional Manual of the Parenting Stress Index (Abidin, 1995), and the composite score was used in this study.
Anxiety during pregnancy
Anxiety related to pregnancy and birth was assessed by the 10-item Pregnancy Related Anxiety Questionnaire—Revised (PRAQ-R; Huizink et al., 2004). Each item is measured on a 5-point scale. PRAQ-R yields three subscales (fear of giving birth, fear of bearing a physically or mentally handicapped child, and concerns about one’s own appearance). In this study, mean scores across all 10 items were computed to obtain an indication of overall level of anxiety related to pregnancy and birth (Cronbach’s alpha = .84, range: 10–50).
Childhood trauma
Childhood traumas were assessed retrospectively by the Adverse Childhood Experiences Scale (ACE), a self-report measure of childhood abuse, neglect, and household dysfunction (Dong et al., 2004). It lists 10 types of adverse childhood experiences and asks whether they have been experienced during their childhood. ACE has shown good test–retest reliability (Dong et al., 2004). Dong et al. (2004) showed that experiencing one type of adverse childhood event increased the odds of having additional adverse childhood experiences, and highlighted the importance of looking at the extent of such experiences rather than effects of a specific type. In this study we used the sum of reported types, ranging from 0 to 10.
Statistical Analysis
Statistical analyses were conducted in two steps. First, the time course of depressive symptoms from midpregnancy through 1-year postpartum was modeled, and subgroups of women with distinct longitudinal courses of depressive symptoms were identified. For this purpose, latent growth curves (LGC) were modeled based on EPDS composite scores at all seven time points (Bollen & Curran, 2006). To represent birth as a major event, a linear three-piece piecewise growth curve model was estimated (Flora, 2008) with the first transition point at the end of the pregnancy period (i.e., T4) and the second 6 weeks after birth (i.e., T5). The three-piece model yielded three phases of symptom development: a pregnancy phase, a peripartum phase, and a postpartum phase. By allowing for sharp transitions at these specific time points the statistical model was able to represent the theoretical expectation of differential change rates during these phases (Ram & Grimm, 2007), such as a pattern of rapid change in symptom levels during the peripartum phase and relatively slower change during the pregnancy and postpartum phases. Two-piece models with only one transition point at either T4 or T5 were also modeled to examine whether the three-piece model with its capacity of detecting slopes with rapid change in close proximity to birth in fact showed superior fit compared with growth models that did not allow for such patterns. Based on these growth curves, latent growth trajectory classes were estimated by means of growth mixture modeling (GMM; Muthén, 2004). GMM can account for heterogeneity in longitudinal patterns of depressive symptomatology as latent classes correspond to qualitatively distinct trajectories. Variances were constrained to be equal across classes, as convergence issues emerged when models with unique variances across classes were estimated.
Second, class membership was regressed on the potential psychosocial adversity factors by estimating multinomial logistic regression models using the three-step modal ML approach accounting for class assignment uncertainties (Asparouhov & Muthén, 2014; Vermunt, 2010). This was done to examine the association of possible predictor variables with class probabilities. Such associations were initially investigated with a univariate approach and subsequently with a multivariate approach to reach the most robust set of predictor variables. Only significant predictors (p < .05) from the univariate analyses were entered in the multivariate model. The scales of the continuous predictor variables were z-transformed, to make them more readily comparable (with the exception of age and education which were measured in years).
Model fit of basic growth models was evaluated by inspecting χ2-square statistics, Confirmatory Fit Index (CFI), Tucker–Lewis Index (TLI), and the root mean square error of approximation (RMSEA). According to recommendations in the literature, CFI and TLI values of .95 or greater and RMSEA values of .06 or lower are considered as indicating good fit (Hu & Bentler, 1999). To decide on number of classes, the bootstrapped likelihood ratio test (BLRT), the Lo–Mendell–Rubin adjusted likelihood ratio test (LMR-LRT), and the Bayesian information criteria (BIC)/sample size adjusted BIC (SABIC) were used (Nylund, Asparouhov, & Muthén, 2007). Entropy values, which represent the quality of classification of individuals into latent classes, were also inspected. Finally, overall interpretability was evaluated, and we excluded models with classes comprising less than 20 women.
Missing data were handled by the full information maximum likelihood procedure (FIML) accounting for missing at random (MAR) assumptions. Moreover, because missing data due to dropout in longitudinal studies may not fulfill MAR assumptions, we additionally tested models handling dropout that is not missing at random (NMAR; Muthén, Asparouhov, Hunter, & Leuchter, 2011). All data analyses were performed in Mplus 7.3, using maximum likelihood estimation with robust standard errors (Muthén & Muthén, 2015).
ResultsMeans, standard deviation, and a correlation matrix of all variables used in the study are presented in Table 1. The table shows mean EPDS scores to range from 2.88 to 4.54, well below clinical cut-off, with generally higher EPDS mean levels during pregnancy compared with the postpartum period. Correlations between assessments were moderate to high (.40 ≤ r ≤ .72) and followed a pattern of stronger correlations among assessments closer in time. The means of life stress index (7.08), ECR anxiety and avoidance (44.23 and 30.05, respectively), PRAQ (22.70), and ACE (0.75) were at the lower end of the scales, which would be expected in a community-based sample.
Means, Standard Deviations, and Correlation Matrix of All Measures
Latent Growth Curve Models
LGC models were fitted based on EPDS mean scores at all seven time points. Initially, a basic model allowing for linear development across all time points with an intercept (estimated initial status early in pregnancy) and a slope (estimated change in depressive symptoms) was estimated. The parameterization of the slope was coded as the number of weeks that had passed since the first measurement, thus reflecting the uneven time intervals between measurements. The basic LGC model had a mean intercept (I) of 4.65 (p < .01) and a mean slope (S) of −0.03 (p < .01), indicating an estimated mean score of depressive symptoms of 4.65 at T1 and a decrease of 0.03 scores each week. However, model fit was poor, χ2(23) = 188.04; CFI = 0.860; TLI = 0.872; RMSEA = 0.083; 90% CI [0.072, 0.094]. A linear three-piece LGC model with transition points at the end of the pregnancy period and 6-weeks postpartum was then estimated (Means: I = 4.38, p < .01; S1 [pregnancy slope] = 0.02, p < .05; S2 [peripartum slope] = −0.10, p < .01; S3 [postpartum slope] = −0.02, p < .01), yielding excellent fit, χ2(14) = 27.00; CFI = 0.989; TLI = 0.983; RMSEA = 0.030; 90% CI [0.012, 0.047]. Results indicate an estimated EPDS mean score of 4.38 at T1, with a slight symptom increase of 0.02 scores each week during pregnancy, a sharper weekly decrease of −0.10 scores in the peripartum phase, with a continued but small weekly decrease of −0.02 scores in the postpartum phase.
Two two-piece models with transition points at the end of pregnancy or 6-weeks postpartum, respectively, were also estimated to investigate if more parsimonious models would yield equivalent fit. However, both models yielded a poor fit, χ2(19) = 173.23; CFI = 0.869; TLI = 0.855; RMSEA = 0.089; 90% CI [0.077, 0.101]; and χ2(19) = 157.35; CFI = 0.882; TLI = 0.870; RMSEA = 0.084; 90% CI [0.072, 0.096]. The linear three-piece model was thus selected for further analyses as it was in accordance with the a priori theoretical model and yielded the best fit.
Growth Mixture Modeling
Next, a series of GMM models was fitted to the three-piece piecewise LGC model for assessment of the optimal number of classes. As Table 2 shows, the two- and three-class solutions were not optimal, as all the fit indices indicated that more classes yielded a better fit. Nor did the six-class solution seem to be adequate as the LMR-LRT indicated fewer classes and the solution included two classes with less than 10 women in each. It was less clear whether a four-class or a five-class solution yielded the best fit, and as neither the BLRT nor the BIC/SABIC provided conclusive answers, we based our decision on LMR-LRT, overall interpretability, and entropy values. LMR-LRT and the entropy values both favored the four-class solution. An inspection of these two solutions showed that the five-class solution in most part reflected the four-class solution, with the exception of one new class (6%) characterized by a high initial level, rapidly dropping to stable low levels. Taking all these aspects into consideration, a four-class solution was finally decided upon. The entropy value was .89 for this model, which indicates good separation of latent classes (Celeux & Soromenho, 1996).
Fit of Growth Mixture Models
Estimated trajectories of the four-class model are depicted in Figure 2, with corresponding parameters found in Table 3. As depicted in Figure 2, the pregnancy-only class (4.4%) represents a heightened initial symptom level early in pregnancy with a steep increase of symptoms during pregnancy, peaking at the last time point before delivery. The symptom level then rapidly dropped during the peripartum period with a continued downward trend postpartum. The postpartum-only class (2.2%) closely resembles the traditional PPD pattern, with low levels during pregnancy, a rapid peripartum onset of symptoms, followed by a gradual postpartum decrease, reaching low symptom levels at the end of the first postpartum year. A third class termed moderate-persistent (10.5%), showed elevated symptoms at a subclinical level with a flat trend during pregnancy. The symptom level dropped slightly during the peripartum period; however, this pattern was reversed in the postpartum period with a steady increase of symptoms the first year after childbirth. The majority of women (82.9%) were categorized into a minimum symptoms class, characterized by low levels of depressive symptoms during pregnancy and with slight, but significant declines after birth.
Figure 2. Estimated mean trajectories of the GMM Four-class model of depressive symptoms from pregnancy to 12-months postpartum.
Parameters of the Four-Class Growth Mixture Model
Because conventional GMM models are based on MAR assumptions, additional analyses under NMAR assumptions were modeled as well. More specifically, we reran our models in the framework of Diggle-Kenward selection model, Roy latent dropout pattern mixture modeling, and Muthén-Roy modeling with latent subgroups of subjects with respect to the piecewise LGC model and the GMM model (Muthén et al., 2011). The estimated parameters, as well as number and proportions of classes did not differ substantially from those in the original models; thus only results from the GMM model under MAR assumptions are reported.
Predictors of Membership in Latent Trajectory Classes
In the next analytic step, the associations of sociodemographic factors, stress, partner attachment, pregnancy-related anxiety, and childhood adversity with class membership were investigated by regressing class membership on these factors in multinomial logistic regression analyses. First, each predictor was included one by one in separate regression models (see Table 4). Second, all significant predictors were included simultaneously in one multiple multinomial logistic regression analyses, to investigate their unique contributions. The minimum symptoms class was chosen as the reference class.
Predictors of Class Membership: Results From Multinomial Logistic Regression Models
In the univariate models, membership in the pregnancy-only class was predicted by several psychosocial factors, as fewer years of education, previous psychopathology, attachment-related anxiety and avoidance, pregnancy-related anxiety, and adverse childhood experiences all increased the odds of belonging to this class compared with the minimum symptoms class. For the postpartum-only class, only previous psychopathology showed a significant increase in odds ratios. Several psychosocial factors predicted class membership in the moderate-persistent class, including previous psychopathology, fewer years of education, increased scores on attachment-related anxiety and avoidance, stressful life events, pregnancy-related anxiety, and childhood adversities. Age and parity were unrelated to class membership.
In the multivariate model, only pregnancy-related anxiety remained a significant predictor of the pregnancy-only class. However, the odds ratio for previous psychopathology remained elevated (OR = 2.32, p = .09), although not significant, possibly indicating low statistical power. For the postpartum-only class results were similar to the univariate analysis, as only previous psychopathology significantly increased the odds of class membership as compared with the minimum symptoms class. Several predictors still distinguished the moderate-persistent class from the minimum symptoms class, as previous psychopathology, fewer years of education, as well as increases in partner-related anxiety and life stress showed significantly elevated odds ratios.
When comparing the three classes with elevated trajectories to one another by means of multinomial logistic regression analyses, some significant associations emerged (see supplementary Table 1). Individuals in the pregnancy-only class reported higher pregnancy-related anxiety relative to both the postpartum-only and the moderate increasing class in both univariate and multivariate analyses, even though the difference between the pregnancy-only and the moderate increasing class was only marginally significant in the multivariate analysis (p = .056). The most notable finding for the for the postpartum-only class was that it had significantly lower odds of both attachment anxiety and avoidance relative to the two other elevated trajectory classes in univariate analyses, as well as avoidance in multivariate analyses. The postpartum-only class thus resembled the minimum symptoms class with regard to partner-related attachment.
DiscussionIn this study, a growth mixture model of four distinct latent piecewise trajectory classes accounted for the heterogeneity of depressive symptom course among women during pregnancy and 12-months postpartum. The four classes were labeled according to trajectory characteristics as pregnancy-only (4.4%), postpartum-only (2.2%), moderate-persistent (10.5%), and minimum symptoms (82.9%). Referring back to our initial hypothesis about trajectory features, we found: (a) one trajectory with elevated symptoms limited to the pregnancy period; (b) one trajectory with stable low symptoms during pregnancy, rapidly increasing after birth with a gradual recovery the first postpartum year, termed postpartum-only; (c) a trajectory characterized with moderately elevated symptom levels during pregnancy, with a slight increase in symptom burden postpartum; (d) no class with a high chronic trajectory, contrary to our expectations; and (e) one trajectory including the majority of women without elevated depressive symptoms, as evident in the minimum symptoms class.
Thus, with the notable exception of not identifying a class characterized by persistent, severely elevated depressed mood, all expectations regarding class trajectories were met. Not finding a chronically elevated class with a high symptom burden has at least two possible explanations: Our sample did not include a sufficient number of participants with severe depressive symptoms. Alternatively, our statistical modeling choice of a three-piece piecewise model facilitated a close mapping of symptom change, whereas other statistical models may overestimate the stability of symptoms in women reporting high levels of depressive symptoms at several, but not all occasions.
Regarding our second aim, all psychosocial adversity factors as well as education distinguished the elevated trajectory classes from the minimum symptoms class. Further, in accordance with previous research distinguishing between remitting and chronic courses of PPD (Vliegen et al., 2014), the moderate-persistent class showed the highest number of associated psychosocial adversity factors. Overall, our findings were consistent with our hypothesis of heterogeneity in pathways of elevated depressive mood during pregnancy and the first postpartum year, connecting distinct trajectories of time courses with differential psychosocial adversity factors.
The pregnancy-only class consisted of women with an elevated initial level of depressive symptoms that rises steeply throughout pregnancy. After birth, however, symptoms are ameliorated and the women did not report further elevated depressed mood. Pregnancy-related anxiety seemed to be of particular importance for this class, as anxiety was the only adversity factor differentiating between the pregnancy-only class and all other classes, both in univariate and multivariate analyses. One potential explanation for this finding may be that depressive symptoms that are limited to pregnancy are a result of negative emotions and cognitions related to pregnancy and birth. This would fit the pattern of rapid symptom increase as the due date approaches followed by a quick amelioration of symptom burden after child delivery.
The second trajectory class follows a typical PPD-pattern (Wisner et al., 2010) comparable with Mora et al.’s (2009) early postpartum class and corresponds with studies of increased risk the first few weeks postpartum (Munk-Olsen et al., 2006). Surprisingly, the various measures associated with membership in the other trajectory classes did not increase odds of belonging to the postpartum-only class. Of the psychosocial factors measured in this study, only previous psychopathology increased the odds—by threefold. Further, higher partner-related attachment avoidance and anxiety decreased the odds of belonging to this group compared with the other two elevated trajectory classes. A tenable interpretation of this might be that this class represents a subgroup of women for whom the development of depressive symptoms is associated with factors not belonging to the psychosocial domain, or alternatively that there are other psychosocial antecedents not covered in this study.
The moderate-persistent class is characterized by a consistently elevated symptom level, with increasing symptoms as time passes after birth. This is in line with Patton et al. (2015) finding that for a large proportion of women with PPD, it represents a continuation of earlier mental health problems, as well as studies identifying symptom onset during pregnancy (Wisner et al., 2013). A noteworthy finding is that the mean estimate trajectory for this group is close to the threshold between subclinical and clinical levels, and—as within-class variation is allowed in the analyses—individual trajectories included in this group will be located both above and below the clinical cut-offs. This emphasizes the importance of subclinical variance, and of including dimensional approaches in this area of research. Several psychosocial adversity factors increased the odds of belonging to this group, as fewer years of education, previous psychopathology, anxious attachment orientation, and stress all increased the odds of following the moderate-persistent trajectory relative to the minimum symptoms class. Notably, stress further distinguished this class from the pregnancy-only class in multivariate analysis, in accordance with Vliegen et al. (2014) who found life stress to be one of the factors distinguishing a persistent course of PPD from a remitting course.
About 83% of the women, belonging to the minimum symptoms class, reported consistently low levels or no symptoms of depression throughout the period of study. This is in accordance with most prevalence reports (Biaggi et al., 2016; Gavin et al., 2005), although direct comparisons are difficult due to differences in assessment periods, methods, and populations (O’Hara & Wisner, 2014). The proportion of the minimum symptoms class in this study is also comparable with previous reports of heterogeneous time courses (Cents et al., 2013; Mora et al., 2009; Sutter-Dallay et al., 2012).
Limitations, Strengths, and Conclusions
There are important limitations of this study. First, the representativeness of the sample can be questioned. Figures from Statistics Norway (2014) indicate that our sample has a higher educational level than the general population. The response rate was 50.7%. As in any community-based research, there is a possibility of self-selection bias with an overrepresentation of healthy and resourceful participants; in this particular study there is a threat of underrepresentation of women with heightened levels of depressive symptoms as they might find participation in research too demanding. This might limit the generalizability of results.
A related concern is selective dropout, and analyses indeed showed that some demographic and psychosocial factors, including depressive symptoms, predicted attrition. However, by using contemporary missing data routines, including FIML and models not assuming MAR, we attempted to reduce the impact of such selective attrition. Yet another concern regarding representativeness is the specific cultural context. There is evidence of considerable variation of PPD prevalence rates across nations and cultures (Halbreich & Karkun, 2006), and it is possible that the relatively generous social welfare policies in Norway (i.e., free prenatal care, a year of paid parental leave) might have a preventive effect on PPD symptoms, thus potentially limiting generalizability in countries with less generous welfare policies. On the other hand, like many Western societies individualistic values are emphasized in Norway, whereas other societies may provide protective factors in endorsing cultural patterns that reinforce the maternal role and effectively relieve new mothers of burdens (Halbreich & Karkun, 2006).
Second, particularly the pregnancy-only and the postpartum-only classes were small in size, including n = 41 and n = 20 participants, respectively. Consequently, our study is limited by the resulting low statistical power to detect differences between these and other classes in multinomial logistic regression analyses. For example, the nonsignificant finding despite relatively high odds ratios regarding previous psychopathology in the pregnancy-only class may be due to low power.
Third, previous psychopathology was assessed by means of self-reports, based on a single item, including all kinds of psychopathology. The severity, timing, and nature of earlier mental health problems thus remain unknown. Ideally, one ought to have objective measures of the participants’ histories of affective disorders. Relatedly, depressive symptoms were measured by self-report only, and therefore provide no information about a clinical diagnosis of depression.
Fourth, stress exposure was measured by a self-reported life event checklist. Although there are reports of satisfying reliability and validity of the instrument we have used (Abidin, 1995), in general this assessment method has received criticism for having methodological limitations such as assuming that the life events listed have the same meaning across contexts and individuals (Harkness & Monroe, 2016). Some caution in the interpretation of stress is therefore warranted.
Fifth, we only included predictors at enrollment, and did not investigate the potential influence of time-varying covariates such as treatment received, birth complications, and infant health.
Despite these limitations, the present study has identified four trajectory classes of depressive symptom course in the pregnancy and postpartum period, as well as predicted class membership on the basis of psychosocial factors. Findings suggest that pregnancy and postpartum depressive symptom onset and development do not follow a uniform course, nor are predicted by a singular set of factors, but rather support a model of differential time courses associated with diverse psychosocial adversities.
Researchers and clinicians should be aware of possible heterogeneous symptom development trajectories, and subsequently inquire about diverse underlying mechanisms, pathogenic pathways and prognosis, in order to refine theories and develop targeted prevention and intervention. Future research is needed to test differential diathesis-stress models for trajectory classes. An important next step would be to investigate the differential outcomes of trajectory classes on maternal health, child development, and family functioning.
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Submitted: May 6, 2016 Revised: November 4, 2016 Accepted: November 4, 2016
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Source: Journal of Abnormal Psychology. Vol. 126. (2), Feb, 2017 pp. 173-183)
Accession Number: 2016-58118-001
Digital Object Identifier: 10.1037/abn0000246
Record: 112- Title:
- Person-environment transactions in youth drinking and driving.
- Authors:
- Pedersen, Sarah L.. Department of Psychological Sciences, University of Missouri, Columbia, MO, US
McCarthy, Denis M.. Department of Psychological Sciences, University of Missouri, Columbia, MO, US, mccarthydm@missouri.edu - Address:
- McCarthy, Denis M., University of Missouri, 213 McAlester Hall, Columbia, MO, US, 65211, mccarthydm@missouri.edu
- Source:
- Psychology of Addictive Behaviors, Vol 22(3), Sep, 2008. pp. 340-348.
- NLM Title Abbreviation:
- Psychol Addict Behav
- Page Count:
- 9
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- Bulletin of the Society of Psychologists in Addictive Behaviors; Bulletin of the Society of Psychologists in Substance Abuse
- Other Publishers:
- US : Educational Publishing Foundation
Society of Psychologists in Addictive Behaviors - ISSN:
- 0893-164X (Print)
1939-1501 (Electronic) - Language:
- English
- Keywords:
- drinking and driving, alcohol, personality, adolescents, health risk behavior, environment
- Abstract:
- Drinking and driving is a significant health risk behavior for adolescents. This study tested mechanisms by which disinhibited personality traits (impulsivity and sensation seeking) and aspects of the adolescent home/social environment (parental monitoring and alcohol accessibility) can influence changes in drinking and driving behavior over time. Two hundred two high school age youths were assessed at 2 time points, approximately 8 months apart. Zero-inflated Poisson regression analyses were used to test (a) an additive model, where personality and environmental variables uniquely predict drinking and driving engagement and frequency; (b) a mediation model, where Time 2 environmental variables mediate the influence of disinhibited personality; and (c) an interaction model, where environmental factors either facilitate or constrain the influence of disinhibited personality on drinking and driving. Results supported both the additive and interaction model but not the mediation model. Differences emerged between results for personal drinking and driving and riding with a drinking driver. Improving our understanding of how malleable environmental variables can affect the influence of disinhibited personality traits on drinking and driving behaviors can help target and improve prevention/intervention efforts. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Alcohol Drinking Patterns; *Driving Behavior; *Personality Traits; *Risk Taking; Home Environment; Social Environments
- Medical Subject Headings (MeSH):
- Adolescent; Adolescent Behavior; Adult; Age Factors; Alcohol Drinking; Alcoholic Beverages; Automobile Driving; Female; Humans; Male; Models, Psychological; Parenting; Personality; Personality Inventory; Risk Factors; Risk-Taking; Social Environment; Social Facilitation; Students; Surveys and Questionnaires
- PsycINFO Classification:
- Psychosocial & Personality Development (2840)
- Population:
- Human
Male
Female - Location:
- US
- Age Group:
- Adolescence (13-17 yrs)
Adulthood (18 yrs & older)
Young Adulthood (18-29 yrs) - Tests & Measures:
- Zuckerman-Kuhlman Personality Questionnaire DOI: 10.1037/t06537-000
Drinking Styles Questionnaire DOI: 10.1037/t03954-000 - Grant Sponsorship:
- Sponsor: National Institute on Alcohol Abuse and Alcoholism
Grant Number: R03 AA13399
Recipients: McCarthy, Denis M. (Prin Inv)
Sponsor: National Institute on Alcohol Abuse and Alcoholism
Grant Number: T32 AA13526
Recipients: No recipient indicated - Methodology:
- Empirical Study; Longitudinal Study; Quantitative Study
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- Accepted: Feb 6, 2008; Revised: Feb 4, 2008; First Submitted: Apr 9, 2007
- Release Date:
- 20080908
- Correction Date:
- 20120827
- Copyright:
- American Psychological Association. 2008
- Digital Object Identifier:
- http://dx.doi.org/10.1037/0893-164X.22.3.340
- PMID:
- 18778127
- Accession Number:
- 2008-11981-003
- Number of Citations in Source:
- 67
- Persistent link to this record (Permalink):
- http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2008-11981-003&site=ehost-live
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- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2008-11981-003&site=ehost-live">Person-environment transactions in youth drinking and driving.</A>
- Database:
- PsycINFO
Person–Environment Transactions in Youth Drinking and Driving
By: Sarah L. Pedersen
Department of Psychological Sciences, University of Missouri—Columbia
Denis M. McCarthy
Department of Psychological Sciences, University of Missouri—Columbia;
Acknowledgement: This research was supported by National Institute on Alcohol Abuse and Alcoholism Grants R03 AA13399 (Denis M. McCarthy, principal investigator) and T32 AA13526.
Youth drinking and driving is a significant public health problem. Motor vehicle accidents are the most common cause of death for high school age youths in the United States (Centers for Disease Control and Prevention, 2004), and statistics for 2005 indicate that 23% of drivers ages 15–20 killed in motor vehicle crashes had a blood alcohol concentration (BAC) at or above .08 (National Highway Traffic Safety Administration, 2006). Recently, O'Malley and Johnston (2007) found that 14.2% of high school seniors report engaging in drinking and driving behavior in the past 2 weeks, and 20.9% report riding with a drinking driver. Although young people are less likely to report driving after alcohol use than older drivers are (Royal, 2003), they consume a greater amount of alcohol before driving and consider it safe to drive at higher BAC levels than do older drivers (Hingson & Winter, 2003). This is of particular concern because the relative risk of fatal car accidents is higher for young drivers at all BAC levels, and risk increases faster for youths as BAC increases (Zador, Krawchuk, & Voas, 2000).
Changes in alcohol control policy, such as increasing the minimum drinking age and lowering BAC limits, have led to significant reductions in youth drinking and driving (O'Malley & Johnston, 2003; Wagenaar, O'Malley, & LaFond, 2001). The effectiveness of these policies makes clear the impact of environmental contingencies on youth drinking and driving decisions. On the other hand, there is evidence that drinking and driving prevalence has become relatively stable (O'Malley & Johnston, 2003, 2007; Sweedler et al., 2004). Drinking and driving also has a high rate of recidivism (Nochajski & Stasiewicz, 2006). The stability of this behavior highlights the potential role of individual difference characteristics that can put youths at risk for drinking and driving.
The present study tested an integrated model of personality and environmental influences on youth drinking and driving. Personality and developmental psychology theory (Buss, 1987; Caspi & Roberts, 2001; Scarr & McCartney, 1983) has emphasized the importance of mechanisms by which heritable individual difference characteristics, such as personality traits, can influence or interact with environmental/contextual factors across the lifespan. Although often referred to as Gene × Environment interactions, following Caspi and Roberts (2001), we use the term person–environment transactions to describe these processes, as this term is neutral regarding the genetic basis of the characteristics under study, as well as the statistical/analytic model of how person characteristics and environments are associated. The present study tested person–environment transactions between disinhibited personality traits (sensation seeking, impulsivity) and aspects of the adolescent home/social environment (parental monitoring, alcohol accessibility) in determining drinking and driving behavior.
Personality Characteristics: Sensation Seeking and ImpulsivityA number of personality characteristics are associated with substance use and risk-taking behaviors in adolescence (Caspi et al., 1997; Elkins, King, McGue, & Iacono, 2006). The personality domain of impulsivity/disinhibition has been found to have the strongest and most consistent relationship with alcohol-related and antisocial behaviors (Sher & Trull, 1994). Recent conceptual work has argued that two of the most studied facets of this domain, impulsivity and sensation seeking, should be considered distinct constructs (Whiteside & Lynam, 2001) that are only moderately correlated (Zuckerman, 1994). Impulsivity can be defined as the tendency to experience and act on strong impulses (Whiteside & Lynam, 2001), while sensation seeking can be defined as desiring new and intense experiences (Zuckerman & Kuhlman, 2000). Impulsivity and sensation seeking have been shown to predict different externalizing behaviors and psychiatric diagnoses (Fischer, Smith, & Anderson, 2003; Lynam & Miller, 2004; Miller, Flory, Lynam, & Leukefeld, 2003; Smith et al., 2007; Whiteside, Lynam, Miller, & Reynolds, 2005). Even when these traits predict similar risk-taking behaviors, it has been argued (Whiteside & Lynam, 2001) that they may do so for different reasons. For example, individuals high in sensation seeking may engage in risk taking as a means of experiencing excitement or thrills, while an impulsive individual may engage in the same behavior in response to strong affect.
There is considerable evidence for both sensation seeking and impulsivity as predictors of alcohol-related behaviors (Hittner & Swickert, 2006; White, Bates, & Buyske, 2001). A literature review of sensation seeking and risky driving behavior (Jonah, 1997) found that most studies reported significant relations between sensation seeking and drinking and driving behavior. Results were consistent between studies of adults and adolescents. High impulsivity has been associated with drinking and driving, riding with a drinking driver, and binge drinking (Ryb, Dischinger, Kufera, & Read, 2006). Impulsivity is also correlated with drinking and driving violations in adult men (Eensoo, Paaver, Harro, & Harro, 2005).
Relatively little is known about specific mechanisms by which personality characteristics might influence adolescent drinking and driving behavior. In adults, Stacy and colleagues conducted both cross-sectional (Stacy, Newcomb, & Bentler, 1991) and prospective (Stacy & Newcomb, 1998) studies that found that disinhibited personality traits influence drinking and driving through alcohol use behavior. Turrisi, Jaccard, and McDonnell (1997) found that the influence of emotional control, a combination of impulsivity and sensation seeking, on drinking and driving was mediated by cognitions about drinking and driving and drinking and driving alternatives. To our knowledge the current study is the first to test potential mechanisms of personality risk for drinking and driving involving two aspects of the adolescent home/social environment: parental monitoring and alcohol accessibility.
Parental Monitoring and Alcohol AccessibilityParenting characteristics are thought to play a significant role in the development of problem behavior in youths. In particular, low levels of parental monitoring are associated with increased risk for a variety of adolescent risk-taking behaviors, including unsafe sexual activity and drug use (Li, Stanton, & Feigelman, 2000) as well as stealing, fighting, and destroying property (Curran & Chassin, 1996). Youth report of parents' knowledge of their behavior is associated with their alcohol use (Chassin, Pillow, Curran, Molina, & Barrera, 1993; Curran & Chassin, 1996) and frequency of heavy episodic drinking (Wood, Read, Mitchell, & Brand, 2004). Longitudinal studies of drinking and driving behavior have shown that low parental monitoring in high school prospectively predicted increased likelihood of drinking and driving (Bingham & Shope, 2004) and increased rate of serious driving offenses (Shope, Waller, Raghunathan, & Patil, 2001).
The accessibility of alcohol in adolescents' social/community environment has also been shown to have considerable impact on their alcohol-related behavior. Self-reported ability to obtain alcohol has been found to be related to alcohol consumption in adolescence (Jones-Webb et al., 1997). At the community level, studies have shown that the number of registered alcohol vendors (Treno, Grube, & Martin, 2003), reported use of alcohol vendors, and perceived community enforcement of underage drinking laws (Dent, Grube, & Biglan, 2005) are related to youth drinking and drinking and driving behavior.
Integrating Environmental and Personality RiskThe present study tested person–environment transactions in the development of youth drinking and driving behaviors. We hypothesized that disinhibited personality traits not only exert a direct influence on drinking and driving behavior but can be mediated by or interact with other important risk factors, such as parenting and social/contextual factors.
A sample of high school age youths was assessed at two time points, approximately 8 months apart. We first tested an additive model, where both personality (impulsivity and sensation seeking) and environmental (parental monitoring and alcohol accessibility) factors were hypothesized to make unique contributions to the prediction of drinking and driving and riding with a drinking driver, controlling for prior alcohol use, license status, gender, and drinking and driving behavior. Models were tested separately for drinking and driving and riding with a drinking driver, as prior studies have indicated that these are distinct behaviors that may have different risk mechanisms (McCarthy & Brown, 2004; Poulin, Boudreau, & Asbridge, 2007; Yu & Shacket, 1999).
We then tested a mediation model, examining potential indirect effects of personality on drinking and driving behavior through their influence on parental monitoring and alcohol accessibility. This model reflects the hypothesis that disinhibited personality traits can influence the response of others in youths' environment, as well as the environments that youths select. For example, disinhibited youths may be more difficult for parents to monitor and less likely to disclose information to parents. Disinhibited youths might also be more likely to select environmental contexts such as a deviant or substance-using peer group, which allow for easier access to alcohol. Although this hypothesis has not been tested directly elsewhere, recent longitudinal studies have demonstrated that youth delinquent behavior can alter parental monitoring/knowledge over time (Laird, Petitt, Bates, & Dodge, 2003) and that personality traits can influence deviant peer affiliation (Yanovitzky, 2005).
Finally, we tested an interaction model, examining whether the association of personality factors with drinking and driving behavior is moderated by parental monitoring and/or alcohol accessibility. There is some evidence that aspects of the home/social environment can constrain or exacerbate substance-related behavior in adolescents. For example, parental involvement has been found to moderate the influence of other family factors on child internalizing problems (Burstein, Stanger, Kamon, & Dumenci, 2006), while maternal support and discipline interact with peer substance use in the development of adolescent substance use over time (Marshal & Chassin, 2000). We hypothesized that high levels of parental monitoring would constrain drinking and driving behavior, such that youths high in sensation seeking or impulsivity would be less likely to drink and drive when parental monitoring is high. For alcohol accessibility, we hypothesized that impulsive or sensation seeking youths would be more likely to drink and drive when alcohol is easily obtained in their environment.
Method Participants
Study participants were 266 high-school-age youths. Of the original sample, 202 (76%) completed the Time 2 survey approximately 8 months later. Participants who did not complete the second survey did not differ from those who did in age, gender, ethnicity, license status (Time 1), or drinking and driving behavior (Time 1). Attriters were more likely to report drinking behavior at Time 1 (77% vs. 60%), χ2(1, N = 266) = 5.85, p < .05, and were more likely to be African American (57% vs. 19%), χ2(1, N = 266) = 24.14, p < .01.
The final sample of 202 participants was primarily Caucasian (85%), with 7% African American and 8% of other racial backgrounds. The sample was 66% female and had a mean age of 16.15 years (SD = 1.00, range 13–18) at Time 1. During the first assessment, 45% of the sample were nondrivers. At Time 2, 20% were nondrivers, 25% were recently licensed drivers, and the remaining 55% were established drivers, driving independently at both time points of the study.
Procedures
Participants were recruited from local high schools through flyers passed out during lunch breaks and after school. Study flyers were also posted in locations frequented by youths (stores, theaters, etc.) throughout the community. Interested participants contacted the research lab and were given more information about the study. For participants under age 18, verbal parental consent was obtained. Participants were then mailed a packet with questionnaires, consent forms, assent form (if under age 18), postage-paid return envelope, and a cover letter. Upon returning completed study materials, participants were each mailed a $20 gift certificate to the local mall. Participants were contacted approximately 7 months later and asked if they would like to participate in a follow-up study. Procedures were otherwise identical to those for Time 1. Participants were again compensated with a $20 gift certificate to the local mall upon completion of the materials. Study procedures were approved by the University of Missouri—Columbia Institutional Review Board.
Measures
Demographic information
A self-report questionnaire was used to collect demographic information, including age, gender, and ethnicity.
Alcohol use
The Drinking Styles Questionnaire (DSQ; Smith, McCarthy, & Goldman, 1995) was used to assess alcohol use at Time 1 and Time 2. The DSQ collects information about drinking status, quantity and frequency of drinking, frequency of drinking to intoxication, and typical drinking situations. Typical frequency of alcohol consumption at Time 1 and past month frequency of alcohol use at Time 2 were used as covariates in the present study. The DSQ has demonstrated good reliability and validity in adolescent and college-age samples (McCarthy, Miller, Smith, & Smith, 2001; Smith et al., 1995).
Drinking and driving behaviors
Participants were asked to report frequency of driving after consuming any alcohol and riding with a driver who had consumed alcohol. Participants retrospectively reported on drinking and driving behaviors over the past 3 months at both time points.
Personality characteristics
Sensation seeking and impulsivity were measured at Time 1 with the Zuckerman–Kuhlman Personality Questionnaire (ZKPQ: Zuckerman, Kuhlman, Joireman, Teta, & Kraft, 1993). The ZKPQ is a 38-item self-report measure with a dichotomous response format. The 19 items that comprise the Impulsivity and Sensation Seeking subscales were included in the present study (8 items on impulsivity, 11 items on sensation seeking). The mean of each subscale (range 1–2) was calculated for each participant, with higher scores representing higher levels of impulsivity and sensation seeking. Internal consistency reliabilities for the impulsivity (α = .63) and sensation seeking (α = .71) subscales in this sample were adequate.
Alcohol accessibility
Alcohol accessibility was assessed at Time 2. Three questions were adapted from a previous study (Smart, Adlaf, & Walsh, 1996) of perceived access to and procurement of alcohol by youths. Two questions asked youths to rate the likelihood that they would be able to obtain alcohol themselves if they wanted. A 6-point Likert-type scale was used, with responses ranging from no chance to certain to happen. Youths were also asked, on a 6-point scale ranging from never to 20 or more times, how often they had obtained alcohol in the past year. The scale mean (range 1–6) was calculated for each participant, with higher scores representing easier accessibility of alcohol. Internal consistency reliability for these items in this sample was adequate (α = .74).
Parental monitoring
Parental monitoring was assessed at Time 2. A six-item measure used in previous studies (Li et al., 2000) asked youths to rate their parents' knowledge of their activities (e.g., “my parents know where I am after school”). A 5-point Likert-type scale was used, with responses ranging from disagree strongly to agree strongly. The scale mean (range 1–5) was calculated for each participant with reverse-coded items so that higher scores represented lower levels of parental monitoring. This measure has been found to be internally consistent and valid in studies of adolescents (Li et al., 2000). Internal consistency reliability for these items in this sample was very good (α = .87).
Analytic Strategy
We tested the study hypotheses using zero-inflated Poisson (ZIP) regression analyses in Mplus 4.2 (Muthén & Muthén, 1998–2007). Poisson regression is appropriate when the dependent variable is a count of the number of events over a fixed period of time, such as the number of times engaging in drinking and driving behavior during a given time span. The ZIP model includes a correction for overdispersion that occurs when the most common frequency count is zero. Mplus estimates two components in a ZIP model. The first, a zero-inflation component, is similar to logistic regression and estimates the odds of being in the zero class, or not reporting engagement in the specified behavior (e.g., the odds of not drinking and driving). The second component is a Poisson regression analysis, which estimates the predicted rate (PR) of engaging in that behavior if the individual is able to assume a nonzero status (e.g., the frequency of drinking and driving among those who drink and drive).
To simplify reporting, we inverted odds ratios (ORs) from the logistic regression component so that higher values indicated greater likelihood of being in the nonzero class, or engaging in drinking and driving behavior. For the Poisson regression component, Poisson regression coefficients were used to calculate a PR value, which indicates the expected rate of increase in the dependent variable under different combinations of the independent variables (Cohen, Cohen, West, & Aiken, 2003). To ease interpretation of Poisson results, we standardized personality and environmental variables as z scores. Models predicting drinking and driving behavior included only participants who were licensed drivers at Time 2, while models for riding with a drinking driver included all study participants. To control for differences in license status across analyses, we created dummy-coded variables of license groups for drinking and driving models (newly licensed/established drivers) and models predicting riding with a drinking driver (never licensed/licensed, new or nonlicensed/established drivers).
Results Descriptive Statistics
Table 1 presents mean levels of sensation seeking, impulsivity, alcohol accessibility, parental monitoring, percent reporting lifetime alcohol use, percent reporting drinking and driving and riding with a drinking driver, and frequency of drinking and driving behaviors for those who engaged in the behavior. No significant gender differences were found for engagement in drinking and driving behaviors, alcohol use, personality characteristics, or environmental factors at either time point.
Descriptive Statistics for Personality and Environmental Variables, Lifetime Alcohol Use, and Drinking and Driving Behaviors
Additive Risk Model of Drinking and Driving Behaviors
We first tested whether impulsivity (Time 1), sensation seeking (Time 1), alcohol accessibility (Time 2), and parental monitoring (Time 2) uniquely predicted frequency of drinking and driving or riding with a drinking driver at Time 2 over and above Time 1 drinking and driving behaviors, license status, frequency of alcohol use (Time 1), and gender. Results for the logistic regression portion of the model indicated that when all study variables were included in the model, only Time 1 alcohol use frequency (OR = 1.63, p < .05) uniquely predicted Time 2 engagement in drinking and driving. Time 1 alcohol use frequency (OR = 1.74, p < .01), frequency of riding with a drinking driver (OR = 1.16, p < .05), and sensation seeking (OR = 2.12, p < .01) predicted Time 2 engagement in riding with a drinking driver. License status variables were not related to engagement for either drinking and driving or riding with a drinking driver.
Results for the Poisson regression portion of the model are presented in Table 2. Time 1 alcohol use frequency, gender, and sensation seeking predicted frequency of both drinking and driving and riding with a drinking driver at Time 2. Frequency of riding with a drinking driver at Time 1 predicted frequency of this behavior at Time 2. Impulsivity predicted frequency of drinking and driving but not riding with a drinking driver. Parental monitoring and alcohol accessibility at Time 2 predicted frequency of riding with a drinking driver but not drinking and driving.
Additive Model Predicting Time 2 Frequency of Drinking and Driving Behaviors
To test whether the influence of personality and environmental variables on drinking and driving behaviors was accounted for by concurrent alcohol use, we also ran analyses including frequency of past month drinking at Time 2. For the logistic regression portion of the model, Time 2 alcohol use frequency was not significantly associated with engagement in either behavior. As a result, the pattern of significant results for this model remained unchanged. For the Poisson regression portion of the model, Time 2 alcohol use frequency was associated with frequency of both drinking and driving (PR = 0.05, p < .001) and riding with a drinking driver (PR = 0.97, p < .001). The inclusion of Time 2 alcohol use frequency did not change the pattern of results for riding with a drinking driver: Sensation seeking (PR = 0.60, p < .001), alcohol accessibility (PR = 0.99, p < .001), and parental monitoring (PR = 1.10, p < .001) remained significantly associated with frequency of this behavior. However, for drinking and driving, sensation seeking (PR = 0.05, p < .01) was associated with frequency over and above Time 2 alcohol use frequency, while impulsivity was no longer related (PR = 0.04, ns).
Indirect Effects of Personality on Drinking and Driving Behaviors
We then examined whether the prediction of engagement and frequency of drinking and driving behaviors by disinhibited personality traits was mediated by parental monitoring or alcohol accessibility. Several conditions must be present for mediation to be indicated (MacKinnon, Lockwood, Hoffman, West, & Sheets, 2002). One condition is that the independent and mediator variables must be associated. Correlation analyses indicated that Time 1 sensation seeking was moderately associated with Time 2 alcohol accessibility (r = .21, p < .01) and parental monitoring (r = .18, p < .05), while impulsivity was not. Another requirement for mediation is that the mediator and dependent variable be associated. Results from the additive model indicated that neither alcohol accessibility nor parental monitoring was related to engagement in either behavior. For the Poisson regression portion of the model, both alcohol accessibility and parental monitoring predicted frequency of riding with a drinking driver, but neither predicted drinking and driving. These results indicate that the influence of sensation seeking on frequency of riding with a drinking driver could be mediated by alcohol accessibility or parental monitoring.
A final condition for mediation is that the association between the independent and dependent variables either drop significantly or reduce to zero when the mediator is included in analyses. Results of the additive model do not support full mediation of sensation seeking's influence on riding with a drinking driver. To test potential partial mediation, we compared the Poisson regression coefficients for sensation seeking predicting riding with a drinking driver when either parental monitoring or alcohol accessibility was included in analyses. Mediation analyses controlled for license status, alcohol use frequency at Time 1, gender, and Time 1 drinking and driving behavior. Results indicated that these coefficients did not differ when either parental monitoring or alcohol accessibility was included in the model. Results therefore do not support mediation of sensation seeking's association with riding with a drinking driver.
Personality × Environment Interactions
Finally, we tested potential interactions between disinhibited personality traits and alcohol accessibility or parental monitoring in the prediction of drinking and driving behaviors. We estimated separate ZIP models for each of four potential interactions (Sensation Seeking or Impulsivity × Alcohol Accessibility or Parental Monitoring). Centered variables were used to create product terms for each potential interaction. For each model, study covariates, the relevant personality and environmental variables, and the corresponding product term were entered as predictors. Results indicated several significant interactions in the prediction of drinking and driving behavior and one interaction in the prediction of riding with a drinking driver.
Impulsivity interacted with both parental monitoring (p < .05) and alcohol accessibility (p < .05) in predicting Time 2 drinking and driving frequency. These interactions were probed by estimating models at 1 SD above and below the mean on parental monitoring and alcohol accessibility (see Figure 1). For ease of interpretability, analyses for probing and graphing interactions did not include study covariates. For youths who reported high alcohol accessibility, increases in impulsivity were associated with greater increases in drinking and driving frequency (PR = 6.56) than for those who reported low alcohol accessibility (PR = 3.64). For youths who reported low parental monitoring, increases in impulsivity were associated with greater increases in drinking and driving frequency (PR = 6.55) than for those reporting high parental monitoring (PR = 4.35).
Figure 1. Graphs represent interactions of impulsivity with parental monitoring and alcohol accessibility from Poisson regression analyses for frequency of drinking and driving. Lines depict predicted rate differences at 1 SD above and below the mean for parental monitoring and alcohol accessibility. PM = parental monitoring; AA = alcohol accessibility. ps < .05.
Results also indicated that sensation seeking interacted with alcohol accessibility in predicting Time 2 drinking and driving in both the logistic regression (p < .05) and Poisson regression (p < .001) components. Additionally, sensation seeking interacted with parental monitoring to predict Time 2 engagement in riding with a drinking driver (p < .05). Probing these interactions indicated that increases in sensation seeking were associated with a greater frequency of drinking and driving for youths reporting high alcohol accessibility (OR = 1.24; PR = 6.54) than for youths reporting low alcohol accessibility (OR = 0.52; PR = 4.23; see Figure 2). Higher sensation seeking was also associated with increased likelihood of riding with a drinking driver for youths reporting low parental monitoring (OR = 1.32) compared to youths reporting high parental monitoring (OR = 0.72).
Figure 2. Graph represents the interaction of sensation seeking with alcohol accessibility from Poisson regression analyses for frequency of drinking and driving. Lines depict predicted rate differences at 1 SD above and below the mean for alcohol accessibility. p < .001.
DiscussionThe goal of the current study was to test potential mechanisms by which personality traits and environmental risk factors might influence adolescents' drinking and driving behaviors. Our results provide support for an additive model, where both personality and environmental factors make unique contributions to drinking and driving behaviors over time. In the additive model, high sensation seeking youths reported increased frequency of both personal driving after drinking and riding with a drinking driver. Importantly, results were significant while controlling for license status, frequency of alcohol use at both time points, and Time 1 drinking and driving behaviors. Although impulsivity predicted frequency of drinking and driving in the additive model, it did not uniquely predict drinking and driving over concurrent alcohol use. This is consistent with prior studies in adults (Stacy & Newcomb, 1998; Stacy et al., 1991) and may indicate that the influence of impulsivity on drinking and driving is mediated by its association with drinking behavior.
There was also evidence for interaction effects, such that disinhibited personality traits led to more frequent drinking and driving in youths for whom alcohol is easily accessible or who reported low parental monitoring of their behavior. Results did not support mediation of risk from disinhibited personality traits by the aspects of adolescents' home/social environment that were assessed in this study.
Several differences emerged between models predicting drinking and driving and riding with a drinking driver. Parental monitoring and alcohol accessibility were related to only the frequency of accepting a ride from a drinking driver and not personal drinking and driving behavior. Also, Time 1 alcohol use frequency predicted later drinking and driving but was not related to riding with a drinking driver. As noted, prior studies have found evidence for distinct risk mechanisms for these two behaviors. For example, adolescents are less likely to ride with a drinking driver when they have a driver's license (McCarthy & Brown, 2004; Poulin et al., 2007). Results of the present study provide evidence that personal drinking and driving decisions are more strongly influenced by individual difference characteristics, such as desiring intense or stimulating experiences. Riding with a drinking driver appears to be more strongly influenced by external factors such as parental monitoring, and these differences remained even after controlling for the effect of license status. However, there was also evidence for moderation of the influence of disinhibited personality traits on personal drinking and driving. The accessibility of alcohol and degree of parental monitoring either facilitated or constrained drinking and driving risk for disinhibited youths.
Personality characteristics, parental monitoring, and alcohol accessibility did not predict increased likelihood of engagement in drinking and driving behaviors once prior drinking and driving behaviors and alcohol use were accounted for. The exception was sensation seeking, which predicted engagement in riding with a drinking driver. These results may be due, in part, to the relatively brief time period of the study (approximately 8 months), which limited the number of youths who initiated drinking and driving over the course of the study. These findings may also be the result of strong bivariate associations of Time 1 alcohol use frequency (OR = 2.20, p < .001) and drinking and driving (OR = 1.85, p < .001) with engagement in drinking and driving at Time 2, making it difficult for study variables to add unique prediction.
We also did not find support for mediation of personality risk for drinking and driving by environmental factors. Impulsive personality traits do not appear to influence either parental monitoring or alcohol accessibility. Although sensation seeking youths reported lower parental monitoring and greater access to alcohol at Time 2, this did not explain sensation seeking's influence on drinking and driving behaviors. However, these results provide some evidence that sensation seeking might influence changes in these environmental characteristics over time. While the magnitude of sensation seeking's bivariate association with parental monitoring and alcohol accessibility was modest, it is similar to that observed between five-factor personality traits and parent and peer support (Asendorpf & van Aken, 2003) in longitudinal studies supporting transactional associations over time. One direction for future research is to examine whether sensation seeking is associated with changes in alcohol accessibility and parental monitoring over a longer developmental period. Having additional time points would also allow for a more stringent test of how personality may influence environmental factors and subsequent drinking and driving behaviors over time.
Another direction for future research is to examine other environmental and contextual factors that might mediate the influence of disinhibited personality traits on drinking and driving. For example, sensation seeking has been found to influence alcohol and drug use in part through influencing youths' associations with deviant or substance-using peers (Yanovitzky, 2005). Associating with deviant peers may help explain why sensation seeking was related to riding with a drinking driver in the current study. There is also evidence that drinking context (e.g., outdoors, at bars; Usdan, Moore, Schumacher, & Talbott, 2005) and lack of transportation planning prior to drinking (Nelson, Kennedy, Isaac, & Graham, 1998) are associated with drinking and driving behaviors. Further research is required to test whether disinhibited youths are more likely to drink in situations where drinking and driving is likely to occur or whether they are less likely to plan for transportation prior to drinking.
The current study also showed changes in the frequency and engagement of drinking and driving over time (see Table 1). One potential reason for this pattern of results is that youths who just begin drinking and driving over the course of the study may initially engage in this behavior less frequently than do those individuals with more established drinking and driving behaviors. Data from the current study tentatively support this possibility. Youths who did not report drinking and driving at Time 1 but did at Time 2 reported an average of 2.8 drinking and driving occasions. Individuals who reported drinking and driving at both time points reported an average of 6.0 drinking and driving occasions at Time 2. Future studies could more directly explore how the rate of increase of drinking and driving frequency changes over time.
There are several limitations to the generalizability of the current study. Participants were primarily recruited from high school campuses in the central Missouri area. There are significant regional differences in the prevalence of drinking and driving behavior, with higher rates in the Midwest (Chou et al., 2006). In addition, although efforts were made to recruit high school age youths from community sources, the use of school-based recruitment can introduce sample biases due to absenteeism, truancy, or disengagement from academics by some youths, particularly disinhibited or substance-involved youths. Female adolescents were also overrepresented in our sample. Although there is ample evidence that male adolescents are at greater risk for engaging in and experiencing consequences of drinking and driving (Hingson & Winter, 2003; Wechsler, Lee, Nelson, & Lee, 2003), rates of drinking and driving behaviors did not differ by gender. Although gender was controlled for in study analyses, our sample size prevented us from conducting study analyses separately by gender.
The study relied on self-report for assessing all study variables. Studies have demonstrated that self-report measures of alcohol use and related behavior can be valid in youths, particularly when data collection is confidential or anonymous and when no consequences are associated with the report (Smith et al., 1995; Wilson & Grube, 1994). For parental monitoring and alcohol accessibility, youth report may not provide an accurate representation of these two constructs. However, there is some evidence that youths' perceptions of the home environment, such as parental behaviors, are most relevant in determining youth behavior (Smith, Miller, Kroll, Simmons, & Gallen, 1999). Nevertheless, studies of both parental monitoring (Laird et al., 2003) and alcohol accessibility (Dent et al., 2005) have demonstrated that supplementing youth report with parent report or community level information can provide a fuller assessment of these constructs. Additionally, parental monitoring and alcohol accessibility were assessed only at Time 2, which limited our ability to test how changes in these environmental factors influence drinking and driving behaviors over time.
Recent research on parental monitoring has also indicated greater complexity of this construct than is reflected in this study. Studies have found differences in the influence of what parents know (parental knowledge) and how they know it (active efforts to monitor behavior, child disclosure) on youth behavior (Kerr & Stattin, 2000). In studies of youth substance use and delinquency, youth self-disclosure and parental knowledge have been found to mediate the influence of parenting style on youth behavior (Soenens, Vansteenkiste, Luyckx, & Goossens, 2006), although there is also evidence for direct effects of parental control and monitoring (Fletcher, Steinberg, & Williams-Wheeler, 2004). It is important for future studies to test whether disinhibited personality traits have distinct influences on youth self-disclosure and parental knowledge.
Results of this study provide evidence for person–environment transactions in the development of youth drinking and driving behavior. It is well known that disinhibited personality traits predict drinking and driving and other risk-taking behaviors. Results of this study suggest that the disinhibited traits of impulsivity and sensation seeking may have different implications for driving after drinking or riding with a drinking driver. This is consistent with prior research demonstrating differential prediction of externalizing behaviors by these traits (e.g., Lynam & Miller, 2004; Miller et al., 2003). Improving our understanding of mechanisms by which these personality traits lead to specific risk-taking behaviors can improve prevention/intervention efforts. For example, alcohol control policies (Babor et al., 2003) and parental intervention strategies that increase communication and parental awareness (Turrisi, Jaccard, Taki, Dunnam, & Grimes, 2001) have been found to be effective in reducing youth alcohol use. Our results suggest that these may also be effective adolescent drinking and driving interventions, particularly when targeting disinhibited youths. As study results suggest that parental monitoring may be of particular importance for riding with a drinking driver, interventions that increase parental awareness and communication may also benefit from targeting youths prior to their obtaining a driver's license.
Footnotes 1 For each mediation test, one of the component paths is assessed as a standard regression/correlation coefficient (e.g., sensation seeking and alcohol accessibility), while the other is assessed as a Poisson regression coefficient (e.g., alcohol accessibility and drinking and driving). This lack of correspondence made several of the standard methods of testing mediation (product of coefficients, estimation of indirect effects) inappropriate in the current study. Instead, Poisson regression coefficients were compared with and without the mediator included in the model. In each case, the 95% confidence interval of the two coefficients overlapped considerably. Although this method is not ideal, for the present study we believe it was sufficient to demonstrate absence of mediation.
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Submitted: April 9, 2007 Revised: February 4, 2008 Accepted: February 6, 2008
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Source: Psychology of Addictive Behaviors. Vol. 22. (3), Sep, 2008 pp. 340-348)
Accession Number: 2008-11981-003
Digital Object Identifier: 10.1037/0893-164X.22.3.340
Record: 113- Title:
- Posttraumatic stress in deployed Marines: Prospective trajectories of early adaptation.
- Authors:
- Nash, William P.. Boston VA Research Institute, MA, US
Boasso, Alyssa M.. VA Boston Healthcare System, Jamaica Plain, MA, US
Steenkamp, Maria M.. VA Boston Healthcare System, Jamaica Plain, MA, US
Larson, Jonathan L.. VA Boston Healthcare System, Jamaica Plain, MA, US
Lubin, Rebecca E.. VA Boston Healthcare System, Jamaica Plain, MA, US
Litz, Brett T.. VA Boston Healthcare System, Jamaica Plain, MA, US, brett.litz@va.gov - Address:
- Litz, Brett T., VA Boston Healthcare System, 150 South Huntington Avenue, 13- B74, Jamaica Plain, MA, US, 02130, brett.litz@va.gov
- Source:
- Journal of Abnormal Psychology, Vol 124(1), Feb, 2015. pp. 155-171.
- NLM Title Abbreviation:
- J Abnorm Psychol
- Page Count:
- 17
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- The Journal of Abnormal and Social Psychology; The Journal of Abnormal Psychology; The Journal of Abnormal Psychology and Social Psychology
- ISSN:
- 0021-843X (Print)
1939-1846 (Electronic) - Language:
- English
- Keywords:
- military, trajectory, PTSD, dissociation, coping
- Abstract:
- We examined the course of PTSD symptoms in a cohort of U.S. Marines (N = 867) recruited for the Marine Resiliency Study (MRS) from a single infantry battalion that deployed as a unit for 7 months to Afghanistan during the peak of conflict there. Data were collected via structured interviews and self-report questionnaires 1 month prior to deployment and again at 1, 5, and 8 months postdeployment. Second-order growth mixture modeling was used to disaggregate symptom trajectories; multinomial logistic regression and relative weights analysis were used to assess the role of combat exposure, prior life span trauma, social support, peritraumatic dissociation, and avoidant coping as predictors of trajectory membership. Three trajectories best fit the data: a low-stable symptom course (79%), a new-onset PTSD symptoms course (13%), and a preexisting PTSD symptoms course (8%). Comparison in a separate MRS cohort with lower levels of combat exposure yielded similar results, except for the absence of a new-onset trajectory. In the main cohort, the modal trajectory was a low-stable symptoms course that included a small but clinically meaningful increase in symptoms from predeployment to 1 month postdeployment. We found no trajectory of recovery from more severe symptoms in either cohort, suggesting that the relative change in symptoms from predeployment to 1 month postdeployment might provide the best indicator of first-year course. The best predictors of trajectory membership were peritraumatic dissociation and avoidant coping, suggesting that changes in cognition, perception, and behavior following trauma might be particularly useful indicators of first-year outcomes. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Disease Course; *Military Deployment; *Posttraumatic Stress Disorder; Avoidance; Coping Behavior; Dissociative Disorders; Social Support; Symptoms; Trauma
- PsycINFO Classification:
- Neuroses & Anxiety Disorders (3215)
Military Psychology (3800) - Population:
- Human
Male - Location:
- US
- Age Group:
- Adulthood (18 yrs & older)
- Tests & Measures:
- Posttraumatic Stress Disorder Checklist
World Health Organization Disability Assessment Scale-II
Life Events Checklist
General Post-Deployment Support Scale
Brief COPE
Beck Depression Inventory–II DOI: 10.1037/t00742-000
Childhood Trauma Questionnaire DOI: 10.1037/t02080-000
Clinician-Administered PTSD Scale DOI: 10.1037/t00072-000
Deployment Risk and Resilience Inventory DOI: 10.1037/t04522-000
Peritraumatic Dissociative Experiences Questionnaire DOI: 10.1037/t07470-000 - Grant Sponsorship:
- Sponsor: VA Health Service Research and Development, US
Grant Number: SDR 09-0128
Recipients: No recipient indicated
Sponsor: U. S. Marine Corps, US
Recipients: No recipient indicated
Sponsor: Navy Bureau of Medicine and Surgery, US
Recipients: No recipient indicated - Methodology:
- Empirical Study; Longitudinal Study; Quantitative Study
- Supplemental Data:
- Tables and Figures Internet
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- First Posted: Nov 24, 2014; Accepted: Oct 6, 2014; Revised: Oct 5, 2014; First Submitted: Feb 8, 2014
- Release Date:
- 20141124
- Correction Date:
- 20150216
- Copyright:
- American Psychological Association. 2014
- Digital Object Identifier:
- http://dx.doi.org/10.1037/abn0000020; http://dx.doi.org/10.1037/abn0000020.supp(Supplemental)
- PMID:
- 25419860
- Accession Number:
- 2014-49228-001
- Number of Citations in Source:
- 90
- Persistent link to this record (Permalink):
- http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2014-49228-001&site=ehost-live
- Cut and Paste:
- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2014-49228-001&site=ehost-live">Posttraumatic stress in deployed Marines: Prospective trajectories of early adaptation.</A>
- Database:
- PsycINFO
Posttraumatic Stress in Deployed Marines: Prospective Trajectories of Early Adaptation
By: William P. Nash
Boston VA Research Institute
Alyssa M. Boasso
VA Boston Healthcare System and Massachusetts Veterans Epidemiology Research and Information Center
Maria M. Steenkamp
VA Boston Healthcare System, Massachusetts Veterans Epidemiology Research and Information Center, and Boston University School of Medicine
Jonathan L. Larson
VA Boston Healthcare System and Massachusetts Veterans Epidemiology Research and Information Center
Rebecca E. Lubin
VA Boston Healthcare System and Massachusetts Veterans Epidemiology Research and Information Center
Brett T. Litz
VA Boston Healthcare System, Massachusetts Veterans Epidemiology Research and Information Center, and Boston University School of Medicine;
Acknowledgement: This study was funded by VA Health Service Research and Development (SDR 09-0128) and by the U. S. Marine Corps and Navy Bureau of Medicine and Surgery. The authors acknowledge the Marine Resiliency Study (MRS) team, General John M. Paxton Jr., USMC, and Debbie Paxton, RN, who made this work possible. We also thank Kevin Grimm, who provided statistical feedback and suggestions.
Posttraumatic stress disorder (PTSD) is a psychopathological condition for which the course of symptoms, as they evolve over time, is of particular theoretical and practical importance. It is one of a small group of mental disorders, termed trauma- and stressor-related disorders in the fifth edition of the Diagnostic and the Statistical Manual of Mental Disorders (DSM-5; American Psychiatric Association, 2013), whose onset is temporally linked to a specific triggering event. To be diagnosed, symptoms must persist beyond the first 30 days following trauma exposure, and one of the two subtypes of PTSD, delayed PTSD, is characterized by symptoms that first appear 6 months after the event. Moreover, the time course of PTSD symptoms is central to the study of adaptation to trauma, and key concepts such as risk, resilience, and recovery have clear meanings only when defined in terms of changes in symptoms and functioning over time (Layne, Warren, Watson, & Shalev, 2007). Tracking the course of PTSD symptoms over time might identify the processes that determine long-term outcomes, discriminating between normative and pathological responses to experiences of trauma and recognizing the points at which early interventions might positively influence outcomes.
The theoretical and practical implications of the time course of PTSD symptoms might be especially salient for the military, which bears primary responsibility for preventing negative psychological health outcomes such as PTSD in service members whose occupations place them at high risk for exposure to potentially traumatic events. For military prevention and early intervention programs, one key question can only be answered in terms of the time course of posttraumatic stress symptoms: among service members exposed to potentially traumatic events, at which point in their symptom courses can those at greatest risk for developing chronic PTSD be identified so that their further exposure to potentially traumatic events can be limited and early interventions to promote recovery can be provided?
Early cross-sectional and retrospective research on PTSD in both military and nonmilitary populations divided groups of people who had been exposed to potentially traumatic events into those who exceeded diagnostic thresholds for the disorder and those who did not, ignoring potential heterogeneity within those two categories. More recent longitudinal studies in many different populations suggest that posttraumatic stress symptoms might trace a number of distinct trajectories over time, which can be grouped into clusters of approximately similar patterns (Bonanno, 2004). The research question addressed by these studies is whether between-individual differences in adaptation to exposure to potentially traumatizing events generate groups of people following similar within-individual courses. A number of statistical tools can be used to answer this question, including growth models, growth mixture models, and latent profile analysis; each procedure has strengths and weaknesses. PTSD researchers have chosen to use growth mixture modeling (GMM), a data-driven statistical procedure that groups participants on the basis of their intra-individual change patterns (e.g., McArdle & Epstein, 1987; Muthén, 2004). In this article, we report the results of GMM analyses of posttraumatic stress trajectories in two cohorts of U.S. Marines enrolled in the Marine Resiliency Study (MRS; see Baker et al., 2012), assessed approximately 1 month prior to their 7-month deployments to Afghanistan, then reexamined at three time points during the 8 months immediately following their return from deployment. We chose GMM chiefly to maximize the comparability of the results with prior studies of deployed service members, all of which employed GMM.
To date, four longitudinal studies have used GMM to identify latent PTSD trajectories in military populations: U.S. Army soldiers followed for 9 months after they deployed to Kosovo on a 6-month NATO-led peacekeeping mission in 2002 (Dickstein et al., 2010); U.S. Army soldiers assessed within 5 days of their return from the Gulf War in 1991 and again at 1.5 and 6 years later (Orcutt, Erickson, & Wolfe, 2004); Danish soldiers followed for 7 months after their return from a 6-month deployment to Afghanistan in 2009 (Berntsen et al., 2012); and a large and heterogeneous cohort of military service members who deployed one or more times to Iraq or Afghanistan between 2001 and 2008 (Bonanno et al., 2012). All of these studies found evidence for qualitatively distinct trajectories of PTSD symptoms, the exact nature of which depended on the sample, methodology, and context. Trajectories characterized by low symptom levels at all time points were modal in all four studies, comprising between 57% and 84% of each sample. Chronic symptoms or new-onset symptoms were uncommon, each representing less than 10% of the sample, when present. Currently, there are no studies of the course of PTSD symptoms in a sample of U.S. service members from a single military unit deployed together for combat duties in Iraq or Afghanistan. Bonanno et al.’s (2012) sample was more heterogeneous, comprising members of all service branches and all occupational fields, many of whom did not serve in combat roles. No existing studies have reported PTSD symptom trajectories in a cohort of U.S. service members deployed to a war zone specifically to engage in ground combat, a population at high risk for combat-related PTSD and, therefore, of great interest to leaders of military PTSD prevention, screening, and treatment programs.
In this study, we chose five sets of self-reported predictor variables relevant to the military that have been repeatedly found to correlate with posttraumatic stress outcomes: combat-related stressor exposures experienced during deployment, lifetime stressor exposures experienced outside the index deployment, perceived social support during and after deployment, peritraumatic dissociation, and avoidant coping. Combat exposure has consistently been shown to be a leading risk factor for PTSD in military personnel, typically in a dose-response fashion (e.g., Dohrenwend et al., 2006; Foy, Sipprelle, Rueger, & Carroll, 1984; Green, Grace, Lindy, Gleser, & Leonard, 1990; King, King, Foy, Keane, & Fairbank, 1999). As predicted by diathesis–stress models of PTSD (e.g., McKeever & Huff, 2003), greater prior life span trauma exposure has been found to confer heightened risk for combat-related PTSD, and high rates of predeployment trauma are present in military personnel (e.g., Clancy et al., 2006; Vogt, Pless, King, & King, 2005; Vogt et al., 2011; Zaidi & Foy, 1994). Because we are interested in the relationship between these two variables, we examined the influence of the interaction between prior trauma and combat exposure on PTSD symptom course. Consistent with the diathesis–stress model, we expected prior life span trauma to moderate the relationship between combat exposure and new-onset or chronic PTSD symptom trajectories. We expected social support during and after deployment to buffer adverse psychological outcomes because it mitigates distress and promotes shared meaning making (e.g., Brailey, Vasterling, Proctor, Constans, & Friedman, 2007), and veterans with PTSD consistently report lower unit support and postdeployment social support (e.g., Keane, Scott, Chavoya, Lamparski, & Fairbank, 1985; Pietrzak et al., 2010).
Peritraumatic dissociation, which entails transient alterations in the normal integration of cognitive, emotional, somatic, and behavioral processes during or immediately after a potentially traumatic event, was included as a predictor variable because it is a marker for stressors experienced by a given person at a given point in time that exceeded their current adaptive capacity, as predicted by the stress injury model of PTSD (Nash, 2007; Nash, Silva, & Litz, 2009; Nash et al., 2010). The stress injury model does not predict which variables confer risk or are protective per se, but focuses on the relationship between moment-to-moment stress levels and ideographic stress breaking points determined by fluctuating biological, psychological, and social functional capacities. According to this model, stress outcomes that follow superthreshold stressor exposures, including subclinical stress injuries and mental disorders such as PTSD, are more likely to be pathological than outcomes that follow less extreme stressor experiences, which are more likely to be normative. Previous studies have found peritraumatic dissociation to be associated with more adverse postevent outcomes, and trauma-exposed people with PTSD are more likely to report having experienced peritraumatic dissociation than did those without PTSD (e.g., Bremner & Brett, 1997; Marmar et al., 1994; O’Toole, Marshall, Schureck, & Dobson, 1999; Tichenor, Marmar, Weiss, Metzler, & Ronfeldt, 1996). Avoidant coping, characterized as utilizing distraction, denial, or disengagement as mechanisms to manage problems, is hypothesized to increase risk for PTSD in two ways: habitual avoidant coping leading up to exposure to a traumatic stressor might contribute to vulnerability for PTSD, and, in the aftermath of trauma, overgeneralized avoidant coping and self-soothing repertoires might lessen the likelihood of corrective recovery-promoting experiences. Avoidant coping has repeatedly been found to correlate with PTSD symptom severity (e.g., Pietrzak, Harpaz-Rotem, & Southwick, 2011; Sharkansky et al., 2000; Solomon, Mikulincer, & Benbenishty, 1989). Service members with PTSD have also been shown to be more likely to use avoidant rather than nonavoidant coping strategies (e.g., Sutker, Davis, Uddo, & Ditta, 1995), whereas decreased use of avoidant coping over time has been associated with recovery from combat stress (Solomon, Mikulincer, & Avitzur, 1988).
On the basis of previous longitudinal studies of PTSD symptom courses, we predicted that six trajectories of PTSD symptom severity would best describe the data: (1) a quadratic recovery course characterized by low predeployment symptoms, followed by high initial postdeployment symptoms, and then a marked decrease in symptoms toward baseline; (2) a relatively flat low–stable course with low symptom levels across all time points; (3) a new- onset course characterized by high and relatively unremitting symptoms across all postdeployment time points that follow a low level of PTSD symptoms prior to deployment; (4) a preexisting–improving course characterized by high levels of PTSD prior to deployment followed by a decrease in symptom levels postdeployment; (5) a preexisting–chronic course characterized by high levels of PTSD prior to deployment that do not decrease during the postdeployment period; and (6) a delayed course characterized by low symptom levels before and immediately after deployment, but an increase in PTSD-symptom burden during the 8 months following return from deployment. Given the high frequency of significant warzone stressors expected in Afghanistan and the high levels of resilience expected in highly trained Marines, we predicted that the recovery trajectory would be most prevalent. We expected the next most prevalent courses to be low–stable and new-onset courses. Given prior warzone deployments and other predeployment stressor exposures in our cohort, we expected the two preexisting PTSD courses to also occur in significant percentages of participants.
We predicted that combat-related experiences during the index deployment would best predict membership in the new-onset, delayed, and preexisting–chronic trajectories and that prior life span stressor exposures would best predict membership in the preexisting–improving and preexisting–chronic trajectories. We expected prior life span trauma and combat experiences during the index deployment to interact to increase vulnerability for the new-onset, chronic, and delayed PTSD courses. That is, we predicted that Marines with extensive trauma histories would be affected by relatively lower doses of combat exposure, tracing worse PTSD outcomes over time than Marines with similar levels of combat exposure but no prior trauma. We expected peritraumatic dissociation and avoidant coping to confer risk for all persistently negative outcomes, including the new-onset, delayed, and preexisting–chronic trajectories. Conversely, we expected perceived social support to serve a protective function, with Marines in the recovery, low–stable, and preexisting–improving trajectories reporting greater perceived social support than Marines in the new-onset, delayed, and preexisting–chronic trajectories. Assessment of the relative importance of these predictors, using relative weights analysis, was largely exploratory.
Method Design and Participants
The data source for this study was the MRS, a longitudinal field study of four consecutive all-male cohorts (named Cohorts 1, 2, 3, and 4) of active-duty ground-combat Marines, each recruited primarily from a single infantry battalion scheduled to deploy to Iraq or Afghanistan between 2008 and 2011 from either Marine Corps Base Camp Pendleton or Marine Corps Air Ground Combat Center, Twenty-Nine Palms, both in California (Baker et al., 2012). Four assessment time points were planned for each cohort: 1 month prior to its 7-month deployment, and 1 week, 3 months, and 6 months postdeployment. Overall, 2,593 Marines completed the Time-0 (T0) predeployment assessment; 2,317 (89.3%) completed the Time-1 (T1) assessment; 1,901 (73.3%) completed the Time-2 (T2) assessment; and 1,634 (63.0%) completed the Time-3 (T3) assessment. Participation at each assessment was voluntary and individual informed consent was obtained before enrollment at baseline with no senior unit leaders present.
For this study, full analyses focused exclusively on Cohort 4, whereas Cohort 3 was used for a post hoc comparison of latent trajectory patterns. Cohorts 1 and 2 were excluded because their PTSD scores at baseline were indexed exclusively to military events, whereas their postdeployment PTSD scores were indexed to any currently distressing lifetime events; this threat to internal validity made an examination of PTSD symptom trajectories problematic across time in Cohorts 1 and 2. In Cohorts 3 and 4, PTSD symptoms were indexed at all time points to any currently distressing lifetime event. Cohorts 3 and 4 were analyzed separately to avoid two other internal validity problems. The first of these arose because modal postdeployment assessment times differed by as much as 3 months between these cohorts. The second reason we analyzed Cohorts 3 and 4 separately was because these cohorts predominantly comprised members of two distinct Marine infantry battalions that trained, deployed, and then returned as units from two very different sets of warzone challenges. Combining them into a single larger sample might introduce a number of uncontrolled between-unit variances. Cohort 4 deployed to Helmand Province in Afghanistan in late 2010, when U.S. forces sustained their highest causality rates. Cohort 3, having deployed earlier than did Cohort 4, before the heaviest fighting began, reported significantly lower combat exposure than Cohort 4, t(1,926) = 14.27, p < .001. For this study, Cohort 4 (N = 892) offered the best opportunity to examine the course of PTSD in highly combat-exposed U.S. service members. We used Cohort 3 (N = 673) to compare the GMM results of Cohort 4 with a sample of similarly assessed Marines with less overall combat exposure.
For this study, we removed Marines who did not deploy (9 in Cohort 3 and 4 in Cohort 4) and those who died during deployment (2 in Cohort 3 and 17 in Cohort 4). To address variability around the modal postdeployment assessment times, which differed between Cohorts 3 and 4, we used the following procedures, outlined by King et al. (2006). For the three postdeployment assessments, scores on all measures were assigned to three follow-up date ranges determined by the count of days since the date of return from deployment. We aimed to minimize the dispersion of days within each date range and to maximize the number of included participants. This was done separately for Cohorts 3 and 4. The date ranges across Cohorts 3 and 4 differed only with respect to the second postdeployment assessment (T2). The ranges of days that best fit the data were 20 to 40 days for T1 (Cohort 3: M = 30, SD = 6; Cohort 4: M = 30, SD = 4), 80 to 100 days for T2 for Cohort 3 (M = 84, SD = 3), but 140 to 160 days for T2 for Cohort 4 (M = 153, SD = 4), and 240 to 260 days for T3 (Cohort 3: M = 251, SD = 2; Cohort 4: M = 249, SD = 5). In other words, on average, assessments for Cohort 4 occurred 1 month predeployment (T0) and 1-month (T1), 5-months (T2), and 8-months (T3) postdeployment. For Cohort 3, on average, assessments occurred 1 month predeployment (T0) and 1-month (T1), 2-months (T2), and 8-months (T3) postdeployment. Once the data were redistributed according to actual date ranges, 4 Marines in Cohort 4 and 4 Marines in Cohort 3 were missing data at all time points and were excluded from analyses. For Cohort 4, the final subsample of responders consisted of 867 Marines: 859 at baseline, 554 at T1, 328 at T2, and 287 at T3. For Cohort 3, the final subsample of responders consisted of 658 Marines: 653 at baseline, 377 at T1, 382 at T2, and 215 at T3.
Table 1 displays statistical comparisons at baseline (T0) between Marines whose PTSD symptom severity data were available at each subsequent time point (responders) and Marines missing PTSD outcome data at those time points (nonresponders). Nonresponders at T1, T2, and T3 were more likely to have previously deployed. In addition, nonresponders at T2 were more functionally impaired and had more prior lifetime trauma. Nonresponders at T3 were more educated, and nonresponders at T2 and T3 were older. In the final Cohort 4 sample, Marines were primarily Caucasian (83.1%). At baseline, participants had served an average of 3.10 (SD = 3.15) years in the military and 51.54% had deployed at least once before. Participants’ ages at baseline ranged from 18 to 43 (M = 23.16, SD = 3.67); 68.1% had no more than a high school diploma, and 41.1% were married (see Table 2). Sample information for Cohort 3 can be found in Tables 1 and 2 of the supplemental materials.
Responder and Nonresponder Comparisons on Variables Reported at Time 0 (T0)
Risk, Resilience, and Mental Health Factors Throughout the Deployment Cycle
Outcome Measures
PTSD
PTSD symptom severity was assessed using both a structured clinical interview, the Clinician Administered PTSD Scale (CAPS; Blake et al., 1995), and a self-report questionnaire, the Posttraumatic Stress Disorder Checklist (PCL; Weathers, Litz, Herman, Huska, & Keane, 1993). The CAPS was used at T0, T2, and T3, but was not used at T1 to minimize participant burden in the early weeks postdeployment. The PCL was used at all time points. At every time point, CAPS and PCL assessments were indexed to any lifetime traumatic event endorsed by the participant as currently most distressing. Consequently, index events were allowed to change during the course of the study, ensuring the capture of maximum symptom burden at each time point.
The CAPS assesses the frequency and intensity of PTSD symptoms, each rated on a Likert-type scale. ranging from 0 (“Never” or “None”) to 4 (“Daily or almost daily” or “Extreme, incapacitating distress, cannot dismiss memories, unable to continue activities”). Total CAPS PTSD symptom severity was calculated by summing the frequency and intensity scales for each item (yielding a range of 0 to 136; Blake et al., 1995). Raters were systematically trained and certified doctoral-level personnel. All CAPS interviews were audio recorded and 15% were randomly selected and co-rated to determine interrater reliability (intraclass correlation coefficient [ICC] = .99; Shrout & Fleiss, 1979). The PCL assesses the severity of PTSD symptoms on a 1 (not at all bothersome) to 5 (extremely bothersome) Likert-type scale. Total PTSD symptom burden was calculated by summing across all 17 symptoms (yielding a range of 17 to 85). The CAPS and the PCL have been shown to have excellent psychometric properties in numerous studies with varied populations (see Weathers et al., 2001).
Convergent outcome indicators
To substantiate the class solutions generated by the GMM, we compared membership in the PTSD trajectories we found with four classes of outcomes we believed would covary with PTSD trajectory: full or subthreshold PTSD caseness based on diagnostic criteria, depression, anxiety, and overall functioning. Using the CAPS at T0, T2, and T3, we defined a PTSD diagnosis as meeting the minimum type and number of symptoms required by DSM–IV criteria (American Psychiatric Association, 2000), each rated at least at a frequency of 1 and a severity of 2 (Weathers et al., 1999). We defined subthreshold PTSD conservatively, by requiring a participant to meet the DSM–IV criteria for Category B symptoms and either Category C or D symptoms (e.g., Blanchard et al., 1995). Because the CAPS was not administered at T1, the PCL was used to determine full and subthreshold PTSD caseness at the initial postdeployment time point; a required symptom was considered present if it was endorsed on the PCL at a severity level of moderately (a value of 3) or above. We assessed depression using the Beck Depression Inventory-II (BDI-II; Beck, Steer, & Brown, 1996), a 21-item questionnaire that assesses symptoms of depression. The internal consistency of the BDI-II in our study was uniformly high (T0: α = .90; T1: α = .89; T2: α = .91; T3: α = .90). We assessed anxiety using the Beck Anxiety Inventory (BAI; Beck, Epstein, Brown, & Steer, 1988), which was also found to have high levels of internal consistency in the MRS (T0: α = .90; T1: α = .92; T2: α = .92; T3: α = .94). Summary scores for both scales at each time point were created by summing across all 21 items. We assessed overall functioning and self-reported disability using the 17-item World Health Organization Disability Assessment Scale-II (WHODAS; Smith & Epping-Jordan, 2000). Summary scores were calculated at each time point by summing across 12 core items that were endorsed on a five-point Likert-type scale, ranging from “None” to “Extreme/cannot do”. The internal consistency of the WHODAS was very high in the MRS (T0: α = .90; T1: α = .92; T2: α = .90; T3: α = .93). The reliabilities for the BDI-II, BAI, and WHODAS for Cohort 3 are shown in Table 3 of supplemental materials.
Fit Statistics for Class Solutions
Predictor Variables
Life span trauma
We assessed a history of previous highly stressful, potentially traumatic experiences using two measures at T0 only: the Childhood Trauma Questionnaire (CTQ; Bernstein et al., 1994) and the Life Events Checklist (LEC; Gray, Litz, Hsu, & Lombardo, 2004). The CTQ is a 28-item questionnaire that assesses the frequency of experiences of abuse or neglect during childhood, each endorsed on a five-point Likert-type scale, ranging from 1 (never true) to 5 (very often true). Childhood trauma summary scores are normally created using the CTQ by summing across its 25 items, grouped into five subscales of five items each. The MRS employed a modified 22-item version of the CTQ, with one item intentionally missing from each of the emotional abuse, physical abuse, and physical neglect subscales, as recommended by our institutional review board. To make modified 22-item CTQ summary scores comparable to childhood trauma scores calculated using all 25 items, we weighted all four-item subscales as if they also reflected responses to five items, then summed all subscale scores to create a composite. This total score was then divided by 25 to obtain a mean childhood trauma score (α = .92).
The LEC assesses lifetime exposure to 16 specific classes of highly stressful, potentially traumatic events. Response options are: happened to me, witnessed it, learned about it, not sure, and doesn’t apply. A prior lifetime trauma composite score was created by first assigning a “1” to each item endorsed as happened to me or witnessed it and a “0” to all other responses and then summing across the 16 events.
Warzone stressor exposures
We used two measures of warzone stressor exposure taken from the Deployment Risk and Resilience Inventory (DRRI; King, King, Vogt, Knight, & Samper, 2006), a collection of questionnaires assessing military deployment-related experiences. To assess exposure to combat events, we used a modified version of the Combat Experiences scale, a 15-item yes/no scale that assesses individual- or unit-level exposure to warzone-related stressors such as “I fired my weapon at the enemy.” The MRS modified the DRRI Combat Experiences scale by changing response choices to a Likert-type scale based on frequency of exposure, ranging from 0 (never) to 4 (daily or almost daily), as suggested by Vogt, Proctor, King, King, and Vasterling (2008); it changed item wording to restrict focus to the personal experiences of the respondent; and added an additional item assessing participation in logistical support convoys. A combat exposure composite was created by averaging across all items (α = .91). We assessed perceptions of danger during deployment using the Perceived Threat scale, a 15-item questionnaire that assesses fear for personal safety and well-being in the warzone, with Likert-type response choices, ranging from 1 (strongly disagree) to 5 (strongly agree). A perceived threat composite was calculated as the mean of all 15 items (α = .84). The two DRRI scales were administered at T1 only.
Peritraumatic dissociation
The Peritraumatic Dissociative Experiences Questionnaire (PDEQ; Marmar, Weiss, & Metzler, 1997) is a 10-item measure of dissociative experiences that uses Likert-type scale response choices that range from 1 (not true at all) to 5 (extremely true). The MRS version asked participants to report dissociative experiences that occurred during the worst event from their most recent deployment, assessed only at T1 (α = .88). An index of peritraumatic dissociation was calculated as the mean of all 10 items.
Social support
We used two questionnaires from the DRRI to measure perceived social support: The Unit Support scale and the General Post-Deployment Support scale. The Unit Support scale includes 12 items that assess perceived levels of cohesion and camaraderie within the military unit during deployment, each rated on a five-point Likert-type scale, ranging from 1 (strongly disagree) to 5 (strongly agree). We calculated unit support composite scores by averaging across all items at T1 (α = .93). The General Post-Deployment Support scale uses 15 items to assess perceptions of social support from all sources, including family, friends, and community, since returning from deployment, each rated on a five-point Likert-type scale, ranging from 1 (strongly disagree) to 5 (strongly agree). We created a composite of general social support by averaging all 15 items across all three postdeployment time points, T1–T3 (T1: α = .87; T2: α = .89; T3: α = .89).
Avoidant coping
We assessed avoidant coping using subscales from the Brief COPE, a 28-item questionnaire that assesses 14 different coping styles, each rated on a four-point Likert-type scale, ranging from 1 (I have not been doing this at all) to 4 (I have been doing this a lot; Carver, 1997). The Brief COPE does not specify a time frame in which coping strategies should be self-assessed and reported but asks to what extent each identified coping strategy seems to have been habitual at each assessment time point. To create an index of avoidant coping across the deployment cycle, we averaged scores on five 2-item subscales of the Brief COPE (Schnider, Elhai, & Gray, 2007) across all four data-collection time points: self-distraction, denial, behavioral disengagement, self-blame, and substance use (T0: α = .81; T1: α = .80; T2: α = .81; T3: α = .82).
Data Analysis Plan
It is worth underscoring limitations to the use of mixture models (the underlying statistical framework for GMMs) and the approaches we took to minimize their impact. The first limitation is that the latent classes in mixture models are not necessarily real entities. As noted, the mixture modeling approach to classification is data-driven: The model attempts to maximize a likelihood function that involves mixture distributions, with each component of the mixture distribution referred to as a latent class, even though these derived groups might not be entirely distinct. To minimize the challenges of using mixture modeling, we examined the interpretability of the latent classes we found and attempted to replicate the pattern of latent classes we found in a second sample. The second limitation is that mixture models are sensitive to starting values. To minimize the impact of this sensitivity, we fit the mixture models with multiple random sets of starting values to ensure that the global maximum was reached. Additionally, the same set of parameter estimates were obtained from multiple sets of starting values suggesting that the solutions were stable. The third limitation involves how the relative fitness of alternative models is compared. Mixture models with a different number of classes are not statistically nested. Thus, researchers are limited to using information criteria (e.g., Bayesian information criterion, Akaike information criterion) and a variety of additional comparative fit indices (e.g., bootstrap likelihood ratio test, Lo-Mendell-Rubin likelihood ratio Test), which often converge to different recommendations. Our approach to model comparison involved a thorough examination of all fit indices and weighed substantive interpretation and classification quality (e.g., entropy). The fourth limitation is the reliance of the mixture model on non-normality. Mixture models attempt to account for non-normal distributions with mixture distributions; however, non-normality might be due to a variety of issues (e.g., poor sampling, poor measurement, etc.; see Bauer & Curran, 2003; Grimm & Ram, 2009). To address this limitation, we thoroughly examined the interpretability of the latent classes, attempted to replicate the solution in a second sample, and fit second-order models to limit the impact of poor measurement. Additionally, we ensured that our chosen model contained latent classes that were distinguishable in terms of initial status, change trajectories, and predictor variables and outcomes (Erosheva, Matsueda, & Telesca, 2014).
Second-Order Growth Mixture Model
GMM is a person-centered analysis that identifies subgroups within a given sample that are defined by a common pattern of change in an outcome variable over time (Jung & Wickrama, 2008). We used a second-order growth mixture model (SOGMM) that combines GMM with factor models comprising multiple measurements of the outcome. SOGMM, compared with GMM, produce outcomes that better control for measurement error and limit the likelihood that an inappropriate class solution will be identified (Bauer & Curran, 2003; Grimm & Ram, 2009). This analysis strategy capitalized on the multiple assessments of PTSD (CAPS and PCL) across all time points and allowed us to create a latent PTSD variable across all time points, obviating the problem of not having CAPS data at T1. Keeping data from all four assessment time points allowed us to examine quadratic effects.
Under the assumption of ignorable missingness (Schlomer, Bauman, & Card, 2010), using Mplus (Version 7.1) we employed full information maximum likelihood (FIML) estimation for all procedures leading up to and including the unconditional SOGMM. To configure the SOGMM, we first created a longitudinal factor model with latent variables representing PTSD for each time point (the first-order model). Each PTSD latent variable comprised a CAPS and a PCL severity score, except for T1 when the CAPS was not administered. The missing CAPS data point was accommodated by creating a missing variable placeholder within the factor model (e.g., King et al., 2009; McArdle & Woodcock, 1997). Following recommendations for fitting multiple indicator models by Muthén and Muthén (2010) and Grimm and Ram (2009), we imposed strict measurement invariance across time (invariant loadings, residuals, and measurement intercepts) and conducted a confirmatory factor analysis (CFA). Next, we applied the GMM to the longitudinal factor model (see Figure 1). To identify the SOGMM the intercepts of the CAPS were set to 0. Within-class variance of the intercept and growth factors were freely estimated, whereas between-class variances were held equal. On the basis of previous studies, we estimated both linear and quadratic terms for the unconditional SOGMM assuming between one and six classes would best fit the data. Model estimation was an iterative process wherein modifications were made to account for estimation difficulties. Specifically, to correct for negative variances that were not significantly different from 0, the variances of the latent slope were set to 0 for the one-, three-, and six-class solutions; the variances of the quadratic variable were set to 0 for all except the four-class solution; the residual variances for the PCL were set to 0 for Class Solutions 4 and 6; and the residual variances of the latent T0 PTSD variable were set to 0 for Class Solutions 4 through 6.
Figure 1. Path diagram for the second-order growth mixture model (SOGMM). The model was constrained as follows: residual variances for the Clinician-Administered PTSD Scale (CAPS) and PTSD Checklist (PCL) were set equal across time, as were intercepts for the PCL, and factor loadings for the PCL on the latent posttraumatic stress disorder (PTSD) variable, intercepts for the CAPS were constrained to 0 for each time point, and factor loadings for the latent PTSD variables on the latent growth factors were set to 0 for the intercept.
Once we obtained properly estimated models, the best-fitting model was selected on the basis of prior research, class size (at least 5% of the total sample in the smallest class), parsimony, interpretability, formal fit indices, and classification quality. Models were considered to have a better fit and more accurate class assignment when they had a lower Bayesian information criterion (BIC), a lower sample size adjusted BIC (SSA-BIC), a significant Lo-Mendell-Rubin likelihood ratio test (LMR-LRT), a significant bootstrapped likelihood ratio test (BLRT), and higher entropy and average posterior probabilities (indices of classification certainty; for index accuracy see Nylund, Asparouhov, & Muthén, 2007; Jung & Wickrama, 2008). For Cohort 3, these same procedures for selecting the best fitting SOGGM were used. Details of individual model modifications to rectify estimation difficulties for Cohort 3 class solutions are in Table 4 of the supplemental materials.
Predictors by Trajectory Comparison
After selecting the unconditional model for Cohort 4, predictors of class membership were assessed by using two separate, supplemental analytic techniques: multinomial logistic regression and relative weights analysis. Using Mplus, we conducted multinomial logistic regression analyses following the three-step method developed by Vermunt (2010) and delineated by Asparouhov and Muthén (2013). This technique accounts for measurement error associated with assignment to latent classes while allowing class membership and trajectory structure to remain intact, which previous analysis strategies could not do.
The relative importance of predictors was determined using relative weights analysis. The goal was to examine the contribution each variable makes to the prediction of PTSD class membership both alone and in combination with the other variables in the model (Johnson & LeBreton, 2004). Multinomial logistic regression analysis fails to sufficiently account for predictor collinearity and is thus not ideal for determining the relative impact of predictors. Relative weights analysis more accurately partitions variance among correlated predictors by creating orthogonal factors that are maximally correlated with the original variables (LeBreton, Hargis, Griepentrog, Oswald, & Ployhart, 2007). We conducted relative weights analyses for logistic regression using an R (R Core Team, 2013) macro developed by Tonidandel and LeBreton (2010). This macro produces weights that are interpreted as a measure of relative effect size and confidence intervals that demonstrate whether the impact of a given predictor is significantly different from 0 (Tonidandel, LeBreton, & Johnson, 2009). It should be noted that although this analysis strategy more accurately partitions variance, unlike the multinomial logistic regression analysis conducted in Mplus, we were unable to compensate for measurement error in the assigned latent class membership. However, when measurement error is low as indicated by high entropy values, this analysis strategy produces relatively unbiased estimates.
Together, the multinomial logistic regression and the relative weights analysis provide information about the ability of predictors to account for variance in trajectory membership, but prediction does not necessarily translate into an ability to screen for trajectory membership, which might have greater practical significance in the military context. To test the classification rates of the predictors, we conducted a multivariable multinomial logistic regression analysis in SPSS (Version 20) where all predictor variables were entered simultaneously as predictors of trajectory membership.
Finally, we conducted two hierarchical multinomial logistic regression analyses in Mplus (Version 7.1) to separately test the interactive effects of combat exposure and prior lifetime trauma, and combat exposure and childhood trauma, on PTSD symptom course. Predictor variables were centered prior to analysis.
Results Multivariate Measurement Model
The strict measurement-invariant longitudinal factor model fit the data well, χ2(17, 867) = 59.78, p < .001 (Tucker Lewis index [TLI]: 0.976, comparative fit index [CFI]: 0.981, Root mean square error of approximation [RMSEA]: 0.054 [.039–.069], standardized root mean square residual [SRMR]: 0.032). Both CAPS and PCL scores were related to the latent construct of PTSD with standardized factor loadings across the four time points ranging from .88 to .96. The factor loadings for the CAPS were higher than the PCL at each time point in the original factor model, and were lower than the PCL in the full SOGMM.
SOGMM
Table 3 shows the fit statistics of the class solutions for the PTSD factor model. The BIC and SSA-BIC fit indices suggested relative improvement in fit with increasing number of classes. Significant LMR-LRTs, however, indicated the three- and four-class solutions were a comparatively better fit to the data. The three- and four-class solutions had high entropy (.82 and .95, respectively) and high average posterior probabilities (all >86%), indicating good classification. The four-class solution included a class populated by approximately 1% of the total count. Consequently, we selected the three-class solution as optimal.
In the three-class solution (see Figure 2), 677 Marines (78.1%) had relatively low symptoms over all time points (labeled low–stable symptoms), 108 (12.5%) had relatively increasing symptoms over the course of deployment (labeled new-onset symptoms), and 81 (9.4%) had relatively high symptoms at the initial, predeployment time point that decreased slightly across all subsequent assessments (labeled preexisting symptoms). Quadratic trends fit the data best for the low–stable symptoms trajectory (b = −1.57, SE = 0.18, p < .001) and the new-onset symptoms trajectory (b = −3.63, SE = 0.88, p < .001), whereas a linear trend best defined the preexisting symptoms trajectory (b = −3.13, SE = 0.77, p < .001).
Figure 2. Posttraumatic stress disorder (PTSD) severity over time by latent class for Cohort 4. CAPS = Clinician-Administered PTSD Scale.
Descriptive Statistics by Trajectory
Means and standard deviations of all study variables for each trajectory and for the full sample are reported in Table 2. PTSD cases were most prevalent in the new-onset symptoms trajectory for T1 through T3, and subthreshold cases were most prevalent in the new-onset symptoms trajectory for T2 and T3. The preexisting symptoms trajectory had the second-highest rates of PTSD and subthreshold PTSD at all postdeployment time points and the highest rates of PTSD and subthreshold PTSD at T0. Ratings of depression, anxiety, and functional impairment exhibited the same pattern, paralleling the severity of PTSD symptoms at each time point. Notably, history of previous deployments did not predict trajectory membership, χ2(2, 859) = 2.95, p = .229.
SOGMM Comparison in Cohort 3
The longitudinal factor model for Cohort 3 had an adequate fit to the data, χ2(17, 658) = 79.79, p < .001 (TLI: 0.957, CFI: 0.965, RMSEA: 0.075 [.059–.092], SRMR: 0.031), and both the CAPS and PCL scores were related to the latent construct of PTSD with standardized factor loadings across the four time points ranging from .85 to .97. For the SOGMM, the BIC and SSA-BIC fit indices suggest relative improvement in fit with increasing number of classes (see Supplemental Materials, Table 4). Significant LMR-LRTs indicated that the four-class solution was a comparatively better fit to the data; however, the four-class solution had one trajectory populated by only approximately 1% of the total sample. The three-class solution also had one trajectory populated by less than 5% of the sample, and this trajectory paralleled one of the other trajectories. In the two-class solution, the two parallel trajectories in the three-class solution collapsed into a single trajectory comprising 9%, of the total sample. Consequently, the two-class solution was selected because it provided the most parsimonious and interpretable solution.
In the two-class solution (see Figure 3), 602 Marines (91.5%) had relatively low symptoms over all time points (low–stable symptoms), and 56 (8.5%) had high symptoms at the initial, predeployment time point that decreased substantially across subsequent assessments (preexisting symptoms). A quadratic trend fit the data best for the low–stable symptoms trajectory (b = −0.32, SE = 0.10, p = .001) and a linear trend best defined the preexisting symptoms trajectory (b = 1.08, SE = 0.53, p = .040). Convergent outcomes, including rates of PTSD and subthreshold PTSD, PTSD severity, and ratings of depression, anxiety, and functional impairment, were generally as expected given the courses of the preexisting symptoms and the low–stable symptomstrajectories (see Supplemental Materials, Table 1).
Figure 3. Posttraumatic stress disorder (PTSD) severity over time by latent class for Cohort 3. CAPS = Clinician-Administered PTSD Scale.
Predictors of PTSD Symptom Trajectories in Cohort 4
New-onset PTSD symptoms versus preexisting PTSD symptoms
Multinomial logistic regression analysis revealed that compared with the preexisting symptoms group, participants in the new-onset symptoms trajectory experienced higher levels of combat exposure (b = 1.26, p = .001) and less prior lifetime trauma (LEC; b = −.15, p = .028; see Table 4). The relative weights analysis confirmed that combat exposure and prior lifetime trauma were two of the strongest predictors, accounting for 13.5% and 3.7% of the total variance, respectively. Collectively, all analyzed predictors accounted for 26.1% of the total variance.
New-onset PTSD symptoms versus low–stable symptoms
Multinomial logistic regression analysis revealed that Marines in the new-onset symptoms trajectory were more likely to report peritraumatic dissociation (b = 1.39, p < .001), use avoidant coping (b = 2.45, p = .001), and experience more prior lifetime trauma (b = .13, p = .036) than participants in the low–stable symptoms group. The results of the relative weights analysis mirrored these findings: peritraumatic dissociation accounted for 10.7% of the total variance, avoidant coping accounted for 8.0%. Prior lifetime trauma, however, was not significant. Collectively, all analyzed predictors accounted for 31.7% of the total variance.
Preexisting PTSD symptoms versus low–stable symptoms
Multinomial logistic regression analysis showed that compared when the low–stable symptoms group, participants in the preexisting symptoms trajectory reported more prior lifetime trauma (b = .28, p < .001), greater peritraumatic dissociation (b = 1.01, p = .002), and avoidant coping (b = 2.47, p < .001). In the relative weights analysis, prior lifetime trauma (7.9%), avoidant coping (5.2%), and peritraumatic dissociation (3.3%) each emerged as significant predictors. Collectively, all predictors together accounted for 20.1% of the total variance.
Classification rates of all predictors
Inspection of the classification tables in the multivariable logistic regression analyses revealed that all predictors simultaneously correctly classified 29.2% of those in the new-onset symptoms trajectory, 10.4% of those in the preexisting symptoms trajectory and 96.9% of those in the low–stable symptom trajectory suggesting the model has high specificity but low sensitivity. The model gave an overall classification success rate of 80.9%.
Interaction between prior trauma and combat exposure
Hierarchical multinomial logistic regressions failed to reveal a synergistic effect on trajectory membership of combat exposure and childhood trauma or combat exposure and prior lifetime trauma (see Table 5).
Synergistic Effects: Combat Exposure-by-Personal History
DiscussionWe examined the heterogeneity of PTSD symptom course in a cohort of concurrently deployed, highly combat-exposed Marines from pre- to early postdeployment throughout one deployment cycle. We aimed to elucidate the longitudinal patterns of posttraumatic stress symptoms that might be expected in the wake of a high-exposure deployment. Three trajectories of PTSD symptoms best fit the data, each representing subgroups of Marines: (a) a low–stable symptom trajectory characterized by persistently low symptoms; (b) a new-onset symptoms trajectory, consisting of Marines who reported clinically significant PTSD symptoms after deployment that had not existed prior to deployment; and (c) a preexisting symptoms trajectory characterized by high PTSD symptoms reported prior to deployment that gradually decreased but remained moderate through eight month postdeployment. A GMM analysis of a separate MRS cohort of deployed Marines replicated these trajectory patterns, except for the absence of the new-onset group. The lack of a new-onset PTSD trajectory in the validation cohort might be explained by their lower overall (unit-wide) level of stressor exposure during deployment.
Contrary to our expectations, a recovery trajectory was not modal in our primary sample. A low–stable symptom trajectory of persistently low PTSD symptoms across all data-collection time points was the most prevalent outcome (79%), suggesting that this trajectory is normative even among military service members with high levels of combat exposure. This low–stable symptom trajectory was similar to resilience trajectories found in previous studies of service members (e.g., Bonanno et al., 2012; Dickstein et al., 2010), except that in contrast to the flat resilience trajectories that were modal in them, Marines in our low–stable symptom group reported a clinically meaningful 10-point rise in PTSD symptom severity between T0 and T1 (e.g., Schnurr et al., 2007), followed by a decline in symptom severity postdeployment. The low–stable course in our study might have differed from similar trajectories in previous studies because our sample was arguably more uniformly exposed to significant stressor events, such that fewer participants in our study were unaffected. This finding contributes to the recent debate in the field about whether persistently low symptoms attributed to resilience might, in some cases, be confounded with low exposure (e.g., Bonanno, 2013; Steenkamp, Litz, Dickstein, Salters-Pedneault, & Hofmann, 2013). Although it is difficult to compare levels of combat exposure across studies because of methodological differences, the high combat fatality rate experienced by our cohort over the span of only 7 months in theater (17/867 = 1.9%) suggests a comparatively high level of combat exposure. In contrast, the U.S. Department of Defense’s Defense Casualty Analysis System (2013) reported that of 2,147,375 U.S. service members deployed to Iraq or Afghanistan between 2001 and 2010 (cited in Institute of Medicine, 2013), 4,585 (0.2%) were killed by hostile action, whereas the U.S. military casualty rate in the 1990–1991 Gulf War was 0.02% (Leland & Oboroceanu, 2010). Overall, our findings suggest that although the prevalence of persistently low symptoms might not be dependent on the degree of combat exposure (it remains the modal outcome), the degree of exposure appears to alter the form that this course takes; in our highly combat-exposed sample, resilience appeared to entail an elastic rebounding from meaningful posttraumatic stress symptoms rather than the absence of those symptoms.
The trajectory of most concern for military prevention and early intervention programs is the new-onset symptoms trajectory (12.5%), consisting of Marines who reported very low symptoms prior to deployment but persistently high symptoms at all postdeployment time points. At 1 month postdeployment, 83.6% of Marines in this group met criteria for full (61.2%) or subthreshold (22.4%) PTSD. Seven months later, the percentage meeting full criteria for PTSD had declined to 39.5%, but the percentage meeting criteria for subthreshold PTSD had increased to 47.5%, suggesting that 87% still reported clinically noteworthy symptomatology 8 months after deployment. The prevalence of our new-onset symptoms trajectory was higher than similar new-onset trajectories reported by Berntsen et al. (2012) and Bonanno et al. (2012), who found prevalence rates of 4% and 6.7% respectively, which might partially reflect differences in levels of combat exposure across studies. It is noteworthy that predeployment symptom levels were virtually identical for our low–stable symptom and new-onset symptoms trajectories suggesting, importantly, that screening for PTSD symptoms prior to a deployment would fail to predict the development of new-onset PTSD postdeployment.
The least prevalent trajectory derived from this GMM, preexisting symptoms (8%), comprised Marines reporting high PTSD symptoms immediately prior to deployment that gradually declined over the study period. At predeployment, 69% of Marines in this trajectory met criteria for full or subthreshold PTSD. By T3, this percentage had dropped to 50%, suggesting slight symptom improvement over the deployment cycle. Marines in this trajectory reported greater prior lifetime trauma than the other two trajectories (but levels of childhood trauma that were similar to the new-onset symptoms trajectory), suggesting that their symptoms were trauma-related rather than solely nonspecific distress or anticipatory anxiety (cf. Dickstein et al., 2010).
The trajectories we did not find might be as important as the trajectories we did. We found no recovery trajectory, which would have consisted of an initial deployment-related increase to superthreshold levels of distress followed by a gradual decrease toward baseline during the early postdeployment period. Dickstein et al. (2010) found a recovery trajectory making up 4% of their sample, and Bonanno et al. (2012) found a similar moderate–improving trajectory that made up 8% of their sample. Perhaps our cohort did not have a recovery trajectory because Marines in our study experienced higher levels of combat exposure than peace-keeping soldiers studied by Dickstein et al. (2010), and our postdeployment follow-up period was relatively short (8 months) compared with the 6-year follow-up reported by Bonanno et al. (2012). It is possible that our new-onset symptoms trajectory, given time, would bifurcate into chronic and recovery courses. We also did not find a delayed trajectory, perhaps again because our postdeployment follow-up was too brief; delayed PTSD, by definition, has an onset of at least 6 months after the trauma, but typically emerges years after the trauma (Andrews, Brewin, Stewart, Philpott, & Hejdenberg, 2009). Finally, unlike Bonanno et al. (2012), we also did not find a persistently chronic trajectory (persistently high symptoms across all time points; 2.2% in their sample), given that Marines in our preexisting symptoms group experienced a decrease in symptom severity over time.
Not surprisingly, combat exposure predicted membership in the new-onset symptoms trajectory relative to the preexisting symptoms course. However, contrary to prior cross-sectional and some longitudinal research, combat experiences did not distinguish the new-onset symptoms trajectory from the low–stable symptoms course. Also, perceived threat did not predict membership between any trajectory and prior trauma history (childhood trauma, prior lifetime trauma, and prior deployments) did not confer as much risk as has been found in previous cross-sectional studies (e.g., Cabrera, Hoge, Bliese, Castro, & Messer, 2007; Clancy et al., 2006; Dedert et al., 2009; Iversen et al., 2008). In addition, combat experiences did not interact with prior life span trauma to enhance risk. Also surprising, given the literature identifying unit cohesion and other sources of social support as significant protective factors for combat-related PTSD, neither unit support (perceived social support from peers and leaders during deployment) nor general postdeployment social support uniquely predicted trajectory membership. On the one hand, this null finding diminishes the validity of the putatively divergent courses found in this study. On the other hand, perhaps our findings differ from those of previous studies and appear to contradict models of deployment-related PTSD that assign primacy to combat experiences as a risk factor and to social support as a protective factor, because our sample comprised members of a single elite combat unit in which both stressor exposures and social support were likely both uniformly high, allowing for relatively less variation than existed in studies using other recruitment strategies. Also, perhaps our measures of social support were too coarse-grained to capture cohesive bonds that might impact stress outcomes in the military, such as trust under fire or shared meaning making. Because the combat exposure scale indexes only warzone dangers, future research should examine other important dimensions of combat exposure (e.g., loss, moral transgression). It might also be useful to augment service member reports of interpersonal behaviors, which might be biased, with family, leader, or peer observations.
Our finding that peritraumatic dissociation and avoidance coping were the strongest predictors of membership in the new-onset symptoms trajectory relative to the low–stable symptoms trajectory might lend support for the stress injury model in that both might serve as markers of stress that exceeded individual adaptive limits in Marines whose apparent risk might have been no greater than their peers. Both peritraumatic dissociation and avoidant coping might serve as targets for indicated prevention programs (e.g., Nash & Watson, 2012) through either self-report screening or observation by leaders, peers, family members, and care providers who are sufficiently familiar with service members to recognize changes in behaviors consistent with these processes.
Our study extended previous military GMM studies of PTSD in several ways. We employed both clinician-administered structured interviews and self-report questionnaires to assess PTSD symptoms, rather than relying solely on either. We assessed PTSD symptom burden at each data-collection time point in reference to participants’ currently most distressing event, regardless of its context, thereby not artificially constraining the trauma-related symptom burden in our sample. In contrast to previous longitudinal studies of service members, the cohort employed in our analyses was a relatively highly exposed group of U.S. military service members who deployed and returned together after performing a warzone mission that was arguably prototypical for the U.S. Global War on Terror: countering insurgency on the ground in a highly contested province of Afghanistan at the peak of conflict there.
Our findings converge with those of previous military GMM studies in two key areas, despite differences in methodology, population, and era. First, we confirmed that most service members report low symptoms throughout the deployment cycle (Bonanno et al., 2012; Berntsen et al., 2012; Dickstein et al., 2010); this was the case in both our primary (78%) and validation (91%) cohorts. Other investigators have argued that rates of psychopathology might be lower than expected among deployed service members because significantly symptomatic or at-risk service members might never have been sanctioned to deploy (Larson, Highfill-McRoy, & Booth-Kewley, 2008; Wilson, Jones, Fear, Hull, Hotopf, Wessely, & Rona, 2009). Our study also confirmed the findings of other GMM studies in service members that PTSD symptoms that are high prior to deployment do not necessarily worsen as a result of a combat deployment, despite the literature suggesting that prior trauma history (e.g., Dedert et al., 2009; LeardMann, Smith, & Ryan, 2010) and predeployment PTSD symptom burden (Franz et al., 2013) can be significant risk factors for postdeployment PTSD symptoms. Perhaps deployment-related protective factors might mitigate the risk posed by preexisting PTSD symptoms, such as unit cohesion, military training, and an increased sense of purpose and meaning while deployed (e.g., Rona et al., 2009).
Findings across longitudinal studies highlight the complex role played by combat exposure on PTSD symptom course and the limitations of traditional dose-response conceptualizations. We confirmed the findings of Orcutt et al. (2004) and Bonanno et al. (2012) that service members who experienced significant worsening of PTSD symptoms from predeployment to postdeployment endorsed the highest levels of combat exposure. On the other hand, peritraumatic dissociation and avoidant coping were stronger predictors of PTSD course than combat exposure, suggesting that deployed service members’ overall stressor dose might be less salient than how they were affected by those stressors. More research is needed to better understand the relationships between person-specific predictors, such as peritraumatic dissociation and avoidant coping and environment-specific predictors such as stressor exposures and social support.
Several limitations of our study deserve mentioning. Because Marines were not randomly selected for inclusion in the study, and our sample included no women, the generalizability of our results is uncertain. In particular, attrition and reranging of data resulted in significant missing data and affected the representativeness of the sample; Marines excluded because of attrition were older and more likely to have previously deployed, and had greater functional impairment and more prior lifetime trauma. The siphoned sample also limited the number of predictors that could be examined, and it prevented detection of potentially finer-grained differences between smaller proportioned trajectories. The duration of our postdeployment follow-up was too short to predict long-term PTSD symptom burdens. Finally, in practical terms, the predictors we chose were good at identifying Marines who are able to bounce back over the deployment cycle but not at predicting those at risk to develop enduring postdeployment PTSD.
In the Method section, we highlighted limitations of using mixture models and the steps we took to minimize these limitations. Notably, we used a second-order model to minimize the impact of poor measurement and the identification of spurious classes. Also, we substantiated our class solutions by testing covariates, which distinguished classes as anticipated. In addition, we were able to compare class solutions in Cohort 4 with those in Cohort 3, partly replicating our results. Nevertheless, our findings need to be confirmed by other prospective studies of highly exposed service members using various statistical approaches to modeling intra-individual change.
Alternative approaches to examining differential trajectories of adaptation to warzone exposure that should be explored include multiple group growth models, structural equation modeling trees with a growth model (Brandmaier, von Oertzen, McArdle, & Lindenberger, 2013), and recursive partitioning with longitudinal data (Abdolell, LeBlanc, Stephens, & Harrison, 2002). Multiple group growth models allow for in-depth examinations of group differences in change trajectories but require a priori classification of individuals into groups. Structural equation modeling trees and recursive partitioning with longitudinal data, like growth mixture models, are exploratory approaches to studying group differences in longitudinal trajectories. They create classes of change over time through repeated binary splits on observed variables to determine which individuals change in similar ways and which change in disparate ways. Finally, it is also possible that analyses extracting a single continuous intercept and slope might be at least as useful as those that recover trajectory groups.
ConclusionIn this article, we examined heterogeneity in PTSD symptom course using GMM in a sample of highly combat-exposed U.S. Marines. We found that three symptom trajectories best characterized the data and that peritraumatic dissociation and avoidant coping (person-level variables indexing trauma-related perceptions and behaviors) best distinguished these trajectories. We repeated our methodology in a separate cohort of less highly combat exposed Marines, in whom we found similar trajectories except for the absence of a new-onset course. Overall, our findings revealed that in highly exposed U.S. Marines, limited and temporary PTSD symptoms might be the most prevalent course, and that significant changes in PTSD symptoms from predeployment to 1-month postdeployment might provide the best indicator of ultimate first-year course.
Footnotes 1 A one-way ANOVA and a post hoc Tukey’s test revealed that Cohort 4 had significantly higher combat exposure than had all other cohorts, F(3, 2205) = 371.87, p < .001.
2 Additional steps for preliminary model testing were prohibited by the missing CAPS variable. Strict measurement invariance was necessary to identify the model, preventing tests of the progressively invariant measurement models recommended. Separate comparable GMMs for each indicator were also not possible.
3 We attempted to use a latent basis GMM to elucidate variability across class solutions, but the added complexity of these models prohibited an identifiable solution.
4 Whereas FIML addresses missingness on the dependent variable, it does not address predictor missingness. Generally, multiple imputation is used to address predictor missingness, but because significance testing of relative weights analysis prohibits the use of multiple imputation (Shao & Sitter, 1996) to keep predictor analyses comparable, participants missing data on any predictor were deleted from all predictor analyses.
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Submitted: February 8, 2014 Revised: October 5, 2014 Accepted: October 6, 2014
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Source: Journal of Abnormal Psychology. Vol. 124. (1), Feb, 2015 pp. 155-171)
Accession Number: 2014-49228-001
Digital Object Identifier: 10.1037/abn0000020
Record: 114- Title:
- Posttreatment motivation and alcohol treatment outcome 9 months later: Findings from structural equation modeling.

- Authors:
- Cook, Sarah. London School of Hygiene & Tropical Medicine, London, United Kingdom, sarah.cook@lshtm.ac.uk
Heather, Nick. Department of Psychology, Faculty of Health and Life Sciences, Northumbria University, United Kingdom
McCambridge, Jim. London School of Hygiene & Tropical Medicine, London, United Kingdom - Address:
- Cook, Sarah, London School of Hygiene & Tropical Medicine, Keppel Street, London, United Kingdom, WC1E 7HT, sarah.cook@lshtm.ac.uk
- Source:
- Journal of Consulting and Clinical Psychology, Vol 83(1), Feb, 2015. pp. 232-237.
- NLM Title Abbreviation:
- J Consult Clin Psychol
- Page Count:
- 6
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- Journal of Consulting Psychology
- Other Publishers:
- US : American Association for Applied Psychology
US : Dentan Printing Company
US : Science Press Printing Company - ISSN:
- 0022-006X (Print)
1939-2117 (Electronic) - Language:
- English
- Keywords:
- treatment, motivation, alcohol problems, readiness to change, outcome predictors
- Abstract (English):
- Objective: To investigate the association between posttreatment motivation to change as measured by the Readiness to Change Questionnaire Treatment Version and drinking outcomes 9 months after the conclusion of treatment for alcohol problems. Method: Data from 392 participants in the United Kingdom Alcohol Treatment Trial were used to fit structural equation models investigating relationships between motivation to change pre- and posttreatment and 5 outcomes 9 months later. The models included pathways through changes in drinking behavior during treatment and adjustment for sociodemographic information. Results: Greater posttreatment motivation (being in action vs. preaction) was associated with 3 times higher odds of the most stringent definition of positive outcome (being abstinent or entirely a nonproblem drinker) 9 months later (odds ratio = 3.10, 95% confidence interval [1.83, 5.25]). A smaller indirect effect of pretreatment motivation on this outcome was seen from pathways through drinking behavior during treatment and posttreatment motivation (probit coefficient = 0.08, 95% confidence interval [0.03, 0.14]). A similar pattern of results was seen for other outcomes evaluated. Conclusion: Posttreatment motivation to change has hitherto been little studied and is identified here as a clearly important predictor of longer term treatment outcome. (PsycINFO Database Record (c) 2018 APA, all rights reserved)
- Impact Statement:
- What is the public health significance of this article?—This study found that those individuals who reported that they were ready to change their drinking at the end of a treatment program were much more likely to show positive outcomes 9 months subsequently than were persons not indicating such a readiness to change. This suggests that attempting to enhance motivation throughout the process may be an important component of successful alcohol treatment. (PsycINFO Database Record (c) 2018 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Alcohol Rehabilitation; *Motivation; *Readiness to Change; *Structural Equation Modeling; *Treatment Outcomes
- Medical Subject Headings (MeSH):
- Adult; Alcoholism; Female; Follow-Up Studies; Great Britain; Humans; Male; Motivation; Surveys and Questionnaires; Treatment Outcome
- PsycINFO Classification:
- Drug & Alcohol Rehabilitation (3383)
- Population:
- Human
Male
Female - Location:
- United Kingdom
- Age Group:
- Adulthood (18 yrs & older)
- Tests & Measures:
- Leeds Dependence Questionnaire
Readiness to Change Questionnaire—Treatment Version DOI: 10.1037/t01789-000
Alcohol Problems Questionnaire DOI: 10.1037/t61043-000 - Grant Sponsorship:
- Sponsor: Medical Research Council
Grant Number: G9700729
Other Details: United Kingdom Alcohol Treatment Trial was funded by the aforementioned sponsor.
Recipients: No recipient indicated
Sponsor: Sponsor name not included
Grant Number: WT086516MA
Other Details: Wellcome Trust Research Career Development Fellowship in Basic Biomedical Science
Recipients: McCambridge, Jim - Methodology:
- Empirical Study; Quantitative Study
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- First Posted: Sep 22, 2014; Accepted: Aug 4, 2014; Revised: May 8, 2014; First Submitted: Mar 8, 2013
- Release Date:
- 20140922
- Correction Date:
- 20180215
- Copyright:
- The Author(s). 2014
- Digital Object Identifier:
- http://dx.doi.org/10.1037/a0037981
- PMID:
- 25244390
- Accession Number:
- 2014-39094-001
- Number of Citations in Source:
- 25
- Persistent link to this record (Permalink):
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- Cut and Paste:
- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2014-39094-001&site=ehost-live">Posttreatment motivation and alcohol treatment outcome 9 months later: Findings from structural equation modeling.</A>
- Database:
- PsycINFO
Posttreatment Motivation and Alcohol Treatment Outcome 9 Months Later: Findings From Structural Equation Modeling / BRIEF REPORT
By: Sarah Cook
London School of Hygiene & Tropical Medicine;
Nick Heather
Department of Psychology, Faculty of Health and Life Sciences, Northumbria University
Jim McCambridge
London School of Hygiene & Tropical Medicine
On Behalf Of: the UKATT Research Team
Acknowledgement: The United Kingdom Alcohol Treatment Trial was funded by the Medical Research Council (Project Grant G9700729). Jim McCambridge is supported by a Wellcome Trust Research Career Development Fellowship in Basic Biomedical Science (WT086516MA). The authors have no conflicts of interest.
Identifying how motivation to change is related to positive outcomes is important for understanding how treatment for alcohol or other behavioral problems can work. Pretreatment stage of change has been found to be an important predictor of outcome for a wide range of disorders (Norcross, Krebs, & Prochaska, 2011), including several aspects of treatment for alcohol problems (Connors et al., 2000; Hernandez-Avila, Burleson, & Kranzler, 1998; Isenhart, 1997; Project MATCH Research Group, 1998).
Three studies have previously investigated three different motivational measures, all based on stages of change, assessed at the conclusion of treatment for alcohol problems. A profile analysis generated from stage of change variables in Project MATCH identified a relationship between more strongly endorsing action posttreatment, measured using the University of Rhode Island Change Assessment (DiClemente & Hughes, 1990), and longer term abstinence (Carbonari & DiClemente, 2000). Another analysis of data from two cognitive behavior therapy alcohol treatments for women found posttreatment motivation, as measured by the Stages of Change Readiness and Treatment Eagerness Scale (Miller & Tonigan, 1996), was a mediator of the relationship between social support for drinking and drinking frequency 6 months later (Hunter-Reel, McCrady, Hildebrandt, & Epstein, 2010).
A third study that was based on data from the United Kingdom Alcohol Treatment Trial (UKATT; UKATT Research Team, 2005) found that posttreatment, but not pretreatment, stage of change, measured by the Readiness to Change Questionnaire Treatment Version (Heather & Hönekopp, 2008), was predictive of drinking outcomes at follow-up 9 months after treatment ended (Heather & McCambridge, 2013). The associations in this study were greatly reduced after adjusting for drinking behavior during treatment: Effect sizes were smaller and did not obtain statistical significance on the most stringent definitions of positive outcome (Heather & McCambridge, 2013). However, motivation to address alcohol problems will be highly interconnected with drinking behaviors before, during, and after treatment, making study of their effects complex (Rollnick, 1998).
The conceptual framework guiding the present study posits that interconnected pathways between variables are structured by time and that mediator and moderator variables may have proximal or distal impacts on one another, the strength of which may vary with time. Structural equation modeling is a flexible statistical technique that can be used to analyze interconnected pathways between variables and thus provide more detailed information on their relationships throughout the treatment process. Our primary hypothesis was that motivation to change drinking posttreatment predicts outcome of treatment for alcohol problems. Our aim in this study was to use structural equation modeling to further investigate the associations previously observed in the UKATT data between posttreatment motivation to change drinking and drinking outcomes roughly 9 months after the conclusion of treatment (Heather & McCambridge, 2013), including delineation of pathways through changes in drinking behaviors, paying careful attention to temporal sequencing in the context of an explicitly longitudinal perspective on change.
Method Study Sample and Design
The UKATT (UKATT Research Group, 2005) was a multicenter randomized controlled trial carried out in five treatment centers in the United Kingdom that compared two different treatments for alcohol problems: motivational enhancement therapy (MET) and social behavior and network therapy (SBNT). This was a pragmatic trial and the study population comprised clients who would normally receive an offer of treatment for alcohol problems in publicly funded treatment services in the United Kingdom. No differences were found between the two treatment groups on any of the drinking outcomes (UKATT Research Team, 2005). Motivation to change and drinking behaviors were measured pretreatment and then at 3 months (when all treatment was ended) and 9 months later, that is, 12 months after entry to the trial. UKATT recruited 742 clients (MET = 422, SBNT = 320) attending treatment voluntarily. Because our research question was related to treatment process, only clients who attended at least one session were included (n = 590). We also examined those with complete data available on the variables of interest at all three time points because the aim of this study was to model the interrelationship between these variables over time. This resulted in a sample of 392 clients included for the present study. There were some differences between this subsample and those who were not included in terms of education (those included were more likely to have been educated to degree level or equivalent [12.2% vs. 7.4%, p = .036] and less likely to have no educational qualification [30.4% vs. 41.7%, p = .002]). The included subsample also had somewhat less severe problems at baseline (lower mean scores on the Leeds Dependence Questionnaire [15.1 vs. 16.4, p = .030] and Alcohol Problems Questionnaire [10.4 vs. 11.7, p < .001]).
Measures
Outcome variables were derived from Form 90 data on alcohol consumption in the past 90 days (Miller, 1996) and the Alcohol Problems Questionnaire (APQ; Drummond, 1990). Data from the Form 90 and APQ were combined to derive three binary treatment outcome variables based on a composite categorical variable developed by Heather and Tebbutt (1989):
- Outcome 1: Abstinent or nonproblem drinker (no alcohol consumption in the past 90 days or drinking with a score of zero on the APQ indicating no evidence of any alcohol problems)
- Outcome 2: At least much improved (abstinent or drinking with a reduction in APQ score from baseline to follow up of at least two thirds)
- Outcome 3: At least somewhat improved (abstinent or drinking with a reduction in APQ score from baseline of at least one third)
These outcomes are principally concerned with the resolution of alcohol problems and vary in the stringency of the definition of a positive outcome. The additional outcomes investigated were two continuous measures of drinking behavior derived from the Form 90 data:
- Outcome 4: Drinks per drinking day (DDD) in the past 90 days, with abstinent clients given a score of zero
- Outcome 5: Percentage of days abstinent (PDA)
These were the same outcome measures used by
Heather and McCambridge (2013).
Motivation to change was assessed using the revised edition of the Readiness to Change Questionnaire Treatment Version, which is designed for use in alcohol-treatment seeking populations (Heather & Hönekopp, 2008) and which refers to both quitting and cutting down on alcohol consumption. This 12-item instrument was used to calculate scores on three stages of change: precontemplation, contemplation, and action. Clients are assigned a stage of change based on the scale on which they score highest, with ties being decided in favor of the stage farthest along the cycle of change. As no clients were in the precontemplation stage at pretreatment and only three were at posttreatment, we defined actively changing drinking (action stage) versus not actively changing drinking (precontemplation + contemplation stages = preaction) as a binary variable.
Sociodemographic variables measured pretreatment were age (coded into 5-year groups), education (coded as no qualifications, some qualifications, and degree or equivalent qualifications), and marital status (married and/or cohabiting or not). Pretreatment score on the Leeds Dependence Questionnaire (Raistrick et al., 1994) assessing the severity of dependence at treatment entry was also included in the model as a predictor of drinking behavior during treatment.
Statistical Analyses
The relationship between motivation to change (pre- and posttreatment) and treatment outcomes 9 months posttreatment was assessed using the structural equation model shown in Figure 1 for Outcome 1. This model was specified a priori by considering the likely temporal relationship between variables. For example, effects of pretreatment motivation to change on drinking outcomes at 9 months posttreatment were considered to be through effects on intermediate variables (drinking during treatment and posttreatment motivation to change). This hypothesis was tested by adding in a direct effect of pretreatment motivation to change on treatment outcomes at 9 months in a sensitivity analysis.
Figure 1. Structural equation model examining relationship between actively changing drinking following treatment and long term drinking outcomes. N = 392. Coefficients are linear regression coefficients for continuous outcomes and probit coefficients for binary outcomes. SBNT = social behavior and network therapy; MET = motivational enhancement therapy; CFI = comparative fit index; TLI = Tucker–Lewis index; RMSEA = root-mean-square error of approximation.
The model is divided by time into four sections—pretreatment, within treatment, posttreatment, and 9 months follow-up—to help elucidate the interrelationships between variables over time. Being abstinent or a nonproblem drinker (Outcome 1) was identified a priori as the main treatment outcome of interest, with models also fitted for the other two binary outcomes comprising less stringent definitions of positive outcome and the continuous outcomes (PDA and DDD) at 9 months posttreatment. Models were fitted separately for each outcome but with the same specified associations between the pre- and posttreatment variables because there was no reason to believe these relationships would differ between treatment outcomes. All models were adjusted for potential confounding by sociodemographic variables (age, sex, education, and marital status). Study site was also included as a confounder as this could represent both differences related to treatment services and geographic location.
Models were estimated using weighted least squares with mean and variance adjusted (WLSMV) but with maximum-likelihood estimation used to calculate odds ratios for the direct effects of posttreatment stage of change on binary drinking outcomes at 12 months. Model fit was assessed using the comparative fit index (CFI), Tucker–Lewis index (TLI), and the root-mean-square error of approximation (RMSEA). CFI and TLI values greater than .95 indicate good model fit, with a minimum of .90 indicating acceptable fit (Streiner, 2006; Tabachnik & Fidell, 1996). For the RMSEA, values greater than 0.10 indicate a bad fit, whereas values less than 0.08 indicate a reasonable fit and less than 0.05 indicate a good fit (Streiner, 2006).
ResultsThe study sample included 392 clients (74.7% male). Mean age was 42.2 years (SD 9.9). 46 clients were nonproblem drinkers at 9 months follow up, and 55 were abstinent. Overall, 153/392 clients overall met the criteria for being much improved and 225/392 were at least somewhat improved.
The results for the most stringent definition of positive treatment outcome (Outcome 1, abstinent/nonproblem drinker at 9 months) are shown in Figure 1. Model fit was very good. Greater posttreatment motivation (being in action vs. preaction) was associated with 3.10 (95% CI [1.83, 5.25]) higher odds (equivalent probit coefficient = 0.44, 95% CI [0.29, 0.59]) of positive outcome at 9 months. There was also good evidence for an indirect effect of pretreatment motivation on being abstinent or a nonproblem drinker at 9 months via effects on DDD and PDA at 3 months and posttreatment motivation (probit coefficient = 0.08, 95% CI [0.03, 0.14]). This was not reduced by including a direct effect of pretreatment motivation on treatment outcome in the model. There was no evidence for a direct effect of pretreatment motivation (probit coefficient = −0.19, 95% CI [−0.10, 0.48]).
The same pattern of results was seen for Outcome 2 (at least much improved; odds ratio for posttreatment motivation = 2.84, 95% CI [1.85, 4.38], and probit coefficient for indirect effect of pretreatment motivation = 0.09, 95% CI [0.03, 0.16]) and for Outcome 3 (at least somewhat improved; odds ratio for posttreatment motivation = 3.27, 95% CI [2.21, 4.84], and probit coefficient for indirect effect of pretreatment motivation = 0.11, 95% CI [0.04, 0.18]). Model fit for Outcomes 2 and 3 was reasonable (for Outcome 2, CFI = .95, TLI = .72, RMSEA = 0.06; for Outcome 3, CFI = .93, TLI = .60, RMSEA = 0.08).
Findings were also similar for Outcomes 4 and 5. Drinks per drinking day at 9 months were 4.14 (95% CI [3.45, 4.82]) fewer in those in action versus preaction posttreatment and 0.93 (95% CI 0.31, 1.55) drinks fewer in those in action versus preaction pretreatment. Those in action versus preaction posttreatment had 12.03% (95% CI [9.11, 14.95]) more abstinent days during Months 10–12. Those in action versus preaction at the beginning of treatment had 3.19% (95% CI [0.86, 5.52]) more abstinent days. Models for continuous outcomes had poorer model fit (for DDD, CFI = 0.64, TLI = −0.88, RMSEA = 0.19; for PDA, CFI = .83, TLI = .12, RMSEA = 0.12).
There was no evidence for Outcomes 2–5 of any direct effects of pretreatment motivation to change. Estimates for indirect effects of pretreatment motivation to change on treatment outcome did not substantively change by adding in a direct effect to the model for any of the treatment outcomes.
In contrast to all previous UKATT findings, there was some evidence (p < .05) of a treatment effect: Those who received SBNT were more likely than those in the MET group to be actively trying to change their drinking at the end of treatment for three out of five of the treatment outcomes (Outcomes 1, 2, and 5).
DiscussionMotivation to change, comparing those in action versus preaction at the conclusion of treatment for alcohol problems, was strongly associated with being abstinent or a nonproblem drinker at follow-up 9 months after treatment ended, approximately trebling the odds of this outcome. Pretreatment motivation had a lesser but nonetheless statistically significant indirect effect via effects on drinking behavior during treatment and posttreatment motivation. The same pattern of results was found for all other longer term treatment outcomes. These results, using a more sophisticated modeling approach, support and extend previous analyses of the same data set (Heather & McCambridge, 2013) by producing a more precise and indeed larger estimate of the effect of posttreatment motivation. Unlike the previous study, our study reveals an indirect effect of pretreatment motivation under the assumptions of no unmeasured confounders (Muthèn, 2011; VanderWeele, 2012) and no direct paths from the measured pretreatment variables to the outcomes, which shows the importance of considering change over time. Using a structural equation modeling approach enabled us to estimate more realistically the relationships between drinking behavior and motivation to change throughout the entire study period, taking account of the temporal nature of likely associations. These data add to the meager literature, comprising only two other treatment cohorts, for which different motivational measures were used.
This study used a binary motivational measure because almost all clients providing data at all three time points were in either the contemplation or the action stage of change, both pre- and posttreatment, and therefore there seemed little added benefit in using a more complex measure. The subsample used in this study had slightly less severe alcohol problems than did the overall UKATT study sample, which was broadly representative of the U.K. treatment population at the time the study was undertaken (Heather & McCambridge, 2013; UKATT Research Team, 2005), with implications for the generalizability of these data. Although measured motivation at treatment entry was similar among members of this group and the group not included in this study, the need to include those who attended at least one treatment session and also provided follow-up data posttreatment and at 12 months may mean there was differential loss to follow-up by motivation postrandomization, although it is difficult to assess this. Using a binary measure of motivation and restricting analyses to a subgroup of the UKATT population thus entails restrictions on the capacity to make inferences about the entire treatment population. In addition, although we have used here the Readiness to Change Questionnaire to measure motivation, there are different constructs and measures of motivation, including those not based on the stages of change (see Gaume, Bertholet, Daeppen, & Gmel, 2013). There is, therefore, a need for replication of analyses using different measures of motivation to fully understand motivation’s importance in treatment for alcohol problems.
These findings describe how drinking behavior changes over time and, notwithstanding that temporal sequencing rules out reverse causality, we make no direct causal inferences from these data given the observational nature of this study. Drinking measures for the 90 days prior to treatment (PDA and DDD) predict these same measures for the period during treatment. Pretreatment motivation also strongly predicts both of these measures during treatment. Reducing drinking is then associated with posttreatment motivation, which, in turn, predicts outcome 9 months later. If there is an underlying causal chain, capitalizing on motivation at the beginning of treatment and making progress during treatment thus appears important to longer term outcome, as is how treatment ends for clients and specifically their motivation to change their drinking at that point.
There was somewhat consistent evidence of a small treatment effect on posttreatment motivation favoring SBNT over MET. This counterintuitive finding could be explained if increased social support for change elicited by SBNT is more effective in motivating change efforts by the client than the mainly psychological processes targeted by MET. However, our finding contrasts with the previously reported analyses of UKATT outcomes, including no differences in the proportion of clients in the action stage of change posttreatment by treatment group (Heather & McCambridge, 2013). The reasons for these differences are not clear and further investigation is warranted.
Posttreatment motivation has been found to be a mediator of the relationship between baseline social support and drinking outcomes (Hunter-Reel et al., 2010). However, as far as we are aware, formal analyses of the role of motivation as a possible mediator of treatment effects has not been undertaken in alcohol treatment studies. In a related area, a brief motivational intervention was found to be more effective in decreasing negative drinking consequences through changes in motivation among emergency department attendees with injuries only in those who were already motivated to change before intervention (Stein et al., 2009). Further process studies are needed to test hypotheses about mediation and moderation of the effects of treatment for alcohol problems.
Korcha, Polcin, Bond, Lapp, and Galloway (2011) have drawn attention to the surprising absence of a longitudinal perspective on motivation in almost all existing alcohol and drug research, despite studies showing its importance for other behaviors such as smoking cessation (e.g., Boardman, Catley, Mayo, & Ahluwalia, 2005). Although it is possible that the lack of prior published studies may, to some degree, reflect publication bias, with null findings not reported, it is clear that posttreatment motivation is a neglected target for study in relation to treatment outcome. Further investigation of this somewhat novel candidate for mechanisms of behavior change and the application of a longitudinal perspective more broadly have potential for deepening the understanding of how alcohol treatment works.
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Submitted: March 8, 2013 Revised: May 8, 2014 Accepted: August 4, 2014
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Source: Journal of Consulting and Clinical Psychology. Vol. 83. (1), Feb, 2015 pp. 232-237)
Accession Number: 2014-39094-001
Digital Object Identifier: 10.1037/a0037981
Record: 115- Title:
- Prediction of psychopathology and functional impairment by positive and negative schizotypy in the Chapmans’ ten-year longitudinal study.
- Authors:
- Kwapil, Thomas R.. Department of Psychology, University of North Carolina at Greensboro, Greensboro, NC, US, t_kwapil@uncg.edu
Gross, Georgina M.. Department of Psychology, University of North Carolina at Greensboro, Greensboro, NC, US
Silvia, Paul J.. Department of Psychology, University of North Carolina at Greensboro, Greensboro, NC, US
Barrantes-Vidal, Neus, ORCID 0000-0002-8671-1238. Department of Psychology, University of North Carolina at Greensboro, Greensboro, NC, US - Address:
- Kwapil, Thomas R., University of North Carolina at Greensboro, Department of Psychology, P.O. Box 26170, Greensboro, NC, US, 27402-6170, t_kwapil@uncg.edu
- Source:
- Journal of Abnormal Psychology, Vol 122(3), Aug, 2013. pp. 807-815.
- NLM Title Abbreviation:
- J Abnorm Psychol
- Page Count:
- 9
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- The Journal of Abnormal and Social Psychology; The Journal of Abnormal Psychology; The Journal of Abnormal Psychology and Social Psychology
- ISSN:
- 0021-843X (Print)
1939-1846 (Electronic) - Language:
- English
- Keywords:
- negative schizotypy, positive schizotypy, psychosis-proneness, schizophrenia, psychopathology, functional impairment
- Abstract:
- The present study examined the predictive validity of psychometrically assessed positive and negative schizotypy in the Chapmans’ 10-year longitudinal data set. Schizotypy provides a useful construct for understanding the etiology and development of schizophrenia and related disorders. Schizotypy and schizophrenia share a common multidimensional structure that includes positive and negative symptom dimensions. Recent cross-sectional studies have supported the validity of psychometric positive and negative schizotypy; however, the present study is the first to examine the predictive validity of these dimensions. The Chapmans’ longitudinal data provided an ideal opportunity because of the large sample size, high reassessment rate, and extended interval between assessments. A total of 534 psychometric high-risk and control participants were initially assessed, and 95% of this sample was reinterviewed 10 years later. As hypothesized, positive and negative schizotypy uniquely predicted the development of schizophrenia-spectrum disorders. At the reassessment, both positive and negative schizotypy predicted psychotic-like, schizotypal, and paranoid symptoms, as well as poorer adjustment. The positive dimension was associated with mood and substance use disorders and mental health treatment. Negative schizotypy was associated with schizoid symptoms and social impairment at the follow-up. The results extend the growing validity findings for psychometrically assessed positive and negative schizotypy by demonstrating that they are associated with the development of differential patterns of symptoms and impairment. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Psychopathology; *Schizophrenia; *Schizotypy; Prediction; Psychosis
- Medical Subject Headings (MeSH):
- Adolescent; Adult; Case-Control Studies; Female; Humans; Logistic Models; Longitudinal Studies; Male; Mass Screening; Predictive Value of Tests; Psychometrics; Schizophrenic Psychology; Schizotypal Personality Disorder; Surveys and Questionnaires; Young Adult
- PsycINFO Classification:
- Schizophrenia & Psychotic States (3213)
- Population:
- Human
Male
Female - Location:
- US
- Age Group:
- Adulthood (18 yrs & older)
- Tests & Measures:
- Revised Social Anhedonia Scale
Physical Anhedonia Scale
Schedule for Affective Disorders and Schizophrenia–Lifetime version
Wisconsin Manual for Assessing Psychotic-like Experiences
Social Adjustment Scale interview
Wisconsin Schizotypy Scales
Personality Disorder Exam
Global Adjustment Scale
Impulsive Nonconformity Scale DOI: 10.1037/t10480-000
Perceptual Aberration Scale DOI: 10.1037/t20156-000
Magical Ideation Scale DOI: 10.1037/t02328-000 - Methodology:
- Empirical Study; Longitudinal Study; Quantitative Study
- Supplemental Data:
- Tables and Figures Internet
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- Accepted: Jun 3, 2013; Revised: May 29, 2013; First Submitted: Oct 15, 2012
- Release Date:
- 20130909
- Copyright:
- American Psychological Association. 2013
- Digital Object Identifier:
- http://dx.doi.org/10.1037/a0033759; http://dx.doi.org/10.1037/a0033759.supp(Supplemental)
- PMID:
- 24016018
- Accession Number:
- 2013-30852-015
- Number of Citations in Source:
- 46
- Persistent link to this record (Permalink):
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- Cut and Paste:
- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2013-30852-015&site=ehost-live">Prediction of psychopathology and functional impairment by positive and negative schizotypy in the Chapmans’ ten-year longitudinal study.</A>
- Database:
- PsycINFO
Prediction of Psychopathology and Functional Impairment by Positive and Negative Schizotypy in the Chapmans’ Ten-Year Longitudinal Study
By: Thomas R. Kwapil
Department of Psychology, University of North Carolina at Greensboro;
Georgina M. Gross
Department of Psychology, University of North Carolina at Greensboro
Paul J. Silvia
Department of Psychology, University of North Carolina at Greensboro
Neus Barrantes-Vidal
Department of Psychology, University of North Carolina at Greensboro, Universitat Autònoma de Barcelona, and Sant Pere Claver–Fundació Sanitària, Barcelona, Spain
Acknowledgement: The authors thank Loren and Jean Chapman for use of their longitudinal data.
As schizotypy enters its sixth decade, it remains a valuable and evolving construct for considering individual differences and specifically for understanding vulnerability for schizophrenia-spectrum psychopathology (Claridge, 1997; Lenzenweger, 2010; Meehl, 1962, 1990; Rado, 1956). Since the time of Paul Meehl’s landmark address to the American Psychological Association in 1962, schizotypy has evolved from a relatively homogenous phenotype linked to a single-gene model of liability for schizophrenia to a broader, multidimensional construct. Although the exact nature of schizotypy is not universally agreed on (see landmark texts by Claridge [1997] and Lenzenweger [2010] for opposing viewpoints), we conceptualize schizotypy as a continuum of schizophrenia-like manifestations ranging from minimal impairment, to subclinical deviance, to personality pathology, to full-blown psychosis (Kwapil, Barrantes-Vidal, & Silvia, 2008). Thus, schizotypy conveys the vulnerability for schizophrenia-spectrum disorders, although the majority of schizotypic individuals are not expected to develop psychosis. The study of schizotypy is useful for understanding the etiology and development of schizophrenia and related disorders, in part because it avoids third-variable confounds such as medication, stigma, and institutionalization arising from schizophrenia. The reliable identification of schizotypic individuals should facilitate our understanding of relevant etiological factors and developmental trajectories, clarify risk and protective agents, and provide a necessary step toward development of preventative treatments.
Schizotypy and, by extension, schizophrenia are heterogeneous. This heterogeneity occurs at the phenotypic level, with symptoms and impairment ranging from marked diminution (e.g., alogia) to marked excesses (e.g., hallucinations) in behavior. Furthermore, this heterogeneity is evident at the etiological, developmental, and treatment-response levels. Thus, treating schizotypy and schizophrenia as homogenous constructs impedes our ability to understand the origins, development, and expression of these complex conditions (Kwapil & Barrantes-Vidal, 2012). The heterogeneity of schizotypy and schizophrenia appear to be characterized by a common multidimensional structure. Factor analytic studies suggest that positive, negative, and disorganized dimensions underlie schizophrenia (Lenzenweger & Dworkin, 1996; Liddle, 1987). Consistent with the factor structure of schizophrenia, positive and negative factors of schizotypy are the most replicated dimensions (Cicero & Kerns, 2010; Kwapil et al., 2008; Vollema & van den Bosch, 1995). The reliable identification and measurement of these dimensions is essential for parsing the heterogeneity of schizotypy and schizophrenia.
Recent research indicates that two factors underlie the Wisconsin Schizotypy Scales, which are comprised of the Perceptual Aberration (Chapman, Chapman, & Raulin, 1978), Magical Ideation (Eckblad & Chapman, 1983), Physical Anhedonia (Chapman, Chapman, & Raulin, 1976), and Revised Social Anhedonia (Eckblad, Chapman, Chapman, & Mishlove, 1982) Scales. Exploratory and confirmatory factor analyses reliably identify positive and negative schizotypy dimensions that account for approximately 80% of the variance in the measures (e.g., Lewandowski et al., 2006; Brown, Silvia, Myin-Germeys, Lewandowski, & Kwapil, 2008; Kwapil et al., 2008). This factor structure has been replicated in cross-cultural studies (e.g., Kwapil, Ros-Morente, Silvia, & Barrantes-Vidal, 2012). Furthermore, studies indicated that the positive and negative schizotypy dimensions are associated with differential patterns of symptoms and impairments in cross-sectional questionnaire studies (e.g., Lewandowski et al., 2006), interview studies (e.g., Kwapil et al., 2008; Barrantes-Vidal et al., 2013), laboratory studies (Kaczorowski, Barrantes-Vidal, & Kwapil, 2009), and experience sampling studies (e.g., Kwapil, Brown, Silvia, Myin-Germeys, & Barrantes-Vidal, 2012). Consistent with deficits reported in positive and negative symptom schizophrenia, Kwapil et al. (2008) indicated that the positive and negative schizotypy dimensions were differentially related to psychopathology, personality, and social functioning. Both schizotypy dimensions were associated with schizotypal and paranoid personality disorder symptoms. Positive schizotypy was uniquely related to psychotic-like experiences, substance abuse, mood disorders, and mental health treatment, whereas negative schizotypy was specifically associated with negative and schizoid symptoms. Both dimensions were associated with poorer overall and social functioning, but negative schizotypy was associated with decreased likelihood of intimate relationships. Furthermore, Barrantes-Vidal et al. (2013) indicated that the schizotypy dimensions are associated with prodromal symptoms in a nonclinically identified sample.
These initial findings support the construct validity of psychometrically assessed positive and negative schizotypy dimensions. However, this work has been limited to cross-sectional studies. The present study examined the predictive validity of the schizotypy dimensions using data from the Chapmans’ 10-year longitudinal study of psychosis proneness (e.g., Chapman, Chapman, Kwapil, Eckblad, & Zinser, 1994; Kwapil, 1998). The Chapmans’ study was the first longitudinal assessment of psychometric high risk and it was notable for its large sample, high reassessment rate, and 10-year follow-up interval. They interviewed 534 college students at the initial assessment and reassessed 95% of the sample 10 years later. The study used a psychometric high-risk approach in which participants were assigned to five groups: (1) high scorers on Perceptual Aberration or Magical Ideation (PerMag) Scales, (2) high scorers on the Impulsive-Nonconformity Scale (Chapman et al., 1984), (3) high scorers on the Physical Anhedonia Scale, (4) a combined-risk group, and (5) a control group. However, the study did not examine the dimensional structure underlying the psychometric measures.
At the cross-sectional assessment, the high-risk groups exceeded the control participants on psychotic-like experiences and schizotypal symptoms. Chapman et al. (1994) noted that the PerMag group was especially deviant. Note that none of the participants were psychotic at the time of the initial assessment. At the 10-year reassessment, 14 participants had developed DSM–III–R (American Psychiatric Association, 1987) psychotic disorders and 30 met criteria for schizophrenia-spectrum disorders including schizotypal, schizoid, and paranoid personality disorders. The PerMag group exceeded the control group on rates of psychotic disorders, as well as on ratings of psychotic-like, schizotypal, and paranoid symptoms. They also had poorer overall functioning and elevated rates of mood and substance use disorders. None of the other groups exhibited elevated rates of psychotic disorders. Although participants in the longitudinal study were not selected based on scores on the Revised Social Anhedonia Scale, Kwapil (1998) reported that scores on the scale predicted elevated rates of schizophrenia-spectrum disorders, as well as psychotic-like, schizotypal, schizoid, and paranoid symptoms.
The primary goal of the present study was to investigate the predictive validity of psychometrically assessed positive and negative schizotypy using data from the Chapmans’ longitudinal study. The validity of these dimensions has been supported in a variety of cross-sectional studies, but this provided the first examination of their predictive validity. This longitudinal data set provides an ideal vehicle for this purpose. Based on cross-sectional findings (e.g., Kwapil et al., 2008; Gross, Silvia, Barrantes-Vidal, & Kwapil, 2013), it was hypothesized that the positive and negative schizotypy dimensions would predict differential patterns of psychopathology and impairment at both assessments. Specifically, it was hypothesized that both dimensions would predict schizotypal and paranoid symptoms and functional impairment. Further, it was expected that positive schizotypy would predict psychotic-like experiences, mood disorder symptoms, and substance abuse at both time points and that negative schizotypy would predict schizoid symptoms. Most importantly, it was hypothesized that both dimensions would predict the development of schizophrenia-spectrum disorders at the reassessment.
Method Participants
The present study used data from the Chapmans’ longitudinal study of psychosis-proneness. The method is described below, but additional details can be found in Chapman et al. (1994) and Kwapil (1996, 1998).
Initial assessment
A total of 534 students enrolled at the University of Wisconsin-Madison participated in the initial assessment (mean age = 19.3 years, SD = 1.4; 52% female). These participants were initially selected from a pool of approximately 8,000 undergraduates who completed the Wisconsin Schizotypy Scales in mass screening sessions over the course of seven semesters. High-risk participants were recruited based on standard scores of at least 1.96 on the Perceptual Aberration or Magical Ideation Scales (n = 193), Physical Anhedonia Scale (n = 75), or Impulsive-Nonconformity Scale (n = 74). A combined risk group included 33 participants whose sum of their standard scores on the four scales was at least 3.0. Additionally, 159 control participants were included who had standard scores on each of the four scales of less than 0.5. Note that consistent with our hypotheses there was not any group assignment used in the present study or any group comparisons conducted.
10-year follow-up assessment
A total of 508 of the original participants (95%) were reinterviewed (mean age = 30.0 years, SD = 1.7; 52% female). Participants who completed the reassessment did not differ from those lost to attrition on positive and negative schizotypy scores. The mean interval between the assessments was 10.7 years (SD = 1.0). The positive and negative schizotypy dimensions were unassociated with interval length (rs = −.04 and .06, respectively).
Materials and Procedures
Initial assessment
The 534 participants who took part in the initial assessment completed face-to-face interviews and were administered the Revised Social Anhedonia Scale and a questionnaire measure of paranoia that contained 36 true/false items, including 10 items from the Minnesota Multiphasic Personality Inventory Scale 6 (University of Minnesota, 1943).
The structured interview included the Schedule for Affective Disorders and Schizophrenia–Lifetime version (SADS-L; Spitzer & Endicott, 1977) sections covering mood, psychotic and substance use disorders, and schizotypal features. The SADS-L was modified to obtain additional information about psychotic-like experiences. The Wisconsin Manual for Assessing Psychotic-like Experiences (Chapman & Chapman, 1980; Kwapil, Chapman, & Chapman, 1999) was used to quantify seven classes of psychotic symptoms across a range of clinical and subclinical deviancy. Kwapil et al. (1999) reported that the highest rating across the seven classes provides a useful index that predicts the development of psychotic disorders. Each participant’s rating of schizotypal symptoms was the total number of the 18 criteria endorsed. The Social Adjustment Scale interview (Weissman & Paykel, 1974) was used to quantify social impairment. It produced a total score and subscale scores for social functioning in school, social and leisure, and family settings (with higher scores indicating greater impairment). Participants were assessed for substance use disorders and assigned quantitative ratings of impairment associated with drug and alcohol use (Kwapil, 1996).
10-year follow-up assessment
The follow-up interview assessed overall functioning, psychosis, schizophrenia-spectrum personality disorders, psychotic-like experiences, mood disorders, substance abuse, and mental health treatment. Note that the Wisconsin Schizotypy Scales were not readministered at the follow-up assessment. The interview included a modified SADS-L, the Wisconsin Manual for Assessing Psychotic-like symptoms, and portions of Loranger’s (1988) Personality Disorder Exam (PDE) that assessed schizotypal, schizoid, and paranoid personality disorders. The PDE provided both DSM–III–R diagnoses and dimensional ratings of the disorders. The Global Adjustment Scale (Endicott, Spitzer, Fleiss, & Cohen, 1976) was used to assess overall functioning for each subject. Participants were rated on a six-point scale of the closeness and quality of intimate relationships. Substance use was assessed in the same manner as at the initial interview.
The interviews, as well as the scoring and diagnosis at both assessments, were conducted by clinical psychologists and advanced graduate students who had received extensive diagnostic training. Interviewers and raters at both assessments were unaware of the subjects’ scores on the schizotypy scales. Interviewers and raters at the follow-up were unaware of participants’ responses at the initial assessment.
Results Schizotypy Dimension Scores
Positive and negative schizotypy dimension scores were computed for all 534 participants in the 10-year follow-up study. Schizotypy scores were assigned based on formulae derived from a principal components analysis with a promax rotation of the four Wisconsin Schizotypy Scales using the sample of 6,137 young adults described in Kwapil et al. (2008). Note that this factor structure accounts for 80% of the variance in the Wisconsin Schizotypy Scales, correlates .99 with confirmatory factor analytic derived scores from Kwapil et al., and appears invariant across samples. This is the same procedure used in other studies from our laboratory examining the differential expression of positive and negative schizotypy dimensions (e.g., Barrantes-Vidal et al., 2013; Kaczorowski et al., 2009; Kwapil, Brown, et al., 2012). The formulae (based on raw scores on the scales) are as follows:
Consistent with the selection process for the longitudinal study, the mean for the positive schizotypy dimension was higher than for the negative schizotypy dimension; however, the range of scores was comparable for the two dimensions (Positive schizotypy: M = .95, SD = 1.53, range = −1.96 to 4.63; Negative schizotypy: M = −.27, SD = 1.05, range = −1.85 to 4.91). The two dimensions were modestly inversely correlated, r = −.23, p < .001. The positive and negative schizotypy dimensions were uncorrelated with age at each assessment, and with parental socioeconomic status measured at the initial assessment. The positive schizotypy dimension scores were significantly higher in women (women: M = 1.17, SD = 1.48; men: M = .72, SD = 1.56, p < .01, Cohen’s d = .30), and the negative schizotypy dimension scores were significantly higher in men (women: M = −.47, SD = .93; men: M = −.04, SD = 1.12, p < .001, Cohen’s d = .49).
Associations of Positive and Negative Schizotypy at the Initial Assessment
In order to assess the validity of the schizotypy dimensions, a series of hierarchical linear and binary logistic regression analyses were computed examining the variance accounted for by the positive and negative schizotypy dimensions and their interaction in the prediction of measures of psychopathology and functioning at the initial and 10-year follow-up assessments. The positive and negative schizotypy dimensions were entered simultaneously in the regression at the first step to examine the relative contribution of each factor. The interaction term was entered at the second step to assess its effect over-and-above the main effects. Note that the Chapman et al.’s (1994) longitudinal study initially used an extreme groups design. However, we believe that regression analyses are appropriate because (a) there were continuous and uninterrupted distributions for the four Wisconsin Schizotypy Scales in the Chapman et al. study, (b) positive and negative schizotypy factor score assignments were based on a large unselected sample, and most importantly, (c) the distributions of the positive and negative schizotypy dimensions were continuous and uninterrupted. Given that a number of the variables had non-normal distributions, bootstrap procedures with 10,000 samples were used for the linear regression analyses. Note that statistical significance for linear regression analyses was only indicated at the .05 and .01 level, because Mplus does not provide bootstrap confidence interval (CI) levels for the upper and lower .05% cutoffs.
Schizophrenia-spectrum psychopathology and functioning
Table 1 presents the linear and logistic regressions at the initial assessment. Positive schizotypy was associated with ratings of psychotic-like, schizotypal, and paranoid symptoms. Negative schizotypy was associated with schizotypal and paranoid symptoms. The positive × negative schizotypy interaction predicted paranoid symptoms over-and-above the main effects. Simple slopes analysis of the interaction term revealed that positive schizotypy significantly predicted paranoid symptoms at all levels of negative schizotypy, but this relation strengthened as negative schizotypy increased. This was the case for low (β = 0.38), moderate (β = 0.51), and high (β = 0.64, all slopes p < .001) levels of negative schizotypy (low reflects −1 SD, moderate is the mean, and high is +1 SD). Both positive and negative schizotypy dimensions were associated with impaired functioning as assessed by the Social Adjustment Scale total and subscale scores.
Linear and Logistic Regressions of Measures at the Initial Assessment (n = 534)
Mood symptoms and substance abuse
As hypothesized, positive, but not negative, schizotypy was associated with mood disturbances and substance abuse. Positive schizotypy was associated with increased ratings of depressive and manic symptoms. In addition, it was associated with elevated rates of substance use disorders and with quantitative ratings of impairment associated with alcohol and drug use.
Associations of Positive and Negative Schizotypy at the Reassessment
Schizophrenia-spectrum psychopathology
Table 2 presents the linear and logistic regressions of positive and negative schizotypy at the initial assessment predicting outcomes at the 10-year follow-up assessment. Positive schizotypy was associated with the development of psychotic disorders at the follow-up, whereas both positive and negative schizotypy were significantly associated with the development of schizophrenia-spectrum disorders (including both psychotic disorders and cluster A personality disorders). Note that the odds ratios (ORs) for the prediction of psychotic disorders were comparable for positive and negative schizotypy, but only attained statistical significance for positive schizotypy. This may reflect that the sample had a higher rate of high scorers on positive than on negative schizotypy and thus provided a more stable estimate of the effects for positive, than for negative, schizotypy. Both schizotypy dimensions were associated with ratings of psychotic-like experiences and schizotypal and paranoid personality traits. In addition, negative schizotypy was associated with ratings of schizoid personality traits. Consistent with the initial interview, the positive × negative schizotypy interaction predicted paranoid traits. Simple slope analysis revealed that the relation between positive schizotypy and paranoid personality traits was significant at moderate (β = 0.22; p < .001) and high (β = 0.35; p < .001) levels of negative schizotypy, but not at low levels (β = 0.094).
Linear and Logistic Regressions of Measures at the 10-Year Follow-Up (n = 508)
The same interview measure of psychotic-like experiences was administered at both assessments and correlated .36 across the two interviews. In order to examine whether the schizotypy dimensions predicted worsening psychotic-like experiences across the 10-year interval, we recomputed the regression analysis predicting psychotic-like experiences at the follow-up after partialing out variance associated with psychotic-like experiences at the initial interview. The prediction of follow-up psychotic-like experiences remained significant for both positive (β = 0.25; p < .001) and negative (β = 0.08; p < .05) schizotypy. Although different measures were used at the two assessments, the correlations across assessments of ratings of schizotypal (r = .34) and paranoid (r = .34) symptoms were significant. We recomputed the regression analyses partialing out the baseline measures. The prediction of schizotypal symptoms remained significant for both positive (β = 0.20; p < .001) and negative (β = 0.15; p < .01) schizotypy. Likewise, the prediction of paranoid symptoms remained significant for both positive (β = 0.10; p < .05) and negative (β = 0.11; p < .05) schizotypy.
Functioning and mental health treatment
Both positive and negative schizotypy predicted impaired functioning as assessed by the Global Adjustment Scale. Negative, but not positive, schizotypy was associated with diminished closeness of significant relationships and with diminished likelihood of having married. Positive schizotypy was associated with increased likelihood of receiving mental health treatment (including hospitalization, pharmacotherapy, or psychotherapy).
Mood disorders and substance abuse
As hypothesized, positive, but not negative, schizotypy was associated with mood disturbances and substance abuse. Positive schizotypy was associated with increased likelihood of major depressive and manic or hypomanic episodes, as well as with increased rates of suicide attempts. Family members indicated that two participants committed suicide between the initial and follow-up assessments. Both of these participants had elevated scores on the positive, but not the negative, schizotypy dimension (positive schizotypy standard scores of 2.48 and 2.65). Positive schizotypy was associated with elevated rates of substance abuse and dependence disorders and with quantitative ratings of impairment associated with alcohol and drug use.
As hypothesized, the positive and negative schizotypy dimensions were associated with differential patterns of symptoms and impairment. However, one question is whether the dimensions actually perform better than the original group assignment. Therefore, we reran the regression analyses for five primary dependent measures at the 10-year follow-up (global adjustment, psychotic-like experiences, and schizotypal, schizoid, and paranoid personality disorder symptoms) after partialing out variance associated with group membership. Specifically, we created four dummy codes that compared Chapman et al.’s (1994) Perceptual Aberration/Magical Ideation, Physical Anhedonia, Impulsive-Nonconformity, and combined groups with the control group, following guidelines from Cohen, Cohen, West, and Aiken (2003). We entered the dummy codes as a block in the regression analysis prior to entering the schizotypy dimension scores. The findings for the positive and negative schizotypy dimensions were unchanged (negative schizotypy still significantly predicted all five criteria and positive schizotypy significantly predicted all the criteria except schizoid symptoms). In contrast, almost none of the dummy codes remained significant after entering the schizotypy dimensions.
Post Hoc Analyses
We conducted a number of post hoc analyses in response to recommendations of the reviewers to further examine the nature of the associations of positive and negative schizotypy with outcomes at the assessments. First, we examined whether the schizotypy dimensions predicted psychotic or schizophrenia-spectrum disorders at the follow-up assessment over-and-above the effects of family history of psychosis in first degree relatives. Note that 15 participants reported a first-degree relative with psychosis, and neither the positive schizotypy nor negative schizotypy dimensions were associated with family history, r = −.03 and .06, respectively. In each logistic regression analysis family history of psychosis in a first-degree relative was entered at Step 1, and the schizotypy factors were entered together at Step 2. Family history significantly predicted psychotic disorders at the follow-up assessment, OR = 6.17, 95% CI [1.25, 30.40], p < .05. Furthermore, positive schizotypy still predicted psychotic disorders, OR = 1.53, 95% CI [1.06, 2.20], p < .05, although negative schizotypy did not, OR = 1.47, 95% CI [0.92, 2.33]. Similarly, family history significantly predicted schizophrenia-spectrum disorders at the follow-up assessment, OR = 6.53, 95% CI [1.95, 21.92], p < .01. Both positive schizotypy, OR = 1.53, 95% CI [1.18, 1.99], p < .01, and negative schizotypy, OR = 1.86, 95% CI [1.35, 2.56], p < .001, still predicted spectrum disorders over-and-above family history.
Given that the Chapmans’ original sample was initially selected using an extreme groups approach, a reviewer recommended analyses of weighted data to correct for sampling bias. Therefore, we computed a sampling weight for each subject based on the product of the probability of their positive and negative schizotypy scores (using norms from our original derivation sample). We then recomputed the regression analyses for our primary schizotypy-dependent measures at the follow-up (Global Assessment of Functioning score, psychotic-like experiences, schizotypal, schizoid, and paranoid personality dimensional scores) using the Mplus WEIGHT option. The results were substantively unchanged, with the exception that negative schizotypy no longer significantly predicted psychotic-like experiences at the follow-up assessment. These results are presented in Supplemental Table S1.
We also calculated the prediction of quantitative outcome measures at the initial and follow-up assessment separately for the positive and negative schizotypy factors (as opposed to our planned analyses that entered them simultaneously into the regression models). Note that the statistical significance of these zero-order associations was largely unchanged from the initial regressions. These results are reported in Supplemental Table S2.
Following a reviewer’s recommendation, we recalculated all of the linear and logistic regressions after removing variance associated with gender. Note that in every reanalysis, gender (coded 1 = men, 2 = women) was entered at the first step, and the positive and negative schizotypy dimension scores were entered simultaneously at the second step. Note that none of the effects for positive and negative schizotypy were substantively changed after partialing out variance associated with gender (see Supplemental Table S3). Thus, although there are gender differences in positive and negative schizotypy, the cross-sectional and longitudinal predictions of psychopathology and impairment by psychometric schizotypy were not accounted for by gender.
The primary goal of the study was to examine the association of the positive and negative schizotypy dimensions with symptoms and impairment at the initial and 10-year follow-up assessments. However, a reviewer raised concerns about the need to test the relative predictive strength of the two schizotypy dimensions (i.e., whether the positive and negative schizotypy regression coefficients differed significantly). The most elegant method is to examine whether the 95% CIs around one standardized coefficient include the other coefficient. In other words, if the 95% CI around the beta for positive schizotypy’s prediction of psychotic-like experiences does not include the beta value for negative schizotypy, we can reject that null that βpositive = βnegative. However, MPlus does not provide bootstrapped CIs for standardized coefficients (beta) in its output. As a solution, we computed nonbootstrapped CIs around the standardized coefficients and examined whether they overlapped. Note that this appears to be an acceptable solution given that (a) bootstrapping does not change the coefficient values, just the estimation of standard errors and (b) the statistical significance did not change for any of the regression coefficients when the bootstrapped and nonbootstrapped results were compared. The results with the nonbootstrapped CIs are presented in Supplemental Table S4. The betas for positive and negative schizotypy were significantly different for 14 of the 19 analyses. However, we caution readers to consider the larger pattern of findings across multiple studies, given that this study was not specifically designed to assess the positive and negative schizotypy dimensions.
DiscussionEarly psychiatric models suggested that psychosis represented a discontinuity such that one either did or did not have a psychotic illness (and “never the twain shall meet”). However, increasing evidence from multiple sources such as community studies (e.g., van Os, Hanssen, Bijl, & Ravelli, 2000), family studies (e.g., Kendler, McGuire, Gruenberg, & Walsh, 1995), and studies of the prodrome (e.g., Woods et al., 2009) and high-risk designs (e.g., Gooding, Tallent, & Matts, 2005) indicates that brief, transient, and subclinical psychotic symptoms are not uncommon and that these symptoms may presage the development of schizophrenia-spectrum disorders. Schizotypy provides a powerful unifying framework for integrating subclinical manifestations, the prodrome, spectrum disorders, and full-blown psychosis. Schizotypy also allows us to consider risk and protective factors, facilitates the search for endophenotypes, and involves a multidimensional structure that takes into account the heterogeneous nature of etiology, expression, and treatment response. Furthermore, consideration of a multidimensional model of schizotypy should facilitate the mapping of psychosis and psychotic-like symptoms onto comprehensive models of psychopathology (e.g., Markon, 2010; Wright et al., 2013) and dimensional models of personality pathology (e.g., Krueger et al., 2011). However, reliable and valid measurement of these dimensions is essential for furthering our understanding of schizotypy and schizophrenia.
The concurrent validity of psychometrically assessed positive and negative schizotypy has been supported in interview, questionnaire, laboratory, and daily life studies. However, the present findings provided the first evidence of the predictive validity of these dimensions by demonstrating that positive and negative schizotypy are associated with hypothesized patterns of symptoms and impairment in a 10-year follow-up of nonclinically ascertained young adults. The Chapmans’ longitudinal data set provides an ideal starting place for assessing the predictive validity of the dimensions because of its large sample size, high reassessment rate, 10-year time interval, and inclusion of criteria relevant to the construct of schizotypy. Although the results are not completely surprising in light of the findings for the individual scales in Chapman et al. (1994) and Kwapil (1998), the present findings make a unique contribution over those original results by assessing and supporting the validity of a conceptually driven dimensional model of schizotypy. Nevertheless, new prospective studies should be launched to attempt to replicate these findings in independent samples. Furthermore, such future studies would benefit from inclusion of measures of negative symptoms and the prodrome, as well as consideration of other schizotypy dimensions such as cognitive and behavioral disorganization. However, given the cost and time required to conduct longitudinal assessments, use of the Chapman’s longitudinal sample provided a unique opportunity to assess the validity of the positive and negative dimensions to predict psychopathology and impairment, and most importantly, the development of schizophrenia-spectrum disorders at the 10-year follow-up.
As hypothesized, the dimensions showed differential patterns of associations at both the initial and follow-up assessments, such that positive schizotypy was associated with psychotic-like symptoms, mood disorders, substance abuse, and mental health treatment, whereas negative schizotypy was related to schizoid traits and diminished closeness of significant relationships. Furthermore, additional analyses indicated that positive schizotypy predicted the development of psychotic disorders and both dimensions predicted the development of schizophrenia-spectrum disorders over-and-above family history of psychosis. As expected, both dimensions were associated with schizotypal and paranoid traits and impaired functioning. These findings are consistent with Kwapil et al.’s (2008) cross-sectional interview study of 430 young adults, but also provide evidence that these dimensions are useful in longitudinally predicting schizophrenia-spectrum disorders. The results also indicated that the dimensions provided superior prediction relative to the original nominal groups used in Chapman et al. (1994). Converging evidence indicates that positive and negative schizotypy are related but qualitatively different phenotypes, with different etiologies and underlying pathophysiology. Despite this, researchers often treat schizotypy and schizophrenia as homogenous constructs. We suggest that failure to differentiate the multidimensional structure of schizotypy and schizophrenia will confound signal and noise and impede our ability to elucidate relevant etiological factors.
Consistent with previous findings in the schizotypy literature (e.g., Miettunen & Jääskeläinen, 2010; Raine, 1992), women scored higher than men on the positive schizotypy dimension (small effect size) and men scored higher on the negative schizotypy dimension (medium effect size). However, these gender differences did not account for the association of the positive and negative schizotypy dimensions with measures of symptoms and impairment at the cross-sectional or longitudinal assessments. Note that the analyses of gender differences and effects in this study should be interpreted cautiously as these were largely post hoc examinations and the study was not specifically designed to examine gender differences.
The positive and negative schizotypy dimensions predicted schizophrenia-spectrum symptoms and disorders at the 10-year follow-up. An obvious concern is that this may simply reflect baseline effects at the initial assessment; however, several factors speak against this. First, the participants were all functioning well enough at the initial assessment to attend a major university and had only just entered the age of greatest risk for developing spectrum disorders. As noted, both schizotypy dimensions predicted psychotic-like, schizotypal, and paranoid ratings at the follow-up over-and-above ratings at the initial interview. In terms of disorders, none of the participants met criteria for psychotic illnesses at the initial assessment, although 14 had done so at the time of the follow-up. Unfortunately, Chapman et al. (1994) did not diagnose schizophrenia-spectrum personality disorders at the initial assessment, so we cannot definitively state the extent to which spectrum personality disorders diagnosed at the follow-up assessment were present at the initial assessment. However, several lines of evidence suggest that the rates at the initial assessment would likely be low. Using a subset of 180 participants from the Chapman et al. study, Kwapil (1998) used extant information to make DSM–III–R schizotypal, schizoid, and paranoid personality disorder diagnoses for participants at the initial assessment. Only one of 180 (.6%) met criteria for a schizophrenia-spectrum personality disorder diagnosis at the initial assessment. Similarly, two cross-sectional interview studies assessed large samples of college students with an overrepresentation of high scorers on the positive and negative schizotypy dimensions. Kwapil et al. (2008) reported that only seven of 430 (1.6%) met criteria for schizotypal, schizoid, or paranoid personality disorders, and Barrantes-Vidal et al. (2013) reported a rate of five of 214 (2.3%) for these disorders. So, the evidence suggests that the dimensions predicted symptoms at the cross-sectional assessment and the development of symptoms and disorders at the follow-up assessment.
Chapman et al. (1994) reported that their group identified by high scores on the Physical Anhedonia Scale did not have elevated rates of psychotic disorders, elevated ratings of psychotic-like or schizophrenia-spectrum personality disorder traits, or impaired functioning compared with the control group at the follow-up assessment. In contrast, the negative schizotypy dimension, which includes comparable loadings from both anhedonia scales, was significantly associated with schizophrenia-spectrum disorders, symptoms, and impairment (over-and-above variance accounted for by positive schizotypy). Furthermore, the findings for the negative schizotypy dimensional score are as good or superior to the findings for the Revised Social Anhedonia Scale reported by Kwapil (1998), suggesting that the effectiveness of the Revised Social Anhedonia Scale as a predictor is not “diluted” by the inclusion of variance from the Physical Anhedonia Scale. We suggest that the combination of variance from the two anhedonia scales provides a richer assessment of the negative schizotypy dimension than either scale individually.
The finding that the positive and negative schizotypy interaction term generally did not account for additional variance is consistent with our previous studies and suggests that the effects of the dimensions tend to be additive. This additive effect is clearly demonstrated in Barrantes-Vidal, Lewandowski, and Kwapil’s (2010) findings of marked deviancy for a combined positive and negative schizotypy cluster. Of note, significant interactions were found at both assessments for measures of paranoia—despite the fact that these were assessed 10 years apart and that the initial assessment used a trait-based questionnaire of paranoia, whereas the follow-up used an interview for paranoid personality disorder. It is not entirely clear why this interaction occurred specifically for paranoia. Bentall et al. (2009) described that paranoia has a wide variety of emotional (e.g., negative affect, low self-esteem) and social–cognitive (e.g., poor ability to reason about the mental states of others) mechanisms that appear to be differentially related to positive and negative schizotypy. Thus, high levels of paranoia may require this synergistic combination of affective and cognitive deficits associated with positive and negative schizotypy. However, this bears further investigation in both cross-sectional and longitudinal studies.
One possible criticism is that the assignment of factor scores for positive and negative schizotypy involved the use of formulae based on college students norms from data collected in another state and approximately two decades after the participants in the Chapmans’ sample were assessed. Unfortunately, it was not possible to assess the factor structure of the screening cohort from which the longitudinal samples was drawn. However, we have found that the factor structure is robust and invariant across time, location, and language. In fact, the factor scores from our formulae correlated .999 with factor scores derived from principal component analyses of recent samples collected in Spain (Kwapil, Ros-Morente, et al., 2012) and from unpublished data collected in Wisconsin in the early 1990s. Obviously, the most robust demonstration of the utility of the dimension scores comes from the validity findings in the present and recent studies.
An additional concern is that the differential findings for positive and negative schizotypy are simply due to psychometric differences in discriminating power between the dimensions. However, we believe that is not the case. The schizotypy dimensions have been replicated in both exploratory and confirmatory factor studies. The factor structure is stable, and both factors account for a large portion of the variance in the underlying measures. Furthermore, the individual scales used to derive the factors all have good internal consistency and test–retest reliability. Second, Gross et al. (2013) reported that the 10-week test–retest reliabilities of the positive and negative schizotypy dimensions are .81 and .82, respectively. Furthermore, it is important to note that the present findings are part of a larger series of studies that have reported hypothesized differential patterns of associations of the positive and negative schizotypy dimensions with questionnaire, interview, biobehavioral, and daily life experiences. If the results were simply due to one of the dimensions being more psychometrically discriminating, we would expect to primarily find significant effects for that dimension.
In summary, we believe that advancement of our understanding of schizotypy and schizophrenia requires conceptual and empirical consideration of the underlying multidimensional structure of these constructs. In turn, this requires reliable and valid measurement of these dimensions. The present study provided the first evidence of the predictive validity of psychometrically assessed positive and negative schizotypy, and it points the way for continued conceptualization and validation.
Footnotes 1 Unpublished test copies of the Revised Social Anhedonia Scale are available from the corresponding author Thomas R. Kwapil.
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Submitted: October 15, 2012 Revised: May 29, 2013 Accepted: June 3, 2013
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Source: Journal of Abnormal Psychology. Vol. 122. (3), Aug, 2013 pp. 807-815)
Accession Number: 2013-30852-015
Digital Object Identifier: 10.1037/a0033759
Record: 116- Title:
- Predictive validity of callous–unemotional traits measured in early adolescence with respect to multiple antisocial outcomes.
- Authors:
- McMahon, Robert J.. Department of Psychology, University of Washington, Seattle, WA, US, robert_mcmahon@sfu.ca
Witkiewitz, Katie. Department of Psychology, Washington State University, Pullman, WA, US
Kotler, Julie S.. Department of Psychiatry & Behavioral Medicine, Seattle Children’s Hospital Research Institute, University of Washington, Seattle, WA, US - Institutional Authors:
- The Conduct Problems Prevention Research Group
- Address:
- McMahon, Robert J., Simon Fraser University, 8888 University Drive, Burnaby, BC, Canada, V5A 1S6, robert_mcmahon@sfu.ca
- Source:
- Journal of Abnormal Psychology, Vol 119(4), Nov, 2010. Oppositional Defiant Disorder and Conduct Disorder: Building an Evidence Base for DSM-5. pp. 752-763.
- NLM Title Abbreviation:
- J Abnorm Psychol
- Page Count:
- 12
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- The Journal of Abnormal and Social Psychology; The Journal of Abnormal Psychology; The Journal of Abnormal Psychology and Social Psychology
- ISSN:
- 0021-843X (Print)
1939-1846 (Electronic) - Language:
- English
- Keywords:
- Antisocial Process Screening Device, antisocial personality disorder, callous–unemotional traits, delinquency, predictive validity
- Abstract:
- This study investigated the predictive validity of youth callous–unemotional (CU) traits, as measured in early adolescence (Grade 7) by the Antisocial Process Screening Device (APSD; Frick & Hare, 2001), in a longitudinal sample (N = 754). Antisocial outcomes, assessed in adolescence and early adulthood, included self-reported general delinquency from 7th grade through 2 years post–high school, self-reported serious crimes through 2 years post–high school, juvenile and adult arrest records through 1 year post–high school, and antisocial personality disorder symptoms and diagnosis at 2 years post–high school. CU traits measured in 7th grade were highly predictive of 5 of the 6 antisocial outcomes—general delinquency, juvenile and adult arrests, and early adult antisocial personality disorder criterion count and diagnosis—over and above prior and concurrent conduct problem behavior (i.e., criterion counts of oppositional defiant disorder and conduct disorder) and attention-deficit/hyperactivity disorder (criterion count). Incorporating a CU traits specifier for those with a diagnosis of conduct disorder improved the positive prediction of antisocial outcomes, with a very low false-positive rate. There was minimal evidence of moderation by sex, race, or urban/rural status. Urban/rural status moderated one finding, with being from an urban area associated with stronger relations between CU traits and adult arrests. Findings clearly support the inclusion of CU traits as a specifier for the diagnosis of conduct disorder, at least with respect to predictive validity. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Antisocial Personality Disorder; *Juvenile Delinquency; *Screening; Statistical Validity
- Medical Subject Headings (MeSH):
- Adolescent; Aggression; Antisocial Personality Disorder; Child; Conduct Disorder; Crime; Emotions; Humans; Violence
- PsycINFO Classification:
- Behavior Disorders & Antisocial Behavior (3230)
- Population:
- Human
Male
Female - Location:
- US
- Tests & Measures:
- Self-Report of Delinquency
Antisocial Process Screening Device DOI: 10.1037/t00032-000 - Grant Sponsorship:
- Sponsor: National Institute of Mental Health, US
Grant Number: R18 MH48043; R18 MH50951; R18 MH50952; R18 MH50953; K05MH00797; K05MH01027; R01MH050951-15S1
Recipients: No recipient indicated
Sponsor: Center for Substance Abuse Prevention
Recipients: No recipient indicated
Sponsor: National Institute on Aging, US
Other Details: Fast Track through a memorandum of agreement with the NIMH
Recipients: No recipient indicated
Sponsor: US Department of Education, US
Grant Number: S184U30002
Recipients: No recipient indicated - Conference:
- Meeting of the Society for Research on Adolescence, Mar, 2010, Philadelphia, PA, US
- Conference Notes:
- Portions of this article were presented at the aforementioned conference.
- Methodology:
- Empirical Study; Quantitative Study
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- First Posted: Oct 11, 2010; Accepted: Apr 15, 2010; Revised: Apr 13, 2010; First Submitted: Jul 13, 2009
- Release Date:
- 20101011
- Correction Date:
- 20120827
- Copyright:
- American Psychological Association. 2010
- Digital Object Identifier:
- http://dx.doi.org/10.1037/a0020796
- PMID:
- 20939651
- Accession Number:
- 2010-21298-001
- Number of Citations in Source:
- 86
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- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2010-21298-001&site=ehost-live">Predictive validity of callous–unemotional traits measured in early adolescence with respect to multiple antisocial outcomes.</A>
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Predictive Validity of Callous–Unemotional Traits Measured in Early Adolescence With Respect to Multiple Antisocial Outcomes
By: Robert J. McMahon
Department of Psychology, University of Washington;
Katie Witkiewitz
Department of Psychology, Washington State University
Julie S. Kotler
Department of Psychiatry & Behavioral Medicine, Seattle Children's Hospital Research Institute, University of Washington
Acknowledgement: Members of the Conduct Problems Prevention Research Group, in alphabetical order, include Karen L. Bierman, Department of Psychology, Pennsylvania State University; John D. Coie, Department of Psychology, Duke University; Kenneth A. Dodge, Center for Child and Family Policy, Duke University; Mark T. Greenberg, Department of Human Development and Family Studies, Pennsylvania State University; John E. Lochman, Department of Psychology, The University of Alabama; Robert J. McMahon, Department of Psychology, University of Washington; and Ellen E. Pinderhughes, Department of Child Development, Tufts University.
Portions of this article were presented at the March 2010 meeting of the Society for Research on Adolescence, Philadelphia, Pennsylvania. This work was supported by National Institute of Mental Health (NIMH) Grants R18 MH48043, R18 MH50951, R18 MH50952, and R18 MH50953. The Center for Substance Abuse Prevention and the National Institute on Drug Abuse also have provided support for Fast Track through a memorandum of agreement with the NIMH. This work was also supported in part by Department of Education Grant S184U30002 and NIMH Grants K05MH00797, K05MH01027, and R01MH050951-15S1. For additional information concerning Fast Track, see http://www.fasttrackproject.org.
We are grateful for the close collaboration of the Durham Public Schools, the Metropolitan Nashville Public Schools, the Bellefonte Area Schools, the Tyrone Area Schools, the Mifflin County Schools, the Highline Public Schools, and the Seattle Public Schools. We greatly appreciate the hard work and dedication of the many staff members who implemented the project, collected the evaluation data, and assisted with data management and analyses. We also appreciate the consultation provided by Paul J. Frick and Patrick J. Curran on an earlier version of the manuscript.
In the past several decades, a wide range of risk factors has been identified that is associated with the development and persistence of conduct problems in children and adolescents. Additionally, a growing body of longitudinal data has demonstrated that there is substantial heterogeneity in the developmental trajectories leading to conduct problem behavior (see Lahey, Moffitt, & Caspi, 2003; McMahon, Wells, & Kotler, 2006, for reviews). Increased understanding of these trajectories has contributed to a more accurate conceptualization of youth conduct problems, which, in turn, has provided a foundation for more successful intervention and prevention efforts.
However, even with this progress, youth conduct problems (which frequently result in disruptions at home and school and can also lead to crime and violence) continue to represent a serious and costly societal problem (e.g., Aos, Lieb, Mayfield, Miller, & Pennucci, 2004; M. A. Cohen, 1998). Thus, conduct problem behaviors and their sequelae have continued to be a focus of public concern and a priority for the field of psychology (e.g., Dodge, 2008). In this context, researchers have continued efforts to identify new causal factors and developmental pathways, especially for youth with severe and often early-onset conduct problems who have not consistently responded to currently available treatments and preventive efforts.
Some researchers have looked to the adult literature to identify constructs that have been useful in conceptualizing and predicting antisocial behavior, with the assumption that these constructs might first appear in childhood and/or adolescence and might also be important in identifying unique etiological pathways to severe youth conduct problems. One such construct, psychopathy, has been extensively studied in adults (e.g., Cleckley, 1976; Hart & Hare, 1997; see Patrick, 2006, for a review). Traditional descriptions of the psychopathy construct include interpersonal aspects (e.g., superficial charm, grandiosity, manipulation, and lying), affective aspects (e.g., shallow emotions, callousness, and lack of guilt and empathy), and a behavioral dimension (e.g., impulsivity, irresponsibility, need for excitement, using others, and lack of realistic long-term goals; Cooke & Michie, 2001). In samples of adults, psychopathic traits predict a particularly serious and violent pattern of antisocial behavior that has been shown to be quite resistant to treatment (e.g., Hart, Kropp, & Hare, 1988; Patrick, 2006; Serin, 1993). Furthermore, the antisocial behavior associated with psychopathy in adults is widely thought to have a relatively different etiology from antisocial behavior in nonpsychopathic adults (e.g., Lykken, 1995).
These findings in adult populations prompted interest in whether conduct problems in some youth might be explained by a similar “youth psychopathy” correlate (e.g., Moffitt, Caspi, Dickson, Silva, & Stanton, 1996). Just as this discussion began to take hold, Lynam (1996, 1997, 1998) proposed that psychopathy, widely theorized to be a personality attribute, ought to be recognizable prior to adulthood. Additionally, citing evidence that attempts to treat psychopathy in adulthood had proven unsuccessful (e.g., Hart et al., 1988) and that psychopathic individuals often had antisocial and criminal histories beginning prior to adulthood (Hart & Hare, 1997), Lynam concluded that efforts to interrupt and arrest the development of antisocial and criminal behavior would be aided by the early identification of psychopathic traits in youth. Taken together, these theoretical advancements prompted significant interest in a youth psychopathy construct and in the explicit testing of models of youth psychopathy (e.g., Frick, O'Brien, Wootton, & McBurnett, 1994; Forth, Hart, & Hare, 1990). As a result, in the past 15 years, several research groups have independently worked toward (a) adapting and modifying the construct of adult psychopathy within a developmental context, (b) creating age-appropriate measurement tools to parallel measurement in adult populations, and (c) developing and testing models of youth psychopathy in a variety of cross-sectional and longitudinal youth samples (see Kotler & McMahon, 2005; Lynam & Gudonis, 2005, for reviews).
It should be noted that there remain significant concerns about whether the concept of psychopathy should be applied to youth (e.g., Hart, Watt, & Vincent; 2002; Seagrave & Grisso, 2002; Skeem & Petrila, 2004). Some of the debate surrounding this issue includes (a) conflict about whether delineating psychopathic traits in youth is developmentally appropriate given the malleability of personality during development and the heterogeneity of antisocial youth, (b) questions about the stability of psychopathic traits from youth to adulthood, and (c) concerns about the psychopathy label and its use in legal settings.
As noted above, researchers have made an effort to more accurately assess dimensions of youth psychopathy. Child and adolescent psychopathy measures have been developed by either directly adapting adult assessment tools (primarily the Psychopathy Checklist—Revised [PCL-R]; Hare, 1991, 2003) or creating new screening measures (Forth, Kosson, & Hare, 2003; Frick & Hare, 2001; Lynam, 1997; see Kotler & McMahon, in press, for a review of youth psychopathy assessment methods and issues). The Psychopathy Checklist: Youth Version (PCL:YV; Forth et al., 2003), a direct adaptation of the PCL-R for adolescents, and the Antisocial Process Screening Device (APSD scale; originally called the Psychopathy Screening Device; Frick & Hare, 2001), which includes all elements of the PCL-R unless absolutely not relevant for youth (e.g., multiple marriages), are the tools most commonly used to assess youth psychopathy. However, all of the currently used assessment tools purport to measure a psychopathy construct that is consistent with that described by Hare and colleagues. Furthermore, most of the measures have items/scales that address the affective, interpersonal, and behavioral dimensions of the psychopathy construct. Thus, these youth measures can be viewed as attempting to capture aspects of the psychopathic personality (affective/interpersonal components) as well as the deviant lifestyle and antisocial behaviors that are typically associated with that personality. Moreover, although significant measurement issues continue to be debated, the pattern of relations between the youth psychopathy measures and temperamental and behavioral characteristics suggest that, overall, youth psychopathy assessment tools capture a construct that appears similar to adult psychopathy.
As theory development and research in the domain of juvenile psychopathy have progressed, increasing attention has been paid to the affective/interpersonal component of the psychopathy construct, typically referred to as callous–unemotional (CU) traits in the youth psychopathy literature. In part, this focus on CU traits may have come about as an effort to capture the unique components of the psychopathy construct that are not embedded in established behavioral descriptions of youth antisocial behavior. Furthermore, data suggest that CU traits may be particularly useful in identifying a subgroup of antisocial youth with stable and severe antisocial behavior (Frick & White, 2008) who may differ in their social/emotional, cognitive, and biological functioning (Frick & Viding, 2009). In fact, Frick and colleagues (e.g., Frick, Cornell, Bodin, et al., 2003; Frick & Viding, 2009) proposed that CU traits are the key component of the juvenile psychopathy construct with respect to identifying a unique etiological pathway for early-onset conduct problems. Often, CU traits are operationalized using the CU subscale from the APSD scale (Frick & Hare, 2001). More recently, measurement tools specific to CU traits have also been developed (e.g., interpersonal callousness, Pardini, Obradović, & Loeber, 2006; Inventory of Callous–Unemotional Traits, Frick, 2004).
Using both CU trait-specific approaches and multidimensional youth psychopathy measures, researchers have documented relatively robust and consistent relations (see Frick, 1998; Frick & Marsee, 2006; Lynam & Gudonis, 2005, for reviews) between measures of child and adolescent psychopathy and a range of conduct problems in juvenile offender populations, clinic-referred populations, and community samples (e.g., Christian, Frick, Hill, Tyler, & Frazer, 1997; Dadds, Fraser, Frost, & Hawes, 2005; Forth, 1995; Lynam, 1997, 1998; Salekin, 2008). Taken together, these findings indicate that higher scores on measures of youth psychopathy are positively related to a more severe, pervasive, and stable constellation of conduct problems.
The majority of research on youth psychopathy has utilized concurrent measurements of psychopathy and conduct problems. Although the lack of longitudinal data in this domain is a notable weakness (Moffitt et al., 2008), measures of psychopathy are increasingly being included in longitudinal conduct problem data sets. For example, Loeber and colleagues (2001) assessed psychopathy using the Child Psychopathy Scale (CPS; Lynam, 1996, 1997, 1998) as part of the Pittsburgh Youth Study. The full-length version of the CPS was administered at one time point in the middle cohort of boys (12–13 years of age), while a short version of the CPS (composed of 18 items drawn directly from the Child Behavior Checklist; Achenbach, 1991) was available at all assessment points. Boys with high scores on the CPS were the most frequent, severe, aggressive, and temporally stable delinquent offenders. They were impulsive and prone to externalizing behavior disorders. Moreover, psychopathy predicted serious, stable, antisocial behavior in adolescence above and beyond other known predictors and classification approaches. A recent mixed-model analysis (utilizing the short form of the CPS) indicated that youth psychopathy was relatively stable from childhood through adolescence (i.e., from 7 to 17 years old; intervals examined for stability analyses ranged from 6 months to 5 years) and that both measurement reliability and predictive validity were maintained throughout this lengthy developmental period (Lynam et al., 2009). Lynam, Caspi, Moffitt, Loeber, and Stouthamer-Loeber (2007) also conducted a follow-up assessment of psychopathy in a subsample of the boys from the Pittsburgh Youth Study (n = 271) at the age of 24 using the Psychopathy Checklist: Screening Version (Hart, Cox, & Hare, 1995). These authors reported that psychopathy from early adolescence to early adulthood was moderately stable (r = .31), irrespective of initial risk status or initial psychopathy level and after controlling for 13 other constructs (e.g., demographic information, parenting, and delinquency).
Also using data from the Pittsburgh Youth Study, Pardini and colleagues (2006) constructed a measure of interpersonal callousness and found that higher scores on this measure predicted delinquency persistence in the adolescent cohort. Pardini and Loeber (2008) further identified trajectories of interpersonal callousness over a 4-year period in adolescence and reported that boys with higher initial levels of interpersonal callousness and those with trajectories that increased or did not decline had the highest level of antisocial personality characteristics at age 26.
Recent studies have also examined scores on the PCL:YV (Forth et al., 2003) as a predictor of future recidivism. Schmidt, McKinnon, Chattha, and Brownlee (2006) examined the PCL:YV in a multiethnic community sample of 130 adjudicated male and female adolescents. At a mean follow-up of 3 years, the PCL:YV predicted general and violent recidivism in male Caucasian and Native Canadian youth. Examining a sample of 130 youth involved in court assessments, Salekin (2008) showed that, after controlling for a host of variables relating to offending, PCL:YV scores predicted general and violent recidivism over a 3- to 4-year period from mid-adolescence to young adulthood.
Several studies utilizing community samples have also provided valuable longitudinal outcome data. For example, Frick, Stickle, Dandreaux, Farrell, and Kimonis (2005) followed a sample of 98 youth (Grades 4–7 at baseline) for 4 years. They found that youth with both baseline conduct problems and CU traits subsequently demonstrated the highest rates of conduct problems, self-reported delinquency, and police contacts. Compared to youth without initial conduct problems, youth with baseline conduct problems who did not evidence CU traits also showed higher rates of conduct problems, but rates of self-reported delinquency were not elevated. Piatigorsky and Hinshaw (2004) constructed a psychopathy prototype using items from the California Child Q-Set and found that children with a high degree of similarity to the prototype had more severe delinquency at a 5- to 7-year prospective follow-up, even after controlling for baseline conduct problems. Similarly, Dadds and colleagues (2005) found that, after accounting for initial antisocial behavior, CU traits predicted antisocial behavior for boys (ages 4–9 years) and older girls (ages 7–9 years) at a 12-month follow-up. Examining a very large community sample in Great Britain (n = 7,636 youth ages 5–16 years), Moran et al. (2009) found that CU traits were positively associated with psychopathology at a 3-year follow-up.
Overall, the currently available longitudinal data suggest that measures of youth psychopathy account for significant variation in later conduct problem outcomes and even adult antisocial behavior. However, it is notable that the magnitude of these relations has varied widely across studies and tends to be larger in offender populations.
In the context of this limited but growing body of longitudinal findings, there has been significant concern about overlap between youth psychopathy (both the multidimensional construct and the CU traits component) and conduct problem constructs, especially when psychopathy is measured in nonoffender populations where baseline levels of conduct problems vary widely (e.g., Burns, 2000; Dadds et al., 2005). In particular, it is possible that many relations between youth psychopathy and subsequent conduct problems are due to significant shared variance between measures of psychopathy and other established measures of conduct problem severity (e.g., initial severity of conduct problems, timing of conduct problem onset). Consequently, some researchers have questioned whether psychopathy constructs can provide added value to existing conduct problem models and current Diagnostic and Statistical Manual of Mental Disorders (4th ed. [DSM–IV]; American Psychiatric Association, 1994) and proposed DSM–V CD and subtyping criteria (e.g., Burns, 2000; Moffitt et al., 2008). To provide an accurate response to this question, dimensions of youth psychopathy must be evaluated in the context of other commonly used predictors of conduct problem outcomes (Burns, 2000; Dadds et al., 2005; Frick, 2000). As noted in the review of extant longitudinal data, several authors have begun to address this issue. For example, Dadds et al. (2005), Moran et al. (2009), and Piatigorsky and Hinshaw (2004) found that psychopathy measures predicted significant variance in conduct problem behavior after controlling for baseline conduct problems. However, not all studies have yielded this pattern of results. Salekin, Neumann, Leistico, DiCicco, and Duros (2004) found that, although the PCL:YV (Forth et al., 2003) predicted previous offenses above and beyond oppositional defiant disorder (ODD) and CD diagnoses, the APSD scale (Frick & Hare, 2001) did not do so.
In addition, whether CU traits contribute incremental utility over information provided by comorbid attention-deficit/hyperactivity disorder (ADHD) has not been well established (Frick & Moffitt, 2010). ADHD is the comorbid condition most commonly associated with conduct problems and is thought to precede the development of conduct problems in the majority of cases. In fact, some investigators consider ADHD (or, more specifically, the impulsivity or hyperactivity components of ADHD) to be the motor that drives the development of early-onset conduct problems, especially for boys (e.g., Burns & Walsh, 2002; Loeber, Farrington, Stouthamer-Loeber, & Van Kammen. 1998). Coexisting ADHD also predicts a more negative life outcome than do conduct problems alone (see Waschbusch, 2002).
The current study was designed to specifically address the issue of whether the youth psychopathy construct provides added value to existing models of conduct problems, including the diagnostic subtyping criteria of CD in the DSM–IV and the presence of comorbid ADHD. We focused our investigation on CU traits because this affective/interpersonal component of the psychopathy construct can be more clearly differentiated from behavioral definitions of conduct problems and because CU traits are under consideration as a specifier for CD in the DSM–V (Frick & Moffitt, 2010). In particular, we examined the predictive validity of CU traits measured in early adolescence (Grade 7) for subsequent antisocial outcomes and early adult antisocial personality disorder characteristics in the context of existing predictors of conduct problem severity. Three primary research questions were evaluated: (a) Do CU traits predict later antisocial outcomes above and beyond existing measures of childhood conduct problems and ADHD? (b) How accurately do CU traits identify individuals who engage in antisocial behavior in young adulthood compared to other established predictors of antisocial behavior, and does a CU trait specifier (as proposed for DSM–V) add predictive value to an existing CD diagnosis? (c) Does the predictive validity of CU traits vary as a function of youths' sex, race, or urban/rural status?
To address these questions, CU traits were measured in Grade 7 using the CU traits subscale of the parent-report version of the APSD scale (Frick & Hare, 2001). Antisocial outcomes, measured in adolescence and early adulthood, included (a) self-reported delinquency from seventh grade through 2 years post–high school; (b) self-reported serious crimes through 2 years post–high school, as well as both juvenile and adult arrest records through 1 year post–high school; and (c) antisocial personality disorder symptoms and diagnosis at 2 years post–high school. We controlled for earlier measures of conduct problems (e.g., ODD and CD criterion counts, childhood onset of CD) and ADHD (criterion count). Finally, there is a significant shortage of research on girls and ethnic minority youth who exhibit CU traits (Moffitt et al., 2008); furthermore, to our knowledge, urban versus rural status of these youth also has not been investigated. Thus, sex, race, and urban/rural status were explored as potential moderators.
Method Participants
Participants came from the control schools of a longitudinal multisite investigation of the development and prevention of childhood conduct problems, the Fast Track project (Conduct Problems Prevention Research Group, 1992, 2000). Schools within four sites (Durham, North Carolina; Nashville, Tennessee; Seattle, Washington; and rural Pennsylvania) were identified as high risk based on crime and poverty statistics of the neighborhoods that they served. Within each site, schools were divided into sets matched for demographics (size, percentage free or reduced lunch, ethnic composition), and the sets were randomly assigned to control and intervention groups. Using a multiple-gating screening procedure that combined teacher and parent ratings of disruptive behavior, 9,594 kindergarteners across three cohorts (1991–1993) from 55 schools were screened initially for classroom conduct problems by teachers, using the Teacher Observation of Child Adjustment—Revised Authority Acceptance score (Werthamer-Larsson, Kellam, & Wheeler, 1991). Those children scoring in the top 40% within cohort and site were then solicited for the next stage of screening for home behavior problems by the parents, using items from the Child Behavior Checklist (Achenbach, 199l) and similar scales, and 91% agreed (n = 3,274). The teacher and parent screening scores were then standardized and summed to yield a total severity-of-risk screen score. Children were selected for inclusion into the high-risk sample based on this screen score, moving from the highest score downward until desired sample sizes were reached within sites, cohorts, and groups. Deviations were made when a child failed to matriculate in the first grade at a core school (n = 59) or refused to participate (n = 75) or to accommodate a rule that no child would be the only girl in an intervention group. The outcome was that 891 children (control = 446, intervention = 445) participated. In addition to the high-risk sample of 891, a stratified normative sample of 387 children was identified to represent the population normative range of risk scores and was followed over time. From among the control schools (n = 27), teachers completed ratings of child disruptive behavior to identify a normative, within-site stratified sample of about 10 children within each decile of behavior problems.
The current study utilized data from the high-risk control group (65% male; 49% African American, 48% European American, 3% other race) and normative sample (51% male; 43% African American, 52% European American, 5% other race). Because 79 of those recruited for the high-risk control group were also included as part of the normative sample, the final sample for the current analyses included 754 participants. Weighting was used in all analyses to reflect the oversampling of high-risk children. Participants from the high-risk intervention sample were not included in this study.
Measures
Antisocial Process Screening Device (APSD)
Youth psychopathy was assessed in the summer after seventh grade using the parent version of the APSD scale (Frick & Hare, 2001). Scoring on this 20-item rating scale of youth behaviors is based on a 3-point scale: 0 (not at all true), 1 (sometimes true), or 2 (definitely true). The APSD scale has been shown to have adequate test–retest reliability (Christian et al., 1997). We used the APSD scale three-factor structure identified by Frick, Bodin, and Barry (2000) that includes CU traits, narcissism, and impulse control/conduct problems factors. A confirmatory factor analysis indicated that this factor structure adequately fit the APSD scale data from participants in the current study (Kotler, McMahon, & the Conduct Problems Prevention Research Group, 2002; comparative fit index = .91; goodness-of-fit index = .92). Only the CU factor score was employed in the present investigation. Similar to findings reported by Frick et al. in their examination of the APSD scale in a large community sample, CU scores in our normative sample were moderately skewed (skewness = 0.241). In contrast, CU scores in our high-risk sample were not significantly skewed (skewness = −0.004). This finding is consistent with results from other studies utilizing the APSD scale with high-risk populations (e.g., Pardini, Lochman, & Powell, 2007). In addition, a CU trait specifier was calculated for use in the sensitivity analyses. This specifier was developed using the criteria described by Frick and Moffitt (2010) and proposed for DSM–V. The Frick and Moffitt criteria include a CD diagnosis as well as the presence of two or more of the following CU traits for at least 12 months and in more than one setting: (a) lack of remorse or guilt, (b) callous–lack of empathy, (c) unconcerned about performance, and (d) shallow or deficient affect. In the current study, four items from the CU factor of the APSD scale that correspond to the four traits described by Frick and Moffitt were used to create the CU specifier: (a) does not feel guilty, (b) unconcerned about the feelings of others, (c) unconcerned about school/work, and (d) does not show emotion. For the purposes of the current study, we calculated a CU trait cutoff, defined as having a score of 2 (definitely true) on at least two of the four items from the APSD CU traits scale. This cutoff, in combination with a CD diagnosis, was utilized as the CU trait specifier in the sensitivity analysis.
Self-Report of Delinquency
The Self-Report of Delinquency (SRD; Elliott, Huizinga, & Ageton, 1985) measure was administered from Grades 7 through 12, as well as the 2 years following high school, and captured the number of times in the past year the respondent committed 34 different offenses. Offenses range from lying about one's age to get something to attacking someone with the intent to hurt. Following earlier use of the measure (e.g., Elliott et al., 1985), the items in each grade were capped at three to avoid creating an extremely skewed distribution. The SRD general delinquency outcome measure was defined as the mean of all 34 items within each year, with a possible range of 0–1. A count measure of serious crimes in the 2 years following high school was created by summing the number of 13 items from the SRD that represent serious offenses, including stealing, physical violence toward others, and selling drugs. Thus, the possible range for the serious crimes variable was 0–13.
Court records
Juvenile and adult arrest information was collected from the court system in the child's county of residence and surrounding counties through 1 year post–high school. A court record of arrest indicates any crime for which that youth was arrested and adjudicated, with the exception of probation violations (which were inconsistently reported in courts across the four sites) and referrals to youth court diversion programs for very young first-time offenders (starting at age 11). Other offenses leading to youth diversion programs were included as long as there was an identified arrest in the records.
The data collected from the courts included a description of the offense, the date of offense, the adjudication date for the arrest, and the outcome of the arrest. To capture both frequency and severity of the crimes for which youth were arrested, we created a lifetime severity-weighted frequency of juvenile and adult arrests (Cernkovich & Giordano, 2001). Each offense for each arrest was assigned a severity score ranging from 1 to 5. Level 5 included all violent crimes, such as murder, rape, kidnapping, and first-degree arson. Level 4 contained crimes involving serious or potentially serious harm and included assault with weapon and first-degree burglary. Level 3 crimes reflected medium severity, such as simple assault, felonious breaking and entering, possession of controlled substances with intent to sell, and fire setting. Level 2 included low-severity crimes such as breaking and entering, disorderly conduct, possession of controlled substance, shoplifting, vandalism, and public intoxication. Level 1 involved status and traffic offenses. We then summed the severity level of the most severe offense from each arrest from Grade 6 through 1 year post–high school (separately for adult and juvenile arrests).
Psychiatric criterion counts and disorders
The Parent Interview version of the NIMH Diagnostic Interview Schedule for Children (DISC) is a well-validated, highly structured, laptop computer–administered, clinical interview to assess DSM–IV symptoms in children and adolescents ages 6 to 17 years. We used Version 2.3 in Grade 3 (and the published anticipated DSM–IV criteria for diagnosis at that time) and Version IV in Grades 6, 9, and 12 (Shaffer & Fisher, 1997; Shaffer et al., 1996; Shaffer, Fisher, Lucas, & Comer, 2003). Lay interviewers, unaware of control/normative status, were trained until they reached reliability. Administration took place in the child's home with the primary parent, usually the mother, during the summer following Grades 3, 6, 9, and 12. Variables were computed for past-year criterion counts and diagnoses for CD, ODD, and ADHD. Criteria were solicited for the past 6 months for ODD and for the past 12 months for CD and ADHD. CD scores were based on 15 criteria derived from 23 symptom items, with actual scores ranging from 0 to 9. ODD scores were based on eight criteria derived from 12 symptom items, with scores ranging from 0 to 8. ADHD scores were based on 18 criteria derived from 21 symptom items, with scores ranging from 0 to 18. Diagnoses for Grade 3 followed from Diagnostic and Statistical Manual of Mental Disorders (3rd ed., rev.; American Psychiatric Association, 1987) criteria, and diagnoses for Grades 6, 9, and 12 followed from DSM–IV criteria.
The DISC–Young Adult version (DISC-YA; Shaffer, Fisher, Lucas, Dulcan, & Schwab-Stone, 2000) was administered to the youth at 2 years post–high school. Antisocial personality disorder diagnosis was based on having three or more criteria derived from seven symptom items, with actual scores ranging from 0 to 7 (M = 0.95, SD = 1.45).
Analysis Plan
To address the primary research questions described earlier, we conducted three sets of analyses to examine the relation between CU traits measured in Grade 7 using the APSD scale and six antisocial outcome measures: self-reported delinquency averaged across Grade 7 through 2 years post–high school, self-reported serious crimes in the 2 years following high school, severity-weighted juvenile and adult arrests, and antisocial personality disorder criterion count and diagnoses 2 years following high school. In the first set of analyses, we estimated the relation between CU traits and antisocial outcomes, while controlling for measures of conduct problems (i.e., CD and ODD criterion counts or diagnosis, childhood onset of CD, assessed in Grades 3, 6, 9, and 12) and ADHD (criterion score or diagnosis, assessed in Grades 3, 6, 9, and 12). For continuous outcomes (self-reported delinquency, serious crimes, juvenile and adult arrests, and antisocial personality disorder criterion count), the criterion counts of CD, ODD, and ADHD were included as covariates. For the binary outcome (antisocial personality disorder diagnosis), CD, ODD, and ADHD diagnoses were included as covariates.
In the second set of analyses, we examined the predictive accuracy of CU traits and other measures of conduct problems for successfully identifying individuals who engaged in antisocial behavior in young adulthood. In the third set of analyses, we incorporated three demographic measures to determine whether the predictive validity of CU traits applied equally to all individuals, regardless of sex, race, or urban/rural status.
All of the continuous antisocial outcome measures were count measures with significant positive skew (skewness ranged from 1.73 for antisocial personality disorder criterion count to 6.10 for self-reported serious crimes post–high school). To accommodate the distributions, we used a negative binomial regression model (Hilbe, 2007), which is an extension of the Poisson model that allows for overdispersion (when the variance of the outcome is greater than the mean of the distribution). For the dichotomous outcome—antisocial personality disorder diagnosis—a logistic regression model was used. The second set of analyses was a binary classification test to determine the sensitivity, specificity, and predictive value of the various measures of conduct problems and ADHD in the prediction of antisocial outcomes (Altman & Bland, 1994). Sensitivity represents the proportion of individuals who exhibited antisocial outcomes, given the predictor (i.e., CU traits) was present. One minus the sensitivity provides the false-negative rate, or the rate of missing the prediction of antisocial outcomes. Specificity is the proportion of individuals who did not exhibit antisocial outcomes, given the predictor was absent. One minus the specificity provides the false-positive rate, which is the proportion of individuals who were inaccurately predicted to exhibit antisocial outcomes. The positive predictive value of an indicator was calculated as the proportion of individuals exhibiting antisocial outcomes who were predicted to exhibit antisocial outcomes. Negative predictive value was the proportion of individuals not exhibiting antisocial outcomes who were not predicted to exhibit antisocial outcomes. Ideally, both sensitivity and specificity will be high, which would indicate that the predictor correctly identifies those who will develop antisocial outcomes and correctly identifies those who will not develop antisocial outcomes.
For the third set of analyses, we conducted moderated regression analyses (Aiken & West, 1991) to examine whether sex, race, and urban/rural status moderated the relation between CU traits and antisocial outcomes. The interaction between CU traits and the potential moderators (sex, race, and urban/rural status) was calculated by mean-centering the CU traits score and multiplying the centered CU traits scale score by dummy-coded moderators. The regression models described above for the first set of analyses were repeated with the main effects of CU traits, the main effects of each moderator, and the interaction between CU traits and each moderator. Given the large number of tests, we used a corrected alpha of p < .01.
Regression models were conducted with Mplus Version 5.2 (Muthén & Muthén, 2007). Missing outcome data were accommodated using full-information maximum likelihood (ML) with robust standard errors and numerical integration, which provides an estimate of the variance–covariance matrix using all of the available information from the observed data (Schafer, 1997; Schafer & Graham, 2002). ML assumes data are missing at random (MAR), which means the function by which data are missing can be characterized (probabilistically) by the observed data. By controlling for observed variables that predict the missingness function, the conditional likelihood of the missing value becomes independent of the outcome of interest (Rubin, 1976). The most missing data occurred on the DISC-YA measure, with 33% (n = 250) missing at 2 years post–high school. Attrition analyses indicated that race, urban/rural status, and CU trait scores were significantly associated with whether data were missing; therefore, data were assumed to be MAR with these variables included in all models. Our effective sample sizes were 754 for the SRD and arrests models and 504 for the antisocial personality disorder criterion counts/diagnosis models.
ResultsDescriptive information (i.e., means and standard deviations of continuous measures) and the bivariate correlations for all measured variables are provided in Table 1. As shown with boldfacing, many of the predictor variables were significantly correlated (p < .01) with the six primary outcome variables. There were also significant correlations within each scale (e.g., DISC, SRD). Overall, self-report of general delinquency from Grade 7 to 2 years post–high school was most highly correlated with CD criterion count. Self-report of serious crimes during the 2 years post–high school was most highly correlated with CD and ADHD criterion counts. Adult arrests were most highly correlated with CD criterion count and CU traits. Juvenile arrests were most highly correlated with ADHD and CD criterion counts. Antisocial personality disorder criterion count and diagnosis were most strongly correlated with CD criterion count and child onset of CD. Table 1 also provides the frequencies (percentage of sample endorsing) of antisocial personality disorder diagnosis as well as child onset of CD.
Bivariate Correlations, Means, and Standard Deviations for All Measured Variables
Predictive Validity of CU Traits
The first set of analyses evaluated whether CU traits predicted additional variance in later antisocial outcomes over existing measures of childhood conduct problems and ADHD. As seen in Table 2, the CU traits subscale of the APSD was significantly associated with average SRD scores (i.e., general delinquency), juvenile and adult arrests, and both antisocial personality disorder criterion count and diagnosis. The direction of the effects was such that higher levels of CU traits predicted higher levels of self-reported general delinquency, more juvenile and adult arrests, greater number of antisocial personality disorder criteria met, and a higher likelihood of antisocial personality disorder diagnosis. CD criterion count significantly predicted self-reported general delinquency scores and juvenile and adult arrests. ODD criterion count and ADHD criterion count significantly predicted self-reported serious crimes, and child onset of CD predicted antisocial personality disorder criterion count and diagnosis.
Standardized Regression Coefficients for CU Traits Predicting Outcomes, Including CD, ODD, ADHD, and Child-Onset Criteria as Covariates
Positive Predictive Value and Specificity of CU Traits
The level of the predictive accuracy of the CU traits scale of the APSD, in comparison to other predictors of antisocial outcomes, was evaluated by calculating the sensitivity, specificity, positive predictive value, and negative predictive value of each predictor (Altman & Bland, 1994). A total antisocial index, defined by one or more antisocial outcomes (i.e., the presence of any severity-weighted juvenile or adult arrests, at least one serious crime, or a diagnosis of antisocial personality disorder), was used as the outcome in the sensitivity/specificity analyses. Forty-seven percent of the sample (n = 356) qualified for at least one antisocial outcome.
Table 3 provides each value. ODD diagnosis had the highest level of sensitivity (.43), whereas CD diagnosis with the CU traits cutoff score had the highest specificity (.99). The CD diagnosis with the CU traits cutoff score also had the highest positive predictive value (.89). Therefore, incorporating the CU traits specifier for those with a diagnosis of CD improves positive prediction of antisocial outcomes, with a very low false-positive rate (.01). In the current sample, only one of the nine individuals who were diagnosed with CD and exhibited CU traits did not also exhibit later antisocial outcomes. Negative predictive values and sensitivity were relatively low across the conduct problem and ADHD predictors because of the large number of individuals with antisocial outcomes.
Sensitivity and Specificity of Antisocial Outcomes for Each Conduct Problem/ADHD Predictor
Consistency of Effects Across Sex, Race, and Urban/Rural Status
In the final set of analyses, sex, race, and urban/rural status were examined as moderators of the relation between CU traits and each antisocial outcome using moderated regression analyses. CU traits scale scores were centered and multiplied by the dichotomized sex, race, and urban/rural status variables to create separate interaction terms (Aiken & West, 1991). The only significant interaction effect was an interaction between CU traits and urban/rural status in the prediction of adult arrests (β = −0.84, p < .001). Probing the interaction using simple slopes indicated that the association between CU traits and adult arrests was significantly greater among individuals from urban areas (r = .26, p < .001) as compared to rural areas (r = .11, p = .17).
DiscussionThis study focused on the predictive validity of CU traits, measured in early adolescence, with respect to multiple antisocial outcomes in adolescence and young adulthood. We employed a longitudinal sample with 15 years of annual data collection beginning in kindergarten and extending through 2 years post–high school. Multiple antisocial outcomes were measured, including general delinquent behavior from seventh grade through 2 years post–high school (approximately age 20) and serious crimes in the 2 years following high school, both derived from youth self-report; juvenile and adult arrests through 1 year post–high school, as measured by both youth self-report and court records; and antisocial personality disorder criterion count and diagnosis, as measured by youth self-report.
Three primary research questions were addressed using analytic models designed to focus on assumptions regarding the underlying distribution of the data in the population: (a) Do CU traits predict later antisocial outcomes above and beyond existing measures of childhood conduct problems and ADHD? (b) How accurately do CU traits identify individuals who engage in antisocial behavior in young adulthood compared to other established predictors of antisocial behavior, and does a CU trait specifier (as proposed for DSM–V) add predictive value to an existing CD diagnosis? (c) Does the predictive validity of CU traits vary as a function of youths' sex, race, or urban/rural status? Our findings with regard to each of these questions are discussed below.
Does the CU Traits Construct Provide Added Value to Existing Models of Conduct Problems?
Overall, the results indicated that the measure of CU traits administered to parents in seventh grade (i.e., from the APSD scale) was highly predictive of five of the six antisocial outcomes: self-reported general delinquency, juvenile and adult arrests, and both early adult antisocial personality disorder criterion count and diagnosis. Of import, however, was whether information about CU traits provided incremental value in terms of predictive validity over other well-established predictors of antisocial outcomes, such as criterion counts of ODD and CD, childhood-onset status of CD, and ADHD criterion count, assessed from Grade 3 to Grade 12. Surprisingly, the measure of CU traits was more predictive of later antisocial outcomes than any of these other predictors. This was the case for general delinquency, juvenile and adult arrests, and antisocial personality disorder criterion count and diagnosis.
The current findings add to the existing literature in several key ways. First, there have been conflicting results regarding the added predictive value of CU traits and psychopathy data taken from psychopathy screening measures above and beyond frequently used predictors of antisocial behavior (e.g., baseline conduct problems, ODD and CD diagnoses). While several research groups have found that psychopathy measures predict significant variance in conduct problem behavior after controlling for baseline conduct problems (e.g., Dadds et al., 2005; Moran et al., 2009; Piatigorsky & Hinshaw, 2004), Salekin et al. (2004) looked specifically at the predictive validity of the APSD scale above and beyond ODD and CD diagnoses and did not find a significant effect. Thus, our results lend further weight to the contention that data from CU traits and psychopathy screening measures can provide added predictive validity in the context of a rigorous analytic design including multiple often-used predictors of antisocial behavior. Second, in the extant CU traits and psychopathy literature, insufficient attention has been paid to ADHD as a primary predictor of antisocial behavior (Frick & Moffitt, 2010). Thus, our findings demonstrating the incremental predictive validity of CU traits above and beyond an ADHD measure also augment the current knowledge base in this regard. Finally, results from the current study move beyond demonstrating that CU traits provide incremental predictive validity, in that, with respect to a number of key antisocial outcomes, CU traits were shown to be a more salient predictor than other frequently used conduct problem measures. Establishing CU traits as a key predictor of antisocial outcomes is of primary importance when considering the addition of a CU traits specifier to the diagnosis of CD in DSM–V.
It is important to note that only 5% of participants in the current sample met the criteria for CU traits described by Frick and Moffitt (2010), suggesting that children who meet the CU traits criteria are at extremely high risk for engaging in antisocial acts. Considering that recent prevalence estimates of antisocial personality disorder in the general population are only 3.6% (Grant et al., 2004) and the high degree of specificity for CU traits in predicting antisocial outcomes, it is not surprising that so few participants in the current sample reported CU traits. Nonetheless, the results from the current study should be interpreted with some caution until replicated in a larger sample.
How Accurately Do CU Traits Identify Individuals Who Engage in Antisocial Behavior in Young Adulthood Compared to Other Established Predictors of Antisocial Behavior, and Does a CU Traits Specifier (as Proposed for DSM–V) Add Predictive Value to an Existing CD Diagnosis?
To examine the predictive accuracy of the CU traits scale, in comparison to other predictors of antisocial outcomes, we calculated the sensitivity, specificity, positive predictive value, and negative predictive value of each predictor with respect to a total antisocial index (Altman & Bland, 1994). This index was defined by one or more of four antisocial outcomes (i.e., the presence of any severity-weighted juvenile or adult arrests, at least one serious crime, or a diagnosis of antisocial personality disorder). Almost half of the sample displayed at least one antisocial outcome, providing a wide range of individuals who engaged in antisocial behavior. However, the high rate of antisocial outcomes resulted in low sensitivity (below .43) across all predictors, primarily because of the high number of false negatives (meaning youth who engaged in antisocial behavior but did not meet diagnostic criteria).
Incorporating the CU traits specifier for those with a diagnosis of CD improved positive prediction of antisocial outcomes, with a very low false-positive rate (.01) and with the highest positive predictive value (.89). Only one of the nine individuals who were diagnosed with CD and exhibited CU traits did not also exhibit later antisocial outcomes. As noted previously, several recent studies have evaluated the predictive validity of CU traits and the psychopathy construct in the context of other predictors of antisocial outcomes (e.g., Dadds et al., 2005; Piatigorsky & Hinshaw, 2004; Salekin, 2008). However, to our knowledge, this is the first investigation to specifically address the predictive accuracy of CU traits in relation to other commonly used predictors of antisocial behavior (cf. Frick & Moffitt, 2010). When combined with the findings from our first analysis showing that, compared to other commonly used measures, CU traits provide superior prediction with respect to a number of antisocial outcomes, results demonstrating that the inclusion of CU traits data improves predictive accuracy lend additional weight to the assertion that a CU traits specifier for the diagnosis of CD would be a valuable addition to the diagnostic framework.
It is also important to note that child onset of CD, which is currently a subtype of CD in the DSM–IV, also had a low false-positive rate (.04) and good positive predictive value (.82). These findings provide support for the current proposal to retain the age-of-onset distinction in the DSM–V (Frick & Moffitt, 2010).
Does the Predictive Validity of CU Traits Vary as a Function of Youths' Sex, Race, or Urban/Rural Status?
As noted above, there has been a paucity of research concerning whether or not various demographic variables might serve to moderate the predictive validity of CU traits on antisocial outcomes. To our knowledge, this is the first study to examine sex, race (African American vs. non–African American), and urban/rural status in the same sample.
There was minimal moderation of the effects of CU traits by sex, race, or urban/rural status. The relation between CU traits and adult arrests was somewhat stronger for urban participants than it was for rural participants. This interaction can be partially explained by the significantly higher rate of adult arrests among youth from urban areas and the correspondingly significantly higher scores on the CU traits scale among African Americans from urban areas. To our knowledge, this is the first study to examine urban/rural status as a potential moderator of the effects of CU traits on later antisocial outcomes. The failure of these demographic variables to moderate the predictive relationship between CU traits and nearly all antisocial outcomes (measured up to 7 years later) underscores the robustness of the link between CU traits and antisocial outcomes. However, it is important to note that detecting significant interaction effects can be extremely difficult, particularly when variable distributions are skewed (McClelland & Judd, 1993). Future research should continue to examine whether demographic characteristics may moderate the association between CU traits and antisocial outcomes.
Implications for DSM–V
Findings clearly support the inclusion of presence of CU traits as a possible specifier for the diagnosis of CD (Frick & Moffitt, 2010), at least with respect to predictive validity. Higher levels of CU traits (measured in seventh grade) were associated with a more negative prognosis on five of six antisocial outcomes employed in this study, including self-reported general delinquency, juvenile and adult arrests, and antisocial personality disorder criterion count and diagnosis. Of even greater significance, our indicator of CU traits provided incremental value in terms of predictive validity over other well-established predictors of antisocial outcomes, including previous and current criterion counts of ODD, CD, and ADHD and childhood-onset status of CD. The superior performance of CU traits in predicting later antisocial outcomes strongly suggests that they may have a place in the diagnostic system for CD in the forthcoming DSM–V, along with retention of the age-of-onset subtyping distinction currently in place.
Finally, the findings supported the general robustness of the relation between CU traits and later antisocial outcomes. This was the case during adolescence, with no evidence of moderation by sex, race, or urban/rural status found for either general delinquency or juvenile arrests, as well as early adulthood, with no evidence of moderation for serious crimes or antisocial personality disorder criterion count and diagnosis. The only evidence of moderation was that the connection between CU traits and adult arrests was stronger for urban participants than for rural participants. Future research should be conducted to examine the interaction between living in urban areas and CU traits in the prediction of adult arrests.
Overall, these findings are supportive of serious consideration of the inclusion of CU traits as a specifier for the diagnosis of CD in the upcoming DSM–V.
Footnotes 1 Although less explicit in nature, previous attempts have been made to extend the psychopathy construct to youth populations. Notably, the Diagnostic and Statistical Manual of Mental Disorders (3rd ed. [DSM–III]; American Psychiatric Association, 1980) differentiated children with conduct disorder (CD) who were socialized or undersocialized. The undersocialized type was connected to traditional views of the adult psychopathic personality (primarily the interpersonal/affective factor), while the socialized type of CD focused more on an environmental/behavioral etiology of conduct problems. Within this system, youth were also categorized as aggressive/nonaggressive. See Frick and Ellis (1999) for a detailed discussion of this DSM–III subtyping approach and its association with the youth psychopathy construct.
2 Research findings indicating that the youth psychopathy construct actually seems fairly stable across multiple-year intervals (e.g., Frick, Cornell, Barry, Bodin, & Dane, 2003; Lynam et al., 2009) suggest that this concern may be less relevant than initially thought.
3 It is notable that Murrie, Boccaccini, McCoy, and Cornell (2007) recently found that while behavioral history and personality descriptions influenced judges' decisions, the psychopathy label itself did not.
4 Validity studies for the DISC-YA, including the antisocial personality disorder module, have not been conducted (P. Fisher, personal communication, March 24, 2010). However, support for the construct validity of the DISC-YA antisocial personality disorder criterion count and diagnosis comes from their positive and statistically significant associations with measures of self-reported general delinquency and serious crimes and with juvenile and adult arrests in the current sample (coefficients ranging from .25 to .39, all ps < .01).
5 Initially, latent growth models were estimated for the SRD scores from Grade 7 through Grade 12; however, the results indicated nonsignificant change in SRD over time and a main effect for mean level of SRD. Thus, to simplify the results, we report the relations between conduct problems, CU traits, and mean SRD over time.
6 There is no way of determining whether the MAR assumption holds in any one data set. Fortunately, Collins, Schafer, and Kam (2001) showed that inaccurately assuming MAR, when data are missing not at random, has a minor impact on the ML estimates and standard errors.
7 In the context of the regression analyses, the regression coefficient for ODD predicting self-reported serious crimes was in the opposite direction to the bivariate correlation, indicating a suppression effect (J. Cohen, Cohen, West, & Aiken, 2003) due to high intercorrelations between CD, ODD, and ADHD criterion counts and the stronger associations between serious crimes and both CD and ADHD criterion counts.
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Submitted: July 13, 2009 Revised: April 13, 2010 Accepted: April 15, 2010
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Source: Journal of Abnormal Psychology. Vol. 119. (4), Nov, 2010 pp. 752-763)
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Digital Object Identifier: 10.1037/a0020796
Record: 117- Title:
- Predictor combination in binary decision-making situations.
- Authors:
- McGrath, Robert E.. Fairleigh Dickinson University, Teaneck, NJ, US, mcgrath@fdu.edu
- Address:
- McGrath, Robert E., School of Psychology T-WH1-01, Fairleigh Dickinson University, Teaneck, NJ, US, 07666, mcgrath@fdu.edu
- Source:
- Psychological Assessment, Vol 20(3), Sep, 2008. pp. 195-205.
- NLM Title Abbreviation:
- Psychol Assess
- Page Count:
- 11
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- Psychological Assessment: A Journal of Consulting and Clinical Psychology
- ISSN:
- 1040-3590 (Print)
1939-134X (Electronic) - Language:
- English
- Keywords:
- linear regression, Bayes's theorem, predictive power, clinical decision making, heuristics
- Abstract:
- Professional psychologists are often confronted with the task of making binary decisions about individuals, such as predictions about future behavior or employee selection. Test users familiar with linear models and Bayes's theorem are likely to assume that the accuracy of decisions is consistently improved by combination of outcomes across valid predictors. However, neither statistical method accurately estimates the increment in accuracy that results from use of additional predictors in the typical applied setting. It was demonstrated that the best single predictor often can perform better than do multiple predictors when the predictors are combined using methods common in applied settings. This conclusion is consistent with previous findings concerning G. Gigerenzer and D. Goldstein's (1996) 'take the best' heuristic. Furthermore, the information needed to ensure an increment in fit over the best single predictor is rarely available. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Decision Making; *Linear Regression; *Prediction; *Psychometrics; *Statistical Probability
- Medical Subject Headings (MeSH):
- Bayes Theorem; Decision Making; Humans; Models, Psychological; Predictive Value of Tests; Psychology
- PsycINFO Classification:
- Statistics & Mathematics (2240)
- Population:
- Human
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- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- Accepted: Jun 12, 2008; Revised: Jun 12, 2008; First Submitted: Jun 27, 2007
- Release Date:
- 20080908
- Copyright:
- American Psychological Association. 2008
- Digital Object Identifier:
- http://dx.doi.org/10.1037/a0013175
- PMID:
- 18778156
- Accession Number:
- 2008-12234-001
- Number of Citations in Source:
- 29
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Predictor Combination in Binary Decision-Making Situations
By: Robert E. McGrath
Fairleigh Dickinson University;
Acknowledgement: I am deeply grateful to Robyn Dawes, Joshua Dwire, Lewis Goldberg, William Grove, and John Hunsley for their comments on earlier drafts of this article.
Professional psychologists are often faced with the practical task of classifying people into one of at least two categories. Examples include whether to implement a treatment, whether to hire a person, or whether each of a series of diagnoses applies to an individual. The practical need to dichotomize cases often exists even when the variables used to make the decision are inherently dimensional, though the dichotomization of dimensional data is a problematic undertaking from a formal statistical perspective (e.g., Dwyer, 1996; MacCallum, Zhang, Preacher, & Rucker, 2002; but see Farrington & Loeber, 2000). This paradox highlights the importance of considering both practical considerations and formal statistical issues when one intends data to reveal something about real-world practices (McGrath, 2001).
For example, statistical methods familiar to applied psychologists tend to suggest that the accuracy of predictions is consistently improved by combination of multiple valid predictors. This article presents evidence that this is not necessarily the case, particularly when the pragmatics of predictor combination for purposes of classifying individuals in applied settings are taken into consideration. It demonstrates that under certain common circumstances, psychologists may be better served by basing their classification on the best single predictor and ignoring additional sources of information.
Statistical Methods Relevant to Predictor CombinationTwo statistical methods commonly familiar to psychologists—linear regression and Bayes's theorem—can be taken to suggest that additional predictors will consistently improve prediction. This section provides a brief review of each model and discusses their limitations as a basis for such a conclusion in applied settings.
Linear Regression
Linear regression involves the identification of an optimal set of weights for generation of an additive composite of predictors. It is one of various computationally intensive methods that have been developed for combining data from multiple predictors. Other such methods are available—for example, classification trees, discriminant function analysis, neural networks, cluster analysis, boosting, and methods based on receiver operating characteristic curves (e.g., Friedman, Hastie, & Tibshirani, 2000; Hogarth & Karelaia, 2005; Swets, Dawes, & Monahan, 2000)—but linear regression is clearly the method most familiar to psychologists and so has the greatest influence on psychologists' beliefs about the advantages of multiple predictors.
The use of linear regression for combining information across predictors in applied settings is often discussed in terms of incremental validity (Hunsley & Meyer, 2003; Sechrest, 1963). Incremental validity may be defined as the extent to which additional predictors enhance the proportion of overlapping variance with the criterion. Some form of hierarchical regression is the standard statistical method for evaluation of the degree of incremental validity provided by additional predictors, and some variant of the correlation coefficient usually provides the corresponding effect-size index.
Because this article focuses on dichotomous decisions, subsequent discussion of linear regression focuses on logistic regression. Table 1 contains four examples of results from incremental validity studies that used logistic regression. Various statistics that provide an analogue to the correlation coefficient are available for logistic regression. SAS offers two, the Cox and Snell (1989) generalized coefficient of determination (R2) and an adjusted version that corrects for possible range restriction in R2 (max R2; Nagelkerke, 1991). Each example provides the incremental validity of adding two predictors, B and C, over predictor A. Subsequent discussion focuses on cases of three predictors but at times addresses issues of two predictors.
Examples of Hierarchical Logistic Regression Incremental Validity Analyses
In the first example in Table 1, adding B and C to A increases the proportion of the variance of the criterion predicted by .06 according to the generalized coefficient and by .07 according to the adjusted coefficient. An important mathematical attribute of both statistics is that the coefficient cannot decrease as more predictors are added. That is, the multiple correlation for a set of predictors will always be at least equal to that of any subset of the predictors included in the set. Including additional valid predictors always enhances prediction (or at least does no harm). This attribute can foster the belief that, when the costs of additional testing are minimal and the results of linear regression can be considered reliable (i.e., shrinkage has already been accounted for), it is always desirable to increase the number of valid predictors.
Despite its familiarity, linear regression is rarely used as a combination method in applied settings, for practical reasons. Consider the conditions that must be met before linear regression can be used as the basis for binary decisions:
- A sample that is sufficiently large relative to the number of predictors must be gathered to allow derivation of reliable weights and a cut score for the predicted scores.
- Any changes in the set of predictors will require a new set of weights.
- For optimal fit, future cases must reflect the same population as does the derivation sample.
- In high-stakes decision-making situations, application of the combination method to the individual may need to be accomplished quickly.
It has been argued that in the case of multiple regression, the first condition can be avoided by the method called equal weighting or tallying (Dawes & Corrigan, 1974; Hogarth & Karelaia, 2005; Wainer, 1976). This method involves weighting those predictors positively correlated with the criterion by 1 and those predictors negatively correlated with the criterion by −1 after they have been standardized. Equal weighting can produce results superior to multiple regression under circumstances in which shrinkage is possible. However, application of this strategy to the case of dichotomous decisions would still require identification of an optimal cut score for the weighted combination of predictors and standardizing statistics; such a requirement reintroduces the need for a sizable derivation sample.
The second condition is unrealistic in applied settings in which the battery of predictors is tailored to the respondent on the basis of variations in the goals of the assessment, time constraints, respondent limitations, or issues of cost. The third condition is an untestable assumption in the individual case, and the fourth condition suggests that the application of linear models may be particularly unwieldy in precisely those settings in which accuracy in prediction is most important. Given the practical obstacles, test users almost always rely on less intensive methods of data combination in applied settings.
Bayes's Theorem
A more practical option for combining predictors in applied settings is referred to here as the vote-counting heuristic. If a predictor is not inherently dichotomous, it is first dichotomized with a cut score derived specifically for that predictor. The decision is based on the majority outcome across predictors. This heuristic is mathematically equivalent to tallying but has two modifications that make implementation of the heuristic more practical:
- Each predictor is dichotomized as X− = 0 and X+ = 1 prior to aggregation.
- The cut score for the aggregate is pragmatic rather than optimized; it is based on the value that is half the maximum possible score.
The vote-counting heuristic should not be considered to be specific to psychological evaluation. It is generally applicable to settings in which decision making is based on standardized data-gathering procedures. For example, medical professionals often dichotomize outcomes on dimensional indicators (e.g., body temperature or white blood cell count) according to whether they fall within the normal or abnormal range and make a judgment based on the preponderance of evidence.
Bayes's theorem provides a second statistical method familiar to most psychologists that can be used in conjunction with the vote-counting heuristic to estimate the improvement in accuracy resulting from use of multiple predictors. As background to a discussion of the application of Bayes's theorem to vote counting, a classification table (see Figure 1) is used to introduce some concepts from probability theory relevant to the case in which both a predictor and a criterion are dichotomous. The criterion variable Y is a dichotomous indicator of whether an individual falls in the targeted (Y+) or complement (Y−) population (e.g., whether the person meets or does not meet standards for employment). This criterion is predicted by dichotomized indicators X = A, B, and C, on which a respondent may produce a positive outcome (X+), predictive of membership in the targeted population, or a negative outcome (X−). The probability of belonging to the targeted population can be referred to as p(Y+), though the more familiar term base rate (BR) is used here instead. The probability of being simultaneously a member of the targeted population and negative on predictor B, which would be a prediction error, is symbolized p(B−Y+). The conditional probability of a positive outcome on B among members of the targeted population is symbolized p(B+|Y+). The proportion of predictions that are correct is frequently referred to in the psychological literature as the hit rate, after Meehl and Rosen (1955), but this term has a different meaning in the general statistical literature, so the more contemporary term correct fraction (CF) is used instead.
Figure 1. The symbols displayed are used to represent various outcomes of a decision-making process and the associated probabilities. The upper right and lower left cells indicate various ways to present the probabilities associated with incorrect decisions; the upper left and lower right cells indicate the probabilities of correct decisions. Sens = sensitivity; PPP = positive predictive power; Spec = specificity; NPP = negative predictive power; BR = base rate; CF = correct fraction.
Table 2 provides computational formulas for several statistics relevant to the analysis of prediction in 2 × 2 tables of this type, often referred to as diagnostic efficiency statistics. Sensitivity (Sens) is the probability of a positive test result given membership in the targeted population, or p(X+|Y+). Specificity (Spec) is the probability of a negative result within the complement population, or p(X−|Y−). These statistics reflect the probability of a correct decision within each population.
Computational Formulas for Diagnostic Efficiency Statistics
Positive predictive power (PPP), also referred to as the positive predictive value, is the probability the individual is a member of the targeted population, given a positive result, p(Y+|X+), and negative predictive power (NPP) is the corresponding statistic concerning correct outcomes among individuals who are negative on the predictor, p(Y−|X−). These statistics reflect the probability of a correct decision within each test outcome.
Sens and Spec have a statistical advantage over predictive power statistics in terms of sampling variability. Sens and Spec vary as a function of BR, at least under certain circumstances having to do with the causal model explaining the relationship between predictor and criterion (Choi, 1997). However, they tend to vary less than PPP and NPP, because predictive power is a direct function of the BR, Sens, and Spec. Consider the following restatement of the formulas for PPP and NPP for a given predictor, X:
These formulas indicate that, even if Sens and Spec remain constant, PPP will increase and NPP will decrease as the BR increases (see Meehl & Rosen, 1955). As a result, they demonstrate substantially greater sampling variability as a function of BR than do Sens or Spec (Brenner & Gefeller, 1997).
Even so, predictive power is often of greater interest than are Sens and Spec in applied settings, because the results are directly relevant to circumstances in which a conclusion must be drawn about the respondent's population membership (Y) on the basis of test results (X). As suggested in the preceding discussion of dichotomizing dimensional indicators, practical considerations are not always coincident with the optimal statistical approach.
Equations 1 and 2 are also interesting because they represent restatements of Bayes's theorem in terms of the symbols introduced here. From a Bayesian perspective, BR can be treated as the prior probability of membership in the targeted population, that is, the probability of membership in the absence of additional information from the indicator. PPP represents the corresponding posterior probability (i.e., the probability of membership in the targeted population after a positive outcome has been found on a predictor). Similarly, p(Y−) is the prior probability of membership in the complement population and NPP is the posterior probability, given a negative outcome on X.
One implication of Bayes's theorem is that, if X is a valid predictor of Y, a positive outcome on X will result in a posterior probability of membership in Y+ (PPP) that is greater than the prior probability (BR). In other words, a positive outcome should increase one's confidence that the individual is a member of the targeted population. A reasonable extrapolation is that the iterative use of multiple predictors should incrementally improve PPP to the extent that the respondent produces positive results on each predictor (Waller, Yonce, Grove, Faust, & Lezenweger, 2006). Again, the statistic can be taken as implying that more is almost always better.
The application of Bayes's theorem to the case of multiple dichotomous predictors is demonstrated in Figure 2. In these examples, BR = .10 and SensX = SpecX = .70 for all three predictors. The figure's left panel demonstrates the results for case A+B+C+, in which the respondent generated positive outcomes on all three predictors. A positive outcome on A suggests that the probability of membership in the targeted population is .21. When this value is used as the new prior probability of membership in the population, a positive outcome on B raises that value further to .38. A third positive outcome on C raises the posterior probability of membership in the targeted population to .59.
Figure 2. Two examples of the iterative application of Bayes's theorem to the estimation of PPP. (a) Computing the probability of membership in the targeted population (Y+) if A, B, and C are all positive. A positive outcome on A increases the probability of Y+ from .10 to .21, of a positive outcome on B from .21 to .38, and of a positive outcome on C from .38 to .59. (b) Computing the probability of membership in the targeted population (Y+) if A and C are positive but B is negative. Notice that the results for A and B cancel each other.
Three comments are worth making about the application of Bayes's theorem to vote counting. First, finding that all three outcomes were positive justifies assigning greater confidence to the assertion that the respondent is a member of the targeted population than does finding one positive outcome, but it would probably surprise many applied test users that there is still such a sizable probability (.41) that the respondent is not a member of that population. This finding reflects the low initial BR, so that even a substantial increase in the probability of membership in the targeted population does not approach certainty. The tendency to overestimate the confidence afforded by test results when the initial BR is ignored has been noted many times (e.g., Meehl & Rosen, 1955; Wiggins, 1972), but it continues to bedevil psychological (and medical) practice.
Second, it is worth noting that the enhancement of diagnostic efficiency resulting from the use of multiple predictors varies depending on the pattern of outcome across predictors. The right panel of Figure 2 represents the case in which predictor B is inconsistent with the other two predictors. As these three predictors are equivalent in validity, the divergent outcome for B offsets exactly the increment in PPP due to C, so the overall result is no better than that from A alone. When the predictors differ in their Sens and Spec, each pattern of outcomes can be associated with a unique value for PPP or NPP.
Finally, the estimate of predictive power resulting from the application of Bayes's theorem to the vote-counting heuristic is very likely to be wrong. An extreme example demonstrates why this would be the case. Suppose that predictors A, B, and C all correlate perfectly. If so, the information about Y provided by each predictor is redundant and the posterior probability of membership in the targeted population is still only .21, even if all three test outcomes are positive. The iterative application of Bayes's theorem produces inaccurate results because it ignores dependencies among the predictors (Katsikopoulos & Martignon 2006; see also Waller et al., 2006).
Direct Computation of Probabilities
A more accurate method of determining the effectiveness of the vote-counting heuristic involves computation of the overall probability of a correct decision, given a positive outcome (PPP) or a negative outcome (NPP), on the basis of the majority of test outcomes. For example, the overall PPP in the three-predictor case can be generated by determining the proportion of cases in which at least two of the predictors are positive that involve members of the targeted population.
This approach was first considered in the context of the two-predictor case. This case immediately presents a problem for the vote-counting heuristic. If both predictors are positive or both predictors are negative, the majority decision is clear. The decision becomes uncertain when A is positive and B is negative or vice versa. One reasonable heuristic for breaking the tie suggests a bias in favor of the predictor with the higher level of criterion-related validity; this option has been described, for example, by Ganellen (1996, pp. 72–73). That is, if rYA > rYB, the decision is positive if both A and B are positive or A alone is positive and is negative if both A and B are negative or A alone is negative. Although this rule seems to be intuitively reasonable and may well reflect what test users do in applied settings, an analysis of the implications of this strategy for PPP and NPP produces a surprising result. Assume that A is the more valid predictor. If so, the heuristic suggests that if A is positive the decision based on both predictors will always be positive, whereas if A is negative the two-predictor decision will always be negative. In other words, the diagnostic efficiency of combining A and B is no different than is the diagnostic efficiency of using A alone. This suggestion may seem counterintuitive, because the PPP for the case in which both A and B are positive should be greater than the PPP for either A or B alone, if it is assumed that A and B do not correlate so highly that they are essentially redundant. However, this gain is offset by the lower PPP for the case in which A is positive but B is negative or vice versa. The same pattern holds for NPP and HR. The point is demonstrated mathematically in the Appendix.
If the decision is the same regardless of the outcome on B, then B adds nothing but psychological comfort to the overall predictive power of the assessment. What seemed to be a reasonable, relatively complete, and practically useful heuristic for the integration of results from two predictors offers no incremental validity over the predictor that is awarded dominance for purposes of tie breaking. This conclusion holds even if the second predictor demonstrates incremental validity according to hierarchical regression.
Application of the vote-counting heuristic is more straightforward in the three-predictor case. One reasonable option would be to declare the individual positive when at least two out of three predictors are positive and negative when at least two of the three predictors are negative. An important variant of this heuristic is commonly used in medical diagnostics, when two tests are administered (or the same test is administered twice) and a third is administered as a tiebreaker if they disagree.
The analytic development of this heuristic is provided in the Appendix, and the results are equally unintuitive. If predictor A is more valid than predictors B and C, the analysis demonstrates it would not be unreasonable to find that the PPP, NPP, and HR for A alone are greater than are the corresponding values based on combining all three predictors. More specifically, if p(A+B−C−Y+) > p(A−B+C+Y+), or p(A−B+C+Y−) > p(A+B−C−Y−), or both are true, then A by itself will outperform the vote-counting heuristic on the basis of all three predictors. This finding suggests an alternative heuristic for applied decision making, which is referred to as the best single predictor (BSP).
The conclusion that the BSP can outperform multiple predictors echoes similar conclusions drawn concerning Gigerenzer and Goldstein's (1996) “take the best” (TTB) heuristic, which they presented as one method people use for comparison of pairs of objects or options when time and/or information is limited. Because TTB was developed as a model of naturalistic decision making and BSP is proposed as a model for decision making in more formal testing situations, TTB differs from BSP in several important ways. TTB specifically describes a method for predicting ordinal placement within pairs of objects, whereas BSP is a method for predicting dichotomous placements of objects one at a time. Gigerenzer and Goldstein proposed that the first cue used in TTB is always recognition of the options and that the tendency is to reject those options unfamiliar to the decision maker. Presumably, all tests used will be familiar to the test user. Finally, TTB incorporates the possibility that in a particular comparison, the best single environmental cue may not provide a clear preference for one object over the other, in which case the decision maker is expected to proceed through additional cues until a decision is possible. In contrast, BSP relies on a dichotomous predictor, so placement on the basis of a single predictor is always possible.
Despite the differences, there are enough similarities that evidence concerning the accuracy of TTB should provide some support for the potential of BSP. In circumstances in which information is limited, TTB is often as effective as or more effective than methods based on linear regression and Bayesian methods as a basis for decision making (Gigerenzer, Czerlinski, & Martignon, 2002; Hogarth & Karelaia, 2005; Martignon & Laskey, 1999). To evaluate whether the same was true for BSP, the researcher created a series of data simulations to compare the various approaches that have been reviewed.
Generating SimulationsThe simulations were created with an algorithm intended to sample from the universe of combinations of dichotomous predictors and criteria that could reasonably occur in well-designed applied settings. Each simulation was based on a set of 16 probabilities drawn from two 2 × 2 × 2 contingency tables; each table represented one of the two criterion populations. The first cell of the first table represented the probability that all three predictors were positive in the targeted population, or p(A+B+C+|Y+). The other seven cells in the table reflected conditional probabilities for the other possible combinations of predictor outcomes, given membership in the targeted population. The second table reflected conditional probabilities for the complement population.
The probability for each cell was iteratively increased from 0 to .80 by .10. The BR was similarly set to p(Y) = .02, .10, .30, and .50. BR values > .50 were omitted, as they would have simply mirrored the results for smaller BRs, with PPP and NPP switched. When the BR and the 16 conditional probabilities were used, it was possible to compute the diagnostic efficiency statistics for each predictor, the correlation between each predictor and the criterion, and the correlations between the predictors. Simulations were eliminated if they failed to meet any of the following criteria:
- The sum of the eight probabilities within each of the two tables equaled 1.0.
- The sum of the probabilities that determined the Sens for each of the three predictors fell within the interval .50 ≤ SensX ≤ .90.
- For each of the three predictors, .50 ≤ SpecX ≤ .90.
- For each predictor, either SensX or SpecX was > .50.
- Correlations with the criterion fell in the interval .10 ≤ rYX ≤ .70.
- Correlations between predictors fell in the interval 0 ≤ rXX ≤ .70.
- rYA ≥ rYB and rYB ≥ rYC.
The first criterion restricted the simulations so they were consistent with the mathematical requirements for conditional probability tables. Criteria 2–6 were used to limit the simulations to the types of outcomes likely to occur in well-designed applied settings. The last criterion assured that predictors were ordered from most to least correlated with the criterion. This process generated 186,301 unique simulations.
For each simulation, logistic regression was computed for predictor A and for all three predictors with SAS Version 9.1. In addition to the correlational statistics described earlier, the researcher generated a classification table that assumed a prior probability equal to BR. Results from this table were used to generate estimates of PPP, NPP, and CF for the case in which three predictors are combined.
The Bayesian estimate of CF for three predictors was computed with procedures described by Waller et al. (2006). To generate an overall Bayesian estimate of PPP, the researcher used the same procedures to compute the PPP for each combination of test outcomes that would lead to a positive prediction according to the vote-counting heuristic (at least two of three predictors positive). These PPPs were weighted by the probability of that combination occurring and were averaged. The same process was used to generate the Bayesian estimate of NPP.
Equations A7, A9, and A11 (see the Appendix) were used to directly compute diagnostic efficiency statistics for the three-predictor case. Finally, to evaluate the BSP heuristic, the researcher determined the diagnostic efficiency of predictor A alone.
ResultsDescriptive statistics for the simulations may be found in Table 3. Results are presented for regression-based correlational statistics. The table also contains diagnostic efficiency statistics generated with four methods: the BSP heuristic and logistic regression, as well as the application of Bayes's theorem and direct computation to the vote-counting heuristic. For logistic regression, the mean value for the two correlational statistics is provided for predictor A and for all three predictors combined.
Descriptive Statistics
The findings were generally consistent with expectation. The addition of B and C increased the mean proportion of variance predicted for both the generalized coefficient and the adjusted version. The proportion of variance accounted for increased as the BR approached .50 (see McGrath & Meyer, 2006). For the BSP heuristic, correlations between CF and correlational statistics were higher when the latter was based on one predictor rather than three, whereas the reverse was true for the other classification methods based on three predictors.
One unexpected finding was the relatively low correlations between CF based on logistic regression and the correlational statistics. These correlations were substantially lower than were those for CF estimates based on the BSP heuristic or Bayes's theorem. This finding seemed to be a function of the differential effects of BR on the correlational statistics versus CF derived via logistic regression. After BR had been partialed, the correlations between CF and the correlational statistics increased to a level consistent with those for direct computation. The finding suggests that expectations about the value of additional predictors derived from literature on incremental validity may not generalize even to regression-based diagnostic efficiency statistics.
In the remainder of Table 3, comparisons are organized by diagnostic efficiency statistic. As expected, mean statistics derived with logistic regression and Bayes's theorem were consistently higher than were those based on direct computation or the BSP heuristic. However, their improvement over BSP was not substantial, and mean values were consistently larger for BSP than for direct computation. In other words, the BSP on average generated diagnostic efficiency statistics almost as good as those based on combinations of three predictors and better than the actual diagnostic efficiency associated with the popular vote-counting heuristic. BSP diagnostic efficiency statistics also tended to correlate well with those statistics resulting from the combination of three predictors.
The correlation matrices provided in the table highlight the important role of BR in diagnostic efficiency. Excluding logistic regression, BR alone accounted for 46%–66% of variability in PPP and NPP. Because these effects are in opposite directions, the finding that their combined effect on the HR was attenuated is not surprising.
Table 4 contains the results of direct comparisons with BSP. The top panel is based on all simulations. For reference purposes, the first two columns of statistics include information about the incremental validity of three predictors when compared with one predictor according to logistic regression. The mean increments in the correlational statistics are restatements of the information in Table 3. Across simulations, none were associated with a decrement in effect size when the set of predictors was increased from one to three, and only .02% remained the same. It is worth noting that these results ignore the potential for shrinkage. Under that condition, linear regression correlational statistics consistently suggest an improvement in fit over the BSP.
Improvement Over Best Single Predictor (BSP)
The results are very different when diagnostic efficiency is considered. Though logistic regression and the application of Bayes's theorem to the vote-counting heuristic both were associated with a mean increase in all three diagnostic efficiency statistics, the use of three predictors was associated with a decline in diagnostic efficiency in 14%–43% of comparisons with BSP. The results were substantially poorer for the direct computation of diagnostic efficiency. Across the three statistics examined, BSP did at least as well as three predictors in 70% or more of the simulations.
To demonstrate the conclusions drawn earlier about the circumstances under which three predictors would prove better than one, the researcher repeated the analyses using only those simulations in which p(A−B+C+Y+) ≥ p(A+B−C−Y+) and p(A+B−C−Y−) ≥ p(A−B+C+Y−). The results are given in the lower panel of Table 4. This restriction generally enhanced the increment in fit resulting from use of three predictors. As expected, this enhancement was particularly evident for direct computation. This finding eliminated simulations in which the use of three predictors reduced diagnostic efficiency.
Unfortunately, the joint probabilities one needs to determine whether multiple predictors combined via vote counting will improve over a single predictor are not available to test users. To offer some guidance on circumstances in which the vote-counting heuristic can potentially offer some benefit over the BSP heuristic, the study next addressed the question of whether it is possible to identify circumstances in which additional predictors are likely to increase diagnostic efficiency by using commonly available statistics. For this purpose, it was assumed that the following statistics would be available to a test user or at least estimable: BR, the correlation of each predictor with the criterion, and the correlations between the predictors. Simulations were dichotomized according to whether ΔPPP for direct computation was > 0 versus ≤ 0. The same was done for NPP and HR. Point–biserial correlations were then computed with the statistics assumed to be available, as well as various combinations of those statistics based on similar analyses by Hogarth and Karelaia (2005). The largest point–biserial correlations were associated with the criterion-related validity coefficient for the least valid predictor, rYC, varying between .26 and .34. The best cut scores proved to be .393 for PPP, .40 for NPP, and .41 for HR.
On the basis of these results, it would be reasonable to suggest adding predictors if the validity coefficient for the least valid predictor is .40 or higher. This validity coefficient may strike the reader as improbably high for the least valid of three predictors in psychological settings. It should also be noted that the CFs based on this heuristic varied between .70 and .78. In particular, 57% or more of simulations in which additional predictors were useful were misclassified when the cut score of .40 was used. Finally, the test user must consider the costs of collecting additional tests for this minimal payoff. It would seem then that additional predictors are only desirable when they demonstrate relatively high validity and relatively low cost.
DiscussionWhen reading the literature on applied assessment, one can occasionally find warnings that more information is not necessarily better than less (e.g., Faust, 1989). Even so, when charged with making decisions that have potentially life-altering consequences, psychologists and other users of standardized testing procedures cannot be faulted for associating a greater sense of subjective comfort with larger amounts of information. This association is particularly apt given familiar statistical methods (e.g., linear regression and Bayes's theorem) that reinforce this belief. In fact, linear regression and Bayesian methods in general provide an optimal approach to quantitative prediction under optimal circumstances. What is one to do, though, when conditions are suboptimal, in particular, when information is incomplete about whether each individual is in fact a member of the population used to generate the statistical model? The results of these analyses suggest that the BSP according to zero-order correlation with the criterion can be a better option than is the vote-counting heuristic in cases of three predictors.
An important question to consider is how often in practice the conditions are met under which BSP would trump vote counting. Unfortunately, there is no way to answer this question, because the information about the relative size of certain key joint probabilities is never available in practice. The simulations that served as the basis for the outcomes in Tables 3 and 4 sampled from the array of possible real-world scenarios but were not weighted according to the probability of those scenarios. Furthermore, because they permit the correlations between variables to be as large as .70, the rules used to identify acceptable simulations can be faulted for including too many cases in which the relationships between variables are unusually high. This is particularly true in the case of dichotomous variables, because dichotomization tends to attenuate the size of correlations (MacCallum et al., 2002). The fact that two thirds of the simulations demonstrated a decrement in diagnostic efficiency when vote counting was used instead of BSP does not imply that the same would be true in two thirds of applied testing situations. It should raise serious concerns about the possibility of a decrement in any testing situation, however.
As noted previously, the statistics that are likely to be available to the test user are not particularly helpful for determining whether additional predictors will contribute to diagnostic efficiency. An example of this point is provided in Table 5. Three simulations are presented that are equivalent on BR and the six zero-order correlations. In the first case, the proportion of cases in which A and Y are positive but B and C are negative is larger than is the proportion of cases in which all predictors but A are positive. The result is a decline in all three diagnostic efficiency statistics when B and C are added to A. In the second case, the difference between the first pair of joint probabilities is offset by the difference between the second pair. In this case, PPP is reduced but NPP increases and HR is stable. In the third case, the probability of B, C, and Y being negative is higher than is the probability that only A and Y will be negative; this results in an increase in all three diagnostic efficiency statistics when B and C are considered. The correlations are the product of the 16 joint probabilities and BR; thus, the correlations are equivalent across simulations, because the differences between the joint probabilities listed in the table are offset by differences in other joint probabilities.
Sample Simulations
This article does not really address utility issues as they apply to practical decision making, but the findings raise serious questions about the “more is better” philosophy, at least in the case of two to three predictors. The analysis offered for the three-predictor case in the Appendix does suggest that as the number of predictors is expanded further, to four to five predictors per criterion, the probability that a single predictor will prove equal or superior to the vote-counting method declines substantially. However, one must consider the issue of cost when using so many predictors for a single criterion.
It should be noted that heuristic test aggregation in applied settings can take more complicated forms than vote counting. One common alternative modifies the interpretation on the basis of unique characteristics of each predictor. For example, a positive outcome on a valid performance-based measure of thought disorder combined with a negative outcome on a self-report measure of the same construct might be interpreted as evidence of a lower level disorder than full-blown psychosis or of a lack of insight into the oddity of one's thinking. Such an approach could potentially provide more accurate information than could the purely statistical methods discussed here. It also offers some insight into why practitioners often prefer broadband scales that are sensitive to multiple related psychological constructs (Cronbach & Gleser, 1957). In employee development or clinical settings, inconsistencies in outcomes on such measures can be perceived as the starting point for a more fine-grained analysis of the respondent.
Although this approach to aggregating inconsistent test findings can produce intriguing conclusions, it demonstrates a troubling similarity with ad hoc approaches to explaining inconsistent results in significance testing. For example, Schmidt (1996) hypothesized that the use of post hoc explanations based on moderator variables to understand inconsistent outcomes across significance tests, rather than treatment of those inconsistencies as a logical outcome of insufficient power, tends to result in overly complex interpretations of findings. This unnecessary complexity in turn interferes with the accumulation of knowledge in psychology. Similarly, the ad hoc approach that modifies the interpretation of the tests when results seem inconsistent overlooks the possibility that such disparities are due to random variation in indicator outcomes. The result can lead to overly complex and incorrect person descriptions. This analysis is not intended to suggest that the ad hoc approach to integration of inconsistent outcomes is necessarily invalid, just as Schmidt could not be accused of claiming that differences in outcomes across significance tests never occur because of moderators. It does suggest that test users may be insufficiently skeptical about the modified interpretation of tests as a means of explaining inconsistencies in outcomes across multiple measures. This problem is particularly salient when test outcomes are dichotomized, so that small differences in scores can translate into substantial differences in the interpretation of a test.
The findings also raise questions about the appropriate statistical standard for an adequate predictor. The usual standard is a history of significant correlations with the criterion. However, this evidence alone is insufficient to assure that a test enhances prediction. The simulations were limited to cases in which Sens and Spec were each at least .50, because lower values for either would mean that the test could actually result in more errors in one population than could random placement. The truth, however, is that a test with very high Spec but very low Sens can easily produce significant correlations in a sample of reasonable size. Inspection of diagnostic efficiency statistics provides a more reasonable basis for judgments about the use of tests for decision-making purposes. If an estimate of BR is available, direct estimation of PPP and NPP can be particularly useful as a means of avoiding excessive confidence in the implications of a particular test outcome.
The final point to be raised here is the broad applicability of the terms test and predictor, as used in this article. They are not restricted to formal procedures, such as standardized instruments, but can include interviews, discrete or global clinical impressions, biographical data, and information gathered from significant others. The psychologist who assumes that the issues raised in this article are relevant only to standardized data-gathering procedures is sadly mistaken. Informal procedures demonstrate the same statistical properties as do formal procedures, though there is the added complication that those statistical properties are unknown. For example, when the best psychometric predictor of a construct demonstrates greater criterion-related validity than an interview, if one's goal in gathering data is to make judgments about the test taker and if the outcomes will be combined via vote counting, one must question from a cost–benefit perspective whether there is any practical benefit to interviewing at all. On the other hand, there are often legal and personal expectations about interviewing that might mandate its continued use, even though it may actually reduce accuracy. The combination of predictors as an alternative to the BSP always warrants justification, no matter what the nature of those predictors.
Footnotes 1 This practice violates a common recommendation for the use of local cut scores over global or standard cut scores (e.g., Meehl & Rosen, 1955). The recommendation is largely ignored in applied settings because it is often considered impractical to generate local cut scores, for the same reasons that impede applied use of linear regression. Also, local cut scores create the uncomfortable possibility that a person classified one way in one setting will merit reclassification in a subsequent setting. For example, an individual declared suicidal in an inpatient setting might not meet criteria for classification as suicidal at a later group-home placement because of a change in local cut score, even though the test outcome is the same. Such practices would be extremely problematic from a liability perspective. It is also worth noting that Hsu (1985) found that local cut scores are not necessarily superior to global cut scores, but the truth is that resistance to local cut scores is more practical than it is statistical.
2 Following Meehl and Rosen (1955), expository writing on diagnostic efficiency for psychologists often notes that in cases where the BR is extremely low or high, use of the test may result in a lower CF than does “betting the base rate” (i.e., always predicting that the respondent is a member of the modal population; Hsu, 1985; Waller, Yonce, Grove, Faust, & Lezenweger, 2006). Although this is technically true, betting the BR is unacceptable in applied settings for very practical reasons. Consider the potential consequences for a clinical psychologist who refuses to predict that anyone is at risk of committing suicide because, given the very low BR for suicide, this practice results in the best CF.
3 In recent years, the study of Bayesian networks has allowed researchers to consider dependencies among predictors when they estimate posterior probabilities (e.g., Almond, DiBello, Moulder, & Zapata-Rivera, 2007). However, this method is even more computer intensive than is linear regression. Furthermore, as the focus here is on those statistical models that contribute to the presumption among psychologists that more predictors is always better, Bayesian networks will not be considered further.
4 This heuristic is still technically incomplete, as it ignores the case in which rYA = rYB. This case is probably rare enough that it deserves to be relegated to a footnote, but it could still be addressed by, for example, randomly awarding precedence to A or B. So long as one predictor is treated as dominant, the conclusions drawn in the text remain valid.
5 In 5,523 simulations with low BR, logistic regression did not identify any cases as positive. Statistics presented in Tables 3 and 4 were computed twice, once setting the PPP in these cases to 0 and once setting it to missing. The exclusion of those simulations had no effect on the interpretation of the results, so the tables present results based on all simulations.
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APPENDIX APPENDIX A: Analytic Approach to the Two-Predictor Case
The PPP for the two-predictor case can be restated as follows, assuming that A has been awarded dominance over B:
Compare this to the formula for the PPP of A alone when the formula for the PPP of a single predictor (see Table 2) is expanded in consideration of there being a second predictor that is ignored:
That is, the formulas are exactly the same, and the addition of a second predictor B offers no improvement in the overall PPP. The same relationship holds for NPP2 versus NPPA,
and for the CF:
The formula for PPP with three predictors is
That is, the numerator represents the probability of at least two predictors being positive and the individual being a member of the targeted population. The denominator represents the probability of at least two predictors being positive.
In contrast, the formula for the PPP of A alone when there are three predictors expands to
That is, the numerator represents the probability of at least A being positive and the individual being a member of the targeted population, and the denominator represents the probability of at least A being positive.
The two formulas are surprisingly similar. Only the underlined terms differ. Comparison of the formulas suggests the following conclusion: If p(A+B−C−Y+) > p(A−B+C+Y+), or especially if p(A−B+C+Y−) > p(A+B−C−Y−), then PPPA > PPP3. These conditions are particularly likely if A is the best single predictor of population.
Similar comparisons can be offered for NPP and HR, as indicated by the following equations:
In all three cases, the same sets of joint probabilities determine whether three predictors offer an improvement over one. Specifically, if p(A+B−C−Y+) > p(A−B+C+Y+) and/or p(A−B+C+Y−) > p(A+B−C−Y−), the diagnostic efficiency of the first indicator exceeds that of all three predictors. The only difference across the three statistics is the relative influence of the two comparisons. For PPP, the comparison between p(A−B+C+Y−) and p(A+B−C−Y−) is the more salient to the size of the difference. For NPP, it is the comparison between p(A+B−C−Y+) and p(A−B+C+Y+) that matters most, and the two are equipotent for the CF.
Submitted: June 27, 2007 Revised: June 12, 2008 Accepted: June 12, 2008
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Source: Psychological Assessment. Vol. 20. (3), Sep, 2008 pp. 195-205)
Accession Number: 2008-12234-001
Digital Object Identifier: 10.1037/a0013175
Record: 118- Title:
- Predictors and outcomes of drinkers’ use of protective behavioral strategies.
- Authors:
- Jongenelis, Michelle I.. School of Psychology and Speech Pathology, Curtin University, Perth, WAU, Australia, michelle.jongenelis@curtin.edu.au
Pettigrew, Simone. School of Psychology and Speech Pathology, Curtin University, Perth, WAU, Australia
Pratt, Iain S.. Cancer Council Western Australia, Subiaco, WAU, Australia
Chikritzhs, Tanya. Faculty of Health Sciences, National Drug Research Institute, Curtin University, Perth, WAU, Australia
Slevin, Terry. Cancer Council Western Australia, Subiaco, WAU, Australia
Liang, Wenbin. Faculty of Health Sciences, National Drug Research Institute, Curtin University, Perth, WAU, Australia - Address:
- Jongenelis, Michelle I., School of Psychology and Speech Pathology, Curtin University, GPO Box U1987, Perth, WAU, Australia, 6845, michelle.jongenelis@curtin.edu.au
- Source:
- Psychology of Addictive Behaviors, Vol 30(6), Sep, 2016. pp. 639-647.
- NLM Title Abbreviation:
- Psychol Addict Behav
- Page Count:
- 9
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- Bulletin of the Society of Psychologists in Addictive Behaviors; Bulletin of the Society of Psychologists in Substance Abuse
- Other Publishers:
- US : Educational Publishing Foundation
Society of Psychologists in Addictive Behaviors - ISSN:
- 0893-164X (Print)
1939-1501 (Electronic) - Language:
- English
- Keywords:
- alcohol, protective behavioral strategies, harm minimization
- Abstract:
- While protective behavioral strategies (PBSs) have the potential to reduce alcohol-related harm, there is a lack of understanding of the factors influencing adults’ use of these strategies. The present study assessed the frequency of enactment of a range of PBSs among Australian adults and identified factors associated with their use and the implications for alcohol harm minimization. A sample of 2,168 Australian drinkers (1,095 males and 1,073 females) recruited via a web panel provider completed an online survey that included items relating to quantity and frequency of alcohol consumption, beliefs about the health consequences of alcohol consumption, use of 5 specific PBSs (e.g., counting drinks and eating while drinking), and demographic characteristics. In general, use of these PBSs was negatively associated with overall alcohol consumption. However, usage rates were relatively low, especially among the heaviest drinkers. Refusing unwanted drinks and alternating between alcoholic and nonalcoholic beverages were identified as especially important strategies in the Australian context, accounting for a substantial proportion of the variance in alcohol consumption. Greater efforts to increase awareness and use of PBSs are warranted. In particular, the results suggest that information relating to the importance of refusing unwanted drinks and alternating between alcoholic and nonalcoholic beverages should be actively disseminated to the drinking public. In addition, the reliance on specified numbers of standard drinks in national drinking guidelines suggests encouraging drinkers to count their drinks should be a further focus of interventions given low reported prevalence of this behavior. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Alcohol Drinking Patterns; *Harm Reduction; *Protective Factors; *Change Strategies; Alcohol Abuse; Prediction
- PsycINFO Classification:
- Drug & Alcohol Usage (Legal) (2990)
Substance Abuse & Addiction (3233) - Population:
- Human
Male
Female - Location:
- Australia
- Age Group:
- Adulthood (18 yrs & older)
Young Adulthood (18-29 yrs)
Thirties (30-39 yrs)
Middle Age (40-64 yrs)
Aged (65 yrs & older) - Tests & Measures:
- Alcohol Intake Survey
Engagement in Protective Behavioral Strategies Measure [Appended] - Grant Sponsorship:
- Sponsor: Western Australian Health Promotion Foundation (Healthway), Australia
Grant Number: 20338
Recipients: No recipient indicated - Methodology:
- Empirical Study; Quantitative Study
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- First Posted: Jul 25, 2016; Accepted: Jun 13, 2016; Revised: May 22, 2016; First Submitted: Mar 12, 2016
- Release Date:
- 20160725
- Correction Date:
- 20160915
- Copyright:
- American Psychological Association. 2016
- Digital Object Identifier:
- http://dx.doi.org/10.1037/adb0000194
- PMID:
- 27454371
- Accession Number:
- 2016-36394-001
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- Database:
- PsycINFO
Predictors
and Outcomes of Drinkers’ Use of Protective Behavioral Strategies
By: Michelle I. Jongenelis
School of Psychology and Speech Pathology, Curtin University;
Simone Pettigrew
School of Psychology and Speech Pathology, Curtin University
Iain S. Pratt
Cancer Council Western Australia, Subiaco, Western Australia and School of Psychology and Speech Pathology, Curtin University
Tanya Chikritzhs
Faculty of Health Sciences, National Drug Research Institute, Curtin University
Terry Slevin
Cancer Council Western Australia, Subiaco, Western Australia and School of Psychology and Speech Pathology, Curtin University
Wenbin Liang
Faculty of Health Sciences, National Drug Research Institute, Curtin University
Acknowledgement: This study was supported by the Western Australian Health Promotion Foundation (Healthway), research grant 20338.
Protective behavioral strategies (PBSs) are actions that can reduce alcohol-related harm by decreasing the total amount of alcohol consumed and the amounts consumed on single drinking occasions (Grazioli et al., 2015; Napper, Kenney, Lac, Lewis, & LaBrie, 2014; Pearson, 2013). They can be categorized as either limiting/stopping drinking (e.g., deciding on a limit prior to drinking), modifying the manner of drinking (e.g., eating while drinking), or serious harm reduction (e.g., arranging a designated driver; Martens, Ferrier, & Cimini, 2007; Treloar, Martens, & McCarthy, 2015). A lack of awareness of or adherence to PBSs may prevent drinkers from restricting their alcohol intake to low-risk levels (Kelly, Chan, & O’Flaherty, 2012; Palmer, Corbin, & Cronce, 2010).
Most previous research investigating the use of PBSs has focused on American college students and young adults (Arterberry, Smith, Martens, Cadigan, & Murphy, 2014; Barry & Goodson, 2011; Braitman, Henson, & Carey, 2015; DeMartini et al., 2013; Grazioli et al., 2015; Kelly et al., 2012; Kenney & LaBrie, 2013; Kenney, Napper, LaBrie, & Martens, 2014; Linden-Carmichael, Braitman, & Henson, 2015; Linden, Lau-Barraco, & Milletich, 2014; Martens et al., 2007; Martens et al., 2004; Napper et al., 2014; Palmer et al., 2010; Pearson, D’Lima, & Kelley, 2013; Pearson, Kite, & Henson, 2013; Prince, Carey, & Maisto, 2013). These studies have typically found that PBSs reduce alcohol consumption and alcohol-related harms in these target groups and are more likely to be enacted by females. There is still much work to be done in this domain due to a general lack of research involving other drinker segments and the need for a more comprehensive understanding of the factors influencing PBS use (Lewis, Rees, & Lee, 2009), including relevant moderating and mediating relationships (Pearson, 2013; Pearson, Kite et al., 2013). For example, factors such as gender and attitudes to alcohol and its potential harms have been nominated as being potentially relevant to the decision to use PBSs (Pearson, 2013). Identifying the factors associated with use of PBSs at a population level and determining whether these vary for different PBSs can facilitate the development of appropriate interventions to advise drinkers of the nature and desirability of these strategies and overcome potential barriers to use.
A further important issue is whether different PBSs exhibit varying levels of effectiveness. To optimize their outcomes, future public education programs relating to PBSs need to focus on those strategies that have the greatest potential to reduce alcohol-related harms. Previous studies have tended to aggregate PBSs within categories or overall, preventing identification of relative effects of individual strategies (e.g., Arterberry et al., 2014; DeMartini et al., 2013; Napper et al., 2014; Treloar et al., 2015). Identifying the individual strategies that are most strongly associated with reduced alcohol intake can inform future efforts to minimize alcohol-related harms (Barry & Goodson, 2011).
The present study addresses these research gaps in the context of Australia, where average consumption levels are relatively high at approximately 10 L of pure alcohol per capita per year (World Health Organization, 2014) and alcohol-related harms are estimated to cost AUD$36 billion per annum (Foundation for Alcohol Research & Education, 2011) for a population of just 24 million (Australian Bureau of Statistics, 2016). The specific aims of the study were to (a) assess prevalence of engagement in protective behavioral strategies by alcohol consumption risk status and (b) identify factors associated with higher and lower levels of enactment of specific strategies and the associated implications for alcohol consumption.
MethodThe study was part of a broader examination of Australians’ alcohol-related beliefs and behaviors via a national online survey (Pettigrew et al., 2016). Ethics approval for the study was obtained from a University Human Research Ethics Committee.
Recruitment and Participants
A large web panel provider, PureProfile, provided access to a sample of 2,168 drinkers. The panel is comprised of 350,000 Australians who represent a broad range of geographic and socioeconomic groupings. Panel members are recruited via diverse strategies, including radio and Internet advertising, publicity, and referrals. Potential sample members for individual studies can opt in by either clicking through to the survey from a link provided to them in an invitation e-mail or by selecting the survey from a list of those made available to panel members on the PureProfile website. Panel members receive small financial incentives for participating in surveys and IP addresses are monitored to avoid multiple completions by the same individuals. PureProfile hosts the surveys, and no identifying information is contained in the data files provided to researchers.
Eligibility criteria for the present study were 18+ years of age and alcohol consumption at least twice per month. As a result, the sample was on average older and characterized by heavier drinkers compared to the periodic Australian national alcohol surveys conducted by the Australian Institute of Health and Welfare (AIHW, 2011, 2014) that use broader inclusion criteria (14+ years and any level of alcohol consumption (including nil) within the previous 12 months). Quotas were used to ensure equal proportions of males and females. The resulting sample profile is presented in Table 1.
Sample Profile (n = 2168)
Measures and Procedure
After providing informed consent, respondents completed an online survey that included items relating to quantity and frequency of alcohol consumption, beliefs about the health consequences of alcohol consumption, frequency of use of five PBSs, and demographic characteristics (age, gender, SES, education, income). Alcohol consumption was assessed as per the items used in national alcohol intake surveys (AIHW, 2011, 2014): “In the last 12 months, how often did you have an alcoholic drink of any kind?” and “On a day that you have an alcoholic drink, how many standard drinks do you usually have?” A figure sourced from the National Health and Medical Research Council (NHMRC, 2009) depicting standard drink quantities (with a standard drink containing 10 g of alcohol) across a broad range of beverage and container types was presented to respondents prior to the reporting of intake levels to facilitate accurate measurement.
Based on reported consumption, the NHMRC (2009) alcohol guidelines were used to determine whether respondents were at low or high risk of alcohol-related harm. Accordingly, low risk was defined as an average of two or fewer standard drinks per day and no more than four standard drinks in a single sitting and high risk was defined as an average of more than two standard drinks per day and/or more than four standard drinks in a single sitting. In addition, a further “very high risk” category was constructed that included individuals who consumed 11+ standard drinks in a single sitting at least once per month. This level of intake (110 g of alcohol) is associated with substantial risk of alcohol dependence and abuse (Greenfield et al., 2014).
Engagement in PBSs was also assessed as per the items used in national alcohol intake surveys (AIHW, 2011, 2014), with respondents asked how often they engaged in the following five strategies: Count the number of drinks you have, Deliberately alternate between alcoholic and nonalcoholic drinks, Make a point of eating while consuming alcohol, Quench your thirst by having a nonalcoholic drink before having alcohol, and Refuse an alcoholic drink you are offered because you really do not want it. Frequency of engagement in each behavior was rated on a scale of 1 (Never) to 5 (Always). A composite score was generated by calculating the grand mean of all items. Cronbach’s alpha was used to assess the internal consistency of the five PBS items selected for inclusion in the study, resulting in a coefficient of .76. In accordance with existing guidelines (George & Mallery, 2003; Kline, 2011), this was deemed adequate.
As per MacKinnon, Nohre, Pentz, and Stacy (2000) and Nohre, MacKinnon, Stacy, and Pentz (1999), beliefs about the proximal risks associated with alcohol consumption (i.e., those relating to negative outcomes that are more likely to occur in the short term) were assessed by asking respondents: “Can drinking alcohol impair your ability to work with machinery?” and “Can drinking alcohol impair your ability to drive a car?” Response options ranged from 1 (No, not at all) to 5 (Yes, definitely). As per Pettigrew et al. (2016), a composite “proximal health risk” belief score was created by calculating the grand mean of both items. As per Kozup, Burton, and Creyer (2001), beliefs about the distal risks associated with alcohol consumption (i.e., those associated with longer term health problems) were assessed by asking respondents to indicate whether they perceived alcohol consumption to be favorable or unfavorable for heart disease, high blood pressure, cancer, stroke, and liver damage. Response options ranged from 1 (Unfavorable) to 5 (Favorable), with items reverse-scored for analysis purposes. A composite “distal health risk” belief score was created by calculating the grand mean of all five items (as per Pettigrew et al., 2016). General beliefs about alcohol were assessed by asking respondents “Overall, what is your attitude or opinion about alcohol consumption?” (5-point response scale Extremely negative to Extremely positive; Peters et al., 2007) and “Do you consider alcohol to be part of a healthy diet?” (5-point response scale No, not at all to Yes, definitely; Kozup et al., 2001).
Statistical Analyses
Descriptive analyses were conducted to calculate the degree to which respondents engaged in each PBS. Pearson chi-square analyses were used to compare engagement in PBSs by risk level, with a Bonferroni-adjusted alpha level of .016 used to control for the familywise error rate and determine significance.
Univariate regression analyses were conducted to identify factors associated with higher and lower levels of enactment of specific strategies and the associated implications for alcohol consumption. Further univariate regression analyses were then conducted to identify variables that were significantly associated with average weekly alcohol consumption. Factors entered as independent variables were gender (1 = male, 2 = female), age, SES (derived from postcode), education (1 = non-tertiary educated, 2 = tertiary educated), household income, overall attitude to alcohol, whether alcohol is considered part of a healthy diet, and beliefs about the potential proximal and distal risks of alcohol consumption. Use of individual PBSs and average engagement across all five PBSs were also entered as factors associated with consumption.
Structural equation modeling was then used to examine the adequacy of a model linking predictors of use of PBSs with predictors of alcohol consumption. Of particular interest was whether use of PBSs influenced alcohol consumption over and above other potential influencing factors and whether there were any indirect effects of the included variables on reported consumption. Several fit indices from the maximum likelihood estimator output were inspected (model chi-square, Tucker–Lewis Index [TLI; ≥ 0.95], Comparative Fit Index [CFI; ≥ 0.95], the root mean square error of approximation [RMSEA; ≤ 0.06] and the standardized root-mean-square residual [SRMR; < 0.08]). This was conducted in Mplus 7.4 (Muthén & Muthén, 1998–2015).
Results Prevalence of Use of PBSs by Alcohol Consumption Risk Status
Respondents’ reports of the extent to which they use the included PBSs are shown in Table 2. Fewer than half of the respondents reported regularly using most of the strategies, with the average usage rate across all strategies being around one third of respondents. For all the strategies, reported usage was lower at higher levels of alcohol-related risk. Of particular note is that on average only half the sample reported refusing drinks they did not want, ranging from one third of high-risk drinkers to two thirds of low-risk drinkers (p < .001).
Engagement in Protective Behavioral Strategies by Risk Status
Factors Associated With PBS Use and Associated Levels of Alcohol Consumption
Univariate regression analyses identified gender, age, tertiary education, attitude to alcohol, and proximal risk beliefs as factors significantly associated with use of PBSs. Univariate regression analyses identified use of PBSs, gender, age, tertiary education, household income, SES, attitude to alcohol, proximal harm beliefs, and belief that consumption of alcohol is part of a healthy diet as factors significantly associated with alcohol consumption.
Based on these results, a model was created that linked use of PBSs at an aggregate level and its predictors with reported alcohol consumption and its predictors (see Figure 1). The model fit the data well with a nonsignificant model chi square, χ2(3) = 7.06, p = .070. All other fit indices met criteria for adequate to excellent fit (χ2/degrees of freedom = 2.35, TLI = 0.95, CFI = 0.99, RMSEA = 0.03 [90% CI = 0.00, 0.05], SRMR = .01). The model accounted for 5% of the variance in engagement in PBSs and 19% of the variance in alcohol consumption.
Figure 1. Path analysis (with standardized regression coefficients and standard errors) of factors affecting engagement in PBSs and alcohol consumption. Nonsignificant paths are depicted by the dashed lines. * p < .05. *** p < .001.
Standardized coefficients and unique errors associated with the observed variables are included in Figure 1. All independent variables other than overall attitude to alcohol (depicted by the dashed line in Figure 1) emerged as significant factors associated with PBS use, with gender exhibiting the largest standardized regression coefficient. PBS use, gender, age, tertiary education, and overall attitude to alcohol emerged as significant factors associated with alcohol consumption. Of these, use of PBSs exhibited the largest standardized regression coefficient.
To examine the mediating relationships proposed by this model, a test of indirect effects was conducted as per Baron and Kenny (1986). All of the indirect effects emerged as significant: gender to alcohol consumption via engagement in PBSs (b’ = −0.05, SE = 0.01, z = −6.30, p < .001), age to alcohol consumption via engagement in PBSs (b’ = 0.02, SE = 0.01, z = −2.21, p < .001), tertiary education to alcohol consumption via engagement in PBSs (b’ = −0.03, SE = 0.01, z = −4.17, p < .001), and proximal risk beliefs to alcohol consumption via engagement in PBSs (b’ = −0.03, SE = 0.01, z = −3.58, p < .001).
Each of the PBSs was then examined individually. After univariate regression analyses identified factors significantly associated with use of each practice, a path analysis was conducted linking use of each PBS and its predictors with alcohol consumption and its predictors (see Figure 2). Although the chi-square associated with this model was significant, χ2(23) = 50.52, p < .001, other fit indices for this model met the criteria outlined for adequate to excellent fit: χ2/degrees of freedom = 2.20, TLI = 0.97, CFI = 0.99, SRMR = .02, and RMSEA = 0.03 (90% CI = 0.02, 0.04). The model accounted for 22% of the variance in alcohol consumption.
Figure 2. Path analysis (with standardized regression coefficients and standard errors) of factors affecting engagement in each of the PBSs and alcohol consumption. Bolded solid lines indicate the presence of a significant direct and indirect effect from the independent variable to alcohol consumption via the various PBSs. Nonbolded solid lines indicate the presence of a significant direct effect. Dashed lines depict nonsignificant pathways. Directionality is only shown for significant pathways. *** p < .001.
Standardized coefficients and unique errors associated with the observed variables are presented in Figure 2. PBSs were allowed to covary. Gender was found to be associated with all five PBSs, with females reporting greater use compared to males in all cases. Beliefs about the proximal risks associated with alcohol consumption also emerged as a significant factor for all individual PBSs, with the exception of Deliberately alternate between alcoholic and nonalcoholic drinks. The PBS Refuse an alcoholic drink you are offered because you really do not want it had the strongest relationship with alcohol consumption, followed closely by Deliberately alternate between alcoholic and nonalcoholic drinks. Make a point of eating while consuming alcohol was least closely associated with alcohol consumption and was not significant. Unexpectedly, the PBS Quench your thirst by having a nonalcoholic drink before having alcohol had a significant positive relationship with alcohol consumption.
To examine the mediating relationships proposed by this model, a test of indirect effects was conducted as per Baron and Kenny (1986). The bolded solid lines in Figure 2 indicate the presence of a significant direct and indirect effect from the independent variable to alcohol consumption via the various PBSs. There were few distinct patterns in the indirect effects, with the primary points of note being the strong effect of gender and the important role of proximal beliefs in influencing respondents’ use of multiple PBSs.
DiscussionThe results of the present study support previous U.S. studies finding that enactment of various PBSs is associated with lower levels of alcohol consumption (Arterberry et al., 2014; Braitman et al., 2015; DeMartini et al., 2013; Linden-Carmichael et al., 2015; Martens et al., 2007; Napper et al., 2014; Palmer et al., 2010; Prince et al., 2013). A substantial proportion of variance in alcohol consumption (19–22%) was found to be accounted for by the tested models. The results extend previous research and contribute to the very limited body of previous work by demonstrating that this effect occurs in a general population sample of drinkers and is therefore not confined to college students.
Of particular importance is the finding that the tested PBSs demonstrated different degrees of association with alcohol consumption. Previous studies have tended to aggregate PBSs within categories, which prevents identification of relative effects of individual behaviors (e.g., Arterberry et al., 2014; DeMartini et al., 2013; Napper et al., 2014; Treloar et al., 2015). The results of the present study suggest that compared to the other PBSs included in the study, encouraging drinkers to refuse drinks they do not want may have the greatest potential to reduce consumption, along with encouraging alternating alcoholic and nonalcoholic beverages. These two strategies accounted for the most variance in reported alcohol consumption. However, changing these behaviors will be a challenging task given the highly social nature of much alcohol consumption, entrenched rituals of pressing alcohol on others to demonstrate hospitality, and turn-taking in purchasing alcohol for group members when out drinking (Borsari & Carey, 2001; Emslie, Hunt, & Lyons, 2013). Such rituals serve to enhance social bonding and encourage continued drinking regardless of desire for alcohol, thereby reducing the likelihood of individuals engaging in the recommended PBSs.
Of note is that commencing a drinking session by quenching thirst with a nonalcoholic beverage was associated with higher levels of alcohol consumption, indicating that encouraging greater enactment of this particular behavior may be counterproductive. It may be that having a nonalcoholic beverage first is seen by drinkers to justify or ameliorate heavy subsequent drinking. Alternatively, the action of drinking, albeit a nonalcoholic product, may prime further drinking behavior, especially in drinking-focused environments (Aarts, Dijksterhuis, & de Vries, 2001). This may occur via multiple mechanisms. In the first instance, intentionally suppressing the desire to consume alcohol, as would occur when initially selecting a nonalcoholic beverage in an alcohol environment, may increase the accessibility of alcohol-related information in the memory, potentially resulting in increased subsequent alcohol consumption (Palfai, Monti, Colby, & Rohsenow, 1997). Second, craving for alcohol at a point in time is also associated with higher subsequent alcohol intake (McHugh, Fitzmaurice, Griffin, Anton, & Weiss, 2016), indicating that triggering craving by encouraging drinkers in high-alcohol environments to delay drinking commencement may result in unintended consequences.
Gender and age were the demographic variables most strongly associated with both PBS enactment and alcohol consumption. In terms of gender, females were significantly more likely to use all five of the strategies and reported lower levels of alcohol consumption. It thus appears that males would be the most appropriate target for future efforts to increase uptake of the recommended behaviors. The tendency for males in this sample to demonstrate lower rates of enactment of PBSs and higher alcohol intake is consistent with prior research in the United States on college and youth samples (DeMartini et al., 2013; Kelly et al., 2012; Prince et al., 2013), indicating that the results may have potential relevance to other national and subpopulation contexts.
The results for age were mixed in the present study, with older drinkers being more likely to engage in two of the PSBs (eating while drinking and refusing unwanted drinks) but also demonstrating higher overall levels of alcohol consumption. This is likely to reflect riskier but less frequent alcohol consumption among younger people relative to more consistent drinking among older people (AIHW, 2014). This suggests that both older and younger drinkers would benefit from greater awareness of the benefits associated with frequent enactment of PBSs. PBSs are not currently actively promoted in Australia, and greater efforts to increase awareness of and motivation to use PBSs are warranted given low take-up levels of most of the recommended strategies.
The results highlight the need to emphasize the importance of monitoring the number of drinks consumed given the NHMRC guidelines’ reliance on recommended numbers of standard drinks to minimize the risk of short- and long-term harm (Parry, Patra, & Rehm, 2011). Many other countries also express alcohol intake recommendations in terms of number of standard drinks, including the United States (U.S. Department of Agriculture & U.S. Department of Health & Human Services, 2010), the United Kingdom (House of Commons, 2012), Canada (Butt, Beirness, Gliksman, Paradis, & Stockwell, 2011), and various parts of Europe (Mongan & Long, 2015). Around two thirds of drinkers in the present study reported that they do not regularly keep count of the number of drinks they consume, which renders alcohol intake guidelines largely futile for these drinkers. Encouraging self-monitoring thus appears to be a vital first step in working toward greater compliance with the guidelines. This will also entail ensuring drinkers are able to accurately assess a standard drink to enable them to accurately count their drinks, which will constitute a substantial communications challenge given the tendency for drinkers to overpour and underestimate intake (Kerr & Stockwell, 2012).
Several alcohol-related health beliefs were included in this study as possible predictors of PBSs. These included general attitudes relating to the healthiness of alcohol as a product and awareness of specific alcohol-related harms, some of which are more likely to manifest in the short term (e.g., the consequences of drinking while driving or operating machinery) or the longer term (e.g., illnesses such as heart disease and cancer). Of the beliefs tested, those relating to proximal risks emerged as most influential, exhibiting a significant association with four of the five PBSs. Such beliefs are potentially modifiable, suggesting that raising awareness of the proximal risks associated with alcohol consumption may assist in promoting greater enactment of PBSs. This approach would be consistent with learnings from tobacco control, where campaigns focusing on the proximal negative consequences of smoking have proven to be effective (Amonini, Pettigrew, & Clayforth, 2015).
The relatively small proportion of variance in aggregated PBS enactment accounted for by the predictor variables included in the study (5%) needs explanation. Recent research suggests that PBSs are unlikely to be enacted unless group norms are supportive (Previte, Russell-Bennett, & Parkinson, 2015). In heavy drinking cultures, group norms favor excessive consumption (Castro, Barrera, Mena, & Aguirre, 2014; Jones, 2014), indicating that future efforts to increase prevalence of PBSs will need to actively counter these norms. In particular, the tendency of many respondents to regularly accept alcoholic beverages they do not want highlights the importance of specifically addressing this apparently counterintuitive behavior in harm-minimization campaigns. Similar to work that has been done to inform the public of the risks associated with drinking during pregnancy and to promote strategies that pregnant women can use to deflect pressure to consume alcohol (France et al., 2014), the results of the present study suggest that it may be beneficial to undertake interventions designed to promote the rights of individuals to decline offers of alcohol and to provide recommendations for effective refusal strategies.
Limitations and Future Research Directions
The present study has several limitations that could be addressed in future research. In the first instance, cross-sectional data were used to investigate the relationships between (a) various predictor variables and enactment of PBSs and (b) individual and aggregated PBSs and alcohol consumption. Although cross-sectional studies are useful for identifying factors that can be definitively examined in subsequent longitudinal or experimental studies (Jacobi, Hayward, de Zwaan, Kraemer, & Agras, 2004), they cannot determine whether the putative factor or the outcome occurred first. There is therefore a need for longitudinal research that tracks individuals over time to provide more robust evidence of factors influencing PBSs and alcohol intake. Such work would be ideally undertaken using a range of sampling strategies across multiple geographical areas to overcome the limitations to generalizability that are associated with web panels and country-specific data.
Due to the analytical approach of the present study, just five PBSs were selected for inclusion. Future research could include a larger number of PBSs to provide greater insight into the differential effects of different strategies. In particular, the strategies included in the present study belong to the “limiting/stopping” and “manner of drinking” PBS categories, and represent just a small number of possible strategies within each of these categories. Future research could include coverage of a broader range of strategies across and within all three categories: limiting/stopping drinking, modifying the manner of drinking, and serious harm reduction (Martens et al., 2007; Treloar et al., 2015). Further, it has been recently suggested that PBS measures may be improved by asking respondents to report actual numbers of times behaviors are enacted, rather than using the broader frequency response options that have been used in the present study and previous research (Braitman et al., 2015). Future work in this area may incorporate such modified measures to obtain more precise data relating to the extent to which different categories of drinkers engage in specific PBSs.
Although the large sample size and coverage of major demographic groups assists in addressing concerns regarding representativeness, the use of a web panel provider to recruit the sample of drinkers for the present study means population representativeness cannot be assumed. Future research could seek to source representative samples via different forms of recruitment that also access very light drinkers to assess whether PBS engagement plays a role in the drinking strategies of these individuals.
Conclusion
Overall, the present study supports the importance of PBSs in influencing alcohol intake. Different PBSs were found to be associated with varying levels of consumption, illustrating the importance of determining which PBSs should be most actively promoted to the drinking public. One PBS was found to be associated with higher levels of alcohol consumption, further highlighting the need to ensure drinking guidelines are evidence based and are not likely to contribute to alcohol-related harm. The results suggest that refusing unwanted drinks and alternating alcoholic and nonalcoholic beverages could be primary target PBSs in the Australian context, with potential application to other countries sharing similar drinking cultures characterized by peer pressure to consume alcohol and cultural norms associated with heavy drinking during social events (Hogan, Perks, & Russell-Bennett, 2014; Kuntsche, Rehm, & Gmel, 2004; Kuntsche et al., 2014). Males and heavier drinkers are likely to be especially important target groups for these interventions. The low reported frequency of counting drinks highlights the need to also encourage this behavior given the emphasis in current drinking guidelines on monitoring the number of standard drinks consumed.
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Submitted: March 12, 2016 Revised: May 22, 2016 Accepted: June 13, 2016
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Source: Psychology of Addictive Behaviors. Vol. 30. (6), Sep, 2016 pp. 639-647)
Accession Number: 2016-36394-001
Digital Object Identifier: 10.1037/adb0000194
Record: 119- Title:
- Predictors of engaging in problem gambling treatment: Data from the West Virginia Problem Gamblers Help Network.
- Authors:
- Weinstock, Jeremiah. University of Connecticut Health Center, Farmington, CT, US, jweinsto@slu.edu
Burton, Steve. Problem Gamblers Help Network of West Virginia, Charleston, WV, US
Rash, Carla J.. University of Connecticut Health Center, CT, US
Moran, Sheila. Problem Gamblers Help Network of West Virginia, Charleston, WV, US
Biller, Warren. Problem Gamblers Help Network of West Virginia, Charleston, WV, US
Krudelbach, Norman. Problem Gamblers Help Network of West Virginia, Charleston, WV, US
Phoenix, Natalie. University of Connecticut Health Center, CT, US
Morasco, Benjamin J.. Portland Veterans Affairs Medical Center, Portland, OR, US - Address:
- Weinstock, Jeremiah, Department of Psychology, Saint Louis University, 3511 Laclede Avenue, St. Louis, MO, US, 63103-2010, jweinsto@slu.edu
- Source:
- Psychology of Addictive Behaviors, Vol 25(2), Jun, 2011. pp. 372-379.
- NLM Title Abbreviation:
- Psychol Addict Behav
- Page Count:
- 8
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- Bulletin of the Society of Psychologists in Addictive Behaviors; Bulletin of the Society of Psychologists in Substance Abuse
- Other Publishers:
- US : Educational Publishing Foundation
Society of Psychologists in Addictive Behaviors - ISSN:
- 0893-164X (Print)
1939-1501 (Electronic) - Language:
- English
- Keywords:
- gambling, initiation, treatment, West Virginia Problem Gamblers Help Network, help-lines, telephone counseling
- Abstract:
- Gambling help-lines are an essential access point, or frontline resource, for treatment seeking. This study investigated treatment engagement after calling a gambling help-line. From 2000–2007 over 2,900 unique callers were offered an in-person assessment appointment. Logistic regression analyses assessed predictors of (a) accepting the referral to the in-person assessment appointment and (b) attending the in-person assessment appointment. Over 76% of callers accepted the referral and 55% of all callers attended the in-person assessment appointment. This treatment engagement rate is higher than typically found for other help-lines. Demographic factors and clinical factors such as gender, severity of gambling problems, amount of gambling debt, and coercion by legal and social networks predicted engagement in treatment. Programmatic factors such as offering an appointment within 72 hr also aided treatment engagement. Results suggest gambling help-lines can be a convenient and confidential way for many individuals with gambling problems to access gambling-specific treatment. Alternative services such as telephone counseling may be beneficial for those who do not engage in treatment. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Counseling; *Hot Line Services; *Online Therapy; *Pathological Gambling; *Treatment
- Medical Subject Headings (MeSH):
- Adult; Aged; Behavior, Addictive; Counseling; Female; Gambling; Humans; Interviews as Topic; Male; Middle Aged; Patient Acceptance of Health Care; West Virginia
- PsycINFO Classification:
- Health & Mental Health Treatment & Prevention (3300)
Behavior Disorders & Antisocial Behavior (3230) - Population:
- Human
Male
Female - Location:
- US
- Age Group:
- Adulthood (18 yrs & older)
Young Adulthood (18-29 yrs)
Thirties (30-39 yrs)
Middle Age (40-64 yrs)
Aged (65 yrs & older) - Grant Sponsorship:
- Sponsor: Sponsor name not included
Grant Number: R21-AA017717
Recipients: Weinstock, Jeremiah
Sponsor: Sponsor name not included
Grant Number: T32-AA07290
Recipients: Rash, Carla J.
Sponsor: Sponsor name not included
Grant Number: K23-DA023467
Recipients: Morasco, Benjamin J. - Methodology:
- Empirical Study; Quantitative Study
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- First Posted: Apr 11, 2011; Accepted: Feb 16, 2011; Revised: Jan 10, 2011; First Submitted: Mar 17, 2010
- Release Date:
- 20110411
- Correction Date:
- 20110613
- Copyright:
- American Psychological Association. 2011
- Digital Object Identifier:
- http://dx.doi.org/10.1037/a0023240
- PMID:
- 21480678
- Accession Number:
- 2011-07481-001
- Number of Citations in Source:
- 44
- Persistent link to this record (Permalink):
- http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2011-07481-001&site=ehost-live
- Cut and Paste:
- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2011-07481-001&site=ehost-live">Predictors of engaging in problem gambling treatment: Data from the West Virginia Problem Gamblers Help Network.</A>
- Database:
- PsycINFO
Predictors of Engaging in Problem Gambling Treatment: Data From the West Virginia Problem Gamblers Help Network
By: Jeremiah Weinstock
University of Connecticut Health Center, Farmington, Connecticut;
Department of Psychology, Saint Louis University;
Steve Burton
Problem Gamblers Help Network of West Virginia, Charleston, West Virginia
Carla J. Rash
University of Connecticut Health Center, Farmington, Connecticut
Sheila Moran
Problem Gamblers Help Network of West Virginia, Charleston, West Virginia
Warren Biller
Problem Gamblers Help Network of West Virginia, Charleston, West Virginia
Norman Krudelbach
Problem Gamblers Help Network of West Virginia, Charleston, West Virginia
Natalie Phoenix
University of Connecticut Health Center, Farmington, Connecticut
Benjamin J. Morasco
Portland Veterans Affairs Medical Center and Oregon Health and Science University, Portland, Oregon
Acknowledgement: We thank the First Choice Health Systems, Inc., West Virginia Problem Gamblers Help Network and their callers for sharing the data that made this manuscript possible. The following grants also supported work on this manuscript: R21-AA017717 (Jeremiah Weinstock); T32-AA07290 (Carla J. Rash), K23-DA023467 (Benjamin J. Morasco).
Pathological gambling is an impulse-control disorder characterized as maladaptive gambling behavior that persists despite its many adverse consequences (American Psychiatric Association, 2000). Individuals with pathological gambling endorse at least five of 10 symptoms related to preoccupation, tolerance, withdrawal, and negative financial and social consequences of gambling. The prevalence of the disorder is approximately 1% of the general population (Petry, Stinson, & Grant, 2005). Individuals with subclinical pathological gambling who endorse three or four symptoms are often called problem gamblers and account for an additional 2–3% of the general population (Shaffer, Hall, & Vander Bilt, 1999). Unfortunately, the vast majority of individuals (80% to 95%) with gambling problems never seek professional help (Slutske, 2006; Suurvali, Hodgins, Toneatto, & Cunningham, 2008). While a proportion of those untreated will recover naturally without professional intervention (Slutske, 2006; Cunningham, Hodgins, & Toneatto, 2009), many continue gambling problematically despite the availability of empirically supported interventions. Engaging problem and pathological gamblers in treatment can reduce the adverse consequences of the disorder.
The treatment options for gambling problems are expanding, and range from brief interventions and internet chat-lines to inpatient and residential treatment (Pallesen et al., 2005; Westphal, 2008). Gambling help-lines are an essential access point, or frontline resource, for those seeking help, as they are advertised widely and accessible. Moreover, this medium overcomes some perceived barriers via convenience and anonymity. While several studies have investigated the demographic characteristics and the relationship between gambling severity and psychiatric comorbidity of help-line callers (Griffiths, Scarfe, & Bellringer, 1999; Ledgerwood, Steinberg, Wu, & Potenza, 2005; Potenza et al., 2004; Potenza, Steinberg, & Wu, 2005), little is known about treatment engagement via gambling help-lines. Two studies have found that the proportion of callers agreeing to a referral to in-person treatment from a gambling help-line vary from less than 50% to as high as 75% (Dickerson, 2004; Shandley & Moore, 2008), and approximately two-thirds of those offered a referral make an appointment. The likelihood that individuals will follow through and attend the appointment is not known.
The decision to seek treatment for gambling problems is multi-faceted and often hindered by perceived barriers. Many problem and pathological gamblers cite financial, social and legal pressures as the reasons why they seek treatment (Pulford et al., 2009). Attitudinal factors (i.e., stigma, shame, desire to handle a problem without professional intervention) and environmental barriers (i.e., availability, costs) are common barriers reported by problem and pathological gamblers (Clarke, Abbott, DeSouza, & Bellringer, 2007; Hodgins & el-Guebaly, 2000; Suurvali, Cordingley, Hodgins, & Cunningham, 2009). Demographic characteristics, including male gender, younger age, and less formal education are also identified as barriers to gambling treatment (Clarke et al., 2007). Evans and Delfabbro (2005) describe the process of help-seeking as “crisis driven,” indicating that individuals seek help when the situation is perceived as dire and treatment is seen as a last resort. Although these factors are associated with treatment attendance and engagement in prior studies, they have not been systematically examined in the context of referral from a gambling help-line. Understanding factors related to treatment engagement from a gambling-helpline is paramount, as help-lines become more prevalent and a primary access point for treatment; a call to a gambling help-line is an unique opportunity to provide services to an individual in need that should not be squandered.
The aim of the present study is to investigate treatment engagement of problem and pathological gamblers following an initial gambling help-line call. Treatment engagement in the context of this study is defined as a two-stage process of (a) agreeing to the referral, and (b) accessing the referred services. Using a sample of gambling help-line callers, this study examines demographic, clinical, and contextual characteristics associated with acceptance and attendance of gambling treatment referrals. Based on research concerning perceived barriers of gambling treatment and studies of help-line initiated referrals for other health-related problems (Curry, Grothaus, McAfee, & Pabiniak, 1998; De Coster, Quan, Elford, Li, Mazzei, & Zimmer, 2010; Gould, Kalafat, Munfakh, & Kleinman, 2007; McAfee, 2007), we expect older individuals, females, and those with higher education to accept and follow through on referrals more often than younger individuals, males, and those with less education. Additionally, we hypothesize that greater problem severity and a history of gambling treatment will predict treatment engagement (Pulford et al., 2009).
Method Participants
Data used in this study were from a total of 3,453 unique callers to the Problem Gamblers Help Network of West Virginia (PGHN) from August 2000 until October 2007. Only data from individuals who were offered an in-person assessment (N = 2,912; 84.3%) were analyzed. Callers who were offered an in-person assessment were predominately those with a gambling problem (98.5%; n = 2,865) or a significant other, spouse, or family member of a person with a gambling problem (1.3%; n = 39). In-person assessments were not offered if (a) the caller was not a West Virginia resident, (b) it was deemed an inappropriate call (e.g., prank) by PGHN staff, or (c) the caller ended the contact prematurely without providing any contact information.
Procedure
The PGHN operates a 24-hour toll-free telephone help-line staffed by trained, licensed clinicians. All help-line staff is credentialed at either the national or international gambling counselor level. The number is advertised throughout West Virginia at various gambling venues via billboards, lottery website, public service announcements, and by stickers placed on slot machines. Callers completed a standardized telephone interview with a clinician. If appropriate, a two-hour in-person diagnostic assessment with a licensed counselor trained specifically to work with gambling problems was offered. For those accepting the referral, a “warm transfer” procedure was used. A local clinician was selected by the caller from a list of PGHN providers, and while the caller was holding, help-line staff called the clinician to schedule an appointment. Attempts were made to schedule the in-person assessment appointment within 72 hr of the call to the help-line. In most cases, the caller had an appointment time and directions before the call ended. Help-line staff made a preappointment reminder call 24 hr before the scheduled appointment. All callers were offered information about local Gamblers Anonymous meetings, the Consumers Credit Counsel, and an information packet about problem gambling.
Individuals accepting the referral to the in-person assessment provided a release of information such that the help-line could track their attendance to the in-person assessment appointment and could gather follow-up information resulting from the in-person assessment. The help-line reimbursed the provider for the in-person assessment, which was provided at no cost to the caller. Use of de-identified data for this study was reviewed and approved by the lead author's university institutional review board.
Measures
A standardized telephone interview assessed demographic information, pathological gambling diagnostic criteria, current gambling behavior and debt, history of prior problem gambling help-seeking, current suicidal ideation, and psychiatric history. Information was not collected in the same order for all callers, but responses were primarily coded in a fixed format response (e.g., yes/no, ordinal categories for levels of debt) and used to provide callers with appropriate referrals. The PGHN performs quality assurance assessments on its counselors to ensure help-line staff is following guidelines and collecting accurate information.
Data Analysis Plan
Univariate analyses examined differences between the groups on demographic and clinical characteristics using chi-square tests for categorical data and ANOVA for continuous data. Two separate binary logistic regression analyses assessed predictors of referral (a) acceptance (coded 0,1; 1 = accepted referral), and (b) attendance (coded 0,1; 1 = attended session). As the analyses were exploratory, all independent variables (IVs) were included in the logistic regressions. We used a hierarchical approach for the regression analyses. Block 1 contained demographic characteristics and Block 2 contained gambling and clinical characteristics, as outlined in Tables 1 and 2.
Demographic Characteristics of Gambling Helpline Callers by Referral Status
Clinical Characteristics of Gambling Helpline Callers by Referral Status
The data had a high percentage of missing data, with only 22% (650 of 2,912) of cases having complete data from all 15 IVs. Ninety percent of the sample (2,621 of 2,912) had missing data on ≤ 5 out of the 15 IVs under consideration. Percent of missing data on each IV ranged from 0–35% (with 9 variables missing < 10%): gender (0.3%), age (8%), marital status (5%), employment (7%), annual income (35%), education (32%), gambling frequency (7%), preferred gambling activity (3%), gambling debt (22%), precipitating problem (5%), prior problem gambling help-seeking (14%), recent suicidal ideation (0%), history of comorbid psychiatric disorders (22%), DSM-IV symptoms (5%), and assessment within 72 hr following call (27%). Dependent variables were 100% observed. The main reason for missing data was the clinician failing to ask the item, which is assumed to be missing at random (MAR; Donders, van der Heijden, Stijen, & Moons, 2006). We examined the data for differences among callers in terms of missing data for each variable; with the exception of a significant association between missingness status and number of DSM-IV pathological gambling symptoms endorsed, no other significant differences were present between the missing data groups on demographic and clinical variables. Rates of missingness were higher for those refusing referral versus those accepting the referral, and for those who did not attend the in-person assessment versus those who did. The fact that missing data status can be predicted by other measured variables indicates that MAR is a reasonable assumption for this dataset.
A multiple imputation procedure was implemented in which missing values for any variable are estimated using existing values from other variables. This method assumes data are MAR, an assumption that is not directly testable (Allison, 2003). Multiple imputation using five or more imputations produces less biased estimates than single imputation strategies or complete case analysis under the MAR mechanism (Schafer & Graham, 2002). Additionally, we note that multiple imputation may produce more accurate estimates than complete case analyses even when data do not satisfy MAR assumptions (Graham, 2009). Finally, we considered imputing more than five datasets similar to that recommended by Graham and colleagues (2007). However, given our large sample size and amount of missing data, estimates suggest little efficiency is gained with additional imputations as power would increase only by 0.003 by doubling the number of imputed datasets.
AMELIA II version 1.2-12 (Honaker, King, & Blackwell, 2009) with R version 2.9.1 (R Development Core Team, 2009) was used for the imputation, with all demographic, clinical, and dependent variables included. In order to ensure a high rate of relative efficiency based upon the amount of missing data (Newgard & Haukoos, 2007), five separate imputed datasets were created using a 9% ridge prior. Ridge priors of ≤ 10% are considered reasonable (Honaker et al., 2009). Nominal variables and most ordinal variables were restricted to integer values; however, ordinal variables that represented a continuous variable (e.g., income, gambling debt) were imputed as continuous variables (Honaker et al., 2009). Diagnostics on the imputed datasets suggested imputations were plausible and stable. Logistic regressions were run separately in the imputed datasets and values from each imputed dataset were combined according to Rubin (1987) as outlined by Newgard and Haukoos (2007), resulting in a single set of regression coefficients, standard errors, and confidence intervals. Model fit statistics, combining across the five imputed datasets, were calculated according to Allison's (2001) formulas.
Two separate logistic regression analyses were conducted to evaluate predictors of (a) accepting the referral and (b) attending the in-person assessment. For these analyses, the following demographic variables were entered in Block 1: gender, age, marital status, annual income employment status, and education. Block 2 contained the clinical variables gambling frequency, preferred gambling activity, gambling-related debt, precipitating problem, prior gambling treatment, suicidal ideation, history of psychiatric comorbidity, and number of DSM-IV pathological gambling symptoms. Pathological gambling diagnostic status was not included in the model as number of DSM-IV pathological gambling symptoms is more informative. All data analysis, aside from the multiple imputation procedure, was completed using SPSS (v.15.0).
Results Demographic and Clinical Characteristics
Approximately 81.5% (n = 2,256) of the sample endorsed five or more DSM-IV symptoms of pathological gambling, indicating a likely diagnosis of pathological gambling, 15.1% (n = 417) of the sample endorsed three or four symptoms indicating problem gambling, and 3.4% (n = 95) endorsed zero to two symptoms. About three-quarters of callers to the help-line (n = 2,215) accepted the referral to the in-person assessment and 24% declined (n = 697). For those accepting the referral, 57.1% (n = 1,220) were scheduled within 72 hr of calling the help-line. As noted in Table 1, univariate analyses found significant demographic and clinical differences between those declining, accepting but not attending, and accepting and attending the referral for the in-person assessment, p < .05.
Referral Acceptance
Table 3 displays the final model for predictors of referral acceptance. Both blocks and the overall model were significant, p < .001. After controlling for all other variables in the model (final block), callers whose preferred gambling activity was categorized as “other” had a significantly decreased likelihood of referral acceptance compared with slot machine players. Individuals who had previously sought help for gambling problems were significantly less likely to accept a referral compared to those who had not previously sought gambling treatment. Married or cohabitating individuals and divorced or separated individuals were significantly more likely to accept the referral compared to individuals with single marital status. Similarly, those whose precipitating problem involved legal or spousal pressures were more likely to accept the referral compared to those whose calls were motivated by financial concerns. History of comorbid psychiatric disorders, greater gambling debt, and greater severity of pathological gambling symptoms were positively and significantly related to referral acceptance.
Odds Ratios of Demographic and Clinical Characteristics on Accepting Referral to In-Person Assessment
Appointment Attendance
Of the 2,215 callers who accepted the in-person assessment referral, 72.1% attended the appointment (n = 1,595), 26.3% did not attend the appointment (n = 582), and for 38 individuals it is unknown whether they attended the appointment (1.7%; excluded from subsequent analyses). Logistic regression assessed the relationship between demographic and clinical characteristics and attendance at the in-person assessment. Table 4 displays the final model for predictors of attending the in-person assessment appointment from the logistic regression analysis. Both blocks and the overall model were significant, p < .001. After controlling for all variables in the analysis (Block 2), females were less likely to attend the in-person assessment. Factors associated with an increased likelihood of attending the in-person assessment included age, education, prior gambling treatment, greater severity of pathological gambling symptoms, and in-person assessments scheduled within 72 hr of the call. Additionally, all precipitating problems with the exception of problem recognition were associated with increased odds of appointment attendance compared to those whose call was precipitated by finances.
Odds Ratios of Demographic and Clinical Characteristics on Attending In-Person Assessment
DiscussionOverall, the West Virginia PGHN was able to facilitate engagement in treatment for about 55% of all calls to the help-line. Over 75% of callers accepted the referral, and of those, 72% attended the in-person assessment. These utilization rates are similar or exceed those typically found for other help-line services and attendance rates of initial appointments for other mental health services, which generally range from 35% to 77% (De Coster et al., 2010; Gould et al., 2007; Hser, Maglione, Polinsky, & Anglin, 1998; McKay, & Bannon, 2004; Sherman, Barnum, Nyberg, & Buhman-Wiggs, 2008).
Administrative aspects of the help-line may have facilitated the attendance rate. The help-line staff is specifically trained in the arena of pathological gambling and in help-line intervention techniques that build rapport while collecting all the pertinent information from the caller. “Warm transfer” procedures were used to facilitate the referral to an extensive list of gambling-specific providers across the state, thereby lowering the barrier of knowing where to get professional help and reducing some constraints of travel and geographic limitations. Other help-lines that use warm transfer procedures have seen increases in referral attendance rates (e.g., Curry et al., 1998; Sherman et al., 2008). Additionally, as demonstrated by this study and others (e.g., Compton, Rudisch, Craw, Thompson, & Owens, 2006), scheduling the appointments within 72 hr of the telephone call greatly increased the likelihood of the individual attending the in-person assessment. Finally, the foot-in-the-door technique of a small request (i.e., attend a single session at no cost) may be associated with increased likelihood of compliance (Dillard, 1991). Overall, the way in which a help-line interacts with its callers impacts referral utilization.
Demographic and clinical characteristics were associated with referral acceptance and attendance. Callers with more severe problems and possibly experiencing coercion, such as legal problems or being compelled to call by a family member, were significantly more likely to accept and attend the referral to an in-person assessment. While coercion is a common factor for seeking treatment (Pescosolido et al., 1998), it does not appear to negatively affect clinical outcome (Snyder & Anderson, 2009; Wild, Cunningham, & Ryan, 2006). Another factor associated with the likelihood of attending the in-person assessment was gender. While more women than men called the gambling help-line and more women than men accepted the referral, women were significantly less likely to attend the in-person assessment. Female pathological gamblers tend to have more disruptive and unstable home environments in comparison to male pathological gamblers (Ladd & Petry, 2002), and certain barriers, such as lack of childcare and transportation, may have more of an impact on women than men, therefore contributing to the lower attendance rate (de Figueiredo, Boerstler, & Doros, 2009).
Unfortunately, the help-line was not able to capitalize on the opportunity presented to all callers. Individuals who declined a referral to services tended to have less severe problems in terms of diagnostic symptoms, debt, and psychiatric comorbidity. These individuals may not recognize their gambling as a problem (i.e., precontemplative stage of change) or desire professional help. Brief telephone interventions or mailed self-help materials may still be appropriate and beneficial for these individuals (Hodgins, Currie, Currie, & Fick, 2009). Overall, 45% of callers did not engage in treatment via the help-line. Smoking cessation quitlines offer a successful model of telephone-based counseling (McAfee, 2007) that gambling help-lines could adopt for those who refuse the referral or do not attend the appointment. Quitlines deliver counseling immediately over the telephone when motivation for help is high, and obstacles for treatment such as the delay in getting an appointment and transportation are removed.
It is interesting to note that individuals who had previously sought help for gambling problems were less likely to accept the referral, but those who did were more likely to attend the in-person assessment than individuals who had not previously sought gambling help. Potential reasons for declining the referral may have to do with prior treatment experiences with a specific provider and/or feeling as if treatment does not work. Conversely, experience with gambling treatment and previously acknowledging the need for help to overcome their gambling problems may reduce or remove these barriers that first-time help-seekers may still experience.
Unfortunately, this investigation provides only a static or episodic view of help-seeking from the perspective of the gambling help-line rather than a dynamic or pathways view. Individuals may have called the help-line and then decided to seek help elsewhere through other resources. It is not known how often this occurred and how successful individuals were in utilizing other resources. Another potential limitation of this study included missing data. We used multiple imputation to overcome this limitation. The use of multiple imputed datasets reduced uncertainty in our logistic regression analyses and allowed full use of the dataset (Donders et al., 2006). All reports of prior help-seeking and psychiatric comorbidity were based upon self-report. No objective or verified reports were obtained in regard to these variables, and either under- or overreporting of these events may have occurred. Lastly, barriers such as distance to the clinic and scheduling availability were not assessed and are potential barriers that impact follow-through with the referral.
In summary, a large percentage of the sample accepted and then attended a gambling treatment referral from a help-line; both demographic and clinical characteristics predicted these outcomes. Understanding factors related to not following through with formal treatment has important implications for the field, because a significant portion of treatment seekers first make contact through help-lines. Women were less likely to attend the in-person assessment indicating additional barriers for these individuals. Conversely, gambling problem severity, coercion, and administrative procedures positively influenced referral acceptance and attendance. Additional research is needed to evaluate whether the factors identified in this study generalize to other settings (e.g., help-lines lacking warm transfer procedures), are associated with long-term treatment adherence, and have an impact on treatment outcomes.
Footnotes 1 Gambling is widely available in West Virginia with lottery, slot machines (casino and non-casino based), and horse racing.
2 The PGHN maintains an extensive network of licensed counselors in order to offer referrals in a caller's local area.
3 In 2001, a self-help workbook was incorporated into the help-line's information packet mailed to callers.
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Submitted: March 17, 2010 Revised: January 10, 2011 Accepted: February 16, 2011
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Source: Psychology of Addictive Behaviors. Vol. 25. (2), Jun, 2011 pp. 372-379)
Accession Number: 2011-07481-001
Digital Object Identifier: 10.1037/a0023240
Record: 120- Title:
- Predictors of initiation of hookah tobacco smoking: A one-year prospective study of first-year college women.
- Authors:
- Fielder, Robyn L.. Center for Health and Behavior, Syracuse University, Syracuse, NY, US, rlfielde@syr.edu
Carey, Kate B.. Center for Health and Behavior, Syracuse University, Syracuse, NY, US
Carey, Michael P.. Center for Health and Behavior, Syracuse University, Syracuse, NY, US - Address:
- Fielder, Robyn L., Department of Psychology, Syracuse University, 430 Huntington Hall, Syracuse, NY, US, 13244, rlfielde@syr.edu
- Source:
- Psychology of Addictive Behaviors, Vol 26(4), Dec, 2012. pp. 963-968.
- NLM Title Abbreviation:
- Psychol Addict Behav
- Page Count:
- 6
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- Bulletin of the Society of Psychologists in Addictive Behaviors; Bulletin of the Society of Psychologists in Substance Abuse
- Other Publishers:
- US : Educational Publishing Foundation
Society of Psychologists in Addictive Behaviors - ISSN:
- 0893-164X (Print)
1939-1501 (Electronic) - Language:
- English
- Keywords:
- college students, hookah, predictors, tobacco, waterpipe
- Abstract:
- Hookah tobacco smoking has become increasingly prevalent among American college students over the past decade. Hookah smoking is associated with poor health outcomes and exposes users to high levels of nicotine, carbon monoxide, and smoke. Research on the correlates of hookah use has begun to emerge, but all studies thus far have been cross-sectional. Little is known about hookah use during the transition to college, psychosocial factors related to hookah smoking, or prospective predictors of hookah initiation and frequency of use. This longitudinal cohort study examined risk and protective factors predicting initiation of hookah tobacco smoking during the first year of college. First-year female college students (n = 483; 64% White) provided data on demographic, behavioral, and psychosocial variables and precollege hookah use at baseline; they then completed 12 monthly online surveys about their hookah use from September 2009 to August, 2010. Among the 343 participants who did not report precollege use, 79 (23%) initiated hookah tobacco smoking during the year after college entry. Zero-inflated negative binomial regression showed that alcohol use predicted the likelihood of initiating hookah use; impulsivity, social comparison orientation, and marijuana use predicted the frequency of hookah use. These findings suggest that hookah prevention and intervention efforts may need to address other forms of substance use as well as hookah use. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *College Students; *Human Females; *Tobacco Smoking; Prediction
- Medical Subject Headings (MeSH):
- Adolescent; Cohort Studies; Female; Humans; Impulsive Behavior; Longitudinal Studies; Prevalence; Prospective Studies; Risk Factors; Smoking; Students; Universities; Young Adult
- PsycINFO Classification:
- Drug & Alcohol Usage (Legal) (2990)
- Population:
- Human
Female - Location:
- US
- Age Group:
- Adulthood (18 yrs & older)
Young Adulthood (18-29 yrs) - Tests & Measures:
- Brief Multidimensional Measure of Religiousness/Spirituality
Impulsiveness–Monotony Avoidance Scale
Health Value Scale
Iowa–Netherlands Comparison Orientation Measure
Rosenberg Self-Esteem Scale DOI: 10.1037/t01038-000
Perceived Stress Scale DOI: 10.1037/t02889-000
Patient Health Questionnaire-9 DOI: 10.1037/t06165-000 - Grant Sponsorship:
- Sponsor: National Institute on Alcohol Abuse and Alcoholism
Grant Number: R21-AA018257
Recipients: Carey, Michael P. - Methodology:
- Empirical Study; Quantitative Study
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- First Posted: May 7, 2012; Accepted: Mar 20, 2012; Revised: Feb 1, 2012; First Submitted: Jun 20, 2011
- Release Date:
- 20120507
- Correction Date:
- 20151019
- Copyright:
- American Psychological Association. 2012
- Digital Object Identifier:
- http://dx.doi.org/10.1037/a0028344
- PMID:
- 22564201
- Accession Number:
- 2012-11652-001
- Number of Citations in Source:
- 41
- Persistent link to this record (Permalink):
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- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2012-11652-001&site=ehost-live">Predictors of initiation of hookah tobacco smoking: A one-year prospective study of first-year college women.</A>
- Database:
- PsycINFO
Predictors of Initiation of Hookah Tobacco Smoking: A One-Year Prospective Study of First-Year College Women
By: Robyn L. Fielder
Center for Health and Behavior and Department of Psychology, Syracuse University;
Kate B. Carey
Center for Health and Behavior and Department of Psychology, Syracuse University
Michael P. Carey
Center for Health and Behavior and Department of Psychology, Syracuse University
Acknowledgement: Kate B. Carey is now at the Department of Behavioral and Social Sciences, Program in Public Health, and the Center for Alcohol and Addiction Studies, Brown University. Michael P. Carey is now at the Centers for Behavioral and Preventive Medicine at The Miriam Hospital, the Department of Psychiatry and Human Behavior, and the Department of Behavioral and Social Sciences, Brown University.
We thank Annelise Sullivan for her assistance with data collection and Jennifer Walsh, PhD, for statistical consultation. This research was supported by grant R21-AA018257 from the National Institute on Alcohol Abuse and Alcoholism to Michael P. Carey.
Hookah, or waterpipe, tobacco smoking involves the passage of smoke through water (in the body of the hookah) before inhalation (Maziak, 2008). Hookah use is associated with reduced lung function (Raad et al., 2011) and increased odds of lung cancer, respiratory illness, and periodontal disease (Akl et al., 2010). Compared with smoking one cigarette, a typical session (45–60 minutes) of hookah tobacco smoking confers a higher nicotine dose, greater carbon monoxide exposure, and almost 50 times the smoke volume (Eissenberg & Shihadeh, 2009).
The prevalence of hookah use has increased among youth worldwide over the past two decades (Akl et al., 2011). In the United States, lifetime hookah tobacco smoking is now almost as common among college students as cigarette smoking (Sutfin et al., 2011). One-third to one half of students report lifetime hookah use, and 10–20% report use during the past 30 days (Eissenberg, Ward, Smith-Simone, & Maziak, 2008; Primack et al., 2008; Sutfin et al., 2011). The increasing prevalence of hookah use and its adverse health effects suggests the need for education and prevention, ideally targeted toward those most likely to take up this practice.
Studies of the correlates of hookah use have focused on demographic factors and substance use behaviors. Hookah smoking is common among Arab Americans (Grekin & Ayna, 2008), but it is less popular among other ethnic minority groups, such as African Americans (Barnett, Curbow, Weitz, Johnson, & Smith-Simone, 2009). Adolescents and college students who smoke cigarettes, use other tobacco products (e.g., cigars), smoke marijuana, and drink alcohol are all more likely to report hookah tobacco use (Eissenberg et al., 2008; Jordan & Delnevo, 2010; Sutfin et al., 2011). Less is known about psychosocial determinants of hookah use, such as personality characteristics. Given the correlation between hookah use and other forms of substance use, one might expect that psychosocial correlates of cigarette smoking and alcohol use might also be associated with hookah use, including some personality traits (e.g., impulsivity), stress, religiosity, and depression (Kashdan, Vetter, & Collins, 2005; Patterson, Lerman, Kaufmann, Neuner, & Audrain-McGovern, 2004; Rigotti, Lee, & Wechsler, 2000).
Psychological theory can help to understand hookah initiation and use. From a substance use perspective, Jessor's (1991) problem behavior theory invokes the notion of risk factors, which might include role models for problem behavior, greater access and opportunity to engage in it (e.g., due to location of hookah lounges in college towns; Barnett, Curbow, Soule, Tomar, & Thombs, 2011), and personal and contextual vulnerability for its occurrence (e.g., peer pressure). Because hookah use often co-occurs with other forms of substance use, it may be part of a cluster of co-occurring substance use risk behaviors. Similarly, from a developmental perspective, Arnett's (2000) theory of emerging adulthood suggests that experimenting with hookah might be part of a normative process of seeking new experiences, as rates of other substance use peak during the 18–25 age range (Arnett, 2005). From both theoretical frameworks, the transition to college is a likely time for hookah initiation given the increased freedom enjoyed by residential students, the popularity of hookah lounges in college towns (Sutfin et al., 2011), the ability of students under age 21 to get into hookah lounges but not regular bars (Barnett et al., 2011), media portrayals of hookah smoking as exotic and trendy (Maziak, 2008), permissive social norms about substance use typical of the college environment (Perkins, Meilman, Leichliter, Cashin, & Presley, 1999), and the developmental task of identity exploration (Arnett, 2005).
Given the paucity of research on the predictors of hookah use, we sought to identify risk and protective factors for hookah initiation and subsequent use. Building on empirical precedent (e.g., Windle, Mun, & Windle, 2005) and theory, we investigated demographics, intrapersonal functioning, values, personality, and other substance use as possible predictors of hookah use among female college students. We expected Black and Asian race, academic achievement, religiosity, health value, and self-esteem to be protective factors for hookah initiation and use. We expected impulsivity, sensation-seeking, depression, anxiety, stress, social comparison, marijuana use, cigarette smoking, and alcohol use to be risk factors for hookah initiation and use.
We focused on females because tobacco use patterns, risk, and protective factors differ by gender (Rigotti et al., 2000). We focused on first-year college students because the transition to college is an important developmental period when risky behaviors, such as alcohol and marijuana use, often increase (Fromme, Corbin, & Kruse, 2008). Our research improves upon prior efforts by (a) using a prospective longitudinal design and (b) assessing frequency (rather than just dichotomous indicators) of hookah use. This design allowed us to identify predictors of hookah initiation and of greater involvement with hookah over one year.
Method Study Design and Procedures
All procedures were approved by the Institutional Review Board. Data were from a larger study, conducted from August 2009 to August 2010 at a private university in upstate New York, on health behaviors, relationships, sexual behavior, and adjustment among first-year college women. Participants for the larger study (n = 483) were recruited via a mass mailing sent to incoming female students who would be at least 18 years old by the start of the study and were not international students or scholarship athletes (excluded because of postal lags and policies of the National Collegiate Athletic Association, respectively). Campus fliers, word of mouth, and the psychology department participant pool were also used to bolster recruitment. Most participants (61%) were recruited from the mass mailing, 28% from the participant pool, and 11% from word of mouth or flyers. During their first three weeks on campus, interested students attended brief in-person orientation sessions, during which research staff explained study procedures and obtained written informed consent, and participants completed the baseline survey.
Participants were also invited to complete 12 monthly follow-up surveys. Invitations were sent by email on the last day of each month with an embedded link to a confidential survey website; the surveys required 10 to 20 minutes to complete, and participants had one week to complete them. Participants who missed surveys were allowed to resume participation with the next survey. Participants received $20 (or credit for one hour of research if from the department participant pool) for the baseline survey, $10 for each of the next 10 surveys, and $15 and $20 for the final two surveys; higher compensation was offered at baseline because that survey was the longest, and for the final two surveys to reduce attrition during the summer.
Measures
All predictors were assessed at baseline unless otherwise noted. Participants provided their age, sexual orientation (reduced to heterosexual or other), race (Asian, Black, White, or other/multiple), and ethnicity (Hispanic). Socioeconomic status (SES) was assessed at wave eight using a 10-point SES ladder (Adler, Epel, Castellazzo, & Ickovics, 2000).
Protective factors
Participants indicated their high school grade point average (GPA) on a 4.0 scale. Religiosity was measured with the global religiosity self-ranking item from the Brief Multidimensional Measure of Religiousness/Spirituality (Fetzer Institute, 1999). Health value was measured with the four-item Health Value Scale (Lau, Hartman, & Ware, 1986). Self-esteem was measured with the 10-item Rosenberg Self-Esteem Scale (RSES; Rosenberg, 1965).
Risk factors
Impulsivity and sensation-seeking were each measured using six items (Magid, MacLean, & Colder, 2007) from the impulsiveness and monotony avoidance subscales, respectively, of the Impulsiveness–Monotony Avoidance Scale (Schalling, 1978). Depression was measured with the Patient Health Questionnaire-9 (Spitzer, Kroenke, & Williams, 1999), anxiety with the Generalized Anxiety Disorder-7 (Spitzer, Kroenke, Williams, & Löwe, 2006), and perceived stress with the four-item Perceived Stress Scale (Cohen, Kamarck, & Mermelstein, 1983). Social comparison orientation, or the extent to which individuals compare themselves with others (e.g., peers), was measured with the six items that constitute the ability factor of the Iowa–Netherlands Comparison Orientation Measure (Gibbons & Buunk, 1999).
Substance use during the month before college entry was assessed, and specific anchor dates (i.e., August 1–31) were provided to facilitate recall. Participants indicated the number of days on which they used marijuana, the number of cigarettes they smoked each day in a typical week (summed for a measure of cigarettes per week), and the number of standard drinks they consumed each day in a typical week (summed for a measure of drinks per week).
Hookah use
Precollege hookah use was assessed at baseline; participants indicated how many times they “ever smoked hookah before starting college (before August 26, 2009).” At each follow-up, participants indicated on how many days in the last month they “used hookah to smoke tobacco.” All last-month intervals were specified with anchor dates to facilitate recall.
Because the study spanned a year and involved 13 assessments, 2–18% of participants had missing data on hookah use at each follow-up. To maintain the entire sample, we used multiple imputation, which is preferred over traditional approaches such as listwise deletion (Schafer, 1999). We imputed binary indicators of monthly hookah use (based on all observed data used in the analyses) because it was not feasible to impute count data for the number of days of hookah use. We then created a summary variable by summing the total number of months in which participants reported hookah tobacco smoking during the year-long follow-up (i.e., between waves 2–13); this count data outcome served as a measure of sustained hookah use over time. We imputed five complete datasets (Schafer, 1999) with the R package mi (Su, Gelman, Hill, & Yajima, 2011). Analyses were conducted with all five datasets, and parameter estimates were pooled using the imputation algorithms in Mplus 6 (Muthén & Muthén, 2010).
Analytic Approach
Count data tend to violate the assumptions needed for unbiased ordinary least squares regression (Atkins & Gallop, 2007). However, count regression accommodates non-negative integer outcomes with high positive skew. We first tested each variable as a univariate predictor of hookah initiation using zero-inflated negative binomial (ZINB) regression. Negative binomial regression was more appropriate than Poisson regression because of overdispersion, and ZINB regression was used instead of negative binomial regression because of the proportion (77%) of zeroes as well as a theoretical rationale that two processes likely lead to zeroes (i.e., some participants will never use hookah, whereas some will but simply did not do so during the period we observed). ZINB regression includes a logistic portion predicting nonoccurrence of the outcome and a count portion predicting the frequency of the outcome when it occurs (Atkins & Gallop, 2007). Following recommendations for exploratory multivariate models, univariate predictors with p < .25 were candidates for the multivariate model (Hosmer & Lemeshow, 2000). We entered these candidates into a multivariate ZINB regression, with simultaneous entry of all predictors for both the logistic and count portions of the model. In the interest of parsimony, we removed variables with p > .10 in this model and then calculated the final multivariate model.
ResultsPrecollege hookah use was reported by 29% of participants (n = 140), for whom the mean number of days of use was 7.0 (SD = 11.7, median = 3, range: 1–100). These 140 participants were excluded from the present study of hookah initiation.
Participants were 343 first-year female college students who reported no precollege hookah use. The average age at baseline was 18.1 years (SD = 0.3, range: 18–21), and 96% identified as heterosexual. The racial/ethnic distribution was 64% White, 14% Asian, 11% Black, and 12% other/multiple; 7% identified as Hispanic. Participants completed an average of 10.9 of 12 (SD = 2.3, median = 12) follow-up surveys; response rates ranged from 86–98% during the academic year and 83–90% during the summer afterward. Descriptive statistics for all other predictors are presented in Table 1, along with Cronbach's alpha for all scales.
Descriptive Statistics for Predictors (n = 343)
Seventy-nine participants (23%) initiated hookah use during the year-long follow-up. The average total number of months of hookah use for those who initiated was 2.0 (SD = 1.3, median = 2, range: 1–7). Results from the univariate ZINB regressions appear in Table 2. Based on the univariate results, we entered age, sexual orientation, high school GPA, religiosity, self-esteem, impulsivity, social comparison orientation, marijuana use, and alcohol use as predictors in both portions of a preliminary ZINB multivariate model; next, we removed predictors that were not statistically significant at p < .10 in either portion of the model and reran a more parsimonious final model. In the final multivariate model (see Table 3), alcohol use was the only significant predictor in the zero-inflation portion of the model. As alcohol use increased, the likelihood of being a zero (i.e., nonoccurrence of the outcome) decreased; that is, the more alcohol use participants reported at baseline, the more likely they were to initiate hookah tobacco use. Impulsivity, social comparison orientation, and marijuana use were significant predictors in the negative binomial, or count, portion of the model; the higher these variables, the greater the predicted number of months of hookah tobacco use during the year-long follow-up.
Univariate Predictors of Hookah Initiation (n = 343)
Multivariate Predictors of Hookah Initiation (n = 343)
DiscussionHookah tobacco smoking, which has become increasingly common among college students (Sutfin et al., 2011), is an emerging public health concern (Akl et al., 2010). In this study, almost one third of incoming first-year female college students had smoked hookah before college entry, consistent with the results of a study conducted in 2010 (Smith et al., 2011b) but higher than the 10% prevalence found in three previous studies (Barnett et al., 2009; Jordan & Delnevo, 2010; Primack, Walsh, Bryce, & Eissenberg, 2009). The higher rates found by Smith et al. (2011b) and in the current study are likely the result of sampling (i.e., our sample comprised first-year college students, who would have had more time and opportunities to use hookah than 9th–12th grade students sampled in earlier studies) and an ongoing cultural trend; that is, prevalence rates from these two recent studies (data collected in 2010) are higher than rates obtained in studies with data collection in 2005, 2007, and 2008. These and other studies suggest that the popularity of hookah has increased over the past few years; indeed, rates of hookah use among adults in California increased by 40% from 2005 to 2008 (Smith et al., 2011a).
We found that 23% initiated hookah tobacco smoking during their first year of college. Among women who initiated hookah smoking during the study, 45% reported use in only one month, suggesting that many young women who try hookah will experiment with it but not become frequent or regular users. The low-level involvement of many hookah users is consistent with the assertion that substance use is experimental for most emerging adults (Arnett, 2005). More concerning from a health perspective are women who report frequent use. Because hookah smokers ingest nicotine, report increased pleasant subjective effects, and exhibit decreased tobacco abstinence symptoms (Maziak et al., 2009), abuse and dependence are possible.
The prospective design of this study allowed us to examine a variety of demographic, behavioral, and psychosocial predictors of hookah initiation. The strongest risk factors were alcohol and marijuana use; alcohol use predicted initiation, and marijuana use predicted frequency of use. These results corroborate prior research showing strong correlations between hookah and other substance use (e.g., Sutfin et al., 2011) and support the hypothesis of a cluster of substance use behaviors put forth in problem behavior theory (Jessor, 1991). Explanations for the connections among these forms of substance use may include the social aspect, predisposing personality traits, and the (“deviant”) act of smoking. Moreover, youth may have “socially organized opportunities to learn risk behaviors together and normative expectations that they be performed together” (Jessor, 1991, p. 600). Different forms of substance use may also co-occur because they have similar functions (e.g., coping, social affiliation, hedonistic benefits). Prevention and intervention efforts targeting hookah use will need to take into account the probable co-occurrence of multiple forms of substance use (Jessor, 1991).
Relatively few psychosocial risk or protective factors predicted hookah use. Impulsivity and social comparison orientation were positively associated with frequency of hookah use. When presented with the opportunity to use hookah, impulsive students may acquiesce to social influences more easily. Students who compare their behavior with others' may be affected by perceived social norms (Perkins et al., 1999), especially in settings where hookah use is more prevalent and hookah lounges provide more access. College students overestimate the degree to which their peers use substances; only 9% of students used hookah in the last month, but 68% perceived that the typical student had (American College Health Association [ACHA], 2011).
Limitations of the Research
Recruitment from one university may limit the generalizability of our findings; however, the racial/ethnic distribution of our sample is equivalent to that of national samples of female students (e.g., ACHA, 2011). Future research should include men and study gender differences. Our sample had low rates and frequencies of cigarette smoking. Research should be conducted with a sample in which there is higher rates of, and more frequent, cigarette smoking. Although we improve on past research by using continuous rather than dichotomous indicators of hookah use, our outcome was number of months of use rather than a more sensitive daily measure. We assessed only a subset of possible predictor variables and followed participants for only their first year of college. Finally, given the state of the field, our analyses were necessarily exploratory.
Strengths of the Research
To our knowledge, this is the first prospective study of the predictors of hookah initiation. We surveyed almost 500 women about their hookah use during a key developmental period, the transition to college. Patterns of use of other tobacco products differ by gender (Rigotti et al., 2000), so it is useful and novel to investigate hookah use among women. We addressed a gap in the literature by investigating psychosocial predictors of hookah use, and our data clarify the ratio of experimenters to more frequent users. By using ZINB regression to accommodate the count outcome, we determined predictors of any use and of more frequent use.
Implications and Future Research
Because many youth only experiment with hookah a few times and because the strongest predictors of hookah tobacco smoking were other forms of substance use, prevention and intervention efforts should (a) focus on regular users and (b) jointly target hookah, marijuana, and alcohol use to optimize the public health impact. Future research should include males and noncollege attending emerging adults, explore social norms related to hookah use, and investigate why some youth only experiment and others proceed to regular use.
Footnotes 1 Per university data, the sample represented 26% of all incoming female students at the university, with an equivalent ethnic breakdown.
2 Also, two participants had missing data for sexual orientation, one for high school GPA, one for health value, one for social comparison orientation, and 35 for SES; these missing data were also imputed.
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Submitted: June 20, 2011 Revised: February 1, 2012 Accepted: March 20, 2012
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Source: Psychology of Addictive Behaviors. Vol. 26. (4), Dec, 2012 pp. 963-968)
Accession Number: 2012-11652-001
Digital Object Identifier: 10.1037/a0028344
Record: 121- Title:
- Predictors of patient retention in methadone maintenance treatment.
- Authors:
- Proctor, Steven L.. Department of Psychology, Louisiana State University, Baton Rouge, LA, US, sproct2@tigers.lsu.edu
Copeland, Amy L.. Department of Psychology, Louisiana State University, Baton Rouge, LA, US
Kopak, Albert M.. Department of Criminology and Criminal Justice, Western Carolina University, NC, US
Hoffmann, Norman G.. Department of Psychology, Western Carolina University, NC, US
Herschman, Philip L.. CRC Health Group, Inc., Cupertino, Cupertino, CA, US
Polukhina, Nadiya. CRC Health Group, Inc., Cupertino, Cupertino, CA, US - Address:
- Proctor, Steven L., Department of Psychology, Louisiana State University, 236 Audubon Hall, Baton Rouge, LA, US, 70803, sproct2@tigers.lsu.edu
- Source:
- Psychology of Addictive Behaviors, Vol 29(4), Dec, 2015. pp. 906-917.
- NLM Title Abbreviation:
- Psychol Addict Behav
- Page Count:
- 12
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- Bulletin of the Society of Psychologists in Addictive Behaviors; Bulletin of the Society of Psychologists in Substance Abuse
- Other Publishers:
- US : Educational Publishing Foundation
Society of Psychologists in Addictive Behaviors - ISSN:
- 0893-164X (Print)
1939-1501 (Electronic) - Language:
- English
- Keywords:
- methadone, predictors, opioids, maintenance treatment, illicit drug use
- Abstract:
- This study sought to determine whether select pretreatment demographic and in-treatment clinical variables predict premature treatment discharge at 6 and 12 months among patients receiving methadone maintenance treatment (MMT). Data were abstracted from electronic medical records for 1,644 patients with an average age of 34.7 years (SD = 11.06) admitted to 26 MMT programs located throughout the United States from 2009 to 2011. Patients were studied through retrospective chart review for 12 months or until treatment discharge. Premature discharge at 6- and 12-month intervals were the dependent variables, analyzed in logistic regressions. Clinical predictor variables included average methadone dosage (mg/d) and urinalysis drug screen (UDS) findings for opioids and various nonopioid substances at intake and 6 months. Pretreatment demographic variables included gender, race/ethnicity, employment status, marital status, payment method, and age at admission. UDS findings positive (UDS+) for cocaine at intake and 6 months were found to be independent predictors of premature discharge at 12 months. UDS+ for opioids at 6 months was also an independent predictor of premature discharge at 12 months. Higher average daily methadone dosages were found to predict retention at both 6 and 12 months. Significant demographic predictors of premature discharge at 6 months included Hispanic ethnicity, unemployment, and marital status. At 12 months, male gender, younger age, and self-pay were found to predict premature discharge. Select demographic characteristics may be less important as predictors of outcome after patients have been in treatment beyond a minimum period of time, while others may become more important later on in treatment. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Methadone; *Methadone Maintenance; *Opiates; Drug Therapy; Drug Usage
- PsycINFO Classification:
- Drug & Alcohol Rehabilitation (3383)
- Population:
- Human
Male
Female - Location:
- US
- Age Group:
- Adulthood (18 yrs & older)
Young Adulthood (18-29 yrs)
Thirties (30-39 yrs)
Middle Age (40-64 yrs) - Grant Sponsorship:
- Sponsor: CRC Health Group, Inc., US
Recipients: No recipient indicated - Methodology:
- Empirical Study; Longitudinal Study; Retrospective Study; Quantitative Study
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- First Posted: Jun 22, 2015; Accepted: Apr 1, 2015; Revised: Mar 30, 2015; First Submitted: Oct 29, 2014
- Release Date:
- 20150622
- Correction Date:
- 20160104
- Copyright:
- American Psychological Association. 2015
- Digital Object Identifier:
- http://dx.doi.org/10.1037/adb0000090
- Accession Number:
- 2015-27693-001
- Number of Citations in Source:
- 74
- Persistent link to this record (Permalink):
- http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2015-27693-001&site=ehost-live
- Cut and Paste:
- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2015-27693-001&site=ehost-live">Predictors of patient retention in methadone maintenance treatment.</A>
- Database:
- PsycINFO
Predictors of Patient Retention in Methadone Maintenance Treatment
By: Steven L. Proctor
Department of Psychology, Louisiana State University;
Amy L. Copeland
Department of Psychology, Louisiana State University
Albert M. Kopak
Department of Criminology and Criminal Justice, Western Carolina University
Norman G. Hoffmann
Department of Psychology, Western Carolina University
Philip L. Herschman
CRC Health Group, Inc., Cupertino, California
Nadiya Polukhina
CRC Health Group, Inc., Cupertino, California
Acknowledgement: This project was supported in part by CRC Health Group, Inc. The funding source was not involved in the study design, analysis, interpretation, or writing of the article. All authors contributed in a significant way to the article and have read and approved the final version. One of the authors (Philip L. Herschman) is the former Chief Clinical Officer at CRC Health Group, Inc. None of the authors have any additional real or potential conflicts of interest, including financial, personal, or other relationships with organizations or pharmaceutical/biomedical companies that may inappropriately influence the research and interpretation of the findings.
Opioid use and opioid use disorders remain serious public health concerns. According to estimates from the 2010 National Survey on Drug Use and Health (Substance Abuse and Mental Health Services Administration [SAMHSA], 2011), approximately 2.2 million persons aged 12 years or older in the U.S. general population met current Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM–IV; American Psychiatric Association [APA], 1994) criteria for an opioid use disorder (i.e., dependence or abuse). Opioids, including prescription pain relievers and heroin, had the second highest rate of past year drug dependence or abuse, behind only cannabis, and rates of current opioid dependence or abuse have also increased since 2002 (SAMHSA, 2011). Opioid use and opioid use disorders have also been associated with a variety of negative outcomes including hospitalization, economic burden, increased vulnerability to other serious medical conditions or infections, additional substance use and psychiatric comorbidity, cognitive impairment, and mortality (Brooner, King, Kidorf, Schmidt, & Bigelow, 1997; Fals-Stewart, 1997; Hulse, English, Milne, & Holman, 1999; Mark, Woody, Juday, & Kleber, 2001; Pilowsky, Wu, Burchett, Blazer, & Ling, 2011; Strain, 2002; SAMHSA, 2008).
In light of the range of impairment and adverse consequences associated with opioid use and opioid use disorders, effective treatment placement and completion is an important goal. One potential treatment option is methadone maintenance treatment (MMT), which is the most widely used form of treatment for problematic opioid use in the United States (Parrino, 2002). Systematic reviews of the vast opioid use treatment literature have shown that maintenance treatment with methadone is associated with increased treatment retention, reduced opioid use, decreased craving, and improved social functioning (e.g., Bart, 2012). The efficacy of MMT in reducing illicit opioid use among opioid-dependent patients is well-documented (for reviews see Amato et al., 2005; Marsch, 1998). However, considerable research has also demonstrated a consistent, statistically significant relationship between MMT retention and various additional favorable outcomes beyond abstinence from opioids (Hartel & Schoenbaum, 1998; Marsch, 1998; Sorensen & Copeland, 2000). For example, not only is the mortality rate for patients receiving MMT substantially lower than that of regular opioid users in the U.S. general adult population not in treatment, but unfavorable or premature discharge from MMT is associated with increased mortality (Caplehorn, Dalton, Cluff, & Petrenas, 1994; Gibson et al., 2008; Hulse et al., 1999; Zanis & Woody, 1998). High rates of MMT attrition are problematic and warrant the need to identify patients at elevated risk for premature discharge. Thus, identification of various pretreatment demographic and clinical variables that may impact MMT retention remains of paramount importance if opioid-dependent patients, treatment providers, and society in general aspire to more favorable outcomes.
A multitude of demographic and individual difference variables have been found to negatively impact various MMT outcomes (Abramsohn, Peles, Potik, Schreiber, & Adelson, 2009; Alterman, Rutherford, Cacciola, McKay, & Boardman, 1998; Avants, Margolin, & McKee, 2000; Goehl, Nunes, Quitkin, & Hilton, 1993; Hser et al., 2011; Lehmann, Lauzon, & Amsel, 1993; Shirinbayan, Rafiey, Roshan, Narenjiha, & Farhoudian, 2010; Simpson, Joe, & Rowan-Szal, 1997; Wong & Longshore, 2008). Select demographic characteristics including male gender, membership to an ethnic-minority group, unmarried, and unemployment have all been found to negatively influence MMT retention (Ball, Lange, Myers, & Friedman, 1988; Deck & Carlson, 2005; Del Rio, Mino, & Perneger, 1997; Hser, Anglin, & Liu, 1990; Judson & Goldstein, 1982; Mancino et al., 2010; Saxon, Wells, Fleming, Jackson, & Calsyn, 1996). Method of payment for MMT services has also been found to result in differential outcome expectations (Maddux, Prihoda, & Desmond, 1994; Murphy & Rosenbaum, 1988). Specifically, patients assigned to a fee-status treatment condition (i.e., required to pay a daily methadone dispensing fee) demonstrated a significantly lower retention rate at 12 months compared with patients who paid nothing for treatment services (34% vs. 54%, respectively; Maddux et al., 1994). Patient fees have long been considered one of the major barriers to MMT (Anglin, Speckart, Booth, & Ryan, 1989; Muhleisen, Clark, Teo, & Brogan, 2005) and the inability to fund one’s own treatment services has been associated with increased admission delays to outpatient MMT (Gryczynski, Schwartz, Salkever, Mitchell, & Jaffe, 2011). Thus, consideration of select pretreatment patient characteristics at treatment admission including method of payment appear to be a requisite for future research efforts aimed at identifying patients at elevated risk for poor MMT response.
One demographic variable in particular that has consistently been found to predict premature discharge from MMT is age, with younger patients evincing higher rates of attrition, for up to 2 years following MMT admission in some studies (Ball et al., 1988; Brown, Watters, Iglehart, & Aikens, 1982; Deck & Carlson, 2005; MacGowan et al. 1996; Magura, Nwakeze, & Demsky, 1998; Mancino et al., 2010; Saxon et al., 1996; Strike et al., 2005; Torrens, Castillo, & Perez-Sola, 1996). However, with the exception of age, many studies investigating pretreatment demographic predictors of premature MMT discharge have failed to identify variables that reliably predict MMT retention, presumably due to the relatively small samples and/or the brief and variable follow-up periods utilized. The limitation pertaining to sample size is particularly salient given small sample sizes have the potential to result in marginally significant effect sizes and may have an additional impact when there is multicollinearity among predictor variables. Furthermore, many of the estimates relating to the various identified demographic predictors of MMT discharge have been imprecise and tend to account for only a fraction of the variance. In light of these disparate findings and methodological constraints, additional research is warranted.
Beyond pretreatment demographic predictor variables, several clinical variables including opioid and nonopioid substance use both prior to and during MMT, as well as average daily methadone dosage, have been found to predict MMT retention. For instance, greater opioid use history in terms of years of use prior to MMT has been found to predict retention, whereas continued use of opioids at 3 months following MMT admission has been shown to significantly predict attrition (Brown et al., 1982; Del Rio et al., 1997; MacGowan et al., 1996). Ongoing use of alcohol and cocaine following MMT admission have also been found to negatively impact retention rates (Brands et al., 2008; Brown et al., 1982; Judson & Goldstein, 1982; Magura et al., 1998; Torrens et al., 1996). Another important clinical variable relates to the type and intensity of MMT services (i.e., appropriate methadone dosage indicated for long-term retention). In fact, accumulating evidence points to the value of higher methadone dosage prescription practices, with dosages between 80 and 100 mg/d typically found to be more effective than lower dosages (e.g., in the range of 60–80 mg/d) in retaining patients (Faggiano, Vigna-Taglianti, Versino, & Lemma, 2003; Ling, Wesson, Charuvastra, & Klett, 1996; Maremmani, Pacini, Lubrano, & Lovrecic, 2003; Strain, Bigelow, Liebson, & Stitzer, 1999; Torrens et al., 1996). Methadone dosages greater than or equal to 100 mg/d have also been shown to result in favorable treatment outcomes with regard to MMT retention compared with lower dosages (Peles, Linzy, Kreek, & Adelson, 2008). Recent findings from a meta-analysis of 18 randomized controlled trials investigating the influence of different dosage ranges on MMT retention rates suggest that favorable outcomes may also be achieved with dosages greater than or equal to 60 mg/d relative to dosages less than 60 mg/d (Bao et al., 2009). Specifically, across dosing strategies (i.e., flexible vs. fixed), 60+ mg/d was associated with greater retention than dosages < 60 mg/d at both 3–6 months (62.5% vs. 50.6%, respectively) and 6–12 months (57.0% vs. 42.5%, respectively). However, it is notable that approximately half of patients maintained on < 60 mg/d were retained through 6 months, and nearly as many were retained in treatment through 12 months. Thus, although dosages in the 60+ mg/d range appear indicated, methadone dosage guidelines, practices, and subsequent retention rates vary and suggest the need for future work.
In sum, increasing rates of opioid use disorders coupled with a resultant public health concern warrant further investigation to determine significant predictors of MMT outcomes and identify patients at elevated risk for poor treatment response at 6 and 12 months. In general, a large number of studies have failed to identify robust pretreatment demographic and clinical predictors of MMT attrition. Studies reporting significant independent predictors of outcome, although promising, require replication in a well-powered investigation. Furthermore, many studies have included relatively small samples and/or brief or limited follow-up periods, and some have relied on self-reported indices of illicit drug use. The limitation pertaining to sample size appears nearly universal across studies and is particularly salient given the potential to result in marginally significant effect sizes. Further, although it is widely accepted that MMT retention is a function of methadone dosage, additional work is warranted to confirm the appropriate dosage range indicated for favorable treatment response. Given these issues, the present study sought to replicate and extend previous findings in an effort to fill the apparent gaps in the MMT research literature using data from a large, multisite MMT population.
The present retrospective longitudinal study has two aims. The first is to assess the impact of select demographic and clinical variables on premature patient discharge at 6 and 12 months. We tested this by identifying significant predictors of treatment discharge after adjustment for relevant variables to determine the effects of both pretreatment demographic variables and treatment performance variables (i.e., urinalysis drug screen [UDS] findings for opioids, cocaine, amphetamines, benzodiazepines, and cannabinoids at intake and 6 months) on attrition at the two follow-up intervals (i.e., 6 and 12 months). The second aim is to replicate prior work in an effort to best delineate the average daily methadone dosage most prudent for favorable treatment response at 6 and 12 months. We tested this in two ways. First, we tested whether patient retention could be predicted by six a priori average daily methadone dosage categories (i.e., 10.1–60.0 mg, 60.1–120.0 mg, 10.1–80.0 mg, 80.1–120.0 mg, 60.1–80.0 mg, and 80.1–100.0 mg), analyzed in logistic regressions. Second, we conducted a bivariate correlation to determine the relationship between average daily methadone dosage prescribed throughout the course of treatment (when examined as a continuous variable) and length of stay (LOS) in MMT. It was hypothesized that a higher average daily methadone dosage would be associated with increased retention.
MethodDemographic and clinical data for the present study were derived from patient records utilizing the management information system of a large U.S. health care provider. A total of 9,212 active and discharged patients admitted to a CRC Health Group-operated substance use treatment program during the period of January 1, 2009 through April 30, 2011 were initially identified based on the following specified inclusionary criteria: (a) minimum length of stay of 15 days; (b) presented for medication-assisted maintenance treatment (as opposed to temporary placement or detoxification); and (c) received methadone (as opposed to one of two buprenorphine formulations). However, only those patients for whom complete demographic data were available (i.e., gender, race/ethnicity, employment status, age, and marital status) were included in the final dataset. The largest proportion of cases were excluded due to missing employment status data (n = 5,408). Next, cases with missing marital status data were excluded (n = 1,375), followed by those with missing or unknown data relating to the reason for treatment discharge (n = 754). In addition, one transgendered patient was excluded. Further, to define reliable measures using aggregated patient data, we followed the recommendation of Simpson et al. (1997), and excluded treatment programs for whom relatively small patient sample sizes were found (i.e., only programs including 50 or more patients were selected); which resulted in a net sample of 1,644 patients. The final sample was comprised of all remaining patients admitted to 26 treatment programs located throughout the United States (e.g., California, Oregon, Virginia, Louisiana, West Virginia, North Carolina, Kansas) during the aforementioned observational period. Given that the 26 treatment programs utilized in the present study were operated by the same national health care provider, all programs followed similar MMT practices as outlined in a common Policy and Procedures manual.
Patients were studied through retrospective electronic chart review for 12 months or until treatment discharge; whichever came first. Our rationale for following patients through the a priori 12-month observational period is consistent with the standard timeframe generally examined in MMT retention research (e.g., Deck & Carlson, 2005; Del Rio et al., 1997; Lehmann et al., 1993). Although there remains disagreement regarding the most appropriate duration of treatment, which depends largely on both the individual patient and the specific goals of treatment, 12 months has commonly been accepted as the minimum timeframe necessary to achieve clinical benefit for most MMT patients (Moolchan & Hoffman, 1994; Simpson et al., 1997). Accordingly, this treatment goal was explicitly conveyed to all patients upon admission to the 26 MMT programs. It is important to note, however, that in select cases, patients “successfully” completed treatment prior to 12 months. In instances in which patients were able to achieve their treatment goals in a relatively short period of time, the treatment team collaboratively arrived at the decision to discharge them due to successful treatment completion. Release of the de-identified dataset was approved by the CRC Health Group, Inc. Institutional Review Board for use in secondary analyses.
Participants
Demographic and clinical characteristics for the total sample at intake are detailed in Table 1. The total sample was comprised of 1,644 patients (63.1% male) with an average age of 34.7 years (SD = 11.06) and a range of 18 to 74 years; although 40.9% were between the ages of 25 and 34 years. Racial composition was predominately Caucasian (75.0%) and Hispanics constituted the largest ethnic-minority group (18.2%). Slightly more than half (52.0%) of the patients were single at the time of admission, and 29.3% indicated that they were either married or had a “significant other.” More than half (57.0%) of the patients were unemployed, and 39.1% were employed at the time of admission. Regarding payment method for MMT services, approximately three fourths (72.1%) of the sample were classified as self-pay.
Demographic and Clinical Characteristics at Intake
Measures
UDS testing was conducted at the discretion of the various MMT programs for individual treatment planning purposes or, in some cases, as a mandate in partial fulfillment of the terms of a patient’s parole. Thus, testing was performed at various intervals, defined by both the state and type of patient, and the timing and frequency of testing varied across sites. However, standard procedures at all facilities required that a minimum of eight UDS tests be conducted per year for each patient. In fact, despite the variability in UDS testing procedures across sites, the frequency of UDS testing for opioids was quite consistent in that more than 99.4% of active patients received a UDS for opioids at the 6- and 12-month intervals. Similarly, nearly all (99.6%) patients received a UDS for the various nonopioid substance categories at the two follow-up intervals, with the exception of cannabinoids. However, even UDS testing for cannabinoids was performed, on average, 92.2% of the time at the various intervals across MMT sites. The methadone dispensing software utilized by all of the MMT programs identified patients due for a UDS on a specific day on a random interval schedule and the dispensing of an individual patient’s prescribed methadone dosage was contingent on UDS submission. Collection of specimens was observed via nonrecording camera observation in accordance with each respective program’s state requirements to ensure authenticity. The type of testing performed and the panel chosen was dictated by the state’s requirements, the certification of the program, and the compliance requirements of the individual facility. Thus, upon request, specimens were subjected to an initial Immunoassay screen to assess for recent use of methadone, alcohol, amphetamines, barbiturates, benzodiazepines, cannabinoids, cocaine, heroin, and oxycodone. Immunoassay class results for the various substances at intake and 6 months were utilized as the predictor variables, analyzed in logistic regressions for the present study’s analyses.
Data Analyses
Patient retention in MMT was the outcome variable of interest. The term “retention,” as it is presented in the context of the current investigation, is defined as the proportion of active patients at the 6- and 12-month follow-up interval. Conversely, treatment attrition (or premature treatment discharge) refers to any situation in which patients are prematurely discharged from treatment prior to the two follow-up intervals, irrespective of the specific reason, and encompasses both patient- and organizational-level factors. That is, in the instance of patients discharged due to financial constraints or against medical advice, treatment discharge may be considered a patient-level variable, while patients discharged due to administrative reasons (e.g., not participating in treatment, failure to comply with program policies) would suggest treatment discharge to be an organizational-level variable. Patients were dichotomized as either treatment successes or premature treatment discharges at the 6- and 12-month follow-up intervals based on their LOS in treatment (measured in days). Thus, patients with an LOS > 179 and 364 days at the 6- and 12- month intervals, respectively, were classified as treatment successes. In an effort to avoid artificially inflating the attrition rate at 6 and 12 months, patients who successfully completed treatment or were transferred to another MMT facility (presumably to a higher level of care) prior to the two follow-up intervals were excluded and subsequently not classified as premature treatment discharges at each respective follow-up interval. This procedure revealed that 12.6% (n = 207) and 13.7% (n = 225) of the total sample completed treatment or were transferred, respectively, during the 12-month observational period. Patients discharged after 179 days due to successful treatment completion or transfer to another MMT facility, however, were still classified as treatment successes at 6 months.
All UDS findings (i.e., obtained at intake and 6 months) were dichotomized to indicate the detection of the presence or absence of the various substances for which a UDS was administered at each respective interval. Alcohol and barbiturates were detected in less than 2% of cases at intake, so these substances were not considered as potential individual predictors of treatment attrition. Similarly, all patients were positive for methadone at the various intervals following MMT admission, so this variable was excluded from the respective models. A variable was constructed based on UDS findings for each of the specified substances at intake and 6 months, and included all findings from which a UDS was administered within 15 days of each interval for the various substances. For example, for the 6-month cocaine UDS variable, all patients administered a UDS for cocaine between 165 and 195 days following treatment admission were included. An algorithm was also utilized to place patients into a composite “opioids” UDS category based on UDS findings for both heroin and oxycodone at the 6-month interval. Thus, if a patient produced a positive UDS finding for heroin, oxycodone, or both at 6 months, they received a positive UDS designation when grouped in the composite opioids UDS category. The algorithm utilized to classify patients at intake, however, included positive findings for methadone in addition to heroin or oxycodone given methadone may have been used recreationally prior to MMT admission.
Patients were grouped into six a priori, nonmutually exclusive categories based on average daily methadone dosage received throughout the duration of their treatment (i.e., 10.1–60.0 mg, 60.1–120.0 mg, 10.1–80.0 mg, 80.1–120.0 mg, 60.1–80.0 mg, and 80.1–100.0 mg). The rationale for this categorization procedure was to first examine the differential outcome expectations for patients receiving an average methadone dosage greater than 60.0 mg/d relative to those receiving 60.0 mg/d or less. Second, in an effort to isolate the specific methadone dosage range associated with increased retention in MMT, those patients receiving greater than 60.0 mg/d were divided into two groups representing those receiving greater than 80.0 mg/d (but less than 100.1 mg/d) and those receiving less than 80.1 mg/d (but still greater than 60.0 mg/d). The methadone dosage categories described here are consistent with those commonly examined in the MMT retention literature (e.g., Bao et al., 2009; Magura et al., 1998; Peles et al., 2008; Strain et al., 1999; Torrens et al., 1996).
Separate hierarchical binary logistic regression models were fitted to the data to test the hypotheses regarding whether premature MMT discharge could be predicted at the 6- and 12-month intervals by: (a) pretreatment demographic variables alone; and (b) pretreatment and in-treatment clinical performance variables (i.e., UDS findings for cocaine, amphetamines, benzodiazepines, and cannabinoids obtained at intake and the 6-month interval) after adjustment for relevant demographic variables and average daily methadone dosage received throughout the duration of treatment. The dependent variable for the logistic regressions was a binary variable coded as 1 if discharged due to various reasons (i.e., administrative, financial, or medical) or against medical advice prior to 6 or 12 months and 0 if the patient was still enrolled in MMT at the various a priori follow-up intervals (i.e., 6 and 12 months); this provided for a measure of premature treatment discharge. Logistic regressions involving average daily methadone dosage as a predictor, however, utilized a binary dependent variable indicative of MMT retention (i.e., coded as 1 = enrolled in MMT at the two a priori follow-up intervals and 0 = discharged). Inclusion of relevant demographic variables in the various models was determined based on significant findings from chi-square analyses. Goodness-of-fit statistics were examined to assess the fit of each respective logistic model against actual outcome (i.e., whether patients were classified as premature treatment discharges at 6 and 12 months). One inferential test (i.e., Hosmer-Lemeshow) and two additional descriptive measures of goodness-of-fit (i.e., R2 indices defined by Cox & Snell [1989] and Nagelkerke [1991]) were utilized to determine whether the various models fit to the data well. Finally, a positive UDS finding for opioids at intake was not included as a predictor variable given a positive finding for this substance was nearly universal for the total sample at intake and the resultant lack of variance precluded identifying a relationship with premature treatment discharge at the 6- and 12-month interval.
Separate binary logistic regressions were also conducted to further assess the impact of various pretreatment demographic characteristics on 6- and 12-month retention rates, as well as delineate the average daily methadone dosage category most prudent for increasing retention in MMT at 6 and 12 months. In terms of racial/ethnic groups, only two groups (i.e., Caucasian and Hispanic) were of sufficient size to justify inclusion in the models as predictor variables. Thus, the total sample was dichotomized in order to classify patients based on group membership (Hispanic vs. non-Hispanic, Caucasian vs. non-Caucasian). Logistic regressions involving these two binary categorical variables were utilized to ascertain whether particular racial/ethnic groups were more strongly associated with premature MMT discharge at the 6- and 12-month intervals. A similar procedure was performed for the patient payment method, marital status, and employment status pretreatment variables.
Results UDS Findings and Retention Rates
Based on UDS findings at intake, nearly all (93.7%) of the patients produced a positive finding for opioids (i.e., heroin, oxycodone, or methadone). The remaining positive UDS findings obtained at intake that predominated were as follows: cannabinoids, 31.7%; benzodiazepines, 26.5%; cocaine, 11.8%; and amphetamines, 10.8%. Examination of the UDS findings at 6 months revealed that only 6.9% of the patients produced a positive finding for opioids (i.e., heroin or oxycodone). Regarding the remaining UDS results at 6 months, 4.3% produced a positive UDS finding for only one nonopioid substance, and 2.5% were found positive for more than one nonopioid substance. Specifically, 4.8% were positive for benzodiazepines, 3.7% for cannabinoids, 1.9% for cocaine, and 2.5% for amphetamines.
With respect to the observed retention rates, 46.8% of patients were retained at 6 months and 20.3% were retained at 12 months. At 6 months, the percentages regarding the total number of patients classified as premature treatment discharges due to the various specific reasons for discharge were as follows: 49.1%, against medical advice; 25.7%, administrative discharge; 23.9%, financial constraints; and 1.3%, medical discharge. However, it is important to note that as discussed earlier, patients discharged due to successful treatment completion (n = 99) or transfer to another MMT facility prior to the 6-month interval (n = 101) were excluded in an effort to avoid inflation of the attrition rate. Similar to the 6-month estimates, the percentages and specific reasons for discharge regarding the total number of premature treatment discharges at 12 months were as follows: 47.1%, against medical advice; 27.1%, administrative discharge; 24.3%, financial constraints; and 1.5%, medical discharge. Over one third of patients were excluded due to successful treatment completion (n = 108) or transfer to another MMT program (n = 124) during the 6- to 12-month interval.
Demographic Variables
Results from separate logistic regressions revealed that the risk of premature MMT discharge at 6 months was significantly higher for Hispanics (OR: 1.37, 95% CI [1.03, 1.81]), Model χ2(1) = 4.738, p < .05, R2 = .01 (Cox & Snell), R2 = .01 (Nagelkerke), unemployed patients (OR: 1.26, 95% CI [1.03, 1.56]), Model χ2(1) = 4.832, p < .05, R2 = .01 (Cox & Snell), R2 = .01 (Nagelkerke), and patients not married or having a significant other at intake (OR: 1.27, 95% CI [1.01, 1.59]), Model χ2(1) = 4.143, p < .05, R2 = .01 (Cox & Snell), R2 = .01 (Nagelkerke), not adjusting for other factors. Patient gender, Caucasian race, age, and method of payment were not found to significantly predict premature MMT discharge at 6 months. At the 12-month interval, the risk of premature discharge was significantly higher for self-pay patients (OR: 1.44, 95% CI [1.08, 1.93]), Model χ2(1) = 6.029, p < .05, R2 = .01 (Cox & Snell), R2 = .01 (Nagelkerke), male patients (OR: 1.33, 95% CI [1.01, 1.75]), Model χ2(1) = 4.110, p < .05, R2 = .01 (Cox & Snell), R2 = .01 (Nagelkerke), and patients younger than 35 years of age (OR: 1.36, 95% CI [1.04, 1.79]), Model χ2(1) = 4.831, p < .05, R2 = .01 (Cox & Snell), R2 = .01 (Nagelkerke), not adjusting for other factors. Employment status, Hispanic ethnicity, Caucasian race, and marital status were not found to significantly predict MMT attrition at 12 months.
In summary, unemployment and being Hispanic increase initial premature discharge risks while being married seems to decrease such risks. However, by the 12-month mark, younger age, male gender, and self-pay status seem to become greater factors in failure to continue in MMT during the second 6 months of treatment.
Clinical Variables
Hierarchical binary logistic regressions were also fitted to the data to assess the impact of various clinical variables on premature MMT discharge at 6 and 12 months after adjustment for relevant covariates (see Table 2). The only intake UDS finding entered into the model that was found to significantly predict premature MMT discharge at 6 months was a positive finding for cocaine, after controlling for employment status, ethnicity, marital status, and average daily methadone dosage, Model χ2(8) = 211.122, p < .001, R2 = .19 (Cox & Snell), R2 = .26 (Nagelkerke). Further, the Hosmer-Lemeshow goodness-of-fit test was insignificant, χ2(8) = 8.401, p > .05, suggesting that the model was fit to the data well. Specifically, patients found positive for cocaine at intake were 1.79 times (95% CI [1.18, 2.72]) more likely to be prematurely discharged at 6 months, compared with patients found negative for cocaine at intake. At the 12-month interval, the only independent clinical variables found to significantly predict MMT discharge were a positive UDS finding for cocaine at intake and a positive UDS finding for opioids at 6 months, Model χ2(13) = 52.605, p < .001, R2 = .17 (Cox & Snell), R2 = .23 (Nagelkerke), after controlling for patient gender, age, method of payment, and average daily methadone dosage. The Hosmer-Lemeshow goodness-of-fit test was also insignificant, χ2(8) = 4.510, p > .05. In fact, patients found positive for cocaine at intake were 3.71 times (95% CI [1.35, 10.17]) more likely, and patients found positive for opioids at 6 months were 2.13 times (95% CI [1.10, 4.12]) more likely to be prematurely discharged at 12 months, compared with patients found negative for cocaine and opioids at intake and 6 months, respectively. The remaining intake and 6-month UDS findings were not found to significantly predict MMT discharge at the 12-month interval. In other words, it appears that a positive UDS finding for cocaine at intake and a positive UDS finding for opioids at 6 months were the only clinical variables found to independently contribute to study outcome (i.e., MMT attrition at 12 months) after adjustment for relevant covariates.
Clinical Predictors of Premature Treatment Discharge at 6 and 12 Months
Average Daily Methadone Dosage
Regarding the average daily methadone dosage prescribed for the total sample, nearly one third (32.2%) of patients were prescribed a dosage between 40.1 and 60.0 mg/d, and nearly as many (29.0%) were prescribed a dosage between 60.1 and 80.0 mg/d throughout the duration of treatment. The balance of the cases was as follows: 40.0 mg/d or less, 20.4%; 80.1–100.0 mg/d, 13.2%; 100.1–120.0 mg/d, 3.7%; and only 25 patients (1.5%) were prescribed an average daily dosage of 120.1 mg or greater.
Results from logistic regressions revealed that patients prescribed an average methadone dosage of 60.1–120.0 mg/d were 4.01 times (95% CI [3.27, 5.10]) more likely to be retained in MMT at 6 months than patients prescribed an average dosage of 10.1–60.0 mg/d, Model χ2(1) = 163.539, p < .001, R2 = .11 (Cox & Snell), R2 = .15 (Nagelkerke). At the 12-month interval, patients prescribed an average methadone dosage of 60.1–120.0 mg/d were 3.58 times (95% CI [2.66, 4.82]) more likely to be retained in MMT than patients prescribed an average dosage of 10.1–60.0 mg/d, Model χ2(1) = 76.397, p < .001, R2 = .06 (Cox & Snell), R2 = .09 (Nagelkerke). Further examination of the specific dosage range most prudent for favorable treatment response found that patients prescribed an average methadone dosage of 80.1–100.0 mg/d were 4.47 times (95% CI [2.93, 6.80]) more likely to be retained in MMT at 6 months than patients prescribed an average dosage of 60.1–80.0 mg/d, Model χ2(1) = 57.621, p < .001, R2 = .09 (Cox & Snell), R2 = .12 (Nagelkerke). Similarly, patients prescribed an average methadone dosage of 80.1–100.0 mg/d were 3.32 times (95% CI [2.23, 4.94]) more likely to be retained in MMT at 12 months than patients prescribed an average dosage of 60.1–80.0 mg/d, Model χ2(1) = 35.127, p < .001, R2 = .06 (Cox & Snell), R2 = .09 (Nagelkerke). When comparisons involved 80.1–120.0 mg/d versus 10.1–80.0 mg/d, the differences in outcome were even more pronounced, such that those in the higher methadone dosage group (i.e., 80.1–120.0 mg/d) were 7.73 times (95% CI [5.49, 10.88]) more likely to be retained in MMT at 6 months than patients in the lower methadone dosage group, Model χ2(1) = 179.994, p < .001, R2 = .12 (Cox & Snell), R2 = .16 (Nagelkerke). At 12 months, patients in the higher methadone dosage group were 6.25 (95% CI [4.57, 8.55]) times more likely to be retained in MMT than patients in the lower methadone dosage group d, Model χ2(1) = 128.894, p < .001, R2 = .10 (Cox & Snell), R2 = .15 (Nagelkerke). Thus, higher average daily methadone dosages were found to predict MMT retention at both 6 and 12 months. Finally, there was a moderate, positive correlation found between average daily methadone dosage prescribed throughout the course of treatment (when examined as a continuous variable) and LOS, r = .357, p < .001, with higher dosages associated with increased retention.
DiscussionThe findings replicate and extend prior work which indicated that various pretreatment demographic and clinical variables were associated with MMT retention. Unlike prior published longitudinal MMT research, however, the present study utilized a substantially larger treatment sample, examined a longer timeframe, and controlled for relevant demographic and clinical characteristics that have the potential to impact outcome. This strategy yielded several important implications in that the present findings revealed that certain pretreatment demographic characteristics were associated with differential outcome expectations at both the 6- and 12-month intervals.
Demographic variables appeared to exert their influences either early, during the first 6 months of treatment, or later during the second 6 months. Membership in an ethnic-minority group (i.e., being of Hispanic ethnicity), unemployment, and not being married or having a significant other were the only significant and independent predictors of premature MMT discharge at 6 months. However, none of these variables were found to predict discharge at 12 months; presumably because these factors had already exerted their influence at the 6-month mark. In fact, examination of the observed 6-month attrition rates for these three predictor variables revealed that 59.6% of Hispanic patients, 55.8% of unemployed patients, and 54.9% of patients not married or having a significant other (i.e., single, separated, divorced, or widowed) had already been discharged from treatment prior to 6 months. Conversely, demographic variables found to predict discharge at 12 months included male gender, method of payment for treatment services (i.e., self-pay), and being younger than 35 years of age. Thus, it appears that select demographic variables may be more important early, during the initial 6 months of MMT, while others may be more important later on in the MMT process.
For instance, with regard to patient employment status and ethnicity, our findings are in accord with prior studies which found that unemployed patients and patients of an ethnic-minority group were more likely to experience a poor outcome with respect to treatment retention (Ball et al., 1988; Hser et al., 1990; Judson & Goldstein, 1982). The finding that unemployment was found to significantly predict premature MMT discharge at 6 months was not surprising given that patient fees represent a major obstacle to successful MMT outcomes (Anglin et al., 1989; Gryczynski et al., 2011; Muhleisen, Clark, Teo, & Brogan, 2005), and the risk of dropout is higher for patients with no stable source of income prior to treatment admission (Del Rio et al., 1997). Additional correlates of unemployment, beyond simply a lack of income, may explain the observed findings considering that unemployed patients often present with co-occurring issues known to impact substance use treatment outcomes (for review see Henkel, 2011). Patients in the present study may have also been unemployed due to any number of potential contributing factors (e.g., a more severe opioid use disorder, lack of transportation, lower motivation), which would undoubtedly create barriers to successfully completing MMT. Regardless of the co-occurring issues and underlying reasons for unemployment, the development of relationships with job placement agencies or the inclusion of vocational promotion and rehabilitation services for appropriate patients at the outset of MMT may be indicated if programs aspire to impact the relatively poor retention rates among unemployed patients.
Further, patients not currently married or having a significant other at treatment admission demonstrated poorer retention in MMT at 6 months. Potential reasons for the differential outcome expectations for married/significant other patients compared with members of the other marital status categories (i.e., single, divorced, widowed, and separated) include several factors found to predict MMT retention (Shirinbayan et al., 2010; Torrens et al., 1996). That is, the presence of more immediate access to a stable social network and additional support in the form of encouragement from their partner, as well as an overall increased level of perceived social support may explain the observed findings. Therefore, ethnic-minority patients, unemployed patients, and those patients not currently married or having a significant other at admission may require additional services from the staff or the consideration of alternative treatment regimens early on in the treatment process to help thwart the problem of MMT attrition. Attention to the unique needs of these subgroups of patients has the potential to improve retention.
Although well-documented in the MMT literature (e.g., Brown et al., 1982; Deck & Carlson, 2005; Hser et al., 1990; MacGowan et al., 1996; Magura et al., 1998; Mancino et al., 2010; Saxon et al., 1996; Strike et al., 2005), the present study also replicated prior work in that younger patients were found to evince a significantly higher rate of treatment attrition at 12 months, which suggests that younger patients may be less prepared for extended treatment. This presumably may be due to younger patients’ lower maturity level or less cumulative substance-related negative consequences in their lifetime relative to older patients. Additionally, the finding that the risk of premature discharge at 12 months was significantly higher for male (OR: 1.33, 95% CI [1.01, 1.75]) than female patients may be indicative of important gender-specific differentials relating to MMT prognostic indicators or it may simply be an artifact of the sample composition. Given that women with more severe substance use problems have traditionally been found to seek treatment less often than men, arguably due to a history of trauma and the presence of more barriers to treatment (e.g., childcare responsibilities, inadequate health insurance), further investigation is warranted (Ashley, Marsden, & Brady, 2003; Hodgins, El-Guebaly, & Addington, 1997).
Together, the various demographic variables found to significantly predict premature MMT discharge at both 6 and 12 months suggest that more intensive and/or supplemental services may be appropriate for select subgroups of patients. Although the predictors of treatment discharge by 12 months (i.e., < 35 years of age, male, and self-pay) may not require immediate attention relative to the 6-month predictors and related constructs (e.g., unemployment, limited support), MMT programs should consider early intervention with members of these select groups if 12-month retention rates are desired. From a clinical standpoint, one potential treatment option would be to incorporate motivational enhancement techniques (e.g., motivational interviewing) into standard treatment programming (Miller & Rollnick, 1991). At the program level (and assuming local resources permit such a strategy), MMT programs may consider determining the composition of select psychotherapeutic groups on the basis of age or gender, and supplementing standard programming with topics or techniques designed to increase treatment engagement. At the individual level, both appointments with counselors or case managers and visits with prescribing physicians also represent a suitable context to elicit motivation from patients at elevated risk for premature discharge, which in turn may improve treatment outcomes.
The finding that patients’ method of payment for MMT services predicted premature discharge at the 12-month interval warrants additional comment. Viewed from a sheer economical perspective, the finding that self-pay status eventually became associated with decreased retention in treatment relative to non-self-pay patients over time is hardly surprising given the cumulative out-of-pocket expenses that self-pay patients would have acquired had they remained in treatment through 12 months. The differential outcomes appear to be more an issue of the apparent inability to sustain payment for treatment services, and suggest that cost may not become a statistically significant treatment barrier to successful outcomes until self-pay patients have been in treatment beyond a minimum of 6 months. The additional finding that an estimated one in four premature MMT discharges at both the 6- and 12-month mark were discharged due to financial constraints further confirms the notion that cost may be a significant barrier to MMT completion. Thus, the observation that self-pay status comes into play after a period of being in MMT suggests that economic factors beyond employment alone may be an impediment to long-term treatment. The data are compatible with the conjecture that those for whom the costs of treatment are an economic strain may be more likely to discontinue their treatment. While the trends for employment and payment source are in the consistent direction, it may be the case that one is more important during the two time intervals.
The general finding that positive UDS results for substances other than opioids at intake are associated with increased attrition risks has implications for treatment planning. However, the most important implication concerns the fact that a positive UDS finding for cocaine at intake and 6 months were both found to independently predict premature treatment discharge, after adjustment for relevant demographic variables and additional UDS findings obtained at intake and 6 months. Specifically, patients found positive for cocaine at intake were nearly two times more likely to be discharged at 6 months and almost four times more likely to be discharged by the 12-month mark. From a clinical standpoint, these findings suggest that MMT programs should allocate time and resources toward the treatment of cocaine use and related problems in addition to opioid dependence, rather than simply focusing on the treatment of opioid-related problems alone. In fact, concomitant cocaine use is common among patients presenting for MMT (Chaisson et al., 1989; DeMaria, Sterling, & Weinstein, 2000) and the inclusion of cognitive–behavioral or reinforcement-based interventions designed specifically for cocaine use into standard MMT practices has been found to positively impact clinical outcomes (Barry, Sullivan, & Petry, 2009; Rawson et al., 2002; Silverman et al., 1998). Thus, MMT protocols which incorporate additional psychosocial approaches for cocaine use may improve patient retention in treatment.
Another key finding is the confirmation of previous work that higher dosages of methadone consistently produce better results. Our findings are consistent with previous research (Bao et al., 2009; Faggiano et al., 2003; Ling et al., 1996; Maremmani et al., 2003; Strain et al., 1999; Torrens et al., 1996) in that higher average methadone dosages were associated with increased retention in treatment. Of particular interest were the 6- and 12-month outcomes when average methadone dosage was dichotomized at 80.0 mg/d (i.e., 60.0–80.0 vs. 80.1 vs. 100.0). Specifically, results from logistic regressions revealed that there was over a fourfold increase in the likelihood of MMT retention for patients prescribed the higher dosage at 6 months, and more than three times as likely to be retained in treatment at 12 months. Therefore, the findings suggest that MMT retention appears to be a function of average daily methadone dosage and support the hypothesis that higher daily methadone dosages may positively impact retention in MMT. Although including average daily dosage as a predictor of MMT outcome in regression models is consistent with previous research, when analyses were conducted with peak methadone dosage as a predictor, the observed findings remained generally the same.
It is important to note, however, that although there may be some pharmacological basis for the observed differential findings, the outcomes are likely to be multiply determined, and as such, require additional discussion regarding alternative interpretations. That is, there may be clinical expectancies and biases operating that are not apparent in the data but that played a role in patient retention in treatment. For instance, the findings regarding the associations between lower dosage ranges and decreased retention may be the product of less treatment engagement as opposed to simply a matter of dosage. That is, given higher dosages of methadone have the potential to attenuate or block the reinforcing effects of opioids (SAMHSA, 2005), patients may have intentionally requested lower dosages in an effort to continue using illicit opioids. Similarly, patients may have been aware of methadone’s relative ease of cessation at lower dosages due to decreased withdrawal symptoms. Considering that many programs are responsive to patient requests for lower dosages, both patient and physician biases—although not apparent from patient data derived from electronic medical records—may be important sources of variance in terms of outcomes, which warrant the need for further investigation.
Study Limitations
The findings from the present study should be considered in light of several limitations that suggest the need for additional work in the area of identifying predictors of MMT retention. First, the present study utilized a predominately Caucasian convenience sample comprised exclusively of patients presenting for long-term methadone maintenance in the United States. Despite the relatively large geographical coverage relating to the MMT programs utilized in the present investigation, some caution is warranted in generalizing the findings to other programs, particularly those serving populations with a more varied racial/ethnic composition. Furthermore, the finding that nearly three fourths of the sample funded their own treatment (i.e., were self-pay) and all 26 MMT programs were ��for-profit,” represent another potential limitation pertaining to the generalizability of the findings given estimates from several large-scale MMT studies indicate that generally less than half of patients presenting for MMT are self-pay (Banta-Green, Maynard, Koepsell, Wells, & Donovan, 2009; Bradley, French, & Rachal, 1994). The present study design also consisted of retrospective longitudinal electronic chart review and therefore, warrants further prospective longitudinal work.
Another limitation involved the issue of missing or incomplete demographic data for a sizable number of patients included in the initial data set. That is, in the instance of unavailable data for select demographic variables for a substantial number of patients, it is possible that more complete demographic data might have altered the results; although a larger sample size has the potential to reinforce the present findings as well. Thus, the present findings should be considered as a minimum dataset, consisting of lower bound estimates of demographic predictors of outcome within the current sample. The breadth of clinical data included in the present dataset represents another limitation. Although the present study examined the impact of various UDS findings obtained at various intervals as well as average daily methadone dosage on MMT retention, additional clinical factors found to impact retention, including program philosophy and ancillary services data, as well as extent of prior substance use and treatment admissions history data (Brown et al., 1982; Deck & Carlson, 2005; Saxon et al., 1996), were not included. Moreover, motivation and readiness to change, as well as perceived self-efficacy are important individual difference factors to consider in future work given their influence on MMT retention and various clinical outcomes (Hser et al., 2011; Joe, Simpson, & Broome, 1998; Li, Ding, Lai, Lin, & Luo, 2011; Nosyk et al., 2010; Wong & Longshore, 2008).
Given the large variation in average daily methadone dosage, another limitation is that overall dosage-level recommendations may not provide clinical staff with sufficient information to adequately guide treatment practice. Future research should focus on identifying the most effective processes of dosage determination practices (e.g., examination of serum methadone levels; Leavitt, Shinderman, Maxwell, Eap, & Paris, 2000) rather than simply delineating specific dosage levels most prudent for favorable treatment response. However, inclusion of average daily methadone dosage as a predictor of outcome in regression models is consistent with previous MMT research (Hallinan, Ray, Byrne, Agho, & Attia, 2006; Soyka et al., 2008). Consideration of various individual difference (e.g., sexual abuse history, mental health conditions) and treatment delivery (e.g., guideline adherence, tendency to encourage dosage reductions) factors found to correlate with the dosage of methadone at which patients achieve positive clinical outcomes is also a requisite for future studies (Trafton, Minkel, & Humphreys, 2006). Finally, the observed findings are predictive associations and as such, causal interpretations cannot be assumed.
Conclusions
As the number of U.S. adults receiving treatment for opioid dependence continues to increase annually (SAMHSA, 2011), coupled with the resultant public health concern, the challenge of identifying patients in need of specialized services at the outset of treatment and measures to optimize positive outcomes is of paramount importance. Specific modifications to treatment regimens early on in the process for certain subgroups of patients based on select pretreatment characteristics and intake UDS findings have the potential to forestall premature treatment discharge. Despite the short-term predictive value of select factors at treatment admission, consideration of additional variables might also serve as equally important indicators to guide subsequent treatment planning beyond a minimum interval of time. In sum, the current findings provide indications that consideration of demographic and economic factors along with clinical factors, such as the use of other substances (i.e., cocaine), may provide strategies for enhancing retention in MMT. Improvements in retention are essential to reduce the occurrence of repeated treatment episodes and improve the overall clinical outcomes of these patients.
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Submitted: October 29, 2014 Revised: March 30, 2015 Accepted: April 1, 2015
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Source: Psychology of Addictive Behaviors. Vol. 29. (4), Dec, 2015 pp. 906-917)
Accession Number: 2015-27693-001
Digital Object Identifier: 10.1037/adb0000090
Record: 122- Title:
- Premeditation moderates the relation between sensation seeking and risky substance use among young adults.
- Authors:
- McCabe, Connor J.. Department of Psychology, University of Washington, Seattle, WA, US, cmccabe@uw.edu
Louie, Kristine A.. Department of Psychology, University of Washington, Seattle, WA, US
King, Kevin M.. Department of Psychology, University of Washington, Seattle, WA, US - Address:
- McCabe, Connor J., Department of Psychology, University of Washington, 119A Guthrie Hall, UW Box 351525, Seattle, WA, US, 98195, cmccabe@uw.edu
- Source:
- Psychology of Addictive Behaviors, Vol 29(3), Sep, 2015. pp. 753-765.
- NLM Title Abbreviation:
- Psychol Addict Behav
- Page Count:
- 13
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- Bulletin of the Society of Psychologists in Addictive Behaviors; Bulletin of the Society of Psychologists in Substance Abuse
- Other Publishers:
- US : Educational Publishing Foundation
Society of Psychologists in Addictive Behaviors - ISSN:
- 0893-164X (Print)
1939-1501 (Electronic) - Language:
- English
- Keywords:
- sensation seeking, impulse control, substance use, externalizing behaviors, individual differences
- Abstract:
- Young adulthood is a peak period for externalizing behaviors such as substance abuse and antisocial conduct. Evidence from developmental neuroscience suggests that externalizing conduct within this time period may be associated with a 'developmental asymmetry' characterized by an early peak in sensation seeking combined with a relatively immature impulse control system. Trait measures of impulsivity—sensation seeking and premeditation—are psychological manifestations of these respective systems, and multiple prior studies suggest that high sensation seeking and low premeditation independently confer risk for distinct forms of externalizing behaviors. The goal of the present study was to test this developmental asymmetry hypothesis, examining whether trait premeditation moderates the effect of sensation seeking on substance use and problems, aggression, and rule-breaking behavior. Using a cross-sectional sample of college-enrolled adults (n = 491), we applied zero-inflated modeling strategies to examine the likelihood and level of risky externalizing behaviors. Results indicated that lower premeditation enhanced the effect of higher sensation seeking on higher levels of positive and negative alcohol consequences, more frequent drug use, and more problematic drug use, but was unrelated to individual differences in antisocial behaviors. Our findings indicate that the developmental asymmetry between sensation seeking and a lack of premeditation is a risk factor for individual differences in problematic substance use among young adults, and may be less applicable for antisocial behaviors among high functioning individuals. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Antisocial Behavior; *Drug Abuse; *Externalization; *Sensation Seeking; *Developmental Neuroscience; Impulsiveness; Individual Differences; Risk Factors
- Medical Subject Headings (MeSH):
- Adolescent; Alcohol Drinking; Alcohol Drinking in College; Antisocial Personality Disorder; Cross-Sectional Studies; Female; Humans; Impulsive Behavior; Male; Risk Factors; Risk-Taking; Sensation; Students; Substance-Related Disorders; Universities; Young Adult
- PsycINFO Classification:
- Substance Abuse & Addiction (3233)
- Population:
- Human
Male
Female - Location:
- US
- Age Group:
- Adulthood (18 yrs & older)
Young Adulthood (18-29 yrs) - Tests & Measures:
- Urgency, Premeditation, Perseverance and Sensation Seeking Impulsive Behavior Scale
Achenbach Adult Self Report
Positive Drinking Consequences Questionnaire DOI: 10.1037/t17576-000
Young Adult Alcohol Problems Screening Test DOI: 10.1037/t02795-000 - Methodology:
- Empirical Study; Quantitative Study
- Supplemental Data:
- Text Internet
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- Accepted: Feb 18, 2015; Revised: Feb 17, 2015; First Submitted: Aug 18, 2014
- Release Date:
- 20150928
- Copyright:
- American Psychological Association. 2015
- Digital Object Identifier:
- http://dx.doi.org/10.1037/adb0000075; http://dx.doi.org/10.1037/adb0000075.supp(Supplemental)
- PMID:
- 26415063
- Accession Number:
- 2015-43528-006
- Number of Citations in Source:
- 102
- Persistent link to this record (Permalink):
- http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2015-43528-006&site=ehost-live
- Cut and Paste:
- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2015-43528-006&site=ehost-live">Premeditation moderates the relation between sensation seeking and risky substance use among young adults.</A>
- Database:
- PsycINFO
Premeditation Moderates the Relation Between Sensation Seeking and Risky Substance Use Among Young Adults
By: Connor J. McCabe
Department of Psychology, University of Washington;
Kristine A. Louie
Department of Psychology, University of Washington
Kevin M. King
Department of Psychology, University of Washington
Acknowledgement:
Late adolescence and young adulthood is a developmental period characterized by the highest rates of externalizing behavior relative to any other period. Of those between 18 and 25 years of age, for instance, 40% are classified as binge drinkers (drinking 5 or more drinks in a single episode at least monthly), and 21% report current illicit drug use (U.S. Department of Health and Human Services, 2011). Aggression and delinquency—as well as more extreme forms of antisocial conduct such as violent criminal activity (Snyder, 2012)—also peak in adolescence and remain high in young adulthood (Loeber & Hay, 1997). Moreover, these are thought to presage more extreme antisocial behaviors and may serve as a marker for life-course-persistent antisocial conduct (Burt, Donnellan, Iacono, & McGue, 2011; Moffitt, 1993). Indeed, many—if not most—externalizing behaviors at all levels reach peak population prevalence between the ages of 16 and 25 (Steinberg, 2013).
Peaks in externalizing behavior in late adolescence and young adulthood may be explained by differential development of reward and control systems. Neurological studies suggest that reward sensitivity develops in a curvilinear pattern across puberty and young adulthood, generally peaking in midadolescence and declining through young adulthood (Galvan et al., 2006; Van Leijenhorst et al., 2010). Concurrently, structural magnetic resonance imaging (MRI) studies investigating the development of the prefrontal cortex (responsible for cognitive control over impulses) have shown that full maturation of this area follows a linear and protracted course through the third decade of life (Giedd, 2004; Gogtay et al., 2004; Paus, 2005; Somerville, Jones, & Casey, 2010). The disparate courses of development of these two systems produce a developmental asymmetry, and this asymmetry has been forwarded as an explanation for the high rates of risk behavior during adolescence (Casey, Jones, & Somerville, 2011; Steinberg, 2010). Specifically, adolescents may be particularly vulnerable to problematic engagement in externalizing behaviors because they have a higher propensity toward reward-driven behavior while their capacity to control such behavior is relatively immature.
Similar to neurological studies, studies using trait and behavioral measures suggest that adolescence reflects a time of increased sensation seeking and slowly developing impulse control. Increases in reward sensitivity are thought to increase trait sensation seeking, defined as the propensity to actively seek out novelty and excitement regardless of associated risks (Steinberg, 2013; Steinberg et al., 2008). Survey and behavioral measures of sensation seeking have shown similar patterns of development across the life span, peaking in midadolescence and generally declining after age 20 (Harden & Tucker-Drob, 2011; Romer, Duckworth, Sznitman, & Park, 2010; Steinberg et al., 2008). Evidence from cross-sectional and longitudinal studies suggest a similar course of development in survey and behavioral measures of impulse control, showing a linear increase across adolescence and young adulthood (Galvan, Hare, Voss, Glover, & Casey, 2007; Harden & Tucker-Drob, 2011; Romer & Hennessy, 2007; Steinberg, 2010).
However, it is also the case that a substantial proportion of individuals within this age range tend to abstain from externalizing behavior altogether (Romer, 2010), and that much externalizing behavior during this period may be driven by only a minority of individuals. For instance, National Household Survey (NHS) data report that among 12- to 20-year-olds, approximately 66% of drunk driving, 72% of criminal arrests, and 87% of all drug-related health problems were accounted for by only 18% of youths sampled (Romer, 2003; Biglan, Brennan, Foster, & Holder, 2004). This suggests that although adolescents and young adults do engage in problem behaviors more frequently on average, these figures may not reflect unilateral shifts in externalizing conduct, and a critical step is to translate the predictions of the developmental asymmetry model into predications about individual differences. Specifically, this model implies that the individuals who are highest on sensation seeking and lowest on impulse control will exhibit the greatest level of multiple externalizing behaviors. That is, regardless of developmental level, asymmetry between systems may predict high risk behaviors. However, to our knowledge, few studies have tested this hypothesis.
Sensation Seeking and Risk BehaviorSensation seeking has been a consistent predictor of externalizing behaviors in adolescent and young adult samples (Whiteside & Lynam, 2001, 2009; Zuckerman, 1979). Those high on sensation seeking may pursue risky activities as a result of a hedonic drive toward novel and rewarding activities, and measures of sensation seeking within this developmental period are indeed associated with engagement in multiple externalizing behaviors (Zuckerman & Kuhlman, 2000). Among young adults, sensation seeking is associated with greater drinking frequency (Coskunpinar, Dir, & Cyders, 2013; Stautz & Cooper, 2013), higher rates of drug use, aggression, and other forms of risky externalizing behaviors (see Roberti, 2004 for a review).
Although sensation seeking may reflect a broad propensity toward involvement in risky behavior, it is less clear whether sensation seeking confers direct vulnerability for problematic levels of risk behavior. For example, prior research has frequently demonstrated that there are often different predictors of substance use versus substance related problems (King, Karyadi, Luk, & Patock-Peckham, 2011; Simons, 2003; Stice, Barrera, & Chassin, 1998), particularly among young adults. Separately, relations between sensation seeking and problem behaviors may be confounded by a failure to disaggregate sensation seeking from other traits that confer risk (Whiteside & Lynam, 2009). For instance, trait measures of sensation seeking (such as the Zuckerman Sensation-Seeking Scale; Zuckerman et al., 1993) have frequently included items that reflect both a tendency toward novelty as well as a propensity toward acting on impulse, yet sensation seeking may reflect only one of a number of distinct, weakly correlated definitions of impulsivity (Cyders et al., 2007; Smith et al., 2007; Whiteside & Lynam, 2009). When controlling for these distinct factors in more recent studies, sensation seeking does independently predict risk behaviors such as the frequency of alcohol and drug use, count of sexual partners, and gambling frequency—but less consistently predicts problem levels of these activities per se (Cyders, Flory, Rainer, & Smith, 2009; Hawkins, Catalano, & Miller, 1992; Miller, Flory, Lynam, & Leukefeld, 2003; Quinn & Harden, 2013; Smith et al., 2007). This notion is supported by research that has observed no association between sensation seeking and problem behaviors above and beyond separate forms of trait impulsivity, such as urgency, lack of perseverance, and a lack of premeditation (Verdejo-García, Bechara, Recknor, & Pérez-García, 2007). Relatedly, in more recent meta analyses, sensation seeking was a moderate predictor of alcohol use (r values = 0.27 and 0.28), but was less strongly related to problem use (r values = 0.17 and .24; Coskunpinar et al., 2013; Stautz & Cooper, 2013). Whiteside and Lynam (2009) have further noted that sensation seeking may be associated with alcohol-related use and problems in adolescents (Bates & Labouvie, 1995; Wood, Cochran, Pfefferbaum, & Arneklev, 1995) but not in older adults (Lejoyeux, Feuché, Loi, Solomon, & Adès, 1998; Virkkunen et al., 1994), which may imply a potential role for psychological maturity to buffer the risk of sensation seeking among adults relative to younger populations. Taken together, these observations suggest that although a relation between sensation seeking and problem externalizing behaviors may exist, the nature of this relation may be moderated by separate contextual and psychological risk factors.
Impulse Control as a ModeratorOne potential moderator implied by the developmental asymmetry model—and a frequent predictor of risk behavior consequences—is trait impulse control. Impulse control has frequently been operationalized as the ability to think before acting and plan ahead (Whiteside & Lynam, 2001, 2009; Wills, Ainette, Stoolmiller, Gibbons, & Shinar, 2008), and is commonly referred to as planning, premeditation, or “good self-control,” with similar or identical items used as indicators of these parallel constructs (King, Patock-Peckham, Dager, Thimm, & Gates, 2014; Sharma, Markon, & Clark, 2014). A wide body of personality literature suggests a direct and inverse relation between impulse control and problem behaviors. For instance, low impulse control predicts higher rates of externalizing and conduct problems (Luengo, Carrillo-de-la-Peña, Otero, & Romero, 1994; Monahan, Steinberg, Cauffman, & Mulvey, 2009; Whiteside & Lynam, 2009); alcohol problems and heavy drinking (Coskunpinar et al., 2013; Stautz & Cooper, 2013); and drug use (Verdejo-García, Lawrence, & Clark, 2008), among others. These relations further persist above and beyond other known dispositional risk factors, such as sensation seeking, perseverance, and emotion-based impulsivity (i.e., urgency; Smith et al., 2007; Whiteside & Lynam, 2009).
More recent evidence suggests that impulse control (or a lack thereof) can also buffer (or enhance) the effects of other known risk factors for externalizing problems, though the construct has frequently been quantified heterogeneously. For instance, Wills and colleagues (2008) operationalized “good self-control” as a combined measure of planning and problem solving, and found that high levels of this trait reduced the effect of risk factors such as peer use and family events on frequency of cigarette, alcohol, and marijuana use in adolescents. Related findings have been observed in a daily diary study of college students (Neal & Carey, 2007), in which higher scores on the Eysenck Impulsiveness Scale (Eysenck, Pearson, Easting, & Allsopp, 1985)—a broad measure of acting without forethought and making hasty decisions—enhanced the relation between daily intoxication and the likelihood of experiencing consequences as a result of drinking. In a more recent study examining premeditation—or the tendency to think before acting—as a moderator, higher levels of the trait buffered the effect of depressive symptoms in predicting levels of alcohol problems, and enhanced this relation at low levels of premeditation (King et al., 2011).
Although numerous prior studies have tested the unique effects of impulse control and sensation seeking in the prediction of risk behaviors (e.g., Malmberg et al., 2010; Quinn & Harden, 2013; Roberts, Peters, Adams, Lynam, & Milich, 2014), few studies to date have tested interactions between these traits in predicting risk behaviors, and these studies have not reported evidence for the developmental asymmetry proposed in the present study (i.e., high sensation seeking and poor impulse control). For instance, in assessing levels of risky sexual behaviors, impulsive decision-making—a measure that encompasses aspects of both negative urgency and planning—was less strongly associated with sexual activity while intoxicated at higher levels of sensation seeking, suggesting that being high on either trait is risky, though being high on both traits predicts no greater risk (Charnigo et al., 2013). A separate study using a measure of self-control that included items measuring “breaking habits, resisting temptation, and keeping good self-discipline” (Tangney, Baumeister, & Boone, 2004, p. 275) found that good self-control had a buffering effect on unprotected sex and with a monogamous partner, as well as on alcohol problems among heavy drinkers, at lower levels of sensation seeking, but this study did not report whether the co-occurrence of poor self-control and high sensation seeking conversely predicted higher risk (Quinn & Fromme, 2010). By using measures of sensation seeking and impulse control that are clearer analogues of reward and control systems proposed by the developmental asymmetry model that also have well-established psychometric properties (e.g., Whiteside & Lynam, 2001), we aim to test whether the co-occurrence of high sensation seeking and poor impulse control characterizes synergistic risk for externalizing behaviors across multiple forms of externalizing conduct.
In the present study, we examined the moderating role of impulse control—operationalized in the present study as premeditation—on sensation seeking in predicting indicators of high risk substance use and delinquent behavior in a cross-sectional cohort of college-age young adults. Based on prior studies of known trait predictors of externalizing behavior, we expected that sensation seeking would likely predict externalizing behavior engagement (Magid & Colder, 2007), and a lack of premeditation would be associated with both engagement and more problematic levels of such behaviors (Smith et al., 2007; Stautz & Cooper, 2013). However, a lack of premeditation may additionally reflect a deficit in regulating reward drive, and may further have an enhancing effect on sensation seeking in predicting both frequency and problem levels of externalizing behavior. As such, we hypothesized that the co-occurrence of risk levels of these traits—that is, high sensation seeking and a lack of premeditation—would further characterize those with the most problematic levels of externalizing behavior.
Method Participants
Participants (n = 491) were undergraduate students in Psychology at the University of Washington who received course credit for survey participation. Participants completed the study in a single in person computer assisted interview session. A total of 34 participants were removed from antisocial behavior analyses due to missing data in criterion variables. Excluded participants did not significantly differ from retained participants in age (b = −.37, p = .08), gender, χ2(1 df) = .55, p = .46, or Asian American versus non-Asian American ethnicity, χ2(1 df) = .31, p = .57. A total of 56.6% of the participants were female. Approximately 55% were of Caucasian ethnicity, 33% of Asian or Pacific Islander ethnicity, and the remaining 12% reported being of other ethnicities. Participant age ranged from 18 to 24 with a median age of 19; 86% of participants were between 18 and 20.
Measures
Covariates
Gender, Asian/Asian American ethnicity, and age and were entered into the models as covariates to control for potential demographic differences in risk behavior outcomes. Gender was coded 0 for females and 1 for males. Because of the relatively high proportion of Asian American participants in our sample, and the lower mean prevalence rates of alcohol and drug use among Asian American populations, we included Asian American ethnicity as a covariate, coded as 1 = Asian/Pacific Islander ethnicity, and 0 for all other ethnicities. Although prevalence rates of substance use and risk behavior may be similarly lower for other minority groups, only a small proportion of participants reported minority status (e.g., 1.8% of the sample were African American, 4.9% were Hispanic/Latino) and meaningful comparisons for these groups could not be made. Current age was measured with a single self-report item.
Sensation seeking and premeditation
Premeditation (11 items) and sensation seeking (12 items) were measured via self-report from the Urgency, Premeditation, Perseverance and Sensation Seeking (UPPS) Impulsive Behavior Scale (Whiteside & Lynam, 2001). Participants were instructed to rate how well each statement described them. Response options for all facets were on a 5-point Likert scale ranging from not at all to very much. Sample premeditation items included “My thinking is usually careful and purposeful” and “I don’t like to start a project until I know exactly how to proceed.” Sample sensation seeking items included “I like sports and games in which you have to choose your next move very quickly” and “I would enjoy fast driving.” Facet scores were computed by taking the mean of the items for that facet. Internal consistency coefficients were high for both premeditation and sensation seeking (α = .87, α = .91, respectively). We coded premeditation such that higher scores reflected higher levels of premeditation, and higher sensation seeking scores reflected higher sensation seeking. Consistent with previous findings (e.g., Cyders et al., 2009), sensation seeking and premeditation were only modestly correlated in the present sample (r = −.26).
Substance Use
Alcohol use
Participants self-reported their frequency and quantity of alcohol consumption in the past year with four items. Frequency was assessed using two items (one for beer/wine and one for hard liquor) with responses ranging from never to every day. Quantity was assessed with two items (one for beer/wine, one for hard liquor) asking how much the participant drank in the past year on a “typical” occasion, ranging from 1 to 9 or more drinks per occasion. A single alcohol use variable was computed as the sum of the products of the beer/wine quantity*frequency and the hard liquor quantity*frequency variables.
Alcohol consequences
We assessed the consequences of alcohol use in two ways. First, to assess the immediate risk posed by alcohol use, participants self-reported on 39 negative consequences related to alcohol use in the past year. A total of 27 items were from the Young Adult Alcohol Problems Screening Test (YAAPST; Hurlbut & Sher, 1992), including items such as “Have you ever been arrested for drunk driving, driving while intoxicated, or driving under the influence of alcohol?,” “Have you ever felt like you needed a drink just after you’d gotten up?,” and “Have you ever had “the shakes” after stopping or cutting down on drinking?,” reflecting traditional symptoms of alcohol abuse and dependence. Twelve negative consequences were taken from Mallett, Bachrach, and Turrisi (2008) to reflect alcohol consequences that may be common to young adults, but may or may not be traditionally represented in indices of alcohol disorders. These items include “Have you ever urinated on yourself because of your drinking?,” “Have you ever been embarrassed socially because of your drinking?,” or “Have you ever lost personal items because of your drinking?”
We also measured positive alcohol-related consequences, to assess for potentially reinforcing effects of alcohol use that might presage later escalations in drinking (Logan, Henry, Vaughn, Luk, & King, 2012; Park, Kim, & Sori, 2013). We used 14 items from the Positive Drinking Consequences Questionnaire (PDCQ; Corbin, Morean, & Benedict, 2008), and included items such as “Have you ever stood up for a friend or confronted someone who was in the wrong while drinking?” and “Have you ever felt especially confident that other people found you attractive while you were drinking?”
For both scales, 10 response options ranged from never or not in the past year to 1 time in the past year to 40 or more times in the past year. We computed pseudocounts for negative and positive alcohol consequences, reflecting the sum of past year perceived frequency of these alcohol-related consequences. We computed coefficient alphas as a measure of consistency among items for each of these scales in order to assess whether items for each reflect an underlying construct of alcohol dyscontrol (e.g., Hurlbut & Sher, 1992; Read, Merrill, Kahler, & Strong, 2007); alphas for these scales were .94 and .92, respectively.
Drug use
Illicit drug use was self-reported using 11 items measuring the frequency of using marijuana, inhalants, cocaine, stimulants, club drugs, hallucinogens, opiates, and steroids within the past year. The seven response options for past year consumption ranged from not at all to everyday. Total past year drug use was computed as a sum of drug use frequency across all the items. Reliability for this measure was .62. Because 31.4% of all participants reported marijuana use, and few reported use of illicit substances other than marijuana (17.1%), we also examined the single-item frequency of past year marijuana use as a separate risk outcome.
Drug consequences
Participants self-reported the frequency of negative drug consequences experienced within the past year. A total of 39 items assessed the number of times a consequence occurred in the past year, with 10 categories ranging from never or not in the past year to1 time in the past year to 40 or more times in the past year. Items were derived from the YAAPST (Hurlbut & Sher, 1992) and Mallett et al. (2008), and were modified to apply to drug use outcomes. Sample items included “Have you gotten into physical fights when using drugs?” and “Have you ever been arrested for driving under the influence of drugs (besides alcohol)?” Similar to the alcohol consequences variable, we computed a pseudocount variable by summing across all 39 consequence items. Reliability for this scale was .88.
Antisocial Behavior
Aggression and rule-breaking behavior
Aggression and rule-breaking behaviors were measured using the Achenbach Adult Self Report (ASR; Achenbach & Rescorla, 2003). These subscales consisted of 15 and 14 self-reported items, respectively, ranging on a 3-point scale from not true to very true or often true. Sample aggressive behavior items included “I get along badly with my family” and “I get in many fights.” Sample rule-breaking behavior items included “I hang around people who get in trouble” and “my behavior is irresponsible.” Subscale scores were computed as means, and were transformed into T scores based on national norms (Achenbach & Rescorla, 2003). Reliability for these scales were .79 and .77, respectively.
Results Analytic Strategy
Our outcome measures of annual drug and alcohol use frequency, drug and alcohol consequences, and externalizing behavior scores were highly overdispersed, with a substantial proportion of the sample reporting no occurrence of many risk-taking behaviors and few reporting at or near the maxima of these outcomes (see Table 1 for summary). As such, we explored a variety of analytic methods for modeling nonnormally distributed data. Although sum scores of ordinal data (such as self-report frequency of past year alcohol-related consequences collected on an ordered categorical scale) do not reflect true counts, their distributions behaved very much like zero-inflated count distributions, in that there were excessive zeroes, many participants with low scores, and very high skew with very few high-scoring participants. Although modeling approaches for treating ordinal data as counts exist for single-item indicators (McGinley, Curran, & Hedekr, n.d.), these methods have not yet been extended to sums of ordinal items (McGinley, personal communication). Thus, we modeled these data treating outcomes as “pseudocount” data (e.g., counts of past year alcohol and drug use and consequences), analyzed using zero-inflated Poisson (ZIP), zero-inflated negative binomial (ZINB), and negative binomial hurdle modeling (NBH) for highly zero-inflated count data (Atkins, Baldwin, Zheng, Gallop, & Neighbors, 2013; Bandyopadhyay, DeSantis, Korte, & Brady, 2011). These zero-inflated strategies are used to analyze outcome variables in two separate regression models: a logistic regression predicting the logged-odds of a binary zero versus nonzero value of the outcome, and a separate model predicting counts among those reporting nonzero values of the outcome variable. In the present context, this allowed us to predict the relative likelihood of experiencing any risk behavior compared with experiencing none at all, and separately, the “count” of these outcomes among those engaging in these behaviors. We tested all substance use and substance-related problem outcomes assuming a normal (Gaussian) distribution as a baseline model, then specified models using ZIP, ZINB, and NBH. Final model selection was determined by comparing Akaike information criteria (AIC) and Bayesian information criteria (BIC), and were tested formally using likelihood ratio tests for nested models and Vuong tests for non-nested models (Vuong, 1989). Models with lower information criteria were selected when likelihood tests were nonsignificant. Based on these results, NBH was selected for models of past year alcohol, drug, and marijuana use, and ZINB was used to model negative and positive drinking consequences as well as drug use consequences. For highly skewed continuous measures of aggression and rule-breaking behavior, semicontinuous (or two-part) regression was used for analysis (Gottard, Stanghellini, & Capobianco, 2013), and coefficients produced by this modeling strategy are interpreted similarly to NBH. Comparisons of model fit using likelihood ratio and Vuong tests for each outcome are provided in Table 2.
Sample Descriptive Statistics
Model Selection Criteria
We included age, Asian American ethnicity, and gender as covariates to control for expected group differences in problem risk behaviors. We probed significant interactions using the pick-a-point approach (Aiken & West, 1991). Probing interactions between sensation seeking and premeditation were conducted at high (+1 SD), mean, and low (−1 SD) levels of premeditation. All main predictors within the models were centered at zero to simplify the interpretation of regression coefficients (Cohen, Cohen, West, & Aiken, 2003). Effect sizes are reported as odds ratios (OR) in the likelihood portions of our models and rate ratios (RR) in the count portions, which refer to factor increases in the odds of a dichotomous outcome, and factor increases in the predicted count outcome resulting from single-unit increases in predictor values, respectively (Atkins et al., 2013). Descriptive data analyses were performed using Statistical Packages for the Social Sciences (SPSS) 20.0, and all other analyses were performed using R (R Development Core Team, 2014). Zero-inflated count models were estimated using package “pscl” (Zeileis, Kleiber, & Jackman, 2008), and semicontinuous outcomes using “mhurdle” (Carlevaro, Croissant, & Hoareau, 2012).
Premeditation as a Moderator of Sensation Seeking on Alcohol Use and Consequences
We first examined the effects of sensation seeking, premeditation, and their interaction on the quantity and frequency of past year alcohol use, negative alcohol consequences, and positive alcohol consequences. Results from these models are reported below and summarized in Table 3.
Sensation Seeking, Premeditation, and Drinking Behavior
Higher sensation seeking increased the likelihood of past year alcohol use, while premeditation decreased in the likelihood of use. In the count portion of our model, premeditation was associated with lower levels of use among alcohol-using participants. No other effects were significant.
Similar to alcohol use, sensation seeking increased the likelihood of experiencing negative alcohol consequences, and premeditation decreased the likelihood of consequences. The effects on positive alcohol consequences were similar: higher sensation seeking was associated with a higher likelihood of positive consequences, and higher premeditation decreased the likelihood.
Moreover, we observed a significant interaction between sensation seeking and premeditation predicting the counts of both negative and positive alcohol consequences. At mean levels of sensation seeking, premeditation was associated with fewer positive and negative consequences in the count portion of our model. Further, sensation seeking was associated with alcohol consequences when premeditation was low, but was unrelated to consequences when premeditation was at mean or high levels. When premeditation was low, a unit increase in sensation seeking was associated with a nearly significant 15% increase in negative consequences, RR = 1.15, p = .057, 95% CI [1.00, 1.34], and a 12% increase in positive consequences, RR = 1.12, p = .036, 95% CI [1.01, 1.25]. Figure 1 illustrates these effects.
Figure 1. The synergistic effects of sensation seeking and premeditation on substance use outcomes. Lines represent relations between sensation seeking and alcohol consequences at low (−1 SD) and high (+1 SD) levels of premeditation. Shaded regions represent simulated 95% confidence intervals. * p ≤ .05. ** p ≤ .01. *** p ≤ .001.
Premeditation as a Moderator of Sensation Seeking on Drug Use and Consequences
We next tested premeditation as a moderator of the effects of sensation seeking on drug use and consequences. Results are reported below and in Table 4.
Sensation Seeking, Premeditation, and Drug Behavior
Similar to the effects of alcohol outcomes reported above, sensation seeking was associated with a higher likelihood of drug use, while premeditation was associated with a lower likelihood. Results for the likelihood of marijuana use were similar for both sensation seeking and premeditation. Although neither sensation seeking nor premeditation predicted the level of drug use alone, we found evidence of an interaction. When premeditation was low, a unit increase in sensation seeking was associated with a 69% increase in the predicted count of past year drug use, RR = 1.69, p = .002, 95% CI [1.21, 2.38], but predicted no change in drug use when premeditation was at mean and high levels. These effects are shown in Figure 1. We did not observe this interaction when marijuana was a sole outcome.
For our drug consequences outcome, results largely mirrored those reported for drug use. Higher sensation seeking was associated with a higher likelihood of consequences, and premeditation was associated with a lower likelihood, and we observed an interaction predicting levels of drug consequences. At mean and high levels of premeditation, sensation seeking was unrelated to the level of drug consequences; however, when premeditation was low, sensation seeking trended toward an association with drug consequences, predicting a 28% increase in the count for every unit change, RR = 1.28, p = .058, 95% CI [0.99, 1.65], but this pattern was not significant—and in fact trended toward the reverse—at higher levels of premeditation, RR = 0.75, p = .096, 95% CI [0.54, 1.05]. That is, sensation seeking trended toward being protective against higher drug consequences when premeditation was high, but was a risk factor when premeditation was low. Figure 1 illustrates these effects.
Premeditation as a Moderator of Aggression and Rule-Breaking Behaviors
Finally, we tested the effects of sensation seeking, premeditation, and their interaction on aggression and rule-breaking behavior (see Table 5 for summary). Unlike our substance use outcomes, we found no main effects or interactions for aggression. Sensation seeking was a risk factor for higher levels of rule-breaking behavior, and similarly predicted a greater likelihood of reporting any rule-breaking behavior. Premeditation was unrelated to rule-breaking behavior among those reporting any rule-breaking behavior, but was protective against the likelihood of exhibiting such behavior. The interaction between sensation seeking and premeditation was not significant.
Sensation Seeking, Premeditation, and Antisocial Behavior
DiscussionThe goal of the present study was to test whether the interaction between sensation seeking and premeditation was associated with externalizing behaviors in young adulthood. Our main effect findings yielded consistent results: higher sensation seeking characterized individuals who were more likely to be alcohol and drug users within the past year, while premeditation characterized those who were less likely to use alcohol and drugs and those who drank less among alcohol-initiated participants. Moreover, our results provided moderate support for the developmental asymmetry hypothesis among substance use outcomes: a combination of high sensation seeking and a lack of premeditation characterized those with the highest rates of drinking consequences, drug use, and drug consequences, suggesting that an asymmetry between high sensation seeking and a lack of premeditation may be a critical risk indicator for heightened substance abuse among young adults.
Although previous findings have reported mixed or weak relations between sensation seeking and problematic drinking (Coskunpinar et al., 2013; Smith et al., 2007; Stautz & Cooper, 2013), our findings clarified and extended these results: the relation between sensation seeking and problem levels of drinking may be stronger among individuals who also lack premeditation. Importantly, the effects were similar whether we examined negative alcohol consequences, which likely reflect risky behavior that directly results from alcohol use, or positive alcohol-related consequences, which may serve as a marker of those at higher risk for heavier alcohol involvement (Park et al., 2013). The present findings were the first to provide direct evidence for these synergistic associations. One prior study failed to detect this interaction (Quinn & Fromme, 2010). It may be that using modeling strategies that address zero inflation and overdispersion in outcomes provided increased sensitivity to detect these effects. Second, we used trait predictors designed to measure orthogonal constructs of impulsivity (Whiteside & Lynam, 2009) that may be more appropriate psychological analogues to the neurobiological dual systems model proposed by Steinberg (2010). Third, we did not covary for drinking quantity/frequency to avoid the concern of collinearity between drinking levels and drinking consequences. Although some research suggested that sensation seeking increased the risk of substance related problems merely by increasing substance use frequency (Magid & Colder, 2007; Quinn & Fromme, 2010), our previous work suggested that high sensation seekers experience more consequences for a given level of alcohol use (King et al., 2011). Future research should attempt to disaggregate the mediating/moderating role of alcohol use more directly.
The present study was also, to our knowledge, the first to test this interaction predicting drug use. Multiple prior studies have reported relations between multidimensional constructs of trait impulsivity and multiple forms of drug use, including heavy ecstasy use (Parrott, Milani, Parmar, & Turner, 2001), cocaine use (Coffey, Gudleski, Saladin, & Brady, 2003; Moeller et al., 2002), and heroin use (Kirby, Petry, & Bickel, 1999; Madden, Petry, Badger, & Bickel, 1997), while sensation seeking has been previously associated with stimulant use (Leland & Paulus, 2005) and heroin dependence (Dissabandara et al., 2014). The present findings add to this literature: risk levels of both traits predicted a higher probability of drug use engagement, and their co-occurrence was synergistically associated with more polydrug use and problems. Moreover, although marijuana use was the single most commonly used illicit substance in our sample and both sensation seeking and premeditation predicted higher likelihoods of having used marijuana, we did not find evidence of significant main or interactive effects among marijuana-using college students. Although some previous findings have observed main effects of trait impulsivity on problematic marijuana use (e.g., Day, Metrik, Spillane, & Kahler, 2013), still others have failed to observe these relations (Butler & Montgomery, 2004; Dvorak & Day, 2014; Simons, Neal, & Gaher, 2006; Verdejo-García et al., 2008). It is possible that these and the present results may be confounded by distinctions in motivations toward marijuana use versus other illicit substances. Although young adults may use certain substances primarily to seek stimulation (e.g., MDMA; Peters, Kok, & Abraham, 2008), a proportion of marijuana users may separately use to cope with negative emotional states such as anxiety (Bonn-Miller, Zvolensky, & Bernstein, 2007); thus, the relation between marijuana use, sensation seeking, and premeditation may be particularly attenuated by separate predictors of use such as coping motives, and we encourage future studies to address these relations. Moreover, we encourage others to address transactions and interactions between sensation seeking and premeditation predicting patterns of drug use in a longitudinal sample; some prior research suggests that impulsivity may be an earlier marker for drug abuse while sensation seeking a product of abuse (Ersche, Turton, Pradhan, Bullmore, & Robbins, 2010), yet the precise nature of these relations has yet to be investigated.
The present dual systems framework hypothesizes that the developmental asymmetry explains the relative peak in criminal and antisocial conduct such as robbery, burglary, and forcible rape among young adults (Steinberg, 2013), among other risky behaviors. Although relations between aggressive behavior and poor impulse control and sensation seeking have been reported previously (Monahan et al., 2009; Wilson & Scarpa, 2011), we did not observe a synergistic interaction between these traits. There may be two reasons why we did not observe this result. First, the present data was drawn from a community sample of college-enrolled young adults, with very few respondents reporting clinically relevant levels of aggression (3.1%) or rule-breaking behavior (7.7%). Future research investigating this developmental asymmetry in more vulnerable populations, such as among incarcerated samples, may find evidence among “riskier” young adults exhibiting higher rates of antisocial conduct. Second, our measures of self-reported aggression and rule-breaking behavior may not have been sufficiently sensitive constructs to properly measure “antisocial” behavior in this population. Although these measures are considered ecologically valid (Achenbach et al., 2003), they may not capture the range of subthreshold antisocial behavior that may be observed in non- or preclinical populations. Relatedly, measures separate from antisocial conduct that are more relevant to lower-level risky behavior in college may yield more promising results, such as risky sexual conduct (Charnigo et al., 2013) and risky driving (Pharo, Sim, Graham, Gross, & Hayne, 2011).
Our findings indicate that sensation seeking increases the likelihood of engaging in multiple forms of externalizing behaviors and is also a conditioned risk indicator of externalizing problems (i.e., is unrelated directly to levels of substance use unless coupled with a lack of premeditation). These findings are consistent with a more nuanced perspective of the trait (e.g., Ravert et al., 2013): sensation seeking may confer risk for engaging in novel (and sometimes risky) behaviors, but may not be a direct indicator of risk for consistent and problematic engagement in itself. Moreover, some prior research has observed positive developmental outcomes among sensation-seekers, such as higher IQ (Bayard, Raffard, & Gely-Nargeot, 2011; Raine, Reynolds, Venables, & Mednick, 2002), psychological well-being (Ravert et al., 2013), and age-related improvements in the ability to delay gratification (Romer, 2010), suggesting that a propensity toward novelty may be adaptive and yield positive results in certain environments. We found marginal evidence of this in the present study among those experiencing drug consequences: the nature of the interaction between sensation seeking and premeditation was such that the effect of sensation seeking on drug consequences reversed in its direction depending on level of premeditation, implying that high levels of these traits might constitute protection in the context of problematic drug use. It may be the case that separate contextual factors drive sensation-seekers toward more adaptive forms of reward-driven behaviors (Romer & Hennessy, 2007); this interpretation warrants caution given that this effect was not the focus of the present analyses, though we encourage this question be addressed directly in future research.
Our results reflect an estimate of the associations between developmental asymmetry and externalizing behaviors during young adulthood. It may be that this interaction differs across development. Several factors highlight the importance of considering development in predicting externalizing behaviors from asymmetry. First, although longitudinal reports indicated that sensation seeking remains high through the mid-twenties (Harden & Tucker-Drob, 2011), studies suggested that sensation seeking peaks earlier in development, specifically during midadolescence (Romer et al., 2010; Steinberg et al., 2008). Given the protracted and linear development of impulse control, asymmetry between these two systems might therefore be greatest on average during this developmental period. Second, engaging in substance use earlier in development is less normative than drinking at older ages and may reflect greater propensity toward delinquency than college-age drinking. Evidence has indicated that earlier initiation and problematic use within adolescence were markers for substance disorder throughout the life span (King & Chassin, 2007; McGue & Iacono, 2005), and we might expect that asymmetry within this period is a particularly critical indicator of delinquency both within the period and in later life. Third, although we did not find evidence of an association between developmental asymmetry and antisocial behavior among young adults, developmental asymmetry may capture antisocial behavior that is limited to adolescence (Moffitt, 1993) and largely desists by young adulthood (Steinberg, 2013). Taken together, the effect of the developmental asymmetry on multiple forms of delinquency may be stronger during adolescence on average and may predict other period-specific externalizing behaviors, though translation of these population-level predictions to examining individual differences within adolescence remains unexplored.
The present study provides evidence that the dual systems framework does predict individual differences in problematic substance use among college-enrolled adults (Strang, Chein, & Steinberg, 2013). The present study has a number of strengths, including the application of advanced quantitative methods to appropriately specify models for low base-rate risk behavior and the application of psychometrically validated measures of trait impulsivity. The present study also has a number of limitations that should be considered. First, our results are cross-sectional, and precise causal inferences between trait impulsivity and risk behavior cannot be made. For instance, substance use may result in an increase in impulsiveness or sensation seeking over time (Ersche et al., 2011; Littlefield, Vergés, Wood, & Sher, 2012), and correlational data in either cross-sectional or longitudinal designs is not sufficient for ensuring causal precedence of impulsive traits. Second, as mentioned previously, these results represent findings among a community sample of college-enrolled young adults; two critical future directions include extending these findings to younger adolescent populations—among whom the developmental asymmetry is theoretically paramount in predicting externalizing and other risk behavior—as well as vulnerable populations such as clinical samples and incarcerated youth. Third, the present study sought to test the specific interaction between trait measures of sensation seeking and premeditation, but other dispositions toward impulsive behavior may explain additional variance in substance abuse outcomes, or may buffer or enhance the effects observed in the present study. For instance, positive and negative urgency—which reflect dispositions toward positive and negative emotion-based rash action—are critical and independent determinants of problem behaviors in themselves (Cyders & Smith, 2007) and may similarly interact with impulsive traits specified in the present study. Relatedly, we encourage future research to examine the developmental asymmetry model using behavioral measures of these constructs as well. Although support for direct overlap between laboratory-based behavioral measures and self-reported trait measures is modest, both methods of measuring these constructs have independently predicted externalizing behavior outcomes (Cyders & Coskunpinar, 2011; Sharma et al., 2014). Thus, examining interactions between reward sensitivity and impulse control measured using laboratory tasks may provide additional support for the developmental asymmetry model. Our findings underscore that dispositions toward impulsive behavior are critical in the assessment, prevention, and intervention of risk-taking behavior, and that examining the synergistic impact of these traits can provide additional insight in understanding individual differences in problem behaviors.
Footnotes 1 Negative and positive drinking consequences were highly correlated with alcohol use in the present sample (r values = 0.83 and 0.81, respectively). When use was included as a covariate, fitted probabilities for consequences were numerically 0 or 1 in the zero inflation portion due to perfect collinearity between use and consequences, and reliable estimates for predictors of interest could not be obtained.
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Submitted: August 18, 2014 Revised: February 17, 2015 Accepted: February 18, 2015
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Source: Psychology of Addictive Behaviors. Vol. 29. (3), Sep, 2015 pp. 753-765)
Accession Number: 2015-43528-006
Digital Object Identifier: 10.1037/adb0000075
Record: 123- Title:
- Prevalence and correlates of cannabis use in an outpatient VA posttraumatic stress disorder clinic.
- Authors:
- Gentes, Emily L.. VA Mid-Atlantic Mental Illness Research, Education, and Clinical Center, Durham, NC, US
Schry, Amie R.. VA Mid-Atlantic Mental Illness Research, Education, and Clinical Center, Durham, NC, US
Hicks, Terrell A.. Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC, US
Clancy, Carolina P.. Durham VA Medical Center, Durham, NC, US
Collie, Claire F.. Durham VA Medical Center, Durham, NC, US
Kirby, Angela C.. VA Mid-Atlantic Mental Illness Research, Education, and Clinical Center, Durham, NC, US
Dennis, Michelle F.. VA Mid-Atlantic Mental Illness Research, Education, and Clinical Center, Durham, NC, US
Hertzberg, Michael A.. Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC, US
Beckham, Jean C.. VA Mid-Atlantic Mental Illness Research, Education, and Clinical Center, Durham, NC, US
Calhoun, Patrick S.. VA Mid-Atlantic Mental Illness Research, Education, and Clinical Center, Durham, NC, US, Patrick.calhoun2@va.gov - Address:
- Calhoun, Patrick S., VA Mid-Atlantic MIRECC, Durham VA Medical Center, 508 Fulton Street, Durham, NC, US, 27705, Patrick.calhoun2@va.gov
- Source:
- Psychology of Addictive Behaviors, Vol 30(3), May, 2016. pp. 415-421.
- NLM Title Abbreviation:
- Psychol Addict Behav
- Page Count:
- 7
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- Bulletin of the Society of Psychologists in Addictive Behaviors; Bulletin of the Society of Psychologists in Substance Abuse
- Other Publishers:
- US : Educational Publishing Foundation
Society of Psychologists in Addictive Behaviors - ISSN:
- 0893-164X (Print)
1939-1501 (Electronic) - Language:
- English
- Keywords:
- veterans, PTSD, cannabis, trauma, substance use
- Abstract:
- Recent research has documented high rates of comorbidity between cannabis use disorders and posttraumatic stress disorder (PTSD) in veterans. However, despite possible links between PTSD and cannabis use, relatively little is known about cannabis use in veterans who present for PTSD treatment, particularly among samples not diagnosed with a substance use disorder. This study examined the prevalence of cannabis use and the psychological and functional correlates of cannabis use among a large sample of veterans seeking treatment at a Veterans Affairs (VA) PTSD specialty clinic. Male veterans (N = 719) who presented at a VA specialty outpatient PTSD clinic completed measures of demographic variables, combat exposure, alcohol, cannabis and other drug use, and PTSD and depressive symptoms. The associations among demographic, psychological, and functional variables were estimated using logistic regressions. Overall, 14.6% of participants reported using cannabis in the past 6 months. After controlling for age, race, service era, and combat exposure, past 6-month cannabis use was associated with unmarried status, use of tobacco products, other drug use, hazardous alcohol use, PTSD severity, depressive symptom severity, and suicidality. The present findings show that cannabis use is quite prevalent among veterans seeking PTSD specialty treatment and is associated with poorer mental health and use of other substances. It may be possible to identify and treat individuals who use cannabis in specialty clinics (e.g., PTSD clinics) where they are likely to present for treatment of associated mental health issues. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Comorbidity; *Military Veterans; *Posttraumatic Stress Disorder; *Substance Use Disorder; Cannabis; Drug Usage; Symptoms; Trauma
- PsycINFO Classification:
- Psychological & Physical Disorders (3200)
Military Psychology (3800) - Population:
- Human
Male
Outpatient - Location:
- US
- Age Group:
- Adulthood (18 yrs & older)
- Tests & Measures:
- Alcohol Use Disorders Identification Test DOI: 10.1037/t01528-000
Beck Depression Inventory–II DOI: 10.1037/t00742-000
Quality of Life Inventory DOI: 10.1037/t03748-000
Clinician-Administered PTSD Scale DOI: 10.1037/t00072-000
Combat Exposure Scale - Grant Sponsorship:
- Sponsor: VA Mid-Atlantic Mental Illness Research, Education, and Clinical Center, US
Recipients: No recipient indicated
Sponsor: Durham VA Medical Center, US
Recipients: No recipient indicated - Methodology:
- Empirical Study; Longitudinal Study; Quantitative Study
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- Accepted: Dec 2, 2015; Revised: Nov 1, 2015; First Submitted: May 5, 2015
- Release Date:
- 20160523
- Copyright:
- American Psychological Association. 2016
- Digital Object Identifier:
- http://dx.doi.org/10.1037/adb0000154
- Accession Number:
- 2016-24705-005
- Number of Citations in Source:
- 25
- Persistent link to this record (Permalink):
- http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2016-24705-005&site=ehost-live
- Cut and Paste:
- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2016-24705-005&site=ehost-live">Prevalence and correlates of cannabis use in an outpatient VA posttraumatic stress disorder clinic.</A>
- Database:
- PsycINFO
Prevalence and Correlates of Cannabis Use in an Outpatient VA Posttraumatic Stress Disorder Clinic / BRIEF REPORT
By: Emily L. Gentes
VA Mid-Atlantic Mental Illness Research, Education, and Clinical Center, and Durham VA Medical Center, Durham, North Carolina
Amie R. Schry
VA Mid-Atlantic Mental Illness Research, Education, and Clinical Center, and Durham VA Medical Center, Durham, North Carolina
Terrell A. Hicks
Department of Psychiatry and Behavioral Sciences, Duke University
Carolina P. Clancy
Durham VA Medical Center, Durham, North Carolina
Claire F. Collie
Durham VA Medical Center, Durham, North Carolina
Angela C. Kirby
VA Mid-Atlantic Mental Illness Research, Education, and Clinical Center, and Durham VA Medical Center
Michelle F. Dennis
VA Mid-Atlantic Mental Illness Research, Education, and Clinical Center, and Department of Psychiatry and Behavioral Sciences, Duke University
Michael A. Hertzberg
Department of Psychiatry and Behavioral Sciences, Duke University, and Durham VA Medical Center
Jean C. Beckham
VA Mid-Atlantic Mental Illness Research, Education, and Clinical Center, and Department of Psychiatry and Behavioral Sciences, Duke University
Patrick S. Calhoun
VA Mid-Atlantic Mental Illness Research, Education, and Clinical Center, and Department of Psychiatry and Behavioral Sciences, Duke University;
Acknowledgement: Emily L. Gentes is now at Butler Hospital, Providence, Rhode Island.
This work was supported by the VA Mid-Atlantic Mental Illness Research, Education, and Clinical Center and Durham VA Medical Center. Emily L. Gentes’s and Amie R. Schry’s contributions to this article were also supported by the Department of Veterans Affairs Office of Academic Affiliations Advanced Fellowship Program in Mental Illness Research and Treatment, and Jean C. Beckham’s contributions were also supported by a Research Career Scientist Award from the Clinical Science Research and Development Service of the VA Office of Research and Development. The Department of Veterans Affairs had no involvement in the study design, in the collection, analysis, and interpretation of data, in the writing of the report, or in the decision to submit the paper for publication. The views expressed in this article are those of the authors and do not necessarily reflect the position or policy of the VA or the U.S. government or any of the institutions with which the authors are affiliated.
Cannabis is the most frequently used illicit substance in the United States (Substance Abuse & Mental Health Services Administration, 2014) and has been associated with a wide range of health issues, particularly related to cardiopulmonary and mental health (Goldman et al., 2010; Moussouttas, 2004). In particular, recent research has documented high rates of comorbidity between cannabis use disorders and posttraumatic stress disorder (PTSD; Agosti, Nunes, & Levin, 2002) across both civilian and veteran populations. Among U.S. adults, PTSD is associated with increased odds of cannabis use, even when adjusting for sociodemographic variables, alcohol use disorders, nicotine dependence, co-occurring anxiety and mood disorders, and trauma type frequency (Cougle, Bonn-Miller, Vujanovic, Zvolensky, & Hawkins, 2011). Among veterans, rates of PTSD are higher in those with a cannabis use disorder compared to those with other substance use disorders (Bonn-Miller, Harris, & Trafton, 2012).
The veteran population may be at particular risk for elevated rates of cannabis use, as well as its negative effects on physical and mental health because veterans tend to report higher rates of the medical and psychological problems associated with problematic cannabis use (Hoerster et al., 2012; Kessler et al., 2014). In particular, veterans who have been exposed to combat and have PTSD may use cannabis to cope with symptoms such as anxiety, insomnia, and depression (Boden, Babson, Vujanovic, Short, & Bonn-Miller, 2013) with attempts at self-medication resulting in high rates of cannabis use in this population. Overall, rates of cannabis use disorder within the Veterans Affairs (VA) Health Care System have increased more than 50% (from 0.66% to 1.05%) from 2002 to 2009 (Bonn-Miller et al., 2012). However, utilization of specialty treatments for substance use disorders has decreased among those with a cannabis use disorder (Bonn-Miller et al., 2012). It may therefore become important to identify individuals who seek treatment in other VA clinics and may also struggle with cannabis use. Despite possible links, relatively little is known about the prevalence of cannabis use and its demographic and psychiatric correlates in veterans who present for PTSD specialty treatment.
Furthermore, research examining the consequences of cannabis use among veterans has focused almost exclusively on individuals who meet diagnostic criteria for a cannabis use disorder. However, individuals may experience negative physical health, mental health, psychosocial, and legal effects resulting from cannabis use without meeting full criteria for cannabis use disorder (Calhoun, Malesky, Bosworth, & Beckham, 2005; Fergusson, Horwood, & Beautrais, 2003; Goldman et al., 2010; Moussouttas, 2004), highlighting the importance of studying cannabis use among a broader veteran population. One recent study found that 11.5% of veterans being referred from primary care for initial behavioral health assessment reported past-year cannabis use. Age, gender, other past-year drug use, presence of alcohol use disorders, smoking status, depressive disorders, PTSD, anxiety disorders, and psychotic symptoms were each found to independently predict veterans’ cannabis use over the past year. After adjusting for demographic variables (age, race, and gender), only other substance use including past-year drug use, alcohol use disorders, and cigarette use remained associated with past-year cannabis use (Goldman et al., 2010).
The purpose of the present study was to extend Goldman and colleagues’ (2010) findings by examining the prevalence of cannabis use and its psychological and functional correlates among a large sample of veterans seeking treatment at a VA PTSD specialty clinic.
Method Participants and Procedures
Archival data were analyzed from 719 male veterans who presented at a specialty outpatient PTSD clinic at a VA hospital in the southeastern United States. Patients presenting to this clinic completed a diagnostic evaluation to assess the presence and severity of Diagnostic and Statistical Manual of Mental Disorders (4th ed.; DSM–IV; American Psychiatric Association [APA], 2000) PTSD symptoms. Participants completed all measures as part of their standard clinic evaluation. Participants who completed an evaluation between 1998 and 2008 were included in the current study. The study was determined by the institutional review board to be exempt from review because data were collected as part of standard clinic evaluation and did not include any identifying information. Demographic data are presented in Table 1. Only male veterans were included in the present study because only 23 females had available data in the sample, which limited the ability to examine possible gender differences.
Demographic, Substance Use, and Clinical Characteristics of Cannabis Users and Non-Users
Measures
As part of clinic procedures, demographic data including age, race, marital status (married vs. unmarried), employment status (employed vs. unemployed), and number of health problems experienced over the past year were collected. Additional self-report data were collected on difficulty controlling violent behavior in the past month (yes or no).
The presence of PTSD symptoms was assessed using the Clinician-Administered PTSD Scale (CAPS; Blake et al., 1995), a structured clinical interview that evaluates the frequency and intensity of the 17 symptoms of PTSD as defined in the DSM–IV (APA, 2000). Scores from the CAPS interview have been shown to demonstrate excellent reliability and validity within multiple trauma populations, and it is widely accepted as the gold standard for PTSD assessment (Weathers, Keane, & Davidson, 2001; Weathers, Ruscio, & Keane, 1999). A clinical psychologist supervised all evaluations. Interrater agreement among clinicians for PTSD diagnosis was excellent (κ = .92). The CAPS total score both overall and in each cluster (i.e., Cluster B reexperiencing symptoms, Cluster C avoidance symptoms, Cluster D hyperarousal symptoms) was computed by summing the frequency and intensity ratings for all items in each cluster. Clinicians also rated the global severity of the patient’s PTSD symptoms on the following 5-point scale: 0 (none), 1 (mild), 2 (moderate), 3 (severe), and 4 (extreme). Interrater reliability for global severity ratings was high (κ = .82).
Self-report data on cannabis and other drug use were collected through the use of a questionnaire that asked about the frequency of use of specific drugs (e.g., cannabis, amphetamines, cocaine, heroin) during the past 6 months. Response options for each drug included the following: no use, daily use, weekly use, use once every 2 weeks, use once every 3 weeks, use once every month, use once every 3 months, and use once every 6 months. There were no negative consequences directly attached to reporting substance use, although patients were instructed that all information collected during their evaluations would be made part of their medical record. Drug use self-reports have been demonstrated to be highly valid in veterans seeking help for PTSD (Calhoun et al., 2000).
The Alcohol Use Disorders Identification Test (AUDIT; Saunders, Aasland, Babor, de la Fuente, & Grant, 1993) is a 10-item measure assessing three factors: alcohol consumption, alcohol dependence, and adverse consequences of alcohol use. The range of possible scores is 0–40, with higher scores indicating increased probability of an alcohol use disorder. The AUDIT has been found to have a high level of agreement with other measures of alcohol use disorders (Babor, Higgins-Biddle, Saunders, & Monteiro, 2001). The internal consistency (i.e., Cronbach’s alpha) of the AUDIT in the current sample was .92.
The Combat Exposure Scale (CES; Keane et al., 1989) was used to assess combat exposure. The CES is a widely used 7-item, Likert-type scale designed to measure wartime trauma exposure. The total score ranges from 0 to 41 and is a sum of weighted scores. Cronbach’s alpha of the CES in the current sample was .89.
The Beck Depression Inventory—II (BDI-II; Beck, Steer, & Brown, 1996) was used to assess current depression symptoms. The BDI-II is a 21-item measure with total scores ranging from 0 to 63, where higher scores are indicative of more severe depression symptoms. Suicidality was measured using an item from the BDI-II, which participants rated on a scale from 0 to 3, where higher scores are indicative of greater suicidality. Scores from the BDI-II have been shown to be valid and reliable (Beck et al., 1996). The internal consistency of the BDI-II in the current sample was excellent (α = .91).
The Quality of Life Inventory (QOLI; Frisch, 1994, 1998) includes 32 items that assess satisfaction across 16 important life areas including health, self-esteem, goals and values, money, work, play, learning, creativity, helping, love, friends, children, relatives, home, neighborhood, and community. Participants first rate the importance of each domain on a 3-point scale from 0 (not important) to 2 (extremely important). Then they rate their satisfaction on a 7-point scale from −3 (extremely dissatisfied) to 3 (extremely satisfied). Scores range from −6 to 6, with higher scores indicative of greater quality of life. The internal consistency (i.e., Cronbach’s alpha) of the QOLI in the current sample was .89.
Statistical Analyses
All variables were screened for outliers. Descriptive statistics were calculated to characterize demographic, substance use, and psychological attributes of participants. In order to facilitate interpretation of results from logistic regression, z scores were calculated for continuous variables including combat exposure (CES), hazardous alcohol use (AUDIT total score), PTSD symptom severity (CAPS total score, CAPS reexperiencing, CAPS avoidance, CAPS hyperarousal, CAPS clinician-rated global severity score), depression (BDI-II), suicidality (BDI-II item 9), and quality of life (QOLI).
Unadjusted and adjusted logistic regression analyses were used to examine the association between marijuana use and demographic, substance use, and psychological variables. No past 6-month cannabis use served as the reference category in each model. Adjusted models examined the association of marijuana use with each variable after adjusting for age, race, service era, and combat exposure.
Results Participant Characteristics
The majority of participants (n = 658, 91.5%) in the present sample met DSM–IV diagnostic criteria for PTSD at the time of assessment. One hundred five participants (14.6%) reported using cannabis at least once during the past 6 months. Among those who reported use of cannabis in the past 6 months, 27.6% reported daily use, 35.2% reported weekly use, 8.6% reported biweekly use, 3.8% reported use every 3 weeks, 9.5% reported monthly use, 3.8% reported use every 3 months, and 11.4% reported use every 6 months.
Predictors for Past Six Months Cannabis Use
Descriptive statistics are presented in Table 1. Bivariate analyses were used to examine the relationship between demographic, substance use, and psychological variables and cannabis use (see unadjusted results in Table 2). There were no significant associations among age, race, service era, employment status, violent behavior, or number of self-reported health problems and cannabis use. Veterans who were unmarried and those who smoked cigarettes or used at least one other drug were more likely to report use of cannabis in the past 6 months. Higher levels of combat exposure, lower quality of life, and more symptoms of hazardous alcohol use, depression, and suicidal ideation were also associated with increased likelihood of using cannabis. Higher clinician-rated global PTSD severity ratings and higher levels of PTSD Cluster C avoidance symptoms were associated with increased likelihood of using cannabis, but CAPS total score, reexperiencing, and hyperarousal symptoms were not associated with cannabis use.
Association of Demographic, Substance Use, and Clinical Characteristics With Cannabis Use Among Veterans Seeking Help for PTSD
Next, logistic regression models controlling for age, race, service era, and combat exposure were run (see adjusted results in Table 2). After accounting for these variables, marital status, smoking, other drug use, hazardous alcohol use, PTSD clinician-rated global severity, depressive symptoms, and suicidality remained associated with past 6-month cannabis use. Specifically, veterans who were unmarried, those who smoked, and those who used drugs other than cannabis were more likely to report use of cannabis in the past 6 months. Greater hazardous alcohol use, greater clinician-rated global PTSD severity, more severe depressive symptoms, and higher levels of suicidality were also all associated with increased likelihood of using cannabis.
Post hoc analyses were done to further examine the correlates of daily cannabis use compared to less frequent use among participants who reported using cannabis in the past 6 months (n = 105). In this series of logistic regression analyses, less-than-weekly cannabis use served as the reference category. In unadjusted models, younger veterans and those who served in Operation Enduring Freedom (OEF)/Operation Iraqi Freedom (OIF) (compared to Vietnam) were more likely to report daily use. Veterans who smoked cigarettes were less likely to report daily cannabis use. The association between cigarette smoking and daily cannabis use remained when controlling for age, race, service era, and combat exposure.
DiscussionThe purpose of this study was to examine the prevalence of cannabis use and its psychological and functional correlates among a large sample of veterans seeking treatment at a VA PTSD specialty clinic. Overall, 14.6% of participants reported using cannabis in the past 6 months. After controlling for age, race, service era, and combat exposure, past 6-month cannabis use was associated with marital status, smoking, other drug use, hazardous alcohol use, clinician-rated global PTSD severity, depressive symptoms, and suicidality. While multiple studies have documented associations between cannabis use disorders and trauma-related symptoms (Boden et al., 2013; Bonn-Miller et al., 2012), relatively few have examined the prevalence and correlates of cannabis use outside of samples with a diagnosed substance use disorder, particularly in veteran populations. Results from the present study showed that cannabis use among veterans seeking specialty treatment for PTSD was associated with severity of mood and trauma-related symptoms, as well as with use of other substances.
These results are consistent with a self-medication theory (Boden et al., 2013) in which cannabis use may serve an avoidance function for veterans struggling with symptoms of PTSD. In addition, the association between cannabis use and Cluster C avoidance is consistent with research showing an association between substance use history and avoidance coping, which may put individuals at greater risk of PTSD (Hruska, Fallon, Spoonster, Sledjeski, & Delahanty, 2011). These findings underscore the importance of identifying individuals whose cannabis use may be serving to self-medicate, and perhaps inadvertently to perpetuate symptoms of PTSD (Bonn-Miller, Boden, Vujanovic, & Drescher, 2013).
Previous research has consistently found associations between hyperarousal symptoms and cannabis use (Bonn-Miller et al., 2013; Bremner, Southwick, Darnell, & Charney, 1996), which was not replicated in the present study. This discrepancy may reflect differences in the sample used in the present study (i.e., individuals seeking PTSD specialty treatment), compared to previous studies that have primarily included individuals diagnosed with a cannabis use disorder. Furthermore, it is notable that cannabis use in the present study was associated with clinician-rated global PTSD severity, but not with CAPS total score. This may indicate that clinicians took into account other comorbid conditions (e.g., depression, substance use) in rating global PTSD severity, which may have artificially increased its association with cannabis use.
This study extends previous research documenting past-year prevalence and correlates of cannabis use among veterans referred from primary care for behavioral health assessment. Past 6-month prevalence rates in the current study (14.6%) were slightly higher than the past-year prevalence found in previous research (11.5%; Goldman et al., 2010), which may be attributable to differences in the sample composition, including the exclusion of female participants, as well as the possibility that individuals presenting to a PTSD specialty clinic may show more severe anxiety and mood symptoms than those being referred from primary care. Furthermore, a large percentage (27.6%) of participants who reported cannabis use in the present study reported daily use. However, the sample size for daily cannabis users was relatively small and differences between daily and other users should continue be examined in future studies.
Findings from the present study may have important implications for treatment of cannabis use and associated problems within the VA system, particularly in light of recent research showing that utilization of specialty substance use treatments has decreased even as use of cannabis has increased (Bonn-Miller et al., 2012). Given that many cannabis users may not present in substance use clinics, perhaps cannabis use disorder can be identified and treated in other clinics (e.g., PTSD clinics) where these individuals are likely to present for treatment of associated mental health issues. Results from this study may also have policy implications, in an era when marijuana has been suggested as a potential treatment for PTSD and other mental health conditions, and when at least one state has PTSD as an approved condition for the use of medical marijuana. Although results from the present study are correlational and cannot imply a causal relationship, the data presented here suggest that veterans who are using marijuana may be doing more poorly across several important life domains (e.g., marital status, mental health symptoms) compared with those who report no cannabis use.
Several limitations of the present study deserve mention. First, analyses were restricted to male participants because of the small number of female veterans in the sample. In addition, data were not available to determine how many individuals in the current sample met criteria for a substance use disorder. Furthermore, additional research on the validity of veteran self-reports is needed, as prior research in this area (e.g., Calhoun et al., 2000) may not apply given the current military and VA climate surrounding substance use. Although many veterans in the present study reported on their substance use, it remains possible that the observed prevalence is an underestimate. In addition, there is a risk of Type I error. Effect sizes (odds ratios) are included to indicate the magnitude of each effect and it is encouraging that results are largely consistent with those found in previous studies. Nevertheless, associations should be replicated in future study.
Despite these limitations, the present study extends previous work by examining the prevalence and correlates of cannabis use among individuals seeking specialty PTSD treatment. It provides some of the first data on cannabis use in this patient group. More work is needed to determine the causes and consequences of cannabis use in this population.
Footnotes 1 Additional analyses were conducted to test whether marital status, smoking, other drug use, hazardous alcohol use, depressive symptoms, and suicidality remained associated with marijuana use after adjusting for clinician-rated global PTSD severity. All findings remained significant.
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Submitted: May 5, 2015 Revised: November 1, 2015 Accepted: December 2, 2015
This publication is protected by US and international copyright laws and its content may not be copied without the copyright holders express written permission except for the print or download capabilities of the retrieval software used for access. This content is intended solely for the use of the individual user.
Source: Psychology of Addictive Behaviors. Vol. 30. (3), May, 2016 pp. 415-421)
Accession Number: 2016-24705-005
Digital Object Identifier: 10.1037/adb0000154
Record: 124- Title:
- Prevalence and correlates of transactional sex among an urban emergency department sample: Exploring substance use and HIV risk.
- Authors:
- Patton, Rikki. The Substance Abuse Research Center, University of Michigan, MI, US, rpatton@uakron.edu
Blow, Frederic C.. Department of Psychiatry, University of Michigan and Department of Veteran's Affairs, Health Services Research and Development, US
Bohnert, Amy S. B.. Department of Psychiatry, University of Michigan and Department of Veteran's Affairs, Health Services Research and Development, US
Bonar, Erin E.. Department of Psychiatry, University of Michigan and Department of Veteran's Affairs, Health Services Research and Development, US
Barry, Kristen L.. Department of Psychiatry, University of Michigan and Department of Veteran's Affairs, Health Services Research and Development, US
Walton, Maureen A.. Department of Psychiatry, University of Michigan and Department of Veteran's Affairs, Health Services Research and Development, US - Address:
- Patton, Rikki, Department of Counseling, 27 South Forge Street, Akron, OH, US, 44325, rpatton@uakron.edu
- Source:
- Psychology of Addictive Behaviors, Vol 28(2), Jun, 2014. pp. 625-630.
- NLM Title Abbreviation:
- Psychol Addict Behav
- Page Count:
- 6
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- Bulletin of the Society of Psychologists in Addictive Behaviors; Bulletin of the Society of Psychologists in Substance Abuse
- Other Publishers:
- US : Educational Publishing Foundation
Society of Psychologists in Addictive Behaviors - ISSN:
- 0893-164X (Print)
1939-1501 (Electronic) - Language:
- English
- Keywords:
- HIV, emergency department, gender, substance use, transactional sex, risk
- Abstract:
- Men and women involved in transactional sex (TS) report increased rates of HIV risk behaviors and substance use problems as compared with the general population. When people engaged in TS seek health care, they may be more likely to utilize the emergency department (ED) rather than primary care services. Our goal was to examine the prevalence and correlates of TS involvement among an ED sample of men and women. Adults ages 18–60 were recruited from an urban ED, as part of a larger randomized control trial. Participants (n = 4,575; 3,045 women, 1,530 men) self-administered a screening survey that assessed past 3-month substance use (including alcohol, marijuana, illicit drugs, and prescription drugs) and HIV risk behaviors, including TS (i.e., being paid in exchange of a sexual behavior), inconsistent condom use, multiple partners, and anal sex. Of the sample, 13.3% (n = 610) reported TS within the past 3 months (64.4% were female). Bivariate analysis showed TS was significantly positively associated with alcohol use severity, marijuana use, and both illicit and prescription drug use, and multiple HIV risk behaviors. These variables (except marijuana) remained significantly positively associated with TS in a binary logistic regression analysis. The prevalence of recent TS involvement among both male and female ED patients is substantial. These individuals were more likely to report higher levels of alcohol/drug use and HIV risk behaviors. The ED may be a prime location to engage both men and women who are involved in TS in behavioral interventions for substance use and sexual risk reduction. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Drug Usage; *Emergency Services; *HIV; *Human Sex Differences; *Sexual Risk Taking
- Medical Subject Headings (MeSH):
- Adult; Emergency Service, Hospital; Female; HIV Infections; Humans; Male; Middle Aged; Prevalence; Prostitution; Risk Factors; Substance-Related Disorders; Unsafe Sex; Urban Population
- PsycINFO Classification:
- Inpatient & Hospital Services (3379)
- Population:
- Human
Male
Female - Location:
- US
- Age Group:
- Adulthood (18 yrs & older)
Young Adulthood (18-29 yrs)
Thirties (30-39 yrs)
Middle Age (40-64 yrs) - Tests & Measures:
- HIV Risk-Taking Behavior Scale
Alcohol Use Disorders Identification Test DOI: 10.1037/t01528-000
Alcohol, Smoking and Substance Involvement Screening Test DOI: 10.1037/t01526-000 - Grant Sponsorship:
- Sponsor: National Institutes of Health
Grant Number: T32 DA007267
Other Details: Ruth L. Kirschstein National Research Service Award
Recipients: No recipient indicated
Sponsor: National Institute on Drug Abuse
Grant Number: 026029
Recipients: No recipient indicated - Methodology:
- Empirical Study; Quantitative Study
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- Accepted: Sep 16, 2013; Revised: Aug 6, 2013; First Submitted: Apr 23, 2013
- Release Date:
- 20140623
- Correction Date:
- 20140728
- Copyright:
- American Psychological Association. 2014
- Digital Object Identifier:
- http://dx.doi.org/10.1037/a0035417
- PMID:
- 24955680
- Accession Number:
- 2014-24742-020
- Number of Citations in Source:
- 25
- Persistent link to this record (Permalink):
- http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2014-24742-020&site=ehost-live
- Cut and Paste:
- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2014-24742-020&site=ehost-live">Prevalence and correlates of transactional sex among an urban emergency department sample: Exploring substance use and HIV risk.</A>
- Database:
- PsycINFO
Prevalence and Correlates of Transactional Sex Among an Urban Emergency Department Sample: Exploring Substance Use and HIV Risk / BRIEF REPORT
By: Rikki Patton
The Substance Abuse Research Center, Department of Psychiatry, and School of Social Work, University of Michigan;
Frederic C. Blow
Department of Psychiatry, University of Michigan and Department of Veteran’s Affairs, Health Services Research and Development, Ann Arbor, Michigan
Amy S. B. Bohnert
Department of Psychiatry, University of Michigan and Department of Veteran’s Affairs, Health Services Research and Development, Ann Arbor, Michigan
Erin E. Bonar
Department of Psychiatry, University of Michigan and Department of Veteran’s Affairs, Health Services Research and Development, Ann Arbor, Michigan
Kristen L. Barry
Department of Psychiatry, University of Michigan and Department of Veteran’s Affairs, Health Services Research and Development, Ann Arbor, Michigan
Maureen A. Walton
Department of Psychiatry, University of Michigan and Department of Veteran’s Affairs, Health Services Research and Development, Ann Arbor, Michigan
Acknowledgement: Rikki Patton is now at the University of Akron.
This investigation was supported by the National Institutes of Health under Ruth L. Kirschstein National Research Service Award T32 DA007267 and NIDA #026029. Its contents are solely the responsibility of the authors and do not necessarily represent the official views of the NIH.
Transactional sex (TS) involvement, defined as the exchange of a sexual behavior for money, drugs, or other needs, is associated with increased likelihood of substance use issues (Clarke, Clarke, Roe-Sepowitz, & Fey, 2012; Wechsberg et al., 2009), and greater risk of contracting sexually transmitted infections (STIs), including HIV (Bobashev, Zule, Osilla, Kline, & Wechsberg, 2009; Shannon et al., 2008). The relationship between TS involvement and substance abuse is complex, with research suggesting that substance abuse may act as the antecedent behavior to TS involvement for some individuals, but as a consequence to being involved in TS for others (Belcher & Herr, 2005; Potterat et al., 1998). Further, substantial proportions of substance users report TS involvement, with 25% of male crack users and over 40% female crack-cocaine users in various samples reporting TS within the past 30 days (Edwards, Halpern, & Wechsberg, 2006; Leukefeld, 1999; Logan & Leukefeld, 2000). Regardless of the motivation for use, these prevalence rates suggest individuals who engage in TS are a population vulnerable to substance abuse problems and engaging TS-involved individuals into substance abuse prevention and intervention programs may provide a crucial avenue for addressing both their substance use and their risky sexual behaviors.
TS-involved individuals are considered a difficult-to-reach population (Benoit, Jansson, Millar, & Phillips, 2005). Prior research suggests that substance-abusing women involved in TS tend to seek health care services through the emergency department (ED) compared with other substance-abusing women (Burnette, Lucas, Ilgen, Frayne, Mayo, Weitlauf, 2008). For instance, 39%–56% of women who self-identified as sex workers reported visiting the ED recently or as a result of their TS involvement (Raymond, Hughes, & Gomez, 2001; Shannon, Bright, Duddy, & Tyndall, 2005). These findings suggest that the ED may be a prime location for engaging individuals involved in TS. Less is known about the degree to which men engaged in TS utilize health care services, although prior reports have suggested that, among a sample of men receiving substance abuse treatment, TS-involved men who were involved in TS reported greater likelihood of use of inpatient mental health services compared with other patients, but not emergency services (Burnette et al., 2008).
One study examining the utility of a brief intervention with drug-positive men and women recruited through the ED who reported either cocaine or heroin use stated that approximately 12% of patients reported TS involvement in the past 30 days (Bernstein et al., 2012). Although this study highlights the prevalence of TS involvement within an ED setting among a high-risk substance-using group, findings are limited in their generalizability and applicability to the entire ED patient population.
Present StudyThe current study aims to address the gaps in the literature by, first, evaluating the prevalence of TS involvement among broad a sample of both men and women seeking care in an ED. In addition, given the prior research regarding increased HIV risk and substance abuse associated with TS involvement (Bobashev et al., 2009; Shannon et al., 2008), we evaluated whether substance abuse and HIV risk indicators differed among individuals involved in TS compared with other ED patients. We hypothesized that those ED patients who reported TS involvement would also report higher rates of substance use and HIV-related risk behaviors than those who did not report TS.
Method Study Design and Setting
The current study used data collected as part of a screening survey aimed at identifying patients eligible for a randomized control trial of adult patients (ages 18–60) presenting to an ED located in an Academic Level 1 Trauma Center a Midwestern city with similar rates of crime and poverty as other large cities. The study was approved and conducted in compliance with Institutional Review Board (IRB) requirements. A Certificate of Confidentiality was obtained for this study. Data were collected from February, 2011 to March, 2013. Research staff approached participants and described the study; those who were interested in participating provided written informed consent and self-administered a 15-min computerized screening survey. Participants were compensated for screening with a gift valuing $1 (e.g., playing cards or hand lotion). Patients who presented with acute psychosis, acute sexual assault, medically unstable, or who were in police custody were excluded from screening.
Measures
Transactional sex involvement
TS involvement was assessed using the participant’s responses to the following question from the HIV Risk-taking Behavior Scale (HRBS; Ward, Darke, & Hall, 1990)—“In the past three months, how often have you used condoms when you have been paid for sex?” Respondents who answered “no paid sex” were recoded into the group labeled non-TS group and all other respondents were recoded into the group labeled as TS group.
Demographic variables
Gender, race, age, household income, marital status, current employment, sexual orientation, and education level were queried using items from validated surveys (e.g., National Survey of Drug Use and Health, Office of Applied Studies, 2009; Psychiatric Outcomes Module: Substance Abuse Outcomes Module, Smith et al., 1996; Global Appraisal of Individual Needs, version 5.4.0., 2006).
Risk Variables
Reason for ED visit
Participants were asked a yes/no question regarding whether or not their visit to the ED was injury related, referring to cuts, bruises, broken bones, and so forth.
Alcohol and drug use
Alcohol use severity over the past 3 months was assessed with the 10-item Alcohol Use Disorders Identification Test (AUDIT; Saunders, Aasland, Babor, De La Fluente, & Grant, 1993). Questions assessed frequency of alcohol use, number of drinks consumed on a typical drinking day, frequency of having five or more drinks, and negative consequences due to drinking. Participants’ responses on these items were summed to create a single total score reflecting alcohol use severity (Saunders et al., 1993). Cronbach’s alpha for this measure in the current sample was .90.
The Alcohol, Smoking and Substance Involvement Screening Test (ASSIST; WHO ASSIST Working Group, 2002) was used to measure frequency of drug use. Respondents were asked if they had used the following drugs at least once in the past 3 months—cocaine, marijuana, methamphetamines, hallucinogens, inhalants, prescription stimulants, prescription sedatives, prescription opioids, street opioids, or any other drugs. Due to the distribution of the data, with low frequencies of use for all illicit drugs, excluding marijuana, and nonmedical use of prescription drugs, these variables were refined into the following groups representing using at least once in the past 3 months: (a) marijuana, (b) other illicit drugs, and (c) nonmedical use of prescription drugs.
HIV risk behaviors
Sexual and drug use HIV risk behaviors during the past 3 months were assessed using items selected from the HIV Risk-Taking Behavior Scale (HRBS; Ward, Darke, & Hall, 1990). Specifically, injection drug use, number of sexual partners, inconsistent condom use, and anal sex were queried. Each variable was dichotomized to denote engaging in the risky behavior within the past 3 months. STIs were measured by asking respondents if a doctor or other medical professional ever told the patient that they had an STI, such as chlamydia, gonorrhea, herpes, or syphilis. Respondents answered yes or no to this question (see Substance Abuse & Mental Health Services Administration, 2009).
Data Analysis Plan
Data were analyzed using SAS version 9.3 (SAS Institute Inc., 2012). First, demographic characteristics of the sample were examined. Variable distributions were examined for normality and appropriate statistics were used. Bivariate analyses, including chi-square and independent samples t tests, were conducted to determine if there were significant differences between the TS group and other participants on demographic characteristics, substance use, and HIV risk behaviors. Finally, a hierarchical logistic regression was conducted to determine the associations of TS involvement with demographic and risk variables. A hierarchical model was used in order to control for demographic characteristics in later steps. Demographic factors were entered on Step 1, followed by substance use variables on Step 2, with sex risk behaviors entered on Step 3. All variables that were significant in bivariate analyses were retained in the regression model. Model fit statistics indicated no evidence of multicollinearity.
Results Sample Characteristics
As part of the larger randomized controlled trial (RCT), 6,161 individuals were approached for screening, of whom, 4,575 (74.3%) agreed to complete the screening survey. Respondents completing the screening survey were compared with those who were missed and who refused on gender and race. Males were more likely to be missed (χ2 = 94.1; p < .0001) and to refuse participation (χ2 = 30.95; p < .0001). There were no significant differences by race based on those missed (χ2 = 2.576; p = .11) or refused (χ2 = 3.526; p = .06). Per privacy-related policies of the local IRB, we were not able to collect any additional data about characteristics of patients who refused without written informed consent. See Table 1 for sample characteristics.
Frequencies and Bivariate Analysis for the Whole Sample and Subsamples
Bivariate Analysis
See Table 1 for a full summary of findings from the bivariate analyses. Chi-square/t test analyses indicated there were several significant differences between the TS group and the non-TS group. Participants in the TS group were more likely to report an educational level of high school completion or below (χ2 = 22.9; df = 1), an annual income more than $20,000, and to be married (χ2 = 10.8; df = 1). The TS group had higher scores on all substance use and HIV risk measures as compared with the non-TS group. There were no significant differences regarding gender, race, age, or sexual orientation between the two groups.
Regression Analysis
The hierarchical logistic regression analysis was conducted in order to examine the relationship between transactional sex involvement and Step 1: demographic characteristics (gender, race, educational level, income, marital status, and ED presentation); Step 2: substance use variables (alcohol use severity, any marijuana use, any other illicit drug use, and any prescription drug use); and Step 3: sexual risk behaviors (inject drugs, diagnosed with an STI, number of sexual partners, inconsistent condom use, and anal sex). Although gender and race were not significant in the bivariate analysis, these variables were included in the multivariate analysis due to the strong evidence in prior literature regarding the higher probability of women and racial minorities engaging in transactional sex behaviors. Model fit statistics indicated that each step improved the model (see Table 2). In Step 1, being Caucasian and income (making less than $20,000) were negatively associated with TS involvement (AOR = 0.80 and 0.83, respectively). Having a high school education or less (AOR = 1.59) and being married or living together as married (AOR = 1.43) were positively associated with TS involvement. No other demographic variables were significant.
Hierarchical Binary Logistic Regression Assessing Correlates of Transactional Sex Involvement
With the addition of substance abuse variables in Step 2 the following variables were positively associated with TS involvement: alcohol use severity (AOR = 1.03), illicit drug use (AOR = 2.62), prescription drug use (AOR = 1.53), educational status (AOR = 1.58), and marital status (AOR = 1.53). Race (being Caucasian) and income were negatively associated with TS involvement (AOR = 0.78 and 0.79, respectively).
In the final model including HIV-risk variables, the following variables were positively associated with TS involvement: alcohol use severity (AOR = 1.03), illicit drug use (AOR = 2.19), prescription drug use (AOR = 1.60), ever injecting drugs (AOR = 1.70), inconsistent condom use (AOR = 9.75), and engaging in anal sex (AOR = 1.77). Individuals involved in TS were less likely to be Caucasian (AOR = 0.75), earn less than $20,000 annually (AOR = 0.68), and to ever be diagnosed with an STI (AOR = 0.58), and were more likely to report an education level of high school diploma or less (AOR = 1.65). Nonsignificant variables in the final model included gender, marital status, reason for presenting to the ED, marijuana use, and having multiple sexual partners in the past 3 months.
DiscussionThis study presents novel findings regarding the prevalence and correlates of TS involvement within an urban ED sample of adult men and women. Findings indicated that 13.3% of patients sampled had engaged in TS within the past 3 months and, of those, gender distribution was similar to the larger sample. Findings also showed that patients involved in TS were more likely to report substance use and HIV-risk behaviors compared with other patients.
That more than one in 10 patients presenting to this urban ED reported recent TS involvement has several implications for engagement and treatment. Most research examining TS involvement includes substance-abusing samples only, with rates of TS involvement ranging from 12%–44% (e.g., Bernstein et al., 2012; Burnette et al., 2008). The current sample included individuals with and without substance abuse problems and the prevalence was still within the range of substance-abusing only samples. Thus, the ED may be a useful venue for engaging individuals involved in transactional sex in brief interventions without limiting services to only those individuals who also present with substance abuse problems. Additionally, the current findings showed that TS involvement was not associated with gender, suggesting that both men and women involved in TS may be reached in one location—in the ED.
The present findings for ED patients also support previous reports of increased alcohol and illicit drug use among individuals involved in TS in the general population (Clarke et al., 2012; Wechsberg et al., 2009). A unique finding from this study is that TS involvement was related to increased odds of misusing prescription drugs, including opioids and sedative/hypnotics. Given these findings and the recent increases of prescription drug misuse throughout the United States (Gu, Dillon, & Burt, 2010; SAMHSA, 2009), additional research is needed to understand this association in order to appropriately tailor intervention strategies to address this emerging pattern of substance use. Prior research suggests that individuals who engage in TS may have higher levels of substance use for multiple reasons, including coping with the stresses related to TS involvement (Belcher & Herr, 2005; Burnette et al., 2008; Wechsberg et al., 2009). Alternatively, TS may be partially a result of greater involvement in substance use among impoverished populations as a means to maintain substance use (Clarke et al., 2012; Potterat et al., 1998). Although the causal nature of this relationship requires future study, our findings suggest that the ED may be an appropriate venue for interventions addressing substance use and TS, both of which are associated with risk for STIs and HIV infection, among this vulnerable population.
Finally, the present findings also showed a significant association between TS and HIV-risk behaviors, including injecting drugs, STI diagnosis, inconsistent condom use, and anal sex among patients recruited from the ED, which are consistent with prior work with substance-using samples (Bobashev et al., 2009; Shannon et al., 2008). Interestingly, current findings indicate that men and women who engage in TS are less likely to have received a formal diagnosis of a STI, despite also being more likely to report inconsistent condom use. This may not necessarily reflect a true decreased risk of STI in this population, however, because it is possible that individuals from this vulnerable population may have more barriers to STI screening and treatment services (Kurtz, Surratt, Kiley, Marion, & Inciardi, 2005). Overall, current findings suggest that the ED may be a relevant site for intervention efforts focused on sexual risk reduction among men and women, particularly those who engage in TS given the high prevalence (91.8%) of inconsistent condom use, one of the most potent predictors of HIV/STI.
Limitations and Future Directions
Although the present study augments current literature by examining the prevalence and correlates of TS involvement among men and women in the ED, several limitations should be noted. The criterion we used to define TS was limited by the use of a single item assessment regarding condom use with partners who paid for sex. Although this question asked about recent involvement in exchanging sex for money, the depth of involvement in sex work for these participants is unknown. In addition, this question only inquired about being paid for sex, and may not have identified those who trade sex for drugs or other goods. Further, one of the exclusion criteria for the original study was presenting to the ED for acute sexual assault. Individuals involved in exchanging sex for money experience repeated sexual assaults (Dalla, Xia, & Kennedy, 2003; Karandikar & Prospero, 2010) and these exclusion criteria may have biased the sample, producing a conservative estimate of the prevalence of TS involvement among ED patients. Despite these limitations, the results showed that 13.3% of individuals attending the ED, including both men and women, report engaging in TS. Additionally, although our sample was racially diverse, these results may not be generalizable to other populations or racial/ethnic groups (e.g., Hispanics, Asians), or to other geographic locales due to recruitment from a single site within an ED located in an economically depressed urban area. Further, the analysis was cross-sectional in design, thereby limiting our ability to understand causal relationships among TS and substance use or other factors.
To address these limitations, future research should explore the relationship between TS involvement and use of the ED longitudinally in order to determine how these individuals use the ED to meet their needs and their connection to other services, such as substance abuse treatment. More in-depth assessment of TS involvement is warranted to allow further understanding of how interventions can be developed to effectively intervene with individuals in the ED who engage in high-risk sexual behaviors and substance use. Additionally, given that so many men receiving services in the ED also reported TS involvement, it may be helpful to explore gender differences in TS to inform prevention, behavioral intervention, and medical screening and treatment of STIs. Focusing on these individuals, defined as a difficult-to-reach population, for intervention services in the ED may prove to be a fruitful area for research and practice. Future research is needed to develop and implement combined HIV risk and substance use intervention programs tailored for patients in the ED engaged in both behaviors.
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Submitted: April 23, 2013 Revised: August 6, 2013 Accepted: September 16, 2013
This publication is protected by US and international copyright laws and its content may not be copied without the copyright holders express written permission except for the print or download capabilities of the retrieval software used for access. This content is intended solely for the use of the individual user.
Source: Psychology of Addictive Behaviors. Vol. 28. (2), Jun, 2014 pp. 625-630)
Accession Number: 2014-24742-020
Digital Object Identifier: 10.1037/a0035417
Record: 125- Title:
- Problem gambling and violence among community-recruited female substance abusers.
- Authors:
- Cunningham-Williams, Renee M., ORCID 0000-0002-3940-3216. George Warren Brown School of Social Work, Washington University, St. Louis, MO, US, williamsr@wustl.edu
Ben Abdallah, Arbi. Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, US
Callahan, Catina. Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, US
Cottler, Linda. Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, US - Address:
- Cunningham-Williams, Renee M., George Warren Brown School of Social Work, Washington University, One Brookings Drive, St. Louis, MO, US, 63130, williamsr@wustl.edu
- Source:
- Psychology of Addictive Behaviors, Vol 21(2), Jun, 2007. pp. 239-243.
- NLM Title Abbreviation:
- Psychol Addict Behav
- Page Count:
- 5
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- Bulletin of the Society of Psychologists in Addictive Behaviors; Bulletin of the Society of Psychologists in Substance Abuse
- Other Publishers:
- US : Educational Publishing Foundation
Society of Psychologists in Addictive Behaviors - ISSN:
- 0893-164X (Print)
1939-1501 (Electronic) - Language:
- English
- Keywords:
- problem gambling, substance abuse, minority women, violence, depression
- Abstract:
- Problem gambling (PG) may be associated with depression, victimization, and violence characterizing a substance-abusing lifestyle. The study explored associations of PG with these correlates among heavy-drinking and drug-using out-of-treatment women recently enrolled in 2 National Institutes of Health-funded, community-based HIV prevention trials. Female substance abusers with PG (n = 180) and without PG (NPG; n = 425) were examined according to the criteria of the Diagnostic and Statistical Manual of Mental Disorders (4th ed.; American Psychiatric Association, 1994). Whereas PGs had higher rates of each correlate than did NPGs, significant associations existed for antisocial personality disorder, specifically for violent tendencies. Logistic regression indicated that substance abusers with violent tendencies were about 3 times as likely as those without such tendencies to be PGs, after controlling for sociodemographics. Future research addressing whether underlying constructs, confounding variables, or interactions exist will further specify PG risk and inform prevention and intervention efforts. (PsycINFO Database Record (c) 2017 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Drug Abuse; *Female Criminals; *Major Depression; *Pathological Gambling; *Violence
- Medical Subject Headings (MeSH):
- Adult; Alcoholism; Comorbidity; Crime Victims; Depressive Disorder; Female; Gambling; Humans; Logistic Models; Substance-Related Disorders; Violence
- PsycINFO Classification:
- Behavior Disorders & Antisocial Behavior (3230)
- Population:
- Human
Female - Location:
- US
- Age Group:
- Adulthood (18 yrs & older)
- Tests & Measures:
- Washington University--Risk Behavior Assessment
Diagnostic Interview Schedule
Violence Exposure Questionnaire - Grant Sponsorship:
- Sponsor: National Institute on Drug Abuse
Grant Number: K01 DA 00430
Recipients: Cunningham-Williams, Renee M.
Sponsor: National Institute on Drug Abuse
Grant Number: R01 DA 11622
Recipients: Cottler, Linda
Sponsor: National Institute on Alcohol Abuse and Alcoholism
Grant Number: R01 AA 12111
Recipients: Cottler, Linda
Sponsor: National Institute of Mental Health
Grant Number: 5P30 MH068579
Recipients: No recipient indicated
Sponsor: Washington University School of Medicine, US
Other Details: Karen L. Dodson, Managing Editor and Director of Academic Publishing Services
Recipients: No recipient indicated - Conference:
- Annual Meeting of the College on Problems of Drug Dependence, Jun, 2004, San Juan, PR, US
- Conference Notes:
- An earlier version of this article was presented at the aforementioned conference.
- Methodology:
- Empirical Study; Quantitative Study
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- Accepted: Sep 3, 2006; Revised: Sep 1, 2006; First Submitted: Nov 14, 2005
- Release Date:
- 20070611
- Correction Date:
- 20170306
- Copyright:
- American Psychological Association. 2007
- Digital Object Identifier:
- http://dx.doi.org/10.1037/0893-164X.21.2.239
- PMID:
- 17563144
- Accession Number:
- 2007-08148-013
- Number of Citations in Source:
- 19
- Persistent link to this record (Permalink):
- http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2007-08148-013&site=ehost-live
- Cut and Paste:
- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2007-08148-013&site=ehost-live">Problem gambling and violence among community-recruited female substance abusers.</A>
- Database:
- PsycINFO
Problem Gambling and Violence Among Community-Recruited Female Substance Abusers
By: Renee M. Cunningham-Williams
George Warren Brown School of Social Work, Washington University;
Arbi Ben Abdallah
Department of Psychiatry, Washington University School of Medicine
Catina Callahan
Department of Psychiatry, Washington University School of Medicine
Linda Cottler
Department of Psychiatry, Washington University School of Medicine
Acknowledgement: An earlier version of this article was presented at the annual meeting of the College on Problems of Drug Dependence, San Juan, Puerto Rico, June 16, 2004. This work was supported in part by National Institute on Drug Abuse Grant K01 DA 00430 to Renee M. Cunningham-Williams and Grant R01 DA 11622 to Linda Cottler and by National Institute on Alcohol Abuse and Alcoholism Grant R01 AA 12111 to Linda Cottler. Technical assistance was provided by the Center for Mental Health Services Research at the George Warren Brown School of Social Work, Washington University, with funding from National Institute of Mental Health Grant 5P30 MH068579 and by Karen L. Dodson, Managing Editor and Director of Academic Publishing Services, Washington University School of Medicine. We also acknowledge the contributions of the project staff and the participants of the Women Teaching Women and Sister-to-Sister projects.
High rates of co-occurring disorders are clearly evident among users of illegal substances (Compton, Thomas, Conway, & Culliver, 2005). Several investigations have found high rates of depression and suicidal ideation (Cottler, Campbell, Krishna, Cunningham-Williams, & Ben-Abdallah, 2005), antisocial and criminal activity (Fishbein, 2000), violent perpetration and victimization (Chermack, Walton, Fuller, & Blow, 2001), and trauma and posttraumatic stress disorder (Fullilove et al., 1993). In fact, there appears to be an intersection of several of these behaviors and disorders for African American female substance abusers (Johnson, Cunningham-Williams, & Cottler, 2003).
According to the Diagnostic and Statistical Manual of Mental Disorders (4th ed.; DSM–IV; American Psychiatric Association, 1994), substance abusers are also at increased risk for gambling problems (National Research Council, 1999), with rates of gambling problems and pathological gambling disorder (PGD) being 22% and 11%, respectively (Cunningham-Williams, Cottler, Compton, Spitznagel, & Ben Abdallah, 2000). Additional vulnerabilities for gambling problems include racial/ethnic minority status, poverty, and male gender (Welte, Barnes, Wieczorek, Tidwell, & Parker, 2002). There is a dearth of research on problem gambling (PG) risk specifically for women, particularly those of color who abuse substances, despite their differing gambling profiles (Tavares et al., 2003), although the National Epidemiologic Survey on Alcohol and Related Conditions found associations of DSM–IV PGD with substance use disorder and internalizing disorders, such as major depressive episode and generalized anxiety disorder, that were stronger for women than for men (Petry, Stinson, & Grant, 2005).
This article explores and describes for the first time the association of PG with depression, victimization, and violence in a sample of out-of-treatment, female substance abusers who are predominately poor, young, and African American. We specifically hypothesize that female substance abusers with PG will have significantly higher rates of depression, victimization, and violence exposure compared to those without PG (NPG).
Method Sample and Study Description
Community-based outreach methods (Cunningham-Williams et al., 1999) were used to recruit women 18 years of age and older for enrollment in one of two National Institutes of Health–funded, community-based, HIV prevention trials developed and conducted from 1998–2004 in St. Louis, Missouri: (a) Women Teaching Women (n = 550), which targeted cocaine-, opiate-, and amphetamine-using women determined by a positive urinalysis or fresh injection track marks; and (b) Sister-to-Sister (n = 376), which targeted urine drug-negative women who were heavy alcohol users with scores ≥4 on the Alcohol Use Disorders Identification Test (Babor, de la Fuente, Saunders, & Grant, 1992).
Trained nonclinicians administered the computerized version of the Washington University–Risk Behavior Assessment at Week 1 (Baseline 1) and the computerized Diagnostic Interview Schedule (C-DIS, Version 4; Robins et al., 1999) at Week 2 (Baseline 2). Interviewers also collected additional information at Week 2 on psychiatric disorders (including gambling problems, depression, antisocial personality disorder [ASPD], victimization, and other aspects of violence, life events, and cocaine-related locus of control and expectancies). After the assessments, at Week 2, women received sexually transmitted disease test results and standard posttest counseling, with referrals offered for HIV-positive women (n = 22) who were not randomized for the study intervention. Participants were reinterviewed at 4 and 12 months postintervention and were remunerated for their time. All procedures were approved by the Washington University Institutional Review Board.
For this article, we used three composite measures as independent variables, namely lifetime major depression, violence, and victimization. Using the C-DIS for PGs (i.e., with 1–10 DSM–IV PGD criteria) and for NPGs (i.e., 0 criteria), we assessed lifetime reports of DSM–IV major depression that includes five items assessing suicidality. For this article, we were also interested in two additional composite measures, namely violence and victimization. Therefore we not only assessed DSM–IV ASPD but also characterized violence in terms of nonviolent antisocial acts and violent tendencies, using selected items from both these criteria and from the self-developed Violence Exposure Questionnaire, a compilation of violence-related items from several investigations by others in the field. Violence was also characterized by the final composite variable of childhood victimization before age 15 as operationalized by additional Violence Exposure Questionnaire items.
Statistical Analyses
We used SAS Release 8.2 for univariate and chi-square analysis of the association of PG with composite measures of depression, violence, and victimization. We also tested the hypothesis of increased risk for PG compared to NPG, using four ordered logistic models based on the proportional odds assumption. In Model I, we controlled for the effects of sociodemographics and included only those composite measures found to be significantly related to PG after post hoc Bonferroni correction. We then further specified Model I in three subsequent models (Models II–IV) by examining the individual items comprising the significant composite measures included in Model I. We then compared with Model I the model fit statistics of these latter models in order to determine the best-fitting and most parsimonious model of PG risk in this sample.
Because women enrolled in both studies were recruited from the same target areas and did not significantly differ in sociodemographics or gambling behaviors, we combined them into one sample for further analysis. The final combined study sample, n = 849 (Sister-to-Sister, n = 348; Women Teaching Women, n = 501), excludes 55 women with incomplete baseline interviews. For this article, we further excluded women with incomplete gambling data (n = 12). Additionally, we excluded those who reported gambling or betting five or fewer times during their lifetimes (n = 232) because this was the screening threshold that admitted respondents to this section of the C-DIS. We also wanted to be consistent with the addiction section of the C-DIS. Further, this threshold was chosen at the time of the Epidemiological Catchment Area study (Robins & Regier, 1991) to represent a group that did not have clinically meaningful symptoms. The final data analysis sample is 605.
ResultsSociodemographically, PGs did not differ significantly from NPGs in that these female substance abusers were young, never-married mothers who were jobless in the previous 12 months, poor, and with low education (mean age: NPG = 36.3 years, SD = 9.0; PG = 36.0 years, SD = 8.1; never married: NPG = 35.6%, PG = 35.6%; ≥1child: NPG = 78.4%, PG = 84.4%; jobless in past 12 months: NPG = 49.7%, PG = 56.1%; annual household income ≥$3,999: NPG = 43.2%, PG = 45.0%; >high school education: NPG = 50.6%, PGs = 57.2%). Yet, this predominately African American sample was statistically more represented among the PGs than the NPGs (87.8% vs. 76.2%; χ2 = 10.40, p = .0013).
The majority of the sample did not experience any gambling problems (0 criteria: 70.25%, n = 425). However, 21.16% of the sample met between 1 and 4 DSM–IV PGD criteria (1 criterion: 7.93%, n = 48; 2 criteria: 5.79%, n = 35; 3 criteria: 4.63%, n = 28; 4 criteria: 2.81%, n = 17), and the remaining 8.59% of the sample met the DSM–IV PGD threshold of 5 or more criteria (8.59%, n = 52). The first gambling problem was experienced as early as age 10, and the average age of onset was 28.6 years (SD = 8.86; range = 10���50). Although less than 10% of PGs met criteria for DSM–IV PGD (n = 52), PGs averaged a clinically significant number of criteria, falling just below the diagnostic cutoff of 5 criteria for PGD (M = 3.47; SD = 2.45; range = 1–10).
Associations of Gambling Problems With Depression, Victimization, and Violence
Although PGs had a higher lifetime major depression rate than NPGs, this difference was not statistically significant (Table 1). Similarly, although nearly half of the sample experienced being a victim of violence, there was no significant variation by PG status. However, we also considered a final composite variable of violence that we broadly conceptualized as nonviolent and violent antisocial behaviors operationalized by DSM–IV ASPD criteria. ASPD was significantly associated with PG (at the Bonferroni-adjusted significance level of p ≤ .02). In examining the items individually, our findings showed that PGs were no more likely than NPGs to have higher rates of the nonviolent antisocial acts, yet the opposite was true for items indicating violent tendencies. For example, even after post hoc correction, we found that although PGs were no more likely to own a gun, they were more likely than NPGs to have access to a gun and to carry one. Furthermore, compared to NPGs, PGs in this sample were more likely to be irritable or aggressive, to lack remorse, to threaten to hit or throw something at someone, and to have used drugs before their last fight.
Lifetime Depression, Victimization, and Violence Among Female Substance Abusers With and Without Gambling Problems (n = 605)
Logistic Model of Risk for Gambling Problems
Table 2 shows four separate logistic regression models predicting risk for PG, using NPG as a reference category. In the first multivariate model examined (Model I), we controlled for sociodemographics (i.e., age, race/ethnicity, education) and more closely examined the unique contribution of ASPD, as it was the only significant composite variable, even after Bonferroni correction, among the three composite variables bivariately examined. Logistic regression results indicated that after controlling for sociodemographics, there was an increased risk for PG among those with ASPD (odds ratio = 3.25; Model I). We further specified these results by examining the violent tendency subscale both with (Model II) and without (Model III) the subscale of nonviolent antisocial acts. Although Model III had a slightly better fit for these data than did Model II, neither model was superior to Model I with its larger likelihood ratio χ2(30.36, p < .0001); larger Nagelkerke’s R2 (0.07); and smaller Akaike information criterion (712.385).
Logistic Regression Predicting PG Status of Female Substance Abusers (n = 605)
Finally, we used Model IV to investigate whether the inclusion of only the individual violent tendency items (rather than the subscale) would provide a superior model of PG risk in this sample. Our findings show that although Model IV was the strongest model examined, in that it had better model fit statistics (likelihood ratio χ2 = 56.76, p < .0001; Nagelkerke’s R2 = 0.1369; AIC = 650.328), with 14 degrees of freedom, it was not the most parsimonious model. Furthermore, among the 11 violent tendency items in Model IV, only drug use before engaging in a fight remained as a significant predictor of increased PG risk. Thus, Model I, which showed that those with ASPD were about three times as likely as those without to be a PG, was the best model of PG risk for this sample of female substance abusers.
DiscussionThis is the first report testing the hypothesized association of PG with depression, victimization, and violence among a sample of primarily African American, low-income, out-of-treatment, female substance abusers. We found that violence in many forms, particularly in the form of antisocial behaviors and ASPD, permeates the lives of female substance abusers, which concurs with findings from other research (Fullilove et al., 1993). Although we did not find a significant association of victimization with PG, we did find that after controlling for sociodemographics, a model of violence in the form of ASPD was significantly associated with the increased likelihood of PG in this sample, thus supporting our hypothesis. Furthermore, although this model was the best model tested for understanding PG risk in this sample, it was primarily driven by items relating to violent tendencies, particularly the intersection of drug use and fighting behavior. These results imply that screening and intervention efforts need to be specifically targeted to out-of-treatment female substance abusers at highest risk for PG, particularly those who are African American and have violent tendencies. Assessing ASPD generally and violent tendencies specifically may be prudent not only for preventing potential aggressive and violent behavior among female substance abusers but also for impacting PG behavior among them. Future research that teases out underlying factors and/or significant interactions may further specify PG risk among female substance abusers.
The data are consistent with other investigations of gambling behavior within a substance abusing population showing a high prevalence of both PG and PGD (McCormick, 1993). Our finding of high rates of depression among substance abusers and among substance abusers who gamble is supported by the work of others using different measurement tools and samples drawn from treatment settings (Maccullum & Blaszczynski, 2003) and from the general population (Petry et al., 2005). Yet, the hypothesized relationship of significant associations of PG with depression was unsupported in this sample, potentially due to methodological differences in measurement and in sampling strategies used in this study and in the work of others.
These results are presented in the context of several important limitations. First, although this is a unique and understudied sample, the results cannot be generalized to other substance abusers (e.g., men and substance abusers in treatment) or to the general population of gamblers. Also, although the average number of criteria met in this sample fell within the subsyndromal level (i.e., <5 criteria), the small sample of those meeting criteria for DSM–IV PGD precluded a plan that separately analyzed risk specifically for PGD. Although effective for showing increased risk at the binary level (i.e., no gambling problems vs. gambling problems), combining the two groups (those with ≥5 criteria and those with 1–4 criteria) into a single PG group does not allow for additional specificity regarding increased risk according to PG severity. Future research with a larger sample of more severe problem gamblers is warranted for further delineation of risk for PGD.
Additionally, these findings result from a secondary analysis using data combined from two studies that were neither designed as gambling studies nor exclusively comorbidity studies among substance abusers. We also did not have data on more specific explanatory variables (e.g., gambling activity type, frequency, personality traits, and risk-taking), and some included variables lacked psychometric data, thus potentially affecting the results. Including some otherwise excluded variables could have allowed for an exploration of the propensity for behavioral disinhibition, to which depression, victimization, violence, and PG may each contribute (Martins, Tavares, da Silva, Galetti, & Gentil, 2004).
Despite these limitations, our findings underscore the need for increased attention to the role of PG in the lives of minority female substance abusers, given that nearly 10% of them have met enough criteria to be diagnosed with lifetime DSM–IV PGD. Furthermore, our results show that not only are the lives of those in the sample characterized by violence in general but also that ASPD (specifically tendencies toward violence in the form of using drugs before fighting) is especially predictive of PG even after controlling for sociodemographic factors. Future research may be able to explore whether PG is the result of “escape gambling” for female substance abusers who are attempting to deal with experiences of violence in their lives or just one of several risky/antisocial behaviors accompanying untreated substance abuse among minority women.
References American Psychiatric Association. (1994). Diagnostic statistical manual of mental disorders (4th ed.). Washington, DC: Author.
Babor, T. F., de la Fuente, J. R., Saunders, J., & Grant, M. (1992). AUDIT. The Alcohol Use Disorders Identification Test. Guidelines for use in primary health care. Geneva, Switzerland: World Health Organization.
Chermack, S. T., Walton, M. A., Fuller, B. E., & Blow, F. C. (2001). Correlates of expressed and received violence across relationship types among men and women substance abusers. Psychology of Addictive Behaviors, 15, 140–151.
Compton, W. M., Thomas, Y. F., Conway, K. P., & Culliver, J. D. (2005). Developments in the epidemiology of drug use and drug use disorders. American Journal of Psychiatry, 162, 1494–1502.
Cottler, L. B., Campbell, W., Krishna, V. A. S., Cunningham-Williams, R. M., & Ben-Abdallah, A. (2005). Predictors of high rates of suicidal ideation among drug users. Journal of Nervous and Mental Disease, 193, 431–437.
Cunningham-Williams, R. M., Cottler, L. B., Compton, W. M., Desmond, D. P., Wechsberg, W., Zule, W. A., & Deichler, P. (1999). Reaching and enrolling drug users for HIV prevention: A multi-site analysis. Drug and Alcohol Dependence, 54, 1–10.
Cunningham-Williams, R. M., Cottler, L. B., Compton, W. M., Spitznagel, E. L., & Ben-Abdallah, A. (2000). Problem gambling and comorbid psychiatric and substance use disorders among drug users recruited from drug treatment and community settings, Journal of Gambling Studies, 16, 347–375.
Fishbein, D. (2000). Neuropsychological function, drug abuse, and violence: A conceptual framework. Criminal Justice and Behavior, 27, 139–159.
Fullilove, M. P., Fullilove, R. E., Smith, M., Winkler, K., Michael, C., Panzer, P. G., & Wallace, R. (1993). Violence, trauma and posttraumatic stress disorder among women drug users. Journal of Traumatic Stress, 6, 533–543.
Johnson, S. J., Cunningham-Williams, R. M., & Cottler, L. B. (2003). A tripartite of HIV-risk for African American women: The intersection of drug use, violence, and depression. Drug and Alcohol Dependence, 70, 169–175.
Maccullum, F., & Blaszczynski, A. (2003). Pathological gambling and suicidality: An analysis of severity and lethality. Suicide and Life-Threatening Behavior, 33, 88–98.
Martins, S. S., Tavares, H., da Silva, L., Galetti, A. M., & Gentil, V. (2004). Pathological gambling, gender, and risk-taking behaviors. Addictive Behaviors, 29, 1231–1235.
McCormick, R. A. (1993). Disinhibition and negative affectivity in substance abusers with and without a gambling problem. Addictive Behaviors, 18, 331–336.
National Research Council. (1999). Pathological gambling: A critical review. Washington, DC: National Academy Press.
Petry, N. M., Stinson, F. S., & Grant, B. F. (2005). Comorbidity of DSM-IV pathological gambling and other psychiatric disorders: Results from the National Epidemiologic Survey on Alcohol and related conditions. Journal of Clinical Psychiatry, 66, 564–574.
Robins, L. N., Cottler, L. B., Bucholz, K. K., Compton, W. M., North, C. S., & Rourke, K. M. (1999). The National Institute of Mental Health Diagnostic Interview Schedule, Version IV (DIS-IV). St. Louis, MO: Washington University School of Medicine, Department of Psychiatry.
Robins, L. N., & Regier, D. A. E. (1991). Psychiatric disorders in America: The Epidemiologic Catchment Area Study. New York: Free Press.
Tavares, H., Martins, S. S., Lobo, D. S. S., Silveira, C. M., Gentil, V., & Hodgins, D. C. (2003). Factors at play in faster progression for female pathological gamblers: An exploratory analysis. Journal of Clinical Psychiatry, 64, 433–438.
Welte, J. W., Barnes, G. M., Wieczorek, W. F., Tidwell, M. C., & Parker, J. (2002). Gambling participation in the U.S.: Results from a national survey. Journal of Gambling Studies; 18, 313–337.
Submitted: November 14, 2005 Revised: September 1, 2006 Accepted: September 3, 2006
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Source: Psychology of Addictive Behaviors. Vol. 21. (2), Jun, 2007 pp. 239-243)
Accession Number: 2007-08148-013
Digital Object Identifier: 10.1037/0893-164X.21.2.239
Record: 126- Title:
- Prognostic significance of spouse we talk in couples coping with heart failure.
- Authors:
- Rohrbaugh, Michael J.. Department of Psychology, University of Arizona, Tucson, AZ, US, michaelr@u.arizona.edu
Mehl, Matthias R.. Department of Psychology, University of Arizona, Tucson, AZ, US
Shoham, Varda. Department of Psychology, University of Arizona, Tucson, AZ, US
Reilly, Elizabeth S.. Department of Psychology, University of Arizona, Tucson, AZ, US
Ewy, Gordon A.. Department of Cardiology, University of Arizona, Tucson, AZ, US - Address:
- Rohrbaugh, Michael J., Department of Psychology, University of Arizona, P.O. Box 210068, Tucson, AZ, US, 85721, michaelr@u.arizona.edu
- Source:
- Journal of Consulting and Clinical Psychology, Vol 76(5), Oct, 2008. pp. 781-789.
- NLM Title Abbreviation:
- J Consult Clin Psychol
- Page Count:
- 9
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- Journal of Consulting Psychology
- Other Publishers:
- US : American Association for Applied Psychology
US : Dentan Printing Company
US : Science Press Printing Company - ISSN:
- 0022-006X (Print)
1939-2117 (Electronic) - Language:
- English
- Keywords:
- marriage, close relationships, heart disease, coping, text analysis, marital quality
- Abstract:
- Recent research suggests that marital quality predicts the survival of patients with heart failure (HF), and it is hypothesized that a communal orientation to coping marked by first-person plural pronoun use (we talk) may be a factor in this. During a home interview, 57 HF patients (46 men and 16 women) and their spouses discussed how they coped with the patients' health problems. Analysis of pronoun counts from both partners revealed that we talk by the spouse, but not the patient, independently predicted positive change in the patient's HF symptoms and general health over the next 6 months and did so better than direct self-report measures of marital quality and the communal coping construct. We talk by the patient and spouse did not correlate, however, and gender had no apparent moderating effects on how pronoun use predicted health change. The results highlight the utility of automatic text analysis in couple-interaction research and provide further evidence that looking beyond the patient can improve prediction of health outcomes. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Coping Behavior; *Heart Disorders; *Marital Satisfaction; *Marriage; *Interpersonal Relationships; Spouses
- Medical Subject Headings (MeSH):
- Adaptation, Psychological; Aged; Communication; Female; Heart Failure; Humans; Male; Marriage; Middle Aged; Prognosis; Quality of Life; Semantics; Sick Role; Spouses; Ventricular Dysfunction, Left; Verbal Behavior
- PsycINFO Classification:
- Cardiovascular Disorders (3295)
- Population:
- Human
Male
Female - Location:
- US
- Age Group:
- Adulthood (18 yrs & older)
- Tests & Measures:
- Constructive Communication Scale
Hopkins Symptom Checklist DOI: 10.1037/t06011-000
Relationship Assessment Scale DOI: 10.1037/t00437-000 - Grant Sponsorship:
- Sponsor: American Heart Association, US
Grant Number: 0051286Z
Other Details: Award
Recipients: No recipient indicated - Methodology:
- Empirical Study; Qualitative Study; Quantitative Study
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- Accepted: Jun 23, 2008; Revised: Apr 7, 2008; First Submitted: Oct 22, 2007
- Release Date:
- 20081006
- Correction Date:
- 20140811
- Copyright:
- American Psychological Association. 2008
- Digital Object Identifier:
- http://dx.doi.org/10.1037/a0013238
- PMID:
- 18837595
- Accession Number:
- 2008-13625-007
- Number of Citations in Source:
- 53
- Persistent link to this record (Permalink):
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- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2008-13625-007&site=ehost-live">Prognostic significance of spouse we talk in couples coping with heart failure.</A>
- Database:
- PsycINFO
Prognostic Significance of Spouse We
Talk in Couples Coping With Heart Failure
By: Michael J. Rohrbaugh
Department of Psychology, University of Arizona;
Department of Family Studies/Human Development,
University of Arizona;
Matthias R. Mehl
Department of Psychology, University of Arizona
Varda Shoham
Department of Psychology, University of Arizona
Elizabeth S. Reilly
Department of Psychology, University of Arizona
Gordon A. Ewy
Department of Cardiology, University of Arizona;
Sarver Heart Center,
University of Arizona
Acknowledgement: Elizabeth S. Reilly is now with the
British National Health Service, Glasgow, Scotland.
This research was supported by Award
0051286Z from the American Heart Association, Dallas, Texas.
We thank Christopher Wenner for his help in administering
the Arizona Family Heart Project; Mary Brown, Lisa
Hoffman-Konn, Mary-Frances O'Conner, Josh Schoenfeld, and
Sarah Trost for conducting the interviews; Paul Fenster,
Lorraine Macstaller, Brendan Phipps, Edna Silva, Marti
Simpson, and Lawton Snyder for their help in recruiting
participants; and Jeffrey Berman for suggestions about
statistical analysis.
A growing body of research highlights the key role of close
relationships in successful coping with heart disease and other
forms of chronic illness. For example, in a study of 189
heart-failure (HF) patients and their spouses from Michigan, a
composite measure of marital quality predicted the patient's
survival over the next 8 years independent of baseline illness
severity (Rohrbaugh,
Shoham, & Coyne, 2006). The two
marital quality components predicting best in this
study—the observed affective quality of the couple's
actual interaction (positivity/negativity ratio) and the
reported frequency of their “useful
discussions” about the patient's
illness—illustrate potentially distinct processes
through which marital quality may have its effect.
On the one hand, consistent with other research on marriage and
health, receipt of social emotional support or exposure to
marital conflict could buffer or exacerbate stress via direct
physiological pathways (Gallo, Troxel, Matthews, & Kuller,
2003; Kiecolt-Glaser & Newton,
2001; Ryff & Singer, 2000).
The useful-discussion finding, on the other hand, highlights a
less studied and more instrumental dimension of couple coping
behavior reminiscent of what Lyons, Mickelson, Sullivan, and Coyne
(1998) called “communal
coping” (p. 579; cf. Acitelli & Badr, 2005;
Berg &
Upchurch, 2007; Bodenmann, 2005;
Lewis et al.,
2006). In essence, such a communal or
cooperative problem-solving process involves appraising a
stressor (the patient's heart condition) as
“our” issue rather than
“yours” or “mine” and
taking cooperative “we”-based action to
address it (Lyons et
al., 1998).
Although marital researchers have used both observational and
self-report methods to link communal aspects of specific dyadic
relationships to relationship outcomes (Acitelli & Badr,
2005; Buehlman, Gottman, & Katz,
1992; Mills, Clark, Ford, & Johnson,
2004), recent developments in the arena of
automatic text analysis may hold special promise for
illuminating connections between close relationships and health
(Pennebaker, Mehl,
& Niederhoffer, 2003;
Simmons, Gordon,
& Chambless, 2005;
Slatcher &
Pennebaker, 2006). In particular, use of
first-person plural pronouns (we,
us, our) in the context of
couple communication appears to mark relational commitment,
shared identity, and effective problem solving by relationship
partners (Agnew, Van
Lange, Rusbult, & Langston,
1998; Simmons et al., 2005). In the
present study, we hypothesize that partners' we
talk might also mark an effective communal approach to coping
with a serious health problem such as chronic heart failure,
paying dividends in terms of predicting a favorable course of
the patient's illness.
At an individual level, research using automatic text analysis
software such as Linguistic Inquiry and Word Count (LIWC;
Pennebaker,
Francis, & Booth, 2001) has
shown that easily countable linguistic features of transcribed
narratives can predict (or postdict) such diverse aspects of
adaptation as physical health change (Pennebaker, Mayne, & Francis,
1997), responses to trauma
(Cohn, Mehl,
& Pennebaker, 2004;
Stone &
Pennebaker, 2002), recovery from
anorexia (Lyons, Mehl,
& Pennebaker, 2006), and even
whether poets commit suicide (Stirman & Pennebaker,
2001). It is important to note that a unique
methodological advantage of a text analysis approach to studying
coping and support processes may be that it is less vulnerable
to social desirability bias than traditional interview and
questionnaire approaches are, especially for measuring a highly
evaluative construct such as the quality of social relations
(Pressman &
Cohen, 2007).
In a review of word-use research, Pennebaker et al.
(2003) emphasized the value of studying
particles, that is, filler parts of speech (e.g., prepositions,
articles, and especially pronouns) that linguistically carry
little or no conversational content but on a psychological level
often serve to mark emotional states, social identity, and
cognitive styles. Because pronouns and other particles reflect
linguistic style rather than content, they may be less a product
of conscious word choice than regular verbs or nouns (e.g.,
emotion words) are and thus may reflect more fundamental
psychosocial processes (Pennebaker et al., 2003). Thus, in one
of few applications of text analysis to heart patients,
Scherwitz and colleagues linked
self-involvement, defined by elevated frequency
and density of first-person singular pronoun use, to Type A risk
behavior and clinical outcomes of coronary heart disease
(Scherwitz
& Canick, 1988). Other studies
show shifts from first-person singular to plural pronoun use by
bloggers (Cohn et al.,
2004) and former mayor Rudolph Giuliani
(Pennebaker
& Lay, 2002) coincident with the
communal crisis of September 11, 2001, suggesting that
I talk and we talk may be
subtle markers of individual versus communal self-construal, at
least in the context of talking about an upsetting life event.
Indirect evidence that pronoun use could have adaptive
significance for couples came from Buehlman et al.'s (1992)
observational coding of we-ness versus
separateness during conjoint oral-history interviews.
Judges' ratings of couple we-ness, based largely on partners'
tendency to use we rather than
he, she, or I
when recounting their marital history, correlated with
concurrent positive interaction behavior and predicted both
marital satisfaction and divorce over the next 4 years (cf.
Gottman &
Levenson, 1999). Unfortunately, because
pronoun use was only one component of the we-ness measure, the
Buehlman et al. results do not demonstrate that
we talk alone predicted positive marital
outcomes.
More definitive data on pronouns in marital interaction come
from Simmons et al.'s (2005) study comparing LIWC pronoun counts
with observed and reported marital quality in a sample of 59
couples where one spouse had a diagnosed anxiety disorder.
During face-to-face problem-solving discussions, first-person
plural pronoun use by both partners correlated with
independently coded positive problem-solving behavior during the
same interaction, even after controlling for general behavioral
negativity, which correlated strongly with second-person
you talk. Although we
talk was not related to marital satisfaction, Simmons et al.
(2005) concluded that first-person plural
pronoun use does predict (or at least reflect) better
cooperative problem solving—and, consistent with our
communal coping hypothesis, they suggested that partners who
used such pronouns more often “had a greater sense of
shared responsibility or stake in the problem discussed, which
may have helped them collaborate more effectively” (p.
935).
The present study differs from and extends previous plural
pronoun research in a number of ways. First, our dependent
variables concern the status and course of a serious physical
health problem rather than concurrent marital functioning or
later marital outcomes. Chronic HF, the end stage for many forms
of heart disease, is an increasingly prevalent, costly condition
that makes stringent demands on patients and their families
(MacMahon &
Lip, 2002; Rohrbaugh et al.,
2002). Although treatments have improved
dramatically, HF continues to be associated with shortened life
expectancy (up to 50% mortality 5 years after diagnosis),
frequent hospitalizations, and diminished quality of life
(Chin &
Goldman, 1998; Masoudi et al.,
2004).
Second, rather than studying problem-solving discussions about
relationship conflicts in the laboratory (as Simmons et al.,
2005, did), we examined partners'
first-person pronoun use during an open-ended home interview
about how they cope(d) with the patients' heart failure. Thus,
in line with Buehlman et
al. (1992), the conversations were more
interviewer focused than partner focused and typical of what a
clinician might encounter during an assessment or therapy
session.
Third, we compared nonreactive pronoun markers of communal
coping derived from automatic text analysis with a direct
self-report measure of the same construct. If the text-analysis
approach is indeed more implicit and less vulnerable to
social-desirability reporting bias (Pennebaker et al.,
2003; Pressman & Cohen,
2007), the we talk measures
may have greater prognostic value than self-reported communal
coping or relationship quality for predicting future health
change.
Fourth, in addition to analyzing singular and plural
first-person pronouns in the aggregate, we distinguished their
active and passive forms (I talk vs.
me/my talk,
we talk vs.
us/our
talk). As Pennebaker et al. noted in their
(2003) review, interest in the active–passive
pronoun-use distinction goes back at least to William
James
(1890), although its pragmatics remain
largely uninvestigated. Here, in keeping with the distinction
between active and passive forms of coping with health problems,
we examined possible differences in the adaptive implications of
we talk and
us/our talk (as well as
I talk and
me/my talk) for patients with
HF.
Fifth, we considered the possibility that personal pronoun use
by the patient's spouse, as well as by the patient, makes an
independent contribution to predicting the course of cardiac
illness. Precedence for distinguishing such partner effects and
actor effects with HF patients comes from our studies of the
Michigan sample referenced in the first paragraph, where the
spouse's confidence in the patient's ability to manage the
illness predicted 4-year survival over and above what the
patient's own self-efficacy ratings could predict
(Rohrbaugh et al.,
2004). Would a spouse's communal
orientation to coping with HF likewise have as much or more
adaptive significance than a communal orientation by the
patient? Or does effective communal coping require balanced
we talk by both partners (i.e., is it a
true couple-level phenomenon)? These, too, are questions the
present study attempts to address.
Finally, although the present (Arizona) sample included
relatively few female patients, gender implications of
we talk are difficult to ignore. Apart from
apparent gender differences in overall pronoun use
(Mehl &
Pennebaker, 2003; Newman, Groom, Handelman, &
Pennebaker, 2008) and possibly inherent
gender differences in communal orientation (Taylor, 2006), the
Michigan HF studies found marital functioning more important to
the survival of female patients than male patients
(Coyne et al.,
2001; Rohrbaugh et al., 2006; cf.
Krumholz et al.,
1998). The latter finding is consistent
with a broader literature suggesting that associations between
marital quality and health tend to be stronger for women than
for men (Kiecolt-Glaser
& Newton, 2001;
Saxbe, Repetti,
& Nishina, 2008). If marital
we talk does predict how patients adapt to
heart disease, the results might also suggest possible gender
differences that could involve either the recipient or the
provider of communal coping.
To summarize, we hypothesized that first-person plural pronoun
use (we talk) by one or both partners during an
open-ended conjoint interview would predict the course of the
patient's HF symptoms over a 6-month period and do so better
than (or at least independently from) direct self-reports of
marital quality and communal coping. Additional exploratory
questions, for which specific hypotheses would be premature,
concerned (a) the relative contributions of we
talk by patients and spouses (whether independent actor or
partner effects emerge and whether adaptive we
talk is a couple-level phenomenon), (b) relative contributions
of the active first-person plural pronoun form
(we) compared with passive first-person plural
forms (us, our), and (c) the
possible role of gender in moderating predictive associations
between pronoun use and patient health change.
Method Overview
HF patients and their spouses
participated in a brief, open-ended interview focusing on
how they had coped with the patients' heart conditions. We
then used first-person pronoun patterns (singular vs.
plural, active vs. passive) derived from LIWC analysis of
each partner's interview responses to predict changes in the
patient's symptom severity and general health over the next
6 months. Baseline self-report measures of marital quality,
communal coping, and psychological distress were available
as well, and we examined these as potential covariates of
pronoun use that might explain predictive associations with
the patient's well-being.
Participants
Participants were 60 HF patients (43 men,
17 women) and their opposite-sex spouses recruited primarily
from University of Arizona cardiology clinics. All patients
carried a confirmed HF diagnosis and had a left ventricular
ejection fraction (LVEF), usually documented by
echocardiogram during the previous 6 months, of less than or
equal to 40 (M = 29.1, SD
= 8.7). At the time of the home interview, mean New York
Heart Association (NYHA) functional class was 2.3
(SD = 0.8) on a 1–4 scale,
with 13.3%, 55.0%, 20.0%, and 11.6% of the patients in
Classes I, II, III, and IV, respectively (Domanski, Garg, & Yusuf,
1994). On average, HF had been
diagnosed 4.8 years earlier (SD = 5.1) and
heart problems 11.5 years earlier (SD =
9.8). HF is a complex diagnosis with diverse etiology, and
we do not have systematic information on the variety of
etiological pathways represented in the sample. Available
data do, however, indicate that almost half (42%) of the
patients had experienced myocardial infarction, and
prevalence rates for diabetes and hypertension were 32% and
25%, respectively. Although 50% of the patients had been
hospitalized and 42% had made an emergency room visit in the
previous 6 months, all were outpatients at the time of the
home interview.
Mean ages of patients and spouses were 67
(SD = 11.7) and 65.6
(SD = 10.7) years, respectively, and
couples had been married an average of 34.8 years
(SD = 16.7). The patient sample was
predominantly White (85%), well-educated (40% were college
graduates), and affluent (M zip code income
was at the 65th percentile in the year 2000).
Procedure
During visits to each couple's home,
research assistants interviewed the patient and spouse both
separately and conjointly. Speech samples for LIWC analysis,
transcribed separately for each partner, came from responses
to two open-ended questions asked in conjoint format near
the end of the home visit: (a) “As you think back
on how the two of you have coped with the heart condition,
what do you think you've done best? What are you most proud
of?” and (b) “Looking back on your own
experiences, what suggestions or advice could you offer
other heart patients and their families?”
Interviewers directed these questions to the couple,
encouraged elaboration with reflective prompts, and allowed
time for both partners to answer. Two couples did not
participate in the conjoint coping interview because of
patient fatigue, and the audio recorder malfunctioned for a
third couple, so a total of 57 transcripts (from 41 male-
and 16 female-patient couples) were available for analysis.
The baseline home visit also provided
detailed assessments of the patient's HF symptoms (rated by
both partners) and his or her general health, measured by
the 36-Item Short-Form Health Survey (SF-36;
McHorney, Ware,
& Raczek, 1993). Other
psychosocial variables included the patient's perceived
self-efficacy to manage the illness and parallel
(individual) reports of communal coping, marital quality,
and psychological distress obtained from each partner. Six
months later, in separate telephone interviews with both
partners, we again assessed the patient's HF symptoms and
general health.
Participants provided their informed
consent at the beginning of the home interview. All aspects
of the study were conducted in compliance with procedures
established by the University of Arizona Human Subjects
Committee.
Measures
Pronoun use
Automatic text analyses performed
with the LIWC software (Pennebaker et al.,
2001) produced separate counts of
all pronoun types used by the patient and spouse in each
couple. The LIWC presents variables in a relative
metric, as percentages of a participant's total number
of transcribed words, and a separate pronoun dictionary
permitted further distinctions among active versus
passive personal pronouns. To address the main research
questions, we focused narrowly on first-person pronouns
and based statistical analyses on proportion variables
calculated to represent (a) each partner's use of
first-person plural (we talk) and
singular (I talk) pronouns relative to
all personal (first-, second-, and third-person)
pronouns; (b) each partner's use of first-person
pronouns that were plural rather than singular
(we/I ratio), with total
first-person pronouns as the denominator; and (c) each
partner's use of pronouns that were plural active
(we active) plural passive
(us/our passive), singular active
(I active), and singular passive
(me/my passive), again with total
first-person pronouns as a denominator. The first set of
pronoun variables allowed for examining patient and
spouse I talk and we
talk independently in the same analysis, whereas the
remaining variables (we/I ratio,
we active,
us/our passive,
etc.) captured the relative balance of plural versus
singular first-person pronouns. We also examined total
words and total pronouns as covariates of the proportion
variables, although this had little impact on the
results.
Report measures of communal coping
The individual patient and spouse
interviews each included two questions based on the
Lyons et
al. (1998) communal coping
construct. The patient items were (a) “When
you think about problems related to your heart
condition, to what extent do you view those as ′our
problem' (shared by you and your spouse equally) or
mainly your own problem?” and (b)
“When a problem related to your heart
condition arises, to what extent do you and your partner
work together to solve it?” Both items had a
1–5 response scale, bipolar in the first case
(from 1 = my problem to 5 = our
problem) and unipolar in the second (from 1
= not at all to 5 =
always). Items for the spouse were
directly parallel but referred to “your
partner's heart condition.” Although the two
items correlated only modestly (r = .41
for patients and .26 for spouses), we nonetheless
averaged them to provide a self-report communal
coping score for each partner (patient
M = 4.1, SD = 1.0;
spouse M = 4.6, SD =
0.6).
Patient outcomes
The two dependent variables, each
related to the patient's health and assessed at both
baseline and follow-up, were HF
symptoms and SF-36 health
status. The measure of HF symptoms reflected
patient and spouse ratings of the extent to which the
patient experienced eight specific symptoms in the
previous month. The symptoms were (a) fatigue or lack of
energy for normal activities; (b) difficulty breathing,
especially with exertion; (c) waking up breathless at
night; (d) swelling in ankles and feet; (e) chest pain;
(f) heart flutter (fibrillation); (g) dizziness or
fainting; and (h) nausea, with abdominal swelling or
tenderness. The patient and spouse conjointly rated the
presence of each symptom on a three-level scale
(not at all, some,
a lot) during the baseline
interview, and they did so again separately at
follow-up, with good interrater agreement (intraclass
r = .73). Half of the patients
(n = 30) also completed the Kansas
City Cardiomyopathy Questionnaire (Green, Porter, Bresnahan,
& Spertus, 2000) at
baseline, and the Functional-Status
scale from this validated instrument correlated highly
with our HF symptoms measure (r =
−.79). Internal consistency of the HF symptoms
scale was good at both baseline (α = .79) and
follow-up (α = .84). Mean symptom scores, based
on summing averaged patient and spouse scores, were 15.0
(SD = 3.7) at baseline and 12.3
(SD = 2.8) at follow-up.
To capture the patient's overall
health, we used physical and mental component summary
scores from the widely used SF-36 (McHorney et al.,
1993; Ware, 2000). At
baseline, norm-based scores for physical and mental
health were 43.0 (SD = 19.1) and 56.7
(SD = 19.4), and, at follow-up, the
respective scores were 49.2 (SD = 24.2)
and 67.0 (SD = 21.1). Because scores
for the physical and mental components correlated highly
at baseline (r = .63) and follow-up
(r = .78), we averaged them into a
composite SF-36 health status variable for the main
analyses.
Not surprisingly, HF symptoms and
SF-36 health status also correlated significantly and
substantially with each other at both baseline
(r = −.57,
p < .001) and follow-up
(r = −.66,
p < .001). Test–retest
stability for the two measures was moderately high
(rs = .64 and .71, respectively),
and residual scores reflecting change from baseline to
follow-up correlated as well (r = .53,
p < .01). Less expected were
significant improvements from baseline to follow-up for
both HF symptoms, t(56) =
−8.76, p < .001, and
SF-36 health, t(56) = 4.05,
p < .001.
Marital quality and psychological distress
The patient and spouse also completed
two brief measures of marital quality:
Hendrick's
(1988) seven-item Relationship
Assessment Scale (RAS) and Heavey, Larson, Zumtobel, and
Christensen's (1996) seven-item
Constructive Communication Scale (CCS). Internal
consistency was good for both measures (all αs
> .80). In addition, mean RAS item scores for
patients and spouses were near the upper end of the
1–5 response scale (Ms = 4.4
and 4.4, SDs = 0.6 and 0.7,
respectively), suggesting that couples in this sample
tended to be fairly well satisfied with their
longstanding marriages. Because correlations between RAS
and CCS scores were moderately high for both patients
(r = .61) and spouses
(r = .64), we used the mean
z score for these two variables as an
index of marital quality for each
partner.
The last variable examined as a
possible covariate of first-person pronoun use was
psychological distress,
operationalized via a 25-item version of the Hopkins
Symptom Checklist (HSCL-25) used in previous research
with HF couples (Rohrbaugh et al.,
2002). As in previous studies,
internal consistency was high (α = .93), and a
nontrivial proportion of participants (37% of the
patients and 20% of their spouses) scored in a range
associated with a diagnosis of anxiety or depression
(Hesbacher,
Rickels, Morris, Newman, & Rosenfeld,
1980).
Results Patterns of Pronoun Use
Descriptive statistics for patient and
spouse pronoun variables appear in Table 1. In the top panel, raw
pronoun proportions based on total word counts indicate that
first-person plural pronouns (we,
us, our) occurred with
relatively low frequency, making up less that 2.5% of all
transcribed words from the interview. In fact, 7 patients
and 3 spouses used no plural first-person pronouns at all.
The relative first-person proportion variables in the bottom
part of Table
1 (we talk,
I talk, we/I
ratio, etc.) have higher values, reflecting different
denominators, and were used in the main analyses. Because
the distributions of these variables tended to be negatively
skewed, we applied arcsine transformations to improve
normality and used these transformed values in all analyses.
Means, Standard Deviations, and Ranges of Pronoun Variables for
Patients and Spouses
To examine mean-level differences in
pronoun use and total word use, we performed mixed-model
analyses of variance (ANOVAs) with couple as the unit of
analysis (Maguire,
1999) using the SPSS 16 general
linear model statistical module. The speaker's role (patient
vs. spouse) was a within-couple effect in these models,
whereas patient gender was a between-couple effect. Some of
the models also included pronoun type (e.g.,
I talk vs. we talk,
we active vs. we
passive) as a within-case variable to examine possible main
effects and interactions involving this factor. Although
ANOVAs for total word count and total personal pronouns
revealed no significant main effects or interactions, those
comparing pronoun types found greater use of first-person
pronouns by patients than spouses, F(1, 56)
= 12.2, p < .001, and greater use of
both second-person pronouns, F(1, 56) =
7.36, p = .009, and third-person pronouns,
F(1, 56) = 4.83, p =
.032, by spouses compared with patients.
ANOVAs focusing specifically on
first-person singular and plural pronouns found significant
within-case effects for role and pronoun type(s) as well as
several Role × Type interactions. Thus, including
transformed we talk and I
talk proportions in the same analysis confirmed the higher
prevalence of I talk, F(1,
55) = 166.06, p < .001, and yielded
a significant Role × Type interaction,
F(1, 55) = 20.57, p
< .001. Tests for simple effects related to this
interaction revealed significant differences for all pairs
of means, with patients showing more I talk
than spouses (M = 43.0 vs. 28.8,
p < .001; see Table 1) and
spouses showing more we talk than patients
(M = 12.7 vs. 9.3, p =
.024). A similar ANOVA incorporating the
active–passive dimension (e.g.,
we active,
us/our passive) likewise
indicated a preponderance of active over passive pronouns,
F(1, 55) = 246.28, p
< .001. In addition, a significant Role ×
Type (active–passive) interaction for plural
pronouns reflects differential use of we
active and us/our passive
pronouns by spouses and patients, F(1, 55)
= 13.76, p < .001, with spouses
exceeding patients in we active talk
(M = 25.1 vs. 14.4, p
< .001) but not in
us/our talk
(M = 4.3 vs. 3.2, p =
.167). Strikingly, the patient's gender, either alone or in
interaction with role and/or pronoun type, had no
significant statistical effects in any of these analyses
(ps > .1). In other words,
gender appeared to make little difference in how patients
and spouses used personal pronouns when discussing the
patients' illness.
Further pronoun analyses examined
correlations between the proportion measures and between the
spouses. Not surprisingly, the we talk and
I talk proportions tended to correlate
negatively with each other, although somewhat more so for
patients (r = −.44,
p < .001) than for spouses
(r = −.20, p
> .1). However, there was essentially no association
between patient and spouse pronoun use for
we talk (r =
−.09), I talk (r
= .05), or the we/I ratio
(r = −.05). Thus, if plural
pronouns do mark communal coping, they appear to do so in a
manner that does not represent a reciprocal, couple-level
process.
Other Correlates of We Talk and Patient Health Change
The last set of preliminary analyses
aimed at identifying correlates of the we
talk predictor variables and of change in the two dependent
patient-health variables from baseline to follow-up. Here we
were interested not only in demographic and clinical
characteristics but also in the self-report measures of
communal coping and marital quality that relate most
directly to the study hypotheses. One reason for doing this
was to identify possible control variables (covariates) that
might later explain links between we talk
and health change.
On the one hand, correlational analyses
found essentially no relationships between the
we talk indices and either partner's
gender, age, or education, nor were there any significant
associations between we talk and concurrent
clinical variables such as HF symptoms, SF-36 health status,
NYHA class, LVEF, illness duration, recent hospitalization,
or the patient's psychological distress. On the other hand,
the self-report measure of communal coping did tend to
correlate with the we/I
ratio for patients (r = .32,
p < .05), although not for spouses
(r = .13, ps >
.1), and the we/I ratio
correlated with the marital quality scores of both partners
(rs = .28 and .26, respectively,
ps < .05). It is interesting
that actor–partner regression analyses indicated
also that one partner's plural pronoun use predicted the
other's perception of marital quality over and above any
actor effect (bs for both
we/I ratios = .32,
ps < .05).
To identify possible predictors of
patient health change, we computed partial correlations with
the respective dependent variables, controlling their status
at baseline. Although some clinical variables (e.g., NYHA
class, LVEF, psychological distress, marital quality)
correlated substantially with patient health at baseline,
only two—marital quality and psychological
distress—appeared to predict HF symptoms or SF-36
health status prospectively: Partial rs
were −.48 and −.42 for patient and
spouse marital quality predicting HF symptom change
(ps < .01), .35 and .35 for
HSCL-25 patient and spouse distress predicting HF symptom
change (ps < .05), and .30 and .40
for marital quality predicting SF-36 health change (spouse
p < .05).
Of note, the self-report measure of
communal coping was unrelated to HF symptoms at baseline
(rs = −.15 and −.09
for patients and spouses), and it did not predict change in
HF symptoms during the follow-up period (partial
rs = −.03, −.01).
Communal coping did, however, correlate with reported
marital quality (rs = .59 and .39 for
patients and spouses, ps < .05),
suggesting that, in the self-report domain, these are
related but distinct constructs (cf. Bodenmann,
2005).
Pronoun Predictors of Patient Health Change
To address the main research questions,
we first performed a multiple regression analysis for each
of the two patient outcomes, with patient and spouse
we talk, patient and spouse
I talk, and the patient's gender as
predictors. The regression models also included a baseline
measure of the relevant outcome to capture residualized
change, and predictors were centered to minimize
collinearity of interaction terms (Aiken & West,
1991). Despite our interest in gender as
a putative moderator, no two- or three-way interactions
involving pronoun variables and the patient's biological sex
were statistically significant in these (or any other)
analyses, so we report simplified regression results with
gender excluded. Similarly, because including total word
count and/or personal pronouns as covariates had no
appreciable effect on regression results, we exclude those
variables as well.
Table 2 presents standardized beta weights
from the regression analyses for the two patient health
outcomes. Note here that negative beta weights reflect
positive (healthy) change for HF symptoms (i.e., symptom
reduction), whereas the opposite applies for SF-36 health
status. For both dependent variables, we
talk by the spouse predicted positive changes in the
patient's health independent of what the patient's own
plural pronoun use predicted. In fact, the spouse's
we talk was the only significant
predictor of change in the patient's HF symptoms, although
betas for I talk by both partners were also
in the direction of predicting positive change in the
patient's general health. In neither case, however, was the
patient's we talk associated with his or
her own health change, and partner (spouse) effects on the
patient's well-being were generally more in evidence than
actor effects.
Standardized Betas and Significance Levels for Pronoun Predictors
of Patient Health Change
Follow-up analyses narrowed the focus of
prediction to relative use of plural versus singular
first-person pronouns. Including both partners'
we/I ratio scores appeared to sharpen the
spouse (partner) effect for HF symptoms (b
= −.33, p = .002) but weaken it
for the SF-36 measure of general health (b
= .10, p > .10). Incorporating the
active–passive dimension suggested further that
we active pronouns predicted HF
symptoms (b = −.24,
p = .034), whereas
us/our passive
pronouns did not (b = −.13,
p > .10), although parallel
effects were again not evident for SF-36 health status (both
bs = .02 and .15, respectively,
ps > .10).
Additional regression analyses explored
the relative contributions of patient and spouse
we talk directly, along with the predictive
potential of combining the two partners' scores. For HF
symptoms, a partner discrepancy score created by subtracting
the patient's we/I ratio
score from the spouse's score showed a significant effect on
patient health change (b = −.27,
p = .011), whereas a couple-level
(mean) we/I ratio had a
somewhat weaker effect (b = −.18,
p = .085). As with other analyses based
on the we/I ratio, there
were no associations between these couple-level variables
and changes in the patient's general (SF-36) health. In
summary, we found little evidence that patient and spouse
we talk had interchangeable
consequences for the patient's health.
Pronoun Prediction With Additional Covariates
A final set of regression analyses tested
the possibility that third (control) variables might account
for the associations between we talk and HF
symptom change and compared the prognostic significance of
pronoun and self-report indicators of the communal coping
construct. To pursue this, we focused on the
we/I ratio, the
pronoun variable most predictive of HF symptom change, and
used stepwise regression to examine the effect of adding a
control variable to the actor–partner model. On
the basis of finding relatively few substantial correlates
of we talk and/or symptom change, one might
expect these analyses to have low yield, which, in fact, was
the case. None of the clinical variables we examined as
potential covariates (e.g., NYHA class, LVEF, illness
duration, psychological distress) reduced the statistical
partner effect of spouse
we/I ratio, nor did either
the self-report measure of communal coping, which, as noted
above, had no direct main effect on symptom change, or the
partners' reports of marital quality that, when taken
separately, had predicted the symptom-change criterion.
DiscussionThe results suggest that we talk in couples
coping with HF has prognostic significance for the patient's
health. As hypothesized, the use of first-person plural pronouns
during a conjoint discussion about coping with the patient's
heart condition predicted positive change in HF symptoms over
the next 6 months. Strikingly, however, this result appeared for
we talk by the spouse and not the patient,
creating a statistical partner effect in the absence of a
corresponding actor effect. Exploratory analyses suggested
further that the spouse using the active first-person plural
pronoun (we) contributed more to predicting
symptom change than passive first-person plural forms
(us, our), although this
was not the case for predicting change in the patient's general
health. The patient's (or spouse's) gender, however had no
moderating effects on any results obtained.
More broadly, the we talk findings provide
further evidence that a communal orientation by at least one
partner in a committed relationship can have adaptive
consequences, not only for the dyad as a unit
(Buehlman et al.,
1992; Simmons et al., 2005) but also
for the other partner's health. This highlights an instrumental
dimension of coping with chronic health problems, grounded in
specific dyadic processes (Berg & Upchurch, 2007;
Revenson, Kayser,
& Bodenmann, 2005), that
compliments a much larger body of research on marital conflict
and receipt of social emotional support. From a methodological
perspective, the results also add to growing evidence of
transitive partner effects on individual health
(Ruiz, Matthews,
Scheier, & Shulz, 2006; cf.
Rohrbaugh et al.,
2004), wherein one person's behavior
predicts another person's health outcome over and above what the
same behavior by the actor can predict (Kenny, 1996). The
presence of such statistical partner effects implies
interpersonal influence but usually leaves the mechanism of that
influence unclear.
Perhaps the most important methodological implication of this
preliminary study concerns the potential utility of automatic
text analysis in research on couples and health. Because most
studies in this area rely on self-reports of key constructs such
as marital quality and coping styles, measurement may be
vulnerable to social-desirability reporting bias in ways that
automatic text analysis is not (Pennebaker et al., 2003;
Pressman &
Cohen, 2007). Consistent with this idea,
our results suggest that we talk as an implicit
marker of communal coping had greater prognostic value in
predicting the course of HF symptoms than either direct
self-reports of the same construct (communal coping) or reports
of general marital quality. However, this finding held for only
one partner in the couple (the spouse), and ambiguities remain
about how best to validate plural pronoun use as a marker of the
communal coping construct (e.g., we talk did
correlate significantly with direct reports of communal coping,
but only for patients).
It seems likely that adaptive implications of marital pronoun
use depend on situational factors such as the nature of
predicted outcomes and the interactional contexts from which
speech samples are derived. Extrapolating from previous
research, one would expect we talk by both
partners to predict future marital stability (Buehlman et al.,
1992) or correlate with concurrent adaptive
problem solving (Simmons et al., 2005). But when the
criterion (outcome) variable concerns one person's health
problem and the pronoun speech sample focuses on that, an
asymmetrical pattern of prediction from partner pronoun use may
be less unusual.
Other important boundary conditions for our findings involve
the manner of eliciting partner speech samples. For example, in
contrast to Simmons et
al.'s (2005) procedure, where couples
discussed a disagreement with no interviewer present, the
pronoun samples in our study came from responses to supportive,
open-ended interview questions about how the couple had coped
with the patient's illness. Compared with a conflict discussion,
our interview questions probably pulled for more positive,
collaborative verbalizations—perhaps especially from a
patient's spouse, whose role as a helper may be implicitly
defined by questions about coping with the patient's illness. In
any case, the present results leave open the question of whether
we talk sampled in a different, less
collaborative context would similarly predict patient health
outcomes. Further, it is not known how stable (traitlike)
we talk proportions might be across
different interactional contexts—for example, when
partners talk to versus about each other with an interviewer
absent versus present during a conflictual versus cooperative
task. These are questions for future research.
The results also hint that the relative frequency of
we talk and I talk (the
we/I ratio variable)
predicted change in HF symptoms better than it did change in the
patient's general health, a difference that was less evident
when we analyzed we talk and I
talk separately. It may be, therefore, that we
talk in the context of discussing a specific illness has
particular prognostic significance for coping with that illness.
Consistent with this idea, the patient's response to a single
self-efficacy follow-up question (“How confident are
you that you can do what you need to do to manage your
illness?”) showed essentially the same partner effects
for spouse we-ratio as the HF symptoms measure
did.
Still, the results are ambiguous about the extent to which
we talk marks the kind of communal coping
construct we envisioned. On the basis of the Michigan study
findings, particularly those linking useful discussions to
patient survival (Rohrbaugh et al., 2006), we had
conceptualized communal coping as a key couple-level component
of marital quality, but this does not fit well with either the
asymmetry of patient and spouse prediction results (partner
effects with no actor effects) or the fact that patient and
spouse we talk scores were essentially
uncorrelated. An alternative interpretation is that
we talk in the context studied here says most
about the caretaking posture of an individual
partner—in this case, the patient's spouse. This was
especially evident in the higher rates of I
talk by patients and we talk by spouses, which
fits the idea that interview questions about the patient's
illness served to highlight the spouse's support role.
Also unexpected was the absence of detectable gender
differences: Male- and female-patient couples did not differ in
how often the partners used various types of pronouns, and, more
relevant to clinical concerns, there was no gender moderation of
any predictive association between we talk and
patient health change. On the basis of previous research (e.g.,
Kiecolt-Glaser
& Newton, 2001;
Rohrbaugh et al.,
2006), one would expect a marital
process reflecting relationship quality (if we
talk indeed represents that) to have greater consequences for
the health of women than for the health of men. Unfortunately,
the imbalance of male and female patients in the sample (41 vs.
16) probably limited our ability to detect such
effects—for example, by restricting the heterogeneity
of spousal support for female patients.
This study has several additional limitations, the most
important of which may be the lack of hard outcome measures not
dependent on participants' self-reports. Although the HF
symptoms measure combined the reports of both partners (and
therefore may be on firmer ground than the patient's solo SF-36
score), we have no assurance that reported symptom change over
only 6 months is itself prognostic of long-term survival, nor is
it clear why the patients' symptoms and general health appeared
to improve over that time. One possible explanation is
methodological, in that the first interview was face-to-face and
the second telephonic. A second is that periodic assessment
contacts with project staff were in some way
therapeutic.
Another study limitation is that the sample was small and
probably not representative of the larger population of HF
patients, even those who are married. For example, compared with
the Michigan sample we studied earlier (Rohrbaugh et al.,
2006), the Arizona patients were more
educated and affluent and apparently also more stable medically,
as suggested by the fact that far fewer of them died in the 2
years following the initial assessment. We may therefore have
sampled a restricted range of (high) couple functioning,
although it is unclear if this would bias the results toward or
away from our findings.
A final limitation, inherent in automatic text analysis itself,
is that simple word counts cannot account for semantic
contextual markers related to dimensions such as irony, sarcasm,
and multiple meanings of the same word (Mehl, 2006).
However, this limitation may be offset by the relative
imperviousness of text analysis to potentially biasing effects
of social desirability and shared method variance. It could even
be that the latter feature helped to make possible our detection
of the dominant (spouse we talk) partner
effect, as shared method variance in self-report studies usually
biases results toward actor effects.
The main clinical implication of this study is that looking
beyond the patient can help to predict the likely course of a
heart patient's health. Specifically, it may be valuable for
clinicians to pay close attention to a spouse's use of
first-person plural pronouns when he or she discusses the
patient's heart condition; open-ended interview prompts such as
those used by our research interviewers should be sufficient for
eliciting this. It is unclear if the results generalize to other
health problems, and we do not yet know how we
talk by one or both partners maps onto other coping attitudes or
behaviors. To the extent that we talk does
reflect a communal orientation to coping, interventions that
specifically attempt to promote such a posture—for
example, by attending to reinforcing partners' recollections of
how they have successfully resolved difficulties together in the
past—may have special benefit for couples coping with
chronic illness (Martire & Schulz, 2007;
Revenson et al.,
2005; Shoham, Rohrbaugh, Trost, & Muramoto,
2006).
Footnotes 1 First-person singular pronouns
(I-focus) in the
Simmons et al.
(2005) study were unrelated to partners'
interaction quality but tended to correlate positively with marital
satisfaction. The latter result is inconsistent with findings by
Sillars, Shellen,
McIntosh, and Pomegranate (1997), who
counted pronouns during a nonclinical marital interaction task that
may have pulled less negativity.
2 Although linguists distinguish these as subjective
and objective pronoun types, we believe
active and passive better
capture their difference at a psychological level.
3 Conceptually, this finding approximates what Kenny (1996) and
others called a statistical partner effect,
distinguished here from the complementary actor
effect represented by the patient's score predicting his or
her own health outcome independent of the spouse's score.
4 This single-item self-efficacy measure was less adequate
psychometrically than the two main dependent variables, and we
excluded it from the main analyses to avoid inflating Type I
error.
5 In
addition to the baseline and follow-up interviews, research staff
had regular phone contact with each patient and spouse for a period
of 2 weeks, beginning about 2 months after the initial assessment,
to collect daily-diary data.
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Submitted: October 22, 2007 Revised: April 7, 2008 Accepted: June 23, 2008
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Source: Journal of Consulting and Clinical Psychology. Vol. 76. (5), Oct, 2008 pp. 781-789)
Accession Number: 2008-13625-007
Digital Object Identifier: 10.1037/a0013238
Record: 127- Title:
- Project INTEGRATE: An integrative study of brief alcohol interventions for college students.
- Authors:
- Mun, Eun-Young. Center of Alcohol Studies, Rutgers, The State University of New Jersey, Piscataway, NJ, US, eymun@rci.rutgers.edu
de la Torre, Jimmy. Center of Alcohol Studies, Rutgers, The State University of New Jersey, Piscataway, NJ, US
Atkins, David C.. Department of Psychiatry and Behavioral Sciences, The University of Washington, WA, US
White, Helene R.. Center of Alcohol Studies, Rutgers, The State University of New Jersey, Piscataway, NJ, US
Ray, Anne E.. Center of Alcohol Studies, Rutgers, The State University of New Jersey, Piscataway, NJ, US
Kim, Su-Young. Department of Psychology, Ewha Womans University, Republic of Korea
Jiao, Yang. Center of Alcohol Studies, Rutgers, The State University of New Jersey, Piscataway, NJ, US
Clarke, Nickeisha. Center of Alcohol Studies, Rutgers, The State University of New Jersey, Piscataway, NJ, US
Huo, Yan. Center of Alcohol Studies, Rutgers, The State University of New Jersey, Piscataway, NJ, US
Larimer, Mary E.. Department of Psychiatry and Behavioral Sciences, The University of Washington, WA, US
Huh, David. Department of Psychiatry and Behavioral Sciences, The University of Washington, WA, US - Institutional Authors:
- The Project INTEGRATE Team
- Address:
- Mun, Eun-Young, Center of Alcohol Studies, Rutgers, The State University of New Jersey, 607 Allison Road, Piscataway, NJ, US, 08854, eymun@rci.rutgers.edu
- Source:
- Psychology of Addictive Behaviors, Vol 29(1), Mar, 2015. pp. 34-48.
- NLM Title Abbreviation:
- Psychol Addict Behav
- Page Count:
- 15
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- Bulletin of the Society of Psychologists in Addictive Behaviors; Bulletin of the Society of Psychologists in Substance Abuse
- Other Publishers:
- US : Educational Publishing Foundation
Society of Psychologists in Addictive Behaviors - ISSN:
- 0893-164X (Print)
1939-1501 (Electronic) - Language:
- English
- Keywords:
- meta-analysis, integrative data analysis, brief motivational interventions, alcohol interventions, college students
- Abstract:
- This article provides an overview of a study that synthesizes multiple, independently collected alcohol intervention studies for college students into a single, multisite longitudinal data set. This research embraced innovative analytic strategies (i.e., integrative data analysis or meta-analysis using individual participant-level data), with the overall goal of answering research questions that are difficult to address in individual studies such as moderation analysis, while providing a built-in replication for the reported efficacy of brief motivational interventions for college students. Data were pooled across 24 intervention studies, of which 21 included a comparison or control condition and all included one or more treatment conditions. This yielded a sample of 12,630 participants (42% men; 58% first-year or incoming students). The majority of the sample identified as White (74%), with 12% Asian, 7% Hispanic, 2% Black, and 5% other/mixed ethnic groups. Participants were assessed 2 or more times from baseline up to 12 months, with varying assessment schedules across studies. This article describes how we combined individual participant-level data from multiple studies, and discusses the steps taken to develop commensurate measures across studies via harmonization and newly developed Markov chain Monte Carlo (MCMC) algorithms for 2-parameter logistic item response theory models and a generalized partial credit model. This innovative approach has intriguing promises, but significant barriers exist. To lower the barriers, there is a need to increase overlap in measures and timing of follow-up assessments across studies, better define treatment and control groups, and improve transparency and documentation in future single intervention studies. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Alcohol Rehabilitation; *Brief Psychotherapy; *Intervention; *Motivation; Analysis; College Students; Meta Analysis
- Medical Subject Headings (MeSH):
- Adult; Alcohol-Related Disorders; Data Interpretation, Statistical; Female; Follow-Up Studies; Humans; Male; Meta-Analysis as Topic; Psychotherapy; Research Design; Students; Universities; Young Adult
- PsycINFO Classification:
- Drug & Alcohol Rehabilitation (3383)
- Population:
- Human
Male
Female - Location:
- US
- Age Group:
- Adulthood (18 yrs & older)
- Tests & Measures:
- Group Motivational Interview
Brief Alcohol Screening and Intervention for College Students
Positive and Negative Consequences Experienced Questionnaire
National College Health Assessment Survey
Self-Control Strategies Questionnaire
Drinking Restraint Strategies Scale
Drinking Strategies Scale
Daily Drinking Questionnaire
University of Rhode Island Change Assessment
Phenotypes and eXposures Toolkit
NIH Toolbox for Assessment of Neurological and Behavioral Function
Alcohol Dependence Scale DOI: 10.1037/t00030-000
Alcohol Use Disorders Identification Test DOI: 10.1037/t01528-000
Readiness to Change Questionnaire DOI: 10.1037/t00434-000
Protective Behavioral Strategies Scale DOI: 10.1037/t01849-000
Rutgers Alcohol Problem Index DOI: 10.1037/t00517-000
Young Adult Alcohol Problems Screening Test DOI: 10.1037/t02795-000
Brief Young Adult Alcohol Consequences Questionnaire DOI: 10.1037/t03955-000
Patient-Reported Outcomes Measurement Information System DOI: 10.1037/t06780-000 - Grant Sponsorship:
- Sponsor: National Institute on Alcohol Abuse and Alcoholism
Grant Number: R01 AA019511
Recipients: No recipient indicated - Methodology:
- Meta Analysis
- Supplemental Data:
- Tables and Figures Internet
Text Internet - Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- First Posted: Dec 29, 2014; Accepted: Nov 10, 2014; Revised: Nov 8, 2014; First Submitted: Feb 20, 2014
- Release Date:
- 20141229
- Correction Date:
- 20150615
- Copyright:
- American Psychological Association. 2014
- Digital Object Identifier:
- http://dx.doi.org/10.1037/adb0000047; http://dx.doi.org/10.1037/adb0000047.supp(Supplemental)
- PMID:
- 25546144
- Accession Number:
- 2014-57147-001
- Number of Citations in Source:
- 108
- Persistent link to this record (Permalink):
- http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2014-57147-001&site=ehost-live
- Cut and Paste:
- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2014-57147-001&site=ehost-live">Project INTEGRATE: An integrative study of brief alcohol interventions for college students.</A>
- Database:
- PsycINFO
Project INTEGRATE: An Integrative Study of Brief Alcohol Interventions for College Students
By: Eun-Young Mun
Center of Alcohol Studies, Rutgers, The State University of New Jersey
Jimmy de la Torre
Department of Educational Psychology, Rutgers, The State University of New Jersey
David C. Atkins
Department of Psychiatry and Behavioral Sciences, The University of Washington
Helene R. White
Center of Alcohol Studies, Rutgers, The State University of New Jersey
Anne E. Ray
Center of Alcohol Studies, Rutgers, The State University of New Jersey
Su-Young Kim
Department of Psychology, Ewha Womans University
Yang Jiao
Center of Alcohol Studies, Rutgers, The State University of New Jersey
Nickeisha Clarke
Center of Alcohol Studies, Rutgers, The State University of New Jersey
Yan Huo
Department of Educational Psychology, Rutgers, The State University of New Jersey
Mary E. Larimer
Department of Psychiatry and Behavioral Sciences, The University of Washington
David Huh
Department of Psychiatry and Behavioral Sciences, The University of Washington
Center of Alcohol Studies, Rutgers, The State University of New Jersey;
Department of Educational Psychology, Rutgers, The State University of New Jersey;
Department of Psychiatry and Behavioral Sciences, The University of Washington;
Center of Alcohol Studies, Rutgers, The State University of New Jersey;
Department of Psychology, Ewha Womans University;
Center of Alcohol Studies, Rutgers, The State University of New Jersey;
Department of Educational Psychology, Rutgers, The State University of New Jersey;
Department of Psychiatry and Behavioral Sciences, The University of Washington
Acknowledgement: The project described was supported by Award Number R01 AA019511 from the National Institute on Alcohol Abuse and Alcoholism (NIAAA). The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIAAA or the National Institutes of Health. We thank Lisa A. Garberson, Caressa Slocum, and Yue Feng for their helpful comments on earlier drafts of this paper and for their help with data management. The Project INTEGRATE Team consists of the following contributors in alphabetical order: John S. Baer, Department of Psychology, The University of Washington, and Veterans’ Affairs Puget Sound Health Care System, Seattle, WA; Nancy P. Barnett, Center for Alcohol and Addiction Studies, Brown University; M. Dolores Cimini, University Counseling Center, The University at Albany, State University of New York; William R. Corbin, Department of Psychology, Arizona State University; Kim Fromme, Department of Psychology, The University of Texas, Austin; Joseph W. LaBrie, Department of Psychology, Loyola Marymount University; Matthew P. Martens, Department of Educational, School, and Counseling Psychology, The University of Missouri; James G. Murphy, Department of Psychology, The University of Memphis; Scott T. Walters, Department of Behavioral and Community Health, The University of North Texas Health Science Center; and Mark D. Wood, Department of Psychology, The University of Rhode Island.
This article provides an overview of a collaborative study entitled Project INTEGRATE. Project INTEGRATE is the first behavioral treatment research project to embrace recent advances in psychometrics and statistical methods (e.g., meta-analysis using individual participant-level data [IPD] or integrative data analysis [IDA]). The overall goals are to provide answers to evasive research questions (e.g., identification of mediational paths and subgroup differences), as well as to provide a built-in replication for the reported efficacy of brief motivational interventions (BMIs) for college student populations. The term IDA was coined by Curran and Hussong (2009) to highlight some of the unique promises, as well as challenges, that arise when combining studies in the psychological sciences. The term “meta-analysis using IPD” has been utilized more frequently in evaluating randomized control trials (RCTs) in medical research. We interchangeably use IDA and meta-analysis using IPD (or IPD meta-analysis) in the present article. This article does not report clinical treatment outcomes. Rather, we provide an overview of this research project and discuss the challenges encountered, steps taken to overcome these challenges, and lessons learned thus far. This overview sets the stage for articles that focus on clinical outcomes and mechanisms of behavior change to follow.
Available reviews of BMIs for college students have documented that BMIs (e.g., the Brief Alcohol Screening and Intervention for College Students; Dimeff, Baer, Kivlahan, & Marlatt, 1999) are effective in reducing alcohol use and related problems at least on a short-term basis (Carey, Scott-Sheldon, Carey, & DeMartini, 2007; Cronce & Larimer, 2011). Furthermore, those delivered in person provide more enduring effects compared with computer-delivered feedback interventions, including computer-delivered normative feedback interventions and computer-delivered educational alcohol interventions (Carey, Scott-Sheldon, Elliott, Garey, & Carey, 2012). However, the estimated effect sizes of these brief interventions are fairly small (e.g., Cohen’s d ranging from 0.04 to 0.21 from random-effects models for outcome variables at short-term follow-up [4 to 13 weeks postintervention] of individually delivered interventions; Carey et al., 2007), and vary from study to study across key outcome variables such as alcohol use and alcohol-related problems. Furthermore, only a small subset of studies had a statistically significant effect when reanalyzed in a meta-analysis (Carey et al., 2007). Thus, there appears to be incongruence in the strength of the overall effect between single studies and meta-analysis studies.
Emerging evidence suggests that single studies may be more susceptible to biased statistical inference than previously thought. For example, recent meta-analytic studies examining the efficacy of antidepressant medication aptly demonstrate the potential pitfalls of relying on evidence only from single studies. Turner, Matthews, Linardatos, Tell, and Rosenthal (2008) meta-analyzed aggregated data (AD; e.g., effect size estimates) on antidepressant medication submitted to the Food and Drug Administration (FDA) and in published articles from 74 trials (12 drugs and 12,564 patients) that were registered with the FDA between 1987 and 2004. Their analyses indicated that effect sizes had been substantially overestimated in published articles. For example, whereas 94% of the 37 published studies reported a significant positive result, only 51% had a positive outcome according to the meta-analysis of the FDA data. On average, Turner et al. found a 32% difference in effect sizes between the FDA data and the published data. Moreno et al. (2009) further showed that this false-positive outcome bias was associated with publications, and found that deviations from study protocol, such as switching from an intent-to-treat analysis to a per-protocol analysis (i.e., excluding dropouts and/or those who did not adhere to treatment protocol), accounted for some of the discrepancies between the FDA and published data. Subsequent meta-analyses examined this controversy further. Fournier et al. (2010) obtained raw IPD from six of the 23 short-term RCTs of antidepressant medication (a total of 718 patients). Using IPD, these authors found that antidepressant drugs were minimally effective for patients with mild or moderate depressive symptoms (Cohen’s d = 0.11), but their effects were better for those with severe (d = 0.17) or very severe (d = 0.47) depression. The controversy regarding the efficacy of antidepressant medication illustrates that quantitative synthesis, especially utilizing IPD, can play a unique role in drawing unbiased and robust inference in treatment research.
Unfortunately, controversies like this are not limited to pharmaceutical clinical trials. A recent review of meta-analytic studies published in psychological journals also reveals a clear publication bias (Bakker, van Dijk, & Wicherts, 2012). Bakker et al. demonstrated in a simulation study that it is easier to find inflated and statistically significant effects in underpowered samples than larger and more powerful samples, especially when the true effect size is small. This may be because smaller samples capitalize on chance variations in effect sizes (Tversky & Kahneman, 1971), and also because questionable research practices (e.g., failing to report data on all outcomes) make it more likely to discover statistically significant effects. This may explain the paradox in which typical psychological studies are underpowered, yet 96% of all articles in the psychological literature report statistically significant outcomes (Bakker et al., 2012). Overall, there is evidence of generally larger effects in smaller, compared with larger, studies (Borenstein, Hedges, Higgins, & Rothstein, 2009, p. 291; see also Kraemer, Mintz, Noda, Tinklenberg, & Yesavage, 2006).
In sum, findings from single studies may not provide sufficient, unbiased evidence as to the true magnitude of the effect of an intervention and the extent to which the effect can be applied (Ioannidis, 2005). In addition, published findings in the biomedical, as well as psychological, research fields have poor replicability (Begley & Ellis, 2012; Ioannidis, 2005; Nosek, Spies, & Motyl, 2012). Given that serious negative implications are associated with such poor reproducibility, calls have been made to raise standards for clinical trials (Begley & Ellis, 2012) and psychological research in general (Simmons, Nelson, & Simonsohn, 2011), as well as to improve transparency in reporting methodology and findings (Schulz, Altman, Moher, & the CONSORT Group, 2010; Tse, Williams, & Zarin, 2009). Accordingly, integrative studies synthesizing IPD may be one promising alternative to a large-scale, multisite RCT.
Project INTEGRATE: Data and DesignProject INTEGRATE was motivated to overcome limitations of single studies and AD meta-analyses via pooling IPD from multiple college alcohol intervention trials. More specifically, Project INTEGRATE was developed to examine (a) whether BMIs are efficacious for bringing about changes in theory-based behavior targets, such as normative perceptions about peer alcohol use and the use of protective behavioral strategies while drinking; (b) whether positive changes in behavior targets predict greater reductions in alcohol use and negative consequences; (c) whether subsets of interventions are more promising; and (d) whether subgroups exist for whom different interventions are more efficacious.
The present article (a) provides a summary of the Project INTEGRATE data and its unique design characteristics; (b) describes how we established commensurate measures across studies; and (c) discusses lessons learned and offers practical recommendations for single intervention studies. Once commensurate measures across studies are established, the stated project goals can be examined using a number of appropriate analytical methods. Thus, this article does not delve into any specific analytical models, as they would depend on the research questions being examined.
A group of investigators who had published studies assessing the efficacy of BMIs for college students were contacted in the spring of 2009, asking for their willingness to contribute their deidentified data. All but one agreed, resulting in a total of 24 studies (Studies 1 through 7, 8a through 8c, and 9 through 22; see Table 1 and the online supplemental materials). Note that Studies 7 and 10 are single studies, each with two distinct subsamples. In addition to examining BMIs, all 24 studies sampled college or university students in the United States and assessed alcohol use outcome measures. Existing review studies provide some perspective about our sample of 24 studies as it relates to the body of work on college alcohol BMIs as a whole. Larimer and Cronce (2002, 2007) and Cronce and Larimer (2011) systematically searched the literature covering the combined period from 1984 to early 2010 on individual-focused preventative intervention studies, and summarized results from a combined total of 110 studies, of which approximately one third came from the last 3 years (2007 to 2010). Similarly, Carey et al. (2007) meta-analyzed data from 62 studies that focused on individual-level interventions published between 1985 and early 2007. Thus, the sample of 24 studies included in Project INTEGRATE represents a good proportion of the existing BMIs conducted between 1990 and 2009 (published between 1998 and 2010). These studies are diverse in terms of original investigators, college campuses from which participants were recruited, demographic characteristics, and intervention study designs. Our combined data set also includes data from unpublished studies (Studies 8b, 8c, and 9) and unreported data from published studies (e.g., additional cohorts; Study 20). Investigators who contributed data provided clarifications about study design and data, documentation, and intervention content for their studies.
Study Design Characteristics (N = 12,630)
Study Design Characteristics (N = 12,630)
Combined Sample
Data pooled from all 24 studies consisted of 12,630 participants. All studies included one or more BMI conditions, with the majority (21 studies) including either a control condition or other comparison condition (i.e., alcohol education). Because condition labels varied across studies, we relabeled them based on shared intervention characteristics to one of the following five categories for Project INTEGRATE (Ray et al., 2014): motivational interview plus personalized feedback (MI + PF; n = 10), stand-alone personalized feedback (PF; n = 11), group motivational interview (GMI; n = 11), alcohol education (AE; n = 6), and control (n = 19). There were three unique conditions that did not fit these categories: an MI + PF combined with an AE intervention, an MI without PF, and an MI + PF combined with a parent-based intervention (see Table S1 of the online supplemental materials for all 60 intervention groups and their new labels included in Project INTEGRATE). Participant recruitment and selection also varied across studies, ranging from volunteer students recruited with flyers to students who were required to complete an alcohol program because they violated university rules about alcohol. Although some studies (i.e., Studies 8a, 8b, 8c, 10, 20, and 22) had assessments beyond 12 months postbaseline, we decided to focus only on follow-up data up to a year, as there was a considerable lack of overlap in timing of assessments beyond this point. Each study assessed participants at least twice from baseline up to 12 months. More details on participant characteristics, assessment schedules, and classification of study conditions can be found in Table 1.
More than half of the combined sample is comprised of women (58%) and first-year or incoming students (58%). The majority of the sample is White (74%), with 12% Asian, 7% Hispanic, 2% Black, and 5% belonging to other or mixed ethnic groups. Approximately 15% are college students mandated to complete a university program as a result of alcohol-related infractions, 27% are members (or pledged to be a member) of fraternities and sororities, and 13% are varsity athletes or members of club sports. Two studies of mandated students (Studies 2 and 7.1) utilized a waitlist control within the 12-month follow-up period. To preserve the original randomized group assignment at baseline, we excluded data from those control cases who were waitlisted initially at baseline and received an intervention at a time that the follow-up assessment took place for other treatment groups (i.e., 119 from Study 2 at the 6-month follow-up; 24 from Study 7.1 at the 6-month follow-up; see Table 1). The majority of the individual studies included in Project INTEGRATE have been previously described in the published literature. Additional study details that were not described previously are provided in the online supplemental materials.
In addition to this combined intervention data set, there were additional participants who were not part of the original intervention studies. Adding these screening or nonrandomized participants resulted in a total of 24,336 participants (60% women; 48% first-year or incoming students). This larger data set was used for item response theory (IRT) analysis and sensitivity analysis, as well as for research questions that did not involve intervention efficacy (e.g., racial and gender differences in alcohol-related problems; Clarke, Kim, White, Jiao, & Mun, 2013).
Study Design Characteristics and Analytic Considerations
IDA studies can be developed for specific research questions and there are a number of appropriate analytical approaches that can be utilized, depending on the nature of those questions as well as characteristics of the pooled data itself. Nonetheless, a discussion of some of the challenges and our counter measures to overcome them may be helpful for other IDA studies. The Project INTEGRATE data have a three-level data structure in which multiple repeated assessments are nested within individuals who are nested within studies. If no adjustment is made, any resulting standard error from the nested data tends to be underestimated and the power to detect any effects tends to be overestimated. This nested, correlated data structure can be measured using an intraclass correlation coefficient (ICC). Although the study ICC may be relatively small in our pooled data, the average cluster size (i.e., study sample size) is large, and the design effect, which is estimated as 1 + ICC × (average cluster size −1), can be substantial. In one analysis of a subsample, ICCs were small, ranging from 0.05 to 0.26, but the design effects were huge, ranging from 34.6 to 166.0, because of the large average cluster size (n = 648). To address this issue, we can use a sandwich variance estimator (see Hardin, 2003, for a review) suited for complex survey data. In conjunction with complex survey analysis, we can weight or scale data at the individual level (e.g., by using a weight of 1 over the square root of the sample size of each study) to prevent large studies from exerting overly dominating influences on overall estimates (see Table 1 for discrepant sample sizes across studies). In principle, large studies contribute more information and should count more toward estimates. However, a weighting strategy like this places slightly higher value on individuals’ information from smaller studies relative to individuals’ information from larger studies. An alternative approach is to utilize the multilevel modeling framework using either fixed-effects or random-effects models, which weight data differently when combining effects across studies (see DerSimonian & Laird, 1986) due to different assumptions involved in each modeling approach. This multilevel modeling approach can also accommodate weights, although the best practice may differ for each research project. Both complex survey analysis and multilevel analysis can readily be implemented by using commercially available software programs.
Project INTEGRATE: MeasuresOne of the most important challenges in conducting IDA or meta-analysis using IPD is to ensure that measures are comparable across studies (Cooper & Patall, 2009; Curran & Hussong, 2009; Hussong, Curran, & Bauer, 2013). To address this issue, we utilized harmonization and developed innovative IRT models. Table S2 of the online supplemental materials provides a list of our key constructs and overlap across studies, as well as the approach taken for each construct. For IRT analysis, some harmonization steps were taken to find common response options or to derive items that could be collapsed and linked across studies. Note that the overlap in measures across studies was excellent at the level of construct, but not at the item level. Within each study, most of the conceptual mediator variables were assessed at the same time as outcome measures.
Hierarchical IRT Approaches
When a construct was assessed using multiple items or scales that are well established in the literature, and when there was a subset of construct items that could be linked across studies, we conducted IRT analysis to obtain commensurate measures across studies. IRT, or latent trait theory (Lazarsfeld & Henry, 1968; Lord & Novick, 1968), has been used extensively in the area of educational testing and measurement, and with increasing frequency in psychological research (e.g., Gibbons et al., 2012). Unlike classical test theory, in the IRT framework, item parameters are independent of parameters describing individuals (or studies), which is a critical advantage for the current project, for which item subsets vary by individual and by study. Given the unique qualities of the Project INTEGRATE data, existing IRT methods were extended to handle sparse data, take into account study-level information (e.g., different trait means across studies), and borrow information, when possible, from related or higher order dimensions. More specifically, we developed several IRT models adapted from hierarchical, multiunidimensional, as well as unidimensional two-parameter logistic IRT (2-PL IRT) models, and developed MCMC algorithms to fit these IRT models within a hierarchical Bayesian perspective. Huo et al. (2014) provides the theoretical and technical details of the 2-PL IRT models and MCMC algorithms, as well as the findings of two simulation studies and real data analysis. The MCMC codes were written in Ox (Doornik, 2009), a matrix-based, object-oriented programming language, and are available upon request.
Scoring of latent trait scores across time
For each construct, item parameters were calibrated using baseline data, and these calibrated item parameters were then used to estimate latent trait scores for baseline and subsequent follow-up data. Prior to longitudinal scoring, we checked whether different items were assessed at different time points, and whether different sets of items used at different time points could have introduced bias in our estimation of latent trait scores. Furthermore, not all individuals assessed at baseline were followed up, either by study design or due to attrition. Therefore, we conducted sensitivity analyses by recalibrating data using different sets of items and different subsets of participants across time. We compared the descriptive statistics (e.g., means and standard deviations) of the estimated item parameters, structural parameters, and trait scores by using different sets of items calibrated and checked their correlations (r = .99), which led us to conclude that the differences in items and participants over time did not exert any meaningful influence on our estimates. Next, we give examples of how latent trait scores—often called “theta (θ) scores” in IRT—were established for two key constructs.
Alcohol-related problems
A total of 71 individual items were assessed in all 24 studies. Of the 71 items, three pairs of very similarly worded items were combined (e.g., “I have become very rude, obnoxious, or insulting after drinking”; “Have you become very rude, obnoxious, or insulting after drinking?”) and 68 unique items were subsequently analyzed. Items came from the Rutgers Alcohol Problem Index (RAPI; White & Labouvie, 1989), the Young Adult Alcohol Problems Screening Test (YAAPST; Hurlbut & Sher, 1992), the Brief Young Adult Alcohol Consequences Questionnaire (BYAACQ; Kahler, Strong, & Read, 2005), the Alcohol Use Disorders Identification Test (AUDIT; Saunders, Aasland, Babor, de la Fuente, & Grant, 1993), the Positive and Negative Consequences Experienced questionnaire (D’Amico & Fromme, 1997), and the Alcohol Dependence Scale (Skinner & Allen, 1982; Skinner & Horn, 1984). For each item, responses were dichotomized to indicate 1 = “yes” and 0 = “no,” because this response format was the common denominator across studies. When someone did not drink during the time frame referenced, their score was recoded as zero.
Several existing psychometric studies on alcohol-related problems have used a single-factor structure (RAPI: Neal, Corbin, & Fromme, 2006; YAAPST: Kahler, Strong, Read, Palfai, & Wood, 2004; BYAACQ: Kahler et al., 2005; Diagnostic and Statistical Manual of Mental Disorders (4th ed.): American Psychiatric Association, 1994; alcohol use disorder symptoms: Martin, Chung, Kirisci, & Langenbucher, 2006). Thus, we derived latent trait scores using a unidimensional 2-PL IRT model, which assumes that a single overall severity latent trait gives rise to item responses. We also estimated a four-dimensional 2-PL IRT model (the four related, but distinct, dimensions were Neglecting Responsibilities, Interpersonal Difficulties, Dependence-Like Symptoms, and Acute Heavy Drinking). The estimated correlations among the four dimensions exceeded 0.8. For two small studies (Studies 13 and 14; combined N = 138), only sum scores of the RAPI, but not individual item scores, were available. We matched latent trait scores for these participants using their RAPI sum scores with those from studies that had both latent trait scores and RAPI sum scores.
In the factor analysis environment, items are evaluated in terms of their factor loadings and thresholds (intercepts for continuous indicators), whereas in IRT analysis, items are typically evaluated by their discrimination and difficulty (or severity) item parameters. The item discrimination parameter is the slope of the item characteristic curve that indicates an item’s ability to discriminate among respondents, and how strongly an item is correlated to the underlying latent trait. Items with steeper slopes indicate better discrimination. For example, Item C (“The quality of work suffered because of drinking”) in Figure 1 discriminates respondents better than Item E (“Getting into trouble because of drinking at work or school”). The item difficulty parameter indicates the location of the item along the latent trait continuum where the probability of endorsement of the item is 0.5, and indicates how easy or difficult the item is to endorse. Items with higher difficulty are less frequently endorsed. We examined the total information curve (see Figure S1 in the online supplemental materials), which provides the overall performance of the measure at each level of an underlying latent trait (Markon, 2013). Overall, the items for alcohol-related problems provided less reliable or precise information about individuals whose underlying latent traits were at the lower end of the spectrum, but more reliable information for individuals whose traits were at the higher end of the spectrum (e.g., θ scores between 1 and 3). This also reflects that few alcohol-related problems items are easy to endorse in the present study, and that the majority of these items are more sensitive and informative for those who report high levels of alcohol-related problems, which is similar to findings from a previous analysis (Neal et al., 2006).
Figure 1. Item characteristic curves of several items in a two-parameter logistic item response theory model. A = “While drinking, I have said or done embarrassing things”; B = “Said things while drinking that you later regretted”; C = “The quality of my work or school work has suffered”; D = “Told by a friend or neighbor to stop or cut down on drinking”; E = “Gotten into trouble at work or school”; F = “Almost constantly think about drinking alcohol.” Numbers in parenthesis indicate item discrimination and severity parameters, respectively. See the online article for the color version of this figure.
It is worth mentioning that in deriving latent trait scores, there was a need to reconcile different referent time frames across studies. Most of the studies used a short referent time frame (3 months or shorter) for alcohol-related problems at baseline. More specifically, 20 studies out of 24 used a 1- to 3-month time frame, and three studies (Studies 4, 10, and 12) used a 6-month time frame. Only one study (Study 3) measured past-year alcohol-related problems using the YAAPST items and also included the AUDIT items, which ask about the last year (see Table S3 of the online supplemental materials for measure overlap and referent time frames at baseline across studies). A few studies asked about problems that occurred in two or three different referent time frames, and we examined their responses. Study 20, in particular, had 1-month, 6-month, and 1-year referent time frames for each RAPI item. Because there is a part–whole relationship between answers for the 1-month time frame and answers for the 6-month time frame, item endorsement rates should be higher for items assessed over the longer time frame. However, the differences across the three time frames were relatively small in magnitude, and also depended on item characteristics. For example, for a relatively easy item to endorse, such as “Got into fights, acted bad, or did mean things,” endorsement rates went up progressively across time frames (i.e., 15%, 23%, and 28%, respectively). For a relatively more difficult or severe item, such as “Felt that you had a problem with alcohol,” endorsement rates tended to be stable regardless of the referent time frame (i.e., 8%, 10%, and 11%, respectively). Correlations between 1-month and 6-month responses were also high (0.78 for the easy item; and 0.90 for the more difficult item). Most of the studies had a 1- to 6-month referent time frame at baseline, and follow-up assessments utilized a 1- to 3-month time frame in all studies. Note also that through IRT analysis, the measurement perspective was changed from the number of alcohol-related problems that occurred within a given time frame (i.e., a count variable) to the severity of alcohol-related problems (i.e., a trait score in a normal distribution).
The correlations between the original scale sum scores (e.g., the RAPI or YAAPST sum scores) and latent trait scores within studies were, on average, 0.83, suggesting that the rank orders of individuals within studies were similar across the two approaches (i.e., summed scale scores and theta scores from the IRT analysis). However, these two approaches are based on different measurement models and items, and are not directly comparable.
Protective behavioral strategies
Protective behavioral strategies refer to specific cognitive–behavioral strategies that can be employed to reduce risky drinking prior to and during drinking, and to limit harm from drinking (Martens et al., 2005). A total of 58 protective behavioral strategy items assessed by 13 studies were analyzed. Items came from the 10-item Protective Behavioral Strategies (PBS) measure taken from the National College Health Assessment survey (American College Health Association, 2001), the 15-item Protective Behavioral Strategies Scale (PBSS; Martens et al., 2005), the 37-item Self-Control Strategies Questionnaire (SCSQ; Werch & Gorman, 1988), a seven-item Drinking Restraint Strategies scale used in Wood, Capone, Laforge, Erickson, and Brand (2007), and a nine-item Drinking Strategies (DS) scale reported in Wood et al. (2010). We removed items that indicated either abstinence (i.e., “Chose not to drink alcohol”) or risky (as opposed to protective) drinking behaviors (i.e., “Drink shots of liquor”), as well as items that were used in only one study, as they could not be linked to measures of other studies for our IRT analysis. Of the remaining items, 20 were combined into five individual items because they were very similarly worded (e.g., “use a designated driver”; “used a designated driver”; “use a safe ride or taxi service when you have been drinking”; “make arrangements not to drive when drinking”). Forty-three remaining items were analyzed via hierarchical IRT, specifying a single, underlying dimension of protective behavioral strategies. Although the literature varies as to the dimensionality of these behaviors (e.g., three dimensions for the PBSS in Martens et al., 2005; four dimensions for the PBSS in Walters, Roudsari, Vader, & Harris, 2007; seven factors for the external SCSQ in Werch & Gorman, 1988; one summed score for the DS in Wood et al., 2010), we used a unidimensional IRT model because of lack of overlap in items across studies and also because protective behavioral strategies are often used as a single overall score (e.g., Benton, Downey, Glider, & Benton, 2008; Martens, Ferrier, & Cimini, 2007). Furthermore, the three dimension scores of the PBSS are similarly related to alcohol use, alcohol-related problems, and depressive symptom scores (Martens et al., 2005; Martens et al., 2008). Only data for individuals who reported recent drinking (i.e., past 1 to 3 months) were included.
Items were recoded to indicate 0 = never; 1 = rarely, seldom, occasionally, or sometimes; 2 = usually or often; and 3 = always. Although some of the past studies dichotomized item responses (0 and 1 vs. 2 and 3; e.g., Walters, Roudsari, et al., 2007), we deemed it important that a protective behavior that is often used be differentiated from one that is always used, and that this difference be reflected in estimating latent trait scores. Thus, we used a generalized partial credit model (Muraki, 1992) to assign partial credit for polytomous items. Unlike the previous IRT model, the single difficulty parameter of an item is replaced by three step difficulty parameters, each of which can be interpreted as the intersection point of two adjacent item response curves (0 and 1, 1 and 2, 2 and 3; see Figure 2). These intersection points are the points on the latent trait scale axis (x-axis), in which one response (e.g., 2 = usually or often) becomes relatively more likely than the preceding response (e.g., 1 = rarely).
Figure 2. Category response curves of two items (Figure 2A and Figure 2B) of protective behavioral strategies from the generalized partial credit item response theory model. Item A (“Stop drinking at a predetermined time”) in Figure 2A had a slope parameter of 0.99, with item step difficulty parameters of −0.69 (from 0 to 1), 1.62 (from 1 to 2), and 2.24 (from 2 to 3), respectively. Item B (“Eat before and/or during drinking”) in Figure 2B had a slope parameter of 0.49, with item step difficulty parameters of −3.85 (from 0 to 1), −0.48 (from 1 to 2), and 1.29 (from 2 to 3), respectively. See the online article for the color version of this figure.
Figure 2 shows category response curves for two protective behaviors under the partial credit model. It is relatively easy for participants to endorse “rarely” or “usually” as opposed to “never” for Item B (“Eat before and/or during drinking”), compared with Item A (“Stop drinking at a predetermined time”). Most of the responses to Item A were either “never” or “rarely.” In contrast, most of the responses to Item B occurred between “rarely” and “always.” Item step difficulty parameter estimates reflect this relative difficulty. Item step difficulty parameter estimates for Item A were higher than those for Item B at intersection points (e.g., 1.62 vs. −0.48 for Item A vs. Item B for the intersection between “rarely” and “usually”). In sum, it is relatively more difficult to stop drinking at a predetermined time than to eat food during or before drinking. Polytomous items, therefore, can meaningfully be interpreted in terms of how difficult one item is to endorse compared with other items. The correlations between the original scale sum scores (e.g., the PBS, PBSS) and latent trait scores within studies exceeded 0.96.
Differential item functioning (DIF) and latent traits
DIF tests examine whether participants with the same level of a construct but different backgrounds respond similarly to the same items, and are often conducted in IDA research (Curran et al., 2008; Hussong et al., 2007). Likewise, important covariates, which can be different for different IDA studies, can be included in measurement models when estimating latent traits (e.g., moderated nonlinear factor analysis; Bauer & Hussong, 2009; Curran et al., 2014). Each IDA study may also make certain assumptions about data and item performance.
In deriving item parameters and latent trait scores in the current study, we initially made an assumption that the same items administered in different studies had the same item parameters as specified in the item response function, after taking into account different average trait levels across studies. We reasoned that it is a sensible assumption because all participants were college students who were assessed within a narrow window of assessment (i.e., 12 months). In addition, we had a high proportion of overall missing data at the item level, which can be attributed to the large number of both studies and items that were pooled. Note also that we treated many similarly worded items as different items, which increased the number of items and, consequently, the amount of missing data. The high proportion of missing data for this large-scale IDA data set made it very difficult, if not impossible, to examine DIF for many items (see Huo et al., 2014, for an example of item overlap across studies [in their Table 5] and findings from a simulation study on missing data). In addition, the amount of missingness prevented us from using existing software programs, such as Mplus (Muthén & Muthén, 1998–2014), to compute the tetrachoric correlation from which further analyses (e.g., factor analysis, structural equation modeling analysis) can be conducted.
We should note that there exists an indeterminacy between DIF and group (study) differences in latent traits, which has been well known among psychometricians for some time (e.g., Thissen, Steinberg, & Gerrard, 1986), and that DIF depends on the items or a set of items that serve as a point of reference (i.e., an anchor) because the choice of invariant items within a pool of items affects how remaining items behave (Bechger, Gunter, & Verstralen, 2010; see also Byrne, Shavelson, & Muthén, 1989, for the nonindependence of these tests in the context of confirmative factor analysis). That is the reason why DIF items within a set of items can change depending on search strategies and measurement models (Kim & Yoon, 2011; Yoon & Millsap, 2007). In other words, DIF is only relative to the reference point, which can be set in several ways in a large pool of items. Consequently, latent trait scores can also shift up and down along the theta scale depending on the invariant items or DIF items, although relative positions of individuals on this scale may remain the same across groups. Even when DIF items exist across groups within a pool of items, as long as there are invariant anchor items that provide linkage across groups, latent trait scores can reasonably be estimated. Some in the literature have stated that only one invariant (i.e., non-DIF) item is necessary to establish partial invariance across different groups (studies) for a single unidimensional construct (e.g., Steenkamp & Baumgartner, 1998; also briefly noted in Bauer & Hussong, 2009). Due to this nature of DIF, it may not be best to focus on which items show DIF.
What is central for IDA is whether trait scores are unbiased in relation to a key design variable (i.e., study). Thus, we conducted an additional IRT analyses for alcohol-related problems to examine this question. We compared the latent trait scores from the original IRT model (no-DIF model) with those from alternative models that subset a portion of items to take different item parameters (i.e., DIF items) across studies. If latent trait scores resulting from these different approaches (i.e., no-DIF model vs. DIF models) are equivalent, we can be assured that our trait scores, as a whole, are invariant to study. Our strategy is essentially equivalent to the IRT strategy adopted by Hussong et al. (2007), with the exception that in Hussong et al., DIF items were specifically specified, whereas we allowed some items to have DIF across studies. Results indicated that no meaningful differences existed in latent trait scores between these two IRT approaches (see Figures S2 and S3 in the online supplemental materials). The rank orders of individuals within and across studies were preserved across the two IRT models (rs ≥ 0.95). In addition, the rank orders of the studies in terms of their observed theta means were also largely the same. However, our original no-DIF model was the simpler, more parsimonious model, and had the lower deviance information criterion (DIC; Spiegelhalter, Best, Carlin, & van der Linde, 2002) than the alternative DIF model. The DIC is appropriate for comparing models that are estimated from MCMC analysis. It can be considered as a generalized version of the Akaike information criterion (Akaike, 1974) and Bayesian information criterion (Schwarz, 1978) for Bayesian models. Thus, through the use of novel IRT methods, we were able to combine different items from different scales across the studies included in Project INTEGRATE. The result of these IRT analyses is that all participants could be placed on the same underlying trait scale, although these traits were assessed with different scales, items, and/or response options in the original studies.
Given that we have intervention and control groups, we also checked latent trait scores obtained from our IRT analysis to make sure no systematic bias exists in separating these groups within studies. With the exception of the three studies that did not have a control group, all individual studies utilized random assignment. Thus, the treatment and control groups should be, and were, mostly equivalent at baseline when comparing either original scale scores or latent trait scores. Table S4 of the online supplemental materials provides a list of important considerations and actions that we have made to estimate latent trait scores.
Harmonization
Harmonization can be described as the recoding of variables so that values from different variables assessing the same construct can be made comparable. More broadly, harmonization refers to a general approach in which measures are retrospectively made comparable to synthesize large data sets, and it is increasingly utilized in biomedical epidemiological research (e.g., Fortier et al., 2010). Harmonization can be straightforward if standard measures, such as the Daily Drinking Questionnaire (DDQ; Collins, Parks, & Marlatt, 1985), are utilized. The DDQ asks respondents to indicate the number of drinks they consumed on each day of a typical week in the last month. In the present study, the majority of studies utilized the DDQ, which allowed us to create several key alcohol use frequency and quantity measures. Although the usual time frame for the DDQ is past month, a few studies utilized the past 3 months as a referent time frame. We assumed that this different time frame does not bias the self-reported number of drinks consumed on each day of a typical drinking week for college students.
Trade-off between item overlap and information
In the absence of standard measures, however, one needs to weigh a gain in item overlap across studies against a loss of information that can result when trying to find a common denominator for items. In our research, for example, three studies assessed the number of drinking days (frequency of alcohol use) in the past month as an open-ended question, and six studies collected daily drinking diaries for a 30-day window, which could then be used to compute the number of drinking days in the past month. In contrast, six other studies assessed the frequency of drinking using the AUDIT, which had the following ordinal response options: 0 = never; 1 = monthly or less; 2 = 2–4 times a month; 3 = 2–3 times a week; and 4 = 4 or more times a week. In this case, the AUDIT ordinal response format provides the “lowest common denominator” among response options, but using it would lead to a loss of information for those studies that used more detailed assessments. Thus, deriving a comparable measure using harmonization required striking an appropriate balance between item overlap across studies and information (i.e., greater overlap but loss of information vs. more information retained for fewer studies). For many of the secondary outcome measures, we derived dichotomous outcome measures (e.g., any driving after three drinks or more in the past year) to ensure the broadest possible measurement coverage across studies.
Limits of harmonization
For some constructs, it was not possible to derive a common measure that could be meaningfully compared across studies. One such example was heavy episodic drinking, a well-known and widely used outcome measure in studies of college student drinking. Heavy episodic drinking, or binge drinking, is defined by the National Institute on Alcohol Abuse and Alcoholism (2004) as a pattern of drinking alcohol that brings blood alcohol concentration to 0.08 g percent or above, which corresponds to consuming five or more drinks (men) or four or more drinks (women) in 2 hours. Questions actually used in studies were (a) “How many times have you drank 5 drinks or more?” (for men; 4 or more drinks for women); (b) “How many times have you drank 5 drinks or more?” (i.e., regardless of sex); (c) “How many times have you drank 6 drinks or more?” (i.e., regardless of sex); (d) “How many times have you drank 5 drinks or more in one sitting?” (or in a row); and (e) “How many times have you drank 5 drinks or more within two hours?” These questions were also asked for different referent time frames: in the past 2 weeks, 4 weeks, or 1 month. Thus, key differences across all items were the referent time period (2 weeks vs. 4 weeks/1 month), number of drinks (four, five, or six drinks), sex (sex-specific vs. sex-nonspecific), and duration of a drinking episode (unspecified hours, one sitting or in a row, or two hours). Two studies assessed both 2-week and 1-month heavy episodic drinking measures (five or more drinks for men and four or more drinks for women). Examining means and correlations of these items for these studies led us to conclude that heavy episodic drinking in the past 2 weeks could not be multiplied by 2 to create a measure for the past month (see Table 2). Although these two measures were highly correlated (r > .7), their means were more difficult to map onto a common metric. The 1-month question tended to be an underestimate of the 2-week measure that was multiplied by 2, except in one case in which it was overestimated (i.e., women in Study 22). Thus, any between-study differences in heavy episodic drinking would be confounded with the way heavy episodic drinking was assessed.
Heavy Episodic Drinking (HED) Variable as an Example When Harmonization Was Not Feasible
Similar to the heavy episodic drinking measure, we concluded that readiness to change, a key mediator variable, could not be made comparable across studies. Readiness to change refers to the degree to which an individual is motivated to change problematic drinking patterns, and is measured by assessing different stages of cognitive and affective processes that lead to an initial change effort (Carey, Purnine, Maisto, & Carey, 1999). Although this construct was measured in the majority of the studies, each study included only one scale or a single item assessing this construct, and there was little overlap across the studies. Eight studies used the Readiness to Change Questionnaire (Heather, Rollnick, & Bell, 1993); four studies used the University of Rhode Island Change Assessment (Heesch, Velasquez, & von Sternberg, 2005); and seven studies used different variations of a single-item, readiness to change ruler (LaBrie, Quinlan, Schiffman, & Earleywine, 2005) or contemplation ladder (Herzog, Abrams, Emmons, & Linnan, 2000) that differed in response ranges (1 to 5, 1 to 10, or 0 to 10), as well as anchor points to mark different stages of change. With just one measure for each study and with no overlap in items across scales (and studies), we concluded that any differences in measures would be confounded with between-study differences (e.g., sample or design characteristics). Thus, any analyses using “readiness to change” will have to be replicated across the different scales that are available rather than using the pooled data set. The steps taken and outcomes from these steps thus far demonstrate that even with latest advances in analytical modeling and well-established measures for key constructs, there are some limits. In the next section, we provide a discussion of how to better design individual studies, especially intervention studies, with IDA in mind.
Lessons Learned Thus Far and RecommendationsOne of the most striking lessons that we have learned thus far is that this innovative approach to synthesizing information from multiple studies is very labor-intensive and time-consuming. To meaningfully conduct IDA studies for clinical outcomes, such as intervention efficacy and moderated efficacy, the number of studies included should be sufficiently large to examine study-level, as well as individual-level, differences. However, IDA demands significant time and resources to pool data from studies, clean and check data, and establish commensurate measurement scales. Citing the work by Steinberg et al. (1997) and personal communication with one of the investigators, Cooper and Patall (2009) noted that IPD meta-analysis probably costs 5 to 8 times more than AD meta-analysis, and takes several years from start to publication in the field of medical research. When standard measures are less commonly used or difficult to establish across studies, which is typical for psychological research, the cost may be even greater than what has been estimated for medical research, in which the primary outcome (e.g., death) can often be clearly defined.
For Project INTEGRATE, we developed new MCMC algorithms to estimate item parameters and latent trait scores across studies, which took an enormous amount of time and effort, because commercially available software programs did not sufficiently meet our needs. Our first-hand experience suggests that the application of IPD meta-analysis may require further methodological developments, whereas AD meta-analysis procedures are fairly well-established at the present time. Furthermore, unlike in medical trials in which treatment and control conditions can clearly be defined (i.e., a specific procedure or drug), we learned that treatment and control groups may not be equivalent across studies, which required a closer examination of these groups to ensure that similarly labeled groups in original studies had many critical features in common (Ray et al., 2014).
The capabilities to reexamine effect sizes using more appropriate analytical methods and to peruse intervention procedures to appropriately compare treatment groups are important advantages of IDA over single studies or AD meta-analysis. In the context of IPD meta-analysis, multiple RCTs are typically conceptualized as a sample of studies. The findings can then be generalized to a broader population. A recent IPD meta-analysis study that examined the efficacy of BMIs for Project INTEGRATE is one such research application (Huh et al., 2014). In Huh et al., we utilized Bayesian multilevel, overdispersed Poisson hurdle models to examine intervention effects on drinks per week and peak drinking, and Gaussian models for alcohol problems. This analytic approach accommodated the sampling, sample characteristics, and distributions of the pooled data while overcoming some of the challenges associated with being an IDA study, one of which was the unbalanced RCT design (i.e., 21 interventions vs. 17 controls across 17 studies) of the pooled data set. Although the study by Huh et al. highlights some of the promises of IDA, for this type of investigation, a large enough number of studies are needed to obtain sufficient precision about point estimates and standard errors. Others have said that at least 10 to 20 studies may be needed for population representation and proper model estimation (e.g., Hussong et al., 2013). As the number of studies included for IDA goes up, however, so does the demand on time and other resources.
Having emphasized the need for greater resources for IDA, we remain enthusiastic that IDA is a better research strategy for examining low-base-rate behaviors, such as marijuana or drug use outcomes (White et al., 2014), and for finding subgroups who may respond to treatment differently (i.e., moderators of treatment outcomes), which is widely considered as one of the most important strengths of IDA (e.g., Brown et al., 2013). Thus, IDA holds special promises for the field. We would also like to note that the resources needed may be highly specific to the research goals of individual IDA studies. Other notable strengths of IDA, compared with single studies, include larger, more heterogeneous samples and more repeated measures for longer observed periods. Depending on the specific research questions, the pooled data set from just two studies may be better than data from a single study, as long as the replicability of measurement models can hold across studies.
Emerging analytical and technological advances may provide more favorable environments for pooling and analyzing IPD in the future. In the present moment, our experiences suggest ways to lower barriers to IDA by planning single intervention studies differently. We have several recommendations for future single intervention studies.
Increase Overlap in Measures
The simplest option to increase overlap in measures across studies is to use standardized and common measures for a given construct in future single studies. If there is a need to include a newly developed questionnaire or instrument, it would be quite helpful to include other established measures of the same construct to link items from different measures. Note that the overlap needs to exist not just at the level of the constructs but also at the level of items (and response options). When a concern arises about burdening participants with multiple items, it may be better to administer a portion of items from one measure and a portion of items from other measures (e.g., two versions, A-B and B-C, administered to two groups), as is done in a planned missingness design (Graham, Hofer, & MacKinnon, 1996). This strategy, a common practice in educational research, is better for IDA because items can be linked across studies. In theory, a single item may be used to provide such a chain. However, the level of precision or trustworthiness of the chain will improve with more shared items across studies. Our experience also suggests that, with more work, item banks may be developed for key constructs for this college population, which may make it feasible to derive latent trait scores across studies in the future without the needed overlap in items. At present, there is no such known item bank specifically aimed at this population.
Based on our experience, the importance of common, standard items may be greater for single-item measures, such as heavy episodic drinking, which are often utilized in alcohol research. Our experience is by no means unique. Other investigators have also noted the difficulty of harmonizing alcohol measures across studies (e.g., analysis of twin studies: Agrawal et al., 2012; genome-wide association studies: Hamilton et al., 2011). Future investigations could utilize measures from well-researched and accessible research tools, such as the Phenotypes and eXposures (PhenX) Toolkit (Hamilton et al., 2011; https://www.Phenxtoolkit.org/), the NIH Toolbox for assessment of neurological and behavioral function (http://www.nihtoolbox.org/Pages/default.aspx), or the Patient-Reported Outcomes Measurement Information System (PROMIS; http://www.nihpromis.org/; see Pilkonis et al., 2013, for the development of item banks for alcohol use, consequences, and expectancies).
Increase Overlap in Follow-Ups
The ability to extend the range of observations in terms of the observed time period is one of the advantages of IDA over single studies. However, this can lead to a greater portion of missing data in the combined data set. Two types of missing data exist in IDA: items that were not assessed by study design, and are thus missing at the level of studies; and items that were included but not answered by the participant (Gelman, King, & Liu, 1998). Table 1 provides a glimpse of the sparse nature of pooled data across time, especially at longer-term follow-ups (e.g., 6 to 12 months postintervention). Table S2 of the online supplemental materials shows available constructs for each study. In both tables, missing data are due to different study designs across studies. Within studies, there were also missing data at the individual level due to omitted responses. Although missing values may be random in nature (i.e., missing at random [MAR]) and ignorable (Schafer, 1997) for this project, the pattern of missingness was unique for some studies, and the overall proportion of missing data was substantial.
The missing data challenge can be mitigated if there is better overlap in follow-up assessments across intervention studies. Overall, the power to detect a group difference goes up with increases in the duration of observations, the number of repeated assessments, and the sample size. Of those, the duration has the greatest effect, and the number of repeated assessments has the smallest effect, on power (Moerbeek, 2008). Despite the small effect on power, to capture a change immediately following an intervention and a slower subsequent rebound, one has to have at least four (preferably more) repeated assessments to estimate polynomial growth models without the need to impose restrictive constraints. Although it is reasonable to assess outcomes more frequently, for example, in the first 3 months following a BMI, it is also desirable to assess outcome data beyond the initial phase to see whether, and for how long, the intervention effect is sustained. We recommend that future alcohol intervention trials extend the period under observation to intermediate or long-term follow-ups (e.g., 6 to 12 months postintervention), as this longer-term follow-up is needed from both substantive and methodological perspectives. Assuming that missing data at follow-ups (i.e., dropouts) meet the MAR assumption, the extension of the observed duration should improve power to detect intervention efficacy and other mediational effects. Similar to the case of increasing item overlap by design, the use of planned missingness may prove to be useful in estimating patterns of change for intervention studies. A design of 1-, 3-, 6-, and 9-month follow-ups for a random half sample, and 1-, 2-, 6-, and 12-month follow-ups for the other half, for example, would provide up to seven time points, including baseline, for up to a year, with overlap at baseline, 1-month follow-up, and 6-month follow-up.
Reduce Heterogeneity in Treatment and Control Groups Across Trials
Project INTEGRATE includes interventions that varied, for example, in the number and type of content topics covered and the manner in which they were delivered (e.g., in-person one-on-one, in-person group, by mail) to participants across studies. Therefore, we developed detailed coding procedures for all intervention and control conditions, which allowed us to determine whether similarly labeled groups are indeed equivalent (see Ray et al., 2014, for detail). Based on the content analysis of these components across conditions and the subsequent analysis of those components, we relabeled some of the groups and removed others from the main data set (see Table S1 of the online supplemental materials). This observation highlights a need to develop detailed documentation on the proposed mechanisms and protocols for any new treatment and for any new variant of an existing, evidence-based treatment in the future. In designing future single studies, one should also carefully consider a treatment group and a comparison group for their comparability and overlap with other studies.
Improve Transparency and Documentation
In general, it would be helpful to have greater transparency and better documentation in published articles, as well as in unpublished supporting materials. General reporting guidelines, such as the CONSORT statement (Schulz et al., 2010) and the Journal Article Reporting Standards by the APA Publications and Communications Board Working Group on Journal Article Reporting Standards (2008), have provided a minimum reporting standard for various types of studies, including RCTs. ClinicalTrials.gov, an online registry and results database for Phases 2 through 4 intervention studies, provides easy access to some of the critical, scientific information about clinical studies (i.e., participant flow, baseline characteristics, outcome measures, statistical analyses, and adverse events; Tse et al., 2009). However, the required minimum information for ClinicalTrials.gov focuses on the overall efficacy and adverse events of a treatment, and does not go far enough to facilitate future IDA investigations.
We recommend that any additional outcome measures and covariates at each assessment point, follow-up schedules (beyond posttreatment), and any additional groups (treatment arms) be publicly accessible if they are omitted in published articles. This supplementary information, which could be publicly accessible and searchable, would facilitate IDA studies in the future by helping to select studies for IDA or determining feasibility of such investigations. More detailed and accurate documentation will decrease the need, for example, to pore over codebooks, questionnaires, and data to examine the nature of variation in key outcome measures and covariates. Making this information publicly available may also help to increase awareness among investigators as to the potential overlap with other studies when planning a single study.
ConclusionsProject INTEGRATE was launched to generate robust statistical inference on the efficacy of BMIs for college students, and to examine theory-supported mechanisms of behavior change. The detailed account outlined in this article illustrates both the promises and challenges of this particular IDA project and of IDA in general. The promises of IDA are attractive in the current research environment, in which limited resources are maximized by taking advantage of more efficient designs and analyses. Moreover, IDA investigations are well positioned to confront current outcries about replication failures and potentially overstated treatment benefits in the era of evidence-based-treatment decision making. At the same time, these notable promises are coupled with significant challenges. IDA is not a single analytic technique per se. Rather, it is a set of advanced methods that can be tailored and implemented to address specific goals and challenges of each IDA study, which can be seen clearly in the present article. Our strategies and procedures differed from those of others (e.g., Hussong et al., 2013), which can be attributed to the different data characteristics and different assumptions made about item performance in our study. More methodological research is needed to test these assumptions and to develop guidelines for IDA research, which is expected to increase in the future. Nonetheless, the specific recommendations that we have for single intervention studies may be helpful not only for more robust research practice but also for large-scale research synthesis, such as IPD meta-analysis and IDA.
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Submitted: February 20, 2014 Revised: November 8, 2014 Accepted: November 10, 2014
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Source: Psychology of Addictive Behaviors. Vol. 29. (1), Mar, 2015 pp. 34-48)
Accession Number: 2014-57147-001
Digital Object Identifier: 10.1037/adb0000047
Record: 128- Title:
- Providing intensive addiction/housing case management to homeless veterans enrolled in addictions treatment: A randomized controlled trial.
- Authors:
- Malte, Carol A., ORCID 0000-0002-6126-0733. Center of Excellence in Substance Abuse Treatment and Education (CESATE), Veterans Affairs (VA) Puget Sound Health Care System, Seattle, WA, US, carol.malte@va.gov
Cox, Koriann. Center of Excellence in Substance Abuse Treatment and Education (CESATE), Veterans Affairs (VA) Puget Sound Health Care System, Seattle, WA, US
Saxon, Andrew J.. Center of Excellence in Substance Abuse Treatment and Education (CESATE), Veterans Affairs (VA) Puget Sound Health Care System, Seattle, WA, US - Address:
- Malte, Carol A., Center of Excellence in Substance Abuse Treatment and Education, VA Puget Sound Health Care System, 1660 South Columbian Way, S-116 ATC, Seattle, WA, US, 98108, carol.malte@va.gov
- Source:
- Psychology of Addictive Behaviors, Vol 31(3), May, 2017. pp. 231-241.
- NLM Title Abbreviation:
- Psychol Addict Behav
- Page Count:
- 11
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- Bulletin of the Society of Psychologists in Addictive Behaviors; Bulletin of the Society of Psychologists in Substance Abuse
- Other Publishers:
- US : Educational Publishing Foundation
Society of Psychologists in Addictive Behaviors - ISSN:
- 0893-164X (Print)
1939-1501 (Electronic) - Language:
- English
- Keywords:
- homelessness, veterans, substance use disorders, intensive case management, health care utilization
- Abstract:
- This study sought to determine whether homeless veterans entering Veterans Affairs (VA) substance use treatment randomized to intensive addiction/housing case management (AHCM) had improved housing, substance use, mental health, and functional outcomes and lower acute health care utilization, compared to a housing support group (HSG) control. Homeless veterans (n = 181) entering outpatient VA substance use treatment were randomized to AHCM and HSG and received treatment for 12 months. AHCM provided individualized housing, substance use and mental health case management, life skills training, and community outreach. The control condition was a weekly drop-in housing support group. Adjusted longitudinal analyses compared groups on baseline to month 12 change in percentage of days housed and functional status, substance use, and mental health outcomes (36-Item Short-Form Health Survey; Addiction Severity Index [ASI]). Negative binomial regression models compared groups on health care utilization. Both conditions significantly increased percentage of days housed, with no differences detected between conditions. In total, 74 (81.3%) AHCM and 64 (71.1%) HSG participants entered long-term housing (odds ratio = 1.9, 95% confidence interval [0.9, 4.0], p = .088). HSG participants experienced a greater decrease in emergency department visits than AHCM (p = .037), whereas AHCM participants remained in substance use treatment 52.7 days longer (p = .005) and had greater study treatment participation (p < .001) than HSG. ASI alcohol composite scores improved more for HSG than AHCM (p = .006), and both conditions improved on ASI drug and psychiatric scores and alcohol/drug abstinence. AHCM did not demonstrate overarching benefits beyond standard VA housing and substance use care. For those veterans not entering or losing long-term housing, different approaches to outreach and ongoing intervention are required. (PsycINFO Database Record (c) 2017 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Case Management; *Drug Rehabilitation; *Health Care Utilization; *Homeless; *Substance Use Disorder; Mental Health; Military Veterans
- PsycINFO Classification:
- Health & Mental Health Treatment & Prevention (3300)
Military Psychology (3800) - Population:
- Human
Male
Female
Outpatient - Location:
- US
- Age Group:
- Adulthood (18 yrs & older)
- Tests & Measures:
- Housing History Form
Veteran’s SF-36
Timeline Follow-Back Structured Interview
Addiction Severity Index DOI: 10.1037/t00025-000
Client Satisfaction Questionnaire
36-Item Short Form Health Survey DOI: 10.1037/t07023-000 - Grant Sponsorship:
- Sponsor: VA Health Services Research & Development, US
Grant Number: SDR 11-231
Recipients: No recipient indicated
Sponsor: VA Puget Sound Center of Excellence in Substance Abuse Treatment and Education, US
Recipients: No recipient indicated - Clinical Trial Number:
- NCT01346514
- Methodology:
- Clinical Trial; Empirical Study; Quantitative Study
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- Accepted: Feb 28, 2017; Revised: Feb 22, 2017; First Submitted: Aug 10, 2016
- Release Date:
- 20170508
- Digital Object Identifier:
- http://dx.doi.org/10.1037/adb0000273
- Accession Number:
- 2017-19539-001
- Persistent link to this record (Permalink):
- http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2017-19539-001&site=ehost-live
- Cut and Paste:
- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2017-19539-001&site=ehost-live">Providing intensive addiction/housing case management to homeless veterans enrolled in addictions treatment: A randomized controlled trial.</A>
- Database:
- PsycINFO
Record: 129- Title:
- Psychiatric and psychological factors in patient decision making concerning antidepressant use.
- Authors:
- Dijkstra, Arie. University of Groningen, Department of Social and Organizational Psychology, Groningen, Netherlands, arie.dijkstra@rug.nl
Jaspers, Merlijne. Department of Health Sciences, University Medical Center Groningen, University of Groningen, Groningen, Netherlands
van Zwieten, Marianne. Research Division, Intraval, Groningen, Netherlands - Address:
- Dijkstra, Arie, University of Groningen, Department of Social and Organizational Psychology, Grote Kruisstraat 2/1, 9712 TS, Groningen, Netherlands, arie.dijkstra@rug.nl
- Source:
- Journal of Consulting and Clinical Psychology, Vol 76(1), Feb, 2008. pp. 149-157.
- NLM Title Abbreviation:
- J Consult Clin Psychol
- Page Count:
- 9
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- Journal of Consulting Psychology
- Other Publishers:
- US : American Association for Applied Psychology
US : Dentan Printing Company
US : Science Press Printing Company - ISSN:
- 0022-006X (Print)
1939-2117 (Electronic) - Language:
- English
- Keywords:
- antidepressant use, patient decision making, goal intentions, temporal comparisons
- Abstract:
- The observation that the use of antidepressants has strongly increased during the past decade implies that on a micro level doctors and patients more often decide that antidepressants are the appropriate treatment. Therefore, it is important to increase insight into patients' decision making regarding the use of antidepressants. The decision making model used in the present study was based on A. Bandura's (1986) social cognitive theory. Two cohorts of patients were recruited and followed for 9 months. Among patients who use antidepressants (N = 166), the stronger pros and weaker cons of discontinuation and self-efficacy predicted more proximal goal intentions. Goal intentions predicted discontinuation after 9 months. Among patients who had used antidepressants in the past (N = 73), stronger pros of discontinuation and the weaker perceived functions of antidepressants predicted a more positive evaluation of their present state, compared with when they still used antidepressants. These temporal comparisons, in turn, predicted renewed use of antidepressants after 9 months. The results provide a framework for supporting and influencing decision making with regard to the use of antidepressants. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Antidepressant Drugs; *Client Attitudes; *Decision Making; *Drug Therapy; *Intention
- Medical Subject Headings (MeSH):
- Adult; Antidepressive Agents; Anxiety Disorders; Combined Modality Therapy; Counseling; Decision Support Techniques; Depressive Disorder; Female; Health Knowledge, Attitudes, Practice; Humans; Logistic Models; Male; Middle Aged; Patient Compliance; Personality Inventory; Psychotherapy; Retreatment; Self Efficacy; Substance Withdrawal Syndrome; Surveys and Questionnaires; Treatment Refusal
- PsycINFO Classification:
- Clinical Psychopharmacology (3340)
- Population:
- Human
Male
Female - Location:
- Netherlands
- Age Group:
- Adulthood (18 yrs & older)
- Tests & Measures:
- Beck Depression Inventory-Dutch validated version
State Trait Anxiety Inventory - Methodology:
- Empirical Study; Quantitative Study
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- Accepted: Jun 25, 2007; Revised: Jun 20, 2007; First Submitted: Dec 5, 2006
- Release Date:
- 20080128
- Copyright:
- American Psychological Association. 2008
- Digital Object Identifier:
- http://dx.doi.org/10.1037/0022-006X.76.1.149
- PMID:
- 18229992
- Accession Number:
- 2008-00950-017
- Number of Citations in Source:
- 49
- Persistent link to this record (Permalink):
- http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2008-00950-017&site=ehost-live
- Cut and Paste:
- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2008-00950-017&site=ehost-live">Psychiatric and psychological factors in patient decision making concerning antidepressant use.</A>
- Database:
- PsycINFO
Psychiatric and Psychological Factors in Patient Decision Making Concerning Antidepressant Use
By: Arie Dijkstra
Department of Social and Organizational Psychology, University of Groningen, Groningen, The Netherlands;
Merlijne Jaspers
Department of Health Sciences, University Medical Center Groningen, University of Groningen
Marianne van Zwieten
Research Division, Intraval, Groningen, The Netherlands
Acknowledgement:
The use of antidepressant medication has increased greatly during the past decade (Ciuna et al., 2004; Hemels, Koren, & Einarson, 2002; Rigler et al., 2003). In the United States, the percentage of people among the noninstitutionalized population who purchased at least one prescription of antidepressants increased from 5.6% in 1997 to 8.5% in 2002 (Stagnitti, 2005). In Europe, antidepressant use has also increased; it rose 8.6% in France and 10.6% in Portugal, for example, during the period from 2000 to 2002. Moreover, a recent study on the “treatment gap” in mental illnesses by the World Health Organization concludes that for depressive illnesses, over 50% of the patients are still untreated (Kohn, Saxena, Levav, & Saraceno, 2004). All these data suggest that the increase in the use of antidepressants on a global level is still in full progress.
Although this increase can be explained from different perspectives, it must be concluded that on a micro level, doctors and their patients more often decide that antidepressants are the appropriate treatment. The doctor is guided by professional experience and guidelines, but patients also have increasingly more knowledge about pharmacological treatments, and they exert their influence on the decisions concerning the use of medication. For example, experimental data show that the information provided by a pharmaceutical company on antidepressants increased the patients' perception of the desirability to treat depression pharmaceutically (Frankenberger et al., 2004) and that doctors prescribed antidepressants more often when patients asked for them (Kravitz et al., 2005). To be able to regulate the use of antidepressants professionals need to have insight into the decision making processes with regard to the use of antidepressants. The focus of the present study is patients' decision making.
Patient Information NeedsIn the modern doctor–patient relationship, the patient should be closely involved in the decision to start or discontinue antidepressant use (Elwyn, Edwards, Kinnersley, & Grol, 2000; Epstein, Alper, & Quill, 2004; Hulka, Cassel, Kupper, & Burdette, 1976; Liaw, Young, & Farish, 1996; Whitney, 2003). Within this relationship, the doctor provides the patient with information needed to make a well-informed decision. Several studies have been published on the doctor–patient relationship and the information that patients need (for a review see Kiesler & Auerbach, 2006). For example, studies deal with the benefits and dangers that should be communicated to the patient (Greenhalgh, Kostopoulou, & Harries, 2004; Lewis, Robinson, & Wilkinson, 2003; Say & Thomson, 2003). Although most studies provide directions for informing patients, the validity of the data is limited: None of these studies tested whether the information that was presented as being important for patients' decision making was related to actually discontinuing or starting medication.
These studies addressed decision making with regard to pharmacological treatment in general. With regard to decision making concerning antidepressants, only a few studies are available, and those often report negative attitudes. For example, Jorm, Christensen, and Griffiths (2005) showed that around a quarter of adults among the general population considered antidepressant treatment harmful. The idea that antidepressants are addictive was found in other studies (Boath, Bradley, & Henshaw, 2004; Demyttenaere et al., 2001; Hoencamp, Stevens, & Haffmans, 2002; Kessing, Hansen, Demyttenaere, & Bech, 2005; Stone, Durrance, Wojcik, Carson, & Sharpe, 2004). These data suggest that to support patient decision making, doctors should provide patients with information on the addictiveness of antidepressants.
Although informative, these studies did not show that the opinions and beliefs were related to actual decision making. There are some studies, however, that show a relation between patients' psychological factors and actual adherence. Higher adherence was related to reported social rapport (Voils, Steffens, Flint, & Bosworth, 2005), in other words, the quality of the doctor–patient relationship; to more positive beliefs about antidepressants (Bultman & Svarstad, 2000); and to higher scores on “necessity” than on “concerns” with regard to antidepressants (Aikens, Nease, Nau, Klinkman, & Schwenk, 2005). Lower adherence or higher chances of discontinuation were related to higher scores on the Specific Concerns scale of the Beliefs About Medicines Questionnaire (C. Brown et al., 2005) and to lower ratings on the acceptability of using antidepressants when feeling sad (Aikens, Nease, et al., 2005). Because the psychological factors are related to adherence, these studies make a stronger case that they are involved in decision making regarding adherence.
Theoretical FrameworkIn several ways the present study is an attempt to further develop this line of research on patients' decision making with regard to antidepressant use. First, past studies assessed opinions, beliefs, or sets of beliefs, but none used a comprehensive psychological theory on decision making. Therefore, in the present study we used a decision making model based on the social cognitive theory (Bandura, 1986), which outlines how psychological factors are related to each other and to behavior. This theory defines considerations about the expected or perceived positive and negative outcomes of a behavior as the core of the decision making process. The expectations and interpretations refer to rewards and punishments, respectively, and are the cognitive derivates of well-established operant conditioning processes; expectations or experiences of positive outcomes that result from a behavior stimulate the behavior (increasing the probability that it will occur again), whereas expectations or experiences of negative outcomes that result from a behavior inhibit the behavior (decreasing the probability that it will occur again).
Second, most earlier studies were cross-sectional in nature, and when prospective data were used, the follow-up was short. Therefore, in the present study T1 psychological factors were used to predict T2 behavior after 9 months. Third, concerning the few studies that related psychological factors to actual behavior, the only behavior was adherence. Therefore, in the present study we assessed decision making with regard to two behaviors: discontinuation of antidepressant use among current patients (AD users) and renewed antidepressant use among former patients (former AD users).
With regard to patients' discontinuing or renewing use of antidepressants, both behaviors have positive and negative outcomes (see Table 1), and both may be important. In considering whether to discontinue or to start again, first patients may take into account the perceived functions of antidepressants. In antidepressant medication, the functions will mainly refer to regulating mood and anxious arousal. For both AD users and former AD users, their perception is expected to be based on mainly their own experience with the medicine. Weak perceived functions may stimulate AD users to discontinue use (Gartlehner et al., 2005), and strong perceived functions may stimulate former AD users to restart. The experience of the functions of the medicine can be relative: When patients feel better, the functions of the medicine may decline. Indeed, Demyttenaere et al. (2001) report that 55% of patients stopped usage because they felt better.
Expected Outcomes in Patient Decision Making With Regard to Renewed Use and Discontinuation of Antidepressants
Second, the expected side effects could be taken into account. Strong experienced side effects might stimulate AD users to discontinue use (Cheung, Levitt, & Szlai, 2003; Gartlehner et al., 2005), whereas the anticipation of strong side effects may inhibit former AD users from restarting.
Third, the pros of discontinuation could be taken into account. For example, patients may associate not using antidepressant medication with better cognitive functioning or feeling better about themselves. Especially the latter factor may be strong. Patients may feel less dependent and evaluate themselves more positively when they discontinue use because they no longer violate a (widely held) norm that rejects the use of medication (Kessing et al., 2005; Laubler, Nordt, & Rossler, 2003). To illustrate, Hoencamp et al. (2002) showed that 17% of depressed patients saw using antidepressant drugs as a sign of weakness. For them, discontinuation would avert this threat to their self-esteem. Such expectations of the benefits of discontinuation may stimulate discontinuation in AD users, whereas the experiences of these positive effects in former AD users may inhibit them from restarting.
Fourth, the perceptions of the cons of discontinuation on the shorter and longer term could be taken into account. For example, discontinuation may be associated with the eventual recurrence of psychological or psychiatric symptoms on the longer term. Moreover, given that many people fear dependence on antidepressants, they also may believe that discontinuation is associated with withdrawal symptoms (Broekhoven, Kan, & Zitman, 2002; Hoencamp et al., 2002). The expectation of these cons may inhibit AD users from discontinuing, whereas the actual experience of these cons may stimulate former AD users to restart.
Note that in the present psychological framework, decision making, and thus behavior, is governed by the perceived and experienced outcomes rather than by actual outcomes. Thus, it is not so important whether antidepressants are really effective or discontinuation really leads to withdrawal; rather, it is important whether patients think the antidepressant is effective or think that they will experience withdrawal symptoms.
Social cognitive theory defines one additional psychological factor that is important in decision making: self-efficacy expectations (Voils et al., 2005). Self-efficacy expectations (or perceived control) refer to estimates of one's ability to accomplish a certain task successfully (Bandura, 1986) and is based on attributions of control (J. D. Brown & Siegel, 1988). With regard to antidepressant use, the critical task is to cope with a depressed mood, dysfunctional arousal, or stressful event without antidepressant medication. Stronger self-efficacy expectations will stimulate the decision to discontinue or to not restart antidepressant use because patients can imagine themselves coping with the difficulties without medication. This means that the expected pros of discontinuation—the primary reasons why they want to discontinue—will become available and will exert their motivational power.
Thus, AD users could consider the above types of expected outcomes and self-efficacy expectations in deciding whether to discontinue usage, and former AD users could consider them in deciding whether to restart usage. Eventually, the decision making process results in a psychological “end conclusion” about the best course of action. For AD users and former AD users, the end conclusion differs.
In AD users, the process of deciding to discontinue medication results in a goal intention with regard to discontinuation. Thus, the perceptions of the positive and negative consequences of discontinuation and of self-efficacy expectations will eventually be translated into a goal intention, or a plan to discontinue (Ajzen, 1988; Bandura, 1986). One central aspect of a goal intention is the amount of time a person takes to actually execute the behavior (discontinue antidepressant use); in other words, the shorter the term on which the patient plans to stop, the higher the probability that he or she will actually stop.
In former AD users, perceptions of the positive and negative consequences of renewed use and self-efficacy expectations will eventually be considered in evaluating their present situation. Because former users have a strong source of information about the effects of antidepressants—their own past experience—the decision making process concentrates on comparing the present situation of not using antidepressants with the past situation of using antidepressants; they make temporal comparisons. When these temporal comparisons indicate that the past was better than the present, this will increase the probability of renewed use.
Besides the above psychological factors, psychiatric factors are also expected to influence one's decision to discontinue or restart using antidepressants. Negative emotional states (i.e., depressed mood and anxious arousal) are expected to play a prominent role in a person's decision making. These negative emotions may be related to stronger perceived functions (e.g., necessity) of the antidepressants' use or to stronger perceptions of the cons of discontinuation. In AD users, they may inhibit the formation of goal intentions to discontinue. In former AD users, present negative emotional states may stimulate renewed use because their present situation is worse than the past when they used antidepressants.
Present StudyThe goal of the present study is to identify the psychological and psychiatric factors that are involved in patients' decision to discontinue or to restart antidepressant use. Therefore, two samples of patients will be studied. For AD users, the factors involved in the decision to discontinue will be studied. The psychological and psychiatric factors are expected to predict goal intentions, and goal intentions are expected to predict actual discontinuation during 9 months. For former AD users, the factors involved in the decision to restart antidepressant use will be studied. The psychological and psychiatric factors are expected to predict temporal comparisons, and temporal comparisons are expected to predict actual restarting during 9 months. Whereas self-efficacy expectations are expected to contribute to goal intentions in AD users, they are expected to interact with temporal comparisons (Dijkstra & Borland, 2003) in former AD users.
Method Recruitment
Participants were recruited through advertisements in local newspapers and on Internet sites. In one advertisement, patients who currently used antidepressants were invited to join a study on the use of antidepressants. In another advertisement, patients who had used antidepressants in the past were invited to join a study on the use of antidepressants. Participants completing and returning the two assessments were offered the chance of winning one of five prizes amounting to 20 euros (approximately $27). The study was approved by the ethical review board of the Faculty of Behavioral and Social Sciences.
After participants had phoned the university in order to register, they were sent the Time 1 (T1) questionnaire, which could be returned in a prepaid envelope. The Time 2 (T2) questionnaires, used to assess antidepressant use, were sent after 9 months.
Eventually, 253 patients who currently used antidepressants registered, of whom 197 (78%) returned the T1 questionnaire. Of these questionnaires, 7 had many missing data, leaving 190 usable questionnaires at T1. At T2, 166 (87% of 190; 66% of 253) participants returned the questionnaire.
Of the 120 registered patients who had used antidepressants in the past, 106 (88%) returned the T1 questionnaire. Of the returned questionnaires, 10 were from patients who still used antidepressants, leaving 96 usable questionnaires at T1. At T2, 73 (84% of 87; 61% of 120) participants returned the questionnaire.
T1 Questionnaire
Demographics measured were gender, age, marital status, family situation, and level of education (low, medium, or high). In the diverse schooling system in the Netherlands, a low level of education refers to vocational training, medium level to advanced vocational training, and high level to college or university training. Patients who were currently using antidepressants were asked, “In total, how long have you been using antidepressant medication?” and their answers were recoded into months. Patients who did not use antidepressants anymore were asked, “Since how long have you not been using antidepressant medication?” and their answers were also recoded into months.
Depressive complaints were assessed with the Dutch validated version of the Beck Depression Inventory (BDI–II; Van Der Does, 2002, based on Beck, Steer, & Ball, 1996). This commonly used instrument consists of 21 questions with four explicit answering categories. For example, I do not feel sad (0); I feel sad (1); I am sad all the time and can't snap out of it (2); I am so sad or unhappy that I can't stand it (3). The item sum score was the scale score. Higher scores mean higher levels of depression. Internal consistency (Cronbach's α) for AD users was .94, for former AD users .91. With the help of BDI scores, patients can be classified according to the level of depression as normal (scoring 0–13), mildly depressed (scoring 14–19), moderately depressed (scoring 20–28), or severely depressed (scoring 29–63).
Anxious constitution was assessed with the State–Trait Anxiety Inventory (STAI). This commonly used instrument consists of 20 questions on the frequency of anxious (or relaxed) experiences in general. Answering categories are hardly ever (1); sometimes (2); often (3); almost always (4). The item sum score was the scale score (AD users α = .89; former AD users α = .83). Higher scores mean higher levels of trait anxiety.
The below measures used common item formats to assess the different psychological constructs (e.g., “If I would… then… ,” to be answered on a 5-point agree–not agree scale). The content of the items was based on findings in the literature, interviews with patients, and clinical experience.
The perceived functions of antidepressant use were assessed with the following four questions on the perceived benefits of antidepressants: “The use of antidepressants…” 1) “leads to more positive feelings”; 2) “leads to less negative feelings”; 3) “lessens my complaints”; 4) “improves my functioning.” Each item could be answered as follows: yes (1), no (0) or I don't know (0). The item sum score was the scale score (AD users α = .70; former AD users α = .79). Higher scores mean that patients perceive stronger functions of antidepressants.
The cons of discontinuing antidepressants were assessed with 11 items that began with, “If I do not use antidepressants anymore, in the long run this will cause .…” Examples of possible outcomes are “sleeping problems,” “the feeling of being worthless,” and “the feeling of being out of control.” The items could be answered on a 5-point scale that included the following: not agree at all (1); not agree (2); neutral (3); do agree (4); and do agree strongly (5).The mean items score was the scale score (AD users α = .90; former AD users α = .88). Higher scores mean that patients have stronger expectations that depressive symptoms will return when they discontinue antidepressants.
The pros of discontinuing antidepressants were assessed with 17 items that began with, “If I do not use antidepressants anymore… .” The scale included items on self-evaluative outcomes (e.g., “I am satisfied about myself”), cognitive outcomes (e.g., “I can think more clearly”), and social–mobility outcomes (e.g., “I will have more social contacts and I can work better”). The items could be answered on the above-mentioned 5-point agree–not agree scale. The mean items score was the scale score (AD users α = .91; former AD users α = .88). Higher scores mean that patients expect more pros of discontinuation.
Withdrawal symptoms in the case of discontinuation were assessed with 11 items that began with, “If I stop using antidepressants, I will experience the following withdrawal symptoms.” Participants were presented with a list of possible experiences or sensations and were asked to indicate to what extent they agreed that they would experience the symptoms on the above-mentioned 5-point agree–not agree scale. Examples of experiences or sensations are “feeling restless,” “trembling,” “dizzy,” “anxious or tense,” and “problems concentrating” (AD users α = .85; former AD users α = .88). Higher scores mean that patients expect stronger withdrawal symptoms in the case of discontinuation.
Side effects of antidepressants were assessed with 14 items after the question, “To what extent would you be/are you bothered by the following side effects of antidepressants?” Participants were presented with a list of possible experiences or sensations and were asked to indicate the frequency of the sensation or experience with one of the following answers: never (0), sometimes (1), regularly (2), often (3), and very often (4). Examples of experiences or sensations are “dry mouth,” “dizziness,” “blurred vision,” “nausea,” and “restlessness” (AD users α = .81; former AD users α = .86).
Self-efficacy was assessed by providing participants with short descriptions of eight situations that could potentially activate the desire for (renewed) use of antidepressants. The question was, “Are you able to not use or start using antidepressants in this situation?” The items could be scored from certainly not (1); probably not (2); neutral (3); probably will (4); certainly will (5). For patients who did use antidepressants, one additional instruction stated, “Imagine that you are motivated to discontinue the use of antidepressants.” The eight situations were composed of four emotional situations (“feeling bad,” “having complaints,” “feeling depressed,” and “feeling anxious”) times two durations of the situations (1 month or 6 months). The mean items score was the scale score (AD users α = .94; former AD users α = .93). Higher scores mean that patients have more confidence that they are able to not use antidepressants in situations where they have negative emotional experiences for some time.
Goal intentions were assessed only for patients who used antidepressants. Patients were asked to indicate one out of nine plans that best described their own plan with regard to the discontinuation of antidepressant use (Dijkstra, Bakker, & De Vries, 1997). The categories were: “I am planning to quit within 10 days” (9); “I am planning to quit within 1 month” (8); “I am planning to quit within 6 months” (7); “I am planning to quit within 1 year” (6); “I am planning to quit within 5 years” (5); “I am planning to quit within 10 years” (4); “I am planning to quit sometime in the future but not within 10 years” (3); “I am planning to keep on using but to cut down” (2); “I am planning to keep on using and not to cut down” (1). The shorter the term until patients intend to quit, the stronger their intention.
Temporal comparisons were assessed only in patients who did not use antidepressants anymore. Temporal comparisons were assessed by asking participants whether they felt better or worse off concerning three dimensions of general outcomes or states. The format of the three two-sided 7-point scales was, “Do you think you are doing better/worse as compared to when you still used antidepressants?” This format was specified for three different potential global outcomes that had content-specific anchors. The first was, “Is your ability to have control over your life better or worse, compared to when you still used antidepressants?” The anchors ranged from much better (1) to much worse (7). The other two items were, “Is your ability to be satisfied with your life better or worse compared to when you still used antidepressants?” and, “In general, are you doing better or worse compared to when you still used antidepressants?” The mean items score was the scale score (α = .88). Higher scores mean that patients evaluate their present situation of not using antidepressants negatively compared with when they still used antidepressants.
T2 Questionnaire
In this short questionnaire, antidepressant use was assessed with the question, “Do you take antidepressants at the moment?” (yes–no). The present study is a “low demand” study (see Velicer, Prochaska, Rossi, & Snow, 1992). This implies that it doesn't put the participants under any social pressure to change their behavior in either direction. Therefore, the above self-report is expected to be valid.
Statistical Analyses and Data Recoding
Because goal intention is the most proximal determinant of discontinuation in AD users, it will be regressed on the above eight variables by means of linear regression analysis on the T1 data. Subsequently, all these T1 variables will be used to predict actual discontinuation at T2 by means of logistic regressions analysis. Because temporal comparisons are considered an important determinant of renewed use in former AD users, temporal comparisons will be regressed on the above eight variables by means of linear regression analysis on the T1 data. Subsequently, all these T1 variables will be used to predict actual renewed use at T2 by means of logistic regressions analysis. The interaction between temporal comparisons and self-efficacy will also be tested.
For all analyses, the significance level was set at .05 for two-sided tests. In order not to lose statistical power in the prospective analyses, we manually removed variables when they were not significant, each time choosing the highest p value for removal.
Results Results in AD Users
Sample characteristics of AD users
Of the 166 AD users for whom two measurements were available, 81.1% were women, 19.4% had a low education level, 38.8% a medium level, and 41.9% had a high level. Their mean age was 44.3 years (SD = 12), 65.2% were married or lived together, and 60.9% had children. According to the self-reports, 85.5% of the participants indicated that they used their antidepressants for depressive complaints. On average, the total duration of antidepressant use was 76.7 months, ranging from 2 months to 468 months (39 years; SD = 84.3 months). Forty-seven percent of the participants presently received psychotherapy or counseling for the same complaints they use the antidepressants for. Of the 166 AD users, 13.4% reported using classical tricyclic medicines, 81.9% used modern antidepressants (SSRIs and SNRIs), and the remaining participants used MAO blockers (n = 5) or lithium (n = 2). The mean score on the STAI was 49.6, with scores ranging from 21 to 76.
The mean score on the BDI was 16.6 (SD =10.9), with scores ranging from 0 to 52. Participants were classified according to the four categories of the level of depression. Forty-six percent of the participants were classified as normal, 20% as mildly depressed, 20.5% as moderately depressed, and 13% as severely depressed. In addition, 48% of the patients were in psychotherapy or counseling.
Predicting goal intentions
Linear regression was used to predict goal intentions cross-sectionally (see Table 2) among the 166 participants who would be included in the prospective analyses. In a first step, the following variables were entered as covariates: age, level of education, gender, and duration of antidepressant use. These variables explained 21% of the variance in goal intentions. Adding the STAI and the BDI scores to the model did not improve it significantly; both factors were not related to goal intentions. Finally, in the third step, the six psychological factors—the pros of discontinuation, the cons of discontinuation, withdrawal symptoms, side effects, the benefits (functions) of the use, and the perceived ability to cope with depressed mood without antidepressants (self-efficacy)—were entered. This step added another 27.4% to the explained variance, improving the model significantly (p < .001). The pros of discontinuation (β = .40; p < .001), the cons of discontinuation (β = –.23; p = .003), and self-efficacy (β = .17; p = .01) were significantly related to goal intentions.
Predicting Goal Intentions With Regard to Discontinuation From Psychiatric and Psychological Factors in Patients Who Use Antidepressants
Predicting discontinuation
A logistic regression analysis that was based on T1 variables was used to predict who would report that he or she was not using antidepressants anymore at T2. Of the 166 AD users, only 7.3% did not use antidepressants at T2 anymore. To limit the number of independent variables in the model to preserve statistical power, the nonsignificant variables were removed step by step manually, in each step removing the variable with the highest (nonsignificant) p value. Thus, in the original model the following variables were entered: age, level of education, gender, duration of antidepressant use, STAI, BDI, and the six psychological factors. However, none of these variables predicted discontinuation significantly. Finally, goal intentions was entered as an independent variable. It was the only variable that significantly predicted discontinuation (odds ratio = 2.08; 95% confidence interval 1.41–3.07; p < .001): The shorter the term decided at T1 until the intended discontinuation of use, the higher the chance that the patients would not use antidepressants anymore at T2. In this last model, expected withdrawal symptoms and perceived functions of AD use approached significance (p < .10) and were related to discontinuation in the expected directions (the higher the scores, the lower the probability that patients had discontinued).
Results in Former AD Users
Sample characteristics of former AD users
Of the 73 former AD users for whom two measurements were available, 82.2% were female, 11% had a low education level, 46.6% a medium level, and 42.5% had a high level. Their mean age was 41.2 years (SD = 13.6), 53.4% were married or lived together, and 54.8% had children. On average, the total duration of past antidepressant use was 35.6 months, ranging from 3 months to 168 months (SD = 33.1). The average period since the participants had last used antidepressants was 20.4 months, ranging from 1 to 204 months, and 31.5% of the participants presently received psychotherapy or counseling for the same complaints for which they had used antidepressants. The mean score on the STAI was 49.6, with scores ranging from 27 to 66.
The mean score on the BDI was 14.5 (SD = 9.13), with scores ranging from 0 to 45. Participants were classified according to the four categories of the level of depression. Fifty-one percent of the participants were classified as normal, 25% as mildly depressed, 16.5% as moderately depressed, and 8% as severely depressed.
Predicting temporal comparisons
Linear regression was used to predict temporal comparisons cross-sectionally (see Table 3) among the 73 participants who would be included in the prospective analyses. In a first step, the following variables were entered as covariates: age, level of education, gender, and duration of not using antidepressants. These variables explained 5.2% of the variance in temporal comparisons, mainly because the “duration of not using” was related significantly to temporal comparisons (β = –.23; p < .05): The longer patients were off antidepressants, the more positively they evaluated their present situation.
Predicting Temporal Comparisons From Psychiatric and Psychological Factors in Patients Who Have Discontinued Antidepressant Use
Adding the STAI and the BDI scores improved the model significantly, adding 20.3% to the explained variance. Only the BDI was related to temporal comparisons significantly (β = .38; p = .006): The more depressed the participants were, the less favorable they evaluated their present situation compared with when they still used antidepressants. Finally, in the third step, the six psychological factors were entered; this step added another 28.8% to the explained variance, improving the model significantly. Two psychological variables predicted temporal comparisons: the stronger the perceived functions of AD use (β = .21; p < .05) and the weaker the perceived pros of discontinuation (β = –.45; p < .001), the more negative participants evaluated their present condition in relation to when they still used antidepressants.
Predicting renewed use
A logistic regression analysis was used to predict, on the basis of T1 variables, who would report that he or she was using antidepressants again at T2. Of the 73 participants at T1, 15.1% used antidepressants at T2. To limit the number of independent variables in the model to preserve statistical power, the nonsignificant variables were removed step by step manually, in each step removing the variable with the highest (nonsignificant) p value. Thus, in the original model the following variables were entered: age, level of education, gender, duration of not using antidepressants, STAI, BDI, and the six psychological factors. In addition, temporal comparisons, self-efficacy, and the interaction between both variables were entered to predict renewed use. Only the BDI score (odds ratio = 0.31; 95% confidence interval 0.13–0.79; p < .05) and the interaction between temporal comparisons and self-efficacy (odds ratio = 0.46; 95% confidence interval 0.22–0.98; p < .05) were significant. To search for the meaning of the interaction (see Table 4), the data were used to model a group scoring low on self-efficacy (1 SD below the mean) and a group scoring high on self-efficacy (1 SD above the mean). Only when self-efficacy was high did temporal comparisons significantly predict renewed use (odds ratio = 0.20; 95% confidence interval 0.04–0.92; p < .05). Thus, the more negatively participants evaluated the present compared with the time when they still used antidepressants, the higher the chance that they would use antidepressants again 9 months later. This effect occurred over and above the effect of depressed mood as assessed by the BDI.
Predicting Renewed Use of Antidepressants After 9 Months From Temporal Comparisons in Interaction With Self-efficacy
DiscussionThe goal of this study was to investigate the psychiatric and psychological factors that are involved in patient decision making regarding antidepressant use. The focus was on two decisions: whether to discontinue use and whether to restart use, both of which were analyzed on the basis of a comprehensive psychological framework and a prospective design.
For patients who currently used antidepressants it was found that the shorter the term until patients intended to discontinue use (the more proximal the goal intentions), the higher the chance that they would no longer use antidepressants after 9 months. Thus, despite all the other factors and circumstances to which the patient must have been exposed—such as fluctuations in complaints, information from the media and social environment, and doctor's visits—the relationship between goal intentions and discontinuation maintained primacy. Second, the perceived pros and cons of discontinuation and the perceived ability to cope with depressed mood without antidepressants (self-efficacy expectations) predicted goal intentions. Together, these psychological factors explained over 27% of the variance in goal intentions. These results coincide with our expectations. However, in contrast to goal intentions, the other psychological and psychiatric factors assessed at T1 were not related to discontinuation at T2. Whereas the effect of goal intentions as the “end conclusion” of the decision making process survived the other influences on patients' use of antidepressants between T1 and T2, the other psychological and psychiatric factors were overruled by these influences. This suggests that goal intentions are not only mirroring changes in the psychological factors that are involved in decision making but they have a certain stability. This stability can be conceptualized as commitment to a certain goal, which goes beyond the specific reasons that initially underlie the goal.
The findings with regard to goal intentions are consistent with main theories on behavior and a large body of research showing intentions are the most proximal predictors of behavior, always explaining more variance in behavior than the other psychological factors do (Armitage & Conner, 2001; Godin & Kok, 1996).
For former AD users it was found that the worse off they felt at present compared with when they still used antidepressants (temporal comparisons), the higher the chances that they started using antidepressants again. This effect was present only in patients with a strong perceived ability to cope with depressed mood without antidepressants (self-efficacy expectations). The effect of temporal comparisons was over and above the effect of the person's level of depression. Thus, besides the absolute level of depression that patients experienced at T1, renewed use was predicted by evaluation of the perceived changes since they stopped using antidepressants.
Temporal comparisons are, together with social comparisons, fundamental means to evaluate reality when objective standards are lacking (Albert, 1977; Festinger, 1954; Wilson & Ross, 2000). The present data suggest that the former AD users based their temporal comparisons partly on the experienced pros of discontinuation and the expected benefits of the use of antidepressants (the functions). These factors explained almost 29% of the variance in temporal comparisons. Similar to goal intentions, temporal comparisons are considered to be an “end product” of the decision making process, an evaluative conclusion. Also similar to goal intentions, the other psychological factors did not predict renewed use; only temporal comparisons seemed to have survived all other influences on the decision to start using antidepressants again. Thus, once the idea about how the present compared with the past was formed (at T1), it became partly independent of the psychological factors that this evaluation was based on.
These data on AD users and former AD users also provide some evidence for the validity of the psychological measures: All the relations among the measures were in the expected directions. Future research might further test these measures with a shorter follow-up, so that fewer external factors interfere with the psychological determination.
Another interesting finding is that expected or experienced side effects of antidepressants and expectations of withdrawal symptoms in the case of discontinuation were not related to patients' decisions in any way. However, this may have to do with the phase of the use of antidepressants that the patients in our present samples were in: Experienced or expected side effects may only play a role in decision making in patients just starting use of antidepressants. Experienced or expected withdrawal symptoms may play a role in decision making only in patients who are actually discontinuing use. Thus, when patients are in a stable state of using or not using antidepressants, memories on past side effects and withdrawal symptoms and expectations about these experiences do not seem to play a prominent role in decision making anymore.
Both samples were recruited through regional mass media, and probably a selection of patients occurred. Possibly, patients with less severe complaints joined the studies. However, substantial percentages of patients in both samples fell into more severe categories of depression, and many were currently in psychotherapy or counseling. Thus, the levels of problems were significant in both samples. Furthermore, the mean BDI scores of the participants at T1 were 16.6 and 14.5 for AD users and former users, respectively. In patients who are currently treated with antidepressants and in patients who no longer needed to use antidepressants this is exactly what could be expected. Several studies show that different types of antidepressant treatments lead to similar or even lower BDI scores. For example, Corney and Simpson (2005) report a drop in BDI scores from 21.5 to 14.5; Salkovskis, Rimes, Stephenson, Sacks, and Scott (2006) report a drop from 27 to 13; Cahill et al. (2003) report a drop from 27.6 to 10.3; and Stamenkovic et al. (2001) report a drop from 18.6 to 7. Thus, with regard to the level of depression we argue that our samples are not selective.
In conclusion, the present findings are in line with contemporary insights into the psychological determinants of behavior, and they illustrate that patients' perceptions of using or not using antidepressants influence the actual decision to discontinue or to restart antidepressants beyond psychiatric, environmental, health care, and social factors.
Insight into these patients' perceptions opens the possibility to effectively support patient decision making in the direction that is best for other patients' health. For example, the doctor might want to motivate some patients to discontinue. These patients should be informed about the pros of discontinuation, such as feeling good about oneself, experiencing improved cognitive functioning, and feeling stronger. In addition, the perceived cons of discontinuation should be lowered (“your symptoms may not reoccur at all”) and the confidence in their ability to cope with depressed mood without antidepressant medication should be increased. The latter could be done by applying self-help skills to cope with stress and depressive feelings. Lastly, these patients should be supported in planning to discontinue (“what is a convenient moment in the future to discontinue?”). When it is medically desirable that the patient not discontinue, the same psychological factors can be targeted in the opposite direction.
On the other hand, the doctor might want to motivate some former patients to start using antidepressants again. This might be done by reminding the patient of positive past experiences with antidepressants (“remember how you improved last time?”) or stress the benefits (functions) of this antidepressant (“this new antidepressant might really make you feel better”). In addition, the patient's perception of the pros of discontinuation might be lowered (relatively and temporarily): The functions of antidepressant medication might be presented as currently outweighing the pros of discontinuation (“although there are benefits of not using antidepressants, it may be more important now to control your mood”). When it is medically desirable that the patient not start using antidepressants again, the same psychological factors can be targeted in the opposite direction.
Using social cognitive theory as the theoretical framework, we were able to predict behavior change in patients during and after a period of 9 months. These data increase our understanding of patient decision making, thereby empowering health care professionals to further provide the health care that is best for the patient according to their expert opinions.
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Submitted: December 5, 2006 Revised: June 20, 2007 Accepted: June 25, 2007
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Source: Journal of Consulting and Clinical Psychology. Vol. 76. (1), Feb, 2008 pp. 149-157)
Accession Number: 2008-00950-017
Digital Object Identifier: 10.1037/0022-006X.76.1.149
Record: 130- Title:
- Psychological processes and repeat suicidal behavior: A four-year prospective study.

- Authors:
- O'Connor, Rory C.. Suicidal Behavior Research Laboratory, Institute of Health and Wellbeing, College of Medical, Veterinary & Life Sciences, University of Glasgow, Glasgow, United Kingdom, rory.oconnor@glasgow.ac.uk
Smyth, Roger. Department of Psychological Medicine, Royal Infirmary of Edinburgh, Edinburgh, United Kingdom
Ferguson, Eamonn. School of Psychology, University of Nottingham, Nottingham, United Kingdom
Ryan, Caoimhe. School of Psychology, University of St Andrews, St Andrews, United Kingdom
Williams, J. Mark G.. Department of Psychiatry, University of Oxford, Oxford, United Kingdom - Address:
- O'Connor, Rory C., Suicidal Behavior Research Laboratory, Institute of Health and Wellbeing, College of Medical, Veterinary & Life Sciences, University of Glasgow, Glasgow, United Kingdom, G12 0XH, rory.oconnor@glasgow.ac.uk
- Source:
- Journal of Consulting and Clinical Psychology, Vol 81(6), Dec, 2013. pp. 1137-1143.
- NLM Title Abbreviation:
- J Consult Clin Psychol
- Page Count:
- 7
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- Journal of Consulting Psychology
- Other Publishers:
- US : American Association for Applied Psychology
US : Dentan Printing Company
US : Science Press Printing Company - ISSN:
- 0022-006X (Print)
1939-2117 (Electronic) - Language:
- English
- Keywords:
- cognition, defeat, entrapment, longitudinal, suicidal, risk, depression, hopelessness, suicidal ideation
- Abstract:
- Objective: Although suicidal behavior is a major public health concern, understanding of individually sensitive suicide risk mechanisms is limited. In this study, the authors investigated, for the first time, the utility of defeat and entrapment in predicting repeat suicidal behavior in a sample of suicide attempters. Method: Seventy patients hospitalized after a suicide attempt completed a range of clinical and psychological measures (depression, hopelessness, suicidal ideation, defeat, and entrapment) while in hospital. Four years later, a nationally linked database was used to determine who had been hospitalized again after a suicide attempt. Results: Over 4 years, 24.6% of linked participants were readmitted to hospital after a suicidal attempt. In univariate logistic regression analyses, defeat and entrapment as well as depression, hopelessness, past suicide attempts, and suicidal ideation all predicted suicidal behavior over this interval. However, in the multivariate analysis, entrapment and past frequency of suicide attempts were the only significant predictors of suicidal behavior. Conclusions: This longitudinal study supports the utility of a new theoretical model in the prediction of suicidal behavior. Individually sensitive suicide risk processes like entrapment could usefully be targeted in treatment interventions to reduce the risk of repeat suicidal behavior in those who have been previously hospitalized after a suicide attempt. (PsycINFO Database Record (c) 2017 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Cognition; *Risk Factors; *Suicide; Hopelessness; Major Depression; Suicidal Ideation
- Medical Subject Headings (MeSH):
- Adolescent; Adult; Aged; Culture; Depressive Disorder; Female; Follow-Up Studies; Hope; Humans; Internal-External Control; Longitudinal Studies; Male; Middle Aged; Models, Psychological; Motivation; Patient Readmission; Prospective Studies; Recurrence; Risk Factors; Scotland; Suicidal Ideation; Suicide, Attempted; Young Adult
- PsycINFO Classification:
- Behavior Disorders & Antisocial Behavior (3230)
- Population:
- Human
Male
Female - Location:
- Scotland
- Age Group:
- Adolescence (13-17 yrs)
Adulthood (18 yrs & older)
Young Adulthood (18-29 yrs)
Thirties (30-39 yrs)
Middle Age (40-64 yrs)
Aged (65 yrs & older) - Tests & Measures:
- Defeat Scale
Entrapment Scale
Readmission to Hospital with a Suicide Attempt Measure
Beck Hopelessness Scale
Hospital Anxiety and Depression Scale DOI: 10.1037/t03589-000
Suicide Probability Scale DOI: 10.1037/t01198-000 - Grant Sponsorship:
- Sponsor: Chief Scientist Office, Scottish Government
Grant Number: CZH/4/449
Recipients: No recipient indicated
Sponsor: Wellcome Trust
Grant Number: GRO67797
Recipients: Williams, J. Mark G. - Methodology:
- Empirical Study; Longitudinal Study; Prospective Study
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- First Posted: Jul 15, 2013; Accepted: May 17, 2013; Revised: Apr 30, 2013; First Submitted: Nov 28, 2012
- Release Date:
- 20130715
- Correction Date:
- 20170220
- Copyright:
- American Psychological Association. 2013
- Digital Object Identifier:
- http://dx.doi.org/10.1037/a0033751
- PMID:
- 23855989
- Accession Number:
- 2013-25313-001
- Number of Citations in Source:
- 48
- Persistent link to this record (Permalink):
- http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2013-25313-001&site=ehost-live
- Cut and Paste:
- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2013-25313-001&site=ehost-live">Psychological processes and repeat suicidal behavior: A four-year prospective study.</A>
- Database:
- PsycINFO
Psychological Processes and Repeat Suicidal Behavior: A Four-Year Prospective Study / BRIEF REPORT
By: Rory C. O’Connor
Suicidal Behavior Research Laboratory, Institute of Health and Wellbeing, College of Medical, Veterinary & Life Sciences, University of Glasgow, Glasgow, United Kingdom;
Roger Smyth
Department of Psychological Medicine, Royal Infirmary of Edinburgh, Edinburgh, United Kingdom
Eamonn Ferguson
School of Psychology, University of Nottingham, Nottingham, United Kingdom
Caoimhe Ryan
School of Psychology, University of St Andrews, St Andrews, United Kingdom
J. Mark G. Williams
Department of Psychiatry, University of Oxford, Oxford, United Kingdom
Acknowledgement: This research was supported by funding from Chief Scientist Office, Scottish Government (CZH/4/449). J. Mark G. Williams is supported by Grant GRO67797 from the Wellcome Trust. We thank Andrew Duffy of NHS National Services Scotland for conducting the data extraction for the linkage component of the study.
Suicide and self-injurious behavior represent global public health concerns. Previous suicidal behavior is one of the most robust predictors of future suicide and, consequently, it is often the focus of research efforts to better understand the etiology of suicide (Suominen et al., 2004). Although it is generally accepted that distal suicide risk mechanisms may arise from a complex interaction of genetic and environmental factors (Mann, Waternaux, Haas, & Malone, 1999), there is increased recognition that researchers need to move beyond the classic psychiatric diagnostic categories if they are to further understand the etiology of suicide, because the diagnostic categories are not sufficiently sensitive to differentiate the vast majority of people with mental health disorders who do not take their own lives from those who do (Bostwick & Pankratz, 2000; van Heeringen, 2001).
More basic science research into the identification of individually sensitive suicide risk mechanisms (Baumeister, 1990; Joiner, 2005; Nock & Banaji, 2007; Nock et al., 2010; O’Connor, Fraser, Whyte, MacHale, & Masterton, 2008, 2009; Rudd, Joiner, & Rajad, 1996; Van Orden, Witte, Gordon, Bender, & Joiner, 2008; Van Orden et al., 2010; Williams, Barnhofer, Crane, & Beck, 2005; Williams, Van Der Does, Barnhofer, Crane, & Segal, 2008) is vital to inform the development of evidence-informed treatment interventions in this area. One attempt to take account of this literature in a comprehensive way, to specify in detail the development of suicide risk, has been a three-phase psychological model of suicidal behavior, the integrated motivational–volitional model (IMV; O’Connor, 2011).
This model of suicidal behavior (O’Connor, 2011) draws from Williams (1997) and Baumeister (1990) and assumes that both environmentally and biologically mediated risk variables shift individuals through a final common pathway involving a high sensitivity to cues in the environment signaling defeat and a sense of entrapment. It is unique in that it conceptualizes suicide attempts as health behaviors (Ajzen, 1991) with motivational (i.e., factors associated with the development of suicidal thoughts) and volitional (i.e., factors that govern whether suicidal thoughts will be acted on) determinants. It also endeavors to incorporate the key constructs from existing predominant models of suicidal behavior into a process model to inform the development of psychological interventions that reduce the risk of suicide. In the present context, drawing from social rank theory (e.g., Price, Sloman, Gardner, Gilbert, & Rhode, 1994), defeat is characterized by a failed struggle, when an individual has been defeated by a triggering event or circumstances. Entrapment results when one’s attempt to escape from high stress or defeating circumstances (which can be internal or external) is blocked (arrested flight; Gilbert & Allan, 1998; O’Connor, 2003; Pollock & Williams, 2001; Williams, 1997).
Although defeat and entrapment are not new constructs in the psychopathology literature (Baumeister, 1990; Gilbert & Allan, 1998), the findings from a number of independent research groups suggest that they have special relevance in the etiology of suicide (O’Connor, 2011; Rasmussen et al., 2010; Taylor, Gooding, Wood, & Tarrier, 2011; Taylor, Gooding, Wood, Johnson, & Tarrier, 2011; Williams, 1997). In particular, we posit that it is this motivation to escape from the defeating circumstances that drives the search for solutions to end the unbearable psychological pain (Shneidman, 1996) that often characterizes the suicidal mind. Accordingly, as entrapment increases and no solutions are found, the likelihood of suicide being considered an escape strategy increases (O’Connor, 2011; Taylor, Gooding, Wood, & Tarrier, 2011).
Present StudyIn this study, therefore, we aimed to conduct a robust test of the central tenet of the model. Specifically, we aimed to investigate whether, as posited in the IMV model, defeat and entrapment would predict suicide attempts prospectively and that entrapment would be the strongest predictor of repeat suicidal behavior. We have focused on those who have attempted suicide previously because they comprise a high-risk group for suicide. Indeed, a history of repeat suicide attempts is one of the strongest predictors of whether someone dies by suicide (Hawton & van Heeringen, 2009; Owens, Horrocks, & House, 2002). Specifically, we hypothesized that defeat and entrapment would be significant univariate predictors of future suicide attempts (Hypothesis 1). Crucially, though, we also hypothesized (Hypothesis 2) that entrapment would add incrementally to the prediction of suicide attempts, beyond the explanations offered by established predictors of suicidal behavior (e.g., depression, suicide ideation, hopelessness, past suicide attempts).
Method Participants and Procedure
Seventy patients who were seen by the liaison psychiatry service the morning after presenting at a Scottish hospital following a suicide attempt were recruited to the study. The sample was drawn from a larger sample of 136 intentional self-harm patients who were admitted to the hospital. Eighteen participants were excluded because they had been discharged or transferred to another hospital before they could be invited to participate, six were unfit for interview, 33 reported no suicidal intent, and nine declined to participate. The vast majority of patients presented after an overdose (93%; International Classification of Diseases [ICD] Codes ×60–X69), with episodes of self-cutting (n = 3; ICD Codes ×78) and mixed presentations of self-cutting and overdose (n = 4; ICD Codes ×60–X69, ×78) accounting for the remainder of cases. There were 41 females and 29 males with an overall mean age of 35.6 years (SD = 13.24, range: 16–69 years). The men (M = 33.66 years, SD = 11.34) and women (M = 37.07 years, SD = 14.40) did not differ significantly in age, t(68) = 1.07, ns. We did not record ethnicity; however, the overwhelming majority of participants were White.
Baseline data were collected in hospital, usually within 24 hr of admission. The Information Services Division of the National Health Service Scotland maintains a national database of hospital records and mortality data. This nationally linked database is a powerful resource as it allowed us to determine whether a patient was readmitted to hospital in Scotland with intentional self-harm at any time since their index episode. We asked the Information Services Division to extract hospital admissions for intentional self-harm (ICD Codes ×60–X84) in the period between the index suicide attempt and 48 months later for each patient. For this data set, the Information Services Division successfully linked 87% of the sample (61/70). We also reviewed the electronic medical records of those patients who were hospitalized again after intentional self-harm during the follow-up period to determine whether the repeat self-harm episode was a suicide attempt.
Baseline Measures
Depression
The seven-item depression scale from the Hospital Anxiety and Depression Scale (HADS; Zigmond & Snaith, 1983) was used to assess depression. The HADS is a well-established, widely used, reliable, and valid measure of affect (Bjelland, Dahl, Haug, & Neckelmann, 2002; Mykletun, Stordal, & Dahl, 2001) that assesses depression (and anxiety) in psychiatric as well as primary care and general populations. Cronbach’s alpha for the present sample was .80.
Suicidal ideation
Suicidal ideation was assessed using the Suicidal Ideation subscale of the Suicide Probability Scale (SPS; Cull & Gill, 1988). The scale is reliable and valid (Cull & Gill, 1988). The SPS measures an individual’s self-reported attitudes that are related to suicide risk, and the scale has been shown to predict suicide attempts prospectively (Larzelere, Smith, Batenhorst, & Kelly, 1996). Internal consistency for the present study was very good (Cronbach’s α = .86).
Hopelessness
Hopelessness was measured using the 20-item Beck Hopelessness Scale (BHS; Beck, Weissman, Lester, & Trexler, 1974). This reliable and valid measure has been shown to predict eventual suicide (Beck, Steer, Kovacs, & Garrison, 1985; Beck et al., 1974). In the present study, internal consistency was very good (Kuder–Richardson formula 20 = .92).
Defeat
Feelings of defeat were assessed via the Defeat Scale (Gilbert & Allan, 1998). This is a 16-item self-report measure of perceived failed struggle and loss of rank (e.g., “I feel defeated by life”). The Defeat Scale has good psychometric properties (Gilbert & Allan, 1998; Gilbert, Allan, Brough, Melley, & Miles, 2002). It has good test–retest reliability and has been shown to predict suicidality over 12 months independent of baseline levels of depression (Taylor, Gooding, Wood, Johnson, & Tarrier, 2011). Cronbach’s alpha for the present sample was very good (α = .93).
Entrapment
Entrapment represents the sense of being unable to escape feelings of defeat and rejection and is measured by the Entrapment Scale (Gilbert & Allan, 1998). This 16-item self-report measure taps internal entrapment (perceptions of entrapment by one’s own thoughts and feelings) and external entrapment (perceptions of entrapment by external situations). The Entrapment Scale has good psychometric properties (Gilbert & Allan, 1998; Gilbert et al., 2002). It has good test–retest reliability (Taylor, Gooding, Wood, Johnson, & Tarrier, 2011), and it has been shown to distinguish between clinical patients with and without suicide attempt histories (Rasmussen et al., 2010). Cronbach’s alpha for the present study was .91.
Outcome Measure
Readmission to hospital with a suicide attempt
An episode of self-harm was recorded if a patient was admitted to any hospital in Scotland with self-harm in the 48 months after their index episode (ICD Codes ×60–X84 (intentional self-harm). When a patient was readmitted to a hospital with self-harm during the study period, we reviewed their medical records to ascertain whether this episode was a suicide attempt. On admission to the ward, members of the psychiatric team routinely assess suicidal intent. Two trained coders independently rated the medical records and agreed on all 15 positive cases. Coders of repeat suicidal behavior were unaware of all of the baseline measures.
Statistical analyses
We conducted a series of univariate logistic regression analyses for each predictor of a future suicide attempt. Although we are interested specifically in the entrapment and defeat logistic regression analyses, we present the findings for other established predictors of suicidal behavior (i.e., depression, hopelessness, suicide ideation, past suicide attempts). To test the second hypothesis, we conducted a hierarchical multivariate logistic regression including all significant univariate predictors. All analyses were conducted in SPSS 20 and Stata 11.
Results Linked Sample
There were 35 women and 26 men with an overall mean age of 35.6 years (SD = 13.16, range: 16–69 years) in the linked sample. At baseline, 41.4% of these participants (n = 29) reported no previous suicide attempts, 25.7% of participants (n = 18) reported one previous attempt, 10.0% (n = 7) reported two previous attempts, and 22.9% (n = 16) reported three or more previous episodes.
Repeat Suicide Attempt During Follow-Up
Between Time 1 and Time 2 (48 months after the index episode), 32.8% (n = 20) of the linked participants were readmitted to hospital, presenting with intentional self-harm. One participant died by suicide in this time. Of the 20 participants who self-harmed between Time 1 and Time 2, 75% (n = 15) presented with a suicide attempt at follow-up. There was insufficient information to determine suicidal intent for three of the patients and 10% (n = 2) did not report suicide intent at follow-up admissions. Consequently, in the subsequent analyses, these five participants were coded as having made no suicide attempt between baseline and follow-up. In short, 15 participants engaged in a repeat suicide attempt between Time 1 and Time 2. As anticipated, all continuous study measures were intercorrelated (see Table 1).
Correlations, Means, and Standard Deviations for All of the Study Variables for All Participants
Individual and Multivariate Predictors of Suicide Attempts Between Time 1 and Time 2
None of the demographic variables emerged as significant univariate predictors of future suicidal behavior (see Table 2). However, all of the other variables (i.e., defeat and entrapment as well as frequency of previous suicide attempts, suicidal ideation, depression, and hopelessness) individually predicted suicidal behavior between Time 1 and Time 2 (see Table 2).
Univariate Logistic Regression Analyses Investigating Associations Between Baseline Predictors and Hospital-Treated Suicide Attempts or Suicide Between Time 1 (T1) and Time 2 (T2)
To test the prediction that entrapment adds incrementally over depression, we specified suicide ideation, suicide attempt history, and hopelessness in a hierarchical logistic regression, with entrapment and defeat entered at Step 2. Given that there are significant correlations between the predictors raising concerns about multicollinearity, a series of multicollinearity diagnostics were conducted. First, an examination of the correlations derived from the fitted model variance–covariance matrix shows none were greater than .5 (regardless of sign, the mean correlation was .15, the median was .14, with a range of .00 to .33), indicating no collinearity problems. Next, variance inflation factors (VIFs) were examined. These indicate the extent to which the standard errors are inflated because of collinearity. Various rules of thumb for VIFs exist, with some suggesting that VIFs greater than 10 (Hair, Anderson, Tatham, & Black, 1995) and others suggesting VIFs greater than 4 (Menard, 1995) indicate multicollinearity problems. For these analyses, VIFs ranged from 1.16 to 2.93 (M = 2.26) with the square roots of the VIF all less than 2 (M = 1.48, range: 1.08–1.73), indicating that, on average, the standard errors are only inflated 1.48 times because of multicollinearity (Stewart, 1987). Finally, if multicollinearity is a major problem, then odds ratios will be extremely large. This was not the case in these analyses. Therefore, on the basis of the above analyses, there are no problems with multicollinearity.
The results of the hierarchical logistic regression are reported in Table 3 and show that entrapment adds incremental predictive validity over depression, hopelessness, suicide ideation, and the frequency of previous suicide attempts. In the final model, both entrapment and the frequency of previous suicide attempts predict the occurrence of a future suicide attempt. To aid interpretation, Table 3 also reports standardized coefficients for logistic models (see King, 2007; Long, 1997; Long & Freese, 2006; Menard, 2011). The standardized coefficients allow us to examine the relative magnitude of the effects. Given the variety of potential standardized coefficients for logistic regression (King, 2007; Menard, 2011), Winship and Mare (1984) recommended using fully standardized coefficients, and we report fully standardized coefficients as defined by Long and Freese (2006). For the two significant effects, these show that a one standard deviation increase in entrapment results in just over a half a standard deviation increase (.59) in log odds of attempting suicide and a one standard deviation increase in the number of previous attempts results in an increase of one fifth (.20) in log odds of attempting suicide. We also examined if the effect of entrapment in the final model was significantly different from the number of previous attempts. The results showed that although it was stronger, this effect only approached significance, χ2(1), p = .09. However, given the nonlinearity of the logistic model, another way to assess the importance of a predictor is in terms of the discrete change in the predicted probabilities (Long & Freese, 2006). If the predicted probabilities show a large change across the predictor, then it is likely to be an important predictor. For the predictor variables in this model, the largest change was for entrapment, with a predicted probability change of .63 from the minimum value to the maximum of the scale. The next largest was for the number of suicide attempts (.14); the rest ranged from −.02 to .07. This shows entrapment as an important predictor. Examining the effect for entrapment in terms of standard deviation changes from the mean (holding all other variables at their mean) revealed that a single standard deviation increase in entrapment results in a .08 increased probability of attempting suicide.
Hierarchical Multivariate Logistic Regression Analyses Investigating Associations Between Predictors and Hospital-Treated Suicide Attempts or Suicide Between Time 1 and Time 2
DiscussionThis was the first study to investigate the predictive utility of defeat and entrapment among suicide attempters. The findings clearly showed that both defeat and entrapment were significant univariate predictors of suicidal behavior 4 years after an index suicide attempt, alongside depression, hopelessness, suicidal ideation, and previous suicide attempts. It is important to note, though, that consistent with the IMV model (O’Connor, 2011), entrapment was a unique predictor of suicidal behavior when considered together with the other univariate predictors. As frequency of past suicide attempts was the only other significant predictor in the multivariate analysis, entrapment was the only potentially modifiable risk factor for repeat suicidal behavior in this study. The predictive utility of entrapment is consistent with a central tenet of the IMV model of suicidal behavior (O’Connor, 2011), which states that entrapment is a unique predictor of suicidal behavior. According to Gilbert and Allan (1998), it is the thwarted motivation to escape that distinguishes entrapment from hopelessness. Indeed, we posit that as entrapment beliefs become stronger, the motivation to escape increases, and if no solution to the state of entrapment is found, beliefs about suicide become more likely, with suicide being viewed as the only solution to escape the painful feelings of entrapment.
Clinically, these data suggest that it may be useful to incorporate entrapment, together with established predictors, into the psychosocial risk assessment of repeat suicide attempts in patients who have previously been hospitalized after a suicide attempt. Our findings highlight that the former, in particular, may play a unique role within the suicidal process. It may represent part of the final common pathway to suicide. However, little is known about the development of entrapment. Future research, therefore, is required to specify the factors that lead to entrapment as well as the mechanisms accounting for the strong relationship between entrapment and suicidal behavior. Theoretically, the present findings also suggest that the IMV model is a useful new framework that warrants further empirical and clinical investigation. Although there has been a recent suggestion that defeat and entrapment are not distinct constructs (Taylor, Wood, Gooding, Johnson, & Tarrier, 2009), this study reinforces the utility of operationalizing the constructs separately.
Although these findings are promising and the sample size was adequate, the results do require replication and extension. It is also worth noting that this study was set up to investigate the repetition of medically serious suicide attempts: It will have missed low lethality attempts that did not require hospitalization. It also did not record suicide attempts that may have been captured at outpatient clinics, primary care settings, or other nonclinical settings. Researchers conducting future studies should also investigate whether the findings are generalizable to people with baseline attempts that did not result in hospitalization severe enough to result in initial hospitalization. Also, given that the majority of the sample had attempted suicide at least once prior to entry into the study, it would be useful to determine the predictive validity of entrapment in a homogeneous sample of first-time suicide attempters. As entrapment may underpin different types of self-injurious behavior (Nock, 2010; Williams, 1997), future research ought to investigate whether it differentially predicts suicidal versus nonsuicidal self-injury. Finally, large-scale studies are required to determine whether entrapment on its own is predictive of suicide beyond established risk factors.
ConclusionsThis study extends the understanding of individually sensitive mechanisms of suicide risk. The IMV model of suicidal behavior may provide a useful theoretical framework on which clinical formulations and treatment interventions could be based. Entrapment in particular should be included in clinical assessment and considered for inclusion in treatment trials as an index of clinical change. It should also be thought of as potentially part of the final common pathway to serious suicidal behavior.
Footnotes 1 The IMV model is similar to Joiner’s interpersonal–psychological theory (Joiner, 2005; Van Orden et al., 2010), in that both models endeavor to discriminate between those who think about suicide (but do not act on these thoughts, i.e., ideators) and those who act on their thoughts (i.e., suicide attempters). Both models also aim to provide a detailed map of the pathway to suicidal ideation and suicidal behavior, with belongingness and burdensomeness being highlighted in the interpersonal–psychological theory versus defeat and entrapment in the IMV model, in the final common pathway to suicide risk.
2 Intentional self-harm is the terminology used in the ICD and refers to acts of suicidal and nonsuicidal self-harm.
3 We also used receiver operating characteristic curve analysis to identify a cutoff score for each predictor that maximized that predictor’s sensitivity and specificity with respect to predicting a future suicide attempt. The cutoff scores and areas under the curve (AUC) for each predictor were, (a) for entrapment, 51+, AUC = .83; (b) for defeat, 52+, AUC = .83; (c) for hopelessness, 17+, AUC = .82; (d) for depression, 15+, AUC = .01; (e) for ideation, 20+, AUC = .69; and (f) for frequency of previous attempts, 2+, AUC = .79. The hierarchical logistic regression reported in Table 3 was repeated using the predictor score’s case and noncase at these cutoffs. Entrapment and defeat added significantly over the other four variables (Step χ2 = 8.3, p = .016; Model χ2 = 36.2, p < .001). In the final model, both entrapment (B = 2.3, βstdxy = .33, p = .035) and the frequency of previous suicide attempts (B = 2.3, βstdxy = .32, p = .031) were the only significant predictors. Thus, the results were identical to those in Table 3. There were no multicollinearity problems, with a mean VIF of 1.79 (range: 1.56–2.16), and no correlations derived from the fitted model variance–covariance matrix were greater than .5. Although these results replicate the main findings on the basis of continuous scores using binary cutoff scores, we have to caution strongly that these cutoff scores should not, at present, be used for clinical diagnostic purposes, because of the small sample size and number of repeat suicide attempts. These analyses were conducted to show the potential clinical applications of including entrapment as a key predictor of repeat suicide attempts; however, much more work is needed to show that these scales are indeed taxonic and that the cutoffs vary meaningfully with external criteria (see Ferguson, 2009; Ferguson et al., 2009).
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Submitted: November 28, 2012 Revised: April 30, 2013 Accepted: May 17, 2013
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Source: Journal of Consulting and Clinical Psychology. Vol. 81. (6), Dec, 2013 pp. 1137-1143)
Accession Number: 2013-25313-001
Digital Object Identifier: 10.1037/a0033751
Record: 131- Title:
- Psychometric analysis and validity of the daily alcohol-related consequences and evaluations measure for young adults.
- Authors:
- Lee, Christine M.. Department of Psychiatry and Behavioral Sciences, University of Washington, Seattle, WA, US, leecm@u.washington.edu
Cronce, Jessica M.. Department of Psychiatry and Behavioral Sciences, University of Washington, Seattle, WA, US
Baldwin, Scott A.. Department of Psychology, Brigham Young University, Provo, UT, US
Fairlie, Anne M.. Department of Psychiatry and Behavioral Sciences, University of Washington, Seattle, WA, US
Atkins, David C.. Department of Psychiatry and Behavioral Sciences, University of Washington, Seattle, WA, US
Patrick, Megan E., ORCID 0000-0003-3594-4944. Institute for Social Research, University of Michigan, MI, US
Zimmerman, Lindsey. National Center for PTSD Dissemination and Training Division, VA Palo Alto Health Care System, Menlo Park, CA, US
Larimer, Mary E.. Department of Psychiatry and Behavioral Sciences, University of Washington, Seattle, WA, US
Leigh, Barbara C.. Alcohol and Drug Abuse Institute, University of Washington, Seattle, WA, US - Address:
- Lee, Christine M., Department of Psychiatry and Behavioral Sciences, University of Washington, Box 354944, Seattle, WA, US, 98195, leecm@u.washington.edu
- Source:
- Psychological Assessment, Vol 29(3), Mar, 2017. pp. 253-263.
- NLM Title Abbreviation:
- Psychol Assess
- Page Count:
- 11
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- Psychological Assessment: A Journal of Consulting and Clinical Psychology
- ISSN:
- 1040-3590 (Print)
1939-134X (Electronic) - Language:
- English
- Keywords:
- psychometrics, alcohol-related consequences, daily questionnaire
- Abstract:
- College students experience a variety of effects resulting from alcohol use and evaluate their experiences on a continuum from negative to positive. Using daily reports collected via cell phone, we examined the psychometric properties of alcohol use consequences and evaluations of those consequences. Participants were 349 undergraduate students (mean age 19.7 [SD = 1.26], 53.4% female). Data were analyzed using a multilevel factor analysis framework, incorporating binary items (consequences) and normally distributed items (evaluations). Our model converged on 2 factors—positive and negative—with similar loadings between- and within-persons. Intraclass correlation coefficients for positive consequences and their evaluations ranged from .30 to .40, whereas values for negative consequences were more variable. Intraclass correlation coefficients for negative evaluations were higher, suggesting evaluations were more trait-like compared to experience of consequences which may be context dependent. Generalizability coefficients on the whole were good to excellent, suggesting highly reliable scales at both person-mean and daily-mean levels. However, likely due to binary scale and infrequency, the generalizability coefficients for negative consequences at the daily level was somewhat low. Convergent validity was demonstrated by (a) positive associations between baseline Rutgers Alcohol Problem Index and Alcohol Use Disorders Identification Test scores with latent factors for daily positive and negative consequences, and (b) positive associations between daily drinking and daily consequences and evaluations of consequences. Overall, this measure demonstrated good psychometric properties for use in studies examining daily and lagged relationships between alcohol use and related consequences. (PsycINFO Database Record (c) 2017 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Alcohol Drinking Patterns; *Psychometrics; *Questionnaires; *Test Validity; *Consequence
- PsycINFO Classification:
- Health Psychology Testing (2226)
Drug & Alcohol Usage (Legal) (2990) - Population:
- Human
Male
Female - Age Group:
- Adulthood (18 yrs & older)
Young Adulthood (18-29 yrs) - Tests & Measures:
- Daily Alcohol Use Measure [Appended]
Daily Alcohol Expectancy Measure
Alcohol-Related Consequences Measure
Alcohol Use Disorders Identification Test DOI: 10.1037/t01528-000
Rutgers Alcohol Problem Index DOI: 10.1037/t00517-000 - Grant Sponsorship:
- Sponsor: National Institute on Alcohol Abuse and Alcoholism, US
Grant Number: R01 AA016979
Recipients: No recipient indicated - Methodology:
- Empirical Study; Quantitative Study
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- First Posted: May 19, 2016; Accepted: Mar 14, 2016; Revised: Mar 8, 2016; First Submitted: Jul 13, 2015
- Release Date:
- 20160519
- Correction Date:
- 20170306
- Copyright:
- American Psychological Association. 2016
- Digital Object Identifier:
- http://dx.doi.org/10.1037/pas0000320
- PMID:
- 27196690
- Accession Number:
- 2016-24939-001
- Persistent link to this record (Permalink):
- http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2016-24939-001&site=ehost-live
- Cut and Paste:
- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2016-24939-001&site=ehost-live">Psychometric analysis and validity of the daily alcohol-related consequences and evaluations measure for young adults.</A>
- Database:
- PsycINFO
Psychometric Analysis and Validity of the Daily Alcohol-Related Consequences and Evaluations Measure for Young Adults
By: Christine M. Lee
Department of Psychiatry and Behavioral Sciences, University of Washington;
Jessica M. Cronce
Department of Psychiatry and Behavioral Sciences, University of Washington
Scott A. Baldwin
Department of Psychology, Brigham Young University
Anne M. Fairlie
Department of Psychiatry and Behavioral Sciences, University of Washington
David C. Atkins
Department of Psychiatry and Behavioral Sciences, University of Washington
Megan E. Patrick
Institute for Social Research, University of Michigan
Lindsey Zimmerman
National Center for PTSD Dissemination and Training Division, VA Palo Alto Health Care System, Menlo Park, California
Mary E. Larimer
Department of Psychiatry and Behavioral Sciences, University of Washington
Barbara C. Leigh
Alcohol and Drug Abuse Institute, University of Washington
Acknowledgement: Jessica M. Cronce is now at the Department of Counseling Psychology and Human Services, College of Education, University of Oregon.
Data collection and manuscript preparation were supported by a grant from the National Institute on Alcohol Abuse and Alcoholism (R01 AA016979). The content of this article is solely the responsibility of the author(s) and does not necessarily represent the official views of the National Institute on Alcohol Abuse and Alcoholism or the National Institutes of Health.
Alcohol use and its associated consequences have been widely studied in young adult and college student populations. The term consequence has a definitively negative connotation, most often referring to the damage caused by alcohol misuse (e.g., Hingson, Zha, & Weitzman, 2009; Perkins, 2002), which impacts the individual drinker (e.g., hangovers, blackouts) as well as others (e.g., perpetration of physical assault and sexual violence). For some (but not all) drinkers, identifying previously experienced consequences motivates behavior change (see Cronce & Larimer, 2011, for a review of brief motivational interventions). This is consistent with an operant learning theory perspective, where consequences are assumed to be punishers, and their occurrence is expected to decrease undesirable drinking behavior (Bouton, 2000). The theories of reasoned action (Fishbein, 1980) and planned behavior (Ajzen, 1991) suggest that attitudes about behaviors, including evaluations of whether a consequence is good or bad, are considered when forming behavioral intentions and predicting behavior.
Evaluations of alcohol consequences are, however, not uniform (Patrick & Maggs, 2011). For example, vomiting and blackouts are routinely included in measures of negative consequences and used to predict decreases in drinking, but close to 25% and 50% of students, respectively, consider these consequences to be positive or neutral (Mallett, Bachrach, & Turrisi, 2008). Understanding how and when certain consequences act as reinforcers (i.e., positive consequences that tend to increase behavior) may be just as, if not more, important in predicting and intervening in drinking behavior (Patrick & Maggs, 2008). Of course, evaluations of whether or not a given consequence was experienced as positive, neutral, or negative may vary over time as circumstances change. For example, having a hangover on a day that you can sleep in is likely less negative than having a hangover the morning you have to take a test.
It is important to consider how students evaluate potential future consequences as well as experienced past consequences. Research supports the relation between college students’ evaluations of potential future consequences (i.e., how positive [good] or negative [bad] a consequence would be, regardless of their intent to drink) and alcohol use and problems (Gaher & Simons, 2007; Neighbors, Walker, & Larimer, 2003; Patrick & Maggs, 2011). Experiencing more positive consequences is associated with evaluating future positive consequences as more positive; experiencing more negative consequences is associated with perceiving future negative consequences as less negative (Logan, Henry, Vaughn, Luk, & King, 2012). Other research has focused on students’ evaluations of experienced consequences, namely their evaluations of how positive or negative a consequence was based on their actual experience of it (Merrill, Read, & Barnett, 2013; Merrill, Read, & Colder, 2013; White & Ray, 2014). However, prior studies have chiefly relied on retrospective assessment methods that are subject to recall bias. Moreover, they fail to account for individual variation in evaluations of consequences over time (see Merrill, Read, & Barnett, 2013), and often fail to obtain reports on all of the consequences experienced (e.g., they focus only on negative consequences).
In fact, prior research considering the experience of both positive and negative consequences simultaneously is scant (e.g., Park, 2004; Park & Grant, 2005; Park & Levenson, 2002; Patrick & Maggs, 2008). Examining positive consequences in addition to negative consequences seems critical, as students report experiencing positive consequences significantly more frequently than negative consequences across drinking occasions (Park, 2004; Park & Grant, 2005; Park & Levenson, 2002; Patrick & Maggs, 2008), and alcohol consumption is related to the extent to which students’ most positive consequence is considered positive, but not to the extent that their most negative consequence is negative (Park, 2004). Unfortunately, extant studies examining positive consequences have used measures that classified consequences as positive a priori, consistent with extant negative consequence measures (e.g., Hurlbut & Sher, 1992; Kahler, Strong, & Read, 2005; White & Labouvie, 1989).
Building off our own and others’ prior work, we developed a program of research to examine a daily process model of alcohol use, alcohol expectancies, experienced consequences, and evaluations of experienced consequences (Corbin, Morean, & Benedict, 2008; Lee, Atkins, Cronce, Walter, & Leigh, 2015). The daily process model accounts for different types of associations among the key constructs, including alcohol expectancies predicting later day alcohol use and experienced consequences, as well as feed-forward processes that may help describe the maintenance or change in future alcohol use (e.g., consequences experienced today may predict next-day expectancies and alcohol use). Daily process models using daily reports of behaviors and psychological constructs, like evaluations of experienced consequences, are important for several reasons. First, daily process models can distinguish between-person effects from within-person effects. Between-person effects refer to person-level characteristics that may partially explain variation in an outcome, such as gender partially accounting for variation in the number of drinks consumed on a given day. Within-person effects refer to characteristics (e.g., situational or psychological) that may differ from occasion to occasion for a given individual, and these characteristics can partially account for day-to-day changes in an outcome. For instance, daily fluctuations in negative mood may partially explain day-to-day changes in alcohol consumption for a given individual over time. Second, daily process models allow for the examination of the temporal ordering of psychological constructs and/or behaviors. Daily reports of evaluations of negative consequences may predict reductions in drinking the following day when the consequences are perceived as especially negative (see Merrill, Read, & Barnett, 2013). Third, unlike retrospective reports, daily reports are less adversely affected by the inability to remember events or feelings. Daily reports also minimize bias that may be introduced by the passage of time, as in the case of reevaluating an experience after a few weeks have passed.
To date, there has not been an established psychometrically-sound daily measure of experienced consequences. Using a multilevel factor analysis framework, incorporating up to 56 days of data within person across 349 individuals, the present study establishes the psychometric properties of a 13-item daily positive and negative consequences measure, which includes evaluations of experienced consequences, at both the between- and within-person levels, while taking into account the key features of this type of data including (a) repeated measures nested within individuals, (b) different item scales (i.e., binary items for consequences and continuous scales for evaluations), and (c) evaluation scores being contingent upon whether an individual consequence was experienced.
To psychometrically evaluate the proposed measure, we proceeded in four steps. First, we evaluated the intraclass correlations of each consequence and evaluation to determine how much items varied between- and within-persons. Large between-person variance indicates that consequences and evaluations are fairly constant across time for a given person. Conversely, large within-person variance indicates that consequences and evaluations fluctuate notably within-persons, over time. Second, we conducted separate exploratory multilevel factor analyses to examine between- and within-person factors for consequences and evaluations. We hypothesized two factors (i.e., positive and negative factors) both between- and within-person for consequences and evaluations. Third, based on results of the previous step, we used a multilevel confirmatory factor analysis, which included the latent factors for both consequences and evaluations simultaneously at both between- and within-person levels of the data. This model was necessary in it allowed us to account for the fact that participants only rated a consequence if they reported experiencing the consequence. Thus, we obtained unbiased estimates of factor loadings, factor variances, and factor correlations. With respect to between-factor correlations, we hypothesized that positive consequences would be positively associated with positive evaluations and negative consequences at both levels. We anticipated that negative consequences would be negatively associated with negative evaluations (i.e., greater endorsement of negative consequences is associated with less favorable evaluations), at both between- and within-person levels. Finally, convergent validity was assessed by extending the prior confirmatory factor analysis model to include baseline predictors (i.e., negative consequences as measured by the Rutgers Alcohol Problem Index [RAPI; White & Labouvie, 1989] and the Alcohol Use Disorders Identification Test [AUDIT; Babor, Higgins-Biddle, Saunders, & Monteiro, 2001]) of the latent variable constructs for the daily-level consequences and evaluations. We hypothesized that the proposed daily measure of alcohol-related consequences and evaluations of consequences would demonstrate good convergent validity with relevant baseline measures of alcohol-related consequences and daily measures of alcohol use.
Method Participants
Participants for the present analyses were part of a larger study examining a daily process model of alcohol use, alcohol expectancies and consequences and included 349 undergraduate students (mean age 19.7 [SD = 1.26], 53.4% female). Due to the longitudinal nature of the larger research study, enrollment was only open to students of freshman, sophomore, and junior standing. Most participants (74.2%) were Caucasian, with the remainder Asian American (8.5%), multiracial (11.1%), or other (6.2%).
Procedures
Undergraduate freshman, sophomore, and junior students between the ages of 18–24 (N = 8,923) at a large public university were randomly selected from the University Registrar’s enrollment list and invited to participate in a larger longitudinal study of daily alcohol use and related consequences. Students were invited via e-mail and mailed letter to complete a short confidential screening survey to assess study eligibility, which included owning a mobile phone with a service contract and text messaging, being at least 18 years old, and drinking at least twice a week over the past month. Those who met eligibility criteria were then invited to participate in an online baseline survey assessing demographics, alcohol use, and other psychosocial measures. Of the 3,210 students who completed the screening survey, 539 met criteria and were invited to the baseline survey. Of those, 516 completed the baseline survey, and 352 came to an in-person training session at the study offices, which included consent procedures, as well as an overview of the study procedures and training in the data collection method, and were enrolled in the longitudinal study (i.e., consented and completed at least one daily interview). Of the 164 participants who completed baseline and did not enroll in the study, 58 individuals declined to come in for a training session, one completed the training but did not start the daily interviews, and 105 either never attended their scheduled training session or did not schedule a training session. The main reasons participants cited for missing a scheduled training session and/or not rescheduling a session was that they were too busy or that the available times did not work for them. No significant differences were found between those eligible participants who enrolled versus didn’t enroll in the study based on age, t(364.42) = −1.48, p = .14, gender, χ2(1, N = 516) = 1.19, p = .28, total drinks per week, t(489) = −0.66, p = .51, AUDIT sum scores, t(500) = 0.47, p = .64, and negative consequences, t(260.65) = −0.33, p = .74, at baseline. Participants were compensated $10 for completing the screening survey and $30 for completing the baseline assessment.
Participants used their mobile phones to complete daily telephone interviews via an Interactive Voice Response system. Participants completed three interviews a day for four bursts of 2-week periods over the course of 1 academic year. Daily interviews included a morning interview (9 a.m.–noon), afternoon interview (3–6 p.m.) and evening interview (9 p.m.–midnight). Each interview took less than 10 min to complete and participants were compensated $2 for each complete interview, plus a bonus of $16 if they completed 36 of the 42 possible interviews for each 2-week period. Of the 352 participants who came in for a training session, three participants did not report drinking during the daily portion of the study, resulting in a sample of 349 for the present analyses. All procedures were approved by the University IRB and a federal Certificate of Confidentiality was obtained from the National Institutes of Health.
Measures
Daily alcohol-related consequences and evaluations for young adults
Item development and selection of consequences occurred in conjunction with the development of a daily alcohol expectancy measure (Lee et al., 2015), based on review of the alcohol expectancies and alcohol effects literatures, expert review of items, and cognitive interviews with college students (see Lee et al., 2015, for specific details of the selection of initial items). We conducted cognitive interviews with 14 college student drinkers, exploring whether the proposed consequence items were representative of the alcohol effects that college students would experience and what important effects might have been missed.
The purpose of the broader study was to examine a daily process model of alcohol expectancies, alcohol use, and related consequences and we opted to measure the same consequences as expectancies, due to the fact that the measure of consequences needed to be brief, assess both positive and negative consequences and evaluations, and evidence sufficient between- and within-person variability (with a varying level of endorsement throughout the entire study). For example, severe consequences (e.g., alcohol poisoning, getting arrested due to drinking and driving) may occur and have influence on future drinking and alcohol expectancies; however, the base rates for these consequences are comparatively low and would contribute zero or very little variability between- and within-person, and would therefore not be useful toward the development of a daily measure of consequences. Thus, for the purpose of the present analyses, we used 15 alcohol-related consequences and evaluations.
In each morning interview, participants who reported drinking the previous day were asked, “Did any of the following things happen to you as a result of your drinking yesterday?” Participants could respond yes or no to 15 different alcohol-related consequence items (e.g., I felt relaxed, I became aggressive). For the consequences endorsed, the list was then repeated with the question, “How bad or good was that, from 1 to 9, where 1 is extremely bad and 9 is extremely good?” (see Table 1 for scale items). An open-ended question regarding whether there were any additional consequences that occurred that day as a result of their drinking was also included, as a way to catch more serious consequences. Preliminary coding of those consequences did not yield any consequences that were not represented in the 15 items.
Descriptive Statistics for Consequence Items and Consequence Evaluation Items
Daily alcohol use
In the morning interview, participants reported on their alcohol use on the previous day. Participants were asked “Did you drink any alcohol yesterday, from the time you got up to the time you went to sleep?” For those who reported drinking, they were then asked “How many drinks did you have in total yesterday?” Participants reviewed standard drink definitions during the initial training session.
Baseline alcohol consequences
Participants were asked to indicate how many times in the last 3 months they had experienced each of 23 alcohol-related consequences, as measured by the RAPI (White & Labouvie, 1989). Items were dichotomized to reflect whether the participant experienced the item in the past 3 months (coded 1) or not (coded 0). Items were summed together to create the number of different alcohol-related consequences the participant had experienced in the last 3 months at baseline.
Baseline high-risk alcohol use
The AUDIT (Babor et al., 2001) was administered at baseline to provide a measure of harmful or hazardous alcohol use. The AUDIT consists of 10 items covering consumption, drinking behavior/dependence, and alcohol-related problems, and the items were summed to create a total score. The AUDIT has been found to have reasonable psychometric properties among samples of college students for use in determining high-risk drinking (Kokotailo et al., 2004).
Data Analyses
The present analyses focused on the psychometric and substantive evaluation of two types of daily reports: (a) alcohol-related consequences and (b) evaluations of the consequences, when experienced. Thus, the data analyses had to incorporate several aspects of the resulting data, including (a) repeated measures nested within individuals, (b) differing item scales (i.e., binary items for consequences and continuous scales for evaluations), and (c) the absence of an evaluation score if the consequence did not occur. On the latter point, the evaluation data can be thought of as having a structural missing data pattern, or as subject to a selection process (Kim & Muthén, 2009). The evaluation occurs only after a certain threshold has been passed, namely that the related consequence occurred.
Data were analyzed using a multilevel factor analysis framework, allowing for between- and within-person factors and associations of latent variables. In addition, given the mixture of binary and continuous items, the final model is a type of multimodal multilevel model, in which the likelihood incorporates both binary and normally distributed data. Finally, the joint model functions like a selection model, in which a consequence must first be experienced prior to an evaluation being observed, and the factor structure and loadings of evaluation items are conditional on the consequences. Due to its complexity, the resulting model was fit using a fully Bayesian analysis, including minimally informative priors and Markov chain Monte Carlo estimation (Song & Lee, 2012).
The reliability of the resulting scales was examined with generalizability coefficients (GC; Shavelson & Webb, 1991), which are an extension of classic internal reliability to research designs with multiple sources of error (e.g., longitudinal data). Generalized linear mixed models were used to estimate the following variance components:
where P indexes persons, D indexes days, I indexes items, multiple subscripts indicate interactions (e.g., PD represents the interaction of persons and days), and the final term represents the residual variance. Items and persons are crossed factors as each person completes the same set of items; thus, it is possible to estimate the variance of persons by items, which reflects whether certain people systematically differ in certain items. However, days (i.e., repeated measures) represent a nested factor (and are functionally unique within individuals). Because of this, it is not possible to identify the person by day or item by day variances. Using the variance terms from Equation 1, GCs for both person means and daily means can be estimated via
Similar to Cronbach’s alpha, GCs are measures of true variability due to persons as a proportion of total variance, where the additional terms in the denominators of Equations 2 and 3 represent error variance. Equations 2 and 3 differ in that in the former we are taking an average GC over days, and thus divide the days-variance term by the number of days. Analyses were conducted using Mplus Version 7.11 (Muthén & Muthén, 2012) and R Version 3.0.1 (R Core Team, 2013).
Results Descriptive Statistics
Due to larger study aims of examining daily process models of alcohol use, expectancies and consequences, our goal was to recruit frequently drinking college students who would report experiencing a variety of alcohol-related consequences on any given occasion. Of the 352 participants who completed the baseline survey, completed the in-person training session, and began the longitudinal daily diary portion of the study, 88% engaged in heavy episodic drinking (drank four or more drinks at a sitting for women; five or more for men) at least once in the past week at baseline, and 74% exceeded the National Institute on Alcohol Abuse and Alcoholism (NIAAA) recommendations for weekly drinking (reported drinking eight or more drinks for women and 15 or more drinks for men in a typical week). On average, participants drank 18.82 (SD = 11.60) total drinks per week in the past 3 months and experienced 11.71 (SD = 8.79) negative consequences in the last 3 months at baseline. Further, the average score on the AUDIT was 13.72 (SD = 5.48).
Descriptive statistics for consequences and evaluations can be found in Table 1. As seen in the table, items with generally positive connotations (e.g., being more social) were endorsed more frequently and rated more positively, whereas items with generally negative connotations (e.g., vomiting) were less frequent and rated less positively. Figure 1 contains a dot plot of intraclass correlation coefficients (ICCs) for each item for both consequences and consequence evaluations. The ICC is the proportion of the total item variance that is attributed to the variance between people, and hence values close to one reflect strong, trait-like qualities, whereas values close to zero indicate highly variable responses within individuals. As seen in the figure, positive items are relatively consistent with ICCs between .30 and .40 across both consequences and their evaluations. Negative items’ ICCs are notably more variable. Interestingly, for several items the consequence ICC is quite low, whereas the corresponding evaluation ICC is quite high (e.g., injuring oneself, being aggressive). This would suggest that the consequence itself is more context dependent (i.e., varies notably within people over time), but a person’s evaluation of the consequence is more stable.
Figure 1. Intraclass correlations of positive and negative consequences and their evaluations.
Exploratory multilevel factor analyses were used separately on consequences and evaluations. Based on fit indices and interpretability, results suggested a two factor model at both between- and within-person levels. Fit indices for the two-factor model were root mean square error of approximation (RMSEA) = 0.02, comparative fit index (CFI) = 0.95, Tucker–Lewis index (TLI) = 0.93, and standardized root mean square residual (SRMR) = 0.06. By way of comparison, the fit indices for the one-factor model at both levels were RMSEA = 0.04, CFI = 0.78, TLI = 0.75, and SRMR = 0.13. Most items cleanly loaded on one of two factors, largely conforming to positive versus negative alcohol-related consequences. Two exceptions to this pattern were the items “being unable to study” and “having more desire for sex.” Item loadings for these items were generally lower, and in one instance similar across factors. The loadings for these two items were similar to earlier analyses focused on alcohol expectancies and expectancy evaluations (Lee et al., 2015), and they were not included in the following analyses.
The multimodal, multilevel factor model described earlier was fit to the remaining items and is shown in Figure 2. Based on the preceding exploratory factor analyses, the joint model included positive and negative latent factors for both consequences and consequence evaluations at both between-person and within-person levels of the data. Maximum likelihood methods typically run until a model has “converged”—that is, until there is minimal change in the estimates. Bayesian methods do not have such a criterion, rather an explicit number of iterations must be specified and the resulting estimates must be evaluated for convergence. Current analyses used the Gelman-Rubin diagnostic and traceplots (Gelman & Hill, 2006) to assess convergence of the posterior distributions of parameter estimates, and these diagnostics were consistent with convergence. Factor loadings from the Bayesian model (i.e., mean of the posterior distribution) and 95% credible intervals (CI) are shown in Figure 3 and listed in Tables 2 and 3. All factor loadings are significantly different than zero, though the positive items are estimated much more precisely relative to the negative items. The width of the CI is directly related to the amount of information in the data, where positive consequences were reported much more often, and hence evaluations of positive consequences were observed more frequently. Negative consequences were reported far less often, and their evaluations were observed less frequently. Nonetheless, the point estimates show that items reliably load on their corresponding factors and that in general, items have similar loadings between- and within-persons.
Figure 2. Path diagram for the multilevel, multimodal confirmatory factor model. Rectangles represent observed variables and circles/ovals represent latent variables. Labels beginning with a “c” represent consequences and labels beginning with “e” represent evaluations. Numbers within labels represent the items. The items are 1 = “Relaxed,” 2 = “Hangover,” 3 = “Sociable,” 4 = “Aggressive,” 5 = “Better Mood,” 6 = “Vomit,” 7 = “Injury,” 8 = “Buzz,” 9 = “Forget,” 12 = “Energy,” 13 = “Rude,” 14 = “Express,” and 15 = “Embarrass.”
Figure 3. Factor loadings and credible intervals for positive and negative consequences and their evaluations.
Unstandardized Loadings
Standardized Loadings
The multimodal, multilevel factor model also provides variance-covariance matrices of the latent factors for both between- and within-levels of the model. A dotplot of factor correlations and CI is presented in Figure 4, which shows an interesting pattern of associations among consequences and their evaluations. Not surprisingly, positive consequences and their evaluations show a strong, positive association. Similarly, positive and negative consequences are positively correlated. Between persons, there is no association between evaluations of positive consequences and evaluations of negative consequences. However, within persons (i.e., in day-to-day fluctuations) there is a small, positive correlation between these two different types of evaluations. Finally, within persons, the number of negative consequences and their evaluation had a moderately strong negative correlation. Between-persons negative consequence endorsement and their evaluation were not associated. However, the difference in these two correlations is moderately large and bounded away from zero (rdiff = .62, 95% CI [.27, .99]).
Figure 4. Factor correlations between positive and negative consequences and their evaluations.
GCs were used to estimate the internal consistency of scales for both person means and daily means and are found in Table 4. GCs on the whole were good to excellent, suggesting highly reliable scales at both person-mean and daily-mean levels. The one exception to this was that the GC for negative consequences at the daily level was somewhat low. This reflects in part the scaling of consequences (i.e., binary) as well as their somewhat infrequent nature.
Generalizability Coefficients for Between-Person and Within-Person Scales for Consequences and Consequence Evaluations
Convergent Validity
We examined the convergent validity of the daily-level consequences and evaluation scales in two ways. First, we extended the confirmatory factor analysis model to include baseline predictors of the latent variable constructs for the daily-level consequences and evaluations. Specifically, baseline RAPI and AUDIT scores were included as predictors of the between-persons consequences and evaluation latent variables (see Figure 2). Baseline RAPI was significantly and positively related to positive consequences (β̂ = 0.02, 95% CI [0.01, 0.04]) and negative consequences (β̂ = 0.04, 95% CI [0.03, 0.06]). These coefficients as well as the other validity coefficients can be interpreted as follows. For the positive consequences latent variable, which is standardized with a mean of 0 and standard deviation of 1, a one-unit increase in baseline RAPI is associated with a 0.02 standard deviation increase in positive consequences. For the negative consequences latent variable, a one-unit increase in baseline RAPI is associated with a 0.04 standard deviation increase in negative consequences. One could also interpret the relationships in terms of standard deviation change in the RAPI. For example, a 1 SD increase in baseline RAPI (SD = 8.8) is associated with a 0.4 (8.8 × 0.04) SD increase in negative consequences.
Baseline RAPI was not significantly related to positive evaluations (β̂ = 0.01, 95% CI = −0.001, 0.03) or negative evaluations (β̂ = −0.02, 95% CI [−0.05, 0.01]). The relationships between the RAPI and positive and negative evaluations were in the expected direction, but not significant. This was likely due to the fact that there was less data for evaluations than the consequences, because participants only evaluated the consequences when the consequences occurred. Baseline AUDIT was significantly and positively related to positive consequences (β̂ = 0.03, 95% CI [0.01, 0.05]) and negative consequences (β̂ = 0.07, 95% CI [0.04, 0.10]). Baseline AUDIT was not significantly related to positive evaluations (β̂ = −0.001, 95% CI [−0.03, 0.02]) or negative evaluations (β̂ = −0.01, 95% CI [−0.06, 0.04]).
The second method for evaluating convergent validity was examining whether daily drinking predicted scale scores for consequences and evaluations. The positive and negative consequences scale scores were the sum of positive or negative consequences, respectively, on a given day. The evaluations of positive and negative consequences were the mean positive or negative evaluations, respectively, on a given day. When predicting daily consequences, we used a multilevel Poisson model with robust standard errors. When predicting daily evaluations, we used a multilevel normal model.
Daily drinking had a positive relationship with positive consequences (incident rate ratio [IRR] = 1.07, p < .01, 95% CI [1.06, 1.08]) and negative consequences (IRR = 1.23, p < .01, 95% CI [1.21, 1.26]). Thus, a one-drink increase in daily drinking is associated with a 7% increase in the number of positive consequences and 23% increase in the number of negative consequences experienced on a given day. Daily drinking also had a positive relationship with positive evaluations (β̂ = 0.04, p < .01, 95% CI [0.03, 0.05]), showing consuming more alcohol was associated with more favorable evaluations of positive consequences. Daily drinking had a negative relationship with negative evaluations (β̂ = −0.03, p < .01, 95% CI [−0.05, −0.01]), showing consuming more alcohol was associated with less favorable evaluations of negative consequences.
DiscussionThe current research was designed to develop and examine the psychometric properties of a daily positive and negative alcohol-related consequences measure. Our ultimate goal with this measure is to use it to examine the daily and lagged relationships with alcohol use and alcohol-related consequences. Results from multilevel factor analyses support a 13-item scale with good psychometric properties. Two factors emerged, representing a subscale for positive consequences and a subscale for negative consequences at both between- and within-person levels of data. Reliabilities of the two subscales were high, despite the psychometric difficulties associated with binary items that assess infrequent consequences. In addition, there was variance in the experience and evaluation of positive and negative consequences both between persons on average and within individuals across days. The findings from the present study support the use of this new measure in research utilizing intensive repeated measures designs.
The present results offer some interesting descriptive findings. Consistent with other cross-sectional research (Park, 2004; Park & Grant, 2005; Park & Levenson, 2002; Patrick & Maggs, 2008), we found that positive consequences were endorsed more frequently and more positively across measurement occasions. With the exception of feeling energetic and expressing feelings more, all the positive items were endorsed 50% or more of the time. Negative consequences were much less frequently endorsed, despite this being a college sample recruited for drinking at least twice per week.
The current results demonstrate that both the likelihood of occurrence and the evaluation of consequences vary between and within individuals. This finding has implications for alcohol interventions based on operant learning theories (Bouton, 2000; Monti, Kadden, Rohsenow, Cooney, & Abrams, 2002; Marlatt & Donovan, 2009), which assume behavior is shaped by its consequences. For example, understanding variability in the likelihood of different consequences, as well as individual evaluations of those consequences, is important for utilizing this information in motivational interventions (Cronce & Larimer, 2011). In addition, this information is important to more broadly understand how, when, and under what circumstances consequences impact subsequent drinking. Surprisingly, we found there was less variability in how students evaluated the negative consequences, particularly for hurting or injuring oneself by accident and forgetting what they did while drinking, than there was in how they evaluated the positive consequences. This suggests that evaluations for negative consequences may be more dependent on the person or on prior learning experiences and learning contexts than on the immediate context or extremity of the consequence, while positive evaluations are more situational. If negative consequences are presumed to operate as punishers that would decrease subsequent drinking behavior, it is valuable to understand whether more trait-like negative consequence evaluations are associated with same-day or next-day drinking. Further research on these daily processes in conjunction with alcohol use measures would shed light on whether experiences of specific consequences are particularly aversive and/or for whom they are more likely to be associated with decreases in alcohol use. Identifying associations between negative consequences and within-person changes in alcohol use behaviors would help to identify opportune times and situations in which to intervene.
The present results should be evaluated in light of study limitations. The sample consisted of college students of freshman, sophomore, and junior standing from one university. Further, the sample inclusion criteria limited the participants to those who drank at least twice per week in the last month and to those owning a cell phone with a text messaging plan. Thus the results may not generalize to older individuals, those not in college, or to those with less experience with alcohol. It should be noted, however, that despite our initial drinking inclusion criteria, examination of drinking patterns over the course of the year indicated light to heavy patterns of use. Additional limitations include that the temporal relationship of the reinforcing consequences (e.g., getting a buzz) and the punishing consequences (e.g., having a hangover) differ, with the rewarding consequences more proximal to the behavior. This presents a challenge for future research. Finally, for the purposes of our larger study, we opted to include a very brief measure of consequences and evaluations with item selection based on alcohol effects that would evidence within-person (i.e., occasion-level variability) as well as between-person variability. The measure presented here does not reflect all positive or negative consequences that college students may experience, and in particular does not include serious alcohol-related negative consequences, which might have immediate and long-lasting changes to one’s health, well-being, and future decisions about alcohol use (e.g., alcohol poisoning, alcohol-related traffic accidents, alcohol-related arrests).
We developed a scale to measure the consequences of drinking alcohol with several novel features: the inclusion of both positive and negative consequences and their evaluations, a focus on scale items that describe consequences of discrete drinking episodes, and the tailoring of scale items to be used in daily measures of drinking and its consequences. The scale demonstrated both within- and between-person variability in the consequences reported and the evaluations of those consequences, indicating both trait-like and state-like variability and demonstrating the utility of the scale in examining the relationships between alcohol use and alcohol-related consequences across days.
Footnotes 1 Consequence items were analyzed using a logistic mixed model, which does not include an error term. For these scales, the variance of the logistic distribution (
) was used for the residual error.
2 The exploratory factor analysis for consequence evaluations had difficulties converging due to sparse data. Results from the joint model described later also strongly suggest two factors.
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Submitted: July 13, 2015 Revised: March 8, 2016 Accepted: March 14, 2016
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Source: Psychological Assessment. Vol. 29. (3), Mar, 2017 pp. 253-263)
Accession Number: 2016-24939-001
Digital Object Identifier: 10.1037/pas0000320
Record: 132- Title:
- Psychometric properties for the Balanced Inventory of Desirable Responding: Dichotomous versus polytomous conventional and IRT scoring.
- Authors:
- Vispoel, Walter P.. Department of Psychological and Quantitative Foundations, University of Iowa, Iowa City, IA, US, walter-vispoel@uiowa.edu
Kim, Han Yi. Department of Psychological and Quantitative Foundations, University of Iowa, Iowa City, IA, US - Address:
- Vispoel, Walter P., Department of Psychological and Quantitative Foundations, University of Iowa, 361 Lindquist Center, Iowa City, US, 52242-1529, walter-vispoel@uiowa.edu
- Source:
- Psychological Assessment, Vol 26(3), Sep, 2014. pp. 878-891.
- NLM Title Abbreviation:
- Psychol Assess
- Page Count:
- 14
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- Psychological Assessment: A Journal of Consulting and Clinical Psychology
- ISSN:
- 1040-3590 (Print)
1939-134X (Electronic) - Language:
- English
- Keywords:
- BIDR, polytomous IRT modeling, socially desirable responding, scoring methods, validity, reliability
- Abstract:
- [Correction Notice: An Erratum for this article was reported in Vol 26(3) of Psychological Assessment (see record 2014-16017-001). The mean, standard deviation and alpha coefficient originally reported in Table 1 should be 74.317, 10.214 and .802, respectively. The validity coefficients in the last column of Table 4 are affected as well. Correcting this error did not change the substantive interpretations of the results, but did increase the mean, standard deviation, alpha coefficient, and validity coefficients reported for the Honesty subscale in the text and in Tables 1 and 4. The corrected versions of Tables 1 and Table 4 are shown in the erratum.] Item response theory (IRT) models were applied to dichotomous and polytomous scoring of the Self-Deceptive Enhancement and Impression Management subscales of the Balanced Inventory of Desirable Responding (Paulhus, 1991, 1999). Two dichotomous scoring methods reflecting exaggerated endorsement and exaggerated denial of socially desirable behaviors were examined. The 1- and 2-parameter logistic models (1PLM, 2PLM, respectively) were applied to dichotomous responses, and the partial credit model (PCM) and graded response model (GRM) were applied to polytomous responses. For both subscales, the 2PLM fit dichotomous responses better than did the 1PLM, and the GRM fit polytomous responses better than did the PCM. Polytomous GRM and raw scores for both subscales yielded higher test–retest and convergent validity coefficients than did PCM, 1PLM, 2PLM, and dichotomous raw scores. Information plots showed that the GRM provided consistently high measurement precision that was superior to that of all other IRT models over the full range of both construct continuums. Dichotomous scores reflecting exaggerated endorsement of socially desirable behaviors provided noticeably weak precision at low levels of the construct continuums, calling into question the use of such scores for detecting instances of 'faking bad.' Dichotomous models reflecting exaggerated denial of the same behaviors yielded much better precision at low levels of the constructs, but it was still less precision than that of the GRM. These results support polytomous over dichotomous scoring in general, alternative dichotomous scoring for detecting faking bad, and extension of GRM scoring to situations in which IRT offers additional practical advantages over classical test theory (adaptive testing, equating, linking, scaling, detecting differential item functioning, and so forth). (PsycINFO Database Record (c) 2016 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Impression Management; *Inventories; *Item Response Theory; *Scoring (Testing); Classical Test Theory; Psychometrics; Test Reliability; Test Validity
- Medical Subject Headings (MeSH):
- Adolescent; Deception; Female; Humans; Male; Motivation; Psychological Theory; Psychometrics; Reproducibility of Results; Research Design; Social Desirability; Surveys and Questionnaires; Young Adult
- PsycINFO Classification:
- Tests & Testing (2220)
Social Psychology (3000) - Population:
- Human
Male
Female - Location:
- US
- Age Group:
- Adulthood (18 yrs & older)
Young Adulthood (18-29 yrs) - Tests & Measures:
- Self-Perception Scale for College Students
Self-Description Questionnaire III DOI: 10.1037/t06009-000
Balanced Inventory of Desirable Responding DOI: 10.1037/t08059-000 - Methodology:
- Empirical Study; Quantitative Study
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- First Posted: Apr 7, 2014; Accepted: Feb 12, 2014; Revised: Nov 27, 2013; First Submitted: Jun 11, 2013
- Release Date:
- 20140407
- Correction Date:
- 20140901
- Copyright:
- American Psychological Association. 2014
- Digital Object Identifier:
- http://dx.doi.org/10.1037/a0036430
- PMID:
- 24708082
- Accession Number:
- 2014-12154-001
- Number of Citations in Source:
- 94
- Persistent link to this record (Permalink):
- http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2014-12154-001&site=ehost-live
- Cut and Paste:
- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2014-12154-001&site=ehost-live">Psychometric properties for the Balanced Inventory of Desirable Responding: Dichotomous versus polytomous conventional and IRT scoring.</A>
- Database:
- PsycINFO
Psychometric Properties for the Balanced Inventory of Desirable Responding: Dichotomous Versus Polytomous Conventional and IRT Scoring
By: Walter P. Vispoel
Department of Psychological and Quantitative Foundations, University of Iowa;
Han Yi Kim
Department of Psychological and Quantitative Foundations, University of Iowa
Acknowledgement: We thank Linan Sun and Yi He for their help with keypunching, scoring, and data file creation and management.
Developers and users of self-report questionnaires routinely deal with problems of socially desirable responding (SDR) and have long sought ways to assess, control for, or eliminate such response tendencies. One common way to address these problems is to measure SDR directly along with the other targeted constructs of interest. Some instruments, such as the Minnesota Multiphasic Personality Inventory–2 (Butcher, Dahlstrom, Graham, Tellegen, & Kreammer, 1989) and the California Psychological Inventory (Gough & Bradley, 1996), have items that measure SDR interspersed among other items within the inventory, whereas others, such as the Marlowe-Crowne Social Desirability Scale (Crowne & Marlowe, 1960) and the Balanced Inventory of Desirable Responding (BIDR; Paulhus, 1991, 1999, 2002), are used separately as companions to other questionnaires.
Factor analytic research into the constructs measured by SDR instruments as a whole has revealed two primary dimensions of SDR that Paulhus (1991) described as self-deceptive enhancement and impression management (Cattell & Scheier, 1961; Edwards, Diers, & Walker, 1962; Jackson & Messick, 1962; Paulhus, 1991; Wiggins, 1964). Self-deceptive enhancement represents honest but overly positive beliefs about one’s abilities, whereas impression management may reflect more calculated attempts to look good or bad to the user of the questionnaire results. Measuring both facets of SDR can provide important insights into the credibility and nature of responses to all questionnaire items.
Paulhus (1991) demonstrated that most measures of SDR assess primarily self-deceptive enhancement, primarily impression management, or a combination of the two dimensions. He developed the BIDR to alleviate this ambiguity by providing separate and distinct measurement of self-deceptive enhancement and impression management in the Self-Deceptive Enhancement (SDE) and Impression Management (IM) subscales, respectively. Items on the BIDR are answered initially on either 1–5-point or 1–7-point scales depending upon the form. Either way, Paulhus recommended that the polytomous item scores then be dichotomized so that only extreme responses are scored as socially desirable.
Dichotomous scoring has been used in most but not all research conducted with the BIDR (see, e.g., Li & Bagger, 2007). Nevertheless, in the limited number of studies to date in which dichotomous and polytomous scoring have been compared directly, polytomous scoring has fared better. For example, Stöber, Dette, and Musch (2002), who compared polytomous and dichotomous scoring of a German version of the BIDR, found that polytomous scoring yielded higher alpha-reliability estimates and higher convergent validity coefficients, and that polytomous IM scores were sensitive to both fake good and fake bad instructions, whereas dichotomous IM scores were sensitive only to fake good. Similarly, Vispoel and Tao (2013), using a generalizability framework to compare dichotomous and polytomous scoring of the BIDR, found that polytomous scoring provided higher alpha, test–retest, and generalizability coefficients for both subscales and higher dependability coefficients for using the IM scale to detect fake bad responses.
Another, and rarely applied, way to score the BIDR is to use methods based on item response theory (IRT). Such methods have been used predominantly with achievement and aptitude tests but are applicable to affective measures as well (see, e.g., De Ayala, 1993; Embretson & Reise, 2000; Reise & Henson, 2003; Reise & Waller, 1990). IRT models in educational and psychological testing were initially applied to dichotomously scored (i.e., correct vs. incorrect) multiple-choice items but were later extended to polytomously scored options for multiple-choice items and performance assessments (e.g., essay items). Although findings are mixed, some researchers, using conventional and/or IRT methods to compare dichotomous and polytomous scoring of tests, have found that polytomous scoring can provide higher overall reliability, more precise ability estimation, better pass/fail decisions, and more accurate equating of scores (see, e.g., Haladyna, 1990; Lee, Kolen, Frisbie, & Ankenmann, 2001; Si & Schumacker, 2004; Thissen, 1976). With self-report measures of individual differences, the choice between dichotomous and polytomous IRT models would typically depend on the nature of the response metric used. True–false, yes–no, agree–disagree, and other two-choice items would be scored dichotomously, whereas Likert-style items with three or more choices (e.g., strongly disagree, moderately disagree, slightly disagree, slightly agree, moderately agree, strongly agree) would be scored polytomously.
Benefits of IRT scoring include indices of overall item and item option precision (e.g., information) beyond those offered by classical test theory (Dodd, De Ayala, & Koch, 1995; Fletcher & Hattie, 2004; Lord, 1977), ability to reference scores to both latent trait and raw score metrics (Kolen, Zeng, & Hanson, 1996; Lord, 1977; Wang, Kolen, & Harris, 2000), ability to place individuals from qualitatively diverse groups on common scales (Lord, 1977; Reise, Ainsworth, & Haviland, 2005; Reise & Henson, 2003), adaptive administration of items (Dodd et al., 1995; Lord, 1977; Reise & Henson, 2003; Roper, Ben-Porath, & Butcher, 1995; Simms & Clark, 2005; Scullard, 2007; Waller & Reise, 1989), and a refined foundation for investigating differential item functioning (Lord, 1977; Huang, Church, & Katigbak, 1997; Reise & Henson, 2003; Robie, Zickar, & Schmit, 2001).
Researchers have fit IRT models to results from various personality inventories but have rarely fit both dichotomous and polytomous IRT models to the same set of data (see, e.g., Chernyshenko, Stark, Chan, Drasgow, & Williams, 2001; Cooper & Petrides, 2010; De Ayala, Dodd, & Koch, 1992; Egberink & Meijer, 2011; Flannery, Reise, & Widaman, 1995; Fletcher & Hattie, 2004; Gray-Little, Williams, & Hancock, 1997; Harvey & Murry, 1994; Huang et al., 1997; Reise & Waller, 1990, 2003; Robie et al., 2001; Simms & Clark, 2005). This makes sense because most measures entail either dichotomous or polytomous scoring but not both. However, unlike those measures, the BIDR allows for both types of scoring and opportunities to determine the preferred method for making various types of decisions.
One-parameter IRT dichotomous and polytomous scoring of the BIDR’s SDE was examined by Cervellione, Lee, and Bonanno (2009) using a sample of 315 university students. The Rasch model was applied to dichotomous scores, and the rating scale model (RSM), a generalized version of the Rasch model, was applied to polytomous scores. Overall, they concluded that the polytomous RSM model provided more reliable scores but noted weaknesses in both models in discriminating among trait levels at the low and high ends of the distribution.
This InvestigationIn the study reported here, we extended previous research into IRT modeling and scoring of the BIDR in several important ways. First, we included a considerably larger sample of respondents than did Cervellione et al. (2009; i.e., n = 1,835 vs. 315), which allowed for not only more trustworthy and stable modeling of responses but also expansion to two parameters that permit variation in both item difficulty (e.g., mean endorsement) and item discrimination typically expected in measuring affective characteristics (Reise & Waller, 1990). Cervellione, et al. reported that the fit for the 2PLM to their sample data was not adequate and that the polytomous partial credit model (PCM) did not converge, due largely to the smaller than adequate size sample they used.
Second, we performed analyses for both the IM and SDE scales. Inclusion of the IM scale was particularly important because it is usually considered the more crucial dimension of SDR to assess because scores can reflect willful distortion or faking of responses (either good or bad) to questionnaire items as a whole.
Third, we evaluated two dichotomous procedures for scoring the BIDR. One was the standard prescribed procedure in which scores of “1” were assigned only to exaggerated high endorsement of socially desirable behaviors and scores of “0” to low and moderate endorsement of such behaviors. The other was to assign scores of “1” to moderate and high endorsement and scores of “0” only to low endorsement. This latter procedure was intended to provide more accurate measurement of denial of socially desirable behaviors and thereby address weaknesses in the standard dichotomous scoring procedure noted by Stöber et al. (2002) and Vispoel and Tao (2013) in detecting possible instances of “faking bad.”
Fourth, we derived detailed IRT-based information plots for all dichotomous and polytomous scoring procedures that qualify measurement error at each point across the construct continuums. Such plots highlight places where each scale provides its best and worst precision.
Finally, we compared internal consistency, test–retest reliability, convergent validity, and divergent validity for IRT-based and conventional scoring of both subscales.
We addressed the following research questions:
- How do IRT-based scoring methods compare in model fit and measurement precision (internal consistency estimates, information plots)?
- How do IRT-based and conventional scoring methods compare in short-term test–retest reliability, convergent validity, and divergent validity.
Method Participants
Data were collected from 1,835 undergraduate and graduate students enrolled in educational psychology classes at the University of Iowa (75.0% female; 92.5% White; and 90.9% between the ages of 18 and 24, M = 20.85). Students volunteered to participate to receive research credit counting toward their final course grades.
Instruments and Procedure
All participants completed the BIDR (Version 6; Paulhus, 1991, 1999) and either the Self-Description Questionnaire–III (SDQ-III; Marsh, 1992; n = 1,405) or the Self-Perception Scale for College Students (SPPCS; Neemann & Harter, 2012; n = 430), under anonymous conditions in private cubicles in a research lab on campus. A subset of 602 participants also completed the same measures under the same conditions a week later. These administration procedures are in keeping with Paulhus’s (1999) stated preference for administering the BIDR along with other assessment measures in the same setting. Although the questionnaires could have been administered in either order, we chose to administer the BIDR after the SDQ-III or SPPCS so that validity scales would not be the first items respondents encountered. Respondents also completed a brief demographics questionnaire following the other measures during the first administration session.
The BIDR is a 40-item inventory with two 20-item subscales: Self-Deceptive Enhancement (SDE) and Impression Management (IM). The SDE scale includes items intended to measure truthful but overly positive self-presentation. Individuals who score high on these scales view themselves through “rose-colored” glasses by routinely overestimating their true abilities. The IM scale includes items that describe desirable but atypical behaviors. Respondents who strongly endorse or deny a high number of these behaviors may be attempting to look good or look bad to the user of the questionnaire results. Items on both subscales are statements to which participants rated their level of agreement on a 7-point continuum (1 = not true, 7 = very true). To control for possible acquiescence bias, items are balanced equally in the positive versus negative direction of the keyed response. After appropriate reversals are made, items from each subscale are summed to derive raw polytomous scale scores. The first set of dichotomous scores was created using the conventional procedure of assigning a score of “1” to each response of “6” or “7” to the polytomous scale and a score of “0” to any other responses to that scale. Paulhus (1999) noted that this dichotomous scoring assures that high scores reflect exaggerated rather than accurate self-descriptions of socially desirable behavior. However, this scoring procedure would yield low scores for either low or moderate responses. To address this problem, a second set of dichotomous scores was derived in which low scores reflected only exaggerated denial of socially desirable behaviors by assigning a score of “0” to a polytomous score of 1 and 2 and a score of “1” to any other response. IRT-based theta (θ) scores for the models described later in this section were derived by combining raw score and item parameter information (e.g., difficulty, discrimination indices).
The BIDR has been administered in many contexts, ranging from low-stakes uses in applied research to high-stakes applications in personnel selection. Its four primary uses are for (a) score validation (Davies, French, & Keogh, 1998; Hirschfeld, Feild, & Bedeian, 2000; Kroner & Weekes, 1996; McFarland, 2003; Stöber et al., 2002; Zaldívar, Molina, López Ríos, & García Montes, 2009), (b) statistical control (Hirschfeld et al., 2000; Vispoel & Forte Fast, 2000), (c) outcome assessment (Booth-Kewley, Edwards, & Rosenfeld 1992; Wilkerson, Nagao, & Martin, 2002), and (d) flagging possible invalid responding (Holden, Starzyk, McLeod, & Edwards, 2000; Paulhus, 1998; Paulhus, Bruce, & Trapnell, 1995; Vispoel & Tao, 2013). In score validation, the BIDR is typically used in examining convergent and/or divergent validity (i.e., determining whether BIDR scores and those from other measures correlate in hypothesized ways). In statistical control, the effects of SDR are removed from scores from other measures to provide purer indicators of the constructs of interest. In outcome assessment, BIDR scores typically serve as dependent variables in controlled experiments designed to highlight situations most likely to elicit SDR. Finally, in flagging possible invalid responding, cutoff scores from the IM scale are used to identify instances in which respondents may be intentionally trying to make a favorable or unfavorable impression to the user of questionnaire results.
Psychometric information for BIDR scores reported by Paulhus (1999) includes alpha coefficients for subscales ranging from .70 to .84 and several sources of evidence in support of construct validity. This evidence includes (a) confirmatory factor analyses verifying the hypothesized two-factor structure of responses to the instrument; (b) logical patterns of convergent and divergent correlation coefficients with measures of adjustment, optimism, self-esteem and related constructs; (c) modest correlations (.20–.23) between the SDE and IM scales themselves; (d) expected changes in BIDR scores under experimental manipulations (e.g., greater changes in IM than SDE scores from private to public conditions, fake to not fake conditions, anonymous to identified conditions); (e) high hit rates in using the IM scale to detect fakers and nonfakers in simulation studies; (f) larger differences in self–other ratings for SDE than for IM scales; (g) larger differences between actual and self-rated performance for higher scorers on SDE than for lower scorers; and (h) negligible correlations (.11–.18) between BIDR scores with indices of extremity bias (see Paulhus, 1991, 1999, 2002, for further details).
Results for three subscales (General-Self, Emotional Stability, and Honesty) from the SDQ-III and for one subscale (Global Self-Worth) from the SPPCS are reported here to evaluate the convergent and divergent validity of BIDR scores. Respondents answer SDQ-III items by indicating how true (or false) a given self-descriptive statement is for them along an 8-point metric (1 = definitely false, 8 = definitely true). The General-Self and Honesty subscales have 12 items; the Emotional Stability subscale has 10. The SPPCS’s Global Self-worth scale has six items. For each SPPCS item, a pair of conflicting descriptions is presented (e.g., “Some students like the kind of person they are BUT Other students wish that they were different”). Respondents decide which statement in the pair better describes them, and then whether that statement is really true for me or sort of true for me. Responses to the sample item shown here would be scored as 4 (really true) or 3 (sort of true) for choosing the first statement and as 2 (sort of true) or 1 (really true) for choosing the second statement. Items for all subscales used here are balanced evenly for positive and negative phrasing and appropriately reversed before summing the responses to create total subscale scores. Evidence supporting the reliability and validity of subscale scores from the SDQ-III and SPPCS is provided in their manuals (Marsh, 1992; Neemann & Harter, 1986, 2012) and in Byrne (1996) and Vispoel (1995, 2000, 2014). This evidence includes internal consistency estimates ranging from .76 to .96, factor analyses verifying that the subscales from each instrument measure distinguishable constructs, and logically consistent relationships of subscale scores with external criteria.
The SDQ-III’s General-Self and SPPCS Global Self-worth scales are designed to measure perceptions of overall self-esteem. Both scales are modeled after Rosenberg’s (1965, 1979) Self-Esteem Scale (SES), in which item content is targeted at perceptions of self at a general level in relation to self-acceptance, worth, confidence, respect, and so forth. Research in which scores from these three measures have been correlated is very limited but generally supportive of their convergent validity. Marsh, Byrne, and Shavelson (1988), for example, reported a correlation between SDQ-III General-Self and SES scores of .79 for a sample of 991 Canadian 11th and 12th grade students, and Vispoel and He (2010) found a correlation of .79 between SDQ-III General-Self and SPPCS Global Self-Worth scores in a study of 445 college students.
The SDQ-III’s Emotionality Stability subscale has items linked to emotions such as calmness, relaxedness, happiness, optimism, worry, restlessness, anxiety, depression, and nervousness. Marsh, Trautwein, Lüdtke, Köller, and Baumert (2006) reported interfactor correlations of .71 between Emotionality Stability and Extraversion and of –.82 between Emotionality Stability and Neuroticism in a confirmatory factor analysis of responses to German versions of the SDQ-III (Schwanzer, Trautwein, Lüdtke, & Sydow, 2005) and NEO Five-Factor Inventory (Borkenau & Ostendorf, 1993; Costa & McCrae, 1985, 1989). Correlations between Emotional Stability and either General-Self or Global Self-Worth scores in research reported by Vispoel (1995, 2000), Vispoel and Forte Fast (2000), and Vispoel and He (2010) have ranged from .61 to .68.
The SDQ-III’s Honesty subscale has items focused on integrity, reliability, cheating, lie telling, truthfulness and so forth. Marsh and Richards (1988), in a validation study involving co-administration of the Tennessee Self-Concept Scales (TSCS) and SDQ-III, confirmed a priori hypotheses that scores from the TSCS Personal scale, which measures perceptions of personal worth and adequacy, would correlate higher with SDQ-III General-Self (r = .71) and Emotional Stability (r = .60) scores than with Honesty scores (r = .38) and that scores from the TSCS Moral scale, which taps perceptions of moral worth and viewing self as a good versus bad person, would correlate higher with SDQ-III Honesty (r = .53) than with General-Self (r = .41) and Emotional Stability (r = .36) scores.
We chose to report results for these particular SDQ-III and SPPCS scores due to theoretical and previously observed empirical relationships with BIDR SDE and IM scores. Paulhus (2002; also see Paulhus & Trapnell, 2008) provides an extended hierarchical model of SDR that separates egoistic bias (exaggerations of one’s attributes) and moralistic bias (exaggerated claims of saintliness). SDE falls under egoistic bias and is expected to correlate with measures of adjustment (e.g., self-esteem, emotional stability), whereas IM falls under moralistic bias and is expected to correlate with measures of perceived virtue (e.g., honesty). These relationships are consistent with findings from Vispoel and Forte Fast (2000) in which SDQ-III General-Self and Emotional Stability scores correlated higher with SDE than with IM scores (rs = .45 and .41 vs. .16 and .15) and SDQ-III Honesty scores correlated higher with IM than with SDE scores (r = .59 vs. .25). Similarly, Huang (2013), who conducted a meta-analysis of relations between self-esteem and SDR, reported a mean correlation of .40 between self-esteem (measured by the SES or SDQ-III) and BIDR SDE scores compared to a mean correlation of .16 between self-esteem and BIDR IM scores. We expected the same patterns of relationships to emerge in this study.
Data Analyses
Before applying IRT-based scoring to the data, we evaluated the assumption of unidimensionality that governed all of the IRT models we considered, using MicroFACT 2.0 (Waller, 2001). MicroFACT provides results from factor-analyzing responses to dichotomous and polytomous items using tetrachoric and polychoric correlations, respectively (Finger, 2004). The output includes the Tanaka-Huba unweighted least squares statistic (GFI-ULS; Tanaka & Huba, 1985), which has widely accepted cutoffs of .90 for an adequate fit and .95 for a good fit (Finger, 2004; McDonald, 1999). Dichotomous and polytomous scoring for both subscales yielded GFI-ULS values greater than or equal to .95, except for alternative dichotomous SDE scores, which yielded a fit index of .94. We interpreted these results as providing reasonable support for the single-factor IRT modeling used in subsequent analyses.
The specific IRT models we examined were restricted to one and two parameters because the third, “pseudo guessing,” parameter is typically considered irrelevant in responding to questionnaire items where there are no “correct” answers. Instead, items are keyed so that higher scores are indicative of higher levels of the targeted construct. Dichotomous models included the one-parameter logistic model (1PLM; Birnbaum, 1968; Lord, 1977; Rasch, 1960), which allows for variation in item difficulty only, and the two-parameter logistic model (2PLM; Birnbaum, 1968; Lord, 1977), which allows for variation in both item difficulty and discrimination. The polytomous models we used paralleled the dichotomous ones in that the partial credit model (PCM; Masters, 1982) allows for variation in difficulty, whereas the graded response model (GRM; Samejima, 1969, 1996) allows for variation in both difficulty and discrimination (De Ayala, 1993). When items are scored dichotomously, the PCM will reduce to the 1PLM, and the GRM to the 2PLM. All of the IRT models can be expressed as a single equation denoting the probability of an individual at a given point on the latent construct continuum θ endorsing a particular response to a questionnaire item having a set of prespecified item parameters.
To illustrate, the formula for calculating the probability of getting an item endorsement score of 1 for the 2PLM is
where θi is the latent construct parameter for person i; aj and bj are item parameters for item j that reflect discrimination and difficulty, respectively; and D is a scaling constant, typically set to 1.7 to have the logistic model approximate a normal ogive (Birnbaum, 1968; Lord, 1977, 1980). The formula for the 1PL is identical to the one above except that all aj values are set to the same value.
The PCM is defined as the following category response function reflecting the probability of person i obtaining a score on item j at category k, where mj refers to the number of categories for item j:
The GRM is a cumulative category response function reflecting the probability (p*) of person i obtaining a score at or above category k. When k = 1, the GRM is defined as
For both polytomous models, θi represents the latent construct parameter for person i; aj and bjk are parameters for item j representing discrimination and difficulty for category k, respectively; and D is a scaling constant typically set to equal 1.7.
BILOG-MG 3 (Zimowski, Muraki, Mislevy, & Bock, 2006) was used to fit the 1PLM and 2PLM to dichotomous responses, and MULTILOG 7 (Thissen, Chen, & Bock, 2003) to fit the PCM and GRM to polytomous responses. These programs were also used to generate the IRT-based (θ) scores used in the analyses reported here. The fit for each model was evaluated initially by comparing graphs of observed and expected marginal subscale score distributions and corresponding aggregate root-mean-square error (RMSE) values representing the square root of the squared differences between the observed and expected proportions for each raw score weighted by the observed proportion of each raw score as shown in
where
refers to observed proportion for a raw score of xi,
is the observed proportion of participants scoring a score of xi, f(xi) is the expected proportion of participants scoring a score of xi, and n is the number of score points that participants can obtain. Further comparisons between the 1PLM and 2PLM and between the PCM and GRM were made using the Akaike information criterion (AIC; Akaike, 1973, 1974; Burnham & Anderson, 2002) and Bayesian information criterion (BIC; Schwarz, 1978; Kass & Raftery, 1995).
Measurement precision across IRT models was examined over a wide range of the measured latent construct (θ) continuums using the test information function. Test information values on the θ metric for all models were derived using the following formula for a given theta value θg:
where k = number of items, mj = number of categories for item i, Pix(θ) = the probability of a response in category x, and P′ix(θ) is the first derivative of Pix(θ). Higher test information values reflect lesser error in measuring the latent construct (Lord, 1980).
Estimates of internal consistency reliability for comparing the IRT models were computed by dividing the mean of the squared standard error of measurement for the estimated θ scores by the observed variance of the θ scores and subtracting that result from 1, as shown in Equation (7). Such indices are routinely provided and labeled as marginal reliability coefficients in the output from BILOG-MG 3 and MULTILOG 7 (see, e.g., Green, Bock, Humphreys, Linn, & Reckase, 1984).
We further compared the reliability of IRT-based and conventional BIDR scores using test–retest reliability coefficients based on the 602 participants who completed the BIDR on two occasions, a week apart. We also compared convergent and divergent validity coefficients for all BIDR scores from the first administration of the questionnaires using the General-Self, Emotional Stability, and Honesty subscales from the SDQ-III and Global Self-Worth subscale from the SPPCS as criterion measures. As noted earlier, SDE scores were expected to correlate higher with General-Self, Emotional Stability, and Global Self-Worth subscale scores, and IM higher with Honesty subscale scores.
Results Descriptive Statistics
Means, standard deviations, and internal consistency coefficients for BIDR, SDQ-III, and SPPCS scores are given in Table 1. BIDR means for conventional dichotomous scoring (5.570 and 5.724 for SDE and IM, respectively) are low in relation to the possible range of scores (0–20), whereas those for alternative dichotomous scoring are high (16.30 and 13.55 for SDE and IM, respectively). These results are not surprising, because most respondents would not be expected to provide exaggerated low or high responses in the present setting. Polytomous score means (84.904 for SDE and 76.875 for IM), in contrast, fall closer to the middle of the (20–140) scale continuum, which also makes sense because the polytomous scores are not confined to only extreme responses. Alpha coefficients for BIDR conventional scores varied from .654 to .786 (mdn = .728), and those for SDQ-II and SPPCS scores from .627 to .956 (mdn = .882). Marginal reliability coefficients for IRT-based (θ) scores were highest for polytomous scoring (.769 to .834, mdn = .812), followed by alternative dichotomous scoring (.624 to .759, mdn = .700), followed by original dichotomous scoring (.482 to .618, mdn = .621).
Descriptive Statistics and Reliability Coefficients for BIDR and Self-Concept Scores
Correlations among BIDR scores appear in Table 2. In general, correlations among scaling methods (conventional and IRT) were higher for original and alternative dichotomous scoring (rs ranged from .97 to 1.00, mdn = .99) than for polytomous scoring (rs ranged from .77 to .98, mdn = .88). Polytomous raw and GRM scores were more strongly correlated with each other (rs = .96 for SDE and .98 for IM, mdn = .97) than either was with PCM scores (rs ranged from .77 to .89, mdn = .83). Polytomous scores as a group were more highly correlated with both types of dichotomous scores (rs ranged from .48 to .83, mdn = .75) than were the original and alternative dichotomous scores with each other (rs ranged from .20 to .42, mdn = .27). Correlations among scoring methods as a whole (as shown in the two triangular areas of data that are not in italics in Table 2) were also typically higher for IM (rs ranged from .40 to 1.00, mdn = .79) than for SDE (rs ranged from .20 to 1.00, mdn = .65).
Correlations Among BIDR Scores (n = 1,835)
Research Question 1: How Do IRT-Based Scoring Methods Compare in Model Fit and Measurement Precision?
Model fit
Model fit initially was examined by comparing the observed and expected proportions of BIDR scores for each IRT scoring method. Graphs of these relationships, shown in Figure 1, reveal similar trends for the 1PLM and 2PLM with each type of dichotomous scoring and for the PCM and GRM with polytomous scoring. The modeled distributions for each scoring method are unimodal, but positively skewed for original dichotomous scoring, negatively skewed for alternative dichotomous scoring, and symmetrical for polytomous scoring. In Table 3, aggregate RMSE values, which show roughly the average difference between observed and expected proportions of raw scores, ranged from .006 to .010 for the dichotomous models in relation to their 0- to 20-point scales and from .003 to .006 for the polytomous models in relation to their 20- to 140-point scales. Differences in aggregate RSME values between the simpler and more complex IRT models (i.e., 1PL vs. 2PL or PCM vs. GRM) are very small for each combination of scoring method and subscale (maximum difference = .002).
Figure 1. Model fit (probability distribution functions) for Self-Deceptive Enhancement (SDE) and Impression Management (IM). Org = original; Org-1PL = one-parameter logistic model (original); Org-2PL = two-parameter logistic model (original); Alt-1PL = one-parameter logistic model (alternative); Alt-2PL = two-parameter logistic model (alternative); GRM = graded response model; PCM = partial credit model.
Fit Indices for IRT Models
Fits between the simpler and more complex IRT models for each scoring method and subscale were compared more precisely using AIC and BIC values, both of which penalize model complexity (see Table 3). Differences in these values between the 1PLM and 2PLM and between the PCM and GRM all exceed the 10-point cutoffs for superior model fit between nested models suggested by Burnham and Anderson (2002) for AIC and by Kass and Raftery (1995) for BIC. In each case, the IRT model that allowed for differences in item discrimination (i.e., 2PLM, GRM) provided a better fit than did the corresponding model that did not (i.e., 1PLM, PCM).
Measurement precision
The reliability estimates presented previously in Table 1 revealed that internal consistency was higher for polytomous than for dichotomous scoring, and higher for IM than for SDE using either conventional or IRT scoring. Among the IRT methods, and for both subscales, GRM provided the highest marginal reliability coefficients (.812 for SDE and .834 for IM), followed by the PCM (.769 for SDE and .812 for IM), 2PLM (.579 for SDE and .618 for IM original dichotomous scores, and .649 for SDE and .759 for IM alternative dichotomous scores), and 1PLM (.552 for SDE and .462 for IM original dichotomous scores, and .624 for SDE and .750 for IM alternative dichotomous scores). For both subscales, marginal reliability coefficients were highest for polytomous scoring and lowest for original dichotomous scoring. The test information plots appearing in Figure 2 reveal that the GRM provided greater measurement precision than all other models across the full range of θ scores for both SDE and IM. In most instances, IRT original dichotomous scoring provided greater information at above-average construct levels, whereas alternative dichotomous scoring provided greater information at below-average levels. Overall, these results imply that a combination of dichotomous scoring procedures would provide more information collectively than either dichotomous method would by itself, but still less information than the GRM would provide at any given construct level.
Figure 2. Test information plots for Self-Deceptive Enhancement and Impression Management. GRM = graded response model; PCM = partial credit model; Org-1PL = one-parameter logistic model (original); Org-2PL = two-parameter logistic model (original); Alt-1PL = one-parameter logistic model (alternative); Alt-2PL = two-parameter logistic model (alternative).
Research Question 2: How Do IRT-Based and Conventional Scoring Methods Compare in Short-Term Test–Retest Reliability, Convergent Validity, and Divergent Validity?
Indices in Table 4 show that test–retest coefficients were highest for polytomous raw scores (.841 for SDE and .847 for IM) and GRM scores (.826 for SDE and .794 for IM), in-between and similar for original dichotomous scores (.769–.771 for SDE and .775–.790 for IM), and lowest for PCM (.688 for SDE and .746 for IM) and alternative dichotomous scores (.689–.705 for SDE and .740–.748 for IM). When considered separately, all scoring methods showed good evidence of convergent and divergent validity, with higher correlations for SDE with Global Self-Worth, Emotional Stability, and General-Self than with Honesty, and higher correlations for IM with Honesty than with Global Self-Worth, Emotional Stability, and General-Self. Evidence of convergent validity for SDE was strongest for the GRM (rs ranged from .501 to .590), followed closely by polytomous raw scores (rs ranged from .490 to .572), followed, respectively, by original dichotomous (rs ranged from .407–.504), PCM (rs ranged from .385 to .441), and alternative dichotomous scores (rs ranged from .297 to .356). Convergent validity for IM was similar and higher for original dichotomous, raw polytomous, and GRM scores (rs ranged from .555 to .578) than for alternative dichotomous and PRM scores (rs ranged from .369 to .449). For both SDE and IM, alternative dichotomous and PRM scores yielded consistently lower convergent validity coefficients than did the other scoring methods on all criterion measures. Taken as a whole, these results indicate that GRM and raw polytomous scores yielded more psychometrically sound results than did the other scoring methods.
Test–Retest Coefficients and Correlations of BIDR Scores With Self-Concept Scores
DiscussionOur primary goal in the study reported here was to evaluate the feasibility and potential benefits of applying IRT techniques to the scoring of the BIDR. We expanded upon prior research by using a much larger calibration sample of respondents, including models that allowed for variation in item discrimination as well as difficulty, analyzing results for the IM in addition to the SDE subscale, comparing the standard to a new method of dichotomous scoring, and comparing psychometric results for IRT scoring to those obtained from conventional scoring. IRT scoring for all models with both BIDR subscales was supported by analyses of dimensionality and comparisons of expected and observed proportions of raw scores. However, the 2PLM provided a better overall fit to dichotomous responses than did the 1PLM, and the GRM provided a better fit to polytomous responses than did the PCM based on AIC and BIC index values. Correlations among raw and corresponding IRT-based dichotomous scores were uniformly high (≥.98), and these results were congruent with comparable values for test–retest and validity coefficients for each type of dichotomous score. Similar relationships in intercorrelation
, test–retest, and validity coefficients were observed between polytomous raw and GRM scores, but not between those scores and PCM scores. PCM scores showed good evidence of internal consistency (i.e., marginal reliability) but weaker evidence of stability and convergent validity than did GRM or raw polytomous scoring. Such results would seem to render the PCM as the least desirable polytomous scoring method for general use among the three considered here.
One of the most important findings and one consistent with prior research (Cervellione et al. 2009; Stöber et al., 2002; Vispoel & Tao, 2013) was the superior evidence of reliability and validity for polytomous over dichotomous scoring. In comparing conventional dichotomous and polytomous scoring of a German version of the BIDR, Stöber et al. (2002) found higher alpha reliability coefficients, higher convergent validity coefficients with other measures of SDR and targeted traits, and more consistent effects under fake good and fake bad instructions with polytomous scoring. Similarly, when using the present form of the BIDR, Vispoel and Tao (2013) found higher alpha, test–retest, and generalizability coefficients with polytomous than with dichotomous conventional scoring for both BIDR subscales, and Cervellione et al. (2009) found higher reliability for polytomous than for dichotomous IRT scoring of SDE. Our results for polytomous raw scoring replicate these prior findings of higher alpha, test–retest, and convergent validity coefficients in relation to standard dichotomous raw scoring but highlight the superiority of GRM over all other methods of IRT scoring considered here.
The IRT information plots, which quantify places along the latent-construct continuums where scores have their best and worst precision, provided particularly strong and detailed evidence supporting the use of GRM scoring. GRM scoring yielded higher information than did all other IRT scoring methods across the full latent construct continuums for both SDE and IM. In contrast to the other methods, information for GRM was consistently high and reasonably similar across the full range of the construct continuums. Original dichotomous scores provided their strongest information at middle to high levels of SDE and IM but very weak information for discriminating among respondents at low levels. This pattern reveals a reasonably high ceiling for high IM scores but a strong floor effect for low IM that would support using prescribed BIDR dichotomous scores to flag for instances of faking good but not for faking bad. The floor effect for low IM scores was evident in the Stöber et al. (2002) study and may have contributed to the statistically nonsignificant 1-point mean difference they observed between the standard and fake bad conditions.
The likely reason for the floor effect is that a dichotomous BIDR item score of 1 reflects exaggerated high endorsement of a socially desirable behavior, whereas a score of 0 represents a possible range from low to moderate endorsement of the behavior. A respondent who endorses all IM items moderately would still get a raw score of 0, which would be flagged as “probably” faking bad, according to guidelines in the most recent BIDR manual (Paulhus, 1999, p. 10). One extreme item endorsement and the rest moderate would result in a raw score of 1, which would be labeled as “may be” invalid. Such a weak precision at the low end of the IM raw score scale could lead to excessive and potentially invalid flagging of fake bad responding. In contrast, if percentile equivalents for the dichotomous scale cutoff scores for faking bad and good were derived for the polytomous scale using the present respondents, the cutoff scores would be 59 for probably faking bad, 67 for may be faking bad, 96 for may be faking good, and 110 for probably faking good, all of which are well within the range between the minimum and maximum possible raw scores of 20 and 140.
We anticipated this problem of weak discrimination at low construct levels for original dichotomous scoring and therefore explored the use of an alternative dichotomous scoring procedure that emphasized exaggerated denial rather than exaggerated endorsement of socially desirable behaviors. This procedure did provide much better discrimination at low construct levels, thereby offering a potentially superior alternative for measuring low levels of SDR and using the IM scale to detect possible instances of faking bad. However, the information plots revealed that the use of two separate dichotomous scoring procedures for detecting faking good and faking bad, while better than a single dichotomous score, is still less effective than GRM scoring overall, which takes into account seven possible levels of endorsement for each item. Dichotomizing, while convenient and logically defensible with the BIDR, inevitably sacrifices information and possible discrimination by reducing the number of available score points (see MacCallum, Zhang, Preacher, & Rucker, 2002 for an in-depth analysis of further problems arising from dichotomization of continuous individual difference variables).
In light of findings from this and previous studies of the psychometric properties of BIDR scores (overall reliability, convergent validity, and precision of individual scores), we would recommend conventional polytomous scoring over conventional dichotomous scoring, and IRT GRM scoring over IRT PCM, 1PLM, and 2PLM scoring for general use in validation studies and measuring individual differences in self-presentation. The same recommendations might be made, although more tentatively, for using raw polytomous and GRM scores for statistical control, outcome assessment, and flagging faked responses. These latter recommendations would be bolstered with additional evidence supporting the construct validity of polytomous scores and their sensitivity to fake good and fake bad instructions. The item parameters calibrated in this study would provide the necessary baseline information for conducting such follow-up research with GRM scores and allow for computation of IRT scores for other purposes.
Selection between GRM and conventional polytomous scoring would be tailored to more specific goals of score users. The present analyses highlight the advantages of IRT in representing scores on a latent construct continuum and showing the precision by which scores are estimated along the full range of that continuum via test information indices. Such plots can be used to pinpoint places on the construct scale where a measure provides its best and worst precision, which in turn can help in determining its appropriateness for aiding particular decisions and in redesigning an instrument to target places on the construct continuums where high precision is particularly important.
IRT also can be used with the BIDR and other self-report measures in many additional ways not examined here but worthy of note. Such applications include but are not limited to computerized adaptive administration of items (Dodd et al., 1995; Lord, 1977; Reise & Henson, 2003; Roper et al., 1995; Scullard, 2007; Simms & Clark, 2005), linking items together in creating item pools from nonequivalent groups (Lord, 1977; Reise et al., 2005; Reise & Henson, 2003), developing and equating forms of an instrument (Lord, 1977), creating special forms of an instrument for specific decisions such as clinical classifications (Lord, 1977; Waller & Reise, 1989), determining how well an individual’s responses conform to the IRT model used (i.e., person fit; Cervellione et al. 2009; Reise & Waller, 1993), and investigating differential item functioning (Huang et al., 1997; Lord, 1977; Reise et al., 2005; Reise & Henson, 2003; Robie et al., 2001).
When interpreting results from this and any other study involving the BIDR, we further emphasize that its two subscales (SDE and IM) do not represent all facets of SDR as recently conceptualized and that situational demands can affect the extent to which responses reflect substantive versus stylistic tendencies. Paulhus and Trapnell (2008; also see Paulhus, 2002) provided a model of SDR that distinguishes the responsiveness of a measure to audience manipulation (i.e., private vs. public) and its content focus (i.e., agentic or self-oriented vs. communal or other-oriented). This partitioning yields four forms of SDR that they described as asset exaggeration (private, agentic), deviant denial (private, communal), agentic image (public, agentic), and communal image (public, communal).
The BIDR’s SDE subscale measures asset exaggeration, whereas the IM subscale measures communal image. Paulhus (1998) had developed a 20-item Self-Deceptive Denial Scale (SDD) to measure deviant denial some time ago but excluded it from the standard BIDR due to the possible offensiveness of some items, high correlations with the IM scale under many conditions, and added administrative burden to respondents. He subsequently developed a 20-item Agentic Management Scale (AM) to measure agentic image (see Paulhus, 2002) and later combined all four scales to form the Comprehensive Inventory of Desirable Responding (CIDR; Paulhus, 2006) and labeled them as Agentic Enhancement (SDE), Communal Enhancement (SDD), Agentic Management (AM), and Communal Management (IM). Recently, Blasberg, Rogers, and Paulhus (2013) developed abbreviated 10-item forms of the Agentic Management and Communal Management scales and combined them into a new 20-item inventory called the Bidimensional Impression Management Index (BIMI). Collectively, these instruments allow for more comprehensive assessment of SDR when desired and further opportunities to evaluate the merits of conventional and IRT dichotomous and polytomous scoring procedures. We encourage uses of the BIDR and these related instruments that might take advantage of the strengths of IRT scoring; more comprehensive evaluation of the reliability and validity of polytomous scoring; and investigation of the derivation, use, and effectiveness of polytomous and alternative dichotomous cutoff scores (both conventional and IRT-based) for detecting possible invalid responding.
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Submitted: June 11, 2013 Revised: November 27, 2013 Accepted: February 12, 2014
This publication is protected by US and international copyright laws and its content may not be copied without the copyright holders express written permission except for the print or download capabilities of the retrieval software used for access. This content is intended solely for the use of the individual user.
Source: Psychological Assessment. Vol. 26. (3), Sep, 2014 pp. 878-891)
Accession Number: 2014-12154-001
Digital Object Identifier: 10.1037/a0036430
Record: 133- Title:
- 'Psychometric properties for the Balanced Inventory of Desirable Responding: Dichotomous versus polytomous conventional and IRT scoring': Correction to Vispoel and Kim (2014).
- Authors:
- No authorship indicated
- Source:
- Psychological Assessment, Vol 26(3), Sep, 2014. pp. 1062-v.
- NLM Title Abbreviation:
- Psychol Assess
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- Psychological Assessment: A Journal of Consulting and Clinical Psychology
- ISSN:
- 1040-3590 (Print)
1939-134X (Electronic) - Language:
- English
- Keywords:
- BIDR, polytomous IRT modeling, socially desirable responding, scoring methods, validity, reliability
- Abstract:
- Reports an error in 'Psychometric properties for the Balanced Inventory of Desirable Responding: Dichotomous versus polytomous conventional and IRT scoring' by Walter P. Vispoel and Han Yi Kim (Psychological Assessment, Advanced Online Publication, Apr 7, 2014, np). The mean, standard deviation and alpha coefficient originally reported in Table 1 should be 74.317, 10.214 and .802, respectively. The validity coefficients in the last column of Table 4 are affected as well. Correcting this error did not change the substantive interpretations of the results, but did increase the mean, standard deviation, alpha coefficient, and validity coefficients reported for the Honesty subscale in the text and in Tables 1 and 4. The corrected versions of Tables 1 and Table 4 are shown in the erratum. (The following abstract of the original article appeared in record 2014-12154-001.) Item response theory (IRT) models were applied to dichotomous and polytomous scoring of the Self-Deceptive Enhancement and Impression Management subscales of the Balanced Inventory of Desirable Responding (Paulhus, 1991, 1999). Two dichotomous scoring methods reflecting exaggerated endorsement and exaggerated denial of socially desirable behaviors were examined. The 1- and 2-parameter logistic models (1PLM, 2PLM, respectively) were applied to dichotomous responses, and the partial credit model (PCM) and graded response model (GRM) were applied to polytomous responses. For both subscales, the 2PLM fit dichotomous responses better than did the 1PLM, and the GRM fit polytomous responses better than did the PCM. Polytomous GRM and raw scores for both subscales yielded higher test–retest and convergent validity coefficients than did PCM, 1PLM, 2PLM, and dichotomous raw scores. Information plots showed that the GRM provided consistently high measurement precision that was superior to that of all other IRT models over the full range of both construct continuums. Dichotomous scores reflecting exaggerated endorsement of socially desirable behaviors provided noticeably weak precision at low levels of the construct continuums, calling into question the use of such scores for detecting instances of 'faking bad.' Dichotomous models reflecting exaggerated denial of the same behaviors yielded much better precision at low levels of the constructs, but it was still less precision than that of the GRM. These results support polytomous over dichotomous scoring in general, alternative dichotomous scoring for detecting faking bad, and extension of GRM scoring to situations in which IRT offers additional practical advantages over classical test theory (adaptive testing, equating, linking, scaling, detecting differential item functioning, and so forth). (PsycINFO Database Record (c) 2016 APA, all rights reserved)
- Document Type:
- Erratum/Correction
- Subjects:
- *Impression Management; *Inventories; *Item Response Theory; *Scoring (Testing); Classical Test Theory; Psychometrics; Test Reliability; Test Validity
- PsycINFO Classification:
- Tests & Testing (2220)
Social Psychology (3000) - Tests & Measures:
- Balanced Inventory of Desirable Responding DOI: 10.1037/t08059-000
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- First Posted: May 5, 2014
- Release Date:
- 20140505
- Correction Date:
- 20160218
- Digital Object Identifier:
- http://dx.doi.org/10.1037/pas0000005
- PMID:
- 24796342
- Accession Number:
- 2014-16017-001
- Persistent link to this record (Permalink):
- http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2014-16017-001&site=ehost-live
- Cut and Paste:
- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2014-16017-001&site=ehost-live">'Psychometric properties for the Balanced Inventory of Desirable Responding: Dichotomous versus polytomous conventional and IRT scoring': Correction to Vispoel and Kim (2014).</A>
- Database:
- PsycINFO
Correction to Vispoel and Kim (2014)
In the article “Psychometric Properties for the Balanced Inventory of Desirable Responding: Dichotomous Versus Polytomous Conventional and IRT Scoring” by Walter P. Vispoel and Han Yi Kim (Psychological Assessment, Advanced online publication. April 7, 2014. doi: 10.1037/a0036430), the SDQ-III Honesty subscale was scored incorrectly, with two items erroneously reversed. The mean, standard deviation and alpha coefficient originally reported in Table 1 should be 74.317, 10.214 and .802, respectively. The validity coefficients in the last column of Table 4 are affected as well. Correcting this error did not change the substantive interpretations of the results, but did increase the mean, standard deviation, alpha coefficient, and validity coefficients reported for the Honesty subscale in the text and in Tables 1 and 4. The corrected versions of Tables 1 and Table 4 are shown below with the corrected values in bold.
Descriptive Statistics and Reliability Coefficients for BIDR and Self-Concept Scores
Test-Retest Coefficients and Correlations of BIDR Scores With Self-Concept Scores
This publication is protected by US and international copyright laws and its content may not be copied without the copyright holders express written permission except for the print or download capabilities of the retrieval software used for access. This content is intended solely for the use of the individual user.
Source: Psychological Assessment. Vol. 26. (3), Sep, 2014 pp. 1062-v)
Accession Number: 2014-16017-001
Digital Object Identifier: 10.1037/pas0000005
Record: 134- Title:
- Psychometric properties of the MMPI-2-RF Somatic Complaints (RC1) Scale.
- Authors:
- Thomas, Michael L.. Department of Psychology, Arizona State University, Tempe, AZ, US, michael.t@asu.edu
Locke, Dona E. C.. Division of Psychology, Mayo Clinic Arizona, Scottsdale, AZ, US - Address:
- Thomas, Michael L., Department of Psychology, Arizona State University, 950 South McAllister, P.O. Box 871104, Tempe, AZ, US, 85287, michael.t@asu.edu
- Source:
- Psychological Assessment, Vol 22(3), Sep, 2010. pp. 492-503.
- NLM Title Abbreviation:
- Psychol Assess
- Page Count:
- 12
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- Psychological Assessment: A Journal of Consulting and Clinical Psychology
- ISSN:
- 1040-3590 (Print)
1939-134X (Electronic) - Language:
- English
- Keywords:
- Minnesota Multiphasic Personality Inventory-2 Restructured Form, confirmatory factor analysis, item response theory, psychogenic nonepileptic seizures, taxometrics, psychometrics
- Abstract:
- The MMPI-2 Restructured Form (MMPI-2-RF; Tellegen & Ben-Porath, 2008) was designed to be psychometrically superior to its MMPI-2 counterpart. However, the test has yet to be extensively evaluated in diverse clinical settings. The purpose of this study was to examine the psychometric properties of the MMPI-2-RF Somatic Complaints (RC1) scale in a clinically relevant population. Participants were 399 patients diagnosed with either epilepsy or psychogenic nonepileptic seizures on the basis of video–electroencephalograph monitoring. The internal structure of the MMPI-2-RF was evaluated using taxometric, confirmatory factor analysis, and item response theory procedures. Data from 4 content-specific scales directly related to RC1 (Malaise, Gastrointestinal Complaints, Head Pain Complaints, and Neurological Complaints) indicated that the latent construct of somatization is a dimensional variable with a bifactor structure. However, consistent with the scale's construction, a unidimensional model also provided adequate fit. A 2-parameter logistic item response theory model better accounted for observed item responses than did 1- or 3-parameter models. Results suggest that the RC1 scale is most precise for T score estimates between 55 and 90. Overall, the scale appears to be well suited for the assessment of somatization. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Factor Analysis; *Item Response Theory; *Minnesota Multiphasic Personality Inventory; *Psychometrics; Epilepsy; Seizures
- Medical Subject Headings (MeSH):
- Adult; Electroencephalography; Epilepsy; Factor Analysis, Statistical; Female; Gastrointestinal Diseases; Headache; Humans; MMPI; Male; Models, Psychological; Psychometrics; Psychophysiologic Disorders; Reproducibility of Results; Somatoform Disorders
- PsycINFO Classification:
- Clinical Psychological Testing (2224)
Neurological Disorders & Brain Damage (3297) - Population:
- Human
Male
Female - Location:
- US
- Tests & Measures:
- Minnesota Multiphasic Personality Inventory-2 Restructured Form
Wide Range Achievement Test-3 reading level
WAIS-III
Minnesota Multiphasic Personality Inventory-2 DOI: 10.1037/t15120-000 - Grant Sponsorship:
- Sponsor: University of Minnesota Press, US
Grant Number: MMPI-2/MMPI-2-RF
Recipients: No recipient indicated - Methodology:
- Empirical Study; Quantitative Study
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- Accepted: Jan 28, 2010; Revised: Jan 25, 2010; First Submitted: Oct 29, 2009
- Release Date:
- 20100906
- Correction Date:
- 20121015
- Copyright:
- American Psychological Association. 2010
- Digital Object Identifier:
- http://dx.doi.org/10.1037/a0019229
- PMID:
- 20822262
- Accession Number:
- 2010-18043-002
- Number of Citations in Source:
- 46
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Psychometric Properties of the MMPI-2-RF Somatic Complaints (RC1) Scale
By: Michael L. Thomas
Department of Psychology, Arizona State University;
Dona E. C. Locke
Division of Psychology, Mayo Clinic Arizona, Scottsdale, Arizona
Acknowledgement: This work was supported by an MMPI-2/MMPI-2-RF rescoring grant from the University of Minnesota Press. The authors wish to thank Lynn Autry, Jessie Jacobsen, Allyson Jensen, Jennifer Pichon, and Jeanne Young for their help with record reviews.
The arrival of the Minnesota Multiphasic Personality Inventory-2 Restructured Form (MMPI-2-RF; Tellegen & Ben-Porath, 2008) represents a paradigm shift in the development of clinical measures. The test is more than a revision of its predecessor; it carries with it an emphasis on psychometric theory not typically embraced in the assessment of psychopathology. Yet it has also been met with criticism from psychologists who remain skeptical toward refinements based on modern psychometrics (e.g., Caldwell, 2006; Nichols, 2006). Given this increased focus on psychometric theory, it is critical to examine the characteristics of the MMPI-2-RF with clinical data. In this study, we examined the psychometric properties of the MMPI-2-RF Somatic Complaints (RC1) scale in a clinically relevant population: patients suffering from chronic seizures with either neurologic or psychogenic etiology.
Previous editions of the MMPI were developed, in part, with an empirical keying approach. That is, items were selected on the basis of their observed correlations with a clinical criterion (e.g., depressed vs. nondepressed status). The strength of the approach, known to some as blind or dust bowl empiricism, lies in its overt focus on measurement validity. However, it has long been noted that test scores from scales developed with this method are subject to poor reliability and limited discriminant validity (e.g., Rubin, 1948). Tellegen et al. (2003) sought to improve the MMPI-2 clinical scales by combining items with better psychometric properties. Specifically, the authors wanted to create homogeneous scales with strong divergence and discriminability. Recent research suggests that they were successful in their efforts (Tellegen, Ben-Porath, & Sellbom, 2009).
Locke et al. (2010) examined the MMPI-2-RF scales in patients admitted to an epilepsy monitoring unit for differential diagnosis based on continuous video-electroencephalograph (EEG) monitoring. A diagnosis of epilepsy is indicated when seizures occur in the presence of epileptiform activity on EEG. Conversely, a diagnosis of psychogenic nonepileptic seizures (NES) is indicated when the overt symptoms of seizures (e.g., loss of consciousness, tremors, and convulsions) occur in the absence of EEG abnormalities or other physiological causes for seizurelike behavior (e.g., cardiac abnormality). The consequences of misdiagnosing NES as epilepsy can include inappropriate treatment, increased health care utilization, and delay in appropriate mental health care (Reuber & Elger, 2003). Studies have also shown that prompt diagnosis of NES leads to a better prognosis (Selwa et al., 2000), thus increasing the importance of accurate assessment.
Video-EEG remains the diagnostic gold standard for differentiating epilepsy from NES. However, neuropsychologists routinely play a supportive role in the diagnostic process through the use of personality testing. Previously researchers have demonstrated that MMPI-2 clinical scales can be used to distinguish between NES and epilepsy with moderate accuracy (see Cragar, Berry, Fakhoury, Cibula, & Schmitt, 2002). Locke et al. (2010) found that the RC1 scale was particularly utilitarian for this purpose. In addition, their results suggest that four new content-specific scales directly related to RC1—the Malaise (MLS), Gastrointestinal Complaints (GIC), Head Pain Complaints (HPC), and Neurological Complaints (NUC) scales—also have diagnostic utility.
Although the RC1 scale does appear to be sensitive to symptoms of NES, it was not specifically designed to capture the disorder. Rather, the scale “consists of 27 items that describe a range of somatic complaints often associated with somatoform disorders” (Tellegen & Ben-Porath, 2008, p. 36). The Diagnostic and Statistical Manual of Mental Disorders (4th ed., text rev.; DSM–IV–TR; American Psychiatric Association, 2000) defines the common feature of somatoform disorders (hereafter referred to as somatization [i.e., the state of the somatoform condition]) as “the presence of physical symptoms that suggest a general medical condition… and are not fully explained by a general medical condition, by the direct effects of a substance, or by another mental disorder” (p. 485). A subtype of somatization known as conversion disorder comprises any unexplained impairment in motor or sensory function that would otherwise suggest a neurological condition (American Psychiatric Association, 2000). NES, then, is a type of conversion disorder under the umbrella of somatization. Accordingly, we should expect that the latent construct underlying the RC1 scale is closely related to the disorder.
The present study consists of three types of analyses. The first, taxometrics, uses the MLS, GIC, HPC, and NUC scales to determine whether the latent construct of somatization is best thought of as being discrete or dimensional in nature. Confirmatory factor analysis (CFA) is then used to explore the latent structure of the RC1 scale. Of central investigation is the scale's factor structure. Finally, item response theory (IRT) is used to estimate item and test parameters. In doing so, we assess the accuracy of scores from the scale.
Method Participants and Measures
From April 2001 to April 2009, 650 patients completed the second edition of the Minnesota Multiphasic Personality Inventory (MMPI-2; Butcher et al., 2001) as part of their admission to Mayo Clinic Arizona's epilepsy monitoring unit (MMPI-2s were not administered when precluded by seizure frequency, reading level, or overall cognitive ability). Final consensus diagnoses were determined from a video-EEG discharge summary. After we excluded patients with indeterminate admissions, comorbid epilepsy and NES, or physiological nonepileptic diagnoses, the sample consisted of 214 patients with epilepsy and 215 patients with NES. The groups were comparable on age, education, ethnicity, handedness, history of substance abuse, WAIS-III scores (Wechsler, 1997), and Wide Range Achievement Test-3 reading level (Wilkinson, 1993). However, the NES group had more women, had been experiencing seizures for a shorter length of time, was on fewer antiepileptic medications but more likely to be on psychotropic medications, and was more likely to have a history of psychiatric treatment. The NES group was also reporting more frequent seizures than was the epilepsy group. For full details on this sample and diagnostic criteria, see Locke et al. (2010). The MMPI-2-RF profiles were then screened for evidence of invalidity due to incomplete items (“Cannot Say” > 15) or random responding (Variable Response Inconsistency > 80 or True Response Inconsistency > 80). Removing all invalid profiles resulted in a final sample of 399 participants (196 patients diagnosed with epilepsy and 203 patients diagnosed with NES). The mean age of remaining participants was 43 years (SD = 14.33), and 74% were female. Participants were primarily Caucasian (94%), followed by Latino (3%), African American (3%), Asian (<1%), and American Indian (<1%).
The MMPI-2 consists of 567 dichotomous items. Examinees are asked to read a brief statement and then decide whether the account is a true or false representation of their personality. Items from the MMPI-2 were used to compute MMPI-2-RF scales by the publisher of the test. This rescoring of protocols is possible because the 338-item MMPI-2-RF draws from the same item pool as does its predecessor. The RC1 scale of the MMPI-2-RF consists of 27 items (11 keyed true and 16 keyed false). Test scores from the scale have demonstrated good internal consistency (normative sample: α = .73 for men, α = .79 for women; outpatient mental health center sample: α = .89 for men, α = .89 for women; inpatient community hospital sample: α = .87 for men, α = .88 for women), test–retest reliability (r = .79), and convergent/divergent validity with measures of similar/dissimilar constructs (Tellegen & Ben-Porath, 2008). Scores from the RC1 scale demonstrated good internal consistency in the current study as well (α = .86). Four content-specific scales directly related to somatization were also included in the analyses: the MLS (eight items; α = .73 in the current study), GIC (five items; α = .75 in the current study), HPC (six items; α = .74 in the current study), and NUC (10 items; α = .73 in the current study) scales.
Analysis 1: Taxometrics
Diagnostic rules in the DSM–IV–TR function as though psychological disorders comprise discrete taxons (i.e., categories). That is, mental illnesses are believed to be either present or absent, with no continuum in between. Some researchers have questioned the wisdom of this approach, arguing that psychopathology should be characterized by degrees rather than by types of impairment (e.g., Carson, 1991). Depression, for example, appears to be best thought of as dimensional rather than discrete in nature (Ruscio & Ruscio, 2000). Critics of taxons point to the arbitrary nature of classification schemes, which may be tools of convenience rather than reality (Millon, 1991). However, Meehl (1995) notably disagreed with this position, asserting that critics of categorical approaches focus too much on the pitfalls of classification systems. The inability to successfully categorize people may be a function of lack of understanding and ability rather than an indication that such categories do not exist.
Meehl (2006) believed that questions of taxonicity could be answered with applied mathematics. He and his colleagues (Meehl & Yonce, 1994; Waller & Meehl, 1998) developed a family of taxometric procedures (sometimes referred to as coherent cut kinetics [Waller, 2006]) designed to, as Plato put it, “carve nature at its joints” (Meehl, 1995, p. 268; i.e., determine the underlying characteristics of latent variables). General introductions to taxometric theory can be found elsewhere (see Schmidt, Kotov, & Joiner, 2004). Briefly, taxometric procedures rely on the principles of local independence. They seek to identify variables that are associated in the general population yet are independent within distinct groups. For example, Schmidt et al. (2004) note that although height and baldness are correlated in populations consisting of both men and women, the two are statistically independent in populations of just men or just women (i.e., the association is explained by the correlation between height and gender).
Studies of the underlying characteristics of psychopathology can increase our understanding of disorders (e.g., Harris, Rice, & Quinsey, 1994) and guide our analyses of clinical instruments (e.g., Thomas, Lanyon, & Millsap, 2009). Popular methods for evaluating scales—including CFA, IRT, and latent class analysis—all make assumptions about the taxonicity of latent variables. Before one can proceed with psychometric analyses of the RC1 scale, an assumption must be made as to whether the construct under investigation is dimensional or discrete. There have been no published attempts to examine the taxonicity of somatization. Dimensional structure has been found in the study of health problem overestimation (Walters, Berry, Lanyon, & Murphy, 2009), feigned psychopathology (Walters et al., 2008), and feigned neurocognitive deficit (Walters, Berry, Rogers, Payne, & Granacher, 2009). However, although volitional symptom overreporting may be related to somatization, the two remain conceptually distinct. The former is assumed to represent conscious deception, whereas the latter is believed to be the unintended result of emotional factors.
Medical diagnoses of NES are consistent with a discrete disorder. That is, EEG recordings of seizures are either indicative of abnormal neural activity or they are not. Yet this does not necessarily imply that somatization is also discrete; NES and somatization are not one and the same (recall that conversion is thought to be a subtype of somatization). The relation between somatization and NES is not well understood; it may be one of moderation, mediation, direct cause, or perhaps even spurious association. Given these unknowns, there is little empirical evidence to guide an assumption about the taxonicity of the construct. It is reasonable, then, to conduct an exploratory analysis.
Data analysis
Meehl (2006) recommended that statistical analyses rely on consistency to establish findings. As such, taxometric analyses typically involve the use of multiple procedures. The following three were chosen for the present study: (a) mean above minus below a cut (MAMBAC; Meehl & Yonce, 1994), performed with 50 evenly spaced cuts and 10 internal replications; (b) maximum eigenvalue (MAXEIG; Waller & Meehl, 1998), performed with 25 sliding windows, 90% overlap, and 10 internal replications; and (c) latent mode factor (L-Mode; Waller & Meehl, 1998). Further details about these procedures are provided by Schmidt et al. (2004). All taxometric analyses were carried out with Ruscio's (2009) taxometric program for the R language (R Development Core Team, 2008).
Much like CFAs, taxometric analyses require multiple indicators of a latent variable. The procedures are most accurate when performed with continuous scales measuring diverse clusters of symptoms (Ruscio, Haslam, & Ruscio, 2006). Therefore, for the present analyses we used the MLS, GIC, HPC, and NUC scales of the MMPI-2-RF (rather than individual items from the RC1 scale). Although each scale assesses distinct components of physiological distress, all are related by their shared assessment of persistent and varied complaints of physical symptoms (i.e., somatization). In addition, each shares at least one item with the RC1 scale. The Cognitive Complaints scale—which is normally grouped with the MLS, GIC, HPC, and NUC scales—was excluded from the analyses due to its apparent nonsignificant relation with NES (Locke et al., 2010), its independence from the RC1 scale (i.e., no shared items), and its conceptual distinction from somatoform disorders (i.e., complaints related to cognitive ability are generally excluded from DSM–IV–TR diagnostic criteria).
Evidence of taxonicity is commonly inferred by examining the plots produced from each procedure. Inverted U-shaped graphs for the MAMBAC procedure, peaked graphs for the MAXEIG procedure, and bimodal distributions of factor scores for the L-Mode procedure are all suggestive of taxonic structure. Low variability among base rate estimates across subanalyses can also indicate discrete types. A standard deviation for base rate estimates below 0.10 is thought to be indicative of taxonic data (Schmidt et al., 2004); however, such inferences are subject to considerable error (Ruscio et al., 2006). Finally, the comparison curve fit index (CCFI; Ruscio et al., 2006) is a bootstrap procedure that can be used to compare observed data with simulated taxonic and dimensional comparison data. The CCFI functions as an indicator of relative fit ranging from 0 to 1. Values closer to 0 are indicative of dimensional structure, and values closer to 1 are indicative of taxonic structure.
Results and discussion
Descriptive statistics and validity estimates for the indicator variables are presented in Table 1. The validities (Cohen's d) for the MLS, GIC, HPC, and NUC were all higher than the 1.25 threshold recommended by Meehl (1995). One variable, the GIC, demonstrated a large positive skew. We decided to leave the variable in the analysis to maintain conceptual continuity; however, it should be acknowledged that positive skew can lead to curves that are falsely suggestive of low base rate taxons on the higher end of latent distributions.
Descriptive Statistics and Validity Estimates for the Malaise, Gastrointestinal Complaints, Head Pain Complaints, and Neurological Complaints Scales
The top left panel of Figure 1 presents the MAMBAC curves for each indicator along with an overall average curve (all are presented without smoothing). The curves are ambiguous but somewhat more suggestive of dimensional structure. Analyses including the GIC scale suggest a small taxon at the higher end of latent distributions: possibly an artifact due to the indicator's skew. The standard deviation for base rate estimates produced by each of the indicators (M = 0.39, SD = 0.10) does not clearly indicate dimensionality or taxonicity. Visual inspection of the top middle and top right panels in Figure 1, along with a MAMBAC CCFI of 0.44, indicates that the data are slightly more consistent with dimensional rather than taxonic structure. The bottom left panel of Figure 1 presents the MAXEIG curves for each indicator, along with an overall average curve (also presented without smoothing beyond the use of overlapping windows). The curves are largely suggestive of dimensional structure. The standard deviation for base rate estimates produced by each of the indicators was relatively large (M = 0.35, SD = 0.20) and suggestive of dimensional structure. Visual inspection of the bottom middle and bottom right panels in Figure 1, along with a MAXEIG CCFI of 0.27, also indicates that the data are more consistent with dimensional rather than taxonic structure. Finally, the L-Mode graph presented in Figure 2 reinforces the dimensional nature of the construct, as does the L-Mode CCFI of 0.32.
Figure 1. Top row: left panel—average mean above minus below a cut (MAMBAC) curve (solid line) plotted with the four curves for each indicator (dashed lines); center panel—average MAMBAC curve for the observed data (dark line) in comparison to simulated taxonic data (light lines representing one standard deviation above and below the mean); right panel—average MAMBAC curve for the observed data (dark line) in comparison to simulated dimensional data (light lines representing one standard deviation above and below the mean). Bottom row: left panel—average maximum eigenvalue (MAXEIG) curve (solid line) plotted with the four curves for each indicator (dashed lines); center panel—average MAXEIG curve for the observed data (dark line) in comparison to simulated taxonic data (light lines representing one standard deviation above and below the mean); right panel—average MAXEIG curve for the observed data (dark line) in comparison to simulated dimensional data (light lines representing one standard deviation above and below the mean).
Figure 2. Latent mode factor analysis curve for the observed data (dark solid line), simulated taxonic data (light solid line), and simulated dimensional data (light dotted line) as indicated by the Gastrointestinal Complaints, Head Pain Complaints, and Neurological Complaints scales.
The MAXEIG and L-Mode analyses suggest that somatization—as indicated by the MLS, GIC, HPC, and NUC scales of the MMPI-2-RF—is a dimensional variable. However, the MAMBAC procedure was more ambiguous, with only slight evidence in favor of dimensionality. The data revealed limited evidence of a low base rate taxon on the higher end of the latent distribution; however, this finding is more likely attributed to the skewed GIC scale rather than a true taxon. Given that no a priori assumptions were made about the nature of somatization, the present analyses cannot confirm that the construct is dimensional. Rather, for the purposes of further analysis, we may simply conclude that the observed data are more consistent with dimensionality.
Analysis 2: CFA
The MMPI-2-RF RC1 scale was designed to serve as a relatively unidimensional marker of somatization (Tellegen & Ben-Porath, 2008). However, the presence of domain-specific scales (i.e., the MLS, GIC, HPC, and NUC) suggests that a more complex structure underlies the construct. From a psychometric perspective, applying a unidimensional model to a test that is truly multidimensional would violate the local independence assumption. That is, it must be assumed that covariation between items will disappear once the underlying latent cause of statistical association is accounted for (i.e., the latent factor). Violations of this assumption are not uncommon for clinical tests. Indeed, scales are often broken down into domain-specific components.
Strategies for managing local dependencies in confirmatory factor models include allowing primary factors to correlate, allowing measurement errors to correlate, and including additional factors. When the latent structure of a construct is truly multidimensional, the latter option is preferred because it leads to a more accurate partitioning of error variance (Rindskopf & Rose, 1988) and produces more precise parameter estimates (DeMars, 2006). Bifactor models have seen growing application to clinical research for this purpose (e.g., Gibbons et al., 2007; Gibbons, Rush, & Immekus, 2009; Reise, Morizot, & Hays, 2007). Bifactor structure exists when items are influenced by both general and domain-specific factors (where all factors are fixed to be uncorrelated).
Bifactor models can also be useful in determining the suitability of unidimensional models for tests with complex structures. Researchers have demonstrated that by comparing factor loadings from a bifactor model with those produced from a more constrained unidimensional model, one can determine whether the latter meaningfully distorts analyses (Parsons & Hulin, 1982; Reise et al., 2007). That is, computationally and conceptually simpler unidimensional models can serve as proxies for more complex multidimensional models when they do not significantly alter item and person parameters. It is likely that clinicians will examine elevations on the RC1 scale based on assumptions of scale homogeneity out of practical convenience (standard practice is to convert raw total scores to transformed standard scores [T scores]). It is of substitutive interest, then, to determine whether such interpretations are psychometrically appropriate.
Data analysis
Twenty-seven items from the MMPI-2-RF make up the RC1 scale (Tellegen & Ben-Porath, 2008). Accordingly, a unidimensional model was specified in which each of these items (listed in Table 2) loaded onto the latent factor of Somatization. The metric of the latent factor was defined by fixing its variance to unity. Measurement errors were not allowed to correlate. Of the 27 items that make up the scale, 4 are shared with the GIC scale, 6 are shared with the HPC scale, and 8 are shared with the NUC scale (only 1 item is shared with the MLS scale, and thus the item is expected to be locally independent). Accordingly, a bifactor model was specified in which each item loaded onto the general Somatization factor, and shared items (18 in all) also loaded onto one of the three domain-specific factors (Gastrointestinal, Head Pain, and Neurological). The metric of the latent factors were defined again by fixing their variances to unity. Correlations between factors (e.g., Somatization with Gastrointestinal) were all fixed at 0, as were correlations between measurement errors. The model is depicted in Figure 3.
Completely Standardized Factor Loadings (With Standard Errors) for the Unidimensional and Bifactor Models
Figure 3. Diagram for the bifactor model of the MMPI-2-RF Somatic Complaints (RC1) scale. Latent factors are Somatization, Gastrointestinal, Head Pain, and Neurological. Factors and measurement errors do not correlate in the model.
Data were analyzed using Mplus Version 5 (Muthén & Muthén, 2006). Due to violations of normality created by dichotomous item responses (i.e., the true vs. false response format of the MMPI-2-RF), a robust limited-information weighted least squares estimator with a pairwise present approach for missing data (11 out of 10,773 responses; 0.01%) was used for each model. Limited-information estimators are not as efficient as full-information estimators (nor are they preferred when data are missing at random), but they are more appropriate for evaluating model fit when observed data consist of numerous, discrete item responses. Overall goodness of fit was evaluated using root-mean-square error of approximation (RMSEA), the comparative fit index (CFI), and the Tucker–Lewis index (TLI). Hu and Bentler (1999) suggested that an RMSEA ≤ .06, a CFI ≥ .95, and a TLI ≥ .95 are indicative of acceptable fit.
Results and discussion
The overall goodness-of-fit indices suggested that the unidimensional model provided marginal fit for the data, χ2(136) = 395.56, p < .001, RMSEA = .07, CFI = .86, TLI = .91. Inspection of modification indices suggested localized points of ill fit for error covariances between items of the same content-specific scales. That is, consistent with prior theory, there appear to be local dependencies on the RC1 scale. The overall goodness-of-fit indices suggested that the bifactor model provided acceptable fit for the data, χ2(134) = 235.03, p < .001, RMSEA = .04, CFI = .94, TLI = .96. Furthermore, the chi-square difference test (adjusted for Satorra-Bentler scaling), χ2(14) = 191.86, p < .001, suggested that the bifactor model provided significantly better fit for the data than did the unidimensional model. Loadings and standard errors for both models are presented in Table 2. Inspection of modification indices and expected parameter change (EPC) values indicated that a correlation between the Neurological and Head Pain factors (EPC = .35) and loadings for Item 328 onto the Gastrointestinal factor (EPC = .23), Items 227 and 313 onto the Head Pain factor (EPC = .26, and .28, respectively), and Items 2 and 174 onto the Neurological factor (EPC = .34, and .26, respectively) would have improved the fit of the bifactor model. However, given the purpose of this study, and a lack of theoretical justification for freeing these parameters, no further modifications were made.
The data indicated that a bifactor model provided better fit than did a unidimensional model. However, given that the scale was designed to be unidimensional, the burden of proof is on falsifying this baseline hypothesis—a result that is not necessarily evident in our data. Alternatively, one might consider the detrimental effects of constraining sources of item variances; that is, one can look for evidence of grossly distorted item loadings within the unidimensional case. The loadings presented in Table 2 reveal some signs of systematic bias. For example, items that were affected just by the Somatization factor (i.e., the bottom nine items in Table 2) have weaker loadings in the unidimensional model compared with their loadings in the bifactor model. Conversely, items that were affected both by the Somatization factor and by one of the domain-specific factors (i.e., the top 18 items in Table 2) tended to have stronger loadings in the unidimensional model compared with the bifactor model. That is, within a unidimensional framework, variances accounted for in the multidimensional items may have been overestimated, whereas variances accounted for in the unidimensional items may have been underestimated. This likely occurred because the loadings in the unidimensional model reflect both the variance of the Somatization factor and a conglomeration of common variance from each of the domain-specific factors.
In clinical practice, the effects of this violation of local independence (i.e., the bifactor structure) can be relatively benign. Disease estimates are typically computed using total scores (i.e., simple, unweighted sums of items). As such, misspecified loadings are of little practical consequence in clinical settings. It should be noted that in the context of advanced statistical analyses (e.g., structural equation models), this bias could detrimentally affect the estimation of structural parameters, item parameters, and standard error. Yet the discrepancies between parameter estimates in the current study were not particularly large. Indeed, our conclusions regarding the magnitude and direction of item loadings onto Somatization would change little between the unidimensional and bifactor models. The choice between the two may best be made on theoretical and practical grounds. In clinical settings, for example, the RC1 scale is interpreted as a unidimensional entity and thus may best be evaluated as homogeneous in content.
Overall, the results suggest that the MMPI-2-RF test developers were moderately successful in their efforts to create a unidimensional measure of somatization. As the results in Table 2 show, nearly all of the items on the scale loaded highly onto a single latent factor. When items were allowed to load onto additional, orthogonal dimensions (i.e., the domain-specific factors of the bifactor model), item loadings on the primary factor remained strong. The data revealed little evidence to suggest that the construct identified within this sample is fundamentally different from that described by the authors of the MMPI-2-RF.
Analysis 3: IRT
IRT comprises a set of psychometric models that can be used to study item and scale properties in great detail (see Embretson & Reise, 2000). Although closely related to—and at times mathematically equivalent to—CFA, IRT provides tools specifically designed for the analysis of item and scale accuracy. For example, item parameters in IRT can be used to study item characteristic curves (ICCs), graphs of the probability of passing an item conditional on a latent distribution (i.e., the distribution of the latent factor). ICCs take their shape on the basis of item parameters. The discrimination parameter (a), which functions much like a factor loading, governs the slope of the ICC. With all things being equal, higher values equate with more precise relations between items and factors. The difficulty or location parameter (b) governs the ICC's point of inflection. Item difficulty—perhaps best thought of as item severity in clinical settings—loosely represents the amount of a latent factor that examinees must possess before they are willing to endorse the symptom of disease. The pseudoguessing parameter (c) governs the lower asymptote of the ICC (i.e., the probability of examinees endorsing an item when they are void of the latent factor). In clinical settings, a nonzero pseudoguessing parameter can be indicative of response bias.
IRT can also be used to estimate item and scale information functions—a psychometrically advanced notion of scale accuracy. Higher information equates with lower standard error and higher reliability. In IRT, however, information is allowed to vary along the latent distribution. Thus, the ability of an item or scale to accurately estimate examinees' factor scores varies within specific regions of the latent distribution. Information functions can inform researchers and clinicians how to best employ measurement tools. That is, it can be determined which populations are accurately measured by the scale and which populations are not.
Finally, although not particularly useful for practitioners, IRT analyses can be used to guide future development of testing instruments (e.g., computer-adaptive tests). Unlike the case with classical test theory, item parameters produced from IRT are not population dependent. Therefore, assuming a common metric can be found (and that there is no evidence of differential item functioning), the item parameters produced in one analysis can be directly compared with the item parameters produced in another. Test developers can use the results from past analyses to guide their efforts in developing future measures of somatization.
Data analysis
The previously described CFAs indicated that somatization, as measured by the RC1 scale of the MMPI-2-RF, is consistent with a bifactor structure. However, the results also revealed that item parameter estimates were not severely distorted by a unidimensional model. The practice of estimating unidimensional IRT models is currently more manageable (both computationally and conceptually) in comparison to the practice of estimating multidimensional models. Furthermore, unidimensional models are more consistent with clinicians' interpretations of clinical scales (i.e., unidimensional T scores). Thus, we assumed a unidimensional model for the RC1 scale out of both practical and conceptual benefit. Yen's Q3 (Yen, 1984)—the correlation between item residuals after partialing out the influence of a latent variable (see Embretson & Reise, 2000)—was used to verify the accuracy of this assumption. Q3s greater than .20 can be indicative of local dependencies (see de Ayala, 2009).
Three IRT models were compared in the analyses. A one-parameter logistic (1PL) model was specified in which item difficulties were freely estimated but item discriminations were constrained to be equal and item lower asymptotes were fixed at 0. Next, a two-parameter logistic (2PL) model was specified in which item difficulties and discriminations were freely estimated but again item lower asymptotes were fixed at 0. Finally, a three-parameter logistic (3PL) model was estimated in which item difficulties, discriminations, and lower asymptotes were all freely estimated. The models are nested within each other; the 3PL model is the least restrictive, and the 1PL model is the most restrictive. Therefore, model comparisons were conducted using likelihood ratio tests (ΔG2), relative changes in variability accounted for (RΔ2), and Akaike information criteria (AICs). The ΔG2 is evaluated as a significance test distributed as a chi-square statistic (α = .05), RΔ2 is interpreted as relative improvement in the proportion of variability accounted for, and AIC decreases as model fit improves (see de Ayala, 2009). Item fit was assessed using Bock's chi-square index (α = .05; see Embretson & Reise, 2000)—a statistic that compares observed and expected proportions correct within intervals of the latent distribution.
All IRT analyses were carried out using Rizopoulos's (2006) IRT program for the R language (R Development Core Team, 2008). Item parameters were generated with marginal maximum likelihood estimation. A normal prior distribution was specified, and factor scores were computed using expected a posteriori estimation. Falsely keyed items were reverse-scored so that item discrimination values would all be positive (as is the tradition in IRT).
Results and discussion
The 1PL and 2PL models converged without difficulty; however, the 3PL model appeared to produce improper parameter estimates. Therefore, we constrained the lower asymptote parameters for offending items to be 0 in order to achieve convergence. Model comparisons are presented in Table 3. Likelihood ratio tests revealed that the 2PL model provided significantly better fit than did the 1PL model, ΔG2(26) = 155.44, p < .001; however, the 3PL model did not significantly improve fit beyond the 2PL model, ΔG2(21) = 16.62, p = .73. The AIC and relative change statistics further support the 2PL model. Item parameter estimates and fit statistics for the 2PL model are presented in Table 4. The data suggest that the model adequately accounted for the observed responses, with only three items (227, concerning heart rate and shortness of breath; 254, concerning keeping one's balance while walking; and 313, concerning feeling paralyzed or unusually weak) demonstrating significantly poor fit. Inspection of the ICCs for observed and expected response frequencies (not reported here) did not indicate that additional parameters (e.g., c) or alternative models (e.g., nonmonotone) would have improved fit. Rather, the discrepancies may have been due to sampling error, poor model–item fit, or poor item construction (e.g., confusing wording).
Fit Statistics for the 1PL, 2PL, and 3PL Logistic Models
Item Parameter Estimates (With Standard Errors) and Fit Statistics for the Two-Parameter Logistic (2PL) Model
The average correlation between pairs of residuals (Q3s) was relatively small for items from the GIC scale (r = .18), the HPC scale (r = .08), and the NUC scale (r = .08), raising only minor concerns of local dependencies (the complete correlation matrix between item residuals is not reported here due to space limitations). The results are consistent with those from the CFAs described earlier; that is, RC1 can be regarded as a unidimensional scale with only minor ill effect.
The difficulty and discrimination estimates presented in Table 4 and depicted graphically through ICCs in Figure 4 indicate that items from the RC1 scale tend to be strong indicators of somatization within the mid to high range of the latent distribution. This can be seen more precisely through the total information and standard error functions presented in Figure 5: a plot of the information and standard error associated with standardized estimates of somatization along the latent distribution. As can be seen in Figure 5, the RC1 scale is most accurate between –1 and + 2 standard deviations from the latent distribution's mean. Indeed, over two thirds (67%) of the information provided by the scale is found within this region. The results of Figure 5 are made more interpretable when taken into account along with the test characteristic curve in Figure 6: a plot of expected raw scores and T scores for standardized estimates of somatization along the latent distribution. As can be seen in Figure 6, a member of the current study with an average standardized score on the Somatization factor (i.e., Somatization = 0) would be expected to endorse 10 items on the RC1 scale, earning a T score equivalent of 68 (i.e., participants in the current study tended to score higher on average than did the MMPI-2-RF's normative sample). Combining results from the total information and standard error functions (see Figure 5) with the test characteristic curve (see Figure 6) reveals that the RC1 scale is most accurate between T scores of 55 and 90. The scale appears to achieve peak accuracy at a T score equivalent close to 75.
Figure 4. Item characteristic curves for all 27 items of the MMPI-2-RF Somatic Complaints (RC1) scale. Functions were produced with a two-parameter logistic item response theory model. The x-axis represents Somatization as a normally distributed latent factor.
Figure 5. Test information (y-axis) and standard error (z-axis) functions plotted against Somatization represented as a normally distributed latent factor (x-axis).
Figure 6. Test characteristic curve plotting expected total raw scores (y-axis) and T scores (z-axis) on the MMPI-2-RF Somatic Complaints (RC1) scale associated with standardized estimates of Somatization along the latent distribution (x-axis). The z-axis does not represent interval units of measurement.
The correlation between factor estimates and T scores from the RC1 scale was nearly perfect (r = .99). This implies that there may be little diagnostic benefit to weighting item responses when the RC1 scale is viewed as a unidimensional construct. That is, the practice of transforming unweighted item sums into T scores appears to be psychometrically appropriate. Indeed, the mean difference between standardized estimates of somatization for the epilepsy group (M = –0.37, SD = 0.87) and the NES group (M = 0.35, SD = 0.84), t(397) = –8.40, p < .001, r2 = .15, as well as the mean difference between T scores for the epilepsy group (M = 63.13, SD = 12.79) and the NES group (M = 73.72, SD = 12.45), t(397) = –8.40, p < .001, r2 = .15, were both statistically significant. Because group differences existed between T scores of 63 and 74, it is ideal that the RC1 scale provides peak accuracy within this region. Overall, the results suggest that the scale is well suited for the assessment of somatization within epilepsy and NES populations.
Summary and Concluding DiscussionThe results of the present analyses indicate that somatization—as measured by the MMPI-2-RF somatic scales (MLS, GIC, HPC, and NUC)—is a dimensional construct. The RC1 scale itself appears to have a bifactor structure. We recommend that researchers who are primarily interested in structural relations between the RC1 scale and external variables (e.g., treatment outcomes) consider the use of a bifactor model, because it would likely improve predictive utility (see Chen, West, & Sousa, 2006). However, gains in predictive accuracy must be carefully weighed against the added complexity in multidimensional models. In our analyses, a unidimensional model did not severely distort item parameter estimates. Thus, researchers and clinicians who are primarily interested in the scale's properties in clinical practice may instead want to consider the use of a unidimensional model—the internal structure intended by the MMPI-2-RF's authors. For such analyses, homogeneous constructs would provide practical and conceptual convenience and would not appear to greatly bias parameter estimates.
Items from the RC1 scale were most consistent with a 2PL IRT model. Items had unique discrimination and difficulty parameters, but a lower asymptote was not required to accurately estimate response probabilities. The RC1 scale appears to be most accurate for estimates of latent factors in mid to high regions of the latent distribution (within this particular clinical population). In the present study, this implied that T score estimates between 55 and 90 were most precise. Because clinical scales are often evaluated on the basis of cutoff scores of 65, 70, or 75 T, the RC1 scale appears to have been appropriately constructed for use in clinical assessment. Conversely, the scale appears to provide imprecise estimates of somatization for individuals low on the construct. Researchers and clinicians who are interested in nonimpaired populations should consider using alterative measures.
The results presented in this article may have been biased by the inclusion of patients who engaged in volitional symptom overreporting. Although profiles were screened for evidence of invalidity due to incomplete items or random responding, we chose to include the responses of patients who scored high on content-based validity scales (e.g., the Symptom Validity [FBS-r] and Infrequent Somatic Responses [Fs] scales). These profiles were included in the analyses for two primary reasons. First, somatization is inherently related to unexpected and profuse reports of physical distress. As mentioned earlier, a key element in the diagnosis of the disorder is nondeliberate reports of symptoms (as occurs in factitious disorder or malingering). Given that both somatization and volitional overreporting result in exaggerated profiles of physical symptoms, differentiating between the two conditions becomes complex. Second, volitional overreporting is typically associated with potential for secondary gain (e.g., civil litigation). In such contexts, the base rate of deliberate deception can approach 50% (e.g., Thomas & Youngjohn, 2009). In the current context, however, the base rate of volitional overreporting is thought to be much less common (Martin, Bortz, & Snyder, 2006). By Bayes' theorem, even if one assumes generous 0.90 estimates of sensitivity and specificity for differentiating between volitional overreporting and somatization (by means of an MMPI-2-RF validity scale) and a 10% base rate of factitious disorder and/or malingering in the current sample, this results in a positive predictive value of just 50%. Thus, excluding profiles due to apparent content-based invalid responding would have likely eliminated as many valid as invalid profiles.
Although our sample size was relatively large for a clinical data set and met some suggested minimum requirements for the analyses (e.g., 300 participants for taxometric procedures; Meehl, 1995), larger samples are generally preferred in psychometric analyses of latent constructs. A cross-validation sample would have served to bolster the results. But splitting the sample in two would have seriously threatened the validity of the procedures. Our sample was also restricted by the types of participants included in the analyses. All were suffering from relatively severe physical complaints (whether neurologic or psychogenic in origin). This can be seen by the high mean standardized factor estimate for participants in the current study (equivalent to a T score of 67). Our analyses could have been limited by the relatively rare sample of patients. For example, whereas the present analyses did not find taxonic structure for somatization in a restricted population of hospital patients, the results may differ in a more diverse population consisting of both healthy and sick individuals. Also, the impact of content-specific factors on the RC1 scale could pose a much more serious threat to the assumption of local independence in a population with greater diversity of somatic or physical illnesses (e.g., patients with a nonconversion somatoform disorder). Replication of the results in the present study with a more diverse sample is warranted to bolster our tentative conclusions. Finally, it should be noted that our analyses speak primarily to the reliability and internal structure of test scores from the RC1 scale. Although some evidence of external validity was found in correlations between latent factor estimates, T scores, and NES status, diagnostic and predictive utility of the scale was not the direct focus of this article.
Our study is one of the first empirical examinations of the psychometric properties of the MMPI-2-RF RC1 scale in a clinically relevant population. Overall, the results indicate that the scale has strong psychometric properties. Clinical researchers and practitioners should feel confident that the RC1 scale accurately assesses the latent construct of somatization for individuals in the impaired range of the distribution.
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Submitted: October 29, 2009 Revised: January 25, 2010 Accepted: January 28, 2010
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Source: Psychological Assessment. Vol. 22. (3), Sep, 2010 pp. 492-503)
Accession Number: 2010-18043-002
Digital Object Identifier: 10.1037/a0019229
Record: 135- Title:
- Psychometric properties of the Short Inventory of Problems (SIP) with adjudicated DUI intervention participants.
- Authors:
- Morse, David T.. Department of Counseling, Educational Psychology, and Foundations, Mississippi State University, MS, US, dmorse@colled.msstate.edu
Robertson, Angela A.. Social Science Research Center, Mississippi State University, MS, US - Address:
- Morse, David T., Department of Counseling, Educational Psychology, and Foundations, Mississippi State University, P.O. Box 9727, MS State, MS, US, 39762, dmorse@colled.msstate.edu
- Source:
- Psychology of Addictive Behaviors, Vol 31(1), Feb, 2017. pp. 110-116.
- NLM Title Abbreviation:
- Psychol Addict Behav
- Page Count:
- 7
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- Bulletin of the Society of Psychologists in Addictive Behaviors; Bulletin of the Society of Psychologists in Substance Abuse
- Other Publishers:
- US : Educational Publishing Foundation
Society of Psychologists in Addictive Behaviors - ISSN:
- 0893-164X (Print)
1939-1501 (Electronic) - Language:
- English
- Keywords:
- validity, reliability, assessment of consequences, short inventory of problems, factor analysis
- Abstract:
- We used responses of two large samples of court-ordered participants from a statewide alcohol/driving safety program to investigate factor structure, score reliability, and criterion-related validity of the Short Inventory of Problems (SIP). Exploratory and confirmatory factor analyses, using both item-level and subscore-level data, support a one-factor structure for the SIP. Internal consistency score reliability estimates were consistent across samples and high enough to warrant use for making decisions about individuals. Item response theory model calibration of the scale, using a two-parameter logistic model, yielded consistent estimates of location and discrimination (slope) across samples. Estimated scale scores correlated moderately with an independent indicator of alcohol problems and poorly with an indicator of risky driving behavior, lending evidence of convergent and discriminant validity. We judge the SIP as adequately described by a single factor, that the joint person-item scale is coherent, and scores behave consistently across samples. (PsycINFO Database Record (c) 2017 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Driving Under the Influence; *Factor Structure; *Inventories; *Test Reliability; *Test Validity; Factor Analysis
- PsycINFO Classification:
- Clinical Psychological Testing (2224)
Substance Abuse & Addiction (3233) - Population:
- Human
Male
Female - Location:
- US
- Age Group:
- Adulthood (18 yrs & older)
- Tests & Measures:
- Short Inventory of Problems
Inventory of Drug Use Consequences
Short Inventory of Problems—Drug Use
Alcohol Use Disorders Identification Test DOI: 10.1037/t01528-000
Shortened Inventory of Problems--Alcohol and Drugs DOI: 10.1037/t17417-000
Alcohol Problems Scale [Appended] DOI: 10.1037/t21013-000
Drinker Inventory of Consequences DOI: 10.1037/t03945-000 - Methodology:
- Empirical Study; Quantitative Study
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- First Posted: Jan 12, 2017
- Release Date:
- 20170112
- Correction Date:
- 20170206
- Copyright:
- American Psychological Association. 2017
- Digital Object Identifier:
- http://dx.doi.org/10.1037/adb0000249
- PMID:
- 28080093
- Accession Number:
- 2017-01384-001
- Persistent link to this record (Permalink):
- http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2017-01384-001&site=ehost-live
- Cut and Paste:
- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2017-01384-001&site=ehost-live">Psychometric properties of the Short Inventory of Problems (SIP) with adjudicated DUI intervention participants.</A>
- Database:
- PsycINFO
Psychometric Properties of the Short Inventory of Problems (SIP) With Adjudicated DUI Intervention Participants / BRIEF REPORT
By: David T. Morse
Department of Counseling, Educational Psychology, and Foundations, Mississippi State University;
Angela A. Robertson
Social Science Research Center, Mississippi State University
Acknowledgement:
The Short Inventory of Problems (SIP) measure comprises 15 items from the 50-item (45 scored; 5 control) Drinker Inventory of Consequences (DrInC; Miller, Tonigan, & Longabaugh, 1995). The DrInC was distinctive in focusing on adverse consequences of drinking rather than on drinking behavior or alcohol dependence. Five rationally identified domains of consequence are included: physical (8 items), intrapersonal (8), social responsibility (7), interpersonal (10), and impulse control (12). Five control items are also embedded, as a check on careless responding, although these do not otherwise contribute to scoring. The SIP was constructed by selecting, from each domain, the three items having the highest item-domain correlations. Blanchard, Morgenstern, Morgan, Lobouvie, and Bux (2003) created another version, choosing the 15 items with strongest item-total correlations. Two primary versions of SIP exist.
The lifetime version (“Has this EVER happened to you?”) is dichotomously scored. The recent consequences version uses a 4-point, frequency score scale with a 3-month window. The Inventory of Drug Use Consequences (InDUC; Miller et al., 1995) uses DrInC items reworded as “drinking or using drugs” instead of “drinking.” Allensworth-Davies, Cheng, Smith, Samet, and Saitz (2012) neatly summarized many SIP variants in their Figure 1 (p. 259).
Figure 1. Person score-item location map of Short Inventory of Problems (SIP) responses for Sample 1 (N = 10,312). Higher scale values represent items that were endorsed less often or persons with higher scaled scores; lower scale values represent items that were endorsed more often or persons with lower scores. Each “X” represents 50 respondents.
A number of studies have evaluated the structure of the SIP, either as part of the full DrInC/InDUC or specifically on the SIP or a variant (Allensworth-Davies et al., 2012; Alterman, Cacciola, Ivey, Habing, & Lynch, 2009; Anderson, Gogineni, Charuvastra, Longabaugh, & Stein, 2001; Blanchard et al., 2003; Bender, Griffin, Gallop, & Weiss, 2007; Feinn, Tennen, & Kranzler, 2003; Forcehimes, Tonigan, Miller, Kenna, & Baer, 2007; Gillespie, Holt, & Blackwell, 2007; Hagman et al., 2009; Kenna et al., 2005; Kiluk, Dreifuss, Weiss, Morgenstern, & Carroll, 2013; Marra, Field, Caetano, & von Sternberg, 2014; Tonigan & Miller, 2002). Table 1 summarizes outcomes for these studies of SIP structure.
Summary of Prior Factor Studies of SIP
To summarize, efforts to support anything more than a one-factor structure for the SIP have generally not fared well or, at best, not appreciably better than a unidimensional model. Miller et al. (1995), Forcehimes et al. (2007), and Alterman et al. (2009) explicitly attempted to confirm five-factor structures, with generally poor results. Kiluk et al. (2013) claimed better model-data fit for the five-factor over the one-factor model, although the differences in fit indices were generally slight, as was the case for Kenna et al. (2005). Beyond differences in wording (“drinking,” “drug use,” “drinking or drug use,” “bipolar disorder”) and sample characteristics, choices of how to analyze SIP data may have affected the results being reported, as well as the population sampled and the sample size. We have some methodological concerns with many of the studies that could account for some of the inconsistencies of results in studies of structure.
First, principal components analysis differs in important ways from common factor analysis. Gorsuch (1983) and Thompson (2004) point out that principal components extraction presumes a false reality (e.g., all observed variance is common; no unique or measurement error variance exists) and inflates observed variable-component loadings, especially with few variables. Second, for item-level data, tetrachoric (dichotomous) or polychoric (general form for ordered scales) correlations are preferred (Bock, Gibbons, & Muraki, 1988; Lorenzo-Seva & Ferrando, 2006; Muraki & Carlson, 1995). Third, most of the exploratory factor analysis (EFA) studies relied on dated criteria for determining the number of factors or components to extract (e.g., eigenvalue > 1.0, or scree plot). Simulation studies show better performance for parallel analysis or minimum average partial correlation methods to identify the number of latent variables (Kaufman & Dunlap, 2000; Lorenzo-Seva & Ferrando, 2006; O’Connor, 2000; Velicer, 1976). Fourth, although the SIP structure studies have generally used item-level data, researchers have occasionally used domain subscores (e.g., Anderson et al., 2001). Finally, for some of the EFAs and confirmatory factor analyses (CFAs), the sample sizes would be judged too small by many. For these reasons, and to help furnish more evidence toward understanding the latent structure of the SIP, we report here on the results of analyses of SIP responses from two massive samples of persons arrested for driving under the influence (DUI) and attending mandated alcohol safety training.
Method Participants
Sample 1 comprised 10,312 court-ordered DUI training participants having complete SIP data from July 2013 through October 2014. Most were male (78%), employed full-time (51%), or part-time/self-employed (20%). By ethnicity, 54% were Caucasian and 38% were African American. Hispanic/Latinos represented 2%. For marital status, most were never married (50%), followed by currently married (22%) or divorced (20%). Median age was 34 (M = 36.4, SD = 13.3). Blood alcohol concentration at arrest had a median value of 0.12% (n = 4,144; values above 0.499 were censored). Sample 2 was 21,670 adjudicated participants having complete SIP data from September 2008 through November 2011. Aside from year of court-ordered training, the two samples were quite similar in characteristics. As we used existing data, without personal identifiers, our university institutional review board (IRB) declared this research exempt from IRB review.
Instruments
The 15-item lifetime consequences version of the SIP was completed during the first class session of training for both samples. Both samples responded to the “drinking or drug use” version of the SIP, derived from the InDUC of Miller et al. (1995). For example, Item 11 is as follows: “A friendship or close relationship has been damaged by my drinking or drug use.”
Some ancillary indicators, to investigate criterion-related validity of SIP scores, were (a) Risky Driving Behaviors score (Sample 1 only), (b) Alcohol Consumption, and (c) Alcohol Problems score. The Risky Driving Behaviors score was the summed score of a nine-item set of poor driving behaviors with self-reported frequency on a 1–5 scale within the past 3 months. Two examples are (a) driven 20+ mph over the limit and (b) driven while using a cell phone. Estimated internal consistency reliability was .86. The Alcohol Consumption score was obtained by summing responses of frequency to three items: (a) number of days per week that one drinks, (b) number of drinks per occasion, and (c) frequency of having 5–6 or more drinks. Specific wording was slightly different; for Sample 2, we used wording from the Alcohol Use Disorders Identification Test (AUDIT, second edition; Babor, Higgins-Biddle, Saunders, & Monteiro, 2001). Estimated internal consistency reliabilities were .75 and .76 for Samples 1 and 2, respectively.
The Alcohol Problems Scale was obtained by summing responses of frequency to three items, also from the AUDIT: (a) “How often during the last year have you found that you were not able to stop drinking once you had started?” (b) “How often during the past year have you been unable to remember what happened the night before because you had been drinking?” and (c) “Has a relative, friend, doctor, or other healthcare worker been concerned about your drinking or suggested you cut down?” The response scale for the first two items was 0 = never, 1 = less than monthly, 2 = monthly, 3 = two to three times per week, and 4 = four or more times per week. The third item’s response scale was 0 = no; 1 = yes, but not in the last year; and 2 = yes, during the last year. Estimated internal consistency reliability values for the Alcohol Problems Scale were .71 (Sample 1) and .62 (Sample 2).
Data Analysis
Sample 1 responses were used to conduct an EFA using tetrachoric correlations among the 15 SIP items, calculated via the R library, psych (Revelle, 2016). Maximum likelihood extraction was used, via SPSS version 24. Upon determining a preferred structure, the data from Sample 2 responses were used to run a CFA, using LISREL 9.2 (Jöreskog & Sörbom, 2016). Regardless of the EFA result, the CFA was to compare at least three alternate models: a one-factor model, a five-factor model, and a five-factor model having a single, second-order factor.
We also conducted CFA analyses on both samples, using the five domain scores (physical, social, intrapersonal, impulse control, and interpersonal), to determine whether relationships among domain scores yielded comparable results to those derived from relationships among item scores. We used polychoric correlations among domain scores. With only five scores involved, we were only able to test fit to a one-factor model.
Criteria for good model-data fit in the CFA were (a) normed fit index and comparative fit index values of .90 or better, (b) root mean squared error of approximation (RMSEA) of .08 or less, and (c) standardized root mean square residual (SRMR) of .05 or less (Bollen, 1989; Hair, Black, Babin, & Anderson, 2010). We chose to ignore the overall chi-square test as a criterion, since it is so sensitive to sample size (Bollen, 1989).
We then conducted item response theory (IRT) estimates of SIP item parameters, via both the two-parameter logistic (2PL) and one-parameter logistic (1PL) models (Hambleton, Swaminathan, & Rogers, 1991). The ltm package for R (Version 1.0–0; Rizopoulos, 2006) was used to generate the two-parameter (item location or difficulty and item discrimination or slope) and one-parameter (item location or difficulty) estimates. We compared the overall fit of the 1PL versus 2PL results using the method of Kang and Cohen (2007). Finally, we determined relationships of SIP scores with selected drinking, risk, and behavioral indicators, mentioned in the Instruments section above.
ResultsSummary statistics for rates of item endorsement and for the five domain scores are given for each sample in Table 2. From these, we see that endorsement rates across samples were close, with a few exceptions. Endorsement of Item 5 (taken foolish risks) differed by more than 15% across samples, as did Item 6 (done impulsive things), while Item 14 (spent/lost too much money) differed by 9%. Other items showed lower differences, typically about 4% or less. The social and physical domain items tended to be endorsed less frequently than interpersonal and intrapersonal items. Overall, impulse control items tended to be endorsed most frequently.
Summary Statistics for Lifetime Consequence SIP Items, Domain, and Total Scores by Sample
EFA
Initial checks for the suitability of the SIP scores for factoring were promising. The Kaiser-Meyer-Olkin measure of sampling adequacy was high, .956. Bartlett’s test for an identity matrix was statistically significant (p < .001), indicating the presence of relationships. The minimum observed pairwise correlation was .396, with a median value of .653. Maximum likelihood extraction yielded results clearly favoring a one-factor structure. This conclusion was based on parallel analysis, although both the scree plot and eigenvalues affirm only one factor.
Item-factor loadings are given in Table 3. All were strong; the minimum loading was .556 (item 15) and median loading = .830. Regarding the adequacy of the structure to reproduce observed correlations, 85% of the 105 pairwise relationships yielded absolute residuals < |.05|. The SRMR was .048. We judge these results as supportive of a one-factor structure.
One-Factor Model Factor Loadings and Item Parameter Estimates
CFA: Item Scores
Analysis of the one-factor, five-factor, and second-order five-factor models yielded results favoring a modified one-factor model (see Table 4). With the exception of the RMSEA value, the original one-factor model as tested met all other criteria for acceptable model-data fit. Inspection of residuals suggested five item pairs might share some common variance in their errors: Items 1 and 4 (both were intrapersonal domain items on the DrInC), Items 5 and 6 (impulse control), Items 7 and 9 (physical), Items 8 and 14 (social), and Items 10 and 11 (Interpersonal). We thought it reasonable to estimate these common error covariances. Lest we be accused of back-pedaling our way to a five-factor model, we note that (a) other item pairs, drawing from the same domains, did not warrant such modifications, and (b) neither of the five-factor models yielded fit indices as good or was as parsimonious. Of all models, the modified one-factor model was the only one to meet all set criteria for model-data fit. Estimated loadings from this model are given in Table 2 and match very closely those from the EFA.
Fit Indices for Factor Models Compared Using Item-Level and Domain-Level Scores
We applied the modified one-factor model to the original data set (Sample 1), as no other data were available. The resulting fit indices were good and were superior—albeit not by a great margin—to those for the five-factor and five-factor with second-order factor models (see Table 4).
CFA: Domain Scores
Specification of a one-factor model yielded excellent indices of model-data fit (see Table 4) when the five item-domain scores were used as data points instead of item responses. For both samples, fit index values were excellent. The combination of item-level results with the domain-score level results affirms a one-factor model as suitable for the SIP.
Other Psychometric Information
Classical test theory results
Estimated internal consistency reliability (Cronbach’s alpha) of total SIP scores was very good for both samples. Each sample yielded an estimate of .91. That value is sufficiently high to warrant use of the SIP for making judgments about individuals, according to the guidelines of Cronbach (1990), and is consistent with those reported by others. Corrected item-total correlations yielded correlations ranging from .40 (Item 15) to .69 (Item 3) in Sample 1. Sample 2 yielded comparable values, from .39 (Item 15) to .68 (Item 12), which is unsurprising, given the similarity of factor structure information across samples reported earlier.
IRT results
Estimated item location and discrimination parameters for Sample 1 are reported in Table 3. Separate estimates were derived for Sample 2 and matched to a very high degree; R2 = .95 for location estimates across samples. SIP items differ both in location, with items such as Item 15 (had accident) and Item 9 (physical appearance harmed) being among the least likely to be endorsed, and items such as Item 6 (done impulsive things) and Item 5 (taken foolish risks) being the most likely to be endorsed. Discrimination, which relates to the certainty with which respondents can be distinguished at specific points along the scale continuum, varied considerably as well. Highly discriminating items included Item 3 (failed to do what’s expected), Item 11 (friendship/relationship damaged), and Item 12 (inhibited growth as person), whereas Item 15 was noteworthy for having much lower discrimination than other items. Items having low discrimination yield less information about a respondent’s location on the scale. We compared model fit for the simpler 1PL versus the 2PL, using the method of Kang and Cohen (2007), as implemented in the ltm package for R (Rizopoulos, 2006). For both samples, the likelihood ratio test was statistically significant (p < .001), favoring the 2PL model for use with the SIP items.
The match of item locations relative to participant scaled scores was such that the SIP scale appears to give reasonably good coverage of items to the range of scores observed on the SIP (see Figure 1). One obvious gap in the location of SIP items not matching well to the scaled scores obtained is for scaled scores below 0.3 (logits, or log units). Only four items have locations anywhere within the window of −1.3 through 0.3 (Items 4–6, 14). Better precision of respondent score estimates in that range could be possible with more items at the lower end of challenge (e.g., consequences that were more frequently endorsed). Similarly, there are no items having location higher than 1.11, corresponding to Item 15, which had the lowest discrimination. Thus, higher challenge consequences could also be helpful for the scale.
Other Validity Information
SIP scaled scores, derived from the IRT calibrations, were correlated with the Risky Driving index scores (Sample 1 only) as well as with the Alcohol Consumption and Alcohol Problems scores described earlier (see Table 5). In general, there was a noteworthy correlation of SIP scaled scores with Alcohol Problems scores (about .50 in both samples). Given that the nature of the DrInC/InDUC/SIP was to capture adverse consequences, this relationship is consistent with the purpose of the SIP. Correlation of SIP scores with Alcohol Consumption values was lower, .24–.25 in the samples. Risky Driving Behavior scores correlated lower yet with SIP scaled scores, r = .16. That the SIP scores correlate more strongly with alcohol use and problems than with a completely different domain of behavior (driving) makes intuitive sense. In our judgment, the pattern of relationships observed offers evidence for convergent (e.g., Alcohol Problems) and discriminant (e.g., Risky Driving) validity for the SIP scale.
Correlation of SIP Scaled Scores With Risky Driving, Alcohol Consumption, and Alcohol Problem Scores
DiscussionThe primary question prompting this research was that of the factor structure of the SIP. Both our literature review and our analyses persuade us that a one-factor model is the preferred structure. This is true whether working from item scores or the domain scores. Earlier studies favoring the more complex five-factor model (Kenna et al., 2005; Kiluk et al., 2013) showed only tiny differences in fit index values between competing models—not enough, in our opinion, to warrant the more complex model. Others attempting to confirm a five-factor model found little supporting evidence (Alterman et al., 2009; Forcehimes et al., 2007; Miller et al., 1995). Our study indicates that sample differences, drawing from the same population, have little impact on evidence for SIP structure, psychometrics, or scaling. Despite all the versions of the SIP (Allensworth-Davies et al., 2012), the behavior of the scale is remarkably consistent. That is not to say that the scale could not be improved, however.
Via IRT methods, we believe the scale could be improved for locating respondents with precision. Specifically, the SIP scale could be improved by adding items more likely to be endorsed by lower-scoring respondents, as well as items that would be endorsed by only higher-scoring respondents. Doing so would yield a SIP scale that could be helpful as a screener for “at-risk” clients, for predicting future outcomes, or to gauge response to treatment.
There are some limitations to acknowledge. First, we examined only the lifetime consequences version of the SIP. We believe this an issue more for IRT scaling of the items than for investigation of factor structure or criterion-related validity. Second, our investigation of structure yielded results for the competing models that were—like those of others—similar. What swayed our judgment were the considerations of parsimony and the congruence of item-level and subscore or domain-level support for the one-factor model. Third, our samples were, like the majority of the studies reviewed, predominately male. Only Gillespie et al. (2007), relying on college volunteers, had a sample that was predominately female. Although mean scores by sex have been reported (Anderson et al., 2001; Feinn et al., 2003; Miller et al., 1995), only the sample of Miller et al. (1995) was large enough to justify the exercise. Differential item functioning or structure by sex still remains to be addressed.
In sum, one can appreciate the versatility of the SIP over its 20-year plus existence. There is ample evidence for score reliability, usefulness with a variety of respondent populations, and construct and criterion-related validity. As a brief version of the DrInC/InDUC that is quickly administered, the SIP continues to serve ably the goals outlined by Miller et al. (1995).
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Submitted: October 11, 2016 Revised: December 6, 2016 Accepted: December 9, 2016
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Source: Psychology of Addictive Behaviors. Vol. 31. (1), Feb, 2017 pp. 110-116)
Accession Number: 2017-01384-001
Digital Object Identifier: 10.1037/adb0000249
Record: 136- Title:
- Psychometrics of shared decision making and communication as patient centered measures for two language groups.
- Authors:
- Alvarez, Kiara. Disparities Research Unit, Department of Medicine, Massachusetts General Hospital, Boston, MA, US
Wang, Ye. Disparities Research Unit, Department of Medicine, Massachusetts General Hospital, Boston, MA, US
Alegria, Margarita. Disparities Research Unit, Department of Medicine, Massachusetts General Hospital, Boston, MA, US, malegria@mgh.harvard.edu
Ault-Brutus, Andrea. Health Equity Research Lab, Cambridge Health Alliance, Cambridge, MA, US
Ramanayake, Natasha. Disparities Research Unit, Department of Medicine, Massachusetts General Hospital, Boston, MA, US
Yeh, Yi-Hui. Disparities Research Unit, Department of Medicine, Massachusetts General Hospital, Boston, MA, US
Jeffries, Julia R.. Icahn School of Medicine at Mount Sinai, Mount Sinai Hospital, New York, NY, US
Shrout, Patrick E.. Department of Psychology, New York University, NY, US - Address:
- Alegria, Margarita, MA General Hospital Disparities Research Unit, Department of Medicine, 50 Staniford Street, Suite 830, Boston, MA, US, 02114, malegria@mgh.harvard.edu
- Source:
- Psychological Assessment, Vol 28(9), Sep, 2016. Special Issue: Assessment in Health Psychology. pp. 1074-1086.
- NLM Title Abbreviation:
- Psychol Assess
- Page Count:
- 13
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- Psychological Assessment: A Journal of Consulting and Clinical Psychology
- ISSN:
- 1040-3590 (Print)
1939-134X (Electronic) - ISBN:
- 1-4338-9006-2
978-1-4338-9006-2 - Language:
- English
- Keywords:
- patient-centered, psychometrics, patient−provider communication, shared decision making, behavioral health
- Abstract:
- Shared decision making (SDM) and effective patient−provider communication are key and interrelated elements of patient-centered care that impact health and behavioral health outcomes. Measurement of SDM and communication from the patient’s perspective is necessary in order to ensure that health care systems and individual providers are responsive to patient views. However, there is a void of research addressing the psychometric properties of these measures with diverse patients, including non-English speakers, and in the context of behavioral health encounters. This study evaluated the psychometric properties of 2 patient-centered outcome measures, the Shared Decision-Making Questionnaire−9 (SDM-Q) and the Kim Alliance Scale−Communication subscale (KAS-CM), in a sample of 239 English and Spanish-speaking behavioral health patients. One dominant factor was found for each scale and this structure was used to examine whether there was measurement invariance across the 2 language groups. One SDM-Q item was inconsistent with the configural invariance comparison and was removed. The remaining SDM-Q items exhibited strong invariance, meaning that item loadings and item means were similar across the 2 groups. The KAS-CM items had limited variability, with most respondents indicating high communication levels, and the invariance analysis was done on binary versions of the items. These had metric invariance (loadings the same over groups) but several items violated the strong invariance test. In both groups, the SDM-Q had high internal consistency, whereas the KAS-CM was only adequate. These findings help interpret results for individual patients, taking into account cultural and linguistic differences in how patients perceive SDM and patient-provider communication. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Client Attitudes; *Decision Making; *Health Care Psychology; *Therapeutic Processes; Communication; Psychometrics
- PsycINFO Classification:
- Tests & Testing (2220)
Health & Mental Health Treatment & Prevention (3300) - Population:
- Human
Male
Female
Outpatient - Location:
- US
- Age Group:
- Adulthood (18 yrs & older)
Young Adulthood (18-29 yrs)
Thirties (30-39 yrs)
Middle Age (40-64 yrs)
Aged (65 yrs & older) - Tests & Measures:
- Shared Decision-Making Questionnaire 9 [Appended]
Kim Alliance Scale-Revised
Kim Alliance Scale [Appended] DOI: 10.1037/t32396-000 - Grant Sponsorship:
- Sponsor: Patient-Centered Outcomes Research Institute
Grant Number: CD-12-11-4187
Other Details: The Effectiveness of DECIDE in Patient Provider Communication, Therapeutic Alliance & Care Continuation study was supported by the research Grant
Recipients: No recipient indicated
Sponsor: National Institute of Mental Health, US
Grant Number: R01MH098374-03S1
Other Details: Research Grant
Recipients: Alvarez, Kiara - Methodology:
- Empirical Study; Quantitative Study
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- Accepted: Apr 27, 2016; Revised: Mar 24, 2016; First Submitted: Sep 1, 2015
- Release Date:
- 20160818
- Copyright:
- American Psychological Association. 2016
- Digital Object Identifier:
- http://dx.doi.org/10.1037/pas0000344
- Accession Number:
- 2016-40116-005
- Number of Citations in Source:
- 54
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- Database:
- PsycINFO
Psychometrics of Shared Decision Making and Communication as Patient Centered Measures for Two Language Groups
By: Kiara Alvarez
Disparities Research Unit, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, and Harvard Medical School
Ye Wang
Disparities Research Unit, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, and Harvard Medical School
Margarita Alegria
Disparities Research Unit, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, and Harvard Medical School;
Andrea Ault-Brutus
Health Equity Research Lab, Cambridge Health Alliance and Harvard Medical School
Natasha Ramanayake
Disparities Research Unit, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts
Yi-Hui Yeh
Disparities Research Unit, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts
Julia R. Jeffries
Icahn School of Medicine at Mount Sinai, Mount Sinai Hospital, New York, New York
Patrick E. Shrout
Department of Psychology, New York University
Acknowledgement: The Effectiveness of DECIDE in Patient−Provider Communication, Therapeutic Alliance & Care Continuation study was supported by the Patient-Centered Outcomes Research Institute (PCORI) research Grant CD-12-11-4187. Dr. Alvarez was supported by Research Grant R01MH098374-03S1, funded by the National Institute of Mental Health.
The Institute of Medicine (2001) asserted that the quality chasm in health care services could be closed if providers sought the patient’s perspective about their illness, shared power and responsibility, and improved their communication. Such shared decision making (SDM) and enhanced communication can improve the quality of behavioral health care (Patel, Bakken, & Ruland, 2008; Wills & Holmes-Rovner, 2006). SDM is “a form of patient-provider communication where both parties bring expertise to the process and work in partnership to make a decision” (Duncan, Best, & Hagen, 2010). SDM allows patients to report the “lived experience of their disorder” and the provider to bring to bear expertise about the “science informed processes of medical diagnosis and treatment” (Patel et al., 2008). Previous research shows that SDM augments patient satisfaction and positively corresponds with receipt of quality behavioral care (Swanson, Bastani, Rubenstein, Meredith, & Ford, 2007).
Nevertheless, a major obstacle for patients of color is that providers rarely receive training in how to motivate minority patients to voice their treatment concerns or preferences, nor engage them in the care-related decisions. Providers display fewer patient-centered behaviors (Cooper et al., 2003), are less receptive to question asking, and tend to demonstrate greater verbal dominance (Cooper et al., 2012) with minorities than with White patients. These actions often result in misunderstandings in communication, inadequate services, and failed treatment alliances (Kirmayer, Groleau, Guzder, Blake, & Jarvis, 2003). Minority patients may infer prejudice or perceive a negative attitude from their provider, thus reducing the likelihood that they perceive receiving quality care (Balsa & McGuire, 2003).
Language barriers can also be detrimental to patient-provider communication (Gilmer & Kronick, 2009); patients who do not speak the same language as their providers report worse outcomes (Pippins, Alegría, & Haas, 2007) and higher dropout rates (DuBard & Gizlice, 2008). It is fundamental that there be an understanding within patient-centered care to ensure that the patient can be actively engaged in health care decision making (Barry & Edgman-Levitan, 2012). Measuring shared decision making and communication between patients and providers requires consideration of three perspectives: the patient, the provider, and an unbiased outside observer who simply codes the verbal and nonverbal behaviors. Special attention is needed when developing patient-reported measures and assessing patients’ perspectives, because patients might not understand the intent of questions about shared decision making, particularly if they have not been exposed to them, and might be reluctant to appear critical of their providers. Moreover, the psychometrics of measures need to be reevaluated whenever new patient groups are studied, new languages are used in the assessment, or new clinical contexts are examined. In other words, a measure that is reliable and valid for measuring a patient-centered outcome for English-speaking patients may not be appropriate with Spanish-speaking patients (Mead & Bower, 2000). Yet there has been little research assessing the psychometric properties of patient-centered measures in behavioral health, especially among non-English-speaking patients. This study addresses these two gaps in the literature. Using two measures to evaluate patient-centered outcomes, level of shared decision making (SDM) and quality of patient−provider communication (KAS-CM), we assess the psychometric properties of these measures among English and Spanish-speaking patients receiving behavioral health treatment.
Shared Decision MakingOne of the recent criticisms in the way that behavioral health care providers practice is their lack of shared-decision making (SDM) between patients and providers (Patel et al., 2008). There is some evidence of discomfort expressed by minority patients and providers regarding SDM and race due partly to the power that inherently exists in a patient-provider relationship (Peek et al., 2009). Providers are perceived to hold immense power, authority, and knowledge over their patients. This unequal distribution of power may be exacerbated by societal and racial hierarchies, especially between African American patients and white providers (Peek et al., 2009). However, there is less research regarding how minority status might influence SDM in behavioral health care. One reason cited as the cause for an inadequate usage of SDM in behavioral health care settings has been the inability of patients to engage when suffering from severe behavioral health conditions such as schizophrenia (Hamann, Leucht, & Kissling, 2003; Hamann et al., 2009). Studies have shown that some patients with these disorders often report being pressured by their providers to agree to certain treatment recommendations without sufficient involvement in the decision (Quirk, Chaplin, Lelliott, & Seale, 2012). As a consequence of the authority and power that behavioral health care providers hold in these interactions, increasing attention has been given to patients with behavioral health conditions to help them better navigate and advocate for themselves during SDM interactions with their providers (Polo, Alegría, & Sirkin, 2012). Patients with behavioral health conditions have been noted to want greater access to information on diagnoses, symptoms, medications, and clinician’s rationale for treatment decisions (Patel et al., 2008). In a recent study involving patients with a variety of behavioral health disorders ranging from schizophrenia to depression, focus groups of providers and patients noted that patients’ openness with providers combined with active provider involvement during consultation sessions provided a safer and more comfortable environment that facilitated SDM (Hamann et al., 2015).
Patient−Provider CommunicationPatient−provider communication is a key element impacting the outcomes of clinical care. Good communication between patients and providers has been associated with improved health outcomes (Stewart, 1995) and increased patient satisfaction (Clever, Jin, Levinson, & Meltzer, 2008). One meta-analysis found a 19% higher risk of nonadherence when providers communicated poorly, and indicated that physician communication training resulted in 1.62 times greater increase in the odds of patient adherence when compared to no physician training (Zolnierek & DiMatteo, 2009). However, fostering open communication between patients and providers has historically been a challenge. Patients tend not to state their concerns or ask necessary questions during medical visits (Beisecker & Beisecker, 1990; Korsch, Gozzi, & Francis, 1968; Roter et al., 1997). Minority patients in particular are less likely to engage in collaborative communication with providers (Alegría et al., 2008; Johnson, Roter, Powe, & Cooper, 2004) and to report lacking needed information about treatment (Levinson, Stiles, Inui, & Engle, 1993; Rooks, Wiltshire, Elder, BeLue, & Gary, 2012). Furthermore, once patients are trained to more actively engage in the clinical encounter, providers are not necessarily receptive to improving their own communication (Alegría et al., 2008). Providers have been found to be more verbally dominant and less patient-centered with African American patients, with one study finding that physicians talked 43% more than African American patients during the clinical encounter as compared to only 24% more than White patients (Johnson et al., 2004). Studies of Latino patients have found that a perception of the provider as an authority figure leads to less comfort taking an active role in the encounter and less likelihood the patient will engage in collaborative communication with a provider (Cortes, Mulvaney-Day, Fortuna, Reinfeld, & Alegría, 2009). Communication practices and perceptions on the part of patients ultimately influence the interpersonal dynamic of patient-provider encounters.
Poor functional health literacy has also been found to impact oral communication between patients and providers. English and Spanish-speaking patients with diabetes with poor functional health literacy reported significantly worse communication than those with adequate functional health literacy, particularly in the areas of clarity of the discussion, explanation of health condition, and explanation of processes of care (Schillinger, Bindman, Wang, Stewart, & Piette, 2004). Thus, the experience of patient−provider communication encompasses both functional (e.g., provision of key information) and affective (e.g., rapport between provider and patient) aspects of the encounter. Patient−provider communication is posited to impact health outcomes indirectly via its influence on patient and provider behaviors that directly impact health outcomes, such as identifying the correct diagnosis and treatment plan and increasing patient commitment to treatment (Street, Makoul, Arora, & Epstein, 2009), and can be considered a mechanism through which shared decision making takes place.
Measures The SDM-Q
The SDM-Q (also referred to as SDM-Q-9 in prior studies) was developed as a brief version of a longer questionnaire (Simon et al., 2006). The SDM-Q (see Appendix A) evaluates patient-reported SDM (Kriston et al., 2010) from a patient-provider visit based on the patient’s perception of nine steps deemed essential to SDM in the clinical encounter: disclosing that a decision needs to be made, establishing the equality of both parties, presenting treatment options, informing on benefits and risks of treatment, investigating patient’s understanding and expectations, identifying both parties’ preferences, negotiating, reaching a shared decision, and arrangement of follow-up. Representative items include the following: My provider wanted to know exactly how I want to be involved in making the decision. The nine items are rated on six 6-point scale ranging from 0 (completely disagree) to 5 (completely agree). The sum of the rating ranges between 0 and 45, but this is conventionally transformed to a scale that ranges from 0 to 100, where 0 indicates the lowest level and 100 the highest.
The psychometrics of the SDM-Q have been studied using a sample of German patients who reported on their interaction with primary care providers (Kriston et al., 2010). The scale had a Cronbach’s alpha of .98 in a sample used to develop the measure and an alpha of .94 in a separate cross-validation sample of German primary care patients. A factor analysis was conducted and a one-factor solution was found. The scale was translated into English and then was administered to a national U.S. sample of adults ages 21 to 70, who reported about decision making with their medical providers. Again Cronbach’s alpha was .94 (Glass et al., 2012). The measure has also been studied in Spanish and Dutch primary care samples (De Las Cuevas et al., 2014; Rodenburg-Vandenbussche et al., 2015). To our knowledge, the psychometrics of the scale have not been studied for patients who see a behavioral health provider.
The Kim Alliance Scale−Communication Subscale (KAS-CM)
The Kim Alliance Scale (KAS; Kim, Boren, & Solem, 2001) was developed to measure therapeutic alliance between patient and provider from the patient’s perspective. It includes four subscales designed to measure aspects of alliance: Communication, Collaboration, Integration (equalizing power differential between provider and client), and Empowerment (Kim et al., 2001). In this report we focus on the communication subscale, the KAS-CM, which measures communication from the perspective of the patient (see Appendix B). The KAS-CM is intended to measure both instrumental and affective attributes of the patient-provider encounter such as bonding/rapport, provision of information, and expression of concerns. Sample items include I have a good rapport with my provider, I feel my provider gives me enough information and I can express negative feelings freely. The responses to this 11-item measure are provided on a 4-point Likert scale ranging from 1 (never) to 4 (always), with higher scores indicating higher quality of communication.
The KAS, including KAS-CM, was developed by nurse researchers by developing and rating items, assembling them into a scale based on conceptual consistency with the literature, and rating the items for content validity (Kim et al., 2001). The resulting scale was then validated using a convenience sample of 68 nurses (68% Caucasian, 88% female, and 67% with a master’s degree or more) who reported having had encounters as a patient in the prior 2 years. A factor analysis was conducted in which the initial factor in an unrotated factor solution was examined and items loading at .40 or above were retained within each factor. This resulted in a 30-item KAS (α = .94) which included an 11-item communication subscale (α = .87) with item-total correlations ranging from 0.38 to 0.73. The KAS subscales had high positive correlations with each other (r ranging from 0.74 to 0.86, p < .01). A later validation of the KAS used a 16-item revised version, the KAS-R, with only four of the original KAS communication items included (Kim, Kim, & Boren, 2008). To our knowledge, further psychometric analyses of the KAS or KAS subscales have not been conducted and the scale’s properties have not been investigated among behavioral health patients.
Method Study Patients and Setting
We recruited 351 patients from September 2013 through September 2015 through direct contact in waiting rooms at nine community outpatient behavioral health clinics in Massachusetts. Five clinics are a part of a public safety-net hospital system, two clinics are a part of private hospital system, and the remaining two clinics are a part of private, nonprofit community health centers. These clinics generally serve a high proportion of low-income minority patients. Clinics offered individual and group therapy for a range of mental health and substance abuse issues. Behavioral health services offered in the clinics varied, but included psychotherapy, cognitive-behavioral therapy, psychiatric medication management, substance abuse treatment, and case management. The study was presented to patients as helping them to ask questions and improve communication between the patient and their provider. Eligible patients were between the ages of 18 and 80, spoke English, Spanish or Mandarin, and were enrolled in individual behavioral health care treatment (e.g., psychotherapy or psychopharmacology). Exclusion criteria for patients included screening positive for mania, psychosis, or active suicidality. Patients over the age of 65 were assessed with a brief cognitive function screen and excluded if cognitive impairment was indicated. Based on these criteria and after providing written consent, 271 eligible patients were enrolled in the study. The sample used in this paper includes 160 English-speaking patients and 79 Spanish-speaking patients who enrolled in the study and completed the questionnaire, for a total of 239 patients in the study sample.
Any provider delivering behavioral health services (e.g., psychotherapy, psychopharmacology, and counseling) at participating clinics, with a minimum caseload of 6–8 patients, was eligible to participate in the study. Providers were recruited through presentations of the study at clinics by project staff. Forty-six providers agreed to participate. The study was approved by the Institutional Review Board of the Cambridge Health Alliance.
Research Procedures
Bilingual research assistants in each clinic approached patients prior to their appointments with their behavioral health providers and invited them to participate in the study in their preferred language. Since the screening tool included a suicidality assessment, patients provided written consent (including language about safety protocols) prior to being screened. Ineligible patients were compensated $10 for their time. Eligible participants who consented to participate in the study completed a 1- to 1.5-hr research interview in which they provided demographic information and completed self-report measures on their satisfaction with care, decision-making strategies, and general psychosocial well-being. Provider participants also provided written consent, and completed a 45-min self-report assessment on corresponding measures. All interviews were audio-recorded. Patients received a $25 gift card for either a grocery store or discount retailer as compensation for their time. Providers received a $50 general gift card as compensation.
Process of Translation of Instruments
The Spanish translation of the patient SDM-Q measure was performed by De Las Cuevas and colleagues (2014), who translated the measure to Spanish from German using the five-step methodology of cross-cultural adaptation of self-reported measures (Beaton, Bombardier, Guillemin, & Ferraz, 2000). This process includes having two independent translators, both competent in German and Spanish, translate the questionnaire from German into Spanish and then back-translated. This process also includes assessing the translated questionnaire for cultural appropriateness, content validity testing, and equivalence testing.
The English version of the KAS-CM used in this study was translated to Spanish and back-translated into English by trained Spanish-speaking research assistants and the study’s principal investigator using the Matías-Carrelo et al. (2003) process to achieve semantic, construct, and technical equivalence. The Spanish translation was reviewed by the principal investigator and team to ensure that the translation maintained a focus on the constructs of the English measure. The team reviewed the content equivalence of translated items to ensure they remained relevant to Spanish speakers. The Spanish translations were also pilot tested with Spanish-speaking behavioral health patients to confirm whether the translations were understood by the population with whom the measure would be used. After pilot-testing with Spanish-speaking patients, the principal investigator and team simplified the translations in order to meet the literacy level of patients.
Study Design
Data for the current study came from an ongoing study (NCT01947283) assessing the effectiveness of psychoeducational interventions for patients and providers on question-asking and shared decision making in treatment. All of the providers participating in the study completed the research interview in English. For the purposes of this paper, we excluded the Mandarin patient sample due to the small number of participants available for psychometric testing of the measures. Information regarding the Mandarin sample is available from the authors on request.
Statistical Analysis
We describe sociodemographics (age, gender, education, socioeconomic status, and country of origin) for the full sample of patients and providers and for two patient subgroups, disaggregated by the language version of the measures (English or Spanish). We also provide descriptive statistics on household income, type of behavioral health services received in the past, and the average length of time the patient has seen their behavioral health provider for the patient sample only. We next report statistics on the distributions of the items in the SDM-Q and KAS-CM in each scale in the combined sample, and each language sample separately.
Our psychometric analysis followed the procedure outlined by (Gregorich, 2006), which focuses on the question of whether there is measurement invariance across English and Spanish samples. Measurement invariance, or equivalence, of a measure indicates that the same construct is being measured across different groups. Systematic testing of measurement invariance across at least two distinct groups (in this case, two language groups) allows for analysis of whether the measure and its component items measure the same construct in the two groups. When measurement invariance is established, differences in scores between the two groups can be interpreted as reflecting actual differences in the construct being measured (rather than reflecting bias due to language, cultural, and other group differences). We used confirmatory factor analysis to systematically assess configural invariance (the nature of the factor structure in each group), metric invariance (the equivalence of factor loadings after allowing latent variable variances to vary), strong invariance (the equivalence of item means, after allowing latent variable means to vary), and strict invariance (the equivalence of the item error variation). If strong or strict invariance is supported, it is appropriate to compare groups with the simple summed item responses. We carried out these analyses sequentially, first for SDM-Q and then for KAS-CM.
Mplus Version 7.4 (Muthén & Muthén, 1998-2015) was used to fit all the measurement invariance models with the two-group approach. When it was appropriate to treat the item responses as continuous variables, we used a robust maximum likelihood estimation method (MLMV) and when it was necessary to treat the item responses as categorical, we fit a probit item response model with weighted least squares (WLSMV). Mplus was used to make adjustments to the chi-square difference tests that were used to compare the nested measurement equivalence models. The robust estimation methods required listwise deletion of missing data (SDM) or pairwise deletion (KAS-CM), but the amount of missing data was minimal. Missing data in SDM-Q were less than 9% and less than 2% in KAS-CM. Data were sometimes missing due to patients correctly skipping a question because it was not applicable to them or because the patient felt that a particular item in the measure was not applicable to them. When we computed simple summed scale scores for the correlational analysis, we prorated the scaled measure of SDM-Q and KAS-CM, which essentially imputes the missing item to have the mean of the available items within the scale. When more than two items in a scale were missing, we assigned a missing value to the scale score. This entailed dropping 2 cases from the SDM-Q correlational analysis.
ResultsTable 1 shows the demographic characteristics of the English- and Spanish-speaking patients, along with tests of group differences. The average length of time a patient saw their behavioral health provider (i.e., the provider who participated in the study) was 13 months. A little over 80% of the sample of patients lived near or below the U.S. poverty level. Roughly 76% of patients were hospitalized in the past for behavioral health issues and roughly 76% had past emergency room use for behavioral health issues. About 23% of patients used a prescription drug in the past for behavioral health issues. Compared with the English-speaking patients in the sample, there were more participants in the Spanish-speaking sample who were female, between ages 50 and 64, who had less education, and who were not employed. The majority of Spanish-speaking participants reported being from Central and South America, followed by those who reported being from the Caribbean islands. Table 2 summarizes the sociodemographic information of the providers in the study, who were mostly non-Latino White, between ages 18 and 34, and female. The providers were mostly psychologists, psychiatrist, and social workers.
Demographic Characteristics of Patients for the Overall Sample and Two Language Groups
Demographic Characteristics of Provider Participants
Table 3 shows the means, standard deviations, skewness/kurtosis, and minimum and maximum values for the SDM-Q and KAS-CM items in the total sample and each of two subgroups. For all items pertaining to SDM, participants in the Spanish-speaking sample reported higher levels of shared decision making than the English-speaking sample (Spanish M = 82.0; English M = 72.7; p < .001). The distributions of the responses generally had negative skew, with the majority of patients reporting 4 or 5 to the items. Patients reported the highest ratings for SDM 5 (My provider helped me understand all the information; M = 4.28) and the lowest ratings for SDM 1 (My provider made clear that a decision needs to be made; M = 3.23).
Descriptive Statistics of SDM-Q and KAS-CM Item Scores for the Overall Sample and Two Language Groups
The distributions of the KAS-CM were even more skewed than the SDM-Q items. Although patients could use a scale ranging from 1 to 4, virtually all the means were larger than 3, and six of the 11 items had means greater than 3.9 in both English and Spanish samples. This pattern suggests that the primary decision that patients made regarding these items was whether to report a perfect communication score of 4 or a score that was less than perfect. The English-speaking sample reported slightly lower ratings (relative to the Spanish-speaking) of patient−provider communication on two items: KAS-CM 4 (My provider spends lots of time educating me) and KAS-CM 7 (I feel my provider gives me enough information). The mean KAS-CM score was significantly lower for the English-speaking sample (Spanish M = 42.8, English M = 41.7, p < .001).
Measurement Invariance: SDM
We tested measurement invariance levels for SDM-Q by first assessing configural invariance across the English and Spanish samples, using exploratory factor analysis (EFA) and then we tested metric, strong and strict invariance models using confirmatory factor analysis. The initial one factor EFA revealed that the first item (My provider made it clear that a decision needed to be made) was unrelated to the general SDM dimension in the Spanish sample. This problem had also been reported in a different Spanish-speaking sample (De Las Cuevas et al., 2014), and so we removed this item to enable further tests of configural invariance.
After dropping the first SDM item, the pattern of eigenvalues (“scree plot”) in both English and Spanish samples suggested one factor, as shown in Figure 1. All eight items had standardized loadings of .45 to .90 in magnitude in both samples. The fit of the one factor model, however, was not good according to conventional standards for formal fit statistics (statistics for English and Spanish samples are respectively, RMSEA bounds (0.15 to 0.21; 0.15 to 0.23), TLI (0.79; 0.86), CFI (0.85, 0.91). Hu and Bentler (1999) recommend RMSEA values less than .06 and TLI and CFI values greater than .95. We explored two and three factor models, and found that these had better fit statistics, but there was no coherent interpretation of the factors. For example, the two-factor solution resulted in only one item (SDM 9: My provider and I reached an agreement on how to proceed) loading onto a second factor. We also explored one factor models with correlated residuals, and were able to improve the fit, although the values continued to be worse than the recommended values (for the full sample, RMSEA bounds 0.04 to 0.12, TLI 0.88, CFI 0.93). When criteria for model selection disagree, Marsh, Hau, and Wen (2004) and Gregorich (2006) recommended taking theoretical and substantive concerns into account. For this reason, we continued the investigation of item invariance in the SDM with a one factor model, but we carried out sensitivity analyses with the one factor model with correlated residuals and found similar results. The one factor model allows us to adjust, at least approximately, for latent differences in English-speaking participants and Spanish-speaking participants when considering the item loadings, intercepts and residual variances. The one-factor solution was also consistent with prior research indicating that the SDM-Q scale represents a single latent construct of shared decision-making.
Figure 1. Eigenvalue plots for Shared Decision-Making Questionnaire−9 (SDM) factor analysis (scree plot).
We report the standardized factor loadings from one-factor models that were fit using robust maximum likelihood estimation in Table 4 (combined sample, English-language sample, and Spanish-language sample). For SDM-Q, the loadings ranged from .88 to .52, with a median loading of .72. The item with the highest loading was My provider and I thoroughly weighed the different treatment options, which is a canonical expression of shared decision making. The lowest loading item was the second, which was My provider wanted to know exactly how I want to be involved in making the decision. This pattern was seen in both the English- and Spanish-speaking samples.
Standardized Factor Loadings for Single Factor Models of SDM-Q and KAS-CM for 2 Language Groups
As seen in Table 5, we found evidence that the SDM-Q factor loadings were equivalent across groups (metric invariance; indicated in the table by a nonsignificant chi-square difference test comparing nested models) and that the individual item means were equivalent across groups, after adjusting for latent variable differences (strong invariance). In other words, the Spanish-language sample reported overall higher levels of shared decision-making, but this was not due to differential item functioning. Variation of item errors (strict invariance) was not equivalent across groups. The summed item responses can therefore be used to compare groups.
Factorial Invariance Tests
Internal Consistency (Cronbach’s α) of SDM-Q and KAS-CM for 2 Language Groups
Measurement Invariance: KAS-CM
Just as we did for SDM, we first examined configural invariance issues in KAS-CM. Because the variation in responses to the KAS-CM items was concentrated in the distinction between less than best and best response, we carried out the factor analysis treating all 11 items as binary. We used Mplus Version 7.4 to fit a binary item factor model with a probit link function, which essentially fit tetrachoric correlations based on the binary items. These correlations represent the associations of latent continuous processes reflecting the likelihood of endorsing the KAS-CM items with the best possible endorsement. A one-factor model for the KAS-CM demonstrated good model fit according to fit statistics for the Spanish sample (RMSEA bounds 0 to 0.06, TLI 1.02, CFI 1.00), but good to poor model fit depending on the fit statistics interpreted for the English sample (RMSEA bounds 0.03 to 0.07, TLI 0.88, CFI 0.91). Again, a two-factor solution provided slightly improved fit, but the results were not easily interpretable (e.g., two of the items had equivalent loadings on both factors) and did not result in a clinically meaningful scale. Like the SDM-9, the one factor model for the KAS-CM accounted for the majority of the shared variance (see Figure 2). Interpreting theoretical, practical, and statistical significance of the potential solutions, we selected a one-factor solution for subsequent analyses of measurement invariance. We also carried out sensitivity analyses with the one factor model with correlated residuals, finding good fit across groups and similar results to the original one-factor solution on subsequent tests of measurement invariance.
Figure 2. Eigenvalue plots for Kim Alliance Scale−Communication subscale (KAS-CM) factor analysis (scree plot).
The factor loadings for the one-factor solution ranged from .94 to .38 with a median of .68. The highest loading was for KAS-CM 6 (My provider listens to me without judgment) which is prototypic of effective communication. The lowest loading was for KAS-CM 8 (My provider does not allow me to state my opinion) which is one of the two reverse coded items. The loadings for the Spanish language sample appeared to be somewhat larger (median .81 vs. .64 in the English language sample) since the residual variances were smaller in the Spanish sample for all items.
We continued measurement invariance analyses and first found equivalent factor loadings across English and Spanish language groups (metric invariance). However, we next found that item means were not all equivalent across the groups (strong factorial invariance was not supported), indicating that influences other than the latent factor of patient−provider communication might be causing higher or lower item responses for one group when compared to the other. In order to identify if particular items might be affected more than others, we examined differences in item thresholds and found three large differences. Even adjusting for the latent variable, Spanish-speaking patients were more likely to endorse KAS-CM 4 (My provider spends a lot of time educating me), than English-speaking patients, whereas English speakers were relatively more likely to endorse KAS-CM 1 (Plain/clear language is used by my provider) and KAS-CM 8 (My provider does not allow me to state my opinion, reverse-coded) than Spanish speakers with the same level of overall communication. We hypothesized that these items might account for the systematic difference in item means across groups, and proceeded with an additional test of partial strong invariance in which we found that the means for the remaining items are equivalent across groups once Items 1, 4, and 8 are excluded. The summed item responses of the 8 remaining items can be used to compare groups.
We next estimated Cronbach’s alpha for each of the scales (see Table 6). The alpha for SDM-Q is 0.89 for the combined sample, and was similar in the individual samples (English, α = .88; Spanish, α = .90). Alphas were lower for KAS-CM, with items scored as binary (total sample α = .66; English, α = .61; Spanish, α = .78). Correlation between SDM-Q and KAS-CM revealed that the two constructs of shared decision making and patient−provider communication are related, r = .39, p < .001. The correlation is almost the same in the English-speaking sample, r = .40, p < .001 and lower in the Spanish-speaking group, r = .23, p < .05; however, this difference is not statistically significant.
Internal Consistency (Cronbach’s α) of SDM-Q and KAS-CM for 2 Language Groups
We investigated the convergence of the patient’s reported SDM measure with the provider’s reported SDM on the equivalent scale, the SDM-Q-Doc (Scholl, Kriston, Dirmaier, Buchholz, & Härter, 2012). In parallel, we examined the convergence of the patient’s reported KAS-CM with the provider’s report using a modified KAS-CM (the one item that was added for the provider measure was not included). In both cases, the correlations between providers’ reports and patients’ reports appeared low for the overall sample (r = .04 for SDM-Q and r = .02 for KAS-CM). The trend was for patients to report higher ratings than their provider did in the same encounter.
DiscussionMinority patients face new demands connecting with providers with different customs, values and experiences, and addressing these challenges by improving SDM and patient-centered communication could lead to better quality care. Our findings suggest that a revised eight-item version of the SDM-Q (first item deleted) performs well in both English and Spanish with behavioral health patients, and is a useful patient-centered measure for clinical practice with both Spanish and English-speaking populations. We also found that the KAS-CM is a promising measure for collecting information about patient-provider communication among behavioral health patients, but that in our sample most respondents were near the ceiling of perceived good communication. We identified three KAS-CM items that should be excluded from group comparisons of Spanish- and English-speaking patients due to differential item functioning across groups. Given that patient perceptions of the patient-provider encounter have been linked to overall health and behavioral health outcomes (Little et al., 2001; Stewart et al., 2000), use of these measures is a time-efficient way to collect data necessary to improve the clinical encounter for both English- and Spanish-speaking patients.
Psychometric results for the SDM-Q are consistent with what has been previously reported in the literature for English-speaking, Dutch, German, and Spanish primary care patients (De Las Cuevas et al., 2014; Glass et al., 2012; Kriston et al., 2010; Rodenburg-Vandenbussche et al., 2015). Similar to these studies but with a more diverse population, the factor analyses for our English- and Spanish-speaking samples indicated a one-dimensional structure for the patient’s measure. Our finding that Item 1 on the scale did not have a significant loading for Spanish-speaking patients and that an eight-item version of the SDM-Q demonstrated better fit is consistent with the results in a Spanish sample of primary care patients from Spain (De Las Cuevas et al., 2014). Factor loadings were above 0.48 for all SDM-Q items in both languages. Our results suggest that the scale has factorial validity in both languages, and that Item 1 (My provider made clear that a decision needs to be made) does not correlate well with the overall scale for Spanish-speaking patients, independently of site or sector of care. The finding that this item showed the lowest mean score when compared to other items on the scale was not surprising, given that patients may not view the clinical encounter as a setting where they can make decisions about their care. As has been previously described in the literature (Hamann et al., 2015), patients, particularly non-English-speaking patients, rarely get an opportunity to evaluate decisions in care, and even the conceptualization of sharing power can seem alien in behavioral health care visits (Cortes et al., 2009). This item also seems to diverge from the others in the scale given that it does not describe a decision-making process but rather a direct communication from a provider.
Nevertheless, Spanish-speaking patients rate their providers higher in terms of overall SDM as compared with their English counterparts. This might be tied to a more pronounced barrier to access to behavioral health care for Spanish-speaking patients (DuBard & Gizlice, 2008), who might find that having a provider who offers respect and warmth is a more salient feature of communication than attempting SDM with them (Eliacin, Salyers, Kukla, & Matthias, 2015). As a consequence of the power that behavioral health care providers hold in these interactions and the cultural value of respeto (respect) for Spanish-speaking patients, some behavioral dimensions of SDM (like asking the patient about what should be covered in the session or what are his or her options in treatment) might be experienced and rated differently depending the cultural values and expectations for empowerment of diverse patients (Cortes et al., 2009).
The SDM-Q demonstrated strong factorial invariance, leading us to conclude that the summed item means of the SDM-Q scale can be compared across English and Spanish-speaking patient groups. The reliability of the SDM patient measure was also confirmed, with acceptable alphas in both languages and comparable results to those observed for English-speaking primary care patients (Glass et al., 2012). This seems to expand the generalizability of the SDM measure for behavioral health patients in both English and Spanish. Yet there was no convergent validity with provider ratings, as illustrated by the low correlations of the patient SDM measure with the provider SDM measure for the same encounter. These differences of perspectives reinforce the importance of having a patient’s perspective separate from that of the provider’s when evaluating SDM in the clinical encounter. Several explanations could account for our results. It is possible that patients who would rate their providers low on SDM quickly drop out of care and were not represented in our study. Alternatively, these patients may stay and accept what is offered, without discriminating on specific behavioral dimensions of shared decision making. Some prior data seem to indicate the tension inherent between the patient and the provider’s view of what constitutes optimal shared decision-making in the clinical encounter. For example, one study found that while both patients and providers were in favor of collaborating in the decision-making process, patients viewed providers as responsible for final decisions in treatment, and vice versa (De Las Cuevas, Rivero-Santana, Perestelo-Pérez, Pérez-Ramos, & Serrano-Aguilar, 2012). The low correlation between measures by providers and patients in our study indicate that providers perceived less decision-making and communication in the same encounter when compared to patients, which may reflect differing views of what specific behaviors constitute patient-centered care.
The KAS-CM subscale also demonstrated a one-factor structure of the underlying construct for the total sample and for both language groups, and partial strong factorial invariance, indicating that the summed item means of eight of the scale items can be compared across English and Spanish-speaking patient groups. While the KAS-CM has not been studied as a stand-alone scale, this finding is consistent with the conceptual framework of the KAS as containing a separate communication construct (Kim et al., 2008). Similar to results of Kim and colleagues (2008), where 58% of patients gave perfect scores on the Communication subscale, we also encountered highly skewed endorsements of KAS-CM items from behavioral health patients, indicating that patients tend to rate their providers highly on communication. This led us to modify the measure as a binary item scale rather than treat item responses as continuous in the outcome measure. While we consider that it is possible that our ceiling effect is related to our average patient having seen their provider for more than a year at the time of data collection, we also note that expanding the number of response options for the KAS-CM may provider more variability in responses and improve the scale’s psychometric properties.
On basis of our results, we presume that the KAS-CM subscale represents a unidimensional construct, with adequate reliability in both languages. The factor loadings for the KAS-CM subscale were acceptable (greater than 0.4) for both Spanish- and English-speaking patients. However, we find that three items on the KAS-CM may reflect differences in cultural or language characteristics of the two groups, rather than true differences in latent construct of patient-provider communication. As a result, we see loadings that are different across the two language subgroups, emphasizing how the experience of the communication might vary by language groups. For example, Spanish-speaking patients reported more frequently that their provider spent time educating them and less frequently that their provider allowed them to state their opinion. English-speaking patients reported more frequently that their provider uses plain/clear language.
One potential way of explaining this finding is that communication in health care visits itself varies by language of the patient (Alegría et al., 2013). Perhaps bilingual U.S. providers treating monolingual Spanish-speakers have more difficulty using easily understood wording in behavioral health sessions, due to less proficiency in the language when compared with the patient or to differences in colloquial terms depending on the patient’s country of origin. A more thought-provoking explanation might be that what is valued of communication diverges by language or cultural group (Alegría, Sribney, Perez, Laderman, & Keefe, 2009; Mulvaney-Day, Earl, Diaz-Linhart, & Alegría, 2011). In the case of the KAS-CM, it may be that cultural expectations about the provider’s role influence patient’s ratings of individual items but not of the patients’ overall perception of their level of communication. In the case of whether or not a provider spends time educating the patient, this connotes a one-way communication from the provider (as expert) to the patient (as student). In a qualitative study of communication preferences among patients with depression receiving outpatient behavioral health care, the overall themes expressed by African American, Latino, and White patients did not differ, and centered on good relationships in which patients felt listened to and felt understood (Mulvaney-Day, Earl, Diaz-Linhart, & Alegría, 2011). However, the descriptions and understandings of how these qualities were expressed in the clinical encounter were different across the different ethnic and racial groups. Notably with regards to our findings, Latino patients preferred a directive, authoritative approach from their providers when compared to African American and non-Latino White respondents, who preferred that providers actively work to diminish the power differential inherent in the provider−patient relationship.
This study has several limitations. Patients represented in the study sample agreed to participate in an intervention study aimed at helping them ask questions and make decisions with their providers; thus, the study sample may have been more interested than the average patient in the constructs measured by the study. The sample is predominantly minority patients receiving behavioral healthcare in safety-net clinics, which allows us to present novel results regarding the validity of measures in this population and care setting; however, results may differ in other patient populations. Additionally, the ceiling effect observed on the KAS-CM indicates that a measure with additional response options might better capture variability among patient perceptions of communication with providers. Another limitation has to do with the limited Mandarin speaking sample participating in the study, restricting the possibility of estimating the psychometric properties with this group. The lack of instruments to evaluate the convergent validity of these measures also is considered a limitation. Future studies may further address the reasons why patient and provider reports were not significantly correlated, perhaps by completing an item analysis to identify specific behaviors viewed differently by the two groups.
It is important to note that the one-factor models selected for each measure demonstrated poorer fit than potential two- and three-factor models; however, we took into account theoretical and practical considerations along with statistical considerations in selecting the one-factor models. We did find improved fit when allowing some residuals to correlate, indicating the influence of background factors other than the latent constructs of SDM and KAS, respectively, on these variables. Given that our question of interest was the evaluation of whether the scales can be accurately used in clinical practice with the two language groups, we concluded that the factor structure for each scale was sufficiently established to allow for comparison across groups and recommendations for clinical practice.
Not withstanding these limitations, our study emphasizes the importance of evaluating the psychometric properties of patient-centered outcomes with diverse patients and in different languages. Our results provide evidence of the suitability of revised eight-item versions of both the SDM and the KAS-CM for identifying patient perceptions of shared decision making and patient-provider communication among both Spanish- and English-speaking respondents receiving behavioral health care. The findings are also suggestive of the need to carefully examine how self-report measures perform among non-English-speaking patients, in order to ensure that self-report measures used to track patient outcomes in health services research and clinical practice accurately reflect the experiences of diverse patient populations.
Footnotes 1 As a sensitivity check of the effects of listwise deletion of missing data, we redid the analyses using MLR estimation in MPlus, which allows the partially incomplete observations to inform an EM algorithm estimate of the sample variance covariance. The conclusions about measurement invariance were identical to those presented in the body of the article.
2 To deal with these extreme distributions statistically, we recoded the KAS-CM to be binary (≤3 = 0, 4 = 1) and used statistical methods for binary responses in the factor analysis.
3 In the strong invariance model, the Spanish group had a significantly higher latent mean of communication than the English group (mean difference(se) = 1.13 (.44), Cohen’s d = 1.13).
4 In practice, we conducted the tests of partial strong invariance systematically by first freeing the threshold for Item 4 (the item with the largest difference), then also freeing Item 1, then also freeing Item 8. The difference in chi-square was significant for the first two models and not significant for the last one, in which the thresholds for Items 4, 1, and 8 were all free and the remaining item thresholds constrained to be equal.
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APPENDICES APPENDIX A: SDM-Q
| Item | English | Spanish |
|---|
| Note. SDM-Q = Shared Decision Making Questionnaire-9. This measure was introduced with the following verbal introduction (English version): “Now I’m going to ask you some questions about how decisions were made with your mental health care or substance abuse provider during the last visit you had with your provider. If you didn’t see your provider today, think back to the last appointment you had with your provider. For each statement, please indicate how much you agree or disagree.” |
| SDM1 | My provider made clear that a decision needs to be made. | Mi proveedor me dijo expresamente que debía tomarse una decisión. |
| SDM2 | My provider wanted to know exactly how I want to be involved in making the decision. | Mi proveedor quería saber exactamente cómo me gustaría participar en la toma de decisiones. |
| SDM3 | My provider told me that there are different options for treating my behavioral health or substance abuse condition. | Mi proveedor me informó de que existen distintas opciones de tratamiento para mi problema de salud. |
| SDM4 | My provider explained the advantages and disadvantages of the treatment options. | Mi proveedor me explicó con exactitud las ventajas y desventajas de las distintas opciones de tratamiento. |
| SDM5 | My provider helped me understand all the information. | Mi proveedor me ayudó a entender toda la información. |
| SDM6 | My provider asked me which treatment option I prefer. | Mi proveedor me preguntó qué opción de tratamiento prefiero. |
| SDM7 | My provider and I thoroughly weighed the different treatment options. | Mi proveedor y yo valoramos con detenimiento las distintas opciones de tratamiento. |
| SDM8 | My provider and I selected a treatment option together. | Mi proveedor y yo elegimos juntos/as una opción de tratamiento. |
| SDM9 | My provider and I reached an agreement on how to proceed. | Mi proveedor y yo llegamos a un acuerdo sobre el modo de proceder. |
APPENDIX B: KAS-CM
| Item | English | Spanish |
|---|
| Note. KAS-CM = Kim Alliance Scale, Communication Subscale. This measure was introduced with the following verbal introduction (English version): “Now I am going to read to you some sentences that describe how you and your provider communicate with each other. Some questions will be about how your provider behaves with you. Again, please tell me how often this applies to you: never, rarely, sometimes, or always.” |
| KAS-CM1 | Plain/clear language is used by my provider. | Mi proveedor usa un lenguaje claro/sencillo. |
| KAS-CM2 | I have a good rapport/relationship with my provider. | Yo tengo una buena relación con mi proveedor. |
| KAS-CM3 | I feel my provider criticizes me too much. | Yo siento que mi proveedor me critica mucho |
| KAS-CM4 | My provider spends lots of time educating me. | Mi proveedor emplea bastante tiempo en educarme. |
| KAS-CM5 | I can express negative feelings freely to my provider. | Yo puedo expresarle con libertad mis sentimientos negativos a mi proveedor. |
| KAS-CM6 | My provider listens to me without judgment. | Mi proveedor me escucha sin juzgarme. |
| KAS-CM7 | I feel my provider gives me enough information. | Yo siento que mi proveedor me da suficiente información. |
| KAS-CM8 | My provider does not allow me to state my opinion. | Mi proveedor no me permite expresar mi opinión. |
| KAS-CM9 | It is easy to understand my provider’s instructions. | Es fácil entender las instrucciones de mi proveedor. |
| KAS-CM10 | My provider gives me positive feedback. | Mi proveedor me hace observaciones positivas. |
| KAS-CM11 | I am able to talk to my provider about anything | Yo puedo hablar con mi proveedor de cualquier cosa. |
Submitted: September 1, 2015 Revised: March 24, 2016 Accepted: April 27, 2016
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Source: Psychological Assessment. Vol. 28. (9), September, 2016 pp. 1074-1086)
Accession Number: 2016-40116-005
Digital Object Identifier: 10.1037/pas0000344
Record: 137- Title:
- Psychophysiologic reactivity, subjective distress, and their associations with PTSD diagnosis.
- Authors:
- Pineles, Suzanne L.. National Center for PTSD, VA Boston Healthcare System, Boston, MA, US, Suzanne.Pineles@va.gov
Suvak, Michael K.. Psychology Department, Suffolk University, MA, US
Liverant, Gabrielle I.. VA Boston Healthcare System, Boston, MA, US
Gregor, Kristin. National Center for PTSD, VA Boston Healthcare System, Boston, MA, US
Wisco, Blair E.. National Center for PTSD, VA Boston Healthcare System, Boston, MA, US
Pitman, Roger K.. Department, Massachusetts General Hospital, Boston, MA, US
Orr, Scott P.. Department, Massachusetts General Hospital, Boston, MA, US - Address:
- Pineles, Suzanne L., VA Boston Healthcare System (116B-3), 150 South Huntington Avenue, Boston, MA, US, 02130, Suzanne.Pineles@va.gov
- Source:
- Journal of Abnormal Psychology, Vol 122(3), Aug, 2013. pp. 635-644.
- NLM Title Abbreviation:
- J Abnorm Psychol
- Page Count:
- 10
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- The Journal of Abnormal and Social Psychology; The Journal of Abnormal Psychology; The Journal of Abnormal Psychology and Social Psychology
- ISSN:
- 0021-843X (Print)
1939-1846 (Electronic) - Language:
- English
- Keywords:
- PTSD, imagery, psychophysiology, self-reported distress, trauma cues, reactivity
- Abstract:
- [Correction Notice: An Erratum for this article was reported in Vol 124(2) of Journal of Abnormal Psychology (see record 2015-06738-001). In Table 1 the sample of participants included in Orr et al.’s (1998) paper was incorrectly described as 100% male, rather than 100% female.] Intense subjective distress and physiologic reactivity upon exposure to reminders of the traumatic event are each diagnostic features of posttraumatic stress disorder (PTSD). However, subjective reports and psychophysiological data often suggest different conclusions. For the present study, we combined data from five previous studies to assess the contributions of these two types of measures in predicting PTSD diagnosis. One hundred fifty trauma-exposed participants who were classified into PTSD or non-PTSD groups based on structured diagnostic interviews completed the same script-driven imagery procedure, which quantified measures of psychophysiologic reactivity and self-reported emotional responses. We derived four discriminant functions (DiscFxs) that each maximally separated the PTSD from the non-PTSD group using (1) psychophysiologic measures recorded during personal mental imagery of the traumatic event; (2) self-report ratings in response to the trauma imagery; (3) psychophysiologic measures recorded during personal mental imagery of another highly stressful experience unrelated to the index traumatic event; and (4) self-report ratings in response to this other stressor. When PTSD status was simultaneously regressed on all four DiscFxs, trauma-related psychophysiological reactivity was a significant predictor, but physiological reactivity resulting from the highly stressful, but not traumatic script, was not. Self-reported distress to the traumatic experience and the other stressful event were both predictive of PTSD diagnosis. Trauma-related psychophysiologic reactivity was the best predictor of PTSD diagnosis, but self-reported distress contributed additional variance. These results are discussed in relation to the Research Domain Criteria framework. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Diagnosis; *Distress; *Posttraumatic Stress Disorder; *Psychophysiology
- Medical Subject Headings (MeSH):
- Adult; Aged; Electromyography; Emotions; Facial Muscles; Female; Galvanic Skin Response; Heart Rate; Humans; Imagination; Logistic Models; Male; Middle Aged; Psychophysiology; Stress Disorders, Post-Traumatic; Stress, Psychological; Surveys and Questionnaires
- PsycINFO Classification:
- Neuroses & Anxiety Disorders (3215)
- Population:
- Human
Male
Female - Age Group:
- Adulthood (18 yrs & older)
- Tests & Measures:
- Structured Clinical Interview for Diagnostic and Statistical Manual of Mental Disorders-IV-revised, non-patient version, Vietnam
Structured Clinical Interview for DSM III-R
Clinician-Administered PTSD Scale, Diagnostic Version - Grant Sponsorship:
- Sponsor: Department of Veterans Affairs, Clinical Sciences R&D Service
Other Details: VA Career Development Award
Recipients: Pineles, Suzanne L. (Prin Inv) - Methodology:
- Empirical Study; Quantitative Study
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- Accepted: Apr 12, 2013; Revised: Apr 12, 2013; First Submitted: Sep 14, 2012
- Release Date:
- 20130909
- Correction Date:
- 20150511
- Digital Object Identifier:
- http://dx.doi.org/10.1037/a0033942
- PMID:
- 24016006
- Accession Number:
- 2013-30852-003
- Number of Citations in Source:
- 37
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- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2013-30852-003&site=ehost-live">Psychophysiologic reactivity, subjective distress, and their associations with PTSD diagnosis.</A>
- Database:
- PsycINFO
Psychophysiologic Reactivity, Subjective Distress, and Their Associations With PTSD Diagnosis
By: Suzanne L. Pineles
National Center for PTSD, VA Boston Healthcare System, Boston, Massachusetts and Psychiatry Department, Boston University School of Medicine (BUSM);
Michael K. Suvak
Psychology Department, Suffolk University
Gabrielle I. Liverant
VA Boston Healthcare System and Psychiatry Department, BUSM
Kristin Gregor
National Center for PTSD, VA Boston Healthcare System and Psychiatry Department, BUSM
Blair E. Wisco
National Center for PTSD, VA Boston Healthcare System and Psychiatry Department, BUSM
Roger K. Pitman
Psychiatry Department, Massachusetts General Hospital, Boston, Massachusetts and Harvard Medical School
Scott P. Orr
Psychiatry Department, Massachusetts General Hospital, Boston, Massachusetts and Harvard Medical School
Acknowledgement: Support for this work was provided by a VA Career Development Award (PI: Suzanne L. Pineles) from the Clinical Sciences R&D Service, Department of Veterans Affairs.
A fundamental challenge to the study of emotional experience, whether normal or pathological, is to determine what constitutes a reliable and valid index of emotion. For psychopathology research, this is a particularly salient issue because emotional experience provides the basis for establishing the presence of most forms of psychopathology and for differentiating among diagnoses. In a seminal article on this topic, Miller (1996) noted that researchers “assume (at least implicitly) that self-report is the gold standard for measures of emotional state (p. 623).” This assumption is commonly made in the study of posttraumatic stress disorder (PTSD). As described by Miller (1996), psychophysiological activity also reflects emotion and has the advantage of being relatively independent of an individual’s ability to accurately discern and describe their own emotional state.
Negative emotion in response to traumatic reminders is a hallmark symptom of PTSD that can be manifest as either “intense psychological distress” (DSM-IV-TR PTSD symptom B.4) or “physiological reactivity” (DSM-IV-TR PTSD symptom B.5) to internal or external trauma-related cues (American Psychiatric Association, 2000). The presence and reliability of these diagnostic markers is supported by a large literature demonstrating heightened subjective emotional distress (e.g., Blanchard, Hickling, Taylor, Loos, & Gerardi, 1994; McDonagh-Coyle et al., 2001; Wolf, Miller, & McKinney, 2009), as well as heightened psychophysiologic (e.g., skin conductance [SC], heart rate [HR], and facial electromyogram [EMG] reactivity to cues reminiscent of a traumatic event in individuals with current PTSD; see Pole, 2007, for a review and meta-analysis). Several studies have shown that overall physiological responsiveness (as measured by combining the responses of multiple psychophysiologic measures) can reliably differentiate individuals with PTSD from trauma-exposed individuals without the disorder (e.g., Keane et al., 1998; Laor et al., 1998; Shalev, Orr, & Pitman, 1993). Aligning with research on phobias and other fear-based disorders, increased psychophysiologic reactivity in individuals with PTSD appears to be specific to fear-relevant targets, in this case the trauma-related scripts (e.g., McNeil, Vrana, Melamed, Cuthbert, & Lang, 1993; Shalev et al., 1993). Recent evidence also suggests that psychophysiological assessment of trauma-related cues is less influenced by response bias, or a general tendency to endorse distress, than semistructured interviews (Bauer et al., 2013). Accordingly, there is mounting evidence that psychophysiologic reactivity during script-driven imagery is a reliable, uniquely valid, and useful methodology for assessing PTSD.
Composite measures of overall psychophysiological responsiveness to trauma-related stimuli tend to have high specificity but lower sensitivity for identifying PTSD (see Keane et al., 1998; Laor et al., 1998; Pole, 2007 for review) Thus, psychophysiologic measures are much more successful in correctly identifying individuals without, rather than with, current PTSD as diagnosed with structured clinical interviews. This suggests that there exists a sizable minority of individuals who meet diagnostic criteria for PTSD but who do not react physiologically while recalling their traumatic events. However, these individuals may report distress upon exposure to trauma-related cues even though they do not respond physiologically. Incorporating self-report measures of distress, in addition to physiological measures, may help increase the sensitivity of the script-driven imagery protocol. The few studies that have examined both psychophysiologic and self-reported emotions in response to script-driven imagery suggest that these measures may complement each other (McDonagh-Coyle et al., 2001; Pitman et al., 2001). For example, self-reported negative affect and psychophysiologic reactivity during a trauma-imagery task were not significantly correlated in McDonagh-Coyle et al.’s study and Pitman et al. showed that individuals with PTSD were more psychophysiologically reactive to trauma scripts than individuals without PTSD, but the groups did not differ on self-reported emotional distress. Thus, it may be that measures of subjective and physiological reactivity to trauma-related cues have unique or complementary predictive value for determining the presence of PTSD among individuals exposed to trauma.
Research examining co-occurrence or shared symptoms of mood and anxiety disorders consistently shows that there are common factors across diagnoses along with factors unique to different diagnoses. Specifically, negative affect is a shared global distress factor found in all mood and anxiety disorders, including PTSD, and is usually measured by self-report (Clark, Watson, & Mineka, 1994; Simms, Watson, & Doebbeling, 2002). It is possible that the broad construct of negative affect has a greater influence on subjective emotional experience than it has on the measures of psychophysiologic reactivity used in the script-driven imagery protocol (HR, SC, and frontalis EMG). These measures of psychophysiologic reactivity may be more specifically related to fear or arousal. These differential relationships might contribute to the discrepant findings often found between subjective and psychophysiologic measures. Relatedly, report of subjective levels of distress to trauma cues might be associated with subjective report of distress to other stressful events because of the pervasive nature of negative affect. This contrasts with the psychophysiologic findings, which are specific to the feared targets (e.g., McNeil et al., 1993; Shalev et al., 1993).
The current study combined data from five studies that used the same well-validated, script-driven imagery procedure to assess the relative contribution of psychophysiologic reactivity and self-reported distress in response to both trauma reminders and other stressful, but nontraumatic events in distinguishing trauma exposed individuals with and without a PTSD diagnosis (Carson et al., 2000; Orr et al., 1998; Orr, Pitman, Lasko, & Herz, 1993; Pitman et al., 1990; Pitman, Orr, Forgue, de Jong, & Claiborn, 1987). We hypothesized that psychophysiologic reactivity and self-reported distress to the trauma-related scripts would both significantly predict PTSD diagnosis. Additionally, because PTSD is associated with generalized negative affect and/or distress, we predicted that self-reported distress to the other stressful script (most stressful life experience not related to the traumatic event) would also significantly predict PTSD (Bauer et al., 2013; Simms et al., 2002). Finally, we examined a model that included psychophysiologic reactivity to trauma-related scripts, self-reported distress to trauma-related scripts, psychophysiologic reactivity to other stressful scripts, and self-reported distress to other stressful scripts to assess the relative contribution of each in predicting the PTSD diagnosis. Based on the strong research support for increased psychophysiologic reactivity to trauma-related scripts in PTSD, we hypothesized that this measure would account for the most variance in the PTSD diagnosis.
Method Participants
Participants from five published script-driven imagery studies (Carson et al., 2000; Orr et al., 1993, 1998; Pitman et al., 1987, 1990) are combined in the current study (n = 150). Seventy-eight participants met criteria for current PTSD, and 72 participants had experienced a traumatic event, but never developed PTSD. Sample information for the five studies that comprise the data set are reported in Table 1. In brief, approximately two thirds of participants included in the current study are women, and the mean ages for the studies ranged from late thirties to late sixties. No significant age differences between the PTSD and non-PTSD groups were observed in any of the studies. Mean education level ranged from high school graduate to college graduate, and there were no significant education level differences between groups, with the exception of one study in which participants in the PTSD group reported lower education levels than did the non-PTSD group (Orr et al., 1998).
Descriptive Data of Studies Included in Current Study Sample
Measures and Procedures
Script-driven imagery procedure
Script preparation for the script-driven imagery procedure was conducted according to published procedures (e.g., Pitman et al., 1987). Two personalized “scripts” approximately 30 s in length, composed in the second person, present tense, were created portraying each individual’s traumatic events. In addition, three scripts related to other types of personal experiences, including stressful, positive, and neutral experiences, were also created. Participants also were presented with six standard scripts portraying various hypothetical experiences (two neutral, two fear, one positive, and one action) (Miller et al., 1987). Although participants heard all of the aforementioned scripts, only the personalized scripts portraying each individual’s traumatic and stressful events were analyzed for the current study.
After the electrodes were attached, participants first listened to a 3-min recording of relaxation instructions and then began the script-driven imagery task. Participants were instructed to listen to the audio recorded scripts and vividly imagine the described events as though they were actually happening until they heard a tone. HR, SC, and facial (left lateral frontalis) EMG were measured throughout this imagery period. At the tone, participants were told to stop imagining the script and to relax until they heard a second tone. At the second tone, participants rated the degree to which they had experienced six basic emotions (i.e., happiness, sadness, anger, fear, disgust, and surprise) while listening to and imagining the events, using a 13-point (range 0–12) Likert-type scale (Izard, 1972). In addition, participants used similar 13-point Likert-type scales to rate the valence (unhappy/displeased-happy/pleased), arousal (calm/unaroused-excited/aroused), and vividness (not vivid/unclear-vivid/clear) of their imagery for each script (Lang, 1985). Following the ratings, there was a baseline period before the onset of the next script. The next script was initiated when at least 1 min had passed and HR had returned to within 5% of its value during the previous baseline period.
Scripts were recorded and played back to participants for each event. With minor variations, the scripts were presented in the following order: a standard neutral script was presented first, followed by two blocks of five scripts each. Each block included the following scripts: (a) a personalized trauma-related script, (b) a standard neutral script (sitting in a lawn chair, looking out a living room window, (c) a standard positive script (at a beach), (d) either a personalized other stressor script or a standard fear script (speaking in public), and (e) either the action (riding a bicycle) or the other standard fear script (speaking in public). The order of script presentation was randomized within block.
In all of the studies, HR, SC, and left lateral frontalis EMG, were recorded using a Coulbourn modular system (Coulbourn Instruments LLC, Whitehall, PA) and stored on a Microsoft Windows-based computer system. Electrodes attached to the participant were connected via wires to the Coulbourn system, which was located in an adjoining control that also contained the computer used to record physiologic responses, play back the scripts, and control presentation of the self-report scales.
Interbeat intervals were recorded using 8-mm Ag/AgCl electrodes filled with electrolyte paste and placed on each forearm and then converted to HR. SC was measured by a Coulbourn Isolated Skin Conductance coupler using a 0.5-V constant DC through 8-mm Ag/AgCl surface electrodes filled with isotonic paste and placed on the hypothenar surface of the subject’s nondominant hand, according to published guidelines (Fowles et al., 1981). EMG responses of the left lateral frontalis muscle were recorded using 4-mm Ag/AgCl surface electrodes filled with electrolyte paste and integrated using a 200-ms time constant. The EMG electrodes were placed on abraded skin and were located according to published specifications (Fridlund & Cacioppo, 1986).
PTSD diagnosis
PTSD diagnostic status was based on Diagnostic and Statistical Manual of Mental Disorders, Third Edition, Revised (DSM-III-R; American Psychiatric Association, 1987) criteria in four of the five studies (Orr et al., 1993; 1998; Pitman et al., 1987, 1990). In the Carson et al. (2000) study, PTSD diagnostic status was based on Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM-IV; American Psychiatric Association, 1994) criteria.
The Structured Clinical Interview for Diagnostic and Statistical Manual of Mental Disorders-IV-revised, non-patient version, Vietnam (DSM-III-R-NP-V), which was designed for use with Vietnam veterans (Spitzer & Williams, 1985), was used in three of the studies (Orr et al., 1993; Pitman et al., 1987, 1990). A slightly different version of the Structured Clinical Interview for DSM-III-R (SCID-P; Spitzer, Williams, Gibbon, & First, 1989) was used to classify participants into groups according to their PTSD diagnostic status in the Orr et al. (1998) study. The Clinician-Administered PTSD Scale, Diagnostic Version (Blake et al., 1995) was used in the Carson et al. (2000) study. In all studies, the interviews were administered by doctoral-level psychologists trained in administration of the diagnostic instrument.
Data Reduction
A response score was calculated for each psychophysiologic dependent variable (i.e., HR, SC, and EMG) by subtracting the average baseline period value that preceded each script presentation from the average value during the respective imagery period, separately for each of the two trauma-related scripts and the other stressful script. The psychophysiologic response scores for each physiologic measure were averaged for the two trauma-related scripts. Emotion self-report scores were represented by the Likert-scale ratings for each of the nine subjective measures separately for the two trauma-related scripts and the other stressful script. The emotion self-report scores for the respective subjective measure were averaged for the two trauma-related scripts.
Data Analytic Plan
Discriminant analysis is used for two primary purposes: (a) to identify variables that best discriminate members of two or more groups (e.g., clinician-diagnosed PTSD vs. non-PTSD) and (b) to predict group membership by computing a discriminant function that produces weights (i.e., discriminant coefficients) for each variable that leads to the most accurate classification of each case into one of the groups (Silva & Stam, 1995). Predicted probabilities of group membership (based on values of the predictor variables) can be derived for each participant and analyzed in subsequent analyses. For the current study, four discriminant functions that maximally separated the clinician-diagnosed PTSD from non-PTSD group were derived from the combined data from the five studies. This methodology has the advantage of using all available data to empirically derive models that best predict PTSD diagnosis. First, a trauma-related psychophysiologic discriminant function was derived from the SC, HR, and lateral frontalis EMG responses during personalized trauma-related imagery (average of two scripts). This procedure mathematically determined the optimal weightings for the combination of HR, SC, and lateral frontalis EMG responses during trauma-related imagery that best predicted the PTSD diagnosis (see Orr, Metzger, Miller, & Kaloupek, 2004). Using similar procedures, we derived a self-report-based discriminant function that maximally separated the PTSD from the non-PTSD group based on items assessing subjective emotional experience and arousal in response to trauma-related scripts. This trauma-related, self-report discriminant function was derived from the nine self-report scores to trauma-related imagery using the same procedure as for the psychophysiologic reactivity scores described above. Discriminant functions were likewise derived for the psychophysiologic and subjective emotional responses to a personal, non-PTSD-related stressful event to assess the presence and predictive contribution of reactivity and emotional distress during nontraumatic mental imagery in individuals with PTSD. Predicted probabilities of a PTSD diagnosis, based on each of the discriminant functions, were saved and used in subsequent analyses.
Bivariate associations among the predicted probabilities of the discriminant functions were examined. In addition, their unique associations with PTSD diagnosis were tested using a series of univariate logistic regressions. A multiple logistic regression was also conducted to evaluate the predictive ability of the four predicted probabilities from each discriminant function (trauma-related psychophysiologic reactivity; other stressful psychophysiologic reactivity; trauma-related self-reported distress; and other stressful self-reported distress) simultaneously for PTSD diagnosis.
Finally, to provide a more fine-grained analysis of the prediction of PTSD diagnostic status by the respective probability measures, a communality analysis was conducted. Communality analysis (Nimon & Reio, 2011; Reichwein Zientek & Thompson, 2006) consists of a series of regressions that decompose the variance in the outcome into variance that is (a) accounted for and (b) unaccounted for by the predictor variables. The shared, or overlapping, variance is then further partitioned into unique and common effects. Unique effects identify how much variance is uniquely accounted for by an observed variable, and common effects identify how much variance is accounted for by the overlap among two or more predictors.
ResultsThe means and standard deviations for the psychophysiologic and self-reported emotional responses during script-driven imagery of the traumatic event(s) and other stressful event are presented in Table 2. As discussed above, these measures were entered into the different discriminant functions examined in this study. Participants with PTSD had greater physiologic reactivity during trauma-related script-driven imagery (as measured by HR, SC, and frontalis EMG response scores) and reported feeling more sadness, anger, fear, disgust, and surprise than individuals without PTSD (ps < .05). Individuals with PTSD also described the trauma scripts as more unpleasant than did individuals without PTSD (p < .05). In contrast, the only significant group difference for the other stressful imagery was for anger ratings; individuals with PTSD reported more anger than those without PTSD (p < .05).
Psychophysiologic and Self-Reported Emotional Responses to Trauma-Related and Other Stressful Scripts in Individuals Who Met Criteria for a Diagnosis of PTSD Versus Those Who Did Not Meet Criteria for PTSD
Discriminant Function Analyses
Psychophysiologic response scores for trauma-related script-driven imagery
The trauma-related psychophysiologic discriminant function produced a sensitivity of 59% and a specificity of 90%. In other words, psychophysiological activity in response to trauma scripts is able to correctly identify 59% of individuals diagnosed with PTSD and correctly identify 90% of participants without PTSD.
Emotion self-report scores for trauma-related script-driven imagery
The trauma-related, self-report discriminant function produced a sensitivity of 67% and a specificity of 61%. Thus, subjective “distress” in response to trauma scripts is able to correctly identify 67% of individuals diagnosed with PTSD and correctly identify 61% of participants without PTSD.
Psychophysiologic response scores for other stressful script-driven imagery
A discriminant function was derived from the SC, HR, and lateral frontalis EMG responses during personalized imagery of the other stressful event. This other stressor psychophysiologic discriminant function produced a sensitivity of 46% and a specificity of 72%.
Emotion self-report response scores for other stressful script-driven imagery
A discriminant function was derived from the nine subjective ratings during personalized imagery of the other stressful event. This other stressor self-report discriminant function for the other stressful event produced a sensitivity of 64% and a specificity of 63%.
Bivariate Associations Among the Measures of Psychophysiologic and Self-Reported Distress
Bivariate associations among the predicted probabilities derived from the four discriminant functions (i.e., psychophysiologic and self-report functions for the trauma-related and other stressor-related scripts) are presented in Table 3. The predicted probabilities derived from trauma-related psychophysiologic reactivity and other stressful psychophysiologic reactivity were significantly and moderately correlated (r = .50), as were probabilities derived from the trauma-related self-reported distress and other stressful self-reported distress (r = .42). The probabilities derived from trauma-related psychophysiologic reactivity and trauma-related self-reported distress exhibited a small but significant association (r = .18). None of the other three bivariate associations was significant.
Descriptive Statistics and Bivariate Associations Among Discriminant Function-Derived Predicted Probabilities
Logistic Regression Predictions of PTSD Diagnosis
Table 4 displays the coefficients from the logistic regression analyses. At a bivariate level (i.e., coefficients from the univariate logistic regressions), each of the discriminant function predictive probabilities was significantly associated with PTSD diagnostic status; the odds ratios ranged from 1072.13 for the trauma-related psychophysiological reactivity to 76.63 for the trauma-related, self-reported distress. Cox & Snell R2 values ranged from .24 for the trauma-related psychophysiologic reactivity to .03 for the other stressful psychophysiologic reactivity. In the multiple logistic regression with each measure simultaneously predicting PTSD diagnosis, trauma-related psychophysiologic reactivity, trauma-related self-reported distress, and other stressful self-reported distress remained significant predictors of PTSD diagnosis, whereas other stressful psychophysiologic reactivity was no longer significantly associated with PTSD diagnosis. The Cox & Snell R2 value for the multiple logistic regression was .34.
Summary of the Logistic Regression Equations Predicting PTSD Status
Communality Analysis
The results of the communality analysis are summarized in Table 5. The multiple logistic regression reported above indicated that all of the measures together account for approximately 34% of the variance in PTSD diagnosis. The communality analysis indicated that 48% of the total accounted-for variance (i.e., 48% of 34%) was due to the unique effect of the trauma-related psychophysiologic reactivity. The next largest contributor to the prediction of PTSD diagnosis status was the common effect of trauma-related and other stressful self-reported distress, which accounted for 11% of the total accounted for variance (i.e., 11% of 34%). Of the 34% total variance accounted for by the combination of the four measures, the total contribution of the trauma-related psychophysiologic reactivity (i.e., the sum of its unique effect and all common effects that included this measure) was 68%, and the total contribution of the trauma-related self-reported distress, other stressful self-reported distress, and other stressful psychophysiologic reactivity was 37%, 27%, and 9%, respectively.
Summary of the Communality Analysis
DiscussionThe current study used a combined dataset of five studies to assess the relative utility of psychophysiologic reactivity versus self-reported emotional distress in predicting PTSD diagnosis. This study further examined whether the predictive power of these two different measures of emotional response was specific to trauma-related stimuli or generalized to other highly stressful, but not trauma-related, events. To accomplish these aims, we developed a series of discriminant functions (DiscFxs) that maximally separated participants with clinician-diagnosed PTSD from trauma-exposed individuals who did not meet criteria for PTSD. The first DiscFx measures psychophysiologic response to trauma-related, script-driven imagery procedures and is similar to the DiscFxs previously used in several studies (Carson et al., 2000; Orr et al., 1993, 1998; Pitman et al., 1987, 1990; Shalev et al., 1993). Following the same method, three additional DiscFxs were developed. The first included the nine self-report items that participants use to describe their subjective emotional responses to the trauma-related scripts. The remaining two DiscFxs were based on psychophysiologic and self-report emotional responses to another stressful, but not trauma-related, event.
Notably, the two DiscFxs comprised of self-reported measures of distress, which were based on participants’ subjective emotional responses to the trauma-related and other stressful scripts, were similarly sensitive (67% and 64%, respectively) for the PTSD diagnosis. These DiscFxs were slightly more sensitive than the psychophysiologic DiscFx for the trauma-related script (59%) and notably more sensitive than the psychophysiologic DiscFx for the other stressful script (46%). Interestingly, one might have anticipated even higher sensitivities for the self-report-based DiscFxs, given that the PTSD diagnosis being predicted is also based to a large extent on self-reported emotional experiences.
Although the self-report-based discriminant functions were slightly more sensitive predictors of PTSD, the psychophysiologic DiscFx for the trauma-related script had substantially better specificity (90%) than the three other DiscFxs. Specificity for the DiscFx based on psychophysiologic response scores to the other stressful script (72%) also outperformed both self-report DiscFxs (61% and 63%). Although 90% of individuals who did not meet criteria for PTSD as assessed by a semistructure interview were correctly classified as not meeting PTSD based on patterns of physiological activity in response to trauma scripts, only 61% of individuals without semistructure interview based PTSD diagnosis were correctly classified as such based on their self-reports of emotional distress. Thus, it appears that although self-reported distress may be slightly more likely to identify “true” PTSD diagnoses, psychophysiologic reactivity is notably less likely to produce false positives.
After developing these discriminant functions, we further tested their relative usefulness in predicting PTSD diagnosis with a series of regression equations followed by communality analyses. Perhaps not surprisingly, both self-reported distress and psychophysiologic reactivity in relation to the trauma scripts were significant predictors of PTSD diagnosis. These measures were significant predictors when evaluated separately in univariate regression equations and when the four DiscFxs were entered simultaneously in a multiple regression equation. These findings align with current DSM-IV criteria and the substantial literature documenting both increased self-reported distress and psychophysiologic reactivity to trauma cues in individuals with PTSD, compared with those without a diagnosis (Blanchard et al., 1994; Keane et al., 1998; Laor et al., 1998; McDonagh-Coyle et al., 2001; Shalev et al., 1993; Wolf et al., 2009); see Pole, 2007 for a review. As hypothesized, the self-reported distress to the other stressful script was also a significant predictor of PTSD diagnosis even after controlling for the three other measures. This suggests that individuals with PTSD report heightened emotional distress to negative events that extend beyond their traumatic experiences, perhaps reflecting more general negative affect (Simms et al., 2002). The finding that increased self-reported distress generalizes to other stressful events stands in contrast to the psychophysiologic reactivity results. Heightened psychophysiologic reactivity associated with PTSD appears to be specific to trauma memories and does not generalize to other highly stressful emotional events. These findings are congruent with the literature documenting that heightened psychophysiologic reactivity appears to characterize anxiety disorders that are associated with a specific fear, as in PTSD or specific phobia (McNeil et al., 1993; Shalev et al., 1993).
Because the multiple regression equation can only indirectly address the relative contributions of psychophysiologic reactivity versus self-reported distress in predicting PTSD diagnosis, we conducted a communality analysis. Notably, almost half of the total variance in PTSD diagnosis that was accounted for by the four measures was due to the unique effect of the trauma-related psychophysiologic reactivity. Self-reported emotional distress to the trauma-related script and other stressful script were also important contributors in explaining the total variance accounted for by the four measures; the combination of the overlapping variance and each measure’s unique variance accounted for an additional 29% of the total explained variance.
Taken together, our findings suggest that psychophysiologic reactivity to trauma-related memories is a robust predictor of PTSD diagnosis. These findings are consistent with the extensive literature documenting heightened psychophysiologic reactivity to trauma cues in individuals with PTSD as compared to those without a diagnosis (Pole, 2007). These data also suggest that psychophysiologic and self-report measures of emotional response are not duplicative. Although significant, the correlation coefficient between the psychophysiologic reactivity and self-reported emotional distress to the trauma-related script was small to moderate (r = .18). Furthermore, the common effects of trauma-related psychophysiologic reactivity and self-report distress only accounted for 8% of the total variance explained by the four probability measures in predicting PTSD diagnosis. This is in stark contrast to the unique effects of these measures; trauma-related psychophysiologic reactivity accounted for 48% and trauma-related, self-reported distress accounted for 10% of the total explained variance in the PTSD diagnosis.
As exemplified by the Research Domain Criteria (RDoC) framework, there is a movement in psychopathology research to shift focus away from particular clinical diagnoses to the identification of phenotypes of psychological and biological processes that may explain psychiatric symptoms (Sanislow et al., 2010). Consistent with this framework, heightened psychophysiologic reactivity to script-driven imagery may represent a biological process that reflects acquisition of an intense emotional response to trauma-related cues and/or impaired extinction of these emotional responses. Thus, psychophysiologic reactivity to script-driven imagery is a potential experimental paradigm that could be used to index the acute threat construct of the negative valence system domain within the RDoC. The potential usefulness of this paradigm is supported by its objectivity, standardized procedure, established algorithms to best differentiate individuals with and without PTSD, and good test–retest reliability (Bauer et al., 2013). Furthermore, unlike psychological diagnoses that change over time with each subsequent DSM, psychophysiologic reactivity measures do not. Future research may extend the use of this paradigm to other populations. For example, it is possible that individuals with other fear-based disorders (e.g., specific phobia, agoraphobia) would exhibit similar patterns of reactivity to scripts describing their fears (cf., McNeil et al., 1993; Shalev et al., 1993).
Heightened psychophysiologic reactivity appears to be distinct from self-reported emotional distress to the trauma-related and other stressor-related scripts. Self-reported emotional distress may be an index of negative affect, that is, a shared global distress factor found in all mood and anxiety disorders (Clark et al., 1994; Simms et al., 2002). As applied to the RDoC framework, self-reported distress may either fall within the specific construct of potential harm (i.e., anxiety related to potential harm rather than imminent threat) or might overlap more broadly with the various components of the negative valence system domain, which includes responses to acute threat, potential harm, sustained threat, frustrative nonreward, and loss (National Institute of Mental Health, 2011, March).
It is an interesting possibility that individuals who experience trauma-related sequelae characterized by subjective reports of distress to trauma cues in the absence of heightened psychophysiological reactivity may be qualitatively different from individuals who experience trauma-related sequelae that include heightened psychophysiological reactivity to trauma reminders. This distinction could have important implications for our understanding and treatment of individuals diagnosed with PTSD. It is possible that these phenotypes might inform future conceptualizations of PTSD symptom clusters. Perhaps individuals with PTSD characterized primarily by psychophysiologic reactivity to trauma-related stimuli would be best described as having a fear-based disorder, whereas individuals with PTSD characterized primarily by self-reported distress would be best described as having a distress-based disorder. In turn, these subtypes, fear-based PTSD versus distress-based PTSD, would likely warrant different treatment approaches. If future research supports these subtypes, script-driven imagery procedures could be implemented in PTSD clinics as part of a pretreatment assessment battery. A probability score denoting the likelihood of meeting PTSD diagnostic criteria could be developed by applying the discriminant functions described in the current study to an individual patient’s psychophysiological response scores. Comparing the probability scores for the emotional distress and psychophysiological reactivity discriminant functions would provide an indication of the relative role of fear versus distress in their PTSD diagnostic profile.
An important limitation of the present study is the assumption that the measures and methodology used to assess subjective emotional distress are as valid as the measures and methodology used to assess psychophysiologic reactivity. When rendering their emotion self-reports, the subjects tended to answer at the extremes of the Likert scales, thereby potentially diminishing their accuracy. If these scales could be refined to provide a more sensitive measure of subjective/self-reported emotions, a different pattern of results might be observed. An additional potential limitation is the use of DSM-III-R diagnostic criteria in several of the studies and DSM-IV criteria in another. However, the possible impact seems mitigated by the relatively minor differences in the PTSD criteria as defined by DSM-III-R and DSM-IV. Prior research has also demonstrated good agreement between DSM-III-R and DSM-IV diagnoses (Schwarz & Kowalski, 1991). Regardless, these findings await replication with independent datasets in which PTSD was diagnosed using DSM-IV (and eventually DSM-5) PTSD diagnostic criteria. The current study’s findings align with proposed changes in DSM-5 including the elimination of the subjective reaction component of the index traumatic event and the new four-symptom cluster conceptualization that includes reexperiencing, avoidance, negative cognitions and mood, and arousal. A related limitation is the use of different structured clinical interviews across studies. However, this limitation is also mitigated by the high diagnostic agreement shown in research comparing the clinician-administered PTSD scale and SCID (Barlow, 2002). It is also important to acknowledge that although the present study includes a diverse range of participants with regard to gender, age, and type of trauma experiences, all participants for whom there were available race or ethnicity data reported their race to be Caucasian and had very chronic PTSD. Research is needed to determine the extent to which the present findings generalize to ethnically diverse samples and samples with more acute PTSD. In addition, because this study was a secondary analysis of archival data, we did not have access to person-level data on comorbid diagnoses, and therefore, could not examine how comorbid diagnoses such as depression or panic disorder would impact the pattern of associations among psychophysiological reactivity, emotional distress and PTSD. Future research should examine comorbid diagnoses and other potential moderators of these relationships such as trauma type, chronicity of PTSD, and gender.
Despite its limitations, the present study provides compelling evidence for the distinctiveness of emotional experience as indexed by measures of psychophysiologic reactivity and subjective reports as well as their unique contributions to the prediction of the PTSD diagnosis. Of the four indices examined, psychophysiologic reactivity to trauma-related cues appears to be the most robust predictor. Furthermore, the relatively weak relationship between psychophysiologic reactivity and self-reported emotion to script-driven imagery and the different predictive relationships of these emotion measures to PTSD provide a strong rationale for identifying and exploring different posttrauma phenotypes. For PTSD specifically, it appears that phenotypes of self-reported negative affect in combination with heightened psychophysiologic arousal to script-driven imagery would be particularly promising avenues to pursue.
Footnotes 1 DSM-III-R and DSM-IV diagnoses for PTSD differ in the following ways: (a) slight differences in the definition of a potentially traumatic event; (b) the addition of Criterion A2 (i.e., the person’s response to the trauma involved intense fear, helplessness, or horror); and (c) the symptom of physiologic reactivity to trauma reminders was moved from the arousal cluster (Cluster D) to the reexperiencing cluster (Cluster B).
2 Because the common effects include multiple measures, the sum of these percentages exceeds 100%.
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Submitted: September 14, 2012 Revised: April 12, 2013 Accepted: April 12, 2013
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Source: Journal of Abnormal Psychology. Vol. 122. (3), Aug, 2013 pp. 635-644)
Accession Number: 2013-30852-003
Digital Object Identifier: 10.1037/a0033942
Record: 138- Title:
- Quantifying the impact of recent negative life events on suicide attempts.
- Authors:
- Bagge, Courtney L.. Department of Psychiatry and Human Behavior, University of Mississippi Medical Center, MS, US, cbagge@umc.edu
Glenn, Catherine R.. Department of Psychology, Stony Brook University, US
Lee, Han-Joo. Department of Psychology, University of Wisconsin–Milwaukee, WI, US - Address:
- Bagge, Courtney L., Department of Psychiatry and Human Behavior, University of Mississippi Medical Center, Jackson, MS, US, 39216, cbagge@umc.edu
- Source:
- Journal of Abnormal Psychology, Vol 122(2), May, 2013. pp. 359-368.
- NLM Title Abbreviation:
- J Abnorm Psychol
- Page Count:
- 10
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- The Journal of Abnormal and Social Psychology; The Journal of Abnormal Psychology; The Journal of Abnormal Psychology and Social Psychology
- ISSN:
- 0021-843X (Print)
1939-1846 (Electronic) - Language:
- English
- Keywords:
- negative life events, suicidality, suicide attempts, timeline follow-back method, trigger, interpersonal, spouse or partner
- Abstract:
- The extent to which a specific negative life event (NLE) is a triggering factor for a suicide attempt is unknown. The current study used a case-crossover design, an innovative within-subjects design, to quantify the unique effects of recent NLEs on suicide attempts. In an adult sample of 110 recent suicide attempters, a timeline follow-back methodology was used to assess NLEs within the 48 hours prior to the suicide attempt. Results indicated that individuals were at increased odds of attempting suicide soon after experiencing a NLE and that this effect was driven by the presence of an interpersonal NLE, particularly those involving a romantic partner. Moreover, the relation between interpersonal NLEs and suicide attempts was moderated by current suicide planning. Interpersonal NLEs served as triggers for suicide attempts only among patients who were not currently planning their attempt. Findings suggest the importance of considering potential interpersonal NLEs when evaluating imminent risk for suicide attempts. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Attempted Suicide; *Life Experiences; *Risk Factors; Spouses; Sexual Partners; Interpersonal Relationships
- Medical Subject Headings (MeSH):
- Adolescent; Adult; Cross-Over Studies; Female; Humans; Intention; Life Change Events; Logistic Models; Male; Middle Aged; Motivation; Risk Factors; Suicidal Ideation; Suicide, Attempted; Young Adult
- PsycINFO Classification:
- Behavior Disorders & Antisocial Behavior (3230)
- Population:
- Human
Male
Female
Inpatient - Location:
- US
- Age Group:
- Adulthood (18 yrs & older)
Young Adulthood (18-29 yrs)
Thirties (30-39 yrs)
Middle Age (40-64 yrs) - Tests & Measures:
- Timeline Follow-Back Interview
Personality Assessment Inventory-Borderline Features Scale
Alcohol Use Identification Test
Drug Abuse Screening Test-10
Center for Epidemiological Studies Depression Screening Index-10
Psychiatric Epidemiology Research Interview Life Events Scale DOI: 10.1037/t23838-000
Suicide Intent Scale DOI: 10.1037/t15303-000 - Methodology:
- Empirical Study; Longitudinal Study; Retrospective Study; Interview; Quantitative Study
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- First Posted: Oct 22, 2012; Accepted: Sep 5, 2012; Revised: Sep 5, 2012; First Submitted: Feb 9, 2012
- Release Date:
- 20121022
- Correction Date:
- 20140120
- Copyright:
- American Psychological Association. 2012
- Digital Object Identifier:
- http://dx.doi.org/10.1037/a0030371
- PMID:
- 23088374
- Accession Number:
- 2012-28390-001
- Number of Citations in Source:
- 51
- Persistent link to this record (Permalink):
- http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2012-28390-001&site=ehost-live
- Cut and Paste:
- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2012-28390-001&site=ehost-live">Quantifying the impact of recent negative life events on suicide attempts.</A>
- Database:
- PsycINFO
Quantifying the Impact of Recent Negative Life Events on Suicide Attempts
By: Courtney L. Bagge
Department of Psychiatry and Human Behavior, University of Mississippi Medical Center;
Catherine R. Glenn
Department of Psychology, Stony Brook University and Department of Psychiatry and Human Behavior, University of Mississippi Medical Center
Han-Joo Lee
Department of Psychology, University of Wisconsin–Milwaukee
Acknowledgement:
In the United States alone, one individual will die by suicide every 15 minutes (CDC, 2010), and it is estimated that for each completed suicide, there are 25 suicide attempts (Goldsmith, Pellmar, Kleinman, & Bunney, 2002). Identification of those at greatest risk for attempting suicide is critical for effective prevention and intervention efforts. To this end, research has focused on identifying risk factors, or risk markers, for suicide attempts (e.g., Joiner, 2005; Mann, Waternaux, Haas, & Malone, 1999; Nock et al., 2008). A risk factor is broadly defined as, “anything that increases the probability of developing a pathology” and “is simply correlated with the development of pathology, and may not be causally implicated in the pathogenesis” (Millon & Davis, 1999, p. 29). Risk factors can be either distal from, or proximal to, a target event—for instance, a suicide attempt. Distal risk factors are temporally distant from a suicide attempt, occurring in the years, months, or weeks prior to an attempt (Bagge & Sher, 2008; Hufford, 2001). Although these distal factors may indicate who is more likely to attempt suicide, they do not indicate when an individual may be at greatest risk for attempting.
In contrast, proximal risk factors are temporally close to a suicide attempt and exert their influence in the day, hours, or minutes prior to an attempt (Bagge & Sher, 2008; Hufford, 2001). These proximal factors are closely linked to the timing of the attempt and thus may suggest when an individual may be at imminent risk for attempting suicide. Moreover, the term “trigger” is defined as a specific type of proximal risk factor that is assessed within (as opposed to between) individuals and determines whether a risk factor is unusual for a particular individual (Maclure & Mittleman, 2000). In this case, a trigger is unique to the time period when an individual attempted suicide compared with another similar time period when he or she did not attempt. Identification of triggers may help answer the question: Why did the individual attempt suicide today compared with a previous day?, and thereby aid in determining imminent risk for suicide.
Negative Life EventsThe presence of recent negative life events (NLEs) is one such risk factor, and potential trigger, that may be useful for determining imminent risk for suicide attempts. However, the vast majority of research to date has focused on NLEs as distal risk factors for suicide attempts. This large literature has spanned more than two decades and has consistently found evidence for a positive association between NLEs and suicidal behavior (i.e., suicide attempts and completions) in various populations: adolescents (Beautrais, Joyce, & Mulder, 1997; Brent et al., 1993; Cooper, Appleby, & Amos, 2002) and adults (Cavanagh, Owens, & Johnstone, 1999; Conner et al., in press; Cooper et al., 2002; Heikkinen, Aro, & Lonnqvist, 1992; Weyrauch, Roy-Byrne, Katon, & Wilson, 2001; Yen et al., 2005), as well as across multiple suicidal behavior phenotypes: both attempted suicide (Beautrais et al., 1997; Conner et al., in press; Weyrauch et al., 2001; Yen et al., 2005) and completed suicide (Brent et al., 1993; Cavanagh et al., 1999; Cooper et al., 2002; Heikkinen et al., 1992). Although some studies examined NLEs in the weeks leading up to the attempt (e.g., Cooper et al., 2002), the majority of studies have included a very long exposure window for the NLE assessment in relation to the suicide attempt (e.g., the year prior; Beautrais et al., 1997). However, arguably, if our goal is to determine whether a NLE is a trigger for a suicide attempt, research should focus on NLEs occurring within a time period prior to, but also closely surrounding, the attempt (e.g., within hours of the suicidal act).
Purely descriptive research suggests that NLEs are perceived as precipitating events, or triggers, for suicide by next of kin (Heikkinen et al., 1992). However, to our knowledge, only one previous controlled study has focused on NLEs that occurred specifically on the day of the suicide attempt (Conner et al., in press). This study found that a NLE was more likely to occur on the day of a suicide attempt among patients with alcohol use disorders (AUD) compared with a corresponding day for nonsuicidal AUD controls. Triggering effects of NLEs were also observed: Attempters were more likely to experience a NLE on the day of the suicidal act compared with a previous nonsuicidal day. Given that suicide is related to a number of Axis I disorders beyond AUD (Nock et al., 2008), further examination of NLEs as specific triggers for suicidal behavior in clinically diverse samples is needed. Therefore, the first goal of the current study was to determine the triggering effects of any acute NLE on suicide attempts in a clinically heterogeneous sample of psychiatric patients. This first aim addressed two limitations of previous research by: (a) using a methodology that facilitates assessing proximal, as opposed to distal, NLEs; and (b) enhancing generalizability of results using a more diverse psychiatric sample.
NLE Type
There is both empirical and theoretical evidence to suggest that various NLE categories may have differential importance for suicidal behavior. For instance, previous research suggests that suicidal behavior is often preceded by certain NLEs, such as physical health problems (Cavanagh et al., 1999; Heikkinen et al., 1992), legal problems (Brent et al., 1993), financial and job difficulties (Heikkinen et al., 1997), loss events (Brent et al., 1993; Cheng, Chen, Chen, & Jenkins, 2000; Heikkinen et al., 1997), and interpersonal difficulties (Beautrais et al., 1997; Cavanagh et al., 1999; Heikkinen et al., 1997; Weyrauch et al., 2001).
Studies examining specific types of NLEs occurring within months of an attempt find that interpersonal NLEs, in particular, pose specific risk for suicide attempts. For instance, in an AUD sample, Conner et al. (in press) found that major (severe) interpersonal events (e.g., divorce), but not major noninterpersonal events (e.g., physical injury), occurred more often within a 3-month period among suicide attempters than among nonsuicidal controls. In addition, Yen et al. (2005) examined a wide range of NLEs in the month preceding a suicide attempt and found that two specific categories—love/marriage and crime/legal—were related to attempts. And finally, Cooper, Appleby, and Amos (2002) found that forensic and interpersonal events were more common for suicide completers in the week prior to the suicidal act, as compared with a similar week for controls.
Taken together, despite evidence suggesting that specific domains of NLEs (e.g., interpersonal) may confer differential risk for suicidal behavior, we do not know whether these events are also triggers for suicide attempts. Therefore, a second goal of the current study was to examine specific NLE domains—interpersonal (spouse/partner, family/social) and noninterpersonal (crime/legal, financial, work/school, health)—as triggers for suicide attempts.
Moderation by Suicide Planning
Importantly, these NLEs may not impose uniform risk for all people and, therefore, it is crucial to examine potential moderators of this relation. Current suicide attempt planning (the degree of forethought about the attempt prior to carrying it out; Conner, 2004) is one factor that may influence the extent to which NLEs impact risk for suicide attempts. Examining moderation by attempt planning is particularly important because degree of attempt planning has seen related to attempt severity. For instance, more attempt planning has been associated with greater attempt lethality (Baca-Garcia et al., 2001; Mann et al., 1996). Further, by definition, less suicide planning indicates less forethought (e.g., less than 5 minutes of contemplation; Simon et al., 2001), leaving little time for typical interventions. Thus, the degree of planning surrounding an attempt has different implications for suicide prevention (see review, Conner, 2004).
Indeed, previous research suggests that having a significant NLE, or a certain type of NLE, may be a more relevant trigger for suicidal behavior among individuals who were not currently planning their suicidal act. For instance, research indicates that experiencing a recent NLE (i.e., 2 days prior to suicide) was associated with less planning of that fatal act (Conner, Phillips, & Meldrum, 2007). Moreover, Weyrauch, Roy-Byrne, Katon, and Wilson (2001) demonstrated that certain past-year interpersonal NLEs, but not noninterpersonal NLEs, were related to less planning of a recent attempt. However, although previous results are suggestive of a NLE-planning interaction, little explanation has been provided for why NLEs may be particularly relevant for nonplanners. It is possible that NLEs may not initiate action for individuals who are currently planning a suicide attempt because these individuals may have already made their preparations and are waiting for a predetermined “right” time. In contrast, for individuals who are not currently planning a suicide attempt, NLEs may serve as a catalyst to engage in suicidal behavior; these individuals may have had thoughts of suicide but have not yet planned when, so “Why not now?” Moreover, studies have not yet examined whether interpersonal NLEs are more likely to specifically trigger (as opposed to surround) a suicide attempt among individuals not currently planning their attempt. To address this gap in the literature, the current study examined whether planning moderated the triggering effect of recent NLEs on suicide attempts.
The Case-Crossover Design
To determine which factors were unusual for an individual on the day of their attempt, and to most adequately answer the ultimate question of “Why today?” a within-subjects design is necessary. The case-crossover design is a within-subjects technique that uses each individual case as his or her own control (Maclure & Mittleman, 2000). Initially developed to detect triggers for myocardial infarction, this design measures factors that change from day-to-day (e.g., life events) during a time period surrounding a target event (e.g., suicide attempt). For each individual, factors on the day of the target event are then compared with the same factors on a day more distant from the target event. This design is advantageous because it provides the most conservative control—the same individual on a day when the target event did not occur (e.g., when he or she did not attempt suicide). Thus, the case-crossover design controls for all stable risk factors (e.g., gender, history of a mood disorder, childhood abuse), which do not change daily and instead allows for a controlled examination of time-varying and unique triggers of the specific target event (e.g., suicide attempt).
As discussed above, there has only been one case-crossover study to date examining the impact of NLEs on suicide attempts. Conner et al. (in press) provides preliminary evidence for NLEs as triggers for suicide attempts, in an AUD patient sample. The current study built upon this study by examining a range of NLE categories as triggers for suicide attempts using a case-crossover design, in a clinically heterogeneous sample of psychiatric patients. In addition, the current study also extended previous research by determining whether a NLE (i.e., any NLE and any interpersonal NLE) differentially triggered a suicide attempt among those who are, and who are not, currently planning their attempt. The present study tested the following hypotheses: (a) individuals are more likely to attempt suicide following a proximal NLE; (b) interpersonal NLEs, and spouse/partner NLEs in particular, will serve as specific triggers for suicide attempts; and (c) interpersonal NLEs are more likely to trigger a suicide attempt among individuals who are not currently planning their attempts.
Method Participants
Participants, between the ages of 18 and 64, who presented to a hospital within 24 hours after a suicide attempt (i.e., a self-inflicted behavior with some intent to die; Silverman, Berman, Sanddal, O’Carroll, & Joiner, 2007) were recruited from the only Level 1 trauma hospital in Mississippi. We recruited recent suicide attempters from all areas of the hospital (e.g., inpatient, ER, medical floors) to increase the psychiatric heterogeneity of the sample. To be included in the study, patients also had to report that (a) the suicide attempt was their reason for hospital admission, and (b) they had at least some intent to die at the time of the act. Exclusion criteria included the presence of factors that would interfere with the capacity to provide informed consent or complete the study (e.g., intoxication or disorganized speech/thought content). One-hundred ten suicide attempters (59% female) were enrolled in the present study (85.2% of those approached about the study) between October 2008 and October 2010. Mean age of the sample was 36.39 years (SD = 11.31) and the ethnic composition of the sample was 68% White, 28% Black, and 4% Other Race/Ethnicity.
Procedure
Written consent (approved by an institutional review board) was obtained prior to study initiation. Patients were approached after initial medical/psychological evaluations and assessment sessions occurred close to discharge, and also within 7 days of their suicide attempt. The assessment session required approximately 2.5 hours to complete and included a battery of self-report questionnaires and semistructured interviews. The sequence of assessment measures was counterbalanced to control for possible order effects. Participants volunteered for the current study without compensation.
Interviewers underwent 2 months of training before collecting data for the study. This article focuses on data collected from two interviews: a modified Timeline Follow-Back Interview (TLFB; Sobell & Sobell, 1992) and the Suicide Intent Scale (SIS; Beck, Schuyler, & Herman, 1974). For the TLFB, interviewers included the Principal Investigator (PI; author CLB) and advanced undergraduate students in psychology trained to reliability by the PI; all interviews were reviewed by the PI, with ratings confirmed in consensus meetings. The PI conducted all SIS interviews.
Measures
Suicide descriptives
Prior history of attempts and method of the current attempt were obtained by interviewers asking participants, “How many suicide attempts have you made in your lifetime?” (Nock, Holmberg, Photos, & Michel, 2007) and for your most recent attempt, “What method(s) did you use?” (Kessler & Ustun, 2004). Participants reporting more than one suicide attempt were considered multiple attempters, whereas participants reporting only one lifetime attempt were considered first-time attempters.
TLFB assessment of NLEs
Similar to the methods employed by Conner et al. (in press), a TLFB methodology (Sobell & Sobell, 1992) was used to gather retrospective information on the timing of NLEs during a specified time period prior to a suicide attempt. Given our focus on acute life events, the TLFB used an hourly calendar (e.g., Vinson, Maclure, Reidinger, & Smith, 2003), as opposed to a daily calendar, to assess the presence and timing of NLEs during each hour of the 48 hours prior to the attempt. First, interviewers assessed the date and time of the recent suicide attempt. Based on this information, participants were given the day/dates/times of both the start and ending point of the 48-hr period of interest. Basic contextual information was gathered (e.g., where they were, who they were with, what they were doing) to serve as anchors for recall. Next, participants were presented with a list of 33 acute NLEs and asked whether any of these events occurred during the 48 hours prior to the attempt. Interviewers confirmed with participants that each endorsed event was viewed as being negative in nature. After basic information was gathered to serve as anchors for recall, the exact timing (i.e., start and stop time) of each endorsed NLE event was determined.
Content of NLE
Consistent with the work by Yen et al. (2005), the list of NLEs was adapted from the Psychiatric Epidemiology Research Interview Life Events Scale (Dohrenwend, Krasnoff, Askenasy, & Dohrenwend, 1978). The NLE assessment included 33 acute events or circumstances, grouped into six stress domain categories (Yen et al., 2005) and two broad categories based on the interpersonal nature of the event (Conner et al., in press); chronic stressors were not included in the NLE assessment. For the last item, participants reported any “other important NLE.” If the response content was similar to existing items, the “other NLE” was recategorized to its appropriate category. Discarded “other” NLEs included chronic stressors that spanned the whole 48-hr period.
The assessment focused on whether the specific NLEs occurred during the “case period” (the day of, or 24 hours prior to, the attempt) or the “control period” (the day before, or hours 24 to 48 prior to, the attempt). Categories were rated as present if any item within the category was endorsed. Across the entire 48-hr period prior to the attempt, NLEs were placed in the following categories: (a) interpersonal, including: spouse/partner relationships (four items; e.g., broke up with romantic partner) and family/social community relationships (six items; e.g., argued with relative); and (b) noninterpersonal, including: work/school (six items; e.g., fired), crime/legal (six items; e.g., law violation), financial (six items; e.g., evicted), and health (four items; e.g., seriously injured). Interrelations between stress domain categories were low (rs = −.03–.23), suggesting that these are relatively independent NLE categories.
Planning of suicide attempt
The SIS (Beck et al., 1974), a reliable and valid interview schedule (Beck et al., 1974; Kaslow, Jacobs, Young, & Cook, 2006) that evaluates the severity of an individual’s wish to die following a suicide attempt, was utilized to assess planning of the participant’s current attempt. Consistent with previous studies, two items (suicide preparation and suicide premeditation) were used to assess planning of the suicide attempt (e.g., Baca-Garcia et al., 2001; Suominen, Isometsa, Henriksson, Ostamo, & Lonnqvist, 1997). For the current study, because moderation within conditional logistic regression is only possible using dichotomous variables, a dichotomous suicide planning item (1 = at least some preparation or contemplation for 3 hours or more; 0 = no preparation and contemplation for less than 3 hours) was created from the original SIS response options. SIS data was collected after the first 17 participants and, therefore, is only available for 93 attempters.
Psychiatric symptoms
To examine the extent to which psychiatric symptoms moderated the NLE-attempt association, the study also included the following measures: the Personality Assessment Inventory-Borderline Features Scale (PAI-BOR; Morey, 1991), the Alcohol Use Identification Test (AUDIT; Saunders, Aasland, Babor, de la Fuente, & Grant, 1993), the Drug Abuse Screening Test-10 (DAST-10; Bohn, Babor, & Kranzler, 1991), and the Center for Epidemiological Studies Depression Screening Index-10 (CESD-10; Andresen, Malmgren, Carter, & Patrick, 1994). In line with previous research, the following threshold scores were used to measure clinically significant symptoms in the moderation analyses: 38 or above (66.6%) for borderline personality disorder features (Bagge et al., 2004; Morey, 1991), 8 or above (38.5%) for problematic alcohol use (Saunders et al., 1993), 4 or above (33.0%) for drug use (Bohn et al., 1991; Cocco & Carey, 1998), and 10 or above (90.7%) for depression (Andresen et al., 1994). Consistent with prior work (e.g., Nock et al., 2008), the majority of suicide attempters had significant depressive symptoms; therefore, given the instability of estimates with low cell counts, moderation by depression was not examined.
Data Analytic Plan
Univariate and multivariate analyses
A series of conditional logistic regression analyses (see Stokes, Davis, & Koch, 2000) were used to test our hypotheses. Conditional logistic regression analysis is similar to traditional logistic regression, except that the case period (24 hours prior to the attempt) and control period (hours 25 to 48) are pair-matched within individuals. The dependent variable was the presence (coded 1) or absence (coded 0) of a suicide attempt in the case versus control period. Presence of NLEs within the case period (compared with the control period) was the independent variable used to predict risk of attempt. NLEs were parameterized (1 = present and 0 = absent) in three ways: (a) any NLE, (b) any interpersonal NLE, and (c) specific types of NLEs (i.e., the six stress domain categories) in the 24-hour time periods. Two series of conditional logistic regression analyses (univariate and multivariate) were conducted for each parameterization of NLEs.
Moderation analyses
Moderation within a case-crossover design concerns an interaction effect or “difference of differences” (e.g., the difference between NLEs in case and control periods is hypothesized to be greater among one subgroup [nonplanners] than among another subgroup [planners]). Factors such as characteristics of the index (current) attempt do not vary within individuals and cannot serve as a traditional independent variable to predict risk of attempt (as in case-control studies). These variables can, however, serve as grouping variables (or effect modifiers) to determine whether a NLE (i.e., any NLE and any interpersonal NLE) differentially triggers a suicide attempt among those who are, and who are not, currently planning their attempt. Therefore, the conditional logistic regression analyses described above were modified with a strategy commonly used in the multilevel analysis of couple data to incorporate categorical between-subjects variables into the analysis. Finally, we used the TEST command in SAS Proc Logistic to determine whether the two estimates differed significantly across subgroups.
Results General Descriptive Information
Approximately half of suicide attempters reported prior planning for the current attempt (53.76%). Sixty percent reported having a history of suicide attempts (number of prior attempts among repeat attempters: M = 5.18, SD = 6.81). The most common index suicide attempt methods were overdose of medications (75.45%), overdose of alcohol or other drugs (12.73%), sharp instrument (13.64%), and gun (5.45%). Other methods (7.27%) included hanging, jumping from high places, motor vehicle crash, or immolation.
Exposed Cases
A participant is considered exposed if he or she reported a NLE during the period of interest. Results indicate that 69 patients (62.73% of the sample) experienced a NLE; 50% (n = 55) of the sample reported having at least one interpersonal NLE and 25.45% (n = 28) reported at least one noninterpersonal NLE during the case period. The most common specific types of NLEs during the case period were family/social (28.18%) and spouse/partner (27.27%), followed by financial (11.82%), crime/legal and work/school (both 6.36%), and health (4.55%).
Univariate Conditional Logistic Regression Analyses
The first series of analyses included univariate associations between all parameterizations of NLEs and suicide attempts (i.e., dependent variable (DV) is the presence or absence of an attempt on the case vs. control days; see Table 1). Results indicated that experiencing a NLE was associated with a 2.35 times greater risk of attempting suicide (p < .01). Further, this relation was driven by the presence of an interpersonal NLE (OR = 2.85, p < .01); a noninterpersonal NLE did not increase risk for a suicide attempt (OR = 1.29, ns). When further dividing interpersonal and noninterpersonal NLEs into specific NLE categories, only having a spouse/partner NLE (OR = 6.00; p < .01) and a family/social NLE (OR = 2.18, p < .05) increased risk for a suicide attempt. All remaining specific (noninterpersonal) NLE categories were not related to a suicide attempt (ORs ranged from 1.00 to 1.50, ns).
Univariate and Multivariate Conditional Logistic Regression Analyses Predicting Suicide Attempts From Negative Life Events
Multivariate Conditional Logistic Regression Analyses
The second series of analyses included unique associations (within each NLE parameterization) between NLEs and suicide attempts (see Table 1). Results were consistent with the first series of analyses, such that an interpersonal NLE (OR = 2.82, p < .01) was uniquely related to a suicide attempt when controlling for a noninterpersonal NLE (OR = 1.20, ns). However, only a spouse/partner NLE (OR = 5.37 p < .01) uniquely predicted suicide attempts when controlling for other specific NLE categories (ORs range from 0.75 to 2.09, ns).
Moderation Analyses
Table 2 presents the results of the moderation analyses examining the effect of any NLE on a suicide attempt as a function of index attempt planning and psychiatric symptoms. Results indicated significant moderation by current attempt planning: Experiencing an acute NLE was a trigger for a suicide attempt among individuals not currently planning their attempt (OR = 6.00, p < .001), but not among those currently planning their attempt (OR = 1.00, ns). The any NLE suicide attempt association did not differ by psychiatric symptoms. Next, the same pattern of results was observed when examining moderation of the any interpersonal NLE suicide attempt relation (see Table 3; nonplanning subgroup: OR = 11.00, p < .01; planning subgroup: OR = 1.38, ns). Finally, the lack of relation between any noninterpersonal NLE and a suicide attempt did not differ as a function of current attempt planning or psychiatric symptoms, all ps > .10 .
Conditional Logistic Regression Analyses Examining the any NLE-Suicide Attempt Relation as a Function of Current Suicide Planning and Psychiatric Symptoms
Conditional Logistic Regression Analyses Examining the any Interpersonal NLE-Suicide Attempt Relation as a Function of Current Suicide Planning and Psychiatric Symptoms
DiscussionThe goals of this study were to (a) determine the triggering effects of any acute NLE on suicide attempts, (b) examine a range of NLE categories as triggers for suicide attempts, and (c) examine whether the association between acute NLEs and suicide attempts varied as a function of current attempt planning. This study is the first, to our knowledge, to use the TLFB design to provide initial estimates of the triggering effect of NLEs on suicide attempts among a psychiatrically diverse sample. First, consistent with previous research (Cheng et al., 2000; Weyrauch et al., 2001; Yen et al., 2005), the current study found that NLEs were proximal risk factors for suicide attempts. Moreover, based on the use of a case-crossover design, results also indicated that NLEs were triggers for suicide attempts, consistent with Conner et al. (in press): NLEs occurred more often on the day of, as opposed to the day before, a suicide attempt. Notably, a case-crossover design is ideal for separating acute from chronic effects on suicide attempts because it provides estimates of intermittent NLEs over and above baseline risk associated with past history of NLEs.
Rates of NLEs in the current study are consistent with previous research finding that the majority of suicide attempters report a significant NLE in the months and weeks leading up to an attempt. For instance, Yen et al. (2005) found that almost all suicide attempters (99.8%) experienced a NLE in the month prior to their attempt and Heikkinen et al. (1997) found that 70% of suicide completers experienced a NLE in the week prior to their suicide. In addition, although Weyrauch et al. (2001) did not provide overall rates of NLEs, 47% of attempters reported experiencing an interpersonal NLE with a romantic partner, and 71% experienced a financial concern, in the week prior to their attempt.
However, the current study and Conner et al. (in press) found large differences in rates of NLEs on the day of the suicide attempt (current study: 63% vs. Conner et al.: 11%). Although similar in content, timing of the NLE assessment could be one reason for higher rates of NLEs in the current sample. Conner et al. included AUD patients who attempted suicide within 90 days of entry to residential treatment, which means that some participants may have been asked to report NLEs from 3 months prior. In contrast, participants in the current sample attempted suicide within 24 hours of hospital admission and NLEs were assessed within 7 days of admittance. In addition, Conner et al. assessed NLEs by day in the 90 days prior to the suicide attempt, whereas the current study assessed NLEs by hour in the 48 hours prior to the attempt. Using hour versus day units in the assessment may have enhanced recall of NLEs on the day of the attempt. Alternatively, the current study may have used a lower threshold for determining the presence of a NLE, thereby increasing the prevalence of NLEs. However, if these less severe, but still significant, NLEs are helpful in predicting when an individual will attempt suicide, future studies may consider varying NLE thresholds. Taken together, methodology differences and difficulties with retrospective recall may have contributed to the discrepant rates between studies.
Second, and also in line with previous research (Conner et al., in press; Cooper et al., 2002; Yen et al., 2005), the present study’s results suggest that interpersonal NLEs might be particularly important risk factors for suicide attempts. This study is the first, to our knowledge, to use a case-crossover design to empirically demonstrate that interpersonal NLEs are specific triggers for suicide attempts. Notably, results varied by current planning of the suicide attempt. For attempters with current suicide planning, the presence of a NLE did not further their suicidal plans, or trigger action. However, for attempters not currently planning their attempt, a NLE served as a trigger for engaging in suicidal behavior. The current study’s results are consistent with growing evidence suggesting that NLEs, particularly interpersonal NLEs, are associated with less suicide planning (Conner et al., 2007; Weyrauch et al., 2001).
In line with the interpersonal–psychological theory of suicide (IPT; Joiner, 2005), these findings suggest that interpersonal NLEs, in particular, may engender feelings of less belongingness and greater perceived burdensomeness—two constructs thought to increase suicidal desire. For individuals without prior suicide planning, an interpersonal NLE was relatively unusual and could have led to substantial increases in feelings of burdensomeness and less belongingness which triggered their attempts. But why weren’t these NLEs also triggers for the planners? Interestingly, planners were just as likely to experience a NLE on the day of, as the day before, their attempt. Perhaps, during the days leading up to an attempt, NLEs are not unusual for these individuals and thus, do not have a triggering effect. Alternatively, planners may want to follow through with their previous suicide plan and, therefore, are not as impacted by the timing of NLEs. Future research should consider examining whether interpersonal NLEs are associated with key components of the IPT using an event-based suicide assessment, as well as identify potential triggers for the subgroup of attempters that plan their attempts.
Third, the current study is unique due to its more refined examination of particular categories of NLEs as within-person triggers for suicide attempts. In particular, consistent with previous research (Conner et al., in press; Yen et al., 2005), the current study found that a specific type of interpersonal NLE—romantic/partner events—were triggers for suicide attempts. One potential interpretation of these findings is that the loss or disruption of certain interpersonal relationships may confer greater risk for suicide attempts than other relationships. That is, perhaps the impact of NLEs is proportionate to the perceived emotional bond or tolerability of interpersonal disruptions in romantic versus other relationships. It will be important for future studies to consider using larger event-based assessments to clarify the role of specific types of interpersonal NLEs as triggers for suicide attempts.
Although our romantic NLE findings were consistent with previous studies, our null crime/legal (or forensic) NLEs results were not (Cooper et al., 2002; Yen et al., 2005). It is possible that romantic NLEs are more proximal triggers for suicide attempts, whereas crime/legal NLEs are more distal risk factors. For instance, involvement in a court case (the most common crime/legal NLE reported by Yen et al., 2005) is a NLE that may take relatively more time to unfold and, therefore, may exert its influence over the days, weeks, or months, rather than hours, following the event. Therefore, although crime/legal NLEs may put an individual at risk for attempting suicide, these NLEs may not trigger the attempt. Alternatively, crime/legal NLEs may be relatively less common and the small number of crime/legal events reported in the current study may have been too small to detect an effect. Further replication with larger samples is needed.
Finally, given that NLEs, in general, have a greater etiological and pathological association with some psychiatric conditions (e.g., depression, substance disorders; Dohrenwend, 2006) than others, we also examined the effect of psychiatric symptoms on the NLE-suicide association. First, because the majority of participants reported significant depressive symptoms, we were unable to test how the NLE-attempt association was moderated by depression. Insofar as most participants met the threshold for significant depression, it stands to reason that the main study findings are not attributable solely to depression. We also wanted to ensure that psychological conditions characterized by trait impulsivity did not account for our observed moderation results by attempt planning. Because borderline personality disorder (BPD) and substance use disorder are two disorders characterized by high levels of trait impulsivity (Trull, Sher, Minks-Brown, Durbin, & Burr, 2000; Whiteside & Lynam, 2001), we conducted analyses with BPD features and problematic substance use replacing current suicide planning as moderators of the interpersonal NLE-suicide attempt relation. Results revealed that these variables were not significant moderators, and thus, do not account for the moderating role of attempt planning. In addition, we examined whether planning differed as a function of psychiatric group. The only difference was for BPD: Individuals with significant BPD features were more likely to plan their suicide attempt than those without BPD features (OR = 2.59, p = .04). These results are consistent with previous research indicating that individuals high in trait impulsivity do not necessarily engage in more impulsive (or less planned) suicide attempts (e.g., Baca-Garcia et al., 2005) and that the opposite direction of results has also been observed (e.g., Witte et al., 2008).
Taken together, findings from the current study have important implications for both suicide research and clinical work with suicidal patients. First, in regard to research design, there are numerous ways that cases and controls may differ and it is not feasible to control for all group differences using a standard case-control methodology. Importantly, the case-crossover design is an ideal solution to this problem because each case serves as its own control, thereby allowing for examination of factors that are unusual for the individual on the day of the suicide attempt. In addition, it is important to note that it is quite difficult, and arguably near impossible, to conduct longitudinal research on acute triggers on the days, and hours, immediately prior to a suicide attempt. Although some events (distal predictors) confer lifetime risk for suicide, results from the current study suggest that other events, specifically interpersonal events, may put an individual at heightened short-term risk for suicidal behavior. Second, the TLFB method is similar to chain analysis used in dialectical behavior therapy (DBT; Linehan, 1993) to help patients with BPD understand the events, thoughts, and feelings that triggered engagement in self-injurious behaviors. Thus, the TLFB procedure may be useful clinically for highlighting the impact of individual triggers in an effort to prevent future suicidal behavior, and perhaps tell us when an individual may be more likely to attempt suicide. Moreover, the importance of interpersonal NLEs suggests that it may be essential to (a) target interpersonal effectiveness, a core component of DBT (Linehan, 1993), in which patients learn how to anticipate and effectively handle interpersonal conflict; and (b) include romantic partners and other significant social supports in treatment to enhance prevention efforts for at-risk patients.
Further, findings suggest that these interpersonal NLEs may hold significant relevance for those who do not report current planning for a suicide attempt. Currently, the presence of a suicidal plan is one index of increased risk for suicidal behavior (Beck et al., 1974). However, consistent with other studies (e.g., Borges et al., 2006), almost half of our sample reported little to no planning for their attempt. Results suggest that individuals without current suicide planning may still be at heightened risk for attempting suicide if an interpersonal NLE is likely to occur. Therefore, for patients who report no current suicide planning, clinicians may still consider enacting suicide preventative measures, such as creating a suicide safety plan for handling NLEs effectively. In addition, it has been suggested by others (e.g., Conner, 2004) that, given the small intervention window for attempters who do not make a suicide plan, more global efforts to restrict access to lethal means may be the most effective prevention strategy for at-risk individuals.
Although the current study adds to the growing literature examining the NLE-attempt association, there are limitations to this study that deserve comment and suggest areas for future research. First, although the case-crossover methodology holds all stable, and between-person, risk factors constant, there is a possibility that a third within-person variable (varying within days) could have caused both the NLE and suicide attempt. One possible contender is day-to-day fluctuations in negative affect. Research indicates that acute stressors produce negative affect (Dickerson & Kemeny, 2004) and that negative affect increases with daily stress (Watson, 1988). However, it is unclear whether daily fluctuations in negative affect are a precipitant and/or consequence of NLEs. Future research is needed to flesh out the temporality of these associations.
Second, both state and trait negative emotionality could have interfered with participants’ recall of NLEs. For instance, participants may have been in a distressed state during the study assessment, which could have potentially biased their recall of NLEs. However, research indicates that emotional memories (e.g., significant NLEs) are remembered more accurately than neutral memories (Dolcos, LaBar, & Cabeza, 2004; Reisberg & Heuer, 1992). Therefore, the timing of the NLE assessment (close to the suicide attempt) is arguably beneficial to the current study because it minimized retrospective biases and forgetting that may have occurred if the assessment was weeks, months, or even years after the attempt. In addition, trait negative emotionality, such as depression, can impact recall of life events. Given that the majority of the sample reported significant depressive symptoms, it is unlikely that this recall bias contributed to the main study findings. However, given that the TLFB interview is based on self-report, future studies would benefit from also obtaining informant reports (and other records) about these NLEs that are not impacted by the attempters’ biases.
Third, the current study used validated self-report screening measures to assess clinical symptoms. Future studies should replicate these findings with validated structured interviews to assess diagnostic features. Fourth, although suicide planning in the current study was operationalized using a method consistent with previous research (Baca-Garcia et al., 2001; Suominen et al., 1997), there is no agreed upon definition of what constitutes low current suicide planning (cutoffs range from 5 minutes to 24 hours; see review: Conner, 2004). Future research is needed to examine the significance of different conceptualizations of suicide planning on the NLE-suicide association.
Fifth, although the current study did not use the same assessment measure as Conner et al. (in press), we did (a) gather contextual information about the NLE to serve as anchors for recall, (b) use a measure that has demonstrated good interrater reliability in similar samples , and (c) confirm that each event was not simply a daily hassle and conformed to a predetermined list of events. However, unlike other standard assessment measures for NLEs (e.g., the Life Events and Difficulties Schedule; Brown & Harris, 1978), the current study did not use contextual details to judge whether the events met threshold for inclusion and to assess their severity. Therefore, included NLEs could have been impacted by participant reporting biases.
Finally, this study’s design was not prospective. However, the current study’s methodology does provide continuous hourly snapshots prior to the suicide attempt, quite close to when it happened. Therefore, the TLFB design may be a particularly good option for helping to pinpoint triggers for imminent risk of suicide attempts, as well as for aiding in the development of intervention strategies to help prevent future suicidal behavior.
Footnotes 1 Conner et al. (in press) examined whether the presence of any NLE was a trigger for a suicide attempt, but did not examine specific NLE domains as potential triggers using the case-crossover design.
2 We also tested whether the association between NLEs (i.e., any NLE, any interpersonal NLE, any noninterpersonal NLE) and suicide attempts was moderated by gender and history of attempts (i.e., first vs. repeat attempts). No moderation was observed.
3 Using identical procedures to the current study, with the exception of adding financial compensation and audio taping interviews, we conducted a small study (n = 77) and determined the interrater reliability of the NLE categories. Twenty-two recent suicide attempters’ interviews were randomly selected; one interviewer conducted all original TLFB interviews and a second interviewer reviewed the audiotape and provided independent ratings. Results indicated high percent agreement for having any NLE (Kappa = .82), any interpersonal NLE (Kappa = .81), and any noninterpersonal NLE (Kappa = .70).
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Submitted: February 9, 2012 Revised: September 5, 2012 Accepted: September 5, 2012
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Source: Journal of Abnormal Psychology. Vol. 122. (2), May, 2013 pp. 359-368)
Accession Number: 2012-28390-001
Digital Object Identifier: 10.1037/a0030371
Record: 139- Title:
- Quick Delay Questionnaire: Reliability, validity, and relations to functional impairments in adults with attention-deficit/hyperactivity disorder (ADHD).
- Authors:
- Thorell, Lisa B., ORCID 0000-0002-7417-6637. Department of Clinical Neuroscience, Division of Psychology, Karolinska Institutet, Solna, Sweden, lisa.thorell@ki.se
Sjöwall, Douglas, ORCID 0000-0002-8320-3609. Department of Clinical Neuroscience, Division of Psychology, Karolinska Institutet, Solna, Sweden
Mies, Gabry W.. Behavioural Science Institute, Radboud University, Nijmegen, Netherlands
Scheres, Anouk. Behavioural Science Institute, Radboud University, Nijmegen, Netherlands - Address:
- Thorell, Lisa B., Department of Clinical Neuroscience, Division of Psychology, Karolinska Institutet, Nobels Väg 9, SE-17165, Solna, Sweden, lisa.thorell@ki.se
- Source:
- Psychological Assessment, Vol 29(10), Oct, 2017. pp. 1261-1272.
- NLM Title Abbreviation:
- Psychol Assess
- Page Count:
- 12
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- Psychological Assessment: A Journal of Consulting and Clinical Psychology
- ISSN:
- 1040-3590 (Print)
1939-134X (Electronic) - Language:
- English
- Keywords:
- self-report, neuropsychology, attention-deficit/hyperactivity disorder, delay discounting, reliability and validity
- Abstract (English):
- The Quick Delay Questionnaire (QDQ) is a self-report measure of delay-related behaviors in adults, and the present study aimed at investigating the psychometric properties of QDQ scores, how well they can discriminate between ADHD adults and both clinical and nonclinical controls, as well as their relations to measures of functional impairments. In the present study, QDQ ratings, a laboratory measure of delay discounting, and ratings of functional impairments were collected from adults diagnosed with attention-deficit/hyperactivity disorder (ADHD; n = 51), a clinical control group with other psychiatric disorders (n = 46), and a nonclinical control group (n = 105). Results showed that the QDQ scores showed adequate reliability. Adults with ADHD had higher scores compared with normal controls on both QDQ subscales, and they also reported higher levels of delay aversion than the clinical controls. Logistic regression analyses showed high specificity but low sensitivity when trying to discriminate between adults with ADHD and nonclinical controls. QDQ scores were not associated with a laboratory measure of delay discounting, but with functional impairments such as substance use, criminality, and money management. Our findings indicate that QDQ scores are reliable, but this instrument should be regarded as a complement rather than as a replacement for laboratory measures. The relatively low sensitivity of QDQ scores is in line with current models of heterogeneity stating that only a subgroup of individuals with ADHD has high levels of delay-related behaviors. Our findings further indicate that this subgroup may be at particularly high risk for problems in everyday life. (PsycINFO Database Record (c) 2018 APA, all rights reserved)
- Impact Statement:
- Public Significance Statement—This study shows that the QDQ is a rating instrument that can be used to identify a subgroup of adults with ADHD who show high levels of delay-rated behaviors (i.e., negative feelings/attitudes toward waiting and delaying rewards). These behaviors are more common in adults with ADHD compared with controls. Delay-related behaviors are also related to important functional impairments, especially criminality, substance use, and problems with money management. (PsycINFO Database Record (c) 2018 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Attention Deficit Disorder with Hyperactivity; *Questionnaires; *Test Reliability; *Test Validity; *Delay Discounting; Neuropsychology; Self-Report
- PsycINFO Classification:
- Clinical Psychological Testing (2224)
Developmental Disorders & Autism (3250) - Population:
- Human
Male
Female - Location:
- Sweden
- Age Group:
- Adulthood (18 yrs & older)
Young Adulthood (18-29 yrs)
Thirties (30-39 yrs)
Middle Age (40-64 yrs) - Tests & Measures:
- Adult ADHD Self-Report Scale
Delay-Discounting Task
Delis-Kaplan Executive Function System DOI: 10.1037/t15082-000
Wechsler Adult Intelligence Scale--Fourth Edition DOI: 10.1037/t15169-000
Quick Delay Questionnaire DOI: 10.1037/t64746-000 - Grant Sponsorship:
- Sponsor: Swedish Research Council, Sweden
Recipients: Thorell, Lisa B. - Methodology:
- Empirical Study; Quantitative Study
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- First Posted: Dec 19, 2016; Accepted: Oct 26, 2016; Revised: Oct 13, 2016; First Submitted: Dec 22, 2015
- Release Date:
- 20161219
- Correction Date:
- 20180215
- Copyright:
- American Psychological Association. 2016
- Digital Object Identifier:
- http://dx.doi.org/10.1037/pas0000421
- PMID:
- 27991822
- Accession Number:
- 2016-60730-001
- Persistent link to this record (Permalink):
- http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2016-60730-001&site=ehost-live
- Cut and Paste:
- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2016-60730-001&site=ehost-live">Quick Delay Questionnaire: Reliability, validity, and relations to functional impairments in adults with attention-deficit/hyperactivity disorder (ADHD).</A>
- Database:
- PsycINFO
Quick Delay Questionnaire: Reliability, Validity, and Relations to Functional Impairments in Adults With Attention-Deficit/Hyperactivity Disorder (ADHD)
By: Lisa B. Thorell
Department of Clinical Neuroscience, Division of Psychology, Karolinska Institutet;
Douglas Sjöwall
Department of Clinical Neuroscience, Division of Psychology, Karolinska Institutet
Gabry W. Mies
Behavioural Science Institute, Radboud University
Anouk Scheres
Behavioural Science Institute, Radboud University
Acknowledgement: This study was supported by a grant from the Swedish Research Council to Lisa B. Thorell. We thank Ylva Holst and Emelie von Vogelsang Antonsson for their valuable help with the data collection.
During recent decades, it has repeatedly been argued that attention-deficit hyperactivity disorder (ADHD) is a heterogeneous disorder with multiple neuropsychological underpinnings (Castellanos, Sonuga-Barke, Milham, & Tannock, 2006; Nigg, Willcutt, Doyle, & Sonuga-Barke, 2005; Sonuga-Barke & Fairchild, 2012). For example, according to the dual-pathway model, executive deficits constitute one pathway to ADHD, and a unique motivational style constitutes another (Sonuga-Barke, 2002). With regard to empirical support for this model, a fairly large number of studies investigating deficits in executive functioning have found that individuals with ADHD differ from controls with regard to executive functions such as inhibitory control, working memory, and shifting during both childhood (e.g., Willcutt, Doyle, Nigg, Faraone, & Pennington, 2005) and adulthood (e.g., Boonstra, Oosterlaan, Sergeant, & Buitelaar, 2005). However, results are more inconsistent with regard to motivational deficits, and a better understanding of the psychometric properties of the measures used in different studies is needed.
A number of laboratory measures are available that tap into delay-related behaviors, such as delay of gratification (e.g., Mischel, Shoda, & Rodriguez, 1989), delay aversion (e.g., Sonuga-Barke, Taylor, Sembi, & Smith, 1992), and delay discounting (e.g., Critchfield & Kollins, 2001). Meta-analyses have shown that children with ADHD are more delay averse compared with controls (Willcutt, Sonuga-Barke, Nigg, & Sergeant, 2008) and that individuals with ADHD show relatively steeper delay discounting (e.g., Jackson & MacKillop, 2016; Patros et al., 2016). However, it should be noted that there are individual studies that have failed to find significant group differences between children with ADHD and controls on delay aversion and delay-discounting tasks (Bidwell, Willcutt, Defries, & Pennington, 2007; Karalunas & Huang-Pollack, 2011; Scheres et al., 2006; Sjöwall, Roth, Lindqvist, & Thorell, 2013; Solanto et al., 2007). With regard to studies on adults, a link between delay-related behaviors and dimensional measures of ADHD symptoms was found in two studies using nonclinical samples of university students (e.g., Clare, Helps, & Sonuga-Barke, 2010; Scheres, Lee, & Sumiya, 2008). However, a recent meta-analysis of clinical studies concluded that adults with ADHD do not differ significantly from controls on delay discounting, the effect size being only .14 (Mowinckel, Pedersen, Eilertsen, & Biele, 2015).
In the previously mentioned meta-analysis on delay discounting in adults with ADHD (Mowinckel et al., 2015), it was concluded that there are few studies available (n = 6 in the meta-analysis), and that the tests used to study delay-related behaviors might not have been adequately adapted to an adult population. Among children, relations between delay-related behaviors and ADHD have been shown to be stronger in younger compared with older children (cf. Karalunas & Huang-Pollock, 2011; Pauli-Pott & Becker, 2011). This finding could be related to measurement issues, or to the fact that relations between delay-related behavior and ADHD are not as strong after the preschool years. It is interesting that it has also been suggested that delay-related behaviors are expected to be more strongly associated with symptoms of hyperactivity/impulsivity than with symptoms of inattention (Castellanos et al., 2006). In line with this notion, one nonclinical study of children (Thorell, 2007) found that delay aversion was only related to hyperactivity/impulsivity when controlling for the overlap between the two ADHD symptom domains. However, some support for the opposite (i.e., that delay aversion is more strongly related to symptoms of inattention) has also been found (e.g., Paloyelis, Asherson, & Kuntsi, 2009; Sjöwall, Backman, & Thorell, 2015). To our knowledge, the issue to what extent delay-related behavior is specifically related to either one of the two ADHD symptom domains has not been addressed in previous studies of adults.
One final issue that should be taken into consideration when examining the link between delay-related behavior and ADHD is that the tests used to measure delay-related behaviors may not fully capture this construct. According to Sonuga-Barke (2002), delay aversion is a motivational style characterized by the desire to escape delays when possible, because of the negative emotions experienced during waiting periods. Thus, delay aversion refers not only to choices that result in minimal waiting periods, but also to negative subjective experiences during such periods. Therefore, there is a need for subjective delay aversion measures that can be used as a complement to neuropsychological tests.
Based on the limitations of the laboratory measures described previously, the need for an easily administered questionnaire-based measure of response to delay in adulthood has been pointed out. To address this need, Clare and colleagues (2010) introduced the “Quick Delay Questionnaire” (QDQ), a 10-item self-rating instrument for adults that measures delay-related behaviors. The QDQ includes items related to responses and attitudes to delay-related activities and situations that are relevant to everyday adult life such as queuing/waiting and choosing long-term over short-term outcomes (e.g., “Even if I have to wait a long time for something I won’t give up if it is important to me” and “I am usually calm when I have to wait in queues”). In the first study introducing this instrument (Clare et al., 2010), the psychometric properties of the QDQ scores were examined in a convenience sample of university students. A factor analysis showed that the questionnaire has two subscales (i.e., delay aversion and delay discounting) and that the scores had both adequate internal consistency (α = .68/.77) and good test–retest reliability (r = .80/.81). In addition, this study showed that the scores for both subscales were significantly related to symptoms of inattention and hyperactivity in this nonclinical sample. Significant, although somewhat weaker, relations were also found between QDQ scores and symptoms of both anxiety and depression. Significant relations between the QDQ scores and both ratings of ADHD symptoms and depression have also been confirmed in a more recent study (Mies, de Water, & Scheres, 2016). The study by Mies and colleagues also found evidence for convergent validity of the QDQ because high scores on the two QDQ subscales were associated with a higher number of choices for smaller sooner rewards compared with larger later rewards in a delay-discounting task using hypothetical rewards. Except for this study, no previous studies have examined the convergent validity of QDQ scores, and there is only one study available (Holst & Thorell, 2013) that has included individuals with ADHD. This study did not include a nonclinical control group. Thus, it is still unknown to what extent QDQ scores can be used to discriminate between adults with ADHD and adults without psychiatric disorders. The QDQ has, on the other hand, been used in studies investigating gambling (Addicott, Pearson, Kaiser, Platt, & McClernon, 2015), Parkinson’s disease (Torta et al., 2012), and Type 2 diabetes (Williams, Lynch, Knapp, & Egede, 2014). Several adapted parent-rated versions of the QDQ for assessing delay-related behavior in children with ADHD have also recently been presented (Hsu, Benikos, & Sonuga-Barke, 2015; Van Liefferinge et al., in press). Thus, the QDQ has been used in several different areas of research, despite the fact that the psychometric properties of the QDQ scores have not been thoroughly examined.
According to Clare and colleagues (2010), the items included in the QDQ are intended to provide descriptions of responses to and attitudes toward delay-related situations of relevance in everyday adult life. However, the link between QDQ ratings and daily life functioning has not been examined. This means that it is not known to what extent the questionnaire can be used to provide a better understanding of the functional impairments known to affect individuals with ADHD, such as academic underachievement (Frazier, Youngstrom, Glutting, & Watkins, 2007), high unemployment (e.g., Shaw et al., 2012), problematic social relations (e.g., Nijmeijer et al., 2008), problems in daily situations such as handling money (e.g., Graziano et al., 2015), substance abuse (e.g., Lee, Humphreys, Flory, Liu, & Glass, 2011), and criminality (e.g., von Polier, Vloet, & Herpertz-Dahlmann, 2012).
Addressing this issue should be considered of vital importance, particularly because it has been argued that even though measures of neuropsychological deficits are often suboptimal with regard to discriminating between individuals with ADHD and controls, they might provide useful clinical information by identifying individuals with increased risk of functional impairments (Pritchard, Nigro, Jacobson, & Mahone, 2012; Sjöwall & Thorell, 2014).
A final important aspect when examining the link between motivational aspects and ADHD is to what extent these deficits are independent of executive function deficits. According to the dual-pathway model (Sonuga-Barke, 2002, 2003), delay aversion and executive function deficits are two core deficits of ADHD that should be at least partially dissociable. A relatively large number of previous studies on children support this distinction, at least with regard to inhibitory control and delay aversion (e.g., Coghill, Seth, & Matthews, 2014; Dalen, Sonuga-Barke, Hall, & Remington, 2004; Solanto et al., 2001; Sonuga-Barke, Dalen, & Remington, 2003). However, Karalunas and Huang-Pollock (2011) suggested that some executive functions might be more likely than others to be related to a delay-averse motivational style. More specifically, they argued that executive functions that rely on engagement across periods of delay (i.e., working memory) might be more strongly linked to delay aversion than other executive functions (e.g., inhibition). In support of this hypothesis, they found that delay aversion was related to working memory but not to inhibition. Also in support of a link between delay-related behaviors and working memory, it was demonstrated that rates of discounting of delayed rewards were significantly reduced among adults who received memory training but remained unchanged among those who received control training (Bickel, Yi, Landes, Hill, & Baxter, 2011).
In summary, current theoretical propositions as well as the empirical findings referred to previously, suggest that scores on a measure of delay-related behavior such as the QDQ will most likely have high specificity (i.e., few controls will be impaired), but relatively low sensitivity, because only a subgroup of individuals with ADHD will have high levels of delay-related behaviors. This subgroup might be at a particularly high risk of certain functional impairments, and it is therefore important to examine the link between delay-rated behaviors and daily functioning. Finally, it is of importance to further investigate to what extent executive function deficits and delay aversion are truly two dissociable components in relation to ADHD, and such studies should include measures of several different executive functions and not only inhibition.
Aims of the StudyThe overall aim of the present study was to examine the psychometric properties and the clinical utility of QDQ scores for examining delay-related behaviors in adults with ADHD. More specifically, we aimed to:
(1) Investigate the psychometric properties (i.e., internal consistency and test–retest reliability, as well as both convergent and discriminative validity) of QDQ scores.
(2) Examine the association between scores on the QDQ and symptoms of ADHD in nonclinical samples and to what extent QDQ scores can be used to discriminate between individuals with ADHD and nonclinical or clinical controls (i.e., patients with primarily depression and anxiety disorders).
(3) Examine whether scores on the QDQ can increase the prediction rate, above and beyond the influence of executive function deficits (i.e., including measures of inhibition, working memory and set shifting), when trying to discriminate between adults with ADHD and controls.
(4) Examine the association between QDQ scores and measures of daily functioning (i.e., social relations, daily problem areas, academic achievement, unemployment, substance abuse and criminality) in a clinical sample.
Method Participants
Because we were interested in comparing an ADHD group with normal controls as well as with a clinical sample of individuals with psychiatric disorders other than ADHD, the study included two clinical samples and one nonclinical sample of adults in the age range 18–45 years. More detailed information on recruitment is presented under the heading “Procedure” below and demographic information is presented in Table 1.
Results of the Analyses of Covariance (ANCOVAs; Dimensional Variables) or Chi-Square Test/Fisher’s Exact Test (Categorical Variables) Comparing the Attention-Deficit/Hyperactivity Disorder (ADHD) Group (1), The Clinical Control Group (2), and the Nonclinical Control Group With Regard to Background Variables and the Two Quick Delay Questionnaire (QDQ) Subscales
Nonclinical sample
The nonclinical sample was a convenience sample recruited from two different sources. A total of 44 individuals (18 men, 26 women) were part of another ongoing study, and these individuals were recruited from a larger sample of 1000 individuals aged 18–45 years, living in or around the central parts of Stockholm, Sweden. In addition, 61 individuals (26 men, 35 women) were recruited through announcements at a major Swedish university, and this sample consisted of undergraduate students. Thus, the nonclinical sample consisted of 105 individuals in total (44 men, 61 women).
Clinical samples
The first clinical sample included 51 adults (20 men, 31 women) diagnosed with ADHD. The second clinical sample included 46 individuals (13 men, 33 women) diagnosed with clinical disorders other than ADHD (primarily anxiety disorders and depression, see further details below). Participants in both groups were recruited from three outpatient psychiatric clinics in Stockholm, Sweden, using flyers distributed in the waiting room of the clinics. The participants in the ADHD group underwent a neuropsychiatric assessment conducted by a licensed psychologist. The assessment included a clinical judgment using the second version of the Diagnostic Interview for ADHD in Adults (DIVA 2.0; Kooij, 2013). This diagnostic interview has been shown to have a diagnostic accuracy of 100% when compared with the diagnoses obtained with the Conners’ Adult ADHD Diagnostic Interview, and it has also been shown to correlate highly with several self-report scales assessing ADHD severity (e.g., Ramos-Quiroga et al., in press). The interview is freely available as a PDF on the website of the DIVA Foundation (www.DIVAcenter.eu). In addition to DIVA (which assesses both current and childhood symptoms of ADHD), current levels of ADHD symptoms were assessed using the Adult ADHD Self-Report Scale (ASRS-v1.1; Kessler et al., 2005). The psychologist also interviewed a close relative of the participant, in most cases the mother, to obtain a detailed anamnesis. All participants in the ADHD group met the full diagnostic criteria for ADHD combined subtype (n = 38) or inattentive subtype (n = 13) as specified in the DSM–5 (American Psychiatric Association, 2013). Finally, all participants underwent testing of global intellectual ability using the fourth edition of Wechsler Adult Intelligence Scale (WAIS-IV; Wechsler, 2008a). Exclusion criteria were an IQ score of <80 on WAIS-IV and the presence of substance-related disorders. All participants in the ADHD group met at least the minimum symptom criteria for ADHD according to DSM–5 (i.e., 5 symptom of either hyperactivity/impulsivity and/or inattention) based on both the DIVA 2.0 diagnostic interview and self-rating using the ASRS.
In addition to a primary ADHD diagnosis, some participants in the ADHD group also met the DSM–IV criteria for the following comorbid diagnoses: mood disorders including “major depression” (15.8%), bipolar disorder (5.3%), unspecified anxiety disorder (5.3%), panic disorder (3.5%), obsessive–compulsive disorder (1.7%), social phobia (1.7%), and personality disorders (5.3%). Five participants had more than one comorbid diagnosis. The diagnoses in the clinical control group were the following: mood disorders including “major depression” (43.4%), bipolar disorder (11.3%), anxiety disorder UNS (15.1%), social phobia (9.4%), panic disorder (1.8%), obsessive–compulsive disorder (5.7%), general anxiety disorder (5.7%), posttraumatic stress disorder (5.7%), eating disorders (1.8%), and personality disorders (11.3%). Fifteen participants had more than one diagnosis.
Procedure and Measures
Procedure
For the clinical sample, an information letter was sent by mail to individuals who had shown interest in participating in the study after reading the flyers distributed in the waiting rooms of the clinics. For the nonclinical sample, the flyers included a link to a website, where more information about the study was presented. Individuals who wanted to participate in the study were instructed to complete a questionnaire, which assessed ADHD symptom levels, delay-related behavior and functional impairments using the instruments described below (see heading “Questionniare data”), either through an Internet-based platform or completion of a paper-and-pencil version. Two males in the clinical sample, but none of the participants in the two controls groups, showed interest in participating in the study but failed to take part of the neuropsychological testing.
Two to 4 weeks later after completing the questionnaire, participants underwent a neuropsychiatric assessment. For the nonclinical sample, this also included completion of a laboratory measure of delay discounting and completing the QDQ a second time. Participants performed the tests at the clinic (clinical samples and random nonclinical sample) or at the university laboratory (nonclinical student sample). Altogether, the testing took approximately 1 hr. An experimenter was present in the room during the testing, and instructions were standardized and either read out loud to the participants or displayed on a computer screen. The neuropsychological tests were administered in a fixed order, with the QDQ always being administered before the delay discounting task. Exclusion criteria for the clinical group were an IQ score of <80 on WAIS-IV and the presence of any substance-related disorder. Exclusion criteria for the nonclinical group were the presence of any psychiatric disorders. As compensation for participating, the individuals in the two clinical groups received two movie tickets (value approx. 20 Euros), and those in the control group received 50 Euros. The local ethics committee approved the study.
Questionnaire data
The QDQ is a 10-item questionnaire, and all items are presented in the original publication (Clare et al., 2010). As mentioned in the introduction, the QDQ has been used in several previous studies, although the psychometric properties of the QDQ scores have not been thoroughly examined. The items included in the QDQ were originally selected from a larger number of items. The final set of items were selected based on the result of a factor analysis, the need to include both positive and negative items in the scale, and the need to minimize overlap of content (see Clare et al., 2010, for more detailed information). In the present study, all participants filled out the QDQ at the time of entering the study, and the nonclinical sample also completed the QDQ a second time as part of the neuropsychological assessment. Ratings were made on a 5-point Likert scale ranging from 1 (“not like me at all”) to 5 (“very like me”). Because the original version of the QDQ is in English and the present study was conducted in Sweden, the questionnaire was translated and back-translated with the help of two bilingual researchers. It should be noted that translation could have a significant impact on a scale. However, because the items included in the QDQ are very clear, both translation and back-translation was completed without any problems.
In addition to the QDQ, participants’ ADHD symptom levels (both nonclinical and clinical samples) were assessed using the Adult ADHD Self-Report Scale (ASRS; Kessler et al., 2005). The scores used in the dimensional analyses were sums for the nine items measuring hyperactivity/impulsivity (α = .89) and the nine items measuring inattention (α = .88). Scores on the ASRS have been shown to have high test–retest reliability (r = .88), and high correlations have been found between ASRS scores and the DSM-oriented symptom scales included in the Conners Adult ADHD Rating Scales (rs ≥ .66; Kim, Lee, & Joung, 2013). Finally, ASRS scores have been shown to discriminate well between adults with ADHD and controls, with a total classification accuracy of 96.2% (Kessler et al., 2005).
Functional impairment was addressed first of all using questions about educational attainment and unemployment (current and previous). In addition, the ADHD Daily Problem Questionnaire (ADPQ), which was designed within the present project, was used. The ADPQ is similar in design to Barkley’s Functional Impairment Scale (BFIS; Barkley, 2011a), in that it contains a list of daily activities, and participants (or a close relative/friend of the patient) are asked to rate their level of functioning on a scale from 0 (“no problem”) to 9 (“very severe problem”). However, whereas the BFIS contains relatively broad items (e.g., problems “in your home life with your immediate family”), the ADPQ contains more specific items within four problem areas: (1) economic problems (2 items: “handling money in a responsible way” and “paying bills on time”), (2) daily chores/responsibilities (4 items: “cooking,” “cleaning,” “doing laundry,” “grocery shopping”), (3) time management (4 items: “keeping appointments,” “being on time for work/school,” “getting up in time in the morning,” “going to bed on time when having to get up early”), and (4) social relations (2 items: “socializing with friends” and “going to a party when I do not know the other guests well”). The reason for focusing on these four areas is that previous research has shown that the most serious impairments among individuals with ADHD are found in these areas (e.g., Barkley, Murphy, & Fischer, 2008). Finally, we assessed criminality. Ratings were made on a 5-point scale (0 = never, 1 = 1 time, 2 = 2–3 times, 3 = 4–10 times, 5 = more than 10 times) and included the following areas: (1) violent criminal behavior (2 items: “physical abuse” and “hitting someone so severely that he/she needed professional medical help”), (2) nonviolent criminal behavior (4 items: “shoplifting,” “pickpocketing,” “eating and running,” and “breaking and entering”), *3) driving-related problems (3 items: “driving under the influence,” “driving without a license,” and “exceeding the speed-limit by more than 30 kilometers/hour”) and (4) police contact (1 item: “being arrested”). In the present study, we used the mean of all items as a measure of criminality.
Neuropsychological assessment
The measures of executive functioning used in the present study were selected from either the Delis Kaplan Executive System (D-KEFS; Delis, Kaplan, & Kramer, 2001a) or WAIS-IV (Wechsler, 2008a). These are two of the most well-known test batteries for examining executive functioning in adults. In addition, a delay-discounting task was included for participants in the nonclinical sample. Below a detailed description of all included measures is provided.
Delay discounting was measured using a delay-discounting task developed by Scheres, Tontsch, Thoeny, and Kaczkurkin (2010), using the Psytools software (Delosis, London). In this computerized task, participants were instructed to make repeated choices (40) between a small variable reward (values equivalent of approximately 2, 4, 6, or 8 cents in Swedish currency) that would be delivered after 0 s and a large constant reward (approximately 10 cents) that would be delivered after a variable delay (5, 10, 20, 30, or 60 s). For example, on some trials, participants had to choose between 6 cents now or 10 cents after waiting 20 s. Participants were not informed about the delay durations. Instead, they experienced each delay during task practice, giving them a sense of the delay duration associated with each level, without revealing the actual duration. After task completion, participants received the total amount of money won (maximum 40 SEK [approximately €4]). The measure used was the “area under the curve” (AUC; see Myerson, Green, & Warusawitharana, 2001; Scheres et al., 2006, for a detailed description of how to calculate this score). In general, a smaller AUC reflects a steeper discounting function (i.e., less willingness to wait as the delay duration increases). Split-half reliability, estimated using Spearman-Brown coefficient, was excellent (.985) for the scores from the delay-discounting task within the present study. With regard to validity, previous studies have shown that scores on delay-discounting tasks have high discriminative validity, as individuals with impulse-control problems—including ADHD—show increased preferences for immediate rewards compared with typically developing controls (see meta-analyses by Jackson & Mackillop, 2016 and Patros et al., 2016).
Verbal working memory was measured using two subtests from the WAIS-IV: Letter-Number Sequencing and Digit Span. In Letter-Number Sequencing, participants have to repeat a series of randomly mixed letters and numbers, starting with the numbers in ascending order, followed by the letters in alphabetical order. In Digit Span Backward, participants have to repeat the series in the backward order. In Digit Span Sequencing, the numbers are randomly presented and must be repeated in the correct number order. Digit Span Forward was not included, as this test primarily measures short-term memory (STM). According to the WAIS-IV technical manual (Wechsler, 2008b), the test–retest reliability for the scores from the Letter Number Sequencing and Digit Span Task is good (all rs ≥ .80), and the construct validity high, because scores from these two measures of verbal working memory are highly correlated (r = .69).
Inhibition was measured using the third trial (i.e., interference trial) of the Color-Word Interference Test from the D-KEFS. In this part of the task, participants are presented with rows of words printed in incongruent colors and are instructed to inhibit reading the words and, instead, name the colors in which the words are printed. The number of seconds needed to complete the trial was used as a measure of inhibition. The test–retest reliability for the scores from the interference part of the Stroop task is reported to be adequate (r = .75) in the D-KEFS manual, and there are also other studies available showing high test–retest reliability (r = .86; Siegrist, 1997). Regarding construct validity, a significant correlation has been found between scores from this task and scores from for example the executive cluster from the Woodcock-Johnson III Tests of Cognitive Abilities (r = .39; Floyd et al., 2006). High discriminative validity when comparing individuals with ADHD and healthy controls has also been reported (see meta-analysis by Homack & Riccio, 2004).
Set shifting was measured by using the shifting trials from the Color-Word Interference Task from the D-KEFS. In this part of the task, participants are asked to switch back and forth between naming the discordant ink colors and reading the words. Completion time was used as a measure of set shifting. The test–retest reliability for scores from the switching trial has been shown to be moderate (r = .65; Delis, Kaplin, & Kramer, 2001b). Regarding construct validity, scores from this task have been shown to be significantly correlated with, for example, scores from the shifting trial from the Trailmaking subtest from D-KEFS (r = .41; Delis et al., 2001b).
Statistical Analyses
First, the reliability of the QDQ scores was investigated using measures of Cronbach’s alpha to assess internal consistency. Test–retest reliability was investigated by studying correlations between Time 1 and Time 2 (separated by a 2- to 4-week interval). Second, convergent validity was investigated by studying correlations between scores from the QDQ and the delay-discounting task. Third, correlations between the QDQ scores and symptoms of ADHD were investigated within the nonclinical sample. To investigate whether the scores on the two QDQ subscales were specifically related to either one of the two ADHD symptom domains, partial correlation analyses were used to control for hyperactivity/impulsivity when studying relations to inattention and vice versa. Fourth, the ability of QDQ scores to discriminate between individuals with ADHD and either nonclinical or clinical controls was investigated. Here, we used analyses of variances (ANOVA), and these analyses were complemented with measures of effect sizes using ηp2, where ηp2 = .01 is considered to be a small effect, ηp2 = .06 a medium effect, and ηp2 = .14 a large effect (Cohen, 1988). Tukey Honest Significance Differences was used as the post hoc test to compare the ADHD group with either the clinical or nonclinical control group. Effect sizes for these post hoc tests were computed using Hedges g, which uses pooled SDs. In line with recommendations, g = .30 was considered to be a small effect, g = .50 a medium-sized effect, and g = .80 a large effect (Cohen, 1988). In addition, logistic regression analyses were used to obtain measures of sensitivity and specificity for scores on the QDQ. Here, we first of all conducted two regression analyses that only included scores on the QDQ, and thereafter two hierarchical regression analyses that included scores from a set of executive function measures in the first step and scores on the two QDQ subscales in the second step. This allowed us to examine whether adding QDQ scores would increase the sensitivity and/or specificity of belonging to the ADHD group above and beyond the influence of executive functioning deficits. Finally, correlation analyses were used to examine associations between QDQ scores and functional impairments. In line with Cohen (1988), r = .10 was considered a small effect, r = .30 and medium-sized effect, and r = .50 a large effect. Only the two clinical samples had data available for investigating relations to functional impairments, and only the nonclinical sample had data available for studying test–retest reliability and convergent validity. For the remaining analyses, both the two clinical samples and the nonclinical sample were included.
ResultsDescriptive data and group differences with regard to demographic variables are presented in Table 1. No significant group differences were found for age or gender, but the groups differed significantly with regard to educational level. As expected, the clinical ADHD group had the lowest proportion of individuals with a university education and the nonclinical control group the highest. We therefore reran the analyses using educational level as a covariate, but this did not result in any changes in the results.
Reliability and Validity of the QDQ Scores
Internal consistency was found to be adequate for scores on the two QDQ subscales, α = .71 for delay aversion and α = .83 for delay discounting, when including all participants. Similar results were found when studying internal consistency separately for the clinical and nonclinical sample (αs ranging between .71–.81). With regard to test–retest reliability, correlations between QDQ scores collected 2–4 weeks apart were shown to be good for delay aversion, r = .78, p < .001, but less satisfactory for delay discounting, r = .65, p < .001.
Convergent validity of QDQ scores was examined by studying associations to the AUC measure from the laboratory task tapping into delay discounting. The results showed that scores on the two QDQ subscales were not significantly correlated with scores on the delay-discounting task, r = −.07 (delay aversion) and r = −.08 (delay discounting). When plotting the relation between the AUC measure and the scores on the two QDQ subscales, no indications of nonlinear relations (i.e., cubic or quadratic) were found. The mean AUC was .48 (SD = .27, range = .09–.83), which indicates that the data from the delay-discounting task for the nonclinical sample showed enough variance to be able to detect any associations that might be present.
Relations Between the QDQ Scores and ADHD Symptoms
With regard to the dimensional analyses (i.e., correlations between scores on the two QDQ subscales and ADHD symptom levels in the nonclinical samples), the results showed that scores on the two QDQ subscales were both strongly related to inattention (r = .50 for delay aversion and r = .36 for delay discounting, ps < .001) and hyperactivity/impulsivity (r = .61 for delay aversion and r = .35 for delay discounting, ps < .001). When controlling for the overlap between the two ADHD symptom domains, no significant relations were found for delay discounting (both rs < .19, ns). However, for delay aversion, a significant relation was found to hyperactivity/impulsivity, r = .42, p < .001, but not to inattention (r = .19, ns).
With regard to the categorical analyses, the results showed a significant main effect of group for both delay aversion and delay discounting (Table 1). The effect sizes for the overall comparison were large (both ηp2 > .20). Post hoc analyses revealed that participants in both the ADHD group and the clinical control group rated themselves significantly higher with regard to both delay aversion and delay discounting compared with participants in the nonclinical control group. Effect sizes when comparing the ADHD group with the nonclinical control group were large for both delay aversion (g = 1.11) and delay discounting (g = 1.26). Compared with the clinical control group, participants in the ADHD group had significantly higher scores on delay aversion, but not on delay discounting. Effect sizes were medium for delay aversion (g = .68) and small for delay discounting (g = .32).
In the next step, we used a logistic regression analysis to determine how well QDQ scores could discriminate between adults with ADHD and adults without any psychiatric disorder (Model 1). Measures of sensitivity, specificity and the total proportion of participants classified into the correct category are presented in Table 2. Model 1 was shown to be significant, χ2 = (2, n = 156) = 55.69 p < .001, with a sensitivity of 56.9 and a specificity of 91.4. In total, Model 1 could classify 80.1% of the participants into the correct category. Next, adults with ADHD were compared with adults with other psychiatric disorders (Model 2). Model 2 was also shown to be significant, χ2 = (2, n = 97) = 10.83, p < .01, with a sensitivity of 70.6 and a specificity of 54.3. In total, Model 2 could classify 62.9% of the participants into the correct category.
Results of the Logistic Regression Analyses
In addition to examining how well QDQ scores could discriminate between adults with ADHD and either nonclinical or clinical controls, we also wished to examine to what extent QDQ scores could increase the classification rate above and beyond the influence of executive functioning tests. These analyses would also allow us to compare the discriminative validity of the QDQ scores (presented in Model 1 and 2) with scores from a battery of laboratory tests of executive functioning (Step 1 in Model 3 and 4). The results (Table 2) showed that when trying to discriminate between adults with ADHD and nonclinical controls (Model 3), Step 1 of the model was significant, χ2 = (4, n = 150) = 52.14, p < .001, with a sensitivity of 57.8, a specificity of 91.4, and a total classification rate of 81.3%. When adding the scores from the two QDQ subscales, Step 2 was significant, χ2 = (2, n = 150) = 36.47, p < .001, and the sensitivity (68.9), the specificity (93.3), and the total classification rate (86.0) were increased. The influence of both the delay aversion (Wald = 12.69, p < .001) and delay-discounting subscales (Wald = 3.93, p < .05) were significant. Finally, Model 4 tested whether QDQ scores could increase the classification rate also when comparing adults with ADHD to adults from a clinical sample. The results showed that Step 1 of the model was significant, χ2 = (4, n = 89) = 20.38, p < .001, with a sensitivity of 62.2, a specificity of 77.3, and a total classification rate of 69.7%. When adding the QDQ subscales in Step 2, this step was also significant, χ2 = (2, n = 89) = 13.76, p < .001. The sensitivity for Model 4 was 73.3, the specificity was 75.0 and the total classification rate was 74.2. The influence of scores on the delay aversion subscale was significant (Wald = 10.70, p < .001), but not the influence of scores on the delay-discounting subscale (Wald = 0.14, ns).
Relations Between QDQ Scores and Daily Life Functioning
Our last research question concerned to what extent scores on the two QDQ subscales are significantly related to functional impairments among individuals in the two clinical groups. As can be seen in Table 3, QDQ scores on both subscales were significantly related to criminality and problems with money management and daily chores/responsibilities. In addition, scores on the delay aversion subscale were significantly related to problems with daily chores/responsibilities, and scores on the delay-discounting subscale were significantly related to both substance use and problems with time management. However, no significant associations were found to educational level. The direction of all significant effects was as expected, with higher levels of delay-related behavior being related to higher levels of functional impairment. When using the Bonferroni-Holm correction for multiple comparisons, delay aversion was only significantly related to criminality, whereas delay discounting was significantly related to criminality, substance abuse, as well as to money management problems. The sizes of the effects surviving control for multiple comparisons were all in the medium range. When controlling for ADHD symptom levels, delay discounting was significantly related to money management and criminality, but none of the other associations remained significant.
Correlations Between the Quick Delay Questionnaire (QDQ) and Functional Impairments Among Participants in the Two Clinical Groups (n = 95)
DiscussionRecent models of heterogeneity have emphasized the need to not only regard ADHD as an executive disorder, but as a disorder related to multiple neuropsychological deficits (e.g., Nigg et al., 2005). Delay-related behaviors have repeatedly been linked to ADHD in children, but few studies focusing on delay-related behaviors have been conducted on adult ADHD samples. One major reason for this is probably the lack of ratings with good psychometric properties for assessing constructs such as delay aversion and delay discounting in adults. The present study, which aimed at investigating the psychometric properties and clinical utility of QDQ scores, therefore provides highly useful information from both a theoretical and clinical point of view. The results showed that the scores for the QDQ had good test–retest reliability with regard to the delay-discounting subscale, but less satisfactory reliability for scores on the delay aversion subscale. Scores from the two QDQ subscales (i.e., delay aversion and delay discounting) were not significantly related to a laboratory measure of delay discounting. Adults with ADHD were shown to have higher scores on both QDQ subscales compared with normal controls. They were also shown to have higher levels of delay aversion, but not delay discounting, than adults with other psychiatric disorders. When trying to discriminate between adults with ADHD and nonclinical controls, the sensitivity and specificity of scores from the QDQ and the laboratory measures of executive functioning were similar, with very high specificity (> .90), but relatively low sensitivity (< .60). Finally, the results showed that scores on the QDQ correlated highly with some important areas of daily functioning, of which relations to criminality and problems with money management remained significant after controlling for ADHD symptom severity in the clinical sample.
Reliability of the QDQ Scores
Generally, .70 or above is considered adequate test–retest reliability. In the present study, the reliability for scores on the delay-discounting subscale was above this cut-off (r = .78), whereas the reliability for scores on the delay aversion subscale was somewhat below it (r = .65). Low reliability has also been found for other ratings designed to measure neuropsychological functions, such as the self-motivation subscale (r = .61) included in the Barkley Deficits in Executive Functioning Scale (BDEFS; Barkley, 2011b). However, of more importance is the fact that our finding is not in line with what Clare and colleagues (2010) found when they introduced the QDQ, because they reported a test–retest reliability of about .80 for scores from both subscales. In future studies and in clinical practice, it would be of value to adapt the QDQ for use by a close relative/friend to assess the consistency between different raters, and thereby get a better estimate of delay-related behaviors in the patient.
Relations Between Scores on the QDQ and the Delay-Discounting Task
We only found weak, nonsignificant, associations between QDQ scores and scores from a laboratory measure of delay discounting. To our knowledge, only one previous study (Mies et al., 2016) has studied this relation. This study did find significant relations to the two QDQ subscales, but the size of the effect was very small. When discussing this finding, it should be of value to draw a parallel to studies comparing laboratory measures and self-ratings of executive functioning. As shown in a review by Toplak, West, and Stanovich (2013), only 24% of the studies reported a statistically significant relation between tests and ratings of executive functioning, and the overall median correlation was only .19. The authors argue that one important distinction between laboratory measures and self-ratings—and this applies both to executive functions and delay-related behaviors—is that participants are estimating the frequency and typicality of how well they perform in day-to-day situations when they complete self-ratings, whereas neuropsychological measures are instead assessing optimal/maximum performance in a well-structured test situation at one point in time. Another difference that perhaps could explain the weak association between scores on the QDQ and scores on the delay-discounting task is that we used a laboratory task in which all delays were experienced and all rewards were paid. It is likely that there is a discrepancy between how people think they behave, as measured with the QDQ, and how they actually behave, as measured with a delay-discounting laboratory measure (cf. Scheres et al., 2008). As already mentioned in the introduction, another apparent difference between the QDQ and the temporal discounting task is that the test only includes the choice to wait or not to wait, whereas the QDQ includes both items related to choices (e.g., “I often give up on things that I cannot have immediately”) and items measuring the subjective feeling of waiting (e.g., “Having to wait for things makes me stressed and tense”). Finally, it is also possible that the delays in the task we used were too short to measure delay discounting in a similar way as was done with the QDQ (i.e., the delays were a maximum of 60 s, which is usually not comparable to delays people encounter in real life). In line with such an interpretation, Scheres and colleagues (2010) showed that choices in a hypothetical delay-discounting task with long delays and large rewards did not correlate with choices in real and hypothetical tasks with short delays and small rewards. Thus, not even all delay-discounting tasks appear to measure exactly the same construct, and this might explain why Mies and colleagues (2016) did find a relation between scores on the QDQ and scores on a delay-discounting task with hypothetical rewards, whereas QDQ scores were not significantly related to scores on the task with real choices used in the present study.
In summary, scores on the QDQ and scores on the delay-discounting task showed little overlap, and it is difficult to determine what conclusions can be drawn from this finding. One interpretation is that the delay-discounting task should be regarded as the “gold standard” for measuring delay-related behaviors and that the weak association between scores on this task and scores on the QDQ should be taken as support for poor validity of the QDQ. On the other hand, when discussing measures of executive functioning, it has been argued that the scores obtained from ratings have higher ecological validity compared with those obtained from laboratory tasks (e.g., Barkley & Fischer, 2011). In our opinion, ratings and laboratory tasks appear to measure different aspects of delay-related behaviors, and both have limitations and strengths, albeit different ones. Thus, it will be important for future research to conduct further examination of the overlap between different delay-related measures to determine how to best capture the multifaceted nature of this construct.
Associations Between QDQ Scores and ADHD
Regarding the dimensional analyses, scores from the two QDQ subscales were shown to be associated with both symptoms of hyperactivity/impulsivity and symptoms of inattention. However, when controlling for the overlap between the two ADHD symptom domains, our results indicated that delay aversion, but not delay discounting, was primarily related to hyperactivity/impulsivity rather than inattention. These findings are in line with theoretical propositions claiming that “cold” executive functions such as working memory and inhibitory control are primarily related to inattention, whereas “hot” executive functions such as delay aversion and gambling are primarily related to symptoms of hyperactivity/impulsivity (e.g., Castellanos et al., 2006). Some empirical support for this notion has also been found in studies of children (Thorell, 2007), but to our knowledge, the present study is the first study finding support for a specific link between delay aversion and hyperactivity/impulsivity among adults.
With regard to discriminative validity, our results showed that the scores from the QDQ had high specificity, but relatively low sensitivity when trying to discriminate between adults with ADHD and nonclinical controls. Thus, scores on the QDQ show a good ability to correctly classify the controls (i.e., few controls have high levels of delay-related behaviors), but a poor ability to correctly classify individuals with ADHD (i.e., only a subgroup of individuals with ADHD have high levels of delay-related behaviors). Because the present study is the first to examine the discriminative validity of scores on the QDQ, a comparison with previous findings is not possible. However, our finding of high specificity and low sensitivity is similar to several previous studies examining executive functioning in ADHD samples (e.g., Berlin, Bohlin, Nyberg, & Janols, 2004; Doyle, Biederman, Seidman, Weber, & Faraone, 2000; Lovejoy et al., 1999). In addition, these findings are in line with current theoretical models in which it is argued that ADHD is a neuropsychologically heterogeneous disorder, with only some individuals displaying high levels of delay-related behaviors, either alone or in combination with other neuropsychological deficits such as poor executive functioning (e.g., Castellanos et al., 2006; Nigg et al., 2005; Sonuga-Barke & Fairchild, 2012). Based on current models of heterogeneity, we therefore also aimed to investigate to what extent QDQ scores could increase the prediction rate, above and beyond the influence of executive functioning deficits. The results showed that especially sensitivity increased when scores from both the laboratory measures of executive functioning and the QDQ were entered into the model. Thus, a larger number of adults with ADHD were correctly classified when QDQ scores were also included in the assessment. This is exactly what should be expected based on the dual-pathway model, which suggests independent pathways of delay-related behavior and executive functioning in relation to ADHD (Sonuga-Barke, 2002, 2003).
Unlike most previous studies investigating the discriminative validity of different neuropsychological measures, we did not only compare adults with ADHD with nonclinical controls, but also with clinical controls diagnosed with other psychiatric disorders. The results showed that when studying the first step of Model 3 and 4 (i.e., models including only executive functioning measures), the total classification rates were very similar to those found when only including QDQ scores (i.e., Model 1 and 2). Thus, scores from the QDQ is about equally good at discriminating between the two clinical groups as scores from laboratory measures of executive functioning are. With regard to sensitivity versus specificity, the results showed that sensitivity was slightly higher when including only scores from the laboratory measures of executive functioning, whereas specificity was slightly higher when only including QDQ scores.
The low specificity of the QDQ scores when comparing adults with ADHD and clinical controls means that individuals with a diagnosis other than ADHD are quite likely to be incorrectly assigned to the ADHD group on the basis of QDQ scores (i.e., false positives). Comparing different clinical groups should be considered important. However, our findings are not unexpected, considering the fact that most participants in this group suffered from affective disorders, the majority from depression, and that previous research has shown that patients with depression, or individuals with increased levels of depressive symptoms, show steeper delay discounting of rewards (Mies et al., 2016; Pulcu et al., 2014; Takahashi et al., 2008, but see Lempert & Pizzagalli, 2010). This has been attributed to, for example, hyposensitivity to reward, pessimism about the future, but also to delays possibly being perceived as longer than they actually are (e.g., Pulcu et al., 2014), which might explain why the clinical control group scored higher on both subscales of the QDQ compared with nonclinical controls.
Associations Between QDQ Scores and Functional Impairments
When introducing the QDQ, Clare and colleagues (2010) stated that this instrument was intended to capture delay-related activities and situations of relevance to everyday adult life. However, the present study is the first to truly examine the link between scores from the QDQ subscales and measures of daily functioning. Our results showed that the QDQ scores correlated highly with criminality, substance use, as well as with daily functioning, such as time and money management. It is interesting to note that relations to criminality and problems with money management remained significant when controlling for ADHD symptom severity in the clinical sample. Thus, scores on the QDQ are able to capture important associations to functional impairments that are not explained by ADHD symptom severity. Several previous studies investigating nonclinical samples of adults have found relations between delay-discounting tasks and criminality and addictive behavior (e.g., Arantes, Berg, Lawlor, & Grace, 2013; MacKillop et al., 2011). However, to our knowledge this association has not been studied previously in a clinical ADHD sample, and our findings therefore provide important new information by showing that a subgroup of clinically referred adults with high delay-related behavior appear to be at particularly high risk for criminality and problems with money management, above and beyond the influence of ADHD symptom severity. With regard to our failure to find a relation between QDQ scores and academic achievement, this finding is in line with previous studies of children in which executive functioning deficits, but not delay aversion, were shown to mediate the link between ADHD symptoms and academic achievement (e.g., Brock, Rimm-Kaufman, Nathanson, & Grimm, 2009; Thorell, 2007). Thus, QDQ scores are associated with functional impairments, and some of these associations remain after controlling for ADHD symptoms.
Limitations, Future Directions, and Conclusions
One limitation of the present study was that data from the delay-discounting task were only available for the nonclinical sample. However, no clear ceiling effects were found, which means that it should have been possible to detect a relation between QDQ scores and scores on the laboratory task if such a relation had existed. Despite this, it would be valuable for future studies to investigate the relation between QDQ scores and scores on the delay-discounting tasks also in a clinical ADHD sample and thereby gain further information about the associations between ratings and task of delay-related behaviors. In addition, we have already noted that it would be of value to also include a task with hypothetical rewards and to adapt the QDQ to allow relatives or a close friend of the patient to make the ratings. Future studies should also include longitudinal investigations to examine to what extent scores on the QDQ can predict functional impairments over time. Before doing a longitudinal follow-up, the psychometric properties of the scores obtained from our measures used to access functional impairments (e.g., the ADPQ), need to be investigated. Finally, it would also be of value to include a clinical control group with nonaffective disorders, such as antisocial behavior or substance use.
In conclusion, it is interesting to note that scores on the QDQ can help to differentiate between ADHD and controls, above and beyond deficits in executive functioning. However, it should also be noted that when examining the discriminative validity of the QDQ scores, sensitivity was relatively low, suggesting that a substantial number of individuals with ADHD do not show high levels of delay aversion or delay discounting. This is in line with current models of heterogeneity in which ADHD is seen as a disorder related to multiple neuropsychological deficits. Given this heterogeneity, it has been suggested that neuropsychological measures should not primarily be used to differentiate between patients and controls, but rather to identify more homogeneous subgroups of individuals with ADHD, which may show differential susceptibility to functional impairments (Coghill, 2014). Our finding that scores on the QDQ are significantly related to important functional impairments associated with ADHD (i.e., primarily criminality, substance abuse, and economic management) is in line with this suggestion. This could be considered to indicate that the QDQ should be viewed as a valuable clinical instrument for identifying a subgroup of ADHD patients with high levels of delay-related behaviors. This subgroup might be at particularly high risk of certain functional impairments. However, it should also be noted that further research is needed within this area. This is especially important with regard to the association between scores on the QDQ and scores on laboratory tests of delay-related behaviors, as well as longitudinal studies investigating to what extent QDQ scores can predict functional impairment across time.
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Submitted: December 22, 2015 Revised: October 13, 2016 Accepted: October 26, 2016
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Source: Psychological Assessment. Vol. 29. (10), Oct, 2017 pp. 1261-1272)
Accession Number: 2016-60730-001
Digital Object Identifier: 10.1037/pas0000421
Record: 140- Title:
- Ratings of Everyday Executive Functioning (REEF): A parent-report measure of preschoolers’ executive functioning skills.
- Authors:
- Nilsen, Elizabeth S.. Centre for Mental Health Research, Department of Psychology, University of Waterloo, Waterloo, ON, Canada, enilsen@uwaterloo.ca
Huyder, Vanessa. Centre for Mental Health Research, Department of Psychology, University of Waterloo, Waterloo, ON, Canada
McAuley, Tara. Centre for Mental Health Research, Department of Psychology, University of Waterloo, Waterloo, ON, Canada
Liebermann, Dana. Hamilton-Wentworth District School Board, Hamilton, ON, Canada - Address:
- Nilsen, Elizabeth S., Department of Psychology, University of Waterloo, 200 University Avenue West, Waterloo, ON, Canada, N2L 3G1, enilsen@uwaterloo.ca
- Source:
- Psychological Assessment, Vol 29(1), Jan, 2017. pp. 50-64.
- NLM Title Abbreviation:
- Psychol Assess
- Page Count:
- 15
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- Psychological Assessment: A Journal of Consulting and Clinical Psychology
- ISSN:
- 1040-3590 (Print)
1939-134X (Electronic) - Language:
- English
- Keywords:
- preschoolers, parent-report, assessment, executive functioning, cognitive development
- Abstract:
- Executive functioning (EF) facilitates the development of academic, cognitive, and social-emotional skills and deficits in EF are implicated in a broad range of child psychopathologies. Although EF has clear implications for early development, the few questionnaires that assess EF in preschoolers tend to ask parents for global judgments of executive dysfunction and thus do not cover the full range of EF within the preschool age group. Here we present a new measure of preschoolers’ EF—the Ratings of Everyday Executive Functioning (REEF)—that capitalizes on parents’ observations of their preschoolers’ (i.e., 3- to 5-year-olds) behavior in specific, everyday contexts. Over 4 studies, items comprising the REEF were refined and the measure’s reliability and validity were evaluated. Factor analysis of the REEF yielded 1 factor, with items showing strong internal reliability. More important, children’s scores on the REEF related to both laboratory measures of EF and another parent-report EF questionnaire. Moreover, reflecting divergent validity, the REEF was more strongly related to measures of EF as opposed to measures of affective styles. The REEF also captured differences in children’s executive skills across the preschool years, and norms at 6-month intervals are reported. In summary, the REEF is a new parent-report measure that provides researchers with an efficient, valid, and reliable means of assessing preschoolers’ executive functioning. (PsycINFO Database Record (c) 2018 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Cognitive Ability; *Cognitive Development; *Developmental Measures; *Preschool Students; *Test Construction; Parents; Test Reliability; Test Validity
- Medical Subject Headings (MeSH):
- Child; Child Development; Child, Preschool; Executive Function; Factor Analysis, Statistical; Female; Humans; Male; Neuropsychological Tests; Parents; Reproducibility of Results; Surveys and Questionnaires
- PsycINFO Classification:
- Developmental Scales & Schedules (2222)
Cognitive & Perceptual Development (2820) - Population:
- Human
Male
Female - Location:
- Canada
- Age Group:
- Childhood (birth-12 yrs)
Preschool Age (2-5 yrs)
Adulthood (18 yrs & older) - Tests & Measures:
- Ratings of Everyday Executive Functioning [Appended]
Digit Span Task
Bear/Dragon Task
Gift Delay Task
Self-Ordered Pointing Task
Tower of Hanoi Task
Count and Label Task
Flexible Item Selection Task
Truck Loading Task
Day/Night Task
Whisper Task
Wechsler Preschool and Primary Scale of Intelligence-III
Children’s Communication Checklist-2 US Edition
BRIEF-P
Strengths and Weaknesses of ADHD symptoms and Normal Behavior Scale
Childhood Executive Functioning Inventory DOI: 10.1037/t62232-000
Strengths and Difficulties Questionnaire DOI: 10.1037/t00540-000
Children’s Behavior Questionnaire--Short Form DOI: 10.1037/t07622-000 - Methodology:
- Empirical Study; Quantitative Study
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- First Posted: Apr 7, 2016; Accepted: Feb 17, 2016; Revised: Feb 5, 2016; First Submitted: Jun 5, 2015
- Release Date:
- 20160407
- Correction Date:
- 20180315
- Copyright:
- American Psychological Association. 2016
- Digital Object Identifier:
- http://dx.doi.org/10.1037/pas0000308
- PMID:
- 27054618
- Accession Number:
- 2016-16660-001
- Persistent link to this record (Permalink):
- http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2016-16660-001&site=ehost-live
- Cut and Paste:
- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2016-16660-001&site=ehost-live">Ratings of Everyday Executive Functioning (REEF): A parent-report measure of preschoolers’ executive functioning skills.</A>
- Database:
- PsycINFO
Ratings of Everyday Executive Functioning (REEF): A Parent-Report Measure of Preschoolers’ Executive Functioning Skills
By: Elizabeth S. Nilsen
Centre for Mental Health Research, Department of Psychology, University of Waterloo;
Vanessa Huyder
Centre for Mental Health Research, Department of Psychology, University of Waterloo
Tara McAuley
Centre for Mental Health Research, Department of Psychology, University of Waterloo
Dana Liebermann
Hamilton-Wentworth District School Board, Hamilton, Ontario
Acknowledgement: The authors thank Alita Borkar, Yvonne Dewit, Tuyen Le, and Megan Smith for their assistance with data collection and data entry. In addition, the authors greatly appreciate the statistical consultation from Erik Woody as well as the time spent by colleagues in the field of executive functioning who reviewed and provided feedback on early versions of our measure.
Executive functioning (EF) refers to higher order processes that aid in the monitoring and control of thought and action and facilitate goal-directed behavior (Burgess, 1997). Executive functions can involve both “hot” affective aspects as well as “cool” cognitive aspects of self-regulation (Zelazo & Müller, 2011). Though different conceptualizations of EF exist (e.g., Jurado & Rosselli, 2007), core EF skills include inhibitory control (i.e., deliberately supressing dominant yet inappropriate responses), working memory (i.e., actively maintaining important information in mind), and shifting (i.e., considering simultaneous representations of an object or event and/or flexibly alternating between tasks), which are separable yet interrelated and show differential associations with more complex forms of EF, for example, planning (i.e., looking ahead to the attainment of a goal and planning one’s actions accordingly; Miyake et al., 2000). This conceptualization, based on findings from studies with adults (e.g., Miyake et al., 2000), has been replicated in developmental studies of children 6 years and older (Huizinga, Dolan, & van der Molen, 2006; McAuley & White, 2011). In contrast, comparable work with younger children has shown that EF is more elusive earlier in development—with studies suggesting that EF constitutes an undifferentiated resource (Brocki & Bohlin, 2004; Hughes et al., 2009; Wiebe, Espy, & Charak, 2008; Wiebe et al., 2011), or consists of two broad components reflecting working memory and inhibition (Müller & Kerns, 2015), or consists of multiple components that undergo a period of integration during the preschool years before they become separable once again later in development (Howard, Okely, & Ellis, 2015).
Although executive functions have a protracted course of development and are not fully mature until adolescence or even young adulthood (Best, Miller, & Jones, 2009), they emerge in the first few years of life (e.g., behaviors indicative of working memory, inhibitory control, and task shifting are shown before the age of two; Espy, Kaufmann, McDiarmid, & Glisky, 1999; Kochanska, Coy, & Murray, 2001; Reznick, Morrow, Goldman, & Snyder, 2004). As noted by Garon, Bryson, and Smith (2008), the period spanning 3 to 5 years of age is characterized by considerable growth in core executive skills: preschool-aged children are able to retain more information for longer periods of time, are increasingly adept at manipulating information that is being held in mind, develop the ability to withhold inappropriate responses and to generate alternative, less prepotent actions, are increasingly able to shift their attention from one aspect of a stimulus to another, and are more practiced at integrating these core EF skills to engage in more complex forms of behavior.
Indeed, the preschool years may be one of the most important periods in EF development. In addition to undergoing particularly dramatic improvements during this time, individual differences in preschoolers’ EF underlie many areas of normative development, including school readiness, academic skills, language, and social competence (Best, Miller, & Naglieri, 2011; Bull, Espy, & Wiebe, 2008; Blair & Razza, 2007; Hughes, 1998; Hughes & Ensor, 2007, 2011; Sasser, Bierman, & Heinrichs, 2015; Thorell, Bohlin, & Rydell, 2004). Moreover, deficits in EF have been implicated in a host of negative developmental trajectories, including aggression, attention-deficit-hyperactivity disorder (ADHD), autism spectrum disorder (ASD), learning problems, and anxiety and mood disorders (Johnson, Humphrey, Mellard, Woods, & Swanson, 2010; Raaijmakers et al., 2008; Sanders, Johnson, Garavan, Gill, & Gallagher, 2008; Snyder, Kaiser, Warren, & Heller, 2015; Wagner, Muller, Helmreich, Huss, & Tadic, 2015; Willcutt, Doyle, Nigg, Faraone, & Pennington, 2005). Given the centrality of EF to many facets of development, the availability of a psychometrically sound tool that measures the full spectrum of EF is of critical importance—particularly during the preschool period.
Measurement of Executive Functioning in PreschoolersGiven the surge of interest in the role that EF plays in normative and atypical development, the assessment of EF in preschoolers has become increasingly relevant. One approach to the assessment of children’s EF entails the use of laboratory-based measures that are typically administered within a controlled setting (e.g., lab or office). The advantage of this approach is that such tests are designed to capture components of EF and, therefore, can provide process-specific information; however, this approach comes with certain disadvantages. First, the context in which children’s EF skills are assessed is somewhat artificial in that there are few distractors, they receive clear instructions with well-defined goals, their performance is closely monitored, and they often receive continuous feedback. Second, such tests attempt to separate integrated functions into component parts and as such do not represent the multidimensional and priority-based decision making that real-world situations demand (Burgess, 1997). Relatedly, there is a lack of agreement among researchers as to what specific component of EF particular tasks assess, and, in reality, no task is a pure measure of any EF component (Miyake et al., 2000). While latent variable analysis provides a solution to some of these problems, it is time-consuming and resource intensive to administer a comprehensive battery of tasks that adequately assesses EF components. In addition, this approach poses a particular challenge when working with preschool-aged children who may have limited attention spans and be more susceptible to boredom and fatigue. Lastly, when using individual tasks to measure EF skills, there are very few measures that have been validated and standardized for use with preschool-aged children (one notable exception is the NEPSY; Korkman, Kirk, & Kemp, 1998), which limits their clinical utility and the ability to compare findings across studies (Isquith, Gioia, & Espy, 2004). Further, the psychometric properties of many EF tasks are undocumented and for others they can be varied depending on the particulars of the sample (see, e.g., Willoughby, Blair, Wirth, & Greenberg, 2010). Addressing this limitation in the field, recent efforts are being made to promote the use of tools with established psychometric properties (e.g., National Institutes of Health Toolbox; Zelazo & Bauer, 2013).
An alternative approach to capturing children’s EF is to ask observers to report on children’s behaviors using questionnaires. The advantage of assessing EF in this manner is that it may permit the integration of information from a child’s everyday environment (home, school, daycare, etc.), thereby allowing for global aspects of behavior to be determined. Observer ratings also allow for information to be gathered efficiently from multiple sources over different contexts and extended periods of time, which increases the ecological validity of the measure (Mahone & Hoffman, 2007). Questionnaires are easy to administer and, as such, can provide an efficient and useful way for screening EF in children who present with developmental concerns or who may be at risk for developing psychiatric disorders.
There are a number of observer report measures that assess EFs in school-age children (e.g., Behavior Rating Inventory of Executive Function [BRIEF; Gioia, Isquith, Guy, & Kenworthy, 2000]); Childhood Executive Functioning Inventory [CHEXI; Thorell & Nyberg, 2008]; Comprehensive Executive Function Inventory [CEFI; Naglieri & Goldstein, 2013]; Barkley Deficits in Executive Functioning Scale - Children and Adolescents [BDEFS-CA; Barkley, 2012]; behavioral screener for estimating executive functions from the Behavior Assessment System for Children [Garcia-Barrera, Kamphaus, & Bandalos, 2011]); however, there are far fewer measures for preschool-aged children. One measure specifically designed to target this age group is the Behavior Rating Inventory of Executive Function-Preschool Version (BRIEF-P; Gioia, Espy, & Isquith, 2003). The BRIEF-P is a 63-item rating scale completed by parents or teachers for children 2 to 5 years of age. The items map onto five statistically driven scales reflecting the core EF skills of inhibitory control, working memory, and shifting as well as planning and emotional control. Although this measure has excellent internal consistency and temporal stability (Gioia et al., 2003), it is not without limitation. First, similar to the school-age version of the BRIEF, items on the BRIEF-P tend to overlap with the diagnostic criteria for ADHD. It is problematic when the tool being used to predict future outcomes (e.g., ADHD diagnoses) is so similar to the outcome variable. Second, items on the BRIEF-P ask parents to make general evaluations of their children’s behavior as opposed to specific observations, which may provide more room for biases to play a role in reporting and thereby affect the validity of the results. For example, unlike performance-based measures of EF, the BRIEF-P shows little correlation with age (Mahone & Hoffman, 2007). Third, the BRIEF-P subscales tend not to correlate with performance-based measures of EF (e.g., Liebermann, Giesbrecht, & Müller, 2007; Mahone & Hoffman, 2007; Toplak et al., 2013) suggesting that it may be more a measure of children’s behavioral disruption and impairment than deficits in EF per se (McAuley, Chen, Goos, Schachar, & Crosbie, 2010). Indeed, Toplak and colleagues argue that, in general, rating scales of EF capture different underlying mental constructs from performance-based measures (i.e., with the former capturing success in goal pursuit and the latter capturing efficiency of information processing; Toplak, West, & Stanovich, 2013).
Our aim was to develop a new parent rating scale—the Ratings of Everyday Executive Functioning (REEF)—that captures the executive functions of preschool children (3- to 5-year-olds) as demonstrated by their behaviors in a variety of everyday environments. In creating the REEF, we endeavored to overcome some of the disadvantages that are associated with the use of observer report measures. For example, we sought to reduce ratings that are influenced by the general biases of observers or the unrealistic expectations that observers may hold of children’s behavior, as well as ratings that reflect observers’ predictions of children’s behavior rather than children’s actual behavior, all of which may lead to overestimation in some areas and underestimation in others (Isquith et al., 2004). To circumvent these problems, we anchored items on the REEF in well-delineated contexts (e.g., at home, in the community, etc.), required that parents provide specific ratings rather than global impressions, and had parents base their ratings on what they observed their children doing (i.e., function) as opposed to what they felt their children were not able to do (dysfunction). In doing so, we anticipated the REEF would improve upon the validity of existing informant rating scales of preschooler EF—as would be evident, for example, in a higher correspondence between parental ratings on our measure and children’s performance on lab-based EF tasks than has been documented previously. We also sought to create a tool that would be efficient to administer and capable of capturing individual variation within typical development (i.e., rather than serving primarily as a screening tool for dysfunction).
Study 1The primary aim of Study 1 was to administer the REEF to parents of preschoolers to gauge whether initial items were appropriate for this age range. A secondary aim was to determine whether children’s performance on our newly developed measure corresponded to their performance on EF tasks. Given that past research has often not found a relationship between EF tasks and EF rating scales (Liebermann et al., 2007; McAuley et al., 2010; Toplak et al., 2013), it was important to assess at an early stage in the measure’s development whether asking parents to report on specific, observable behaviors might lead to a stronger association with children’s actual EF performance. To achieve these goals, parents completed the REEF and children participated in a battery of laboratory-based EF tasks.
Development of REEF Items
The items for the REEF (see Appendix) were generated following a review of the EF literature regarding the types of everyday behaviors preschoolers display that demonstrate EF growth or success. The goal of item construction was to provide parents with common, observable behaviors that would require some element of EF. Attention was given to identifying behaviors that captured different components of EF: inhibitory control (e.g., “Waits for you to finish on the phone before seeking your attention”), working memory (e.g., “Fetches all items requested by adult [e.g., Does not forget what he/she was asked to get]”), cognitive flexibility (e.g., “Rephrases language when another person doesn’t understand what he/she is saying”), emotion regulation (e.g., “Recovers quickly from a disappointment or change in plans [e.g., the family is no longer going out for dinner]”), and planning (e.g., “Plans ahead when playing games [e.g., what he/she should do on the next turn]”). To provide a contextual frame for the responder to reference, items are separated into different themes. For example, one theme is “around the house” and items within this section refer to activities that children may do when at home. Other contextual themes include: playing games, playing games with others, interacting with others, in the community, in stores, story time. Drawing from observer rating tools of children’s adaptive functioning (e.g., the Adaptive Behavior Assessment System-II [ABAS-II; Harrison & Oakland, 2003]), parents make a forced-choice response of “is not able” (0), “never or almost never” (1), “sometimes” (2), or “always or almost always” (3). Parents are also asked to indicate if they “guessed” in their response for each item.
The language of administration for the REEF was English. This study, and all subsequent studies, were approved through the Office of Research Ethics at the University of Waterloo.
Method
Participants
Forty-two children between the ages of 3- and 5-years-old (20 females, M = 51.7 months, SD = 11.1 month, range = 36.6–72 months) were recruited from local preschools and daycares via information letters that were sent home to parents. Mothers were primarily the respondents on the REEF (two fathers completed the REEF). Data from one participant was excluded from analyses because the number of missing items exceeded the inclusion criterion of less than 10% of items missing.
Procedure
Following the initial generation of REEF items, the measure was sent to three researchers whose primary field of study was the development of EF in children. Items were modified or removed if the researchers determined the item did not reflect EF. The first version of the REEF contained 171 items, which was completed by parents as part of the package that was sent home from the preschool/daycare. Children were tested individually by a researcher in a quiet location within their preschool or daycare over two 45-min sessions.
Executive functioning
The battery of EF tasks was derived from tasks that are commonly used with this age-range and capture skills in areas of working memory, inhibitory control, shifting, and planning/organizing (as well as two tasks of emotion regulation, not coded because of experimenter error). We attempted to assess performance in areas considered to be reflective of “hot” EFs, which involve more emotional significance (e.g., delay tasks) as well as “cold” EFs, which reflect more emotionally neutral, decontextualized tasks (e.g., span tasks). Tasks were administered in a standardized order over two sessions: Digit span, Bear/dragon, Gift delay, Self-ordered pointing, Tower of Hanoi, Count and Label, Flexible Item Selection Task, Truck Loading, Day/Night, and Whisper Task. Task descriptions and references are provided in Table 1.
Description of Executive Functioning (EF) Tasks Used in Study 1
Language
To assess children’s receptive language skills, the Receptive Vocabulary Scale of the Wechsler Preschool and Primary Scale of Intelligence-III (WPPSI-III; Wechsler, 2002) was used. This test was administered according to standardized protocol.
Results and Discussion
Item reduction on the REEF
To refine the scale, items were removed if they, (a) did not yield variable responses (i.e., only two response options were endorsed; 29 items), (b) showed floor or ceiling effects (i.e., had means below 1.0 or above 2.7; 12 items), or (c) engendered a relatively high proportion of guessing (i.e., >15% of parents indicated they had guessed; 12 items). Removal of these items resulted in a 119-item measure. These items were summed to provide a total score, which demonstrated excellent internal consistency (α = .97). Missing data (when less than 10% of items were missing, representing .002% of responses) were replaced using single imputation with an expectation-maximization algorithm in SPSS.
Convergent validity of the REEF and EF tasks
After reverse scoring gift delay, performance on the EF tasks was standardized and summed to create an EF task composite, which was internally consistent (α = .83). As shown in Table 2, children’s performance on the EF composite was positively related to their age and receptive vocabulary. Children’s performance on the EF composite was also positively related to parents’ ratings of children’s EF skills on the REEF, which remained significant even when children’s receptive vocabulary was controlled.
Correlations Between REEF (119-Item and Final 76-Item Version), Executive Function (EF) Tasks, and Language in Study 1 With Partial Correlations Controlling for Receptive Vocabulary in Parentheses
Using total scores from the reduced set of 119 items, we found that children who demonstrated more successful performance on the laboratory-based tasks of EF were rated by their parents as engaging in more EF behaviors within their daily environments. This finding contrasts with previous work that has found observer report measures of EF to be unrelated to children’s EF performance (e.g., Liebermann et al., 2007; McAuley et al., 2010; Toplak et al., 2013). Because other EF measures tend to focus on global statements that are indicative of executive deficits, we speculate that asking parents to report on a wide range of observed behaviors that are anchored within a concrete context (e.g., around the house) reduced reporting biases.
Study 2The goals for Study 2 were twofold. First, we sought to further refine the REEF by removing items that did not meet the aforementioned criteria based on a larger sample. Second, we sought to further assess the convergent validity of the REEF by comparing parents’ responses on the REEF with a measure of preschoolers’ dysfunction, the BRIEF-P (Gioia et al., 2003).
Method
Participants
Parents of 3- to 5-year-old children were recruited through community centers, daycares and preschools. Packages with informed-consent forms, questionnaires, and a $5 gift card were provided to 136 parents. One hundred forms were returned (87 mothers, 11 fathers, and 1 guardian; 1 respondent chose not to answer). The mean age of children being rated by caregivers was 49.79 months (SD = 9.06; range 36 months to 71.8 months; 44 females). In total, there were 49 3-year-olds, 37 4-year-olds, and 14 5-year-olds. The general education of the respondents was high, with 72% reporting that they had received postsecondary education. Ninety-two percent of respondents indicated they primarily spoke English within their homes, though there were other languages spoken, such as languages from Asia (14%), South Asia (10%), and Europe (10%). If parents noted developmental concerns, data were removed from analyses (n = 2).
Procedure
Parents completed the 119-item REEF (i.e., the version of the REEF that was used in the analyses for Study 1), the BRIEF-P (Gioia et al., 2003), and a questionnaire related to parenting stress (not the focus of this study). The BRIEF-P consists of 63 items comprising five scales: Inhibit (16 items), which assesses shortfalls in the child’s ability to inhibit or resist impulsive actions and stop behavior at the appropriate time; Shift (10 items), which measures the child’s difficulty transitioning from one situation or task to another or thinking about a problem in different ways; Emotional Control (10 items), which measures the degree to which the child struggles to modulate emotional responses; Working Memory (17 items), which assesses limitations in the child’s capacity to hold information in mind for the purpose of completing a task or making a response; and Plan/Organize (10 items), which measures the child’s difficulty with managing current and future oriented task demands. Parents responded to items with responses of “never,” “sometimes,” or “often” with higher scores reflecting worse EF. The BRIEF-P is reported to have high internal consistency and established validity (Gioia et al., 2003). Children’s raw scores on the scales as well as on a Global Executive Composite (i.e., reflecting the total of all scales) were included in the analyses.
To control for children’s general language/communication skills when comparing parent ratings on the REEF with the BRIEF-P, respondents also completed the Children’s Communication Checklist-2 US Edition (CCC-2; Bishop, 2003). The CCC-2 is a 70-item instrument that is used to assess children’s communication skills. Items are grouped into 10 subscales that cover language structure and pragmatic skills that can be combined to produce a General Communication Composite. The CCC-2 has strong reliability and validity (Bishop, 2003).
Results and Discussion
Item reduction on the REEF
The process used to inspect and remove items in Study 1 was applied to Study 2. Of the 119-items on the revised REEF, 5 were removed because they, (a) showed insufficient variability (3 items) or, (b) had a high proportion of guessing among parents (2 items). Missing data (.003% of responses) were replaced using single imputation with an expectation-maximization algorithm in SPSS. The resultant 114 items were summed to create a composite, which showed good internal consistency (α = .97).
Convergent validity of the REEF and BRIEF-P
As shown in Table 3, comparison of caregivers’ responses on the REEF and BRIEF-P showed that the total REEF score was significantly correlated with all subscales of the BRIEF-P, even when controlling for the child’s communication skills (all ps < .001). Results indicate that caregivers’ observations of their children’s everyday EF, as assessed per the REEF, were strongly related to their more global assessments of executive dysfunction, as measured per the BRIEF-P.
Bivariate Correlations Between the REEF and BRIEF-P in Study 2 With Partial Correlations Controlling for Reported Language Skills (CCC-2) in Parentheses
In conjunction with the findings of Study 1, these results suggest that the REEF is a tool that holds considerable promise as a measure of preschooler EF: it possess high internal consistency, shows strong convergent validity, and is easily administered to parents. However, the sample sizes of Study 1 and 2 were relatively small (with a particularly low number of 5-year-olds in Study 2) and precluded evaluation of the factor structure of the REEF. Moreover, at a current length of 114 items, over 1,000 respondents would be needed to ensure sufficient statistical power for factor analyses in the future. As such, we set out to further reduce the number of items on the REEF and recruit a large sample to inspect the underlying factor structure of this measure.
Study 3Before collecting data, items from the 114-item REEF were further refined in several ways. Specifically, an item was removed if, based on findings from Study 2, (a) a high percentage (>65%) of parents responded “Never” or “Always or Almost Always” to that item (reflecting a stricter criterion for a “floor/ceiling” effect than used in Study 1 and 2), (b) the item only had two types of responses or a mean above 2.7, (c) the item did not correlate strongly with the BRIEF-P GEC (i.e., p > .25) or, (d) the item showed poorer functions (i.e., all removed items had an item-total correlation below .43). Items were also removed if, (e) there was a significant negative correlation between it and the lab-based EF tasks from Study 1 (with the exception of Gift Delay where a negative relationship was expected). According to these criteria, 18 items were removed.
Next, we solicited the input of three experts (different to those contacted in Study 1) in the area of EF who each had an established research program within an academic setting that focused on the executive skills of children and had published articles in this area. Experts were asked to specify whether or not each of the items on the REEF was reflective of EF and, if so, to identify which component of EF the item assessed. Items were removed if, (f) two or more raters indicated the item was not a measure of EF, did not know where the item belonged, ascribed the item to different EF components, or indicated that they did not know which EF component to ascribe the item (suggesting that the described behavior might not be clear). According to these criteria, 20 items were removed. However, three items, which were previously removed following the stricter criteria applied to the data in Study 2 (e.g., ceiling effects), were readded as they met other inclusion criteria and were thought to represent important behaviors that rely on EF. The resulting REEF had 79 items.
Method
Participants
Parents or guardians of children 3- to 5-years-old were recruited through an online crowdsourcing website, Mechanical Turk, via CrowdFlower. This study was completed by 944 participants; however, participants were removed from analyses if: they resided outside of North America (n = 96); completed the survey in less than the minimal amount of time required to answer all of the questions (i.e., 20 min: n = 210), had more than 10% missing data on the REEF (i.e., 8 or more unanswered items: n = 17), or responded to all items on the REEF with the same response (i.e., no variance in responding: n = 5); indicated that they were the child’s biological mother and were older than 50 years (n = 2); the child was less than 36 months old (n = 46) or older than 72 months (n = 40); or the child was diagnosed with or was suspected to have developmental concerns (n = 22). The remaining sample size was 506.
The mean age of children being rated by their parent or guardian was 50.52 months (SD = 9.85; range 36 months – 71.50 months). The mean age of respondents was 394.99 months (32.83 years; SD = 84.72; range 18.24–68.41 years). The sample was comprised of children identified as White (70.4%), Black (6.9%), Asian (4.2%), Latin American (3.2%), Other (i.e., different ethnicity or multiethnic; 14.3%), or whose ethnicity was not provided (1%). Respondents were biological mothers (62.5%), biological fathers (27.1%), adoptive mothers (1.4%), adoptive fathers (2.0%), guardian females (3.6%), guardian males (2.2%), and other, such as grandparent (1.2%).
Procedure
Parents completed the 79-item REEF, as well as a demographic questionnaire (and other measures intended to assess children’s social and behavioral functioning, not included here). Respondents were provided $1.80 for their participation.
Results and Discussion
Item reduction on the REEF
The items of the REEF were again inspected as per criteria outlined in Study 1 and 2. There were no items with a mean greater than 2.7 or less than 1, no items with only two types of responses or less, and no items that were guessed by more than 15% of responders. Items were inspected further by calculating the corrected-item total correlations. Items were removed if the corrected item-total correlation was less than .43 (n = 3). The resulting 76-item REEF showed good internal consistency (α = .97).
Factor analysis of the REEF
There were 276 participants who had complete data on the REEF. For the remaining participants, missing data were replaced using single imputation with an expectation-maximization algorithm in SPSS. This affected 1.3% of all responses. Because of inconsistent findings in the literature regarding the underlying structure of preschoolers’ EF, exploratory factor analysis with maximum likelihood extraction and oblique (promax) rotation was used to examine the 76-item REEF. Oblique rotation was chosen to permit correlations among multiple components. Inspection of the scree plot, as well as the loadings of each item on the produced factors, suggested that a one-factor solution was in fact most appropriate. Specifically, there were no items that loaded on another factor more than the first factor and all loadings on the first factor were equal to or greater than .40. To ensure that EF behaviors were not situationally bound, we created an average score of items within each of the eight contexts represented in the REEF provided that no more than 20% of an individual’s responses were missing across context-specific items. These average scores were then entered into another exploratory factor analysis with oblique rotation, resulting in a one-factor solution that explained 70% of the variance. Taken together, these findings suggest that parent ratings of preschooler’s behavior on the 76-item REEF reflect a singular EF factor that does not vary with situational demands.
Study 4To further confirm that the REEF is measuring what it is proposed to measure, our final study sought to examine the convergent and divergent validity of the 76-item REEF with other aspects of children’s behavior. In particular, we asked parents to complete measures of their children’s executive dysfunction and symptoms of ADHD, which we anticipated would show strong negative correlations with the REEF. As well, given that previous studies show that EF facilitates appropriate social behavior (Ciairano, Visu-Petra, & Settanni, 2007; Huyder & Nilsen, 2012), we examined the degree to which children’s REEF scores related to social outcomes by having parents report on their children’s social functioning. Finally, we asked parents to report on other behaviors that may be under less executive control, such as children’s general affect. Specifically, we asked parents to report on their child’s degree of fears and sadness as these behaviors tend to fall on separate factors than those related to effortful control, that is, the ability to choose a course of action, plan, and detect errors, which shares much similarity with the executive function system (Rothbart, 2007; Rothbart, Ahadi, Hershey, & Fisher, 2001). In addition, we asked parents to report on their child’s degree of smiling/laughter given that smiling in contexts where emotion regulation is not specifically required tends not to relate to executive functioning (Simonds, Kieras, Rueda, & Rothbart, 2007) and executive functioning tends not to be facilitated by positive affect (Mitchell & Phillips, 2007; although see Qu & Zelazo, 2007). While we anticipated that such affective variables may be significantly related to observer-reports on the REEF (given significant relations between executive functioning and emotional well-being; Wagner et al., 2015), we anticipated that these relations would be weaker than with other EF-specific measures.
Method
Participants
North American parents or guardians of children 3- to 5-years-old were recruited through Mechanical Turk via Crowdflower. This study was completed by 622 participants who had not previously completed Study 3; however, participants were removed from analyses if they: completed the survey in less than the minimal amount of time required to answer all of the questions (i.e., 10 min: n = 12); had more than 10% missing data on the REEF (i.e., 8 or more unanswered items: n = 3); responded to all items on the REEF with the same response (i.e., no variance in responding: n = 2); indicated that they were the child’s biological mother and were older than 50 years (n = 1); indicated the child was less than 36 months old (n = 21) or older than 72 months (n = 8) or had no identifiable birthdate (n = 5); or indicated the child was diagnosed with or suspected to have developmental concerns (n = 12). The remaining sample size was 558.
The mean age of children being rated by their parent or guardian was 50.82 months (SD = 8.64; range 36 months – 71.10 months) and the sample was comprised of 49.5% female, 49.5% male, and 1.0% declined to answer this question. The mean age of respondents was 389.07 months (32.42 years; SD = 81.81; range 18.65–61.92 years). The sample was comprised of children identified as White (70.4%), Black (7.2%), Asian (5.6%), Latin American (4.3%), Other (i.e., different ethnicity or multiethnic; 11.5%), or whose ethnicity was not provided (1.0%). Respondents were biological mothers (49.1%), biological fathers (42.5%), adoptive mothers (1.1%), adoptive father (2.0%), guardian female (2.9%), guardian male (2.2%), other, such as grandparent, (0.1%), and declined to answer (0.1%).
Procedure
Parents completed the 76-item REEF, as well as a demographic questionnaire and other measures intended to assess children’s everyday social and behavioral functioning. Respondents were provided with $3.00 for their participation.
Measures
The Childhood Executive Functioning Inventory (CHEXI) is a 24-item measure of children’s EF that consists of two subscales: Inhibition (11 items), which assesses a child’s difficulties in stopping inappropriate actions and maintaining on-task behavior, and Working Memory (13 items), which assesses a child’s difficulties holding information in mind or planning/organizing activities (Thorell & Nyberg, 2008). Parents rate their child on items using a 5-point scale with the choices being “Definitely not true,” “Not true,” “Partially true,” “True,” and “Definitely true.” This measure is reported to have good test–retest reliability and established validity (Thorell & Nyberg, 2008). While this measure is aimed at children older than our targeted population (i.e., 4-years-old to 12-years-old), items were deemed general enough to apply to a younger population as well.
The Strengths and Weaknesses of ADHD symptoms and Normal Behavior Scale (SWAN) is an 18-item measure that assesses children’s manifestations of ADHD symptoms and consists of two subscales: Inattention and Hyperactivity/Impulsivity (Lakes, Swanson, & Riggs, 2012). Parents rate their child on these items using a 7-point scale to indicate a child’s manifestation in comparison to other children of the same age with the choices ranging from 1 = far above to 7 = far below. Higher scores are reflective of fewer difficulties with Inattention or Hyperactivity. This measure is reported to have good reliability and validity for preschool age children (Lakes et al., 2012).
The Strengths and Difficulties Questionnaire (SDQ) is a 25-item screening instrument to evaluate behavioral and emotional concerns that can be separated into 5 scales (Goodman & Scott, 1999). Of interest to our study were the Peer Problems scale (5 items) and Prosocial Behavior scale (5 items), which reflect a child’s social skills. Parents were administered these 10 items and rated their child on each using a 3-point scale with the choices being “Not true,” “Somewhat true,” and “Certainly true.” The SDQ has been used in the past as a measure of social competence or behavioral adjustment and has been shown to have good test–retest reliability and concurrent validity (Goodman, 2001).
The Children’s Behavior Questionnaire-Short Form (CBQ-SF) is a parent-report measure intended to assess various dimensions of children’s (3- to 7-year-old’s) temperament (Putnam & Rothbart, 2006). Of the 15 possible dimensions on this measure, we selected three subscales reflecting children’s affect (i.e., Fear, Sadness, and Smiling). Parents were administered 25 items that were rated on a 7-point scale from 1 = extremely untrue of your child to 7 = extremely true of your child. This measure is reported to have good reliability and validity (Putnam & Rothbart, 2006).
Results and Discussion
Age-related change in the REEF
There were 508 participants who had complete data on the REEF. For the remaining participants, missing data were replaced using single imputation with an expectation-maximization algorithm in SPSS. This affected 0.2% of all responses. Replicating our findings from Study 3, factor analysis of the 76-item REEF supported a one-factor solution that did not vary across situational contexts (69% of variance explained). These 76 items showed good internal consistency (α = .96). After removing one univariate outlier, the mean for the total REEF score was 160.66 (SD = 29.93). The REEF score was comparable across genders, t(554) = 1.29, p = .20 and so gender was not included in further analyses. As was expected, however, the REEF score was significantly correlated with children’s age, r = .28, p < .001. Age-related differences in the REEF score were further examined by dividing participants in Study 3 and Study 4 into 6 6-month age intervals (36–41, 42–47, 48–53, 54–59, 60–65, and 66–71 months). Age group means are presented in Table 4, excluding 12 participants who were identified as bivariate outliers on the association of age and the REEF score based on inspection of residuals. An analysis of variance (ANOVA) with the REEF score as the dependent measure and age group as a predictor revealed a significant main effect of age in both Study 3, F(5, 484) = 6.92, p < .001, and Study 4, F(5, 551) = 11.02, p < .001 (and combined, F(5, 1041) = 16.67, p < .001). To inspect which of the six age groups differed significantly on their observer report of EF behavior, Tukey’s post hoc tests were performed using the combined data from Study 3 and 4. The results of marginally significant differences are displayed in Table 5 and are summarized as follows: children between 36 to 41 months tended to have lower EF ratings than children between 42 to 47 months; both groups of younger children were generally rated as lower in their EF behavior than the older age groups; children at an intermediate age of 48 to 53 months tended to have lower EF ratings than the oldest children between 60 to 65 months and 66 to 71 months; EF ratings were statistically comparable in children aged 54 months and onward.
Performance on the REEF for Each Age Group Study 3 and Study 4
Summary of Tukey’s Analyses Between the Different Age Groups’ REEF Total Scores Using Data From Study 3 and 4 Combined (N = 1047)
Convergent and Discriminant Validity of the REEF
To assess the convergent and discriminant validity of the 76-item REEF, correlations between the REEF total score and other measures of children’s everyday social and behavioral functioning were examined (see Table 6).
Correlations Between REEF, CHEXI, SWAN, SDQ, and CBQ in Study 4
The REEF total score was significantly correlated with EF as assessed by the CHEXI, including the Inhibition subscale, r = −.53, p < .001, and the Working Memory subscale, r = −.61, p < .001. It was also significantly correlated with behavioral traits associated with EF (e.g., fewer ADHD traits; Willcutt et al., 2005; Nigg, Quamma, Greenberg, & Kusche, 1999), such as the SWAN subscales reflecting Inattention, r = .49, p < .001, and Hyperactivity/Impulsivity, r = .46, p < .001. Replicating previous findings using laboratory tasks of executive functioning (e.g., Ciairano et al., 2007; Huyder & Nilsen, 2012), we found that children who received higher scores on the REEF demonstrated more pro social behavior, r = .49, p < .001, and fewer peer problems, r = −.28, p < .001 on the SDQ.
Reflecting divergent validity, the REEF total score was significantly more strongly correlated with the two CHEXI executive function subscales, than it was correlated to measures of affective functioning, that is, the Fear (r = −.11), Sadness (r = −.09), and Smiling (r = .24) subscales of the CBQ, zs = −7.33 to −15.40, ps < .001. This was also the case when comparing the correlation coefficients for the REEF and traits of ADHD relative to affective functioning, zs = 4.95 to 10.05, ps < .001.
Reanalysis of the 76-item REEF and children’s performance on EF tasks
To further assess the convergent validity of the final REEF, the data from Study 1 were reexamined using a total REEF score based on the 76 items (as opposed to the 119 initial items). There was a significant relation between children’s performance on the lab-based measures of EF and the parent-reported REEF, r = .37, p = .04, even when children’s language skills were controlled, r = .38, p = .04. Thus, our final version of the REEF was shown to positively relate to lab-based measures of EF (see Table 2).
General DiscussionGiven the rapid development of EF during the preschool period (e.g., Garon et al., 2008), the centrality of early executive skills to other areas of development (e.g., Best et al., 2009), and the robust association of EF with various developmental disorders (e.g., Johnson et al., 2010; Sanders et al., 2008; Snyder et al., 2015; Wagner et al., 2015; Willcutt et al., 2005), the availability of a psychometrically sound and well-validated measure of preschool EF is theoretically important and clinically relevant. Although some EF rating scales have been developed for use with this population (e.g., BRIEF-P; Gioia et al., 2003), they tend to place a narrow focus on executive dysfunction rather than the full continuum of executive functioning, consist of general statements that are not tied to clearly observable behaviors, overlap with diagnostic criteria for developmental disorders such as ADHD, and do not correlate with children’s performance on lab-based measures of EF—calling into question what constructs they are measuring. To address these limitations, and thus, fill a critical gap in our corpus of tools that may be used to assess EF in preschool aged-children, we present the REEF—a brief, easily administered, and psychometrically sound parent-report questionnaire that can be used to capture the everyday, observable behaviors of preschool-aged children that are reflective of their executive functioning.
Our studies demonstrate that the REEF has excellent psychometric properties, including high internal consistency and validity. It is particularly noteworthy that our REEF aligns with other laboratory-based measures of EF, given that most studies have failed to find associations between informant-ratings of children’s behavior on EF questionnaires and children’s EF task performance (Liebermann et al., 2007; McAuley et al., 2011; Toplak et al., 2013). Study 1 revealed a significant relationship between the initial 171-item REEF and children’s performance on commonly used EF tasks, which remained significant when this association was reevaluated using the final 76-item version of our questionnaire, even when controlling for language ability. It has been argued that measures that ask individuals to report on specific behaviors yield higher reliability and validity scores than measures that ask individuals to render more global judgments (Sattler & Hoge, 2006). Accordingly, by asking parents to report on their preschoolers’ specific, observable behaviors that are reflective of executive functioning, rather than general statements of executive dysfunction, we believe that the REEF provides a more detailed and accurate picture of a preschooler’s executive skill set. This property of the REEF makes it particularly useful for researchers who often rely on laboratory-based measures of EF in their studies, which is both time consuming and resource intensive (indeed, our battery of tasks took two sessions that each spanned close to an hour). By utilizing a questionnaire that relates to actual EF task performance, researchers will be able to capture the domain they seek to measure (i.e., EF) efficiently. This being said, even though we found a correlation between the REEF and lab-based measures, we cannot rule out the possibility that they are measuring related, but different constructs (an assertion made by Toplak et al., 2013). For instance, even when correlations between lab-based measures and parent-report measures have been found in past research (albeit in an older age group than the present study), these different measures contributed to other constructs, such as academic achievement, in unique ways (e.g., the CHEXI; Thorell & Nyberg, 2008). Moreover, it was not the case that children’s performance on all of the individual executive function tasks was significantly related to the REEF. It may be the case that certain lab-based measures are more predictive of children’s everyday demonstration of EF behaviors than others.
The REEF also demonstrates convergent and divergent validity with other parent-report questionnaires of children’s behavior. Study 2 showed that parent ratings on the REEF were significantly associated with parent ratings on the BRIEF-P, such that parents who rated their preschoolers as demonstrating more advanced executive behaviors on our questionnaire were also rated as showing less evidence of executive dysfunction. This finding is important as it suggests that the specific behaviors that we ask parents to comment on in the REEF are capturing an aspect of children’s EF that is similar to the more global assessment that existing measures capture. Moreover, results from Study 4 highlight the divergent validity of the REEF. That is, the REEF showed a stronger relation with measures of executive functioning and ADHD traits than affective measures. Demonstrating this is important because it suggests that relations between the REEF and other constructs are not simply because of an overall positive (or negative) view parents hold of their children’s behavior. Rather, the REEF targets areas related to executive control.
In addition to developing a psychometrically sound tool that assesses EF in preschool-aged children, we wanted our tool to capture age-related differences in EF. Consistent with this goal, we found that children’s scores on the REEF showed significant differences within the preschool-years, reflecting the sensitivity of our measure to the normative developmental changes in EF that are known to occur early in development (Garon et al., 2008). Specifically, we found that parents rated 3-year-olds as demonstrating behaviors indicative of advanced EF less frequently than 5-year-olds, with the most dramatic differences emerging between 3 year-olds and older children. Similar findings have been demonstrated in studies using performance-based tasks to assess EF in preschoolers (e.g., Carlson, 2005). Taken together, findings from studies using varied approaches to the assessment of EF converge on the view that the preschool period is one of noticeable improvement in EF development. However, it should be noted we did not find that scores significantly differed between the older preschool ages, which suggests that the REEF is more sensitive to age-related differences in EF behavior within the early (rather than later) preschool years.
Finally, items selected for inclusion on the REEF converged on a unitary EF construct in our preschool-aged sample. This is consistent with findings from other studies that have examined the factor structure of EF early in development using performance-based tasks (Hughes et al., 2009; Wiebe et al., 2008, 2011). However, it is in contrast to other work that has found that a two factor structure (i.e., working memory and inhibition) may more adequately capture the pattern of data for preschoolers’ EF performance (Miller, Giesbrecht, Müller, McInerney, & Kerns, 2012; Usai, Viterbori, Traverso, & De Franchis, 2014). Findings also diverge from other studies using informant ratings of executive dysfunction in preschool-aged children, in which multiple factors have emerged in young children (i.e., BRIEF-P; Bonillo, Araujo Jiménez, Jané Ballabriga, Capdevila, & Riera, 2012; Duku & Vaillancourt, 2014; Isquith et al., 2004). The factor structure of preschoolers remains somewhat elusive, though there is converging evidence that EF is differentiated later in development, with separable, though interrelated, EF skills being discernable at least by the early elementary school-age years and remaining separable through young adulthood (Huizinga et al., 2006; McAuley & White, 2011).
Our series of studies provide promising data regarding the utility of the REEF for assessing EF in preschool-aged children; however, there are limitations with regards to the development of our scale that warrant mention. Because Studies 3 and 4 were conducted via an online questionnaire system, a tool that is becomingly increasingly established in the social sciences (Mason & Suri, 2012), we were less able to ensure that participant criteria for inclusion in our study were valid. For example, though we specified that all respondents were required to have a child between 3- to 5-years of age, we were not able to verify that participants met this requirement. Further, though we implemented quality control measures to exclude participants with unusual response patterns (e.g., very short completion times, suggesting that the questionnaire was completed in an overly expedited fashion), we cannot ensure that all participants paid careful attention to all items. Lastly, by virtue of these studies being conducted online, there were no opportunities for participants to seek experimenter input in the event that a question was unclear. With these concerns in mind, we had relatively large samples to try to ensure that any problems—should they have arisen—would have affected only a small proportion of our data. If anything, we expect that the findings that we observed in our studies would be stronger if we conducted them in-person. Another limitation to note is the relatively limited ethnic diversity of our samples. Specifically, the majority of our participants in the last two studies identified as “White.” It would be important for future work to include a more diverse sampling of participants, as well as to include information about respondents’ family income and marital status, to increase the generalizability of the results. A final limitation is that we did not include a measure of IQ in our battery of tasks to determine whether the REEF uniquely relates to EF behaviors, independent of IQ. We did control for children’s vocabulary, which has been found to relate to IQ (e.g., Childers, Durham, & Wilson, 1994); however, future work could more directly assess and control for children’s general cognitive function abilities.
Future Directions
The studies presented here represent the foundational work in creating the REEF. There are a number of future steps that can be taken to further elucidate the psychometric properties and application of this measure. First, though the internal consistency of the REEF is high, test–retest reliability could be assessed to determine how consistent caregivers are in reporting observations of their children’s behavior at different time periods and, similarly, it would be useful to determine the consistency of responses across multiple informants (e.g., different parents, day care providers, etc.) as well as across settings (home, day care, etc.). Second, future work could identify other internal indices, such as those indicative of positive/negative response bias and inconsistent responding, to further increase one’s confidence in ratings that are provided by caregivers. Third, the REEF could be used to assess the concurrent relationship between children’s EF and other academic, cognitive, and social-emotional competencies, and to examine the predictive relationship between children’s EF and their future development in each of these domains. Relatedly, the REEF could also be used to equate typical/atypical groups of children on EF when investigating other correlates of executive functioning (e.g., social communication). While the REEF was designed with research purposes in mind, there are some potentially interesting clinical applications as well. For example, given the importance of preschoolers’ EF for other aspects of functioning, the REEF might be used to screen young preschoolers to determine who might benefit from early intervention. In addition, the REEF could be used to determine whether interventions aimed at bolstering EFs generalize to a preschooler’s everyday behaviors. Whereas most intervention studies look for evidence of transfer using children’s performance on lab-based tasks of EF, inclusion of the REEF in this line of research would enable researchers to determine whether there may also be concomitant improvements in children’s abilities to use their executive skills in their everyday lives. Certainly, though, before using the REEF for such clinical applications, the predictive validity as well as sensitivity/selectivity of the REEF would need to be determined.
ConclusionThe assessment of preschoolers’ EF is important because of the role that EF plays in facilitating development in domains, such as academic skills, cognitive and social-emotional development, and healthy psychological functioning (Best et al., 2009). The REEF is a promising new measure of preschoolers’ executive functioning—one that relies on caregivers’ observations of preschoolers’ behaviors that are indicative of their ability to apply executive skills in their daily lives. The REEF correlates with lab-based measures of EF, shows strong internal consistency, demonstrates convergent and divergent validity, and has scores that reflect variability in the preschool years. We hope that the REEF will be used by researchers as an efficient way of measuring the EF of preschool-aged children and anticipate that our questionnaire will contribute to our understanding of the correlates and consequences of EF that occur early in development.
Footnotes 1 The pattern of data did not change when participants without English as their primary language were removed. Reported data includes all participants.
2 CFA was subsequently used to compare the fit of a unitary executive functioning (EF) model with that of a two-factor model consisting of “hot” and “cool” aspects of EF (i.e., inhibition and emotion regulation vs. working memory, cognitive flexibility, and planning/organization) and a five-factor model in which all five EF skills were specified as separable (i.e., inhibition, emotion regulation, working memory, cognitive flexibility, and planning/organization). In each model error variances for items drawn from the same context were allowed to covary within a factor. The five-factor model did not generate a permissible solution. Fit indices of the one- and two-factor models were comparable (i.e., comparative fit index [CFI] = .82; root mean square error of approximation [RMSEA] (90% confidence interval [CI]) = .05 [.052–.053]); however, given the high correlation between the two aspects of EF (r = .92), the unitary EF model appears to provide a more parsimonious fit to the data.
3 Using CFA, comparison of fit indices of the one factor model with alternative two- and five-factor models yielded results that were almost identical to Study 3 and lent further support to a unitary EF model.
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APPENDIX APPENDIX A: Ratings of Everyday Executive Functioning (76 Items)
| PART A—HOW YOUR CHILD PLAYS GAMES |
| This section will ask you about your child’s abilities related to playing games. |
| 1. Plays “Hide and Go Seek” without cheating (e.g., does not peek when counting). |
| 2. Follows and plays games with two step directions (e.g., in a memory game selects cards and checks if they match) without reminders. |
| 3. Remembers lengthy instructions about how to play games (e.g., board games). |
| 4. Remembers the rules of games (e.g., does not need to be reminded frequently). |
| 5. Persists at games or puzzles even when he/she finds them frustrating. |
| 6. When playing a game, he/she stops and thinks before acting. |
| 7. Follows instructions to a game without needing repeated directions. |
| 8. Learns from trial and error in mastering a new task (i.e., changes strategies when something doesn’t work). |
| 9. Follows verbal instructions to a new game without being shown how to play. |
| 10. Gets all the materials he/she needs before starting an activity. |
| PART B—HOW YOUR CHILD PLAYS GAMES WITH OTHERS |
| This section will ask you about your child’s abilities when playing with others. |
| 1. Can play “I spy” without disclosing the thing he/she is thinking about before the other player guesses the object. |
| 2. Waits his/her turn in games and other activities. |
| 3. Controls his/her anger when another person breaks the rules in a game. |
| 4. Plays games without having disputes with playmates. |
| 5. In role playing games, he/she chooses to play different roles (e.g., does not always want to be “the mommy”). |
| 6. Waits his/her turn and works cooperatively with other players in board games. |
| PART C—HOW YOUR CHILD INTERACTS WITH OTHERS |
| This section will ask you about your child’s abilities when interacting with other children and adults. |
| 1. Is good at keeping secrets (i.e., doesn’t blurt them out). |
| 2. If a companion is being asked a question, your child can withhold giving his/her answer. |
| 3. During conversations waits for his/her turn to speak. |
| 4. Waits until someone has finished talking before leaving the conversation. |
| 5. Waits until a question has been completed before answering it. |
| 6. Is able to wait for a reasonable period of time when asked to do so by an adult (e.g., will not keep talking if you ask him/her to wait for a minute while you finish another conversation). |
| 7. Refrains from hitting/pushing other children when he/she is angry. |
| 8. Regulates his/her own facial expressions so they are socially appropriate |
| 9. Apologizes, without reminders, when he/she has hurt the feelings of others. |
| 10. Resolves small disputes with other children without adult intervention. |
| 11. Appreciates other individuals’ perspectives. |
| 12. Rephrases language when another person doesn’t understand what he/she is saying. |
| 13. Lets you have a conversation with other people without interrupting needlessly. |
| PART D—AROUND THE HOUSE |
| This section will ask you about your child’s behaviours at home. |
| 1. Sits at dinner table for entire meal without fussing or getting up from table. |
| 2. Sits still for extended periods of time (e.g., during movies, performances). |
| 3. Will refrain from taking “goodies” that are left in an accessible location. |
| 4. Completes chores that involve multiple steps (e.g., setting the table). |
| 5. Concentrates on a task (e.g., doing a puzzle) even when there are distractions (e.g., a sibling is crying). |
| 6. Remembers all steps involved in completing tasks (i.e., does not forget half way through activity). |
| 7. When asked to do several things, remembers to do most or all of them (e.g., putting toys and books away). |
| 8. Fetches all items requested by adult (i.e., does not forget what he/she was asked to get). |
| 9. Keeps next step in mind while performing an activity (e.g., remembers to clean up toys after eating snack). |
| 10. When asked to put away toys, does so in an organized manner. |
| 11. Keeps his/her bedroom tidy. |
| 12. Concentrates on a task even when the task is not very appealing to him/her. |
| 13. Sorts multiple items (e.g., clothing, cutlery) easily (i.e., doesn’t need to do only one item at a time). |
| 14. Waits for you to finish on the phone before seeking your attention. |
| PART E—IN THE COMMUNITY |
| This section will ask you about your child’s behaviours while out of the house. |
| 1. Waits in line without complaint (e.g., for his/her turn to go on a ride). |
| 2. When given a time frame, he/she is able to adjust actions accordingly (e.g., he/she does not start reading a new book if about to leave the library). |
| 3. Stops fun activity, without complaint, when he/she is told time is up. |
| 4. Recovers quickly from a disappointment or change in plans (e.g., the family is no longer going out for dinner). |
| 5. Gets over minor disappointments easily (e.g., he/she is not permitted to watch TV because he/she was disobedient earlier). |
| 6. Refrains from talking at inappropriate times (e.g., at the library during story time). |
| 7. Waits for food at restaurants without complaining. |
| PART F—OUT SHOPPING |
| This section will ask you about your child’s behaviours while you are out at the grocery store or at the mall. |
| 1. Doesn’t stay disappointed for long after being told he/she isn’t going to receive a treat at a store. |
| 2. Refrains from making inappropriate comments about other shoppers (e.g., “look at that fat man”). |
| 3. Waits to pay for items without complaint. |
| 4. He/she would forego enjoying an immediate treat for receiving a larger treat later. |
| 5. If asked, stops him/herself from touching objects that look fun to play with (e.g., fragile items in a store). |
| 6. Refrains from touching things he/she is not supposed to approach (e.g., buttons on the elevator). |
| 7. Will remind you about the next step in an activity (e.g., what item you need to pick up next in the grocery store) if you ask him/her. |
| 8. Fetches requested items from grocery store (i.e., doesn’t get distracted by other items). |
| 9. When at the grocery store, only places items that are needed in the cart. (e.g., doesn’t get distracted by other items). |
| 10. Accepts when something happens that he/she doesn’t like (e.g., doesn’t whine when he/she does not get favourite cereal at the grocery store). |
| PART G—STORY TIME |
| This section will ask you about your child’s abilities related to reading and telling stories to others. |
| 1. Tells you a made-up story in an organized manner (e.g., starting at the beginning, finishing at the end). |
| 2. Tells a story about something that has happened so that others can easily understand. |
| 3. When telling a story, real or fictional, links events in a way that makes sense. |
| 4. Repeats stories or jokes he/she has heard from others. |
| 5. If interrupted, will continue from where he/she left off in telling you a story (i.e., doesn’t need to start from the beginning again). |
| 6. Is quiet when you read him/her a story (i.e., doesn’t interrupt you). |
| PART H—GENERAL SKILLS AND BEHAVIOURS |
| This section will ask you about your child’s general abilities. |
| 1. Puts on his/her own clothing. |
| 2. Is able to put the brakes on his/her actions when asked (e.g., can stop acting silly). |
| 3. Can do simple calculations in his/her head (e.g., 2 plus 3). |
| 4. Can do things that require mental effort (e.g., counting backwards). |
| 5. Plans/talks about the next day’s events. |
| 6. Understands concepts of time (e.g., appreciates the difference between 5 minutes and half an hour). |
| 7. Uses the same object for different or novel uses (e.g., using a pencil as chop sticks). |
| 8. Can shift gears and easily adapt behaviours to a new task. |
| 9. Adjusts behavior to different situations (e.g., eating at a restaurant versus eating at home). |
| 10. Uses suggestions from you when trying a new task (e.g., learning to tie his/her shoelaces). |
Submitted: June 5, 2015 Revised: February 5, 2016 Accepted: February 17, 2016
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Source: Psychological Assessment. Vol. 29. (1), Jan, 2017 pp. 50-64)
Accession Number: 2016-16660-001
Digital Object Identifier: 10.1037/pas0000308
Record: 141- Title:
- RCT of web-based personalized normative feedback for college drinking prevention: Are typical student norms good enough?
- Authors:
- LaBrie, Joseph W.. Department of Psychology, Loyola Marymount University, Los Angeles, CA, US, jlabrie@lmu.edu
Lewis, Melissa A.. Department of Psychiatry and Behavioral Sciences, University of Washington, St Louis, MO, US
Atkins, David C.. Department of Psychiatry and Behavioral Sciences, University of Washington, St Louis, MO, US
Neighbors, Clayton. Department of Psychology, University of Houston, Houston, TX, US
Zheng, Cheng. Department of Psychiatry and Behavioral Sciences, University of Washington, St Louis, MO, US
Kenney, Shannon R.. Department of Psychology, Loyola Marymount University, Los Angeles, CA, US
Napper, Lucy E.. Department of Psychology, Loyola Marymount University, Los Angeles, CA, US
Walter, Theresa. Department of Psychiatry and Behavioral Sciences, University of Washington, St Louis, MO, US
Kilmer, Jason R.. Department of Psychiatry and Behavioral Sciences, University of Washington, St Louis, MO, US
Hummer, Justin F.. Department of Psychology, University of Washington, St Louis, MO, US
Grossbard, Joel. Department of Psychiatry and Behavioral Sciences, University of Washington, St Louis, MO, US
Ghaidarov, Tehniat M.. Department of Psychology, Loyola Marymount University, Los Angeles, CA, US
Desai, Sruti. Department of Psychiatry and Behavioral Sciences, University of Washington, St Louis, MO, US
Lee, Christine M.. Department of Psychiatry and Behavioral Sciences, University of Washington, St Louis, MO, US
Larimer, Mary E.. Department of Psychiatry and Behavioral Sciences, University of Washington, St Louis, MO, US - Address:
- LaBrie, Joseph W., Department of Psychology, Loyola Marymount University, 1 LMU Drive, Suite 4700, Los Angeles, CA, US, 90045, jlabrie@lmu.edu
- Source:
- Journal of Consulting and Clinical Psychology, Vol 81(6), Dec, 2013. pp. 1074-1086.
- NLM Title Abbreviation:
- J Consult Clin Psychol
- Page Count:
- 13
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- Journal of Consulting Psychology
- Other Publishers:
- US : American Association for Applied Psychology
US : Dentan Printing Company
US : Science Press Printing Company - ISSN:
- 0022-006X (Print)
1939-2117 (Electronic) - Language:
- English
- Keywords:
- alcohol, college students, personalized normative feedback, social norms, web-based feedback, risky alcohol consumption
- Abstract:
- Objectives: Personalized normative feedback (PNF) interventions are generally effective at correcting normative misperceptions and reducing risky alcohol consumption among college students. However, research has yet to establish what level of reference group specificity is most efficacious in delivering PNF. This study compared the efficacy of a web-based PNF intervention using 8 increasingly specific reference groups against a Web-BASICS intervention and a repeated-assessment control in reducing risky drinking and associated consequences. Method: Participants were 1,663 heavy-drinking Caucasian and Asian undergraduates at 2 universities. The referent for web-based PNF was either the typical same-campus student or a same-campus student at 1 (either gender, race, or Greek affiliation), or a combination of 2 (e.g., gender and race), or all 3 levels of specificity (i.e., gender, race, and Greek affiliation). Hypotheses were tested using quasi-Poisson generalized linear models fit by generalized estimating equations. Results: The PNF intervention participants showed modest reductions in all 4 outcomes (average total drinks, peak drinking, drinking days, and drinking consequences) compared with control participants. No significant differences in drinking outcomes were found between the PNF group as a whole and the Web-BASICS group. Among the 8 PNF conditions, participants receiving typical student PNF demonstrated greater reductions in all 4 outcomes compared with those receiving PNF for more specific reference groups. Perceived drinking norms and discrepancies between individual behavior and actual norms mediated the efficacy of the intervention. Conclusions: Findings suggest a web-based PNF intervention using the typical student referent offers a parsimonious approach to reducing problematic alcohol use outcomes among college students. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Alcohol Drinking Patterns; *Feedback; *Intervention; *Online Therapy; *Social Norms; College Students; Risk Taking
- Medical Subject Headings (MeSH):
- Adolescent; Alcohol Drinking; Feedback, Psychological; Female; Humans; Internet; Linear Models; Male; Social Norms; Social Perception; Students; Young Adult
- PsycINFO Classification:
- Health & Mental Health Treatment & Prevention (3300)
- Population:
- Human
Male
Female - Location:
- US
- Age Group:
- Adulthood (18 yrs & older)
Young Adulthood (18-29 yrs) - Tests & Measures:
- Daily Drinking Questionnaire
Quantity/Frequency Index
Drinking Norms Rating Form DOI: 10.1037/t03956-000
Rutgers Alcohol Problem Index DOI: 10.1037/t00517-000 - Grant Sponsorship:
- Sponsor: National Institute on Alcohol Abuse and Alcoholism
Grant Number: R01AA012547-06A2
Recipients: No recipient indicated - Methodology:
- Empirical Study; Quantitative Study
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- First Posted: Aug 12, 2013; Accepted: Jul 8, 2013; Revised: Jun 18, 2013; First Submitted: Aug 3, 2012
- Release Date:
- 20130812
- Correction Date:
- 20131202
- Copyright:
- American Psychological Association. 2013
- Digital Object Identifier:
- http://dx.doi.org/10.1037/a0034087
- PMID:
- 23937346
- Accession Number:
- 2013-28918-001
- Number of Citations in Source:
- 62
- Persistent link to this record (Permalink):
- http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2013-28918-001&site=ehost-live
- Cut and Paste:
- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2013-28918-001&site=ehost-live">RCT of web-based personalized normative feedback for college drinking prevention: Are typical student norms good enough?</A>
- Database:
- PsycINFO
RCT of Web-Based Personalized Normative Feedback for College Drinking Prevention: Are Typical Student Norms Good Enough?
By: Joseph W. LaBrie
Department of Psychology, Loyola Marymount University;
Melissa A. Lewis
Department of Psychiatry and Behavioral Sciences, University of Washington
David C. Atkins
Department of Psychiatry and Behavioral Sciences, University of Washington
Clayton Neighbors
Department of Psychology, University of Houston
Cheng Zheng
Department of Psychiatry and Behavioral Sciences, University of Washington
Shannon R. Kenney
Department of Psychology, Loyola Marymount University
Lucy E. Napper
Department of Psychology, Loyola Marymount University
Theresa Walter
Department of Psychiatry and Behavioral Sciences, University of Washington
Jason R. Kilmer
Department of Psychiatry and Behavioral Sciences, University of Washington
Justin F. Hummer
Department of Psychology, Loyola Marymount University
Joel Grossbard
Department of Psychiatry and Behavioral Sciences, University of Washington
Tehniat M. Ghaidarov
Department of Psychology, Loyola Marymount University
Sruti Desai
Department of Psychiatry and Behavioral Sciences, University of Washington
Christine M. Lee
Department of Psychiatry and Behavioral Sciences, University of Washington
Mary E. Larimer
Department of Psychiatry and Behavioral Sciences, University of Washington
Acknowledgement: Data collection and manuscript preparation were supported by National Institute on Alcohol Abuse and Alcoholism Grant R01AA012547-06A2.
Heavy drinking among college students is associated with a range of serious primary and secondary consequences (e.g., academic and psychological impairment, risky sexual behavior and victimization, car accidents, and violence; Hingson, Zha, & Weitzman, 2009; Wechsler & Nelson, 2008). A considerable body of research confirms that behavioral decisions, such as the decision to drink heavily, are influenced by normative perceptions of significant referents’ behaviors and beliefs (Berkowitz, 2004; Borsari & Carey, 2003). For example, perceptions of peers’ drinking (descriptive norms) and attitudes toward drinking (injunctive norms) have been identified as among the strongest predictors of personal drinking behavior among college students (Neighbors, Lee, Lewis, Fossos, & Larimer, 2007; Perkins, 2002). Students commonly and consistently overestimate the amount of alcohol peers consume (Borsari & Carey, 2003; Lewis & Neighbors, 2004), with approximately seven in 10 students overestimating the amount of alcohol consumed by typical students at their college (Perkins, Haines, & Rice, 2005).
Interventions to correct normative misperceptions can reduce drinking and negative consequences among college students (e.g., Bewick et al., 2010; LaBrie, Hummer, Neighbors, & Pedersen, 2008; Neighbors, Larimer, & Lewis, 2004; Walters, 2000). Personalized normative feedback (PNF) interventions, which attempt to correct normative misperceptions by presenting students with individually delivered feedback comparing their personal drinking behavior, perceptions of peers’ drinking behavior (perceived descriptive norms), and peers’ actual drinking behavior (actual descriptive norms), have demonstrated considerable success in reducing normative perceptions and alcohol consumption in college student populations (S. E. Collins, Carey, & Sliwinsky, 2002; Cunningham, Humphreys, & Koski-Jännes, 2000; Lewis & Neighbors, 2006a; Murphy et al., 2004; Neighbors et al., 2004; Walters, 2000). In fact, several trials support the efficacy of stand-alone PNF interventions (for a review, see Zisserson, Palfai, & Saitz, 2007), which have evidenced similar effect sizes compared with PNF delivered as part of multicomponent interventions (Walters & Neighbors, 2005).
Despite the growing body of evidence in support of PNF-only interventions, questions remain regarding what level of specificity of referent is most effective. Moreover, limited data exists assessing the utility of web-based PNF outside of the laboratory, but the limited results suggest that this approach can lead to reductions in alcohol consumption (Bewick et al., 2010; Neighbors, Lewis, et al., 2010; Walters, Vader, & Harris, 2007). Web-based PNF has the potential to provide a cost-effective, standardized intervention that can be easily disseminated to large groups, while being appealing to college students who perceive this modality to be unobtrusive and convenient (Neighbors, Lewis, et al., 2010; Riper et al., 2009). In the current study, we aimed to address this gap in the literature by examining the efficacy of web-based PNF using varying levels of reference group specificity.
Specificity of Normative Reference GroupThe majority of PNF initiatives have used typical student-normative referents (S. E. Collins et al., 2002; Murphy et al., 2004; Neighbors et al., 2004; Walters, 2000). However, recent research has indicated that increasing the specificity of normative reference groups (e.g., gender specific) may enhance the efficacy of PNF interventions for certain individuals (Lewis & Neighbors, 2006a). This is consistent with theoretical perspectives that suggest more socially proximal and salient, as compared with more distal, social reference groups have a greater impact on an individual’s behavioral decisions (e.g., social comparison theory, Festinger, 1954; social impact theory, Latané, 1981). Indeed, researchers have found descriptive norms for more socially proximal referents (e.g., close friends, same-sex students) tend to be more strongly associated with alcohol consumption than those of “typical” or “average” students (Korcuska & Thombs, 2003; Lewis & Neighbors, 2004; Lewis, Neighbors, Oster-Aaland, Kirkeby, & Larimer, 2007). Furthermore, Larimer et al. (2009, 2011) reported that even at increasing levels of specificity (i.e., gender, ethnicity, residence), students overestimated descriptive-normative drinking behaviors of proximal peers, and these misperceptions were uniquely related to personal drinking. Targeting more specific reference groups may be particularly effective in communicating feedback that closely resembles the individual respondent, thereby increasing the saliency, believability, and recognition of the information presented and, in turn, more strongly promoting positive behavioral change. In the current study, we focused on normative reference groups derived from combinations of participants’ gender, race, and Greek status.
Gender and Greek Specificity
Gender and Greek status are two levels of specificity that may influence the impact of PNF interventions. Men and women exhibit different drinking behaviors (Kypri, Langley, & Stephenson, 2005) and perceptions of normative beliefs (Lewis & Neighbors, 2004, 2006a; Suls & Green, 2003). Efficacy studies of gender-specific PNF have revealed inconsistent results. For example, Lewis and Neighbors (2007) did not find any overall differences in the short-term efficacy of gender-specific and nongender-specific PNF, although both groups reported reductions in drinking compared with controls. However, gender-specific feedback worked better for women who identified more closely with their gender. Neighbors, Lewis et al. (2010) demonstrated PNF delivered biannually with gender-specific norms reduced weekly drinking, whereas nongender-specific and one-time only gender-specific norms did not.
Students affiliated with fraternities and sororities (Greek systems) hold significantly higher perceived and actual drinking norms (Carter & Kahnweiler, 2000) than non-Greek peers. Larimer et al. (2011) found Greek students’ perceived norms for referents that do not include Greek status tended to be close to, if not lower than, their own drinking behavior. However, Greek students presented with referents that did include Greek status overestimated normative drinking. As such, Greek-specific normative feedback may be particularly beneficial to Greeks, who appear amenable to normative feedback interventions (LaBrie et al., 2008; Larimer et al., 2001; Larimer, Turner, Mallett, & Geisner, 2004).
Ethnicity/Race Specificity
Currently, no studies have addressed the efficacy of race-specific PNF, and limited data are available examining racial and ethnic differences in norms and their relationship to alcohol use (LaBrie, Atkins, Neighbors, Mirza, & Larimer, 2012). The few studies in which ethnic- and race-specific reference groups have been examined suggest perceived norms vary on the basis of the race specificity of the reference group (Larimer et al., 2009, 2011), and perceived norms for same-ethnicity students are positively associated with drinking, particularly for those who identify most strongly with their ethnic group (Neighbors, LaBrie, et al., 2010). The typical American college student is most often viewed as Caucasian, even among non-Caucasian students (Lewis & Neighbors, 2006b), thus perceived typical student norms may be less predictive of drinking among non-Caucasian students. Indeed, Stappenbeck, Quinn, Wetherill, and Fromme (2010) found that although Caucasian and Asian students do not differ in perceived typical student norms, generic norms were predictive of alcohol use and own social group norms for Caucasian, but not Asian students.
Taken together, these findings suggest that PNF interventions may benefit from providing race-specific feedback. In the current study, we extend previous research by examining the impact of race-specific PNF among Caucasians students, the prototypical heavy-drinking racial subgroup in college populations, and Asian American students. Although Asian Americans have higher rates of abstinence than other ethnic groups, Asian American adolescents who do drink have higher rates of binge drinking than any other ethnic group, and this racial subgroup exhibits escalating rates of heavy episodic drinking and alcohol abuse (Grant et al., 2004; Hahm, Lahiff, & Guterman, 2004; Office of Applied Studies, 2008; Wechsler, Dowdall, Maenner, Gledhill-Hoyt, & Lee, 1998; Wechsler et al., 2002). These findings have led to calls for alcohol prevention efforts to specifically target this ethnic minority (e.g., Hahm et al., 2004; LaBrie, Lac, Kenney, & Mirza, 2011). Thus, in the current study we focused on Asian students in order to contribute to prevention efforts for this understudied group as well as to extend work of Stappenbeck and colleagues (2010) to evaluate the efficacy of typical student versus ethnic-specific feedback for diverse populations. We also selected Asian students, as they represent an ethnic minority population of sufficient size and with distinct drinking behavior and norms from the majority population to enable a strong test of our research questions.
Discrepancy of Actual Norm With Behavior and PerceptionsPNF approaches correct normative misperceptions by showing discrepancies between actual norms and students’ perceptions and behaviors in order to motivate behavior change (Rice, 2007). Presumably, for PNF to be effective, inaccurate beliefs must be present (Lewis & Neighbors, 2006a), and the greater the discrepancy between actual norms and perceived norms, and actual norms and behavior, the greater the potential impact of normative feedback (Larimer et al., 2004). Larimer et al. (2011) examined the accuracy of students’ perceived norms using reference groups varying in similarity to the participant, including typical student and combinations of gender, race, and Greek status. Participants rated the referent to have higher levels of alcohol consumption relative to their own drinking, and, in general, as the referent became more similar, mean normative estimates generally decreased. Thus, the greatest discrepancy between perceived norms and actual norms occurred when the typical student referent was used. Although students may find specific normative information more relevant, compelling, and, therefore, motivating, the greater accuracy of descriptive norms for specific reference groups may reduce the discrepancy and decrease the motivating potential of normative feedback. In the current study, we examine whether intervention effects are mediated by discrepancies between actual norms, perceived norms, and drinking behavior.
The Current StudyWe compare the efficacy of web-based PNF using one of eight increasingly specific reference groups (typical student and gender-, race-, Greek status-, gender-race-, gender-Greek status-, race-Greek status-, gender-race-Greek status-specific) compared against a web-based motivational feedback intervention derived from the well-established BASICS intervention (Brief Alcohol Screening and Intervention for College Students; Dimeff, Baer, Kivlahan, & Marlatt, 1999) and a generic feedback control in the current study. The Web-BASICS control provides an opportunity to examine whether addition of comprehensive feedback components offers any advantages over stand-alone PNF, whereas the generic control condition allows us to examine whether completing alcohol-related questionnaires and receiving nonalcohol-related feedback could be responsible for intervention effects. We hypothesized that both PNF and Web-BASICS would outperform an assessment-only control condition in reducing risky drinking (number of weekly drinks, peak drinks in the past month, and days of drinking during the past month) and negative consequences of alcohol use. We further predicted that increasing levels of specificity of feedback would be more effective in reducing risky drinking and consequences such that PNF with three levels of specificity (same-sex, same-race, same Greek membership status) would outperform two and one levels and typical student feedback. Finally, we examined the role of discrepancy between drinking behavior, perceived descriptive norms, and the actual drinking norm for each reference group as a mechanism of intervention efficacy.
Method Participants and Procedure
Participants were undergraduate students from two West Coast universities. A random list of enrolled students (N = 11,069; n1 = 6,495; n2= 4,574) was provided by the registrar’s office. Students were contacted via mail and e-mail to participate in an online screening survey. Of participants contacted, 4,818 (43.5%) responded and completed the screening survey (60.2% female). Campus 1 (n1 = 3,034), a large, public university, has an enrollment of approximately 30,000 undergraduate students. Campus 2 (n2 = 1,784) is a mid-sized private university with enrollment of approximately 6,000 undergraduates. Participants were between 18 and 24 years old (M = 19.86, SD = 1.35). Racial composition was 50.7% Caucasian, 27.4% Asian, 10.7% multiracial, 6.4% “other,” 2.5% African American, 1.6% Hawaiian/Pacific Islander, and 0.5% American Indian/Alaskan Native. Furthermore, 10.9% self-identified as Hispanic. The screening samples were similar to the college populations from which they were drawn with respect to alcohol use. For example, a similar proportion of students reported that they did not drink on a typical week (Campus 1: 35.2% screening, 37.2% population; Campus 2: 25.7% screening, 27.7% population). In terms of demographics, females were slightly overrepresented in the screening sample (Campus 1: 56.7% screening, 51.6% population; Campus 2: 65.6% screening, 57.9% population), and White students were underrepresented in the Campus 1 sample (Campus 1: 45.7% screening, 56.6% population; Campus 2: 59.1% screening, 55.5% population).
A total of 2,034 (42.2%) out of the 4,818 students who completed the screening survey met inclusion criteria for the current study. Inclusion criteria consisted of participants reporting a minimum of one past-month heavy episodic drinking event (HED; consuming at least four [for female] or five [for males] drinks during a drinking occasion) and identifying as either Caucasian or Asian. Of the 2,034 participants who met inclusion criteria, 1,831 (90%) students completed an online baseline survey, and 1,663 were randomized to one of the 10 conditions reported on in this article. Another condition (n = 168) was a minimal assessment control condition comprising students who did not participate in the 1-, 3- or 6-month follow-up periods and therefore was not included in the current analysis. Follow-up rates were 89.7% at 1 month, 86.8% at 3 months, 84% at 6 months, and 85.5% at 12 months. The final sample was 56.7% female, with a mean age of 19.92 years (SD = 1.3). The majority of the sample identified as Caucasian (75.7%) and did not belong to a sorority or fraternity (70.7%).
Study Design
The current study was approved by the Institutional Review Boards of both participating universities, and a Federal Certificate of Confidentiality was obtained to further protect research participants.
Screening
Students randomly selected from registrar rosters at both universities received mailed and e-mailed letters inviting their participation in a study of alcohol use and perceptions of drinking in college. The invitations included a URL to a 20-min online screening survey, which gathered demographic, alcohol use, and descriptive and injunctive norms data. Screening survey completers received a $15 stipend.
Baseline
Students completing the screening survey who met inclusion criteria were immediately invited to participate in the longitudinal trial. Students were presented with a web invitation, which provided a URL directing them to the baseline survey. The baseline survey included additional measures related to study hypotheses such as an assessment of negative consequences of drinking. Baseline survey completers received a $25 stipend. Upon completion of the baseline survey, students were randomly assigned to one of the 10 treatment conditions using a web-based algorithm. A stratified, block randomization was used (Hedden, Woolson, & Malcolm, 2006), in which assignment was stratified by Greek organization membership (yes/no), sex (male/female), race (Asian/Caucasian), and total drinks per week (10 or fewer, 11 or more). Thus, each treatment condition was composed of approximately 82 men and 100 women, 43 Asian Americans and 139 Caucasians, and 55 Greek-affiliated students and 127 non-Greek students.
PNF intervention
Of the 10 conditions examined in the current study, eight provided normative feedback based on differing levels of specificity of the reference group. Condition 1 was provided normative information about the typical student at the same university. Conditions 2–4 were provided matched normative information at one level of specificity based on the participant’s gender, Greek status, or race. Conditions 5–7 were presented two levels of specificity for students at the same university matched to participant’s gender and race (e.g., typical female Asian), gender and Greek status (e.g., typical male Greek-affiliated student), or race and Greek status (e.g., typical Caucasian Greek-affiliated student). The eighth condition provided participants with three levels of specificity for students at the same university matched to participant’s gender, race, and Greek status (e.g., typical female, Asian, Greek-affiliated student). A ninth condition presented Web-BASICS (Dimeff et al., 1999). Finally, the 10th condition was a repeated assessment control group who received generic nonalcohol-related normative feedback about the typical student’s frequency of text messaging, downloading music, and playing video games on their campus.
After completing the baseline survey, participants were immediately provided with Web-based feedback, depending on their randomized condition. Three feedback categories were used: PNF (Conditions 1–8 described above), Web-BASICS (Condition 9), and generic control feedback (Condition 10). Participants were given the option to print their feedback.
The PNF
The PNF contained four pages of information in text and bar graph format. Separate graphs, each including three bars, were used to present information regarding the number of drinking days per week, average drinks per occasion, and total average drinks per week for (a) one’s own drinking behavior, (b) their reported perceptions of the reference group’s drinking behavior on their respective campus, at the level of specificity defined by their assigned intervention condition, and (c) actual college student drinking norms for the specified reference group. Actual norms were derived from large representative surveys conducted on each campus in the prior year as a formative step in the trial. Participants were also provided with their percentile rank comparing them with other students on their respective campus for the specified reference group (e.g., “Your percentile rank is 99%; this means that you drink as much or more than 99% of other college students on your campus”).
Web-BASICS feedback
The Web-BASICS feedback contained a total of 26 pages of interactive comprehensive motivational information based on assessment results, modeled from the efficacious in-person BASICS intervention (Dimeff et al., 1999; Larimer et al., 2001). It addressed quantity and frequency of alcohol use; past-month peak alcohol consumption; estimated blood alcohol content (BAC); and provided information regarding standard drink size, how alcohol affects men and women differently, oxidation, alcohol effects, reported alcohol-related experiences, estimated calories, and financial costs based on reported weekly use, estimated level of tolerance, risks based on family history, risks for alcohol problems, and tips for reducing risks while drinking as well as alternatives to drinking. The feedback also included PNF using typical student drinking norms. Participants were given the option to click links throughout the feedback to obtain additional information on standard drink size, sex differences and alcohol use, oxidation, biphasic tips, hangovers, alcohol costs, tolerance, and protective factors, as well as provided with a link to a BAC calculator.
Generic control feedback
The generic control feedback, which was presented to those in the assessment control condition, contained three pages of information in text and bar graph format. Separate graphs, each including two bars, were used to present information regarding the number of hours spent texting, number of hours spent downloading music, and number of hours spent playing video games per week for (a) one’s own behavior and (b) actual college student behavior. Participants were also provided with their percentile rank comparing them with other students on their respective campus (e.g., “Your percentile rank is 60%; this means that you text as much or more than 60% of other college students on your campus”).
Follow-up
To assess intervention efficacy, participants were invited to take a series of online follow-up surveys at 1-, 3-, 6-, and 12-month time points after their online intervention. Participants received $30 for completing the 1-, 3- and 6-month follow-up surveys and $40 for completing the 12-month follow-up survey. Additionally, students who completed all surveys received a bonus check of $30 at the end of the study.
Measures
All measures were completed at screening/baseline, 1-, 3-, 6-, and 12-month follow-up. A standard drink definition was included for all alcohol consumption measures (i.e., 12 oz. beer, 10 oz. wine cooler, 4 oz. wine, 1 oz. 100 proof [1 [1/4] oz. 80 proof] liquor).
Demographics
The initial section of the screening survey asked participants to report their birth sex, race, and Greek status.
Alcohol consumption
The Daily Drinking Questionnaire (DDQ; R. L. Collins, Parks, & Marlatt, 1985; Kivlahan, Marlatt, Fromme, Coppel, & Williams, 1990) measured one of the primary outcomes: the number of drinks per week. Students were asked to consider a typical week in the last month and indicate the number of drinks they typically consumed on each day of the week. Students’ responses were summed across each of the 7 days to form a composite of total weekly drinks.
The Quantity/Frequency Index is an assessment of alcohol use (Baer, 1993) that measures participant’s drinking during the past month. Participants were asked to think about the occasion when they drank the most and to report how many drinks they consumed on that occasion. In addition, participants reported how many days they drank alcohol in the past month. Response options ranged from 0 (I do not drink at all) to 7 (Every day).
Descriptive norms
The Drinking Norms Rating Form (DNRF; Baer, Stacy, & Larimer, 1991) assessed participants’ perception of the number of drinks consumed each day of the week by a typical student at one’s university and at varying levels of reference group specificity. The levels of specificity referred to a typical student’s gender, race, and Greek status and all combinations of the tree, resulting in eight reference groups for each question.
Alcohol-related negative consequences
The 25-item Rutgers Alcohol Problem Index (RAPI; White & Labouvie, 1989) assessed the frequency of alcohol-related negative consequences. Response options ranged from 0 (never) to 4 (10 or more times). The items included “Passed out or fainted suddenly”; “Caused shame or embarrassment to someone”; and “Felt physically or psychologically dependent on alcohol.” Items were summed to create a composite score for the analysis.
Data Analyses
The first two hypotheses examining the efficacy of PNF compared with Web-BASICS and control conditions, and the efficacy of PNF conditions varying in specificity of feedback, were tested using a quasi-Poisson generalized linear model fit by generalized estimating equations (GEE; Liang & Zeger, 1986). The primary outcomes included number of drinks consumed per week, peak drinks in the past month, drinking days during the past month, and total number of alcohol-related problems. Each of these outcomes represents a type of count variable. Count variables have certain properties (e.g., bounded at zero, integer scaling) that make them ill-suited for statistical methods that assume normality and are more appropriately modeled by count regression methods (see Atkins & Gallop, 2007). Poisson GEE models are appropriate for clustered or longitudinal count data and control for correlated data through estimating a working correlation matrix of the residuals and using robust, cluster-adjusted standard errors. However, the basic Poisson GEE assumes that the mean of the outcome is equal to its variance (conditional on the covariates). This is often violated in real-world data, leading to a condition called overdispersion, which yields biased standard errors and statistical tests (Hilbe, 2011). The quasi-Poisson GEE is a semiparametric mean model that incorporates an overdispersion parameter, yielding unbiased variance estimates in the presence of overdispersion.
The predictors were connected to the outcome through a natural logarithm link function, which is the standard link function for Poisson models and other count regression methods. To interpret quasi-Poisson regression models, the coefficients are typically exponentiated (i.e., eB) to yield rate ratios (RRs). Like odds ratios in logistic regression, a value of 1 is a null value for RRs (i.e., no effect), and RRs larger than 1 are interpreted as a percentage increase in counts (for each unit increase in the predictor). Conversely, RRs less than 1 are interpreted as percentage decreases in the outcome (for each unit decrease in the predictor).
The basic quasi-Poisson model used to test primary hypotheses (using the DDQ outcome as an example) was:
As seen in Equation 1, the baseline level of the outcome was included as a covariate in all analyses, which increases the efficiency of the model (i.e., reduces SE for treatment contrasts and other terms), and a participant’s outcome in the regression models included his or her values of the outcome at 1, 3, 6, and 12 months postbaseline. Time was modeled as a linear association with outcomes, which was confirmed through sensitivity analyses that allowed more flexible, nonlinear associations. In Equation 1, a single treatment indicator is shown (Tx), but in analyses this was replaced by appropriate treatment contrasts, described below in the Results section. Randomization excluded the possibility of baseline confounders, and there were no concerns about treatment comparability at baseline. Hence, models did not adjust for additional covariates. The proportion of missing data were consistent across treatment conditions (see Figure 1), and sensitivity analysis demonstrated no differences based on missing data status. A priori power analyses given the current design indicated that treatment condition sample sizes of n = 141 or greater (accounting for planned attrition of 20%) would yield power of .80 or better to detect treatment contrasts of d = 0.20 (e.g., small effect sizes). All analyses were done in R v2.11.1 (R Development Core Team, 2010).
Figure 1. Participant flow through the study. BASICS = Brief Alcohol Screeing and Intervention for College Students; m = month.
Results Descriptive Analyses
Participants in the randomized control trial sample reported consuming an average of 11.03 drinks (SD = 9.5; males M = 14.23, SD = 11.5; females M = 8.58, SD = 6.6) in a typical week. Furthermore, on the occasion on which participants drank the most in the past month, they reported drinking an average of 8.77 drinks (SD = 4.1; males M = 10.68, SD = 4.3; females M = 7.31, SD = 3.2) on a single occasion. Table 1 has descriptive statistics for each of the 10 treatment conditions (i.e., all PNF conditions are reported separately).
Mean Drinking and Consequences Outcomes for Different Treatment Groups at Five Time Points
Quasi-Poisson GEE Analyses of Control Versus Web-BASICS Versus PNF
Initial inferential statistics focused on treatment comparisons between control, Web-BASICS, and PNF (considered as a single group). As noted earlier, a quasi-Poisson GEE model was fit that included time, indicator variables for Web-BASICS and PNF (compared with control), and the outcome measured at baseline. A model including interactions between time and treatment conditions was examined. These interactions were not significant, indicating that all change occurred from baseline to 1 month with little change following that, and thus the simpler model was retained, including main effects for treatment and time. Moderation of intervention effects by demographic variable (e.g., race, gender, and Greek membership) was also nonsignificant. RRs and 95% confidence intervals (CIs) for RRs are shown in Table 2.
Rate Ratios (RRs) and 95% Confidence Intervals (CIs) for RR From Quasi-Poisson GEE Comparing Control, BASICS, and PNF Participants From 1 to 12 Months
Focusing on total weekly drinking, the intercept term is the estimate of drinking for control participants at baseline (i.e., time = 0) because of the coding of the indicator variables. The RR for that term provides the average outcome for this group (e.g., the mean total drinks per week is 8.7 in the control group). The effect for time presents the adjusted common change across time (i.e., there is approximately a 0.6% decrease in drinks per week every month postintervention in the control group). Compared with the control group, PNF participants showed a 4% reduction in average total drinks, significantly lower than control participants. The Web-BASICS and control conditions were not significantly different from one another. Findings for the other three outcomes are broadly similar: PNF participants reported significantly less peak drinking, drinking days, and drinking-related problems (RAPI) relative to control participants. However, in each case, the differences are modest (between 1% and 8%). Web-BASICS participants reported significantly lower peak drinking and total drinking days relative to control, but RAPI scores were similar between the two groups. Finally, contrasts examined whether there were differences between the two active treatment groups (combined PNF = 0, Web-BASICS = 1), and they revealed no significant differences for total weekly drinks (RR = 0.96, 95% CI [0.85, 1.09]), peak drinks (RR = 1.01, 95% CI [0.94, 1.10]), total drinking days (RR = 1.00, 95% CI [0.91, 1.09]), and drinking-related problems (RR = 0.91, 95% CI [0.67, 1.25]).
Quasi-Poisson GEE of Individual PNF Conditions
We next examined whether a more specific comparison group with PNF might yield better treatment outcomes relative to a generic “typical” student comparison group. Using only the eight PNF conditions, a quasi-Poisson GEE examined whether greater specificity in the normative reference group would lead to greater reductions in drinking. Table 3 presents RR and 95% CI for RR for comparisons among PNF conditions. No PNF condition led to greater change over time in any of the four outcomes as compared with typical student feedback. Surprisingly, just the opposite was found: All RRs comparing more specific PNF references with the typical student were greater than 1, and virtually all were significant. Thus, more specific PNF conditions achieved reliably worse results compared with typical student feedback.
Rate Ratios (RRs) and 95% Confidence Intervals (CIs) for RR From Quasi-Poisson GEE Comparing Typical Student PNF With More Specific PNF Conditions From 1 to 12 Months
Treatment Mediators and Mechanisms
Several analyses examined possible mediators or mechanisms for why typical student feedback might be superior to feedback with more specific reference groups, focusing on total drinks per week during follow-up, as in the earlier PNF-focused treatment analyses. The perceived descriptive drinking norm (measured as the average rating on the DNRF across reference groups at each time) was considered as a mediator of treatment efficacy. The approach to mediation was similar to the classic approach to mediation, in which a total effect of treatment is decomposed into a direct effect of treatment and indirect effect through the mediator. However, we used a bootstrapped, nonparametric method for estimating the quantities (Imai, Keele, & Tingley, 2010). Table 4 shows results for mediation analyses, comparing each of the other seven PNF conditions with typical student PNF. The total effect column reports the estimated mean difference in total weekly drinks between typical student PNF and the specified treatment condition (i.e., basic treatment difference expressed as estimated mean difference), and the indirect effect column reports the amount of the total effect that can be explained by the indirect pathway through the DNRF. These results show that changes in the DNRF account for 11%–51% of the treatment superiority of the typical student PNF relative to other PNF conditions.
Mediation Results for DNRF and Two Different Types of Discrepancy
The putative mechanisms of PNF include the discrepancy between the individual’s own drinking and the actual descriptive drinking norm they are provided during the feedback, as well as the discrepancy between their perception of the norm (i.e., DNRF) and the norm provided during feedback. Conceptually, we consider these to be treatment mechanisms, as opposed to mediators or moderators. They are not moderators as they are directly manipulated as part of the treatment, but they are also not traditional mediators because the treatment does not influence the discrepancy, but rather the discrepancy is part of the intervention itself. However, if the pragmatic goal is to separate the effect of treatment content (i.e., discrepancy) and treatment type, then analytically, we can consider discrepancy as a mediator to achieve this aim. Results are shown in Table 4.
Approximately 5% of the total effect (i.e., estimated mean treatment difference) can be explained by the discrepancy with one’s own drinking and even less by the discrepancy with perceived norm. Thus, relative to the DNRF as a mediator, these treatment mechanisms appear to be somewhat weaker explanations for the treatment difference. Considering the indirect effect as a percentage change in the treatment difference, there is a 2.9% (CI [2.4%–3.5%], p < .001) change in total drinks per week with each unit change in DNRF, a 14% (CI [12%–18%], p < .001) change in total drinks per week with each unit change in discrepancy with one’s own drinking, and a 0.4% (CI [0.0%–0.9%], p = .57) change in total drinks per week with each unit change in discrepancy with perceived norm. Thus, in understanding the difference between typical student feedback versus more specific PNF, both DNRF and discrepancy with own drinking appear to significantly affect the treatment differences. In summary, typical student PNF appears to yield greater changes in typical weekly drinking in part by having greater influence on perceptions of descriptive norms (i.e., DNRF) as well as generating a larger discrepancy with the student’s own drinking relative to other PNF conditions.
DiscussionIn the current study, we evaluated the efficacy of web-based PNF in reducing drinking and alcohol-related negative consequences relative to an active comparison condition (Web-BASICS) and a control condition. Relative to the control condition, PNF (considered as a single group) was associated with reductions in each of the four outcomes (number of weekly drinks, peak drinks, days of drinking, and number of alcohol-related problems). However, the effects of the web-based PNF were modest, with reductions ranging from a 1.0% decrease in number of drinking days to an 8.1% decrease in maximum number of drinks consumed on one occasion. PNF appeared to have more of an effect on the amount students drank (total drinks and peak drinks) than on drinking frequency. Furthermore, compared with control, Web-BASICS was associated with a decrease in number of drinking days (2.5%) and peak number of drinks, but no change in number of alcohol-related negative consequences at the 12-month assessment.
Findings also indicated that the PNF (when considered as a single group) and Web-BASICS interventions did not differ significantly from each other, which suggests that a brief web-based PNF intervention with a focus only on normative comparisons is as efficacious as a more inclusive Web-BASICS intervention that focuses on normative comparisons in addition to a wide range of other feedback components (e.g., blood alcohol, content, expectancies, protective behavioral strategies). Because both interventions were comparable at 12 months, a more parsimonious PNF intervention might be a preference over a more inclusive BASICS intervention, at least with respect to web-based interventions. It is worth noting that Web-BASICS includes PNF feedback. The absence of differences may suggest that components within Web-BASICS other than PNF (e.g., expectancy information, review of risk factors, review of consequences experienced) may not offer unique impact over and above PNF.
We also extended existing research in the current study by examining the influence of specificity of normative referent group on the efficacy of web-based PNF. In contrast to expectations, the PNF intervention was most effective when the typical student (i.e., least specific normative referent) was used as the normative reference group. Thus, students who engaged in HED and were given personalized information highlighting the discrepancy between their own drinking behavior, their perception of typical student drinking norms, and the actual drinking behavior of the typical student reduced their drinking more and experienced fewer negative consequences than when they were given personalized information relative to the drinking behavior of more specific normative referent groups. For example, on average, heavy-drinking Asian men and Greek women reduced their drinking more when they were compared with the typical student rather than with the typical Asian male student or the typical Greek female student.
Mediation analyses indicated that typical student PNF was associated with greater changes in typical weekly drinking, in part, by having a stronger influence on descriptive normative perceptions. Thus, typical student PNF resulted in a greater discrepancy with a student’s own drinking relative to other PNF conditions. One plausible explanation as to why PNF that used the typical student-normative referent was more efficacious is that participants may be more likely to project characteristics that they felt were important or that generalize to a drinking college student onto the nondescriptive typical student referent rather than having those characteristics selected for them. Previous research has shown that students often perceive the typical student as different from themselves (Lewis & Neighbors, 2006b; e.g., the typical student is perceived as male and Caucasian). Greater discrepancies may arise from students’ inability to fully envision or define the “typical student.” Along with projecting characteristics onto this blank slate that may be important to the individual, students may also find it easy to project the highly salient and prototypical behavior of a heavy-drinking college student (hence, the largest perceived norms for this group). In this way, the discrepancy becomes larger, as does the relative importance of the typical student. Students may be more likely to think about how their drinking relates to other students in general rather than to other students who share their specific demographic characteristics. The combination of the two projection effects may result in more compelling feedback, thus promoting greater cognitive dissonance between perceived norms, actual norms, and an individual’s own behavior. Under the tenets of social norms theory, this dissonance would produce greater change. In contrast, students’ schema for drinking norms may not extend to very specific subgroups, and the additional complexity of proximally specific reference information may undermine the otherwise straightforward message conveyed by PNF. Students may feel more confident in their knowledge of the drinking norms of more specific groups, and the lack of a large discrepancy may further reduce the potential for change despite what is theoretically purported to be more meaningful and influential feedback.
Although typical student PNF outperformed more specific PNF conditions in this trial, this does not rule out the importance of considering group characteristics or social identity in the context of norms-based interventions. Perhaps if the feedback highlighted the salience of the participant’s membership to the more relevant referent group, it would have been more efficacious compared with PNF about the typical student. More specific reference groups may be more influential only when they are also accompanied by identification with those groups. Recent studies have shown that the association between perceived norms for specific reference groups and drinking behavior is moderated by degree of identification with, or feelings of connectedness to, the group in question (Hummer, LaBrie, & Pedersen, 2012; Neighbors, LaBrie, et al., 2010; Reed, Lange, Ketchie, & Clapp, 2007). There is considerable variability in the extent to which individuals identify with others who share their demographic characteristics. Furthermore, individuals may identify strongly with one or two demographic dimensions and not at all with others. For example, an Asian sorority woman may strongly identify with her gender and sorority but not her race, or with her race but not her gender or sorority. Thus, specificity of the reference group in normative feedback may only matter to the extent that it is matched to group identification. This explanation is consistent with Lewis and Neighbors’ (2007) study in which gender-specific normative feedback was only more effective than gender nonspecific-normative feedback for women who identified more strongly with their gender. Additional research is needed to evaluate the efficacy of self-defined important normative referents.
Another consideration is the feedback in this study was provided remotely on the web. Previous studies have demonstrated larger effects of computer-based PNF when students are required to come in to the lab (Neighbors, Lewis, et al., 2010). This may be due in part to competing demands for attentional resources while students consider estimates for drinking norms and/or review PNF. Students may pay less attention while completing web-based interventions; they may be simultaneously talking on the phone, texting, watching television, and the like, whereas they would be less likely to engage in distracting activities in a lab-based intervention. If the influence of specificity of norms feedback requires more attention to the material, then we would expect a greater likelihood of effects in a more controlled setting.
Regardless of why we did not find strong effects for PNF that used more specific normative referents, the current findings suggest that web-based PNF that uses the typical student referent group may be an optimal choice and has the added advantage of being more parsimonious for college personnel in collecting norms and designing feedback interventions.
Clinical Implications
In the current study, both Web-BASICS and PNF interventions delivered via the Web are associated with reduced drinking through 12-month follow-up, and PNF is also associated with reduced negative consequences. Although these reductions are relatively small in magnitude, from a public health perspective, the very low-cost and easy-to-implement typical student PNF is associated with sustained reductions and therefore has broad potential for large-scale implementation. This intervention can be implemented with comparable or fewer resources than are currently used for educational or awareness campaigns shown to be ineffective for college drinking prevention (Cronce & Larimer, 2011; Larimer & Cronce, 2007). In the current study, there was no significant advantage of the more comprehensive Web-BASICS intervention relative to PNF alone, providing additional evidence that more is not necessarily better (Kulesza, Apperson, Larimer, & Copeland, 2010; Wutzke, Conigrave, Saunders, & Hall, 2002). More research is needed to evaluate potential moderators of efficacy of Web-BASICS and PNF, as well as moderators of more specific versus less specific PNF feedback efficacy. Nonetheless, the current findings are encouraging and provide further evidence that a low-cost, low-complexity PNF intervention can demonstrate lasting effects on student drinking.
Limitations and Future Directions
This study is not without limitations. One of the most notable limitations of this study is that we defined the specificity of the normative referent group in order to increase relevance to that group. It may be that students did not care or identify with a more specific normative referent group as we defined it. Future research should evaluate whether PNF using self-defined normative referents is more efficacious than PNF using researcher-defined normative referents. For example, it would be interesting to also ask students to generate a list of groups with which they most strongly identify. It would also be feasible to allow students to select from a set of possible reference groups with whom their drinking might be compared. An additional limitation is that the current study was limited to Caucasians and Asians. It is unknown whether findings would generalize to other racial/ethnic groups. Finally, we only evaluated in the current study specificity of the normative referent group relative to descriptive drinking norms. Future research is necessary to evaluate whether more specific normative referent groups are more effective than less specific normative referent groups when presenting feedback based on injunctive drinking norms.
ConclusionsThe current research extends previous implementation of social norms-based interventions for drinking in several ways. This is the first study to evaluate PNF on the basis of specifying the normative referent in regards to race, gender, and Greek status, and to test a PNF intervention with a large sample of Asian ethnic minority students engaged in HED. Asian students, as noted previously, are a growing risk group for heavy drinking and alcohol use disorders (Grant et al., 2004; Hahm et al., 2004; Office of Applied Studies, 2008; Wecshler et al., 2002), and are often underrepresented in alcohol research trials. Furthermore, the study directly tests the extent to which increasing specificity of the reference group across multiple dimensions of demographic similarity improves (or fails to improve) efficacy of PNF, and tests the magnitude of the normative discrepancy as a potential mechanism explaining the advantage we found for typical student PNF in this context. This has both theoretical and practical significance, as it addresses a critical tension in the normative feedback literature between ostensibly enhancing relevance of the feedback through a focus on highly proximal/similar reference group norms versus emphasizing the largest normative discrepancy, which is generally represented by the typical student norm. Furthermore, although typical student norms are often readily available through annual or routine campus alcohol-related surveys, more specific normative information may be less readily available and entail considerable expense to collect. Thus, the benefit of using typical student-normative feedback demonstrated in the current study has important implications for implementation of PNF interventions on college campuses. Additionally, this is the first study to evaluate a direct comparison between PNF and Web-BASICS. Inclusion of two meaningful comparison groups, the Web-BASICS condition and the nonalcohol feedback control condition, increases our understanding of the extent to which typical student PNF is an efficacious and parsimonious approach to reducing alcohol use among ethnic majority and Asian minority students, as well as both males and females and those in Greek organizations. This research extends a growing literature emphasizing the importance of normative comparisons in constructing brief single and multicomponent interventions aimed to reduce drinking. The research further contributes to a small body of studies challenging the conventional wisdom that more comprehensive interventions are superior to minimal interventions in producing drinking reductions. We expect the current study will stimulate additional research in these areas. On the basis of these and previous findings, we would encourage the use of the typical normative referent group when constructing PNF for students identified as heavier drinkers and web-based delivery of feedback.
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Submitted: August 3, 2012 Revised: June 18, 2013 Accepted: July 8, 2013
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Source: Journal of Consulting and Clinical Psychology. Vol. 81. (6), Dec, 2013 pp. 1074-1086)
Accession Number: 2013-28918-001
Digital Object Identifier: 10.1037/a0034087
Record: 142- Title:
- Risk pathways among traumatic stress, posttraumatic stress disorder symptoms, and alcohol and drug problems: A test of four hypotheses.
- Authors:
- Haller, Moira. Department of Psychology, Arizona State University, Tempe, AZ, US, moira.haller@asu.edu
Chassin, Laurie. Department of Psychology, Arizona State University, Tempe, AZ, US - Address:
- Haller, Moira, Department of Psychology, Arizona State University, 950 S. McAllister, P.O. Box 871104, Tempe, AZ, US, 85287-1104, moira.haller@asu.edu
- Source:
- Psychology of Addictive Behaviors, Vol 28(3), Sep, 2014. pp. 841-851.
- NLM Title Abbreviation:
- Psychol Addict Behav
- Page Count:
- 11
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- Bulletin of the Society of Psychologists in Addictive Behaviors; Bulletin of the Society of Psychologists in Substance Abuse
- Other Publishers:
- US : Educational Publishing Foundation
Society of Psychologists in Addictive Behaviors - ISSN:
- 0893-164X (Print)
1939-1501 (Electronic) - Language:
- English
- Keywords:
- comorbidity, self-medication, posttraumatic stress disorder, substance use disorders, traumatic stress, risk factors, alcohol problems
- Abstract:
- The present study utilized longitudinal data from a community sample (n = 377; 166 trauma-exposed; 54% males; 73% non-Hispanic Caucasian; 22% Hispanic; 5% other ethnicity) to test whether pretrauma substance use problems increase risk for trauma exposure (high-risk hypothesis) or posttraumatic stress disorder (PTSD) symptoms (susceptibility hypothesis), whether PTSD symptoms increase risk for later alcohol/drug problems (self-medication hypothesis), and whether the association between PTSD symptoms and alcohol/drug problems is attributable to shared risk factors (shared vulnerability hypothesis). Logistic and negative binomial regressions were performed in a path analysis framework. Results provided the strongest support for the self-medication hypothesis, such that PTSD symptoms predicted higher levels of later alcohol and drug problems, over and above the influences of pretrauma family risk factors, pretrauma substance use problems, trauma exposure, and demographic variables. Results partially supported the high-risk hypothesis, such that adolescent substance use problems increased risk for assaultive violence exposure but did not influence overall risk for trauma exposure. There was no support for the susceptibility hypothesis. Finally, there was little support for the shared vulnerability hypothesis. Neither trauma exposure nor preexisting family adversity accounted for the link between PTSD symptoms and later substance use problems. Rather, PTSD symptoms mediated the effect of pretrauma family adversity on later alcohol and drug problems, thereby supporting the self-medication hypothesis. These findings make important contributions to better understanding the directions of influence among traumatic stress, PTSD symptoms, and substance use problems. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Alcohol Abuse; *Drug Abuse; *Posttraumatic Stress Disorder; *Self-Medication; Comorbidity; Risk Factors; Stress; Trauma; Substance Use Disorder
- Medical Subject Headings (MeSH):
- Adolescent; Adult; Alcohol-Related Disorders; Alcoholism; Case-Control Studies; Child; Child of Impaired Parents; Cohort Studies; Comorbidity; Family; Family Characteristics; Female; Humans; Logistic Models; Longitudinal Studies; Male; Prospective Studies; Psychological Trauma; Risk Factors; Self Medication; Stress Disorders, Post-Traumatic; Substance-Related Disorders; United States; Young Adult
- PsycINFO Classification:
- Psychological Disorders (3210)
- Population:
- Human
Male
Female - Age Group:
- Adulthood (18 yrs & older)
- Tests & Measures:
- Computerized Diagnostic Interview Schedule-III
Adolescent’s Family Adversity Measure
Adolescent Substance Use Problems Questionnaire
Family Process Scale
General Stressful Life Events Schedule for Children
Children of Alcoholics Stressful Life Events Schedule - Grant Sponsorship:
- Sponsor: National Institute on Alcohol Abuse and Alcoholism, US
Grant Number: F31AA020698 and AA016213
Recipients: No recipient indicated - Methodology:
- Empirical Study; Longitudinal Study; Interview; Quantitative Study
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- First Posted: Jun 16, 2014; Accepted: Dec 16, 2013; Revised: Oct 18, 2013; First Submitted: Apr 26, 2013
- Release Date:
- 20140616
- Correction Date:
- 20151207
- Copyright:
- American Psychological Association. 2014
- Digital Object Identifier:
- http://dx.doi.org/10.1037/a0035878
- PMID:
- 24933396
- Accession Number:
- 2014-24382-001
- Number of Citations in Source:
- 46
- Persistent link to this record (Permalink):
- http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2014-24382-001&site=ehost-live
- Cut and Paste:
- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2014-24382-001&site=ehost-live">Risk pathways among traumatic stress, posttraumatic stress disorder symptoms, and alcohol and drug problems: A test of four hypotheses.</A>
- Database:
- PsycINFO
Risk Pathways Among Traumatic Stress, Posttraumatic Stress Disorder Symptoms, and Alcohol and Drug Problems: A Test of Four Hypotheses
By: Moira Haller
Department of Psychology, Arizona State University;
Laurie Chassin
Department of Psychology, Arizona State University
Acknowledgement: This study was supported by grants from the National Institute on Alcohol Abuse and Alcoholism (F31AA020698 and AA016213).
Exposure to traumatic events is surprisingly common. Approximately 61% of men and 51% of women experience at least one traumatic event during their lifetimes (Kessler, Sonnega, Bromet, Hughes, & Nelson, 1995). Traumatic events may lead to the development of not only posttraumatic stress disorder (PTSD), but also alcohol and drug problems. Rates of PTSD among adults with substance use disorders (SUDs) range from 14% to 60% (see Hien, Cohen, & Campbell, 2005). Studies of adolescents with SUDs indicate rates of PTSD ranging up to 20% (Deykin & Buka, 1997). Such high rates of comorbidity suggest that traumatic stress and trauma-related symptomatology may play a role in the etiology of SUDs or vice versa.
Several pathways may underlie the link between PTSD and SUDs. First, the “high-risk hypothesis” states that substance use or abuse may increase risk for exposure to a traumatic event by placing individuals in high-risk situations (e.g., Windle, 1994) or by impairing detection of danger cues in the environment (Davis, Stoner, Norris, George, & Masters, 2009). Second, the “susceptibility hypothesis” states that substance use or abuse may increase risk for developing PTSD among individuals who are exposed to a traumatic event (Chilcoat & Breslau, 1998a). For instance, substance use problems may interfere with the ability to effectively manage negative emotions resulting from the traumatic event, may increase anxiety and arousal levels attributable to substance withdrawal symptoms (Stewart, Pihl, Conrod, & Dongier, 1998), or may facilitate avoidance and lack of processing of trauma material (Kaysen et al., 2011). Third, the “self-medication hypothesis” states that individuals may use substances to cope with symptoms of posttraumatic stress (e.g., Chilcoat & Breslau, 1998b; Reed, Anthony, & Breslau, 2007). Finally, the “shared vulnerability hypothesis” (Stewart & Conrod, 2003) states that shared risk factors may account for both PTSD and alcohol/drug problems, such that PTSD and SUDs are not causally related once shared risk factors are taken into account.
Although these hypotheses are presented separately, they are not mutually exclusive and may be integrated into a larger, developmental model of PTSD-SUD comorbidity. For instance, preexisting adolescent substance use problems may not only increase individuals’ risk for trauma exposure (high-risk hypothesis), but may also make it more likely that they will turn to alcohol/drugs to cope with subsequent PTSD symptoms (self-medication hypothesis); this increased substance use may further exacerbate PTSD symptoms. The present study examines evidence for each of the four pathways that have been proposed to underlie PTSD-SUD comorbidity.
The High-Risk and Susceptibility HypothesesThe high-risk and susceptibility hypotheses both suggest that SUDs causally influence risk for PTSD. Retrospective and prospective studies examining these hypotheses have had mixed findings. Some studies show that substance use problems increase risk for trauma exposure but not PTSD (Bromet, Sonnega, & Kessler, 1998; Kilpatrick, Acierno, Resnick, Saunders, & Best, 1997), others show that substance use problems increase risk for PTSD but not trauma exposure (Acierno et al., 1999), and others show that substance use problems do not increase risk for either trauma exposure or PTSD after controlling for other risk factors (Chilcoat & Breslau, 1998a). Moreover, studies examining onset patterns of PTSD and SUDs in adults tend not to support the high-risk or susceptibility hypotheses, instead indicating that PTSD more often precedes than follows SUD onset (see Stewart & Conrod, 2003). This lack of compelling empirical support has led some researchers to conclude that it is unlikely that substance use problems causally influence risk for trauma exposure or PTSD, especially when considered among other risk factors (e.g., Chilcoat & Breslau, 1998a; Stewart & Conrod, 2003).
However, little prospective research has examined relations between adolescent substance use problems and risk for trauma exposure or PTSD. This limitation is important, given that risk for trauma exposure—particularly assaultive violence exposure—peaks between the ages of 16 and 20 (Breslau et al., 1998). It is currently unclear what role adolescent substance use problems may play in this heightened risk period for assaultive violence exposure. Because adolescent substance use problems may reflect a tendency to engage in particularly risky behavior, they may be more likely to result in trauma exposure or PTSD compared to adult substance use problems. Although retrospective data indicate that adolescents with SUDs are at two to five times the risk for experiencing a traumatic event (risk for exposure to an event involving violence is even higher), and at four to nine times the risk for developing PTSD (Deykin & Buka, 1997; Giaconia et al., 2000; Kilpatrick et al., 2000) than those without a SUD, prospective studies are needed to disentangle the direction of influence. In addition, studies that capture pretrauma risk stemming from subclinical levels of substance use problems are also needed, given that adolescent substance use problems, even if not meeting diagnostic threshold, may create meaningful risk for trauma exposure, PTSD, and posttrauma substance use problems. The current study addresses these limitations by testing the prospective effects of pretrauma adolescent substance use problems on risk for later trauma exposure (as well as risk for assaultive violence exposure, specifically) and PTSD symptoms.
The Self-Medication HypothesisIn contrast to the conflicting results from investigations of the high-risk and susceptibility hypotheses, there is a strong body of evidence supporting the self-medication hypothesis (Breslau, Davis, Peterson, & Schultz, 1997; Breslau, Davis, & Schultz, 2003; Chilcoat & Breslau, 1998b; Reed et al., 2007; Shipherd, Stafford, & Tanner, 2005; Ullman, Filipas, Townsend, & Starzynski, 2005; see also Hien et al., 2005, for review). Indeed, in a review of retrospective and prospective studies on PTSD-SUD comorbidity, Stewart and Conrod (2003, p. 37) summarized that “PTSD has been shown to develop before the SUD in the large majority of comorbid cases in retrospective studies, and PTSD has been shown to increase risk for SUDs in prospective studies.” Theoretically, individuals with PTSD may use alcohol and drugs to reduce irritability, concentration problems, hyperarousal, and so forth, raising risk for the development of SUDs.
The self-medication hypothesis implies a mediating role for PTSD symptoms in the relation between trauma exposure and substance use problems (Stewart, 1996). Support for the mediating role of PTSD comes from studies demonstrating that individuals who develop PTSD appear to be at higher risk for SUDs than do individuals who are exposed to a traumatic event but do not develop PTSD (Chilcoat & Breslau, 1998b; Breslau et al., 1998). However, these studies do not examine the extent to which PTSD symptom severity influences risk for future substance use problems. To address this limitation, the current study simultaneously estimated the unique effects of both PTSD symptoms and trauma exposure on future substance use problems in order to test the extent to which PTSD symptom severity influences risk for substance use problems, controlling for the effects of trauma exposure itself, as well as shared risk factors for PTSD and SUDs.
The Shared Vulnerability HypothesisThe high prevalence of PTSD-SUD comorbidity suggests that PTSD and SUDs may share a common etiological diathesis, including both environmental and genetic factors. There are a number of family-related risk factors that trauma exposure, PTSD, and SUDs may share in common. For instance, parental psychopathology has been shown to increase risk for offspring trauma exposure (Bromet et al., 1998; Koenen et al., 2002), PTSD (Brewin, Andrews, & Valentine, 2000; Bromet et al., 1998), and SUDs (Zhou, King, & Chassin, 2006). Importantly, parental psychopathology is also associated with other familial risk factors, such as higher levels of family conflict and higher levels of stress (Dube et al., 2003), which may further increase risk for trauma exposure and posttrauma psychopathology (Brewin et al., 2000; Deykin & Buka, 1997; Koenen, Moffitt, Poulton, Martin, & Caspi, 2007). Research suggests that individuals who grow up in adverse family environments may be sensitized to the effects of future stressors (Koenen et al., 2007), thus placing them at risk for posttrauma maladjustment. Adolescents from adverse family environments may also be less likely to have the resources and supports necessary for effectively coping with a traumatic event. Therefore, the familial backdrop against which trauma occurs is likely to be a key determinant of posttrauma functioning. Yet, the extent to which pretrauma adversity in the family environment is a shared risk factor for PTSD and later substance use problems is currently unclear because so few studies contain pretrauma measures of family risk factors. The present study thus examines how pretrauma family adversity may influence risk for PTSD symptoms and/or substance use problems in order to better understand the role of family risk factors in PTSD-SUD comorbidity.
In addition to directly increasing risk for PTSD and substance use problems, it is also possible that adolescent family adversity may indirectly influence risk for posttrauma substance use problems by increasing risk for PTSD symptoms (i.e., PTSD symptoms may mediate the influence of preexisting family adversity on later alcohol and drug problems). Although finding that PTSD symptoms mediate the influence of preexisting family adversity on later alcohol and drug problems would support the self-medication hypothesis rather than the shared vulnerability hypothesis, such findings would nonetheless highlight preexisting family adversity as an important developmental antecedent in the PTSD-SUD link.
To test whether family adversity during adolescence accounts for the link between PTSD symptoms and posttrauma substance use problems (i.e., the shared vulnerability hypothesis), it is important to control for preexisting substance use problems that may have already been present and that co-occurred with adolescent family adversity. Indeed, adolescents who grow up in adverse (i.e., high-conflict, high-stress) family environments are also more likely to misuse alcohol and drugs (e.g., Repetti, Taylor, & Seeman, 2002; Zhou et al., 2006), regardless of trauma exposure or PTSD. Thus, to disentangle the directions of influence among traumatic stress, PTSD, and problematic substance use, both preexisting adolescent substance use problems and the confounding influence of the larger constellation of family adversity must be accounted for.
Trauma exposure itself may be conceptualized as a shared environmental risk factor for PTSD and SUD (Yehuda, McFarlane, & Shalev, 1998). Although traumatic events are most often associated with PTSD, they may also precipitate SUDs independent of their effects on PTSD, such that PTSD-SUD comorbidity reflects the co-occurrence of distinct diatheses. For instance, individuals who are predisposed to biological hyper-responsiveness may experience further sensitizations in their stress response systems after trauma exposure and may thus develop PTSD (Yehuda et al., 1998), whereas individuals with other predispositions may experience a range of other stress responses that lead to other disorders, such as SUDs. If this hypothesis were true, traumatic stress would be expected to directly predict substance use problems, separate from its influence on PTSD. Alternatively, a direct effect of traumatic stress on problematic alcohol/drug use (separate from the effects of PTSD) would not be expected if other common risk factors account for the link between PTSD symptoms and alcohol/drug problems. Previous tests of these hypotheses show that trauma-exposed individuals who do not develop PTSD are not at increased risk for subsequent onset of SUDs, but those who develop PTSD are (Breslau et al., 2003; Chilcoat & Breslau, 1998b; Reed et al., 2007; see Fetzner, McMillan, Sareen, & Asmundson, 2011 for an exception). However, the “trauma exposure as a shared risk factor” hypothesis has not yet been tested using analytic strategies other than comparing risk for onset of clinical SUDs among individuals with PTSD to individuals with trauma exposure who do not have PTSD. Therefore, the current study tested the effect of trauma exposure on later substance use problems, while controlling for a count of the number of PTSD symptoms that participants endorsed.
The Present StudyThe purpose of the present study was to better understand the risk pathways that link trauma exposure, PTSD, and alcohol and drug problems. Alcohol and drug problems were examined as separate outcomes based on previous studies that have found that traumatic stress and PTSD symptoms may have differential relations with alcohol versus drugs (e.g., Breslau et al., 2003; Haller & Chassin, 2012; Shipherd et al., 2005). Specifically, this study tested the following hypotheses (see Figure 1), which are not mutually exclusive:
High-risk hypothesis. Do adolescent substance use problems increase risk for trauma exposure or assaultive violence exposure over and above the influence of preexisting family risk factors and demographic predictors?
Susceptibility hypothesis. Do adolescent substance use problems increase risk for PTSD symptoms among individuals exposed to trauma over and above the influence of preexisting family risk factors and demographic predictors?
Self-medication hypothesis. Do PTSD symptoms increase risk for future alcohol and/or drug problems over and above the influences of trauma exposure itself, pretrauma substance use problems, demographic predictors, and preexisting family risk factors that are common to both PTSD and alcohol/drug problems?
Shared vulnerability hypothesis. Do trauma exposure and/or adversity in the family environment account for the link between and substance use problems, such that PTSD symptoms and substance use problems are not related when these potential shared risk factors are accounted for? By testing this hypothesis, this study also addresses a series of related questions: To what extent does adversity in the family environment increase risk for both PTSD and alcohol/drug problems? To what extent is the influence of preexisting family risk factors on adult alcohol and drug problems mediated by PTSD symptoms? Is trauma exposure a shared risk factor for both PTSD and substance use problems, such that trauma exposure increases risk for future alcohol or drug problems, independent of PTSD symptoms?
Figure 1. Simplified depiction of model estimation and paths relevant to hypothesis testing. a Note that Wave 4 measures reflect trauma exposure and PTSD symptoms that occurred between Waves 1 and 4 (average age at exposure was 17.3).
These hypotheses were tested using data from a longitudinal, community-based study of familial alcoholism, which is important given that most research on the overlap between PTSD and substance use problems consists of cross-sectional, retrospective, and clinic-based studies without preexisting measures of substance use and associated family risk factors. By using a high-risk sample with elevated prevalence of risk factors, trauma, and substance use problems, the present study was particularly well-suited for examining the hypothesized pathways.
Method Participants
Participants (n = 377) were from a larger longitudinal study of familial alcoholism (Chassin, Barrera, Bech, & Kossak-Fuller, 1992). The original study had three annual waves of data collection and three additional follow-ups separated by five years. The present study used data from Waves 1 (1988), 4 (≈1995), and 5 (≈2000). Both parents and adolescents were interviewed at each time point. At Wave 1, there were 454 “target” adolescents between the ages of 11 and 15 and their parents; 246 adolescents had at least one biological parent with an alcohol disorder who was also a custodial parent, and the remaining 208 adolescents were demographically matched controls without any biological or custodial parents with an alcohol disorder. Sample retention ranged from 90% to 99% across waves.
Participants reported their history of trauma exposure and PTSD at Wave 4 (seven to 10 years after the initial assessment). Forty-seven participants who were not interviewed at Wave 4 were excluded from the present study. Thirty participants who reported trauma exposure before Wave 1 were also excluded so that Wave 1 measures preceded trauma exposure for all participants. Thus, our final sample consisted of 377 participants (54% male; 52% children of parents with an alcohol disorder; 73% non-Hispanic Caucasian; 22% Hispanic, 5% other ethnicity; mean age = 13.2 at Wave 1, 20.4 at Wave 4, and 25.6 at Wave 5).
Analyses examining differences between the 377 participants included in this study and the 77 excluded participants showed that excluded participants were more likely to be children of alcoholic parents, χ2(1) = 6.65, p = .01. However, included and excluded participants did not differ in gender, ethnicity, parental psychopathology other than alcoholism, family conflict, family life stress, adolescent substance use problems, or adult alcohol or adult drug problems.
Recruitment and Procedure
Families with parental alcoholism were recruited using court DUI records, questionnaires from newly enrolled members of a large HMO, and community telephone surveys. Matched nonalcoholic families (matched on child’s age, family composition, ethnicity, and SES) living in the same neighborhoods as the families with parental alcoholism were recruited via telephone surveys. Potential participants who were successfully contacted did not differ from those who were not contacted on available alcoholism indicators (e.g., blood alcohol level at time of arrest, number of prior alcohol-related arrests). See Chassin et al. (1992) for details. After parents provided informed consent and adolescents provided assent, interviews were conducted in person with computer-assisted interviews (parents and adolescents were interviewed in separate rooms), or via telephone (after verifying private conditions) for out-of-state families. All protocols were approved by the Arizona State University Institutional Review Board.
Measures
Adolescent substance use problems
At Wave 1, adolescents reported on 14 (Cronbach’s alpha = .86) lifetime problems (e.g., receipt of complaints from family or friends) that they may have experienced as a result of alcohol or drug use. Items were adapted from Sher’s (1987) questionnaire assessing substance use problems among college students. Given the low frequency of adolescents with high counts of substance use problems (only 42% of adolescents reported having ever used alcohol or drugs), analyses used a variable that was coded 0 if the adolescent reported no lifetime substance use problems (n = 317; 84.1%), 1 if the adolescent reported one lifetime substance use problem (n = 23; 6.1%), and 2 if the adolescent reported two or more lifetime substance use problems (n = 37; 9.8%).
Adolescent’s family adversity
At Wave 1, family adversity was measured via a cluster of related family variables (family conflict, family stress, parental alcoholism, and other parent psychopathology) associated with trauma exposure, PTSD, and SUDs. These variables are likely to reflect a combination of both genetic and environmental risk. Correlations among the four family factors were all significant (see Table 1; ps < .001). To avoid multicollinearity problems, analyses used a composite “family adversity” variable derived using factor scores (M = 0.00; SD = 0.84, range: −1.91 to 2.36) from a one-factor confirmatory factor analysis. The family adversity factor score was significantly associated with trauma exposure, PTSD symptoms, adult alcohol problems, and adult drug problems (see Table 1). Thus, this variable appeared to appropriately capture shared risk for these outcomes.
Zero-Order Correlations
Both family conflict and familial life stress were measured via adolescent, mother, and father reports. Family conflict was a composite of all reports, each measured via four items (e.g., “we fought a lot in our family”) from Bloom’s (1985) Family Process Scale with responses ranging from Strongly Disagree to Strongly Agree (M = 2.74; SD = 0.60; range: 1.33–4.38; Cronbach’s alphas were .62, .63, and .63 for adolescent, mother, and father report). Familial life stress was a count of 15 independent events (e.g., serious money troubles; M = 3.20; SD = 2.36; range: 0–11) from the General Stressful Life Events Schedule for Children (Sandler, Ramirez, & Reynolds, 1986) and the Children of Alcoholics Stressful Life Events Schedule (Roosa, Sandler, Gehring, & Beals, 1988). Parent alcoholism (Diagnostic and Statistical Manual of Mental Disorders, third edition [DSM–III] abuse or dependence) was measured via parents’ self-reports on the Computerized Diagnostic Interview Schedule (CDIS-III; Robins, Helzer, Croughan, & Ratcliff, 1981), or via spousal report for noninterviewed parents using Family History-Research Diagnostic Criteria, Version 3 (Endicott, Anderson, & Spitzer, 1975; 51.5% of adolescents had at least one biological parent with an alcohol disorder who was also a custodial parent). Other parent psychopathology (DSM–III affective, anxiety, or antisocial personality disorder) was measured via parents’ self-reports on the CDIS-III (39.5% of adolescents had a parent with one of these disorders).
Late adolescent/early adult trauma exposure and PTSD symptoms
At Wave 4, the computerized Diagnostic Interview Schedule (CDIS-III-R; Robins, Helzer, Cottler, & Golding, 1989) was used to assess participants’ lifetime exposure to trauma and PTSD symptoms using DSM–III–R criteria. Participants reported on up to three traumatic events and 17 PTSD symptoms (118 [71%] participants reported one event; 33 [9%] participants reported two events, and 15[4%] participants reported three events). On average, fewer than 3 years (M = 2.65, SD = 1.70) elapsed between the time of the traumatic event and the assessment of PTSD. Among trauma-exposed participants (n = 166; 44%; mean age at exposure = 17.3 years.), 72 (43%) experienced at least one event involving assaultive violence (rape, physical assault or being threatened with a weapon), whereas 94 (57%) experienced other types of events (seeing someone hurt or killed, natural disaster, narrow escape from death/injury, sudden injury/accident, sudden death/injury of someone close, experiencing shock from other’s experience, or other event). See Haller and Chassin (2012) for rates of each type of event. Analyses used a dichotomous measure of trauma exposure, and a count variable that indicated the total number of PTSD symptoms for whichever event produced the highest number of symptoms (M = 5.41 symptoms, SD = 4.11). Thirty-one participants (19% of trauma-exposed) met criteria for PTSD.
Adult alcohol and drug problems
At Wave 5, participants reported on 17 problems (e.g., failed attempts to cut down) as a result of alcohol and drug use (note that three substance use problems were assessed at Wave 5 that were not assessed during adolescence). Follow-up questions assessed the recency of each problem separately for alcohol and drugs. Analyses used count variables indicating the total number of adult alcohol problems and drug problems (separately) experienced in the past two years at Wave 5. The two-year timeframe allowed for prospective prediction of adult alcohol and drug problems from PTSD symptoms. Cronbach’s alpha was .85 for alcohol and .91 for drugs. Participants who drank at Wave 5 (82%) reported drinking on average more than “5 times in the past year” but less than “1–3 times a month.” Twenty-nine percent of participants reported using drugs at Wave 5, with marijuana being the most commonly used drug. At Wave 5, 44% of interviewed participants experienced at least one alcohol problem in the past two years (M = 1.54, SD = 2.58, range: 0–13), and 19% experienced at least one drug problem in the past two years (M = 0.83, SD = 2.33, range: 0–13).
Data Analytic Strategy
All analyses were conducted in MPlus version 6.11 (Muthén & Muthén, 1998–2011). Models were estimated using the maximum likelihood estimator with robust standard errors (MLR) to ensure robustness against heteroscedasticity, non-normality, and model misspecification. Full information maximum likelihood estimation was used to account for missing data for 29 participants who were not interviewed at Wave 5.
Figure 1 presents a simplified depiction of model estimation and those paths that are relevant to hypothesis testing. Adult alcohol problems and adult drug problems were examined in separate models. Each model included three endogenous variables: trauma exposure (binary), PTSD symptoms (count variable), and adult alcohol/drug problems (count variables). Logistic regression was used to predict trauma exposure, and negative binomial regression was used to predict PTSD symptoms and adult alcohol/drug problems. Incidence rate ratios (IRRs) are presented for count outcomes (e.g., an IRR of 1.10 means that for every one unit increase in the predictor, there is a 10% increase in the dependent variable). Because PTSD symptoms are conditional upon trauma exposure, data were specified as missing on the count measure of PTSD symptoms for participants who were not exposed to a traumatic event (i.e., those coded 0 on the binary trauma exposure variable). Paths were specified from adolescent family adversity, adolescent substance use problems, gender (coded 0 for males and 1 for females), and ethnicity (coded 0 for non-Hispanic Caucasians and 1 for other ethnicities) to each endogenous variable. Paths were also specified from trauma exposure and PTSD symptoms to adult alcohol/drug problems. The residual covariance between trauma exposure and PTSD symptoms was estimated to allow for the fact that they may share predictors other than those specified in the model. In addition to the primary models, a separate logistic regression was conducted to test whether adolescent substance use problems significantly increase risk for assaultive violence exposure (i.e., high-risk hypothesis), over and above family adversity, gender, and ethnicity.
Before conducting the main analyses, preliminary analyses tested for significant covariates (gender, ethnicity, parent education, age, age at trauma exposure, and time since trauma exposure), covariate by predictor interactions, and predictor by predictor interactions. Preliminary analyses indicated a significant effect of time since trauma exposure on risk for alcohol problems, an interaction between family adversity and gender when predicting risk for drug problems, an interaction between gender and ethnicity when predicting trauma exposure, and interactions between PTSD symptoms and ethnicity when predicting alcohol and drug problems. These effects were retained in the final models (as shown in Table 2). All other covariate effects and interactions were nonsignificant and were not further considered. However, gender and ethnicity were retained as covariates in all models, given numerous gender and ethnic differences in both the trauma/PTSD and SUDs literatures.
Results From Primary Analyses
ResultsCorrelations among study variables are presented in Table 1. As expected, males were more likely to be exposed to a traumatic event than were females (r = −.15, p = .003), but trauma-exposed females exhibited higher levels of PTSD symptoms (r = .35, p < .001) than trauma-exposed males. Males exhibited higher levels of adult alcohol (r = −.21, p < .001) and drug (r = −.12, p = .029) problems than did females. Trauma-exposed participants were at significantly higher risk for adult alcohol problems (r = .12, p = .024) and at marginally higher risk for adult drug problems (r = .09, p = .096), than were participants who were not exposed to a traumatic event. Among trauma-exposed participants, PTSD symptoms were not significantly associated with adult alcohol problems (r = .11, p = .198) and were only marginally associated with adult drug problems (r = .14, p = .089). However, partial correlations revealed that after controlling for gender, there was a significant association between PTSD symptoms and both alcohol (pr = .22, p = .006), and drug (pr = .21, p = .011) problems. Gender was specified as a covariate in all analyses.
Table 2 presents results from the primary analyses. Results showed that the unique effect of adolescent substance use problems on risk for trauma exposure was nonsignificant (high-risk hypothesis; B = 0.21, p = .33, OR = 1.23), over and above pretrauma family adversity, gender, and ethnicity. However, a separate logistic regression indicated that there was a small unique effect of adolescent substance use problems on risk for assaultive violence exposure (B = 0.38, p = .051, OR = 1.46; results not shown in table).
In terms of the susceptibility hypothesis, adolescent substance use problems did not significantly increase susceptibility for developing PTSD symptoms (B = 0.07, p = .40, IRR = 1.07) among participants exposed to a traumatic event over and above the influence of correlated adversity in the family environment. Follow-up analyses showed that if family adversity were excluded from the model, adolescent substance use problems would have had significant effects on both trauma exposure (B = 0.41, p = .045, OR = 1.50) and PTSD symptoms (B = 0.15, p = .04, IRR = 1.16).
In terms of the self-medication hypothesis, results showed that PTSD symptoms had a significant unique effect on future adult alcohol (B = 0.09, p = .003, IRR = 1.10) and drug problems (B = 0.09, p = .042, IRR = 1.10), over and above the effects of trauma exposure, pretrauma substance use problems, family adversity, and covariates. However, preliminary analyses indicated a significant interaction between ethnicity and PTSD symptoms in the model predicting alcohol problems (B = −0.10, p = .02, IRR = 0.90), and a marginally significant interaction in the model predicting drug problems (B = −0.15, p = .06, IRR = 0.86). Probing these interactions indicated that the influence of PTSD symptoms on both alcohol drug problems was significant for non-Hispanic Caucasians but not for minority ethnicities (see note “c” in Table 2). To allow comparisons specifically between Hispanics and non-Hispanic Caucasians, analyses were repeated while excluding the 18 participants of other ethnicities. Results were consistent; the effect of PTSD symptoms on alcohol and drug problems was significant for non-Hispanic Caucasians but not for Hispanics.
Finally, we examined evidence for the shared vulnerability hypothesis. Because PTSD symptoms were significantly related to both alcohol and drug problems while accounting for family adversity and trauma exposure, this hypothesis was not supported. We subsequently examined the paths from family adversity to trauma exposure, PTSD symptoms, alcohol problems, and drug problems to test the extent to which family adversity increased risk for these outcomes. Although family adversity had a significant effect on both trauma exposure and PTSD symptoms, its direct effect on alcohol problems (the “c” path in a mediational model) was nonsignificant (B = 0.01, p = .92, IRR = 1.01). Mediational analyses showed that PTSD symptoms significantly and fully mediated the effect of pretrauma family adversity on alcohol problems (95% CI = [0.010, 0.038]), while controlling for gender, ethnicity, and pretrauma substance use problems. As for drug problems, the direct effect of family adversity on risk for drug problems was significant for females (B = 1.12, p < .001, IRR = 3.06) but not for males (B = −0.01, p = .95, IRR = 0.99). Thus, although PTSD symptoms significantly mediated the influence of family adversity on drug problems (95% CI = [0.001, 0.057]), there appeared to be full mediation for males but only partial mediation for females.
With respect to the theory that trauma exposure itself may be conceptualized as a shared risk factor, trauma exposure did not have a unique effect on either alcohol (B = 0.17, p = .38, IRR = 1.19) or drug problems (B = 0.20, p = .55, IRR = 1.22).
DiscussionThe present study tested a series of hypotheses to help explain the risk pathways that link traumatic stress, PTSD symptomatology, and alcohol and drug problems. Results provided the strongest support for the self-medication hypothesis, such that PTSD symptoms predicted higher levels of later alcohol and drug problems participants, over and above the influences of pretrauma family adversity, pretrauma substance use problems, trauma exposure, and demographic variables. As for the reverse direction (the influence of substance use problems on risk for trauma exposure or PTSD), the high-risk hypothesis was partially supported but only with respect to trauma exposure that involved assaultive violence. That is, pretrauma adolescent substance use problems did not significantly influence overall risk for trauma exposure over and above the influence of pretrauma family adversity, but did have a marginally significant unique effect on risk for assaultive violence exposure. Moreover, pretrauma binge drinking was significantly associated with increased risk of assaultive violence exposure. There was no support for the susceptibility hypothesis, as pretrauma adolescent substance use problems did not significantly influence risk for PTSD symptoms over and above the influence of pretrauma family adversity. Finally, there was little support for the shared vulnerability hypothesis. Neither trauma exposure nor preexisting family adversity accounted for the link between PTSD symptoms and later alcohol and drug problems. Findings are explored in greater detail below.
High-Risk and Susceptibility Hypotheses
The present study is among the first to test whether adolescent substance use problems prospectively predict increased risk for trauma exposure or PTSD symptoms. Importantly, the nonsignificant effect of adolescent substance use problems on risk for both trauma exposure and PTSD would have been significant if pretrauma family adversity were excluded from the model. This finding suggests that it is the high-risk family context within which problematic adolescent substance use occurs that may increase risk for future trauma exposure and PTSD symptoms, rather than adolescent substance use problems themselves. Trauma-exposed adolescents from adverse family environments may lack the safe context, resources, and social support needed to effectively cope with a traumatic event. These results highlight the importance of considering family adversity as an important contextual risk factor in models of PTSD-SUD risk to avoid making false conclusions about the about the extent to which associated individual behaviors lead to trauma exposure and posttrauma maladjustment. Although previous retrospective data indicate that adolescents with SUDs are at greatly elevated risk for both trauma exposure and PTSD compared to adolescents without SUDs (Deykin & Buka, 1997; Giaconia et al., 2000; Kilpatrick et al., 2000), such findings likely reflect the large body of risk factors associated with adolescent SUDs.
In contrast to the nonsignificant effect of adolescent substance use problems on risk for overall trauma exposure, adolescent substance use problems did have a marginally significant effect on risk for assaultive violence exposure (events involving rape, physical assault or being threatened with a weapon), even after accounting for the significant influence of co-occurring family adversity. Further, post hoc analysis showed that adolescent binge drinking significantly increased risk for exposure to assaultive violence, over and above the effect of family adversity. These findings are important given that risk for assaultive violence exposure, which carries an especially high risk for developing PTSD compared with other types of traumatic events (Kessler et al., 1995), is especially high during late adolescence/early adulthood (Breslau et al., 1998). Adolescent substance misuse, such as binge drinking, may be one factor driving this risk. Importantly, this finding suggests that programs to prevent adolescent substance abuse may have the added benefit of reducing assaultive violence exposure, thus also reducing risk for PTSD.
There are several reasons why substance use problems and binge drinking may place adolescents at risk for assaultive violence exposure. Risky substance use, such as binge drinking, may impair judgment and one’s ability to discern danger cues in the environment. Moreover, compared with adults, adolescents may be more likely to use substances outside of the home to avoid adult supervision, which may place them in dangerous situations. Adolescents may also engage in unsafe activities while under the influence or during their efforts to obtain alcohol and drugs. In addition, adolescent substance abusers are especially likely to associate with deviant peers who engage in delinquent behaviors (Fergusson, Swain-Campbell, & Horwood, 2002), which may thereby increase their risk for assaultive violence. Finally, given that the average age at which adolescent substance use problems were measured was 13.2 years old, it is possible that those individuals who experience substance use problems so early in life constitute a particularly high-risk group that is likely to engage in multiple risk behaviors (e.g., stealing, fighting, early initiation of sex), any of which may increase their risk for being exposed to violence. Indeed, a recent study found that adolescent boys who engaged in high-risk behaviors (i.e., alcohol use, drug use, and delinquent behavior) were at increased risk for exposure to physical assault and/or witnessed violence later in adolescence (Begle et al., 2011).
The Self-Medication and Shared Vulnerability Hypotheses
This study adds to a growing literature in support of the self-medication hypothesis, such that individuals may use alcohol and drugs to cope with PTSD symptoms and are thus at increased risk for substance use problems. Indeed, for each additional PTSD symptom, risk for alcohol and drug problems increased approximately 10%. Findings extend previous research on the self-medication hypothesis in several ways. First, this study accounted for the influence of preexisting, subclinical levels of substance use problems. Previous research has typically examined patterns of onset among PTSD and SUDs, which ignores the role that subclinical levels of substance use problems may play in risk for both trauma exposure and posttrauma maladjustment. Moreover, by including both pre- and posttrauma measures of substance use problems, the present study allowed for inferences regarding the direction of effect.
Second, the present study differentiated between the effects of trauma exposure and PTSD symptoms on future substance use problems. Few studies have recognized that trauma exposure may be a shared risk factor for both PTSD and SUDs. The fact that trauma exposure failed to significantly influence risk for alcohol or drug problems while controlling for subclinical levels of PTSD provides strong evidence that the effects of traumatic stress on substance use problems are mediated by PTSD symptoms. Even though the majority of trauma-exposed individuals do not develop clinically significant PTSD (Kessler et al., 1995), this study suggests that trauma exposure may nonetheless have meaningful effects on one’s risk for future substance use problems to the extent that there are resultant posttraumatic symptoms.
Third, the present study advances previous research on the self-medication hypothesis by controlling for the confounding influence of preexisting adversity in the family environment. Findings highlight adolescent family adversity as an important risk factor for trauma exposure, PTSD, and adult substance use problems, alike. However, there was no evidence that family adversity accounted for the association between PTSD and either alcohol or drug problems. The influence of family adversity on alcohol problems was fully mediated by PTSD symptoms; the influence of family adversity on drug problems was fully mediated by PTSD symptoms for males but only partially mediated by PTSD symptoms for females. Although the effects of family adversity on alcohol and drug problems were generally indirect rather than direct, findings nonetheless suggest that preexisting family adversity plays an important role in the PTSD-SUD link. Indeed, results provided evidence for a causal chain, whereby family adversity increased risk for trauma exposure and PTSD symptoms, which in turn increased risk for later adult alcohol and drug problems. Thus, it appears that family adversity operates as an important contextual risk factor such that trauma-exposed individuals who grow up in adverse family environments are more likely to develop PTSD symptoms and later substance use problems.
Fourth, although previous research has made it clear that substance use problems are prevalent in the aftermath of trauma (Stewart & Conrod, 2003), the present study extends this knowledge by demonstrating that the effects of PTSD on substance use problems persist well into the future. This finding is consistent with a study by Swendsen and colleagues (2010), which showed that PTSD diagnosis prospectively predicted onset of alcohol and drug dependence 10 years later. Finally, the present study provided tentative evidence that the self-medication hypothesis may vary across ethnicity, such that PTSD symptoms increase risk for substance use problems for non-Hispanic Caucasians but not Hispanics. However, given the small sample size, replication of this finding is needed before definitive conclusions can be made.
This study’s finding that PTSD symptoms directly increased risk for both alcohol and drug problems differs from a previous study using this same sample, which examined externalizing and internalizing symptoms as mediators of the influence of PTSD symptoms on alcohol and drug problems (Haller & Chassin, 2012). This previous study found that PTSD symptoms directly influenced risk for adult drug problems, but PTSD symptoms only influenced risk for adult alcohol problems to the extent that PTSD symptoms increased early adult externalizing symptoms. Several methodological differences may help explain the difference in findings between the present study and the Haller and Chassin (2012) study. First, the present study included both individuals who were and were not exposed to trauma in its analysis, whereas the previous study included only trauma-exposed participants. Second, the present study accounted for family adversity, ethnicity, and trauma exposure, whereas the previous study did not. Third, this study used a count of alcohol/drug problems as its outcome variable, whereas the previous study used a composite of frequency of use and problems within a shorter timeframe (only one year). Thus, the outcome variable in the current study reflects a more severe measure of alcohol problems. It is possible that PTSD symptoms are more strongly related to problematic alcohol use than to alcohol use itself.
Despite these methodological differences, findings from the Haller and Chassin (2012) study have important implications for the present study. Haller and Chassin distinguished between a PTSD-specific self-medication mechanism, and a more generalized negative affect self-medication mechanism (e.g., Khantzian, 1985), such that individuals may use substances to reduce negative affect and other internalizing symptoms. Importantly, Haller and Chassin found that PTSD-related increases in internalizing symptoms did not significantly increase risk for either alcohol or drug problems. Thus, it appears to be PTSD symptoms, specifically, that increase risk for substance use problems, rather than broader internalizing symptomatology (e.g., sad mood, low energy, worthlessness) that is often experienced during the aftermath of trauma.
Limitations and Conclusions
Several study limitations should be noted. First, many factors outside the scope of this study (e.g., peritraumatic factors, genetic influences) may influence risk for trauma exposure and/or posttrauma adjustment. Although this study failed to support the shared vulnerability hypothesis with respect to trauma exposure and family adversity, many other shared risk factors may contribute to the association between PTSD and SUDs. Similarly, despite the lack of support for the susceptibility hypothesis, future studies of moderators may find that preexisting substance use problems may indeed increase risk for PTSD for certain individuals or under certain conditions. Second, it was not possible to examine reciprocal relations between PTSD symptoms and substance use problems over time because PTSD symptoms were assessed at only one time point. Third, findings may not generalize to those with very early trauma exposure, given that we excluded participants who experienced trauma before Wave 1. Fourth, adolescent substance use problems were measured at a very young age and, on average, four years before trauma exposure. Measures closer in time to the traumatic event will be better suited to testing the true extent to which preexisting substance use problems are a causal risk factor for trauma exposure and/or PTSD; however, the unpredictable timing of trauma exposure makes it nearly impossible to obtain such a measure. Finally, trauma exposure and PTSD symptoms were assessed using DSM–III–R criteria rather than the current DSM-5 criteria.
In summary, this study is among the few longitudinal, community-based studies to test the directions of influence among trauma exposure, PTSD, and alcohol and drug problems. Results demonstrated that PTSD symptoms may have long-lasting effects on substance use problems, thereby highlighting PTSD symptomatology as an important etiological factor in the development of SUDs. Findings also indicated that family environments characterized by high levels of conflict, stress, and psychopathology may influence risk for posttrauma substance use problems by increasing the likelihood of developing PTSD symptoms after a traumatic event. Finally, this study also provided support for adolescent substance use problems and binge drinking as risk factors for assaultive violence exposure, which conveys an especially high risk for PTSD compared with other traumatic events (Kessler et al., 1995) and may thus increase the likelihood of posttrauma substance use problems. Findings are thus consistent with the notion that multiple, nonmutually exclusive pathways may underlie the link between PTSD and SUDs.
These findings have implications for preventing substance use problems among individuals who present for treatment for PTSD. Clinicians should routinely assess clients’ risk for using alcohol or drugs to self-medicate PTSD symptoms, discuss long-term dangers associated with self-medication, and provide other means of coping. Findings also highlight the need to screen for and treat PTSD symptomatology among individuals who present with substance use problems. Research indicates a low detection rate of PTSD within addiction treatment centers because individuals with substance use problems often to do not report traumatic experiences and PTSD symptoms unless specifically asked (Kimerling, Trafton, & Nguyen, 2006). Individuals with concurrent PTSD symptoms and SUDs are especially hard to treat, and do not optimally benefit from standard SUD interventions (Norman, Tate, Anderson, & Brown, 2007). Findings from the present study suggest that in addition to addressing the functional associations between PTSD symptoms and problematic substance use, resolving distress related to adversity in the family environment may also be a potentially important treatment target. Understanding the development and treatment of co-occurring PTSD symptoms and substance use problems remains an important area for research.
Footnotes 1 Trauma exposure is by definition a risk factor for PTSD symptoms, but this study clarifies whether trauma exposure may also directly increase risk for future substance use problems.
2 Analyses modeled the effects of substance use problems, rather than substance use itself, because problems were expected to be more prognostic of future risk for trauma exposure, PTSD, and adult substance use problems. That is, adolescents who were using substances to such an extent that they were already experiencing abuse or dependence symptoms were theorized to exhibit a high-risk substance use style that may place them at risk for trauma, PTSD, and/or substance use problems. Moreover, modeling the effects of substance use problems allowed us to be longitudinally consistent when predicting adult substance use problems.
3 This study examined risk for substance use problems at Wave 5 rather than Wave 4 (when trauma/PTSD was assessed) to provide a prospective test of PTSD symptoms on future substance use problems. Analyses controlled for Wave 1 (pretrauma) substance use problems when examining risk for Wave 5 substance use problems.
4 The assessment of drug-related problems referred to drugs in general rather than a specific class of drugs (how recently have you used a drug enough so that that you felt like you needed or depended on it?). Before beginning these questions, the interviewer stated When we ask you about drug use we do NOT mean medicines that were given to you by your doctor. We want to know about your use of drugs that were not prescribed by your doctor.
5 Analyses were repeated after deleting the 29 participants who were missing data at Wave 5 (n = 348). Results were unchanged. All findings pertaining to hypothesis testing were identical to those presented below.
6 Preliminary analyses tested whether Poisson, zero-inflated Poisson (ZIP), negative binomial, or zero-inflated negative binomial regression was the most appropriate method of model estimation for each count dependent variable. Results showed the best fit for the negative binomial models. However, to differentiate between risk for using alcohol/drugs and risk for developing alcohol/drug problems among those who use alcohol/drugs, follow-up analyses predicted alcohol and drug problems using ZIP regression. Results were consistent with those presented below. PTSD symptoms had significant unique effects on risk for alcohol problems among those who drink (B = .10, p < .001, IRR: 1.11) and risk for drug problems among those who use drugs (B = .08, p < .001, IRR: 1.08). PTSD symptoms did not significantly influence the probability of being a nondrinker (B = −.02, p = .770, OR: .98) or the probability of being a nondrug user (B = −.08, p = .11, OR: .92). Therefore, these analyses PTSD symptoms significantly increase risk for alcohol and drug problems among those who use alcohol and drugs.
7 We did not believe it was advisable to assume that participants without trauma exposure had zero PTSD symptoms because it is feasible that nontrauma exposed individuals may have similar symptoms (e.g., sleep disturbances, irritability, feeling detached or estranged from others) that are not trauma-induced.
8 Males had more alcohol and drug problems than did females, whereas females had more PTSD symptoms than did males (see Table 1), thus obscuring the relation between PTSD symptoms and alcohol and drug problems.
9 To examine the robustness of this finding, additional analyses modeled the effects of past-year frequency of binge drinking, getting drunk, and using marijuana, on risk for trauma exposure or assaultive violence exposure. Similar to the main analyses, neither binge drinking nor getting drunk predicted overall risk for trauma exposure. However, binge drinking had a significant unique effect on risk for assaultive violence exposure over and above family adversity, gender, and ethnicity (B = 0.30, p = .032, OR = 1.34). Frequency of getting drunk and marijuana use had marginally significant unique effects on risk for assaultive violence exposure.
10 Additional analyses tested whether adolescent substance use problems or binge drinking interacted with two indices of trauma severity—number of traumatic events and type of trauma (assaultive violence exposure vs. other types of events)—to increase susceptibility to developing PTSD symptoms. There were no significant interactions.
11 Additional analyses tested whether PTSD symptoms mediated the influence of trauma severity (as indicated by type of trauma or number of traumas) on substance use problems among the 166 trauma-exposed participants. PTSD symptoms did not significantly mediate the effect of type of trauma, as type of trauma did not have a significant unique effect on risk for PTSD symptoms (B = 0.11, p = .35, IRR = 1.11). PTSD symptoms fully mediated the influence of number of traumas on risk for alcohol problems. For drug problems, the effect of PTSD symptoms on drug problems was marginally significant (B = 0.12, p = .057, IRR = 1.12) after accounting for the significant effect of number of traumas on drug problems (B = 0.57, p = .04, IRR = 1.76). In sum, these analyses supported the self-medication hypothesis, as PTSD symptoms influenced risk for alcohol and drug problems even when controlling for these indices of trauma severity.
12 Post hoc analyses tested the effects of adolescent substance use problems at either Wave 1, 2, or 3—whichever Wave was closest in time but preceding the traumatic event. For participants who were not exposed to trauma, Wave 3 substance use problems were used. Results were identical with respect to hypothesis testing to those presented in the manuscript, thus lending confidence that our findings are consistent even when substance use problems were assessed closer in time to the traumatic event.
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Submitted: April 26, 2013 Revised: October 18, 2013 Accepted: December 16, 2013
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Source: Psychology of Addictive Behaviors. Vol. 28. (3), Sep, 2014 pp. 841-851)
Accession Number: 2014-24382-001
Digital Object Identifier: 10.1037/a0035878
Record: 143- Title:
- Risk, compensatory, protective, and vulnerability factors related to youth gambling problems.
- Authors:
- Lussier, Isabelle D.. Department of Educational and Counselling Psychology, McGill University, International Centre for Youth Gambling Problems and High-Risk Behaviors, Montreal, PQ, Canada, isabelle.lussier@mail.mcgill.ca
Derevensky, Jeffrey. Department of Educational and Counselling Psychology, McGill University, International Centre for Youth Gambling Problems and High-Risk Behaviors, Montreal, PQ, Canada
Gupta, Rina. Department of Educational and Counselling Psychology, McGill University, International Centre for Youth Gambling Problems and High-Risk Behaviors, Montreal, PQ, Canada
Vitaro, Frank. Department of Psychoeducation, University of Montreal, Research Unit on Children's Psychosocial Maladjustment, Montreal, PQ, Canada - Address:
- Lussier, Isabelle D., McGill University, International Centre for Youth Gambling Problems and High-Risk Behaviors, 3724 McTavish Street, Montreal, PQ, Canada, H3A 1Y2, isabelle.lussier@mail.mcgill.ca
- Source:
- Psychology of Addictive Behaviors, Vol 28(2), Jun, 2014. pp. 404-413.
- NLM Title Abbreviation:
- Psychol Addict Behav
- Page Count:
- 10
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- Bulletin of the Society of Psychologists in Addictive Behaviors; Bulletin of the Society of Psychologists in Substance Abuse
- Other Publishers:
- US : Educational Publishing Foundation
Society of Psychologists in Addictive Behaviors - ISSN:
- 0893-164X (Print)
1939-1501 (Electronic) - Language:
- English
- Keywords:
- compensatory factors, development, gambling, protective factors, risk factors, individual resources, environmental risk, vulnerability
- Abstract:
- This study explores the additive (i.e., risk or compensatory) or moderating (i.e., protective or exacerbating) role of individual resources (social bonding, personal competence, and social competence) and environmental risk (family, peers, and neighborhood) in regard to the association between established personal risk attributes (i.e., impulsivity, anxiety) and youth gambling problems. Using a cross-sectional design, regression analyses indicated that among a sample of mostly first-generation immigrant adolescents from low-income homes (N = 1,055; M = 15.03; SD = 1.64), social bonding was associated with a decrease in gambling problems (odds ratio [OR] = 0.15, p < .01) while peer and neighborhood risk were associated with an increase in gambling problems (OR = 2.24, p = .01 and OR = 2.31, p = .01, respectively), net of personal risk attributes. In terms of protective processes, no putative moderating effect was found for composite individual resources. The findings are discussed with respect to the roles of compensatory, risk, and protective processes. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Gambling; *Protective Factors; *Risk Factors; Susceptibility (Disorders)
- Medical Subject Headings (MeSH):
- Adolescent; Anxiety; Child; Cross-Sectional Studies; Depression; Family; Female; Gambling; Humans; Impulsive Behavior; Logistic Models; Male; Peer Group; Poverty; Protective Factors; Residence Characteristics; Risk Factors; Risk-Taking; Social Support
- PsycINFO Classification:
- Behavior Disorders & Antisocial Behavior (3230)
- Population:
- Human
Male
Female - Location:
- Canada
- Age Group:
- Childhood (birth-12 yrs)
School Age (6-12 yrs)
Adolescence (13-17 yrs)
Adulthood (18 yrs & older)
Young Adulthood (18-29 yrs) - Tests & Measures:
- Diagnostic and Statistical Manual for Mental Disorders–Fourth Edition–Multiple Response--Juvenile
Individual Protective Factors Index
Eysenck Impulsiveness Scale
Reynolds Adolescent Depression Scale–2nd Edition
Beck Anxiety Inventory DOI: 10.1037/t02025-000
Gambling Activities Questionnaire DOI: 10.1037/t04212-000 - Grant Sponsorship:
- Sponsor: Ontario Problem Gambling Research Centre
Other Details: studentship award
Recipients: No recipient indicated
Sponsor: Fonds de recherche du Québec - Société et culture
Other Details: doctoral fellowship; Actions concertées: Les impacts socioéconomiques des jeux de hasard et d’argent
Recipients: No recipient indicated - Methodology:
- Empirical Study; Quantitative Study
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- First Posted: Nov 25, 2013; Accepted: Jul 15, 2013; Revised: Jul 12, 2013; First Submitted: Oct 12, 2012
- Release Date:
- 20131125
- Correction Date:
- 20140623
- Copyright:
- American Psychological Association. 2013
- Digital Object Identifier:
- http://dx.doi.org/10.1037/a0034259
- PMID:
- 24274433
- Accession Number:
- 2013-40796-001
- Number of Citations in Source:
- 62
- Persistent link to this record (Permalink):
- http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2013-40796-001&site=ehost-live
- Cut and Paste:
- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2013-40796-001&site=ehost-live">Risk, compensatory, protective, and vulnerability factors related to youth gambling problems.</A>
- Database:
- PsycINFO
Risk, Compensatory, Protective, and Vulnerability Factors Related to Youth Gambling Problems
By: Isabelle D. Lussier
Department of Educational and Counselling Psychology, McGill University, International Centre for Youth Gambling Problems and High-Risk Behaviors, Montreal, Quebec, Canada;
Jeffrey Derevensky
Department of Educational and Counselling Psychology, McGill University, International Centre for Youth Gambling Problems and High-Risk Behaviors, Montreal, Quebec, Canada
Rina Gupta
Department of Educational and Counselling Psychology, McGill University, International Centre for Youth Gambling Problems and High-Risk Behaviors, Montreal, Quebec, Canada
Frank Vitaro
Department of Psychoeducation, University of Montreal, research unit on children’s psychosocial maladjustment, Montreal, Quebec, Canada
Acknowledgement: Isabelle D. Lussier is now at the Children’s Hospital of Eastern Ontario, Ottawa, ON, Canada.
This research was supported by a studentship award from the Ontario Problem Gambling Research Centre and by a doctoral fellowship from the Fonds de recherche du Québec - Société et culture (FQRSC): Actions concertées: Les impacts socioéconomiques des jeux de hasard et d’argent.
Prevalence studies indicate that gambling among youth is widespread (Jacobs, 2004) and that opportunities to gamble are increasing with the expansion of land-based gambling, Internet gambling, interactive lotteries, mobile gambling, and novel slot machines (Griffiths & Wood, 2000). Gambling problems among youth are also relatively prevalent, with reported probable pathological gambling rates ranging between 2% to 7% among Canadian youth (Gupta & Derevensky, 2000; Martin, Gupta, & Derevensky, 2007). Adolescents with gambling problems are more likely to report difficulty in school and truancy (Ladouceur, Boudreault, Jacques, & Vitaro, 1999) as well as financial and personal problems (Burge, Pietrzak, & Petry, 2006; Schissel, 2001). Several studies have identified a number of risk factors that are predictively or concurrently associated with gambling problems in adolescents. These risk factors include personal risk attributes (e.g., disinhibition-impulsivity) and environmental factors (e.g., parenting). However, few studies have examined whether personal and environmental risk factors operate in a cumulative (i.e., additive) or a multiplicative (i.e., interactive) manner in regard to youth gambling problems. Therefore, the first goal of the current study was to determine the additive or interactive role of personal and environmental risk factors in relation to youth gambling problems.
Despite the well-established relationships between personal or environmental risk factors and gambling problems, many youth exposed to personal, environmental, or both risk factors never develop problem behaviors. This suggests the possibility for compensatory-resource factors or protective-moderating factors. Researchers in other areas of youth development have begun to identify factors that may counteract risk factors through a cancellation process in the case of compensatory factors or a mitigating-buffering process in the case of protective factors (Rutter, 1990). However, no study, to our knowledge, has examined this important issue in relation to youth gambling problems despite the important contribution such findings could have to the prevention literature for youth gambling problems. Therefore, the second goal of the current study was to determine whether individual resources could play a compensatory or protective role with respect to the link between risk factors and gambling problems.
A Multidimensional/Ecological Integrative PerspectiveInfluenced by Bronfenbrenner’s ecological (Bronfenbrenner, 1979) and bioecological (Bronfenbrenner, 2005) models, contemporary prevention research often emphasizes relationships and systems, and integrated biological, social, and cultural processes across time. Ecological theory is defined as:
. . . the scientific study of the progressive, mutual accommodation, throughout the life course, between an active, growing human being and the changing properties of the immediate settings in which the developing person lives, as this process is affected by the relations between these settings, and by the larger contexts in which the settings are embedded. (Bronfenbrenner, 2005, p. 107)
According to this model there are four environmental levels that can operate in an additive, mediating or interactive way. The four levels include the microsystem (actions and interactions within the environment that a child is behaving in at any given moment in time, e.g., home and school), the mesosystem (the interrelations among the child’s microsystems, e.g., the relationship between a child’s parents and school), the exosystem (environments that indirectly influence the child’s behavior and development, e.g., a parent’s workplace), and the macrosystem (broad social factors and cultural values that influence the other settings, e.g., social norms and public policies) (Bronfenbrenner, 2005; Kaminski & Stormshak, 2007). Most researchers now agree that a youth may be competent in one context or aspect of life but not another, and/or at one point in development but not another (O’Dougherty Wright & Masten, 2005). This wave of research has led to a search for processes related to positive outcomes (or the reduction or absence of negative outcomes), particularly in fields of substance use and delinquent behavior. This multidimensional framework was used to examine the possible cumulative or interactive role of risk factors across different levels (i.e., personal and environmental) as well as the possible compensatory or protective role of individual resources in regard to youth gambling problems.
Personal Risk Attributes Related to Youth Gambling ProblemsProblem gambling appears to be more prevalent among males than females (NRC, 1999). Males are more likely to gamble more money (Derevensky, Gupta, & Della–Cioppa, 1996), to begin gambling at an earlier age (Derevensky & Gupta, 2001), and to gamble more frequently (Jacobs, 2004). Attributes including physiological, personality, emotional, and coping variables have also been shown to be associated with excessive youth gambling behavior (Hardoon & Derevensky, 2002). Three personality variables often cited as predictors in youth gambling research include impulsivity, anxiety, and depression.
Predictive links have repeatedly been identified in longitudinal studies between poor impulse-control or behavioral disinhibition and youth problem gambling (Vitaro, Arseneault, & Tremblay, 1999; Wanner, Vitaro, Carbonneau, & Tremblay, 2009). In terms of anxiety, cross-sectional youth gambling research has identified a relationship between anxiety and youth problem gambling (Ste-Marie, Gupta, & Derevensky, 2006). However, this relationship may be less straightforward. For example, Vitaro and colleagues (1996) reported that low anxiety during childhood distinguished problem gamblers from non-problem gamblers in adolescence. In regards to depression, research findings have produced mixed results. Certain studies have cited that adolescents with gambling problems report higher levels of depression, suicide ideation, and suicide attempts (Lee, Storr, Ialongo, & Martins, 2011; Nower, Gupta, Blaszczynski, & Derevensky, 2004). However, recent longitudinal research has also demonstrated no significant link between childhood shy/depressed behavior and problem gambling in adulthood, again suggesting that additional factors may play a compensatory or a protective role (Shenassa, Paradis, Dolan, Wilhelm, & Buka, 2012). In turn, there are environmental factors that may also operate as risk factors, adding to or compounding the role of personal risk factors.
Environmental Risk Factors Related to Youth Gambling ProblemsAdolescent problem gamblers are more likely to have a parent who struggles with a gambling problem (Vachon, Vitaro, Wanner, & Tremblay, 2004), and often report having had their first gambling experience at home with a family member (Gupta & Derevensky, 1997). Youth gambling problems also appear to be linked to parental discipline, independent of parent gambling. For example, even after controlling for numerous variables (including socioeconomic status, gender, and impulsivity-hyperactivity problems), main effects were identified between youth gambling problems and poor parental monitoring and disciplinary strategies (Vachon et al., 2004).
Peer modeling and social learning also appear to be involved in the development of gambling problems (Gupta & Derevensky, 1997; Hardoon & Derevensky, 2001). For example, many adolescents report that they gamble because their friends do (Griffiths, 1990). Over time, adolescents with gambling problems have been reported to replace old friends with gambling associates (Gupta & Derevensky, 2000).
Although less research has directly examined predictive links between disadvantaged neighborhoods and gambling behavior, findings from a longitudinal study of boys living in economically deprived neighborhoods revealed an elevated risk of gambling problems among those whose mothers were below the median on maternal occupational prestige (Vitaro et al., 1999). To summarize, several studies reported an association, either longitudinally or concurrently, between personal or environmental factors and youth problem gambling. However, few studies have examined their possible cumulative or interactive role. Even fewer studies have examined whether resource factors could compensate or moderate the association between risk factors and youth problem gambling.
Compensatory and Protective Resource FactorsResource factors can operate in two distinct ways: By decreasing the chances of a negative outcome in the context of adversity (i.e., through a protective-moderating effect), and by decreasing the chances of a negative outcome regardless of exposure to adversity (i.e., through a compensatory-cancellation effect; Rose et al., 2004). In statistical terms, a compensatory factor implies a negative main effect (opposite to risk factors) whereas a protective factor implies a mitigating-buffering effect on the relationship between a risk variable and a maladaptive outcome. Only a small number of investigations have examined resource factors in relation to youth gambling behavior. A few studies examined whether resource factors such as social bonding, personal competence, and social competence played a compensatory role (Dickson, Derevensky, & Gupta, 2008; Lussier, Derevensky, Gupta, Bergevin, & Ellenbogen, 2007). To our knowledge, no study has examined the potentially protective role of these resource factors with respect to the relationship between personal or environmental risk factors and gambling problems. Indeed, these resource factors may serve to buffer youth that would otherwise be at risk of developing gambling problems by providing alternatives for engaging in, and by helping to avoid the negative consequences of excessive problem behavior.
Social Bonding
Social bonding represents the degree to which people feel a positive affect for, involvement with, and motivation toward success in social contexts (e.g., family and school) and acceptance toward conventional values (e.g., prosocial norms; Springer & Phillips, 1992). Studies have consistently denoted the importance of social bonds in relation to various high-risk behaviors (Resnick et al., 1997; Rutter, 1990), and more recently, to gambling problems (Dickson et al., 2008; Lussier et al., 2007). Two specific indicators of social bonding (i.e., family cohesion, and school connectedness) have been identified as compensatory mechanisms in relation to youth gambling problems (Dickson et al., 2008; Magoon & Ingersoll, 2006). As well, based on a large community sample (N = 1,273), using a cross-sectional design, Lussier and colleagues (2007) identified low social bonding to be the strongest predictor of youth gambling problems (over and above personal competence, social competence, family risk, neighborhood risk, and perceived deviant peers), while controlling for gender.
In terms of protective processes, no study has examined the potentially protective role of social bonding on the relationship between relevant risk factors and gambling problems. However, other high-risk behavior research has found school bonding to putatively moderate the relationship between deviant peers and substance use and deviant behavior (Crosnoe, Erickson, & Dornbusch, 2002). As well, family bonding has been found to putatively moderate the relationships between deviant peers and deviant behavior, alcohol, tobacco, and other drug use (Crosnoe et al., 2002) and between stress and low self-esteem and heavy episodic drinking (Jessor, Costa, Kruege, & Turbin, 2006).
Personal Competence
The ability to function effectively with a sense of purpose toward the future may be referred to as personal competence. Personal competence includes dimensions such as self-concept, self-control, self-efficacy, and positive outlook (Springer & Phillips, 1992). It has been identified as a compensatory factor in relation to youth gambling problems in a large cross sectional study (Lussier et al., 2007). However, when it was included in a larger model including other known predictors such as environmental risk and risky behaviors, it was not retained as a significant predictor. In terms of protective processes, no study has examined the potentially protective role of personal competence on the relationship between relevant risk factors and gambling problems. However, other high-risk behavior research has found self-control to putatively moderate the relationships between three predictors (family life events, adolescent life events, peer substance use) and substance use (Wills, Ainette, Stoolmiller, Gibbons, & Shinar, 2008), and self-efficacy beliefs have been found to moderate the relationships between home environment and social behavior, achievement, and overall problems (Bradley & Corwyn, 2001).
Social Competence
The definition of social competence varies among researchers. However, general themes include responsiveness, caring, and flexibility in social situations (Springer, Wright, & McCall, 1997); qualities that are believed to elicit positive responses from others. Three qualities related to social competence include assertiveness, confidence, and cooperation and contribution. Social competence has been investigated as a potential compensatory factor in relation to youth gambling problems (Lussier et al., 2007). Research on social competence among other high-risk behaviors has resulted in mixed results. Although some support exists for social competence as a compensatory mechanism (Sandstrom & Coie, 1999), other findings suggest that an inflated perception of social competence may actually increase externalizing behavior problems (Brendgen, Vitaro, Turgeon, Poulin, & Wanner, 2004; de Castro, Brendgen, van Boxtel, Vitaro, & Schaepers, 2007) and smoking and cannabis use (Veselska, Geckova, Orosova, van Dijk, & Reijneveld, 2009).
Socioeconomic Status (SES)Among many other factors, a child’s macrosystem and exosystem are partly defined by the socioeconomic status (SES) of the family. The term SES broadly refers to personal lifestyle variables including occupation, income, and education. Substantial research exists on the risk mechanisms involved between low SES and maladaptive outcomes. Several meta-analyses have summarized physical health issues, cognitive deficiencies, poor school achievement, and emotional and behavioral problems, including substance use, as effects of economic disadvantage on youth (McLoyd, 1998; Miech & Chilcoat, 2005). The data regarding the relationship between SES and gambling problems is both scarce and inconsistent. Some studies have found a positive association between low SES and gambling problems (Fisher, 1993; Schissel, 2001). Findings from a prospective longitudinal study of boys living in economically deprived neighborhoods, found that those whose mothers were below the median on maternal occupational prestige were significantly more at risk of gambling problems. That is, the poorest adolescent males were at greatest risk (Vitaro et al., 1999). Other studies reveal a more complex relationship between SES and youth gambling problems. For example, a study by Welte and colleagues (2008) reported that although low SES individuals as a whole reported less gambling activity in the past year, those that did indicate having gambled were more likely to meet the criteria for problem gambling. As well, a study by Auger and colleagues (2010), demonstrated that low SES influenced gambling onset primarily among impulsive youth, identifying impulsivity as a risk factor for gambling onset among low but not high SES youth. Because of the uncertainty with respect to the role of SES in reference to gambling problems, and given our interest in protective and compensatory factors, we selected a naturally occurring homogeneous high-risk sample of low SES adolescents. Hence, SES was methodologically controlled.
Current ResearchThe current study was designed to (1) identify whether personal risk attributes (gender, impulsivity, and emotional problems) and environmental risk factors (family, peers, and neighborhood) operate additively or interactively in the prediction of gambling problems in a sample of low SES adolescents (2) identify whether individual resources (social bonding, personal competence, and social competence) operate as compensatory or protective factors in the prediction of youth gambling problems.
Method Participants
The sample included 1,055 participants (535 males, 518 females, 2 were missing gender information) in Grades 7 to 11 (ages 11–18; M = 15.03; SD = 1.64) from three schools in the Montreal area, with an overrepresentation of students from low-income homes (see Table 1). Schools were targeted using the Classification des écoles primaires et classification des écoles secondaires (CES; ranking of 1–27 out of 90; CGTSIM, 2006) and by the Indices de défavorisation par école [decile ranking of 8 to 10 on the low-income cutoff (LICO); MELS, 2006]. The CES is an annual classification system that hierarchically classifies schools according to the proportion of students from underprivileged homes, whereas the Indices de défavorisation par école is a broader school population map that covers the province of Quebec, and classifies schools by decile rankings for two indices, one of which denotes a low-income cutoff. Notably, the student body of the school from which the majority of data was collected (n = 813) was largely made up of first-generation immigrant youth. In fact, only 38% of the school’s student body (at the time of data collection) were born in North America. Next to Quebec (36%), the birthplace for most students in the school was the Republic of China (13.7%).
Distribution by Sex and Developmental Level
Instruments
Outcome variable: Gambling
The Gambling Activities Questionnaire (GAQ; Gupta & Derevensky, 1996) was used to identify Non-Gamblers (failure to endorse any of the 12 gambling activities during the past year). The Diagnostic and Statistical Manual for Mental Disorders–Fourth Edition–Multiple Response - Juvenile (DSM–IV-MR–J) (Fisher, 2000) was used to assess severity of problem gambling among those gambling. Gamblers were classified into three groups; Social, At-Risk, or Probable Pathological. A score of 0 or 1 was indicative of social gambling, a score of 2 or 3 reflected an at-risk level of gambling, and a score of 4 or more was indicative of probable pathological gambling (PPG). The internal consistency reliability for these scales was adequate, with Cronbach’s α = .72 and .75, respectively. French versions of these measures have been previously used in prior research (Dickson et al., 2008).
Individual resources
The Individual Protective Factors Index (IPFI) was designed to assess 61 individual resources on a 4-point Likert scale across three domains; Social Bonding (family bonding, school bonding, and prosocial norms), Personal Competence (self-concept, self-control, positive outlook, and self-efficacy), and Social Competence (assertiveness, confidence, and cooperation and contribution) (Springer & Phillips, 1992).
Social bonding
This domain is comprised of 18 questions distributed evenly across the three dimensions of school bonding (e.g., “Finishing high school is important”), family bonding (e.g., “I can tell my parents the way I feel about things”), and prosocial norms (e.g., “I like to see other people happy”). The social bonding domain score was computed by adding all raw social bonding subscale scores together and dividing by the total number of social bonding items, thus providing a domain score with a range of 1–4. The internal consistency reliability for this scale was adequate, with Cronbach’s α = .77.
Personal competence
The focus of this domain (consisting of 25 questions) is on individual identity, relating to one’s sense of personal development, self-image, and outlook on life (Springer & Phillips, 1992). The four dimensions within this domain include self-concept (e.g., “I like the way I act”), self-control (e.g., “When I am mad, I yell at people”), self-efficacy (e.g., “Other people decide what happens to me”), and positive outlook (e.g., “I am afraid my life will be unhappy”). The personal competence domain score was computed by adding all raw personal competence subscale scores together and dividing by the total number of personal competence items, thus providing a domain score with a range of 1–4. The internal consistency reliability for this scale was adequate, with Cronbach’s α = .79.
Social competence
The elements of this domain include one’s ability to feel responsive, caring, and flexible in social situations. Eighteen questions in this domain are distributed evenly across three subscales including assertiveness (e.g., “If I don’t understand something, I will ask for an explanation”), confidence (e.g., “I will always have friends”), and cooperation/contribution (e.g., “I always like to do my part”). The social competence domain score was computed by adding all raw social competence subscale scores together and dividing by the total number of social competence items, thus providing a domain score with a range of 1–4. The internal consistency reliability for this scale was good, with Cronbach’s α = .80.
For the purpose of this study, the three dimensions of individual resources (Social Bonding, Personal Competence, and Social Competence) were considered separately as potential compensatory factors. In addition, a composite score was created to reflect a global individual resources score. The reason for this composite score was to test the global protective (i.e., moderating) effect of the individual resources in a parsimonious manner. To create a composite-global score, all 10 subscale scores were summed together and divided by the total number of items, as per manual guidelines. The internal consistency reliability for this scale was excellent, with Cronbach’s α = .90.
Personal and Environmental Risk Factors
Impulsivity
Impulsivity was assessed by using the five impulsiveness items from the Eysenck Impulsiveness Scale (EIS) with the highest factor loadings on the original scales (e.g., “Do you generally do and say things without stopping to think?”; Eysenck, Easting, & Pearsons, 1984). All items required yes/no responses and were summed to create a composite score ranging from 0–5. The internal consistency reliability for the current sample was adequate (Cronbach’s α = .76).
Anxiety
The Beck Anxiety Inventory (BAI; Beck, Epstein, Brown, & Steer, 1998) includes 21 items on a 4-point Likert scale, with scores ranging from 0–3 (e.g., “Indicate how much you have been nervous during the past week”). The composite score ranges from 0–63. Although the BAI was initially designed for adults, it has been shown to have acceptable psychometric properties among adolescents (Osman et al., 2002). The internal consistency reliability for the current sample was excellent, with Cronbach’s α = .91.
Depression
The Reynolds Adolescent Depression Scale–2nd Edition (RADS–2) was used to assess depressive symptomatology. The 30 items (e.g., “I feel lonely.”) are rated on a 4-point Likert scale, with composite scores ranging from 30–120 (Reynolds, 2002). The internal consistency reliability for the current sample was excellent, with Cronbach’s α = .93.
Environmental risk
Part two of the IPFI, the EMT-Risk, scores 25 environmental risk questions across six subscales (Springer & Phillips, 1992). Scores were calculated such that high scores reflected greater risk in the domains of Family (supervision [e.g., “The rules in our house are clear”] and interaction [e.g., “You talk to your parents about school”]), Peers (positive peer associations [e.g., “Your closest friends study hard at school”], peer AOD use exposure [e.g., “Your closest friends try drugs like marijuana or cocaine once in a while”]), and Environment (neighborhood environmental risk [e.g., “You see the police arrest someone”] and AOD use exposure [e.g., “You have been around other kids who were drinking alcohol”]). For the purpose of this study, the three risk dimensions of Family, Peers, and Environment were considered separately as potential additional risk factors. In addition, a composite score was created to test their global exacerbating (i.e., moderating) effect. The reason for a composite environmental risk score was to test its global exacerbating (i.e., moderating) effect in a parsimonious manner. To create a composite-global score, all six subscale scores were summed together and divided by the total number of items. EMT-Risk items were rated on a 2-, 3-, or 4-point scale, with composite scores ranging from 1–3.28. Although the EMT-Risk is not standardized, it has demonstrated excellent internal consistency reliability (Cronbach’s α = .91) in prior research using a large sample (Lussier et al., 2007). The internal consistency reliability for the present sample was good, with Cronbach’s α = .80.
Procedure
Data collection was group administered in classrooms or school cafeterias. Students completed the survey in one 50-min period. Consent was obtained from parents and adolescents before their participation. Teachers remained at the front of the room to respect participants’ confidentiality. Questionnaires that were deemed unreliable (e.g., zigzag or patterned responses, illegible responses, or questionable information) were discarded (n = 58).
Data Screening
Two participants were missing more than 10% of responses for the DSM–IV-MR-J. Because this measure was one of the two instruments used to create the main criterion variable, their scores were excluded from the data set. The other instrument that was used to create the gambling criterion variable was the GAQ. No student omitted more than 10% of items on the GAQ. Missing data analysis for this instrument was simplified by the fact that it was used as a filter variable to discriminate those who gambled from those who did not. Participants that endorsed no gambling activities in the past year were classified within the Non-Gambling category. For all other instruments, if a participant failed to respond to >10% of items, their scores were excluded from further analyses pertaining to that instrument. Administration manuals were consulted to address missing values within acceptable limits (<10%). For measures with no suggested protocol, items with significantly skewed distributions were imputed with item medians, whereas items with nonsignificantly skewed distributions were imputed with item means.
Outliers were identified by transforming each instrument composite variable into z-scores. Scores that were >3 SD were converted to a value that equaled the next most extreme score within 3 SD. Bivariate multicollinearity was assessed using a correlation matrix. No pairwise correlations exceeded r = .64, indicating no evidence of multicollinearity. Multivariate correlations were assessed by running a linear regression with the DSM–IV-MR-J as the dependent variable, and all other variables as independent variables. Collinearity diagnostics revealed no Tolerance statistic value <.20 and no VIF value >10, suggesting no evidence of multivariate singularity or multicollinearity. The DSM–IV-MR-J instrument was significantly positively skewed, with a skewness value = 2.04. Logarithmic, square root, and inverse function transformations were unable to significantly improve the shape of the distribution. Consequently, this variable was left as nonnormal and analyzed categorically wherever possible.
Statistical Analyses
Sequential binary logistic regressions were used to determine the combination of personal risk attributes (impulsivity, anxiety, and depression) and environmental risk (family, peers, and neighborhood) that best predict problem gambling and to explore the possible compensatory or interaction effects of individual resources (social bonding, personal competence, social competence) with respect to these two categories of risk factors. The outcome variable was coded as 0 = Non-Gambler or Social-Gambler and 1 = At-Risk or PPG (see details later). Gender and age were considered as potential control variables, and the personal risk attributes of impulsivity, anxiety, and depression were considered as potential predictor variables. However, age and depression were not significantly related to problem gambling and as such were removed from further models. In the first series of regressions, gender, anxiety, and impulsivity were entered at Step 1 and environmental risk domains and/or individual resource domains were entered at Step 2 to test for their respective additive risk or compensatory role. In the second series of regressions, gender, anxiety, and impulsivity were again entered at Step 1 and composite individual resource scores, composite environmental risk scores, and interaction terms were entered at Steps 2, 3, 4, and 5 to explore the potentially putative buffering and exacerbating moderating effects of individual resources and environmental risk on the relationships between the personal risk attributes (impulsivity and anxiety personality variables) and youth gambling problems, while controlling for other known predictors (gender). For all regressions, the Hosmer and Lemeshow test was nonsignificant, indicating an adequate model fit. Collinearity diagnostics revealed no multicollinearity among the examined variables as ascertained by Tolerance and Variance Inflation Factor statistics. Furthermore, tests for outliers revealed only one case with a z-residual score greater than three, which is considered to be acceptable in analyses involving a large sample.
ResultsA majority (60.2%) of respondents gambled at least once in the past year. The most endorsed activities were cards, sports betting, scratch tickets, poker, and bingo. Overall, 39.8% of participants were non gamblers (n = 419; 35.1% male; 44.8% female); 49.6% were social gamblers (score of 0 or 1 on the DSM–IV-MR-J; n = 522; 49.5% male; 49.4% female); 7.9% met the criteria for At-Risk gambling (score of 2 or 3; n = 83; 11.1% male; 4.6% female); and 2.8% met the criteria for PPG (score of 4 or more; n = 29; 4.3% male; 1.2% female). Overall, 10.7% (n = 112) of the sample indicated some form of gambling related problem. As expected, gender differences were pronounced with males reporting more gambling-related problems than females, (χ2 [1, N = 1,051] = 29.47, p < .001). Differences between age groups were not significant (χ2 [9, N = 1,050] = 15.45, p = .079).
Given the inherent limitations of the cross-sectional design of this study, findings only represent correlational associations that may not be interpreted in terms of temporal or causal links. However, for simplicity terms such as predict or predictor are used to refer to main effects.
The first regressions consisting of gender, impulsivity, and anxiety in Step 1, and the three environmental risk domains (Family, Peer Group, and Neighborhood) entered in Step 2, revealed that Peer Group (z = 6.35, p = .01) and Neighborhood (z = 6.76, p = .01) were retained in the model according to the established Wald criterion but Family was not. In addition, impulsivity was related to problem gambling, net of participants’ gender and environmental factors. The second set of regressions consisting of gender, impulsivity, and anxiety in Step 1, and the three individual resources (Social Bonding, Social Competence, and Personal Competence) in Step 2, revealed that the Social Bonding domain was the only resource to be retained in the model (z = 20.96, p < .001). In a third set of regressions, all six environmental risk and individual resource domains were entered into a prediction model to test for their unique additive or compensatory role. As presented in Table 2, the Peer Group, Neighborhood, and Social Bonding domains were again retained, above and beyond the effects of other known contributors, including gender, impulsivity, and anxiety (z = 4.47, p < .05; z = 4.53, p < .05; and z = 7.24, p < .01, respectively). The Family, Social Competence, and Personal Competence domains were again excluded, as they still did not improve the prediction of problem gambling.
Sequential Logistic Regressions of Domains Predicting Problem Gamblers
To explore the possibility of interaction effects, a conceptual model was tested in which environmental risk and individual resources were assumed to respectively exacerbate and buffer the relationship between either impulsivity or anxiety and problem gambling. As well, two three-way interactions between individual resources, personal risk attributes, and environmental risk were anticipated, such that individual resources would be particularly protective in a context where one or the other of the personal risk attributes and global environmental risk conspire interactively in predicting problem gambling. However, no interaction terms were retained in the final prediction model (see Table 3).
Sequential Logistic Regression Models for Problem/Non Problem Gambling Groups
DiscussionBased on a sample of adolescents deriving mostly from low income homes, analyses identified social bonding as a compensatory factor and peer and neighborhood risk as additional salient risk factors in the prediction of youth gambling problems, net of personal risk attributes such as impulsivity and gender, which also made significant contributions. Of all six environmental risk (family, peers, and neighborhood) and individual resource (social bonding, personal competence, and social competence) variables, low social bonding emerged as the strongest predictor of problem gambling, followed by neighborhood and peer environmental risk. No moderating role was identified for global individual resource or global environmental risk scores on the relationships between personal risk attributes (impulsivity and anxiety) and youth gambling problems. As well, the two three-way interaction terms between either personal risk attribute, global environmental risk, and global individual resources were not significant. These results are discussed in turn, after addressing a number of preliminary issues.
Preliminary Issues
Problem gamblers made up 10.7% of the current sample (7.9% At-Risk; 2.8% PPGs). Several Canadian studies have reported PPG rates of adolescents being 3% to 7% (Derevensky & Gupta, 2000; Gupta & Derevensky, 2000), although more recently, lower rates have also been reported (Martin et al., 2007 [2.1%]) The lower rate of PPGs in this sample suggests that youth from low-income homes do not appear to be at increased risk for developing gambling problems. However, the total rate of a little more than 10% of adolescents in the present sample with at least one gambling-related problem is a matter for concern.
As anticipated, impulsivity was identified as a significant predictor of problem gambling. Anxiety was also a predictor of problem gambling but in a less consistent way. However, depression was not. This finding corroborates recent longitudinal research whereby children that exhibited impulsive behavior at age seven were more than three times more likely to report problem gambling in adulthood. However, no significant link was found between childhood shy/depressed behavior and emerging gambling behavior in childhood (Vitaro & Wanner, 2011) or problem gambling in adulthood (Shenassa et al., 2012).
Individual Resources
As anticipated, social bonding was identified as a compensatory mechanism, contributing to the prediction models of gambling problems over and above other known predictors (gender, age, impulsivity, anxiety, and depression). This finding replicates similar findings from other studies that have sought to identify compensatory factors related to youth gambling problems (Dickson et al., 2008; Lussier et al., 2007). Possible mechanisms that could help explain the compensatory effect of social bonding may relate to social control theory, which emphasizes the importance of internalized attachments to conventional role models (e.g., parents), and bonds to institutions (e.g., school) and individuals who discourage maladaptive behavior (Petraitis, Flay, & Miller, 1995). In terms of protective processes, no interaction term was significant in the binary logistic regression models for gambling problems. However, logistic regressions are known to be an insensitive test for such effects (Preacher, MacCallum, Rucker, & Nicewander, 2005), and the power for detecting such differences may be reduced if sample sizes are highly unequal (Fleiss, Tytum, & Ury, 1980), as was the case for the gambling variable in the current study.
Environmental Risk
As anticipated, the neighborhood and peer group variables were identified as important risk factors for gambling problems over and above other known predictors. The link between peer risk and youth gambling behavior supports prior findings that peer modeling and social learning are involved in the onset of gambling problems (Hardoon & Derevensky, 2001). Many adolescents report that they gamble because their peers gamble. The exact mechanisms that could help explain this association, however, remain unclear because both socialization and selection effects can be involved in a cross-sectional study as ours. However, one may suspect modeling and social reinforcement effects, in addition to selection effects of gambling peers.
Although very little research has been conducted regarding the relationship between neighborhood risk and youth gambling behavior, existing literature corroborates the present findings (Lussier et al., 2007). The contribution of neighborhood risk is particularly important in the context of our study given the likely restricted range on this variable as a consequence of our low socioeconomic status sample. Future investigation into this risk factor may help to better understand its relation to the development of youth gambling problems.
Adversity Among Adolescents in the Present Sample
In an attempt to procure a naturally occurring high-risk sample (youth from low income homes), the classification systems of two separate government organizations were consulted to identify schools consisting primarily of youth from low-income homes. Although this population is widely cited as being at high-risk for numerous maladaptive outcomes, the present sample did not appear to engage in elevated levels of gambling behavior. Further, the normal distribution and adequate variability of environmental risk in this sample (M = 1.90, SD = 0.30), as well as significant mean differences in environmental risk among gambling groups provided little justification for considering the present sample “high-risk.”
Of note was that most students in the current sample were first-generation immigrant youth. Although there is little research regarding first-generation immigrant youth, existing literature indicates that parenting practices among Chinese and Latino immigrant parents may place greater emphasis on parental control (Chao, 1994; Domenech Rodríguez, Donovick, & Crowley, 2009). Another consequence of this feature of our sample may have been a reduced variability on the Family risk scale, thus explaining its noncontribution in the analyses.
Implications
The utility of research on compensatory and protective processes lies in its assimilation into prevention programs and subsequent evaluation regarding program efficacy. Environmental micro-, meso-, exo-, and macro-systems should be taken into consideration in the design, implementation, and evaluation stages of such initiatives as these four systems are increasingly recognized as important transactional variables that significantly influence human development (Bronfenbrenner, 2005; Kaminski & Stormshak, 2007). Prevention programs geared toward concurrently fostering compensatory factors, particularly social bonding, and reducing risk factors, particularly neighborhood and peer environmental risk may lead to lower levels of problem gambling, although prospective longitudinal research would be required to confirm these findings.
Limitations of the Current Study and Future Directions
Mean differences in the risk exposure and individual resource scales among gambling groups were in the anticipated directions. However, differences were small in scale, with greatest scaled score difference being only .39 for environmental risk and .22 for individual resources (both between Non-Gamblers and PPGs). Although, this may be due in part to the large sample size (N = 1,053), this finding alone may not translate into practical implications for prevention and intervention efforts. As well, all measures in this study were self-report. As such, correlations may be inflated because of shared method variance. The generalizability of the results of this study is limited by the fact that the majority of participants were derived from low-income homes and that most students in the current sample were first-generation immigrant youth. The EMT-Risk scale is not standardized and future research should consider a standardized measure of this scale. Because this sample does not appear to have been exposed to significant adversity, additional research with a naturally occurring high-risk sample could provide a better a understanding of how risk and vulnerability factors lead to gambling problems in certain youth and not in others and to what extent unique compensatory and protective factors can be identified. Finally, the design of this study was cross-sectional, preventing causal interpretations of the relationships between environmental risk, personal risk attributes, and individual resources, on one hand, and gambling problems on the other hand. Replication of the findings from the present research, as well as prospective longitudinal research is required to determine causal links and to investigate how the relationships between these variables develop over time.
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Submitted: October 12, 2012 Revised: July 12, 2013 Accepted: July 15, 2013
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Source: Psychology of Addictive Behaviors. Vol. 28. (2), Jun, 2014 pp. 404-413)
Accession Number: 2013-40796-001
Digital Object Identifier: 10.1037/a0034259
Record: 144- Title:
- Self-criticism and dependency in female adolescents: Prediction of first onsets and disentangling the relationships between personality, stressful life events, and internalizing psychopathology.
- Authors:
- Kopala-Sibley, Daniel C.. Department of Psychology, Stony Brook University, Stony Brook, NY, US, daniel.kopala-sibley@stonybrook.edu
Klein, Daniel N.. Department of Psychology, Stony Brook University, Stony Brook, NY, US
Perlman, Greg. Department of Psychiatry, Stony Brook University, Stony Brook, NY, US
Kotov, Roman. Department of Psychiatry, Stony Brook University, Stony Brook, NY, US - Address:
- Kopala-Sibley, Daniel C., Department of Psychology, Stony Brook University, Stony Brook, NY, US, 11794-2500, daniel.kopala-sibley@stonybrook.edu
- Source:
- Journal of Abnormal Psychology, Vol 126(8), Nov, 2017. pp. 1029-1043.
- NLM Title Abbreviation:
- J Abnorm Psychol
- Page Count:
- 15
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- The Journal of Abnormal and Social Psychology; The Journal of Abnormal Psychology; The Journal of Abnormal Psychology and Social Psychology
- ISSN:
- 0021-843X (Print)
1939-1846 (Electronic) - Language:
- English
- Keywords:
- personality, self-criticism, dependency, anxiety, depression
- Abstract (English):
- There is substantial evidence that personality traits, such as self-criticism and dependency, predict the development of depression and anxiety symptoms, as well as depressive episodes. However, it is unknown whether self-criticism and dependency predict the first onset of depressive and anxiety disorders, and unclear how to characterize dynamic mechanisms by which these traits, stressful life events, and psychopathology influence one another over time. In this study, 550 female adolescents were assessed at baseline, 528 and 513 of whom were assessed again at Waves 2 and 3, respectively, over the course of 18 months. Self-criticism and dependency were assessed with self-report inventories, depressive and anxiety disorders were assessed with diagnostic interviews, and stressful life events were assessed via semistructured interview. Logistic regression analyses showed that self-criticism and dependency significantly predicted the first onset of nearly all depressive and anxiety disorders (significant polychoric rs ranged from .15–.42). Subsequent path analyses focused on prediction of depression, and supported several conceptual models of personality-stress-psychopathology relationships. In particular, Personality × Stress interactions were evident for both dependency and self-criticism. These interactions took the form of dual vulnerability, such that stressful life events predicted an increased probability of a later depressive disorder only at low levels of each trait. Results suggest the traits of self-criticism and dependency are important to consider in understanding who is at risk for depressive and anxiety disorders. (PsycINFO Database Record (c) 2017 APA, all rights reserved)
- Impact Statement:
- General Scientific Summary—This study examined whether the personality traits of self-criticism and dependency predict the first onset of major depression, dysthymia, social anxiety disorder, specific phobia, generalized anxiety disorder, and panic disorder in a sample of 550 female adolescents who were assessed at baseline, and 528 and 513 of whom were assessed again at Waves 2 and 3, respectively, over the course of 18 months. Self-criticism and dependency predicted the first onset of a range of internalizing disorders. For depressive disorders, results primarily supported Personality × Stress models. (PsycINFO Database Record (c) 2017 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Adolescent Psychopathology; *Onset (Disorders); *Self-Criticism; *Adolescent Characteristics; Anxiety; Dependency (Personality); Internalization; Major Depression; Personality; Stress
- PsycINFO Classification:
- Psychological Disorders (3210)
- Population:
- Human
Female - Location:
- US
- Age Group:
- Adolescence (13-17 yrs)
- Tests & Measures:
- Big Five Inventory
Kiddie Schedule for the Affective Disorder--Past and Lifetime Version
Stressful Life Events Schedule--Adolescent Version
Family History Screen
Interpersonal Dependency Inventory DOI: 10.1037/t20072-000
Stressful Life Events Schedule DOI: 10.1037/t39244-000
Depressive Experiences Questionnaire DOI: 10.1037/t02165-000
Revised Depressive Experiences Questionnaire DOI: 10.1037/t06703-000 - Grant Sponsorship:
- Sponsor: National Institute of Mental Health, US
Grant Number: R01 MH093479
Recipients: Kotov, Roman
Sponsor: National Institute of Mental Health, US
Grant Number: RO1 MH45757
Recipients: Klein, Daniel N.
Sponsor: Social Sciences and Humanities Research Council of Canada, Canada
Other Details: postdoctoral fellowship
Recipients: Kopala-Sibley, Daniel C. - Methodology:
- Empirical Study; Followup Study; Longitudinal Study; Interview
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- Accepted: Jun 20, 2017; Revised: Jun 17, 2017; Aug 26, 2016
- Release Date:
- 20171120
- Copyright:
- American Psychological Association. 2017
- Digital Object Identifier:
- http://dx.doi.org/10.1037/abn0000297
- Accession Number:
- 2017-51268-002
- Persistent link to this record (Permalink):
- http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2017-51268-002&site=ehost-live
- Cut and Paste:
- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2017-51268-002&site=ehost-live">Self-criticism and dependency in female adolescents: Prediction of first onsets and disentangling the relationships between personality, stressful life events, and internalizing psychopathology.</A>
- Database:
- PsycINFO
Self-Criticism and Dependency in Female Adolescents: Prediction of First Onsets and Disentangling the Relationships Between Personality, Stressful Life Events, and Internalizing Psychopathology
By: Daniel C. Kopala-Sibley
Department of Psychology, Stony Brook University;
Daniel N. Klein
Department of Psychology and Department of Psychiatry, Stony Brook University
Greg Perlman
Department of Psychiatry, Stony Brook University
Roman Kotov
Department of Psychiatry, Stony Brook University
Acknowledgement: This research was supported by National Institute of Mental Health (NIMH) Grant R01 MH093479 (Kotov) and NIMH Grant RO1 MH45757 (Klein), as well as postdoctoral fellowship from the Social Sciences and Humanities Research Council of Canada (Kopala-Sibley).
Self-criticism and dependency are associated with a wide variety of indicators of psychosocial functioning including psychopathology (Blatt, 2004; Blatt & Zuroff, 1992), social (Fichman, Koestner, & Zuroff, 1994; Kopala-Sibley, Rappaport, Sutton, Moskowitz, & Zuroff, 2013) and romantic relationship functioning (Lassri & Shahar, 2012), and academic achievement (Zuroff, 1994). Whereas there is substantial evidence that other personality traits are associated with the development of symptoms of depression and anxiety, and with onsets of these disorders (Clark, 2005; Klein, Kotov, & Bufferd, 2011; Krueger & Tackett, 2003), self-criticism and dependency have yet to be studied as predictors of first-onset psychological disorders. Thus, the first aim of this study was to test whether self-criticism and dependency predict the first lifetime onsets of a range of anxiety and depressive disorders in a large sample of female adolescents. In this paper, first-onset refers to the first time a participant met DSM–IV-TR criteria for that disorder, rather than the first time they developed any symptoms of the disorder.
If personality traits predict first onsets, the next step involves understanding how and under what conditions they do so. A number of conceptual models have been proposed to understand personality-psychopathology associations, including the precursor, diathesis-stress, and consequences models (e.g., Klein et al., 2011). Studies testing these models statistically typically examine only one model at a time, which likely reflects the difficulty and burden associated with collecting all the data needed to examine multiple analytic models (i.e., large sample, multiwave, cross-modal). Yet authors often discuss these conceptual models in competing terms, as if one or more were likely to be correct or receive more support than others. Alternatively, several analytic models may be supported by the data, although some may provide more unique explanatory power than others. The second aim of this study is to simultaneously examine these various perspectives in one analytic model predicting later depressive disorders.
Self-Criticism, Dependency and Internalizing DisordersBlatt and colleagues (e.g., Blatt & Luyten, 2009; Blatt & Zuroff, 1992; Kopala-Sibley & Zuroff, 2014) articulated a two-polarities theoretical model, according to which self-definition, or one’s sense of self, and relatedness, or one’s sense of relationships with close others, represent life span developmental tasks that are fundamental to both healthy functioning and the development of psychopathology. Delays or deficits in the development of a healthy sense of relatedness, due to negative developmental experiences, may lead to high levels of a personality style labeled dependency. This trait is characterized by fears of abandonment as well as insecurity regarding close others and a sense of self-worth that is contingent upon the care and support of others. Notably, other theorists have developed similar conceptualizations of interpersonal dependency (Hirschfeld et al., 1977; Bornstein, 1994, 1997), which they characterized as thoughts, feelings, and behaviors involving excessive emotional reliance on others, a strong need for contact with and attachment to others, high needs for approval, and excessive fears of abandonment.
In contrast, delays or deficits in the development of self-definition, again due to adverse developmental experiences, may lead to high levels of a personality style labeled self-criticism. Highly self-critical individuals are permeated with feelings of low self-worth and guilt, and have excessive needs to ascertain, confirm, and preserve status and value in the eyes of important others (see Blatt, D’Afflitti, & Quinlan, 1976; Blatt & Luyten, 2009; Kopala-Sibley & Zuroff, 2014 for reviews).
Although originally formulated as risk factors specific to depression (see Zuroff, Mongrain, & Santor, 2004, for an overview), research has shown that self-criticism and dependency, or closely related variables such as perfectionism, autonomy, and sociotropy, are associated with a range of disorders, including perimenopausal depression (Mauas, Kopala-Sibley, & Zuroff, 2014), social anxiety disorder (Kopala-Sibley, Zuroff, Russell, & Moskowitz, 2014), and generalized anxiety disorder and panic disorder (Antony, Purdon, Huta, & Swinson, 1998). As such, these traits may represent transdiagnostic risk factors for psychopathology. Whereas the bulk of this work has been limited to adults, self-criticism and dependency are also longitudinally associated with higher levels of depressive and anxiety symptoms, and heighten the effect of negative events on depressive symptoms, in children and early adolescents (e.g., Abela, Webb, Wagner, Ho, & Adams, 2006; Kopala-Sibley, Zuroff, Hankin, & Abela, 2015).
Whereas some research in adults has found that dependency predicts the occurrence of depressive episodes in adults (Dunkley, Zuroff, & Blankstein, 2006), only one study has examined whether dependency predicts the first onset of depressive disorders. Hirschfeld et al. (1989) found that interpersonal dependency predicted the onset of major depression in adults aged 31–41 years. This question has not been examined in adolescents, and no studies have examined whether dependency predicts first onsets of anxiety disorders. It is also unknown whether self-criticism predicts the first onset of any internalizing disorders in youth or adults.
Distinguishing Self-Definition and Dependency From NeuroticismConcerns have been raised over whether self-definition and relatedness are distinct from broader personality traits, in particular Neuroticism (e.g., Coyne & Whiffen, 1995, although see Zuroff et al., 2004 for a response). Self-criticism is moderately related to Neuroticism (Pearson rs of approximately .40–.60; Mongrain, 1993; Zuroff, 1994); the association between dependency and Neuroticism is weaker (Bagby & Rector, 1998; Clara, Cox, & Enns, 2003). Despite the moderate overlap of self-criticism with Neuroticism, self-criticism has shown incremental utility in predicting outcomes. For instance, adjusting for Neuroticism, self-criticism uniquely longitudinally predicts depressive symptoms, the occurrence of major depression, and global psychosocial functioning (Clara et al., 2003; Dunkley et al., 2006; Mongrain & Leather, 2006; see Smith et al., 2016 for a meta-analysis), although these studies did not examine dependency. Self-criticism and dependency also predict social anxiety disorder diagnoses (Cox et al., 2000; Kopala-Sibley et al., 2014) and negative affect in borderline personality disorder patients (Kopala-Sibley, Zuroff, Russell, Moskowitz, & Paris, 2012) over and above the effects of Neuroticism. However, no research has examined the incremental utility of dependency and self-criticism in predicting onsets, much less first onsets, of a variety of internalizing disorders, over and above Neuroticism.
Conceptual Models of Personality-Stress-Psychopathology RelationshipsThe interrelationship of personality, stressful life events, and psychopathology can be characterized by several plausible conceptual models (Clark, 2005; Klein et al., 2011; Krueger & Tackett, 2003). The precursor model posits that personality traits are antecedents and predictors of psychopathology. To the extent that other factors, such as life events (i.e., the stress reactivity model), also influence psychopathology, their effects are independent of personality, resulting in an additive model of personality and stress on psychopathology (Kushner, 2015).
Another influential set of theoretical models posit that traits moderate the effect of stressful life events on psychopathology (see Kushner, 2015 for a recent review). The most common conceptual model within this perspective is the diathesis-stress model (e.g., Blatt & Zuroff, 1992), which assumes psychopathology is produced by high levels of both the predisposing trait and life stressors. However, an alternative variant of Trait × Stress moderation models is the social push (Raine, 2002) or dual vulnerability (Morris, Ciesla, & Garber, 2008) model, which posits that either high levels of the trait or high levels of stress can produce psychopathology, but the absence of psychopathology requires low levels of both the trait and life stress.
Stressful life events or other environmental factors may also influence personality development (Klein et al., 2011; Kopala-Sibley & Zuroff, 2014; Ormel, Oldehinkel, & Brilman, 2001). Indeed, multiple studies indicate that adverse developmental and environmental experiences contribute to personality change (e.g., Roberts, Caspi, & Moffitt, 2003; Scollon & Diener, 2006), including change in self-criticism and dependency (Kopala-Sibley & Zuroff, 2014).
Finally, the consequences theoretical model posits that psychopathology may have persisting effects on personality traits (Klein et al., 2011). Results testing the consequences model of personality and depression have been inconsistent, with some evidence indicating that personality traits, including Neuroticism and dependency, are increased following a depressive episode (e.g., Fanous, Neale, Aggen, & Kendler, 2007; Rohde, Lewinsohn, & Seeley, 1990, 1994), whereas others have not found such an effect (e.g., Ormel, Oldehinkel, & Vollebergh, 2004; Shea et al., 1996). We are unaware of any research which has measured self-criticism and episodes of psychopathology repeatedly over time in order to test a consequences model.
Previous research has generally examined these conceptual models separately from one another. In order to make further progress in understanding the roles of personality and life stress in the etiology of psychopathology it is important to examine multiple conceptual models within a single analytic framework. Testing these conceptual models in separate statistical models, and usually in separate samples, cannot determine the contribution of each statistical path to the etiology of psychopathology over and above the effects of other paths.
To our knowledge, only two studies in any population have simultaneously statistically tested multiple conceptual models of personality and psychopathology in a single analytic framework, neither of which included life stressors (De Bolle, Beyers, De Clercq, & De Fruyt, 2012, De Clercq, De Caluwé, & Verbeke, 2016). De Bolle and colleagues assessed a large sample of children and young adolescents three times over the course of two years and found both correlated changes and reciprocal associations of normal range (De Bolle et al., 2012) and pathological (De Bolle et al., 2016) personality traits with internalizing and externalizing symptoms. The present study extends De Bolle et al. (2012, 2016) by including stressful life events and testing a broader range of conceptual models. As such, it represents a potentially important step forward in providing a more comprehensive understanding of the associations between personality and psychopathology.
Overview and HypothesesThe current study seeks to address several gaps in the existing literature. First, in a large sample of female adolescents who were assessed three times over an 18-month period, the current study examined whether the traits of self-criticism and dependency predict the first onset of a range of internalizing disorders. In addition, their predictive power over and above Neuroticism was examined. In all logistic regression analyses, baseline levels of symptoms of the predicted disorder was included as a covariate to rule out the possibility that any effects are due to prodromal cases in which the episode had started but not yet reached diagnostic threshold. This rendered the logistic models highly conservative given that prior subthreshold symptoms are among the most robust predictors of subsequent full threshold disorders (Burcusa & Iacono, 2007; Klein et al., 2013; Klein, Shankman, Lewinsohn, & Seeley, 2009).
Second, by measuring both personality traits and depressive disorders on three occasions, precursor (personality → depression), consequences (depression → personality), stress reactivity (stressful life events → depression), personality development (stressful life events → personality), and Trait × Stress moderation (Personality × Stressful Life Events → depression) conceptual models were examined. It should be noted the path models in this second set of analyses were limited to depression, as anxiety disorders were assessed at only two time-points, precluding testing most of these conceptual models. In addition, unlike in the logistic regression models described above where participants with a history of depression not otherwise (NOS) specified at baseline were excluded in order to predict first onsets of depressive disorders, these participants were not excluded when testing these path models. Rather, these analyses predicted later depression diagnoses, adjusting for the effects of prior depression diagnoses. It should also be noted that these analyses were not designed to test competing models; rather, they were intended to test multiple mutually compatible models under one analytic framework, an approach rarely taken in this literature.
In the logistic regression models, it was expected that both self-criticism and dependency would predict an increased likelihood of depressive (major depressive disorder [MDD], dysthymic disorder, and any depressive disorder) and anxiety disorders (generalized anxiety disorder, social anxiety disorder, panic disorder, specific phobia, and any anxiety disorder). In the path analyses, it was expected that several conceptual models of the relationship between personality and depression would be supported; however, there were no a priori predictions for which given that previous research has not tested them simultaneously.
Method Participants
At baseline, the sample consisted of 550 female adolescents aged 13.5–15.5 (Mage = 14.4, SD = 0.6) who participated as part of the Adolescent Development of Emotions and Personality Traits (ADEPT) project. ADEPT is a longitudinal study aiming to identify predictors of first onset depression and dysthymia. Thus, adolescent girls were excluded from enrollment if they met lifetime Diagnostic and Statistical Manual of Mental Disorders (4th ed., text rev.; DSM–IV–TR; American Psychiatric Association, 2000) criteria for major depressive, dysthymic or bipolar disorder at the initial assessment. Lifetime history of subthreshold depressive symptoms or DSM–IV-TR depression NOS were not exclusion criteria. The age range of 13.5–15.5 was selected because this is the period that immediately precedes the sharp increase in MDD incidence in girls (Hankin et al., 1998), thereby maximizing the yield of first onsets and minimizing the number of girls to be excluded. Adolescents were recruited through several methods, primarily by contacting families whose telephone numbers were purchased from a commercial list broker, but also word of mouth, school presentations, and advertisements. The racial or ethnic distribution was 80.5% Caucasian, 5.1% African American, 8.4% Latino, 2.5% Asian, 0.4% Native American, and 3.1% Other. Median household income was approximately $110,000 per year (adjusting cost of living, this is equivalent to a household income of $81,481 in the average location in the United States). Most (85.6%) participants lived in two-parent homes, and 51.4% of mothers and 46.9% of fathers had graduated from college. Adolescents who did not have a biological parent willing to participate in the study or had significant physical or cognitive disabilities that would prevent completion of all aspects of the study were excluded.
Of the 550 participants, all of whom had completed diagnostic interviews at baseline, 537, 500, and 511completed measures of self-criticism at Waves 1, 2, and 3, respectively. Similarly, 536, 492, and 505 youth completed measures of dependency at Waves 1, 2, and 3, respectively. At baseline, 548 completed a measure of Neuroticism. At Wave 2, 528 participants were interviewed regarding stressful life events and depressive diagnoses, and 513 completed diagnostic measures at Wave 3. In total, 104 participants had missing data on one or more variables at baseline as well as Wave 2 or 3 personality or diagnostic information, or stress at Wave 2. These 104 did not differ significantly from the 446 with complete data on all measures in terms of any demographic, personality, life events, or diagnostic variables in this paper (all ps > .05). This suggests that participants with complete data were representative of the original cohort. For path models, full information maximum likelihood procedures in Mplus v7.3 (Muthén & Muthén, 1998–2012) were used to estimate the means and intercepts to account for missing observations (Schafer & Graham, 2002).
Logistic regressions and polychoric correlations were computed for each specific anxiety disorder after removing participants who had that diagnosis at baseline, thereby allowing prediction of the first onset of each anxiety disorder. For logistic models predicting specific anxiety disorders, participants with a baseline (NOS) diagnosis for that specific disorder were also excluded. For logistic models predicting any anxiety disorder (which included first onsets of anxiety NOS), individuals with any anxiety NOS diagnosis at baseline were excluded. Participants who were diagnosed with depression NOS at baseline were removed in analyses predicting first onset of MDD, dysthymia, and any depressive disorder (the last of which included first onsets of depression NOS). NOS cases were handled differently in logistic models predicting depression than those predicting anxiety disorder because there are no MDD NOS or dysthymia NOS categories. In contrast, path models were computed including all participants. Thus, the logistic regression models predict the first onset of disorders, and the path models predict later diagnostic status after adjusting for the effects of diagnostic status at prior time points.
Procedure
The adolescents were assessed at three waves, each 9 months apart. At all three waves, participants completed a revised version (Bagby, Parker, Joffe, & Buis, 1994) of the Self-Criticism subscale of the Depressive Experiences Questionnaire (DEQ; Blatt et al., 1976), as well as the Emotional Dependency subscale of the Interpersonal Dependency Inventory (IDI; Hirschfeld et al., 1977). Participants completed the Neuroticism subscale of the Big Five Inventory (BFI) at baseline (John, Naumann, & Soto, 2008). At Wave 2, teens were interviewed with the Stressful Life Events Schedule (SLES; Williamson et al., 2003), from which total life events were scored. At Waves 1 and 3, participants were administered the Kiddie Schedule for the Affective Disorders Past and Lifetime version (K-SADS-PL; Kaufman et al., 1997). At Wave 2, the adolescents completed the depressive disorders section of the K-SADS-PL. All procedures were approved by the Institutional Review Board at Stony Brook University.
Materials
Self-criticism
Self-criticism was assessed with Bagby and colleagues’ (1994) 10-item revision of the Self-Criticism subscale of the DEQ (Blatt et al., 1976). An example of a self-criticism item is “There is a considerable difference between how I am now and how I would like to be.” Participants are asked to judge the extent to which they agree or disagree with each statement on a 5-point scale (1 = Disagree strongly, 5 = Agree strongly). The Self-Criticism subscale of the DEQ has shown acceptable internal consistency and excellent test–retest reliability, and discriminated between depressed outpatients and healthy controls (Bagby et al., 1994). In the current study, Cronbach’s alpha for the Self-Criticism scale at Waves 1, 2, and 3, were .86, .88, and .88, respectively. Stability coefficients for self-criticism from Waves 1 to 2, 2 to 3, and 1 to 3, were .69, .70, and .58, respectively.
Dependency
Dependency was assessed by the IDI (Hirschfeld et al., 1977), a widely used measure of trait dependency. Using principal components analysis, Hirschfeld and colleagues (1977) found that the IDI items loaded onto three subscales: Emotional Reliance on Another Person (ER), Lack of Social Self-Confidence, and Assertion of Autonomy. The current paper focused on the emotional reliance subscale, which is comprised of six items (e.g., “Disapproval by someone I care about is very painful for me”). The IDI subscales demonstrated acceptable reliability (Hirschfeld et al., 1977), and retest reliability over intervals ranging from 16 to 84 weeks (Bornstein, 1994, 1997). The IDI distinguishes between depressed and healthy individuals (Hirschfeld et al., 1977), and is associated with other self-report and behavioral measures of dependency (Bornstein, 1994; Hirschfeld, Klerman, Clayton, & Keller, 1983). In addition, emotional reliance has predicted the onset of major depression in adults (Hirschfeld et al., 1989), and adolescents’ scores on the ER subscale predicted subsequent MDD episodes in young adulthood (Lewinsohn, Rohde, Seeley, Klein, & Gotlib, 2000). In the present study, the ER subscale had alphas of .86, .88, and .87 at Waves 1, 2, and 3, respectively. Stability coefficients from Waves 1 to 2, 2 to 3, and 1 to 3, were .62, .57, and .49, respectively.
Neuroticism
Neuroticism was assessed at baseline with the self-report BFI (John et al., 2008; John, Donahue, & Kentle, 1991). The BFI asks participants to rate extent to which a series of statements describes them on a scale of 1 (Disagree strongly) to 5 (Agree strongly). Example items are “is emotionally stable, not easily upset,” (reversed) and “can be moody.” Neuroticism scores on the BFI have been associated with depressive and anxiety symptoms in the general population as well as internalizing diagnoses in psychiatric populations (Gosling, Rentfrow, & Swann, 2003; Kotov, Gamez, Schmidt, & Watson, 2010; Rammstedt & John, 2007). In the current study, the Neuroticism subscale had an alpha of .83.
Life events
Stressful life events were assessed with the SLES, adolescent version (Williamson et al., 2003), a semistructured interview that focuses on life events occurring during the previous 9 months. It covers events from a range of domains of relevance to adolescents, including parents, peers, romantic partners, siblings, and academic performance, as well as other domains, such as health and family finances. Interviews were conducted with the adolescent by undergraduate research assistants and postbachelors and masters-level staff who were trained and supervised by a team of clinical psychologists (GP, DK, RK) and experienced staff members. Training included didactics, supervised role playing, and observing several interviews by trained interviewers. Following established SLES procedures (Williamson et al., 2003), raters met as a group to establish consensus ratings of objective threat for each event. Objective threat was coded on a scale from 1 (little or no effect) to 4 (great effect) using the descriptors provided in the manual. The stress score was the sum of objective threat ratings for all events. In prior research, the SLES has shown good interrater reliability for coding objective threat, and discriminates between children with and without psychopathology (Williamson et al., 2003).
Internalizing disorders
Psychopathology was assessed with the K-SADS-PL (Kaufman et al., 1997), a widely used semistructured diagnostic interview designed to assess current and past episodes of psychopathology in children and adolescents according to DSM–IV-TR criteria. K-SADS interviews were conducted with the adolescent by postbachelors or masters-level staff who were trained and supervised by a team of clinical psychologists (GP, DK, RK). Training included didactics, supervised role playing, and observing several interviews by trained interviewers. Quality control was maintained through weekly supervision meetings for discussion and feedback and reliability of video-recorded interviews. Interviews with parents about cardinal symptoms of depression and anxiety in the child using the Family History Screen (Weissman et al., 2000) were also conducted. If parents described symptoms that their child did not report, interviewers clarified the discrepancies with the teens and revised K-SADS ratings. Depressive disorders were assessed at Waves 1, 2, and 3, whereas anxiety disorders were assessed at Waves 1 and 3. Analyses focus on major depression, dysthymia, any depressive disorder (including NOS), social anxiety disorder, specific phobia, generalized anxiety disorder, panic disorder, and any anxiety disorder (including NOS). An independent rater derived diagnoses from videotapes of 40 interviews to establish interrater reliability. Kappas for specific diagnoses ranged from .62 (depression NOS) to .91 (generalized anxiety disorder), with a median kappa of .79. The reliability of diagnoses of any depressive and any anxiety disorder were Kappas = .81 and .75, respectively.
The prevalence of each diagnosis at baseline and over the follow-up period, as well as the number of first-onset cases, is listed in Table 1. Subthreshold symptoms refer to significant symptoms of a specific disorder that fell short of meeting full criteria for that disorder and were not impairing enough to warrant an NOS diagnosis. Due to the diversity of clinical syndromes subsumed by anxiety disorders, when anxiety NOS diagnoses were assigned, interviewers rated which specific anxiety disorder they corresponded most closely to.
Prevalences at Baseline and Follow Up and Number of First Onsets of Each Disorder
Data Analyses
Analyses consisted of two parts. First, via a series of logistic regression models, diagnostic status for each disorder at Wave 3 was regressed on either self-criticism or dependency. In order to examine first onsets, within each logistic regression model any participants who had a history of that specific diagnosis at baseline were dropped. Thus, the logistic regressions compared the first onset group to the unaffected group, and the effective sample size differed for each disorder. In logistic regression models predicting MDD, dysthymia, and any depressive disorder (including depression NOS), participants with a prior diagnosis of depression NOS were excluded. In logistic analyses predicting each specific anxiety disorder, participants with a prior full or NOS diagnosis for the disorder examined in that model were excluded. In logistic analyses predicting any anxiety disorder (including cases with anxiety NOS), participants with any prior anxiety disorder, including any anxiety NOS diagnosis, were removed from the analysis. As anxiety NOS and depression NOS are not specific clinical syndromes, they were not included as outcomes on their own.
In all logistic regression analyses, baseline subthreshold status of the predicted disorder was included as a covariate. As noted earlier, this is a highly conservative approach, given that prior subthreshold disorders are a robust predictor of subsequent full threshold disorders (Burcusa & Iacono, 2007; Klein et al., 2009, 2013). Baseline personality traits were standardized (M = 0, SD = 1). Odds ratios with confidence intervals, as well as polychoric correlations are reported as measures of effect size. Of note, whereas predictors of first onsets of each disorder were examined separately, some participants experienced first onsets of multiple disorders. Specifically, 36 (7.0%) participants experienced the first onset of both a depressive and anxiety disorder, and 87 (16.4%) experienced the first onset of more than one anxiety disorder during the follow up.
Logistic regressions were repeated after covarying baseline Neuroticism in order to examine whether the effects of predictors showed incremental predictive utility over and above this higher order personality trait. Logistic regressions were again repeated after including both self-criticism and dependency in the same models in order to examine their incremental predictive utility relative to one another as well as any specificity in their effects on the first onsets of internalizing disorders.
The second set of analyses consisted of cross-lagged panel analyses in Mplus 7.3 (Muthén & Muthén, 1998–2012; Figure 1). Path models only examined depressive disorders, as these were assessed at all three time points, whereas anxiety disorders were only assessed at baseline and Wave 3, precluding adequate testing of many of the models described above. Rather than examining first onsets of depression, the path analyses predicted later depressive disorder diagnosis (including NOS) after covarying the effects of prior depressive disorder diagnosis. Path models used the full sample, and baseline depression NOS cases were treated as covariates. This was done so that path models could examine personality-depression relationships, such as consequences effects, yet still ensure that effects of personality, stress, and their interaction on later depressive disorders were not due to baseline depressive disorders. Path models predicting depressive disorders at Waves 2 and 3 examined the consequences (Path A), precursor (Path B), personality development (Path C), Personality × Stress (Path D), and stress-reactivity (Path E) conceptual models (see Figure 1). A path from Wave 2 depressive diagnoses to Wave 3 personality was included to test the consequences models, but not from Wave 1 depression, given that the only possible depression diagnosis at Wave 1 was depression NOS, which does not provide an adequate test of this conceptual model. Effects of baseline personality or depression NOS on Wave 2 stress (i.e., stress generation) were not included because stress was not measured at baseline, and we therefore could not adjust for its baseline levels. Wave 2 stress comprises events that occurred during the interval between Waves 1 and 2. Wave 1 personality, therefore, reflects personality prior to the stressors, whereas Wave 2 personality assesses traits following the stressors. Thus, our models examine the interaction of Wave 1 personality with stress occurring subsequent to the measurement of the personality trait. Moderation was examined via the Johnson-Neyman (JN) index, also known as a regions of significance test (Johnson & Neyman, 1936; Bauer & Curran, 2005). We were primarily interested in the effects of stress on depressive diagnoses at different levels of personality, but also examined the effects of personality at different levels of stress.
Figure 1. Conceptual overview of the analytic models linking personality, stress, and depressive disorders. (Path A) consequences; (Path B) precursor; (Path C) personality development; (Path D) Personality × Stress; (Path E) stress reactivity.
Results Descriptive Statistics and Bivariate Correlations
The prevalence of each disorder and the number of first onsets are shown in Table 1. Specific phobia had the largest, and dysthymia the fewest number of first onsets. Descriptive statistics and bivariate correlations between personality at Waves 1, 2, and 3 and stress at Wave 2 are shown in Table 2. Baseline self-criticism, dependency, and Neuroticism were positively associated with greater levels of stress at Wave 2, whereas Wave 2 stress was positively associated with self-criticism and dependency at Waves 2 and 3. Baseline Neuroticism was positively correlated with dependency and self-criticism at all three waves.
Descriptive Statistics and Bivariate Correlations
Predicting First Lifetime Onsets of Disorders
Results of logistic regression analyses regressing the first onsets of depressive and anxiety disorders on self-criticism and dependency appear in Table 3. Self-criticism significantly predicted the first onset of all disorders except panic and MDD. Dependency also significantly predicted the first onset of all disorders except major depression, and, at a trend level, social anxiety disorder.
Results of Baseline Personality Traits Predicting First Onsets of Disorders
Adjusting for Neuroticism (Table 4), self-criticism predicted the first onset of dysthymia, whereas Neuroticism was nonsignificant. Neuroticism predicted the first onsets of any anxiety disorder and any depressive disorder, whereas in both cases self-criticism exhibited nonsignificant trend-level effects. Neither trait uniquely predicted the first onset of social anxiety disorder, generalized anxiety disorder, panic disorder, specific phobia, or major depression.
Results of Baseline Personality Traits and Neuroticism Predicting First Onsets of Disorders
Adjusting for Neuroticism, dependency significantly predicted the first onset of generalized anxiety disorder, specific phobia, and any anxiety disorder. Neuroticism was significantly related to any anxiety disorder, major depression, and any depressive disorder, adjusting for dependency. Neither trait uniquely predicted social anxiety disorder, panic disorder, or dysthymia.
Adjusting for the effects of both dependency and self-criticism (Table 5), dependency, but not self-criticism, uniquely predicted the first onset of generalized anxiety disorder, specific phobia, and any anxiety disorder. Self-criticism, but not dependency, uniquely predicted the first onset of dysthymia and any depressive disorder.
Results of Dependency Versus Self-Criticism Predicting First Onsets of Disorders
Path Models
Path models simultaneously tested the various conceptual models discussed above and focused on the presence of any depressive disorder at Waves 2 and 3. Dependency and self-criticism were evaluated in separate path models as incorporating both traits at each wave as well their interactions with stress predicting psychopathology while adjusting for baseline Neuroticism would require a much larger sample size for adequate power. Both models adjusted for the effects of baseline Neuroticism on Wave 2 and 3 depressive disorders (Figure 2). Wave 1 and 2 variables were covaried within time points, but Wave 3 variables were not covaried as Mplus cannot estimate covariances between categorical and continuous dependent variables in the presence of missing data. In models with both continuous and categorical endogenous variables and missing data, Mplus employs a Monte Carlo integration algorithm which precludes use of theta parameterization. The default delta parameterization was therefore used.
Figure 2. Interrelationships between personality, stress, and depression. Transparent gray lines indicate nonsignificant paths. Covariances between endogenous variables not included for visual clarity, although Time 3 variables were not covaried. T1 = Time 1; T2 = Time 2; T3 = Time 3; OR = odds ratio. * p < .05. ** p < .01.
Self-criticism and depressive disorders
Wave 1 self-criticism predicted an increased likelihood of a depressive disorder at Wave 2 and Wave 2 self-criticism predicted an increased likelihood of a depressive disorder at Wave 3 (Figure 2A, top panel). Wave 2 life events predicted increased self-criticism at Wave 3 and a greater likelihood of a depressive disorder at Wave 3. There was no significant effect of depressive disorders at Wave 2 on Wave 3 self-criticism. Finally, there was a significant interaction between baseline self-criticism and Wave 2 life events predicting Wave 3 depressive disorders. Life events predicted an increased likelihood of a depressive disorder only when self-criticism was less than 0.3 standard deviations above the mean (Figure 3A, top panel). Results showed that greater life stress predicted an increased likelihood of a depressive disorder at the 10th (β = 1.20, p < .001), 25th (β = 1.01, p < .001), and 50th (β = .70, p < .001) percentiles of self-criticism, but not at the 75th (p = .24) or 90th (p = .55) percentiles. Examining stress as the moderator, greater levels of self-criticism predicted an increased likelihood of a depressive disorder only when stress was less than 0.35 standard deviations above the mean. Greater self-criticism predicted an increased likelihood of a depressive disorder at the 10th (β = .96, p = .003), 25th (β = .82, p < .005), and 50th (β = .53, p = .03) percentiles of life stress, but not at the 75th (p = .87) or 90th (p = .27) percentiles.
Figure 3. Effect of stress on the probability of a depressive disorder at varying levels of baseline self-criticism (Panel A) or dependency (Panel B). For both panels, only slopes at 10th, 25th, and 50th percentile levels are significant. Dep = dependency; SC = self-criticism.
Dependency and depressive disorders
Baseline dependency did not predict Wave 2 depressive diagnoses, although it did predict Wave 3 depressive diagnoses (Figure 2B, bottom panel). Wave 2 depressive disorders did not predict changes in Wave 3 dependency. Stressful life events predicted a greater likelihood of a depressive disorder at Wave 3, but did not predict dependency at Wave 3. Finally, there was a significant interaction between baseline dependency and Wave 2 life events predicting Wave 3 depressive disorders. The JN analysis showed that more life events predicted an increased likelihood of a depressive disorder only when dependency was less than 0.6 standard deviations above the group mean. Specifically, life events (Figure 3B, bottom panel) predicted an increased likelihood of a depressive disorder at the10th (β = .98, p < .001,) 25th (β = .81, p < .001); and 50th (β = .52, p < .001); but not at the 75th (p = .34); or 90th percentile (p = .71). Examining stress as the moderator, greater levels of dependency predicted an increased likelihood of a depressive disorder only when stress was less than 0.6 standard deviations above the mean. Greater dependency predicted an increased likelihood of a depressive disorder at the 10th (β = .69, p < .03) and 25th (β = .59, p = .03) percentiles, and showed a nonsignificant trend at the 50th (β = .38, p = .08) percentiles of life stress, but was not significant at the 75th (p = .89) or 90th (p = .35) percentiles.
DiscussionA series of logistic regression analyses revealed that the personality traits of self-criticism and dependency predict the first lifetime onset of a range of depressive and anxiety disorders over a period of 18 months in a sample of female adolescents. Moreover, a number of the effects, particularly for dependency, remained significant after adjusting for Neuroticism, which, in many cases, was not significant over and above self-criticism or dependency. Results suggest that self-criticism and dependency predict risk for the first onset of internalizing disorders in early female adolescents, thereby informing clinicians’ ability to identify and potentially intervene with young female adolescents prior to such onsets.
Second, path analyses that predicted depressive disorders at Wave 2 or 3 adjusting for prior depression tested a series of conceptual models that could account for these predictive effects. For both self-criticism and dependency, precursor, stress reactivity, and Personality × Stress paths predicted depressive diagnoses. The personality development model was supported for self-criticism, but not dependency, and there was no support for the consequences model in for either trait. Taken together, given that stress-reactivity and precursor effects were qualified by their interaction, results primarily support Personality × Stress models, in the form of dual vulnerability, as well as personality development in terms of the effects of stress on self-criticism.
Predicting First Onsets of Disorders
Rates of depressive and anxiety diagnoses were similar to those found in epidemiological surveys of adolescents (Merikangas et al., 2010), and the total cumulative rates of anxiety and depressive disorders by Wave 3 was similar to longitudinal community surveys (Moffitt et al., 2010), suggesting that the prevalence of internalizing disorders in the current sample is broadly comparable to other community samples. Results from logistic regression analyses showed that both self-criticism and dependency confer an increased risk for the first lifetime onset of most internalizing disorders, although neither significantly predicted major depression or panic disorder on its own. The stronger effects on dysthymia than major depression are consistent with evidence (Klein & Black, 2017; Kotov et al., 2010) that chronic depression is more strongly related to trait vulnerabilities, whereas acute major depression may be more strongly related to life stressors.
We then conducted analyses adjusted for Neuroticism, given concerns that self-criticism or dependency are so highly saturated with this broad trait that they may not have any unique effects (Coyne & Whiffen, 1995). Consistent with previous evidence of the incremental utility of these two personality traits (Smith et al., 2016; Zuroff et al., 2004), after adjusting for Neuroticism, self-criticism continued to predict the first onset of dysthymia and showed a nonsignificant trend toward predicting the first onset of any anxiety disorder and any depressive disorder, whereas dependency continued to predict the first onset of generalized anxiety disorder, specific phobia, and any anxiety disorder. The inclusion of Neuroticism in the logistic regression models eliminated the significant effects of self-criticism on first onsets of social anxiety disorder, generalized anxiety disorder, and specific phobia, and the significant effects of dependency on onsets of panic disorder, dysthymia, and any depressive disorder. Although Neuroticism independently predicted the onset of any anxiety and any depressive disorders, it did not uniquely predict the onset of any specific internalizing disorder other than major depression when self-criticism or dependency was included in the logistic regression model. Thus, despite its broader content, Neuroticism failed to account for additional variance in the onset of most specific internalizing disorders over and above the narrower traits of self-criticism and dependency. Although highly correlated predictors such as Neuroticism and self-criticism or dependency are subject to a degree of fungibility in multivariate analyses, findings suggest that dependency may be a unique predictor of the onset of anxiety disorders, over and above Neuroticism, whereas self-criticism may contribute unique variance in predicting the onset of dysthymia.
It is possible that much of the shared variance between Neuroticism, self-criticism, and dependency is due to each being characterized by emotional dysregulation and tendencies toward negative affect (see Zuroff, 1994; Zuroff et al., 2004). However, although Neuroticism is defined largely in terms of affective tendencies, self-criticism also measures one’s sense of self, personal standards, and expectations of others, and dependency assesses one’s sense and expectations of relationships with close others. As such, it is possible that these aspects of self-criticism and dependency influenced risk for internalizing psychopathology over and above Neuroticism in the current study.
When including self-criticism and dependency in the same models, the results appeared to bear out this relative specificity of dependency for risk of anxiety disorders onset and self-criticism for risk of depressive disorders onset. Indeed, dependency uniquely predicted the first onset of generalized anxiety disorder, specific phobia, and any anxiety disorder, whereas self-criticism uniquely predicted the first onset of dysthymia and any depressive disorder. However, given that there were broader transdiagnostic effects of each personality trait when considered individually and when not adjusting for Neuroticism, future research should further elucidate the shared and unique predictive utility of different personality traits for the first onset of internalizing disorders.
Elucidating the Personality-Life Stress-Psychopathology Relationship
The prospective links between traits, stressful life events, and depressive disorders were examined over 18 months. The conceptual models reflected in these paths are typically tested individually rather than within the same analytic framework, which can lead to a biased or incomplete understanding of dynamic associations among key variables. It is important to note, however, that the path models in this paper were not predicting first onsets of depressive disorders. Rather, they were predicting later diagnostic status after adjusting for the effects of prior diagnostic status.
There was consistent support for the precursor model of personality-depression relationships. That is, while controlling for Neuroticism and prior history of depression NOS at Wave 1, both self-criticism and dependency predicted subsequent depressive diagnoses. Consistent with a large body of literature showing an effect of stress on depression (e.g., Monroe, Slavich, & Georgiades, 2014), there was also support for stress-reactivity models. However, both of these effects were qualified by the interaction between stress and personality, such that stress only predicted depressive disorders at lower levels of self-criticism or dependency. Alternatively, these effects may be interpreted as there being a greater effect of personality on depressive diagnoses at lower levels of stress.
Consistent with a variety of prior developmental studies (see Kopala-Sibley & Zuroff, 2014; Neyer & Asendorpf, 2001; Ormel et al., 2001; Scollon & Diener, 2006), stress predicted change in personality traits, but only for self-criticism. This suggests that high levels of stressful life events in early adolescence may compound this personality-level risk factor for psychopathology. However, contrary to prior work (e.g., Kopala-Sibley et al., 2012, 2015; Kopala-Sibley, Zuroff, Hermanto, & Joyal-Desmarais, 2016; Soenens, Vansteenkiste, & Luyten, 2010), effects of stress on dependency were not found. The reasons for this are unclear. It is possible that only events pertaining to specific life domains may influence dependency, especially relationship-centered stressors (Kopala-Sibley, Zuroff, Leybman, & Hope, 2012; Kopala-Sibley, Zuroff, Hermanto, & Joyal-Desmarais, 2016; Soenens et al., 2010). Moreover, it is possible that stressors may be related to specific aspects of dependency, such as connectedness, which is a more adaptive form, versus neediness, which is more maladaptive (see Rude & Burnham, 1995).
In contrast to the findings for the other conceptual models, results did not support the consequences model, as depression did not predict subsequent self-criticism or dependency. These results are consistent with some research indicating that personality traits are not increased following a depressive episode (e.g., Ormel et al., 2004; Shea et al., 1996), although inconsistent with other work that has found such an effect (Fanous et al., 2007; Rohde et al., 1990, 1994). The reason for these contradictory findings is unclear.
Path models for both self-criticism and dependency revealed significant interactions between personality traits and stressful life events in predicting subsequent depressive disorders. Most studies of personality by stress interactions conceptualize them from a diathesis-stress perspective (e.g., Brown & Rosellini, 2011; Kendler, Kuhn, & Prescott, 2004; Kopala-Sibley, Kotov, et al., 2016). However, the present findings provide strong support for the dual-vulnerability or social push model instead (Kushner, 2015; Morris et al., 2008). That is, individuals with highly elevated levels of self-criticism or dependency showed an increased likelihood of a subsequent depressive disorder, regardless of the level of life stressors they experienced. This would be consistent with the precursor model, albeit only for the subset of adolescents with elevated trait vulnerabilities. In contrast, youth with lower levels of self-criticism or dependency exhibited higher rates of internalizing disorders only when subjected to a high level of stressful life events. Thus, stress reactivity is an appropriate way to understand the relationship between life events and internalizing psychopathology for those lower in self-criticism or dependency.
Finally, results should be interpreted in a developmental context, as participants in this study underwent substantial changes in socioemotional and personality development. Consistent with other studies examining self-criticism and dependency in adolescence (Kopala-Sibley et al., 2015; Thompson, Zuroff, & Hindi, 2012), as well as broader personality traits such as the Big Five (Roberts & DellVecchio, 2000), self-criticism and dependency showed only moderate stabilities over time, suggesting that these traits are more fluid in adolescence relative to adulthood. As noted by Blatt (e.g., Blatt & Luyten, 2009), early adolescence is a key period for the development of self-definition and relatedness. Whereas all adolescents deal with individuation and new forms of relatedness, females may be in a particularly unique developmental context as friendships and romantic relationships take on especially important roles (Blatt & Luyten, 2009). More highly dependent or self-critical female teens appear to be at risk for depression regardless of these stressors, which appear to play a particularly important role in depression for less dependent or self-critical female adolescents. For early adolescents with lower levels of these personality traits, who are also navigating stressful life events that are new or assume greater importance than before, life stressors appear to increase risk for depressive disorders even in the absence of personality-level vulnerabilities.
Clinical ImplicationsResults suggest that practitioners should be cognizant of levels of self-criticism or dependency in female adolescents as these appear to increase risk for internalizing psychopathology, although there are multiple other risk factors to consider as well. Youth may benefit from interventions designed to directly reduce levels of dependency or self-criticism, such as self-compassion-based psychotherapy (Gilbert, 2009; Kelly, Zuroff, & Shapira, 2009). On the contrary, for those lower in dependency or self-criticism, interventions may seek to bolster individuals’ capacity to cope with stress (e.g., social skills training, interpersonal psychotherapy, cognitive-behavioral psychotherapy). Given interactions between these traits and life events, distinct interventions may be beneficial for adolescents who have elevated compared with low levels of these traits but are experiencing high levels of life stress. Further follow-up waves are required to test these models pertaining to anxiety disorders; it is unclear if the same conclusions would apply to anxiety-related psychopathology.
Limitations and Future Directions
Although this study had some notable strengths, including a large sample assessed at three waves over 18 months, as well as the use of semistructured interviews to establish diagnoses and to assess stressful life events, several limitations should be acknowledged. First, female adolescents were enrolled in order to maximize first onsets of depressive disorders. Adolescence is the beginning of the peak risk period for the onset of depression, with rates increasing more rapidly among females than males (e.g., Hankin et al., 1998). However, the present results may not extend to males or to other age groups, such as children and adults. Relatedly, although the sample was representative of the socioeconomic makeup of the geographical region in which this study was conducted, it was somewhat greater in terms of education and income than the national average and had a larger proportion of Caucasians. It is therefore unclear whether results would generalize to other socioeconomic, ethnic, or racial groups.
Second, the number of first onsets of some disorders, such as dysthymia and panic, were small, reducing power to detect disorder-specific effects. Third, the current study examined total stressful life events, so it is unknown whether results would extend to specific types of events (e.g., dependent and independent, or interpersonal and achievement). Fourth, there was primarily a single informant for all measures, which may raise concerns about inflated correlations due to method variance. This may have also resulted in some missing information, especially regarding stress. However, it should be noted that parents were also interviewed about youth’s psychopathology, diminishing concerns about biases or errors in diagnoses.
Finally, to fully elucidate the nature of prospective associations between variables, it is necessary to include all measures at all time points in the analytic model (Maxwell & Cole, 2007). As anxiety diagnoses were assessed only at baseline and Wave 3, they were not examined in path models. In addition, in the depression path models, stress was not measured at baseline precluding examining of the stress-generation conceptual model (Hammen, 2006). Moreover, consequences effects from Wave 1 depressive status to Wave 2 personality were not examined because only depression NOS cases were included at baseline, thereby precluding a proper test of this path.
Conclusion
The personality traits of self-criticism and dependency predicted the first onsets of a range of internalizing disorders in a sample of young female adolescents, a group that is particularly vulnerable to internalizing psychopathology. In addition, path models testing a variety of relationships between traits, life events and depression consistently supported Personality × Stress models, in the form of dual vulnerability. Thus, for the subgroup of participants with elevated self-criticism or dependency, traits appeared to be a precursor of depression, whereas for the subgroup with lower levels of trait vulnerability, depression was explained by stress-reactivity. This suggests that researchers and clinicians should consider personality by stress interactions in understanding depressive disorders, and that these interactions may take alternative forms than the classic diathesis-stress formulation. More broadly, the present findings indicate that self-criticism and dependency predict risk for a range of internalizing disorders in female adolescents, and suggest that a variety of therapeutic strategies may be useful for these vulnerable youth.
Footnotes 1 However, if a path from personality or psychopathology to stress is included, baseline self-criticism, dependency, and depression NOS predict greater levels of stress, adjusting for Neuroticism. Other results are unchanged if paths from baseline personality and depression to stress are included.
2 Given concerns of normative developmental effects, analyses were repeated after including age at each wave in the model, as well as the interaction of age with each variable at each wave predicting outcomes at the subsequent wave. None of these effects were significant, and the pattern of results reported here was unchanged. Age was therefore dropped from our final models.
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Submitted: August 26, 2016 Revised: June 17, 2017 Accepted: June 20, 2017
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Source: Journal of Abnormal Psychology. Vol. 126. (8), Nov, 2017 pp. 1029-1043)
Accession Number: 2017-51268-002
Digital Object Identifier: 10.1037/abn0000297
Record: 145- Title:
- Self-harm and suicidal behavior in borderline personality disorder with and without bulimia nervosa.
- Authors:
- Reas, Deborah L.. Department of Eating Disorders, Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway, deborah.lynn.reas@ous-hf.no
Pedersen, Geir. Department of Personality Psychiatry, Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
Karterud, Sigmund. Institute for Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway
Rø, Øyvind. Regional Department of Eating Disorders, Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway - Address:
- Reas, Deborah L., Regional Department of Eating Disorders (RASP), Division of Mental Health and Addiction, Oslo University Hospital–Ullevål Hospital, P.O. Box 4956 Nydalen, N-0424, Oslo, Norway, deborah.lynn.reas@ous-hf.no
- Source:
- Journal of Consulting and Clinical Psychology, Vol 83(3), Jun, 2015. pp. 643-648.
- NLM Title Abbreviation:
- J Consult Clin Psychol
- Page Count:
- 6
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- Journal of Consulting Psychology
- Other Publishers:
- US : American Association for Applied Psychology
US : Dentan Printing Company
US : Science Press Printing Company - ISSN:
- 0022-006X (Print)
1939-2117 (Electronic) - Language:
- English
- Keywords:
- suicide, self-harm, Bulimia nervosa, borderline personality disorder
- Abstract (English):
- Objective: Few studies have investigated whether a diagnosis of Bulimia nervosa (BN) confers additional risk of life-threatening behaviors such as self-harm and suicidal behavior in borderline personality disorder (BPD). Method: Participants were 483 treatment-seeking women diagnosed with BPD according to the Structured Clinical Interview for DSM–IV Axis II Personality Disorders (SCID-II; First, Gibbon, Spitzer, Williams, & Benjamin, 1997; Diagnostic and Statistical Manual of Mental Disorders, 4th ed.; APA, 1994) and admitted to the Norwegian Network of Psychotherapeutic Day Hospitals between 1996 and 2009. Of these, 57 (11.8%) women met DSM–IV diagnostic criteria for BN according to the Mini-International Neuropsychiatric Interview (M.I.N.I.; Sheehan et al., 1998) and they were compared with women with BPD and other Axis I disorders. Results: We found that comorbid BN is uniquely and significantly associated with increased risk of suicidal behavior among women being treated for BPD. Findings underscore the importance of routinely screening for BN among women seeking treatment for BPD, as co-occurring bulimia appears to be a significant marker for immediate life-threatening behaviors in this already high-risk population, which is a significant public health issue. A significantly greater proportion of women with BPD-BN reported suicidal ideation at intake (past 7 days), engaged in self-harm behavior during treatment, and attempted suicide during treatment. All bivariate associations remained significant in the logistic regression models after controlling for mood, anxiety, and substance-related disorders. Conclusion: The presence of a concurrent diagnosis of BN among women with BPD is significantly and uniquely associated with recent suicidal ideation, and self-harm behavior and suicide attempts during treatment after controlling for major classes of mental disorders. Co-occurring BN appears to represent a significant marker for immediate life-threatening behaviors in women seeking treatment for BPD. Extra vigilance and careful monitoring of suicidal behavior during treatment is important for these individuals, and routine screening for BN is warranted. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
- Impact Statement:
- What is the public health significance of this article?—This study found that co-occurring bulimia nervosa is uniquely and significantly associated with increased risk of suicidal behavior among women being treated for borderline personality disorder. Findings underscore the importance of routinely screening for bulimia nervosa among women seeking treatment for borderline personality disorder, as co-occurring bulimia appears to be a significant marker for immediate life-threatening behaviors in this already high-risk population. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Attempted Suicide; *Borderline Personality Disorder; *Bulimia; *Self-Injurious Behavior; *Suicidal Ideation; Comorbidity; Risk Factors
- Medical Subject Headings (MeSH):
- Adolescent; Adult; Aged; Borderline Personality Disorder; Bulimia Nervosa; Female; Humans; Middle Aged; Self-Injurious Behavior; Suicidal Ideation; Suicide, Attempted; Young Adult
- PsycINFO Classification:
- Psychological Disorders (3210)
- Population:
- Human
Female
Outpatient - Location:
- Norway
- Age Group:
- Adulthood (18 yrs & older)
Young Adulthood (18-29 yrs)
Thirties (30-39 yrs)
Middle Age (40-64 yrs)
Aged (65 yrs & older) - Tests & Measures:
- Structured Clinical Interview for DSM-IV Axis II Personality Disorders
Mini International Neuropsychiatric Interview DOI: 10.1037/t18597-000 - Methodology:
- Empirical Study; Interview; Quantitative Study
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- First Posted: Dec 15, 2014; Accepted: Oct 31, 2014; Revised: Oct 29, 2014; First Submitted: Jun 6, 2014
- Release Date:
- 20141215
- Correction Date:
- 20160512
- Copyright:
- American Psychological Association. 2014
- Digital Object Identifier:
- http://dx.doi.org/10.1037/ccp0000014
- PMID:
- 25495360
- Accession Number:
- 2014-55558-001
- Number of Citations in Source:
- 34
- Persistent link to this record (Permalink):
- http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2014-55558-001&site=ehost-live
- Cut and Paste:
- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2014-55558-001&site=ehost-live">Self-harm and suicidal behavior in borderline personality disorder with and without bulimia nervosa.</A>
- Database:
- PsycINFO
Self-Harm and Suicidal Behavior in Borderline Personality Disorder With and Without Bulimia Nervosa / BRIEF REPORT
By: Deborah L. Reas
Regional Department of Eating Disorders, Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway;
Geir Pedersen
Department of Personality Psychiatry, Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway, and Institute for Clinical Medicine, Faculty of Medicine, University of Oslo
Sigmund Karterud
Institute for Clinical Medicine, Faculty of Medicine, University of Oslo and Department of Research and Development, Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
Øyvind Rø
Regional Department of Eating Disorders, Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway and Institute for Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway
Acknowledgement:
Borderline personality disorder (BPD) is characterized by a pervasive pattern of instability in interpersonal relationships, self-image, and affect (APA, 2013) and is marked by impulsivity and recurrent suicidal and self-mutilating behavior. Suicidal behavior and self-harm also occur among patients with Bulimia nervosa (BN; Corcos et al., 2002; Crow et al., 2009; Franko & Keel, 2006; Preti, Rocchi, Sisti, Camboni, & Miotto, 2011) and a robust and differential pattern of comorbidity has been found between BPD and BN in clinical samples of mixed personality disorder (PD; Reas, Rø, Karterud, Hummelen, & Pedersen, 2013). Significant associations have also been found between purging behavior, personality traits, and self-injury or suicidality in women with eating disorders (ED) (Paul, Schroeter, Dahme, & Nutzinger, 2002) and adolescent psychiatric inpatients (Zaitsoff & Grilo, 2010), consistent with clinical observations linking BN with impulsivity and dysregulation.
The extent to which BN in the presence of BPD might confer additional risk of suicidality and self-injury is unclear. Several studies have investigated the predictive validity of comorbidity associated with suicide risk among individuals with PD, yet these have focused largely on risk attributable to major depression, substance abuse, or anxiety disorders (Soloff, Lynch, Kelly, Malone, & Mann, 2000; Wedig et al., 2012; Yen et al., 2003), with comparatively scarce attention on the independent predictive utility of BN. To our knowledge, only two controlled investigations have specifically addressed the incremental prognostic validity of BN in samples of BPD sufferers (Chen, Brown, Harned, & Linehan, 2009; Dulit, Fyer, Leon, Brodsky, & Frances, 1994). An investigation by Dulit et al. (1994) found that BPD inpatients (N = 124) with comorbid BN were four times more likely to engage in repeated self-injury (≥5 lifetime acts) than BPD inpatients without BN. An investigation of 135 women with BPD observed a significant association between BN and recurrent suicide attempts (≥2 acts), but not other self-injury, after controlling for age and other non-ED Axis I disorders (Chen et al., 2009). A later study by Chen et al. (2011) found equivalent rates of lifetime suicide attempts and self-injury among 166 women and 166 men with and without ED in a diagnostically heterogeneous sample of PD.
Evidence on a cross-sectional level supporting the incremental validity of BN in predicting suicidality in this high-risk population would underscore the importance of screening for BN in treatment settings for BPD. Such an approach is also consistent with recommendations that future researchers consider the effects of comorbidity when elucidating predictive effects of mental disorders on suicide attempts (Nock, Hwang, Sampson, & Kessler, 2010). To be of greatest benefit, further investigations should address previously identified methodological limitations, including (a) the merging of highly select samples from trials recruiting or screening specifically for suicidal BPD or substance-dependent BPD, which may limit ecological or clinical representativeness, and (b) small and/or diagnostically heterogeneous samples which may render effect sizes diminished due to low power. The present study aimed to investigate whether comorbidity-independent associations exist between BN and self-harm, suicidal ideation, and suicide attempts among a naturalistic, treatment-seeking sample of women with BPD.
Method Participants
Participants included 483 women diagnosed with BPD, aged 18–65 years, with an initial admission between 1996 and 2009 to the Norwegian Network of Personality-Focused Treatment Programs. Established in 1992, this is a clinical research network providing mostly long-term, group-based (or concurrent individual–group) treatment (see also Karterud et al., 2003; Reas et al., 2013). All treatment units used uniform and standardized assessment procedures (Pedersen, Karterud, Hummelen, & Wilberg, 2013) following the longitudinal, expert, all-data (LEAD) standard, which is a comprehensive, integrative diagnostic approach using multiple sources of information (e.g., interview data, informants, behavioral observations, and medical records). Data collection is overseen by a central coordinating site responsible for quality assurance, standardization of routines, and screening data for irregularities and missing data. Raters all held professional degrees and skill acquisition, and maintenance included training courses and supervision. Ongoing monitoring of protocol adherence included checklists and periodic site visits (up to 3–4 times annually) and in addition, site coordinators from all 16 units met every 6 months to discuss and calibrate diagnostic and clinic procedures.
Materials and Procedure
Patients were interviewed with the Structured Clinical Interview for DSM–IV Axis II Personality Disorders (SCID-II; First, Gibbon, Spitzer, Williams, & Benjamin, 1997), a well-established diagnostic tool with demonstrated reliability for the assessment of PD (Lobbestael, Leurgans, & Arntz, 2011). Interrater reliability for the Norwegian version of the SCID-II has been established (κ = .66 for BPD; Kvarstein et al., 2014). The Mini-International Neuropsychiatric Interview Version 4.4 (M.I.N.I.; Sheehan et al., 1998) was used to establish Axis I diagnoses, which has demonstrated reliability and validity for the assessment of Axis I disorders, including BN (κ = .78; Sheehan et al., 1998). The Norwegian version of the M.I.N.I is validated and is considered a time-efficient and feasible alternative to the SCID-P (SCID-I/P; First et al., 2002) and CIDI (CIDI; Kessler & Ustün, 2004) (Mordal, Gundersen, & Bramness, 2010).
Assessment of Suicidal Behavior
The assessment of suicidality included clinician- and self-reported data capturing different time epochs. First, self-reported data were systematically collected at intake using a routinely administered sociodemographic questionnaire. Suicidal ideation was assessed dichotomously to capture the past 7 days and past 12 months, that is, “Have you had thoughts about taking your own life?” Self-harm behavior was assessed dichotomously to capture the past 12 months and lifetime, that is, “Have you [ever] physically hurt yourself, for example, cutting, scratching, burning, head-banging, and so forth?” Suicide attempts were assessed dichotomously to capture the past 12 months and lifetime, that is, “Have you ever tried to kill yourself?” The number of lifetime suicide attempts was also rated. Clinician-rated data were systematically collected at discharge using a routine discharge form to assess (a) the occurrence of self-harm behavior during treatment, that is, physically harmful behaviors such as cutting, scratching, burning, banging against hard objects, and so forth; (b) suicidal ideation during treatment, that is, “Did the patient express thoughts about taking one’s life, excluding very fleeting or dramatic expressions regarding the wish to die?” and (c) suicide attempts occurring during treatment, that is, “lethal acts with the intent to die that would have been successful without acute intervention from others.” It should be noted that assessment of self-harm behavior did not specify expectations (e.g., to gain relief from negative feelings, relieve suffering, resolve interpersonal difficulties) as specified in DSM-5 (APA, 2013) for nonsuicidal self-injury; thus, we use the more general term of self-harm. Bulimic behaviors, sometimes conceptualized under the rubric of self-harm, were not covered by assessment. All data were registered in a central, anonymous database administered by Oslo University Hospital. All patients provided written consent and the study was approved by the State Data Inspectorate and the Regional Ethics Committee.
Data Analyses
Analyses were conducted using predictive analysis software (PASW) Version 18.0. Patients were grouped according to the presence of BN (BPD-BN) or non-ED Axis I disorder (BPD-other), in line with grouping methods by Chen et al. (2011). Cases of anorexia nervosa (AN) (N = 7) and eating disorders not otherwise specified (EDNOS) (N = 81) were excluded from BPD-other due to potential confounding of subthreshold BN in the EDNOS group, or lifetime history of BN, because diagnostic fluctuation or crossover is common in DSM-IV ED (Peterson et al., 2011). Chi-square analyses tested differences for categorical variables. Consistent with methods from earlier studies (Bodell, Joiner, & Keel, 2013), logistic regression analyses (ORs and 95% CIs) controlled for mood, anxiety, and substance-use disorders were conducted when significant bivariate associations were detected. Following guidelines detailed in previous research (Chen, Cohen, & Chen, 2010), ORs of 1.68, 3.47, and 6.71 were considered equivalent to small, medium, and large effect sizes (Cohen’s d = 0.2, 0.5, and 0.8, respectively). Analyses were two-tailed (p < .05).
Results Sample Characteristics
A total of 57 patients (11.8%) received a comorbid diagnosis of BN and BPD and were grouped as BPD-BN. All other patients (N = 426) were diagnosed with at least one non-ED Axis I diagnosis (BPD-other). Approximately 68% of both groups had mood disorders; 63.2% versus 52.6% (p = .123) were diagnosed with anxiety disorder; and 28.1% versus 15.1% (p = .014) had substance-use disorder. Table 1 shows no significant baseline differences for age, mean global assessment of functioning (GAF) at intake, length of treatment, mean frequency of non-ED Axis I or Axis II disorders, and number of SCID-II BPD criteria fulfilled. No differences were detected for marital status, χ2(4, 459) = 2.97, p = .562, or work situation, χ2(5, 421) = 4.91, p = .427.
Sample Characteristics for BPD-BN Versus BPD-Other (N = 483)
Clinician and Self-Reported Self-Harm, Suicidal Ideation, and Suicide Attempts
As shown in Table 1, a significantly greater proportion of women in the BPD-BN group demonstrated self-harm behavior during treatment (p < .001). Approximately 50% of both groups engaged in self-harm behavior over the past 12 months. Lifetime self-harm did not show a statistically significant difference between BPD-BN and BPD-other (70.9% vs. 58.2%; p = .079). A significantly higher proportion of BPD-BN reported suicidal ideation during the past 7 days prior to intake (p = .012), but differences in suicidal ideation (past 12 months) were not significant (88.4% vs. 77.4%, p = .101). A trend was detected for greater suicidal ideation during treatment in BPD-BN (p = .058).
A significantly higher proportion of women with BPD-BN attempted suicide during treatment (p = .029). No significant differences were found for suicide attempts during the past 12 months (31.8% vs. 22.8%, p = .192), lifetime, or recurrent suicide attempts (two or more acts). For suicide attempters, mean (SD) number of lifetime attempts was 3.0 (2.24) for BPD-BN versus 2.80 (2.68) for BPD-other, respectively, t(255) = .401, p = .688.
Logistic Regression
Table 2 shows significant associations between BN and self-harm during treatment OR = 3.23, 95% CI [1.76–5.92], suicidal ideation past 7 days, OR = 2.37, 95% CI [1.23–4.55]; and suicide attempts during treatment OR = 2.83; 95% CI [1.05–7.64]. An OR of 1.73, 95% CI [.987–3.04], p = .055 was detected for suicidal ideation during treatment, indicating a small effect.
Logistic Regression Models for the Association Between BN and Self-Harm, Suicidal Ideation, and Suicide Attempts Among Women With BPD Controlling for Mood, Anxiety, and Substance-Use Disorders
DiscussionThese findings indicate that the presence of a comorbid diagnosis of BN in the context of BPD is significantly and uniquely associated with increased risk of recent suicidal ideation at intake and self-harm and suicide attempts during treatment after controlling for mood, anxiety, and substance-related disorders. As such, a concurrent diagnosis of BN among women seeking treatment for BPD appears to represent a strong and significant marker for immediate life-threatening behaviors. Death by suicide occurs in 8–10% of individuals with BPD, which is among the highest rates of all mental disorders (Pompili, Girardi, Ruberto, & Tatarelli, 2005). Our study has indicated that bulimic episodes signal even greater risk of suicide attempts within this high-risk population. Findings echo those by Bodell et al. (2013), who found comorbidity-independent associations between BN and lifetime suicidality among university women, prompting calls for a standard risk assessment of suicide among women with BN.
The sample size and setting were considered study strengths, that is, over 400 consecutively admitted female patients seeking day treatment for BPD. No significant differences were observed in length of admission (i.e., approximately 4.5 months) or length of referral process; thus, these variables were not considered potential confounds. Data collection was part of routine clinic procedure, and although risk of bias cannot be eliminated, only minimal bias owing to clinician expectations regarding the present study aims were expected to influence results.
Several limitations of our study are important to consider. Despite face validity, the reliance on direct, single-item assessments limited the scope of the measurement. Information regarding lethality, intent, or specific methods of self-harm was unavailable, and behaviors such as aborted, interrupted, and low-lethality attempts might have not been captured. Prior research, however, has used single-item, self-report assessments of suicidal ideation and attempt with demonstrated validity (Bodell et al., 2013; Cougle, Keough, Riccardi, & Sachs-Ericsson, 2009). Retrospective self-report data on the occurrence of self-harm and suicidal behavior might be subject to recall bias. This study design was cross-sectional, thereby precluding the ability to infer stability of findings and the longitudinal risk or other clinical outcomes (e.g., suicide completion). Self-harm behavior, suicidal ideation, and past suicide attempts have been documented as important risk factors for future suicide (Klonsky, May, & Glenn, 2013), also in samples with BPD (Wedig et al., 2012). The overall rate of lifetime attempts in our sample was 59%, which is higher than typically observed in BN (25–35%; Franko & Keel, 2006), although within the 40–85% range observed for BPD (Oumaya et al., 2008). Our sample included treatment-seeking women admitted for intensive and specialized day-hospital treatment for PD; thus, results may not generalize to individuals under 18 or over 65 years of age, or to community or nonspecialist treatment settings which serve less severe populations.
Because the pattern of findings indicated significantly elevated risk of suicidality at intake and during treatment, but not prior to treatment, replication is necessary to rule out potential state effects underlying results. Further investigation is needed to explore whether findings relate to appropriateness of therapeutic setting or treatment approach, for example, or whether suicidality might constitute a particularly salient motivator for treatment-seeking among women with BPD-BN. Several alternative classification schemes for subtyping BN according to patterned heterogeneity in comorbidity (i.e., multi-impulsive BN, borderline–nonborderline BN, undercontrolled–externalizing; see Wildes & Marcus, 2013) may have relevance for the contextualization and conceptualization of findings, or our results may speak broadly to cross-cutting behavioral and neurobiologically informed constructs such as trait impulsivity or cognitive control (Insel, 2014).
This was a controlled investigation providing evidence on a cross-sectional level supporting the incremental validity of BN in predicting suicidality beyond mood, anxiety, and substance-related disorders in the high-risk BPD population. Findings warrant routine assessment of BN in treatment-seeking samples of women with BPD, and underscore the importance of high vigilance and fastidious monitoring of suicidal behaviors during treatment for these individuals.
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Submitted: June 6, 2014 Revised: October 29, 2014 Accepted: October 31, 2014
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Source: Journal of Consulting and Clinical Psychology. Vol. 83. (3), Jun, 2015 pp. 643-648)
Accession Number: 2014-55558-001
Digital Object Identifier: 10.1037/ccp0000014
Record: 146- Title:
- Self-injury, substance use, and associated risk factors in a multi-campus probability sample of college students.
- Authors:
- Serras, Alisha. Department of Psychology, Eastern Michigan University, MI, US, aserras@emich.edu
Saules, Karen K.. Department of Psychology, Eastern Michigan University, MI, US
Cranford, James A.. Department of Psychiatry, University of Michigan, MI, US
Eisenberg, Daniel. School of Public Health, University of Michigan, MI, US - Address:
- Serras, Alisha, EMU Psychology Clinic, 611 W. Cross Street, Ypsilanti, MI, US, 48197, aserras@emich.edu
- Source:
- Psychology of Addictive Behaviors, Vol 24(1), Mar, 2010. pp. 119-128.
- NLM Title Abbreviation:
- Psychol Addict Behav
- Page Count:
- 10
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- Bulletin of the Society of Psychologists in Addictive Behaviors; Bulletin of the Society of Psychologists in Substance Abuse
- Other Publishers:
- US : Educational Publishing Foundation
Society of Psychologists in Addictive Behaviors - ISSN:
- 0893-164X (Print)
1939-1501 (Electronic) - Language:
- English
- Keywords:
- co-occurrence, college students, mental health, self-injury, substance use, risk factors
- Abstract:
- This research examined two questions: (1) What is the prevalence of self-injurious behavior (SIB) among college students, overall and by gender, academic level, and sexual orientation? (2) To what extent is SIB associated with different forms of substance use and other risk behaviors? A probability sample of 5,689 students completed an Internet survey on self-injury, mental health, and substance use. Past-year prevalence of SIB was 14.3%, with undergraduates significantly more likely than graduate students to engage in SIB. Drug use and frequent binge drinking were associated with higher rates of SIB. Among those who engaged in any SIB, those who used drugs had higher depression scores, higher prevalence of cigarette smoking, and higher rates of binge eating. In a multiple logistic regression model predicting SIB, depression, cigarette smoking, gambling, and drug use were significant predictors. Information about those at risk for SIB is critical for the design of prevention and intervention efforts as colleges continue to grapple with risky behaviors. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Drug Usage; *Mental Health; *Risk Factors; *Self-Injurious Behavior; College Students; Comorbidity
- Medical Subject Headings (MeSH):
- Female; Humans; Male; Risk Factors; Sampling Studies; Self-Injurious Behavior; Students; Substance-Related Disorders; Universities; Young Adult
- PsycINFO Classification:
- Psychological & Physical Disorders (3200)
- Population:
- Human
Male
Female - Location:
- US
- Age Group:
- Adulthood (18 yrs & older)
- Tests & Measures:
- Patient Health Questionnaire DOI: 10.1037/t02598-000
- Grant Sponsorship:
- Sponsor: University of Michigan Depression Center, US
Recipients: No recipient indicated
Sponsor: Penn State, Children, Youth, and Families Consortium, US
Recipients: No recipient indicated
Sponsor: Eastern Michigan University, Department of Psychology, Graduate School, US
Recipients: Serras, Alisha - Conference:
- American Psychological Association Convention, Aug, 2009, Toronto, Canada
- Conference Notes:
- Data presented in this manuscript were included in a preliminary report presented at the aforementioned conference.
- Methodology:
- Empirical Study; Quantitative Study
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- Accepted: Jul 14, 2009; Revised: Jul 13, 2009; First Submitted: Mar 5, 2009
- Release Date:
- 20100322
- Copyright:
- American Psychological Association. 2010
- Digital Object Identifier:
- http://dx.doi.org/10.1037/a0017210
- PMID:
- 20307119
- Accession Number:
- 2010-05354-013
- Number of Citations in Source:
- 69
- Persistent link to this record (Permalink):
- http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2010-05354-013&site=ehost-live
- Cut and Paste:
- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2010-05354-013&site=ehost-live">Self-injury, substance use, and associated risk factors in a multi-campus probability sample of college students.</A>
- Database:
- PsycINFO
Self-Injury, Substance Use, and Associated Risk Factors in a Multi-Campus Probability Sample of College Students
By: Alisha Serras
Department of Psychology, Eastern Michigan University;
Karen K. Saules
Department of Psychology, Eastern Michigan University
James A. Cranford
Department of Psychiatry, University of Michigan
Daniel Eisenberg
School of Public Health, University of Michigan
Acknowledgement: The data collection for this study was supported by the University of Michigan Depression Center; the Penn State Children, Youth, and Families Consortium; and the universities participating in the study. The first author was supported by the Eastern Michigan University Department of Psychology and the EMU Graduate School. Data presented in this manuscript were included in a preliminary report presented at the 2009 American Psychological Association Convention, August 2009, Toronto.
Self-injury, defined as the infliction of physical harm to one’s body without suicidal intent (Simeon & Favazza, 2001), has become a dangerous trend on college campuses (Whitlock, Lader, & Conterio, 2006). Types of self-injury include, but are not limited to, the following: cutting oneself, burning oneself, scratching oneself, pulling one’s hair, biting oneself, interference with one’s wound healing, carving into one’s skin, rubbing sharp objects into one’s skin, and punching object(s) to inflict bodily self-harm (Gratz, 2001). Despite the growing interest in self-injury, assessment tools to measure self-injurious behavior (SIB) remain in their infancy (Gratz, 2001; Whitlock & Knox, 2007). Historically, research on SIB has focused on borderline personality disorder and developmental disorders. More recently, SIB has begun to manifest in subclinical or nonclinical populations, including college students (Whitlock, Muehlenkamp, & Eckenrode, 2008). Several reasons to be specifically concerned about SIB are increased morbidity because of medical complications, infections, and scarring (Plante, 2006); deficits in emotional regulation (e.g. Klonsky, 2009; Hasking, Momeni, Swannell, & Chia, 2008; Ross, Heath, & Toste, 2009); and the increased risk of suicide (e.g. Prinstein et al., 2008; Whitlock & Knox, 2007).
Self-injury has also been linked with other risky behaviors, such as disordered eating, substance use, and suicide (Haw, Hawton, Casey, Bale, & Shepard, 2005; Ross et al., 2009). The extent to which associations between SIB and risk behavior vary by gender and academic level, however, has not been studied; the present study aims to address this gap. Below we briefly review the literature on SIBs and the associations between self-injury, alcohol and drug use, disordered eating, gambling, and depression, with a focus on studies of college students.
Academic Status and Self-InjuryRecent research suggests that the prevalence of SIB in college students is alarmingly high, with 7% of students reporting past-month SIB (Gollust, Eisenberg, & Golberstein, 2008), and 17% to 38% reporting any self-injury over their lifetime (Gratz, Conrad, & Roemer, 2002; Whitlock, Eckenrode, & Silverman, 2006). Because of the high rates of SIB on college campuses, it is important to understand demographically who is at risk for this behavior so that prevention and intervention efforts can be tailored to those specifically experiencing this problem. While numerous studies have focused on undergraduate students, few have examined the prevalence and co-occurrence of substance use behaviors and self-injury in graduate students. Earlier reports (Jacobson & Gould, 2007) suggested that rates of SIB appear to decline with age. By contrast, Whitlock et al. (2008) reported comparable SIB rates for graduate and undergraduate samples, although the sample was limited to people age 24 or younger.
Gender and Self-InjuryAssociations between gender and the prevalence of SIB among university students are still unclear. Gollust and colleagues (2008) and Heath, Toste, Nedecheva, and Charlebois (2008) reported no significant gender differences in prevalence of self-injury. By contrast, Whitlock, Eckenrode, and Silverman (2006) reported that females engage in repetitive SIB significantly more than males. Moreover, Hawton and Harriss (2008) reported that, in a clinical population, females engaged in SIB more frequently than males (60% and 40%, respectively). A review conducted on extant literature concluded that differences based on gender remain inconclusive (Jacobson & Gould, 2007). Given such discrepant reports, the present study aims to add to our understanding of the relationship between gender and SIB.
Sexual Orientation and Self-InjuryAnother demographic characteristic that has been related to SIB is sexual orientation. Studies have shown that those who endorsed a sexual orientation of bisexual, lesbian, questioning their sexuality, or gay reported higher rates of SIB (e.g. Deliberto & Nock, 2008; Gollust et al. 2008; Whitlock, Eckenrode, & Silverman, 2006). It is important to understand the relationship between sexual orientation and SIB so that universities can properly target these vulnerable groups and reach out to them effectively. Because of the extant literature on the relationship between sexual orientation and SIB, we would expect to replicate the finding of higher rates of SIB among nonheterosexual participants.
Alcohol Use and Self-InjuryBinge drinking among college students has been identified as a major public health problem (Ham & Hope, 2003; Hingson, Heeren, Winter, & Wechsler, 2005). Compared with their noncollege-attending peers, college students have higher rates of past-month alcohol use (Substance Abuse and Mental Health Services Administration [SAMHSA], 2007), binge drinking (Dawson, Grant, Stinson, & Chou, 2004; SAMHSA, 2007; Slutske, 2005; Slutske et al., 2004), and alcohol abuse (but not dependence; Slutske, 2005).
The National Institute on Alcohol Abuse and Alcoholism (NIAAA) defines “a ’binge’ as a pattern of drinking alcohol that brings blood alcohol concentration (BAC) to 0.08 gram-percent or above. For a typical adult, this pattern corresponds to consuming 5 or more drinks (male), or 4 or more drinks (female), in about 2 hours” (http://www.collegedrinkingprevention.gov/1College_Bulletin-508_361C4E.pdf, p. 2, retrieved April 13, 2009). While the 5/4 cutoff measurement has been shown to distinguish some drinkers, it does not distinguish between all drinkers, across all indices (Read, Beattie, Chamberlain, & Merrill, 2008). Furthermore, the 5/4 rule has been criticized as being imprecise because of the exclusion of weight, duration of drinking episode, and pharmacokinetics (NIAAA).
Conversely, research has also indicated that binge drinking may not be associated with mental health problems, perhaps because of the socially normative aspect of binge drinking on college campuses. For example, Cranford, Eisenberg, and Serras (2009) found that major depression was associated with lower odds of frequent binge drinking. Furthermore, results from the NESARC (Dawson, Grant, Stinson, & Chou, 2005) indicated a direct association between major depression and binge drinking among noncollege students but not among college students.
Despite research on the associations between alcohol use and mental health, the relationship between alcohol use and self-injury is largely understudied, and the available literature reports varied findings. For example, Gollust and colleagues (2008) reported no significant relationship between binge drinking and SIB. This study, however, did not account for the variation in frequency of binge drinking in relation to SIB or the effects of drug use (other than marijuana) and binge drinking on SIB. Other studies indicate that as many as one-third to nearly one-half of SIBs occur within 6 hours of alcohol intake (Haw et al., 2005; Hawton & Harriss, 2007; Hawton et al., 2003). In addition, a study on adolescents found that alcohol abuse was a risk factor for SIB (Deliberto & Nock, 2008).
It is unclear whether the association between binge drinking and SIB is a function of the quantity of alcohol consumed or the concurrent drug use that commonly accompanies excessive alcohol use (O’Grady, Arria, Fitzelle, & Wish, 2008). The present study aims to clarify the extent to which each of these variables confers risk for SIB. Based on the literature, we hypothesized that a positive relationship between binge drinking and SIB would exist, and that this association would not vary by gender (see Jacobson & Gould, 2007) but would vary by academic level.
Drug Use and Self-InjuryRelatively high prevalence rates of substance use behaviors such as tobacco and marijuana use have been well documented among undergraduate students (American College Health Association, 2007; Mohler-Kuo, Lee, & Wechsler, 2003; Wechsler et al., 2002). Self-injury is common in clinical samples of substance dependent persons, with prevalence rates reported between 34% and 50% (Evren & Evren, 2005; Oyefeso, Brown, Chiang, & Clancy, 2008). Furthermore, Deliberto and Nock (2008) reported that drug abuse was a significant risk factor for SIB by adolescents. However, the extent to which various forms of substance use behaviors are associated with SIB among college students is not well understood. In the aforementioned paragraph it is noted that frequent binge drinking is associated with increased drug use (O’Grady et al., 2008). In this report, we aim to clarify the relative risk of SIB conferred by illicit drug use versus frequent binge drinking.
Smoking and Self-InjuryNumerous studies have documented linkages between nicotine dependence and mood and anxiety disorders (Breslau, 1995; Breslau, Kilbey, & Andreski, 1991; Grant, Hasin, Chou, Stinson, & Dawson, 2004; Hagman, Delnevo, Hrywna, & Williams, 2008; see Morissette, Tull, Gulliver, Kamholz, & Zimering, 2007, for a review of research on anxiety and smoking). Evidence for associations between cigarette smoking and symptoms of anxiety and depression in college samples has also been reported (Lenz, 2004; Saules et al., 2004; for a review, see Patterson, Lerman, Kaufmann, Neuner, & Audrain-McGovern, 2004). Although the literature is sparse in this area, recent research indicates that self-injurers report higher prevalence rates of smoking than their noninjuring peers (Gollust et al., 2008; Matusmoto & Imamura, 2008). Furthermore, Jacobson and Gould (2007) reported that correlates of SIB among adolescents include a history of smoking. Because of the research suggesting a relationship between negative affect and self-injury and the research on adolescent smoking and prevalence of SIB, we hypothesized that the rates of SIB would be higher among college students who smoke cigarettes than among those who do not.
Disordered Eating and Self-InjuryEmotional regulation is defined as the way people manage and manipulate their emotions so that they remain consistent with their goals and objectives (Selby, Anetis, & Joiner, 2008). Emotional dysregulation is maladaptive emotional regulation. Self-injury and disordered eating have been established as ways that people cope with emotional dysregulation (Muehlenkamp et al., 2008). These behaviors are initiated in an attempt to decrease negative affect (Selby et al., 2008). However, despite research indicating the co-occurrence of self-injury and disordered eating, few studies have examined this relationship in college students. The literature for college students indicates that between 25.9% and 28.1% of those who self-injure also engage in disordered eating behavior (Gollust et al., 2008; Whitlock, Eckenrode, & Silverman, 2006). We hypothesized that rates of SIB would be higher in students who engage in disordered eating behavior; specifically, in this database, we assessed binge eating.
Gambling and Self-InjurySelf-injury and gambling have both been linked to impulse control problems, addictive behaviors, and obsessive-compulsive spectrum disorders, but the extent to which this comorbidity occurs in college students is unknown (Lochner & Stein, 2006). Furthermore, gambling has been linked to other risky behaviors such as excessive drinking, drug use, and binge eating (Engwall, Hunter, & Steinberg, 2004). These other behaviors also overlap with self-injury, and therefore we hypothesized that SIB would be associated with gambling behavior.
Depression and Self-InjuryResearch has shown that self-injury is associated with negative affect and emotional dysregulation. For example, Gollust et al., (2008) found that, among college students who self-injure, 32.5% screened positive for a probable depressive disorder. Furthermore, Briere and Gil (1998) reported that the two most common reasons people self-injured were “to distract themselves from painful feelings” and “to punish themselves” (p. 615). In addition, they reported that people engaged in SIB because they thought it would reduce their emotional pain. Therefore, we hypothesized that higher depression scores would be associated with greater likelihood of SIB.
SummarySelf-injury and substance use among college students have separately received substantial attention from researchers, but few studies have systematically evaluated the associations between these two behaviors. Understanding the association between SIB and substance use is important due to the possibility of increased lethality of SIB while under the influence.
Accordingly, the present study addressed two primary questions: (1) What is the prevalence of self-injury among college students, overall and by gender, academic level (undergraduate vs. graduate status), and sexual orientation? (2) To what extent is self-injury associated with different forms of substance use (cigarette smoking, binge drinking, illicit drug use) and other risk behaviors (gambling, binge eating)?
Method Participants
Our sample was based on an Internet survey of students attending 13 universities across the United States. We randomly selected 1,000 students at each university, or 13,000 total (76.5% undergraduates, 23.5% graduate or professional students), from a database of all enrolled students who were at least 18 years old. These students were sent mail and e-mail invitations to complete the survey on a secure Web site. After reading a description of the study, participants indicated their consent by clicking on the link to begin the survey. The study was approved by each university’s Health Sciences Institutional Review Board.
Accounting for Nonresponse Bias
A total of 5,689 students provided survey data, for a response rate of 44%. We constructed response propensity weights to adjust for differences between respondents and nonrespondents. These weights were equal to one divided by the predicted probability of response, which was estimated using multiple logistic regressions and administrative data on the following characteristics of all students randomly selected for the study: gender, race/ethnicity, year in school, international student status, and grade point average (GPA).
Measures
Self-injury
One question, developed for this study, assessed self-injury in the past year (Gollust et al., 2008). The item asked about the most common forms of SIBs, and was worded as follows: “This question asks about ways you may have hurt yourself on purpose, without intending to kill yourself. In the past year, have you ever done any of the following intentionally? (Select all that apply.)” Response options were: (1) cut myself, (2) burned myself, (3) banged my head or other body part, (4) scratched myself, (5) punched myself, (6) pulled my hair, (7) bit myself, (8) interfered with wound healing, (9) carved words or symbols into skin, (10) rubbed sharp objects into skin, (11) punched or banged an object to hurt myself, or (12) “other” (specify). If respondents specified behaviors exclusively in the “other” category, which were not consistent with self-injury as the deliberate and direct destruction of body tissue resulting in injury severe enough for tissue damage (e.g., alcohol abuse, minor nail biting, or binge eating), we reclassified them as “no, none of these.”
In order to analyze frequency of SIB, we asked: “On average, how often in the past year did you hurt yourself on purpose, without intending to kill yourself?” The response categories included: (1) Once or twice, (2) Once a month or less, (3) 2 or 3 times a month, (4) Once or twice a week, (5) 3 to 5 days a week, or (6) Nearly everyday, or everyday.
The primary SIB variable used in statistical analyses was whether the participant engaged in any form of SIB over the past year, although some analyses focused on specific types of SIB or the total number (range = 0–12) of types of SIB in which participants engaged. We focused on any SIB as the primary outcome because research in this area is in its infancy, and, as such, data on variations between specific types of SIB or the meaning of multiple forms of SIB is lacking.
Substance use behaviors
We asked about the frequency of binge drinking (past 2 weeks) and cigarette smoking (past 30 days). Questions about smoking (“On average, how many cigarettes did you smoke in the past 30 days?”) and binge drinking (“Over the past 2 weeks, on how many occasions did you have [5 if male, 4 if female] drinks in a row?”) were taken from the College Student Life Survey (Boyd & McCabe, 2007) and the College Alcohol Study (Wechsler, Davenport, Dowdall, Moeykens, & Castillo, 1994), respectively. Questions about marijuana use, cocaine use, heroin use, methamphetamine use, ecstasy use, and other drug use without prescription all asked simply about any use within the past 30 days.
Because certain substance use variables were positively skewed, we created binary versions of each variable. For example, on the binge drinking item, 57% of respondents said they had not engaged in any binge drinking in the past 2 weeks, 14.6% said they had 1 episode of binge drinking, 12.4% had 2 episodes, 12.6% had 3 to 5 episodes, 2.8% 6 to 9 episodes, and 0.5% admitted to 10 or more episodes. Because it is difficult to interpret the clinical significance of the difference between 3 to 5 versus 6 or more episodes, we felt it would be easier to interpret the binge drinking data with the binary version of this variable (i.e., none versus any). In addition, we created a binary variable for “frequent binge drinking,” defined as at least 3 binge drinking occasions in the past 2 weeks (McCabe, 2002; Presley & Pimentel, 2006; Wechsler et al., 2002) and cigarette smoking (any cigarette smoking within the past 30 days).
Other risk behaviors
Gambling was assessed by asking “In the past 12 months, on approximately how many days did you make any sort of bet? (By “bet” we mean betting on sports, playing cards for money, playing gambling games online, buying lottery tickets, playing pool for money, playing slot machines, betting on horse races, or any other kind of betting or gambling).” Responses were indicated by frequency of gambling occurrence. Binge eating was assessed by asking a question adapted from the Structured Clinical Interview for DSM-IV (SCID): “Do you have eating binges in which you eat a large amount of food in a short period of time and feel that your eating is out of control?” Responses were indicated based on frequency of episodes per week.
Depression
Symptoms of depression in the past 2 weeks were measured using the Patient Health Questionnaire-9 (PHQ-9), a screening instrument based on the nine DSM-IV criteria for a major depressive episode (Spitzer, Kroenke, & Williams, 1999). Sample items include “Over the past 2 weeks, how often have you been bothered by feeling down, depressed, or hopeless?” “Over the past 2 weeks, how often have you been bothered by little interest or pleasure in doing things?” Response options are not at all, several days, more than half the days, and nearly every day. We used the PHQ-9 to determine the participant’s raw score and used this continuous variable in the analyses. The PHQ measures of mental health symptoms have been validated in a variety of populations (e.g., Diez-Quevedo, Rangil, Sanchez-Planell, Kroenke, & Spitzer, 2001; Henkel et al, 2004; Kroenke, Spitzer, & Williams, 2001; Lowe et al, 2004). For example, Spitzer et al. assessed the sensitivity and specificity of the PHQ in a sample of 585 family medicine and general internal medicine patients. Within 48 hours of completing the PHQ, patients were interviewed and diagnosed by a mental health professional (a clinical psychologist or psychiatric social worker). Evaluated against the clinical diagnoses, the PHQ had a sensitivity of .73 and specificity of .98 for major depressive disorder.
Sociodemographic characteristics
We collected information on the following socio-demographic characteristics: gender, academic level (graduate or undergraduate status), age, race/ethnicity, nationality (United States or international), living arrangement, sexual orientation, current financial situation, financial situation when growing up, and current relationship status.
Statistical Analysis
Using χ2 analyses, we first estimated the prevalence of SIB overall, by academic level, and by university. Second, we examined the prevalence of SIB by gender, race, and sexual orientation. We then investigated the different types of SIB by academic level and substance use.
Next, we estimated univariate logistic regression models predicting SIB and candidate predictor variables including: sexual orientation, student status, binge drinking, drug use, smoking cigarettes, binge eating, gambling, and depression.
Finally, we tested a multivariate logistic regression model predicting SIB as a function of candidate predictor variables. All analyses incorporated the nonresponse adjustment weights described above and were performed using SPSS 16.0.
ResultsA total of 5,689 (43.7%) students completed the main survey. The overall response rate (44%) was similar to other large-scale studies that have been reported (Cook, Heath, & Thompson, 2000; McCabe et al., 2007; Reifman, Watson, & McCourt, 2006; Wechsler et al., 2002).
Response rates were higher among graduate students (53%) than undergraduates (42%) and among females (50%) than males (36%). The sample was comprised of 69.6% undergraduate and 30.4% graduate students. While graduates had higher response rates, there were more undergraduate students total, which is why undergraduates represented a higher percentage within the sample. Among undergraduate students, 61.9% were female, and the race/ethnicity breakdown was 72.3% White, 7.5% Asian, 4.2% African American, 6% Hispanic, and 9.8% were Multi-Racial or Other. Most undergraduate students (86.1%) were in the “18–22 years” age category. Among graduate students, 61.7% were female, and the race/ethnicity breakdown was 62.7% White, 17.8% Asian, 6% African American, 4.1% were Hispanic, and 9% were Multi-Racial or Other. Most graduate students (92.6%) were in the “23 years and older” age category.
Prevalence of SIBs
The overall prevalence of past one-year SIB was 14.3%. Nearly 16% (N = 595) of undergraduate and 10% (N = 176) of graduate students reported any self-injury over the last year, χ2(1) = 68.2, p < .05. As seen in Table 1, undergraduates engaged in all types of self-injury more frequently than graduates. Among those who reported any SIB, the average number of SIBs was 2.0 (SD = 1.5). Significant undergraduate and graduate differences were found in cutting oneself, burning oneself, scratching oneself, biting oneself, carving something into skin, rubbing sharp objects into skin, and punching an object in order to hurt oneself. The most common form of SIB was punching oneself for both undergraduates and graduates (33.6% and 33.0%, respectively). The least common form of SIB was carving something into one’s skin for undergraduates and rubbing sharp objects into one’s skin for graduates (5.7% and 0.3%, respectively). Frequency of SIB was also analyzed (See Table 1). Overall, 84.3% of self-injurers participated in SIB once a month or less and 15.7% of self-injurers participated in SIB two to three times a month or more.
Past-Year Prevalence (%) of Self-Injury Among Undergraduate and Graduate Students
Past-year SIB rates varied significantly across different educational levels, ranging from 8.2% for advanced (beyond fourth year) graduate students up to 17.9% for first year undergraduates χ2(9) = 74.7, p < .01. Past-year SIB rates also differed significantly across the 13 universities, ranging from 9.8% to 19.4%, χ2(12) = 74.9, p < .01. Notably, prevalence rates of SIB were higher at medium (15.2%) and larger (15.1%) versus smaller (11.6%) schools, χ2(2) = 22.7, p < .001. In addition, rates were higher at public (15.0%) versus private (12.1%) schools, χ2(1) = 15.1, p < .001. Prevalence rates did not significantly differ across schools based on U.S. News and World Report (2008) reputation score (on a scale of 0–5, where 5 is the best), with SIB rates of 14.6% for those below the median and 14.0% for those above the median.
There were no gender or race differences in rates of past-year self-injury. Those who identified as GLBT had higher rates of self-injury relative to the heterosexual group. Relative risk in presented in Table 2; actual percentages of any SIB within each category are 13.1% of those who identified as heterosexual, 34.8% of those who identified as bisexual, 23.7% of those who identified as gay/lesbian/queer, and 23.1% of those who identified as “other”, χ2(3) = 57.3, p < .001.
Univariate Logistic Regression Model Odds Ratios for Prediction of Self-Injurious Behavior (SIB) With Sexual Orientation, Student Status, Binge Drinking, Drug Use, Smoking Cigarettes, Binge Eating, Gambling, and Depression
Prevalence of SIBs by Academic Level and Substance Use
Rates of past-year SIB were significantly different for the three levels of substance use (none, 11.0%; binge drink only, 13.7%; drug use, 25.3%), χ2(2) = 257.96, p < .001. Drug use, including marijuana, was associated with higher rates of all forms of self-injury, whereas binge drinking alone was not. This difference was significant for the sample as a whole, and for undergraduates and graduates when analyzed separately.
To address the question of whether frequency of binge drinking or drug use conferred greater risk for SIB, we conducted two logistic regression analyses predicting SIB by each of these variables (i.e., frequent binge drinker with non binge drinkers as the reference group, and drug using students with non drug using students as the reference group). Although frequent binge drinkers had increased risk of SIB (odds ratio [OR] = 1.7, p < .001), drug use was associated with nearly a three-fold risk of SIB (OR = 2.7, p < .001). Therefore, drug use was the focus of subsequent analyses.
Univariate Logistic Regression Analysis Predicting SIB as a Function of Candidate Predictor Variables
Table 2 presents logistic regression analyses predicting SIB with each of the candidate predictor variables covered in the literature review. Specifically, sexual orientation (OR = 1.4), student status (OR = 1.7), binge drinking (OR = 1.5), drug use (OR = 2.5), smoking cigarettes (OR = 2.3), binge eating (OR = 1.7), gambling (OR = 1.3), and depression (OR = 1.1) are all significant predictors of any SIB (p < .01).
Multiple Logistic Regression Analysis Predicting SIB as a Function of Candidate Predictor Variables
Variables with significant univariate relationships with SIB were simultaneously entered into a multiple logistic regression model (Table 3). While all predictors had significant univariate relationships with self-injury (i.e., Table 2), our final logistic regression model indicated that depression (OR = 1.14), cigarette smoking (OR = 1.45), gambling (OR = 1.28), and drug use (OR = 1.75) were significant independent predictors (at p < .05) of past-year self-injury, but binge drinking and binge eating were not. Note that results from a follow-up analysis using each predictor in its full (nonbinary) range yielded results that were highly comparable, but more difficult to interpret.
Final Logistic Regression Model Odds Ratios for Prediction of Self-Injurious Behavior (SIB) With Simultaneous Entry of Sexual Orientation, Student Status, Binge Drinking, Drug Use, Smoking Cigarettes, Binge Eating, Gambling, and Depression
DiscussionThis study addressed two questions: (1) What is the prevalence of self-injury among college students, overall and by gender, academic level (undergraduate vs. graduate status), and sexual orientation? (2) To what extent is self-injury associated with different forms of substance use (cigarette smoking, binge drinking, illicit drug use) and related risk behaviors (gambling, binge eating)?
With respect to our first question, we found no differences in SIB prevalence were observed for either gender or race. Our findings are consistent with previous work showing that the prevalence of SIB is similar across gender and racial groups (Gollust et al., 2008; Heath et al., 2008).
Furthermore, we found that overall, undergraduate students were significantly more likely than graduate students to engage in SIB. This finding replicates earlier studies where rates differed in this same direction (e.g. Gollust et al., 2008). However, more recently, Whitlock et al. (2008) found no differences between undergraduate and graduate prevalence rates. This finding could be in part to the latter study’s truncating the age of participants at 24. Our sample’s age range extended far beyond 24, possibly accounting for the divergent finding. That is, the effect of age on SIB could be because of people maturing out of this behavior. Alternatively, selection factors may be operating, whereby those who engage in this behavior are less likely to attend graduate school. Nonetheless, in our study, the prevalence reported by graduate students reported is still high.
Furthermore, this study replicated previous reports that students who identify as gay, lesbian, queer or transgender tend to have higher rates of SIB than their heterosexual peers (e.g. Deliberto & Nock, 2008; Gollust et al. 2008; Whitlock, Eckenrode, & Silverman, 2006). The higher rate of SIB in the Gay-Lesbian-Transgender-Queer (GLTQ) population, while replicated in many studies, does not have clear supporting evidence regarding why these rates are higher. Perhaps the rates could reflect more negative affect (King et al., 2008; Westefeld, Maples, Buford, & Taylor, 2001) the impact of social stigma and marginalization (DeLiberto & Nock, 2008; Scourfield, Roen, & McDermott, 2008), or conflicting feelings of sexual identity (Beckinsale, Martin, & Clark, 2001). More research is needed in this area to understand the mechanism driving this relationship.
With regard to our second question, those who reported drug use—including but not limited to marijuana use—had significantly higher rates of SIB relative to those who engaged in binge drinking in the absence of drug use or who denied both binge drinking and drug use. This difference was evident within both the graduate and undergraduate subsamples. Furthermore, drug use was associated with higher rates of all types of SIB, again both for undergraduates and graduates. Notably, the highest rate of SIB (62%) was observed among graduate students who reported both smoking cigarettes and using illicit drugs. Substance use is more prevalent in undergraduate students. However, students generally mature out of these behaviors by graduate school. As such, graduate students who continue to engage in substance use are an atypical group. Further it is possible that these graduate students are experiencing higher levels of distress which result in substance use and SIB. Therefore, their behavior could be more pathological in nature. This finding suggests that they might merit more intensive prevention efforts than are typically in place for such students.
Finally, in regard to whether demographic, substance use, and risk behaviors elevate risk of engaging in SIB, we found that in most cases, they do. Specifically, drug use, cigarette smoking, gambling, depression, sexual orientation and undergraduate student status were all associated with increased odds of engaging in SIB, both at the bivariate level and in combination, when collectively entered into a logistic regression model. As mentioned, the relationship between binge eating and SIB was mediated by depression.
Notably, past 2-week binge drinking behavior, per se, was not a significant predictor of SIB, but frequent binge drinking was. The lack of association between SIB and less frequent binge drinking is consistent with research suggesting that, overall, binge drinking among college students is not significantly associated with poor mental health. Dawson et al. suggested that this null association could be a selection effect, reasoning that those with co-occurring disorders are less likely to attend college. Another possibility is that binge drinking is considered a routine part of college life, and as such, it is not tied to affect regulation drinking motives (cf. Chassin, Pitts, & Prost, 2002). These theories have not been implicated in relation to SIB, but they might have the potential to advance our understanding of the relationship between SIB and binge drinking.
Specifically, our results suggest that campus prevention resources, which are typically quite limited, may be best directed at more frequent binge drinkers and those who use illicit drugs, rather than focusing on all college student drinkers.
Limitations
Our results should be interpreted in the context of several limitations to our study. A limitation is that assessment of SIB and psychological correlates were based on self-reports and online screening measures. Although these measures have good psychometric properties, it is not clear if the use of diagnostic interviews would yield similar results.
In addition, the cross-sectional design of our study precluded the identification of temporal order of associations between SIBs, demographic variables, substance use behaviors, and related risky behaviors. It is possible that the risk behaviors and SIB have overlapping initiation. Furthermore, it is plausible that there is significant individual variation in the initiation of these risky behaviors and SIB. Moreover, it is possible that the risk behaviors and SIB share common possible causal links, such as impulsivity. Future research may shed light on these relationships in a way that our cross-sectional survey—which did not assess timing of onset and offset of risk behaviors—does not permit.
The cross-sectional design also precluded examination of whether lower SIB among graduate students might be due to selection (e.g., undergraduates who engage in SIBs are less likely to pursue graduate education; cf. McCabe et al., 2007; Wood, Sher, & McGowan, 2000), socialization, effects of the academic context, age effects, or some combination of these processes. To our knowledge, the theory of “maturing out”, which is often discussed in the binge drinking literature, has not been explored in relation to Hawton and Harriss (2008) did identify that rates of SIB are higher in ages 20 to 34, compared with ages 10 to 19 and 35 to 59, however, this study was completed in a clinical population who presented with suicidal intent. Other researchers have conjectured that SIB peaks during mid-adolescence and declines into adulthood (Jacobson & Gould, 2007). It is possible that SIB follows the same trend as binge drinking, but this is still unclear.
In addition, our binge drinking measure did not account for quantity of alcohol consumed per occasion, beyond the 5/4 cut-off. The most recent binge-drinking literature suggests that a better indicator for problematic alcohol use is defined by quantity of alcohol consumed per occasion. As such, it may not be the frequency of binge drinking that is the most problematic, but the amount of alcohol consumed (Jackson & Sher, 2008). Thus, in future research it would be useful to include items assessing the quantity of alcohol consumed per occasion and more clearly specifying the duration of “an occasion.”
Furthermore, as self-injury increases in prevalence, people have also become more creative in their methods of self-injury. As recently reported in the popular press, hospitals are now seeing a type of self-injury called “self-embedding” where adolescents embed glass, paper clips and other objects into their skin (Peck, 2008). This type of self-injury requires a surgical procedure to remove the item. As SIBs morph, assessment tools likewise must account for these new behaviors. This study assessed many SIBs but perhaps missed other newer types of SIB.
Moreover, assessment of SIB frequency varies across studies. This study quantified frequency of SIB in units of time such as weekly or monthly over the past year. In other studies the frequency of SIB was calculated based on number of times over the lifespan. For example, Heath et al. (2008) assessed frequency of SIB as: once, two to four times, five to ten times, 11 to 50 times, and 100 or more times over the lifespan. Gratz et al. (2002) measured lifetime frequency using a different metric, i.e., more than 10 times in the past and more than 100 times in the past. Without a standardized way to measure frequency of SIB it is difficult to compare rates across populations and studies.
Strengths
Despite these limitations, our study has several methodological strengths. To our knowledge, no studies have examined how the co-occurrence of self-injury, substance use, and other related behaviors varies by academic level. Additionally, data for this study were collected from thousands of students across 13 U.S. universities, and, as such, is one of the first studies of this magnitude to look at SIB among college students.
Examination of subgroup differences, using a sample of this magnitude, may inform development of targeted prevention efforts aimed at reducing self-injury, substance use, and their co-occurrence. Furthermore, the use of a probability sample increases confidence in the generalizability of our findings to this population, and we used response propensity weights to adjust for nonresponse bias. The relatively large sample size allowed us to test gender and academic level as potential moderators of the associations between self-injury and substance use behaviors, and we statistically controlled for several demographic variables and risk factors.
Also, despite its limitations, our study makes several important substantive contributions. First, our results indicate that associations between self-injury and substance use in college samples vary by drug use and academic level. Second, we found that candidate predictors of SIB include sexual orientation, student status, drug use (including marijuana), smoking cigarettes, binge eating, gambling, and depression. Furthermore, our study indicated that binge eating, after analysis controlling for depression, was no longer a predictor of SIB. To our knowledge, this is the first study to report this pattern of associations.
ConclusionsTaken together, results from the current study lend strong support to the heightened risks associated with SIB. This study highlights the importance of distinguishing the effects of academic level, substance use, smoking, binge eating, gambling, and depression on SIB. As such, our findings may allow for more focused prevention and intervention efforts that target subgroups of students at greater risk for particular patterns of co-occurrence.
This is one of the first studies of this magnitude to look at the association between substance use and self-injury in a college student population. Perhaps most notably, the relationship between drug use and well established comorbidities (smoking, binge eating, depression, gambling) are significantly stronger amongst those who self-injure. The heightened association between drug use and self-injury in combination could increase lethality.
Although current prevention and intervention programs focused on binge drinking on college campuses exist, this study provides additional details that will allow campus administrators to target of substance users who are at high risk of self-injury. Instead of a blanket prevention program targeting all binge drinkers, perhaps universities should target those students who are participating in frequent binge drinking and drug use. Capturing these students and screening proactively for SIB and additional risky behaviors could aid in the intervention and treatment before these symptoms are exacerbated.
Results of the present study can inform college campus efforts to establish empirically grounded policy and prevention efforts to reduce SIB among their students. Study findings shed light on the risk factors for college student self-injury and may ultimately inform the design of prevention and intervention efforts. Such information is critical as colleges continue to grapple with risky behaviors such as self-injury and substance use. Finally, because of the gravity of self-injury and the associated risky behaviors it is necessary for clinicians and counseling centers to be appropriately prepared to encounter students manifesting with these issues. Research on the treatment of SIB is needed in nonclinical and subclinical populations to advance our understanding of the most effective ways to combat this behavior and prevent associated deleterious consequences.
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Submitted: March 5, 2009 Revised: July 13, 2009 Accepted: July 14, 2009
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Source: Psychology of Addictive Behaviors. Vol. 24. (1), Mar, 2010 pp. 119-128)
Accession Number: 2010-05354-013
Digital Object Identifier: 10.1037/a0017210
Record: 147- Title:
- Should uncontrollable worry be removed from the definition of GAD? A test of incremental validity.
- Authors:
- Hallion, Lauren S.. Department of Psychology, University of Pennsylvania, Philadelphia, PA, US, hallion@psych.upenn.edu
Ruscio, Ayelet Meron. Department of Psychology, University of Pennsylvania, Philadelphia, PA, US - Address:
- Hallion, Lauren S., Department of Psychology, University of Pennsylvania, 3720 Walnut Street, Philadelphia, PA, US, 19104, hallion@psych.upenn.edu
- Source:
- Journal of Abnormal Psychology, Vol 122(2), May, 2013. pp. 369-375.
- NLM Title Abbreviation:
- J Abnorm Psychol
- Page Count:
- 7
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- The Journal of Abnormal and Social Psychology; The Journal of Abnormal Psychology; The Journal of Abnormal Psychology and Social Psychology
- ISSN:
- 0021-843X (Print)
1939-1846 (Electronic) - Language:
- English
- Keywords:
- DSM-5, excessiveness, generalized anxiety disorder, uncontrollability, worry
- Abstract:
- In its current instantiation in DSM–IV, a diagnosis of generalized anxiety disorder (GAD) requires the presence of excessive and uncontrollable worry. It has been proposed that the uncontrollability criterion be removed from future editions of the DSM, primarily on the basis of empirical and conceptual overlap between excessiveness and uncontrollability and a relative lack of research on uncontrollability. However, no research has directly investigated the incremental validity of the uncontrollability criterion—that is, the extent to which uncontrollability predicts important clinical information over and above excessiveness. This question was examined in a community sample of 126 adults diagnosed with GAD. After controlling for excessiveness, uncontrollability explained a significant proportion of additional variance in a variety of relevant clinical measures, including GAD severity, clinician-rated anxiety, number and severity of comorbid disorders, and use of psychotropic medication and psychotherapy. The results remained statistically significant even when other features of GAD were controlled. By contrast, excessiveness did not significantly predict any clinical measure over and above uncontrollability. These findings suggest that uncontrollability contributes to the validity of the GAD diagnosis and should be retained as a core feature of pathological worry. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Anxiety; *Diagnostic and Statistical Manual; *Generalized Anxiety Disorder
- Medical Subject Headings (MeSH):
- Adult; Anxiety; Anxiety Disorders; Diagnostic and Statistical Manual of Mental Disorders; Female; Humans; Logistic Models; Male; Reproducibility of Results; Young Adult
- PsycINFO Classification:
- Neuroses & Anxiety Disorders (3215)
- Population:
- Human
Male
Female - Location:
- US
- Age Group:
- Adulthood (18 yrs & older)
- Tests & Measures:
- Anxiety Disorders Interview Schedule for DSM–IV
Hamilton Anxiety Rating Scale DOI: 10.1037/t02824-000
Hamilton Rating Scale for Depression DOI: 10.1037/t04100-000
Penn State Worry Questionnaire DOI: 10.1037/t01760-000 - Grant Sponsorship:
- Sponsor: University of Pennsylvania
Other Details: University Research Foundation grant
Recipients: Ruscio, Ayelet Meron - Methodology:
- Empirical Study; Interview; Quantitative Study
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- Accepted: Dec 20, 2012; Revised: Dec 19, 2012; First Submitted: Jul 9, 2012
- Release Date:
- 20130527
- Correction Date:
- 20160606
- Copyright:
- American Psychological Association. 2013
- Digital Object Identifier:
- http://dx.doi.org/10.1037/a0031731
- PMID:
- 23713499
- Accession Number:
- 2013-17531-004
- Number of Citations in Source:
- 32
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- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2013-17531-004&site=ehost-live">Should uncontrollable worry be removed from the definition of GAD? A test of incremental validity.</A>
- Database:
- PsycINFO
Should Uncontrollable Worry Be Removed From the Definition of GAD? A Test of Incremental Validity
By: Lauren S. Hallion
Department of Psychology, University of Pennsylvania;
Ayelet Meron Ruscio
Department of Psychology, University of Pennsylvania
Acknowledgement: This work was supported in part by a University Research Foundation grant from the University of Pennsylvania to Ayelet Meron Ruscio.
Over the last several decades, generalized anxiety disorder (GAD) has evolved from a “wastebasket” diagnosis to a robust and clinically meaningful construct that can be diagnosed as reliably as most other Axis I disorders (Brown, DiNardo, Lehman, & Campbell, 2001). In its current instantiation in DSM–IV (American Psychiatric Association, 1994), GAD is centrally defined by the presence of excessive and uncontrollable worry that persists for 6 months or longer. To warrant a diagnosis, individuals must also experience at least three of six associated symptoms (one associated symptom in children) and clinically significant distress or impairment. In the run-up to DSM-5, several revisions to these criteria were proposed. Arguably the most controversial of these was the proposed removal of the “uncontrollability” criterion (Criterion B), which requires that the individual finds the worry difficult to control. Although GAD will remain largely unchanged in DSM-5, the suggestion that this criterion be eliminated has highlighted questions about the role that uncontrollability should play in defining pathological worry.
The uncontrollability criterion was added in DSM–IV as part of a larger effort to refine the definition of GAD and improve the reliability and discriminant validity of the diagnosis (Brown, Barlow, & Liebowitz, 1994). The recommendation to include this criterion was based primarily on research suggesting that “uncontrollability (or the difficulty of dismissal) seems to be the most central feature of worry” (Borkovec, Shadick, & Hopkins, 1991, p. 31) and that the ability to control worry distinguishes individuals high and low in trait worry (Borkovec, Robinson, Pruzinsky, & DePree, 1983) and individuals with and without DSM–III–R GAD (Abel & Borkovec, 1995; Borkovec, 1994; Craske, Rapee, Jackel, & Barlow, 1989). The requirement that worry be perceived as difficult to control was also considered an improvement over the DSM–III–R requirement that worries be unrealistic, which was discarded because it was unreliable, subjective, and difficult to operationalize (Borkovec et al., 1991), concerns that have since been raised about the excessiveness requirement as well (Ruscio et al., 2005).
Since the inclusion of the uncontrollability criterion in DSM–IV, empirical and theoretical interest in control over worry—and in cognitive control more broadly—has grown. Difficulty controlling anxious thoughts has been linked to heightened anxiety and depression (Peterson, Klein, Donnelly, & Renk, 2009) and has been proposed to play a key role in the onset and maintenance of several anxiety disorders (e.g., Rachman, 1997; Wells, 1995). These findings are consistent with leading theoretical accounts that implicate perceptions of uncontrollability, broadly construed, in the etiology of anxiety disorders (Mineka & Zinbarg, 2006). Negative beliefs about control over worry have been found to be especially relevant for GAD, discriminating individuals with GAD not only from healthy controls (Cartwright-Hatton & Wells, 1997; Hoyer, Becker, & Roth, 2001) but from high worriers without GAD (Hoyer, Becker, & Margraf, 2002; Ruscio & Borkovec, 2004) and from individuals with other anxiety disorders (Cartwright-Hatton & Wells, 1997; Hoyer et al., 2001, 2002) and major depressive disorder (MDD; Barahmand, 2009).
Despite growing evidence for uncontrollability as an important feature of anxiety and GAD, some researchers have questioned its necessity as a diagnostic criterion, citing the conceptual similarity and strong association of uncontrollability with excessiveness (e.g., r = .91; Brown et al., 2001) as indications of redundancy (Andrews et al., 2010). This concern has been underscored by research demonstrating that removing the uncontrollability criterion while retaining the excessiveness criterion would have a small impact on the lifetime prevalence and identified cases of GAD (Andrews & Hobbs, 2010; Beesdo-Baum et al., 2011). If excessiveness and uncontrollability are redundant, discarding one of these criteria could result in improved diagnostic efficiency without compromising validity and clinical utility. Indeed, the recommendation to remove uncontrollability from DSM-5 was based mainly on the premise that this criterion does not contribute unique clinical information to the GAD diagnosis over and above excessiveness. To date, however, no research has tested this assumption directly. This important gap should be addressed before the uncontrollability criterion is discarded.
To that end, the present study evaluated the incremental validity of the uncontrollability criterion. Specifically, we tested the hypothesis that uncontrollability of worry accounts for unique variance in important concurrently assessed clinical measures among GAD cases, over and above information contributed by excessiveness of worry. Whereas a demonstration of incremental validity would suggest that uncontrollability should be retained in the definition of GAD, evidence of redundancy with excessiveness would argue against the need for both criteria.
Method Participants
Participants were 126 adults with DSM–IV GAD recruited from the Philadelphia community (n = 112) and from a private northeastern university (n = 14; see Table 1). Participants were recruited via electronic and posted advertisements for a research study on anxiety and depression and received $10 per hour for their time. GAD was the principal (most severe) diagnosis in two thirds (n = 83) of these cases. The remainder had principal MDD and were included to enhance ecological validity, given the frequent comorbidity of GAD with MDD (Kessler et al., 2008). Participants were excluded if they had a principal diagnosis other than GAD or MDD, were acutely psychotic or suicidal, or had a current substance use disorder.
Sample Characteristics
To evaluate the reliability of these diagnoses, an independent clinical interviewer rated the recorded interviews of 42 cases, including 25 GAD cases randomly selected from the current sample (20%) plus an additional 17 cases without GAD randomly selected from the larger study in which these measures were administered. Interrater agreement was high for the presence of DSM–IV GAD (κ = 1.00). In the subsample with GAD, interrater agreement was acceptable for GAD and MDD clinical severity (ICC = 0.73 and 0.90, respectively) and for the presence of comorbid MDD (κ = 0.92).
Measures
Predictor variables
Uncontrollability and excessiveness
Uncontrollability and excessiveness were assessed using the GAD module of the Anxiety Disorders Interview Schedule for DSM–IV (ADIS-IV; Brown, DiNardo, & Barlow, 1994). In accordance with ADIS-IV administration standards, interviewers rated the uncontrollability of worry reported by participants for each of eight life domains (minor matters, work/school, family, finances, social/interpersonal, health of self, health of significant others, community/world affairs) on a scale of 0 (worry is never difficult to control) to 8 (worry is extremely difficult to control). Interviewers separately rated the excessiveness of worry for the same eight domains on a scale of 0 (no worry/tension) to 8 (constantly worried/extreme tension). Interrater agreement was good for each of the eight domains of uncontrollability (mean ICC = .84; range = .70–.96) and excessiveness (mean ICC = .87; range = .80–.94). Ratings were averaged across domains to create a global uncontrollability score and a global excessiveness score. These uncontrollability and excessiveness composite scores were comparable in terms of range (4.75 and 4.38, respectively) and internal consistency (Cronbach’s alpha = .62 and .60, respectively).
Other features of GAD
Diagnostic features of GAD other than worry were assessed using the ADIS-IV. A Criterion C composite was created by averaging the severity ratings (0–8) for the six associated symptoms of GAD. A clinical significance composite was created by averaging self-reported distress and interference attributed to GAD symptoms (0–8). Interrater agreement was good for these composites (ICC = 0.92 and 0.80, respectively).
Anxiety-related measures
Global anxiety severity
Interviewers rated participants’ anxiety symptoms on the 14-item Hamilton Anxiety Rating Scale (HAM-A; Hamilton, 1959), a widely used clinician-administered scale (Shear et al., 2001).
GAD and worry severity
Interviewers rated GAD clinical severity (0–8) for all participants. Additionally, participants estimated the percent of an average day that they spent worrying (0–100%) on the ADIS-IV, and self-reported their typical (trait) levels of worry on the Penn State Worry Questionnaire (PSWQ; Meyer, Miller, Metzger, & Borkovec, 1990). These measures were used as measures of the day-to-day impact of GAD on participants’ lives.
Comorbidity-related measures
Comorbid disorders
The number and severity of comorbid mental disorders were assessed using the ADIS-IV. We focused on anxiety disorders (panic disorder with and without agoraphobia, social anxiety disorder, obsessive–compulsive disorder, posttraumatic stress disorder, acute stress disorder, specific phobia) and mood disorders (MDD, dysthymic disorder, bipolar disorders), given their common co-occurrence with GAD (Ruscio et al., 2005). Each disorder was assessed for diagnostic status (present/absent) and assigned a clinical severity rating (0–8). Number of comorbid disorders was determined by summing the number of disorders other than GAD for which DSM–IV diagnostic criteria were met. Severity of comorbid disorders was determined by averaging the clinical severity of all disorders other than GAD for which diagnostic criteria were met.
Global depression severity
Interviewers rated participants’ depression symptoms on the 17-item Hamilton Rating Scale for Depression (HAM-D; Hamilton, 1960), a widely used global measure of depression severity (López-Pina, Sánchez-Meca, & Rosa-Alcázar, 2009).
Treatment-related measures
Treatment history
Dichotomous variables representing current use of psychotropic medication, current use of psychotherapy, and lifetime history of psychiatric hospitalization were drawn from the Medical History module of the ADIS-IV.
Procedure
Interviews were administered by Master’s- and Bachelor’s-level interviewers who received extensive training and demonstrated high interrater reliability with the supervising licensed psychologist. Final diagnostic status and clinical severity ratings were determined in weekly team consensus meetings. Interviewers and supervisors were blind to study hypotheses.
Statistical Analyses
Hierarchical multiple and logistic regression analyses were performed to examine the incremental validity of uncontrollability over and above excessiveness. In each analysis, excessiveness was entered on the first step and uncontrollability was entered on the second step. Additionally, outliers that scored ≥2.5 SD above or below the mean for that variable or which exerted an unduly large influence on the model (defined using Cook’s distance) were removed, resulting in the exclusion of 0 to 3 data points (less than 3% of available data) per analysis. Three clinical measures (number and severity of comorbid disorders and MDD severity among depressed participants) had non-normal distributions and consequently were log10 transformed prior to analysis. Variance inflation factors (VIF) were below 4, suggesting that multicollinearity was not a problem in these analyses.
ResultsDescriptive statistics for all measures appear in Table 2. As expected, excessiveness and uncontrollability were highly correlated (r = .83).
Descriptive Statistics for Clinical Measures
Despite this correlation, uncontrollability predicted anxiety-related measures over and above excessiveness (see Table 3). Effect sizes were small to moderate, with uncontrollability explaining an additional 2–11% of the variance over and above excessiveness. These effects were statistically significant for measures of GAD and worry (GAD clinical severity, percent of the day spent worrying, trait worry) and marginally significant for global anxiety severity. Notably, excessiveness was no longer a significant predictor of any measure once uncontrollability was entered into the models.
Incremental Validity of Uncontrollability for Predicting Anxiety-Related Measures
Uncontrollability also significantly predicted both the number and severity of comorbid emotional disorders over and above excessiveness, explaining an additional 4% of the variance in each analysis (see Table 4). Excessiveness no longer predicted either measure once uncontrollability was included in the model.
Incremental Validity of Uncontrollability for Predicting the Number and Severity of Comorbid Emotional Disorders
Given the particularly close relationship of GAD to MDD, we performed additional analyses focusing specifically on depression-related measures. Uncontrollability incrementally predicted greater global depression severity in this GAD sample, explaining an additional 4% of the variance and rendering excessiveness nonsignificant (see Table 5). Uncontrollability did not predict the presence of comorbid MDD (see Table 6), but marginally predicted the severity of the current depressive episode among participants diagnosed with MDD (see Table 5).
Incremental Validity of Uncontrollability for Predicting Depression Severity
Incremental Validity of Uncontrollability for Predicting the Presence of Comorbid MDD
Finally, uncontrollability was incrementally associated with treatment-seeking (see Table 7). The pattern differed for pharmacotherapy versus psychotherapy. Higher uncontrollability was associated with elevated odds of psychotropic medication use (odds ratio [OR] = 2.52) but with reduced odds of psychotherapy use (OR = 0.50). Once uncontrollability was included in the model, excessiveness was no longer a significant predictor of psychotherapy, but became a significant predictor of medication use. Lastly, higher uncontrollability more than doubled the odds of past psychiatric hospitalization (OR = 2.95).
Incremental Validity of Uncontrollability for Predicting Treatment-Related Measures
Sensitivity Analyses
Sensitivity analyses were performed to examine whether the incremental value of uncontrollability held even when controlling for additional features of GAD, including the associated symptoms (Criterion C) and clinical significance (distress and interference) criteria, along with excessiveness. In analyses controlling for all of these features simultaneously, uncontrollability remained a significant predictor of GAD severity (explaining an additional 2% of the variance), percent of the day spent worrying (10%), and trait worry (4%), but was no longer a significant predictor of global anxiety severity (1%). Uncontrollability remained a significant predictor of the number and severity of comorbid disorders (3% in each analysis) and was a marginally significant predictor of global depression severity (2%) in the total sample and depressive episode severity (4%) in the subsample with comorbid MDD. Uncontrollability continued to be positively associated with pharmacotherapy (OR = 2.43) and negatively associated with psychotherapy (OR = 0.47) and was marginally elevated among those with a history of psychiatric hospitalization (OR = 2.29).
DiscussionThe present findings should be interpreted in light of several limitations. First, our sample was restricted to individuals diagnosed with GAD. This precluded a test of the utility of uncontrollability for identifying cases of GAD. Additionally, different results might have been obtained from a sample in which subclinical symptoms were represented. Nevertheless, the predictive power of uncontrollability among these clinically significant cases, observed in spite of the restricted range of uncontrollability scores, argues for the clinical relevance of this criterion. A second limitation was the adult-only sample. Because of their lower metacognitive awareness, children may have difficulty articulating the extent to which their worry is uncontrollable or may not attempt to control their worry (Beesdo-Baum et al., 2011). The generalizability of the present findings to children remains to be determined. Third, we studied clinical measures related to disorder severity and treatment-seeking, with a particular focus on measures of anxiety and depression. Although these measures are well suited for studying the validity of GAD, other important variables such as illness course and treatment outcome were not represented and await future investigation.
With these limitations in mind, our findings challenge the assumption that uncontrollability contributes little clinical information beyond that provided by excessiveness. After controlling for excessiveness, uncontrollability incrementally predicted a wide range of important clinical measures, including measures specific to GAD and more general measures pertaining to clinical severity, comorbidity, and treatment-seeking. In the majority of analyses, excessiveness was no longer a significant predictor after uncontrollability entered the model. These results remained significant or marginally significant for all but one measure in conservative sensitivity analyses wherein other features of GAD were also controlled. There were no analyses in which excessiveness was a stronger predictor than uncontrollability.
Whether the incremental value of uncontrollability demonstrated by these findings is sufficient to retain this criterion in the GAD diagnosis is a matter of judgment. The relatively small effects observed here are consistent with previous research reporting substantial overlap between excessiveness and uncontrollability (Andrews & Hobbs, 2010). However, the present findings also suggest that excluding the criterion may result in a less valid and clinically informative diagnosis. For GAD, whose diagnostic validity has long been a significant concern, this risk should not be taken lightly.
Removing the uncontrollability criterion may also lead to a missed opportunity to link work on GAD to the exciting and rapidly growing cognitive control literature. There is increasing recognition of the important role played by cognitive dyscontrol in psychopathology in general and in GAD in particular. Impaired cognitive control has been linked to higher trait worry (Crowe, Matthews, & Walkenhorst, 2007), difficulty dismissing unwanted thoughts (Brewin & Smart, 2005), and poorer treatment outcome in older adults receiving cognitive–behavioral therapy for GAD (Mohlman & Gorman, 2005). Continued research into the cognitive processes that underlie normal control over thoughts, and how these processes may be disrupted in individuals with GAD, has the potential to advance understanding of this critical yet poorly understood component of anxiety. Such research may, in turn, help lead to the development of novel treatments for uncontrollable anxious thought—a possibility especially important for GAD, which has the lowest treatment success rate of all anxiety disorders (Siev & Chambless, 2007).
The association between uncontrollability and treatment-seeking described here highlights the value of continued research into uncontrollability. We found that individuals reporting higher uncontrollability generally were more severe clinical cases. However, rather than exhibiting an undifferentiated pattern of negative outcomes, these individuals reported less use of psychotherapy and more use of pharmacotherapy than their counterparts with lower levels of uncontrollability. While several explanations may account for this finding, one possibility is that persons who perceive worry as far outside their control may feel less able to reduce worry themselves by applying strategies learned in psychotherapy and consequently may seek medication treatment instead. Although preliminary, our treatment-related findings suggest that additional research into uncontrollability and related metacognitive phenomena may help advance understanding of the factors that influence treatment utilization in GAD.
In light of these considerations, it may be asked whether excessiveness, rather than uncontrollability, should be discarded if the overlap between them is judged too high to retain both criteria in the definition of GAD. Our results hint that this may be the more defensible choice, as we did not find evidence for the incremental validity of excessiveness over uncontrollability for any of the clinical measures considered here. Other shortcomings of the excessiveness criterion have been noted previously, including its definitional ambiguity, its negative impact on diagnostic reliability, and its exclusion of milder but still clinically significant cases from the GAD diagnosis (see Ruscio et al., 2005, for a review). Indeed, the recommendation to retain the excessiveness criterion rather than the uncontrollability criterion seems to have been based more on the limited number of studies that have examined uncontrollability than on the strength of the evidence for excessiveness (Andrews et al., 2010). Nevertheless, given the paucity of research available for excessiveness as well as uncontrollability, it may be premature to recommend the removal of either criterion. Future research investigating the relationship of these criteria to important variables not investigated here (e.g., clinical course, treatment response, family history of GAD), in children as well as adults, will aid in determining whether uncontrollability, excessiveness, or both should remain in future iterations of the DSM. Until this research is conducted, however, we believe the present findings warrant further consideration of uncontrollability as an important but neglected feature of GAD and, perhaps, of pathological worry more generally.
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Submitted: July 9, 2012 Revised: December 19, 2012 Accepted: December 20, 2012
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Source: Journal of Abnormal Psychology. Vol. 122. (2), May, 2013 pp. 369-375)
Accession Number: 2013-17531-004
Digital Object Identifier: 10.1037/a0031731
Record: 148- Title:
- Social connections and suicidal thoughts and behavior.
- Authors:
- You, Sungeun. Chungbuk National University, Cheongju City, Republic of Korea
Van Orden, Kimberly A., ORCID 0000-0001-9439-401X. Department of Psychiatry, University of Rochester Medical Center, Rochester, NY, US, kimberly_vanorden@urmc.rochester.edu
Conner, Kenneth R.. University of Rochester Medical Center, Rochester, NY, US - Address:
- Van Orden, Kimberly A., Department of Psychiatry, University of Rochester Medical Center, 300 Crittenden Blvd., Box PSYCH, Rochester, NY, US, 14642, kimberly_vanorden@urmc.rochester.edu
- Source:
- Psychology of Addictive Behaviors, Vol 25(1), Mar, 2011. pp. 180-184.
- NLM Title Abbreviation:
- Psychol Addict Behav
- Page Count:
- 5
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- Bulletin of the Society of Psychologists in Addictive Behaviors; Bulletin of the Society of Psychologists in Substance Abuse
- Other Publishers:
- US : Educational Publishing Foundation
Society of Psychologists in Addictive Behaviors - ISSN:
- 0893-164X (Print)
1939-1501 (Electronic) - Language:
- English
- Keywords:
- loneliness, social support, suicidal ideation, suicide attempts, thwarted belongingness, social connectedness, substance use disorders
- Abstract:
- Disrupted social connectedness is associated with suicidal thoughts and behaviors among individuals with substance use disorders (SUDs). The current study sought to further characterize this relationship by examining several indices of social connectedness—(a) living alone, (b) perceived social support, (c) interpersonal conflict, and (d) belongingness. Participants (n = 814) were recruited from 4 residential substance-use treatment programs and completed self-report measures of social connectedness as well as whether they had ever thought about or attempted suicide. Multivariate results indicated that interpersonal conflict and belongingness were significant predictors of a history of suicidal ideation, and that belongingness, perceived social support, and living alone were significant predictors of suicide attempt. These results indicate the most consistent support for the relationship between suicidality and thwarted belongingness, and also support the clinical utility of assessing whether individuals live alone. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Drug Abuse; *Social Interaction; *Suicidal Ideation; Attempted Suicide; Dual Diagnosis; Mental Disorders; Suicide; Substance Use Disorder
- Medical Subject Headings (MeSH):
- Adult; Female; Humans; Interpersonal Relations; Logistic Models; Loneliness; Male; Middle Aged; Self Report; Social Support; Suicidal Ideation; Suicide, Attempted; Surveys and Questionnaires
- PsycINFO Classification:
- Behavior Disorders & Antisocial Behavior (3230)
- Population:
- Human
Male
Female - Location:
- US
- Age Group:
- Adulthood (18 yrs & older)
- Tests & Measures:
- National Comorbidity Survey
Physicians Health Questionnaire
Alcohol Use Disorders Identification Test DOI: 10.1037/t01528-000
Interpersonal Needs Questionnaire DOI: 10.1037/t10483-000
Kessler Perceived Social Support Scale DOI: 10.1037/t04511-000
Test of Negative Social Exchange DOI: 10.1037/t01270-000 - Grant Sponsorship:
- Sponsor: National Institute on Alcohol Abuse and Alcoholism
Grant Number: 5R01AA016149-04
Recipients: Conner, Kenneth R.
Sponsor: National Institute of Mental Health
Grant Number: 5T32MH020061-09
Other Details: Yeates Conwell
Recipients: No recipient indicated - Methodology:
- Empirical Study; Quantitative Study
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- First Posted: Dec 13, 2010; Accepted: Jul 11, 2010; Revised: Jul 1, 2010; First Submitted: Nov 11, 2009
- Release Date:
- 20101213
- Correction Date:
- 20151207
- Copyright:
- American Psychological Association. 2010
- Digital Object Identifier:
- http://dx.doi.org/10.1037/a0020936
- PMID:
- 21142333
- Accession Number:
- 2010-25604-001
- Number of Citations in Source:
- 33
- Persistent link to this record (Permalink):
- http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2010-25604-001&site=ehost-live
- Cut and Paste:
- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2010-25604-001&site=ehost-live">Social connections and suicidal thoughts and behavior.</A>
- Database:
- PsycINFO
Social Connections and Suicidal Thoughts and Behavior
By: Sungeun You
Chungbuk National University, Cheongju City, South Korea
Kimberly A. Van Orden
Department of Psychiatry, University of Rochester Medical Center, Rochester, New York;
Kenneth R. Conner
University of Rochester Medical Center and Canandaigua VA Center of Excellence, Rochester, New York
Acknowledgement: This research was supported, in part, by a grant from the National Institute on Alcohol Abuse and Alcoholism to Kenneth R. Conner (5R01AA016149-04) and by a grant from the National Institute of Mental Health to Yeates Conwell (5T32MH020061-09).
Substance dependence confers elevated risk for suicidal ideation (i.e., thoughts about killing oneself; Grant & Hasin, 1999) and suicide attempts (i.e., attempting suicide but surviving; Kessler, Kendler, Heath, Neale, & Eaves, 1992), as well as suicide (i.e., suicide attempts that result in death; Wilcox, Conner, & Caine, 2004). Reviews of the suicide literature have estimated that the risk for suicide among individuals with SUDs is five times greater or more than is that of the general population (Wilcox et al., 2004; Yoshimasu, Kiyohara, & Miyashita, 2008). The identification of psychological and social processes that elevate risk for suicidal thoughts and behaviors (i.e., ideation, attempts, or death) among individuals with substance use disorders (SUDs) represents one avenue for increased understanding of etiological mechanisms, as well as improved prevention efforts.
Several theories of suicide posit a central role for social connectedness in the etiology of suicide. Durkheim's sociological model proposes that too little social integration is one of several dysregulated social forces that causes suicide (Durkheim, 1897), and Shneidman's cubic model of suicide (Shneidman, 1987) proposes that an unmet need for “affiliation” is one of several needs that contribute to suicide when unmet. The interpersonal theory of suicide (Joiner, 2005; Van Orden et al., in press) proposes that the need to belong to caring and supportive relationships (Baumeister & Leary, 1995) is so powerful that, when thwarted, contributes to a desire for suicide. Several studies specifically examining the relationship between belongingness—the degree to which individuals perceive that they are meaningfully connected to satisfying (and positive) relationships or social groups—and suicidal desire have supported the theory (Conner, Britton, Sworts, & Joiner, 2007; Joiner, Hollar, & Van Orden, 2006; Joiner et al., 2009; Van Orden, Witte, Gordon, Bender, & Joiner, 2008; Van Orden, Witte, James, et al., 2008), although only one report used a substance-dependent sample (Conner et al., 2007).
Empirically, indices of social connectedness are related to suicidal thoughts and behavior among individuals with SUDs in several ways. First, living alone is associated with suicide (Murphy, Wetzel, Robins, & McEvoy, 1992) and suicide attempts (Haw, Houston, Townsend, & Hawton, 2001). Second, low social support is associated with suicide attempts (Darke et al., 2007; Johnsson & Fridell, 1997; Kingree, Thompson, & Kaslow, 1999). Third, perceptions of belongingness are also related to a lower likelihood of a past suicide attempt (Conner et al., 2007).
Given the high degree of interpersonal impairment associated with substance dependence (Segrin, 2001), interpersonal factors may be especially important targets for suicide prevention in substance-dependent populations. Indeed, psychological autopsy studies (though uncontrolled) indicate that partner–family relational discord is more common among SUD individuals who died by suicide than among those with mood or anxiety disorders (Duberstein, Conwell, & Caine, 1993; Heikkinen et al., 1994; Rich, Fowler, Fogarty, & Young, 1988). Given the high prevalence of interpersonal problems among individuals with SUDs who die by suicide, the aim of the current study is to examine the relationships between suicidal ideation and attempts with several social connectedness indices simultaneously in order to identify which measure (or measures) of social connectedness may be especially relevant to suicidality among individuals with SUDs. With few exceptions (Conner et al., 2007; Duberstein et al., 1993; Heikkinen et al., 1994), studies of suicidal thoughts and behavior have used only a single measure of social connectedness, precluding comparisons among measures.
We used indices of social connectedness across several levels of analysis as proposed by Berkman, Glass, Brissette, and Seeman (2000). At the first level, structural components of the social network were measured by whether participants lived alone; perceived social support and perceived degree of conflict in relationships were measured at an intermediate level; and at the most microlevel, the inner state of thwarted belongingness was measured, which is presumed to reflect an unmet need to belong to meaningful relationships (Baumeister & Leary, 1995). We hypothesized that all indices of social connectedness would be associated with suicidal behaviors such that greater degrees of connection would be associated with reduced probability of a past suicide attempt and suicidal ideation.
Method Procedure
Participants were recruited from four residential substance-use treatment programs in upstate New York. Following brief announcements, participants who were interested in study participation were scheduled for a one-on-one screening session lasting about 30 min. All participants completed self-report questionnaires and received a $10 gift card. A small proportion of participants went on to complete a more in-depth research battery; the present results focus only on the screening data. The study procedures were approved by the institutional review board of the University of Rochester Medical Center and the University of Buffalo.
Participants
A total of 814 patients participated in the study. There were 584 men and 228 women, and 2 participants did not report their gender. The mean age of participants was 39.0 years (SD = 11.3), and 219 (26.9%) reported having fewer than 12 years of education. Of the sample, 477 (58.6%) identified themselves as non-Hispanic White, 282 (34.6%) as non-Hispanic Black, and 55 (6.8%) as other race/ethnicity. Diagnostic data are not available for the sample, because these data were collected as part of a brief screen.
Measures
Outcomes: suicidal ideation and suicide attempt
Lifetime attempt was assessed using a question (“Have you ever tried to kill yourself or attempt suicide?”) that shows high test–retest reliability (91.8% agreement, κ = .82) in substance-dependent patients (Conner et al., 2007). Lifetime ideation was assessed using a question from the National Comorbidity Survey (“Have you ever seriously thought about committing suicide?”; Kessler, Borges, & Walters, 1999). Three mutually exclusive groups included history of suicide attempt, with or without suicidal ideation (N = 207, 25.4%), no history of suicide attempt but history of suicidal ideation (N = 168, 20.6%), and no history of ideation or attempts (N = 439, 53.9%).
For the secondary analyses of attempters, two mutually exclusive subgroups were created using an item from the National Comorbidity Survey with the procedure described by Nock and Kessler (2006) to discriminate suicidal gestures without intent to die (“My attempt was a cry for help, I did not intend to die”) versus suicide attempts with intent (“I tried to kill myself, but knew the method was not foolproof” or “I made a serious attempt to kill myself and it was only luck that I did not succeed”). An item created for the project asking, “How did you feel after the attempt?” was used to create two mutually exclusive subgroups of those happy to be alive after the attempt (“100% wanted to be alive” or “Mostly wanted to be alive”) versus those who regretted surviving (“Mostly wanted to be dead” or “100% wanted to be dead”).
Assessments of social connectedness
The Interpersonal Needs Questionnaire (Van Orden, Witte, Gordon, et al., 2008) was used to assess belongingness with higher scores indicating more belongingness (internal consistency, α = .81). Participants were asked to rate 10 questions assessing one's beliefs about the degree to which they feel they belong to others on a 7-point Likert scale from not at all true for me to very true for me (α = .81). An example item is “These days I am close to other people.” Perceived social support was assessed with the Kessler Perceived Social Support scale (KPSS; Kessler et al., 1992), with higher scores indicating more social support. The scale asks (a) “How much do the following people listen to you if you need to talk about your worries or problems?”, (b) “How much do the following people understand the way you feel and think about things?”, and (c) “How much do the following people go out of their way to help you if you really needed it?” Participants rate each question for five different social relationships (spouse, family, friends, religious groups, and neighborhood) on a 4-point Likert scale from not at all to a great deal, and rate the overall satisfaction on a 6-point Likert scale from very dissatisfied to very satisfied (“Overall, how satisfied are you with that?”). The sum of all items was used as the overall level of perceived social support (internal consistency, α = .93). Interpersonal conflict was measured with the Test of Negative Social Exchange (TENSE; Ruehlman & Karoly, 1991), with higher scores indicating more frequent negative social exchanges including hostility, insensitivity, interference, and ridicule. Participants were asked to rate how often they have experienced such behaviors in the past 3 months on a 5-point scale from not at all to about everyday (α = .93). To measure living status, participants were asked to report their usual living arrangements during the 90 days prior to inpatient admission. We formed three mutually exclusive groups: (a) living alone; (b) living with family (with partner/significant other, with partner and children, with children, with other family); and (c) other living arrangements (incarcerated/jail/prison, homeless, psychiatric unit, inpatient alcohol/drug treatment, and other). Of the sample, 23.8% (N = 190) reported living alone, 55.2% (N = 440) living with family, and 21.0% (N = 167) other living arrangements.
Assessments of covariates
Demographic covariates included age, gender, ethnicity (non-Hispanic White, non-Hispanic Black, and other race/ethnicity), and education (<12 years or ≥12 years). For primary substance use, participants were asked to answer the question of “Which drug, including alcohol, is your primary substance of use?” We formed three mutually exclusive groups on the basis of the primary substance: alcohol, cocaine, and other. In support of validity, the item was highly correlated with items asking which drug caused “the most difficulty” (r = .90, p = .01) and the drug that was used “most often” in the past year (r = .92, p = .01). For breadth of drug use, the numbers of drugs that were used more than 1–2 times per week were calculated to create a continuous variable of the breadth of drug use (Conner, Swogger, & Houston, 2009). Alcohol-related severity is assessed using the Alcohol Use Disorders Identification Test (AUDIT; Bohn, Babor, & Kranzler, 1995), a 10-item self-report measure of drinking and alcohol-related problems in the past year (α = .92) Although more often used as a screen, the AUDIT has also been validated for use in clinical substance-use populations as a continuous measure of alcohol-related severity (Donovan, Kivlahan, Doyle, Longabaugh, & Greenfield, 2006). The Physicians Health Questionnaire (PHQ; Spitzer, Kroenke, & Williams, 1999) was used to assess the severity of depressive symptoms, excluding the suicide item (α = .87).
Data Analytic Strategy
Using multinominal logistic regression models (Hosmer & Lemeshow, 2000), we compared three mutually exclusive, unordered groups of attempt, ideation, and nonsuicidal participants. The method of profile likelihood (McCullagh & Nelder, 1989) was used to compute odds ratios and 95% confidence intervals. We first conducted univariate tests for each predictor variable and covariates to compare the ideation and attempt groups with the nonsuicidal reference group. Predictors were perceived social support, belongingness, interpersonal conflict, and living alone. Covariates included gender (female, reference), age, ethnicity (White, reference), education (≥12 years, reference), primary substance use (alcohol, reference), breadth of drug use, alcohol-related problem severity, and depressive symptoms. In multivariate analyses, we simultaneously examined the relationships between indices of social connectedness at different levels of analysis and the outcomes of both suicidal ideation and suicide attempts. Variables that were not significantly associated with either ideation or attempt with p > .05 in a univariate test were removed from the subsequent multivariate test. Finally, in secondary analyses of individuals who had made a suicide attempt, we compared subgroups of attempters with low versus high intent to die, as well as subgroups who were glad to have survived versus wished they had died, on the indices of social connectedness. These analyses explore the extent to which the connectedness variables may differ as a function of these clinically relevant aspects of attempts. If connectedness is more strongly associated with more severe attempts (i.e., suicide intent) and with a continued longing for death (i.e., wished had died), then it would suggest the importance of a focus on connectedness in the prevention of more serious acts of suicide.
ResultsThe majority of the sample was male (n = 584, 71.74%) and the average age was 39.0 years (SD = 11.3). Most identified as non-Hispanic White (n = 477, 58.6%) or non-Hispanic Black (n = 282, 34.6%). The majority of the sample reported at least 12 years of education (n = 595, 73.1%). Most reported living with family (n = 451, 55.4%). As is seen in Table 1, concerning the covariates, univariate results (odds ratios, 95% confidence intervals, and p values, respectively) indicate that men were significantly less likely to report a past attempt (0.40, 0.28–0.56, p < .01) and those with less than 12 years of education were significantly more likely to report both ideation (1.50, 1.01–2.23, p < .05) and attempt (1.57, 1.09–2.26, p < .05). Neither age nor ethnicity was predictive of ideation or attempt. Both severity of alcohol-related problems and depressive symptoms were significantly related to ideation (AUDIT score: 1.02, 1.01–1.04, p < .05; PHQ-9 score: 1.09, 1.06–1.12 p < .05) and attempt (AUDIT score: 1.03, 1.02–1.05, p < .05; PHQ-9 score: 1.09, 1.06–1.12 p < .05).
Univariate and Multivariate Results of Multinominal Regression Models Predicting Lifetime Suicide Ideation and Attempt
Concerning the predictors of interest, as is seen in Table 1, univariate results show that decreased levels of perceived social support (0.98, 0.97–0.99, p < .01) and belongingness (0.96, 0.95–0.98, p < .01) were associated with greater probability of ideation. Likewise, decreased levels of perceived social support (0.98, 0.96–0.98, p < .01) and belongingness (0.97, 0.96–0.98, p < .01) were associated with greater probability of attempt. A 1-point decrease on the perceived social support measure increased the probability of having ideation by 2% (1%–3%) and attempt by 2% (1%–4%); a 1-point decrease on the belongingness measure increased the probability of having ideation by 4% (2%–5%) and attempt by 3% (2%–4%). Consistently, increased levels of interpersonal conflict were associated with greater probability of ideation (1.03, 1.02–1.05, p < .01) and attempt (1.02, 1.01–1.03, p < .01). Living alone was associated with greater probability of attempt (1.57, 1.04–2.35, p < .05) but was not associated with ideation at a statistically significant level. Finally, none of the social connectedness indices differentiated between subgroups of attempters with (a) low versus high intent to die or (b) low versus high regret over surviving, suggesting that the interpersonal variables are relevant to attempts broadly but may not distinguish a more severe subgroup of attempter.
Multivariate results are presented in Table 1. Three variables that were not associated with either ideation or attempt in univariate analyses (age, ethnicity, and primary substance of use) were removed from the multivariate analysis. After adjustment, lower levels of belongingness were associated with greater probability of both ideation (0.98, 0.96–1.00, p < .05) and attempt (0.98, 0.97–1.00, p < .05). Lower levels of perceived social support were associated with greater probability of attempt (0.98, 0.97–0.99, p < .01) but not with ideation at a statistically significant level. Individuals living alone were more likely to attempt suicide than were those living with family (1.74, 1.11–2.72, p < .05).
DiscussionThe current study examined the relationships among several indices of social connectedness and lifetime histories of suicidal ideation and suicide attempt among individuals in residential substance-use treatment programs. In line with predictions, all indices of social connectedness—interpersonal conflict, low perceived social support, low belongingness, and living alone—were associated with an increased probability of a history of suicide attempt and history of ideation (with the exception of living alone, which was associated with attempt only). In the multivariate model with all indices of social connectedness included, as well as covariates, interpersonal conflict and belongingness were significant predictors of a history of suicidal ideation, and belongingness, perceived social support, and living alone were significant predictors of suicide attempt. Thus, among individuals with SUDs, indices of current social connectedness at several levels of analyses are associated with lifetime histories of suicidal ideation and attempt. Future research could examine whether these indices may function as indicators of on-going elevated risk for suicidality. Finally, we found the most consistent support for the relationship between suicidal ideation and suicide attempts and belongingness, which is the form of social connectedness posited by the interpersonal theory of suicide to be a key factor in desire for suicide. Thus, our results provide additional empirical support for the theory and its applicability to patients treated for SUDs (Conner et al., 2007).
Our findings should be considered within the context of the study's limitations. Suicidal ideation and attempts were measured retrospectively, thus precluding an examination of temporal and causal relations. Furthermore, associations between social connectedness and current suicidality were not analyzed, and it is possible that some measures of social connectedness may display different relations with current suicidality. Other sources of heterogeneity of suicide attempts were not available; for example, data on the number of past attempts were not available, thus precluding an examination of whether indices of social connectedness function differently for multiple versus single attempters. We do not have diagnostic data for these participants, thus precluding analyses examining whether diagnostic categories function as either distal contributors to—or consequences of—social disconnection, thereby exploring one mechanism whereby mental disorders may elevate risk for suicide. Our sample consisted of adults receiving treatment at residential SUD treatment programs, thus caution must be taken when generalizing these findings beyond this high-risk population. An assessment of burdensomeness, the other key interpersonal predictor in the interpersonal theory, is not available.
Regarding clinical implications, the single-item question measuring whether or not participants lived alone is a quickly and easily administered index of social connectedness, and our data suggest that it is reliably associated with a history of a past attempt. Research is needed to investigate mechanisms whereby living alone confers risk; in the meantime, we suggest that clinicians working with SUD patients should routinely inquire about living status and take into consideration living alone in their suicidal behavior risk formulations. The measure of belongingness (Van Orden, Witte, Gordon, et al., 2008), a straightforward 10-item self-report scale, could also be administered and scored rapidly as part of a risk assessment. Future studies could investigate whether interventions for SUDs that specifically target patients' connectedness, particularly belongingness, reduce the risk for suicidal behavior.
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Submitted: November 11, 2009 Revised: July 1, 2010 Accepted: July 11, 2010
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Source: Psychology of Addictive Behaviors. Vol. 25. (1), Mar, 2011 pp. 180-184)
Accession Number: 2010-25604-001
Digital Object Identifier: 10.1037/a0020936
Record: 149- Title:
- Social influences on smoking in middle-aged and older women.
- Authors:
- Holahan, Charles J.. Department of Psychology, University of Texas at Austin, Austin, TX, US, holahan@psy.utexas.edu
North, Rebecca J.. Department of Psychology, University of Texas at Austin, Austin, TX, US
Holahan, Carole K.. Department of Psychology, University of Texas at Austin, Austin, TX, US
Hayes, Rashelle B.. Division of Preventive and Behavioral Medicine, University of Massachusetts Medical School, MA, US
Powers, Daniel A.. Department of Sociology and Population Research Center, University of Texas at Austin, Austin, TX, US
Ockene, Judith K.. Division of Preventive and Behavioral Medicine, University of Massachusetts Medical School, MA, US - Address:
- Holahan, Charles J., Department of Psychology, University of Texas at Austin, A8000, Austin, TX, US, 78712, holahan@psy.utexas.edu
- Source:
- Psychology of Addictive Behaviors, Vol 26(3), Sep, 2012. pp. 519-526.
- NLM Title Abbreviation:
- Psychol Addict Behav
- Page Count:
- 8
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- Bulletin of the Society of Psychologists in Addictive Behaviors; Bulletin of the Society of Psychologists in Substance Abuse
- Other Publishers:
- US : Educational Publishing Foundation
Society of Psychologists in Addictive Behaviors - ISSN:
- 0893-164X (Print)
1939-1501 (Electronic) - Language:
- English
- Keywords:
- aging, living with smoker, smoking, social support, women's health, social influence
- Abstract:
- The purpose of this study was to examine the role of 2 types of social influence—general social support and living with a smoker—on smoking behavior among middle-aged and older women in the Women's Health Initiative (WHI) Observational Study. Participants were postmenopausal women who reported smoking at some time in their lives (N = 37,027), who were an average age of 63.3 years at baseline. Analyses used multiple logistic regression and controlled for age, educational level, and ethnicity. In cross-sectional analyses, social support was associated with a lower likelihood and living with a smoker was associated with a higher likelihood of being a current smoker and, among smokers, of being a heavier smoker. Moreover, in prospective analyses among baseline smokers, social support predicted a higher likelihood and living with a smoker predicted a lower likelihood of smoking cessation 1-year later. Further, in prospective analyses among former smokers who were not smoking at baseline, social support predicted a lower likelihood and living with a smoker predicted a higher likelihood of smoking relapse 1-year later. Overall, the present results indicate that social influences are important correlates of smoking status, smoking level, smoking cessation, and smoking relapse among middle-aged and older women. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Aging; *Living Arrangements; *Social Support; *Tobacco Smoking; Health; Human Females; Social Influences
- Medical Subject Headings (MeSH):
- Age Factors; Aged; Female; Follow-Up Studies; Humans; Middle Aged; Prospective Studies; Recurrence; Smoking; Smoking Cessation; Social Facilitation; Social Support; Statistics as Topic
- PsycINFO Classification:
- Substance Abuse & Addiction (3233)
- Population:
- Human
Female - Location:
- US
- Age Group:
- Adulthood (18 yrs & older)
Middle Age (40-64 yrs)
Aged (65 yrs & older) - Tests & Measures:
- Medical Outcomes Study Social Support Survey DOI: 10.1037/t04034-000
- Grant Sponsorship:
- Sponsor: National Institute on Drug Abuse, US
Grant Number: 1R03DA025225-01A1
Recipients: No recipient indicated - Methodology:
- Empirical Study; Followup Study; Longitudinal Study; Quantitative Study
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- First Posted: Oct 17, 2011; Accepted: Aug 16, 2011; Revised: Jul 19, 2011; First Submitted: Jun 1, 2011
- Release Date:
- 20111017
- Correction Date:
- 20120917
- Copyright:
- American Psychological Association. 2011
- Digital Object Identifier:
- http://dx.doi.org/10.1037/a0025843
- PMID:
- 22004130
- Accession Number:
- 2011-23443-001
- Number of Citations in Source:
- 51
- Persistent link to this record (Permalink):
- http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2011-23443-001&site=ehost-live
- Cut and Paste:
- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2011-23443-001&site=ehost-live">Social influences on smoking in middle-aged and older women.</A>
- Database:
- PsycINFO
Social Influences on Smoking in Middle-Aged and Older Women
By: Charles J. Holahan
Department of Psychology, University of Texas at Austin;
Rebecca J. North
Department of Psychology, University of Texas at Austin
Carole K. Holahan
Department of Kinesiology and Health Education, University of Texas at Austin
Rashelle B. Hayes
Division of Preventive and Behavioral Medicine, University of Massachusetts Medical School
Daniel A. Powers
Department of Sociology and Population Research Center, University of Texas at Austin
Judith K. Ockene
Division of Preventive and Behavioral Medicine, University of Massachusetts Medical School
Acknowledgement: This work was supported by a grant from the National Institute on Drug Abuse (1R03DA025225-01A1). We acknowledge the contribution of all Women's Health Initiative Centers and their participants, staff, and investigators. In addition, we thank David Collins for assistance in preparing this article.
This article was prepared using a limited access dataset obtained from the National Heart Lung and Blood Institute (NHLBI) of the U.S. National Institutes of Health. The Women's Health Initiative (WHI) is conducted and supported by the NHLBI in collaboration with the WHI Study Investigators. The present manuscript was reviewed and approved for publication by the WHI Publications and Presentation Committee.
The U.S. Surgeon General has emphasized the importance of longitudinal research on smoking among women across adulthood (U.S. Department of Health and Human Services, 2001). Especially needed is an understanding of determinants of smoking in older women (Donze, Ruffieux, & Cornuz, 2007). A growing body of evidence indicates that social relationships shape health behavior throughout adulthood (Umberson, Crosnoe, & Reczek, 2010); however, research on social influences in smoking has focused primarily on adolescence. The purpose of this study was to examine the role of two types of social influence—social support and living with a smoker—in smoking among middle-aged and older women in the Women's Health Initiative (WHI) Observational Study.
Women and SmokingCigarette smoking is an important causal factor in morbidity and mortality among women in adulthood (Husten et al., 1997; LaCroix et al., 1991; Healthy People, 2010 (U.S. Department of Health and Human Services, 2000). From 2000 to 2004, almost 174,000 annual deaths among women were attributed to smoking-related causes, principally from cancer, cardiovascular diseases, and respiratory diseases (Centers for Disease Control and Prevention, 2008). Lung cancer now causes greater cancer-related mortality among women than breast cancer (U.S. Department of Health and Human Services, 2001). Cigarette smoking also contributes to many other cancers, as well as lower bone density and increased risk of hip fractures in postmenopausal women (U.S. Department of Health and Human Services, 2004).
Middle-aged and older women can reap substantial health benefits from smoking cessation (Burns, 2000; Hermanson, Omenn, Kronmal, & Gersh, 1988; Ockene, 1993; U.S. Department of Health and Human Services, 1990, 2001, 2004). Smoking cessation in middle-aged and older individuals is associated with improvement in immediate and longer-term health (Hermanson et al., 1988; Taylor, Hasselblad, Henley, Thun, & Sloan, 2002; U.S. Department of Health and Human Services, 1990). In addition, although fewer older adults attempt to quit smoking as compared with younger persons (U.S. Department of Health and Human Services, 2000), they are just as likely or more likely to be successful in their attempts to quit smoking (Burns, 2000; Sorlie & Kannel, 1990; Whitson, Heflin, & Burchett, 2006).
Social Support and SmokingSocial support has been a central focus in research on social ties and health behavior (Taylor & Repetti, 1997; Umberson et al., 2010). For example, the transtheoretical model of change lists helping relationships as a key process in changing health behaviors (Prochaska, Johnson, & Lee, 2009). However, although research evidence generally suggests a relationship between social support and smoking, effects of social support on smoking cessation have not been consistently significant (Lichtenstein, Glasgow, & Abrams, 1986). Further, while research has tended to focus on smoking-specific social support, general social support from family and friends may play an important role in smoking behavior (Wagner, Burg, & Sirois, 2004). Moreover, few studies have investigated social support and smoking in large samples of women (Väänänen, Kouvonen, Kivimäki, Pentti, & Vahtera, 2008), and fewer have investigated this relationship among middle-aged and older women.
Some cross-sectional evidence from community surveys documents an inverse association between social support and smoking status. Among over 20,000 mixed-aged female employees in Finland, low social support was positively associated with being a current smoker and heavier smoker and with a lower likelihood of reporting having quit smoking (Väänänen et al., 2008). Similarly, among almost 2,000 Lebanese women, those who experienced less trust and felt more isolated were more likely to smoke. However, these effects were observed only among younger women (Afifi, Nakkash, & Khawaja, 2010).
Additional prospective evidence documents a positive association between social support and smoking cessation in the context of smoking cessation interventions. For example, early research with community adults in a university-based smoking cessation program indicated that both perceived partner support and perceived general support predicted quitting smoking (Mermelstein, Cohen, Lichtenstein, Baer, & Kamarck, 1986). Further, a social support group intervention among employees from diverse worksites in the Chicago area predicted increased smoking cessation two years later (McMahon & Jason, 2000). Similarly, a social support addition to a behavioral smoking cessation intervention among residents of Calgary, Canada, improved cessation at three months among both women and men. However, at six months, cessation was maintained only among men (Carlson, Goodey, Bennett, Taenzer, & Koopmans 2002). Moreover, among lower-educated women in a community-based smoking cessation program, high social support weakened the negative relationship between history of depression and smoking cessation (Turner, Mermelstein, Hitsman, & Warnecke, 2008).
Effects of Living With a SmokerAlthough research on social ties and health behavior generally has assumed that social ties play a salutary role, in fact ties to others can also encourage health risk behaviors (Umberson, Crosnoe, & Reczek, 2010). In fact, for both adolescents and adults, negative health behaviors, such as smoking, often are learned and reinforced in group contexts (Taylor & Repetti, 1997; Väänänen et al., 2008). For example, after following over 9,000 middle-aged couples for up to eight years, Falba and Sindelar (2008) found that partners shape one another's health habits for good and for bad. Effects were especially strong for tobacco where a partner's behavior may operate as a smoking cue. Correspondingly, household smoking bans promote attempts to quit smoking and more successful cessation outcomes among community adults (Farkas, Gilpin, Distefan, & Pierce, 1999; Gilpin, White, Farkas, & Pierce, 1999). However, because most research in this area has focused on younger or mixed-age samples, little is known about the effect of living with a smoker among older women smokers.
Large-sample community surveys have shown consistent adverse effects for living with a smoker on smoking level and smoking cessation among younger Danish women (Mueller, Munk, Thomsen, Frederiksen, & Kjaer, 2007), recently married New York couples (Dollar, Homish, Kozlowski, & Leonard, 2009), and mixed-age British householders (Chandola, Head, & Bartley, 2004). Similarly, in the context of smoking cessation interventions, consistent adverse effects for living with a smoker on cessation have been observed in large samples of mixed-aged adults in Britain (Ferguson, Bauld, Chesterman, & Judge, 2005), Australia (Gourlay et al., 1994), and Italy (Senore et al., 1998). Moreover, there is some evidence from smoking cessation interventions with large mixed-aged samples of adults in the U.S. (Bjornson et al., 1995) and Australia (Moshammer & Neuberger, 2006) that women's smoking may be more adversely influenced than men's by living with a smoker.
In addition to its adverse direct effect on smoking, it is also plausible that living with a smoker may diminish the salutary effect of general social support on smoking outcomes. This question may be especially relevant to women, who are more likely to report inconsistency between perceived norms to quit smoking and the smoking behavior of a partner (Dohnke, Weiss-Gerlach, & Spies, 2011). Very little research has examined this question. Pollak and Mullen (1997) studied a small sample of pregnant women who had spontaneously quit smoking. General social support from a partner was positively associated with continued abstinence six weeks postpartum, but only for women whose partners did not smoke. Reflecting on this pattern of findings, the authors concluded that: “partners evidently cannot override their smoking with general social support” (p. 186). In a similar vein, alliance with a buddy to provide social support enhanced quitting in a self-help smoking cessation program in the Chicago area, but only when the buddy was not a continuing smoker (Kviz, Crittenden, Madura, & Warnecke, 1994).
The Present StudyThe present study examined the relation of two types of social influence—general social support in the emotional, informational, leisure, and tangible domains and living with a smoker—on smoking in 37,027 middle-aged and older women using data from the WHI Observational Study. The WHI Observational Study was framed to examine the role of lifestyle factors in the prevention of heart disease, some cancers, and osteoporosis in women who were postmenopausal (Hays et al., 2003). Participants were recruited from urban, suburban, and rural areas surrounding clinical centers in the United States. The WHI Observational Study presents a unique opportunity to examine the separate and interactive effects of social support and living with a smoker on a range of smoking outcomes in a large sample of middle-aged and older women. Smoking outcomes include point-prevalence measures of smoking status and smoking level at baseline and smoking cessation and smoking relapse assessed at a 1-year follow-up.
Two hypotheses were advanced. (a) Extending previous research on the role of social support in smoking in mixed-aged samples (McMahon & Jason, 2000; Väänänen et al., 2008), we hypothesized that social support would be negatively associated with smoking status, heavier smoking, and smoking relapse and positively associated with smoking cessation. (b) Extending previous research on the role of living with a smoker in smoking in mixed-aged samples (Chandola et al., 2004; Falba & Sindelar, 2008), we hypothesized that living with a smoker would be positively associated with smoking status, heavier smoking, and smoking relapse and negatively associated with smoking cessation. In addition, we examined one exploratory question. Extending previous research on the interactive roles of social support and living with a smoker on smoking among young women (Pollak & Mullen, 1997), we examined whether living with a smoker would weaken the hypothesized associations between social support and smoking outcomes.
Methods Sample Selection and Characteristics
The WHI Observational Study included women between the ages of 50 and 79 who were postmenopausal at enrollment between 1993 and 1998 (Hays et al., 2003). The original purpose of the WHI Observational Study was to explore the predictors and natural history of important causes of morbidity and mortality in postmenopausal women related to heart disease, cancers, and osteoporosis. Postmenopausal was defined as not having a menstrual period for at least 6 months if age was 55 years or older, and more conservatively as having no menstrual period for at least 12 months for younger women aged 50–54.
Inclusion criteria included the ability and willingness to provide written informed consent and plans to stay in the same area for at least 3 years. Potential participants were excluded if they had medical conditions that predicted survival of less than 3 years, or if they had conditions such as alcohol or drug dependency, mental illness, including severe depression or dementia, which might affect retention. The WHI sample was healthier and reported a lower prevalence of smoking than the general population of women in their cohort (Langer et al., 2003). The inclusion of participants from racial/minority groups proportionate to their age-group representation in the U.S. population was a priority (Hays et al., 2003). Details of the WHI design have been published previously (Hays et al., 2003; Langer et al., 2003; Women's Health Initiative Study Group, 1998). Of participants who completed the WHI screening form and who were not assigned to a clinical trial, 93,676 (30.9%) were successfully enrolled in the WHI Observational Study (Hays et al., 2003).
Because smoking initiation is unlikely in middle to later adulthood (Moon-Howard, 2003), the present analyses were restricted to participants who reported smoking at some time in their lives. Specifically, the present sample includes the 37,027 (40%) of baseline participants in the WHI Observational Study who reported that they “smoked at least 100 cigarettes” during their entire life and who also provided complete data on the measures used here. At baseline, the participants in the present sample were an average age of 63.3 (SD = 7.19) years. The majority of participants (59%) were married. The sample was predominantly White (86.6%), with the remainder of the sample American Indian/Alaskan Native (0.4%), Asian/Pacific Islander (1.5%), Black (8.0%), Hispanic (2.5%), and Unknown (1.0%). Both White and Black ethnicity categories specified not-of-Hispanic origin. In terms of education, 4.7% of participants had less than a high school education, 15.5% had a high school (or vocational school) education, 39.2% had some education beyond high school but had not completed college, and 40.5% had completed college.
Measures
Sociodemographic factors, social support, and living with a smoker were assessed at baseline and smoking outcomes were assessed at baseline and at a 1-year follow-up by self-report with standardized questionnaires.
Sociodemographic factors
Sociodemographic factors used as control variables included age (in years), educational level, and ethnicity. Educational level was operationalized as less than a high school (or vocational school) education, high school (or vocational school) education, some education beyond high school (or vocational school) but not having completed college, and completed college.
Social support
Social support was measured with 9 items from the Medical Outcomes Study Social Support Survey (Sherbourne & Stewart, 1991). The items tapped emotional, informational, leisure, and tangible dimensions of general support. Items were preceded by a prompt: “How often is each of the following kinds of support available to you if you need it?” A sample item is “Someone you can count to listen to you when you need to talk.” Responses ranged from 1, none of the time, to 5, all of the time. Total scores (range = 9–45) are the sum of scores for the nine items. In the present sample, Cronbach's alpha = .93.
Living with a smoker
Living with a smoker in one's home was indexed by a single item: “Does anyone living with you now smoke cigarettes inside your home?” (no = 0, yes = 1). Among participants living with a smoker, most (57.4%) household smokers were partners. Among participants living with a smoker who was not a partner, most household smokers (62.1%) were a daughter or son versus some “other person.”
Smoking outcomes
Four smoking outcomes were assessed. At baseline, we assessed smoking status and, among current smokers, smoking level. In addition, at a 1-year follow-up, we assessed smoking cessation among baseline smokers and smoking relapse among former smokers who were not smoking at baseline.
Smoking status at baseline was indexed based on responses to an item that asked “Do you smoke cigarettes now” (no = 0, yes = 1). Level of smoking among smokers was indexed based on responses to a question that asked, “On the average, how many cigarettes do you usually smoke each day?” Response choices were: Less than 1, 1–4, 5–14, 15–24, 25–34, 35–44, and 45 or more. Following Hatsukami et al. (2006) and Holahan et al. (in press), we operationalized light smoking as less than 15 cigarettes per day (score = 0) and heavier smoking as 15 or more cigarettes per day (score = 1). Among baseline smokers, smoking cessation at 1 year was operationalized as reporting no smoking (score = 1) versus smoking (score = 0) at the 1-year follow-up. Among former smokers who were nonsmokers at baseline, smoking relapse at 1 year was operationalized as reporting smoking (score = 1) versus no smoking (score = 0) at the 1-year follow-up.
Data Analysis Strategy
Multiple logistic regression analyses were used to analyze the relation of social influences to smoking outcomes. First, separate cross-sectional analyses examined smoking status and, among current smokers, smoking level at baseline. Next, separate prospective analyses examined smoking cessation among baseline smokers and smoking relapse among baseline nonsmokers at a 1-year follow-up. In each analysis, we began with a model that included social support and living with a smoker as predictors. Next, we examined a model that included the interaction between social support and living with a smoker. If the interaction was significant, it was retained in the model; if it was not significant, it was not retained in the model. All analyses controlled for age (in years), educational level (less than a high school education was the reference group), and ethnicity (White was the reference group).
Results Descriptive Statistics
At baseline, mean social support was 35.7 (SD = 7.9), and 4,012 participants (10.8%) lived with a smoker. Current smoking was reported by 4,834 participants (13.1%) at baseline. Baseline smokers began smoking at a median age of 15–19 years and had been regular smokers for a median of 30–39 years. Most baseline smokers (51.7%) were light smokers. Among baseline smokers, smoking cessation was reported by 706 of 4,407 participants (16.0%) who provided data at the 1-year follow-up. Among baseline nonsmokers, smoking relapse was reported by 349 of 30,516 participants (1.1%) who provided data at the 1-year follow-up.
Analyses of Missing Data and Attrition
Missing data
Overall, there were relatively little missing data, except for living with a smoker for which 15.4% of participants had missing data. Among the full sample of 45,304 baseline participants who reported smoking at some time in their lives, we compared participants who provided sufficient data to be included in the present analyses (N = 37,027) with those who did not provide sufficient data (n = 8,277, 18.3%). The only noteworthy difference involved ethnicity, χ2(5, N = 45,185) = 119.67, p > .01, with missing data most likely among Hispanics (27.5%) and least likely among Whites (17.4%).
1-year attrition
In addition, among the 37,027 participants included in the present analyses, we compared surviving participants (n = 34,923) with those who did not participate at the 1-year follow-up (n = 2,104, 5.7%). The only noteworthy differences involved educational level and ethnicity. For educational level, χ2(3, N = 37,027) = 283.03, p > .01, missing data were more likely among participants with less than a high school education (13.8%) compared with other educational groups (average of 5.3%). For ethnicity, χ2(5, N = 37,027) = 820.11, p > .01, missing data were most likely among American Indian/Alaskan Natives (14.6%), Blacks (15.7%), and Hispanics (14.5%), and least likely Whites (4.4%).
Baseline Smoking
Smoking status
We began by examining the cross-sectional association between social influences and current smoking status at baseline in a multiple logistic regression analysis (N = 37,027). Controlling for age, educational level, and ethnicity, both social support (standardized) and living with a smoker were significantly and uniquely associated with current smoking status at baseline. Specifically, a 1 standard deviation increase in social support was linked to a 17% decrease in the odds of being a current smoker. Compared with not living with a smoker, living with a smoker was associated with a more than sixfold increase in the odds of being a current smoker.
Next, we investigated the possible interactive effect of social support and living with a smoker. The interaction was significantly positively related to current smoking status at baseline and was retained in the model. Specifically, living with a smoker attenuated the association between social support and smoking status. Results for the final model are presented in Table 1.
Results of Multiple Logistic Regression Analyses Predicting Current Smoking Status Among Participants Who Smoked at Some Time in Their Lives and, Among Current Smokers, Level of Smoking at Baseline
To illustrate the interactive effect of social support and living with a smoker, we examined the association between social support and smoking status under contrasting levels of living with a smoker. Among individuals who did not live with a smoker, each 1 standard deviation increase in social support was linked to a 20% decrease in the odds of being a current smoker. In contrast, among individuals who lived with a smoker, each 1 standard deviation increase in social support was linked to an 8% decrease in the odds of being a current smoker.
Smoking level
In addition, we examined the cross-sectional association between social influences and current smoking level among current smokers at baseline in a multiple logistic regression analysis (n = 4,834). Controlling for age, educational level, and ethnicity, both social support (standardized) and living with a smoker were significantly and uniquely associated with current smoking level at baseline. Specifically, a 1 standard deviation increase in social support was linked to a 12% decrease in the odds of being a heavy smoker. Compared with not living with a smoker, living with a smoker was associated with a 34% increase in the odds of being a heavy smoker.
Next, we investigated the possible interactive effect of social support and living with a smoker. The interaction was significantly positively related to smoking level at baseline and was retained in the model. Specifically, living with a smoker attenuated the association between social support and smoking level among current smokers. Results for the final model are presented in Table 1.
To illustrate the interactive effect of social support and living with a smoker, we examined the association between social support and smoking level under contrasting levels of living with a smoker. Among individuals who did not live with a smoker, each 1 standard deviation increase in social support was linked to a 16% decrease in the odds of being a heavy smoker. In contrast, among individuals who lived with a smoker, each 1 standard deviation increase in social support was linked to a 4% decrease in the odds of being a heavy smoker.
Prospective Analyses
Smoking cessation
Next, we examined the prospective association between social influences and smoking cessation among baseline smokers in a multiple logistic regression analysis (n = 4,407). Controlling for age, educational level, and ethnicity, both social support (standardized) and living with a smoker were significantly and uniquely associated with smoking cessation among baseline smokers. Next, we investigated the possible interactive effect of social support and living with a smoker. The interaction was not significantly related to smoking cessation and was not retained in the model. Results are presented in Table 2. Each 1 standard deviation increase in social support was linked to a 20% increase in the odds of quitting smoking. Compared with not living with a smoker, living with a smoker was linked to a 26% decrease in the odds of quitting smoking.
Results of Prospective Multiple Logistic Regression Analyses Predicting Smoking Cessation Among Baseline Smokers and Smoking Relapse Among Baseline Nonsmokers at 1-Year Follow-Up
Smoking relapse
We also examined the prospective association between social influences and smoking relapse among former smokers who were not smoking at baseline in a multiple logistic regression analysis (n = 30,516). Controlling for age, educational level, and ethnicity, both social support (standardized) and living with a smoker were significantly and uniquely associated with smoking relapse among former smokers who were not smoking at baseline. Next, we investigated the possible interactive effect of social support and living with a smoker. The interaction was not significantly related to smoking relapse and was not retained in the model. Results are presented in Table 2. Each 1 standard deviation increase in social support was linked to a 20% decrease in the odds of relapsing into smoking. Compared with not living with a smoker, living with a smoker was associated with a 128% increase in the odds of relapsing into smoking.
DiscussionThe present findings demonstrate a consistent link between social influences and negative smoking-related behaviors among middle-aged and older women in the WHI Observational Study who smoked at some point in their lives. Extending previous research on the role of social support in smoking in mixed-aged samples (McMahon & Jason, 2000; Väänänen et al., 2008), we found that social support was consistently inversely associated with all of the smoking outcomes we investigated. Further, extending previous research on the role of living with a smoker in smoking in mixed-aged samples (Chandola et al., 2004; Falba & Sindelar, 2008), we found that living with a smoker was consistently positively associated with all of the smoking outcomes we investigated. The strength of these findings may be due in part to the nature of the sample. Current smokers in the sample were more likely to be light smokers, and light compared with heavier smoking is more likely to be influenced by environmental factors (Shiffman, 2009).
Specifically, general social support was associated with a lower likelihood and living with a smoker was associated with a higher likelihood of being a current smoker and, among smokers, of being a heavier smoker. Moreover, among baseline smokers, social support predicted a higher likelihood and living with a smoker predicted a lower likelihood of smoking cessation 1-year later. Further, among former smokers who were not smoking at baseline, social support predicted a lower likelihood and living with a smoker predicted a higher likelihood of smoking relapse 1-year later. All of these effects were unique contributions for both social support and living with a smoker controlling for one another as well as for age, educational level, and ethnicity.
General social support from family and friends may reduce smoking in several ways. A perception of positive regard from significant others may motivate self-care behaviors (Wagner et al., 2004). In addition, family and friends may explicitly endorse behaviors that enhance the health of loved ones (Väänänen et al., 2008). Further, social support may reduce stress and depressed mood (Umberson et al., 2010; Väänänen et al., 2008). Further, supportive others may further perceptions of self-efficacy toward desired health behaviors (Umberson et al., 2010).
On the other hand, living with a smoker may increase smoking in several ways. At a psychological level, for individuals attempting to quit smoking, abstinence from smoking on the part of partners or housemates may be perceived as a form of social support (Pollak & Mullen, 1997). Partners or housemates who smoke may also foster a household norm that legitimizes smoking behavior and signals a lack of communal commitment to reducing negative health behaviors more generally (Umberson, Crosnoe, & Reczek, 2010). In addition, at a behavioral level, smoking by others in the household provides smoking cues (Falba & Sindelar, 2008). More practically, living with a smoker also results in an easy availability of cigarettes (Chandola, Head, & Bartley, 2004).
Extending previous research on the interactive roles of social support and living with a smoker on smoking among young women (Kviz, Crittenden, Madura, & Warnecke, 1994; Pollak & Mullen, 1997), we found that living with a smoker weakened the cross-sectional inverse association of social support with both smoking status and smoking level. In contrast, the prospective association of social support with both smoking cessation and smoking relapse was independent of living with a smoker.
Social support may have been less reactive to living with a smoker in the context of change in health behavior where social support is especially likely to be sought and valued for promoting change (see Prochaska et al., 2009). Alternatively, social support may have been more stable than housemates' smoking behavior across the 1-year follow-up period. Whereas self-reports of available social support are highly stable across time (Sarason, Sarason, & Shearin, 1986), partners' health behaviors often change in unison (Falba & Sindelar, 2008) and household smokers may themselves have quit smoking at follow-up.
Some limitations should be kept in mind in interpreting these results. The WHI Observational Study measure of smoking relied on self-report. However, several comparisons of self-report with biochemical or cross-informant measures of smoking have found that self-report measures are accurate in most situations, particularly, as in the WHI, in studies of adults who are not in smoking intervention studies (Caraballo, Giovino, Pechacek, & Mowery, 2001; Rebagliato, 2002). Nevertheless, future research would be strengthened by including objective or collateral measures of smoking. Moreover, because participants in the WHI Observational Study were healthier and reported a lower prevalence of smoking than the general population of women in their cohort (Langer et al., 2003), the results may not generalize to all middle-aged and older women. Further, missing data on the variables examined here resulted in an underrepresentation of Hispanics in our baseline analyses and 1-year attrition resulted in an underrepresentation of several ethnic minority groups (Indian/Alaskan Natives, Blacks, and Hispanics), as well as participants with less than a high school education, in our follow-up analyses.
The present study has several strengths. A central contribution is the analysis of social influences and smoking in middle-aged and older women, a population that has been neglected in smoking research. Additional strengths are the large sample, the longitudinal design, and the availability of well-validated measures of social influences. Overall, the present results indicate that social influences are important correlates of smoking status, smoking level, smoking cessation, and smoking relapse among middle-aged and older women. Moreover, our findings demonstrate that the effects of social ties are complex. Whereas, positive social support discourages smoking, living with a smoker maintains it. In fact, as Umberson et al. (2010) have noted, the counter effects of positive and negative influences have likely resulted in an underappreciation of the role of social ties in health behavior.
Our results suggest that addressing social influences can contribute to the effectiveness of smoking intervention programs with middle-aged and older women. Our findings reinforce U.S. Public Health Service clinical practice guidelines for treating tobacco use (Fiore et al., 2000) that encourage incorporating social support both within and outside of treatment. At the same time, our findings underscore the need for a more textured appreciation of the adverse, as well as the salutary, effects of social ties. For example, training in cognitive-behavioral skills for relapse prevention might be tailored to include coping with the adverse effects of living with a smoker. Further, when partners or housemates smoke, group interventions, including household smoking bans, may be especially effective.
Footnotes 1 The WHI did not assess nondaily, intermittent smoking.
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Submitted: June 1, 2011 Revised: July 19, 2011 Accepted: August 16, 2011
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Source: Psychology of Addictive Behaviors. Vol. 26. (3), Sep, 2012 pp. 519-526)
Accession Number: 2011-23443-001
Digital Object Identifier: 10.1037/a0025843
Record: 150- Title:
- Sociodemographic and psychiatric diagnostic predictors of 3-year incidence of DSM–IV substance use disorders among men and women in the National Epidemiologic Survey on Alcohol and Related Conditions.
- Authors:
- Goldstein, Risë B.. Laboratory of Epidemiology and Biometry, Division of Intramural Clinical and Biological Research, National Institute on Alcohol Abuse and Alcoholism, National Institutes of Health, Bethesda, MD, US, goldster@mail.nih.gov
Smith, Sharon M.. Laboratory of Epidemiology and Biometry, Division of Intramural Clinical and Biological Research, National Institute on Alcohol Abuse and Alcoholism, National Institutes of Health, Bethesda, MD, US
Dawson, Deborah A.. Laboratory of Epidemiology and Biometry, Division of Intramural Clinical and Biological Research, National Institute on Alcohol Abuse and Alcoholism, National Institutes of Health, Bethesda, MD, US
Grant, Bridget F.. Laboratory of Epidemiology and Biometry, Division of Intramural Clinical and Biological Research, National Institute on Alcohol Abuse and Alcoholism, National Institutes of Health, Bethesda, MD, US - Address:
- Goldstein, Risë B., Laboratory of Epidemiology and Biometry, Division of Intramural Clinical and Biological Research, National Institute on Alcohol Abuse and Alcoholism, National Institutes of Health, M. S. 9304, 5635 Fishers Lane, Bethesda, MD, US, 20892-9304, goldster@mail.nih.gov
- Source:
- Psychology of Addictive Behaviors, Vol 29(4), Dec, 2015. pp. 924-932.
- NLM Title Abbreviation:
- Psychol Addict Behav
- Page Count:
- 9
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- Bulletin of the Society of Psychologists in Addictive Behaviors; Bulletin of the Society of Psychologists in Substance Abuse
- Other Publishers:
- US : Educational Publishing Foundation
Society of Psychologists in Addictive Behaviors - ISSN:
- 0893-164X (Print)
1939-1501 (Electronic) - Language:
- English
- Keywords:
- incidence, predictors, epidemiology, substance use disorders, gender differences
- Abstract:
- Incidence rates of alcohol and drug use disorders (AUDs and DUDs) are consistently higher in men than women, but information on whether sociodemographic and psychiatric diagnostic predictors of AUD and DUD incidence differ by sex is limited. Using data from Waves 1 and 2 of the National Epidemiologic Survey on Alcohol and Related Conditions, sex-specific 3-year incidence rates of AUDs and DUDs among United States adults were compared by sociodemographic variables and baseline psychiatric disorders. Sex-specific logistic regression models estimated odds ratios for prediction of incident AUDs and DUDs, adjusting for potentially confounding baseline sociodemographic and diagnostic variables. Few statistically significant sex differences in predictive relationships were identified and those observed were generally modest. Prospective research is needed to identify predictors of incident DSM-5 AUDs and DUDs and their underlying mechanisms, including whether there is sex specificity by developmental phase, in the role of additional comorbidity in etiology and course, and in outcomes of prevention and treatment. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Alcohol Abuse; *Demographic Characteristics; *Drug Abuse; *Mental Disorders; *Substance Use Disorder; Diagnosis; Epidemiology; Human Sex Differences
- PsycINFO Classification:
- Substance Abuse & Addiction (3233)
- Population:
- Human
Male
Female - Location:
- US
- Age Group:
- Adulthood (18 yrs & older)
Young Adulthood (18-29 yrs) - Tests & Measures:
- Alcohol Use Disorder and Associated Disabilities Interview Schedule—DSM–IV Versions
- Grant Sponsorship:
- Sponsor: National Institute on Alcohol Abuse and Alcoholism, US
Other Details: National Epidemiologic Survey on Alcohol and Related Conditions (NESARC)
Recipients: No recipient indicated
Sponsor: National Institute on Drug Abuse, US
Other Details: National Epidemiologic Survey on Alcohol and Related Conditions (NESARC)
Recipients: No recipient indicated
Sponsor: National Institutes of Health, NIAAA, US
Recipients: No recipient indicated - Conference:
- Annual Meeting of the American Psychiatric Association, 167th, May, 2014, New York, NY, US
- Conference Notes:
- A preliminary version of parts of this article was presented at the aforementioned conference.
- Methodology:
- Empirical Study; Interview; Quantitative Study
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- Accepted: Mar 3, 2015; Revised: Feb 23, 2015; First Submitted: Dec 11, 2014
- Release Date:
- 20160104
- Digital Object Identifier:
- http://dx.doi.org/10.1037/adb0000080
- PMID:
- 26727008
- Accession Number:
- 2015-58335-004
- Number of Citations in Source:
- 55
- Persistent link to this record (Permalink):
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- Cut and Paste:
- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2015-58335-004&site=ehost-live">Sociodemographic and psychiatric diagnostic predictors of 3-year incidence of DSM–IV substance use disorders among men and women in the National Epidemiologic Survey on Alcohol and Related Conditions.</A>
- Database:
- PsycINFO
Sociodemographic and Psychiatric Diagnostic Predictors of 3-Year Incidence of DSM–IV Substance Use Disorders Among Men and Women in the National Epidemiologic Survey on Alcohol and Related Conditions / BRIEF REPORT
By: Risë B. Goldstein
Laboratory of Epidemiology and Biometry, Division of Intramural Clinical and Biological Research, National Institute on Alcohol Abuse and Alcoholism, National Institutes of Health, Bethesda, Maryland;
Sharon M. Smith
Laboratory of Epidemiology and Biometry, Division of Intramural Clinical and Biological Research, National Institute on Alcohol Abuse and Alcoholism, National Institutes of Health, Bethesda, Maryland
Deborah A. Dawson
Laboratory of Epidemiology and Biometry, Division of Intramural Clinical and Biological Research, National Institute on Alcohol Abuse and Alcoholism, National Institutes of Health, and Kelly Government Services, Rockville, Maryland
Bridget F. Grant
Laboratory of Epidemiology and Biometry, Division of Intramural Clinical and Biological Research, National Institute on Alcohol Abuse and Alcoholism, National Institutes of Health
Acknowledgement: The National Epidemiologic Survey on Alcohol and Related Conditions (NESARC) is funded by the National Institute on Alcohol Abuse and Alcoholism (NIAAA) with supplemental support from the National Institute on Drug Abuse. This research was supported in part by the Intramural Program of the National Institutes of Health, NIAAA. A preliminary version of parts of this article was presented at the 167th Annual Meeting of the American Psychiatric Association, May, 2014, New York, NY. The authors extend their thanks to S. Patricia Chou, PhD, and Tulshi D. Saha, PhD, for invaluable assistance with the revision of this article. The views and opinions expressed in this report are those of the authors and should not be construed to represent the views of sponsoring organizations, agencies, or the U.S. government.
Incidence (first onset) rates of alcohol and drug use disorders (AUDs and DUDs) are consistently higher in men than women across diagnostic systems, regardless of whether abuse and dependence are combined or considered separately (see Table 1). However, few studies have investigated whether predictors of incident AUDs and DUDs differ by sex. Bijl, de Graaf, Ravelli, Smit, and Vollebergh (2002) found no sex difference in prediction by age of incident AUDs in a nationally representative sample of adults in the Netherlands; Wittchen et al. (2008) reported similar findings from epidemiologically ascertained adolescents and young adults in the Munich Early Developmental Stages of Psychopathology (EDSP) cohort. Conversely, Mattisson, Bogren, Horstmann, and Öjesjö (2010) found a tendency toward later onsets of AUDs among women than men in the Lundby cohort.
Previous Incidence Studies Reporting Sex-Specific Rates of Alcohol and Drug Use Disorders
Zimmermann et al. (2003) did not find sex differences in prediction of incident AUD by anxiety disorders over a mean follow-up of 42 months among the Munich EDSP cohort. To our knowledge, however, the sex specificity of other predictors of AUD and DUD incidence, including unmarried status and existing psychiatric disorders (Crum, Chan, Chen, Storr, & Anthony, 2005; de Graaf, ten Have, Tuithof, & van Dorsselaer, 2013; Eaton et al., 1989; Grant et al., 2009; Newman & Bland, 1998; Zimmermann et al., 2003), has not been investigated. Both sociodemographic characteristics and existing psychiatric disorders may contribute differentially in men and women to the etiology and course of chronologically secondary AUDs and DUDs. Sex specificity could reflect differences in risk and protective factors, including gendered patterns of exposure to substances and social acceptability of their use. In addition to informing further etiologic investigations, identification of sex differences in predictors of AUD and DUD incidence may guide appropriate tailoring of preventive and therapeutic interventions for these and chronologically primary psychiatric disorders.
Accordingly, this study’s goals were to: (a) estimate sex-specific incidence rates of DSM–IV AUDs and DUDs in a large, nationally representative U.S. sample, (b) provide sex-specific data on sociodemographic risk factors, and (c) estimate sex-specific prediction of incident AUDs and DUDs by baseline Axis I and Axis II disorders.
Method Sample
The entire research protocol, including informed consent procedures, was approved by the institutional review board of the U.S. Census Bureau and the Office of Management and Budget. Wave 2 (W2) is the 3-year prospective follow-up of the Wave 1 (W1) National Epidemiologic Survey on Alcohol and Related Conditions (NESARC) sample (Grant, Moore, Shepard, & Kaplan, 2003; Grant, Kaplan, Moore, & Kimball, 2007). The W1 NESARC (overall response rate = 81.0%, n = 43,093) represented U.S. residents ≥ 18 years old of households and selected group quarters. Individuals 18 to 24 years old, non-Hispanic Blacks, and Hispanics were oversampled. In-person reinterviews of all W1 respondents were attempted in W2. Among those alive, resident in the U.S., and not incapacitated or on active military duty throughout the follow-up period, the W2 response rate was 86.7% (n = 34,653); the cumulative response rate was 70.2% across the two waves. W2 respondents did not differ from W2 respondents plus eligible nonrespondents sociodemographically or on any W1 lifetime psychiatric disorder (Grant et al., 2009).
Assessments
Substance use disorders
Diagnostic assessments utilized the Alcohol Use Disorder and Associated Disabilities Interview Schedule—DSM–IV Versions (AUDADIS-IV) for Waves 1 (Grant, Dawson, & Hasin, 2001) and 2 (Grant, Dawson, & Hasin, 2004). DSM–IV criteria for alcohol and drug-specific abuse and dependence for 10 drug categories were queried at both waves (Compton, Thomas, Stinson, & Grant, 2007; Grant et al., 2009; Hasin, Stinson, Ogburn, & Grant, 2007). Abuse diagnoses required that ≥ 1 abuse criterion, and dependence diagnoses, ≥ 3 dependence criteria, be met in the same year for the same substance. Drug-specific disorders are aggregated to yield any drug abuse and any drug dependence. Nicotine dependence was diagnosed similarly (Grant, Hasin, Chou, Stinson, & Dawson, 2004). Reliability of AUDADIS-IV AUDs (κ = .70–.84), DUDs (κ = .53–.79), and nicotine dependence (κ = .60–.63), and their validity, are extensively documented in clinical and general population samples (Compton et al., 2007; Grant, Dawson et al., 2003; Hasin et al., 2007).
Other psychiatric disorders
DSM–IV primary mood (MDD, dysthymia, and bipolar I and II) and anxiety (panic, social and specific phobias, and generalized anxiety) disorder diagnoses were assessed at W1 (Grant, Hasin et al., 2005; Grant, Stinson et al., 2005). DSM–IV primary diagnoses excluded substance- and illness-induced cases; MDD diagnoses ruled out bereavement. Lifetime posttraumatic stress disorder (PTSD) and attention-deficit/hyperactivity disorder (ADHD) were assessed at W2 (Ruan et al., 2008) but considered as predictors herein only if prevalent up to W1.
All DSM–IV personality disorders (PDs) were assessed on a lifetime basis: avoidant, dependent, obsessive–compulsive, paranoid, schizoid, histrionic, and antisocial PDs at W1 (Grant, Hasin, Stinson et al., 2004); borderline, schizotypal, and narcissistic PDs at W2 (Grant et al., 2008). Test–retest reliabilities of AUDADIS-IV mood and anxiety (κ = .42-.65), PD (κ = .40-.71), and ADHD (κ = .71) diagnoses were fair to good (Grant, Dawson et al., 2003; Ruan et al., 2008). Convergent validity of mood, anxiety, and PD diagnoses was good to excellent (Grant, Hasin, Stinson et al., 2004; Grant, Hasin et al., 2005; Grant, Stinson et al., 2005).
Statistical Analyses
Incidence of each AUD and DUD was estimated as a percentage, the numerator comprising individuals with no lifetime history of the target disorder (e.g., alcohol dependence) at W1 who developed it during follow-up and the denominator comprising all respondents with no lifetime history of the disorder at W1 (population at risk). Individuals with alcohol dependence can later develop abuse, though this has not been observed for DUDs (Grant et al., 2009). Nevertheless, the hierarchical preemption under DSM–IV by dependence of subsequent abuse was suspended for both AUDs and DUDs.
Because of the low incidence of AUDs and DUDs, particularly among women, 3-year rates were considered so as to have sufficient cases for meaningful analyses. Incidence rates were compared by sociodemographic and psychiatric predictors, stratified on sex, using standard contingency table approaches. All sociodemographic predictors were entered simultaneously into sex-specific logistic regressions for each incident disorder.
Sex-specific logistic regressions estimated prediction of each incident AUD and DUD by specific psychiatric disorders, adjusted for sociodemographic variables and all other psychiatric disorders. Adjustment for diagnostic covariates tests the hypothesis that incidence is predicted by the pure (noncomorbid) form of a specific baseline disorder (Compton et al., 2007; Hasin et al., 2007). Odds ratios (ORs) were considered significant when their 95% confidence intervals excluded 1.00. When incidence is < 10%, as with AUDs and DUDs reported herein, the OR closely approximates the relative risk (Zhang & Yu, 1998).
Sex differences in ORs were assessed in models including Sex × Predictor Interaction Terms among the total sample, with alpha-to-stay = .05. No adjustments were made for multiple comparisons. All analyses utilized SUDAAN (Research Triangle Institute, 2008) to adjust for the NESARC’s complex sample design.
Results Alcohol Use Disorders
Three-year incidence ± SE of alcohol abuse among men and women was 8.39% ± 0.40 and 3.12% ± 0.19, respectively, chi-square(1) = 83.71, p < .0001; of alcohol dependence, 4.62% ± 0.23 and 2.18% ± 0.14, respectively, chi-square(1) = 56.03, p < .0001). Higher rates among men were observed in all sociodemographic subgroups examined (data available upon request). ORs for sociodemographic predictors of AUDs did not differ by sex (see Table 2), except for reduced incidence of dependence among Hispanic women, but no association in Hispanic men, versus non-Hispanic Whites.
Adjusteda Odds Ratios [95% Confidence Intervals] for 3-Year Incidence of DSM-IV Alcohol and Drug Use Disordersb by Wave 1 Sociodemographic Characteristics Among Male and Female NESARC Respondents
Adjusted ORs for prediction of AUDs by most psychiatric disorders did not differ by sex (see Table 3). A significant Sex × Bipolar I interaction identified reduced odds of abuse for men and no association for women. Though significant in both sexes, significantly greater ORs were observed among women than men for prediction of both AUDs by nicotine dependence. A significant Sex × Generalized Anxiety Disorder (GAD) interaction identified reduced odds of dependence for men and no association for women.
Odds Ratios [95% Confidence Intervals] for 3-Year Incidence of DSM-IV Alcohol and Drug Use Disordersa by Wave 1 Lifetime Psychiatric Diagnoses Among Male and Female NESARC Respondents Adjusted for Sociodemographic Characteristics and Additional Baseline Psychiatric Comorbidity
Drug Use Disorders
Three-year incidence ± SE of any drug abuse was 2.24% ± 0.19 among men and 1.22% ± 0.11 among women, chi-square(1) = 19.57, p < .0001; of any drug dependence, 1.16% ± 0.13 and 0.58% ± 0.09, respectively, chi-square(1) = 12.02, p = .0005. Similar to findings for AUDs, higher rates were observed among men in all sociodemographic subgroups (data available upon request); however, ORs for sociodemographic predictors of DUDs did not differ by sex (see Table 2).
Again similar to findings for AUDs, there were few sex differences in adjusted ORs for psychiatric predictors of DUDs (see Table 3). Interactions with sex were noted for prediction of abuse by schizotypal, borderline, avoidant, and obsessive–compulsive PDs. Schizotypal PD positively predicted abuse in women but not men. Borderline PD positively predicted abuse in both sexes, but more strongly in women; avoidant and obsessive–compulsive PDs negatively predicted abuse in men but not women.
The only sex difference in prediction of dependence was observed with obsessive–compulsive PD: reduced odds in men, no association in women.
DiscussionConsistent with our previous findings on lifetime prevalences and psychiatric comorbidity of AUDs and DUDs (Goldstein, Dawson, Chou, & Grant, 2012), incidence rates were higher in men, but there were few significant sex differences in predictors. Predictors could operate similarly despite differential prevalences, whether singly or in joint distributions, between men and women (cf. Huang et al., 2006). The only significant sex difference in sociodemographic predictors, reduced odds among Hispanic women but not Hispanic men, versus non-Hispanic Whites, for alcohol dependence, may reflect gendered norms regarding acceptability of alcohol use and associated behaviors (e.g., Zemore, 2007). Such norms may either protect against dependence, or reduce affected women’s willingness to report symptoms.
Significantly larger ORs were observed in women than men for prediction of AUDs but not DUDs by nicotine dependence. Although part of the externalizing spectrum, nicotine dependence is less informative about broader externalizing liability and less strongly related to the underlying externalizing dimension than antisocial PD and dependence on other substances (Markon & Krueger, 2005). Predictive relationships may also have been subject to “ceiling effects” reflecting comparatively high baseline prevalence of nicotine dependence (men: 20.0%; women: 15.6%).
Borderline PD predicted drug abuse significantly more strongly among women than men. This PD loads on both the internalizing subfactor of distress and the externalizing factor of psychopathology (Eaton et al., 2011). Previous studies have located DUDs at a more severe point than AUDs along the externalizing spectrum (Carragher et al., 2014; Kendler, Prescott, Myers, & Neale, 2003; Krueger, Markon, Patrick, & Iacono, 2005), and prevalences of most externalizing disorders are lower among women than men (Grant & Weissman, 2007). The stronger predictive relationships of borderline PD to incident DUDs among women, despite lack of sex differences in borderline PD prevalence (Grant et al., 2008), may thus reflect higher concentrations of externalizing liability among women than men with borderline PD, and women’s greater vulnerability to more severe externalizing pathology.
ORs for schizotypal PD and drug abuse were also higher in women. Previous studies identified relationships between use and disorders associated with cannabis, the most commonly used drug in the NESARC sample, and schizophrenia spectrum disorders, including schizotypal PD (Davis, Compton, Wang, Levin, & Blanco, 2013; Di Forti, Morrison, Butt, & Murray, 2007; Schiffman, Nakamura, Earleywine, & LaBrie, 2005; Stefanis et al., 2014). However, the directionality of those relationships could not be determined because the studies were cross-sectional. Respondents with schizotypal PD may have been more likely to use drugs before W1, becoming diagnosable with a DUD only during follow-up. To our knowledge, no studies have identified plausible explanations for the sex differences we observed in predictive relationships.
Finally, avoidant PD negatively predicted drug abuse and obsessive–compulsive PD negatively predicted both DUDs in men but not women. With lower prevalence in men (Grant, Hasin, Stinson, et al., 2004), essential features including hypersensitivity to negative evaluation, and correlates including high harm avoidance (Joyce et al., 2003), avoidant PD may protect men from socially disvalued behaviors like problematic drug use, for which they are otherwise at greater risk than women. Similarly, although prevalence of obsessive–compulsive PD does not differ by sex (Grant, Hasin, Stinson, et al., 2004), its essential features including scrupulosity, overconscientiousness, and inflexibility about morality and values may deter DUD development more strongly among men.
Study limitations include its reliance on self-reports. Collateral data sources are particularly important for PD assessment to mitigate potential distortions in respondents’ self-appraisals, including lack of insight into the effects of symptomatic behaviors on role functioning (Clark, 2007; Pedersen, Karterud, Hummelen, & Wilberg, 2013; Zimmerman, 1994). Additionally, the 3-year follow-up yielded relatively few incident cases of AUDs and DUDs, particularly among women.
The NESARC sample was limited by design to general population U.S. adults. Therefore, the applicability of these findings to other populations, and to individuals < 18 years old at baseline in the general U.S. population, is unclear. Moreover, respondents youngest at W2 were 20 years old, beyond the ages of peak hazards for AUDs and DUDs (Compton et al., 2007; Hasin et al., 2007). That relationships between specific baseline disorders and incident AUDs and DUDs may vary across developmental phases (Grant et al., 2009), including by sex, could explain our unexpected findings of reduced risks of incident alcohol abuse among men with bipolar I, of alcohol dependence among men with GAD, of drug abuse among men with avoidant and obsessive–compulsive PDs, and of drug dependence among men with obsessive–compulsive PD. Future longitudinal studies should involve longer follow-up periods, consider including institutional subsamples, and capture earlier developmental phases.
Ideally, all diagnostic predictors would have been assessed at W1, but respondent and interviewer burden made it infeasible. Mitigating this concern for PDs, respondents were explicitly queried about symptoms occurring most of the time, throughout their lives, regardless of the situation or whom they were with (Grant, Hasin, Stinson, et al., 2004). Respondents with each PD were also more impaired than those with no PD on the Mental Component Summary score of the Short Form 12-Item Health Survey, version 2 (Gandek et al., 1998), and more often reported stressful life events such as relationship breakups and financial, interpersonal, or employment problems, at each wave, regardless of when specific PDs were assessed (Skodol et al., 2011).
The tendency of respondents to recall and report onsets as more recent than they actually were for Axis I disorders (Prusoff, Merikangas, & Weissman, 1988), substance use (Johnson & Schultz, 2005), and medical conditions (Raphael & Marbach, 1997) may likewise mitigate time-of-assessment concerns for PTSD. While we are unaware of findings documenting forward telescoping specifically in PTSD, its occurrence is plausible given findings on other disorders.
The few sex differences we identified in sociodemographic and diagnostic predictors of AUD and DUD incidence were modest, yielding limited implications for sex-specific targeting of prevention and early identification efforts. Nevertheless, AUDs and DUDs and their predictors confer substantial burdens on affected individuals, their social networks, and health, social service, and correctional systems (Brown, 2010; Sirotich, 2009; Whiteford et al., 2013). Treatment utilization for AUDs and DUDs is low, despite the availability of a growing range of empirically supported therapies (Compton et al., 2007; Hasin et al., 2007). Despite the lack of strong sex-specific signals, this study’s findings, together with those of previous prospective studies (e.g., de Graaf et al., 2013; Grant et al., 2009; Fergusson, Horwood, & Ridder, 2007) reinforce the need for comprehensive assessment and evidence-based treatment of mental health and substance use disorders in both sexes, regardless of clients’ chief complaints and the clinical settings to which they present.
Prevalences are similar (Dawson, Goldstein, & Grant, 2013; Goldstein et al., 2015) and concordances excellent between DSM–IV dependence and DSM-5 moderate to severe AUDs and DUDs (Compton, Dawson, Goldstein, & Grant, 2013; Goldstein et al., in press). These findings plus similarity in clinical profiles between alcohol dependence and DSM-5 moderate to severe AUD (Dawson et al., 2013) suggest that predictors may be similar across these disorders, despite increases in sociodemographic and substance use diversity of the population since the W1 and W2 NESARC data were collected. Conversely, divergence of clinical profiles (Dawson et al., 2013) suggests caution in extrapolating from abuse to mild DSM-5 disorders. Prospective research is needed to identify sociodemographic and diagnostic predictors of and mechanisms underlying incident DSM-5 AUDs and DUDs. Whether there are sex differences in predictive relationships across developmental phases, the role of additional comorbidity in etiology and course, perceived need and barriers to treatment for AUDs and DUDs, and outcomes of prevention and treatment also warrants examination.
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Submitted: December 11, 2014 Revised: February 23, 2015 Accepted: March 3, 2015
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Source: Psychology of Addictive Behaviors. Vol. 29. (4), Dec, 2015 pp. 924-932)
Accession Number: 2015-58335-004
Digital Object Identifier: 10.1037/adb0000080
Record: 151- Title:
- Stimulant medication use in college students: Comparison of appropriate users, misusers, and nonusers.
- Authors:
- Hartung, Cynthia M.. Department of Psychology, University of Wyoming, Laramie, WY, US, chartung@uwyo.edu
Canu, Will H.. Department of Psychology, Appalachian State University, NC, US
Cleveland, Carolyn S.. Department of Psychology, University of Wyoming, Laramie, WY, US
Lefler, Elizabeth K.. Department of Psychology, University of Northern Iowa, IA, US
Mignogna, Melissa J.. Department of Psychology, Oklahoma State University, OK, US
Fedele, David A.. Department of Psychology, University of Florida, FL, US
Correia, Christopher J.. Department of Psychology, Auburn University, AL, US
Leffingwell, Thad R.. Department of Psychology, Oklahoma State University, OK, US
Clapp, Joshua D.. Department of Psychology, University of Wyoming, Laramie, WY, US - Address:
- Hartung, Cynthia M., Department of Psychology, University of Wyoming, 1000 East University Avenue, Department #3415, Laramie, WY, US, 82071, chartung@uwyo.edu
- Source:
- Psychology of Addictive Behaviors, Vol 27(3), Sep, 2013. pp. 832-840.
- NLM Title Abbreviation:
- Psychol Addict Behav
- Page Count:
- 9
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- Bulletin of the Society of Psychologists in Addictive Behaviors; Bulletin of the Society of Psychologists in Substance Abuse
- Other Publishers:
- US : Educational Publishing Foundation
Society of Psychologists in Addictive Behaviors - ISSN:
- 0893-164X (Print)
1939-1501 (Electronic) - Language:
- English
- Keywords:
- ADHD, college students, emerging adults, psychostimulant misuse
- Abstract:
- While stimulant medication is commonly prescribed to treat Attention-Deficit/Hyperactivity Disorder in children and adolescents (Merikangas, He, Rapoport, Vitiello, & Olfson, 2013; Zuvekas & Vitiello, 2012) and is considered an empirically supported intervention for those groups (Barkley, Murphy, & Fischer, 2008; Pelham & Fabiano, 2008; Safren et al., 2005) surprisingly little is known about the efficacy of stimulants in the slightly older emerging adult population. A focus has emerged, however, on illicit stimulant use among undergraduates, with studies suggesting such behavior is not uncommon (e.g., Arria et al., 2013). Unfortunately, details are lacking regarding outcomes and personal characteristics associated with different patterns of stimulant misuse. The current study compares the characteristics of four groups of college students, including those with stimulant prescriptions who use them appropriately (i.e., appropriate users), those who misuse their prescription stimulants (i.e., medical misusers), those who obtain and use stimulants without a prescription (i.e., nonmedical misusers), and those who do not use stimulant medications at all (i.e., nonusers). Undergraduates (N = 1,153) from the Southeastern, Midwest, and Rocky Mountain regions completed online measures evaluating patterns of use, associated motives, side effects, ADHD symptomatology, and other substance use. Both types of misusers (i.e., students who abused their prescriptions and those who obtained stimulants illegally) reported concerning patterns of other and combined substance use, as well as higher prevalence of debilitating side effects such as insomnia and restlessness. Research and practical implications are discussed. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Attention Deficit Disorder with Hyperactivity; *CNS Stimulating Drugs; *College Students; *Drug Abuse; Adult Development
- Medical Subject Headings (MeSH):
- Adolescent; Adult; Attention Deficit Disorder with Hyperactivity; Central Nervous System Stimulants; Female; Humans; Logistic Models; Male; Multivariate Analysis; Prescription Drug Misuse; Students; Substance-Related Disorders; Surveys and Questionnaires; Universities; Young Adult
- PsycINFO Classification:
- Psychological & Physical Disorders (3200)
- Population:
- Human
Male
Female - Location:
- US
- Age Group:
- Adulthood (18 yrs & older)
Young Adulthood (18-29 yrs) - Tests & Measures:
- Frost Multidimensional Perfectionism Scale DOI: 10.1037/t05500-000
Sensation Seeking Scale - Methodology:
- Empirical Study; Quantitative Study
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- Accepted: Jun 4, 2013; Revised: May 16, 2013; First Submitted: Jan 5, 2012
- Release Date:
- 20130923
- Correction Date:
- 20160414
- Copyright:
- American Psychological Association. 2013
- Digital Object Identifier:
- http://dx.doi.org/10.1037/a0033822
- PMID:
- 24059834
- Accession Number:
- 2013-33297-006
- Number of Citations in Source:
- 53
- Persistent link to this record (Permalink):
- http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2013-33297-006&site=ehost-live
- Cut and Paste:
- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2013-33297-006&site=ehost-live">Stimulant medication use in college students: Comparison of appropriate users, misusers, and nonusers.</A>
- Database:
- PsycINFO
Stimulant Medication Use in College Students: Comparison of Appropriate Users, Misusers, and Nonusers / BRIEF REPORT
By: Cynthia M. Hartung
Department of Psychology, University of Wyoming;
Will H. Canu
Department of Psychology, Appalachian State University
Carolyn S. Cleveland
Department of Psychology, University of Wyoming
Elizabeth K. Lefler
Department of Psychology, University of Northern Iowa
Melissa J. Mignogna
Department of Psychology, Oklahoma State University
David A. Fedele
Department of Psychology, University of Florida
Christopher J. Correia
Department of Psychology, Auburn University
Thad R. Leffingwell
Department of Psychology, Oklahoma State University
Joshua D. Clapp
Department of Psychology, University of Wyoming
Acknowledgement: We thank Erica K. Allen and Collin T. Scarince for their contributions to this project.
Studies estimate a 4–14% yearly incidence of nonprescribed stimulant medication use in college students (American College Health Association [ACHA], 2010; Hall, Irwin, Bowman, Frankenberger, & Jewett, 2005; McCabe, Teter & Boyd, 2006; Weyandt et al., 2009; White, Becker-Blease, & Grace-Bishop, 2006), which is higher than the national prevalence of cocaine, hallucinogen, or inhalant use (SAMHSA, 2011), and approximately double the prevalence of prescribed stimulant use (2–3%; Babcock & Byrne, 2000; Stone & Merlo, 2011) in this age group. In considering stimulant abuse, however, it is important to note that not all who use illicitly are qualitatively similar. While motive (e.g., getting high vs. increasing concentration) is one way to categorize stimulant users (Teter, McCabe, Cranford, Boyd & Guthrie, 2005), means and degree of use differentiate among (a) medical misusers (i.e., those with a prescription who periodically use excessive doses), (b) nonmedical misusers (i.e., those who obtain and use stimulants illegally), and (c) appropriate users (i.e., those who use prescription according to instructions). The need for closer examination of these groups is underscored by the somewhat ambiguous stimulant-related maladjustment (Bogle & Smith, 2009), and infrequent and incomplete differentiation among misuser groups in the literature.
Although prevalence estimates vary widely (e.g., 4%, McCabe, Knight, Teter, & Wechsler, 2005; 38%, Arria et al., 2013; 43%, Advokat, Guidry, & Martino, 2008), it seems likely that a substantial number of college students misuse stimulants (DeSantis, Webb, & Noar, 2008). In contrast to prescribed use of stimulants in college students with Attention-Deficit/Hyperactivity Disorder (ADHD; DuPaul, Weyandt, O’Dell, & Varejao, 2009), which some have suggested ameliorates maladjustment (Staufer & Greydanus, 2005), nonmedical misuse is correlated with lower grades (McCabe et al., 2005), academic concerns (Rabiner et al., 2009), risk for polysubstance abuse (Rozenbroek & Rothstein, 2011), and a desire to improve studying (Stone & Merlo, 2011). However, unaddressed symptoms of ADHD may be linked to nonmedical misuse of stimulants too, with one study finding that 12% of nonmedical misusers believed they had the disorder (Advokat et al., 2008). It is also possible that students without ADHD use stimulants to enhance academic performance (Smith & Farah, 2011), as staying awake and increasing studying efficiency are frequent rationales for misuse (Advokat et al., 2008).
While addressing undiagnosed or undertreated ADHD and related academic problems is a motive for misuse that parallels the intended purpose of prescription stimulants, recreation (i.e., euphoric effects; Teter et al., 2005) and socialization (White et al., 2006) are not uncommonly endorsed as reasons for use. This may be particularly prevalent in nonmedical misusers, as approximately one fifth of this group reports using stimulants while drinking (Low & Gendaszek, 2002), and to prolong intoxication (Rabiner et al., 2009). Some have suggested that stimulant use may be even more reinforcing in social situations, as the resulting alertness may facilitate prolonged social engagement (Hall et al., 2005). However, recreational motives for stimulant abuse do not outrank academic motives among nonmedical misusers, and are uncommonly the sole motive reported (Rabiner et al., 2009).
Specific personality characteristics have also been related to stimulant misuse, with both sensation seeking (Arria, Caldeira, Vincent, O’Grady & Wish, 2008) and perfectionism (Low & Gendaszek, 2002) positively predicting this behavior in college populations. Further, men appear more likely than women to misuse stimulants (Bogle & Smith, 2009; Hall et al., 2005; see exception in McCabe et al., 2005), which may be due to sex differences in risk-taking (Byrnes, Miller & Schafer, 1999) or knowledge about from whom one can illicitly obtain stimulants (Hall et al., 2005).
Immediate adverse consequences of stimulant use have been reported in college student nonmedical misuser samples, including appetite reduction (63%), sleep problems (60%), irritability (45%), and reduced academic self-efficacy (41%; Rabiner et al., 2009). Taken with the potential legal consequences of illicit use of a Schedule II substance (e.g., methylphenidate) and increased risk of polysubstance abuse, this suggests illicit stimulant use is associated with risk across several domains. However, particularly given some studies suggesting relatively mild and circumscribed maladjustment in misusing college students (e.g., Bogle & Smith, 2009), replication and further detailing of the putative adverse consequences associated with illicit stimulant use is a valid aim, especially given the potential downside of overly negative portrayals (e.g., Food and Drug Administration caps on production).
This study examined four college student groups differentiated by type of stimulant use (i.e., nonusers, nonmedical and medical misusers, appropriate users). Given the extant literature, hypotheses were as follows: (a) both misuser groups were expected to more frequently nominate recreational motives for stimulant use; (b) misusers, given their nonprescribed drug use, were expected to endorse high rates of other illicit substance use (i.e., concurrent to stimulant use or at other times in the past year); (c) nonmedical misusers would report more ADHD-related symptomatology (i.e., inattention, hyperactivity) than nonusers, but less than either appropriate users or medical misusers; (d) nonmedical misusers would be distinguished by high sensation seeking and perfectionism. Finally, other planned analyses examined whether groups differed on other motives for use, side effects, and methods of ingestion; however, given a relative dearth of direction from prior research for these variables, specific hypotheses were not made.
Method Participants
Participants were 1,153 undergraduates (65.2% female; 88.4% European American) from four public universities located in the Southeast (n = 2), Rocky Mountain (n = 1), and Midwest (n = 1) regions of the United States who were compensated with class credit. The mean age of these participants was 19.72 years (SD = 1.45; range: 18–25). Distribution by class standing was 46.2% freshmen, 24.0% sophomores, 16.8% juniors, and 13.0% seniors. Based on self-reported stimulant use, groups included (a) nonusers (n = 708), (b) nonmedical misusers (i.e., illicitly obtaining and using stimulant medication without a prescription; n = 274), (c) appropriate users (i.e., taking stimulants according to prescription; n = 146), and (d) medical misusers (i.e., using higher doses or more frequently than prescribed; n = 25). Agreement regarding group assignment was 100% (consensus of first, second, and fourth authors). At two of four universities, stimulant users were overselected via a prescreening questionnaire. Thus, the distributions across user status do not reflect the true prevalence of use and misuse on these college campuses.
Measures and Procedure
Participants completed all rating scales online in a fixed order after providing informed consent. Study procedures were approved by each university’s Institutional Review Board.
Substance use
Participants reported whether they used a variety of legal and illegal substances in the past year (e.g., alcohol, cigarettes, marijuana). They also reported whether they used substances concurrently with prescription stimulants. Previous studies support the reliability and validity of self-reported substance use (Tucker, Murphy, & Kertesz, 2010), and endorsement of 12-month substance use or nonuse is also consistent with prior research in this area (e.g., Johnston, O’Malley, Bachman, & Schulenberg, 2013; Mohler-Kuo, Lee, & Wechsler, 2003; SAMHSA, 2011).
Stimulant use
Students were asked about: (a) use (e.g., “I have a prescription and take accordingly”; “I do not have a prescription but obtain stimulants and use them”; see White et al., 2006), (b) source for obtaining (e.g., received from my doctor/pharmacy, given by a friend/family member, or bought or stolen from someone; based on McCabe, Teter, & Boyd, 2006), (c) method of ingestion (e.g., oral, intranasal, or intravenous; as per Teter et al., 2005), (d) reasons for use (e.g., control ADHD symptoms, suppress appetite, or stay awake; adapted from Low & Gendaszek, 2002), and (e) side effects experienced while taking stimulants (e.g., insomnia, loss of appetite, or weight loss).
ADHD symptoms
ADHD symptoms were measured with an 18-item self-report measure of DSM–IV inattention and hyperactivity (Barkley & Murphy, 2006). Participants indicated whether they never/rarely (0), sometimes (1), often (2), or very often (3) experienced each symptom. Summary scores were created for inattention and hyperactivity. Internal consistency has been good for inattention (α = .80) and adequate for hyperactivity (α = .73) based on college student self-reports (e.g., Fedele, Hartung, Canu, & Wilkowski, 2010). In addition, interrater reliability has been found to be moderately high in adults (e.g., r = .67; Barkley, Knouse, & Murphy, 2011). Convergent and discriminant validity have also been demonstrated for adult self-reports (e.g., Magnusson et al., 2006). Internal consistency in the current sample was good for inattention (α = .87) and adequate for hyperactivity (α = .76).
Personality characteristics
Sensation seeking was measured using a 16-item version (Donohew et al., 2000) of the Sensation Seeking Scale (Zuckerman, 1994). Responses were disagree a lot (0), disagree a little (1), don’t agree or disagree (2), agree a little (3), or agree a lot (4) and were aggregated into a summary score (range = 0 to 64). Previous reports of internal consistency were adequate (α = .79; Donohew et al., 2000) and internal consistency was good in the current sample (α = .82). Perfectionism was measured using a 24-item version (Khawaja & Armstrong, 2005) of the Frost Multi-Dimensional Perfectionism Scale (Frost, Marten, Lahart, & Rosenblate, 1990). This version has been reported to have excellent internal consistency (α = .90) and strong concurrent validity with other measures of perfectionism (Khawaja & Armstrong, 2005). Responses range from strongly disagree (0) to strongly agree (4). There are four subscales: concern over mistakes (10 items), organization (4 items), parental expectations (6 items), and high personal standards (4 items). Internal consistency was adequate for parental expectations (α = .79), good for organization (α = .88) and concern over mistakes (α = .87), but inadequate for high personal standards (α = .64). Accordingly, the latter was omitted from analyses.
ResultsMultinomial logistic regression analyses were conducted to examine relations between predictors and user status. For some analyses, all four user status groups were included. For other analyses, nonusers were not included because the items were not relevant (e.g., reasons for use, side effects). For all analyses, sex and university were entered as covariates due to significant differences across user status. In keeping with prior findings (e.g., Bogle & Smith, 2009), men were more likely to engage in nonmedical misuse than women (p = .009). Alpha corrections were conducted for all analyses and resulting p values are noted in each of the tables. For each regression, likelihood (i.e., χ2) and pairwise odds ratios representing the unique relation between predictor and outcome variable (i.e., user status) are reported.
First, a logistic regression analysis was conducted to examine the relation between reasons for stimulant use and user status (see Table 1). We were particularly interested in using “to get high” as a measure of recreational use. However, we were not able to include this reason in the regression due to low levels of endorsement. Specifically, 13% of nonmedical misusers and 24% of medical misusers indicated using stimulants to get high (compared to none of appropriate users). With regard to other reasons for use, we conducted planned exploratory analyses. Results showed that both types of misusers endorsed some reasons significantly more often than appropriate users. Specifically, nonmedical and medical misusers were more likely to endorse using to stay awake than appropriate users. Also, nonmedical misusers were more likely to report using to study than appropriate users whereas medical misusers were more likely to endorse using to increase academic performance than appropriate users. Finally, both appropriate users and medical misusers were more likely to use “to control ADHD symptoms” than nonmedical misusers.
Multinomial Logistic Regression Analysis for Reasons for Stimulant Use by User Status
Another logistic regression analysis was conducted to examine the relation between use of other substances and user status (see Table 2). Across eight substances, nonusers of stimulants were the least likely to endorse use of other substances, appropriate users were next in terms of likelihood to endorse, and misusers were the most likely to endorse. Although alcohol use was surveyed, it could not be entered in the regression because 100% of medical misusers endorsed it.
Multinomial Logistic Regression Analyses for Use of Other Substances
Next, a logistic regression analysis was conducted to examine the relation between concurrent use of stimulants with other substances and user status (see Table 3). Appropriate users were typically the least likely to endorse concurrent use of additional substances. Medical misusers were significantly more likely to endorse concurrent marijuana use than appropriate users. Nonmedical misusers were more likely to endorse concurrent marijuana and pain medication use than appropriate users. Interestingly, nonmedical misusers were significantly less likely to endorse concurrent alcohol use than appropriate users.
Multinomial Logistic Regression Analyses for Concurrent Use of Other Substances and Stimulants
Next, regressions were conducted to examine how user status related to ADHD and personality variables (see Table 4). Nonusers reported significantly lower levels of inattention and hyperactivity than any other group. In addition, nonmedical misusers reported lower levels of inattention than appropriate users and lower levels of hyperactivity than medical misusers. With regard to personality, nonmedical misusers reported higher parental expectations than nonusers and appropriate users. Moreover, nonmedical misusers reported higher levels of sensation seeking than appropriate users and nonusers.
Multinomial Logistic Regression Analyses for (A) Inattention & Hyperactivity and (B) Perfectionism & Sensation Seeking by User Status
Exploratory analyses were conducted to examine differences across user groups for side effects, stimulant source, and ingestion. An analysis was conducted to examine the relation between side effects and user status (see Table 5). Overall, misusers appeared to experience more side effects; both misuser groups were significantly more likely to endorse exaggerated well-being and restlessness than appropriate users. In addition, nonmedical misusers were more likely to report insomnia and exaggerated well-being—and less likely to report weight loss, anxiety, or gastrointestinal problems—than appropriate users. Finally, medical misusers were more likely to endorse changes in sex drive than nonmedical misusers.
Logistic Regression Analysis for Various Side Effects of Stimulant Medication by User Status
Finally, sources for obtaining stimulants and ingestion methods were examined. No regression analysis could be conducted for these variables because appropriate users obtained their stimulants exclusively from prescriptions and participants reported oral ingestion as their primary method. Notably, among nonmedical misusers, 81% got stimulants from a friend, 45% bought them, and 4% stole them. Additionally, nasal ingestion among nonmedical (17.9%) and medical misusers (20.0%) was much higher than for appropriate users (0.0%) although the difference between the two misuser groups was not significant.
DiscussionThe purpose of this study was to compare characteristics of undergraduates who use, misuse, and do not use prescription stimulants. Overall, those classified as misusers (i.e., medical and nonmedical) presented relatively more concerning correlates than those who used stimulants according to prescription. First, although not statistically analyzed due to nonendorsement by all appropriate users, both medical and nonmedical misusers more frequently equate stimulant ingestion with recreation (i.e., getting high). Further, misusers appeared to experience different side effects. Notably, both misuser groups were more likely to endorse exaggerated well-being and restlessness than appropriate users. Nonmedical misusers were more likely to endorse insomnia than appropriate users, but less likely to have experienced anxiety, weight loss, or digestive problems. Unfortunately, “desirable” side effects (e.g., exaggerated well-being) may encourage misuse by off-setting negative consequences and reinforcing the expectation of euphoria.
Perhaps not surprisingly, misusers reported the highest rates of other substance use. Nonmedical misusers were more likely to report use of marijuana and hallucinogens than nonusers and appropriate users. Medical misusers were the most likely endorsers for all substances but these differences only reached statistical significance when compared to nonusers for cigarettes, amphetamines, and anxiety medication. When examining substances frequently used by college students (e.g., alcohol and marijuana; ACHA, 2010), appropriate users were more likely than nonusers to endorse use of these substances. This finding is consistent with prior research suggesting that ADHD is associated with increased risk for substance use (Wilens, 2004), but seems to contradict a documented protective effect of stimulant treatment (Biederman, 2003; Faraone & Wilens, 2003; Wilens, Faraone, Biederman, & Bunawardene, 2003). However, the current data cannot inform the prospective influence of stimulant intervention in childhood. Overall, it seems reasonable to conclude that stimulant misuse is associated with risk for broader substance use.
With regard to concurrent substance use, misusers were more likely than appropriate users to report marijuana use in combination with stimulants. In addition, nonmedical misusers were significantly more likely to endorse concurrent pain medication use than appropriate users. Such recreational use suggests that the motives of misusers may not be benign (e.g., extra dose for finals). This is consistent with other studies in which students frequently endorsed using stimulants while “partying” (e.g., Teter et al., 2005; White et al., 2006), and those in which short-term positive gain is reported with stimulant misuse (Rabiner et al., 2009) despite low endorsement of long-term academic gain (Hall et al., 2005).
With regard to concurrent alcohol use, the three user groups reported relatively high rates, which is troubling due to potential interactions between alcohol and stimulants. Specifically, using stimulants in combination with alcohol may diminish the experience of alcohol-related effects. This may in turn lead to underestimation of inebriation (Flack et al., 2007; Hingson, Edwards, Heeren & Rosenbloom, 2009; Knight et al., 2002) and poor decisions (e.g., drunk driving, unsafe sexual activity) that could lead to physical harm (e.g., motor vehicle accident, sexually transmitted disease, unplanned pregnancy).
Regarding inattention, nonmedical misusers reported significantly lower levels than appropriate users but higher levels than nonusers. For hyperactivity, nonmedical misusers reported significantly lower levels than medical misusers, but higher levels than nonusers. Thus, nonmedical misusers may be using stimulants to address subthreshold ADHD, and self-medication may be a viable explanation for the behavior of some nonmedical misusers (Rabiner et al., 2009). Misusers endorsed levels of sensation seeking that were significantly higher than nonusers and appropriate users. This is consistent with research linking sensation seeking to substance abuse (Carlson, Johnson & Jacobs, 2010; Dunlop & Romer, 2010; Zuckerman, 1994). Group differences on perfectionism subscales were also evident. Most notable, perhaps, was that nonmedical misusers endorsed higher perceived parental pressure relative to nonusers. Thus, perception of parental expectations for academic success may moderate the misuse of stimulants among those without a prescription. When asked about sources for obtaining stimulants, 81% of nonmedical misusers reported getting them from friends, closely resembling previous findings (77.8%; Barrett, Darredeau, Bordey, & Pihl, 2005). This suggests that some—and potentially many—college students with prescription stimulants are taking their medication in smaller doses or less often than prescribed as there seem to be “leftovers” available to sell or share.
Limitations
First, the medical misuser group was small (n = 25), and this limited power to detect differences between this and other groups. Given that this group reported very high rates of problematic consequences that were often not statistically significantly different from other groups, more research with individuals who misuse stimulant prescriptions is warranted. Next, our assessments of substance use and ADHD symptoms were limited to self-report measures, and future research might use corroborating sources (e.g., biochemical and parent-report measures, respectively). Another limitation was related to reports of type and dose of stimulants. We attempted to gather this information but participant responses reflected confusion or lack of knowledge. Further, data regarding frequency of misuse, duration of use, and amount typically consumed are lacking. Future research should address such details to extend our appreciation for differences among user groups. Another limitation was related to the overselection of stimulant users, which increased power but decreased representativeness. Further, although the current data were derived from four universities, the findings may not fully generalize to groups underrepresented in this sample (see McCabe, Teter, & Boyd, 2004). Finally, while geographic region and Greek affiliation have been shown to potentially add to risk for illicit stimulant use in college (McCabe et al., 2005), we did not consider the impact of these variables in the current study; researchers should include these in the design of future studies.
Conclusions
These findings reinforce that the misuse of stimulants is associated with other risks, such as that for polysubstance misuse. However, stimulant misuse by itself, even for academic reasons, may have concerning side effects (Graham et al., 2011). One university’s decision to change its honor code to include stimulant misuse as an “improper assistance” violation indirectly supports the call to proactively address this issue (Arria & DuPont, 2010; Diller, 2010; Wilens et al., 2008). Additionally, roughly 14% of students in this sample misused a prescription. Further, 81% of nonmedical misusers obtained stimulants from a friend. These two findings emphasize the importance of prescribers closely monitoring consumption and openly discussing consequences of misuse and diversion with college students. For example, if a student reports only taking medication on weekdays, then 30 pills might last 6 weeks rather than 4. Therefore, prescribers may want to evaluate how often students are taking their medication and prescribe accordingly to reduce the quantity of stimulants available to be diverted.
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Submitted: January 5, 2012 Revised: May 16, 2013 Accepted: June 4, 2013
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Source: Psychology of Addictive Behaviors. Vol. 27. (3), Sep, 2013 pp. 832-840)
Accession Number: 2013-33297-006
Digital Object Identifier: 10.1037/a0033822
Record: 152- Title:
- Stimulus control in intermittent and daily smokers.
- Authors:
- Shiffman, Saul. Department of Psychology, University of Pittsburgh, Pittsburgh, PA, US, shiffman@pitt.edu
Dunbar, Michael S.. Department of Psychology, University of Pittsburgh, Pittsburgh, PA, US
Ferguson, Stuart G.. School of Medicine, University of Tasmania, TAS, Australia - Address:
- Shiffman, Saul, Department of Psychology, University of Pittsburgh, 130 North Bellefield Avenue, Suite 510, Pittsburgh, PA, US, 15213, shiffman@pitt.edu
- Source:
- Psychology of Addictive Behaviors, Vol 29(4), Dec, 2015. pp. 847-855.
- NLM Title Abbreviation:
- Psychol Addict Behav
- Page Count:
- 9
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- Bulletin of the Society of Psychologists in Addictive Behaviors; Bulletin of the Society of Psychologists in Substance Abuse
- Other Publishers:
- US : Educational Publishing Foundation
Society of Psychologists in Addictive Behaviors - ISSN:
- 0893-164X (Print)
1939-1501 (Electronic) - Language:
- English
- Keywords:
- smoking, ecological momentary assessment, nondaily smoking, daily smoking, stimulus control
- Abstract:
- [Correction Notice: An Erratum for this article was reported in Vol 29(4) of Psychology of Addictive Behaviors (see record 2015-33590-001). There was an error in the reported value in the Discussion section. The second sentence of the second paragraph should have read, 'Notably, DS also showed strong stimulus control in this analysis, implying 85% accuracy in identifying smoking situations.' This was a result of a transcription error. All versions of this article have been corrected.] Many adult smokers are intermittent smokers (ITS) who do not smoke daily. Prior analyses have suggested that, compared with daily smokers (DS), ITS smoking was, on average, more linked to particular situations, such as alcohol consumption. However, such particular associations assessed in common across subjects may underestimate stimulus control over smoking, which may vary across persons, due to different conditioning histories. We quantify such idiographic stimulus control using separate multivariable logistic regressions for each subject to estimate how well the subject’s smoking could be predicted from a panel of situational characteristics, without requiring that other subjects respond to the same stimuli. Subjects were 212 ITS (smoking 4–27 days/month) and 194 DS (5–30 cigarettes daily). Using ecological momentary assessment, subjects monitored situational antecedents of smoking for 3 weeks, recording each cigarette in an electronic diary. Situational characteristics were assessed in a random subset of smoking occasions (n = 21,539), and contrasted with assessments of nonsmoking occasions (n = 26,930) obtained by beeping subjects at random. ITS showed significantly stronger stimulus control than DS across all context domains: mood, location, activity, social setting, consumption, smoking context, and time of day. Mood and smoking context showed the strongest influence on ITS smoking; food and alcohol consumption had the least influence. ITS smoking was under very strong stimulus control; significantly more so than DS, but DS smoking also showed considerable stimulus control. Stimulus control may be an important influence on maintaining smoking and making quitting difficult for all smokers, but especially among ITS. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Daily Activities; *Tobacco Smoking; Stimulus Control
- PsycINFO Classification:
- Drug & Alcohol Usage (Legal) (2990)
- Population:
- Human
Male
Female - Age Group:
- Adulthood (18 yrs & older)
Young Adulthood (18-29 yrs) - Grant Sponsorship:
- Sponsor: Department of Health and Human Services, National Institutes of Health, National Institute on Drug Abuse, US
Grant Number: R01-DA020742
Recipients: No recipient indicated - Methodology:
- Empirical Study; Quantitative Study
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- First Posted: Feb 23, 2015; Accepted: Nov 23, 2014; Revised: Nov 13, 2014; First Submitted: Sep 5, 2014
- Release Date:
- 20150223
- Correction Date:
- 20160104
- Copyright:
- American Psychological Association. 2015
- Digital Object Identifier:
- http://dx.doi.org/10.1037/adb0000052
- Accession Number:
- 2015-07274-001
- Number of Citations in Source:
- 51
- Persistent link to this record (Permalink):
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- Cut and Paste:
- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2015-07274-001&site=ehost-live">Stimulus control in intermittent and daily smokers.</A>
- Database:
- PsycINFO
Stimulus Control in Intermittent and Daily Smokers
By: Saul Shiffman
Department of Psychology, University of Pittsburgh;
Michael S. Dunbar
Department of Psychology, University of Pittsburgh
Stuart G. Ferguson
School of Medicine, University of Tasmania
Acknowledgement: This work was supported by Grant R01-DA020742 (Shiffman) from the United States Department of Health and Human Services, National Institutes of Health, National Institute on Drug Abuse.
Nicotine dependence is considered the primary determinant of persistent cigarette smoking. This helps explain why most smokers smoke frequently throughout the day, every day, which functions to prevent nicotine levels from sinking below a level where nicotine withdrawal sets in (Benowitz, 2010; Stolerman & Jarvis, 1995). Maintaining nicotine levels (“trough maintenance;” Russell, 1971) is best accomplished by smoking at very regular intervals, but some models allow room for variations from this, for example, smoking in response to situational cues. However, this leeway is limited, as nicotine withdrawal can set in within a few hours of abstinence (Benowitz, 2008).
Nondaily smoking is becoming increasingly prevalent among U.S. adults, however (Cooper et al., 2010; Schane, Glantz, & Ling, 2009; Shiffman, 2009b; Shiffman, Tindle, et al., 2012). As many as 38% of U.S. adult smokers are now nondaily or intermittent smokers (ITS; U.S. Department of Health & Human Services, 2014). ITS smoke an average of 4–5 cigarettes per day on the days they smoke (Gilpin, Cavin, & Pierce, 1997; Shiffman, Tindle, et al., 2012; Wortley, Husten, Trosclair, & Chrismon, & Pederson, 2003), but their defining characteristic is that they frequently go for several days running without smoking (Shiffman, Tindle, et al., 2012), and thus clearly do not maintain nicotine levels (Benowitz, 2008). ITS may be seeking the reinforcing effects of acute doses of nicotine (“peak seeking”; Russell, 1971), rather than trying to maintain a minimal level to avoid withdrawal (“trough avoidance”). Nor is this necessarily just a transient phase en route to dependence: Zhu, Sun, Hawkins, Pierce, and Cummins (2003) reported that a substantial proportion of ITS maintained that status over two years. Similarly, we have studied a sample of ITS who have been smoking for an average of 19 years, and have consumed an average of more than 40,000 cigarettes (Shiffman, Tindle, et al., 2012), yet show little or no dependence (Shiffman, Ferguson, Dunbar, & Scholl, 2012; Shiffman, Tindle, et al., 2012). Nevertheless, ITS have surprising trouble quitting smoking, with failure rates of 78%, only slightly lower than those of daily smokers (DS; Tindle & Shiffman, 2011).
What might account for the difficulty ITS have quitting? One factor might be stimulus control. A behavior (in this case, smoking) is said to be under stimulus control when the presence of a given stimulus (or stimuli) changes the likelihood of that behavior occurring. Such relationships are believed to be established through various learning processes. Stimuli could influence smoking by serving as discriminative stimuli, indicating that smoking will be reinforcing, acting as priming stimuli, and/or as conditioned stimuli eliciting responses instilled by prior associations with stimuli, including the effects of smoking itself (Bickel & Kelly, 1988). If ITS smoking is strongly associated with certain situational cues, exposure to such cues might promote continued smoking and pose a significant barrier to abstinence in the face of exposure to relevant cues. Strong stimulus control is a common feature of casual drug use (Bickel & Kelly, 1988), and we have hypothesized that its diminution is an important step in the development of tobacco dependence (Shiffman & Paty, 2006; Shiffman, Waters, & Hickcox, 2004), as use shifts from particular settings to nicotine maintenance via frequent nicotine intake. Consistent with this, in responses to a questionnaire on a scale assessing smoking motives (Piper et al., 2004), ITS have identified responsiveness to cues as their most important motivation to smoke (Shiffman, Dunbar, Scholl, & Tindle, 2012), and smoking in chippers—very light smokers—has been shown to be under greater stimulus control than that seen in heavy smokers (Shiffman & Paty, 2006).
A useful way to assess individuals’ smoking patterns is via ecological momentary assessment (EMA; Shiffman, 2009a, in press; Stone & Shiffman, 1994)—collection of real-time, real-world data on multiple occasions. Collecting data in subjects’ real-world settings ensures ecological validity, and collecting it in real time avoids problems of recall bias. Collecting data on both smoking and nonsmoking occasions allows one to characterize the associations between smoking and situational antecedents (Paty, Kassel, & Shiffman, 1992; Shiffman, 2009a). This method has been used to study situational associations with smoking in a variety of populations (e.g., Beckham et al., 2008; Cronk & Piasecki, 2010; Mermelstein, Hedeker, Flay, & Shiffman, 2007; Shiffman et al., 2002; Shiffman & Paty, 2006).
We recently used EMA data to compare the particular stimuli associated with smoking for ITS and DS, and found that ITS smoking was more likely to be associated with cues such as being away from home, being in a bar, drinking alcohol, socializing, being with friends and acquaintances, and being where others were smoking (Shiffman et al., 2014a). However, these analyses, although they contribute to our understanding of ITS smoking, only identify the smoking triggers that most ITS share in common; they do not fully reflect the degree of control that various stimuli exert over individual ITS smoking.
To quantify stimulus control, one must abstract from relationships between smoking and the cues that ITS (or DS) share in general, to examine idiographic associations with cues within each person, as these associations can be idiosyncratic, with different smokers having different, even opposite, reactions to the same cue. For example, if some subjects smoke when they are feeling good, whereas others smoke when they feel bad, a group-wise analysis of individual moods might show no effect, even though mood exercises stimulus control over smoking for both groups of subjects. Indeed, data from an EMA study of DS showed such effects, in that the overall group-wise relationship between smoking and mood was estimated as 0 (Shiffman et al., 2002), yet the distribution showed wide variation, with relationships in both directions, and these variations proved meaningful in predicting subsequent relapse (Shiffman et al., 2007). Also, different subjects might respond to different stimuli, even within a given domain. For example, some subjects might respond to how good or bad they feel and others to how aroused they feel. Both might be considered equally under stimulus control by mood, but these associations, too, would be missed or diluted in the analyses of single cues that are typically done (e.g., Shiffman et al., 2014a). Yet such variable idiographic relationships between smoking and antecedent stimuli are to be expected if the associations are due to conditioning (Niaura et al., 1988), because individuals’ learning histories would likely vary.
Accordingly, in this paper we go beyond assessing directional group-wise associations between situational stimuli and smoking to quantify the degree of stimulus control using EMA data to estimate how well various situational characteristics can account idiographically for each individual’s smoking based on analyses within each individual subject. Subsequent comparisons of the resulting parameters are made between ITS and DS.
Method Subjects
Subjects were 212 ITS and 194 DS recruited through advertisement. Participants had to be at least 21 years old, report smoking for at least 3 years, smoking at their current rate for at least 3 months, and not planning to quit within the next month. DS had to report smoking every day, averaging 5 to 30 cigarettes per day. ITS had to report smoking 4 to 27 days per month, with no restrictions on number of cigarettes. We oversampled African American smokers, because national surveys have indicated they are more likely to be ITS (Trinidad et al., 2009); data were weighted to balance ethnic representation. Analyses of association of smoking and particular cues were reported for this sample in Shiffman et al. (2014a), and the sample largely overlaps with one reported in several analyses of other data (Shiffman et al., 2013a; Shiffman et al., 2013b; Shiffman et al., 2012; Shiffman, Ferguson, et al., 2012; Shiffman, Tindle, et al., 2012).
Briefly, DS were 41 years old, 55% male, smoked 15 cigarettes per day, and had been smoking for 26 years on average. ITS were slightly younger (37 years old), 49% male, smoked 4–5 cigarettes per day on smoking days, smoked 4–5 days per week, and had been smoking for 19 years on average (see Shiffman et al., 2014a for additional details).
Procedures
The EMA methods for this study have been described in detail in Shiffman et al. (2014a), and are similar to those in previous studies (Ferguson & Shiffman, 2011; Shiffman, 2009a; Shiffman et al., 2002). Briefly, subjects were provided with a palmtop computer, which they used to monitor smoking for three weeks (average 21.60 ± 4.11 days). Subjects were to record all cigarettes, but to avoid excessive burden, the computer administered an assessment of the surrounding circumstances only for a portion of those smoking occasions, selected at random.
To assess the circumstances of nonsmoking moments, as a necessary contrast for smoking occasions (Paty et al., 1992; Shiffman, 2009a), the computer “beeped” subjects at random about four times a day (but never within 15 min of smoking), and administered a nearly identical assessment. Subjects in the analysis received three to four prompts per day on average (DS: M = 3.52; ITS: M = 3.93) and responded to 88% of them (DS: 87.6%; ITS: 88.2%).
Assessment
All assessments were administered on the computer’s touch screen, with structured responses (no open-ended text) consisting of 0–100-point visual analog scales for mood items and single or multiple selections for other domains. The content of the domains is shown in Table 1. (a) Mood ratings of 14 adjectives (listed in Table 1 note) addressing mood, arousal, and attention, respectively, were summarized as four factor scores: Negative Affect, Positive Affect, Arousal, and Attention Disturbance, each analysis of which included linear and quadratic components; (b) Location (if subjects had moved to smoke, they were asked to describe the setting that first prompted them to smoke, otherwise current location was described); (c) Activity; (d) Social Setting; (e) Smoking Setting, i.e. whether others were smoking (and whether they were part of the group of people they were with or were just in view), and whether smoking was restricted; (f) Consumption of Food or Drink in the past 15 min; and, and (g) Time of Day, which was automatically recorded by the palmtop computer. We also assessed craving on a 0–100 scale.
Summary of Stimulus Domains
Analysis
Data set construction is described in detail in Shiffman et al. (2014a). The data set comprised 406 subjects (212 ITS; 194 DS), each contributing an average of 53.02 (SD = 33.00) smoking assessments (ITS: 36.66 [SD = 30.81]; DS: 70.90 [SD = 25.14]) and 66.28 (SD = 19.78) nonsmoking assessments (ITS: 72.07 [SD = 18.32]; DS: 59.94 [SD = 19.42]).
To illustrate the relevance of idiographic analyses, we report the range across subjects of the association between smoking and four illustrative variables: (a) a summary score of emotional state, as captured by a 5-point bipolar item in which subjects indicated how good or bad they were feeling (very bad, bad, neutral, good, very good); (b) a factor score indexing degree of arousal; (c) an indicator of drinking coffee in the previous 15 min (0, 1); and (d) an indicator of being alone (0, 1, where 1 = alone). For each subject, the association of the variables with smoking was estimated by a within-subject correlation coefficient (point-biserial for mood, phi for “coffee” and “alone”). We display the distributions for DS and ITS separately, and also note the standard deviation of the correlations.
Stimulus control was assessed for each situational domain. The analysis proceeded in two steps (Raudenbush & Bryk, 1992; see Shiffman & Paty, 2006), (a) within-subject idiographic analyses performed separately for each subject, and (b) between-groups analyses of the estimated parameters by smoker type. We first assessed the degree to which each participant’s smoking was under stimulus control of the variables in each of several domains of situational context by conducting separate multivariable logistic regressions for each subject to determine how well the situational variables predicted smoking (in contrast to nonsmoking observations). In other words, for each subject and for each domain, we ran a separate logistic regression with smoking (yes, no) as the dependent variable, and the domain variables (see Table 1) as predictors. To account for potential overfitting of models, analyses omitted cases that demonstrated complete or quasi-complete separation (<5% of all cases in each domain). In addition to fitting models for each domain, we also fitted for each subject an omnibus model, including all the variables listed in Table 1. The within-subject logistic models did not take into account the autocorrelation among a subject’s data; the estimates generated are used descriptively. To quantify the degree of prediction (and thus stimulus control) achieved by each of these models, for each subject and domain, we calculated the area under the curve (AUC) for the receiver operating characteristic curve (ROC). Like an R2 value for ordinary regression, higher AUC-ROC values (also sometimes described as the c statistic) indicate better prediction. AUC-ROC is interpretable as the probability of correctly identifying a smoking (vs. nonsmoking) observation given the situational predictors. Thus, AUC-ROC ranges from 0.5 (random guessing) to 1.0 (perfect prediction; Hanley & McNeil, 1982). Accordingly, each subject had an AUC-ROC value for each domain that quantified the degree of “predictability” of smoking from the variables in that domain.
In the second step, to assess whether DS differed from ITS, we tested the between-groups differences (DS vs. ITS) in AUC-ROC for each domain using mixed-regression models (SAS’s PROC MIXED) specifying variance components’ autocorrelation structure. At this second level, each estimate was weighted by the inverse of its standard error (SE), so that more precise estimates received greater weight (Hanley & McNeil, 1982). The SEs of AUC-ROC values decrease as the number of observations increases, and also decrease as the estimated magnitude of the AUC-ROC increases (Hanley & McNeil, 1982). Note that, although DS reported more smoking events, DS and ITS did not differ in average AUC-ROC SEs across any situational domains, though ITS had lower SEs for an omnibus model with all the variables included. Analyses were also weighted by race to account for oversampling of African American participants. To assess whether the AUC-ROCs in each domain differed between ITS and DS, we computed mixed-regression models, treating AUC-ROC values across situational domains as a within-subjects random effect. The analyses also examined whether the DS-ITS differences varied by domain by assessing the interaction between smoker type and situational domain. We used a mixed model to accommodate cases in which subjects had missing estimates for a particular domain (e.g., due to complete separation).
We also report the AUC-ROC for the relationship between craving and smoking, and test whether the group differences in this relationship mediate the group differences in AUC-ROC for each of the stimulus domains. We used the Sobel test (Preacher & Hayes, 2004) to assess the significance of these mediational relationships using separate ordinary least-square regression models, first assessing smoker type as a predictor of craving AUC-ROC (α path) and then examining subjects’ craving AUC-ROC (β path) covarying for smoker type as a predictor of the AUC-ROC for each stimulus domain. The product of the α and β coefficients was used to assess evidence for mediation of stimulus control within each domain (Preacher & Hayes, 2004).
Results Idiographic Variations in Associations of Smoking With Contexts
Figure 1 shows the distribution of within-subject correlations between smoking and several illustrative variables; the correlations each quantify how each variable relates to smoking for each subject. In all cases, the average correlations are near 0, indicating at most a modest association, on average, although some of these associations were significant in analyses reported in Shiffman et al. (2014a). However, this mean value masks the fact that the distributions extend on either side of 0, indicating that there are individuals who show positive associations, as well as others who show negative associations. For example, as seen in Figure 1, for some ITS, being alone was correlated −0.60 with smoking (i.e., they were considerably more likely to smoke with others); for others, being alone was correlated as high as 0.90 with smoking (i.e., they were much more likely to smoke alone). The average correlation among ITS was −0.02, indicating no relationship with smoking, on average (see also Shiffman et al., 2014a). Notably, the spread of the correlations was consistently wider among ITS, as demonstrated by the higher SDs. (This was true of almost all variables, not just those shown in Figure 1.) Furthermore, for all variables, both positive and negative correlations were observed, with the range of subject-specific correlations averaging 1.0 (for example, −0.5 to +0.5 or −0.4 to +0.6).
Figure 1. Correlations between smoking and various situational characteristics, among daily smokers (DS) and intermittent smokers (ITS). The histograms show the range of correlations, at the level of individual subjects, between selected situational characteristics and smoking, shown separately for DS and ITS. The variables shown are (a) feeling/valence, a rating of feelings from negative to positive; (b) arousal, a factor score whose constituent items include “active,” “calm,” “quiet/sleepy,” and “energetic;” (c) drinking coffee (no, yes); and (d) being alone (no, yes). The figures illustrate that the associations vary widely, even when the average association is near 0. Note that the range of the x axes is kept identical for DS and ITS within a given variable to accommodate different ranges of observed correlations, but the scales differ across variables. Each graph also shows the standard deviation of the correlation coefficients shown in the histogram, illustrating that the associations observed among ITS are consistently more variable than those observed among DS.
Stimulus Control of Smoking
When all the situational variables are considered simultaneously, ITS stimulus control was nearly perfect, with AUC-ROC averaging 0.95; that is, smoking and nonsmoking occasions were distinguishable 95% of the time based on the situation descriptors. This was significantly higher than the average AUC-ROC for DS, but it was also very high at 0.86. Figure 2 shows the AUC-ROC values for particular stimulus domains, and shows that both groups demonstrated considerable stimulus control in all domains, with all ROC values significantly higher than the null value of 0.5. However, ITS showed significantly stronger stimulus control over smoking in all stimulus domains. There was also an overall group main effect and ITS values were higher than DS overall and in every domain.
Figure 2. Receiver operating curve (ROC) values across situational domain and smoker group. Average values for the area under the curve (AUC) of the ROC express the predictability of smoking from situational domains. ITS values were significantly higher for every domain, and all values were significantly greater than 0.5, the null value. DS = daily smokers; ITS = intermittent smokers.
To test whether the ITS–DS difference in AUC-ROCs varied by domain, we evaluated the Group × Domain interaction, which was significant (p < .0001). As shown in Figure 2, the differences were greatest for the smoking-context domain and smallest for consumption. Within each smoker group, analyses revealed significant main effects of domain, indicating that some domains are more tightly linked to smoking than others. Within-group differences in AUC-ROC values between domains were nearly all significant. (The exceptions were that, among ITS, AUC-ROC values did not differ between activity and location; among DS, values did not differ between location and smoking context). The AUC-ROC values indicated that social setting, mood, and activity exercised the greatest stimulus control over smoking in both groups. In addition, smoking context was the strongest predictor of ITS smoking; this was not the case for DS.
Mediation of Group Differences by Craving Responsiveness
An AUC-ROC analysis evaluated the relationship between craving and smoking. ITS had a significantly higher value (0.79 vs. 0.63, p < .0001), indicating that their smoking was more closely linked to craving (see also Shiffman et al., 2014a). Covarying the craving AUC-ROC in separate ordinary least squares regression analyses of smoker type effects on AUC-ROCs within each stimulus domain suggested that craving attenuated but did not fully account for ITS and DS group differences in stimulus control (all group differences remained significant at p < .0001). However, tests of mediation suggested that differences in the craving–smoking link partially mediated smoker group effects in nearly all situational domains (Sobel test ps < .01), with the exception of mood (Sobel test p = .12).
DiscussionDetailed data on smoking contexts, collected by real-time EMA methods, demonstrated that situational contexts exercise greater influence over ITS compared with DS smoking. ITS smoking consistently demonstrated significantly greater stimulus control in every situational domain considered: Time of Day, Social Setting, Affect, Restrictions, Location, Activity, and Consumption of Food and Drink. The absolute magnitude of the associations was striking. For example, just knowing the person’s emotional state allowed us to correctly predict, with over 75% accuracy, whether an ITS was smoking or not. In short, ITS smoking seems to be under tight stimulus control.
Even more striking was the estimated level of stimulus control when all variables were considered: the analysis indicated that one could achieve 95% accuracy in identifying smoking situations among ITS. Notably, DS also showed strong stimulus control in this analysis, implying 85% accuracy in identifying smoking situations. However, the figures from this omnibus analysis should be treated with some caution, because the models included all 26 variables shown in Table 1, and so may have been overfitted, perhaps achieving spurious levels of prediction.
The finding of stronger stimulus control among ITS across a range of domains is consistent with the hypothesis that stimulus control helps to maintain ITS smoking and make quitting difficult in the face of cues associated with smoking, and may help explain why ITS are not much better able to quit than DS (Tindle & Shiffman, 2011), despite the fact that ITS do not maintain nicotine levels, and do not suffer increased craving or withdrawal when they abstain (Shiffman, Dunbar, Tindle, & Ferguson, 2014). It is also consistent with our previously reported finding that ITS smoking is more responsive to craving than DS smoking (Shiffman et al., 2014b): ITS may experience craving, and hence smoke, when in the presence of certain stimuli, but in the absence of such stimuli, they do not experience a drive to smoke. In a sense, strong stimulus control over use may represent another kind of dependence that keeps users of psychoactive drugs from easily stopping. Given that nondaily use is common for other addictive drugs (SAMHSA, 2003), this mode of dependence may be important for understanding the range of drug-use behaviors.
While the observed degree of stimulus control among ITS was particularly striking, DS smoking also showed a substantial amount of stimulus control—more than would be expected under a strict nicotine-regulation model, which implies smoking at regular intervals, determined by the ebb of nicotine, rather than in response to external stimuli. Further, the pattern of stimulus control across stimulus domains (see Figure 2) was strikingly similar for DS and ITS: Across the seven situational domains examined, the profiles of AUC-ROC values for DS and ITS correlated at 0.90. Thus, stimulus control among DS appears to be qualitatively similar to that in ITS, but consistently weaker.
It is widely understood that even DS smoking is initially under stimulus control during early stages of smoking (Russell, 1971), but the emerging need for nicotine maintenance is thought to supplant stimulus control as a driver of smoking (Shiffman & Paty, 2006). These data suggest that stimulus control remains important even for established adult DS. Perhaps the influence of context is not supplanted, but simply diluted, as smokers begin to smoke more of their cigarettes to maintain nicotine levels above the withdrawal threshold, independent of the situation. In this conceptualization, both ITS and DS respond to similar cues, but, whereas this is the dominant influence on smoking among ITS, its influence on DS is masked by the addition of cigarettes smoked for nicotine maintenance. This account is consistent with the boundary model (Kozlowski & Herman, 1984), which conceptualizes dependence as demanding a certain minimum rate of smoking while allowing for additional smoking that might be prompted by situational influences.
This two-factor model of smoking (Withdrawal Avoidance and Stimulus Control) may also have implications for understanding smoking cessation and relapse among DS, who face a dual challenge when quitting smoking. First, they must overcome withdrawal and background craving (West & Schneiders, 1987), which can be mitigated by pharmacological treatment (Ferguson & Shiffman, 2009). But they also must overcome the influence of stimulus control, which is unmasked during cessation, and triggers cue-elicited craving upon exposure to cues (Ferguson & Shiffman, 2009). The role of stimulus control among DS is evident in lapse situations, which are marked by cueing stimuli like the ones seen in our analyses: for example, exposure to other smokers, consumption of alcohol, and so forth (Bliss, Garvey, Heinold, & Hitchcock, 1989; Shiffman et al., 1997; Shiffman, Paty, Gnys, Kassel, & Hickcox, 1996; Shiffman & Waters, 2004). The re-emergent role of cues also helps explain why smokers relapse (albeit at lower rates) even when their nicotine requirements are met by nicotine replacement. In a study where 100% of baseline nicotine levels were met by a high-dose patch (Shiffman, Ferguson, Gwaltney, Balabanis, & Shadel, 2006) and withdrawal was completely suppressed, 62% of smokers still lapsed within 6 weeks (vs. 75% on placebo; Shiffman et al., 2006) when cued by the typical situational triggers (Ferguson & Shiffman, 2010, 2014). Thus, two factors appear to maintain smoking and make quitting difficult for DS: the need to maintain nicotine levels to avoid withdrawal and abstinence-induced craving, and the influence of cues that trigger cue-induced craving and smoking (i.e., stimulus control).
Previous analyses (Shiffman et al., 2014b) showed that ITS smoking was more tightly linked to craving, because ITS reported very little craving when they were not smoking. Analyses in the present paper showed more broadly that ITS smoking was more sensitive to situational craving, but mediational analyses showed that this did not account for the difference between stimulus control in ITS and DS smoking. The actual elicitation of smoking by situational stimuli may still be due to their stimulation of craving; the analysis only suggests that once craving is elicited, differential responsiveness to that craving does not explain differences in stimulus control.
The idiographic n = 1 analyses used here revealed patterns not seen in group-wise nomothetic analyses. It was particularly striking that nomothetic analyses showed almost no relationship between emotional state and smoking among DS, either in this study or in others (Shiffman et al., 2014a; Shiffman et al., 2002; Shiftman, Paty, Gwaltney, & Dang, 2004), and, consistent with this, Figure 1 shows little or no relationship between emotional state and smoking, on average. Yet, considered idiographically, emotional state was among the most important situational influences on DS and ITS smoking, suggesting that emotion does influence smoking, but not in a simple, consistent way. Important to note, the observed influence of affect on smoking is not readily attributable to withdrawal effects, because it includes cases in which smoking was associated with positive emotional states. Indeed, in traditional analyses of the role of affect in smoking, smoking was more likely to occur when subjects—both ITS and DS—were feeling better, rather than worse (Shiffman et al., 2014a).
The study’s limitations include reliance on self-report of smoking status and situational characteristics, potential for reactivity, and possible biasing effects of noncompliance and smoking restrictions (see Shiffman, 2009a). Particularly when there were few smoking observations, the individual logistic regressions could have exploited chance relationships; this was particularly the case for the omnibus models, as they included many predictors. Also, differences in AUC-ROC values across domains could have been due to differences in how domains were assessed, rather than true differences in their influence on smoking. Some stimulus domains may not have been covered as comprehensively or assessed as reliably as others, perhaps resulting in lower average AUC-ROCs due to these measurement factors. Finally, unlike traditional animal studies of stimulus control, we did not control the pairing of specific antecedent stimuli and our target behavior (smoking) and as such, we cannot draw causal conclusions about the associations observed; that is, although the patterns observed are consistent with stimulus control, we cannot conclude that they are caused by it.
The study’s strengths included the use of real-time EMA methods, and a nontreatment-seeking sample with diverse smoking behavior. An important aspect of our work here was the ability to expand the scope of the analysis from unidirectional and univariate nomothetic relationships that were similar across subjects (e.g., all subjects tending to smoke when feeling worse emotionally) to encompass the fact that different individuals have different, indeed opposite, associations (e.g., some subjects smoke when feeling worse emotionally, and some smoke when feeling better; Figure 1). The analysis by domains, which encompassed several related situational characteristics, also allowed variation across subjects in whom particular variables were influential. For example, if some subjects tended to smoke more when drinking alcohol, and others to smoke more when drinking coffee, such effects might be diluted, perhaps to the point of being invisible, in traditional analyses treating alcohol and coffee as separate cues. In contrast, both effects would have been included in our analysis of the stimulus control exerted by consumption. Particularly because such heterogeneity in influential variables, and in their direction of influence, is to be expected if these individual differences resulted from idiosyncratic learning histories (Niaura et al., 1988), this mode of analysis seems important for assessing the influence of situational variables on smoking, that is, stimulus control.
In summary, ITS demonstrated very strong stimulus control over smoking, which may be a dominant driver of their smoking, and may account for their surprising difficulty quitting. DS also showed substantial stimulus control, suggesting that stimulus control also plays a significant role in driving and maintaining smoking even among DS. DS smoking may be maintained by two factors—withdrawal avoidance and stimulus control—whereas ITS smoking may be maintained primarily by stimulus control.
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Submitted: September 5, 2014 Revised: November 13, 2014 Accepted: November 23, 2014
This publication is protected by US and international copyright laws and its content may not be copied without the copyright holders express written permission except for the print or download capabilities of the retrieval software used for access. This content is intended solely for the use of the individual user.
Source: Psychology of Addictive Behaviors. Vol. 29. (4), Dec, 2015 pp. 847-855)
Accession Number: 2015-07274-001
Digital Object Identifier: 10.1037/adb0000052
Record: 153- Title:
- 'Stimulus control in intermittent and daily smokers': Correction to Shiffman, Dunbar, and Ferguson (2015).
- Authors:
- No authorship indicated
- Source:
- Psychology of Addictive Behaviors, Vol 29(4), Dec, 2015. pp. 855.
- NLM Title Abbreviation:
- Psychol Addict Behav
- Page Count:
- 1
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- Bulletin of the Society of Psychologists in Addictive Behaviors; Bulletin of the Society of Psychologists in Substance Abuse
- Other Publishers:
- US : Educational Publishing Foundation
Society of Psychologists in Addictive Behaviors - ISSN:
- 0893-164X (Print)
1939-1501 (Electronic) - Language:
- English
- Keywords:
- smoking, ecological momentary assessment, nondaily smoking, daily smoking, stimulus control
- Abstract:
- Reports an error in 'Stimulus Control in Intermittent and Daily Smokers' by Saul Shiffman, Michael S. Dunbar and Stuart G. Ferguson (Psychology of Addictive Behaviors, Advanced Online Publication, Feb 23, 2015, np). There was an error in the reported value in the Discussion section. The second sentence of the second paragraph should have read, 'Notably, DS also showed strong stimulus control in this analysis, implying 85% accuracy in identifying smoking situations.' This was a result of a transcription error. All versions of this article have been corrected. (The following abstract of the original article appeared in record 2015-07274-001.) Many adult smokers are intermittent smokers (ITS) who do not smoke daily. Prior analyses have suggested that, compared with daily smokers (DS), ITS smoking was, on average, more linked to particular situations, such as alcohol consumption. However, such particular associations assessed in common across subjects may underestimate stimulus control over smoking, which may vary across persons, due to different conditioning histories. We quantify such idiographic stimulus control using separate multivariable logistic regressions for each subject to estimate how well the subject’s smoking could be predicted from a panel of situational characteristics, without requiring that other subjects respond to the same stimuli. Subjects were 212 ITS (smoking 4–27 days/month) and 194 DS (5–30 cigarettes daily). Using ecological momentary assessment, subjects monitored situational antecedents of smoking for 3 weeks, recording each cigarette in an electronic diary. Situational characteristics were assessed in a random subset of smoking occasions (n = 21,539), and contrasted with assessments of nonsmoking occasions (n = 26,930) obtained by beeping subjects at random. ITS showed significantly stronger stimulus control than DS across all context domains: mood, location, activity, social setting, consumption, smoking context, and time of day. Mood and smoking context showed the strongest influence on ITS smoking; food and alcohol consumption had the least influence. ITS smoking was under very strong stimulus control; significantly more so than DS, but DS smoking also showed considerable stimulus control. Stimulus control may be an important influence on maintaining smoking and making quitting difficult for all smokers, but especially among ITS. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
- Document Type:
- Erratum/Correction
- Subjects:
- *Daily Activities; *Tobacco Smoking; Stimulus Control
- PsycINFO Classification:
- Drug & Alcohol Usage (Legal) (2990)
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- First Posted: Jul 27, 2015
- Release Date:
- 20150727
- Correction Date:
- 20160104
- Digital Object Identifier:
- http://dx.doi.org/10.1037/adb0000105
- Accession Number:
- 2015-33590-001
- Persistent link to this record (Permalink):
- http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2015-33590-001&site=ehost-live
- Cut and Paste:
- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2015-33590-001&site=ehost-live">'Stimulus control in intermittent and daily smokers': Correction to Shiffman, Dunbar, and Ferguson (2015).</A>
- Database:
- PsycINFO
Correction to Shiffman, Dunbar, and Ferguson (2015)
In the article “Stimulus Control in Intermittent and Daily Smokers” by Saul Shiffman, Michael S. Dunbar, and Stuart G. Ferguson (Psychology of Addictive Behaviors, Advance online publication. February 23, 2015. http://dx.doi.org/10.1037/adb0000052), there was an error in the reported value in the Discussion section. The second sentence of the second paragraph should have read, “Notably, DS also showed strong stimulus control in this analysis, implying 85% accuracy in identifying smoking situations.” This was a result of a transcription error. All versions of this article have been corrected.
This publication is protected by US and international copyright laws and its content may not be copied without the copyright holders express written permission except for the print or download capabilities of the retrieval software used for access. This content is intended solely for the use of the individual user.
Source: Psychology of Addictive Behaviors. Vol. 29. (4), Dec, 2015 pp. 855)
Accession Number: 2015-33590-001
Digital Object Identifier: 10.1037/adb0000105
Record: 154- Title:
- Structure and measurement of depression in youths: Applying item response theory to clinical data.
- Authors:
- Cole, David A.. Department of Psychology and Human Development, Vanderbilt University, Nashville, TN, US, david.cole@vanderbilt.edu
Cai, Li. Department of Education, University of California-Los Angeles, CA, US
Martin, Nina C.. Department of Psychology and Human Development, Vanderbilt University, Nashville, TN, US
Findling, Robert L.. Department of Psychiatry, Case Western Reserve University, Cleveland, OH, US
Youngstrom, Eric A.. Department of Psychology, University of North Carolina-Chapel Hill, NC, US
Garber, Judy. Department of Psychology and Human Development, Vanderbilt University, Nashville, TN, US
Curry, John F.. Department of Psychiatry and Behavioral Sciences, Duke University School of Medicine, NC, US
Hyde, Janet S.. Department of Psychology, University of Wisconsin–Madison, WI, US
Essex, Marilyn J.. Department of Psychiatry, University of Wisconsin School of Medicine and Public Health, WI, US
Compas, Bruce E.. Department of Psychology and Human Development, Vanderbilt University, Nashville, TN, US
Goodyer, Ian M.. Department of Psychiatry, University of Cambridge, Cambridge, England
Rohde, Paul. Oregon Research Institute, Eugene, OR, US
Stark, Kevin D.. Department of Educational Psychology, University of Texas-Austin, TX, US
Slattery, Marcia J.. Department of Psychiatry, University of Wisconsin School of Medicine and Public Health, WI, US
Forehand, Rex. Department of Psychology, University of Vermont, VT, US - Address:
- Cole, David A., Department of Psychology and Human Development, Vanderbilt University, Peabody MSC 512, 230 Appleton Place, Nashville, TN, US, 37203, david.cole@vanderbilt.edu
- Source:
- Psychological Assessment, Vol 23(4), Dec, 2011. pp. 819-833.
- NLM Title Abbreviation:
- Psychol Assess
- Page Count:
- 15
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- Psychological Assessment: A Journal of Consulting and Clinical Psychology
- ISSN:
- 1040-3590 (Print)
1939-134X (Electronic) - Language:
- English
- Keywords:
- Kiddie–SADS (K–SADS), adolescents, children, depression, item response theory (IRT), Kiddie Schedule of Affective Disorders and Schizophrenia for School-Aged Children, structure, measurement
- Abstract:
- Our goals in this article were to use item response theory (IRT) to assess the relation of depressive symptoms to the underlying dimension of depression and to demonstrate how IRT-based measurement strategies can yield more reliable data about depression severity than conventional symptom counts. Participants were 3,403 children and adolescents from 12 contributing clinical and nonclinical samples; all participants had received the Kiddie Schedule of Affective Disorders and Schizophrenia for School-Aged Children. Results revealed that some symptoms reflected higher levels of depression and were more discriminating than others. Furthermore, use of IRT-based information about symptom severity and discriminability in the measurement of depression severity was shown to reduce measurement error and increase measurement fidelity. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Affective Disorders; *Item Response Theory; *Major Depression; *Measurement; *Test Construction; Adolescent Psychopathology; Child Psychopathology; Schizophrenia
- Medical Subject Headings (MeSH):
- Adolescent; Adult; Child; Child, Preschool; Depression; Depressive Disorder; Diagnostic and Statistical Manual of Mental Disorders; Factor Analysis, Statistical; Female; Humans; Interview, Psychological; Male; Models, Statistical; Psychiatric Status Rating Scales; Psychometrics; Sensitivity and Specificity; Severity of Illness Index
- PsycINFO Classification:
- Clinical Psychological Testing (2224)
Psychological Disorders (3210) - Population:
- Human
- Age Group:
- Childhood (birth-12 yrs)
Preschool Age (2-5 yrs)
School Age (6-12 yrs)
Adolescence (13-17 yrs)
Adulthood (18 yrs & older)
Young Adulthood (18-29 yrs) - Tests & Measures:
- Kiddie Schedule of Affective Disorders and Schizophrenia for School-Aged Children-Present version
Kiddie Schedule of Affective Disorders and Schizophrenia for School-Aged Children-Lifetime version - Grant Sponsorship:
- Sponsor: Institute of Education Sciences, US
Grant Number: R305B080016; R305D100039
Recipients: Cai, Li
Sponsor: National Institute on Drug Abuse, US
Grant Number: R01DA026943; R01DA030466
Recipients: Cai, Li
Sponsor: National Institute of Mental Health, US
Grant Number: R01MH64650
Recipients: Cole, David A.
Sponsor: National Institute of Mental Health, US
Grant Number: R01MH069940; R01MH069928
Recipients: Compas, Bruce E.
Sponsor: National Institute of Mental Health
Grant Number: R01MH066647; P20-MH066054
Recipients: Findling, Robert L.; Youngstrom, Eric A.
Sponsor: Stanley Medical Research Institute
Other Details: Clinical Research Center Grant
Recipients: Findling, Robert L.; Youngstrom, Eric A.
Sponsor: National Institute of Mental Health, US
Grant Number: RO1MH069940; RO1MH069928
Recipients: Forehand, Rex
Sponsor: John D. and Catherine T. MacArthur Foundation, Research Network on Psychopathology and Development
Recipients: Hyde, Janet S.; Essex, Marilyn J.
Sponsor: National Institute of Mental Health, US
Grant Number: R01MH44340; P50-MH52354; P50-MH69315; P50-MH84051
Recipients: Hyde, Janet S.; Essex, Marilyn J.
Sponsor: National Institute of Mental Health, US
Grant Number: R29MH454580; R01MH57822
Recipients: Garber, Judy
Sponsor: National Institute of Child Health and Human Development, US
Grant Number: P30-HD15052
Other Details: Faculty Scholar Award 1214-88
Recipients: Garber, Judy
Sponsor: William T. Grant Foundation
Grant Number: 173096
Recipients: Garber, Judy
Sponsor: National Institutes of Health
Grant Number: K02MH66249
Other Details: Independent Scientist Award
Recipients: Garber, Judy
Sponsor: National Health Service (NHS), Health Technology Assessment Programme
Recipients: Goodyer, Ian M.
Sponsor: Central Manchester and Manchester Children’s University Hospitals NHS Trust, United Kingdom
Recipients: Goodyer, Ian M.
Sponsor: Cambridge and Peterborough Mental Health Trust, United Kingdom
Recipients: Goodyer, Ian M.
Sponsor: National Institute of Mental Health, US
Grant Number: 98-DS-0008
Other Details: Treatment for Adolescents With Depression Study [TADS] to John S. March
Recipients: Curry, John F.
Sponsor: National Institute of Mental Health, US
Grant Number: MH56238; MH67183; MH 56238
Recipients: Rohde, Paul
Sponsor: National Institutes of Health, National Center for Research Resources, US
Grant Number: 1UL1RR025011
Other Details: Clinical and Translational Science Award Program
Recipients: Slattery, Marcia J.
Sponsor: National Institute of Mental Health, US
Grant Number: P50-MH69315
Recipients: Slattery, Marcia J.
Sponsor: National Institute of Mental Health, US
Grant Number: R01MH63998
Recipients: Stark, Kevin D.
Sponsor: National Institute of Mental Health, US
Grant Number: R01MH063852; N01 MH90003
Other Details: Myrna Weissman
Recipients: No recipient indicated - Methodology:
- Empirical Study; Quantitative Study
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- First Posted: May 2, 2011; Accepted: Feb 7, 2011; Revised: Feb 7, 2011; First Submitted: Mar 9, 2010
- Release Date:
- 20110502
- Correction Date:
- 20111128
- Copyright:
- American Psychological Association. 2011
- Digital Object Identifier:
- http://dx.doi.org/10.1037/a0023518
- PMID:
- 21534696
- Accession Number:
- 2011-08825-001
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- 64
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Structure and Measurement of Depression in Youths: Applying Item Response Theory to Clinical Data
By: David A. Cole
Department of Psychology and Human Development, Vanderbilt University;
Li Cai
Departments of Education and Psychology, University of California, Los Angeles
Nina C. Martin
Department of Psychology and Human Development, Vanderbilt University
Robert L. Findling
Department of Psychiatry, Case Western Reserve University
Eric A. Youngstrom
Departments of Psychology and Psychiatry, University of North Carolina at Chapel Hill
Judy Garber
Department of Psychology and Human Development, Vanderbilt University
John F. Curry
Department of Psychiatry and Behavioral Sciences, Duke University School of Medicine
Janet S. Hyde
Department of Psychology, University of Wisconsin–Madison
Marilyn J. Essex
Department of Psychiatry, University of Wisconsin School of Medicine and Public Health
Bruce E. Compas
Department of Psychology and Human Development, Vanderbilt University
Ian M. Goodyer
Department of Psychiatry, University of Cambridge, Cambridge, England
Paul Rohde
Oregon Research Institute, Eugene, Oregon
Kevin D. Stark
Department of Educational Psychology, University of Texas at Austin
Marcia J. Slattery
Department of Psychiatry, University of Wisconsin School of Medicine and Public Health
Rex Forehand
Department of Psychology, University of Vermont
Acknowledgement: This research was supported in part by the following grants. Li Cai: Institute of Education Sciences Grants R305B080016 and R305D100039 and National Institute on Drug Abuse Grants R01DA026943 and R01DA030466; David Cole: National Institute of Mental Health (NIMH) Grant R01MH64650 and by gifts from Patricia and Rodes Hart and from the Warren family; Bruce Compas: NIMH Grants R01MH069940 and R01MH069928 and a gift from Patricia and Rodes Hart; Robert Findling and Eric Youngstrom: NIMH Grants R01MH066647 and P20-MH066054 and a Clinical Research Center Grant from the Stanley Medical Research Institute; Rex Forehand: NIMH Grants RO1MH069940 and RO1MH069928 and a gift from the Heinz and Rowena Ansbacher Professorship; Janet S. Hyde and Marilyn J. Essex: John D. and Catherine T. MacArthur Foundation Research Network on Psychopathology and Development and NIMH Grants R01MH44340, P50-MH52354, P50-MH69315, and P50-MH84051; Judy Garber: NIMH Grants R29MH454580 and R01MH57822 and National Institute of Child Health and Human Development Grant P30-HD15052, Faculty Scholar Award 1214-88 and Grant 173096 from the William T. Grant Foundation, and Independent Scientist Award K02MH66249; Ian Goodyer: National Health Service (NHS) Health Technology Assessment Programme, Central Manchester and Manchester Children's University Hospitals NHS Trust, and the Cambridge and Peterborough Mental Health Trust; John S. March: NIMH 98-DS-0008 (Treatment for Adolescents With Depression Study [TADS]; John F. Curry was a co-investigator who collaborated on this project.); Paul Rohde: NIMH Grants MH56238, MH67183, and MH 56238; Marcia J. Slattery: Grant 1UL1RR025011 from the Clinical and Translational Science Award Program of the National Center for Research Resources in NIH and NIMH grant P50-MH69315; Kevin Stark: NIMH Grant R01MH63998; Myrna Weissman: NIMH Grant R01MH063852 and NIMH Contract N01 MH90003.
Robert Findling receives or has received research support, acted as a consultant, and/or served on a speaker's bureau for Abbott, Addrenex, AstraZeneca, Biovail, Bristol-Myers Squibb, Forest, GlaxoSmithKline, Johnson & Johnson, KemPharm Lilly, Lundbeck, Neuropharm, Novartis, Noven, Organon, Otsuka, Pfizer, Rhodes Pharmaceuticals, Sanofi-Aventis, Schering-Plough, Seaside Therapeutics, Sepracore, Shire, Solvay, Sunovion, Supernus Pharmaceuticals, Validus, and Wyeth.
The application of item response theory (IRT) to semistructured clinical interview data can simultaneously advance the understanding of psychopathology and enhance the fidelity of its measurement. IRT has proven useful when applied to paper-and-pencil measures of depressive symptoms (Bedi, Maraun, & Chrisjohn, 2001; Cassano et al., 2009; Sharp, Goodyer, & Croudace, 2006; Waller, Compas, Hollon, & Beckjord, 2005). For clinical researchers, the closest thing to a gold standard for the assessment of child and adolescent depression is a semistructured clinical interview, typically administered not just to the child but to a parent or other caregiver as well. As such, the semistructured clinical interview is inherently a multimethod assessment system, filtering information from multiple informants through interviewers with clinical training and expertise. Analyzing symptom-level information derived from such measures can provide insights into the structure of the underlying depression construct, lead to the psychometric enhancement of these measures, and eventually enable researchers to derive more information from such interviews of depressed children and adolescents. Although IRT analyses have been conducted with adult samples (e.g., Simon & Von Korff, 2006), relatively few IRT analyses of clinical interview data have been conducted with child or adolescent populations (e.g., Small et al., 2008).
In both child and adult populations, conventional factor analyses have informed researchers' understanding about the relation of specific depression symptoms to the underlying latent variable (Aggen, Neale, & Kendler, 2005; Ryan et al., 1987). IRT provides at least three additional kinds of information. First, in IRT, each symptom is linked to a specific level of depression severity. Consider an analogy. On a math test, some items may be more difficult than others, such that passing a more difficult item may suggest that the respondent has a higher level of math ability than does passing an easier item. The same may be true for depressive symptoms. Some symptoms may be evident at relatively mild levels of the disorder, whereas other symptoms may only emerge at very severe levels. In other words, severe depression may be characterized by symptoms that are not often evident in mild depression. If the severity of depression is assessed simply by counting the number of symptoms, then all symptoms are treated as though they were of equal severity or importance and other valuable information that could be derived from the assessment process potentially is ignored.
Second, IRT allows for the possibility that all symptom ratings may not be equally reliable or discriminating indicators of depression. Some symptoms may be strong indicators of depression, constituting core characteristics of the disorder. Other symptoms may be less strongly related to a depressive disorder or may be relatively nonspecific signs of the disorder. Unlike methods based on classical test theory, IRT-based estimates of item (or symptom) discriminability are not sample dependent once the IRT model is calibrated (Reise & Waller, 2009). That is, the psychometric properties of the items do not vary from sample to sample but generalize to all samples from the same population, revealing something about the structure of the underlying latent dimensions in general. Furthermore, utilization of IRT-derived discriminability information (in conjunction with severity information) can greatly enhance the fidelity of the information that can be derived from clinical interview data.
Third, the application of IRT to a collection of symptoms enables researchers to ascertain the degree to which a measure “covers” the latent variable. That is, IRT reveals how informative a measure is at all levels of the underlying dimension. Some measures may be particularly discriminating at the high end of depression severity and be especially useful in clinical settings. Other measures may be maximally discriminating at the low end of depression severity and be useful as a screening device in nonclinical populations. A measure used in clinical trials should be discriminating along the entire range of severity, because participants typically start at very high levels of the disorder but (it is hoped) end up at much lower levels.
When symptoms of a disorder (as assessed by a semistructured clinical interview) are treated as “items” in an IRT analysis, these three kinds of information simultaneously serve two purposes. First, they provide more information about the relations of symptoms to the underlying depression factor(s). And second, they can be used in the construction of new indices (and even computer adaptive testing methods) that are more efficient and more discriminating across a wider range of the targeted dimension. IRT has often resulted in tests that are shorter and more sensitive to the detection of individual differences (Gibbons et al., 2008; Reeve, Burke, et al., 2007; Reeve, Hays, et al., 2007). Clinical applications of IRT are rare, largely because IRT requires sample sizes that are substantially larger than are available in most clinical data bases. Of the few such studies that do exist, almost all have focused on paper-and-pencil measures of psychopathology, on which large samples can more easily be obtained (Cassano et al., 2009; Fliege et al., 2009; Gardner et al., 2004; Gibbons et al., 2008). To solve the sample size problem, we aggregated data from clinical researchers in the United States and Great Britain who used the Kiddie Schedule of Affective Disorders and Schizophrenia for School-Aged Children (K–SADS) to measure major depressive disorder (MDD) in children and adolescents. We intentionally sought a wide variety of data sets including community samples, high-risk samples, and clinical treatment samples, so that collectively they would represent all levels of depression severity. We also sought samples that would contribute to the demographic diversity of the composite data set, in terms of age, sex, and ethnicity.
Thus, in the present study, we had three goals or hypotheses. First, we anticipated that the presence of some depressive symptoms would reflect a more severe underlying depressive disorder than would the presence of other symptoms. For example, we hypothesized that depressed mood would be a relatively mild symptom (as it is widely regarded as the core or gateway symptom of MDD), whereas suicidal ideation would be a more severe symptom, tending to manifest itself at relatively severe levels of the disorder. Second, we expected items to evince different levels of discriminability, with some being highly reflective of the underlying disorder (e.g., anhedonia; Clark & Watson, 1991; Lonigan, Carey, & Finch, 1994) and others being only moderately reflective of the condition (e.g., weight or appetite disturbance)—perhaps because they are also characteristic of other disorders. Finally, we sought to examine the degree to which an IRT-based scoring of the K–SADS would yield more reliable symptom ratings and would generate more information than conventional methods of scoring the K–SADS to measure depression severity.
Method Data Set Selection
Three criteria were required for a data set to be included in the study. First, it had to contain symptom-level information either about participants' current state or their recent episode of MDD, derived from K–SADS interviews with children and parents. Second, participants had to be from 5 to 18 years old. Third, the K–SADS data must have been collected prior to any treatment or preventive intervention. Prior to data acquisition, we obtained institutional review board approval, arranged for the complete de-identification of data sets, made explicit the limitations on our use of the data, conferred with the principal investigator (PI) and other study collaborators to ensure that no conflicts of interest existed between our research agenda and those of the original investigator(s), discussed authorship, and obtained signed letters of agreement from the PI or co-PI of each project.
In total, we obtained 12 different data sets, yielding a total of 3,403 participants. We refer to each study by the investigator who was our key collaborator on this project. When this person provided access to multiple data sets, we indicate the study title as well. Contributors included the following: Cole (Cole et al., in press), Compas and Forehand (Compas et al., 2009; 2010), Curry (Treatment for Adolescents With Depression Study [TADS], 2003, 2005), Findling (Findling et al., 2005), Garber (multiple data sets: Garber 1 indicates the Development of Depression Project [DODP], Gallerani, Garber, & Martin, 2010; Garber & Cole, 2010; and Garber, Keiley, & Martin, 2002; Garber 2 indicates Parent–Child Project [PCP], Garber, Ciesla, McCauley, Diamond, & Schloredt, 2011), Goodyer (Goodyer et al., 2007, 2008), Hyde and Essex (Essex et al., 2006; 2009; Grabe, Hyde, & Lindberg, 2007; Mezulis, Priess, & Hyde, 2010; Priess, Lindberg, & Hyde, 2009), Rohde (N. Kaufman, Rohde, Seeley, Clarke, & Stice, 2005; Rohde, Clarke, Mace, Jorgensen, & Seeley, 2004; Rohde, Seeley, Kaufman, Clarke, & Stice, 2006), Stark (Fisher, 2010), Weissman (Pilowsky et al., 2008; Weissman, Pilowsky, & Wickramaratne, 2006), and Youngstrom (Youngstrom et al., 2005). Key characteristics of the data sets appear in Table 1.
Sample Characteristics by Study
Measures
Several versions of the K–SADS were used in the contributing studies. These included K–SADS–Present and Lifetime Version (K–SADS–PL; J. Kaufman et al., 1997), K–SADS–PL Version 1.0 (J. Kaufman, Birmaher, Brent, Rao, & Ryan, 1996), K–SADS–Epidemiological Version (K–SADS–E; Orvaschel, 1994), Washington University in St. Louis K–SADS (WASH–U–K–SADS; Geller, Zimerman, & Williams, 2001), and K–SADS–Version IV–Revised (K–SADS–IV–R; Ambrosini & Dixon, 1996). When K–SADS data were available for multiple episodes of major depression, we focused on the current or most recent episode. All five K–SADS versions provide lines of inquiry and example questions for interviewers to use with children (about their own symptoms) and with parents (about their child's symptoms). Slight differences exist in the example questions; however, no version requires that the interviewer adhere to the exact questions that are listed. In fact, all versions recommend that interviewers utilize their clinical skills to probe in ways that the participants can understand.
Because the Diagnostic and Statistical Manual of Mental Disorders (4th ed., text rev.; American Psychiatric Association, 2000) regards irritability or anger as evidence of mood disturbance in children, we treated it as a separate symptom in the current study. We pooled questions for assessment of the depressive symptoms across the five versions of the K–SADS; examples of these questions include:
- Depressed mood. Have you ever felt sad, blue, down, or empty? Did you feel like crying? Did you have a bad feeling all the time that you couldn't get rid of?
- Irritability or anger. Was there ever a time when you got annoyed, irritated, or cranky at little things? Did you ever have a time when you lost your temper a lot?
- Pervasive anhedonia (lack of interest, apathy, low motivation, or boredom). Has there ever been a time you felt bored a lot of the time? Did you have to push yourself to do your favorite activities? Did they interest you?
- Weight or appetite disturbance. (a) Appetite loss: How is your appetite? Do you feel hungry often? Do you leave food on your plate? Do you sometimes have to force yourself to eat? (b) Weight loss: Have you lost any weight since you started feeling sad? Do you find your clothes are looser now? (c) Appetite gain: Have you been eating more than before? Is it like you feel hungry all the time? (d) Weight gain: Have you gained any weight since you started feeling sad? Have you had to buy new clothes because the old ones did not fit any longer?
- Sleep disturbance. (a) Insomnia: Do you have trouble sleeping? How long does it take you to fall asleep? Do you wake up in the middle of the night? Do you wake up earlier than you have to? (b) Hypersomnia: Are you sleeping longer than usual? Do you go back to sleep after you wake up in the morning?
- Psychomotor disturbance. (a) Agitation: Since you've felt sad, are there times when you can't sit still, or you have to keep moving and can't stop? Do people tell you not to talk so much? (b) Retardation: Since you started feeling sad, have you noticed that you can't move as fast as before? Has your speech slowed down? Have you felt like you are moving in slow motion?
- Fatigue, lack of energy, or tiredness. Have you been feeling tired? Do you take naps because you feel tired? Do you have to rest? Do your limbs feel heavy? Is it very hard to get going?… to move your legs?
- Self-perceptions. (a) Worthlessness: How do you feel about yourself? Do you like yourself? Do you ever think of yourself as pretty or ugly? Do you think you are bright or stupid? (b) Excessive or inappropriate guilt: Do you feel guilty about things you have not done? Or are actually not your fault? Do you feel you cause bad things to happen? Do you think you should be punished for this?
- Cognitive disturbance. (a) Concentration, inattention, slowed thinking: Sometimes children have a lot of trouble concentrating, like [list examples]. Have you been having this kind of trouble? Is your thinking slowed down? When you try to concentrate on something, does your mind drift off to other thoughts? Can you pay attention in school? Can you pay attention when you want to do something you like? (b) Indecision: When you were feeling sad, was it hard for you to make decisions?
- Suicide. Sometimes children who get upset or feel bad wish they were dead or feel they'd be better off dead. Have you ever had these types of thoughts? Sometimes children who get upset or feel bad think about dying or even killing themselves. Have you ever had such thoughts? How would you do it? Did you have a plan?
All versions had good interrater reliability in the studies that contributed data. Previously accumulated validity information supports the use of all versions of the K–SADS to measure and diagnose depression (Ambrosini, 2000). The K–SADS–PL and K–SADS–E versions are primarily categorical diagnostic interviews, whereas the WASH–U–K–SADS and K-SADS–IV–R measure symptom severity and are sometimes used to measure degree of treatment response (Ambrosini, 2000). The various versions of the K–SADS also differ in the scaling used to quantify symptom severity. The K–SADS–PL has a 3-point scale, where 1 = symptom is absent, 2 = symptom is present at a subclinical level, and 3 = symptom is severe and frequent enough to be at or above threshold. Other versions of the interview have 4-, 6-, and 7-point scales. All versions provide explicit severity and frequency anchors for their scales. These anchors enabled us to translate all measures onto the 3-point K–SADS–PL scale. We converted the 6-point K–SADS–IV–R scale such that 1–2 = 1, 3 = 2, and 4–6 = 3. We converted the 4-point Orvaschel versions of the K–SADS such that 1 = 1, 2 = 2, and 3–4 = 3. We converted a 7-point version of the K–SADS–PL such that 1–3 = 1, 4 = 2, and 5–7 = 3. And we modified the WASH-U–K–SADS such that 1–2 = 1, 3 = 2, and 4–7 = 3. We used a multigroup approach in our data analytic method (which we will discuss later), enabling us to confirm the psychometric equivalence of the resultant scales across studies.
Variables
We extracted four kinds of variables from the K–SADS data. The first was a collection of symptom-specific variables (on the 3-point scales described earlier). The second was a dichotomous index of presence or absence of MDD, reflecting DSM–IV–TR criteria (using only “above-threshold” symptoms). Third was a raw symptom count variable, ranging from 0–10, reflecting presence or absence of the 10 depression symptoms (also using only above-threshold symptoms). Fourth was a raw symptom sum variable, equal to the sum of the 10 symptom-specific (3-point) variables.
Missing data
Three different patterns of missing data occurred across the contributing data sets. Pattern 1 (10% of the cases) emerged because in some studies, questions about depressed mood, irritability, and anhedonia were used as screening questions, and the remaining depressive symptoms were not addressed (presumably because they did not meet criteria on the screening symptoms). Pattern 2 (12.5%) emerged because in some studies, participants were asked the first screening questions plus the suicide screening question but were not asked about other symptoms. Pattern 3 (5%) consisted of random missing data. Comparisons of participants with each pattern of missing data with the larger pool of participants with no missing data revealed no psychometric differences between the groups. Consequently, we did not exclude participants with partial data but used an expectation-maximization (EM) algorithm for the multiple group full-information maximum marginal likelihood estimation that utilized all available data (Bock & Aitkin, 1981).
Results Descriptive Statistics
Overall, the composite data set contained information on 1,722 boys and 1,678 girls (gender data were missing for three participants). Ages ranged from 5 to 18 years (M = 12.39, SD = 2.99). See Table 1. The sample was ethnically diverse: with 66% White, 24% African American, 4% Hispanic, and 6% other. All were English speaking. Means and SDs for all symptom variables and the total symptom count appear in Table 2.
Sample Descriptive Statistics for Variables Derived From the Kiddie Schedule for Affective Disorders and Schizophrenia
Testing Unidimensionality and Local Independence
Two closely related assumptions of IRT are unidimensionality of the symptoms and the absence of noteworthy local dependencies between the symptoms after accounting for the primary underlying factor (Reise & Waller, 2009). We used categorical weighted least squares confirmatory factor analysis and IRT methods to test these assumptions. Specifically, we constrained all symptoms to load only onto a single underlying factor, allowing no correlations among the disturbances. Although the overall chi-square was significant, χ2(35) = 142.71, p < .001, other fit indices clearly revealed that the fit was excellent: comparative fit index (CFI) = 0.99, normed fit index (NFI) = 0.99, root-mean-square error of approximation (RMSEA) = 0.035, 90% confidence interval (CI) [0.030, −0.059], suggesting that the model fit the data well (Browne & Cudeck, 1993). Factor loadings appear in Table 3. Further, the root-mean square of the residuals was only 0.036. Eigenvalues of the estimated polychoric correlation matrix were 7.54, 0.51, 0.41, 0.30, 0.29, 0.26, 0.23, 0.19, 0.15, and 0.12. Taken together, these results provide strong support for the unidimensionality of the depressive symptoms. We also conducted an exploratory full-information factor analysis (Bock, Gibbons, & Muraki, 1988) using IRT for Patient-Reported Outcomes (IRTPRO; Cai, du Toit, & Thissen, in press) software. Extracting two factors (in an oblique, direct quartimin rotation) revealed evidence of overfactoring (i.e., the second factor had only one large loading, as shown in Table 3). Finally, Chen and Thissen's (1997) local dependence indices showed no discernable pattern across all item pairs, suggesting no evidence of nuisance factors.
Factor Loadings From One- and Two-Factor Analyses of 10 Depression Symptoms
IRT Analyses
General analytic approach
Our primary analytic approach consisted of a multigroup, unidimensional, graded IRT model. We arbitrarily selected one of the contributing data sets (Garber 2 = PCP) to serve as the reference group in this analysis. We used Samejima's (1969) graded response model because it is specifically suited to examining the 3-point ratings for each symptom (absent, subclinical, clinical). We used IRTPRO to estimate these models. We relied on Orlando and Thissen's (2000) summed-score item-fit statistics and plots to test the misfit in the shape of item response characteristic curves. In every case, we found that the model-expected probabilities closely followed the observed response probabilities.
Cross-study comparisons
By design, we selected highly heterogeneous data sets. Examining them directly in a multiple-group model, we demonstrated that we can successfully capture this heterogeneity (see Figure 1). Note that all distributions are plotted on a common metric for the latent depression variable. In IRT (as in common factor analysis), this metric is arbitrary. In the current application, we set the reference group mean at 0 and the SD at 1. We then mapped all the other groups onto this metric. Because many of the other groups contained more seriously depressed participants, the mean and SD of the combined sample were greater than those for the reference group. For the combined sample, the mean of the IRT scale score was 2.60, and the SD was 1.28. Aided by the availability of the MDD diagnosis variable in our data sets, we found that a score of 4 on the latent depression scale corresponded to a level of depression associated with a 0.85 predicted probability of having MDD in a logistic regression of MDD on depression scale scores.
Figure 1. Distributions (probability density functions) of the contributing data sets on the latent depression variable. A = Cole (Cole et al., in press); B = Compas and Forehand (Compas et al., 2009, 2010); C = Curry (Treatment for Adolescents With Depression Study [TADS], 2003, 2005); D = Essex (Essex et al., 2006, 2009; Grabe, Hyde, & Lindberg, 2007; Mezulis, Priess, & Hyde, 2010; Priess, Lindberg, & Hyde, 2009); E = Garber 1 (Development of Depression Project [DODP], Gallerani, Garber, & Martin, 2010; Garber & Cole, 2010; Garber, Keiley, & Martin, 2002); F = Garber 2 (Parent–Child Project [PCP], Garber, Ciesla, McCauley, Diamond, & Schloredt, 2011);G = Goodyer (Goodyer et al., 2007, 2008); H = Hyde (Essex et al., 2006; 2009; Grabe, Hyde, & Lindberg, 2007; Mezulis, Priess, & Hyde, 2010; Priess, Lindberg, & Hyde, 2009); I = Rohde (N. Kaufman, Rohde, Seeley, Clarke, & Stice, 2005; Rohde, Clarke, Mace, Jorgensen, & Seeley, 2004; Rohde, Seeley, Kaufman, Clarke, & Stice, 2006); J = Stark (Fisher, 2010); K = Weissman (Pilowsky et al., 2008; Weissman, Pilowsky, & Wickramaratne, 2006); L = Youngstrom (Youngstrom et al., 2005); M & N = Findling 1 & Findling 2 (Findling et al., 2005). Note that for the present analyses, the combined data set of Essex and Hyde was split into two, and the resulting sets were labeled “Essex” and “Hyde.” The single data set for Findling also was split into two, and the resulting sets were labeled “Findling 1” and “Findling 2.”
Using this metric, we plotted the estimated depression distributions for all contributing data sets, which collectively span the entire range of the underlying latent depressive continuum (with nonclinical samples falling at the lower end of the scale and samples with more seriously depressed participants falling at the higher end; see Figure 1). Such breadth allowed us to assess the relation of symptom to depression across the entire range of the latent variable. More important, such heterogeneity ensures greater generalizability compared with most single-sample investigations.
Next, we conducted differential item function (DIF) tests to detect noninvariance of item parameters across the studies. Of the tested items, we found no significant differences in the item characteristic curves, providing evidence of invariance across samples despite the use of different interviewers and different versions of the K–SADS. To the extent supported by the statistical results, the lack of DIF shows that our conversion of all K–SADS measures to 3-point scales yielded psychometrically equivalent metrics, thereby paving the way for tests of our more substantive hypotheses.
As shown in Figure 2, the study-specific standard error of measurement (SEM) curves convey the precision of the K–SADS at all points along the latent depression continuum. Particularly noteworthy is that the curves are horizontally aligned with one another, revealing that for all of the studies' scores from the K–SADS measure of depression were most reliable between scores of 3.1 and 5.6 on the latent depression variable. Between these values, all studies had small SEMs, ranging from 0.26 to 0.50 on the y axis. Also important is the fact that this “high-reliability window” contains the value of 5.0 on the x axis, the approximate threshold for an MDD diagnosis. At lower and higher levels of depression, the K–SADS symptom scores begin to provide a less reliable index of depression severity, as indicated by the upward curves of the SEM lines. For people with fewer than two symptoms or more than seven, the SEMs begin to exceed 1.0 on the y axis.
Figure 2. Standard error of measurement and Fisher information curves for all contributing studies. Hyde and Essex (Essex et al., 2006; 2009; Grabe, Hyde, & Lindberg, 2007; Mezulis, Priess, & Hyde, 2010; Priess, Lindberg, & Hyde, 2009); Rohde (N. Kaufman, Rohde, Seeley, Clarke, & Stice, 2005; Rohde, Clarke, Mace, Jorgensen, & Seeley, 2004; Rohde, Seeley, Kaufman, Clarke, & Stice, 2006); Garber 1 (Development of Depression Project [DODP], Gallerani, Garber, & Martin, 2010; Garber & Cole, 2010; Garber, Keiley, & Martin, 2002); Findling 2 (Findling et al., 2005); Youngstrom (Youngstrom et al., 2005); Findling 1 (Findling et al., 2005); Curry (Treatment for Adolescents With Depression Study [TADS], 2003, 2005); Garber 2 (Parent–Child Project [PCP], Garber, Ciesla, McCauley, Diamond, & Schloredt, 2011); Goodyer (Goodyer et al., 2007, 2008); Weissman (Pilowsky et al., 2008; Weissman, Pilowsky, & Wickramaratne, 2006); Cole (Cole et al., in press); Compas and Forehand (Compas et al., 2009, 2010); Stark (Fisher, 2010. Note that for present analyses, the combined data set of Essex and Hyde was split into two, and the resulting sets were labeled “Essex” and “Hyde.” The single data set for Findling also was split into two, and the resulting sets were labeled “Findling 1” and “Findling 2.”
Overview of main results
The relation of each symptom and the various K–SADS response options are represented by a set of response curves. As shown in the example curves in Figure 3, each symptom has three curves. The descending curve on the left represents the probability of obtaining a score of 1 (i.e., symptom is absent), as a function of the latent depression level. We would expect these probabilities to drop sharply as the level of depression increases. The rising and falling curve in the middle represents the probability of a 2 (i.e., symptom is subclinical). We would expect these probabilities to be near 0 at both the low and high ends of the depression continuum. The rising curve at the right represents the probability of a 3 (i.e., symptom is present at a clinically significant level). We would expect these probabilities to rise sharply at higher levels of the latent depression variable. The point at which the descending curve reaches .50 is called Threshold 1, and the point at which the rising curve meets .50 is called Threshold 2. These reflect symptom severity. The overall steepness of these curves reflects how sharply a symptom discriminates between different levels of depression. In the hypothetical examples of Figure 3, Panel A represents a low-severity low-discriminability symptom, Panel B represents a high-severity low-discriminability symptom, Panel C represents a low-severity high-discriminability symptom, and Panel D represents a high-severity high-discriminability symptom. Response curves for the actual symptoms appear in Figure 4, and the associated symptom threshold and discrimination parameters are the focus of the next sections.
Figure 3. Hypothetical item response curves, depicting symptoms with low vs. high severity and low vs. high discriminability (1 = symptom is absent, 2 = symptom is present at a subclinical level, and 3 = symptom is present at a clinical level). Panel A represents a low-severity, low-discriminability symptom; Panel B represents a high-severity, low-discriminability symptom; Panel C represents a low-severity, high-discriminability symptom; and Panel D represents a high-severity, high-discriminability symptom
Figure 4. Item response curves for each symptom, where 1 = symptom is absent, 2 = symptom is present at a subclinical level, and 3 = symptom is present at a clinical level.
Question 1: Are some depressive symptoms reflective of more severe depression than others?
We estimated the severity thresholds for each symptom. Then we used the symptom parameter covariance matrix, produced by IRTPRO with a supplemented EM algorithm (Cai, 2008), to compute the standard errors (SEs) around the severity threshold estimates. With this information, we determined the rank order of the depressive symptom severities. Table 4 contains estimates of Thresholds 1 and 2 and their SEs. The final column of Table 4 indicates the rank order of the symptom severities, based on the second set of threshold estimates.
Symptom Severity Parameter Estimates and Standard Errors
According to these data, clinically significant concentration problems, feelings of worthlessness or guilt, and sleep disturbance emerge at the lowest levels of depression severity, followed by problems related to depressed mood, fatigue or lack of energy, irritability, and anhedonia. At still higher levels of depression severity, psychomotor agitation or retardation, weight or appetite disturbance, and suicidal ideation or attempts emerge—with each signaling a significantly higher level of depression severity.
These results raised the possibility that concentration problems, feelings of worthlessness or guilt, and sleep disturbance might serve as a better screening cluster than depressed mood, irritability, and anhedonia (the symptoms used as screeners in some applications of the K–SADS). Consequently, we compared sensitivity and specificity analyses for the two symptom clusters. Using DSM–IV–TR diagnosis of MDD as the criteria, however, would bias these results in favor of the conventional screeners, as DSM–IV–TR requires at least one of these three symptoms for an MDD diagnosis. Instead, we used number of symptoms as the criterion. As shown in Table 5, the unconventional screeners have slightly better sensitivity than the conventional screeners. With the illness criterion set at five or more MDD symptoms, the unconventional screeners would catch 99.3%–98.6% = 0.7% more cases than would the conventional criteria. In the current data set, this translated into eight more cases. Of course, this advantage comes at the cost of lower specificity. With the illness criterion again set at five or more MDD symptoms, the conventional screeners would have correctly categorized 78.6%–70.6% = 8.0% more of people who did not have the illness, compared with the unconventional screeners. In the current data set, this translated into 114 more cases.
Sensitivity and Specificity Analyses for Two Different Screening Tests: Conventional Versus Unconventional
Question 2: Are some symptoms more discriminating indicators of depression than others?
To estimate the strength of relation between each symptom and the underlying latent variable, we examined the discrimination parameters and factor loadings for each symptom (see Table 6). The item discrimination parameters can be interpreted as logistic regression slopes, or log odds-ratios. When we examined these parameters and their associated factor loadings (using conversion formulae in Wirth & Edwards, 2007), we found that all of the K–SADS items are highly discriminating indicators of depression. Even the smallest slope (suicidal ideation) is associated with depression at an odds ratio of 2.36. Examination of the overlap (and the gaps) between the confidence intervals around the slopes revealed that some symptom indicators are more discriminating than others. Depressed mood and anhedonia were the most discriminating indicators. The next most discriminating set of indicators included fatigue or lack of energy, irritability, and concentration problems. The third most discriminating set consisted of sleep disturbance, feelings of worthless and guilt, psychomotor agitation or retardation, followed by weight or appetite disturbance. The least discriminating symptom was suicidal ideation.
Estimates of Symptom Discrimination Parameters and Factor Loadings
Question 3: How much more information can be gleaned from K–SADS interview data using IRT-based estimates of depression?
One way to address this question is to compare four indices of depression severity. First was the raw symptom count (simply the number of DSM–based symptoms of depression that were coded as present). Second was the raw symptom sum (the raw sum of the 10 symptom variables, each on a 3-point scale). Third was called IRT-2, an IRT-based expected a posteriori (EAP) index based on the two-parameter logistic (2-PL) model with only two levels of information about presence or absence of the symptoms. And the fourth was called IRT-3, an IRT-based EAP index based on the graded model utilizing all three levels of severity for each symptom. We made this comparison by estimating the SEM for each index at varying levels of the latent depression variable. We estimated the SEM curves for the two IRT-based indices using the posterior standard deviations of the scale scores (Thissen & Wainer, 2001). We estimated crude SEM curves for the two non-IRT indices by applying the formula, SEM =
, where reliability was Cronbach's alpha for the selected index computed repeatedly for subsamples representing a sliding 2-SD-wide window on the latent depression variable.
The four SEM curves are depicted in Figure 5. At any given level of the latent variable (i.e., various points along the x axis), a smaller SEM signifies greater measurement fidelity. Visual examination of this figure revealed two important findings. First, both of the IRT-based indices had lower SEMs than both of the non-IRT indices at virtually all levels of the latent depression variable. That is, using IRT-derived information about symptom severity and discriminability substantially enhanced precision in the measurement of depression severity. Second, both of the indices that included information about subclinical levels of depressive symptoms (i.e., the raw symptom sum and the IRT-3) were superior to both of the indices that did not include such information (i.e., raw symptom count and IRT-2). That is, both the symptom sum index and the IRT-3 index had lower SEMs than the symptom count and IRT-2 index, respectively, especially at lower levels of the latent depression variable.
Figure 5. Standard error of measurement curves for four indices of depression severity, as a function of the latent depression variable. IRT = item response theory; IRT-2 = IRT-based expected a posteriori (EAP) index based on the 2-PL model utilizing only two levels of information about presence or absence of the symptoms.IRT-3 = IRT-based EAP index based on the graded model utilizing all three levels of severity for each symptom.
A second way to address this question is to examine the amount of information that is lost when one uses more conventional non-IRT-based indices of depression severity. A simple symptom count does not take into consideration the fact that some symptoms reflect greater depression severity than others. One can visualize the degree to which this is true by examining histograms depicting the range of IRT-based latent depression scores at each level of a more conventional symptom-count variable (see Figure 6). For people with a raw symptom count of 1, latent depression scores ranged from 0.7 to 3.6. For people with a raw count of 8, latent depression ranged from 4.1 to 5.8 (with an SD = 1.28 for the latent depression variable). This means that the variability of latent depression scores spanned approximately 1 to 2 SDs at each whole number value of the raw symptom count. That is, the raw symptom count gives identical scores to people with highly discrepant levels of latent depression—a process that results in a substantial loss of information.
Figure 6. Histograms of latent depression levels at each level of symptom count index of depression based on the Kiddie Schedule of Affective Disorders and Schizophrenia for School-Aged Children.
DiscussionFour major findings about the K–SADS and depressive symptoms in children and adolescents emerged from this study. First, our K–SADS depression data were remarkably unidimensional. Second, some symptoms of depression emerged at relatively mild levels of the disorder; others emerged when depression was much more severe. Third, in children and adolescents, all K–SADS symptoms of depression were strongly associated with depression. And fourth, higher fidelity and better coverage of the construct derived from assessment algorithms in which IRT-based estimates of symptom severity and discriminability were taken into account and information about subclinical levels of symptom severity were utilized. These findings have important clinical and theoretical implications.
Our first major finding was that a very strong single latent variable emerged from our K–SADS data on symptoms of depression. Our confirmatory factor analysis showed that loadings for the 10 symptoms were strong, ranging from 0.95 (depressed mood) to 0.73 (suicide). This factor accounted for 75.4% of the covariance among the 10 symptoms. The fact that no evidence of secondary factors emerged (not even nuisance factors) is unusual for measures of depression; however, most measures of depression are questionnaires in which many symptoms are represented by multiple items. For example, the Children's Depression Inventory (Kovacs, 1985) contains three mood items, two anhedonia items, two guilt items, four self-esteem items, and so on. This creates a complex structure with a number of small factors caused by parcels of item content (Cole, Hoffman, Tram, & Maxwell, 2000). Indeed, when such measures are exceptionally unidimensional, one begins to wonder whether the items are too similar to one another, causing the underlying factor to be overly narrow. In the K–SADS, interviewers also ask multiple questions about each symptom, but then they aggregate each cluster of questions into a single appraisal about a particular symptom. This procedure greatly reduces the likelihood that nuisance factors will emerge. As the content of each item is highly distinctive (depressed mood, appetite disturbance, sleep disturbance, suicide, psychomotor agitation or retardation, irritability, fatigue or lack of energy, guilt or low self-esteem, concentration problems, and anhedonia), the resulting factor is anything but narrow. Given the strong, prima facie, one-to-one correspondence of K–SADS depression items to DSM–IV–TR depression symptoms, the emergence of a strong single factor suggests that the core symptoms of depression correlate with one another only because of a single underlying dimension of psychopathology, arguably depression.
Second, some DSM–IV–TR symptoms are present at significantly lower levels of depression severity than are others. At relatively low levels of the latent dimension (below the threshold for a diagnosis of MDD), clinically significant symptoms of concentration problems, feelings of worthlessness or guilt, and sleep disturbance were evident. At slightly higher levels of the latent variable (and still below the MDD threshold), symptoms of depressed mood, fatigue, irritability, and anhedonia were evident. At still higher levels of depression (and above the MDD cutoff), psychomotor agitation or retardation, weight or appetite disturbance, and suicidal ideation or attempts became increasingly likely, with each reflecting a clinically and statistically significant increase in severity on the latent variable.
Our expectation that the required symptoms of MDD (depressed mood, irritability, or anhedonia) would emerge at the lowest levels of the latent variable was not confirmed. In children and adolescents, concentration problems were evident at significantly lower levels of depression than were all three affective symptoms. Feelings of worthlessness or guilt and sleep disturbance were not significantly different from concentration problems. Taken together, these results suggest that concentration problems, feelings of worthlessness or guilt, and sleep disturbance may represent early warning signs for MDD. This possibility, however, would not seem to warrant changing the K–SADS screening criteria, as the relatively small (0.7%) gain in sensitivity comes at a much larger (8.0%) loss of specificity.
In a related vein, our results also showed that the occurrence of some symptoms signifies a much greater level of depression severity than does the occurrence of other symptoms. For example, the occurrence of feelings of worthlessness or guilt or disturbance of sleep patterns represents a very small increase in depression severity over-and-above concentration problems, whereas the presence of psychomotor agitation or retardation, weight and appetite problems, or suicidal ideation or attempts represents substantially higher levels of severity. To our knowledge, no K–SADS measurement algorithm makes use of this kind of information, which could substantially enhance the fidelity of depression severity assessments.
Third, all DSM–IV–TR symptoms were strong indicators of depression in children and adolescents; however, some symptoms were more strongly related to the depression factor than others. Depressed mood was by far the strongest indicator, such that a 1-point change in the latent variable was associated with a 15-fold increase in the probability of the symptom. Anhedonia was the next most discriminating symptom, followed by fatigue or lack of energy, irritability, and concentration problems. Suicidal ideation or attempt was the least discriminating symptom. This kind of information can be used to enhance the measurement of depression severity (Weiss, 1982, 1985).
We found that an interesting trade-off appears to exist between severity and discriminability of depressive symptoms as indicators of depression, with the less discriminating items emerging at higher levels of depression severity. The correlation between severity and discriminability estimates was −0.64. For example, suicidal ideation or attempts, weight or appetite disturbance, and psychomotor agitation or retardation were among the most severe yet least discriminating symptoms. Conversely, depressed mood and concentration problems were among the less severe but more discriminating symptoms. In an ideal method of measurement, both severity and discriminability would be taken into consideration.
Fourth, to do this, we constructed two IRT-based K–SADS indices of depression severity; one was based on just the presence or absence of symptoms, whereas the other utilized information about subclinical symptoms as well. Both indices outperformed more conventional scoring methods that were simply based on symptom counts or summed scores. Psychometric comparisons revealed that scores from the IRT-based measures were more reliable and had lower SEs, especially in the moderate to severe range of depression. Furthermore, utilizing subclinical symptom information extended these psychometric advantages further into the mild range of depression. In other words, using IRT methods and incorporating information about subclinical symptom levels increased both the fidelity and bandwidth of measurement.
These results have two noteworthy implications. First, IRT-based increments in measurement fidelity (i.e., reduced measurement error) can readily translate into larger between- and within-group effect sizes and therefore into greater statistical power to detect treatment effects, as shown in at least one randomized treatment-control study on the effectiveness of antidepressants (Santor, Debrota, Engelhardt, & Gelwicks, 2008). Second, the IRT-based inclusion of subclinical symptom information and the resultant increased bandwidth can be especially helpful in treatment-comparison research. When one treatment is compared with another, a large part of the effect can depend upon differences that reside in the subclinical range of the dependent variable. The inclusion of even one extra response option to indicate the subclinical presence of each symptom can substantially enhance the researcher's capacity to detect a treatment difference. Whether the inclusion of even more response options could generate more power is an interesting question worthy of further investigation.
At least four shortcomings of the current study suggest avenues for future research. First, all of the IRT analyses in this study focused on data obtained by using the K–SADS. Although this measure utilizes information from multiple informants, filtered through the expertise of well-trained clinical interviewers, the K–SADS still represents only a single method for measuring depression. As such, it is possible that the strong latent variable that emerged from our analyses represents not just depression but also this method. Although semistructured clinical interviews like the K–SADS have been touted as the closest thing to a gold standard that mental health researchers have in the assessment of psychopathology (Hersen & Gross, 2008), they are not immune to method effects. Although it is unlikely that demand characteristics or interviewer bias would act similarly across all the investigative teams that contributed data to this study, it is not impossible. For example, eager to fill the quota of depressed participants in a research study, interviewers could have been positively biased in their perception of depressive symptoms. Replication of the current results with multiple, methodologically dissimilar measures of depression would mitigate these concerns.
Second, our analyses carefully established the invariance of the IRT results across the samples that contributed to the aggregate data set. This is a critical first step. It is possible, however, that the results may not be invariant across other ways of subdividing the data. Efforts are currently underway to examine ways that the relation of symptoms to the underlying depression factor may vary as a function of age, gender, and ethnicity.
Third, though we were able to use IRT methods to accomplish the cross-study linkage of latent variable scales, our results are best treated as a first step, in the absence of further evaluations of the quality of linking. Furthermore, we note that the means of the studies are spread out widely across the latent depression scale, which can lead to a deterioration of the quality of linking in the extremes.
Finally, the current study provided very strong evidence that a single underlying factor underlies the 10 symptoms of depression as assessed by the K–SADS. It is possible, however, that this unidimensionality depends upon the level at which the symptoms of depression are examined. We focused on symptom clusters, as recommended in the DSM–IV–TR for the diagnosis of MDD. Specific examples include negative self-perceptions (which consist of low self-esteem and guilt feeling), irritability and anger, sleep disturbance (hypersomnia and insomnia), psychomotor symptoms (agitation and retardation), and weight or appetite disturbance (increase and decrease). Examination of the disaggregated symptoms could reveal evidence of one or more other dimensions.
Footnotes 1 Consultation with experts suggested one exception. Suicide was scaled such that 1 = 1, 2 = 2, and 3–6 = 3.
2 The Hyde and Essex data set and Findling data set were each divided into two data sets, as slightly different versions of the K–SADS were used for different subsets of the participants.
3 Because there are more than a dozen groups in the analysis, the use of IRT-based likelihood ratio (IRT-LR) DIF procedure (Thissen, Steinberg, & Wainer, 1993) was too cumbersome. Instead, we relied on the more flexible and asymptotically equivalent Wald DIF test to examine the degree to which the items exhibited cross-study differences in thresholds or discrimination parameters. For anchoring, we adopted the IRT–LR DIF convention of using all items other than the studied item as the anchor set. Due to the combination of study-specific skip patterns and missing data, some items only had a few observed responses in some studies, leading to some DIF runs with nonconverged solutions. Given this limitation, we were still able to conduct DIF tests for six of the 10 symptoms (depressed mood, irritability, anhedonia, weight and appetite disturbance, sleep disturbance, and feelings of worthlessness or guilt). There was no indication of statistically significant DIF for the symptoms tested.
4 Because some studies in our sample have much larger or smaller variability than the reference group with an assumed variance of 1.0, the study-specific SEMs can be either larger or smaller than 1.0.
5 The availability of the IRT scale scores, as realizations of the “true scores” of the underlying depression latent variable, enabled us to make the comparison between the reliability of raw symptom sums or counts and the reliability of the IRT scale scores. Each IRT scale score, whether IRT-2 or IRT-3, had an associated standard error of measurement. As for the raw symptom sums or counts, we calculated their reliability by treating the symptoms as observed variables in a scale and utilized a traditional summed-score-based internal consistency reliability estimator (Kuder–Richardson Formula 21 or Cronbach's alpha). The curves in Figure 5 for symptom sums or counts were smoothed to eliminate the effect of distributional discontinuities.
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Submitted: March 9, 2010 Revised: February 7, 2011 Accepted: February 7, 2011
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Source: Psychological Assessment. Vol. 23. (4), Dec, 2011 pp. 819-833)
Accession Number: 2011-08825-001
Digital Object Identifier: 10.1037/a0023518
Record: 155- Title:
- Suicidality as a function of impulsivity, callous–unemotional traits, and depressive symptoms in youth.
- Authors:
- Javdani, Shabnam, ORCID 0000-0003-3949-1970. Department of Psychology, University of Illinois at Urbana–Champaign, Champaign, IL, US, javdani2@illinois.edu
Sadeh, Naomi, ORCID 0000-0002-8101-3190. Department of Psychology, University of Illinois at Urbana–Champaign, Champaign, IL, US
Verona, Edelyn. Department of Psychology, University of Illinois at Urbana–Champaign, Champaign, IL, US - Address:
- Javdani, Shabnam, University of Illinois at Urbana–Champaign, Department of Psychology, 603 East Daniel Street, Champaign, IL, US, 61820, javdani2@illinois.edu
- Source:
- Journal of Abnormal Psychology, Vol 120(2), May, 2011. pp. 400-413.
- NLM Title Abbreviation:
- J Abnorm Psychol
- Page Count:
- 14
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- The Journal of Abnormal and Social Psychology; The Journal of Abnormal Psychology; The Journal of Abnormal Psychology and Social Psychology
- ISSN:
- 0021-843X (Print)
1939-1846 (Electronic) - Language:
- English
- Keywords:
- adolescence, externalizing, gender, psychopathy, suicide, suicidality, impulsivity, depressive symptoms, unemotional traits
- Abstract:
- Suicidality represents one of the most important areas of risk for adolescents, with both internalizing (e.g., depression, anxiety) and externalizing–antisocial (e.g., substance use, conduct) disorders conferring risk for suicidal ideation and attempts (e.g., Bridge, Goldstein, & Brent, 2006). However, no study has attended to gender differences in relationships between suicidality and different facets of psychopathic tendencies in youth. Further, very little research has focused on disentangling the multiple manifestations of suicide risk in the same study, including behaviors (suicide attempts with intent to die, self-injurious behavior) and general suicide risk marked by suicidal ideation and plans. To better understand these relationships, we recruited 184 adolescents from the community and in treatment. As predicted, psychopathic traits and depressive symptoms in youth showed differential associations with components of suicidality. Specifically, impulsive traits uniquely contributed to suicide attempts and self-injurious behaviors, above the influence of depression. Indeed, once psychopathic tendencies were entered in the model, depressive symptoms only explained general suicide risk marked by ideation or plans but not behaviors. Further, callous–unemotional traits conferred protection from suicide attempts selectively in girls. These findings have important implications for developing integrative models that incorporate differential relationships between (a) depressed mood and (b) personality risk factors (i.e., impulsivity and callous–unemotional traits) for suicidality in youth. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Adolescent Psychopathology; *Major Depression; *Suicidal Ideation; *Symptoms; Attempted Suicide; Human Sex Differences; Impulsiveness; Personality Traits; Suicide
- Medical Subject Headings (MeSH):
- Adolescent; Child; Depression; Female; Humans; Impulsive Behavior; Male; Mental Disorders; Personality; Risk; Risk Assessment; Self-Injurious Behavior; Sex Factors; Suicidal Ideation; Suicide
- PsycINFO Classification:
- Psychological Disorders (3210)
- Population:
- Human
Male
Female - Location:
- US
- Age Group:
- Childhood (birth-12 yrs)
School Age (6-12 yrs)
Adolescence (13-17 yrs) - Tests & Measures:
- Kiddie Schedule for Affective Disorders and Schizophrenia—Present and Lifetime Version
Lifetime Parasuicide Count
Suicide Behavior Questionnaire--Revised DOI: 10.1037/t20076-000
Antisocial Process Screening Device DOI: 10.1037/t00032-000 - Grant Sponsorship:
- Sponsor: Farris Family Award for Innovation
Recipients: No recipient indicated
Sponsor: Arnold O. Beckman Award
Recipients: No recipient indicated - Methodology:
- Empirical Study; Quantitative Study
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- First Posted: Jan 31, 2011; Accepted: Jul 2, 2010; Revised: Jun 30, 2010; First Submitted: Oct 18, 2009
- Release Date:
- 20110131
- Correction Date:
- 20130520
- Copyright:
- American Psychological Association. 2011
- Digital Object Identifier:
- http://dx.doi.org/10.1037/a0021805
- PMID:
- 21280931
- Accession Number:
- 2011-01897-001
- Number of Citations in Source:
- 99
- Persistent link to this record (Permalink):
- http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2011-01897-001&site=ehost-live
- Cut and Paste:
- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2011-01897-001&site=ehost-live">Suicidality as a function of impulsivity, callous–unemotional traits, and depressive symptoms in youth.</A>
- Database:
- PsycINFO
Suicidality as a Function of Impulsivity, Callous–Unemotional Traits, and Depressive Symptoms in Youth
By: Shabnam Javdani
Department of Psychology, University of Illinois at Urbana–Champaign;
Naomi Sadeh
Department of Psychology, University of Illinois at Urbana–Champaign
Edelyn Verona
Department of Psychology, University of Illinois at Urbana–Champaign;
Acknowledgement: This study was supported by a Farris Family Award for Innovation and an Arnold O. Beckman Award. Portions of the data from this sample were also used in Sadeh, Verona, Javdani, and Olson (2009).
Suicide is the third leading cause of death among youth in the United States (Anderson, 2002), with evidence suggesting that suicide rates are increasing both nationally (Bridge et al., 2006) and internationally (World Health Organization, 2002). The suicidality rates in the United States are among highest in the world (World Health Organization, 2002), with an average of one in five youth reporting serious suicidal ideation or behavior (Grunbaum et al., 2002). Both genders demonstrate risk for suicide, with girls more likely to attempt suicide but boys more likely to die from suicide (Bridge et al., 2006; Ross & Heath, 2002). Growing social and public health concern has led researchers to examine numerous correlates of youth suicide (Bridge et al., 2006; Evans, Hawton, & Rodham, 2004; Gould, Greenberg, Velting, & Shaffer, 2003), including those related to mood disorders and antisocial tendencies. Very few studies to date have examined links between suicide risk and psychopathic tendencies in youth; the latter construct has garnered attention for its usefulness in deconstructing the heterogeneity of youth antisocial behavior (e.g., Frick, Bodin, & Barry, 2000; see Douglas, Herbozo, Poythress, Belfrage, & Edens, 2006). The present study is the first to attend to gender differences in the risk and protection conferred by psychopathic tendencies and depression in relation to three overlapping but distinct measures of suicidality: general risk marked by ideation or plans, self-injury, and suicide attempts.
Mental Health Correlates of Youth SuicideNumerous correlates of youth suicide have been identified, including personality (e.g., impulsivity), biology (e.g., serotonin functioning, pubertal development), psychopathology (e.g., mood, substance disorders), demographics (e.g., sexual orientation, gender, age), social adversity (e.g., abuse, stressful life events), and sociocultural factors (e.g., imitation, contagion, media; for reviews, see Bridge et al., 2006; Evans et al., 2004; Gould et al., 2003). Among these correlates, mood disorders are studied most often. In fact, a diagnosis of major depressive disorder (MDD)—and depressive symptoms more broadly—has been identified as the strongest and/or most prevalent risk factor for suicidality (Gould et al., 2003; Kandel, Raveis, & Davies, 1991; Marttunen, Aro, Henriksson, & Lonnqvist, 1991). Researchers have advanced etiological mechanisms to explain the role of depression, including that depression is one part of a more pervasive affective state that includes feelings of worthlessness (Wichstrøm, 2000), hopelessness (Beck, Steer, Kovacs, & Garrison, 1985), and neuroticism (Beautrais, Joyce, & Mulder, 1999).
Other studies have implicated the role of antisocial or externalizing-spectrum psychopathology and personality factors. This includes links between suicidality and aggression (Brent et al., 1994; Brent & Mann, 2005; Pfeffer, Plutchik, & Mizruchi, 1983), impulsivity (Apter, Plutchik, & van Praag, 1993), antisocial behavior (Marttunen et al., 1991), substance use (Brent, Baugher, Bridge, Chen, & Chiappetta, 1999; Wunderlich, Bronisch, & Wittchen, 1998), and conduct or disruptive disorders (Andrews & Lewinsohn, 1992; Sourander, Helstela, Haavisto, & Bergroth, 2001). Researchers have advanced the idea that suicidal behaviors in particular constitute the externalization of emotions (Tyler, Whitbeck, Hoyt, & Johnson, 2003) and are important indicators of poor self-control or problem solving, a cognitive deficit linked to engaging in suicidal behaviors (Gould et al., 2003). Further, the presence of externalizing psychopathology—and impulsivity in particular—is thought to underlie the intergenerational transmission of suicide attempts (Brent, Bridge, Johnson, & Connolly, 1996; Brent & Mann, 2005; Brent et al., 2002; Mann, 1998).
Research has consistently documented high rates of comorbidity among depression and externalizing tendencies (Bridge et al., 2006; Shaffer et al., 1996; Wunderlich et al., 1998), making it difficult to document their unique roles when it comes to suicide risk. In previous work, the following factors continued to make unique contributions to the prediction of suicidality, even after accounting for the influence of depression: externalizing tendencies (Lewinsohn, Rohde, & Seeley, 1996), conduct problems (Kandel et al., 1991; Sourander et al., 2001), engagement in illegal activity (Tyler et al., 2003), and impulsivity (Kashden, Fremouw, Callahan, & Franzen, 1993). These findings mirror those from the adult literature, which suggest that externalizing psychopathology uniquely contributes to suicide attempts (Hills, Afifi, Cox, Bienvenu, & Sareen, 2009), even after accounting for shared variance with internalizing psychopathology and the comorbidity between internalizing and externalizing psychopathology (Verona, Sachs-Ericsson, & Joiner, 2004). It is important to note that early work by Apter and colleagues conducted with youth (Apter et al., 1993, 1995) stipulated that impulsivity-related disorders (e.g., conduct problems) may serve as risk factors for suicide, regardless of the presence of depression. The present study is one of the first to investigate the differential contributions of depression and antisocial–psychopathic tendencies in youth to disentangle the heterogeneity of suicide risk and protection. Psychopathic traits have been examined in relation to suicidality in adults but have gone relatively unstudied in youth (see Douglas et al., 2006, for an exception).
Psychopathic Tendencies and Youth SuicidalityPsychopathic tendencies share some features with externalizing behaviors and provide a unique opportunity to disentangle the heterogeneity inherent in the broad externalizing spectrum. Psychopathy is often conceptualized as multidimensional, with distinct facets representing affective (emotional detachment, lower capacity for intimacy, immunity to guilt or shame), interpersonal (arrogant, dominant, deceitful), and behavioral (e.g., antisocial, impulsive, aggressive) dimensions (e.g., Cooke & Michie, 2001). While psychopathy has been quite extensively studied in adult populations, evidence has also been mounting for the downward translation of psychopathic tendencies in adolescent populations (e.g., Lynam et al., 2005; Salekin, Leistico, Trobst, Schrum, & Lochman, 2005), suggesting that these facets are important for accounting for extreme manifestations of aggression and deviance in youth. A substantial portion of the literature on youth psychopathic tendencies is based on research with the Antisocial Process Screening Device (APSD; Frick & Hare, 2001). This instrument has proven reliable and valid, yielding a three-factor model similar to adult psychopathy: callous–unemotional (affective), narcissism (interpersonal), and impulsivity (behavioral) dimensions (Frick et al., 2000; Vitacco, Rogers, & Neumann, 2003). In support of its construct validity, research indicates the APSD is useful for assessing and predicting violence and conduct problems in youth (Frick et al., 2000) and evidences similar personality correlates as adult psychopathy (Sadeh, Verona, Javdani, & Olson, 2009).
Although Cleckley's (1976) monograph on psychopathy suggested that individuals with high levels of psychopathic traits rarely engage in suicide, aggressive and antisocial individuals are at heightened risk for suicidality (Bukstein et al., 1993; Goldston et al., 1998). Previous research with adults suggests that this paradox can be reconciled by examining distinct facets of psychopathy. For instance, suicidality was positively linked with the impulsive–antisocial facet but was unrelated to the affective–interpersonal facet of psychopathy in adult male offenders (Verona, Patrick, & Joiner, 2001). In addition, low levels of trait constraint (or impulsivity) account for the relationship between suicidality and the impulsive–antisocial facet of psychopathy in male adult and youth offenders (Douglas et al., 2008; Verona et al., 2001). Further, the impulsive–antisocial facet accords risk for both suicide attempts and nonsuicidal self-injury in male and female adult psychiatric patients (Swogger, Conner, Meldrum, & Caine, 2009). Research has also investigated the affective–interpersonal traits of psychopathy, with two studies finding a negative link to ideation in male offenders (Douglas et al., 2008) and attempts in female offenders (Verona, Hicks, & Patrick, 2005). Other studies have found no association (Swogger et al., 2009; Verona et al., 2001; see also Douglas et al., 2006).
Thus, although a link between suicidality and the impulsive–antisocial facet has been consistently found (Douglas et al., 2006, 2008; Swogger et al., 2009; Verona et al., 2001), findings regarding the interpersonal and affective facets of psychopathy are more equivocal. Mixed findings serve to underscore the importance of further investigating psychopathy–suicide relationships, with attention to whether and to what extent these relationships can be translated downward to both male and female youth. Indeed, no study to date has examined gender differences in the association between suicide and psychopathy, and there has been a paucity of research on youth in particular. Examining different facets of psychopathy and suicide risk indicators can begin to reconcile equivocal findings and reveal different pathways of risk for youth who have similar clinical manifestations (i.e., engagement in antisocial behaviors), where some psychopathy facets confer risk and others confer protection.
Different Suicide Risk IndicatorsSuicidal ideation, self-injury, and attempts are key risk factors for suicide completions (Brent et al., 1999; Shaffer et al., 1996), making each important for understanding death by suicide in youth. The majority of research to date has not distinguished between components of suicide risk when examining mental health correlates (see Bridge et al., 2006, for a review). Some evidence suggests that these thoughts and behaviors may be associated with both overlapping and distinct etiologies (Gould et al., 1998, 2003; Linehan, Chiles, Egan, Devine, & Laffaw, 1986; Wichstrøm, 2009). For example, a growing body of work on nonsuicidal self-injury, or the deliberate destruction of body tissue without explicit intent to die, suggests self-injury is related to but distinct from ideation and attempts (Hooley, 2008; Nock, 2009).
Only a few studies have uncovered specific risk factors for ideation, self-injury, and attempts, with one making this distinction in relation to psychopathy in particular (Swogger et al., 1999). For instance, although both suicidal ideation and attempts run in families, research suggests that Axis I psychopathology predominantly confers risk for the generational transmission of ideation, whereas aggressive and impulsive tendencies facilitate the transmission of suicide attempts in particular (Brent et al., 1996). Another study found that impulsivity was the primary factor that distinguished between suicide attempters and psychiatric and community controls, even after covarying internalizing tendencies (Kingsbury, Hawton, Steinhardt, & James, 1999). Further, suicide attempts reported in the absence of ideation seem to be primarily fueled by impulsivity (Lewinsohn et al., 1996). This latter finding is consistent with theorizing that depression may fuel ideation, but engaging in suicide-related behaviors involves low impulse control (Bridge et al., 2006; Linehan, 1993), sometimes even without the presence of depression or ideation (Apter et al., 1993, 1995).
Fewer studies have been conducted examining unique risk factors for nonsuicidal self injury, with most reporting general associations between self injury and both internalizing and externalizing psychopathology (Jacobson & Gould, 2007; Nock, Joiner, Gordon, Lloyd-Richardson, & Prinstein, 2006), including the impulsive–antisocial traits of psychopathy (Swogger et al., 2009). This relatively small literature suggests that depression may confer risk more broadly for suicide ideation and plans, whereas impulsive–antisocial tendencies may confer risk specifically for behavioral manifestations of suicidalilty (particularly attempts), with less research conducted on predictors of self-injury.
Gender and Suicide RiskThe most consistently investigated gender effects have been in regard to the prevalence of suicide outcomes (e.g., Bridge et al., 2006), with some studies indicating that girls are at greater risk of ideation and engagement in nonsuicidal self-injury or suicide attempts, whereas boys are at greater risk of suicide completion (e.g., Bridge et al., 2006; Ross & Heath, 2002). More rarely, data have also informed gender-specific mental health correlates, but this research has advanced mostly equivocal findings. For instance, a number of studies have linked mood disorders to suicide risk in girls and conduct problems and substance use to suicide risk for boys (e.g., Gould et al., 2003). In contrast, other research concluded that externalizing behaviors (e.g., aggression) in girls and dependent traits (e.g., helplessness) in boys confer the most risk (Gould et al., 2003; Reinherz et al., 1995).
Our study was built partly on a conceptualization highlighting the role of the affective–interpersonal traits in female manifestations of psychopathic tendencies. At a theoretical level, callous–unemotional traits are more gender incongruent for women than for men, because they deviate from traditional gender roles that prioritize emotional responding and empathy in girls (e.g., Keenan & Shaw, 1997; Verona & Vitale, 2005). As such, they may represent hallmark features of deviance in girls. Indeed, the affective–interpersonal facets of psychopathy (e.g., superficial charm, conning) are rated by juvenile justice staff as more prototypical of psychopathic girls, whereas the antisocial deviance features (e.g., aggressive criminal behavior) are rated as more prototypical for boys (Cruise, Colwell, Lyons, & Baker, 2003; Salekin, Rogers, & Machin, 2001). In contrast, engagement in suicide-related behaviors (particularly self-injury and attempts) are considered female gender congruent, as suicide attempts and self-injury are common among girls who display emotional regulation problems (Linehan, 1993; Miller, Rathus, & Linehan, 2006). Girls who display callous–unemotional traits, therefore, may be more immune to socialization factors and be better protected against gendered outcomes, such as suicide attempts or self-injury. Consistent with this hypothesis, some evidence shows that the presence of affective–interpersonal traits is protective of female-relevant symptomatology, including anxiety (Verona et al., 2001) and somatization (Lilienfeld & Hess, 2001). Thus, affective–interpersonal deficits can accord protection to particular mental health outcomes, like suicidality, and to a greater degree in women than men (e.g., Verona et al., 2005). In the present study, we directly investigated the possibility that affective–interpersonal traits would play more of a protective role for suicidal behaviors for girls than for boys, given that they signal girls' greater immunity to socialization forces that encourage empathy and emotionality. This work can also better inform some of the equivocal findings from the adult literature regarding affective–interpersonal links to suicide risk indicators.
Present StudyOur primary goal was to investigate the potentially distinct contributions of antisocial tendencies, expanded to include psychopathic traits, and depressive symptoms in relation to suicide risk across adolescents of both genders. Specifically, we (a) deconstructed the heterogeneity of antisocial tendencies by examining whether the three dimensions of psychopathic traits conferred different levels of risk for suicidality, (b) examined whether depression and psychopathic tendencies are differentially associated with distinct measures of suicidality, and (c) expanded on the scarcity of work examining gender-specific risk by analyzing gender differences. In so doing, we aimed to expand the knowledge base around variables explaining distinct suicide outcomes, with specific attention to (a) an overall measure of suicide risk characterized primarily by ideation, plans, and threats; (b) self-injurious behaviors of all kinds; and (c) suicide attempts where youth reported an intent to die (for similar conceptualization of suicide, see Evans et al., 2004; O'Carroll et al., 1996).
We hypothesized that psychopathic tendencies related to impulsivity would confer risk for suicidality, whereas the affective and interpersonal deficits of psychopathy would be unrelated to or confer protection for suicidality (e.g., Douglas et al., 2008; Verona et al., 2001, 2005). In addition, although depression would be significantly related to general suicide risk marked by ideation and plans, only impulsivity would relate to the behavioral indicators of suicide risk, namely, self-injury and suicide attempts. This hypothesis was based on previous work documenting the potent risk conferred by impulsivity-related problems even in the absence of depressive symptomatology (Apter et al., 1993, 1995). Finally, we expected that the affective–interpersonal deficits (i.e., callous–unemotional traits) would be particularly protective of youth suicidality in girls versus boys (Verona et al., 2005).
Method Participants
Participants included 184 youth ranging in age from 11 to 17 years (see Table 1 for demographic and descriptive information). We engaged in a targeted recruitment strategy to ensure we tested a diverse sample of youth with a range of suicide risk as well as antisocial tendencies. As such, our final sample consisted of two subsamples: (a) a treatment-seeking sample composed of youth receiving services from human service and juvenile justice agencies either at the time of recruitment or in the past (n = 99) and (b) a community sample composed of youth without a treatment history (n = 85). Youth were primarily recruited through newspaper and e-mail advertisements (58%); they were also referred from treatment centers (17%), community fliers (8%), schools (7%), and friends or other/miscellaneous sources (10%).
Demographic Characteristics and Descriptive Statistics for the Total Sample and Girls and Boys Separately
Demographic Characteristics and Descriptive Statistics for the Total Sample and Girls and Boys Separately
The overall sample consisted of 100 girls (54.3%) and 84 boys (45.7%). Table 1 reports the demographic characteristics of the overall sample and boys and girls separately. The parents identified 115 youth as European American (62.8%), 34 as African American (18.6%), 10 as Hispanic (5.5%), five as Asian (2.7%), and 19 as biracial (10.3%). Participants were characterized by a diverse range of income levels reflecting annual median incomes for the geographic region (U.S. Census Bureau, n.d.): $1–$30,000 (n = 60, 33.1%), $30,001–$60,000 (n = 48, 26.6%), and $60,001–$75,000+ (n = 73, 40.3%), with a median household size of 2.6 people. Boys and girls did not differ on any demographic variable, including recruitment, age, ethnicity, or income.
Procedures
A telephone screening process was used to assess eligibility and exclude youth with a history of psychotic symptoms or a pervasive developmental disorder (less than 2% of the potential participants). Youth and parents were informed that the goal of the study was to better understand important aspects of adolescent development, including their feelings, thoughts, and behaviors. If youth were eligible, an appointment was scheduled for a parent or guardian and the adolescent. During this appointment, parents provided informed consent and youth provided assent, and they both completed interviews and questionnaires assessing psychopathic tendencies, depression, suicidality, and demographic information. Youth and parents were interviewed and completed questionnaires separately to enhance their comfort and encourage honest responding. Because of our direct assessment of suicide ideation and behaviors, a screening and extended suicide assessment protocol was in place to ensure the safety of participants (e.g., engagement in safety contracting and referral to appropriate resources).
Depressive Symptoms
Youth and a parent or guardian were administered a revised version of the Kiddie Schedule for Affective Disorders and Schizophrenia—Present and Lifetime Version (K–SADS–PL; Kaufman, Birmaher, Brent, Rao, & Ryan, 1996), a semistructured diagnostic interview based on criteria from the Diagnostic and Statistical Manual of Mental Disorders (4th ed., text revision; DSM–IV–TR; American Psychiatric Association, 2000), to assess youth symptoms of MDD. One of the most widely used clinician-rated instruments for youth, the K–SADS–PL has demonstrated adequate validity and test–retest reliability when used with both treatment and nontreatment samples (Ambrosini, 2000; Kaufman et al., 1996). Following K–SADS–PL guidelines, parents and youth answered diagnostic questions separately during private interviews, and trained clinicians rated all individual symptoms on the basis of information and observations provided by both parents and during interviews. Each symptom of MDD was rated as to its presence on a scale of 1 to 3 (1 = not present, 2 = subthreshold, 3 = threshold), and an index of lifetime symptom counts was obtained by summing the symptoms met at threshold level for past and current symptoms. Secondary ratings for each symptom were completed by trained independent raters for 37% of participants, and intraclass correlations demonstrated high levels of agreement between independent raters (intraclass correlation for lifetime symptom counts of MDD = .89).
For the purpose of analyses, a sum of lifetime threshold symptom counts of MDD was calculated and normalized with a Blom transformation to reduce skewness. Use of continuous indicators to assess psychopathology (Krueger & Finger, 2001), including symptom counts of DSM disorders, has been shown to be a valid indicator of youth psychopathology in previous research (e.g., Yager, Bird, Staghezza-Jaramillo, & Gould, 1993) and excellent predictors of school and community functioning (Stoep, Weiss, McKnight, Beresford, & Cohen, 2002). To prevent criterion overlap between psychopathology and suicide variables, we did not include suicide criteria in our calculation of MDD symptom counts.
Antisocial and Psychopathic Traits
The 20-item APSD (Frick & Hare, 2001) was designed to assess psychopathic tendencies in youth. The APSD was developed as a downward translation of the adult Psychopathy Checklist—Revised (Hare, 1991), a widely used and well-validated measure of adult psychopathy. Items on the APSD were modified to make them developmentally appropriate for use with children and adolescents and capture the affective, interpersonal, and behavioral dimensions of psychopathy identified in the adult literature, which research suggests it does (Vitacco et al., 2003). The APSD contains items scored on a 3-point scale (0 = not at all true, 1 = sometimes true, 2 = definitely true) that form three subscales: Callous/Unemotional (six items), Impulsivity (five items), and Narcissism (seven items). These subscales represent the affective (“Does not show feelings or emotions”), behavioral (“Acts without thinking of the consequences”), and interpersonal (“Seems to think he/she is better or more important than other people”) characteristics of psychopathic tendencies, respectively (Frick et al., 2000). In this study, we used the youth-reported rather than parent-reported scores on the APSD, as recommended (Frick, Bodin, & Barry, 2000) and consistent with previous research conducted with mid- to older adolescent samples (e.g., Kruh, Frick, & Clements, 2005; Salekin et al., 2005). The parent and youth reports were moderately correlated, but we opted for the self-reported APSD given that our sample consisted primarily of mid- to older adolescents (M age = 14 years). The youth self-report version provided a better measure of behaviors and affective experience to which parents and teachers are less privy as observers and has demonstrated moderate levels of stability over time (1–2 years; e.g., Muñoz & Frick, 2007). The internal consistency of the total measure was good (α = .74), and the moderate internal consistencies of each subscale—Callous/Unemotional (α = .56), Impulsivity (α = .53), and Narcissism (α = .66)—characterize values typical for these self-report indices in other studies (Poythress, Dembo, Wareham, & Greenbaum, 2006). Despite these lower internal consistencies, empirical work has supported the three-factor model for the youth version of the APSD (Vitacco et al., 2003). Additionally, evidence suggests adequate criterion-related validity, particularly for the association of Callous/Unemotional traits with disturbances in affect-related experiences and Impulsivity with tendencies for behavioral dyscontrol (e.g., Loney, Frick, Clements, Ellis, & Kerlin, 2003; Vitacco et al., 2003; see also Sadeh et al., 2009). Multiple studies from different research groups have used the self-report APSD (Kruh, Frick, & Clements, 2005; Salekin et al., 2005) and report good validity in adolescent samples (e.g., Vitale et al., 2005).
Suicidality Measures
Youth completed the Suicide Behavior Questionnaire—Revised (SBQ–R; Osman et al., 2001), a four-item measure in which youth reported on suicidal thoughts in the last year (e.g., “How often have you thought about killing yourself in the last year?”), lifetime suicidal verbal threats (e.g., “Have you ever told someone that you were going to commit suicide, or that you might do it?”), and suicide ideation and/or planning (e.g., “Have you ever thought about or attempted to kill yourself?”). These are scored on a 5-point Likert-type scale from lowest (1) to highest (5) frequency. The SBQ–R is a good indicator of broad suicide risk, primarily emphasizing suicidal thoughts, threats, and plans (although one item does include engagement in attempt if endorsed at Level 4 or 5). It has been well validated for use with an adolescent population and demonstrates adequate reliability (Osman et al., 2001). As recommended in the literature on this measure, a total suicide risk score was computed as the composite of all four items, which demonstrated good internal consistency in this sample (Cronbach's α = .73). The SBQ–R represents a broad measure of suicide risk characterized primarily by ideation, plans, and threats.
To obtain specific assessments of suicidal behaviors, we administered to youth a short interview adapted from Linehan and Comtois's (1997) Lifetime Parasuicide Count. This interview asks about several methods used by the adolescent to hurt himself or herself (e.g., stabbing, hanging, burning), as well as the level of intent (to die) during the act (definitely, mostly, somewhat, only a little, no intent). The interview has been shown to be a good predictor of future suicide attempts in adolescents (Goldston et al., 1998) and was developed for use in clinical settings in which distinctions regarding level of intent to die are of primary importance; thus, this measure is designed to be sensitive to distinguishing between our primary suicide-related behaviors of interest (Linehan & Comtois, 1997). Responses to this interview were coded by the researchers to create two dichotomous composite variables (0 = no, 1 = yes). The first variable informs lifetime self-injurious behavior, where a 1 on this variable indicates that youth had engaged in any self-injurious behavior in their lifetime, regardless of intent. A second indicator was more specific and assessed lifetime suicide attempts where youth reported engagement in self-injurious behavior; they also reported any intent to die during commission of this act (O'Carroll, 1996). The self-injury and suicide attempt variables were markers of suicidal behaviors specifically, with suicide attempt being the more severe suicidality indicator. Secondary ratings for each behavior were completed by one trained independent rater, who listened to 15% of the audiotaped suicide interviews. Primary and secondary raters showed 100% concordance (i.e., because these are dichotomous outcomes, secondary ratings indicate that youth rated by primary interviewers as engaging in self-injury and suicide attempt were also rated as engaging in these behaviors by a secondary rater).
The self-injury and suicide attempt indicators do not necessarily imply two mutually exclusive groups of youth. That is, the same youth may have been counted in both of these categories if he or she engaged in one suicide attempt (with intent to die) and another self-injurious behavior (without intent to die). In our sample, 34 youth engaged in any self-injury, regardless of intent, and they constitute the “yes” ratings on the self-injury variable (of those youth, 13 reported never having an intent to die). Twenty-one youth reported intent to die at least once and constitute the “yes” ratings on the suicide attempt variable. Almost half of the youth reported engaging in self-injury and a suicide attempt in the past year (40% and 45%, respectively), whereas most of the remainder reported first engaging in these acts within the last 2–3 years (45% and 25%, respectively). It is important to note that these data do not capture the last time youth participated in self-injury or attempt, only their first engagement in these behaviors. Follow-up analyses show that relationships between the APSD and our suicide indicators were similar for recent (last year) versus past injury and attempt.
Data Analytic Strategy
Although the treatment-seeking and community subsamples differed on measures of psychopathology, initial analyses indicated that recruitment sample (treatment seeking vs. community) did not interact with any of our explanatory variables (depression and APSD facets) to account for the suicidality indicators. Thus, sample type was not included in subsequent analyses. To examine the contribution of depression and psychopathic tendencies to the postdiction of the three suicidality measures, we conducted four-step regressions hierarchically, with the first block including age, family income, and gender; the second block including MDD symptom counts; the third block comprising the three APSD facets; and the fourth block including gender interactions with each APSD facet.
For linear regressions (explaining general suicide risk using total scores on the SBQ–R), beta coefficients and changes in variance accounted for (ΔR2) are reported. For logistic regressions (explaining self-injurious behavior and suicide attempts), the Wald statistic, −2 log likelihood (−2LL), and odds ratios are reported. The Wald statistic is an indicator of the explanatory variable's independent contribution after holding other explanatory variables constant and is calculated as the ratio of the beta coefficient divided by the standard error for that individual explanatory variable (Tabachnick & Fidell, 2001). The Wald statistic is considered appropriate to use even when the probability of obtaining a score of 0 is high (e.g., when most participants do not endorse an outcome; e.g., Afifi, Kotlerman, Ettner, & Cowan, 2007), which is the case in our data. The −2LL is an indicator of model fit, whereby significant decreases from previous blocks represent improvements in variance explained by the model. Odds ratios represent measures of effect size and provide the likelihood or odds of an outcome (e.g., self-injury “yes” vs. “no”) given the participant's level on the independent variable (e.g., gender). An odds ratio of 1 would indicate that the probability of the outcome “yes,” for example, is similar across levels of the independent variable (e.g., males and females). Significantly lower odds would be associated with protection and significantly higher odds would be associated with risk.
Results Means and Intercorrelations
Descriptive and demographic statistics for the APSD and measures of suicidality and self-harm are reported in Table 1 for the total sample as well as for girls and boys separately. It is important to note that the means and range of scores endorsed on the APSD are comparable to those found in other studies, with the treatment-seeking sample having a range similar to that of youth recruited from detention centers (e.g., Murrie & Cornell, 2002). Treatment-seeking and community youth differed in expected ways, including that treatment-seeking youth were more likely to endorse symptoms of MDD, t(176) = −5.27, p < .01, and scored higher on the APSD, t(176) = −2.84, p < .01. Girls and boys were also compared across study variables and evidenced a few expected differences. Specifically, girls were characterized by a greater number of MDD symptoms, t(176) = 19.24, p < .01, and reported significantly more suicide risk marked by suicide ideation and threats, as measured by the SBQ–R, t(181) = 2.00, p < .05. However, girls and boys did not differ on history of self-injurious behavior or suicide attempts (see Table 1). Boys scored significantly higher on total APSD, F(176) = 3.80, p < .05, an effect driven by the APSD Impulsivity facet in particular, F(176) = 3.80, p < .05.
Intercorrelations among the APSD subscales for the total sample ranged from .22 to .56, with a moderate association between Callous/Unemotional and Narcissism (r = .25, p < .01) and a higher association between Narcissism and Impulsivity (r = .55, p < .001). Across the whole sample, MDD was positively related to general suicide risk marked by ideation and threats (r = .44, p < .01), self-injurious behavior (r = .29, p < .01), and suicide attempts (r = .25, p < .01). Further, the Impulsivity facet of the APSD was significantly associated with all indicators of suicidality, including suicidal ideation and threats, self-injurious behaviors, and suicide attempts with intent to die (rs range = .18–.25), whereas Narcissism and Callous/Unemotional traits were not significantly related to any of the suicidality indicators. Within-gender bivariate correlations between each facet of the APSD and suicidality are reported in Table 2. These indicated that similar associations characterize girls and boys, although some of the APSD relationships to suicidality measures only approached significance in the boys.
Correlations Between APSD and Suicidality Indicators (N = 181) for Girls (Below the Diagonal) and Boys (Above the Diagonal)
Depression, Psychopathic Tendencies, and Suicidal Ideation and Threats
Next, we conducted a series of linear and logistic regressions to examine unique associations between MDD symptom counts, psychopathic tendencies, and suicidality indicators (see Table 3). For our first model, a linear regression was conducted with the SBQ–R, our indicator of broad suicide risk marked by ideation and threats. Age (β = .23, p < .01) and income level (β = −.19, p < .05) in the first block and MDD symptom counts (β = .38, p < .001) in the second block were significantly related to the SBQ–R. That is, older youth, those with lower income, and those with more depressive symptoms reported more suicide risk on the SBQ–R. When the APSD Callous/Unemotional, Narcissism, and Impulsivity subscale scores were entered simultaneously in the third block, none of these facets of psychopathic tendencies explained a significant amount of variance in the SBQ–R (ΔR2 = .02), whereas MDD symptom counts remained a significant explanatory variable (β = .35, p < .001). In the final block, the interactions between gender and each APSD facet were entered, and no significant interactions emerged. Thus, results suggest that MDD symptoms are significant contributors to suicide risk marked by ideation, plans, and threats, but psychopathic tendencies, as indexed by facets of the APSD, did not explain a significant amount of variance above that explained by MDD symptoms and demographics. Indeed, when we conducted another regression including the same demographic variables but entering MDD symptom counts after the APSD variables, we found that MDD symptoms explained about 11% of the variance in the SBQ–R above that contributed by the APSD factors (ΔR2 = .11, p < .001).
Regression of Suicidality Indicators on the APSD Facets, MDD Symptom Counts, and Gender
Depression, Psychopathic Tendencies, and Suicidal Behavior
We next conducted logistic regression analyses postdicting self-injurious behavior, and the results are reported in the Self-injurious behavior section of Table 3. Modeling followed the same sequence as above, and results indicated that MDD symptom counts were positively associated with self-injurious behavior when entered only in the context of demographic variables (Wald = 5.56, p < .05, odds ratio [OR] = 1.60). In contrast to results of regressions with the SBQ–R, MDD symptom counts were no longer significantly related to self-injurious behavior once the APSD facets were included in the third block of the model (Wald = 1.69, p = .19, OR = 1.32). Instead, APSD Impulsivity emerged as a significant explanatory variable (Wald = 9.05, p < .01, OR = 2.67), and the inclusion of the APSD variables produced an increment in fit as suggested by a significant drop in −2LL between the second and third blocks (see Table 3). No significant Gender × APSD Facet interactions were found in the final block of modeling. Thus, results suggest that impulsivity explains a significant amount of variance in self-injurious behavior above that accounted for by MDD symptom counts.
The final logistic regression analysis examined suicide attempts with intent to die, reported in the Suicide attempts with intent to die section of Table 3. Results indicated that MDD symptom counts were positively related to suicide attempts when entered in the second block (Wald = 5.58, p < .05, OR = 1.78) but were no longer related once the APSD facets were entered in the third block (Wald = 1.53, p = .22, OR = 1.38). Instead, APSD Impulsivity was significantly associated with suicide attempts (Wald = 9.24, p < .01, OR = 4.08). The model fit improved once APSD facets were entered as explanatory variables, as indexed by a significant drop in the −2LL. Finally, in the last block, a significant Gender × Callous/Unemotional interaction emerged (Wald = 3.98, p < .05, OR = 2.20). This significant Gender × Callous/Unemotional interaction was disentangled by conducting logistic regressions for each gender separately. For girls, Callous/Unemotional traits were negatively linked with suicide attempts (Wald = 6.25, p < .05, OR = 0.24), suggesting that they serve a protective role in relation to suicide attempts for girls. For boys, Callous/Unemotional traits were not significantly related to suicide attempts (Wald = 0.16, p = .69, OR = 1.26).
DiscussionThis study is the first to examine the differential associations between depressive symptoms, psychopathic tendencies, and markers of suicidality in youth. Consistent with previous research, we demonstrated that the impulsivity facet of psychopathic tendencies conferred risk for self-injurious behaviors and attempts across genders. Novel to our study was the finding that the callous–unemotional facet conferred protection from suicide attempts in girls specifically, a finding warranting further replication. These findings help to disentangle the heterogeneity of antisocial–externalizing propensities and their associations with suicide risk, such that some tendencies are risk factors for and some are protective of suicidality, even though they are both associated with antisocial behavior. Findings also replicate and extend research conducted in adults, adding to the construct validity of youth psychopathic tendencies. It is important to note that relationships between psychopathic traits and suicidal behaviors were found above the influence of depressive symptomatology, which was positively linked to general suicide risk marked by ideation, plans, and threats (cf. Bridge et al., 2006). Divergent findings underscore the heterogeneous nature of risk for adolescent suicidality, in terms of (a) psychopathic facets and depression, (b) suicide risk indicators, and (c) gender.
Depression, Impulsivity, and Suicide Risk
These data have implications for future research geared toward clarifying different models of suicide risk in youth. One model that can be gleaned from the present findings is that depression confers the most potent risk for suicidal ideation, threats, or plans but is not the primary driver of suicidal behaviors. Instead, impulsive tendencies may ultimately determine youth engagement in self-injury or attempt. This analysis parallels conceptualizations proposed in models of youth suicidality (Apter et al., 1995; Brent & Mann, 2005; Bridge et al., 2006), which suggest that impulsive or aggressive tendencies heighten risk for engagement in suicide behaviors even without the presence of ideation or depression. Thus, both sets of potentially overlapping vulnerabilities for youth suicidality—those that arise from mood states and those that arise from impulsive traits (e.g., Brent & Mann, 2005)—may be important; however, they each affect suicide risk at different levels (ideation/planning vs. behaviors). It is interesting that the former has been conceptualized as being primarily motivated by “a wish to die,” whereas the latter is motivated by “a wish to not be here for a time” (Apter et al., 1995, p. 912).
A second related model draws from work on the neuropsychology of depression, which has demonstrated that depressive states reduce prefrontal cortex activation generally and the left dorsolateral prefrontal cortex in particular, where executive functions are governed (Heller & Nitschke, 1997; Herrington et al., 2010). Indeed, depression is associated with various executive cognitive deficits, including problems with memory, attention, and problem solving (Levin, Heller, Mohanty, Herrington, & Miller, 2007; Rogers et al., 2004). In this model, depression may exacerbate already deficient regulatory processes, suggesting that depression may itself give rise to impulsive behaviors. This may be particularly relevant to our findings, given that we examined suicidality in adolescence, a period when the prefrontal cortex is still in development and executive functions governing behavior regulation are not fully formed (Blakemore & Choudhury, 2006). It is interesting that callous–unemotional traits are often negatively related to negative affective states (e.g., Sadeh et al., 2009); thus, youth with high levels of these traits would be less likely to suffer from depression and therefore less likely to experience deficient executive functions (Sellbom & Verona, 2007), which, in turn, would protect them from suicidal behaviors. The latter is what we found in the girls in our sample. In essence, depression need not be conceptualized as completely distinct from impulsive and antisocial traits. On the contrary, depression may be particularly important for explaining self-injury and suicide attempts to the extent that it works to reduce capacity for behavioral regulation, consistent with the role that negative affect plays in exacerbating impulsivity (Cooper, Agocha, & Sheldon, 2000) and self-defeating behavior (Baumeister & Scher, 1988).
Finally, an alternative model suggests that personality dimensions related to disconstraint drive the risk for both depression and impulsivity, in that these syndromes both involve difficulties in regulating behavior. Specifically, depressive mood states involve overregulation of appetitive behaviors (e.g., anhedonia) and impulsivity involves underregulation of approach behaviors (e.g., risk taking; Carver, Johnson, & Joormann, 2008). This conceptualization may suggest that a general predisposition toward low self-regulation that fosters depressed mood and impulsivity or aggression is the primary mechanism by which youth suicide occurs. Indeed, regulatory systems involving serotonin have been implicated in both depression and impulsivity, potentially paralleling findings that link impulsive suicide to these same neurotransmitter systems (e.g., Brent & Mann, 2005; Carver et al., 2008). More work is needed to empirically link biological mechanisms, psychopathology, and suicide outcomes.
Psychopathic Tendencies, Suicide Risk, and Gender
In addition to informing the broader literature on suicidality in youth, the present results also contribute to knowledge of the role of psychopathic tendencies in other youth problem behaviors. The extent to which psychopathic traits that emerge in childhood parallel the syndrome in adulthood is a relatively nascent area of study, with additional research needed to establish the nomological network of psychopathic tendencies in youth. This study expands the criterion validity of the psychopathic construct in youth by examining its association with suicidality above the influence of depressive symptoms. Among psychopathic adult inmates, research has linked the antisocial–impulsive dimension to heightened risk for suicide attempts (Verona et al., 2001, 2005) and the affective–interpersonal dimension to reduced risk for suicide attempts and ideation, respectively, in women (Verona et al., 2005) and men (Douglas et al., 2008)—although there have been some null findings in regard to the affective–interpersonal traits (e.g., Verona et al., 2001). The differential associations for impulsivity and callous–unemotional traits in the present study closely replicate these findings, providing additional support for the construct of psychopathic tendencies in youth and potential similarities to the disorder in adulthood. The present findings make conceptual sense, given recent reports of personality correlates of youth psychopathy. Sadeh et al. (2009) found that whereas low anxiety and aggression characterized the Callous/Unemotional dimension of the APSD, low trait constraint differentially characterized the Impulsivity dimension. Further, the finding that narcissism did not differentially explain suicidality in the present study may be due to the fact that narcissism is mostly related to social potency (Sadeh et al., 2009) and extroversion (Hall, Benning, & Patrick, 2004). These latter personality traits may not be as relevant to suicidality as are the constructs of impulsivity and callous–unemotional traits.
The finding that callous–unemotional traits may accord a protective effect for suicide attempts in adolescent girls is in keeping with conceptualizations that psychopathy can be adaptive in some contexts (e.g., Hall & Benning, 2006), including in adolescence (Sadeh et al., 2009). Given that gender differences are rarely studied in relation to psychopathic tendencies in youth (e.g., Verona, Sadeh, & Javdani, 2010), another contribution of this study is the relevance of the findings for understanding how psychopathic tendencies may manifest differently for girls. It is unknown why low levels of emotionality selectively decreased risk for suicidality in girls in our sample. One interpretation is that callous–unemotional traits represent greater deviance and prototypicality of psychopathic tendencies for girls than boys (Cruise et al., 2003; Salekin et al., 2001), as these traits are more likely to be discouraged through socialization in girls versus boys (e.g., girls show greater average empathy than boys). Thus, the presence of callousness signals protection from socialization processes in girls more than boys, which may, in turn, reduce tendencies toward emotional distress and engagement in suicidal behaviors among girls with high levels of callous–unemotional traits. However, it should be noted that we did not find mean differences in the level of callous–unemotional traits in our sample. Nonetheless, callous–unemotional traits had more explanatory power in regard to suicide risk for girls than boys in our sample. Another possibility is that girls at the other end of the emotionality spectrum (i.e., with high emotional dysregulation) are more likely than boys to attempt suicide, whereas boys with similar characteristics are more likely to react to negative emotions in other ways, such as hurting others (e.g., Verona & Kilmer, 2007). This is consistent with the finding that borderline personality disorder, a syndrome associated with suicidality stemming from emotional dysregulation, is more common in women than men (Johnson et al., 2003; Swartz, Blazer, George, & Winfield, 1990). Indeed, a higher base rate of suicide attempts in girls overall accords more opportunity to detect explanatory variables, such as callous–unemotional traits, because there is potentially greater variability to explain. Thus, callous–unemotional traits may be a protective factor in relation to suicidality in girls, because the link between emotional dysregulation and suicidal acts is stronger for girls than boys. Exploring such gender-specific pathways to suicide may be a fruitful avenue for future research.
Finally, an alternative possibility is that callous–unemotional traits represent a different construct altogether in girls and boys, given demonstrated gender differences in emotional processing among psychopathic individuals (Rogstad & Rogers, 2008). For girls, callous–unemotional traits may be particularly related to low levels of trait nurturance. In support of this, research shows that the relationship between girls' psychopathy scores and aggressive outcomes is mediated by girls' experiences of victimization (Odgers, Reppucci, & Moretti, 2005). The authors suggested that girls' victimization experiences may “lead to an interpersonal disposition and interaction style that may resemble psychopathic traits (e.g., appear callous and lacking remorse), but are not linked in the same way to the latent construct” (Odgers, Reppucci, & Moretti, 2005, p. 758). Thus, the callous–unemotional construct may be etiologically distinct from the same construct assessed in boys. Although gender differences in callous–unemotional traits could indicate biased responding (i.e., because callous–unemotional traits are in greater discordance with girls' than boys' gender roles, girls may be less likely to endorse them), true differences that emerge through socialization and/or biological processes also likely play a role. Future research could directly examine the potential for differential item functioning between males and females on measures of callous–unemotional traits, including by using item response theory or multiple indicator multiple cause modeling (see Bolt, Hare, Vitale, & Newman, 2004). These approaches allow one to detect biased responding across genders and disentangle the effects of true differences versus differential item functioning.
Strengths and Limitations
This study has several strengths, including the use of clinician-rated and self-reported risk factors and multiple measures of suicidality. Also, the sample incorporated a relatively wide range of psychopathic traits and was diverse in terms of gender, ethnicity, and socioeconomic status. As with any investigation, however, this study also has limitations. First, there are important considerations regarding the potential representativeness of the sample obtained using two different recruitment strategies (i.e., treatment and community samples combined). Care should be taken to generalize findings to primarily midadolescents who represent youth from both treatment-seeking and community-based samples. In addition, the number of youth who engaged in self-injurious and suicidal behaviors was modest, necessitating replication of the findings with larger clinical samples. However, we were able to obtain relatively good representation of suicide-related behaviors in our sample of youth (e.g., almost 20% had engaged in self-injurious behaviors), despite examining a low base rate phenomenon.
Also, our measure of general suicide risk marked by ideation, plans, and threats (the SBQ–R) included a double-barreled item that asks about thoughts or attempts (“Have you ever thought about or attempted to kill yourself?”), raising concerns about the specificity of this measure. However, analyses on the SBQ–R removing this item produced the same results. We also did not ask participants whether their suicidal behavior was planned out or impulsive in nature, and different results may have emerged if we had examined planned versus impulsive suicidal behaviors. However, given that we studied adolescents, who are likely to be impulsive, the findings of this study may be quite relevant for adolescence.
We must note that our design was cross-sectional and postdictive, because our suicide outcomes occurred in the past (i.e., past year or lifetime), so care should be taken in interpreting results (i.e., explanatory variables are not predictors of suicide in this study). Future work can involve prospective designs to examine the temporal sequence of depression, psychopathic traits, ideation, and suicidal behaviors. Finally, although the APSD is an oft-used instrument recommended for adolescents in particular, it is characterized by moderate stability, although this level of stability is typical (Frick et al., 2000). Indeed, we do not find strong agreement between the parent and youth versions of the APSD, and one of the reasons for this may be instability in the measurement of psychopathic traits using the APSD. It is also possible that our results may be specific to the youth-reported APSD, and thus an important area for future research is replication and extension of these findings using other measures of psychopathy.
Despite these limitations, the results of the current study provide important information about the role of youth-relevant mental health indicators and their relation to suicidal thoughts and behaviors. Specifically, they indicate that depression and impulsivity confer risk for (a) suicidal ideation and (b) self-injurious behavior and suicide attempts in youth, respectively. The importance of psychopathic tendencies in the form of callous–unemotional traits was revealed for girls, in that they were protective of suicide attempts in girls but not boys. These data, thus, extend the lens for risk and protection in regard to youth suicide.
Footnotes 1 Parent- and youth-reported subscales of the APSD were correlated as follows: for the total, r = .41, p < .001; for Impulsivity, r = .37, p < .001; for Callous/Unemotional, r = .26, p < .001; for Narcissism, r = .31, p < . 001. These values are similar to those found in other research (total r = .54 in Kimonis, Frick, Fazekas, & Loney, 2006, and total rs = .47–.57 across three time points in Muñoz & Frick, 2007). Analyses conducted separately with the parent-reported APSD suggest parallel results for suicide ideation, where only MDD is a significant predictor (B = .31, p < .001). In contrast, results for self-injury and attempts are discrepant, such that impulsivity does not explain self-injury (Wald = 2.44, p = .12) or attempts (Wald = 0.05, p = .82) above the influence of depression. Also, no Gender × Callous/Unemotional interaction emerges in relation to suicide attempts (Wald = 0.01, p = .93).
2 Age was significantly positively related to all suicide outcomes. This finding replicates multiple other studies (see Bridge et al., 2006, for a review), with current theories suggesting this results is explained by hormonal factors (i.e., puberty), greater opportunity to engage in these behaviors (i.e., because they are older), and lower levels of monitoring (Bridge et al., 2006). Also, separate analyses were conducted to examine whether any interactions between the APSD factors themselves (e.g., Impulsivity × Callous–Unemotional) were associated with the three suicidality indicators, but no significant two- or three-way interactions emerged.
3 Previous theory suggests that depressive states interact with impulsive dispositions to lead to suicidal behaviors (e.g., Bridge et al., 2006). We examined this by testing whether MDD interacted with the APSD to confer risk for suicide behaviors and found no significant MDD by APSD Impulsivity interactions for self-injury (Wald = 0.01, p = . 91) or attempts (Wald = 0.59, p = .44).
4 We investigated whether impulsivity confers suicide risk regardless of the presence of ideation by examining interactive effects between ideation and impulsivity on self-injury or attempts, but we found no effects for either self-injury (Wald = 0.01, p = .91) or attempts (Wald = 0.03, p = .87).
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Submitted: October 18, 2009 Revised: June 30, 2010 Accepted: July 2, 2010
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Source: Journal of Abnormal Psychology. Vol. 120. (2), May, 2011 pp. 400-413)
Accession Number: 2011-01897-001
Digital Object Identifier: 10.1037/a0021805
Record: 156- Title:
- Suicide attempts in a longitudinal sample of adolescents followed through adulthood: Evidence of escalation.
- Authors:
- Goldston, David B.. Department of Psychiatry and Behavioral Sciences, Duke University School of Medicine, Durham, NC, US, david.goldston@duke.edu
Daniel, Stephanie S.. Center for Youth, Family, and Community Partnerships, University of North Carolina at Greensboro, NC, US
Erkanli, Alaattin. Department of Biostatistics and Bioinformatics, Duke University, Durham, NC, US
Heilbron, Nicole. Department of Psychiatry and Behavioral Sciences, Duke University School of Medicine, Durham, NC, US
Doyle, Otima. Jane Addams College of Social Work, University of Illinois, Chicago, IL, US
Weller, Bridget. Department of Psychiatry and Behavioral Sciences, Duke University School of Medicine, Durham, NC, US
Sapyta, Jeffrey. Department of Psychiatry and Behavioral Sciences, Duke University School of Medicine, Durham, NC, US
Mayfield, Andrew. Center for Youth, Family, and Community Partnerships, University of North Carolina at Greensboro, NC, US
Faulkner, Madelaine. Center for Youth, Family, and Community Partnerships, University of North Carolina at Greensboro, NC, US - Address:
- Goldston, David B., Department of Psychiatry and Behavioral Sciences, Duke University School of Medicine, 2608 Erwin Road, Suite 300, DUMC 3527, Durham, NC, US, 27710, david.goldston@duke.edu
- Source:
- Journal of Consulting and Clinical Psychology, Vol 83(2), Apr, 2015. pp. 253-264.
- NLM Title Abbreviation:
- J Consult Clin Psychol
- Page Count:
- 12
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- Journal of Consulting Psychology
- Other Publishers:
- US : American Association for Applied Psychology
US : Dentan Printing Company
US : Science Press Printing Company - ISSN:
- 0022-006X (Print)
1939-2117 (Electronic) - Language:
- English
- Keywords:
- adolescence, sensitization, escalation, suicide attempts, developmental trends
- Abstract (English):
- Objectives: This study was designed to examine escalation in repeat suicide attempts from adolescence through adulthood, as predicted by sensitization models (and reflected in increasing intent and lethality with repeat attempts, decreasing amount of time between attempts, and decreasing stress to trigger attempts). Method: In a prospective study of 180 adolescents followed through adulthood after a psychiatric hospitalization, suicide attempts, and antecedent life events were repeatedly assessed (M = 12.6 assessments, SD = 5.1) over an average of 13 years 6 months (SD = 4 years 5 months). Multivariate logistic, multiple linear, and negative binomial regression models were used to examine patterns over time. Results: After age 17–18, the majority of suicide attempts were repeat attempts (i.e., made by individuals with prior suicidal behavior). Intent increased both with increasing age, and with number of prior attempts. Medical lethality increased as a function of age but not recurrent attempts. The time between successive suicide attempts decreased as a function of number of attempts. The amount of precipitating life stress was not related to attempts. Conclusions: Adolescents and young adults show evidence of escalation of recurrent suicidal behavior, with increasing suicidal intent and decreasing time between successive attempts. However, evidence that sensitization processes account for this escalation was inconclusive. Effective prevention programs that reduce the likelihood of individuals attempting suicide for the first time (and entering this cycle of escalation), and relapse prevention interventions that interrupt the cycle of escalating suicidal behavior among individuals who already have made attempts are critically needed. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
- Impact Statement:
- What is the public health significance of this article?—Some individuals attempt suicide on multiple occasions during adolescence and young adulthood. As they make repeated attempts, the severity of their intention to die increases, and the amount of time between their suicide attempts decreases on average. These findings underscore the need for effective interventions to prevent and interrupt this cycle of escalation in suicidal behavior. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Attempted Suicide; *Sensitization; *Trends
- Medical Subject Headings (MeSH):
- Adolescent; Adult; Child; Female; Humans; Intention; Longitudinal Studies; Male; Prospective Studies; Suicidal Ideation; Suicide; Suicide, Attempted; Young Adult
- PsycINFO Classification:
- Behavior Disorders & Antisocial Behavior (3230)
- Population:
- Human
Male
Female - Location:
- US
- Age Group:
- Childhood (birth-12 yrs)
School Age (6-12 yrs)
Adolescence (13-17 yrs)
Adulthood (18 yrs & older)
Young Adulthood (18-29 yrs) - Tests & Measures:
- Interview Schedule for Children and Adolescents
Follow-Up Interview Schedule for Adults
Beck Suicide Intent Scale
Lethality of Suicide Attempt Rating Scale
Life Events Checklist
Subjective Intent Rating Scale DOI: 10.1037/t11293-000 - Grant Sponsorship:
- Sponsor: National Institutes of Health, National Institute of Mental Health
Grant Number: R01MH048762; K24MH066252
Recipients: No recipient indicated - Methodology:
- Empirical Study; Longitudinal Study; Prospective Study; Quantitative Study
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- First Posted: Jan 26, 2015; Accepted: Nov 12, 2014; Revised: Oct 31, 2014; First Submitted: Feb 6, 2013
- Release Date:
- 20150126
- Correction Date:
- 20160512
- Copyright:
- American Psychological Association. 2015
- Digital Object Identifier:
- http://dx.doi.org/10.1037/a0038657
- PMID:
- 25622200
- Accession Number:
- 2015-02672-001
- Number of Citations in Source:
- 85
- Persistent link to this record (Permalink):
- http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2015-02672-001&site=ehost-live
- Cut and Paste:
- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2015-02672-001&site=ehost-live">Suicide attempts in a longitudinal sample of adolescents followed through adulthood: Evidence of escalation.</A>
- Database:
- PsycINFO
Suicide Attempts in a Longitudinal Sample of Adolescents Followed Through Adulthood: Evidence of Escalation
By: David B. Goldston
Duke University School of Medicine;
Stephanie S. Daniel
University of North Carolina at Greensboro
Alaattin Erkanli
Duke University
Nicole Heilbron
Duke University School of Medicine
Otima Doyle
University of Illinois, Chicago
Bridget Weller
Duke University School of Medicine
Jeffrey Sapyta
Duke University School of Medicine
Andrew Mayfield
University of North Carolina at Greensboro
Madelaine Faulkner
University of North Carolina at Greensboro
Acknowledgement: David B. Goldston, Department of Psychiatry and Behavioral Sciences, Duke University School of Medicine; Stephanie S. Daniel, Center for Youth, Family, and Community Partnerships, University of North Carolina at Greensboro; Alaattin Erkanli, Department of Biostatistics and Bioinformatics, Duke University; Nicole Heilbron, Department of Psychiatry and Behavioral Sciences, Duke University School of Medicine; Otima Doyle, Jane Addams College of Social Work, University of Illinois, Chicago; Bridget Weller and Jeffrey Sapyta, Department of Psychiatry and Behavioral Sciences, Duke University School of Medicine; Andrew Mayfield and Madelaine Faulkner, Center for Youth, Family, and Community Partnerships, University of North Carolina at Greensboro.
Research reported in this publication was supported by the National Institute of Mental Health of the National Institutes of Health (R01MH048762 and K24MH066252). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
The rate of suicide attempts varies over the life span. For example, in community and population-based studies, the transition to adolescence has been found to be associated with a marked increase in the rates of suicide attempts (Boeninger, Masyn, Feldman, & Conger, 2010; Joffe, Offord, & Boyle, 1988; Kessler, Borges, & Walters, 1999; Lewinsohn, Rohde, Seeley, & Baldwin, 2001; Velez & Cohen, 1988; Wunderlich et al., 2001). Females generally attempt suicide at higher rates than males (Lewinsohn et al., 2001; Nock et al., 2013), and in one study, the increase in suicide attempts during adolescence was found primarily among females (Lewinsohn et al., 2001). In community samples, the rate of suicide attempts has been observed to decline during the transition to young adulthood (Kessler et al., 1999; Lewinsohn et al., 2001). Fewer studies have examined age trends in suicide attempts in clinical or high-risk samples, but results suggest similar patterns in the prevalence of suicidal behavior (Angle, O’Brien, & McIntire, 1983; Kovacs, Goldston, & Gatsonis, 1993).
Despite decreasing rates of suicide attempts from adolescence through early adulthood, a greater proportion of the attempts that do occur may be made by individuals who have attempted suicide on more than one occasion. For example, within 3 to 5 years of previous suicidal behavior, rates of repeat suicide attempts among adolescents and adults who have presented in treatment settings range from 25% to 31% (Christiansen & Jensen, 2007; Goldston et al., 1999; Tejedor, Diaz, Castillon, & Pericay, 1999). Moreover, past suicide attempts have been found to be strongly associated with increased risk for future attempts (Goldston et al., 1999; Leon, Friedman, Sweeny, Brown, & Mann, 1989). Hence, adolescents who have attempted suicide may be at particularly high risk for repeat attempts even as they transition into adulthood.
The increased risk for suicidal behavior among individuals with prior suicide attempts may be in part because of sensitization processes. If such processes were operative, individuals would become more sensitive to and show increased reactivity to the triggers for behaviors or illness with repeated exposures to those triggers (Post, Rubinow, & Ballenger, 1986; Post, 2007). Sensitization processes are reflected in three patterns of response. First, when there is sensitization, individuals become more reactive with repeated exposures, and the magnitude of the behavioral or physiological response to the stress increases in intensity. In the example of suicide attempts, the severity of the suicide attempts (e.g., intent and/or lethality of attempts) would increase with repetition. Second, with increased sensitivity to triggers or precipitants, there are more rapid recurrences of the behavioral response or episode of disorder. For suicide attempts, this would be reflected in a decreasing amount of time between repeated suicide attempts. Third, with repeated exposure to a provocative stimulus or stress, it takes progressively less severe stress to trigger or provoke the reaction. In the case of a suicidal person, the amount of life stress that could trigger a suicide attempt would decrease as the individual made an increasing number of attempts.
Sensitization processes have been posited to be associated with different psychiatric disorders, including affective disorders (Bender & Alloy, 2011; Monroe & Harkness, 2005; Post et al., 1986; Post, 1992). For example, earlier episodes of affective disorder in adults are often less severe than later episodes (Kessing, 2008; Lewinsohn, Zeiss, & Duncan, 1989; Maj, Veltro, Pirozzi, Lobrace, & Magliano, 1992). Several (but not all) studies have found that among adults, there is a decreasing amount of time between affective disorder episodes as the number of episodes increases (Kessing, 1998). Furthermore, in adults and older adolescents, recurrent episodes of affective illness are sometimes precipitated by less severe stresses than initial or earlier episodes (Bender & Alloy, 2011; Monroe & Harkness, 2005; Post, 1992; Stroud, Davila, Hammen, & Vrshek-Schallhorn, 2011).
Despite these findings with affective disorders, research findings have been inconsistent regarding whether recurrent suicide attempts conform to a pattern that would be consistent with a sensitization model. Intent and medical lethality, for example, are often considered to be indices of severity of suicide attempts. To date, there have been mixed findings from studies of adults regarding associations between these indices and patterns of suicide attempts. Specifically, some studies have found that repeat suicide attempts are associated with greater intent to die than first-time suicide attempts (Kaslow et al., 2006; Reynolds & Eaton, 1986), whereas other studies have not found this pattern (Forman, Berk, Henriques, Brown, & Beck, 2004; Michaelis et al., 2003; Ojehagen, Danielsson, & Traskman-Bendz, 1992). Similarly, there have been mixed findings regarding a possible association between higher medical lethality and increasing number of suicide attempts (Forman et al., 2004; Kaslow et al., 2006; Michaelis et al., 2003; Pettit, Joiner, & Rudd, 2004; Reynolds & Eaton, 1986). No studies, to our knowledge, have examined the possibility that there may be decreasing amounts of time between successive suicide attempts. Last, the studies of life stress among individuals with differing numbers of prior suicide attempts have yielded mixed findings. For example, the amount of life stress preceding a suicide attempt has variously been found to not differ between individuals making their first attempts and individuals making repeat attempts (Crane et al., 2007; Joiner & Rudd, 2000; Kaslow, Jacobs, Young, & Cook, 2006; Pompili et al., 2011), to be positively related to the number of past attempts (Pettit et al., 2004), and to be related to severity of suicidal episode among individuals making first but not repeat attempts (Crane et al., 2007; Joiner & Rudd, 2000). Putting these mixed findings in context, it is worth noting that with few exceptions (e.g., Joiner & Rudd, 2000; Ojehagen et al., 1992), the majority of studies pertinent to sensitization models of suicide attempts have been cross-sectional in nature and focused on individuals at treatment entry. Cross-sectional comparisons of suicide attempts that precede initiation of treatment may be biased because these attempts may not be typical of all attempts. Prospective studies of recurrent suicide attempts within the same individuals over significant periods of time may be less biased, and more likely to reflect escalation of suicidal behavior consistent with sensitization models.
Sensitization processes are prominently described in theoretical conceptualizations of suicidal behavior. For example, Joiner (2005), in describing the factors that may contribute to increasing courage for suicide, referred to the process of cognitive sensitization. This occurs when “[an individual] undergoes a provocative experience, and subsequently, images and thoughts about that experience become more accessible and easily triggered . . . As suicidal experience accumulates, suicide-related cognitions and behaviors may become more accessible and active. The more accessible and active these thoughts and behaviors become, the more easily they are triggered (even in the absence of negative events), and the more severe are the subsequent suicidal episodes” (pp. 82–83). To this point, Beck (1996) theorized that the cognitive schemas underlying information processing become integrated with motivational, behavioral, and affective response systems. With repeated exposure to relevant experiences, these “modes” of responding, including a hopeless-suicidal mode, become more accessible and more easily activated. As a result, more severe reactions can result from less serious precipitants. Sensitization conceptualizations have been highly influential in our current thinking about suicidal behavior and in the development of interventions for suicidal individuals (e.g., Brown et al., 2005). However, the suggestions that sensitization processes might account in part for patterns of recurrence of suicidal behavior have not previously been tested among individuals followed over long periods.
If sensitization processes contribute to the recurrence of suicidal behavior, a sensitization model would provide a framework for understanding the course and repetition of suicidal behavior (Post et al., 1986, 1992). It also would provide a framework for understanding the high-risk group of individuals who have made multiple suicide attempts, and whose suicidal behavior has become increasingly more severe over time (Post et al., 1986). A sensitization model additionally would have implications for relapse prevention approaches for working with suicidal individuals, and for the theoretical conceptualizations that provide the basis for these interventions (Segal et al., 1996).
In 1991, we began conducting a naturalistic, prospective study of the risk for suicidal behaviors among adolescents who were psychiatrically hospitalized and then followed through young adulthood. With repeated assessments, we examined patterns in suicide attempts in adolescence and through adulthood after hospitalization, and also retrospectively assessed suicide attempts before hospitalization. This continuous record of suicidal behavior allowed us a rare opportunity to examine the degree to which a sensitization model might account for patterns in recurrent attempts across two developmental periods (adolescence and young adulthood). We hypothesized that (a) the severity of suicidal behavior, as reflected in intent to die and in the medical lethality of suicide attempts, would increase as the number of suicide attempts by an individual increases; (b) there would be decreasing amounts of time between successive pairs of suicide attempts as the number of suicide attempts made by an individual increases, and (c) the degree of association between severe life stresses and suicide attempts would decrease as the number of suicide attempts by an individual increases.
Method Participants and Overview of Procedures
The 180 participants in this study were followed prospectively from adolescence, when they were psychiatrically hospitalized, through young adulthood. To be eligible for the study, youths needed to be: (a) 12–19 years old at index hospitalization, (b) hospitalized for 10 or more days, (c) able to cooperate with and complete the assessments in the hospital, and (d) a resident of North Carolina or Virginia at time of recruitment. Adolescents were excluded from the study if they (a) had a serious physical disease, (b) had intellectual disability, or (c) if their sibling was already enrolled in the study. At the time the study was initiated, the average length of stay in hospitals was 23.6 days (National Association of Psychiatry Health Systems, 2002). Hence, the stipulation of hospital stays of 10 or more days was made because patients with shorter hospital stays were often considered by clinical staff to have less severe problems or to be inappropriate for hospitalization. For example, adolescents with shorter lengths of stays had lower scores on the Beck Depression Inventory than individuals with longer stays (Goldston et al., 1999).
Patients on the inpatient unit participated in a comprehensive intake assessment as part of their psychiatric evaluations, including psychiatric diagnostic interviews and interviews about prior suicidal behavior. To recruit the longitudinal sample, we attempted to contact individuals (and their parents/guardians) who met inclusion and exclusion criteria ∼6 to 8 months after discharge from the hospital. Adolescents and their parents or guardians were contacted in the order of their discharge from the hospital. The total eligible sample consisted of 225 adolescents and their parents or guardians. One adolescent died of cardiac problems before we were able to contact him. We contacted 96% of the remaining sample and of these, 84% (n = 180) agreed to participate. The final sample consisted of 91 girls and 89 boys; 80% were European American, 16.7% were African American, and the other participants were Hispanic American, Native American, or Asian American. The average age of participants was 14 years 10 months (SD = 1 year, 7 months; range = 12 years 0 months to 18 years 5 months) at their index hospitalization. Sixteen percent of youths were in the custody of the Department of Social Services at study entry. For the remaining families, the socioeconomic status as classified by the Hollingshead (1957) index from highest to lowest was as follows: I = 3.3%, II = 12.6%, III = 21.9%, IV = 29.8%, and V = 32.4%. At the time of their index hospitalization, 41.7% (n = 75) of the youths had histories of suicide attempts and another 33.3% (n = 60) reported current suicide ideation (Goldston et al., 1999). Psychiatric disorders at the index hospitalization and over the course of the longitudinal study, and the relationship of these psychiatric disorders to risk for suicide attempts have previously been described (Goldston et al., 1999, 2009).
The design of the follow-up study called for the participants to have their first follow-up assessment 6 to 8 months after hospitalization. After their initial assessment in the study, this schedule was tapered so that assessments were subsequently scheduled every 10 to 12 months, and then annually. The longitudinal methods for this study were modeled after successful longitudinal studies by Kovacs and colleagues, in which the time between follow-up assessments after the initial assessments was also tapered (Kovacs, Feinberg, Crouse-Novak, Paulauskas, & Finkelstein, 1984; Kovacs, Obrosky, Goldston, & Drash, 1997). The more frequent assessments at the beginning of the study allowed us to more closely track the course of psychiatric problems after the hospitalization. The amount of time between assessments was tapered as a practical consideration to reduce burden on participants and to reduce study costs.
The median amount of time preceding the first three follow-up assessments ranged from 8.2 to 10.1 month, whereas the median time preceding assessments 8, 9, and 10 ranged from 10.9 to 11.4 months. The number of assessments and the amount of time between assessments varied both within and across participants because of scheduling conflicts, subject requests, staff shortages, funding lapses, and difficulties locating or contacting participants. These assessments occurred primarily in the homes of participants, but also at a university or medical center, in jails and prisons, or in other settings convenient to participants. A variety of methods were used to maintain contact with the sample including phone calls and correspondence, maintenance of information regarding ancillary contacts, use of publicly available databases to help locate participants, and scheduling of assessments in participants’ homes and communities.
As of June 30, 2009, participants had been followed for a maximum of 17.5 years (M = 13 years 6 months; SD = 4 years 5 months), and participated in a total of 2,270 assessments, including the baseline hospital assessments (M = 12.6 assessments, SD = 5.1, range = 2 to 26). The mean age of participants at the last assessment was 28 years 5 months (SD = 4 years 10 months; range = 13 years 0 months to 34 years, 7 months). By the cutoff date for this article, 20 individuals had dropped out of the study, 6 participants had been administratively withdrawn from the study because of lost contact, and 8 participants had died (none because of suicide). Six of the individuals who were no longer active in the study made posthospitalization attempts.
The subsamples of participants used in analyses of developmental trends and to test the different hypotheses are described in Table 1. For developmental trends, we focused on the 109 participants who attempted suicide at least once in their lives, either before hospitalization or during the follow-up study. Of note, 34 of the 105 (32.3%) participants who had not attempted suicide by the time of their index hospitalization subsequently attempted suicide (total attempts = 65, M = 1.9, SD = 1.4, range = 1 to 6) over the follow-up. To test the hypothesis regarding intent and lethality as a function of number of suicide attempts, we focused on the 41 participants who made more than one suicide attempt at any point over the follow-up or during the 2 weeks before hospitalization. The decision to not examine data regarding intent and lethality of suicide attempts before the 2 weeks that preceded the index hospitalization was made in an effort to reduce potential bias in retrospective reports of clinical characteristics. For the hypothesis regarding the amount of time between suicide attempts, we focused on the 63 participants who had a lifetime history of repeat attempts. Last, to test the hypothesis regarding the association between life events and suicide attempts, we focused on the 36 individuals who made more than one suicide attempt after their discharge from the hospital. This strategy was used because life events were assessed only posthospitalization.
Characteristics of Samples for Examination of Developmental Trends and for Tests of the Sensitization Hypotheses
Research interviewers were master’s and doctoral level mental health professionals. The interviewers were extensively trained (e.g., with role plays, calibration of symptom ratings, and observed interviews) and supervised by the principal investigators for the study (D.G., S.D.).
The institutional review boards of the participating institutions provided approval for this ongoing study, and for use of clinical data from the baseline hospitalization for research purposes. Participants provided assent and their parents or legal guardians provided consent at the time of the hospitalization. Participants who turned 18 while participating in the study provided consent at the next assessment following their 18th birthday. Participants were reconsented an additional time at the beginning of the last funding period for the grant.
Instruments
Assessment of suicide attempts
The Interview Schedule for Children and Adolescents (ISCA; Kovacs, Pollock, & Krol, 1997; Sherrill & Kovacs, 2000) and the Follow-Up Interview Schedule for Adults (FISA; Kovacs, Pollock, & Krol, 1995; Sherrill & Kovacs, 2000) are semistructured clinical interviews developed for longitudinal studies used to assess symptoms of psychiatric disorders. Psychiatric diagnoses obtained with these instruments have been shown to be reliable and to have predictive validity as summarized by Sherrill and Kovacs (2000). In the current investigation, these instruments were used to assess suicide attempts. To aid in this assessment, the ISCA and FISA have standardized questions about the presence/absence of thoughts of death, suicide ideation, and suicide attempts, plans and methods, circumstances and suicidal motivations, and psychological intent (e.g., “Have you ever thought about killing yourself?” “Have you ever done anything to try to kill yourself?” “What did you do?” “What did you think would happen when you ____?”). In these instruments, suicide attempts were defined operationally as potentially self-injurious behaviors associated with some (i.e., nonzero) intent to end one’s life; this definition is consistent with current approaches to classification of suicide-related terms (Crosby, Ortega, & Melanson, 2011; Posner, Oquendo, Gould, Stanley, & Davies, 2007; Silverman, Berman, Sanddal, O’Carroll, & Joiner, 2007). Self-injurious behaviors not associated with at least some intent to kill oneself (e.g., cutting to relieve tension) were not considered as suicide attempts. If reports of self-harm were vague or indefinite (e.g., “I honestly can’t remember what was going through my head,” “she took a bunch of pills but I have no idea if she was trying to kill herself or just get high”), the behavior conservatively was not counted as a suicide attempt.
At the index hospitalization and over the follow-up period, all available information was used to make determinations of the dates of attempts. Sources of information included the semistructured interviews; treatment, legal, and school records; and parent interviews. At the index hospitalization, we obtained information about all previous suicide attempts. In subsequent assessments, we assessed all suicide attempts since last contact. The information obtained at the index hospitalization and follow-up assessments was combined to yield continuous (lifetime) records of participants’ suicidal behavior. The ISCA was used in interviews with adolescents at hospitalization, and in interviews with parents or guardians and adolescents over the follow-up until participants reached the age of 18 or began living independently. After that point, the participants were administered the FISA, but parents and guardians were not interviewed.
When participants could not provide precise dates for suicide attempts, but could describe a likely window of time during which the attempt occurred, the dates were estimated as the midpoint of the defined period of time (Kovacs, Feinberg, Crouse-Novak, Paulauskas, & Finkelstein, 1984). Before the age of 18, suicide attempts (meeting our operational definition of this behavior) were considered to be present when reported by either adolescent or parent. The strategy of counting suicide attempts as present when reported by either adult informants or adolescent participants was used in light of the findings from multiple studies that parents are often not aware of adolescents’ suicide attempts (e.g., Breton, Tousignant, Bergeron, & Berthiaume, 2002; Foley, Goldston, Costello, & Angold, 2006; Walker, Moreau, & Weissman, 1990).
We have conducted two interrater reliability trials of our classifications of suicidal thoughts and behavior in this sample using all information, including interviews with the ISCA and FISA, and treatment records. In the first trial of 40 cases, there was 95.0% agreement in the classification of suicide ideation and suicide attempts (Goldston et al., 2001). In a second trial, 500 cases were classified as to whether there was presence of (a) no suicide ideation, (b) suicide ideation without means envisioned, (c) suicide ideation with means envisioned, (d) a single suicide attempt, or (e) multiple attempts since the last assessment. In this trial, there was excellent agreement between previously determined consensus ratings and the ratings of an independent coder (96.4% agreement; κ = 0.92). In all cases, discrepancies in ratings were discussed and resolved by consensus.
Assessment of suicide intent
The subjective intent of suicide attempts during the follow-up period was assessed on the basis of all available information, using the 4-point Subjective Intent Rating Scale developed by our research group (SIRS; Sapyta et al., 2012). This scale was developed to assess suicide intent independently of related constructs such as impulsivity or factors potentially related to medical lethality such as isolation at the time of the attempt. The construct validity of the SIRS has been demonstrated by the higher correlation with the Subjective index than with the Objective index of the Beck Suicide Intent Scale (Beck, Schuyler, & Herman, 1974). Based on all available information including responses to the ISCA and FISA (Sherrill & Kovacs, 2000), intent was rated from “Mild” (respondent acknowledges a wish to die, but mainly wants to live) to “Very High” (respondent expresses very little ambivalence about wanting to die). There was not a point on this scale corresponding to no intent, because by definition, suicide attempts were associated with at least some intent to die. Two independent coders rated intent, and discrepancies were resolved by consensus. SIRS ratings in this study have been found to have high interrater reliability (ICC = 0.99, p < .05), and the maximum intent of past suicide attempts has been found to be predictive of future attempts (Sapyta et al., 2012). The average unweighted intent score for suicide attempts among participants that made more than one attempt was 2.51 (SD = 0.93).
Assessment of medical lethality
Medical lethality of all suicide attempts during the follow-up was rated on the basis of all available information using the Lethality of Suicide Attempt Rating Scale (Berman, Shepherd, & Silverman, 2003; Smith, Conroy, & Ehler, 1984). Using this scale, the suicide attempts were rated in severity of potential medical consequences from 0 (death is an impossibility) to 10 (death is almost a certainty) by two independent raters, with discrepancies resolved by consensus. This scale has been shown to have high interrater reliability and concurrent validity among adolescents as well as adults (Lewinsohn, Rohde, & Seeley, 1996; Nasser & Overholser, 1999; Sapyta et al., 2012) and the maximum lethality of past suicide attempts was found to be predictive of future suicidal behavior (Sapyta et al., 2012). In this sample, there was high interrater reliability in ratings from this scale (ICC = 0.95, p < .05; Sapyta et al., 2012). Similar to other clinical and epidemiologic samples of young people (e.g., Diamond et al., 2005; Lewinsohn, Rohde, & Seeley, 1994), most of the suicide attempts were in the mild to moderate range of lethality (Sapyta et al., 2012). The average medical lethality score of suicide attempts (unweighted for number of observations per participant) among individuals who made repeat attempts was 2.88 (SD = 1.92).
Assessment of life events
Life events before suicide attempts were assessed using all available information. Sources included (but were not limited to) a modified version of the Life Events Checklist (Johnson & McCutcheon, 1980), the symptom timelines that we developed in conjunction with the semistructured clinical interviews (ISCA and FISA; Sherrill & Kovacs, 2000), the queries regarding legal involvement of the Follow-Up Information Sheet, and precipitant section of the Suicide Circumstances Schedule (Brent et al., 1988). Negative life events in the 3 months before each suicide attempt were coded independently by at least two coders and discrepancies were resolved by consensus between the reviewers. If a participant explicitly described a life event as a precipitant, but was vague about the timing, we counted the life event as though it occurred within the 3-month period. In an interrater reliability trial, agreement between two independent coders regarding the presence/absence of a subset of major life events (loss and legal events) was 92.5% (κ = 0.85). For the events agreed upon by the two coders, there was 97.9% agreement as to the date (within a 2-week period of time). The total severity of life stress preceding suicide attempts was assessed in two different ways. First, we examined the unweighted total number of negative life events in the 3 months before suicide attempts. Second, the magnitude of social adjustment required by different life events (“life change units”) was estimated using the standardized table of life change unit values provided by Miller and Rahe (1997). The table of life change units provided by Miller and Rahe (1997) was derived in a scaling study, and represented a revision of the life change values originally described by Holmes and Rahe (1967). In previous studies, both the unweighted number of life events and life change units have been linked to poorer health outcomes (e.g., De Benedittis, Lorenzetti, & Pieri, 1990; Lantz, House, Mero, & Williams, 2005). The average number of life events and life change units in the 3 months before suicide attempts were 3.78 (SD = 2.61) and 184.55 (SD = 125.81), respectively.
Statistical Method
General approach and covariates
Given the number of observations over time and the multiple suicide attempts, we used longitudinal statistical models that can accommodate different numbers of observations per participant, varying amounts of time between observations, and missing data. The data were not analyzed as a panel study with specific “waves” of data and missing values when a scheduled assessment was delayed or missed. Rather, the data set was organized so that assessments for participants were consecutively numbered, regardless of when they occurred.
There were some missing data that the analyses could not accommodate. Specifically, there were five suicide attempts, all occurring before the index hospitalization, for which precise dates could not be estimated. These suicide attempts were not included in analyses of developmental trends, and of the intervals between consecutive suicide attempts. There were no missing life events or lethality data. There were 11 missing values (for 7.1% of suicide attempts at hospitalization or over the follow-up) regarding intent; in these cases, the participants reported enough information to indicate that there was at least some intent to die, but gave vague or inconsistent reports about the degree of intent or ambivalence. These data were viewed conservatively as missing at random (MAR) rather than being imputed, given that we did not know the mechanisms associated with the missing data (Little & Rubin, 1987; Rubin, 1987, 1996). The statistical models implemented in SAS were able to use full information available from the data, under the assumption of MAR.
Because gender (e.g., Lewinsohn et al., 2001) and race/ethnicity (see Goldston et al., 2008) have been found in previous studies to be related to suicide attempts, they were included as covariates in all analyses to reduce variance attributable to potentially confounding or background variables. Because age also has been noted to be related to the clinical characteristics of suicide attempts (Conwell et al., 1998; Hamdi, Amin, & Mattar, 1991; O’Brien et al., 1987), age was included as a time-varying covariate in models of intent and lethality to disentangle age effects from effects associated with increasing number of suicide attempts.
As preliminary analyses, we used linear regression to evaluate whether demographic variables (age at hospitalization, gender, or race/ethnicity), or number of suicide attempts at baseline hospitalization were related to either number of assessments completed, or the length of time in the study (log transformed to improve normality of distribution). We also used linear regression to examine whether variability in the timing of assessments (i.e., time between the assessment when a suicide attempt was reported, and the prior assessment) was related to four of the outcomes of the study (intent of suicide attempts, lethality of suicide attempts, number of life events before suicide attempts, or life change before suicide attempts). The timing of assessments was not examined in relation to the time between successive suicide attempts because many of the reported attempts occurred before initiation of the follow-up study.
Developmental model
A cubic polynomial logistic regression was used to examine suicide attempts as a function of age (z-transformed for numerical stability). This model was chosen over a linear or quadratic model because of the sharp rise in attempts in adolescence, followed by a tapering off in adulthood. This model was fitted in PROC GLIMMIX with variance-components covariance structure, which was assumed to be different for males and females. This model is equivalent to a generalized estimating equations (GEE) based approach except that GEE models do not have an option for a heterogeneous variance-covariance structure. As a conservative approach to modeling, we used sandwich (robust) variance estimates in the analyses, which provided additional protection against heterogeneity and departures from assumptions. Interactions with gender were explored, but eventually were not included in models because they were not reliably related to suicide attempts, and because of multicollinarity.
For descriptive purposes, the actual proportions of individuals with attempts as a function of age (in 2-year intervals) and gender were graphed in Figure 1. The curves were smoothed using the lowess function (Cleveland, 1981) in R (R Development Core Team, 2010). This Figure was not generated from the polynomial logistic regression model, but was based on aggregate data for clarity of presentation.
Figure 1. Proportion of sample with suicide attempts as a function of age. Curves were smoothed using a lowess function in R. The number of individuals for whom data at each age were available is as follows: ≤12 (n = 180), 13 (n = 178), 14 (n = 178), 15 (n = 174), 16 (n = 172), 17 (n = 171), 18 (n = 166), 19 (n = 162), 20 (n = 169), 21 (n = 159), 22 (n = 157), 23 (n = 156), 24 (n = 155), 25 (n = 151), 26 (n = 147), 27 (n = 143), 28 (n = 129), 29 (n = 106), 30 (n = 76), 31 (n = 55), 32 (n = 35), 33 (n = 20), and 34 (n = 6). Number of suicide attempts = 286.
Sensitization models
To examine whether intent and medical lethality of attempts increased as a function of number of prior suicide attempts, we used GEE implemented in SAS 9.2 (SAS Institute, Inc., 2008). This approach allowed us to account for the within-subject correlations from multiple observations. For both intent and lethality analyses, we used multivariate ordinal logistic models (with a cumulative logit link for the Likert scales).
To address the question of whether the amount of time between suicide attempts decreases as a function of number of suicide attempts, linear regression models using GEE were utilized. These models adjusted for within-subject correlations. The amount of time between successive suicide attempts was transformed to a logarithmic scale because of the nonnormal distribution of these times.
Generalized estimating equations were used to examine the relationship between the number of suicide attempts and life change in the 3 months before the most recent suicide attempt. The primary predictor in these analyses was the number of suicide attempts. In separate analyses, the total number of life events and life change scores in the 3 months before each suicide attempt were dependent variables. For the analyses regarding sum of life events as a dependent variable, we used negative binomial regression (with log link). For the analyses regarding life change scores as a dependent variable, we used a multiple linear regression.
In all analyses examining sensitization, robust (sandwich) variance-covariance estimates were used to adjust for heterogeneity and departures from assumptions. Results with model-based estimates of SEs were also examined and the pattern of results was nearly identical to results with robust estimates. Results with model-based estimates are not presented, but are available upon request.
Results Preliminary Analyses
Demographic variables and history of suicide attempts at baseline hospitalization were not related to the amount of time that participants were followed in this study (p values >.05). History of suicide attempts, age at hospitalization, and race/ethnicity were not related to the number of follow-up assessments (p values >0.05). However, females participated in more assessments than males (b = 1.546, SE = 0.763, t = 2.03, p = .044). The amount of time between a follow-up assessment when a suicide attempt was reported and the previous follow-up assessment was not related to intent or lethality of suicide attempts, number of life events before attempts, or life change before attempts (p values >0.05).
Developmental Trends in Suicide Attempts From Adolescence to Young Adulthood
As seen in Table 2, results from the cubic polynomial regression indicated that females made more suicide attempts than males and that the proportion of the sample with suicide attempts varied as a quadratic function of age. Specifically, as seen in the smoothed curve in Figure 1 using aggregate data, the rates of suicide attempts increased from early adolescence through mid-adolescence, peaked in mid-adolescence, and decreased again until the early 20s, whereupon the rates stabilized.
Age Patterns in Suicide Attempts (Cubic Polynomial Logistic Regression for Suicide Attempts as a Function of Age, z-Transformed)
The proportion of suicide attempts at each age that was made by individuals with prior attempts increased from ages 9–10 through adulthood. For example, between ages 9–10 and 15–16, the proportion of suicide attempts in any 2-year period that were repeated attempts ranged between 0.25 and 0.57. From ages 17–18 through 31–32, the proportion of attempts that were repeated attempts ranged from 0.67 to 0.90.
Suicide Intent and Medical Lethality as a Function of Number of Attempts and Age
Suicide intent was positively related both to number of prior suicide attempts and to increasing age in separate models. In contrast, medical lethality of suicide attempts increased with participants’ age (see Table 3), but was not related to number of suicide attempts. Gender and race/ethnicity were not related to intent or lethality.
Tests of the Sensitization Model for Suicide Attempts
Intersuicide Attempt Intervals as a Function of Number of Attempts
As reflected in Table 3, the log-transformed intersuicide attempt intervals decreased in length as a function of the number of past suicide attempts. Converting to the original duration scale by exponentiation, the associated “hazard ratio” was 0.722 per unit increase in the number of past attempts. That is, the more suicide attempts an individual made, the shorter the period of time before the next repeat suicide attempt on average. Neither gender nor race/ethnicity was related to the amount of time between repeat suicide attempts.
Life Events and Suicide Attempts
As seen in Table 3, neither total number of life events nor the sum of life change scores from the 3 months preceding suicide attempts was related to the number of suicide attempts an individual had made.
DiscussionIn this prospective, naturalistic study, we examined patterns in recurrent suicidal behavior among adolescents and young adults, and the degree to which these patterns were consistent with a sensitization model. In this high-risk sample, the rate of suicide attempts among both males and females increased through mid-adolescence, and then decreased during young adulthood, stabilizing by the mid-20s. These developmental patterns are similar to those noted in epidemiological research (Boeninger et al., 2010; Joffe et al., 1988; Kessler et al., 1999; Lewinsohn et al., 2001; Velez & Cohen, 1988; Wunderlich et al., 2001). However, in an extension of these previous studies, we found that by the transition to adulthood, the majority of suicide attempts were made by individuals who already had a history of attempts. Future studies are needed to establish whether a similar pattern would be observed in larger-scale epidemiologic samples.
Given the high rate of repeat suicidal behavior in this sample, it is critical to examine whether suicidal behavior escalates with recurrences, and whether sensitization processes might account for any escalation. As predicted from a sensitization model, the intent of suicide attempts did increase as individuals made a greater number of attempts. The intent of suicide attempts also increased as participants got older, and the effects of increasing age and the number of prior suicide attempts were confounded to a degree. Previous findings regarding the clinical characteristics of earlier versus subsequent suicide attempts (or first-time as contrasted with repeat attempts) have yielded contradictory findings (Forman et al., 2004; Kaslow et al., 2006; Michaelis et al., 2003; Ojehagen et al., 1992; Reynolds & Eaton, 1986). However, these studies generally have been cross-sectional and focused on suicide attempts that precipitated treatment entry, which may not be a “representative” period of time in the natural history of suicidal behavior. The finding that intent increases with number of attempts contradicts the common clinical myth that individuals who make multiple attempts “are not serious” about killing themselves. To the contrary, these individuals seem to become more determined and have less ambivalence about dying with successive attempts.
Another index of severity, the medical lethality of suicide attempts, increased as a function of age, but was not related to the cumulative number of attempts. This finding was not consistent with what would have been predicted by a sensitization model. On the other hand, the finding of increased lethality with increasing age dovetails with other findings that lethality of suicide attempts in some adult patient populations is positively correlated with age (Shearer et al., 1988), and that individuals at older ages are more likely than individuals at younger ages to die by suicide when they engage in suicidal acts (Friedmann & Kohn, 2008). Sapyta et al. (2012) found that intent and lethality are not strongly correlated among adolescents and young adults, although both maximum intent and maximum lethality of past attempts were predictive of future suicidal behavior. This finding could be due in part to restricted access to more lethal methods at younger ages, the fact that adolescents feel constrained in choice of methods because they live with parents, the lack of knowledge about the medical consequences associated with different methods at younger ages (Brown, Henriques, Sosdjan, & Beck, 2004), or greater planning and premeditation among older individuals (Conwell et al., 1998).
The second prediction from a sensitization model was that there would be decreasing intervals of time between successive suicide attempts. Although the amount of time between suicide attempts was quite variable, overall, there was a decreasing length of time between suicide attempts as the number of suicide attempts increased. This possibility, to our knowledge, has not been evaluated previously. The prospective, repeated assessments design of the current study made it particularly well suited for examining the length of time between attempts. The pattern of decreasing amounts of time between successive attempts highlights the possibility of increasing vulnerability associated with repeated occurrences of suicidal behavior.
Last, the sensitization model is predicated on the notion that individuals become more reactive or sensitive to stress through repeated exposure. In this study, life stress measured in two different ways was unrelated to the number of prior suicide attempts. Although these results are consistent with several findings from cross-sectional studies with adults (Crane et al., 2007; Joiner & Rudd, 2000; Kaslow et al., 2006), it should be noted that the sample size for the life stress analyses (n = 36 with 129 suicide attempts) was smaller than for the other samples used for tests of sensitization hypotheses. Therefore, although there was very little indication of an effect, it could be the case that the sample size was not sufficiently large to detect patterns in reactivity to life stress across individuals. In addition, it is possible that approaches or measures for assessing the relationship between life stress and suicidal behavior to date have not been sufficiently sensitive for detecting patterns or reactivity to stress. It also is possible that the heterogeneity among individuals who attempt suicide is so great that any evidence of a sensitization process is obscured in-group analyses. For example, some suicidal individuals may become more reactive to life stress, whereas others, rather than being more reactive, simply experience an inordinate number of negative life stresses, some of which could even be related to their own mental health difficulties (Conway, Hammen, & Brennan, 2012). Another possibility is that some individuals become especially reactive to only certain types of stresses (e.g., losses or difficulties in relationships), and the sensitization process is not apparent across the whole spectrum of major life stresses. Joiner (2005), for instance, has emphasized life circumstances associated with thwarted belongingness and perceived burdensomeness, and Shneidman (1998) emphasized the importance of circumstances associated with unmet psychological needs in the etiology of suicide.
In summary, the results from this longitudinal study revealed a pattern of escalation of suicidal behavior, with increasing intent and decreasing amounts of time between successive attempts. The data were inconclusive as to whether a sensitization model might account for this escalation. It is possible that repeated exposures to stresses or situations that provoked earlier suicidal behavior change the way individuals think or respond affectively to future situations, and additional research is needed to examine this possibility. Nonetheless, the prospective findings of this study importantly underscore the observation that there is an escalation in suicidal behavior that occurs as individuals make a greater number of attempts. If not because of sensitization, there are multiple possibilities for this escalation of suicidal behavior. For example, increasing suicidal behavior may be reflective of increasing distress with persisting difficulties, or of increasing severity of psychopathology. In addition, Linehan (1993) suggested that individuals who have vulnerabilities with emotion regulation may respond to invalidating and inconsistent responses from others with escalating self-destructive behavior. It also is possible that there is other “scarring” that occurs with prior suicide attempts, which renders individuals more vulnerable for future episodes of suicidal behavior. Kessing, Hansen, Andersen, and Angst (2004) have demonstrated that scarring or episode sensitization is one mechanism that may account for recurrent episodes of affective disorder.
The findings from this study have multiple implications for mental health professionals. In working with suicidal clients, clinicians need to be aware that the intent associated with suicidal behavior may increase with repeated attempts, and that both intent and lethality of suicidal behavior may increase as individuals get older. Clinicians sometimes consider recurrent suicidal behavior with less urgency than they should, mistakenly assuming that individuals who make multiple attempts may not be serious about killing themselves. In fact, the opposite appears to be true. As individuals make repeated attempts, they are on average more intent on dying by suicide.
The finding, that for some individuals there is an escalation of suicidal behavior after an initial attempt, underscores the importance of developing effective prevention programs for at-risk populations before individuals have made their first attempt, and potentially entered into this pattern of escalation. For individuals who already have made attempts, there is a strong need for relapse prevention interventions that can interrupt the cycle of recurrent suicidal behavior before there is further escalation. The use of chain analysis and the focus on development of coping skills in dialectical behavior therapy (Linehan, 1993) and the complementary use of the relapse prevention task in cognitive behavior therapy for suicide prevention (Brown et al., 2005) are two promising approaches to relapse prevention. Nonetheless, suicidal individuals often terminate treatment prematurely or fail to initiate treatment after referrals (Dahlsgaard, Beck, & Brown, 1998; Rudd, Joiner, & Rajab, 1995). When individuals terminate treatment prematurely, they may not learn the skills or alternatives to suicidal behavior they need for forestalling future episodes and interrupting this cycle of escalation. Therefore, it is important that effective approaches (e.g., drawing from motivational enhancement strategies) be developed for facilitating treatment engagement and follow-through so that suicidal individuals maximally benefit from relapse prevention activities (Britton, Patrick, Wenzel, & Williams, 2011).
Several caveats regarding the findings from this study should be acknowledged. First, the sample was recruited from an inpatient adolescent psychiatric unit. The findings from the study may not be generalizable to other populations, including individuals who have not been hospitalized or older populations. Second, there was variability both within and between participants in the intent and lethality of suicide attempts, the number and type of stressful life events preceding suicide attempts, and the length of intersuicide attempt intervals. As such, the effects of sensitization may not always be detectable or apparent on an individual basis. Third, although we had a large number of observations over a relatively long period, which contributed to the power of our analyses and provided opportunities to see unfolding patterns, the actual number of individuals on which some results are based is still smaller than would be ideal. In particular, the analyses regarding life stress focused on only 36 individuals who made 129 attempts over the follow-up. Hence, it will be important for these findings to be replicated. Fourth, no one in this study had died by suicide so the degree to which these processes are applicable to suicide deaths is not clear. Fifth, although severity of life stress preceding suicide attempts was measured in two different ways, using all available information, we did not use specific interviews for assessing life events, which would have provided more contextual information about the life stresses. Sixth, this study did not examine potential psychiatric factors including treatment history, increasing severity of psychiatric and substance use problems, or exposure to childhood adversity that could have shed light on the mechanisms associated with escalation or sensitization. These caveats notwithstanding, the pattern of results from this prospective study provided evidence of escalating suicidal behavior among individuals who make repeat suicide attempts in adolescence and young adulthood, even if the tests of a sensitization model per se were not fully supported. A better understanding of the processes underlying this escalation will be important to inform the design of more effective relapse prevention interventions and intervene in patterns that culminate in repeat suicidal behaviors.
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Submitted: February 6, 2013 Revised: October 31, 2014 Accepted: November 12, 2014
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Source: Journal of Consulting and Clinical Psychology. Vol. 83. (2), Apr, 2015 pp. 253-264)
Accession Number: 2015-02672-001
Digital Object Identifier: 10.1037/a0038657
Record: 157- Title:
- Suicide attempts in women with eating disorders.
- Authors:
- Pisetsky, Emily M.. Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC, US
Thornton, Laura M.. Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC, US
Lichtenstein, Paul, ORCID 0000-0003-3037-5287. Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
Pedersen, Nancy L.. Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
Bulik, Cynthia M.. Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC, US, cbulik@med.unc.edu - Address:
- Bulik, Cynthia M., University of North Carolina at Chapel Hill, CB #7160, Chapel Hill, NC, US, 27599, cbulik@med.unc.edu
- Source:
- Journal of Abnormal Psychology, Vol 122(4), Nov, 2013. pp. 1042-1056.
- NLM Title Abbreviation:
- J Abnorm Psychol
- Page Count:
- 15
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- The Journal of Abnormal and Social Psychology; The Journal of Abnormal Psychology; The Journal of Abnormal Psychology and Social Psychology
- ISSN:
- 0021-843X (Print)
1939-1846 (Electronic) - Language:
- English
- Keywords:
- anorexia, binge eating disorder, bulimia, eating disorders, suicide, psychopathology, comorbidity, personality traits
- Abstract:
- We evaluated whether the prevalence of lifetime suicide attempts/completions was higher in women with a lifetime history of an eating disorder than in women with no eating disorder and assessed whether eating disorder features, comorbid psychopathology, and personality characteristics were associated with suicide attempts in women with anorexia nervosa, restricting subtype (ANR), anorexia nervosa, binge-purge subtype (ANBP), lifetime history of both anorexia nervosa and bulimia nervosa (ANBN), bulimia nervosa (BN), binge eating disorder (BED), and purging disorder (PD). Participants were part of the Swedish Twin study of Adults: Genes and Environment (N = 13,035) cohort. Lifetime suicide attempts were identified using diagnoses from the Swedish National Patient and Cause of Death Registers. General linear models were applied to evaluate whether eating disorder category (ANR, ANBP, ANBN, BN, BED, PD, or no eating disorder [no ED]) was associated with suicide attempts and to identify factors associated with suicide attempts. Relative to women with no ED, lifetime suicide attempts were significantly more common in women with all types of eating disorder. None of the eating disorder features or personality variables was significantly associated with suicide attempts. In the ANBP and ANBN groups, the prevalence of comorbid psychiatric conditions was higher in individuals with than without a lifetime suicide attempt. The odds of suicide were highest in presentations that included purging behavior (ANBN, ANBN, BN, and PD), but were elevated in all eating disorders. To improve outcomes and decrease mortality, it is critical to be vigilant for suicide and identify indices for those who are at greatest risk. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Eating Disorders; *Suicide; Anorexia Nervosa; Bulimia; Comorbidity; Personality Traits; Psychopathology; Binge Eating Disorder
- Medical Subject Headings (MeSH):
- Adult; Age of Onset; Comorbidity; Diseases in Twins; Feeding and Eating Disorders; Female; Humans; Logistic Models; Mental Disorders; Prevalence; Suicide; Sweden; Young Adult
- PsycINFO Classification:
- Eating Disorders (3260)
- Population:
- Human
Female - Location:
- Sweden
- Age Group:
- Adulthood (18 yrs & older)
Young Adulthood (18-29 yrs)
Thirties (30-39 yrs)
Middle Age (40-64 yrs) - Tests & Measures:
- Eysenck Personality Inventory DOI: 10.1037/t02711-000
Frost Multidimensional Perfectionism Scale DOI: 10.1037/t05500-000
Structured Clinical Interview for DSM-IV
Temperament and Character Inventory DOI: 10.1037/t03902-000 - Grant Sponsorship:
- Sponsor: National Institutes of Health
Grant Number: CA-085739
Recipients: Sullivan, P. F. (Prin Inv)
Sponsor: National Institutes of Health
Grant Number: AI-056014
Recipients: Sullivan, P. F. (Prin Inv) - Methodology:
- Empirical Study; Quantitative Study
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- Accepted: Oct 1, 2013; Revised: Oct 1, 2013; First Submitted: Jan 7, 2013
- Release Date:
- 20131223
- Correction Date:
- 20160714
- Copyright:
- American Psychological Association. 2013
- Digital Object Identifier:
- http://dx.doi.org/10.1037/a0034902
- PMID:
- 24364606
- Accession Number:
- 2013-44247-010
- Number of Citations in Source:
- 55
- Persistent link to this record (Permalink):
- http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2013-44247-010&site=ehost-live
- Cut and Paste:
- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2013-44247-010&site=ehost-live">Suicide attempts in women with eating disorders.</A>
- Database:
- PsycINFO
Suicide Attempts in Women With Eating Disorders
By: Emily M. Pisetsky
Department of Psychiatry, University of North Carolina at Chapel Hill
Laura M. Thornton
Department of Psychiatry, University of North Carolina at Chapel Hill
Paul Lichtenstein
Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
Nancy L. Pedersen
Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
Cynthia M. Bulik
Department of Psychiatry, University of North Carolina at Chapel Hill;
Acknowledgement: This study was supported by Grants CA-085739 (P.I.: P. F. Sullivan) and AI-056014 (P.I.: P. F. Sullivan) from the National Institutes of Health. The Swedish Twin Registry is supported by grants from the Swedish Department of Higher Education and the Swedish Research Council.
Eating disorders (EDs) are serious mental illnesses that occur in 1–5% of women (Hudson, Hiripi, Pope, & Kessler, 2007) and can have poor long-term outcomes (Berkman et al., 2006; Hudson et al., 2007). A comprehensive meta-analysis of mortality in EDs including 36 studies reported standardized mortality ratios (SMRs) of 5.86 (95% CI [4.17, 8.26]) for anorexia nervosa (AN) and 1.93 (95% CI [1.44, 2.59]) for bulimia nervosa (BN; Arcelus, Mitchell, Wales, & Nielsen, 2011), suggesting that individuals with either disorder are at increased risk of death compared with their age- and gender-matched peers.
Suicide is a common cause of this elevated mortality in EDs. A meta-analysis by Preti, Rocchi, Sisti, Camboni, and Miotto (2011) analyzed data from 40 studies comprising 16,342 patients with AN followed over a mean of 11.1 years and yielded an SMR = 31.0. A companion analysis of 16 studies on BN comprising 1,768 patients, with a mean follow-up of 7.5 years, yielded an SMR = 7.5 (Preti et al., 2011). Only three studies examining suicide in individuals with binge eating disorder (BED) were available for inclusion in this meta-analysis. With only 246 patients, no completed suicides were identified and an SMR could not be calculated. Purging disorder (PD), characterized by purging in the absence of binge eating behavior, is described in depth by Keel and Striegel-Moore (2009) and is included as a named condition in the Diagnostic and Statistical Manual of Mental Disorders (5th ed.; DSM–5) in the Other Specified Feeding and Eating Disorders section (American Psychiatric Association, 2013). To our knowledge, there are no published data on completed suicides in individuals with PD.
Suicide attempts in individuals with AN are also common, with estimates of lifetime attempts ranging from 3.0% to 29.7% (Bulik et al., 2008; Forcano et al., 2011; Franko & Keel, 2006). Furthermore, these attempts are often serious and are associated with the intention to die. In a sample of 432 non–treatment-seeking participants with AN, of those who had attempted suicide, 78.3% wanted to die from their attempt(s), and 56.5% thought that they would die as a result of the attempt(s). Over half of these attempts required medical attention (Bulik et al., 2008). In another study of a treatment-seeking sample, 79% of those who had attempted suicide endorsed “moderate or severe intent” to die (Bulik, Sullivan, & Joyce, 1999).
Suicide attempts are also common in BN, with between 15% and 40% of individuals indicating a lifetime history of at least one suicide attempt (Bulik et al., 1999; Corcos et al., 2002; Favaro & Santonastaso, 1997; Forcano et al., 2009; Franko & Keel, 2006; Milos, Spindler, Hepp, & Schnyder, 2004). Of individuals with BN who attempted suicide, 34.1% had a “serious” or “extreme” first attempt; the proportion of “serious” or “extreme” attempts increased with the number of attempts. More than 60% of individuals with BN were hospitalized as a result of their first suicide attempt, and 100% of those who endorsed an “extreme” suicide attempt were hospitalized (Corcos et al., 2002).
Whether the risk of suicide attempts across ED subtypes differs remains a matter of some disagreement, with some studies reporting no difference in the prevalence of attempts (Bulik et al., 1999; Herzog et al., 1999; Milos et al., 2004), some reporting higher prevalence of suicide attempts in individuals with BN than in those with AN (Favaro & Santonastaso, 1996, 1997), and others reporting higher prevalence of suicide attempts in individuals with AN than in those with BN (Franko et al., 2004). The differences are likely attributable to differences in subtyping diagnostics, although one fairly consistent finding is higher risk among the binge-purge subtype of AN than individuals with the restricting subtype (Bulik et al., 2008; Favaro, Tenconi, & Santonastaso, 2006; Franko & Keel, 2006). However, most of the research has focused on differences in prevalence of suicide attempts across AN subtypes and BN. Research on the prevalence of suicide attempts in individuals with BED or PD is extremely limited. One recent study of patients with BED presenting for outpatient treatment found that 12.5% had a lifetime history of a suicide attempt, providing initial indication of elevated risk of suicide attempts in this population compared with individuals with no ED (Carano et al., 2012). PD has not been included in any of the large studies of suicide attempts in women with EDs. Additional studies are needed to further clarify the prevalence of suicide attempts in individuals with BED and individuals with PD and compare suicide risk in the other ED groups. Large population-based studies have the potential to clarify differences in suicide risk across the ED diagnostic categories, particularly in these understudied disorders, and inform risk assessment in treatment settings.
Reports have been inconsistent when addressing whether specific patient profiles or characteristics are associated with suicide attempts in individuals with EDs. Factors identified as associated with suicide attempts in isolated studies of AN include older age, longer duration of illness, lower body mass index (BMI), depression, greater number of past treatments, antidepressant use, elevated phobic anxiety, and drug and alcohol abuse (Favaro & Santonastaso, 1997; Forcano et al., 2009). For BN, associated factors include greater general psychopathology; greater number of past treatments; antidepressant use; lower education; lower minimum BMI; family history of alcohol abuse; increased impulsive behaviors including self-injury; lower self-directedness, cooperativeness, and reward dependence; and higher harm avoidance (Favaro & Santonastaso, 1997; Forcano et al., 2009). Current evidence suggests that suicidality is associated with anxious personality traits such as harm avoidance and neuroticism in various psychiatric and community samples of individuals with EDs (Bulik et al., 1999; Engström, Brandstrom, Sigvardsson, Cloninger, & Nylander, 2004; McGirr, Paris, Lesage, Renaud, & Turecki, 2007; Ruchkin, Schwab-Stone, Koposov, Vermeiren, & King, 2003; van Heeringen et al., 2003). Moreover, impulsivity, high novelty seeking, and low self-directedness have also been reported (Forcano et al., 2009; McGirr et al., 2007; Zouk, Tousignant, Seguin, Lesage, & Turecki, 2006). The coexistence of anxious and impulsive traits may converge to increase suicidal risk.
Several comorbid psychiatric disorders have been associated with suicide attempts in individuals who have an ED, including a lifetime history of major depression (Anderson, Carter, McIntosh, Joyce, & Bulik, 2002; Bulik et al., 2008; Corcos et al., 2002; Favaro & Santonastaso, 1997), with more than 80% of individuals with AN who attempted suicide reporting that their worst or only attempt occurred during an active episode of major depressive disorder (Bulik et al., 2008). In addition, anxiety disorders (e.g., posttraumatic stress disorder, panic disorder, and a broad diagnosis of “any anxiety disorder”; Bulik et al., 2008; Milos et al., 2004), substance abuse, and alcohol abuse (Anderson et al., 2002; Corcos et al., 2002; Franko et al., 2004) have been associated with suicide attempts in AN and BN.
The present study clarifies and extends this body of research by employing data from the population-based Swedish Twin Registry in conjunction with the Swedish National Patient Register and the National Cause of Death Register. Conducting research with national registers is a valuable methodological approach, especially in Nordic countries (Allebeck, 2009) where data are reliable and participants are less likely to be lost to follow-up. Moreover, all deaths and hospitalizations in Sweden are captured in national registers (Ludvigsson et al., 2011; National Board of Health and Welfare, 2010). Using Swedish national registers, we evaluated whether suicide attempts/completions were more prevalent in individuals with AN subtypes, BN, lifetime history of both AN and BN (ANBN), BED, and PD than in individuals without EDs and explored whether there were specific psychopathological, temperament, and personality features that were associated with suicide attempts in individuals with EDs.
Method Participants
Participants were female twins born between 1959 and 1985 and assessed as part of the Swedish Twin study of Adults: Genes and Environment (STAGE; N = 13,035; Furberg et al., 2008; Lichtenstein et al., 2006). STAGE data were collected in 2005 using Web-based questionnaires and phone interviews (response rate for full STAGE data set = 59.6%). Questionnaires assessed demographic information; medical history; presence of psychiatric disorders including detailed information on EDs and alcohol and illicit substance use; and personality variables including neuroticism, extraversion, perfectionism, and self-directedness. Participants were between 20 and 47 years of age at the time of assessment.
Determination of zygosity was based on responses to standard twin similarity questions, which were validated with a panel of 47 single-nucleotide polymorphisms in a random sample of 198 twin pairs. Ninety-five percent (n = 188) were correctly classified. This zygosity algorithm has also previously been validated with similar results (Lichtenstein et al., 2002). Of the twins included in the present study, 42.4% were from monozygotic twin pairs, 30.2% were from same-sex dizygotic twin pairs, 25.2% were from opposite-sex twin pairs, and 2.3% were of unknown zygosity.
Identification of Attempted and Completed Suicide
All Swedish citizens since 1947 and, therefore, all participants in STAGE, have an assigned unique personal identification number (national registration number; Ludvigsson, Otterblad-Olausson, Pettersson, & Ekbom, 2009). Via this number, the STAGE database can be linked to any Swedish national register. To identify all recorded suicide attempts and completions, STAGE was linked with following registers:
National Patient Register
The National Board of Health and Welfare maintains the National Patient Register (National Board of Health and Welfare, 2010), which covers all public inpatient hospitalizations in Sweden. Each record contains admission and discharge dates, primary discharge diagnosis, and up to eight secondary diagnoses using the International Classification of Diseases (ICD)-8, ICD-9, or ICD-10 depending on the year of hospitalization (World Health Organization, 1967, 1978, 1992). The attending physician documented the diagnoses. This register captures all inpatient psychiatric care in Sweden since 1973. The register also routinely captures suicide attempts using ICD codes (codes E950–E959 in ICD-8 and ICD-9 and X60–X84 in ICD-10). This register was searched for any discharge diagnoses indicating suicide attempts.
Cause of Death Register
All deaths in Sweden from 1958 to 2009 are contained in the Cause of Death Register (National Board of Health and Welfare, 2010). The diagnoses and causes of death are coded according to ICD codes. The register routinely codes suicide as a cause of death. This database was searched to identify all cases of death by suicide (ICD-10 X60–X84 for years 2005–2009).
Suicide attempts/completions
Information on suicide attempts was extracted from the National Patient Register for the years 1969–2009. Therefore, all suicide attempts not resulting in death prior to participation in STAGE and suicide attempts after participation in STAGE were captured. To participate in STAGE, individuals needed to be alive in 2005 and suicide information was available up to 2009. As such, information on completed suicides was available only for the interval between 2005 and 2009. Given the restricted interval to capture completed suicides, the number of completed suicides was hypothesized a priori to be too small to be adequately powered to conduct independent analyses across groups. Therefore, we created a composite variable that included suicide attempts and completions. In addition, the number of individuals with an initial suicide attempt subsequent to their participation in STAGE was hypothesized a priori to be too small to compose a sufficiently large group to be examined independently (i.e., suicide attempt prior to STAGE participation vs. after STAGE participation). For this reason, we identified the presence or absence of lifetime suicide attempts as the primary outcome variable.
ED Diagnosis
Narrow
Two sets of diagnostic criteria were used to define EDs. The first reflected narrow definitions from the Diagnostic and Statistical Manual of Mental Disorders (4th ed.; DSM–IV; American Psychiatric Association, 2000). Specifically, AN was coded if a participant (a) indicated that she had a period of time when she weighed much less than other people thought she should weigh and reported a BMI < 17.55, (b) indicated being very or extremely afraid of gaining weight or becoming fat, and (c) indicated feeling very or extremely fat when at low weight. Amenorrhea was not required for the diagnosis as it is an unreliable diagnostic criterion for AN (American Psychiatric Association, 1994; Bulik, Sullivan, & Kendler, 2000) and was ultimately eliminated in the DSM–5 (American Psychiatric Association, 2013). Information about binge eating (eating an unusually large amount of food in a short period of time with at least slight loss of control) and purging (defined as vomiting, diuretic use, or laxative use during the time when binge eating was occurring or at least weekly) was also collected and used to further classify women as AN, restricting subtype (ANR; absence of both lifetime binge eating and purging) or AN, binge-purge subtype, (ANBP; presence of lifetime binge eating and or purging).
Narrow BN was defined as meeting DSM–IV Criteria A (binge eating), B (inappropriate compensatory behaviors), C (binge eating episodes occur at least eight times a month for at least 3 months), and D (body weight or shape are important or the most important factors in self-evaluation).
Narrow BED was defined as meeting DSM–IV Criteria A (binge eating), B (endorsing at least three of the following symptoms: eating faster than usual, eating until uncomfortably full, eating large amounts of food when not hungry, eating alone due to embarrassment, and feeling disgusted/depressed/guilty after overeating), C (distress or upset over binge episodes), D (at least eight binge episodes a month for at least 3 months), and E (did not engage in inappropriate compensatory behaviors during the time when they were binge eating). A diagnosis of BED was not made if the participant had a history of either AN or BN.
If a participant indicated that she engaged in vomiting, laxative use, or diuretic use at least weekly, indicated that body weight or shape are important or the most important factors in self-evaluation, had no history of binge eating, and did not have a lifetime history of AN, she was scored positive for narrow PD.
In addition to the specific questions described above, participants were also asked the general prompt “Do you have or have you ever had any of the following problems?” and one of the listed problems was “anorexia/bulimia/eating disorders” as part of a medical checklist (n = 11,117). Participants responded “yes” or “no” to this question. This question was used in the present study only to identify agreement between the response on the medical checklist and ED diagnosis as established by the diagnostic algorithms (κ = 0.40). Most individuals who responded yes to the checklist item were also given an ED diagnosis (n = 261). Of the 10,391women who were not classified as meeting a narrow diagnosis for an ED, 642 (6.10%) self-identified as having an ED. There were 50 individuals who said no to the checklist item, but were classified as having an ED diagnosis by the algorithms.
Broad
For the broad diagnoses, the criteria were modified for each disorder. Specifically, AN was coded if a participant (a) indicated that she had a period of time when she weighed much less than other people thought she should weigh and had a BMI < 18.55; (b) indicated being slightly, somewhat, very, or extremely afraid of gaining weight or becoming fat; and (c) indicated feeling slightly, somewhat, very, or extremely fat when at low weight. For broad BN, Criterion C was modified: A reduced frequency of binge eating of four or more times per month was required. In addition, Criterion D was defined as body weight or shape at least moderately influences self-evaluation. This definition has been used previously (Root et al., 2010) and has been shown to improve the detection of binge eating behavior without significantly increasing the prevalence of the disorder (Trace et al., 2012). Broad BED was defined as meeting DSM–IV criteria with a reduced frequency of at least four binge eating episodes in a 1-month period (broadened Criterion D). For broad PD, vomiting, laxative use, or diuretic use had to occur at least weekly and body weight or shape at least moderately influenced self-evaluation.
Agreement between those identified as having a broadly defined ED diagnosis from the above-described criteria and those who self-identified as having an ED on the medical checklist was calculated (κ = 0.60). Many individuals identified by the algorithms as having an ED diagnosis also answered yes to the ED question on the medical checklist (n = 524). However, 202 women met criteria for an ED using the algorithms but responded no to the checklist item. Of those who were not classified as meeting a broad diagnosis for an ED, 407 self-identified as having an ED.
The ED categories used in the analyses were nonoverlapping. For both the narrow and broad definitions, each person was classified as either having a lifetime diagnosis of ANR, lifetime diagnosis of ANBP (the respondent was scored as positive for binge eating, defined below, and/or indicated engaging in at least one purging behavior weekly or daily), lifetime diagnosis of BN, lifetime diagnosis of BED, lifetime diagnosis of PD, or no lifetime diagnosis of an ED (no ED). Any participant who had a lifetime history of both AN and BN (narrow definition, n = 23; broad definition, n = 103) was classified as ANBN. Participants who met broad criteria for an ED but not narrow criteria (n = 388) were excluded from the analyses for the narrow diagnoses, as those individuals could not be considered cases or controls. Therefore, the final sample size for the analyses of the broad definitions of illness was 13,035.
ED Features
Binge eating
The main binge eating question was “Have you ever had binges when you ate what most people would regard as an unusually large amount of food in a short period of time?” with response options yes, no, and don’t know/refuse. Positive responses were followed by “When you were having eating binges, did you feel your eating was out of control?” Response options were not at all, slightly, moderately, very much, extremely, and don’t know/don’t wish to answer. Binge eating was scored as present if the individual responded yes to the first question and indicated feeling slightly, moderately, very much, or extremely out of control.
ED behaviors
Two questions evaluated weight control methods. Individuals who endorsed binge eating were asked whether they engaged in compensatory behaviors (vomiting, laxative use, diuretic use, diet pills) during the time of binge eating. Those who responded that they engaged in compensatory behaviors during the time of binge eating were scored as positive for the respective method. Individuals who did not endorse binge eating were asked whether they ever engaged in vomiting, laxative use, diuretic use, or diet pill use at any point in their lifetime to control shape or weight. Response options were never, once or twice, weekly, or daily. Those who engaged in any of the behaviors weekly or daily were scored as positive for the respective weight control method.
Each participant was asked whether she ever fasted to control her shape or weight or had not eaten for 24 hr or more (present/absent). Excessive exercise reflected exercising more than 2 hr per day to control shape and weight. Those who endorsed daily were scored positive for excessive exercise.
Amenorrhea
Women were asked to recall age at menarche. Those who got their first period at age 16 or later, those who had not yet experienced menarche prior to AN onset, and those who reported missing three or more periods were classified as having amenorrhea.
BMI
Each participant reported lowest weight in kilograms (kg) since age 18 and current height in meters (m). For women who did not have a history of AN, lowest adult BMI (kg/m2) was calculated. For women with a history of AN, lowest BMI was calculated from lowest weight during AN and height at the time of low weight. Current BMI was calculated using current height (m) and participant-reported current weight (kg). Highest BMI was calculated using current height (m) and participant-reported highest weight (not including pregnancy; kg). BMI difference was calculated by subtracting the lowest BMI value from the highest BMI value.
Age of onset
Age of onset of ANR was defined as age at lowest illness-related weight. Age of onset of BN and of BED was defined as age at first binge. Age of onset of ANBP and ANBN was defined as age at first binge or age at lowest illness-related weight, which ever was younger. No age of onset information was provided for inappropriate compensatory behaviors, so these data are unavailable for participants with PD.
Psychiatric Comorbidity
Other psychiatric disorders were assessed using detailed self-report questionnaires based on the Structured Clinical Interview for DSM–IV (First, Spitzer, Gibbon, & Williams, 2002). Depression was coded as present if Criterion A (five symptoms of depression, including depressed mood and/or anhedonia, associated with a change of functioning) and Criterion C (significant impairment caused by the symptoms) were met. Participants needed to endorse the symptoms of depression for 2 or more weeks in a row and experience these symptoms all day long or most of the day to be coded as meeting criteria for depression. Participants were also asked whether they “have or have ever had … depression” with a response option of yes or no. Agreement between those identified as having lifetime depression from the above-described algorithm and those self-identified as having lifetime depression from the yes–no question was calculated (κ = 0.56).
Generalized anxiety disorder (GAD) was considered present if Diagnostic and Statistical Manual of Mental Disorders (4th ed., text rev.; American Psychiatric Association, 2000) Criteria A (excessive anxiety and worry) and C (at least three symptoms resulting from anxiety and worry) were met. Lifetime prevalence of specific phobias, obsessive–compulsive disorder (OCD), and panic disorder was assessed with the question “Have you ever had any of the following problems?” Each disorder was then listed and response options were “yes” and “no.” A composite “any anxiety disorder” variable was also assessed: If the participant had a history of GAD, phobias, OCD, or panic disorder, she was scored positive for any anxiety disorder.
Alcohol abuse/dependence was assigned based on DSM–IV criteria (American Psychiatric Association, 1994); participants were asked about the presence (and frequency where appropriate) of each abuse and dependence criterion. If a participant responded positively to one abuse criterion or three or more dependence criteria, she was given a positive diagnosis for alcohol abuse/dependence. Substance use was defined as using marijuana/hash, opioids, stimulants, hallucinogens, sedatives, and/or hormones 10 times or more in 1 month.
Temperament and Personality
Concern over mistakes (α = .82), personal standards (α = .81), and doubts about actions (α = .90) were each evaluated using four items from the subscales of the Frost Multidimensional Perfectionism Scale (Frost, Marten, Lahart, & Rosenblate, 1990). Extraversion was evaluated using nine items (α = .78) and neuroticism was evaluated using 18 items (α = .90) of the Eysenck Personality Inventory (Schapiro et al., 2001). Self-directedness was measured using five items (α = .84) from the Temperament and Character Inventory (Cloninger, 1994).
Statistical Analyses
All data management and analyses were conducted using SAS 9.2. The STAGE database was first linked with the Swedish National Patient Register and the Swedish National Cause of Death Register. For each individual, the total number of lifetime suicide attempts resulting in hospitalization was based on the number of unique dates of hospital discharge entries into the Swedish National Patient Register with a suicide attempt code. Individuals who died as a result of a suicide attempt were identified from a cause of death code for suicide in the Swedish National Cause of Death Register. Identified completed suicides were added to the number of suicide attempts generated from the Swedish Hospital Discharge Register if the date of the death was different from the last Swedish National Patient Register entry.
Each individual’s age at first attempt was calculated from the date of discharge or death. ICD codes were used to categorize the method used for each attempt. Methods were classified as “violent” (e.g., stabbing, hanging, jumping from a high place) and “nonviolent” (see Table 1 for classification by ICD code). Any participant who had more than one suicide attempt with at least one attempt classified as violent was coded as having had a violent attempt. The prevalence of at least one lifetime suicide attempt and of a violent suicide attempt among those who had ever attempted suicide was computed for ANR, ANBP, ANBN, BN, BED, PD, and no ED groups by narrow and broad ED definitions. Means and standard deviations for number of attempts and age at first attempt were calculated for all groups.
International Classification of Diseases (ICD) Codes Used to Identify Suicide Attempts and Completions and the Violence Categorization for Each Code
Logistic regression analyses (using PROC GENMOD in SAS) with generalized estimating equations (GEEs) were applied to assess differences in the prevalence of suicide attempts/completion across the ANR, ANBP, ANBN, BN, BED, PD, and no ED groups. GEE was used to account for the nesting of the data within twin pairs in this and all subsequent full-model analyses. GEE assumes a relationship within clusters (twin pairs), and this relationship is modeled and treated as a nuisance variable. For our analyses, the exchangeable correlation matrix was used to model the relationship for the analyses of the ANR, ANBP, ANBN, and PD groups and for the personality features for the BN and BED groups. The identity matrix was used for the analyses of the ED features and comorbidity in the BN groups because the models for these groups would not converge. Models for the ED features and comorbidity for the BED group could not be applied. Age at assessment was entered as a covariate in all models. Type 3 score statistics were used to determine the significance of the independent variable in the models. Post hoc contrasts, which use adjusted means, were requested to assess pairwise group differences for the omnibus tests that were significant. The score statistics are presented as χ2 for all analyses. We used the guidelines presented by Chen, Cohen, and Chen (2010): Odds ratios of 1.68, 3.47, and 6.71 are considered equivalent to Cohen’s d = 0.2 (small), 0.5 (medium), and 0.8 (large), respectively. Therefore, odds ratios greater than 1.68 indicate a small effect size and are presented even if the model did not reach significance.
Among those who ever attempted suicide, differences in the prevalence of violent suicide attempts and in age at first suicide attempt across ED category were assessed using general linear models with GEE corrections. To determine whether ED category was associated with the total number of lifetime suicide attempts, we conducted a Poisson regression with GEE corrections.
Associations between suicide attempts and lifetime lowest BMI, age of onset of ED, specific ED features (including history of vomiting, laxative use, diet pill use, diuretic use, excessive exercise, fasting, other inappropriate compensatory behaviors, and amenorrhea), psychiatric comorbidity, and personality traits were evaluated for each ED group separately using general linear models with GEE corrections. All p values of omnibus tests were corrected for multiple testing using the methods of false discovery (pfdr) (Benjamini & Hochberg, 1995).
In addition, sign tests were conducted to evaluate whether there was a systematic difference in reporting of comorbid conditions between individuals with a history of suicide attempts and those who had never attempted suicide. Specifically, we evaluated whether those with suicide attempts reported higher prevalences across a majority of comorbid conditions than those without suicide attempts. These analyses were stratified by ED group, were one-tailed, and were not corrected for multiple testing.
Results Demographics
Narrow diagnostic groups
Demographic information is presented across the narrow ED categories in the top panel of Table 2. There were no differences in age at assessment across the ED groups. BMI at time of assessment differed across EDs (χ2 = 48.79, p < .0001). Post hoc analyses revealed that the BED group had a significantly higher mean BMI at assessment than all of the other ED categories. In addition, the mean BMIs at assessment for the ANR and ANBP groups were significantly lower than those for the BN, PD, and no ED groups. Education level differed across groups (χ2 = 15.38, p < .02); post hoc analyses revealed that education levels differed between the BN and no ED groups. Civil status differed across groups (χ2 = 13.83, p < .04). The prevalence of being married or cohabiting with a partner was lower in the BN and BED groups than the no ED group.
Age and Body Mass Index (BMI) at Time of Swedish Twin study of Adults: Genes and Environment Assessment and Education and Relationship Status by Eating Disorder
Broad diagnostic groups
The bottom panel of Table 2 presents the demographic information across the broadly defined ED categories. There were no differences in age at assessment across the ED groups. BMI at time of assessment differed across ED groups (χ2 = 99.57, p < .0001). Post hoc analyses revealed that the BED group had a significantly higher mean BMI than all of the other ED categories and the mean BMI for the ANBN group was lower than those for the BN, PD, and no ED groups. Education level differed across groups (χ2 = 19.89, p < .01), with post hoc analyses revealing differences between the BN group and both the PD and no ED groups. Civil status differed across groups (χ2 = 19.32, p < .04). The prevalence of being married or cohabiting with a partner was lower in the BN group than the PD and no ED groups.
Prevalence of Suicide Attempts
Narrow diagnostic groups
Suicide attempts were identified in 260 of the 12,647 (2.06%) individuals included in the analysis applying the narrow ED diagnoses, representing 11.99% of individuals with EDs and 1.74% of the referent group. Three individuals died subsequent to their participation in STAGE as a result of a suicide attempt: One had a lifetime diagnosis of narrowly defined BN and two had no lifetime ED diagnosis.
Table 3 presents the lifetime prevalence of suicide attempts across the ANR, ANBP, ANBN, BN, BED, PD, and no ED groups for narrow and broad definitions of illness. The prevalence of suicide attempts was significantly different across narrow ED groups (χ2 = 31.39, pfdr < .004). Suicide attempts were significantly more common in all ED groups than in the referent group (see Table 4). There were no other significant pairwise differences across the narrow ED groups.
Lifetime Prevalence of at Least One Suicide Attempt by Eating Disorder Group for Narrow and Broad Eating Disorder Definitions
Results of Post Hoc Pairwise Comparison Evaluating Differences in Suicide Prevalence Between Eating Disorder Groups for Narrow and Broad Eating Disorder Definitions
Characteristics of the suicide attempts among those with narrow diagnostic classifications who attempted suicide are presented in Table 5. No significant differences emerged across the narrow ED groups and the referent group in terms of the percentage of individuals who experienced violent suicide attempts (see Table 5). The age at first attempt for individuals with at least one suicide attempt ranged from 13 to 50 years and did not differ across narrow ED and referent groups (see Table 5). The total number of suicide attempts ranged from one to 34 (see Table 5). Although the number of suicide attempts did not differ across groups, there was a small effect observed for the ANBN group (OR = 1.92, 95% CI [0.87, 2.98]) having a higher mean number of attempts than the referent group.
Suicide Characteristics in Women Who Attempted Suicide by Eating Disorder and Results for Models Assessing Differences in These Features Across Eating Disorder for Both Narrow and Broad Eating Disorder Definitions
Broad diagnostic groups
Suicide attempts were identified in 270 of 13,035 (2.07%) individuals included in the analyses when applying broad diagnoses, representing 9.13% of individuals with EDs and 1.56% of the referent group. The prevalence of suicide attempts was also significantly different across the broad ED groups (χ2 = 51.88, pfdr < .004). Post hoc pairwise comparisons (see Table 4) revealed that suicide attempts were significantly more common in all broad ED groups than in the referent group. In addition, suicide attempts were significantly more prevalent in the ANBN, BN, and PD groups than in the ANR group.
No significant differences emerged across the broad ED groups and the referent group in terms of the percentage of individuals who experienced violent suicide attempts (see Table 5). The age at first attempt for all individuals with at least one suicide attempt ranged from 13 to 50 years and did not differ across broad ED and referent groups (see Table 5). The total number of suicide attempts ranged from 1 to 43 and also did not differ across all groups (see Table 5).
ED Features Associated With Suicide Attempts
Narrow diagnostic groups
The top panel of Table 6 presents descriptive statistics for ED features by ED diagnosis for individuals with narrow EDs with and without suicide attempts. None of the ED features was significantly associated with suicide attempts in any of the ED groups. Although all of the models investigating the association between ED features and suicide attempts in each of the ED groups failed to reach statistical significance, several of the models produced medium and large effect sizes. The Cohen’s d for BMI difference was 0.52 and for age of ED onset was 1.24 in the ANR group, with women with suicide attempts having a greater BMI difference and an older age of onset. For the ANBN group, women with suicide attempts had lower lowest BMI values (Cohen’s d = 0.60), higher highest BMI values (Cohen’s d = 0.63), greater BMI differences (Cohen’s d = 0.63), and an older age of ED onset (Cohen’s d = 0.87). A greater BMI difference was also observed for women with suicide attempts in the BN group (Cohen’s d = 0.57). In the BED group, women with suicide attempts had higher lowest BMI values (Cohen’s d = 1.04) and higher highest BMI values (Cohen’s d = 0.75) than women who had no suicide attempts. In the PD group, those with suicide attempts had higher highest BMI values (Cohen’s d = 0.74) and a greater BMI difference (Cohen’s d = 0.84). The remaining models did not produce a remarkable effect size or did not converge. Models that did not converge are indicated in Table 6.
Lifetime Prevalence of Eating Disorder Features and Characteristics of Women With a Lifetime History of a Suicide Attempt by Eating Disorder
Lifetime Prevalence of Eating Disorder Features and Characteristics of Women With a Lifetime History of a Suicide Attempt by Eating Disorder
Broad diagnostic groups
The bottom panel of Table 6 presents descriptive statistics for ED features by group for individuals with broad EDs with and without suicide attempts. None of the ED features was significantly associated with suicide attempts in any of the ED groups. Regarding effect sizes, results were similar to those for the narrow ED definitions: Cohen’s d = 0.80 for age of ED onset for the ANR group; Cohen’s d = 0.68 for lowest BMI and Cohen’s d = 0.64 for BMI difference for the ANBN group; Cohen’s d = 0.52 for BMI difference for the BN group; Cohen’s d = 0.71 for lowest BMI and Cohen’s d = 0.68 for highest BMI for the BED group; and, for the PD group, Cohen’s d = 0.62 for lowest BMI (those with suicide attempts had lower lowest BMI values), Cohen’s d = 0.58 for highest BMI, and Cohen’s d = 0.78 for BMI difference. The remaining models produced effect sizes less than 0.50 or did not converge. Models that did not converge are indicated in Table 6.
Psychiatric Comorbidity and Personality Features Associated With Suicide Attempts
Narrow diagnostic groups
The top panel of Table 7 presents comorbid psychiatric disorders and personality variables across narrow ED groups by the presence or absence of suicide attempts. None of the comorbid psychiatric disorders was significantly associated with suicide attempts. None of the models yielded medium or large effect sizes. Models that did not converge are indicated in Table 7.
Lifetime Prevalence of Comorbid Psychiatric Disorders and Temperament Characteristics by Eating Disorder and by Lifetime Suicide Attempt/Completion Status
Lifetime Prevalence of Comorbid Psychiatric Disorders and Temperament Characteristics by Eating Disorder and by Lifetime Suicide Attempt/Completion Status
Although there were no significant differences in the individual comorbid conditions, the sign test indicated that, in the ANBP and ANBN groups, the prevalence of all seven conditions was significantly higher in the group with suicide attempts compared with the group without suicide attempts (ps < .008).
None of the personality measures was significantly associated with suicide attempts in any of the ED groups. However, several of these measures had medium or large effect sizes. Specifically, concern over mistakes was greater in those with suicide attempts than those without attempts in the ANBP (Cohen’s d = 0.73) and BN (Cohen’s d = 0.85) groups, as were doubts about actions in the ANBP group (Cohen’s d = 0.61) and neuroticism in the BED group (Cohen’s d = 0.69). Personal standards was lower in those with suicide attempts than in those without attempts in the ANBN (Cohen’s d = 0.66) and PD (Cohen’s d = 0.70) groups. Extraversion was also lower in those with suicide attempts in the ANBP (Cohen’s d = 0.58), ANBN (Cohen’s d = 0.93), and BED (Cohen’s d = 1.25) groups. Those with suicide attempts in the ANR, BED, and PD groups also had lower self-directedness (Cohen’s d = 0.88, 1.26, and 0.56, respectively).
Broad diagnostic groups
The bottom panel of Table 7 presents comorbid psychiatric disorders and personality variables across broad ED groups by the presence or absence of suicide attempts. None of the comorbid psychiatric disorders was significantly associated with suicide attempts, and no medium or large effect sizes were observed. Models that did not converge are indicated in Table 7.
The sign test indicated that, in the ANR, ANBP, and ANBN groups, the prevalence of all seven conditions was significantly higher in those with suicide attempts compared with those without suicide attempts (ps < .008).
None of the personality measures was significantly associated with suicide attempts in any of the ED groups. Medium or large effect sizes were observed for each measure in at least one broadly defined ED group. Concern over mistakes was greater in those with suicide attempts than those without attempts in the ANR (Cohen’s d = 0.55) and ANBP (Cohen’s d = 0.76) groups, as was doubts about actions in the BED group (Cohen’s d = 0.54) and neuroticism in the ANBP group (Cohen’s d = 0.66). Similar to the narrowly defined ED groups, personal standards was lower in those with suicide attempts than in those without attempts in the PD group (Cohen’s d = 0.88) and extraversion was lower in those with suicide attempts in the ANBN (Cohen’s d = 0.75) and BED (Cohen’s d = 1.25) groups. Those with suicide attempts in the ANR, ANBP, BED, and PD groups also had lower self-directedness (Cohen’s d = 0.59, 0.59, 0.70, and 0.70, respectively).
DiscussionWe shed further light on the risk for suicide in individuals with EDs by engaging the rich national registers in Sweden. Consistent with previous clinical reports, our results revealed significantly elevated prevalence of suicide attempts in individuals with all forms of eating disorders—ANR, ANBP, ANBN, BN, BED, and PD—with the highest odds relative to the referent being for individuals with narrow ANBN (OR = 10.74) and broad PD (OR = 9.16). In general, the prevalence estimates in this study are somewhat lower than published estimates from clinic-based investigations (Bulik et al., 1999; Corcos et al., 2002; Favaro & Santonastaso, 1997). This is as expected given that (a) ours represents a population-based sample of EDs, and (b) our definition of suicide attempts was rigorous insofar as an attempt had to be sufficiently severe as to be captured by the health care system and entered into the national registers. Clinic-based studies of EDs typically focus on a more severe subset of the ED population. Likewise, studies that estimate the prevalence of suicide attempts based on self-report may also include attempts that do not come to the attention of the health care system. This supposition is supported by data from a non–treatment-seeking sample of individuals with AN, in which only approximately 50% of those who reported a suicide attempt had ever sought medical treatment for the attempt (Bulik et al., 2008). However, the percentage of individuals seeking treatment for a suicide attempt was only slightly higher in a treatment-seeking sample of individuals with EDs (60%; Corcos et al., 2002).
Our results underscore the value of the recommendation of Franko and Keel (2006), who emphasized the importance of subtyping AN into ANR and ANBP when assessing the prevalence of suicide attempts. Risk is elevated in both AN subtypes, narrowly and broadly defined, compared with the no ED group. Although there was no significant difference in prevalence of suicide attempts between the ANR and ANBP groups, either narrowly or broadly defined, the broad ANR group had a significantly lower prevalence of suicide attempts than the broad ANBN, BN, and PD groups.
It has long been known that diagnostic crossover is a common occurrence in individuals with AN, with around 50% of those with initial ANR migrating to a bulimic presentation at some point during the course of their illness (Bulik, Sullivan, Fear, & Pickering, 1997; Eddy et al., 2008; Fichter & Quadflieg, 2007; Tozzi et al., 2005). Suicide risk may fluctuate as the clinical presentation migrates between restricting and binge-purge forms. It is noteworthy that the highest prevalence of suicide attempts (17.39%) and the highest mean number of lifetime suicide attempts (15.50) in the present sample were in the narrow ANBN group, indicating that individuals who experience diagnostic crossover may be at particularly elevated risk. More granular investigations of longitudinal risk are required to confirm whether suicide risk fluctuates with symptom evolution or is more strongly related to trait factors.
We further extend our understanding of suicide and EDs by demonstrating significantly elevated risk for suicide in individuals with BED. Although the number of cases of BED was smaller than would have been expected on the basis of U.S. population data (Hudson et al., 2007), suicide risk was significantly elevated in individuals with both narrow and broad BED diagnoses. As adjustments are made worldwide secondary to BED being a bona fide diagnostic category in DSM–5, clinicians should remain vigilant for suicide risk in individuals with this disorder.
An additional extension of the knowledge base is the observation that individuals with broadly defined PD had elevated risk for suicide attempts relative to individuals with no ED (OR = 9.16) and ANR (OR = 3.40). These initial results are consistent with observations that, within those with AN and those with BN, individuals who purge are at greater risk for suicide attempts than those who do not purge (Favaro & Santonastaso, 1997) and highlight the need for additional research to better understand this high-risk yet understudied population.
Unlike previous investigations, we did not identify specific features of EDs that were associated with suicide attempts. The small cell sizes for some of the suicide groups most likely precluded the detection of significant effects. However, the overall pattern of results indicates that the highest odds ratios for suicide were observed in presentations that included purging (ANBP, ANBN, ANBP, and PD). This observation is consistent with findings that individuals who engage in purging behavior are at particularly elevated risk for suicide attempts.
Also consistent with previous reports, our results confirm that greater comorbid psychiatric burden (i.e., increased prevalence across seven comorbid conditions) is associated with elevated risk for suicide in individuals with ANBP and ANBN (Franko & Keel, 2006). By nature of having an ED presentation with both AN and BN features, these individuals already experience elevated comorbidity burden. Individuals who present with a clinical picture of both low weight plus binge eating and/or purging are at increased risk for adverse medical outcomes (Takakura et al., 2006) and psychiatric comorbidity (Peat, Mitchell, Hoek, & Wonderlich, 2009). Our results reveal that this pattern holds in population-based samples as well clinical samples. In aggregate, these results underscore the critical importance of flagging these individuals in the clinical setting as high risk for an array of adverse outcomes, including suicide attempts.
Unlike previous studies of women who sought treatment for EDs, we did not find clear differences in personality measures between those who did and did not attempt suicide. This may be an artifact of the fact that the personality measures in STAGE were not necessarily completed contemporaneously with the suicide attempts, whereas measurements in treatment-seeking samples capture extremes of personality evident during the acute phase of illness (Perkins et al., 2005). Moreover, some of the personality measures assessed in the STAGE cohort are not ones commonly associated with suicide risk (e.g., perfectionism).
Our sample is uniquely informative by referencing a population-based register to identify ED cases. This approach eliminates the biases inherent in relying on samples of individuals seeking treatment for an ED, which skews observations toward the extreme. By coupling this population-based sample with hospital and cause of death registers, we were able to capture those suicide attempts that were sufficiently severe to warrant medical attention. This methodology yields a clear picture of risk of suicide attempts individuals with EDs in the general population. As there is no evidence of elevated risk of suicide in twins (Statham et al., 1998), our results from the STAGE sample are likely to generalize to the nontwin population.
Strengths
One strength of this study is the sample, which was large and population-based. Such a sample decreases the biases inherent in using treatment-seeking populations that are likely to have a more severe ED and be more psychologically distressed. EDs, particularly BN, often go undetected in the community (Hoek, 1991; Hudson et al., 2007). Therefore, a woman is likely to seek treatment if (a) her disorder is sufficiently severe to be noticed by others who encourage treatment, (b) she is highly distressed by her disorder, (c) she has a comorbid disorder for which she is seeking treatment, or (d) she is hospitalized for a suicide attempt and then referred for treatment of her ED. Therefore, the previous reliance on samples of individuals seeking treatment for an ED may have inflated the estimates of the prevalence of suicide attempts.
By estimating the prevalence of suicide attempts from hospital discharge registers and cause of death registers, we opted for a conservative estimate not subject to self-report bias. Furthermore, the use of the cause of death register, albeit for a restricted interval, allowed for the identification of completed suicides. A longer observation period would allow for direct comparisons of those individuals who completed assessments at a point in time after a suicide attempt with individuals who attempted suicide after completing assessments.
Limitations
Although this study makes a significant contribution to the literature, several limitations should be noted. First, the study is of Swedish twins. Although twin registers are often used to conduct research about the general population, twins may differ from the general population in significant ways. The population of Sweden is also fairly homogeneous in terms of racial and ethnic demographics. Therefore, results must be interpreted within the context of limited generalizability.
By estimating the prevalence of suicide attempts via the hospital register, we were unable to identify those suicide attempts that did not warrant medical attention. This yields a conservative estimate; thus, the findings of this study may apply primarily to individuals with more severe suicide attempts.
STAGE was chosen for this study because of the large sample size and detailed information about ED behaviors. However, we were nonetheless faced with fairly small cell sizes—especially in ANR and BED—which left us underpowered for some comparisons. In addition, as only the lifetime presence of ED behaviors and diagnoses were assessed, we did not have information on illness duration or status of illness (currently ill or recovered) at the time of assessment.
The STAGE assessment covers a wide range of physical and psychological variables. Unfortunately, given the large number of variables being assessed, not all of the constructs were assessed in depth. For example, Criterion B of GAD, difficulty controlling worry, was not assessed and therefore not required for participants to receive a diagnosis of GAD. Specific phobia, OCD, and panic disorder were assessed with a single yes–no self-report item, limiting the validity of these diagnoses. Age of onset and status of illness (currently ill or recovered) at the time of assessment were not assessed for any of the comorbid diagnoses, which precluded the investigation of temporal precedence of eating disorders, comorbid disorders, and suicide attempts. Furthermore, personality disorders were not assessed.
Conclusion
Suicide attempts in women with EDs in the general population are concerningly common. Suicide risk is elevated in all EDs studied here—ANR, ANBP, ANBN, BN, PD, and BED. To improve outcomes and decrease mortality, it is critical to identify individuals who, within this patient population, experience psychological suffering so intense that they feel compelled to take their own lives.
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Submitted: January 7, 2013 Revised: October 1, 2013 Accepted: October 1, 2013
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Source: Journal of Abnormal Psychology. Vol. 122. (4), Nov, 2013 pp. 1042-1056)
Accession Number: 2013-44247-010
Digital Object Identifier: 10.1037/a0034902
Record: 158- Title:
- Suicide risk in older adults: Evaluating models of risk and predicting excess zeros in a primary care sample.
- Authors:
- Cukrowicz, Kelly C.. Department of Psychology, Texas Tech University, Lubbock, TX, US, kelly.cukrowicz@ttu.edu
Jahn, Danielle R., ORCID 0000-0003-0156-9680. Department of Psychology, Texas Tech University, Lubbock, TX, US
Graham, Ryan D.. Department of Psychology, Texas Tech University, Lubbock, TX, US
Poindexter, Erin K.. Department of Psychology, Texas Tech University, Lubbock, TX, US
Williams, Ryan B., ORCID 0000-0001-5718-753X. Department of Agriculture & Applied Economics, Texas Tech University, Lubbock, TX, US - Address:
- Cukrowicz, Kelly C., Department of Psychology, Texas Tech University, Mail Stop 42051, Lubbock, TX, US, 79409-2051, kelly.cukrowicz@ttu.edu
- Source:
- Journal of Abnormal Psychology, Vol 122(4), Nov, 2013. pp. 1021-1030.
- NLM Title Abbreviation:
- J Abnorm Psychol
- Page Count:
- 10
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- The Journal of Abnormal and Social Psychology; The Journal of Abnormal Psychology; The Journal of Abnormal Psychology and Social Psychology
- ISSN:
- 0021-843X (Print)
1939-1846 (Electronic) - Language:
- English
- Keywords:
- interpersonal theory, older adults, suicide, zero-inflated negative binomial model, statistical approaches, death ideation, suicide ideation
- Abstract:
- Research is needed that examines theory-based risk factors for suicide in older adults. The interpersonal theory of suicide (Joiner, 2005; Van Orden et al., 2010) provides specific hypotheses regarding variables that contribute to the development and variability in death ideation and suicide ideation; however, data suggest that older adults may not report suicide ideation in research settings or to treatment providers even when they experience it (Heisel et al., 2006). The purpose of this study was to test theory-based predictions regarding variables that contribute to death ideation (i.e., a passive wish to die) and suicide ideation in older adults. This study introduces the application of zero-inflated negative binomial regression (ZINB) to the study of suicidal behavior. ZINB was used to test theory-based predictions, while also testing a hypothesis regarding variables associated with denial of suicide ideation among participants who endorsed risk factors associated with suicide risk. Participants included 239 adults aged 60 and older recruited from primary care clinics who completed a variety of self-report instruments. The results of this study indicated that perceived burdensomeness and hopelessness were significantly associated with variability in death ideation. Additional results indicated that elevated scores on thwarted belonging, the interaction between perceived burdensomeness and hopelessness, and the interaction between thwarted belonging and perceived burdensomeness were associated with a significant reduction in the probability of a participant being a suicide ideator. These results offer substantial support for the interpersonal theory of suicide. The implications of these findings are discussed. (PsycINFO Database Record (c) 2018 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Mathematical Modeling; *Statistical Analysis; *Suicidal Ideation; *Suicide; *Theories; Aging; Risk Factors
- Medical Subject Headings (MeSH):
- Aged; Attitude to Death; Binomial Distribution; Depressive Disorder; Female; Geriatric Psychiatry; Humans; Interpersonal Relations; Male; Middle Aged; Models, Psychological; Primary Health Care; Regression Analysis; Risk Factors; Suicidal Ideation; Suicide; Surveys and Questionnaires
- PsycINFO Classification:
- Behavior Disorders & Antisocial Behavior (3230)
- Population:
- Human
Male
Female - Location:
- US
- Age Group:
- Adulthood (18 yrs & older)
- Tests & Measures:
- Mini Mental Status Exam
Interpersonal Needs Questionnaire DOI: 10.1037/t10483-000
Geriatric Suicide Ideation Scale DOI: 10.1037/t68661-000
Beck Hopelessness Scale
Center for Epidemiologic Studies Depression Scale
Center for Epidemiological Studies Depression Scale DOI: 10.1037/t02942-000 - Grant Sponsorship:
- Sponsor: American Foundation for Suicide Prevention, US
Recipients: No recipient indicated - Methodology:
- Empirical Study; Quantitative Study
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- Accepted: Oct 7, 2013; Revised: Sep 3, 2013; First Submitted: Nov 16, 2012
- Release Date:
- 20131223
- Correction Date:
- 20180816
- Copyright:
- American Psychological Association. 2013
- Digital Object Identifier:
- http://dx.doi.org/10.1037/a0034953
- PMID:
- 24364604
- Accession Number:
- 2013-44247-008
- Number of Citations in Source:
- 53
- Persistent link to this record (Permalink):
- http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2013-44247-008&site=ehost-live
- Cut and Paste:
- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2013-44247-008&site=ehost-live">Suicide risk in older adults: Evaluating models of risk and predicting excess zeros in a primary care sample.</A>
- Database:
- PsycINFO
Suicide Risk in Older Adults: Evaluating Models of Risk and Predicting Excess Zeros in a Primary Care Sample
By: Kelly C. Cukrowicz
Department of Psychology, Texas Tech University;
Danielle R. Jahn
Department of Psychology, Texas Tech University
Ryan D. Graham
Department of Psychology, Texas Tech University
Erin K. Poindexter
Department of Psychology, Texas Tech University
Ryan B. Williams
Department of Agricultural & Applied Economics, Texas Tech University
Acknowledgement: Funding for this study was provided by the American Foundation for Suicide Prevention. We would also like to thank the following individuals for their contributions to these studies: M. David Rudd, Yeates Conwell, Phillip Smith, Erin Schlegel, Matt Jacobs, Brandy Ledbetter, Sean Mitchell, Justin Stevens, Amy Bryant, Kim Allen, Samantha Strople, Cara Cates, Laura Nelson, Shannon Bracket, and Evan Guidry.
Compared with those in other age groups, older adults are at significant risk for death by suicide (CDC, 2012). The rate of death by suicide increases steadily from age 65 to 85, with the highest rate of suicide deaths among older adults ages 85 and older (CDC, 2012). Furthermore, the ratio of suicide attempts to deaths by suicide is in the range of 25:1 for all ages combined, whereas for adults over age 65 it is 4:1 (CDC, 2012), highlighting the pronounced risk for death by suicide in late life compared with younger cohorts. Paradoxically, previous research has also indicated that the rates of reported suicide ideation and suicide attempts decreases with increasing age in older adults (Duberstein et al., 1999; Lynch et al., 1999). As such, Witte et al. (2006) suggest that any endorsement of suicide ideation should be a strong indicator of risk for suicide attempt and death by suicide in older adults. Given the low attempt to death ratio for older adults (i.e., 4:1), studies including primarily suicide attempters may not allow us to understand the multifaceted nature of suicide risk in older adults who die on a first or early suicide attempt. Therefore, it is critical that studies include community samples of older adults who represent a broad range of suicide risk.
Currently, our knowledge regarding older adult suicide risk is limited in two fundamental ways. First, more research is needed that examines theory-based risk factors for suicide in older adults using falsifiable predictions. Second, earlier research has suggested that older adults may not report suicide ideation in research settings or to treatment providers (e.g., mental health providers, primary care physicians), even when they experience it (Heisel et al., 2006). To date, the vast majority of studies have used continuous scales of suicide risk, assuming accuracy in reporting across the continuum. However, given that some individuals with low scores may be underreporting suicide ideation, this approach may be inadequate. Because older adults may still endorse other risk factors associated with suicide ideation (e.g., depressive symptoms, hopelessness), it may be informative to assess risk using an expanded set of indicators related to theory-based risk factors, allowing prediction of those who are at risk for ideation and those who are not. The current study addressed these limitations through the use of theory-based hypotheses regarding the prediction of death ideation and suicide ideation in older adults, and the use of a novel statistical approach for examining suicide risk among older adults.
Reporting of Suicide Ideation Among Older AdultsA critical consideration for any study examining suicide ideation in older adults is the potential for reporting bias, as noted above. This suggests a need for research that uses novel statistical procedures to identify two types of older adults: nonideators (i.e., individuals who deny suicide ideation and who report nonexistent risk on other variables associated with suicide ideation), and potential ideators (i.e., those who deny suicide ideation while simultaneously reporting other established risk factors for suicide).
To adequately examine variables that may be associated with nonideator or potential ideator status, community samples of older adults are necessary to ensure sufficient representation of the range of suicide risk in the population. The sampling of community participants, however, frequently results in a large percentage of respondents with zero (or equivalent) scores on the outcome measure (e.g., death ideation, suicide ideation). Common approaches to analyzing this type of data include multiple linear regression, Poisson regression, and negative binomial regression, but the use of these approaches results in misspecification, such that the regression coefficients for the predictor variables are unstable (cf., Gurmu & Trivedi, 1996). Binary logistic regression can be used with samples including a large percentage of zeros; however, this approach only predicts the presence or absence of an experience (e.g., suicide ideation vs. no suicide ideation), without explaining differences in severity when risk factors are present.
One approach that is used more frequently in other fields (e.g., economics, biometrics, health-care research, ecological studies; Gurmu & Elder, 2008; Hur, Hedeker, Henderson, Khuri, & Daley, 2002; Lambert, 1992; Minami, Lennert-Cody, Gao, & Román-Verdesoto, 2007) is zero-inflated modeling, for instance, zero-inflated Poisson regression and zero-inflated negative binomial regression (ZINB). Recent publications have also described the application of these approaches to psychological sciences as well (Atkins & Gallop, 2007; Coxe, West, & Aiken, 2009). As summarized by Minami and colleagues (2007), zero-inflated models accomplish two objectives regarding the prediction of the outcome variable in the presence of excess zeros. These models simultaneously estimate a binary logistic regression and a negative binomial or Poisson regression, while also accounting for the existence of two unique types of zeros. In relation to the current study, one type of zero (i.e., excess zeros) occurs in participants who deny suicide ideation and have little or no psychological distress (i.e., nonideators). It is important to note, given the current responses to questions pertaining to suicide-risk factors, individuals with this type of zero are highly unlikely to convert to nonzero on suicide ideation. The other type arises from participants who deny suicide ideation while reporting other empirically based risk factors (e.g., depression, hopelessness) for suicide ideation (i.e., potential ideators). The binary logistic portion of the model provides estimates of the likelihood of the dichotomous outcome (i.e., whether a participant is a nonideator or potential ideator). The negative binomial regression provides an estimate of the continuous relationship between the predictor variables and the outcome measure (i.e., death ideation, suicide ideation), having controlled for the effect of nonideators (i.e., excess zeros) on the estimation of ideation.
To date, no studies have used ZINB to identify variables associated with denial of suicide ideation among older adults. This approach allows for differentiation between zero-ideation responses reflecting the absence of psychological distress associated with suicide risk, and zero-ideation responses occurring in participants who report distress variables that are correlated with suicide ideation. Therefore, we used this statistical approach, in combination with a theory-based model of risk factors (detailed below), to predict denial of suicide ideation.
Risk Factors for Late-Life Suicide: Suicide Ideation, Death Ideation, Depression, and Hopelessness
Prior studies have suggested that suicide ideation is a significant risk factor for suicide deaths in older adults (Conwell, Duberstein, & Caine, 2002; Conwell, Van Orden, & Caine, 2011). Researchers have also found increased risk for suicide in older adults reporting death ideation, symptoms of depression, and hopelessness (e.g., Baca-Garcia et al., 2011; Conwell et al., 1996; Cukrowicz et al., 2009; Rao, Dening, Brayne, & Huppert, 1997; Suokas, Suominen, Isometsa, Ostamo, & Lonnqvist, 2001). Although death ideation has historically been considered a less severe indictor of risk for suicide, several studies have concluded that adults reporting death ideation are similar to those reporting suicide ideation (Baca-Garcia et al., 2011; Rao et al., 1997; Suokas et al., 2001). For example, Baca-Garcia and colleagues (2011) examined death ideation and suicide ideation as predictors of suicide attempt using data from two large nationally representative surveys. The results indicated that the risk for lifetime suicide attempt was not significantly different for those with a history of death ideation, compared with those with a history of suicide ideation. This is consistent with other empirical findings examining outcomes for older adults reporting death ideation (Rao et al., 1997; Suokas et al., 2001), suggesting that researchers should assess for death ideation in studies examining risk for suicide among older adults.
A significant body of literature has also found that depression and hopelessness are associated with suicide risk among older adults (Conwell et al., 1996; Conwell et al., 2002; Cukrowicz et al., 2009; Cukrowicz, Cheavens, Van Orden, Ragain, & Cook, 2011; Scocco, Meneghel, Dello Buono, & De Leo, 2001; Szanto et al., 2007; Turvey et al., 2002). These studies indicate that major depressive disorder is the most common psychiatric disorder in older adults who have died by suicide (Conwell et al., 1996; Conwell et al., 2002). A large 10-year prospective study of predictors for late-life suicide found that depressive symptoms, perceived health status, medical status, cognitive difficulties, and affective functioning predicted suicide deaths (Turvey et al., 2002). Further, several studies have indicated significant associations between depressive symptoms and suicide ideation in community samples of older adults, as well as reduction in suicide ideation following reduction in depressive symptoms for depressed older adults participating in treatment (Cukrowicz et al., 2009; Cukrowicz et al., 2011; Scocco et al., 2001; Szanto et al., 2007; Vannoy et al., 2007). Likewise, numerous studies have indicated that hopelessness plays a significant role in suicide ideation and suicide deaths among older adults (Britton et al., 2008; Szanto, Reynolds, Conwell, Begley, & Houck, 1998). These studies indicate that hopelessness is associated with the presence and severity of suicide ideation (Britton et al., 2008). In addition, hopelessness may remain high in older adult suicide attempters (compared with suicide ideators without attempt histories and nonsuicidal older adults) even following medication treatment for depression (Szanto et al., 1998). Taken together, this research shows that death ideation, depressive symptoms, and hopelessness should all be included in studies examining risk for suicide among older adults.
The interpersonal theory of suicide (Joiner, 2005; Van Orden et al., 2010) has generated additional variables (i.e., thwarted belonging and perceived burdensomeness) that may contribute significantly to suicide or death ideation in older adults. Thwarted belonging is conceptualized as an absence of social relationships that results in feeling disconnected or without a sense of belonging (Joiner, 2005). Furthermore, thwarted belonging may develop when an individual lacks reciprocal caring relationships (i.e., lacking support in times of need or believing that he or she does not provide support to others; Van Orden et al., 2010). The second variable proposed by the interpersonal theory of suicide, perceived burdensomeness, is the perception that a person is a liability to others, which generates feelings of self-hatred (Van Orden et al., 2010). A person may feel like he or she is a burden on family members due to mental illness or physical disability, or due to the perception that he or she is not contributing to others because of unemployment or other limitations (Van Orden et al., 2010). It is important to note, empirical data examining older adult suicide risk supports significant associations between perceived burdensomeness and suicide ideation (Cukrowicz et al., 2011; Jahn, Cukrowicz, Linton, & Prabhu, 2011; Marty, Segal, Coolidge, & Klebe, 2012), as well as between thwarted belonging and suicide ideation (Marty et al., 2012; McLaren, Gomez, Bailey, & van der Horst, 2007).
The Interpersonal Theory Provides Testable Models of Suicide Risk in Older Adults
Van Orden and colleagues (2010) made two predictions regarding the associations between interpersonal theory variables and death or suicide ideation. The first prediction specifically suggests that individuals who feel a lack of connection to others, or perceive themselves as a burden on others, may wish for death as a way to reduce these aversive states (Van Orden et al., 2010). For example, an individual who feels disconnected from others, and that others do not care about him or her, may feel that it would be easier to disappear or not wake up, rather than continue feeling a thwarted sense of belonging. In relation, individuals who perceive that their lives detract from the well-being of others may feel that others would be better off if they were dead. As such, Van Orden et al. (2010) predicted that the presence of either thwarted belonging or perceived burdensomeness would be associated with death ideation. The second prediction suggests that the simultaneous presence of thwarted belonging and perceived burdensomeness would be associated with suicide ideation (e.g., “I want to kill myself”), but only when an individual feels hopeless that these states will change (Van Orden et al., 2010).
While the empirical data outlined above has examined the interpersonal theory’s constructs as correlates of suicide ideation, no research has yet examined these specific predictions in a multifaceted model of suicide risk in older adults. The present study tested these predictions in a model that also included depressive symptoms and hopelessness. The inclusion of empirically based risk factors for death ideation and suicide ideation (i.e., depressive symptoms, hopelessness, thwarted belonging, and perceived burdensomeness) allowed us to better account for the unique predictions proposed by the interpersonal theory (Van Orden et al., 2010).
Thus, our first hypothesis predicted that perceived burdensomeness, thwarted belonging, depressive symptoms, and hopelessness would each be significantly associated with death ideation. We further hypothesized that perceived burdensomeness and thwarted belonging would account for greater unique variance in death ideation than other included predictors. Our second hypothesis predicted the presence of a three-way interaction between thwarted belonging, perceived burdensomeness, and hopelessness, such that those with thwarted belonging and perceived burdensomeness would report the greatest suicide ideation if they also reported elevated hopelessness. Finally, we hypothesized a significant three-way interaction, such that elevated scores on thwarted belonging, perceived burdensomeness, and hopelessness would be associated with significantly reduced probability of being a nonideator.
Method Participants
Participants were 239 adults ages 60 and older (M = 72.4, SD = 6.9) recruited from a primary care setting at a southwestern university health-sciences center (cf. Cukrowicz et al., 2011; Jahn, Poindexter, Graham, & Cukrowicz, 2012; Van Orden, Cukrowicz, Witte, & Joiner, 2012). To identify potentially eligible participants, research personnel reviewed upcoming physician appointments for individuals aged 60 years and older from two primary care settings. Patients who met inclusion criteria (i.e., no physician note of bipolar disorder or mania, psychotic disorder, severe memory impairment, or cognitive difficulties, and had not previously participated or declined participation) were identified as potential participants. In order to maximize variability on study variables, this study did not select participants based on an elevated level of suicide ideation or death ideation, nor on a history of suicide attempt or self-injury. Potential participants were either mailed letters describing the study and subsequently contacted to determine their interest in the study or were approached at a scheduled medical appointment. A total of 436 letters were sent, with 105 patients agreeing to participate; 675 additional participants were approached at physician appointments, with 167 agreeing to participate. Participants came to the first author’s research clinic for study participation or, if they were unable to travel to the clinic, research assistants conducted study sessions at participants’ homes. Following consent, participants completed the Mini Mental Status Exam (MMSE; Folstein, Folstein, & McHugh, 1975), with a required minimum score of 25 for participation. Twenty-three participants were excluded due to MMSE scores and were provided referral information, four participants were missing a significant amount of data for the variables of interest in this study, and six participants with influential data points were dropped (see below).
The final sample consisted of 144 women (60.3%) and 95 men (39.7%). Marital status for this sample was: 66.7% married, 18.4% widowed, 7.9% divorced, 4.2% living with partner, 2.0% never married, 0.8% separated from spouse, and 0.8% in an intimate relationship but not living with partner. Participants were 90.8% Caucasian, 6.3% Hispanic, 1.7% African American, and 1.2% other. The mean total years of education for this sample was 14.4 (SD = 3.5). Fifty-eight participants (24.3%) reported a previous diagnosis of a psychological disorder. At the time of participation, 28 participants (11.7%) had a score greater than or equal to 16 on the Center for Epidemiological Studies Depression Scale (CES-D; Radloff, 1977), suggesting significant symptoms of depression. Within this sample, 12 participants (5.0%) had scores on the death-ideation and suicide-ideation variables greater than or equal to one standard deviation above the mean; 16 participants (6.7%) had scores indicating death ideation, but not suicide ideation, greater than or equal to one standard deviation above the mean, and 19 participants (7.9%) had suicide-ideation, but not death-ideation, scores greater than or equal to one standard deviation above the mean. Seven participants (2.9%) reported a history of suicide attempt. Sixty-four participants (26.8%) reported a lifetime diagnosis of a mental or psychological disorder. Diagnoses included depression (n = 44; 18.4%), anxiety disorder (n = 8; 3.3%), substance-use disorder (n = 4; 1.7%), bipolar disorder (n = 3; 1.3%), cognitive disorder (n = 1; 0.4%), schizophrenia (n = 2; 0.8%), and other or unknown (n = 2; 0.8%). Twenty-three participants (9.6%) indicated that they had been diagnosed with a mental or psychological disorder within the past 12 months. Diagnoses included depression (n = 16; 6.7%), anxiety disorder (n = 2; 0.8%), bipolar disorder (n = 3; 1.3%), substance-use disorder (n = 1; 0.4%), and other (n = 1; 0.4%).
Procedure
All procedures for this study were in accordance with protocol approved by the university institutional review board. Older adults electing to take part in the study provided informed consent and completed self-report questionnaires as well as semistructured clinical interviews. Given that some questions pertained to current and past suicidal behaviors, all research personnel were trained in suicide-risk assessment. Researchers examined all participant responses related to suicide risk. In the event that a participant was identified as at risk, follow-up procedures and interventions were performed in accordance with the approved protocol. When participants completed the study, they were given a referral sheet that provided local mental health resources and were compensated for their time.
Measures
Beck Hopelessness Scale (BHS)
The BHS (Beck & Steer, 1988) is a 20-item true/false self-report questionnaire assessing negative cognitions and emotions about the future (Beck & Steer, 1988). Each item (e.g., “I might as well give up because I can’t make things better for myself”) is scored as either a 0 or 1; a sum total is computed (range 0 to 20), with higher scores reflecting greater hopelessness (Beck & Steer, 1988). Previous research has supported the reliability of this scale across a variety of populations (Glanz, Hass, & Sweeney, 1995). The BHS internal consistency in the current sample was good (Cronbach’s α = .85).
Center for Epidemiologic Studies Depression Scale
The CES-D (Radloff, 1977) is a 20-item self-report questionnaire assessing severity of depressive symptoms. Participants were asked to indicate the frequency of items (e.g., “I thought my life had been a failure”) based on the last 7 days using a Likert scale ranging from 0 (rarely or none of the time) to 3 (most or all of the time). Increasing scores indicate more severe depressive symptoms. Research has supported the psychometric properties of this scale when used with older adults (Beekman et al., 1997; Lewinsohn, Seeley, Roberts, & Allen, 1997). Internal consistency in this sample was good (Cronbach’s α = .89).
Geriatric Suicide Ideation Scale (GSIS)
The GSIS (Heisel & Flett, 2006) is a 31-item self-report measure of suicide ideation designed specifically for use in older adults. It is comprised of four subscales: suicide ideation, death ideation, loss of personal and social worth, and perceived meaning in life (Heisel & Flett, 2006). For the purposes of the current study, only the suicide-ideation (10 items) and death-ideation (five items) subscales were utilized in analyses. In this study, one item was removed from the suicide-ideation subscale (i.e., “I frequently think that my family will be better off when I am dead”) to reduce multicollinearity because it was related to perceived burdensomeness, resulting in a 9-item suicide-ideation subscale. Participants rated each statement (e.g., “I have seriously considered suicide more than once earlier in my life,” “I welcome the thought of drifting off to sleep and never waking up”), using a five point Likert scale ranging from 1 (strongly disagree) to 5 (strongly agree). For the suicide-ideation and death-ideation subscales, total scores range from 9 to 45 and 5 to 25, respectively, with higher scores indicting greater ideation and therefore greater risk. Analyses for this study included a suicide-ideation or death-ideation score with a low-end zero score. For suicide ideation, 9 points were subtracted from all scores; for death ideation, 5 points were subtracted from all scores. Studies with older adults have shown that these subscales have adequate internal consistency reliability (Heisel & Flett, 2006; Marty, Segal, & Coolidge, 2010). Internal consistency in the current sample was adequate for the suicide-ideation subscale (Cronbach’s α= .81), as well as for the death-ideation subscale (Cronbach’s α = .67).
Interpersonal Needs Questionnaire (INQ)
The INQ (Van Orden et al., 2012) is a 15-item self-report questionnaire with two subscales, which measure thwarted belonging and perceived burdensomeness. The thwarted-belonging subscale consists of nine items that assess the degree of belonging that an individual experiences (e.g., “These days I am close to other people,” which is reverse scored). The six-item perceived-burdensomeness subscale measures the extent to which one feels like a burden on others and perceives that his or her death is more valuable than his or her life (e.g., “These days I think I am a burden on society”). Participants rated each statement using a 7-point Likert scale ranging from 1 (not at all true for me) to 7 (very true for me; Van Orden et al., 2012). For each subscale, responses are then totaled such that higher scores indicate greater thwarted belonging and perceived burdensomeness. In this sample, Cronbach’s alpha for the thwarted-belonging subscale was .84. For the perceived-burdensomeness subscale, Cronbach’s alpha was .74.
Data Analysis
We assessed for influential data points using recommendations for Cook’s D (Cook & Weisberg, 1982), Mahalanobis distance (Stevens, 1984), and centered leverage (Chatterjee & Hadi, 1988). Six participants’ data exceeded all three cutoff values. Inspection of the death-ideation and suicide-ideation outcome-variable histograms showed relatively large numbers of zero-value responses as a proportion of the total sample size (death ideation = 66/239; suicide ideation = 104/239). The data from samples in suicide research often contain a relatively large number of zero or near-zero values, making traditional ordinary least-squares regression analysis inappropriate. There has been a growing trend in this literature to employ Poisson regression (e.g., Casey, Gemmell, Hiroeh, & Fulwood, 2012; Chan, Chiu, Lam, Leung, & Conwell, 2006; Fang et al., 2012; Kleiman, Miller, & Riskind, 2012); however, this approach fails to account for the presence of excess zero observations, which are common in community samples in which a large portion of the sample has low suicide risk. The existence of excess zeros may be the result of overdispersion (i.e., when the conditional variance exceeds the mean) or nonlinearities in responses (Gurmu & Trivedi, 1996). In the case of overdispersion, the use of an overdispersed Poisson regression, particularly negative binomial regression, is common (Cameron & Trivedi, 1998; Elhai, Calhoun, & Ford, 2008; Long, 1997); however, the use of an overdispersed Poisson regression does not account for the observed zeros that are the result of nonlinearities in responses (i.e., a large number of zero responses), and its use would lead to inconsistent parameter estimates.
One solution to this problem is the use of a two-part count model or hurdle model. These models treat the zero values of the outcome variable differently than the positive values by assuming that the zeros are the result of a different data-generating process than the nonzeros. For example, it is solely up to the individual whether or not to pursue treatment for a psychological disorder (zero counts vs. positive counts), because the number of treatment sessions is determined by both the patient and the therapist (the magnitude of positive count). These models require two distinct estimations: one for the zero-generating process (logit or probit) and one for the variability in positive counts (typically either Poisson or negative binomial regression). Although this approach is attractive for dealing with excess zeros in the data, the treatment of all zeros as arising from the same process may be inappropriate for our sample. An alternative modified count model capable of addressing this concern is the zero-inflated model (Greene, 2012), which is a more acceptable approach, given our assumptions about the data-generating process for our sample.
The zero-inflated modeling approach allows for simultaneous estimation of both the zero and positive responses in the data. This process assumes that some of the zeros are part of the natural distribution of zero responses (i.e., nonideators), whereas there are additional zeros that are explained by a different process than that yielding the distribution of positive responses (i.e., potential ideators; Atkins & Gallop, 2007). Due to the combination of overdispersion and excess zeros in the data, we employed ZINB regression. In addition, given the potential for bias in the binary logistic regression in the presence of heteroscedasticity, we used robust heteroscedastic standard errors. STATA/MP 12.0 (StataCorp, College Station, TX) was used to estimate the models. The predictor variables for death ideation were perceived burdensomeness, hopelessness, depression, and thwarted belonging. The same predictor variables were included in the model for suicide ideation; however, two-way and three-way interactions between perceived burdensomeness, hopelessness, and thwarted belonging were added to maintain consistency with the theorized relationships.
A statistical test was employed to verify the appropriateness of the ZINB regression for this data (Atkins & Gallop, 2007). Vuong’s test (Vuong, 1989) was used to evaluate the existence of excess zeros by testing the ZINB regression against the standard negative binomial regression. Statistical significance of Vuong’s test indicates that the zero-inflated model would be preferred.
ResultsVariable correlations, means, standard deviations, and internal reliability estimates are provided in Table 1.
Correlations and Descriptive Statistics for Variables of Interest
Death Ideation
Table 2 presents the results for the ZINB regression, with death ideation as the outcome variable. Within the dataset, there were 66 occurrences of zero for death ideation (27.6% of participants). The model with covariates was significant, with Wald χ2 equal to 35.59, p < .001. Vuong’s test (Vuong, 1989) was significant (p < .001), supporting the use of the ZINB. No variables were significantly associated with excess zeros (i.e., nonideators) for death ideation. There was a statistically significant relation between the predictor variables and the level of death ideation. In the negative binomial model, the main effects of perceived burdensomeness (estimate = 0.03, p = .015) and hopelessness (estimate = 0.04, p = .032) were statistically significant, whereas the main effects of depression (estimate = 0.01, p = .092) and thwarted belonging (estimate = 0.00, p = .900) were not.
Zero-Inflated Negative Binomial Regression Results for Death Ideation
Presented in the table are the incidence-rate ratios (IRR). An IRR is the exponent of the parameter estimate and reflects the percentage change in the incidence rate of death ideation associated with a change in the predictor variable, holding the other variables constant. In other words, a 1-unit increase in perceived burdensomeness is associated with an approximately 3.5% increase in the incidence rate of death ideation, holding hopelessness, depression, and thwarted belonging constant. Similarly, a 1-unit increase in hopelessness, holding other variables constant, was associated with a 4.3% increase in death ideation.
Suicide Ideation
The ZINB regression results for suicide ideation are presented in Table 3. Within this dataset, there were 104 occurrences of zero on suicide ideation (43.5% of participants). The model with covariates was significant, with Wald χ2 equal to 113.78, p < .001. Vuong’s test (Vuong, 1989) for the existence of excess zeros was also significant, supporting the use of ZINB regression for this model.
Zero-Inflated Negative Binomial Regression Results for Suicide Ideation
Within the binary logistic regression for zero-inflation, the three predictors significantly associated with nonideation (i.e., excess zeros) were the main effect of thwarted belonging (estimate = −0.11, p = .021), the interaction between perceived burdensomeness and hopelessness (estimate = 0.30, p = .033), and the interaction between perceived burdensomeness and thwarted belonging (estimate = 0.10, p = .038). The main effects of perceived burdensomeness (estimate = −2.57, p = .054) and depression (estimate = −0.09, p = .078), and the three-way interaction between perceived burdensomeness, thwarted belonging, and hopelessness (estimate = −0.03, p = .068) were not significant. The parameter estimates in Table 3 give the log of the change in odds that a participant is a nonideator for a 1-unit increase in the variable; therefore, the exponent of the estimate gives the odds ratio. For example, a 1-unit increase in thwarted belonging conditionally reduces the odds of an individual being a nonideator by 10.5% (whereas a 1-unit decrease in thwarted belonging directly increases the odds by 11.8%). However, if we want to evaluate the total effect of a change in thwarted belonging on the probability of an individual being a nonideator, the predicted value depends upon the level of all covariates.
The predicted probabilities for belonging to the nonideator group given changes in perceived burdensomeness are presented in Figure 1. The curves depicted reflect depressive symptoms held at the mean value for all curves, thwarted belonging at the mean and one standard deviation above the mean, and hopelessness at the mean and one standard deviation above the mean. Figure 2 presents the predicted probabilities of belonging to the nonideator group given changes in thwarted belonging. Again, depression is held at its mean value; hopelessness is at the mean and one standard deviation above the mean; and perceived burdensomeness is at one standard deviation below the mean, at the mean, and one standard deviation above the mean. Above, we noted that the two-way interaction between perceived burdensomeness and hopelessness significantly predicted nonideators. As is evident in Figures 1 and 2, as scores on both perceived burdensomeness and hopelessness simultaneously increase, the probability of being a nonideator (i.e., excess zero) decreases significantly. This suggests that, when individuals report elevated perceptions of being burdens on others and also feel hopeless, these states are likely to change, and the individuals are much more likely to be potential ideators. Likewise, the significant two-way interaction between perceived burdensomeness and thwarted belonging suggests that, as perceived burdensomeness and thwarted belonging simultaneously increase, the probability of being a nonideator decreases significantly. This also suggests that those with higher scores on these variables are much more likely to be potential ideators.
Figure 1. Probability of nonideator status (excess zero) as a function of thwarted belonging and hopelessness along the continuum of scores for perceived burdensomeness. BHS = Beck Hopelessness Scale.
Figure 2. Probability of nonideator status (excess zero) as a function of perceived burdensomeness and hopelessness along the continuum of scores for thwarted belonging. TB = thwarted belonging, BHS = Beck Hopelessness Scale.
As noted above, the tested three-way interaction was not significant (p = .068). This may be due to limited power as a result of sample size. The pattern of results discussed below is based on the parameter estimates obtained from analyses described above; however, we would like to emphasize that the following should be interpreted with caution. Figure 1 depicts the change in probability of being a nonideator as a function of linearly increasing scores on perceived burdensomeness for individuals at differing levels of thwarted belonging and hopelessness. The pattern of results indicates that at lower levels of perceived burdensomeness and mean scores on thwarted belonging, those with hopelessness scores one standard deviation above the mean have a lower probability of being nonideators. Further, for those with thwarted belonging and hopelessness one standard deviation above the mean, the probability of being nonideators is lower. Figure 2 depicts the change in probability of being nonideators as a function of linearly increasing scores on thwarted belonging for individuals at differing reported levels of perceived burdensomeness and hopelessness. The patterns of results suggests that, at lower levels of thwarted belonging, an individual with scores at the mean or one standard deviation above the mean on perceived burdensomeness and hopelessness may have a lower probability of being a nonideator. Taken together, as reported experiences of perceived burdensomeness, hopelessness, and thwarted belonging increase, the probability of the individual being a nonideator (i.e., excess zero) reduces substantially, suggesting that he or she is very likely to be experiencing suicide ideation, even if it is not reported. As with Figure 1, this figure suggests that elevated scores on all three variables are associated with a low probability of an individual being a nonideator (i.e., excess zero). Put another way, elevated scores on perceived burdensomeness, thwarted belonging, and hopelessness are associated with a much greater probability that the individual is experiencing suicide ideation, even if it is not reported.
Within the negative binomial regression portion of the model, none of the predictor variables for suicide ideation were statistically significant, suggesting that variations in these variables do not have a strong association with variations in suicide ideation.
DiscussionA primary contribution of this research has been the use of advanced statistical procedures to identify nonideators (i.e., individuals who deny suicide ideation and who report nonexistent risk on other variables associated with suicide ideation), and potential ideators (i.e., those who deny suicide ideation while simultaneously reporting other established risk factors for suicide), which has significant clinical implications. This study introduced the use of zero-inflated statistical models to the study of suicidal behavior, which offers a wealth of advantages in answering research questions that are relevant to this field. As noted earlier, no research has utilized ZINB analyses to test hypotheses proposed by the interpersonal theory of suicide (Van Orden et al., 2010) in older adults. Our use of more advanced statistical techniques allowed for computation of both the binary logistic and negative binomial regression relations between predictor and outcome variables, as informed by our hypotheses. This allowed us to test theory-based predictions regarding variability in death ideation and suicide ideation, while also predicting participants’ denial of suicide ideation, despite reporting experiences that are associated with suicide risk.
We hypothesized that death ideation would be significantly predicted by perceived burdensomeness, thwarted belonging, hopelessness, and depressive symptoms. Our results indicated that only perceived burdensomeness and hopelessness significantly predicted variation in death ideation in the negative binomial portion of the regression. This analysis was conducted to test the first prediction of Van Orden et al. (2010), that individuals who feel a lack of connection to others, or perceive themselves to be a burden on others, will develop a wish for death. Our results support the association of perceived burdensomeness with death ideation, but do not indicate that thwarted belonging was associated with death ideation. As such, these results provide partial support for the interpersonal theory’s assertion that perceived burdensomeness and thwarted belonging are each associated with death ideation when experienced independent of each other. This may signify that thwarted belonging is exclusively associated with the development of suicide ideation in this population. Although it was surprising that depressive symptoms were not associated with death ideation, participants in this study had low scores on depressive symptoms, which may suggest that older adults in our sample were less likely to report feeling depressed, though they endorsed other psychological experiences. Taken together, the significant relations between perceived burdensomeness and death ideation, as well as between hopelessness and death ideation, may suggest that painful emotional experiences related to perceptions of detracting from others and having little hope for the future are associated with ideations related to death in older adults.
In addition, our pattern of results (though nonsignificant) is consistent with Van Orden et al. (2010)’s prediction that individuals with elevated perceived burdensomeness and thwarted belonging would develop suicide ideation when they feel hopeless that these states will change. These findings provide further support for the interpersonal theory of suicide. Neither the negative binomial regression nor the zero-inflation binary logistic regression that tested this three-way interaction was significant; however, the pattern of results from the logistic regression with zero-inflation indicates that increasing scores on these variables, and especially increasing scores on all three variables simultaneously, are associated with a reduced probability that an individual is a nonideator. Put another way, individuals reporting these states are more likely to be potential ideators. Although these results should be interpreted with caution, this is fascinating in that it may suggest that for older adults, these experiences may be associated with the presence or absence of suicide ideation, but are less important to determining the severity of thoughts of suicide.
It is noteworthy that the correlates of death ideation and suicide ideation were somewhat different. Specifically, none of the predictor variables predicted excess zeros on death ideation, whereas thwarted belonging, the interaction between perceived burdensomeness and hopelessness, and the interaction between perceived burdensomeness and thwarted belonging predicted excess zeros on suicide ideation. Although this is speculative, it may suggest more of a presence/absence relationship among correlates of suicide ideation, whereby experiences such as feeling a thwarted sense of belonging trigger the onset of thoughts of suicide, but do not impact the severity of these thoughts. In contrast, the predictors of death ideation were associated with the severity of death ideation rather than the presence of excess zeros. This suggests that these predictors may not lead to the onset of death ideation (thoughts that are more common among older adults), but instead influence the severity of death ideation. In addition, it was surprising that thwarted belonging was not significantly associated with death ideation in either analysis. This could indicate that thwarted belonging is a psychological state so painful that it is associated with an active wish to take one’s own life, rather than a more passive desire for death.
There are several very important implications to these findings. The results suggest that variables included in the interpersonal theory should be key targets in the determination of whether an older adult might be experiencing thoughts of suicide, regardless of whether the older adult is reporting such thoughts. As we noted above, increasing scores on thwarted belonging, perceived burdensomeness, and hopelessness are all associated with a much greater probability that an individual is experiencing thoughts of suicide, whether or not they are reported. The figures especially highlight this, indicating that the probability is near zero that an individual is not experiencing thoughts of suicide if they are reporting elevated experiences related to these constructs. This information is very important from an assessment perspective. As noted earlier, data suggest that older adults may underreport thoughts of suicide (Heisel et al., 2006); therefore, the identification of variables that are associated with the probability of suicide ideation (but that are not direct questions regarding suicide ideation) may prove invaluable for mental health practitioners and primary care physicians seeking to evaluate suicide risk in older adults who might not directly report suicide ideation. In addition to the assessment implications of these findings, the results of this study indicate that mental health practitioners should target perceptions of being a burden, the sense of thwarted belonging, and hopelessness in older adults in an effort to reduce their risk of developing suicide ideation. This study is the first study to attempt to identify such variables using statistical approaches such as zero-inflated negative binomial regression.
This study, although valuable, has limitations that must be noted. First, the cross-sectional design prevents inference of causal relationships. As such, we have been able to identify variables that are associated with death ideation, as well as patterns of variables that are associated with a decreased probability that an individual is currently not experiencing suicide ideation, but we have not been able to draw conclusions about the role of these variables in the development and maintenance of death ideation or suicide ideation. Longitudinal examinations of the Van Orden et al. (2010) model may provide further elucidation of the relations between variables. Furthermore, investigation of other variables not examined in this study (e.g., acquired capability for suicide, emotion inhibition) may offer additional explanation of the differential increased risk among older adults with greater risk for death by suicide (i.e., older adult men). In addition, limited racial diversity, relatively high education levels, and limited geographic dispersion of the sample all limit generalizability.
In summary, it appears that perceived burdensomeness and hopelessness are critical risk factors for death ideation in older adults. Furthermore, the ZINB results suggest that the experiences of thwarted belonging, perceived burdensomeness, and hopelessness are critical to the identification of older adults who may be experiencing suicide ideation, but are not directly reporting it. Providers should use this information in developing efficient and effective screening methods for suicide ideation, as well as for ensuring accurate assessment of older adults who might underreport to direct questions regarding suicide ideation. Participants who deny a thwarted sense of belonging, perceptions of burdensomeness, and hopelessness are unlikely to be experiencing suicide ideation; however, those who indicate elevated thoughts and emotions related to these variables may be experiencing suicide ideation, at least at a low level, even as they deny it.
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Submitted: November 16, 2012 Revised: September 3, 2013 Accepted: October 7, 2013
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Source: Journal of Abnormal Psychology. Vol. 122. (4), Nov, 2013 pp. 1021-1030)
Accession Number: 2013-44247-008
Digital Object Identifier: 10.1037/a0034953
Record: 159- Title:
- Symptoms of posttraumatic stress predict craving among alcohol treatment seekers: Results of a daily monitoring study.
- Authors:
- Simpson, Tracy L.. VA Puget Sound Health Care System, Seattle, WA, US, Tracy.Simpson@va.gov
Stappenbeck, Cynthia A.. VA Puget Sound Health Care System, Seattle, WA, US
Varra, Alethea A.. VA Puget Sound Health Care System, Seattle, WA, US
Moore, Sally A.. Evidence Based Treatment Centers of Seattle, PLLC, Seattle, WA, US
Kaysen, Debra. Department of Psychiatry and Behavioral Sciences, University of Washington School of Medicine, Seattle, WA, US - Address:
- Simpson, Tracy L., VA Puget Sound Health Care System, 1660 South Columbian Way, Seattle, WA, US, 98108, Tracy.Simpson@va.gov
- Source:
- Psychology of Addictive Behaviors, Vol 26(4), Dec, 2012. pp. 724-733.
- NLM Title Abbreviation:
- Psychol Addict Behav
- Page Count:
- 10
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- Bulletin of the Society of Psychologists in Addictive Behaviors; Bulletin of the Society of Psychologists in Substance Abuse
- Other Publishers:
- US : Educational Publishing Foundation
Society of Psychologists in Addictive Behaviors - ISSN:
- 0893-164X (Print)
1939-1501 (Electronic) - Language:
- English
- Keywords:
- PTSD, alcohol craving, daily monitoring, self-medication hypothesis, alcohol use disorders, posttraumatic stress disorder, treatment seekers
- Abstract:
- Alcohol use disorders (AUDs) and Posttraumatic Stress Disorder (PTSD) commonly co-occur. Craving for alcohol is a common aspect of AUD, with and without PTSD, and is one of the key predictors of continued problematic alcohol use among treatment seekers. The present study sought to investigate the self-medication hypothesis using daily Interactive Voice Response (IVR) reports to examine the relationships between PTSD symptomatology and both same-day and next-day alcohol craving. Twenty-nine individuals with an AUD (26 of whom screened positive for PTSD) entering AUD treatment provided daily IVR data for up to 28 days regarding their alcohol use, craving, and 7 symptoms of PTSD. Given the nested nature of daily data, generalized estimating equations using a negative binomial distribution and a log link function were used to test hypotheses. Results suggest that days with greater overall PTSD severity are associated with greater alcohol craving, and greater reports of startle and anger/irritability were particularly associated with same-day craving. The next-day results suggest that the combination of the 7 PTSD symptoms did not predict next-day craving. However, greater distress from nightmares the previous night, emotional numbing, and hypervigilance predicted greater next-day craving, while greater anger/irritability predicted lower next-day craving. These findings highlight the importance of assessing the relationship between specific symptoms of PTSD and alcohol cravings in order to increase our understanding of the functional interplay among them for theory building. Additionally, clinicians may be better able to refine treatment decisions to more efficiently break the cycle between PTSD-related distress and AUD symptoms. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Alcohol Abuse; *Alcoholism; *Craving; *Posttraumatic Stress Disorder; Health Care Seeking Behavior; Monitoring; Self-Medication; Symptoms
- Medical Subject Headings (MeSH):
- Adult; Alcohol Drinking; Alcoholism; Female; Humans; Male; Middle Aged; Stress Disorders, Post-Traumatic
- PsycINFO Classification:
- Psychological & Physical Disorders (3200)
- Population:
- Human
Male
Female - Location:
- US
- Age Group:
- Adulthood (18 yrs & older)
- Tests & Measures:
- Life Events Checklist
Alcohol Use Disorders Identification Test DOI: 10.1037/t01528-000
Penn Alcohol Craving Scale DOI: 10.1037/t51781-000
Clinician-Administered PTSD Scale DOI: 10.1037/t00072-000
Structured Clinical Interview for DSM-IV
PTSD Checklist—Civilian Version DOI: 10.1037/t02622-000 - Grant Sponsorship:
- Sponsor: VA Puget Sound Health Care System, Mental Illness Research, Education, and Clinical Center, US
Recipients: No recipient indicated
Sponsor: University of Washington, Alcohol and Drug Abuse Institute, US
Recipients: No recipient indicated - Methodology:
- Empirical Study; Quantitative Study
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- First Posted: Feb 27, 2012; Accepted: Dec 14, 2011; Revised: Dec 12, 2011; First Submitted: Jul 22, 2011
- Release Date:
- 20120227
- Correction Date:
- 20161117
- Copyright:
- American Psychological Association. 2012
- Digital Object Identifier:
- http://dx.doi.org/10.1037/a0027169
- PMID:
- 22369221
- Accession Number:
- 2012-05218-001
- Number of Citations in Source:
- 69
- Persistent link to this record (Permalink):
- http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2012-05218-001&site=ehost-live
- Cut and Paste:
- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2012-05218-001&site=ehost-live">Symptoms of posttraumatic stress predict craving among alcohol treatment seekers: Results of a daily monitoring study.</A>
- Database:
- PsycINFO
Symptoms of Posttraumatic Stress Predict Craving Among Alcohol Treatment Seekers: Results of a Daily Monitoring Study
By: Tracy L. Simpson
VA Puget Sound Health Care System, Seattle, Washington;
Department of Psychiatry and Behavioral Sciences, University of Washington School of Medicine;
Cynthia A. Stappenbeck
VA Puget Sound Health Care System, Seattle, Washington
Alethea A. Varra
VA Puget Sound Health Care System, Seattle, Washington;
Department of Psychiatry and Behavioral Sciences, University of Washington School of Medicine
Sally A. Moore
Evidence Based Treatment Centers of Seattle, PLLC, Seattle, Washington;
Department of Psychiatry and Behavioral Sciences, University of Washington School of Medicine
Debra Kaysen
Department of Psychiatry and Behavioral Sciences, University of Washington School of Medicine
Acknowledgement: Cynthia Stappenbeck is currently a postdoctoral fellow at the Department of Psychiatry and Behavioral Sciences, University of Washington School of Medicine.
Funding for this study was provided by pilot grants from the VA Puget Sound Health Care System Mental Illness Research, Education, and Clinical Center (MIRECC) and from the University of Washington Alcohol and Drug Abuse Institute.
Alcohol use disorders (AUDs) are among the most common psychiatric disorders, with approximately 14% of the general population having a lifetime history of alcohol dependence (AD; Kessler et al., 1997). AUDs have a chronic and relapsing course (Brownell, Marlatt, Litchenstein, & Wilson, 1986) and are associated with high personal and societal costs (McGinnis & Foege, 1993; Rice, Kelman, & Miller, 1991). One of the key predictors of continued problematic alcohol use among individuals seeking treatment is craving for alcohol. Craving predicts relapse among abstinent alcoholics (Bottlender & Soyka, 2004; Sinha et al., 2011) as well as the amount of alcohol consumed during treatment (Flannery, Poole, Gallop, & Volpicelli, 2003).
AUDs often present with comorbid disorders (Kessler et al., 1997), including posttraumatic stress disorder (PTSD; Chilcoat & Breslau, 1998; Dansky et al., 1996; McFarlane, 1998). Co-occurring PTSD/AUD is typically associated with worse functioning, greater health care utilization, poorer treatment response, higher dropout, and faster alcohol relapse (Ouimette, Moos, & Finney, 2000; Read, Brown, & Kahler, 2004). Although there is some discrepancy in the extant literature, specific PTSD symptom clusters have been found to be most associated with alcohol problems, including emotional numbing, hyperarousal, and reexperiencing symptoms (Jakupcak et al., 2010; Maguen, Stalnaker, McCaslin, & Litz, 2009; McFall, Mackay, & Donovan, 1992), essentially all aspects of the PTSD diagnosis but behavioral avoidance.
The self-medication theory (Khantzian, 2003) has been posited to explain the frequent co-occurrence of AUDs and PTSD. The general theory asserts that distress associated with symptoms of PTSD results in drinking alcohol to reduce the discomfort associated with these symptoms via negative reinforcement, which then increases the likelihood that the person will use alcohol again in the future to manage PTSD-related distress. Over time, this is thought to lead to the development of an AUD (Chilcoat & Breslau, 1998; Simpson, 2003; Stewart, Conrod, Pihl, & Dongier, 1999). A somewhat more general formulation of this basic idea is the stress–response dampening (SRD) theory, which posits that alcohol may be used to mitigate negative reactions to stress, which, for those with PTSD, could include stressful situations, trauma memories, or PTSD symptoms (see Armeli et al., 2003, for an in-depth discussion of SRD). Moreover, some individuals may drink to dampen or disrupt their self-awareness, narrow their attention, or change their appraisals of stressful situations to relieve discomfort associated with stressors (Sayette, 1993). This specification of hypothesized mechanisms behind the use of alcohol to manage symptoms is known as the attention disruption model. Although some support for this model has been found among moderate to heavy drinkers (Armeli et al., 2003), to our knowledge, it has not been evaluated in PTSD/SUD samples. The self-medication hypothesis is more narrowly focused than the SRD model in its emphasis on symptom relief, as opposed to stress more generally, and it is not as nuanced as the attention disruption model, as it does not specify potential mechanisms of the posited effect (i.e., that relief from distress is due to alcohol's effect of disrupting attention, self-awareness, or changing appraisals). Nonetheless, as a working model through which to evaluate whether there appears to be a reliable relationship between PTSD symptomatology and drinking behaviors, the self-medication model is worthy of evaluation.
Trauma exposure and PTSD have been found to precede AUD development (Stewart et al., 1999; Volpicelli, Balaraman, Hahn, Wallace, & Bux, 1999), suggesting that alcohol use may be a learned coping response. However, data also show that alcohol and other drug abuse or dependence may increase the risk of traumatization (Acierno, Resnick, Kilpatrick, Saunders, & Best, 1999; Chilcoat & Breslau, 1998; Messman-Moore, Ward, & Brown, 2009) as well as impede natural recovery from trauma, thereby increasing the risk of developing chronic PTSD (Kaysen et al., 2006, (2011a); McFarlane et al., 2009). These phenomena suggest that there is perhaps a reciprocal or mutual maintenance relationship between the two. Cross-sectional studies of individuals with AUD with and without PTSD have found that, compared with those with only AUD, those with comorbid AUD/PTSD are more likely to report being motivated to drink to cope with negative affect and stress (Grayson & Nolen-Hoeksema, 2005; Kaysen et al., 2007; Ullman, Filipas, Townsend, & Starzynski, 2005), further suggesting that there is likely a functional relationship between PTSD symptomatology and drinking behavior regardless of the temporal order of onset.
As a negative reinforcement model such as the self-medication theory would predict, alcohol cravings are often triggered by negative emotional cues (Cooney, Litt, Morse, Bauer, & Gaupp, 1997; Rubonis, Colby, Monti, & Rohsenow, 1994) and, specifically, by trauma-related cues and distress for individuals with PTSD (Coffey et al., 2002; Saladin et al., 2003). A laboratory-based study involving cue-exposure paradigms found that individuals with comorbid PTSD and AUD experience increases in craving for alcohol in response to both trauma-related and alcohol cues relative to neutral cues (Coffey et al., 2002). However, a later study using similar methodology found that individuals with comorbid AUD and PTSD experience increased craving in response to trauma cues with or without exposure to an alcohol cue (Coffey et al., 2010). Similarly, baseline PTSD severity has been found to be associated with how much craving is elicited by trauma and alcohol cues, with those reporting more severe baseline PTSD also reporting stronger craving responses (Saladin et al., 2003). Although there is emerging evidence of a reciprocal relationship between alcohol use and PTSD (Kaysen et al., in press), it is not known whether craving for alcohol is linked to subsequent PTSD symptom exacerbation. It is possible that discomfort associated with craving is activating and may lead to worsened PTSD.
Research on the relationship between PTSD symptoms and alcohol craving has typically relied on either laboratory-based alcohol cue and imaginal paradigms such as the ones described here or on retrospective reconstruction of how these phenomena interact with one another in individuals' day-to-day lives (Ouimette, Read, Wade, & Tirone, 2010). While laboratory-craving paradigms help to establish what does and does not elicit craving for different groups of drinkers, they are necessarily limited with regard to ecological validity. Research that relies on retrospective symptom assessment makes it difficult to evaluate the interrelationships between PTSD and alcohol use because these assessments are likely biased by recall errors (McKay, 1999; Simpson et al., 2011). Neither approach can capture the interplay between craving and PTSD or other symptoms as it unfolds over time. For example, simple pre- and posttest assessments cannot identify whether an exacerbation of PTSD symptoms is associated with increased alcohol craving or use on a given day. Although laboratory-based studies utilizing craving inductions have been helpful in examining this issue (Coffey et al., 2002; Saladin et al., 2003), these studies, by their very nature, are artificial and still cannot address how PTSD may affect alcohol cravings in people's day-to-day lives.
In order to better assess these clinically and conceptually important issues, researchers have increasingly begun to utilize daily monitoring of symptoms to examine predictors of substance craving, use, and relapse (Armeli, Conner, Cullum, & Tennen, 2010; Collins et al., 1998; Lukasiewicz, Benyamina, Reynaud, & Falissard, 2005; Shiffman et al., 2007; Warthen & Tiffany, 2009). Such assessments allow for close to real-time examination of temporally ordered event-level relationships and thus can answer questions regarding the daily interrelationships among symptoms, and are less subject to recall errors and bias than traditional retrospective methods (Galloway, Didier, Garrison, & Mendelson, 2009; Leigh, 2000). For example, retrospective reports of smoking lapses are affected by attempts to justify lapses, such as attributing them to stress, whereas prospective analyses of daily data fail to find this relationship (Shiffman & Waters, 2004).
Because of their advantages, daily monitoring protocols are being applied to studies on the relationship between alcohol craving and other phenomena such as relapse, negative affect, and coping. For example, in a study of college students participating in a 12-step oriented recovery program, daily reports of alcohol craving were associated with negative affect and negative social experiences, moderated by avoidance coping (Cleveland & Harris, 2010). In alcohol-dependent individuals involved in residential treatment, latent class analyses revealed that consistently high daily reports of craving were associated with less time to relapse following treatment (Oslin, Cary, Slaymaker, Colleran, & Blow, 2009). These studies highlight the likely utility of capturing daily reports of craving and PTSD symptom severity to further our understanding of the interrelationships between them.
The present study is a preliminary investigation of the self-medication hypothesis in a sample of individuals beginning a new episode of treatment for an AUD, most of whom screened positive for PTSD. The data are from those who were randomly assigned to a daily monitoring condition as part of a larger study that evaluated the feasibility of utilizing 28 days of Interactive Voice Response (IVR) system to monitor alcohol use and cravings, as well as PTSD symptoms, with individuals seeking alcohol treatment (Simpson, Kivlahan, Bush, & McFall, 2005). To our knowledge, this methodology has not been used to examine the interrelations between alcohol craving and PTSD symptom severity among AUD-treatment-seeking individuals. Based on the self-medication and negative reinforcement models, we hypothesized that PTSD symptoms would be positively associated with same-day and next-day alcohol craving, such that higher reports of PTSD symptoms would be associated with greater alcohol craving the same day and the next day. We also evaluated whether there was a predictive relationship between craving and later PTSD symptoms to examine a mutual maintenance pattern, wherein increases in cravings predict increases in same-day PTSD symptom severity. In the current study, alcohol use itself was not examined, due to the low rates of drinking reported during the daily monitoring interval.
Method Participants
Thirty-six individuals with a current alcohol use disorder (American Psychiatric Association, 1994) and who experienced drinking in the past month were randomized to the daily monitoring condition of the larger study (Simpson et al., 2005). Of those, 29 (80% of the original daily monitoring sample) provided information for at least 50% of the 28-day monitoring period and are the focus of this report. They were recruited from either a large VA medical center (n = 24) or a large, urban publicly funded community addiction treatment program in Seattle, Washington (n = 5). The mean age of this sample was 48.0 years (SD = 7.0 years) and 93% were male. The self-identified ethnic composition of the sample was as follows: 41% African American, 10% Native American, 45% non-Hispanic White, and 4% Other. Over half of the sample was currently separated, divorced, or widowed (79.4%), 17.2% were single, and 3.4% were married or partnered. Just under half of the participants lived in their own homes (44.8%), 44.8% were homeless, and 10.3% were in other living situations (i.e., VA domiciliary, assisted care living). Over half of the participants had attended at least some college (62.1%), 24.1% had a high school degree or the equivalent, and 13.8% had not completed high school. The majority of the participants were unemployed (72.4%).
The sample was, on average, in the severe range on the AUD Identification Test (Babor, Higgins-Biddle, Saunders, & Monteiro, 2001; M = 26.2, SD = 8.3, range = 0–40). Additionally, all of the participants experienced at least three of the 16 potentially traumatic events (M = 9.8, SD = 3.6, range = 3 – 16) listed in the Life Events Checklist (LEC). Criterion A1 and A2 (i.e., whether actual or threatened harm or violation of physical integrity occurred, and whether response involving fear, helplessness, or horror occurred at the time, respectively) were not assessed. Based on the PTSD Checklist – Civilian version (PCL-C; Blanchard, Jones-Alexander, Buckley, & Forneris, 1996), 89.7% of the sample screened positive for current PTSD at intake, with a score of 38 or higher for women (Dobie et al., 2002) and 42 or higher for men (Spiro, Hankin, Leonard, & Stylianou, 2000). In addition, when the PCL-C was scored to reflect the Diagnostic and Statistical Manual of Mental Disorders (4th ed., DSM-IV; American Psychiatric Association, 1994) criteria regarding the required number of symptoms endorsed in each symptom cluster, the results were the same, except for one person who scored a 50 but missed the hyperarousal criteria by 1 point.
Procedure
Eligible participants provided informed consent as approved by the University of Washington Human Subjects Division Internal Review Board. Consenting participants completed a detailed interview regarding their recent alcohol and drug use, treatment utilization, and legal involvement, as well as several paper-and-pencil measures. They then received instruction regarding the IVR system and completed a practice call. Participants were paid $25.00 for the baseline and $25.00 for the follow-up assessments.
Compliance with the monitoring protocol was automatically tracked by the IVR system. When participants failed to call the system as scheduled, the study coordinator attempted to contact participants within 2 working days in order to reconstruct the data from missed calls verbally and to answer any questions about the IVR system.
Monitoring incentives
We used the same payment schedule as Searles, Helzer, Rose, and Badger (2002). Participants received $0.50 per call, and if they made all seven required calls in a week, they received a bonus of $10.00. If a participant missed only 2 nonconsecutive days over the entire 28-day study period, they then received a prorated bonus of $7 for weeks with any missing calls. Participants could earn up to $54 for perfect compliance over the 28 days of monitoring.
Measures
AUD status
Current and lifetime DSM–IV AUD status was assessed via the interview version of the Structured Clinical Interview for DSM–IV (SCID; First, Spitzer, Gibbon, & Williams, 1996). The SCID is a widely used structured interview that assesses Axis I psychiatric history. The SCID-I has been shown to have very good reliability (Zanarini et al., 2000) and validity (Kranzler et al., 2003).
Alcohol severity
The Alcohol Use Disorders Identification Test (AUDIT; Babor et al., 2001) is a widely used 10-item screen for evaluating the severity of alcohol misuse during the previous year. The internal consistency of the scale has been found to be good (Cronbach's alpha ≥ .85; Babor et al., 2001).
Alcohol craving
The Penn Alcohol Craving Scale (PACS; Flannery, Volpicelli, & Pettinati, 1999) is a 5-item scale developed to assess various aspects of craving for alcohol over the past week. It was administered at baseline and follow-up. The internal consistency of the PACS was high in the current sample (Cronbach's alpha = .93).
Trauma exposure
The LEC (Blake et al., 1995) consists of a list of 16 potentially traumatic events from the Clinician-Administered PTSD Scale (CAPS; Blake et al., 1995). Respondents indicated whether each event happened to them, they witnessed it, or they learned about the event. For the present study, events were included in our assessment of trauma exposure that either happened to the individual or were witnessed by him or her.
PTSD symptomatology
The PCL-C (Blanchard et al., 1996) is a 17-item questionnaire that directly parallels the Diagnostic and Statistical Manual of Mental Disorders (4th ed.; DSM–IV; American Psychiatric Association, 1994) diagnostic criteria for PTSD. Participants rated how much they were bothered in the past month by each symptom on a 5-point scale ranging from “not at all” to “extremely.” The PCL-C was found to be highly correlated with CAPS, the “gold standard” diagnostic interview of PTSD (r = .93), and to have adequate internal reliability (Blanchard et al., 1996). Moreover, the PCL screening appears to have good specificity and sensitivity in identifying PTSD cases compared with other PTSD measures across trauma-exposed populations (Lang, Laffaye, Satz, Dresselhaus, & Stein, 2003; Ruggiero, Del Ben, Scotti, & Rabalais, 2003).
Daily monitoring protocol
Participants called a prerecorded IVR system using a toll-free telephone number. The 25-item daily monitoring protocol was adapted from the work of Searles, Helzer, & Walter (2000). To assess PTSD symptomatology, we selected seven items from the PCL-C (upsetting dreams, upset due to reminders, avoiding reminders, emotionally numb, hypervigilance, increased startle response, anger/irritability). This limited item set was used to minimize participant burden. The items were chosen by a panel of PTSD experts so as to include symptoms that were likely to vary from day to day and that had been shown in cross-sectional research to be associated with alcohol use (McFall et al., 1992). The PCL-C wording was modified to fit the daily time frame, and the phrase “stressful experience” was replaced with “traumatic experience” to increase the likelihood that the focus was on experiences that fit Criterion A of the PTSD diagnostic requirements. Craving was assessed with the following question, adapted from the PACS, to reflect the daily assessment (Item #2): “At its most severe point, how strong was your craving for alcohol yesterday, with 0 being not at all strong and 7 being very strong?” Daily responses to the craving item were significantly correlated with the PACS assessed at baseline, r = .57, p < .001, and follow-up, r = .70, p < .001. The IVR calls lasted, on average, 3.8 minutes the first week (SD = 1.3) and were down to 2.6 minutes by Week 4 (SD = 1.3).
Data Analytic Approach
The daily alcohol craving data were nested within individuals and positively skewed. Therefore, generalized estimating equations (GEE; Hardin & Hilbe, 2003), using a negative binomial distribution and a log link function, were used to test hypotheses about the daily relations between PTSD symptom severity and craving for alcohol. GEE models provide an alternative approach to modeling multilevel data when the response variables are non-normally distributed. GEEs with a negative binomial distribution permit the use of incidence rate ratios (IRRs) as a measure of effect size. GEE models are useful for handling cases with missing observations. Whereas many other analyses (e.g., repeated measures ANOVAs) would exclude participants with incomplete data, GEE modeling allows participants with missing data to be included in the analyses. Four separate GEE models were run to examine the influence of PTSD symptoms on same-day and next-day alcohol craving: (a) seven combined PTSD symptoms predicting alcohol craving on the same day; (b) each of the seven PTSD symptoms entered individually predicting alcohol craving on the same day; (c) combined symptoms predicting next-day craving, and (d) each symptom entered individually predicting next-day craving. Eight GEE models were run to examine the influence of craving on next-day PTSD symptoms: (a) alcohol craving predicting next-day overall PTSD symptom severity, and (b) alcohol craving predicting next-day individual PTSD symptoms separately. In all of these models, we included indicator variables for each day of the week, with Sunday as the reference day, to control for day-to-day variation in PTSD symptoms and craving for alcohol. We also included a monitoring-day variable to test and control for reactivity effects in the endorsement of craving for alcohol. All analyses were conducted in Stata 10.1 (StataCorp, 2009).
Results Preliminary Analyses
Chi-square analyses were conducted to examine gender and race/ethnicity differences between participants included versus participants excluded from the analyses. There were no differences between those included or excluded from analyses on gender, χ2(1, N = 36) = 0.51, p = .48, or race/ethnicity, χ2(1, N = 36) = 0.99, p = .80. Univariate analysis of variances (ANOVAs) were conducted to examine differences on baseline alcohol use, alcohol craving, and PTSD symptom severity between those included versus excluded from analyses. There were no differences between those included (M = 26.17, SD = 8.32) and excluded (M = 28.00, SD = 10.34) on overall alcohol use, F(1, 34) = 0.25, p = .62. There were, however, differences between the groups on baseline alcohol craving, F(1, 34) = 7.14, p = .01, and PTSD symptoms, F(1, 33) = 5.19, p = .03. Individuals included in the analyses reported lower baseline craving for alcohol (M = 3.45, SD = 1.59) than those excluded (M = 5.29, SD = 1.80), and higher baseline PTSD symptoms (M = 60.04, SD = 13.96) than those excluded (M = 46.71, SD = 13.26) from analyses.
The aggregate summary statistics regarding PTSD symptom severity and alcohol craving for the daily monitoring period are shown in Table 1, along with information about compliance with the IVR system. Among these participants, baseline retrospective reports of PTSD severity (PCL-C scores) were significantly correlated with baseline reports of alcohol craving (PACS scores), r(n = 29) = .393, p = .04, and baseline reports of days drinking (r = .17, p < .001). In order to examine the extent of actual drinking behavior reported during the monitoring period, we reviewed summary statistics regarding the number of days drinking and the number of drinks per drinking day. Among the 29 participants included in the final sample, participants monitored for an average of 25.5 days (SD = 3.03), and data were provided for 741 out of a possible 812 days (91.3%). Alcohol was consumed on 85 days (11.5%). On those drinking days, participants consumed between 1 and 24 drinks, with an average of 5.9 drinks (SD = 3.4) and a mode of 8 drinks. In this sample, daily reports of alcohol use were significantly correlated with daily reports of craving, r = .34, p < .001. Due to the low base rate of drinking days, however, alcohol use itself was not examined.
Aggregate Summary Statistics of PTSD Symptom Severity and Alcohol Craving During the Daily Monitoring Period
PTSD Symptom Severity Predicting Same-Day Alcohol Craving
As shown in Table 2, the GEE model examining the influence of overall combined PTSD severity on same-day alcohol craving found that higher overall PTSD severity was associated with greater craving for alcohol on the same day, b = 0.03, IRR = 1.03, p < .001. Additionally, alcohol craving was higher among women, b = 0.79, IRR = 2.20, p < .05, and on Saturday compared with Sunday, b = 0.21, IRR = 1.23, p < .05. A second GEE model investigating the same-day relations between the severity of the individual PTSD symptoms and alcohol craving found that greater daily reports of startle response, b = 0.10, IRR = 1.10, p < .01, and anger/irritability, b = 0.08, IRR = 1.08, p < .01, were associated with greater craving for alcohol on the same day. Alcohol craving was also higher among women, b = 0.69, IRR = 1.99, p < .05, and on Saturday compared with Sunday, b = 0.21, IRR = 1.23, p < .05.
Overall PTSD Severity and Individual PTSD Symptom Severity Predicting Same-Day Alcohol Craving
PTSD Symptom Severity Predicting Next-Day Alcohol Craving
In the model examining overall PTSD severity and next-day alcohol craving, women reported greater craving for alcohol than men, b = 1.11, IRR = 3.03, p < .01 (see Table 3). Contrary to our hypotheses, overall PTSD symptom severity was not significantly associated with next-day alcohol craving, b = 0.01, IRR = 1.01, p = .11. A different pattern emerged when the PTSD symptoms were examined individually. Greater reports of upsetting dreams, b = 0.08, IRR = 1.03, p < .01, emotional numbing, b = 0.06, IRR = 1.06, p < .05, and hypervigilance, b = 0.07, IRR = 1.07, p < .05, were associated with greater next-day craving for alcohol, whereas greater reports of anger/irritability were associated with lower next-day craving for alcohol, b = −0.06, IRR = 0.94, p < .05.
Overall PTSD Severity and Individual PTSD Symptom Severity Predicting Next-Day Alcohol Craving
Alcohol Craving Predicting Next-Day PTSD Symptom Severity
In order to evaluate whether there might be a reciprocal relationship between craving and PTSD symptomatology, and to better insure that the pattern of results indicating that specific PTSD symptoms predict next-day craving was not spurious, GEE models were also conducted to determine whether craving severity predicted next-day PTSD symptom severity. Neither the overall model involving craving predicting the overall PTSD symptom severity nor the one involving craving predicting each PTSD symptom separately was significant (all ps > .21).
DiscussionThe present study is a preliminary investigation of the day-to-day relationships between posttraumatic symptomatology and alcohol craving among individuals seeking treatment for an AUD, most of whom screened positive for comorbid PTSD. The within-day pattern of results suggests that on days with greater PTSD severity, these individuals also experienced greater craving that same day. PTSD symptoms also predicted next-day craving, which would generally support the self-medication hypothesis, as it suggests that individuals may respond to the emotional distress of PTSD symptom exacerbations with increased urges to use alcohol. Of the symptoms of PTSD that we assessed via the daily IVR monitoring protocol, increased startle and increased anger/irritability were particularly associated with greater craving on a given day. These symptoms are from the hyperarousal cluster of the DSM–IV PTSD diagnostic criteria (American Psychiatric Association, 1994). Although preliminary, due to the small sample size and the restricted symptom pool included in the IVR protocol, these results suggest that the experience of heightened psychophysiological arousal on a given day may be especially problematic, leading to increased alcohol craving. Although we were not able to examine consumption itself, due to the low rate of drinking in this sample of individuals in early recovery, it is possible that this may also lead to increased risk for alcohol consumption and relapse. This pattern of results is consistent with the earlier cross-sectional study by McFall and colleagues (1992), which found that the hyperarousal cluster was the only PTSD symptom cluster significantly associated with alcohol use among Vietnam veterans, although other studies have failed to find a relationship between hyperarousal specifically and drinking (Jakupcak et al., 2010; Maguen et al., 2009; Read et al., 2004). Inconsistencies across these studies may reflect differences in gender of the participants, recency and severity of trauma exposure, treatment-seeking status, and whether the sample was still drinking. Studies of alcohol craving have generally not disaggregated PTSD symptoms, but further studies may wish to elucidate which PTSD symptom changes are most indicative of increased cravings.
The effects of PTSD symptoms on next-day alcohol craving were substantially different from the same-day pattern of results. Specifically, following a day of relatively higher PTSD symptoms, overall symptom severity was not associated with higher alcohol craving. However, on the individual symptom level, more distress from nightmares the night before, more emotional numbing, and higher hypervigilance predicted greater alcohol craving on the next day. Both reexperiencing and hyperarousal symptoms, when measured as symptom clusters, have been associated with increased alcohol use (Maguen et al., 2009; Read et al., 2004), as has emotional numbing (Jakupcak et al., 2010). As all of the research conducted to date has consisted of retrospective data at the between-persons, rather than within-persons, level, these findings suggest that PTSD symptoms and alcohol symptoms may influence each other in complex ways across days. If these findings are replicated and extended to include actual drinking behavior, especially in an acute-trauma-exposed sample, they may further elucidate how PTSD and alcohol use and cravings gradually develop into comorbid presentations over time.
The present results also suggest that greater anger/irritability the day before was associated with lower craving. It is puzzling that greater anger was associated with greater craving within the same day but was associated with lower craving the next day. While we have no ready explanation for this finding, it is possible that, for some participants, this shift was due to having consumed alcohol the same day anger was high, thereby perhaps temporarily mitigating craving the next day. The limited sample size and the generally low rates of alcohol consumption did not allow this possibility to be tested, but it is an important finding to follow up on in future research.
It is also noteworthy that we found no support for the idea that alcohol craving leads to next-day PTSD symptom exacerbation. The overall pattern of findings that greater PTSD symptomatology on a given day is associated with increased craving the next day, but that craving does not appear to be related to next-day PTSD symptom severity, lends more support to the self-medication model than to a mutual maintenance model. However, our inability to test these relationships using actual drinking behavior is unfortunate and tempers any conclusions that may be drawn. Future studies should attempt to examine the relationship between alcohol consumption and PTSD, especially in regard to the possibility of mutual maintenance of symptoms.
While the current study has noteworthy strengths, including the use of GEE to analyze data from an extended daily longitudinal assessment protocol with alcohol-treatment-seeking individuals who complied reasonably well, there are also important methodological limitations. The sample size is small, consists largely of males, and is mostly comprised of veterans. Small numbers of participants make it more likely that findings reflect vagaries of the sample. When looking at subgroups, like women, it is even more important to consider issues regarding a small and nonrepresentative sample. We also did not assess actual PTSD diagnostic status but instead screened for likely PTSD with the commonly used PCL-C. While the PCL-C has been found to be highly correlated with the gold-standard CAPS, caution should be exercised in generalizing the present results to individuals with comorbid PTSD/AUD. In order to follow-up on the findings regarding specific PTSD symptoms and findings regarding differential effects of gender, it is necessary to use event level methodologies with larger sample sizes, with a greater proportion of women, and with civilian samples whose PTSD diagnostic status is established with standard diagnostic assessments.
An additional limitation is that the IVR monitoring protocol included only 7 of the 17 standard PTSD symptoms laid out in the DSM–IV. This decision was carefully considered to reduce measurement burden and because there were no other studies involving treatment-seeking individuals at the time this study was initiated. Based on the novelty of the methodology with this population, we determined that the risk of overburdening participants, causing possible distress, or encouraging nonresponses and increasing missing data were greater than the benefit of attempting to assess all 17 PTSD symptoms. However, this choice does significantly limit the interpretability of the results. Future studies should examine the influence of all 17 PTSD symptoms using a daily assessment protocol for people with comorbid PTSD and AUD to test which PTSD symptoms are associated with same-day and next-day alcohol craving and use. Studies with more participants and a more comprehensive daily measure of PTSD could also be used for survival analyses to examine whether PTSD and increased cravings lead to alcohol lapses. Moreover, our use of the PCL items also does not link the symptoms to particular Criterion A stressors. Thus, it is possible that respondents were endorsing symptoms that were more indicative of depression or of more general distress than PTSD per se. Replication of this study in combination with a diagnostic measure of PTSD would help to address this issue.
Despite these important limitations, these findings have potential clinical implications. Our findings regarding specificity of PTSD symptoms and alcohol cravings highlight the need to assess symptoms of PTSD that may contribute to craving for, and subsequent use of, alcohol rather than simply tracking a client's overall level of PTSD severity. The present results suggest that individuals with comorbid PTSD and an AUD could particularly benefit from interventions that would either ameliorate PTSD symptoms or improve their ability to cope with these symptoms, so as to reduce the risk of craving and relapse. For example, Hien, Cohen, Miele, Litt, and Capstick (2004) demonstrated that two behavioral interventions, Seeking Safety and Relapse Prevention, were both effective at reducing PTSD and AUD symptoms for women with comorbid PTSD/AUD. In general, despite strong evidence of efficacy for treatment of PTSD, there has been little published examining the utility of frontline PTSD treatments for those with comorbid AUD. Early work on a frontline intervention for PTSD, prolonged exposure (PE), involving another SUD group (cocaine dependence) with comorbid PTSD, found unacceptably high rates of dropout (Brady, Dansky, Back, Foa, & Carroll, 2001). However, Foa and colleagues are currently evaluating PE with and without naltrexone for individuals with comorbid alcohol dependence and PTSD, and the results of this trial may indicate that PE is a viable intervention for this group (see Foa & Williams, 2010). Additionally, promising work is being done by Chard and colleagues using cognitive processing therapy (CPT) for PTSD among veterans with comorbid PTSD and SUD in residential treatment settings (Kaysen, Schumm, Pederson, Siem, & Chard, 2011b). An initial open-trial pilot test of CPT by this group found that of the 536 veterans treated with CPT, 49% had current or past AUD. Those with AUD did not differ from the PTSD-only group in treatment participation or in PTSD improvements over the course of treatment. However, this study did not include measures of alcohol use or alcohol problems; therefore, the impact of CPT on alcohol outcomes remains an open question. Thus, interventions have been developed both for AUD and for PTSD that emphasize coping skills and may help break the link between exacerbations in specific PTSD symptoms and increased alcohol cravings. Future studies could consider building on this methodology and using IVR to test whether skills taught in treatment then serve to weaken the associations we have found.
In conclusion, this preliminary study demonstrates that it is possible to evaluate the relationships between PTSD symptoms and alcohol craving in a socially unstable (i.e., over 44% were homeless during their participation in the study) treatment-seeking sample. The findings suggest that the hyperarousal symptoms of anger/irritability and startle are particularly associated with same-day alcohol craving, and that nightmare disturbance, greater emotional numbing, and greater hypervigilance are associated with greater alcohol craving the next day, while greater anger is associated with lower craving the next day. These results, along with the finding that craving did not predict next-day PTSD symptoms, are generally consistent with the self-medication hypothesis. Future research in this area could profitably explore more subtle formulations of the self-medication hypothesis, such as the cognitive dampening attention allocation model (see Armeli, Todd, & Mohr, 2005; Sayette, 1993), to better understand the mechanisms involved. Additionally, identification of moderators of self-medication, such as drinking to cope or drinking to enhance positive affect, would be useful to evaluate via “micro” longitudinal studies to better identify who is particularly at risk for relapse.
Footnotes 1 All GEE analyses were rerun, removing the three participants who did not screen positive for PTSD at intake, as assessed by the PCL-C. Different findings emerged only for the model of individual PTSD symptoms predicting next-day alcohol craving. After removing those participants not screening positive for PTSD, increased distressing dreams, b = 0.07, IRR = 1.08, p < .05, and lower anger/irritability, b = −0.06, IRR = 0.94, p < .05, significantly predicted greater craving the next day. However, increased hypervigilance, b = 0.06, IRR = 1.06, p = .07, and emotional numbing, b = 0.05, IRR = 1.05, p = .10, only marginally predicted greater next-day craving.
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Submitted: July 22, 2011 Revised: December 12, 2011 Accepted: December 14, 2011
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Source: Psychology of Addictive Behaviors. Vol. 26. (4), Dec, 2012 pp. 724-733)
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- Temperamental emotionality in preschool-aged children and depressive disorders in parents: Associations in a large community sample.
- Authors:
- Olino, Thomas M.. Western Psychiatric Institute and Clinic, Pittsburgh, PA, US, olinotm@upmc.edu
Klein, Daniel N.. Department of Psychology, Stony Brook University, Stony Brook, NY, US
Dyson, Margaret W.. Department of Psychology, Stony Brook University, Stony Brook, NY, US
Rose, Suzanne A.. Department of Psychology, Stony Brook University, Stony Brook, NY, US
Durbin, C. Emily. Department of Psychology, Northwestern University, Evanston, IL, US - Address:
- Olino, Thomas M., Western Psychiatric Institute and Clinic, 3811 O’Hara Street, Pittsburgh, PA, US, 15213, olinotm@upmc.edu
- Source:
- Journal of Abnormal Psychology, Vol 119(3), Aug, 2010. pp. 468-478.
- NLM Title Abbreviation:
- J Abnorm Psychol
- Page Count:
- 11
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- The Journal of Abnormal and Social Psychology; The Journal of Abnormal Psychology; The Journal of Abnormal Psychology and Social Psychology
- ISSN:
- 0021-843X (Print)
1939-1846 (Electronic) - Language:
- English
- Keywords:
- child temperament, depression, parents, risk, vulnerability, communities
- Abstract:
- Researchers and clinicians have long hypothesized that there are temperamental vulnerabilities to depressive disorders. Despite the fact that individual differences in temperament should be evident in early childhood, most studies have focused on older youth and adults. We hypothesized that if early childhood temperament is a risk factor for depressive disorders, it should be associated with better established risk markers, such parental depression. Hence, we examined the associations of laboratory-assessed positive emotionality (PE), negative emotionality (NE), and behavioral inhibition (BI) with semistructured interview-based diagnoses of parental depressive disorders in a community sample of 536 3-year old children. Children with higher levels of NE and BI had higher probabilities of having a depressed parent. However, both main effects were qualified by interactions with child PE. At high and moderate (but not low) levels of child PE, greater NE and BI were associated with higher rates of parental depression. Conversely, at low (but not high and moderate) levels of child NE, low PE was associated with higher rates of parental depression. Child temperament was not associated with parental anxiety and substance use disorders. These findings indicate that laboratory-assessed temperament in young children is associated with parental depressive disorders; however, the relations are complex, and it is important to consider interactions between temperament dimensions rather than focusing exclusively on main effects. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Childhood Development; *Major Depression; *Personality; *Risk Factors; Communities; Parents
- Medical Subject Headings (MeSH):
- Adult; Affective Symptoms; Child of Impaired Parents; Child, Preschool; Confidence Intervals; Depressive Disorder; Female; Humans; Logistic Models; Male; Odds Ratio; Psychology, Child; Risk Factors; Temperament
- PsycINFO Classification:
- Psychosocial & Personality Development (2840)
Affective Disorders (3211) - Population:
- Human
Male
Female - Location:
- US
- Age Group:
- Childhood (birth-12 yrs)
Preschool Age (2-5 yrs)
Adulthood (18 yrs & older) - Tests & Measures:
- Four Factor Index of Social Status
Laboratory Temperament Assessment Battery
Preschool Age Psychiatric Assessment DOI: 10.1037/t39097-000
Peabody Picture Vocabulary Test - Grant Sponsorship:
- Sponsor: National Institute of Mental Health
Grant Number: RO1-MH069942
Recipients: Klein, Daniel N.
Sponsor: National Center for Research Resources, General Clinical Research Center (GCRC)
Grant Number: M01-RR10710
Other Details: Stony Brook University
Recipients: No recipient indicated - Methodology:
- Empirical Study; Quantitative Study
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- Accepted: Mar 26, 2010; Revised: Mar 25, 2010; First Submitted: Mar 1, 2009
- Release Date:
- 20100802
- Correction Date:
- 20160114
- Copyright:
- American Psychological Association. 2010
- Digital Object Identifier:
- http://dx.doi.org/10.1037/a0020112
- PMID:
- 20677836
- Accession Number:
- 2010-15289-003
- Number of Citations in Source:
- 57
- Persistent link to this record (Permalink):
- http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2010-15289-003&site=ehost-live
- Cut and Paste:
- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2010-15289-003&site=ehost-live">Temperamental emotionality in preschool-aged children and depressive disorders in parents: Associations in a large community sample.</A>
- Database:
- PsycINFO
Temperamental Emotionality in Preschool-Aged Children and Depressive Disorders in Parents: Associations in a Large Community Sample
By: Thomas M. Olino
Western Psychiatric Institute and Clinic, University of Pittsburgh Medical Center, Oakland, Pennsylvania;
Daniel N. Klein
Department of Psychology, Stony Brook University
Margaret W. Dyson
Department of Psychology, Stony Brook University
Suzanne A. Rose
Department of Psychology, Stony Brook University
C. Emily Durbin
Department of Psychology, Northwestern University
Acknowledgement: This work was supported by National Institute of Mental Health Grant RO1-MH069942 (to Daniel N. Klein) and General Clinical Research Center (GCRC) Grant M01-RR10710 to Stony Brook University from the National Center for Research Resources. We thank H. Hill Goldsmith for consultation on the Lab-TAB and James Jaccard for consultation on data analysis.
Researchers have long hypothesized that temperament is a precursor or predisposing factor for mood disorders (Clark, 2005; Compas, Connor-Smith, & Jaser, 2004; Klein, Durbin, & Shankman, 2009). Currently, the leading temperament model of depressive disorders, proposed by Clark and Watson (e.g., Clark & Watson, 1999), posits that depression develops from low positive emotionality (PE) and high negative emotionality (NE). PE and NE are higher order factors included in most structural models of temperament/personality in children and adults (Caspi & Shiner, 2006; Clark & Watson, 1999; Rothbart & Bates, 2006). PE has a number of facets, including positive affect, reward sensitivity, and sociability, although recent studies indicate that sociability may be less central than the affective and motivational components (Lucas, Diener, Grob, Suh, & Shao, 2000). NE refers to high negative mood reactivity, including fear, sadness, and anger. Clark and Watson (1999) hypothesized that low PE has a relatively specific association with depression, although it may also play a role in several other disorders, such as social phobia and schizophrenia. In contrast, high NE is a nonspecific risk factor that plays a major role in anxiety disorders and a significant, but somewhat smaller, role in externalizing conditions such as substance use, antisocial personality, and conduct disorders (Clark, 2005).
Another temperament construct that may be related to risk for depressive disorders is behavioral inhibition (BI; Kagan, 1989). BI refers to a disposition for wariness, reticence, and decreased exploration in unfamiliar contexts and with unfamiliar people. Although BI is primarily viewed as predisposing to anxiety disorders, it may also play a role in depression (Fox, Henderson, Marshall, Nichols, & Ghera, 2005; Hirshfeld-Becker et al., 2008). BI overlaps with NE in that both include a substantial fear component (Muris & Dietvorst, 2006).
Many cross-sectional studies have reported that youth and adults with depressive disorders or symptoms exhibit diminished levels of PE and elevated levels of NE (e.g., Brown, Chorpita, & Barlow, 1998; Lonigan, Hooe, David, & Kistner, 1999). In addition, prospective studies have found that low PE and high NE predict the onset of depressive episodes and increases in depressive symptoms (e.g., Kendler, Gatz, Gardner, & Pedersen, 2006; Krueger, 1999; Lonigan, Phillips, & Hooe, 2003). However, the role of temperament in depressive disorders cannot be regarded as established due to three critical limitations in the literature.
First, most studies have used adults, and studies of youth have generally focused on older children and adolescents. As temperament is generally defined as individual differences in emotional reactivity and regulation that are evident by early childhood (Caspi & Shiner, 2006; Rothbart & Bates, 2006), it is critical to determine whether associations with risk for mood disorders are present in young children.
Second, with some notable exceptions (Hirschfeld et al., 1989; Kendler et al., 2006; Kendler, Neale, Kessler, Heath, & Eaves, 1993; Ormel, Oldehinkel, & Vollebergh, 2004), most studies have not excluded participants with a current or past history of depression. Thus, they cannot determine whether temperament predicts the onset of disorder, as the assessment of temperament may have been influenced by current or prior depression. Moreover, excluding participants with current or past depression is also problematic if the sample has entered the risk period for depression, as it may remove the individuals at highest risk from the sample (Klein et al., 2009). Hence, a better approach is to study children before the age of risk for depression.
Third, almost all studies have used self-report measures of temperament. As virtually all of these studies have also assessed depression via self-report (either with questionnaires or interviews), this raises the possibility that associations are inflated by shared method variance. In addition, most self-report measures of NE, and to a lesser extent PE, have items that overlap with depressive symptoms (Ormel, Rosmalen, & Farmer, 2004), potentially creating a problem with measurement confounding.
Thus, research is needed to determine whether temperament in early childhood is associated with risk for depressive disorders. As the prevalence of depressive disorders in early childhood is extremely low (Lavigne, LeBailly, Hopkins, Gouze, & Binns, 2009) and as young children cannot complete self-report inventories, studying this population also avoids the other problems discussed above. However, research on temperament in young children and risk for depression poses at least two significant challenges of its own. First, prospective studies of risk for depressive disorders in young children require 20–30 years to complete, as the incidence of mood disorders does not peak until young adulthood (Kessler et al., 2005). Although long-term follow-up studies are ultimately necessary, in the short term a useful and informative strategy is to determine whether early child temperament is associated with known risk factors for depressive disorders. The best established risk factor is parental history of depressive disorder (Hammen, 2009). Indeed, many investigators have hypothesized that the nature of the diathesis in the intergenerational transmission of depression involves a temperamental vulnerability (e.g., Silberg & Rutter, 2002).
Second, temperament in young children cannot be assessed with self-report. Hence, informants, such as parents, or behavioral observations must be used. Parent-reports have several strengths, including low cost and capitalizing on the parent's observations of the child over time and across multiple contexts. However, parent-reports can be influenced by the parent's experience, frame of reference, and expectations (Kagan, 1998). This is particularly problematic in studies of the association between child temperament and parental depression, as the parent contributes information on both variables. In addition to the question of shared method variance, there is evidence that being depressed biases parents' reports of child temperament, in that it is associated with greater discrepancies between parents' reports and both other informants' reports and laboratory measures of temperament (Leerkes & Crockenberg, 2003; Whiffen, 1990).
Observational measures, although time consuming and limited to a single context and relatively brief time frame, provide a more objective and controlled approach to assessing temperament in young children. However, few studies have examined the association between observational measures of early childhood temperament and risk for depressive disorders. In a seminal article, Caspi, Moffitt, Newman, and Silva (1996) examined the associations between examiners' global ratings collected when a large birth cohort of children was 3 years old and psychopathology at age 21. They found that a cluster of behaviors labeled as inhibited, which included a mixture of items tapping aspects of BI, NE, and low PE, predicted the development of major depressive disorder (MDD) but not anxiety and substance use disorders. Unfortunately, the assessment procedures were not specifically designed to assess temperament, and the rating scales were not developed to reflect contemporary temperament models.
Four studies have examined the association between observational assessments of early childhood temperament and parental depressive disorders. Three of these studies focused exclusively on BI. In a sample of 88 2- to 3.5-year-olds, Kochanska (1991) found that the children of parents with bipolar disorder exhibited greater BI than children of nondepressed parents, but neither group differed from children of parents with MDD. In a pilot study (N = 56) and a subsequent study (N = 284), Rosenbaum et al. (1988, 2000) reported that the 2- to 7-year-old children of parents with a history of both panic disorder and MDD exhibited greater BI than children of healthy controls. However, children of parents with MDD alone and children of parents with panic disorder alone did not differ from any of the other groups. Finally, in a community sample of 100 3-year-old children, Durbin, Klein, Hayden, Buckley, and Moerk (2005) found that low child PE, but not NE or BI, was associated with an increased risk of maternal depressive disorders. None of the three temperament dimensions was associated with risk for paternal depressive disorders.
Thus, data on the association between observational measures of temperament in young children and parental mood disorders are quite limited. Samples have generally been small; only one study examined PE and NE; and although more data are available for BI, the results are equivocal. We sought to provide a more conclusive test by conducting a family study of a large community sample of 3-year-old children to determine whether observational measures of child PE, NE, and BI are associated with a lifetime history of depressive disorder in their parents. We hypothesized that higher levels of child NE and BI and lower levels of child PE would be associated with parental depression. In addition, we examined two additional issues.
First, although Clark and Watson's (1999) model emphasizes the roles of both low PE and high NE in risk for depression, they have not explicitly addressed the nature of the relation between the two dimensions (Shankman & Klein, 2003). One possibility is that they are additive and that the sum of the two dimensions determines risk. A second possibility is that they interact, reflecting a nonadditive effect on risk. Although an interaction could take several forms (see Morris, Ciesla, and Garber's, 2008, discussion in the context of cognitive vulnerability to depression), the most plausible is that the combination of high NE and low PE confers greater risk than the sum of the risks associated with each dimension alone. Only a handful of studies, all using adolescents or adults and self-report measures, have tested the interaction of PE and NE in predicting depression. Several studies found that the combination of low PE and high NE was associated with greater depression (Gershuny & Sher, 1998; Joiner & Lonigan, 2000; Wetter & Hankin, 2009), but others failed to find an interaction (Jorm et al., 2000; Kendler et al., 2006; Verstraeten, Vasey, Raes, & Bijttebier, 2009). To our knowledge, no studies have examined the relation between these two temperament dimensions in young children using observational measures, or with respect to their association with parental depression. Hence, we investigated whether the interaction between child PE and NE was associated with parental depressive disorders. Given the close conceptual and empirical association between BI and NE, we also explored the interaction of child PE and BI with parental depression, but we did not test the interaction of BI and NE due to the overlap between these constructs.
Second, to explore the specificity of the relationship between child temperament and parental psychopathology, we examined the associations of child PE, NE, and BI with parental anxiety and substance use disorders. We chose these broad groups for two reasons: (a) The existing literature provides a basis for clear hypotheses regarding their associations with PE and NE; and (b) along with depressive disorders, they are the most prevalent groups of disorders in community samples (e.g., Kessler et al., 2005). Based on Clark and Watson's (1999) original model, we hypothesized that child NE would be associated with both parental anxiety and substance use disorders, whereas child PE would be unrelated to both groups of disorders in parents. However, as a number of studies have found that low PE is also associated with social phobia (e.g., Brown et al., 1998; Naragon-Gainey, Watson, & Markon, 2009), and some studies have suggested that BI may be specifically associated with social phobia, rather than with a broader spectrum of anxiety disorders (Fox et al., 2005; Hirshfeld-Becker et al., 2008), we conducted secondary analyses examining the associations between child temperament and parental social phobia.
MethodThe sample consisted of 559 families from a suburban community. Potential participants were identified using a commercial mailing list and screened by telephone. Families with a child between 3 and 4 years of age who lived with an English-speaking biological parent and did not have significant medical conditions or developmental disabilities were included. Of eligible families, 66.4% entered the study. Families who agreed and families who declined to participate did not differ significantly on child sex and race/ethnicity or parental marital status, education, or employment status. Eighteen families without parental diagnostic data, three families in which one parent had bipolar disorder and the coparent did not have a history of depressive disorder, and two families in which the child had already developed a mood disorder were excluded from the present analyses, leaving a final sample of 536 families.
The mean age of the children was 42.2 months (SD = 3.1); 289 (53.9%) were boys and 247 were girls (46.1%). On average mothers were 36.0 years old (SD = 4.5) and fathers were 38.3 years old (SD = 5.4). Most participants (86.9%) were White and middle class, as measured by Hollingshead's Four Factor Index of Social Status (M = 45.1; SD = 10.9). Approximately half the mothers (54.7%) and fathers (45.7%) had at least a 4-year college degree. Most children (95.0%) lived with both biological parents, and 51.9% of the mothers worked outside the home part time or full time. Children were of average cognitive ability according to the Peabody Picture Vocabulary Test (Dunn & Dunn, 1997; M = 102.9, SD = 13.9). The parent with primary caretaking responsibilities (generally the mother) was interviewed about the child with the Preschool Age Psychiatric Assessment (PAPA; Egger, Ascher, & Angold, 1999), which assesses psychopathology within the past 3 months. The two children with PAPA diagnoses of MDD or dysthymic disorder were excluded from the analyses.
Child Temperament
Each child and a parent (95.0% mothers) visited the laboratory for a 2-hr observational assessment of temperament that included a standardized set of 12 episodes selected to elicit a range of temperament-relevant behaviors. Eleven episodes were from the Laboratory Temperament Assessment Battery (Lab-TAB; Goldsmith, Reilly, Lemery, Longley, & Prescott, 1995) and one was adapted from a Lab-TAB episode. Using an independent sample, we previously reported moderate stability of laboratory ratings of temperament from ages 3 to 7 (rs = .46 and .45 for PE and NE, respectively) and moderate concurrent and longitudinal associations between Lab-TAB ratings and home observations (Durbin, Hayden, Klein, & Olino, 2007). Each task was videotaped through a one-way mirror and later coded. To prevent carryover effects, no episodes presumed to evoke similar affective responses occurred consecutively and each episode was followed by a brief play break to allow the child to return to a baseline affective state. The parent remained in the room with the child for all episodes except “Stranger Approach” and “Box Empty” (see below) but was instructed not to interact with the child (except in “Pop-Up Snakes”) and was seated facing at a right angle from the experimenter and child and given questionnaires to complete.
The episodes, in order of presentation, were as follows:
1. “Risk Room.”
Child explored a set of novel and ambiguous stimuli, including a Halloween mask, balance beam, and black box.
2. “Tower of Patience.”
Child and experimenter alternated turns in building a tower. The experimenter took increasing amounts of time before placing her block on the tower during each turn.
3. “Arc of Toys.”
Child played independently with toys for 5 min before the experimenter asked the child to clean up the toys.
4. “Stranger Approach.”
Child was left alone briefly in the room before a male accomplice entered, speaking to the child while slowly walking closer.
5. “Make That Car Go.”
Child and experimenter raced remote-controlled cars.
6. “Transparent Box.”
Experimenter locked an attractive toy in a transparent box, leaving the child alone with a set of nonworking keys. After a few minutes, the experimenter returned and told the child that she had left the wrong set of keys. The child used the new keys to open the box and play with the toy.
7. “Exploring New Objects.”
Child was given the opportunity to explore a set of novel and ambiguous stimuli, including a mechanical spider, a mechanical bird, and sticky soft gel balls.
8. “Pop-Up Snakes.”
Child and experimenter surprised the parent with a can of potato chips that actually contained coiled snakes.
9. “Impossibly Perfect Green Circles.”
Experimenter repeatedly asked the child to draw a circle on a large piece of paper, mildly criticizing each attempt.
10. “Popping Bubbles.”
Child and experimenter played with a bubble-shooting toy.
11. “Snack Delay.”
Child was instructed to wait for the experimenter to ring a bell before eating a snack. The experimenter systematically increased the delay before ringing the bell.
12. “Box Empty.”
Child was given an elaborately wrapped box to open under the impression that a toy was inside. After the child discovered the box was empty, the experimenter returned with several toys for the child to keep.
Coding procedures
Each display of facial, bodily, and vocal positive affect; fear; sadness; and anger in each episode was rated on a 3-point scale (low, moderate, high). Ratings were summed separately within each channel (facial, bodily, vocal) across the 12 episodes, standardized, and summed across the three channels to derive total scores for positive affect, fear, sadness, and anger. Interest was rated on a single 4-point scale (none, low, moderate, and high) for each episode based on the child's comments about the activity and how engaged the child was in play. Interest ratings were then summed across the 12 episodes. PE consisted of the sum of the standardized total positive affect and interest variables. NE was the sum of the standardized total sadness, fear, and anger variables.
BI was coded using an approach that was similar to most previous studies (e.g., Kagan, 1989; Pfeifer, Goldsmith, Davidson, & Rickman, 2002). The three episodes specifically designed to assess BI (“Risk Room,” “Stranger Approach,” and “Exploring New Objects”) were divided into 20- or 30-s epochs, and a series of affective and behavioral codes were rated for each epoch (Goldsmith et al., 1995). Within each epoch, a maximum intensity rating of facial, bodily, and vocal fear was coded on a scale of 0 (absent) to 3 (highly present and salient). Based on previous studies using the Lab-TAB (Durbin et al., 2005; Pfeifer et al., 2002), BI was computed as the average standardized ratings of latency to fear (reversed); and facial, vocal, and bodily fear (“Risk Room,” “Stranger Approach,” and “Exploring New Objects”); latency to touch objects; total number of objects touched (reversed); tentative play; referencing the parent; proximity to parent; referencing the experimenter; and time spent playing (reversed; “Risk Room” and “Exploring New Objects”); startle (“Exploring New Objects”); sad facial affect (“Exploring New Objects” and “Stranger Approach”); and latency to vocalize; approach toward the stranger (reversed); avoidance of the stranger; gaze aversion; and verbal/nonverbal interaction with the stranger (reversed; “Stranger Approach”).
For each participant, different raters coded PE/NE and BI. Most episodes were coded by different raters. Coders were unaware of information on parental psychopathology. PE and NE had adequate internal consistency (αs = .82 and .74, respectively) and interrater reliability (interrater correlation coefficients [ICCs] = .89 and .82, respectively; N = 35). BI exhibited good internal consistency (α = .80) and interrater reliability (ICC = .88; N = 28). NE correlated –.12 (p < .01) with PE and .40 (p < .001) with BI; the correlation between PE and BI was –.22 (p < .001).
Parental Psychopathology
Children's biological parents were interviewed using the Structured Clinical Interview for DSM–IV, Non-Patient Version (SCID–NP; First, Spitzer, Gibbon, & Williams, 1996). Interviews were conducted by telephone, which yields similar results as face-to-face interviews (Rohde, Lewinsohn, & Seeley, 1997), by two master's-level raters with no knowledge of the temperament ratings. SCID–NPs were obtained from 535 (99.8%) mothers and 443 (82.6%) fathers. When parents were unavailable, family history interviews were conducted with the coparent. Diagnoses based on family history data were obtained for an additional one (0.2%) mother and 83 (15.5%) fathers. Based on audiotapes of 30 SCID–NP interviews, kappas for interrater reliability of lifetime diagnoses were .93 for mood disorder, .91 for anxiety disorder (.87 for social phobia), and 1.00 for substance abuse/dependence.
Of the children, 219 (40.9%) had at least one parent with a lifetime depressive disorder (35.3% MDD; 14.6% dysthymic disorder); 32.3% of mothers and 17.3% of fathers had a lifetime depressive disorder. Only 5.2% of children had a parent with current MDD or dysthymia. There were 237 children (44.2%) with a parent with a lifetime anxiety disorder (10.6% panic, 1.7% agoraphobia without panic, 20.7% social phobia, 18.5% specific phobia, 6.5% posttraumatic stress, 8.2% generalized anxiety, and 4.9% obsessive-compulsive disorder); 34.0% of mothers and 19.0% of fathers had a lifetime anxiety disorder. Finally, 264 children (49.3%) had a parent with a lifetime substance use disorder (44.6% alcohol abuse/dependence, 17.2% cannabis abuse/dependence, 9.5% hard drug abuse/dependence); 22.0% of mothers and 36.9% of fathers had a lifetime substance use disorder.
The clinical characteristics of the depressed parents who were directly interviewed were as follows (when both parents had SCID–NP-diagnosed depression, we selected the more severe value): severity of worst lifetime MDD episode was 28.4% mild, 46.4% moderate, and 25.2% severe; onset of earliest depressive disorder occurred on average at 21.5 years (SD = 8.6); and history of recurrent MDD episodes was 32.5%.
Data Analyses
Preliminary analyses revealed significant gender differences in PE, t(534) = 2.21, p < .05, and BI, t(534) = 3.56, p < .001. Girls exhibited greater PE (M = 0.11, SD = 1.01) and BI (M = 0.16, SD = 1.04) than boys (M = –0.09, SD = 0.99; M = –0.14, SD = 0.95; respectively). Gender was not associated with NE. There were no substantive differences in results when analyses included gender and temperament by gender interactions, so we do not consider child gender further.
Our primary analyses focused on the associations of child PE, NE, and BI with lifetime parental psychopathology. Secondary analyses examined associations with the three NE facets (sadness, fear, and anger) and with maternal and paternal disorders separately. Analyses consisted of hierarchical multiple logistic regression models. Consistent with traditional bottom-up family study designs, and to facilitate testing interactions between temperament dimensions, child temperament variables were treated as independent variables and parental psychopathology was the dependent variable. As these analyses are intended to test cross-sectional associations, we do not make any assumptions regarding direction of causality. Independent variables were centered, and cross-product terms were created by multiplying the relevant independent variables to represent the interaction. Main effects were entered on the first step and interactions were entered on the second step. To interpret significant interactions, one temperament dimension was plotted against the predicted probability of parental depression at 1 SD above the mean, the mean, and 1 SD below the mean of the second dimension, and the significance of the simple slopes was tested (Jaccard, 2001). In interpreting these analyses, it is important to bear in mind that high and low levels of the moderator reflect participants' relative standing in the sample, rather than the clinical significance of the trait.
ResultsFirst, we examined the associations of child PE and NE with parental depressive disorders using hierarchical logistic regression (see Table 1). The main effect for NE was significant; higher levels of child NE were associated with a greater predicted probability of parental depression. However, this was qualified by a significant PE × NE interaction. Interestingly, the interaction did not take the anticipated form of a synergistic effect of low PE and high NE. Instead, simple slopes tests indicated that at high (+1 SD: B = .42, SE = .13, p < .01) and moderate (M: B = .22, SE = .09, p < .05) levels of child PE, increases in child NE were associated with a greater predicted probability of parental depressive disorder. In contrast, at low (–1 SD) levels of child PE, there was no association between child NE and parental depression (B = .03, SE = .11, p = .82; see Figure 1, top left).
Multiple Logistic Regression Models of Associations Between Child Temperament and Parental Depressive Disorder
Figure 1. Top panels: Relation between predicted probability of parental depressive disorder and child negative emotionality (NE) as a function of child positive emotionality (PE). Top left: PE as moderator; top right: NE as moderator. Bottom panels: Relation between predicted probability of parental depressive disorder and child behavioral inhibition (BI) as a function of child PE. Bottom left: PE as moderator; bottom right: BI as moderator.
As the choice of which temperament dimension to use as moderator is arbitrary, we also examined the association of child PE with parental depression at different levels of child NE (see Figure 1, top right). Simple slopes tests indicated that at high and moderate levels of NE, child PE was not associated with parental depression (B = .11, SE = .11, p = .32 and B = –.09, SE = .09, p = .35, respectively). However, at low levels of NE, low child PE was associated with a greater probability of parental depression (B = –.28, SE = .14, p < .02).
We also examined the associations between each of the three facets of child NE (fear, sadness, and anger), child PE, and parental depressive disorder (see Table 1). There was a significant main effect for fear, indicating that higher levels of child fearfulness were associated with a greater probability of parental depressive disorder. In addition, there was an interaction that approached significance (p = .05) between sadness and PE. The form of the interaction was similar to that described above for NE as a whole (figure available upon request). At high (B = .30, SE = .14, p < .05) and moderate (B = .17, SE = .10, p = .07) levels of child PE, increasing child sadness was associated with a greater predicted probability of parental depressive disorder. In contrast, at low levels of child PE, child sadness was not associated with the probability of parental depression (B = .05, SE = .10, p = .65). Reversing the moderator, at high and moderate levels of sadness, child PE was not associated with parental depression (B = .05, SE = .10, p = .66 and B = –.09, SE = .09, p = .36, respectively). However, at low levels of sadness, there was a trend for PE to be inversely associated with rates of parental depression (B = –.22, SE = .13, p < .09). There were no significant main or interaction effects for child anger.
Next, we examined the associations of child PE and BI with parental depression (see Table 1). There was a significant main effect for BI, which was qualified by a significant PE × BI interaction. The form of the interaction was fairly similar to the PE × NE interaction described above. Simple slopes tests indicated that at high (B = .39, SE = .13, p < .01) and moderate (B = .22, SE = .09, p < .05) levels of child PE, increases in child BI were associated with a greater predicted probability of parental depressive disorder. However, at low levels of child PE, there was no association between BI and the probability of parental depression (B = .06, SE = .11, p = .60; see Figure 1, bottom left). Reversing the moderator (see Figure 1, bottom right), at high and moderate levels of BI, PE was not associated with parental depression (B = .12, SE = .12, p = .29 and B = –.04, SE = .09, p = .69, respectively). At low levels of BI, there was a nonsignificant tendency for lower child PE to be associated with higher rates of parental depression (B = –.21, SE = .13, p = .11).
In the next set of analyses, we examined associations of child NE, PE, and BI with maternal and paternal depressive disorders separately. In the model examining child NE and PE and maternal depression, there was a nonsignificant main effect of PE (odds ratio [OR] = 0.92, 95% CI [0.76, 1.12], p = .43), a significant main effect for NE (OR = 1.34, 95% CI [1.11, 1.62], p < .01), and a significant NE × PE interaction (OR = 1.29, 95% CI [1.08, 1.56], p < .01). Similar to the results for the sample as a whole, simple slopes tests indicated that at high (B = .55, SE = .14, p < .001) and moderate (B = .29, SE = .09, p < .01) levels of child PE, increasing child NE was associated with a greater probability of maternal depressive disorder. In contrast, at low levels of child PE, NE was not associated with maternal depression (B = .03, SE = .12, p = .08; see Figure 2, top left). When NE was used as the moderator, the results were also similar to those for both parents considered together. At high and moderate levels of NE, PE was not associated with maternal depression (B = .18, SE = .12, p = .13 and B = –.08, SE = .10, p = .43, respectively). However, at low levels of NE, PE was inversely associated with maternal depression (B = –.34, SE = .15, p < .03; see Figure 2, top right).
Figure 2. Top panels: Relation between predicted probability of maternal depressive disorder and child negative emotionality (NE) as a function of child positive emotionality (PE). Top left: PE as moderator; top right: NE as moderator. Bottom panels: Relation between predicted probability of paternal depressive disorder and child behavioral inhibition (BI) as a function of child PE. Bottom left: PE as moderator; bottom right: BI as moderator.
In the model examining child BI and PE and maternal depression, there was a trend for a main effect for BI (OR = 1.20, 95% CI [0.99, 1.44], p = .06). However, the main effect for PE (OR = 0.99, 95% CI [0.81, 1.19], p = .88) and the BI × PE interaction (OR = 1.10, 95% CI [0.94, 1.29], p = .22) were not significant.
In the model for child PE and NE and paternal depression, the main effects for PE (OR = 0.95, 95% CI [0.75, 1.21], p = .68) and NE (OR = 1.12, 95% CI [0.89, 1.41], p = .32) and the PE × NE interaction (OR = 1.00, 95% CI [0.83, 1.21], p = .95) were all nonsignificant. However in the model examining child BI and PE and paternal depression, although the main effect for PE was nonsignificant (OR = 0.91, 95% CI [0.71, 1.16], p = .46), there was a trend for a main effect for BI (OR = 1.14, 95% CI [0.90, 1.44], p = .06) and a significant PE × BI interaction (OR = 1.31, 95% CI [1.04, 1.63], p < .05). Simple slopes tests indicated that at high levels of child PE, BI was associated with a greater predicted probability of paternal depressive disorder (B = .40, SE = .17, p < .05). However, at moderate (B = .13, SE = .12, p = .27) and low (B = –.14, SE = .16, p = .43) levels of child PE, BI was not associated with the probability of paternal depression (see Figure 2, bottom left). Reversing the moderator (see Figure 2, bottom right), at high and moderate levels of child BI, PE was not associated with paternal depression (B = .17, SE = .16, p = .30 and B = –.09, SE = .13, p = .46, respectively). However, at low levels of BI, child PE was inversely associated with paternal depression (B = –.35, SE = .18, p = .05).
Next, we examined bivariate associations of child PE, NE, and BI with three clinical characteristics of parental depression (severity of worst MDD episode, age of onset of earliest depressive disorder, and history of recurrent MDD) among children with a depressed parent. If both parents had histories of depression, the more severe value was used. None of these associations approached significance (data available upon request).
Finally, we conducted hierarchical logistic regression analyses examining the associations of child temperament with parental anxiety and substance use disorders, as well as with parental social phobia specifically. No significant main or interaction effects were observed (see Table 2).
Multiple Logistic Regression Models of Associations Between Child Temperament and Parental Anxiety Disorder, Social Phobia, and Substance Use Disorder
DiscussionWe conducted a bottom-up family study of the associations between laboratory-assessed PE, NE, and BI in 3-year-old children and a history of depressive disorders in their parents using a large community sample. Although some results were consistent with our hypotheses, others were unexpected. In accord with Clark and Watson's (1999) tripartite model, higher levels of child NE were associated with a higher probability of parental depressive disorder. However, in contrast to the tripartite model and our prior findings with a smaller sample (Durbin et al., 2005), child PE was not directly associated with parental depression.
These results were qualified by a significant interaction between child PE and NE. We had expected that if there was an interaction, greater levels of putative risk on both temperament dimensions (low PE and high NE) would be associated with a multiplicative increase in the probability of parental depression. Instead, we found that each temperament dimension was associated with parental depression only when the other temperament dimension was in the lower risk range. Thus, NE was positively associated with parental depressive disorders among children with moderate and high levels of PE, and child PE was inversely associated with parental depression among those with lower NE.
These findings are suggestive of the dual vulnerability model described by Morris et al. (2008), in which each of two risk factors is sufficient but neither is necessary to predict psychopathology. Thus, Morris et al. found that among adolescent girls with low (but not high) levels of stress, cognitive vulnerability predicted depressive symptoms, whereas among girls with low (but not high) levels of cognitive vulnerability, life stress predicted depressive symptoms.
It is unclear why our findings differed from those of our previous study (Durbin et al., 2005), which used largely similar methods. However, the current sample was over 5 times larger, providing greater power to detect main effects for NE and BI and PE × NE and PE × BI interactions. It is more difficult to explain the failure to find a main effect for PE in the present study. However, one possibility is that although the levels of PE were similar in both samples, the current sample exhibited a higher level of NE. As the present findings indicate that low PE is associated with higher rates of parental depression only at low levels of NE, the lower NE in the Durbin et al. (2005) sample may have obscured the interaction and created the appearance of a PE main effect.
The nature of the PE × NE interaction was also unexpected, although admittedly Clark and Watson (1999) have not addressed the issue of an interaction between these dimensions (Shankman & Klein, 2003), and the few studies that have reported testing this interaction have yielded inconsistent findings (e.g., Verstraeten et al., 2009; Wetter & Hankin, 2009). Our sample consisted of young children, and it is conceivable that the nature of the associations between NE, PE, and parental depression could change over the course of development. It is increasingly recognized that temperament is only moderately stable and that it changes over time (Caspi & Shiner, 2006; Roberts, Walton, & Viechtbauer, 2006). This may, in part, be due to environmental influences, such as life stressors. Stress is, almost by definition, associated with NE and it appears to attenuate PE (Berenbaum & Connelly, 1993; Bogdan & Pizzagalli, 2006). At the same time, PE has been shown to buffer the effects of stress, which could, in turn, reduce levels of NE (Tugade & Fredrickson, 2004; Wichers et al., 2007). As parental depression is a marker of a chronically stressful environment (Hammen, 2009), it is conceivable that, over time, the combination of these processes could lead to both increasingly lower PE and higher NE in children with a depressed parent.
Analyses at the facet level revealed interesting differences in the associations of child fear, sadness, and anger with parental depressive disorders. Child fear was directly associated with parental depression, whereas the association of child sadness with parental depression was moderated by child PE. The former finding is consistent with studies reporting elevated levels of fear-related psychopathology in older offspring of depressed parents (Warner, Wickramaratne, & Weissman, 2008). Finally, child anger was not associated with parental depression in any analyses. These data support the importance of going beyond the superfactor level and examining associations at the facet level (Klein et al., 2009).
Previous studies of child BI and parental depression have yielded ambiguous findings (Kochanska, 1991; Rosenbaum et al., 1988, 2000) but used small samples or focused primarily on parental anxiety disorders. In the present study, the results for BI were fairly similar to those for NE. Thus, there was a significant main effect indicating that higher levels of child BI were associated with higher rates of parental depressive disorders. However, this was qualified by a BI × PE interaction. At moderate and high levels of PE, children with higher levels of BI had higher probabilities of parental depression, whereas at low levels of PE, child BI was not associated with parental depression. Although the simple slope for the inverse association of PE with parental depression at low levels of BI was not statistically significant, it was in the same direction as the corresponding association for NE, and this effect was significant in the analyses for paternal depression.
We observed some differences in associations between temperament and maternal, as opposed to paternal, depression. Maternal depressive disorder was associated with the interaction between child PE and NE, whereas paternal depressive disorder was associated with the interaction between child PE and BI. While this could suggest that the gender of the depressed parent is associated with different temperamental vulnerabilities in children, there was considerable overlap between the confidence intervals of the odds ratios, indicating that these are probably not reliable differences.
Interestingly, the associations between child temperament and parental psychopathology were specific to depressive disorders. On the basis of the larger literature, we had expected child NE and BI to be associated with parental anxiety disorders. However, consistent with Durbin et al. (2005), these associations were not significant. In addition, we examined parental social phobia separately, as a number of studies have reported that social phobia is characterized by both low PE as well as high NE (Naragon-Gainey et al., 2009), and BI appears to predict the development of social phobia more strongly than other anxiety disorders (Hirshfeld-Becker et al., 2008). However, child temperament was also unrelated to parental social phobia.
We were also somewhat surprised that child NE was not associated with parental substance use disorders. However, other temperament dimensions not examined in this report, such as nonaffective constraint/effortful control, appear to play a greater role in externalizing disorders (Clark, 2005; Krueger, Markon, Patrick, Benning, & Kramer, 2007; Rothbart & Bates, 2006).
In this article, we focused on the associations between child temperament and parental depressive disorders, rather than elucidating the underlying causal processes. A variety of plausible mechanisms may be involved. For example, if particular temperament traits predispose one to depression, parents with those traits are more likely to have a history of depression and to transmit the traits to their children. Alternatively, the genetic factors influencing temperament and depression may overlap; hence the liabilities to both would be transmitted together. Indeed, there is evidence for shared genetic factors in the transmission of NE and MDD (e.g., Kendler et al., 2006). Finally, as suggested above, depressed parents may provide an environment that amplifies NE and BI, and/or suppresses PE (e.g., Yap, Allen, & Ladouceur, 2008).
The present study had a number of strengths. Children were 3 years old, the age at which temperament begins to exhibit greater stability (Caspi & Shiner, 2006) and prior to the age of risk for even pediatric depression. Only two children had already developed a depressive disorder, and they were excluded. The sample was large, particularly given the time-intensive nature of the temperament assessments. We used an unselected community sample, avoiding the biases associated with clinical samples. Our measures of child temperament and parental psychopathology were independent, consisting of laboratory observations and semistructured diagnostic interviews with both parents, respectively. Finally, we had sufficient power to examine temperament by temperament interactions.
However, the study also had limitations. First, the design was cross-sectional. It will be necessary to follow the children over time to determine whether temperamental emotionality predicts the onset of depressive disorders. Second, the laboratory observation of temperament was limited to a single session; hence it was influenced by situational, as well as trait, factors and may not provide a representative sample of behavior. However, as noted above, previous studies have demonstrated that laboratory temperament assessments are associated with home observations and exhibit moderate stability over time (Durbin et al., 2007; Pfeifer et al., 2002; Stifter, Putnam, & Jahromi, 2008). Third, in order to examine interactions between temperament traits, we treated temperament as the independent variable and parental psychopathology as the dependent variable. This is counterintuitive, as the direction of the effect probably runs from parent to child. Future studies might deal with this by exploring person-centered approaches within a longitudinal framework. Fourth, we may have included a few children who had recovered from a depressive episode more than 3 months before the assessment (the assessment period for the PAPA; Egger et al., 1999). However, the number of such cases would have been very small given the extremely low prevalence of depression in early childhood (Lavigne et al., 2009). Finally, the sample was primarily middle-class and Caucasian, which may limit generalizability.
In conclusion, this study adds to the nomological net linking early childhood temperament and risk for depression. Specifically, observational measures of child PE, NE, and BI were associated with the best established risk marker for depressive disorders: parental depression. However, the associations were complex, involving interactions between PE and NE/BI. Further work is needed to understand the mechanisms responsible for these associations, to determine whether early temperament actually predicts later depressive disorders, and to delineate the processes and pathways that lead from early temperament to later mood disorders.
Footnotes 1 In the analyses presented in Table 1, the main effects for PE and NE or BI were entered simultaneously. However, the nonsignificant main effects for PE are not due to partialing the effects of NE and BI, as the univariate association between PE and parental depression was not significant.
2 As the scoring system was extensively revised between the two studies, we cannot directly compare the temperament ratings from the videotaped episodes. However, in both studies, the experimenter completed a global rating scale after the laboratory visit that included identical sets of items for PE and NE (three items each; each item scored on a scale of 1–4). The mean PE scores in Durbin et al. (2005) and the present study were 9.86 (SD = 2.44) and 10.01 (SD = 2.30), respectively, t(635) = 0.56, p = .58; the mean NE scores in the two studies were 7.69 (SD = 1.89) and 9.49 (SD = 2.10), respectively, t(635) = 8.09, p < .001. The reason for the difference on NE is unclear, but it may be due to differences in recruitment methods (half the participants in Durbin et al. were recruited using ads and fliers and half were recruited with mailing lists, whereas all participants in this study were recruited through mailing lists; it is conceivable that parents of high NE children are less likely to respond to ads for studies) and/or the laboratory battery (the current battery substituted an episode designed to elicit NE [“Exploring New Objects”] for an episode designed to elicit contentment [“Painting a Picture”]).
3 The ORs and 95% CIs for the associations of maternal and paternal depression with the child PE × NE interaction were 1.29 [1.08, 1.56] and 1.03 [0.85, 1.23], respectively. The ORs and 95% CIs for the associations of maternal and paternal depression with the child PE × BI interaction were 1.10 [0.94, 1.29] and 1.31 [1.04, 1.64], respectively.
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Submitted: March 1, 2009 Revised: March 25, 2010 Accepted: March 26, 2010
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Source: Journal of Abnormal Psychology. Vol. 119. (3), Aug, 2010 pp. 468-478)
Accession Number: 2010-15289-003
Digital Object Identifier: 10.1037/a0020112
Record: 161- Title:
- Temporal variation in facilitator and client behavior during group motivational interviewing sessions.
- Authors:
- Houck, Jon M., ORCID 0000-0002-6565-4481. Center on Alcoholism, Substance Abuse, and Addictions, University of New Mexico, Albuquerque, NM, US, jhouck@unm.edu
Hunter, Sarah B.. RAND Corporation, Santa Monica, CA, US
Benson, Jennifer G.. Center on Alcoholism, Substance Abuse, and Addictions, University of New Mexico, Albuquerque, NM, US
Cochrum, Linda L.. Center on Alcoholism, Substance Abuse, and Addictions, University of New Mexico, Albuquerque, NM, US
Rowell, Lauren N.. Center on Alcoholism, Substance Abuse, and Addictions, University of New Mexico, Albuquerque, NM, US
D'Amico, Elizabeth J.. RAND Corporation, Santa Monica, CA, US - Address:
- Houck, Jon M., Center on Alcoholism, Substance Abuse, and Addictions, University of New Mexico, MSC11 6280, 1 University of New Mexico, Albuquerque, NM, US, 87131-0001, jhouck@unm.edu
- Source:
- Psychology of Addictive Behaviors, Vol 29(4), Dec, 2015. pp. 941-949.
- NLM Title Abbreviation:
- Psychol Addict Behav
- Page Count:
- 9
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- Bulletin of the Society of Psychologists in Addictive Behaviors; Bulletin of the Society of Psychologists in Substance Abuse
- Other Publishers:
- US : Educational Publishing Foundation
Society of Psychologists in Addictive Behaviors - ISSN:
- 0893-164X (Print)
1939-1501 (Electronic) - Language:
- English
- Keywords:
- adolescent, motivational interviewing, psychotherapy process, group intervention, alcohol and drug use
- Abstract:
- There is considerable evidence for motivational interviewing (MI) in changing problematic behaviors. Research on the causal chain for MI suggests influence of facilitator speech on client speech. This association has been examined using macro (session-level) and micro (utterance-level) measures; however, effects across sessions have largely been unexplored, particularly with groups. We evaluated a sample of 129 adolescent Group MI sessions, using a behavioral coding system and timing information to generate information on facilitator and client speech (CT; change talk) within 5 successive segments (quintiles) of each group session. We hypothesized that facilitator speech (open-ended questions and reflections of CT) would be related to subsequent CT. Repeated measures analysis indicated significant quadratic and cubic trends for facilitator and client speech across quintiles. Across quintiles, cross-lagged panel analysis using a zero-inflated negative binomial model showed minimal evidence of facilitator speech on client CT, but did indicate several effects of client CT on facilitator speech, and of client CT on subsequent client CT. Results suggest that session-level effects of facilitator speech on client speech do not arise from long-duration effects of facilitator speech; instead, we detected effects of facilitator speech on client speech only at the beginning and end of sessions, when open questions, respectively, suppressed and enhanced client expressions of CT. Findings suggest that clinicians must remain vigilant to client CT throughout the group session, reinforcing it when it arises spontaneously and selectively employing open-ended questions to elicit it when it does not, particularly toward the end of the session. (PsycINFO Database Record (c) 2017 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Motivational Interviewing; *Psychotherapeutic Processes; *Group Intervention; Alcohol Drinking Patterns; Drug Usage
- Medical Subject Headings (MeSH):
- Adolescent; Adolescent Behavior; Female; Humans; Juvenile Delinquency; Male; Motivational Interviewing; Professional-Patient Relations; Psychotherapeutic Processes; Psychotherapy, Group; Speech
- PsycINFO Classification:
- Drug & Alcohol Rehabilitation (3383)
- Population:
- Human
Male
Female - Location:
- US
- Age Group:
- Adolescence (13-17 yrs)
Adulthood (18 yrs & older)
Young Adulthood (18-29 yrs) - Grant Sponsorship:
- Sponsor: National Institutes of Health, National Institute on Drug Abuse, National Institute on Alcohol Abuse and Alcoholism, US
Grant Number: R01DA019938 and R21AA020546
Recipients: D’Amico, Elizabeth J. (Prin Inv)
Sponsor: National Institutes of Health, National Institute on Drug Abuse, National Institute on Alcohol Abuse and Alcoholism, US
Grant Number: R03DA035690 and K01AA021431
Recipients: Houck, Jon M. (Prin Inv) - Methodology:
- Empirical Study; Quantitative Study
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- First Posted: Sep 28, 2015; Accepted: Jun 4, 2015; Revised: Jun 3, 2015; First Submitted: Feb 11, 2015
- Release Date:
- 20150928
- Correction Date:
- 20170306
- Copyright:
- American Psychological Association. 2015
- Digital Object Identifier:
- http://dx.doi.org/10.1037/adb0000107
- PMID:
- 26415055
- Accession Number:
- 2015-44169-001
- Number of Citations in Source:
- 42
- Persistent link to this record (Permalink):
- http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2015-44169-001&site=ehost-live
- Cut and Paste:
- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2015-44169-001&site=ehost-live">Temporal variation in facilitator and client behavior during group motivational interviewing sessions.</A>
- Database:
- PsycINFO
Temporal Variation in Facilitator and Client Behavior During Group Motivational Interviewing Sessions
By: Jon M. Houck
Center on Alcoholism, Substance Abuse, and Addictions, University of New Mexico;
Sarah B. Hunter
RAND Corporation, Santa Monica, California
Jennifer G. Benson
Center on Alcoholism, Substance Abuse, and Addictions, University of New Mexico
Linda L. Cochrum
Center on Alcoholism, Substance Abuse, and Addictions, University of New Mexico
Lauren N. Rowell
Center on Alcoholism, Substance Abuse, and Addictions, University of New Mexico
Elizabeth J. D’Amico
RAND Corporation, Santa Monica, California
Acknowledgement: Jon M. Houck is a trainer of motivational interviewing language coding systems who is occasionally compensated for the training.
Research reported in this publication was supported by the National Institute on Drug Abuse and the National Institute on Alcohol Abuse and Alcoholism of the National Institutes of Health under award numbers R01DA019938 and R21AA020546, (Principal Investigator: Elizabeth J. D’Amico), and R03DA035690 and K01AA021431 (Principal Investigator: Jon M. Houck). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
Motivational interviewing (MI), a directional, client-centered intervention for problematic health behaviors (Miller & Rollnick, 1991, 2002, 2012) has accumulated considerable evidence of its efficacy, both for adults (Hettema, Steele, & Miller, 2005; Lundahl, Kunz, Brownell, Tollefson, & Burke, 2010) and for adolescents (Cushing, Jensen, Miller, & Leffingwell, 2014; Jensen et al., 2011). Empirical support has been found for a theoretical mechanism for MIs effectiveness (Miller & Rose, 2009), specifically, a causal chain linking within-session facilitator speech, client speech, and substance use outcomes (Moyers, Martin, Houck, Christopher, & Tonigan, 2009). Later studies have also fully or partially replicated this seminal finding (Barnett et al., 2014; Morgenstern et al., 2012; Pirlott, Kisbu-Sakarya, DeFrancesco, Elliot, & MacKinnon, 2012; Vader, Walters, Prabhu, Houck, & Field, 2010). Studies examining specific links of the causal chain have also shown robust effects, such as the link between within-session client and facilitator speech (Barnett et al., 2014; Gaume, Bertholet, Faouzi, Gmel, & Daeppen, 2010; Glynn & Moyers, 2010; Moyers & Martin, 2006) and the link between within-session client speech and outcomes (Apodaca et al., 2014; Barnett et al., 2014; D’Amico et al., 2015; Gaume et al., 2010; Shorey, Martino, Lamb, LaRowe, & Santa Ana, 2015; Vader et al., 2010). Most of this work has been conducted using individual sessions except for a recent study by D’Amico et al. (2015) and a subsequent study by Shorey et al. (2015). Thus, little is known about how facilitator and client speech covary over the course of a Group MI session, and how the group therapeutic intervention may be optimized to support behavior change. This is especially important given that group modalities are commonly used in addiction treatment settings (Price et al., 1991; United States Department of Health and Human Services. Substance Abuse and Mental Health Services Administration. Office of Applied Studies, 2014).
One approach to evaluating the mutual influence of facilitator and client speech is by examining the temporal associations between these behaviors through sequential coding. Specifically, given that a particular behavior has occurred, what is the very next behavior that will occur? The first MI study to apply this approach in an individual session (Moyers & Martin, 2006) found that facilitator speech consistent with MI (i.e., affirmations, support, advice with permission, open questions, and reflections) was significantly more likely than expected by chance to be followed by client change talk (CT), a type of within-session client speech that favors changing a problematic health behavior. In contrast, facilitator speech inconsistent with MI (i.e., confrontation, direction, warning, and advice without permission) was significantly more likely than expected by chance to be followed by client sustain talk (ST), a type of within-session client speech that favors maintaining a problematic health behavior. Subsequent studies examining individual therapy have consistently found that facilitator reflections of CT are likely to be followed by client CT, whereas facilitator reflections of ST are likely to be followed by client ST (Barnett et al., 2014; Gaume et al., 2010; Moyers et al., 2009). These findings have been replicated in the group setting, with the additional finding that open-ended questions (OQ) are likely to be followed by CT (D’Amico et al., 2015). These effects support the immediate (i.e., next utterance) influence of facilitator speech on client speech. However, longer-term effects within a therapeutic session have been relatively unexplored. Understanding how to structure and facilitate talk across a therapeutic session may help to optimize client behavioral change after therapy.
Longer-term associations between facilitator and client speech within sessions can be examined by breaking the session into smaller units, such as fifths (i.e., quintiles) or tenths (i.e., deciles) of a session. A seminal study by Amrhein and colleagues (Amrhein, Miller, Yahne, Palmer, & Fulcher, 2003) used this approach to examine individual motivational enhancement therapy (MET; a variant of MI that incorporates feedback) sessions and found not only that CT strength (i.e., a Likert rating of CT strength) predicted drug use treatment outcomes, but also that this effect was only significant for CT in the 7th and 10th deciles, suggesting that particular portions of the session may represent critical periods of influence on client treatment outcomes. Using similar methodology for individual sessions, Walker, Stephens, Rowland, and Roffman (2011) found that in deciles related to when clients received personalized feedback, client CT was particularly predictive of outcome. However, the particular deciles included were not reported. A study of cocaine use applying the decile approach (Aharonovich, Amrhein, Bisaga, Nunes, & Hasin, 2008) found that overall CT strength predicted use and that the shift in CT strength from the 5th to the 10th deciles predicted treatment retention for individuals. A close replication of Amrhein’s work (Morgenstern et al., 2012) examined the effects of CT at the end of an individual session only (i.e., in deciles 9–10) and did not find a significant association between CT in these deciles and outcomes. Finally, a study of significant other effects in MI examined facilitator, client, and significant other speech across deciles and found that only CT from the significant other, and not facilitator speech, predicted client CT (Apodaca, Magill, Longabaugh, Jackson, & Monti, 2013).
Clearly, studies applying the decile technique have taken diverse approaches and shown inconsistent results. In addition, each of these studies has examined this question in individual, rather than group, sessions. The importance of CT from segments of Group MI sessions, and the association between facilitator and client speech during these segments, remains an open question. Rather than examine effects on outcome, an important first step may be the assessment of how facilitator and client speech relate over time in Group MI sessions to provide a theory-driven rationale for segment selection and an explanation for potential effects on outcomes. For instance, if the association between facilitator speech and client CT is consistent throughout the session, then facilitators can maintain a high level of CT by eliciting and reflecting CT from group members throughout the session. If this association varies depending upon the segment of the session, then particular moments during the session may be more important, requiring specialized strategies to ensure that group member CT is elicited and reflected during these critical times.
The present study addresses this question by examining changes in the mutual influence of within-session facilitator and client speech over the course of Group MI sessions with adolescents. Although sequential analysis would appear an attractive choice, the relative infrequency of client change language (Moyers et al., 2009, see Supplemental Materials) limits the number of transitions involving client change language during a segment. Because of the requirement that at least five instances of the transitions of interest occur (Wickens, 1982), it is not feasible to examine transition probabilities for facilitator speech and client change language by segments. Instead, we examined longer-term effects of facilitator speech by segmenting sessions into five equal parts (i.e., quintiles). Because previous studies of individual MI sessions have found quadratic slopes for CT within sessions (Amrhein et al., 2003), we hypothesized that group sessions would show a similar pattern. In addition, we hypothesized that, because of the natural development of CT as well as variability in facilitator reinforcement of CT, the associations between CT and reflections of CT (i.e., RefCT) and between CT and open questions (i.e., OQ) would vary across quintiles.
Method Study Setting
This study involves secondary analysis of data collected in a randomized clinical trial of a group intervention for adolescents in a Teen Court setting (D’Amico, Hunter, Miles, Ewing, & Osilla, 2013). Youth who committed a first-time alcohol or other drug (AOD) offense and were deemed by the Probation department as not in need of more intensive intervention were offered the chance to participate in the community-based Teen Court diversion program. Given that this was a first-time offense, these youth were not further processed by the Probation department. The Teen Court program is not part of the juvenile justice system (i.e., Teen Court is not a drug court), and youth in the Teen Court program are not considered a prison population as they are not formally on probation. Youth could choose to end their participation in the study at any time without any negative consequences; study participation was not tied to youth status in the juvenile justice system. Youth who elect to participate in the Teen Court program enter into a contract with the Teen Court in which they agree to abide by the decisions of a peer jury. Youth who do not wish to participate in Teen court retain the right to have a closed hearing in Juvenile Court. For those who decide to go the teen court, the peer jury is provided with sentencing guidelines, including sanctions such as community service, service on the Teen Court jury, and fees. However, if a teen does not fulfill their contract, the community-based Teen Court lacks the authority to impose any legal consequences; instead, the consequence that has already been imposed by the justice system remains in place (i.e., the offense remains on the youth’s record.). Youth who chose to participate in the study received six group intervention sessions. The current manuscript is based upon examination of behavior during the group intervention sessions, Free Talk. Youth who chose to end their participation in Free Talk could complete any remaining sessions in usual care. As is typical of early intervention programs (see, e.g., McCambridge, Slym, & Strang, 2008), completion rates for both Free Talk and usual care were high; in both cases around 95%. Despite similar completion rates, the rate of recidivism in the following year was much higher for usual care than for Free Talk participants (D’Amico et al., 2013). Usual care participants and sessions were not audio recorded and were, therefore, not included in the analyses. We coded 135 Free Talk sessions. Six sessions were used in coder training; 129 sessions were used for the analyses.
Participants
Youth were eligible if they were 14–18 years old, chose to participate in the Teen Court program during the study period (January 2009 to October 2011) for a first-time AOD offense, and agreed to be randomized to one of the study conditions and complete the study survey instruments. We excluded youth who did not speak and read English well enough to complete the informed consent and self-administered surveys, as well as youth who had multiple offenses or possession of a medical marijuana card. Study refusals (10%) were mostly because of lack of time or transportation to complete a baseline survey before their first group session (see D’Amico et al., 2013 for the CONSORT diagram). No statistically significant demographic differences were observed between study participants and those that refused to participate. There were 110 youth who participated in the Free Talk group sessions. The average age was 16.75 years (SD = 1.02, range 14–18); 65.5% were male, 51.8% were White, 39.1% were Hispanic, and 9.1% were mixed/other race.
Free Talk group sessions were led by one of five facilitators (all female and White) who were psychology doctoral students with prior at-risk teen work experience. The five facilitators received 40 hrs of MI and Free Talk training delivered by two licensed clinical psychologists affiliated with the Motivational Interviewing Network of Trainers (MINT). All Free Talk groups were digitally audio recorded. These two experts reviewed recordings and provided 1-hr weekly supervision to facilitators. The Motivational Interviewing Treatment Integrity scale (MITI; Moyers, Martin, Manuel, Hendrickson, & Miller, 2005) was used to monitor intervention fidelity and to provide feedback during supervision.
Intervention
Facilitators delivered a six-session manualized group intervention, Free Talk (D’Amico, Osilla, & Hunter, 2010). Detailed study procedures are available elsewhere (D’Amico et al., 2013). The sessions were offered weekly and enrollment in the intervention was based on a rolling admission, so that attending the second session was not contingent upon attending the first session. Facilitators used MI strategies throughout the sessions. For example, facilitators used willingness and confidence rulers, a motivation-building exercise, to facilitate discussions about participants’ willingness and confidence to change. Session content included an interactive discussion of AOD myths (e.g., using alcohol will make me more sociable), the pathway from abstinence to addiction, effective interpersonal communication strategies, the effects of AOD on the brain, and the contribution of AOD on other risk-taking behavior such as unsafe sex and driving under the influence (see D’Amico et al., 2013). Each group session lasted 55 min and the average group size was five adolescents (M = 4.54, SD = 1.96). Because of the rolling admission approximately one adolescent was new to each group session (M = 1.29, SD = 2.28). Ninety-five percent of youth completed all six sessions within the required 90-day time frame.
Procedures
All procedures were approved by the institution’s Human Subjects Protection Committee (HSPC). Audio recordings of intervention sessions were used for coding. Independent coders previously rated sessions using an objective sequential behavior coding system (MISC 2.5; Houck, Moyers, Miller, Glynn, & Hallgren, 2010) and a computerized coding application (CACTI; Glynn, Hallgren, Houck, & Moyers, 2012). Although this approach is novel in the group setting, the sole major difference in the coding approach in the present study compared with prior sequential coding studies was in the handling of client speech. Because it was not possible to determine from the audio recording which teen was speaking, “client speech” could occur from any teen in the group. For example, if a facilitator asked a question of one teen, another teen might respond with CT, the facilitator might reflect this CT, and yet another teen could respond with additional CT. As in prior sequential coding studies (Barnett et al., 2014; Moyers & Martin, 2006; Moyers et al., 2009), each session was sequentially coded in its entirely, from beginning to end.
Interrater reliability was generally good to excellent, with intraclass correlations (ICC; Shrout & Fleiss, 1979) for CT (ICC = .897), ST (ICC = .954), OQs (ICC = .668), and RefCT (ICC = .728) all in the good to excellent range (Cicchetti & Sparrow, 1981). In addition, the utterance-to-utterance reliability of our coders was k = .67, indicating that our coders agreed on the exact sequence of behaviors approximately 73% of the time (Bakeman, Quera, McArthur, & Robinson, 1997). Subsequent research has suggested that this utterance-to-utterance approach is superior to reliability estimates that are based upon counts (Lord et al., 2015). On the whole, these results indicate very high interrater reliability.
Detailed coding procedures and interrater reliability estimates are available elsewhere (D’Amico et al., 2015). The extraction of data for quintiles was possible because sequential coding using CACTI preserves both the temporal sequence of behaviors and the exact time at which behaviors occurred. Quintiles were constructed by calculating the length of the session (i.e., end time of the final utterance minus the start time of the initial utterance) and dividing by five. Coding data from these quintiles were extracted using time codes embedded in CACTI output files. Each session’s CACTI output file was used to create five separate files containing codes corresponding to these quintiles. Summary measures defined in the MISC manual (Houck et al., 2010) were computed for each quintile including the total CT, ST, and RefCT (i.e., simple reflections of CT + complex reflections of CT), and open-ended questions (OQ). The slope of client change language over the five quintiles was tested using repeated measures multivariate analysis of variance (MANOVA) in SPSS version 22. The association between client and facilitator speech across quintiles was evaluated using a cross-lagged panel analysis (path analysis) in Mplus (Muthén & Muthén, 1998–2015), incorporating group size as a covariate.
ResultsAs hypothesized, repeated measures ANOVA indicated significant quadratic trends for CT (F(1, 125) = 34.91, p < .001) and reflections of CT (F(1, 125) = 34.42, p < .001), and cubic trends for CT (F(1, 125) = 26.81, p < .001), reflections of CT (F(1, 125) = 29.45, p < .001) and open-ended questions (F(1, 125) = 25.47, p < .001). No significant linear trends were detected. A plot of mean CT, reflections of CT, and open-ended questions over quintiles is displayed in Figure 1.
Figure 1. Slope of change talk (CT), reflections of change talk (RefCT), and open questions (OQ) over time.
Path analysis was used to evaluate cross-lagged partial regression paths. This approach can distinguish between the effects of client CT on facilitator speech, and the effects of facilitator speech on client CT. In MI, client CT is a relatively rare type of speech (see Supplemental Material in Moyers et al., 2009). Therefore, because of significant zero inflation on the variables of interest, a zero-inflated negative binomial model was used in Mplus 7.2 (Muthén & Muthén, 1998) to evaluate associations between CT and facilitator speech, while also addressing the nonnormal distribution of these measures (for a tutorial, see Atkins, Baldwin, Zheng, Gallop, & Neighbors, 2013). This type of multivariate analysis simultaneously assesses a continuous model (e.g., the association between the number of reflections of CT in one quintile and the number of CT utterances in the next quintile) and a logistic model (e.g., the association between the number of reflections of CT in one quintile and having any CT utterances in the next quintile). Because a zero-inflated model was used, we were also able to examine the association between client CT and having zero utterances of reflections of CT or open-ended questions, and between reflections of CT or open-ended questions and having zero utterances of CT. The analysis was conducted for the five successive quintiles of the session to evaluate causal effects over time (Finkel, 1995; Kenny, 2004; Kenny & Harackiewicz, 1979), separately for reflections of CT and open-ended questions.
Figures 2 and 3 present the model and results for CT and reflections of CT. As hypothesized, significant associations were observed between CT and reflections of CT. However, rather than reflections of CT predicting subsequent CT, the count of CT in several quintiles predicted reflections of CT in subsequent quintiles. The significant paths were between CT and subsequent reflections of CT in quintiles two, three, and four (b = 0.054, t = 2.39, p < .01; b = 0.066, t = 2.711, p < .05; b = 0.078, t = 3.461, p < .05, respectively), and between CT in quintile 4 and CT in quintile 5 (b = 0.062, t = 3.109, p < .05). CT in the second quintile was significantly associated with reflections of CT in the third quintile (b = 0.054, t = 2.390, p < .05). CT in the third quintile was significantly associated with reflections of CT in the fourth quintile (b = 0.066, t = 2.711, p < .05). CT in the fourth quintile was significantly associated with fifth-quintile CT (b = 0.062, t = 3.109, p < .05) and reflections of CT (b = 0.078, t = 3.461, p < .05). Group size was not a significant predictor for client or facilitator speech in any quintile. No other partial regression paths were significant in this model.
Figure 2. Initial cross-lagged panel model of change talk (CT) and reflections of CT (RefCT). Variable names ending in “i” represent the logistic part of the model; all other variables are count variables.
Figure 3. Cross-lagged panel model of change talk (CT) and reflections of CT (RefCT) showing only the significant paths. Variables ending in “i” represent the logistic part of the model; all other variables are count variables. Bayesian Information Criteria (BIC) = 5838.686, log-likelihood = −2779.790. Absolute fit statistics such as root mean square error of approximation (RMSEA) and comparative fit index (CFI) are not available for models incorporating count outcomes. * p < .05, ** p < .025, *** p < .01.
Figures 4 and 5 present the model and results for CT and open-ended questions. As hypothesized, significant associations were observed between CT and open-ended questions. Significant paths were detected between CT and subsequent open-ended questions, between open-ended questions and subsequent CT, between open-ended questions and subsequent open-ended questions, and between CT and subsequent CT. Open-ended questions in the first quintile were negatively associated with CT in the second quintile (b = −0.050, t = −2.35, p < .05), and open-ended questions in the fourth quintile were significantly positively associated with CT in the fifth quintile (b = 0.034, t = 2.313, p < .05). CT in the first quintile was significantly negatively associated with open-ended questions in the second quintile (b = −0.13, t = −2.163, p < .05), and with having zero utterances of CT in the second quintile (b = 0.072, t = 2.067, p < .05). This association with having zero utterances of CT in the subsequent quintile means that sessions with high CT counts in the first quintile were more likely to have no instances of open-ended questions in the second quintile. In addition, CT in the third quintile was positively associated with open-ended questions in the fourth quintile (b = .022, t = 2.404, p < .05), while CT in the fourth quintile was positively associated with CT in the fifth quintile (b = 0.047, t = 2.343, p < .05). Open-ended questions were associated with subsequent open-ended questions across all five quintiles (b = 0.023, t = 2.966, p <. 05; b = 0.025, t = 2.534, p < .05; b = 0.027, t = 3.468, p < .05; b = 0.038, t = 4.228, p < .05, respectively, for first to second, second to third, third to fourth, and fourth to fifth quintile open-ended questions). Finally, group size was positively related to having zero instances of open-ended questions in the first quintile (b = 0.201, t = 2.196, p < .05); that is, larger groups tended to have no open-ended questions in the first quintile. Group size was also positively associated with the number of open-ended questions in the fifth quintile (b = 0.034, t = 2.313, p < .05); that is, larger groups tended to have more utterances of open-ended questions in the fifth quintile. No other partial regression paths were significant.
Figure 4. Initial cross-lagged panel model of change talk (CT) and open questions (OQ). Variable names ending in “i” represent the logistic part of the model; all other variables are count variables.
Figure 5. Cross-lagged panel model of change talk (CT) and open questions (OQ) showing only the significant paths. Variables ending in “i” represent the logistic part of the model; all other variables are count variables. Bayesian Information Criteria (BIC) = 7222.875, log-likelihood = −3416.544. Absolute fit statistics such as root mean square error of approximation (RMSEA) and comparative fit index (CFI) are not available for models incorporating count outcomes. * p < .05, ** p < .025, *** p < .01.
DiscussionThe present study used an advanced behavioral coding approach to assess associations between facilitator and client speech across five segments (quintiles) in a large adolescent sample of Group MI sessions. To our knowledge this is the first published study to apply this technique in adolescent group psychotherapy. We detected significant quadratic slopes for client CT and facilitator open-ended questions and reflections of CT, and cubic slopes for CT and reflections of CT, with decreases from the second to fourth quintiles and an increase from the fourth to fifth quintile, consistent with prior studies (Amrhein et al., 2003). These slopes likely reflect the structure of the sessions, in which evocation of teens’ thoughts about the future and take-home messages from each group occurred at the end of the sessions, leading to increased expression of CT and reinforcement of session material through open-ended questions. Alternatively, high levels of CT at the end of the sessions may simply reflect increased teen engagement in the groups as the sessions drew to a close.
We found evidence of effects of client CT on subsequent client CT and facilitator speech, but saw evidence of cross-quintile effects of facilitator speech on client speech only for open-ended questions, and only at the beginning and end of the sessions. This suggests that the beginning and ending of Group MI sessions may be important in exploring group member ambivalence, and particularly for eliciting client CT. The early portions of the session appear to set the stage for the group, whereas the final portions of the session seem to indicate the direction of the client’s ambivalence, and may relate to subsequent outcomes (Amrhein et al., 2003).
Given previous findings on within-session speech using conditional probabilities (D’Amico et al., 2015; Gaume et al., 2010; Moyers & Martin, 2006; Moyers et al., 2009) one might expect to see an effect of reflections of CT on client CT over time. However, four previous studies suggest an immediate impact (i.e., at lag zero, the very next utterance) of facilitator speech on client speech, and cannot address longer-term effects. In contrast, the present study used a cross-lagged approach to examine associations over time at the quintile level. The absence of any cross-quintile effects of facilitator reflections of CT on client CT, coupled with high correlations between these categories of speech within quintiles, suggests that facilitator influence on client speech via reflections of CT is stronger in the short term. This is consistent with research demonstrating associations between session-level counts of MI-consistent speech and CT (Moyers et al., 2007, 2009). Although skilled facilitators can use reflections of CT to “lend” CT to clients who did not express it spontaneously (Miller & Rollnick, 2012), reflections of CT are more commonly used to reinforce than to elicit CT. The momentum generated by this reinforcement of CT appears not to persist over quintiles, suggesting that facilitators must remain vigilant in their reinforcement of CT throughout the session.
In contrast to findings for reflections of CT, we detected effects of open-ended questions on CT at the beginning and the end of these group sessions. Again this is not surprising given that prior research on within-session speech using conditional probabilities has indicated strong associations between open-ended questions and CT (D’Amico et al., 2015; Moyers et al., 2009). In addition to predicting CT at the beginning (second quintile) and end (fifth quintile) of the session, open-ended questions consistently predicted subsequent OQ across all five quintiles, suggesting that facilitator use of open-ended questions was somewhat more stable than was facilitator use of reflections of CT. However, the direction of the effects of open-ended questions on CT differed across segments: at the beginning of the sessions, open-ended questions suppressed CT, whereas at the end of the sessions open-ended questions enhanced CT. The effects of CT were also negative at the beginning of the session, when first-quintile CT suppressed second-quintile open-ended questions. Some of these effects may be because of session structure. For example, at the beginning of each group, open-ended questions focused on topics such as how teens felt about being in the group and generating rules for the group, whereas at the end of the sessions, open-ended questions were typically about what teens would take away from the group or what stood out to the teens about the group. Thus, evocation about thoughts regarding session materials and activities was more likely to generate CT.
Successful implementation of the coding approach used in the present study requires considerable time, effort, and expertise, and as such has not been previously applied in the group setting. What do we learn about group psychotherapy, then, using this novel approach? First, the structure of the group sessions is apparent both from the slopes of facilitator and teen behavior and from the cross-lagged analyses. Across these 129 groups, CT and reflections of CT increased early in the sessions, dropped in the middle and peaked toward the end of the session; open-ended questions peaked early in the sessions and dropped and remained low through the middle and end of the sessions. Overall, the CT-to-CT effect at the end of the session may reflect the influence of peers in Group MI. Such effects may also be reflected by the high CT-to CT-transition probability in a sequential coding study in Group MI (D’Amico et al., 2015) and in a subsequent Group MI study that specifically examined sequential CT statements from one group member to another, which the authors termed “relatedness” (Shorey et al., 2015). The direct influence of peers in Group MI sessions may be as important as the influence of the facilitator, suggesting that teen-to-teen CT may be an important mechanism of change in Group MI.
In addition, little is known about the influence of other factors in Group MI settings, such as the size of the groups. We found that while group size did not influence youth CT or facilitator reflections of CT, group size did appear to be associated with open-ended questions, such that fewer were asked at beginning of the session and more at the end of the session. We speculate that in larger groups, facilitators may have initially asked fewer open-ended questions at the beginning, when facilitators may have been concerned with managing discussion within the allotted session time, and more open-ended questions at the end, when they were confident that all of the session content had been addressed.
Limitations
Client change language was relatively infrequent in the present sample, as in all prior studies of within-session client speech (e.g., Moyers et al., 2009; see Supplemental Materials). On average, groups offered 136.1 utterances per session (SD = 51.6), of which 28.3 (approximately 22.5%) were classified as CT. When further subdivided into quintiles, the modal frequency of CT is zero, which complicated analyses and interpretation of facilitator-client exchanges. In addition, the group audio recordings did not allow for individuals to be identified; it is unknown whether client speech would follow the same patterns at the individual level.
ConclusionClinician influence on client CT is clearly important at the utterance level, with greater open-ended questions and reflections of CT eliciting more CT and subsequently changing behavior (D’Amico et al., 2015; Moyers & Martin, 2006; Moyers et al., 2009); however, we found no evidence of consistent long-duration effects of facilitator reflections of CT on client speech across segments of Group MI sessions. Instead, we detected effects of facilitator speech on client speech only at the beginning and end of sessions, when OQs, respectively, suppressed and enhanced client expressions of CT. Therefore, results emphasize that in Group MI sessions clinicians cannot coast on the strength of an initial, rewarding exchange, with many client expressions of CT and facilitator reflections of CT, but rather must remain vigilant throughout the session to reinforce client change language, using open-ended questions to elicit CT if the client ceases to offer it spontaneously, particularly near the end of the session.
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Submitted: February 11, 2015 Revised: June 3, 2015 Accepted: June 4, 2015
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Source: Psychology of Addictive Behaviors. Vol. 29. (4), Dec, 2015 pp. 941-949)
Accession Number: 2015-44169-001
Digital Object Identifier: 10.1037/adb0000107
Record: 162- Title:
- The association between nonmedical use of prescription drugs and extreme weight control behavior among adolescents.
- Authors:
- Owens, Sherry L.. Department of Social and Behavioral Sciences, West Virginia University School of Public Health, Morgantown, WV, US
Zullig, Keith J.. Department of Social and Behavioral Sciences, West Virginia University School of Public Health, Morgantown, WV, US, kzullig@hsc.wvu.edu
Divin, Amanda L.. Department of Health Sciences & Social Work, Western Illinois University, IL, US
Johnson, Emily. Vandal Health Education, University of Idaho, ID, US
Weiler, Robert M.. Department of Global and Community Health, George Mason University, Fairfax, VA, US
Haddox, J. David. Purdue Pharma LP, Stamford, CT, US - Address:
- Zullig, Keith J., Department of Social and Behavioral Sciences, School of Public Health, West Virginia University, 1 Medical Center, Morgantown, WV, US, 26506, kzullig@hsc.wvu.edu
- Source:
- Psychology of Addictive Behaviors, Vol 31(5), Aug, 2017. pp. 560-569.
- NLM Title Abbreviation:
- Psychol Addict Behav
- Page Count:
- 10
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- Bulletin of the Society of Psychologists in Addictive Behaviors; Bulletin of the Society of Psychologists in Substance Abuse
- Other Publishers:
- US : Educational Publishing Foundation
Society of Psychologists in Addictive Behaviors - ISSN:
- 0893-164X (Print)
1939-1501 (Electronic) - Language:
- English
- Keywords:
- adolescents, youth, nonmedical use of prescription drugs, weight control behaviors
- Abstract:
- Although extreme weight control behavior (EWCB) is associated with substance use, no research has examined the association between the nonmedical use of prescription drugs (NMUPD) and EWCB. Self-report data were collected from a sample of 4,148 students in Grades 9–12 enrolled in 5 high schools across the United States. Logistic regression models were constructed to examine the nonmedical use of prescription pain relievers, depressants, stimulants, and a composite measure for any NMUPD, and the EWCB of fasting, use of diet pills, powders, or liquids, and vomiting or laxative use. Models were estimated before and after controlling for key covariates for males and females. Approximately 16% of respondents reported any EWCB during the past 30 days, while 11% reported any NMUPD during the past 30 days. After covariate adjustment, any NMUPD was associated with any EWCB in both males and females (p < .05), and all EWCB remained significant in females who reported prescription pain reliever use (p < .01), with 2 out of 3 remaining significant for prescription stimulant and depressant use (p < .01). The only significant association detected for males was between prescription pain reliever use and using diet pills, powders, or liquids (OR = 2.2, p < .01). Results suggest significant associations between NMUPD and EWCB, with variations by sex. These findings provide directions for additional research and point to several potential identification and intervention efforts. (PsycINFO Database Record (c) 2017 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Drug Abuse; *Prescription Drugs; *Weight Control; Analgesic Drugs; Antidepressant Drugs; CNS Stimulating Drugs; Adolescent Characteristics
- PsycINFO Classification:
- Substance Abuse & Addiction (3233)
Health Psychology & Medicine (3360) - Population:
- Human
Male
Female - Location:
- US
- Age Group:
- Adolescence (13-17 yrs)
- Tests & Measures:
- Youth Risk Behavior Survey
- Grant Sponsorship:
- Sponsor: Purdue Pharma LP, US
Grant Number: NED 1022329
Recipients: Weiler, Robert M. - Conference:
- National Meeting of the American School Health Association, 88th, Oct, 2014
- Conference Notes:
- This research was originally presented at the aforementioned conference.
- Methodology:
- Empirical Study; Quantitative Study
- Supplemental Data:
- Tables and Figures Internet
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- First Posted: Jul 13, 2017; Accepted: May 16, 2017; Revised: May 15, 2017; First Submitted: Nov 28, 2016
- Release Date:
- 20170713
- Correction Date:
- 20170807
- Copyright:
- American Psychological Association. 2017
- Digital Object Identifier:
- http://dx.doi.org/10.1037/adb0000296; http://dx.doi.org/10.1037/adb0000296.supp(Supplemental)
- PMID:
- 28703613
- Accession Number:
- 2017-30120-001
- Persistent link to this record (Permalink):
- http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2017-30120-001&site=ehost-live
- Cut and Paste:
- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2017-30120-001&site=ehost-live">The association between nonmedical use of prescription drugs and extreme weight control behavior among adolescents.</A>
- Database:
- PsycINFO
The Association Between Nonmedical Use of Prescription Drugs and Extreme Weight Control Behavior Among Adolescents
By: Sherry L. Owens
Department of Social and Behavioral Sciences, West Virginia University School of Public Health
Keith J. Zullig
Department of Social and Behavioral Sciences, West Virginia University School of Public Health;
Amanda L. Divin
Department of Health Sciences & Social Work, Western Illinois University
Emily Johnson
Vandal Health Education, University of Idaho
Robert M. Weiler
Department of Global and Community Health, George Mason University
J. David Haddox
Purdue Pharma LP, Stamford, Connecticut, and Department of Public Health and Community Medicine, Tufts University School of Medicine
Acknowledgement: We thank Gerry Hobbs of West Virginia University for assistance with data interpretation. Supported in part by Grant NED 1022329 from Purdue Pharma LP, Stamford, Connecticut, to Robert M. Weiler. This research was originally presented at the 88th National Meeting of the American School Health Association, Portland, Oregon, in October 2014.
The nonmedical use of prescription drugs (NMUPD) ranks second only to marijuana as the most commonly used illicit drug in the United States (US) according to the Substance Abuse and Mental Health Association (SAMHSA, 2013). Their perceived safety, purity, predictable dose response, and psychopharmacological specificity make prescription drugs particularly attractive for experimentation and nonmedical use (Cicero, Inciardi, & Munoz, 2005; Friedman, 2006), as does their ease of accessibility (Wright et al., 2014). NMUPD, at the time of this study, was defined as taking prescription pain relievers, stimulants, and/or depressants without a prescription or solely for the feeling or experience caused by the drug (Center for Behavioral Health Statistics & Quality, 2015; Ford & Watkins, 2012). SAMHSA has subsequently replaced nonmedical use with “misuse” (Hughes et al., 2016). In 2013, approximately 57% of drug overdose deaths were attributed to pharmaceuticals. Of these deaths, 75% were attributed to opioids and 53% were attributed to depressants alone or in combination (Jones, Mack, & Paulozzi, 2013). Opioid overdoses result in 91 fatalities per day in the US (Okie, 2010), while some evidence indicates that highly addictive stimulant use is increasing as well among adolescents (Dalsgaard, Mortensen, Frydenberg, & Thomsen, 2014; Center for Behavioral Health Statistics & Quality, 2015).
Prescriptions for opioid-based pain relievers, stimulants, and depressants have increased dramatically in the past 20 years, with opioid prescriptions increasing nearly fourfold nationally (Paulozzi, Budnitz, & Xi, 2006; Paulozzi, Jones, Mack, & Rudd, 2011). Psychiatrists’ visits by youth in the US nearly doubled between 1995 and 1998 and 2007–2010, outpacing the increase in adult visits during the same time periods (Olfson, Blanco, Wang, Laje, & Correll, 2014). As such, psychotropic medicine prescriptions increased overall in this population, including a variety of stimulants and depressants. These trends have specific consequences for adolescents, as nonmedical use of opioids has increased in corresponding increased trends in prescribing (Okie, 2010; Paulozzi et al., 2006) and prescription drug availability within adolescent networks increases opportunities for nonmedical use. In fact, approximately 18% of adolescents prescribed opioid pain relievers in 2011–2012 reported misusing them (McCabe, West, & Boyd, 2013).
This trend in NMUPD among adolescents warrants an investigation of its potential behavioral correlates so that risk factors and points of intervention can be identified. In the current study, we investigate whether NMUPD is a potential risk factor for extreme weight control behavior (EWCB) because of its known associations with a range of other substance use-related behaviors in adolescents, including alcohol, marijuana, cocaine, and cigarettes (Piran & Robinson, 2006a; Kelly-Weeder, 2011; Lange & Fields, 2015; Pisetsky, Chao, Dierker, May, & Striegel-Moore, 2008). Accordingly, these associations suggest that EWCB may also be a comorbidity of the nonmedical use of prescription drugs (Field et al., 2012).
EWCB is a class of disordered eating behaviors that include using diet pills and/or laxatives, vomiting or laxative use after eating, and skipping meals or fasting to lose or control weight (Story, Neumark-Sztainer, Sherwood, Stang, & Murray, 1998). Because both eating- and substance use-related behaviors involve activation of overlapping areas of the brain, the feelings of reward and reinforcement experienced by the user are also quite similar (Grall-Bronnec & Sauvaget, 2014). Moreover, engagement in both behaviors may also share the similar motivation of self-medication of psychological distress (Abbate-Daga, Amianto, Rogna, & Fassino, 2007).
EWCB is a warning signal of potentially life-threatening eating disorders and as such, have historically been studied as early indicators of full syndrome eating disorders such as anorexia nervosa or bulimia nervosa (Steinhausen, Gavez, & Metzke, 2005). Eating disorders have the highest mortality rates of all psychological disorders (Piran & Robinson, 2006a). Of significance, studies have also found that NMUPD and EWCB share many characteristics such as depression/negative affect (Fernandez-Aranda et al., 2007), anxiety (Godart et al., 2006), self- destructive behavior, denial, (American Psychiatric Association, 2013), obsessive–compulsive disorder and behavior, intense cravings, social isolation, increased risk for suicide and other substance use (Boyes, Fletcher, & Latner, 2007; Kelly-Weeder, 2011; Young, Glover, & Havens, 2012). Therefore, understanding whether NMUPD and EWCB co-occur is critical in developing interventions to ameliorate both of these potentially harmful behaviors in adolescents, a population that is particularly vulnerable to both drug use and disordered eating. What remains unknown from the extant literature is whether NMUPD increases the odds of EWCB in adolescents. Given the high mortality for both drug overdoses and eating disorders in adolescents, determining whether an overlap exists could present opportunities for early intervention.
Gender differences are also important to consider when evaluating the potential association between NMUPD and EWCB. For example, females engage in EWCB at roughly twice the rate of males, which is attributed in some degree to differences in cultural body standards, media exposure, and even genetic predisposition (Martin, 2010). Female adolescents are also more likely to diet, report body dissatisfaction, and engage in EWCB at alarming rates, although body dissatisfaction among male adolescents is growing as well (Neumark-Sztainer, Wall, Larson, Eisenberg, & Loth, 2011). In contrast, adolescent males have historically engaged in more frequent substance use than adolescent females (Havens, Young, & Havens, 2011; Schroeder & Ford, 2009).
The association between EWCB, substance use, and gender in adolescents has also been observed in previous research. For instance, cigarette smoking and alcohol use are consistently correlated with EWCB among both genders (Croll, Neumark-Sztainer, Story, & Ireland, 2002; Pisetsky et al., 2008). However, a review of 25 studies by Young et al. (2012) concluded that females were more likely to report the nonmedical use of prescription pain relievers and tranquilizers than males. Thus, there is reason to believe differences might also exist by gender when examining the association between NMUPD and EWCB. Moreover, as body dissatisfaction patterns change over time among both male and female adolescents, it is critical to update the literature on this topic (Martin, 2010).
To address this gap in the literature, the current investigation examined the association between the NMUPD and EWCB among a large, geographically diverse sample of adolescents. Given the known association between substance use and EWCB, we hypothesized that NMUPD would be significantly associated with EWCB. Moreover, given prior research suggesting a that greater proportion of females engage in EWCB than males, analyses were stratified by gender. Understanding whether NMUPD and EWCB co-occur is critical in developing interventions to ameliorate both of these potentially harmful behaviors in adolescents.
Method Participants
Data were collected during fall 2010 and spring 2011 from a convenience sample 4,148 students in Grades 9–12 attending five public high schools in five states (California, Florida, Illinois, New Jersey, and West Virginia) using a group-administered, anonymous, cross-sectional survey. Data were originally collected as part of a psychometric study examining the reliability of NMUPD items designed for the Youth Risk Behavior Survey questionnaire (Centers for Disease Control and Prevention [CDC], 2013). The schools for this study were intentionally selected to collect data from a geographically, racially, ethnically, and culturally diverse group of participants to support the aims of the investigation (Weiler et al., 2012). Month, days, and class periods of data collection differed to accommodate school schedules. Schools received a stipend and a needs assessment report for their participation. Informed consent was obtained using a protocol approved by the University of Florida’s Institutional Review (#2010-U-0960).
Measures
Independent variables included three items designed to measure the nonmedical use of prescription drugs during the past 30-days (Howard, Weiler, & Haddox, 2009): “During the past 30 days, how many times did you use a prescription pain reliever that was NOT prescribed for you or that you took only for the experience or feeling it caused?”; “During the past 30 days, how many times did you use a prescription depressant that was NOT prescribed for you or that you took only for the experience or feeling it caused?”; and “During the past 30 days, how many times did you use a prescription stimulant that was NOT prescribed for you or that you took only for the experience or feeling it caused?” Participants were also provided with examples of specific clinical and slang terms of each of the drugs before each question was asked. Ordinal response options for each item duplicated the 30-day prevalence response options for the substance use items comprising the Centers for Disease and Control’s (CDC) Youth Risk Behavior Survey (YRBS) and were “0 times”, “1 or 2 times”, “3 to 9 times”, “10 to 19 times”, “20 to 39 times”, and “40 or more times”. Owing to small frequencies in categories above “1 to 2 times”, participants who reported “0 times” were coded as 0 while students who reported “1+ times” were coded as 1 for analysis, with the referent group being those who reported not engaging in NMUPD. These questions demonstrated adequate test–retest reliability in previous research (Howard et al., 2009; Weiler et al., 2012). In the most recent reliability study, the NMUPD items demonstrated fair to substantial reliability as measured by Kappa: .34, .43, and .52 for past 30-day use of simulants, depressants, and pain relievers, respectively (Weiler et al., 2012).
Dependent variables included three items from the YRBS designed to measure EWCB: “During the past 30 days, did you go without eating for 24 hours or more (also called fasting) to lose weight or to keep from gaining weight?”; “During the past 30 days, did you take diet pills, powders, or liquids without a doctor’s advice to lose weight or keep from gaining weight? (Do not include meal replacement products such as Slim fast.)”; During the past 30 days, did you vomit or take laxatives to lose weight or to keep from gaining weight?” Categorical response options for each question were also the same as found on the YRBS and were “Yes” or “No.” Students who reported not engaging in any of the EWCBs (i.e., “No”) were coded as 0, while students who reported engaging in any EWCB (i.e., “Yes”) were coded as 1 for analysis, with the referent group being those who did not report engaging in any EWCB. Previous research has demonstrated adequate test–retest reliability for the EWCB items (Brener et al., 2002).
Data Analysis
Data were analyzed using PC-SAS version 9.3. Analyses included descriptive summaries, followed by inferential analyses to examine the association between NMUPD and EWCB. Multiple logistic regression models were constructed. The first included nonmedical use of any of the prescription drugs measured (pain relievers, stimulants, and depressants), followed by models for each of the individual prescription drug class stratified by sex. All models were estimated before (unadjusted) and after controlling for the selected covariates (adjusted). Covariates included race, age, self-reported grade, past 30-day cigarette smoking, past 30-day alcohol use, past 30-day marijuana use, and self-reported depression and were not recoded for analysis. Demographic covariates were selected based on research demonstrating that EWCB varies by age and race (Croll et al., 2002). Behavioral covariates were chosen because adolescents who engage in EWCB are at elevated risk for cigarette smoking, marijuana use, and alcohol use (Kelly-Weeder, 2011; Lange & Fields, 2015; Piran & Robinson, 2006a; Piran & Robinson, 2006b; Pisetsky et al., 2008); and depression (Crow, Eisenberg, Story, & Neumark-Sztainer, 2008; Messina et al., 2014; Perez, Joiner, & Lewisohn, 2004; Santos, Richards, & Bleckley, 2007).
Results Sample Characteristics
Demographic characteristics are presented in Table 1. The sample included 4,148 students, of which 51.7% (n = 2,144) identified as female and 48.3% (n = 2,004) male. The majority of the sample identified as White (48.1%, n = 1,997), Black (19.0%, n = 788), or Hispanic (14.0%, n = 579). Any NMUPD during the past 30 days was reported by 11.0% (n = 458) of the sample with no differences by sex emerging. To put any NMUPD into perspective, 36.3% (n = 1507) reported alcohol use in the last 30 days, 21.9% (n = 908) reported marijuana use, and 16.0% (n = 664) reported smoking cigarettes.
Sample Characteristics
Chi-square tests were used to test for gender differences among demographic, EWCB, NMUPD, and covariate variables. Females were significantly more likely than males to report any EWCB (20.9% vs. 10.4%, p < .01) and all EWCB variables were significantly more prevalent among females than males (p < .01). Fasting was the most common behavior for both females (16.3%, n = 350) and males (7.5%, n = 151).
Overall NMUPD and pain reliever use did not differ by gender, with 11.1% of males and 11.0% of females reporting overall NMUPD use (p = .386) and 9.3% and 9.5% of male and female respondents, respectively, reporting pain reliever use (p = .975) during the past 30 days. However, depressant use was significantly more prevalent among males (4.8% vs. 3.7%, p = .04), as was stimulant use (3.9% vs. 2.5%, p < .01).
Regression Results
Table 2 presents unadjusted and adjusted odds ratios (ORs) between any NMUPD and the three EWCB by gender. All unadjusted ORs revealed significant associations between any NMUPD and EWCB for both males and females (p < .01). Males who reported any NMUPD during the past 30 days were between approximately 2.6 (fasting) and 4.0 (vomiting or using laxatives) times more likely to report EWCB when compared to males who did not report any NMUPD (p < .01). Females who reported any NMUPD during the past 30 days were between approximately 3.2 (fasting) and 4.8 (diet pills, powders, or liquids) times more likely to report EWCB when compared to females who did not report any NMUPD (p < .01). When adjusted for demographic and behavioral covariates, results were attenuated as expected. For example, males who reported any NMUPD during the past 30 days were 1.7 times more likely to report fasting when compared to males who did not report any NMUPD (p < .05). Additionally, all three EWCB variables remained statistically significant for females who reported any NMUPD during the past 30 days with ORs of approximately 1.9 (fasting), 2.4 (vomiting or using laxatives), and 3.1 (diet pills, powders, or liquids; p < .01).
Unadjusted and Adjusted Odds Ratios (ORs) and 95% Confidence Intervals (CIs) for Reporting Extreme Weight Control Behavior (EWCB) in the Past 30 Days for Any Nonmedical Use of Prescription Drugs (Past 30 Days) by Gender
Table 3 presents unadjusted and adjusted ORs for the nonmedical use of prescription pain relievers, stimulants, and depressants and each EWCB, by gender. Consistent with the results in Table 2 for any NMUPD, results in the adjusted models were tempered, but a distinct pattern emerged for females. For example, males who reported the nonmedical use of prescription pain relievers during the past 30 days were about 2.4 times more likely to report using diet pills, powders, or liquids in the past 30 days to lose weight or keep from gaining weight compared to males who did not report any nonmedical use of prescription pain relievers (p < .01). In contrast, all but two associations among females (fasting and nonmedical use of prescription stimulants and vomiting or laxative use and nonmedical use of prescription depressants) remained significant in the adjusted models. For example, females who reported nonmedical use of prescription pain relievers during the past 30 days were about 1.8 times more likely to report fasting, 1.9 times more likely to report vomiting or using laxatives, and 2.8 times more likely to report using diet pills, powders, or liquids in the past 30 days to lose weight or keep from gaining weight compared to females who did not report any nonmedical use of prescription pain relievers (p < .01).
Unadjusted and Adjusted Odds Ratios (ORs) and 95% Confidence Intervals (CIs) for Reporting Extreme Weight Control Behavior (EWCB) in the Past 30 Days, by Nonmedical Use of Pain Relievers, Depressants, and Stimulants, by Gender
These significant associations persisted for nonmedical use of prescription stimulants, where females were about 3.0 times more likely to report vomiting or using laxatives, and 4.5 times more likely to report using diet pills, powders, or liquids in the past 30 days to lose weight or keep from gaining weight compared to females who did not report any nonmedical use of prescription stimulants (p < .01). Finally, females who reported nonmedical use of prescription depressants during the past 30 days were about 2.2 times more likely to report fasting and 3.2 times more likely to report using diet pills, powders, or liquids in the past 30 days to lose weight or keep from gaining weight compared to females who did not report nonmedical use of prescription depressants during the past 30 days (p < .01).
To test of whether the association between NMUPD and EWCB among males was stronger for the demographic or behavioral variables, a separate regression was run adjusting for only the demographic variables and compared to the unadjusted model and fully adjusted model containing all covariates for each prescription drug class. Results from this secondary analysis (see online supplemental table) revealed the demographic variables by themselves did not account for the findings for males, suggesting the behavioral variables accounted nearly all of the observed change.
DiscussionThis preliminary study is the first published investigation to examine the association between the nonmedical use of prescription drugs and extreme weight control behavior. EWCB has already been linked to alcohol and marijuana use, and cigarette smoking (Croll et al., 2002; Kelly-Weeder, 2011; Lange & Fields, 2015; Piran & Robinson, 2006a; Piran & Robinson, 2006b; Pisetsky et al., 2008). Study findings also suggest the nonmedical use of prescription pain relievers, stimulants, and depressants appear related to EWCB.
Study findings support both hypotheses that a) NMUPD would be significantly associated with EWCB and b) that associations would vary by gender, even after controlling for key covariates. Results suggest the odds of endorsing any EWCB were higher in adolescents who endorsed any NMUPD. In addition, NMUPD was significantly associated with EWCB for males and females when analyses were stratified by gender and drug class.
Results from the regression analyses must be considered in conjunction with the finding that males and females did not differ in their reported use of any NMUPD or in their nonmedical use of prescription pain relievers. However, males were slightly more likely to report nonmedical use of prescription depressants (4.8%) and stimulants (3.9%) than females (3.7% and 2.5%, respectively). Nonmedical use of prescription stimulants was associated with vomiting/laxative use and or use of diet pills/powders/liquids, but not fasting in females. This is interesting because appetite and weight control have been listed as motives for prescription stimulant use (Suchankova et al., 2013). Our results also contrast with previous studies reporting higher rates of any NMUPD as well as the nonmedical use of prescription pain relievers and sedatives and/or depressants in females (Young et al., 2012). The 2013 YRBS found a higher prevalence of lifetime NMUPD among females in 9th grade and males in 12th grade (CDC, 2014). This study may reflect a growing trend in males’ NMUPD, or may reflect varying rates that one might expect from different sampling techniques. Conversely, the rates of EWCB in this study were only slightly higher than the YRBS estimates (CDC, 2014), as well as those of Ibrahim, El-Kamary, Bailey, and St George (2014) for both genders. In general, past 30-day prevalence for pain reliever use, stimulant use, and depressant use in our study are also higher than past month prevalence rates in the 2011 Monitoring the Future (MTF) survey (Johnston, O’Malley, Bachman, & Schulenberg, 2012). However, there are challenges in making comparisons between our study and MTF, which include but are not limited to, question wording and operationalization of terms, and grade level referencing. Females in this study also endorsed EWCB at twice the rate as males, which is consistent with the 2014 YRBS estimates. Further differences were revealed when analyses were conducted by the individual prescription drug classes.
Nonmedical Use of Prescription Pain Relievers (NMUPPR) and EWCB
Unadjusted models examining NMUPPR and EWCB were significant for both males and females. However, in adjusted models, while all three EWCB variables remained significant for females, only the use of diet pills, powders, and liquids remained significant for males. A possible explanation is that the decrease in significance observed between unadjusted and adjusted models in males resulted from controlling for covariates, as males had markedly higher prevalence rates of all covariates examined with the exception of depression. Therefore, it is plausible that although NMUPPR is independently associated with EWCB in males, once other forms of substance use are accounted for in the model, a more accurate picture for males who report EWCB is that they may be poly users of a variety of substances. This notion is supported by regression findings that adjusted for only the demographic variables (age, grade, and race) for both males and females separately, which remained unchanged from the unadjusted analyses (see supplementary material for table displaying these results).
The findings that fasting and vomiting/laxative use are associated with NMUPPR in females is not surprising given previous research which has examined EWCB and opioid use in those clinically diagnosed with anorexia. For example, Root, Pinheiro, et al. (2010) found significantly higher odds of opioid use in females with anorexia who also engaged in purging behaviors. One possible explanation is that females can be more sensitive to the effects of prescription pain relievers (Whitley & Lindsey, 2009), and that the reinforcing efficacy of prescription pain relievers is enhanced by food deprivation (Root, Pisetsky, et al., 2010) and/or the NMUPPR on an empty stomach.
In both males and females, the use of diet pills, powders, and liquids was significantly associated with the NMUPPR. Moreover, this was the only significant association in males. Diet pills, powders, and liquids often contain large amounts of caffeine or other substances with stimulant properties, which may perpetuate the perception of their use as a fat burning agent. Although speculative, this may be particularly appealing to adolescent males struggling with muscle dysmorphia (Leone, Sedory, & Gray, 2005). The NMUPPR may induce feelings of euphoria (National Institute of Drug Abuse, 2014), a side effect which can be magnified by concurrent use of stimulants such as those found in diet pills, powders, and liquids. Alternatively, the sedating and/or calming effects of prescription pain relievers (Benyamin et al., 2008) may be attractive to individuals who suffer from anxiety or whose use of diet pills, powders, and liquids may cause anxiety. Unfortunately, the causal pathways linking NMUPPR to EWCB cannot be determined in this study, and may vary by type of weight control behavior.
Nonmedical Use of Prescription Stimulants (NMUPS) and EWCB
Moreover, the NMUPS was significantly associated with vomiting/laxative use and use of diet pills/powers/liquids, but not fasting in females, and none of the EWCB in males. This finding is somewhat congruent with the findings of Jeffers, Benotsch, and Koester (2013) and Jeffers and Benotsch (2014) who found NMUPS to be associated with vomiting/laxative use, and the use of diet pills/powders/liquids in samples including both males and females. Our finding is also consistent with Wiederman and Pryor (1996a) who reported that females with bulimia were more likely to use amphetamines than anorexics, and Wiederman and Pryor (1996b) who found greater amphetamine use in females with bulimia. However, our findings differ with Wiederman and Pryor (1996a) who found caloric restriction (e.g., fasting) predicted amphetamine use, and Piran and Robinson (2006a) who found severity of fasting correlated with amphetamine use.
No significant associations were detected between the NMUPS and fasting in either males or females in the adjusted models. This finding is interesting given a known side effect of prescription stimulants is loss of appetite. Loss of appetite or weight loss is a commonly cited motive for the NMUPS, especially among females (Boys, Marsden, & Strang, 2001; Jeffers & Benotsch, 2014; Jeffers et al., 2013; Teter, McCabe, LaGrange, Cranford, & Boyd, 2006). Prescription stimulant use can temporarily disrupt the body’s production of ghrelin, a hunger-generating hormone (Suchankova et al., 2013). We also cannot discount that some study participants did engage in the NMUPS to aid in fasting, although not for a period of 24 or more hours as specified in the questionnaire. Given that individuals who engage in the NMUPS, regardless of motive, are at elevated risk for alcohol and other drug use (Jeffers & Benotsch, 2014; McCabe, Boyd, & Teter, 2009), as well as the current prevalence of the nonmedical use of stimulants and their increased availability (Teter, Falone, Cranford, Boyd, & McCabe, 2010), the strong associations found in the current study do warrant concern. Future research should investigate longitudinal trends in the NMUPS as it relates to EWCB.
Nonmedical Use of Prescription Depressants (NMUPDep) and EWCB
In the present study, NMUPDep was associated with all forms of EWCB in the unadjusted model, but not with any EWCB in the adjusted model for males. For females, both fasting and use of diet pills, powders, or liquids remained significantly associated with NMUPDep in the adjusted model, but not vomiting/laxative use. The findings on fasting are consistent with those of Piran and Robinson (2006a) who found the use of sleeping pills to be associated with fasting in female undergraduates and a community-based sample of young women reporting EWCB (2006b). The lack of a significant association between NMUPDep and vomiting/laxative use is consistent with Piran and Robinson (2006a) who found sleeping pill use higher in women who binged and dieted, but did not purge. There are no direct comparisons between our findings and other studies examining EWCB. However, our findings contrast with a number of studies using clinical samples which have found significantly greater rates of sedative use in individuals diagnosed with anorexia nervosa binge/purge subtype, bulimia nervosa, binge eating disorder, purging disorder, and eating disorders not otherwise specified (Fouladi et al., 2015), greater rates of tranquilizer use in adolescents diagnosed with bulimia nervosa (Wiederman & Pryor, 1996b), and binge eating predicted tranquilizer use in adult females diagnosed with bulimia nervosa (Wiederman & Pryor, 1996a).
Similar to our findings examining the NMUPPR, the current study found a significant association between the NMUPDep and the use of diet pills, powders, and liquids among females. Specifically, females reporting the NMUPDep were 3.2 times more likely to report using diet pills, powders, or liquids when compared to females who did not report any NMUPDep. Because the use of diet pills, powders, and liquids may cause anxiety in some users due to their often high caffeine or other stimulant content, individuals may use prescription depressants for their sedating and/or calming effects. Prescription depressants have a rapid onset of action and generally produce almost immediate effects (Longo & Johnson, 2000), which may explain why they are often the first choice in the treatment of anxiety and/or insomnia (Bostwick, Casher, & Yasugi, 2012). Alternatively, prescription depressants, particularly benzodiazepines, are known to impair aspects of both memory and attention (Buffett-Jerrott & Stewart, 2002). Hence, the use of diet pills, powders, and liquids may be an attempt by some users to reduce sluggishness or ‘hangover’ symptoms which occur as prescription depressants are eliminated from the body (Ashton, 1986; Longo & Johnson, 2000).
Depressants are often prescribed for anxiety disorders, insomnia, anxiety associated with medical illness, anxiety associated with depression, and impulse control disorders (Bostwick et al., 2012; Longo & Johnson, 2000). Motivations most frequently cited for their nonmedical use are to help with sleep, to decrease anxiety, and to get high (Boyd, McCabe, Cranford, & Young, 2006). Given their pharmacological properties, rapid onset of action, motivation(s) for use, perception as safe, and ease of procurement (The Partnership at Drugfree.org, 2013), particularly among females (Paulozzi, Strickler, Kreiner, & Koris, 2015), prescription depressants may be the ‘go-to’ medication for adolescent females who may be unable to obtain alcohol or marijuana, or because prescription depressants are calorie free and do not stimulate appetite.
Unfortunately, adolescent females are particularly vulnerable to prescription medication sharing, particularly with friends who feel they need to self-medicate (Goldsworthy, Schwartz, & Mayhorn, 2008). Both anxiety and insomnia are comorbidities of EWCB, thus NMUPDep, in this case, may be used by adolescent females to address mental health issues associated with EWCB (Wade, Bulik, Neale, & Kendler, 2000). In adolescent females, poor body image is a strong predictor of anxiety and depression, and is also a motivator for EWCB (Boyes et al., 2007). Depressants are often prescribed to treat eating disorders and, therefore, may be used to self-medicate among adolescents (Wade et al., 2000). Given the reinforcing properties of prescription depressants are amplified with nonmedical use (Hoffman & Mathew, 2008) and food deprivation, the combination of both may exert substantial physiological changes on both cognition and mood (Harrop & Marlatt, 2010). Thus, the potential association between the NMUPDep and EWCB detected in this study is a timely phenomenon to examine owing to the high prevalence of both prescription depressant use and adolescents trying to lose weight.
Limitations
Limitations of the present study include not measuring and/or controlling for body mass index (BMI) and/or body image, eating disorder diagnosis, and the self-reported, cross-sectional study design. In addition, no information on extreme exercising was available, which are common among males with body dysmorphia. Second, results may not be generalizable because data were collected from a convenience sample in five states. However, the overall study sample was large and we were able to control for key covariates, which limit the chance of nonmeaningful statistical significance for the study findings (Fischer et al., 2013). Third, analyses were limited to the questions asked and the information collected by self-report. This may have affected the association between NMUPD and EWCB, in addition to introducing potential bias. e.g., although self-report data on risk behaviors and substance have been shown to be generally valid (Ford, 2008), some participants may have provided inaccurate information due to the sensitive nature of the questions. Fourth, caution should be extended when interpreting the results for males in the study owing to a small sample size reporting EWCB behavior. Finally, the temporal sequence between NMUPD and EWCB cannot be determined through our cross-sectional study design. This question may be useful to researchers in predicting NMUPD, EWCB, and/or the factors leading to the growth of either. Despite these limitations, this current study supports the findings of other published studies documenting the prevalence of either the NMUPD or EWCB among adolescents, and is the first to find significant associations among multiple prescription drug classes and EWCB among adolescents.
ConclusionsGiven rates of NMUPD, EWCB, and subsequent adverse outcomes, understanding the correlates of the NMUPD and EWCB is important. Our findings add to the literature by demonstrating the association between any NMUPD, and the nonmedical use of prescription pain relievers, stimulants, and depressants in adolescents and the EWCB of fasting, vomiting/laxative use, and use of diet pills, powders, or liquids, and that these relationships varied by gender.
Although the temporal sequence remains unclear between the NMUPD and EWCB, study findings a) underline the importance of examining individual prescription drug classes used nonmedically when analyzing EWCB; b) demonstrate significant associations between any NMUPD, and the specific nonmedical use of pain relievers, stimulants, and depressants and EWCB; and c) confirm the association between the NMUPD and EWCB is stronger for females than males after adjusting for key covariates.
Implications
Identifying the association between NMUPD and EWCB among adolescents may help schools, practitioners, and professionals working with adolescents identify students at-risk for a myriad of problematic and risky behaviors. Mulheim (2012) found that early intervention is key in preventing severe physical and psychological consequences of EWCB. Specifically, they note skipping lunch as a warning sign that school personnel have the capacity to identify. Therefore, it may be possible to initially detect behavior consistent with early EWCB during school breakfast and lunch, particularly among female students, allowing concerned school personnel to identify adolescents who are at-risk for disordered eating or eating disorders. Furthermore, our findings suggest that these students, particularly adolescent females, may be referred by school personnel for further evaluation of potential NMUPD on a case-by-case basis to determine whether signs of NMUPD use occur in the home or at school alongside EWCB.
In addition to EWCB identification, school or family level interventions targeting NMUPD may benefit by including information about EWCB identification, treatment, and prevention. For example, a meta-analytic review of eating disorder prevention interventions found that programs with a focus on nonspecific factors that may influence eating pathology deserve further exploration (Stice, Shaw, & Marti, 2007). Moreover, a meta-analysis and systematic review of drug use prevention programs found that programs focusing on social skills such as resisting peer pressure significantly reduced drug use at follow-up (Faggiano et al., 2008). While social skills were not explicitly mentioned by Stice et al. (2007), such skills may be an underlying factor worth exploring. The findings of these two studies, in conjunction with our findings on the associations between NMUPD and EWCB may offer an opportunity to implement interventions designed to enhance the types of social skills needed to prevent initiation of either risk behavior.
However, the complexity of the problem associated with NMUPD and EWCB among adolescents presents many challenges. A multifaceted, collaborative, and coordinated response is required. Study findings suggest significant associations between NMUPD and EWCB, with significant variations by gender. Results provide a fundamental starting point for additional research, while also pointing to several potential identification and intervention efforts.
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Submitted: November 28, 2016 Revised: May 15, 2017 Accepted: May 16, 2017
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Source: Psychology of Addictive Behaviors. Vol. 31. (5), Aug, 2017 pp. 560-569)
Accession Number: 2017-30120-001
Digital Object Identifier: 10.1037/adb0000296
Record: 163- Title:
- The consequences of depressive affect on functioning in relation to Cluster B personality disorder features.
- Authors:
- Miller, Joshua D.. Department of Psychology, University of Georgia, Athens, GA, US, jdmiller@uga.edu
Gaughan, Eric T.. Department of Psychology, University of Georgia, Athens, GA, US
Pryor, Lauren R.. Department of Psychology, University of Georgia, Athens, GA, US
Kamen, Charles. Department of Psychology, University of Georgia, Athens, GA, US - Address:
- Miller, Joshua D., Department of Psychology, University of Georgia, Athens, GA, US, 30602-3013, jdmiller@uga.edu
- Source:
- Journal of Abnormal Psychology, Vol 118(2), May, 2009. pp. 424-429.
- NLM Title Abbreviation:
- J Abnorm Psychol
- Page Count:
- 6
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- The Journal of Abnormal and Social Psychology; The Journal of Abnormal Psychology; The Journal of Abnormal Psychology and Social Psychology
- ISSN:
- 0021-843X (Print)
1939-1846 (Electronic) - Language:
- English
- Keywords:
- Cluster B personality disorders, depressive mood induction, laboratory tasks, functioning
- Abstract:
- The authors examined the effects of depressed affect (DA) on functioning measured by behavioral tasks pertaining to abstract reasoning, social functioning, and delay of gratification in relation to Cluster B personality disorder features (PDs) in a clinical sample. Individuals were randomly assigned to either a DA induction or control condition. Consistent with clinical conceptualizations, the authors expected that Cluster B PD symptoms would be related to maladaptive responding (e.g., poorer delay of gratification) when experiencing DA. As hypothesized, many of the relations between the Cluster B PDs and functioning were moderated by DA (e.g., borderline PD was negatively related to abstract reasoning, but only in the DA condition). However, many of the Cluster B PDs symptom counts were related to more adaptive responses in the DA condition (e.g., less aggressive social functioning, better delay of gratification). The authors speculate that individuals with Cluster B PDs may be more likely to respond maladaptively to alternative negative mood states, such as anger and fear. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Depression (Emotion); *Functional Analysis; *Personality Disorders; *Subtypes (Disorders); Experimental Laboratories; Consequence
- Medical Subject Headings (MeSH):
- Adaptation, Psychological; Adult; Affect; Depression; Diagnostic and Statistical Manual of Mental Disorders; Female; Humans; Male; Neuropsychological Tests; Personality Assessment; Personality Disorders; Psychiatric Status Rating Scales; Risk Assessment; Risk Factors
- PsycINFO Classification:
- Personality Disorders (3217)
- Population:
- Human
Male
Female
Outpatient - Location:
- US
- Age Group:
- Adulthood (18 yrs & older)
Young Adulthood (18-29 yrs)
Thirties (30-39 yrs)
Middle Age (40-64 yrs) - Tests & Measures:
- Positive and Negative Affect Schedule—Expanded Form
Wechsler Adult Intelligence Scale– III, Matrix Reasoning subscale
Hypothetical Money Choice Task
Structured Clinical Interview for DSM-IV Axis II Personality Disorders
Symptom Checklist-90–Revised DOI: 10.1037/t01210-000 - Grant Sponsorship:
- Sponsor: University of Georgia Research Foundation, US
Recipients: Miller, Joshua D. - Methodology:
- Empirical Study; Quantitative Study
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- Accepted: Feb 4, 2009; Revised: Feb 4, 2009; First Submitted: Jul 29, 2008
- Release Date:
- 20090504
- Correction Date:
- 20151207
- Copyright:
- American Psychological Association. 2009
- Digital Object Identifier:
- http://dx.doi.org/10.1037/a0015684
- PMID:
- 19413417
- Accession Number:
- 2009-06385-017
- Number of Citations in Source:
- 36
- Persistent link to this record (Permalink):
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- Cut and Paste:
- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2009-06385-017&site=ehost-live">The consequences of depressive affect on functioning in relation to Cluster B personality disorder features.</A>
- Database:
- PsycINFO
The Consequences of Depressive Affect on Functioning in Relation to Cluster B Personality Disorder Features
By: Joshua D. Miller
Department of Psychology, University of Georgia;
Eric T. Gaughan
Department of Psychology, University of Georgia
Lauren R. Pryor
Department of Psychology, University of Georgia
Charles Kamen
Department of Psychology, University of Georgia
Acknowledgement: This research was supported by a grant from the University of Georgia Research Foundation, awarded to Joshua D. Miller.
Personality disorders (PDs) are associated with “a pattern of inner experience and behavior that deviates markedly from the expectations of the individual’s culture… in two of the following areas”: cognition, affectivity, interpersonal functioning, and impulse control (American Psychiatric Association, 2000, p. 689). Cluster B PDs (i.e., antisocial, borderline, histrionic, narcissistic), in particular, are associated with a number of problematic behaviors, such as deliberate self-harm (Klonsky, Oltmanns, & Turkheimer, 2003) and other behaviors thought to fall on the externalizing continuum, such as aggression (e.g., Westen, Shedler, Durrett, Glass, & Martens, 2003) and substance use (e.g., Trull, Waudby, & Sher, 2004). Despite overlap in some of the behavioral correlates across the Cluster B PDs (e.g., aggression is correlated with all Cluster B PDs), there may be different pathways to these outcomes.
Negative affectivity is one domain that may vary across the Cluster B PDs in terms of both the experience of it (e.g., mean elevations, intensity, lability) and its effect on other domains, such as cognition, interpersonal functioning, and impulse control. For example, individuals with borderline (BPD) and narcissistic (NPD) PDs are thought to behave in an impulsive, risky, and/or aggressive manner when experiencing certain negative affective states. It is important to note, however, that the relevant affective states may differ across these PDs such that anger may be pertinent to both BPD and NPD, whereas depression may be more specific to BPD (e.g., Samuel & Widiger, 2008). In fact, behaving impulsively as the result of negative affect is thought to be pathognomonic to BPD; the Diagnostic and Statistical Manual of Mental Disorders (4th ed., Text Rev.; DSM–IV–TR; American Psychiatric Association, 2000) suggests that individuals with BPD may engage in risky sexual behavior, drive unsafely, overspend, abuse substances, or engage in binge eating when affectively dysregulated. It is interesting to note that BPD is associated with both high mean levels of negative affectivity (e.g., mean r = .49; Saulsman & Page, 2004) and affective instability (e.g., r = .36; Miller & Pilkonis, 2006) such that there are frequent and rapid changes in affective states, such as sadness, fear, and hostility (Trull et al., 2008). It has been suggested that the behaviors associated with BPD, such as deliberate self-harm, may be attempts to express and/or regulate unpleasant affective states (e.g., Brown, Comtois, & Linehan, 2002).
The relations between negative affect and problematic behavioral outcomes is less clear for the remaining Cluster B PDs, as they are not strongly associated with mean levels of negative affectivity (Saulsman & Page, 2004) but are associated with affective instability (Miller & Pilkonis, 2006). For example, although narcissistic individuals are generally aggressive (Reidy, Zeichner, Foster, & Martinez, 2008), these individuals behave more aggressively following an ego threat (e.g., Bushman & Baumeister, 1998), which presumably leads to an increase in negative affective states, such as anger. In contrast, antisocial PD (ASPD) is broadly linked to externalizing behavior due to a combination of higher levels of trait antagonism (Samuel & Widiger, 2008) and disinhibition (Miller & Lynam, 2001); in ASPD, externalizing behaviors may not be as contingent upon affective state.
Although there is ample empirical evidence that Cluster B PDs are related to behaviors such as aggression (e.g., Westen et al., 2003) or self-injury (Klonsky et al., 2003), the causal role of negative affectivity in the relations is unclear. There has been little “in-the-moment” empirical attention paid to the behaviors that individuals with these PDs manifest when distressed. Instead, clinicians and researchers surmise, primarily on the basis of patients’ retrospective recall, that these behaviors (e.g., self-harm) are manifested when these individuals are experiencing emotions such as shame, depression, or anger. It is vital that the relations between affect and behavior be investigated in a manner that allows for an examination of their temporal order. This type of research can be conducted using ecological momentary assessment strategies or using experimental psychopathology paradigms in which a negative mood state is induced.
Experimental paradigms may be particularly helpful, as they provide evidence of the causal nature of the relations among affect and subsequent cognition and behavior, shedding light on potential mechanisms. This methodology is particularly relevant for the study of Cluster B disorders, in which individuals engage in behaviors that might be deemed “self-defeating” in the long term. For example, a recent meta-analytic review of BPD and cognition suggests that “motivational influences and negative affects have a disorganizing influence on executive neurocognitive and memory performance in BPD” (Fertuck, Lenzenweger, Clarkin, Hoermann, & Stanley, 2006, p. 369). The causal role of negative affect on cognition could be tested explicitly in an experimental paradigm in which various forms of negative affect (e.g., sadness, anger) are induced to test whether their induction results in decrements in neurocognitive function for individuals with BPD. Such changes, if they occur, may help explain why these individuals engage in more myopic behavior when experiencing negative affect.
In the current study, we examined whether a depressive affect (DA) induction moderates the relations between Cluster B PDs and abstract reasoning (AR), delay of gratification (DoG), and social functioning using three behavioral tasks. We chose these three broad domains of functioning—cognition, impulsive control, and interpersonal functioning—as they are central to the general DSM–IV criteria for the diagnosis of a PD (American Psychiatric Association, 2000). A DA induction was chosen, as sadness is a frequently experienced aspect of BPD (e.g., Trull et al., 2008) but should be less relevant to the remaining Cluster B PDs (e.g., Samuel & Widiger, 2008). Inducing an affective state thought to be specific to BPD but not the remaining Cluster B PDs allows for a differentiated test of the relations between affect and functioning in relation to these PDs. Ultimately, we expected that all of the Cluster B PDs would demonstrate negative bivariate relations with DoG (e.g., Colvin, Block, & Funder, 1995; Miller et al., in press; Petry, 2002) and positive relations with the use of aggression (e.g., Westen et al., 2003; Saulsman & Page, 2004) in response to hypothetical vignettes depicting interpersonal situations. We also expected ASPD to be negatively related to AR, given the negative relation between antisocial behavior and intelligence (e.g., Lynam, Moffitt, & Stouthamer-Loeber, 1993). In reference to the influence of the DA induction, we expected DA to moderate the relation between BPD and all three areas of functioning such that individuals with BPD features who were experiencing DA would evince poorer AR and DoG and greater aggression. We hypothesized that BPD features would interact with the DA induction to lead to aggression, as negative affect is a significant predictor of aggression (e.g., Berkowitz, 1989). Individuals with BPD features who are in the DA condition should be at a higher risk of aggressive responding given the combination of high trait and state negative affectivity. We did not expect the DA induction to moderate the relations between the remaining Cluster B PDs and the behavioral outcomes, as DA is not a particularly salient affective state in these PDs in comparison with affective states such as anger.
Method Participants
The current study used an outpatient clinical sample of 48 Caucasian individuals (29 women; 19 men; mean age = 31.2 years, SD = 10.5), most of whom had completed some (n = 21; 44%) or all of college (n = 21; 44%). Participants in the two conditions (DA vs. control) did not differ on any relevant demographic variables.
Procedures
Recruitment involved placing advertisements in an outpatient psychology clinic and local newspapers; individuals were screened for eligibility on the basis of the following inclusionary criteria: aged 18–60 years, currently seeing a psychologist or psychiatrist, and absence of psychotic symptoms. Participants completed questionnaires, lab tasks, and a DSM–IV PD interview across two assessments. During the first session, after completing the self-report scales, half the participants took part in an DA induction in which they were asked to write about a time “in which you felt very sad” while listening to a piece of classical music that was played at half speed (e.g., Segal, Gemar, & Williams, 1999). Participants were randomly assigned to the experimental or control condition on the basis of a sequence determined by a random number generator. Affect was assessed prior to and after the mood induction. The order of the tasks immediately following the induction was as follows: abstract reasoning, hypothetical money choice task, and social vignettes. All participants provided informed consent and were debriefed and paid for their participation following completion of the study.
Measures
Structured Clinical Interview for DSM–IV Personality Disorders
The Structured Clinical Interview for DSM–IV Personality Disorders (First, Gibbon, Spitzer, & Williams, 1997) is a semistructured interview that assesses DSM–IV PDs. In the current study, only Cluster B PDs are used. Dimensional PD scores were created by adding the ratings (on a 0 to 2 scale) across symptoms. Alphas ranged from .61 (BPD) to .76 (NPD). Thirteen cases were rated by two judges to examine inter-rater reliability; intraclass correlations ranged from .62 (ASPD) to .78 (histrionic). Mean PD scores were as follows: ASPD (dimensional M = 2.8, SD = 2.9; categorical n = 3), BPD (dimensional M = 3.7, SD = 3.1; categorical n = 3), histrionic (dimensional M = 1.7, SD = 2.0; categorical n = 0), NPD (dimensional M = 3.5, SD = 3.5; categorical n = 1).
Symptom Checklist–90–Revised
The Symptom Checklist–90–Revised (Derogatis, 1975) is a 90-item self-report inventory that assesses a range of current (i.e., within the past 7 days) psychological symptoms; only the Global Severity Index is used in the current study. Scores on the Global Severity Index ranged from 1.08 to 2.80 (M = 1.75, SD = 0.43; α = .97).
Positive and Negative Affect Schedule—Expanded Form (Watson & Clark, 1994)
Items from three scales were used: sadness, joviality, and fatigue. Alphas ranged from .82 to .92. Pre- and postinduction means were as follows: sadness (10.62, SD = 4.2, and 13.29, SD = 4.4, respectively), joviality (15.63, SD = 5.8, and 11.08, SD = 4.6), and fatigue (9.54, SD = 3.75, and 8.83, SD = 3.80).
Abstract reasoning
We administered a shortened version (i.e., 13 of 26 items: items 1, 3, 5, 7, 9, 11, 13, 15, etc.) of the Matrix Reasoning subscale from the Wechsler Adult Intelligence Scale–III (Wechsler, 1997). The mean score was 10.36 (SD =1.65).
Hypothetical Money Choice Task (Rachlin, Raineri, & Cross, 1991)
The Hypothetical Money Choice Task was used as a measure of DoG in that participants were asked to choose between a hypothetical larger amount of money available after a delay (i.e., 1 month) and a smaller hypothetical amount of money that was available immediately. The amount of money available after the delay remained fixed ($1,000), whereas the immediately available amount was varied. Participants repeatedly chose between the larger delayed reward and the immediately available reward, which varied in 30 increments from $1 to $1,000. Participants completed two sets of trials; in one series, the amount of money for the immediate reward increased throughout the trials; in the other series, the amount of money for the immediate reward decreased throughout the trials. The dependent variable is calculated by averaging the values for each of the two sets of trials. The first value is the point at which the participant switched preference from the immediate to the delayed rewards when the immediate rewards were presented in the descending order. The second value is the point at which the participant switched from the delayed to the immediate rewards when the immediate rewards were presented in ascending order. The mean score was $785 (SD = $250).
Social vignettes
Participants read 12 vignettes (Tremblay & Belchevski, 2004) describing a hypothetical interaction in which another person performs a behavior that might be considered provocative to the participant (e.g., “You are at a local dance club. While you are dancing a stranger bumps into you very roughly”); four were “hostile” in nature, four were “ambiguous,” and four were “unintentional.” The participants were then asked questions answered on a 1 (not at all likely) to 11 (extremely likely) scale, which assessed the likelihood of (a) being rude, (b) yelling or swearing, (c) threatening the other person if the situation was not resolved, and (d) using physical force if the situation was not resolved. The answers were summed to yield a total score (M = 103.73, SD = 34.7; α = .87).
ResultsThe DA induction was successful, as indicated by increases in sadness, t(23) = −3.0, p ≤ .01, d = .62, and decreases in joviality, t(23) = 5.80, p ≤ .01, d =.87. As expected, the induction did not affect ratings of fatigue, t(23) = .91, ns, d = .19. Next, we examined the bivariate correlations between the study constructs. Finally, we performed a series of regression analyses in which the outcome variables were regressed on the individual Cluster B dimensional scores, a dichotomous variable representing condition (i.e., induction vs. control), and an interaction term. We centered the PDs and the condition variable prior to creating the interaction term to reduce multicollinearity. Because of the small sample size and the difficulty of finding significant interactions (Aiken & West, 1991), we increased our threshold for statistical significance to p < .10 for the interaction terms. For all other statistical tests, we relied on more traditional significance levels (i.e., p ≤ .05).
Bivariate Relations Between Study Variables
Table 1 provides the interrelations between the study variables. Correlations between the Cluster B PDs ranged from .36 (BPD–histrionic) to .60 (BPD–ASPD), with a median of .46. Correlations between the task variables ranged from −.33 (aggressive social functioning–DoG) to .15 (DoG–AR), with a median of −.15. Condition (experimental vs. control) was uncorrelated with all relevant variables. The Cluster B PDs were positively correlated with aggressive social functioning (rs ranged from .18 to .51) and negatively correlated with DoG (rs ranged from −.24 to −.36). ASPD was the only PD that manifested a significant correlation with AR (r = −.30).
Interrelations Among Study Variables
Cognitive Functioning: Interactions Between Cluster B PDs and DA
As seen in Table 2, there was only one significant PD × Condition interaction for AR, such that BPD symptoms were significantly negatively related to AR in the DA condition (B = −.27, SE =.11, p ≤ .05) but nonsignificantly related in the control condition (B =.09, SE =.10, ns).
Cluster B PDs, DA, and Cognitive and Social Functioning
Delay of Gratification: Interactions Between Cluster B PDs and DA
There was a significant interaction (p ≤ .08) between NPD and condition, such that NPD was significantly negatively related to the size of the monetary amount chosen in the control condition (i.e., narcissistic individuals chose smaller but more immediately available monetary amounts; B = −44.04, SE =14.1, p ≤ .01) but was unrelated to the size of the amount chosen in the DA condition (B = −8.35, SE =13.4, ns).
Social Functioning: Interactions Between Cluster B PDs and DA
There were three significant interactions between the PDs and condition (i.e., BPD, histrionic, and NPD) in the prediction of aggressive social functioning. For all three interactions, the PDs were significantly positively related to aggressive responding in the control condition (BPD B = 7.6, SE = 2.0, p ≤ .01; histrionic B = 9.4, SE = 3.4, p ≤ .01; NPD B = 7.0, SE = 1.9, p ≤ .01) but were unrelated in the DA condition (BPD B = 0.81, SE = 2.15; histrionic B = −3.1, SE = 3.4; NPD B = −0.47, SE = 1.8).
Are the Current Effects Due to Axis I-Related Distress?
To control for the possible effects of psychological distress, we reran the previous regression analyses including the Global Severity Index from the Symptom Checklist–90–Revised. Of the previous 11 significant (or near significant) main effects (6) or interactions (5), 8 remained significant. The change in statistical significance for these three effects once the Global Severity Index was included (i.e., main effect of ASPD on AR; main effect of histrionic on DoG; interaction of NPD and condition on NPD) appeared to be primarily due to a loss of power as the relations remained in the same direction with only small reductions in size.
DiscussionIn the current study, DA had both adaptive and maladaptive effects on the relations between Cluster B PDs and functioning. As hypothesized, BPD was negatively related to abstract reasoning, but only in the DA condition. Previous work has shown that “BPD subjects appear to exhibit non-specific deficits in multiple domains of executive neurocognitive and memory performance in comparison to psychiatric and non-clinical groups” (Fertuck et al., 2006, p. 369). These authors noted that these findings are “tentative,” however, because of the possibility that there may be “state-dependent effects,” which is consistent with the current results. This is significant in that it may partially explain why individuals with BPD symptoms engage in behaviors that may seem counterproductive, dangerous, and/or maladaptive, such as self-harm. Although individuals with BPD symptomatology may engage in these behaviors for a variety of reasons, such as distraction or self-punishment, the potential consequences may be more readily ignored when one is not processing information as clearly because of the presence of DA. This hypothesis warrants further examination given the current evidence for impaired cognitive functioning during periods of DA.
It is worth noting, however, that the DA induction did not differentially affect all areas of functioning in relation to BPD, as one might expect (e.g., BPD was not related to poorer delay of gratification or aggressive social behavior in the DA condition). This was surprising, as BPD is linked with a variety of different forms of impulsivity (e.g., Whiteside, Lynam, Miller, & Reynolds, 2005) and has been linked to risky decision making under normal affective conditions (Kirkpatrick et al., 2007). These findings are not without precedence. Chapman, Leung, and Lynch (2008) found that individuals with BPD symptoms who were currently experiencing negative affectivity performed in a less impulsive manner than did individuals with BPD who were not experiencing concurrent negative affectivity (i.e., d = .74), thus suggesting that certain types of negative emotions may have an inhibitory effect on individuals with BPD symptomatology with regard to certain domains of functioning. As noted by Chapman et al., similar results have been found in studies on psychopathy in which anxiety moderates the relation between psychopathy and passive avoidance errors (e.g., Newman & Schmitt, 1998).
Consistent with Chapman et al.’s (2008) findings with BPD, DA had some adaptive effects on histrionic and NPD (and, to a lesser extent, BPD), in that individuals with these symptoms were better able to delay gratification (i.e., NPD) and reported lower probabilities of acting aggressively in hypothetical social situations when experiencing DA (i.e., BPD, NPD, histrionic). All three of these disorders may be linked to a strong behavioral activation system (Gray, 1987; Foster & Trimm, 2008; Pastor et al., 2007), which suggests that individuals with these disorders or traits may be sensitive to signs of reward and novelty. In the current situation, the induction of DA may have dampened, at least briefly, the strength of the behavioral activation system and/or activated the behavioral inhibition system, which is sensitive to signs of punishment or nonreward (when reward is expected). The current results would suggest that many of the negative behaviors believed to occur in relation to Cluster B PDs and negative affect (e.g., self-harm, aggression) may occur under more activating and arousing affective states, such as anger or anxiety, rather than depression.
As expected (e.g., Moeller et al., 2002; Simonoff et al., 2004), ASPD was related to impaired functioning across the domains, and these effects were not contingent upon DA. Instead, the relations between ASPD and poorer social and intellectual functioning may reflect the influence of stable individual differences in intellect and personality that are unrelated to affectivity. Alternatively, there was some evidence that individuals with ASPD symptoms manifested less change in affect, specifically sadness, as a result of the mood induction (i.e., r = −.40, p < .06). The difficulty of inducing DA in relation to ASPD may have confounded our ability to test these relations. It is possible that a DA induction is likely to fail with individuals with ASPD who experience more externally directed forms of negative affect (e.g., anger) than self-directed forms (e.g., depression; Samuel & Widiger, 2008).
In conclusion, Cluster B PD pathology was related to more problematic functioning (i.e., poorer abstract reasoning in ASPD; poorer delay of gratification in histrionic and NPD; and aggressive responding to social situations in ASPD, NPD, and BPD). However, the induction of DA had a substantial effect on these behaviors, some of which could be viewed as being maladaptive (e.g., poorer abstract reasoning), but several of which could be viewed as being adaptive (e.g., less verbal and physical aggression, greater ability to delay gratification). Given the small size of the current sample, future studies are needed to test the generalizability of these findings. Methodologically, future studies should counterbalance the presentation of the laboratory tasks, as it is possible that the effects of the mood induction varied systematically across the tasks (i.e., stronger for the first task than the final task) because the DA induction may have weakened because of the progression of time or distraction by the preceding tasks. Although we believe that the current mood induction may have been more powerful and long-lasting than most (e.g., Velten statements), this was not explicitly tested and requires empirical examination. In addition, because the current induction resulted in changes in negative and positive affect, we are unable to disentangle their individual effects. Future research should aim to do that, although it may prove difficult to manipulate one without affecting the other (see Westermann, Spies, Stahl, & Hesse, 1996, for a review). It will also be important to test these relations in samples with greater degrees of personality pathology. Finally, it will be important to use similar paradigms to test the effect of the induction of other forms of negative affectivity, such as anger, which may be the most salient and problematic for individuals with Cluster B PD symptoms (e.g., Trull et al., 2008).
Footnotes 1 The findings do not change if gender is included in the regression model at Step 1.
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Submitted: July 29, 2008 Revised: February 4, 2009 Accepted: February 4, 2009
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Source: Journal of Abnormal Psychology. Vol. 118. (2), May, 2009 pp. 424-429)
Accession Number: 2009-06385-017
Digital Object Identifier: 10.1037/a0015684
Record: 164- Title:
- The cultural fairness of the 12-item General Health Questionnaire among diverse adolescents.
- Authors:
- Bowe, Anica. Oakland University, Rochester, MI, US, bowe@oakland.edu
- Address:
- Bowe, Anica, Teacher Development & Educational Studies, Oakland University, 2200 North Squirrel Road, Rochester, MI, US, 48309, bowe@oakland.edu
- Source:
- Psychological Assessment, Vol 29(1), Jan, 2017. pp. 87-97.
- NLM Title Abbreviation:
- Psychol Assess
- Page Count:
- 11
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- Psychological Assessment: A Journal of Consulting and Clinical Psychology
- ISSN:
- 1040-3590 (Print)
1939-134X (Electronic) - Language:
- English
- Keywords:
- GHQ-12, adolescents, cultural sensitivity, differential item functioning, ethnic minority groups
- Abstract:
- The 12-item general health questionnaire (GHQ-12) was used in the Longitudinal Study of Young People in England (LSYPE; N = 15,770) to collect measures on adolescent mental health. Given the debate in current literature regarding the dimensionality of the GHQ-12, this study examined the cultural sensitivity of the instrument at the item level for each of the 7 major ethnic groups within the database. This study used a hybrid approach of ordinal logistic regression and item response theory (IRT) to examine the presence of differential item functioning (DIF) on the questionnaire. Results demonstrated that uniform, nonuniform, and overall DIF were present on items between White and Asian adolescents (7 items), White and Black Caribbean adolescents (1 item), and White and Black African adolescents (7 items), however all McFadden’s pseudo R² effect size estimates indicated that the DIF was negligible. Overall, there were cumulative small scale level effects for the Mixed/Biracial, Asian, and Black African groups, but in each case the bias was only marginal. Findings demonstrate that the GHQ-12 can be considered culturally sensitive for adolescents from diverse ethnic groups in England, but follow-up studies are necessary. Implications for future education and health policies as well as the use of IR-based approaches for psychological instruments are discussed. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Cultural Sensitivity; *Diversity; *Fairness; *Adolescent Characteristics; Minority Groups; Racial and Ethnic Groups
- PsycINFO Classification:
- Culture & Ethnology (2930)
- Population:
- Human
- Location:
- England
- Age Group:
- Adolescence (13-17 yrs)
- Tests & Measures:
- General Health Questionnaire-12 [Appended] DOI: 10.1037/t00297-000
- Methodology:
- Empirical Study; Followup Study; Longitudinal Study; Quantitative Study
- Supplemental Data:
- Text Internet
Other Internet - Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- First Posted: May 5, 2016; Accepted: Mar 16, 2016; Revised: Mar 12, 2016; First Submitted: Sep 19, 2015
- Release Date:
- 20160505
- Correction Date:
- 20161229
- Copyright:
- American Psychological Association. 2016
- Digital Object Identifier:
- http://dx.doi.org/10.1037/pas0000323; http://dx.doi.org/10.1037/pas0000323.supp(Supplemental)
- PMID:
- 27148787
- Accession Number:
- 2016-21963-001
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- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2016-21963-001&site=ehost-live">The cultural fairness of the 12-item General Health Questionnaire among diverse adolescents.</A>
- Database:
- PsycINFO
The Cultural Fairness of the 12-Item General Health Questionnaire Among Diverse Adolescents
By: Anica Bowe
Oakland University;
Acknowledgement:
Child and adolescent mental health is presently a top priority for the government of England & Wales (Department of Health, 2015). In 2014, the Department of Health established the Children and Young People’s Mental Health and Wellbeing Taskforce and, in corollary, England’s Department for Education for the first time in its history made mental health a specific priority and dedicated funds toward health and care projects (Department of Health, 2014, 2015). Reports from the Department of Health written in the last 5 years show a concerted effort to identify mental health needs and bolster support for children and adolescents within schools, social services, communities, and among general clinical practitioners (Children and Young People’s Mental Health and Wellbeing Taskforce, 2015; Department of Health, 2015; YoungMinds, 2014).
To date, the two most recent datasets on adolescent mental health and development used to shape mental health policy in the United Kingdom come from the 2004 national survey of the Mental Health of Children and Young People (Green, McGinnity, Meltzer, Ford, & Goodman, 2005) and the first Longitudinal Study of Young People in England of 2004–2010 (LSYPE; Department for Education, NatCen Social Research, 2013). The 2004 National Survey of the Mental Health of Children and Young People measured the prevalence of conduct disorders, emotional disorders, and hyperkinetic disorders among individuals aged 5 to 16 years. The first LSYPE was a large-scale panel study that measured multiple factors related to adolescent development. During Wave 2 data collection (Year 2005), it measured adolescent mental health using the 12-item general health questionnaire (GHQ-12) created by Goldberg (1978). There are other population studies on health that include mental health and well-being measures on adolescents and adults, such as the 2010 and 2011 Health Survey of England (Craig & Hirani, 2010; Craig & Mindell, 2011), but the numbers of adolescents in these population studies are comparably few.
To address matters of diversity for mental health issues within its society, cultural-sensitive instruments, supports, and interventions for mental health are presently top priorities within England’s Department of Health (Children and Young People’s Mental Health and Wellbeing Taskforce, 2015). These priorities become more poignant given the recent surges in asylum seekers to the U.K. from Sudan, Eritrea, Iran, Syria, Afghanistan, Pakistan, Iraq, Albania, India, Bangladesh, and other countries (Refugee Council, 2015). Child and adolescent refugees have unique mental health needs (Pacione, Measham, & Rousseau, 2013). There is a dearth though of appropriate cross-culturally validated assessment tools, and, further, refugees might have cultural understandings of distress that cannot be measured with existing tools within the receiving countries (Pacione et al., 2013). Culturally sensitive tools allow the government to make accurate diagnosis, identify individuals for services, and in the long run, make investments with good returns on factors associated with mental health such as social services and welfare (Children and Young People’s Mental Health and Wellbeing Taskforce, 2015).
Scholars have raised questions about the measurement equivalence of the GHQ-12 for decades (Goldberg, Oldehinkel, & Ormel, 1998; Simon, Goldberg, Von Korff, & Üstün, 2002; Smith, Fallowfield, Stark, Velikova, & Jenkins, 2010). It is important to verify the extent to which mental health scales used in the United Kingdom’s national surveys are culturally sensitive, not only to demonstrate reliability of the scores and validity of inferences made for all ethnic groups, but also, and more importantly, because findings from such studies are used to inform mental health policy (e.g., see the work of Barnes, Green & Ross, 2011). This study examines the cultural sensitivity of the GHQ-12 for the most recent data available on adolescent mental health in England which comes from the second wave of the first LSYPE project collected in 2005. This database is particularly useful in addressing matters of cultural sensitivity because it contains demographic variables that distinguish 17 ethnic groups in England. Since 2002, the government of England and Wales has made extensive efforts to recognize the presence of ethnic minority groups, report their numbers, and disaggregate data by ethnic group, and not just by race, to identify disparities among its diverse citizens (e.g., see Department for Education & Skills, 2005). Thus, findings here would support the government of England and Wales’ quest to attend to equity issues for adolescent mental health in a more targeted manner.
Measurement EquivalenceThe cultural sensitivity of an instrument can be examined by assessing its degree of measurement equivalence. Measurement equivalence establishes whether or not an item, scale, or instrument designed to assess a particular attitude or behavior has the same meaning across different subgroups of a population. Measurement equivalence is important to establish because a certain level is necessary for specific types of group comparisons to be valid (Rensvold & Cheung, 1998). It is well documented that psychological scales that demonstrate good psychometric properties in the general population may or may not hold true for subgroups of that population or over time (e.g., Fujishiro et al., 2011; Gregorich, 2006; Wicherts, Dolan, Hessen, et al., 2004). Thus, it is critical that measurement equivalence holds for the GHQ-12 among the different ethnic groups within the LSYPE database because policy-level assumptions, decisions, and steps toward equity in mental health are being made based on the data it contains.
There are many ways to assess measurement equivalence at the item level (more commonly referred to as differential item functioning [DIF]). The seminal work of Millsap and Everson (1993) provided a useful way to conceptualize the methods used to detect DIF. They pointed out that typically there were a set of methods used to detect DIF for unobserved conditional invariance models (i.e., latent traits) and a set used to detect DIF for observed conditional invariance models. More recent scholars within the health sciences have further conceptualized the methods for detecting DIF as either parametric or nonparametric (Teresi, Ramirez, Lai, & Silver, 2008). Currently, the two most popular frameworks for studying measurement equivalence for unobserved conditional invariance models are confirmatory factor analysis (CFA) and item response theory (IRT) approaches (Raju, Laffitte, & Byrne, 2002; Stark, Chernyshenko & Drasgow, 2006; Tay, Meade & Cao, 2014).
According to Zumbo (1999), “DIF occurs when examinees from different groups show differing probabilities of success on (or endorsing) the item after matching on the underlying ability that the item is intended to measure” (p. 12). Evidence of DIF across groups would suggest that bias is possibly present; however it is up to a panel of experts to determine whether the bias is real, or rather, merely the phenomenon of impact (Karami, 2012). Item impact is the situation where DIF observed between groups is due to real differences in the underlying trait being measured (Zumbo, 2007). DIF analysis specifically looks at equivalence at the item level, that is, does the item have the same meaning across groups?
Earlier Studies on the Validation of the 12-Item General Health QuestionnaireThe general health questionnaire has various forms of different lengths, for example, 12-item, 20-item, 28-item, 30-item, and 60-item (Jackson, 2007). It was originally designed for screening mental health within the general population or nonpsychiatric patients (Goldberg, 1978; Jackson, 2007). The psychometric properties of sensitivity and specificity for the 12-item version have been established for over two decades among various adolescent and adult populations (classified as nonpsychiatric inpatients, nonpsychiatric outpatients, or general community) in Africa, Europe, India, South America, and the USA (Goldberg et al., 1997). Since the Goldberg et al.’s (1997) study, the GHQ-12 has been validated for specificity and sensitivity in additional countries, for example, Austria (Friedrich, Alexandrowicz, Benda, Cerny, & Wancata, 2011), rural China (Lee, Yip, Chen, Meng, & Kleinman, 2006); and South Korea (Kim et al., 2013). Since the late 1990s though, Goldberg and colleagues pointed out that the cut off scores (also called threshold levels) for identifying individuals with mental health issues for the 12-item questionnaire varied among countries and they speculated that this could be due to either differences in prevalence among the cultures, or because cultures respond differently to items, or because different cultures have different thresholds for mental health (Goldberg et al., 1998; Simon et al., 2002). Their work from that time suggested a possibility of item bias present and called for an examination of measurement equivalence of the scale.
More recently, the issue of dimensionality has been called to question for the GHQ-12. The dimensionality of the GHQ-12 is important to establish because it is a prerequisite to estimating reliability and validity (Shevlin & Adamson, 2005; Shevlin, Miles, Davies, & Walker, 2000). In fact, unsuspected multidimensionality can be erroneously labeled as DIF (Mazor, Hambleton & Clauser, 1998). Further, using the wrong model specification of the questionnaire could potentially lead to erroneous identifications of mental illness (Smith, Fallowfield, Stark, Velikova, & Jenkins, 2010).
To provide a snapshot of what is already known about the GHQ-12, Table 1 summarizes studies that examined its dimensionality, measurement equivalence, and item functioning. These studies are in chronological order outlining the author, country the study took place, whether the study examined the factor structure or measurement equivalence, the participants used, the methodological framework used, and the degree of invariance found for the factor structure imposed. Table 1 presents a thorough (but not exhaustive) list of the factor structures uncovered from various countries to give the reader an opportunity to identify trends and make comparisons at the trans-national level.
Factor Structures of the GHQ-12 for Various Countries
Factor Structures of the GHQ-12 for Various Countries
Table 1 illustrates that the majority of the studies implemented the CFA framework. Three studies did employ an IRT framework, but they examined dimensionality or/and item functioning, not measurement equivalence (Gao et al., 2012; Smith et al., 2010; Uher & Goodman, 2010). Only five of the 23 studies examined measurement equivalence for various subgroups, but none of them focused on diverse ethnic groups within a country. Consequently, the literature base regarding the question of the measurement equivalence of the GHQ-12 among diverse ethnic groups within a country has yet to be firmly established. More importantly, findings from various countries demonstrate that the factor structures among the countries differ. There is a trend affirming the 2 and 3 factor models up until 2007; however, the work of Hankins (2008) incurred a pivotal change to exploring and endorsing a unidimensional model with correlated error terms. Since using the wrong model specification of the questionnaire could potentially lead to erroneous identifications of mental illness (Smith et al., 2010), the findings in Table 1 precipitate an immediate concern and examination of the cultural sensitivity of the GHQ-12 for ethnic minority groups in England.
In short, there are a number of reasons why addressing the measurement equivalence of the GHQ-12 among diverse ethnic groups in England is timely. First, this study supports the government of the U.K.’s priorities of addressing the mental health needs of all ethnic groups. Second, there is a surge of refugee immigrants entering the U.K. thus a rising demand for schools to use mental health screening tools that are culturally sensitive and relevant. Third, ethnic identity studies in the USA and U.K. demonstrate that ethnic identity can moderate the degree to which adolescents engage in maladaptive behaviors or demonstrate well-adjusted psychosocial functioning (Aguado et al., 2012; Umaña-Taylor, 2011). It is possible then that ethnic identity moderates item bias on the GHQ-12. Findings from this study might point to an explanation as to why (item bias or item impact) we observe differences in maladaptive behaviors or well-adjusted psychological functioning among adolescents from different ethnic groups. Fourth, there is an unsettled debate regarding the dimensionality of the GHQ-12. Indeed, Aguado et al. (2012) called for future studies to examine the structural invariance of the factor loadings using cross-gender and/or cross-cultural groups for the unidimensional model under Hankins’ (2008) specification. This study is designed to answer that call within an IRT-based framework. This study chose to use an IRT-based framework because the IRT literature suggests that these approaches might be more superior to CFA methods (Raju et al., 2002; Stark et al., 2006; Tay et al., 2014). It focuses on the item level rather than scale level because mental health is a critical issue and individual level decisions are made using this instrument. Thus the possibility of even a small amount of DIF leading to inappropriate interpretations for a particular ethnic group justifies item level inspection (Meade, 2010). A major strength of this study is that it uses a large nationally representative sample and therefore has the capacity to address psychometric questions about the cultural sensitivity of the GHQ-12.
Method The Database
Data came from the first LSYPE of 2004–2010 (N = 15,770). This was a large-scale longitudinal panel study of young people in England which began in 2004 when the individuals were 13 and 14 years of age. The purpose of the LSYPE was to examine the impact current policies had on this group of young people and to provide an evidence-base for further policy development in England. For more detailed information about the content of the database as well as sampling procedures and the handling of missing data, see the LSYPE user guide manual found at this website: http://www.esds.ac.uk/longitudinal/access/lsype/design.asp.
Participants
Data for this study came from Wave 2. Due to responder drop-out, by Wave 2 there were 13,518 adolescents in the database. Of these, 13,116 had answered at least one item on the 12-item general health questionnaire. In keeping with the specificity of ethnic descriptors for national education data since 2002/2003, the LSYPE denotes 17 ethnic groups but also has a variable that combines the white ethnic groups into one race group and maintains the other minority ethnic groups in their separate categories.
In line with standard practice for DIF analysis, this study denoted White adolescents (n = 8,980) as the reference group because they were the dominant population. In support of this, previous literature has already established that ethnic identity is not a salient feature for members of the dominant race group (Phinney, 1996). The focal groups were as follows: Mixed/Biracial (n = 662), Pakistani (n = 793), Indian (n = 859), Bangladeshi (n = 596), Black Caribbean (n = 445), Black African (n = 456). There was another ethnic minority group called Other (n = 325), but this group was not used as a focal group because it was a conglomerate of other ethnic minority groups. Thus in total, the sample size of this study was N = 12,791.
GHQ-12
The GHQ-12 is a 12-item scale used to measure mental health. Adolescents reported their perceptions of their mental health on a 4-point ordinal scale. Specifics of this instrument can be found in Goldberg (1978). There are four ways the general health questionnaire can be scored: the bimodal scoring scheme (0–0–1–1); the Likert format (0–1–2–3); the modified Likert format (0–0–1–2); and the chronic method (0–0–1–1 for positive worded items and 0–1–1–1- for negative worded items) (Goldberg et al., 1997). This study adopted the Likert format for scoring because the items were ordinal items. It acknowledges though that both Goldberg et al. (1997) and more recently Friedrich et al. (2011) demonstrated that the bimodal method of scoring (0–0–1–1) is just as good or superior to the other 3 methods for determining criterion validity. However, the author argues that a bimodal scoring method would decrease the amount of variation and consequently decrease the amount of information the item can provide. Given that an advantage of IRT is that it capitalizes upon the amount of variation per item, it seemed appropriate to retain a Likert format method of scoring. Smith et al. (2010) demonstrated that only item 11 had a disordering effect; further justifying the use of the Likert format for scoring for analysis.
Analysis
Model examined
The author chose to estimate a unidimensional model because the intent of the GHQ-12 developers was a unidimensional structure. Further, Stout (2002) argued that obtaining a reasonable fit for a multidimensional model to a latent trait does not prove that unidimensionality fails. Rather, the true test of unidimensionality is if there is in existence a parametric unidimensional model that is recoverable. Hankins (2008) suggested a unidimensional model with correlated error terms for items 2, 5, 6, 9, 10, and 11. In a slightly different corollary, Fernandes and Vasconcelos-Raposo (2013) recommended to correlate the errors between (4 and 10), (10 and 11), and (11and 12) and also remove items 1, 2, 3 to recover a unidimensional scale. This study adopted the recommendation of Hankins (2008) because it appeared less drastic to that of Fernandes and Vasconcelos-Raposo (2013).
Configural invariance
Before examining DIF, it was first necessary to establish configural equivalence (Tay et al., 2014) because unanticipated multidimensionality can be mistaken for DIF (Teresi et al., 2008). If a scale demonstrates configural equivalence, this means that it is unidimensional for each group, and therefore measuring a single underlying trait for each group. Configural invariance was examined using multi-group CFA with MPlus 7.1 (Muthén & Muthén, 2012) with the weighted least squares estimator because these items were ordinal.
Method for detecting DIF: Ordinal logistic regression/IRT hybrid
DIF can be examined through a variety of ways including but not limited to the use of log-linear models, standardization, logistic regression, logistic discriminant function analysis (Millsap & Everson, 1993), contingency tables and/or regression models, IRT, and dimensionality testing (Zumbo, 2007). Teresi and her colleagues discussed the use of DIF techniques along with modern software used to implement them and the pros and cons of these techniques (Teresi, 2006; Teresi et al., 2008). This study used one of the most modern parametric approaches they discussed which is a hybrid approach of ordinal logistic regression and IRT (Choi, Gibbons, & Crane, 2011). The major strength of this hybrid is that it uses a latent conditioning variable based on IRT estimation which differs from most logistic regression methods that use observed scores (Choi et al., 2011; Teresi et al., 2008). This hybrid approach was used for two main reasons. First, logistic regression can detect both uniform and nonuniform DIF (Teresi et al., 2008). This is an important point to make because up until the time of the Teresi et al. (2008) study, the majority of studies examining DIF for health data only examined uniform DIF. Second, ordinal logistic regression is not as computationally intensive as pure IRT methods. Computational intensity is a major factor for this study given the sample size (N = 12,791), the number of ethnic groups in this study (n = 7), and the computational limitations of latent trait modeling software. Crane, Gibbons, Jolley, and van Belle (2006) draw our attention to the fact that the polytomous graded response model and the ordinal logistic regression model examining DIF are virtually synonymous if IRT-based ability estimates are used in ordinal logistic regression and if the demographic grouping variable understudy is dichotomous or categorical. Both conditions were satisfied in this study.
The three most common item-level criteria for determining DIF within the IRT framework include Lord’s chi-square approach, the differential functioning of items and tests framework, and the likelihood-ratio approach (Tay et al., 2014). Along with determining the presence of DIF, it is equally important to determine the magnitude of DIF, because even though the amount of DIF displayed might reach statistical significance, it might not be clinically important (Teresi et al., 2008). Common effect size indices for determining DIF are the odds ratio, beta coefficient, or incremental R-squared associated with the DIF term for the item understudy (Teresi, 2006; Teresi et al., 2008). This study used the lordif package (Choi, 2012) in R (R Core Team) which utilizes the likelihood ratio χ2 test along with McFaddens’s pseudo R2 as a measure of effect size. Lordif provides estimates of uniform, nonuniform, and overall DIF for each item. For a detailed explanation of the algorithm used for DIF detection by the lordif package see Choi et al. (2011).
This study was prone to increases in Type I error rates because it involved the multiple testing of items. To account for this, Bonferroni adjustments were applied to the standard alpha level of .05. The formula for the Bonferroni adjustment used in each analysis was .05/[(number of items)* (number of focal groups)]. A Bonferroni adjustment was used to set alpha threshold levels rather than conducting the Monte Carlo simulations advised by Choi et al. (2011) because these simulations would prove extremely computationally intensive given the sample size of this study (N = 12,791) versus their study of N = 768. Bonferroni adjustments were considered a reasonable substitute for Monte Carlo simulations for the generation of threshold values, as Bonferroni adjustments are advised by Stark et al. (2006) to account for multiple testing, the Type I error inflations accrued due to the use of constrained baseline models (vs. free-baseline models) in logistic regression, and the fact that likelihood-ratio chi square tests for statistical significance are greatly influenced by sample size. The use of likelihood-ratio chi square test in conjunction with McFaddens’s pseudo R2 decreases the occurrence of Type I error rates for logistic regression detection of DIF (Jodoin & Gierl, 2001).
For interpreting results, three measures of DIF were examined: uniform DIF, nonuniform DIF, and overall DIF. Overall DIF is an important measure to take into account because even though items might contain DIF for certain groups, it does not mean that the DIF affects the overall scale scores (Crane et al., 2007). Therefore, more weight was given to the interpretations of McFadden’s pseudo R2 for effect sizes rather than the likelihood ratio tests because of the effect large sample sizes have on tests for significance. Jodoin and Gierl (2001) recommended the following effect sizes for examining DIF using logistic regression procedures: .035≤ R2Δ − U < .070 a medium effect size, R2Δ − U ≥.070 as large effect size, and anything less than .035 as negligible.
Scale level bias for each ethnic group was also examined as item-level DIF has the potential of having a cumulative impact on the scale or, conversely, a cancelling effect (Teresi et al., 2008). There are a number of effect size indices that can inform on scale-level bias (Meade, 2010). These indices can be calculated using the Microsoft Excel-based computer program called VisualDF developed by Meade (2010) that can be downloaded from this website (http://www4.ncsu.edu/~awmeade/). This study used the Expected Test Score Standardized Difference (ETSSD) effect size index as it allows DIF cancellation across items and respondents (Meade, 2010).
Limitations
The model had the error terms for items 2, 5, 6, 9, 10, and 11 specified as correlated. Correlating the error terms introduced a limitation to this analysis because an assumption of unidimensional IRT models is conditional local independence (Reckase, 2009). Violation of model assumptions and model misspecifications can lead to false DIF detection (Teresi et al., 2008); however, for estimation purposes though, conditional independence of the error terms is not essential and is actually permissible once the model remains identifiable (Kuha, 2013).
In studies assessing item bias, it is not necessary to have large numbers of items; however, it is necessary to have large sample sizes (Hulin, Lissak, & Drasgow, 1982). Hulin et al. (1982) found that for questionnaires of 15 items, 500 individuals were needed for good recovery of discrimination and difficulty parameters. A second limitation of this study then is that the Black Caribbean and Black African ethnic groups might have biased parameter estimates because their sample sizes were approximately 450.
Results Configural Invariance
On the basis of the guidelines of Hu and Bentler (1999) to interpret goodness-of-fit indices, the results demonstrated reasonable fit at the configural level for all ethnic groups, χmodel fit2 (273) = 2369.80, χbaseline model2 (462) = 93547.42; RMSEA = .065, CI90% = [.062,.067]; CFI = .977; TLI = .962).
DIF Analyses
First, it is important to note that the author experienced estimation difficulties with the sample size of N = 12,791. Although an omnibus test was initially performed for all seven ethnic groups using the lordif package in R, the software produced error messages. Thus, a limitation of lordif is that it is unable to perform an omnibus test for extremely large samples sizes on multiple ethnic groups simultaneously. Upon consulting with the authors of the lordif package, additional preliminary steps were taken to speed up processing time. These steps included examining the presence of DIF among the three Asian groups first, then among the two black groups to see if there was enough evidence to justify collapsing the three Asian groups into one race group and the two black groups into another race group to compare to the dominant population. Preliminary findings demonstrated that there were no items demonstrating DIF between Indian, Pakistani, and Bangladeshi adolescents; therefore, they were collapsed into one race group. Conversely, DIF was present on Items 2 and 11 between Black African and Black Caribbean adolescents. The amount of DIF though was clinically negligible for both items as indicated by the low pseudo R2 values (Item 2: R2 < .01 for uniform, nonuniform, and overall DIF; Item 11: R2 = .02 for nonuniform DIF, R2 < .01 for uniform and overall DIF).
For the main analyses, the authors of the lordif package advised that I compare just one focal group to the referent group at a time. Significant testing demonstrated no DIF between White and Mixed/Biracial adolescents; one item (Item 1) flagged for DIF between White and Black Caribbean adolescents; seven items (Items 1,3,4,7,8,11,12) flagged for DIF between White and Black African adolescents; and seven items (Items 1,2,4,5,7,8,9) flagged for DIF between White and Asian adolescents. To determine whether the amounts of DIF present were meaningful, the effect sizes as indicated by McFadden’s pseudo R2 estimates were evaluated. These are all reported in Table 2.
Effect Sizes of the Amount of Uniform, Non-Uniform, and Overall DIF Present by Item (Reference Group = White)
All pseudo R2 estimates indicated that the amount of DIF present in each case was negligible. Thus, even though uniform, nonuniform, and overall DIF were present on items between White and Asian adolescents, White and Black Caribbean adolescents, and White and Black African adolescents, the magnitudes of DIF did not reach the threshold level for even a small effect size.
The results of the tests for scale level bias are found in Table 3. Interpretations of the ETSSD effect size index should be similar to Cohen’s d guidelines for interpreting effect sizes (Meade, 2010). According to Cohen (1992), effect sizes of .20, .50, and .80 represent small, medium, and large effect sizes respectively. Findings demonstrate that there might be a small scale level effect for Mixed/Biracial, Asian, and African adolescents compared with White adolescents as effect size estimates for these groups just meet the borderline of .20 when rounding up. That is, in general, Mixed/Biracial adolescents have overall scale scores about one fifth of a standard deviation above White adolescents. Conversely, Asian and Black African adolescents have an overall scale score about one fifth of a standard deviation below White adolescents. Thus, there might be a small cumulative effect of overall DIF on the GHQ-12 which manifest at the scale level for Mixed/Biracial, Asian, and Black African adolescents but not for Black Caribbean adolescents.
Effect Sizes for Scale Level Bias on the GHQ-12 for the Four Focal Groups (Reference Group = White)
DiscussionThis study examined the presence of DIF on the GHQ-12 using a nationally representative sample of ethnically diverse adolescents in England. This study demonstrates that although the GHQ-12 is free from any meaningful item level bias for ethnically diverse adolescents there, there may be a small cumulative scale level bias for Mixed/Biracial, Asian, and Black African adolescents. Given that the cumulative scale level bias is marginal, the use of the GHQ-12 among different ethnic groups living in the United Kingdom might be appropriate. Ongoing analysis on item and scale level functioning for adolescents living there is necessary to examine whether these effect sizes remain stable. In light of the marginal scale level bias, policymakers and clinicians need not be too concerned with the cultural sensitivity of this instrument at this point in time. They can continue using the GHQ-12 among their ethnically diverse citizens, but be mindful of the possibility of slight group differences for certain ethnic groups.
The recent surges of asylum seekers to the U.K. make the use of the GHQ-12 more poignant for England’s Department for Education. Child and adolescent asylum seekers will be attending local schools. Given the tumultuous backgrounds asylum seekers often come from, these children and adolescents may have heightened mental health issues that need immediate attention. The GHQ-12 is a simple instrument that can be used by teachers without taking up too much curriculum learning time. An important but perhaps obscure point is that ethnic minority communities (which include immigrant and asylum seekers) tend to live in economically deprived areas where schools are of poor quality (Cassen & Kingdon, 2007). This means then that schools serving these groups might not have adequate resources in place to conduct comprehensive screening of students. Thus, the GHQ-12 might be an ideal tool in lieu of more extensive screening not being available.
In support of its use inside schools, previous research demonstrates that many immigrant and ethnic minority groups are more comfortable using school-based services for their children for a variety of reasons including accessibility, stigmatization, lack of awareness about services or mental health issues, concerns about citizenship status, and lack of confidence in providers among other reasons (Ellis, Lhewa, Charney, & Cabral, 2006; Fazel, Doll, & Stein, 2009; Guruge & Butt, 2015; Malek & Joughin, 2004). Addressing mental health needs is critical to individual health but it is also important to society because although costly, the return from investments made for preventive actions and intervention is high (Children and Young People’s Mental Health and Wellbeing Taskforce, 2015). For example, cost benefit analyses show that every pound (£1) spent on effective school counseling results in a saving of six pounds (£6) for costs associated with social services, welfare, and the criminal justice system (Children and Young People’s Mental Health and Wellbeing Taskforce, 2015).
Consequently, England’s Department of Health is prioritizing primary prevention methods for mental health (Department of Health, 2015). Examples of primary preventive actions delineated are to “improve the quality of teaching about mental health in Personal Social, Health, and Economic lessons in schools, [and to also develop] an evidence-based schools counseling strategy to encourage more and better use of counselors in schools” (Department of Health, 2015, p. 22). Surprisingly though, there is no discussion in recent policy documents written by the Children and Young People’s Mental Health Taskforce on whether primary prevention methods ought to be ethnic-group specific or more generalized. In fact, there is little mention of mental health issues specific to ethnic minority groups in general. This suggests that although they have articulated the goal of providing equitable and culturally sensitive services, they struggle to conceptualize how this process would be realized. The taskforce also does not appear to have a firm grasp on the necessary data needed to be collected to guide their decision-making for this goal because there is no discussion of this in their policy documents. Efforts then can be placed on verifying the extent to which primary preventive measures need to be more group-specific or in fact, can be more generalized. This can occur along with ongoing efforts to provide appropriate interventions for the United Kingdom’s diverse ethnic groups.
England’s Department of Health is poised to administer the Third Mental Health of Children and Young People survey for 2016. (The first occurred in 1999 and the second in 2004.) The scales on this survey demonstrate that multiple factors of mental health will be assessed (https://www.gov.uk/government/organisations/office-for-national-statistics). Moreover, there is an emphasis on measuring positive mental health or mental well-being, and not just mental illness (Department of Health, 2014). Indeed, the government of the United Kingdom has made it one of its long-term goals to see increases in the percentages of its citizens who pass healthy threshold measures over time. The second LSYPE project, now called Next Steps, collected its first wave of data in 2013. A perusal of the variables in the codebook demonstrates that the GHQ-12 was not included in that wave. Whether it would be in the second wave is to be determined, however the Department for Children, Schools and Families would do well to include it, as this would allow the Department of Health to determine mental health changes among its adolescent populations over time. It would also allow the verification of the sensitivity of the instrument over time, giving the changing influxes of immigrant populations.
Clinical Implications for Using IRT-Based Methods on Psychological Scales
Typically, for clinical use, the identification of individuals with or without certain traits depends upon them being above or below a certain cut-score (threshold). IRT-based methods then can be used to improve the specificity and sensitivity of psychological questionnaires, especially if there are many items available to assess a continuous level of the trait (Gordon, 2015). A strength of IRT-based approaches is that these approaches maximize the amount of information available on a given item. Thus, for scoring, it might be better to use Likert scale scoring methods rather than bimodal methods when using IRT models to estimate person parameters for clinical purposes. Second, by estimating item parameters, IRT-based approaches provide information on the relative easiness or difficulty for adolescents to respond to an item. Thus, for primary prevention efforts, IRT-based approaches allow practitioners to identify adolescents who are not “poor mental health” but may perhaps be on the verge of the threshold.
This point is quite relevant to U.K.’s mental health clinicians given the marginally small effect sizes found at the scale level on the GHQ-12 for three of the ethnic groups. These findings suggest that in cases where ethnicity appears to moderate mental health outcomes in the United Kingdom (and possibly the United States), the real underlying cause might be a cumulative item bias effect and not real differences in mental health. The threshold guidelines for the GHQ-12 in England were established in 1983 for adolescents and are therefore quite dated (Banks, 1983). There is a need for U.K. clinicians to reexamine the threshold level for the GHQ-12 for adolescents and determine the extent to which these marginally small effect sizes impact the cut-score criteria for identifying individuals for mental health services.
Methodology Designs to Further Explore
It is important to determine the extent to which the specified dimensionality of the GHQ-12 actually matters. Findings of this study demonstrate that a parametric unidimensional model with correlated error terms holds for all ethnic groups. A more important question though is whether different model specifications for the GHQ-12 are equally accurate in identifying individuals who need mental health services. Misspecification of latent trait models can lead to erroneous identifications; however, it is also possible that different specifications for the GHQ-12 do not significantly change the number of individuals identified. An answer to this question would inform on whether it is worthwhile to continue debating the real structure of the GHQ-12.
The choice of using an IRT-based approach rather than a CFA approach to examining DIF was based on earlier studies pointing out the superiority of IRT-based approaches for examining DIF on latent trait models. It is important to point out though that simulation studies demonstrate that the superiority of IRT might not hold under certain conditions, for example free-baseline approach versus constrained baseline approach in combination with large sample sizes (Love, 2014). Love (2014) provided suggestions for future work on simulation studies examining parameter recovery for CFA versus IRT approaches. This study used nationally representative data that by and large represented three of the unique design characteristics Love (2014) put forth that ought to be further explored. That is, first, this study utilized a sample size more than 10 times of those found in the simulation literature. Second, there were more than three ethnic groups explored which is also atypical in simulation studies. Third, this study had unbalanced sample sizes. In addition to these three design characteristics, it is important to point out that the scale had only 12 items which possibly confounded the purification processes for the identification of stable anchor items. Specifically, future simulation studies can examine the superiority of IRT-based methods over CFA methods under these design conditions: sample sizes between 5,000 to 12,000 individuals, four through seven subgroups, unbalanced sample sizes, and scales of different lengths (e.g., 10 items, 20 items, and 40 items). Likewise, it may also be true that IRT-based approaches do not maintain superiority over CFA-based approaches with datasets such as the LSYPE; thus, future empirical studies can use this database to arrive to a more tenable conclusion regarding this matter.
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Submitted: September 19, 2015 Revised: March 12, 2016 Accepted: March 16, 2016
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Source: Psychological Assessment. Vol. 29. (1), Jan, 2017 pp. 87-97)
Accession Number: 2016-21963-001
Digital Object Identifier: 10.1037/pas0000323
Record: 165- Title:
- The dirty dozen: A concise measure of the dark triad.
- Authors:
- Jonason, Peter K.. Department of Psychology, University of West Florida, Pensacola, FL, US, peterkarljonason@yahoo.com
Webster, Gregory D.. Department of Psychology, University of Florida, Gainesville, FL, US - Address:
- Jonason, Peter K., University of West Florida, Department of Psychology, Bldg. 41, 11000 University Parkway, Pensacola, FL, US, 32514, peterkarljonason@yahoo.com
- Source:
- Psychological Assessment, Vol 22(2), Jun, 2010. pp. 420-432.
- NLM Title Abbreviation:
- Psychol Assess
- Page Count:
- 13
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- Psychological Assessment: A Journal of Consulting and Clinical Psychology
- ISSN:
- 1040-3590 (Print)
1939-134X (Electronic) - Language:
- English
- Keywords:
- Dark Triad, Machiavellianism, measurement, narcissism, psychopathy, human nature, convergent & discriminant validity, test development, structural reliability, test-retest reliability
- Abstract:
- There has been an exponential increase of interest in the dark side of human nature during the last decade. To better understand this dark side, the authors developed and validated a concise, 12-item measure of the Dark Triad: narcissism, psychopathy, Machiavellianism. In 4 studies involving 1,085 participants, they examined its structural reliability, convergent and discriminant validity (Studies 1, 2, and 4), and test–retest reliability (Study 3). Their measure retained the flexibility needed to measure these 3 independent-yet-related constructs while improving its efficiency by reducing its item count by 87% (from 91 to 12 items). The measure retained its core of disagreeableness, short-term mating, and aggressiveness. They call this measure the Dirty Dozen, but it cleanly measures the Dark Triad. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Human Nature; *Machiavellianism; *Narcissism; *Psychopathy; *Test Validity; Factor Structure; Measurement; Test Construction; Test Reliability; Dark Triad
- Medical Subject Headings (MeSH):
- Adolescent; Adult; Antisocial Personality Disorder; Conscience; Extraversion (Psychology); Female; Humans; Machiavellianism; Male; Middle Aged; Narcissism; Neurotic Disorders; Personality; Psychometrics; Reproducibility of Results; Research Design; Sex Distribution; Surveys and Questionnaires
- PsycINFO Classification:
- Personality Scales & Inventories (2223)
Personality Traits & Processes (3120) - Population:
- Human
Male
Female - Location:
- US
- Age Group:
- Adulthood (18 yrs & older)
Young Adulthood (18-29 yrs)
Thirties (30-39 yrs)
Middle Age (40-64 yrs) - Tests & Measures:
- Big Five Inventory
Self-Report Psychopathy Scale-III
Rosenberg's (1965) Self-Esteem Scale
Aggression Questionnaire DOI: 10.1037/t00691-000
Ten-Item Personality Inventory DOI: 10.1037/t07016-000 - Methodology:
- Empirical Study; Quantitative Study
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- Accepted: Feb 10, 2010; Revised: Feb 8, 2010; First Submitted: Sep 8, 2009
- Release Date:
- 20100607
- Correction Date:
- 20151207
- Copyright:
- American Psychological Association. 2010
- Digital Object Identifier:
- http://dx.doi.org/10.1037/a0019265
- PMID:
- 20528068
- Accession Number:
- 2010-10892-021
- Number of Citations in Source:
- 46
- Persistent link to this record (Permalink):
- http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2010-10892-021&site=ehost-live
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- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2010-10892-021&site=ehost-live">The dirty dozen: A concise measure of the dark triad.</A>
- Database:
- PsycINFO
The Dirty Dozen: A Concise Measure of the Dark Triad
By: Peter K. Jonason
Department of Psychology, University of West Florida;
Gregory D. Webster
Department of Psychology, University of Florida
Acknowledgement: The authors thank Jeremy Tost, Kayla Whitworth, Tiffany Rodriguez, Joshua Legarreta, Austin Lee Nichols, and Jessica Darby for help with data collection and entry.
The Dark Triad is a term used to describe a constellation of three socially undesirable personality traits: narcissism, psychopathy, and Machiavellianism (Paulhus & Williams, 2002). Research on the Dark Triad has increased exponentially over the last decade. An analysis of Google Scholar hit counts for “Dark Triad” in scientific works reveals an explosive increase from one in 2002 to at least 38 in 2009. Despite the recent flurry of scientific interest in the Dark Triad, it has a substantial methodological shortcoming: With over 90 items spread across three scales, it is remarkably inefficient for researchers to measure. In this article, we propose a much-needed solution to this problem: We develop and test the psychometric properties of an efficient, 12-item version of the Dark Triad called the Dirty Dozen.
There are at least two reasons to develop a concise measure of the Dark Triad. First, each measure has its own response biases and limitations. For instance, the Mach IV (Christie & Geis, 1970), a measure of Machiavellianism, may be biased by social desirability (Wilson, Near, & Miller, 1996) and has some unclear psychometric properties (Hunter, Gerbing, & Boster, 1982; for an exception, see Jones & Paulhus, 2009). Indeed, across two recent studies (Jonason, Li, Buss, 2010; Jonason, Li, & Teicher, in press), the internal consistency of the Mach IV did not exceed .70, a remarkably low number for a 20-item scale (Carmines & Zeller, 1979). The Narcissistic Personality Inventory (NPI; Raskin & Terry, 1988), a measure of narcissism, is subject to high rates of impression management (Auerbach, 1984) and is composed of a series of dichotomous questions, which can be problematic (Comrey, 1973). In addition, the use of two different measurement techniques further complicates one's ability to measure the Dark Triad, requiring scores on each measure to be standardized (Jonason, Li, Webster, & Schmitt, 2009).
Second, assessing the Dark Triad's 91 items is inefficient, time-consuming, and may cause response fatigue in some participants. When studying the Dark Triad and one or more other measures of interest (e.g., self-esteem, Big Five personality traits), the total number of items in a questionnaire can easily exceed 100. Large-scale surveys have the advantage of providing a plethora of data, but it may come at the cost of response errors resulting from participant fatigue. Concise measures can eliminate redundant items, save time and effort, and thus reduce participant fatigue and frustration (Saucier, 1994). Using measures that are more efficient can therefore be mutually beneficial to both researchers and participants in terms of time and resources saved, all without sacrificing precision, so long as the concise measure adequately reflects its original version.
Traditionally, the Dark Triad is composed of three independent constructs with some overlap (e.g., Paulhus & Williams, 2002). However, recent evidence suggests there are good theoretical and empirical reasons to treat them as different measures of the same latent construct. Specifically, the Dark Triad as a whole can be thought of as a short-term, agentic, exploitive social strategy that may have evolved to enable exploitation when conspecifics are likely to avoid or punish defectors (Jonason et al., 2009; Jonason et al., in press). Empirical evidence suggests that narcissism, psychopathy, and Machiavellianism measure a single, latent construct that accounts for approximately 50% of the variance associated with the three scales (Jonason, Li, & Buss, 2010; Jonason et al., 2009). Moreover, the Dark Triad's correlations with mating motivations (Jonason et al., 2009) and agreeableness (Jakobwitz & Egan, 2006; Paulhus & Williams, 2002) are similar across all three dimensions and sometimes stronger with a single, latent composite (Jonason et al., 2009; Jonason et al., in press). Thus, it is important that any concise measure of the Dark Triad incorporate the flexibility of being scored as either three related subscales or as a single, composite scale.
In addition to retaining flexibility, the Dark Triad Dirty Dozen should behave in ways that the longer measures do. First, the Dirty Dozen should be correlated with the longer, original measures of the Dark Triad. Second, the Dirty Dozen should be correlated negatively with agreeableness (e.g., Paulhus & Williams, 2002) and positively with a short-term mating strategy (Jonason et al., 2009) and aggressiveness (e.g., Bushman & Baumeister, 1998; Paulhus & Williams, 2002). It is common to use the Big Five and other “normal,” lower order personality traits to describe the validity of measures (e.g., Schutte et al., 1998; Seemann, Buboltz, Thomas, Soper, & Wilkinson, 2005). Last, there is ample evidence that suggests that men score higher on all three of these traits than women do (e.g., Jonason, Li, & Buss, 2010; Jonason et al., 2009), and therefore, we expect that men should score higher than women do on the Dirty Dozen measures. These predictions constitute validity tests of our measure (Cronbach & Meehl, 1955).
In the present research, we sought to develop a concise measure of the Dark Triad that improves its efficiency by reducing its item count by 87% (from 91 to 12 items), while simultaneously preserving its flexibility in serving as either a one- or three-dimensional construct. In two studies, we develop this measure through principal components analyses (PCAs) and confirmatory factor analyses (CFAs). We also validate the Dirty Dozen through assessment of the surrounding nomological network (Cronbach & Meehl, 1955) with constructs that have proven important in prior research such as the original, 91-item version of the Dark Triad, the Big Five, mating, self-esteem level and stability, and aggression. In a third study, we assess the test–retest reliability of the measures over a 3-week period. Consistency over time is one of the defining features of personality traits, and therefore, such evidence will bolster our claims about the usefulness of our measure as well as provide support for the treatment of the Dark Triad as personality variables. In a fourth study, we fine tune our measure by simplifying a double-barreled item, improve the internal consistency of the scale, and again confirm the Dark Triad Dirty Dozen's factor structure.
Study 1In Study 1, we developed a new, concise, and psychometrically sound measure of the Dark Triad. We first created 22 candidate items inspired by the original Dark Triad measures that we felt were the most theoretically central to each construct. We tested our measure by correlating it with the original Dark Triad measures, the Big Five, and measures of mating. We also provide evidence for sex differences among these measures.
Method
Participants and procedures
Two hundred seventy-three psychology students (90 men, 183 women) aged 18–47 years (M = 20.08, SD = 3.79) from the Southwestern United States received course credit for completing the surveys described below. Participants completed packets in a lab setup for mass testing where as many as 10 other people could participate at a time. Participants were instructed to ensure that at least one seat separated them from other participants. Once they completed the measures, they were debriefed and thanked for their participation.
Measures
To assess the Big Five personality dimensions, we used the Big Five Inventory (Benet-Martinez & John, 1998), a cross-culturally validated instrument, using a response scale from 1 (strongly disagree) to 5 (strongly agree). Five factors were detected: Extraversion (Cronbach's α = .84, 8 items), Neuroticism (α = .79, 8 items), Openness (α = .76, 10 items), Conscientiousness (α = .69, 9 items), and Agreeableness (α = .72, 9 items).
Narcissism was assessed with the 40-item NPI, a validated and widely used measure (Raskin & Terry, 1988). For each item, participants chose one of two statements they felt applied to them more. One of the two statements reflected a narcissistic attitude (e.g., “I have a natural talent for influencing people”), whereas the other statement did not (e.g., “I am not good at influencing people”). We summed the total number of narcissistic statements the participants endorsed as an index of narcissism (α = .80).
The 31-item Self-Report Psychopathy Scale–III (Paulhus, Hemphill, & Hare, in press) assessed subclinical psychopathy. Participants rated how much they agreed (1 = strongly disagree, 5 = strongly agree) with statements such as “I enjoy driving at high speeds” and “I think I could beat a lie detector.” Items were averaged to create an index of psychopathy (α = .74).
Machiavellianism was measured with the 20-item Mach IV (Christie & Geis, 1970). Participants were asked how much they agreed (1 = strongly disagree, 5 = strongly agree) with statements such as “It is hard to get ahead without cutting corners here and there” and “People suffering from incurable diseases should have the choice of being put painlessly to death.” The items were averaged to create a Machiavellianism index (α = .65).
The three Dark Triad measures can be treated as one measure (Jonason et al., 2009). We standardized (z-scored) the overall scores on each of the three scales and then averaged all three standardized scores together to create a composite Dark Triad score.
Sociosexuality was measured as a tripartite personality construct (Jackson & Kirkpatrick, 2007). We replicated all three dimensions. The items for each dimension were averaged to create a measure of short-term mating orientation (α = .94), long-term mating orientation (α = .91), and sexual experiences (α = .75).
Results and Discussion
Factor structure
We conducted separate PCAs and internal consistency analyses for each measure (see Table 1). All PCAs used oblique rotation, and loadings were from pattern matrices. Eigenvalues greater than one were used to determine factors. The four items with the strongest loadings on the primary factor were chosen from each of the three Dark Triad measures. Together, these 12 items constituted the Dark Triad Dirty Dozen. Using the same methods, we then conducted a single PCA and internal consistency analysis on the Dirty Dozen to test the factor structure. As predicted, three factors emerged: Machiavellianism, Psychopathy, and Narcissism (see Table 2). The correlations among these rotated factors were modest (|r|s ≤ .35; see Table 3). Overall, the Dark Triad Dirty Dozen achieved good internal consistency (α = .83). When evaluated separately, the internal consistency (α) for each component improved after being reduced to four items for both psychopathy (from .62 to .63) and Machiavellianism (from .67 to .72). In contrast, narcissism's internal consistency decreased (from .87 to .79), which is not surprising considering that α increases with the number of items (all else being equal) and that narcissism had the largest decline in items (from 11 to four).
Separate Subscale-Based Principal Components Analysis Using Oblique Rotation of—and Item–Scale Correlations for—a 22-Item Dark Triad Measure in Two Studies
Principal Components Analysis Using Oblique Rotation of and Item–Scale Correlations for the Dirty Dozen Dark Triad Items in Studies 1 and 2 and Item-Level Temporal Reliability in Study 3
Principal Component Correlation Matrix for Study 1 (Above the Diagonal) and 2 (Below the Diagonal)
Along the same lines, although the α of .63 for psychopathy may seem low (Nunnally, 1978), it is respectable for a scale with only four items (see Carmines & Zeller, 1979). For example, for a typical scale that has a mean interitem correlation of .30, α is .63 for a four-item scale, but it increases to .81 for a 10-item scale, or .90 for a 20-item scale, solely on the basis of additional items. Thus, adjusting for number-of-item inflation, αs in the .60s are reasonable for four-item scales. We suspect that psychopathy's comparatively lower internal consistency may be the result of the double-barreled nature of the item, “I tend to not be too concerned with morality or the morality of my actions,” which is an issue we address in Study 4.
Convergent and discriminant validity
Next, we assessed the convergent and discriminant validity of the Dirty Dozen (see Table 4). We first examined the relationships among our concise measures and the original versions of the Dark Triad and its three components using a multitrait–multimethod matrix (Campbell & Fiske, 1959). The top of Table 4 shows the heteromethod block of interest. As expected, the heteromethod block of the multitrait–multimethod matrix revealed a consistent pattern of convergent and discriminant validity among the Dark Triad components, with the on-diagonal correlations (validity diagonals) being stronger than the off-diagonal correlations (heterotrait-heteromethod triangles) with two exceptions. First, the 31-item measure of Psychopathy correlated slightly more strongly with our concise measure of Machiavellianism (.44) than it did with our concise measure of psychopathy (.42). Second, the 31-item measure of Psychopathy also correlated slightly more strongly with the Dirty Dozen (.51) than the Dirty Dozen did with the original 91-item Dark Triad composite measure (.47). Despite these exceptions, the multitrait–multimethod matrix largely supported the expected pattern of convergent and discriminant validity between the original 91-item Dark Triad and our 12-item Dirty Dozen.
Correlations Among the 12-Item Dark Triad Dirty Dozen, Its Components, and the 91-Item Dark Triad, Its Components, and Mating, Self-Esteem, Big Five Personality, and Aggression
Assessments of the nomological network were also telling. The Dirty Dozen measures retained a core of disagreeableness (Paulhus & Williams, 2002), showed low levels of conscientiousness (Jonason et al., in press), and were more closely correlated with a short-term mating orientation than a long-term one (Jonason et al., 2009). This suggests that the Dirty Dozen taps the same personality traits as the unabridged Dark Triad measures.
Sex differences
Tests for sex differences revealed men scored higher than women on our concise scales of Machiavellianism, t(265) = 3.98, p < .01, d = 0.49; narcissism, t(265) = 3.11, p < .01, d = 0.40; and psychopathy, t(265) = 4.95, p < .01, d = 0.62; as well as the Dirty Dozen, t(265) = 4.95, p < .01, d = 0.64.
Study 2In Study 2, we provide additional evidence of the Dirty Dozen's sound psychometric properties and its validity as a concise measure of constructs underlying the Dark Triad. We assess the validity of this measure by correlating it with alternative measures of the Big Five, mating, and a global measure of self-esteem. We again test sex differences to verify that men score higher than women do on these measures.
Method
Participants and procedures
Two hundred forty-six psychology students (101 men, 145 women) aged 18–42 years (M = 20.69, SD = 3.76) from the Southwestern United States received course credit for completing the surveys described below. Procedures from Study 1 were replicated here.
Measures
To measure the Big Five, we used the Ten-Item Personality Inventory (TIPI; Gosling, Rentfrow, & Swann, 2003), which asks two questions for each dimension. Participants were asked, for instance, how much (1 = not at all, 5 = very much) they think of themselves as “extraverted, enthusiastic” and “quiet, reserved” as measures of extraversion. Estimates of internal consistency returned low rates: extraversion (α = .55), agreeableness (α = .22), conscientiousness (α = .44), neuroticism (α = .38), and openness (α = .09), as is to be expected for scales composed of a small number of items (Kline, 2000). Although these estimates are smaller than those reported in Gosling et al. (2003), such estimates are expected because internal consistency estimates are positively related to the number of scale items (Carmines & Zeller, 1979). Indeed, Gosling et al. (2003) made just such a point, noting that the more appropriate test of reliability for a brief measure is test–retest instead of Cronbach's alpha (p. 516), evidence we provide in Study 3. However, because of the lower levels of internal consistency—even by liberal standards (Schmitt, 1996)—we corrected the correlations between the TIPI dimensions and the Dirty Dozen for attenuation from measurement error (see Cohen, Cohen, West, & Aiken, 2003).
Sociosexuality was measured using the seven-item Sociosexual Orientation Inventory (Simpson & Gangestad, 1991), which gauges participants' attitudes and behaviors regarding sexual intercourse with multiple partners. For instance, participants were asked how much they agreed (1 = strongly disagree; 9 = strongly agree) with the statement “I can imagine myself being comfortable and enjoying casual sex with different partners.” Individual items were standardized (z-scored) prior to computing scale means and as averaged into an index (α = .80).
Global self-esteem was measured with the 10-item Rosenberg's (1965) Self-Esteem Scale. Participants were asked how much they agreed (1 = strongly disagree; 4 = strongly agree) with statements like: “I feel that I am person of worth, at least on an equal basis with others.” The 10 items were averaged to create an index of self-esteem (α = .80).
Results and Discussion
Factor structure
Using the same methods as in Study 1, we conducted separate PCAs and internal consistency analyses for each measure based on all 22 items (see Table 1). These results largely confirmed our findings from Study 1: The four items with the highest primary factor loadings for each component were the same. We then conducted a single PCA and internal consistency analysis on the Dirty Dozen to test its factor structure. As predicted, three factors emerged: Machiavellianism, Narcissism, and Psychopathy (see Table 2). The correlations among these rotated factors were modest (|r|s ≤ .35; see Table 3). These findings replicated those of Study 1 with one minor exception: Item 6 (“I tend to not be too concerned with morality or the morality of my actions”) loaded slightly higher on Machiavellianism (.31) than did its predicted dimension, Psychopathy (.30).
Each scale returned highly similar rates of internal consistency as reported in Study 1. When treated as a single scale, internal consistency analyses for the Dark Triad Dirty Dozen returned an identical Cronbach's alpha (α = .83) in Study 2 as in Study 1. Similar to Study 1, when we reduced the number of items to be four for each scale, our Machiavellianism (from .72 to .77) and Psychopathy (from .66 to .69) measures increased in internal consistency. In addition, like in Study 1, the internal consistency of our Narcissism measure decreased (from .85 to .78).
We ran a series of nested CFAs to evaluate our Dirty Dozen Dark Triad items. In the first model, we allowed all 12 items to load on a single, one-dimensional, Dark Triad factor (see Figure 1A). The single-factor measurement model fit the data poorly, χ2(54) = 344.50, p < .01, χ2/df = 6.38; normed fit index (NFI) = .63, comparative fit index (CFI) = .66; root-mean-square error of approximation (RMSEA) = .15, 90% confidence interval (CI) = .13–.16, pclose fit < .01. Because they are structurally equivalent, the three-dimensional (see Figure 1B) and hierarchical (see Figure 1C) models produced identical fits to the data, which were reasonable, χ2(51) = 123.90, p < .01, χ2/df = 2.43; NFI = .87, CFI = .92; RMSEA = .08, 90% CI = .06–.09, pclose fit < .01. The multidimensional and hierarchical models fit the data significantly better than the one-dimensional model, Δχ2(3) = 220.50, p < .01.
Figure 1. One-dimensional (A), three-dimensional (B), and hierarchical (C) confirmatory factor analyses of the Dark Triad Dirty Dozen items in Studies 2 (higher numbers, N = 246) and 4 (lower numbers, N = 470). All loadings and correlations were significant (ps < .05).
Convergent and discriminant validity
Next, we assessed the convergent and discriminant validity of the Dirty Dozen (see Table 4). Again, the Dirty Dozen measures retained a core of disagreeableness (Paulhus & Williams, 2002) and were positively associated with sociosexuality (Jonason et al., 2009; Webster & Bryan, 2007). When we corrected for attenuation for measurement error, we replicated evidence from above that the Dirty Dozen was negatively correlated with conscientiousness. We also found that scores on the Dirty Dozen and the TIPI's neuroticism dimensions were negatively correlated and scores on the Dirty Dozen and the TIPI's openness dimension were positively correlated.
Sex differences
Tests for sex differences revealed men scored higher than women on our concise scales of narcissism, t(242) = 4.76, p < .01, d = 0.62, and psychopathy, t(242) = 3.18, p < .01, d = 0.42, as well as the Dirty Dozen, t(242) = 4.01, p < .01, d = 0.52. Men scored slightly higher on our concise Machiavellianism scale than did women, t(242) = 1.56, ns, d = 0.21. The sex difference in Machiavellianism has proven to be somewhat elusive (e.g., Jonason, Li, & Buss, 2010). Consistent with Study 1, men scored higher on these measures than did women, on average.
Study 3We conducted Study 3 with three purposes in mind. First, we sought to assess the test–retest reliability of the Dark Triad Dirty Dozen and its three component scales. Second, we sought to assess the temporal reliability of each of the Dirty Dozen's 12 items. Third, we sought to expand our understanding of the Dirty Dozen's convergent and discriminant validity by correlating it with measures of aggression and self-esteem. Because aggression is positively related to psychopathy (Jones & Paulhus, 2010) and narcissism (Bushman & Baumeister, 1998; Bushman et al., 2009; Donnellan, Trzesniewski, Robins, Moffitt, & Caspi, 2005; Twenge & Campbell, 2003; Webster, 2006), and because Machiavellians may occasionally use aggression to manipulate others, we predicted that the Dark Triad Dirty Dozen would be positively correlated with self-reported aggression. Such predictions are also consistent with the original formulation of the Dark Triad in that they share an aggressive core (Paulhus & Williams, 2002).
We remained agnostic, however, as to what specific components of the Dark Triad would correlate most strongly with different measures of aggression (i.e., anger, hostility, and verbal and physical aggression). In part, this is because it appears that those who score high on the Dark Triad use tactics of aggression and manipulation as expressions of a “whatever works” strategy for dealing with conspecifics (Jonason, 2010). Therefore, we make no specific predictions about which types of aggressiveness will be correlated with the Dark Triad.
In contrast, showing evidence of discriminant validity, we predicted that the Dark Triad Dirty Dozen would be mostly uncorrelated with measures of self-esteem level and stability, aside from research suggesting that narcissism might represent unstable inflated self-views (Bushman & Baumeister, 1998; Rhodewalt, Madrian, & Cheney, 1998; but see also Webster, Kirkpatrick, Nezlek, Smith, & Paddock, 2007). Such a prediction would replicate results from Study 2 but extends those results by considering instability and change over time, a more psychometrically robust measurement technique for self-esteem.
Method
Participants and procedure
Participants were a convenience sample of 96 undergraduates enrolled in social or evolutionary psychology classes or a research laboratory group in the Southeastern United States. Each participant was asked to complete measures in class or lab once a week for three weeks. In all, 81 (84%), 79 (82%), and 76 (79%) participants completed questionnaires during Weeks 1, 2, and 3, respectively. The modal and median participant completed questionnaires all 3 weeks (M = 2.46, SD = 0.77). In all, 16 (17%), 20 (21%), and 60 (62%) participants completed only one, only two, or all three sessions, respectively. Of the 96 participants, 60 were women and 36 were men; the modal and median age was 20 years (M = 20.44, SD = 1.43), ranging from 18 to 25. Among the 60 participants who provided data for all 3 weeks, 37 were women and 23 were men; the modal and median age was 20 years (M = 20.25, SD = 1.20), ranging from 18 to 23.
Measures
All measures used a response scale from 1 (strongly disagree) to 9 (strongly agree). Study 3 used the same 12-item Dark Triad Dirty Dozen developed previously in Studies 1 and 2.
We measured self-esteem level with the Single-Item Self-Esteem Scale (“I have high self-esteem”; Robins, Hendlin, & Trzesniewski, 2001). Self-esteem instability was measured with two scales: (a) the five-item Labile Self-Esteem Scale (Dykman, 1998; αs = .88, .89, .90) and (b) the five-item Stability of Self Scale (Rosenberg, 1965; reverse-scored to reflect self-esteem instability; αs = .86, .86, .84). The Labile Self-Esteem Scale contained such items as “I notice that how good I feel about myself changes from day to day (or hour to hour)” and “I'm often feeling good about myself one minute, and down on myself the next minute,” whereas the Stability of Self Scale contained such items as “My opinion of myself tends to change a good deal.” Both self-esteem instability scales contained a reverse-scored item.
Aggression was measured using a brief, 12-item version of the Aggression Questionnaire (“I am an even-tempered person” [reverse-scored]; A. H. Buss & Perry, 1992; αs = .75, .83, .82) that has been used effectively in prior research (e.g., Webster, 2006, 2007; Webster et al., 2007). The brief Aggression Questionnaire uses the three highest loading items from each of four subscales found in A. H. Buss and Perry's (1992) original article: Physical Aggression (αs = .85, .85, .89), Verbal Aggression (αs = .55, .62, .68), Hostility (αs = .39, .48, .49), and Anger (αs = .65, .81, .84).
Results and Discussion
Test–retest reliability
First, we examined the test–retest reliability of the Dark Triad Dirty Dozen and its three components in the traditional way by correlating Time 1 with Time 2 scores (r12), Time 2 with Time 3 scores (r23), and Time 1 with Time 3 scores (r13); these correlations are bolded in Table 5. The average test–retest correlation (i.e., the Fisher's r-to-z-based mean of r12, r23, and r13) was .89 for the Dirty Dozen and ranged from .76 to .87 for its three subscales (see Table 6, first column).
Temporal Correlation Matrix for the Dark Triad Dirty Dozen and Its Three Subscales
Test–Retest Reliability Correlations for the Dark Triad Dirty Dozen and Its Three Subscales Across Three Time Points (3 Weeks) Using Four Different Methods
Second, we examined the corrected test–retest reliabilities using Heise's (1969) formula for three time points, rxx = (r12r23)/r13, which estimates test–retest reliability independent of change-over-time effects. The corrected test–retest correlation was .91 for the Dark Triad Dirty Dozen and ranged from .71 to .88 for its subscales (see Table 6, second column).
Third, we used a series of structural equation measurement models to examine the test–retest reliabilities among latent variables for each measure at each time point for the 60 participants with complete data. For example, the measurement model for Machiavellianism was structurally identical to the model shown in Figure 1B, but with Machiavellianism at Times 1, 2, and 3 being the three latent variables. In contrast, the measurement model for the Dark Triad Dirty Dozen was a second-order model in which a latent variable for the Dark Triad at each time point was specified by the three latent factors of its subscales at that time point. We then took the resulting correlations among the three latent factors and submitted them to Heise's (1969) formula. The corrected test–retest correlations based on latent variables was .94 for the Dark Triad Dirty Dozen and ranged from .67 to .92 for its subscales (see Table 6, third column).
Fourth, we reran the analyses above using data from all participants (N = 96) instead of those with complete data (n = 60), because maximum likelihood procedures provide sound estimates for data missing at random. The corrected test–retest correlations based on latent variables using all participants was .97 for the Dark Triad Dirty Dozen and ranged from .79 to .91 for its subscales (see Table 6, fourth column).
Fifth, using Fisher's r-to-z transformation, we averaged across these four types of test–retest correlations to produce grand mean test–retest correlations. The grand mean test–retest correlation was .93 for the Dark Triad Dirty Dozen and ranged from .74 to .89 for its subscales (see Table 6, fifth column).
Item-level temporal reliability
We examined item-level temporal reliability using Cronbach's alpha (α) for participants with complete data (N = 60). That is, we examined the reliability of each of the Dirty Dozen items across three time points to assess each item's stability over time. Items with higher αs show greater temporal reliability, whereas items with lower αs show less. Item-level temporal reliability αs are shown in the rightmost column of Table 2, and ranged from .72 (Item 6) to .94 (Item 3) with a Fisher's r-to-z-based mean of .90. The mean item-level temporal reliability αs for the Machiavellianism, Psychopathy, and Narcissism subscales were .92, .84, and .92, respectively.
Convergent and discriminant validity
Data were averaged across weeks for validity analyses (N = 60). As expected, neither the Dark Triad Dirty Dozen nor its constituent subscales were significantly correlated with the Single-Item Self-Esteem Scale (see Table 4). The Labile Self-Esteem Scale did not correlate significantly with the Dark Triad Dirty Dozen or its subscales; however, the Stability of Self Scale—recoded to reflect instability—was positively correlated with the Dark Triad, but this was driven largely by its positive correlation with Psychopathy. Overall, the Dark Triad showed no pattern of consistent correlation with either self-esteem level or self-esteem stability. In other words, these measures of self-esteem showed some discriminant validity with the Dark Triad Dirty Dozen.
In contrast, the Dark Triad Dirty Dozen showed some convergent validity with measures of aggression (see Table 4). As expected, the 12-item Dark Triad and the 12-item Aggression Questionnaire showed a strong correlation of .51, suggesting that people who score high on the Dark Triad may also use aggression to get what they want. Among subscales, Machiavellianism was positively related to physical aggression, verbal aggression, and hostility, but not anger—a pattern shared with the Dark Triad Dirty Dozen overall. Psychopathy, in contrast, was positively related only to physical and verbal aggression. Narcissism, on the other hand, was positively related only to hostility. Overall, the 12-item Aggression Questionnaire was positively related to Machiavellianism and Psychopathy, but not Narcissism. Nevertheless, the abbreviated Dark Triad and Aggression Questionnaires showed a fairly consistent pattern of convergent validity.
Because men scored higher on the Aggression Questionnaire than did women, t(58) = 2.76, p < .01, d = 0.73, especially on the Physical Aggression subscale, t(58) = 5.26, p < .01, d = 1.38, and because prior research has shown reliable sex differences in self-reported (Webster, 2006; Webster et al., 2007) and behavioral (Eagly & Steffen, 1986) aggression, we also ran the above correlations controlling for participant sex (see Table 4). Controlling for sex had no effect on the pattern of significant correlations with two exceptions: the correlation between Machiavellianism and Physical Aggression became nonsignificant, and the correlation between the Dark Triad Dirty Dozen and Anger became significant.
Sex differences
Tests for sex differences revealed that men scored higher than women on our concise measures of the Dark Triad, t(58) = 2.63, p = .01, d = 0.69, and Machiavellianism, t(58) = 3.00, p < .01, d = 0.79; men scored slightly higher than women on psychopathy, t(58) = 1.34, ns, d = 0.35, and narcissism, t(58) = 1.29, ns, d = 0.34. Consistent with Studies 1 and 2, men scored higher on these measures than did women, on average.
Study 4A key limitation of the Dark Triad Dirty Dozen is the convoluted, double-barreled phrasing of Item 6, “I tend to not be too concerned with morality or the morality of my actions.” Double-barreled items should generally be avoided because respondents could hold different views about different topics within a single item (Simms & Watson, 2007). For example, it is possible for one to be concerned with morality while not being concerned with the morality of one's actions, and such a person would have difficulty responding to Item 6. We suspect that this problem resulted in lower internal consistency coefficients (αs) for the psychopathy subscale compared with the narcissism and Machiavellianism subscales. To address this concern, we rephrased Item 6 to read, “I tend to be unconcerned with the morality of my actions.” The purpose of Study 4 was to examine the psychometric properties of the Dark Triad Dirty Dozen using this new, simplified Item 6. We expected that this new item would improve the psychopathy subscale's internal consistency while replicating the Dark Triad Dirty Dozen's three-dimensional factor structure.
Method
Participants and procedure
Participants were 470 undergraduate psychology students (157 men, 312 women, 1 unknown gender) aged 17 years (n = 2) to “26 and up” (n = 3) years (mode = 18, Mdn = 19, M = 19.00, SD = 1.30) from the Southeastern United States who received course credit for completing an online prescreening survey consisting of multiple questionnaires.
Measures
Study 4 used the same 12-item Dark Triad Dirty Dozen developed in Studies 1–3, with the exception that Item 6 was streamlined to read, “I tend to be unconcerned with the morality of my actions.” Participants responded using a scale from 1 (disagree strongly) to 9 (agree strongly).
Results and Discussion
Factor structure
Following recommendations by John and Soto (2007), we present item-level correlations and descriptive statistics in Table 7. This interitem correlation matrix shows the clustering of the Dark Triad Dirty Dozen items into their respective dimensions.
Study 4 Dark Triad Dirty Dozen Item-Level Correlations and Descriptive Statistics
Consistent with Studies 1 and 2, we conducted a PCA (see Table 8) and a CFA (see Figure 1) on the Dark Triad Dirty Dozen. As predicted, the PCA produced a clear three-factor solution: Machiavellianism, Psychopathy, and Narcissism (see Table 8). The correlations among the three rotated factors and among the three subscales created from the unweighted items were moderately strong (see Table 9). Overall, the Dark Triad Dirty Dozen and its subscales achieved good internal consistency. Revising Item 6 in Study 4 resulted in a markedly stronger α for the psychopathy subscale (.77 vs. .63, .69, .44, .59, and .64 in Studies 1–3).
Principal Components Analysis Using Oblique Rotation of and Item–Scale Correlations for the Dirty Dozen Dark Triad Items in Study 4
Study 4 Principal Component Correlation Matrix (Above the Diagonal), Zero-Order Correlations (Below the Diagonal), and Scale Reliability Coefficients (Along the Diagonal)
Replicating Studies 1 and 2, the single-factor measurement model (see Figure 1A) fit the data poorly, χ2(54) = 822.52, p < .01, χ2/df = 15.23; NFI = .64, CFI = .66; RMSEA = .17, 90% CI = .16–.18, pclose fit < .01. Because they are structurally equivalent, the three-dimensional (see Figure 1B) and hierarchical (see Figure 1C) models produced identical fits to the data, which were reasonable, χ2(51) = 192.02, p < .01, χ2/df = 3.76; NFI = .92, CFI = .94; RMSEA = .08, 90% CI = .06–.09, pclose fit < .01. The three-dimensional and hierarchical models fit the data significantly better than the one-dimensional model, Δχ2(3) = 630.50, p < .01. Nevertheless, the hierarchical model produced one negative variance estimate (the disturbance for the latent Machiavellianism factor). When this variance was constrained to equal zero, the fit remained good, χ2(52) = 194.21, p < .01, χ2/df = 3.74; NFI = .92, CFI = .94; RMSEA = .08, 90% CI = .06–.09, pclose fit < .01, and this constraint did not significantly degrade the model's fit, Δχ2(1) = 2.19, p = .14.
Sex differences
Tests for sex differences revealed that men scored higher than women on our concise measures of the Dark Triad, t(466) = 2.47, p = .01, d = 0.23, and psychopathy, t(466) = 4.99, p < .01, d = 0.46; men scored slightly higher than women on Machiavellianism, t(466) = 0.58, ns, d = 0.05, and narcissism, t(466) = 0.97, ns, d = 0.09. These results are consistent with Studies 1–3 and prior work.
General DiscussionThere is a growing trend in psychological assessment to create concise measures of core personality traits. Concise measures are appealing because they take less time to complete than more protracted personality inventories by eliminating item redundancy and thus reducing participant fatigue and frustration (Burisch, 1984, 1997; Saucier, 1994). Moreover, researchers have been demanding and developing more efficient measures of traditional scales for use in a variety of settings where a premium is placed on time or the number of items used (e.g., daily diary studies, experience sampling studies, mass-testing and prescreening sessions, field studies, research using special populations). In the present research, we developed and validated a concise measure of the Dark Triad. These Dirty Dozen items had reasonable psychometric properties, showed acceptable convergent (e.g., NPI, Big Five, mating, aggression) and discriminant validity (e.g., self-esteem) with the other measures we examined and proved to be reliable over time and across a number of tests (e.g., corrected test–retest reliability). This single measure—the Dirty Dozen—provides a considerable improvement in efficiency compared to the 91-item, three-scale version of the Dark Triad (an 87% reduction in items). The Dirty Dozen version of the Dark Triad will not only reduce participant fatigue, it will also allow all three constructs to be measured using the same response scale format.
We found consistent evidence the Dirty Dozen measures the Dark Triad and shows correlation patterns with other personality traits in the nomological network. Specifically, the Dark Triad Dirty Dozen showed a consistent pattern of disagreeableness (Paulhus & Williams, 2002) and short-term mating (Jonason et al., 2009) across two studies and conscientiousness (Jonason, Li, & Teicher, in press), which may relate to a fast life strategy that underlies the nature of the Dark Triad (e.g., Jonason, Koenig, & Tost, 2010) in Study 2. In addition, after we corrected for measurement error, evidence suggests that the Dirty Dozen measures are negatively correlated with neuroticism and positively correlated with openness; both findings are consistent with work that suggests the Dark Triad reflects a latent dimension of social exploitation (Jonason et al., 2009), where this profile of lower order personality traits is expected (Jonason, Li, & Teicher, in press).
In line with this contention about social exploitation, we found that the Dark Triad tended to be correlated with measures of aggression in Study 3. Individuals may use tactics such as aggression to get what they want in life. One of the defining features of the Dark Triad is its link to aggressiveness (Paulhus & Williams, 2002). Indeed, new evidence suggests that one tactic used by those scoring high on the Dark Triad to get what they want is coercion (Jonason, 2010). However, we did not find much evidence for a narcissism–aggression correlation beyond narcissism's positive correlation with hostility (cf. Bushman & Baumeister, 1998; Bushman et al., 2009; Donnellan et al., 2005; Jones & Paulhus, 2010; Twenge & Campbell, 2003; Webster, 2006). This may be because we did not include an ego threat (e.g., Bushman & Baumeister, 1998; Jones & Paulhus, 2010). Studies 2 (Self-Esteem Scale) and 3 (Single-Item Self-Esteem Scale) also show the lack of relationship between self-esteem and the Dark Triad. Self-esteem is not associated with the Dark Triad, despite the common conceptualization (e.g., Engler, 2009) that those who are, for instance, narcissistic may also have low self-esteem. Indeed, in Study 3, correlations between measures of self-esteem and the Dirty Dozen were some of the weakest we report. However, such evidence is inconsistent with research suggesting a possible link between the instability of self-esteem and narcissism (Bushman & Baumeister, 1998; Rhodewalt et al., 1998). It may be that the reduction of items from the 40-item NPI to the four items we used to measure narcissism caused one or more specific aspects of narcissism that relate to self-esteem instability to be lost (e.g., entitlement, grandiosity, superiority). This is a cost that researchers need to consider because the reduction of items in scales reduces some of narcissism's heterogeneity.
Across numerous studies, men consistently scored higher than women did on the traditional measures of the Dark Triad (e.g., Jonason et al., 2009) and related measures like sociosexuality (e.g., Simpson & Gangestad, 1991). From an adaptionist perspective (D. M. Buss, 2009), men should benefit more from social exploitation (D. M. Buss & Duntley, 2008) and therefore should have higher scores on personality traits that reflect social exploitation. As measures that tap into this latent, exploitive psychology, our Dirty Dozen measures showed some sex differences across all studies, confirming that men tend to be more socially exploitive than women are through personality traits like the Dark Triad. Exploiting others may come at a higher cost for women than for men, because women are more dependent on social networks than men are on average (e.g., Jonason, Webster, & Lindsey, 2008).
Although some of the fit indexes could have been stronger (especially for the one-dimensional model), fit indexes do not have strict, nonarbitrary cutoff criteria (Fan & Sivo, 2007). Moreover, latent measures with fewer items or manifest indicators are expected to have more error than ones with more items, and thus, we did not anticipate the fit indexes to be overwhelmingly strong (Kline, 2000). Nevertheless, the measurement models fit the data well for the (structurally equivalent) three-dimensional and hierarchical models. The fact that the three-dimensional and hierarchical models of the Dark Triad Dirty Dozen fit the data better than the one-dimensional model is not surprising, because overall measures were derived from separate but related subscales (e.g., Bryant & Smith, 2001; Webster & Bryan, 2007). However, a one-dimensional model appears to behave as we would expect, and thus, it may prove useful as Jonason et al. (2009) argued.
The three-component measures of the Dark Triad Dirty Dozen were composed of only four items each, and as such, they are likely to have relatively low levels of internal consistency (Nunnally, 1978; Schmitt, 1996). Because coefficient α is a function, in part, of the number of items in a scale, the αs we reported were reasonable (Carmines & Zeller, 1979). Indeed, the overall Dirty Dozen measure had a higher degree of internal consistency than did the three subscales, consistent with this reasoning.
Perhaps related to measurement error, correlations between the original Dark Triad measures and our measures could have been stronger. For instance, the generation of new items, instead of cherry-picking good items from preexisting scales, may have failed to tap all the aspects of heterogeneous constructs like the Dark Triad. However, there is no agreement about the specific factor structures of measures of the Dark Triad. For instance, the NPI has been treated as both a seven-dimensional (Raskin & Terry, 1988) and a four-dimensional (Emmons, 1987) construct. Future work should attempt to replicate our findings, in hopes of providing stronger evidence for convergent validity, perhaps examining the Dirty Dozen in relation to different factor structures of narcissism to see what aspects of each measure of the Dark Triad that it taps best. Overall, however, we feel that our measure provides a satisfactory compromise between precision and efficiency that are often at odds in measurement (e.g., Gorsuch & McFarland, 1989).
We believe that our concise Dark Triad measure—the Dirty Dozen—will have immediate applications in a variety of settings that value efficient measurement such as large-scale national or international surveys, prescreening packets, longitudinal studies, daily diary studies, experience-sampling studies, and anywhere else researchers may have limited time or funding. Our findings suggest that our concise measure, despite being called the Dirty Dozen, cleanly measures the latent constructs of the Dark Triad.
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Submitted: September 8, 2009 Revised: February 8, 2010 Accepted: February 10, 2010
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Source: Psychological Assessment. Vol. 22. (2), Jun, 2010 pp. 420-432)
Accession Number: 2010-10892-021
Digital Object Identifier: 10.1037/a0019265
Record: 166- Title:
- The effects of extraverted temperament on agoraphobia in panic disorder.
- Authors:
- Rosellini, Anthony J.. Center for Anxiety and Related Disorders, Department of Psychology, Boston University, Boston, MA, US, ajrosell@bu.edu
Lawrence, Amy E.. Center for Anxiety and Related Disorders, Department of Psychology, Boston University, Boston, MA, US
Meyer, Joseph F.. Center for Anxiety and Related Disorders, Department of Psychology, Boston University, Boston, MA, US
Brown, Timothy A.. Center for Anxiety and Related Disorders, Department of Psychology, Boston University, Boston, MA, US - Address:
- Rosellini, Anthony J., Center for Anxiety and Related Disorders, Department of Psychology, Boston University, 648 Beacon Street, 6th Floor, Boston, MA, US, 02215-2013, ajrosell@bu.edu
- Source:
- Journal of Abnormal Psychology, Vol 119(2), May, 2010. pp. 420-426.
- NLM Title Abbreviation:
- J Abnorm Psychol
- Page Count:
- 7
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- The Journal of Abnormal and Social Psychology; The Journal of Abnormal Psychology; The Journal of Abnormal Psychology and Social Psychology
- ISSN:
- 0021-843X (Print)
1939-1846 (Electronic) - Language:
- English
- Keywords:
- agoraphobia, panic disorder, personality, situational avoidance, temperament, extraverted
- Abstract:
- Although situational avoidance is viewed as the most disabling aspect of panic disorder, few studies have evaluated how dimensions of neurotic (i.e., neuroticism, behavioral inhibition) and extraverted (i.e., extraversion, behavioral activation) temperament may influence the presence and severity of agoraphobia. Using logistic regression and structural equation modeling, we examined the unique effects of extraverted temperament on situational avoidance in a sample of 274 outpatients with a diagnosis of panic disorder with and without agoraphobia. Results showed low extraverted temperament (i.e., introversion) to be associated with both the presence and the severity of situational avoidance. Findings are discussed in regard to conceptualizations of conditioned avoidance, activity levels, sociability, and positive emotions within the context of panic disorder with agoraphobia. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Agoraphobia; *Avoidance; *Extraversion; *Panic Disorder; *Personality
- Medical Subject Headings (MeSH):
- Adolescent; Adult; Aged; Agoraphobia; Anxiety; Chi-Square Distribution; Extraversion (Psychology); Female; Humans; Male; Middle Aged; Models, Psychological; Panic Disorder; Personality Assessment; Psychiatric Status Rating Scales; Regression Analysis; Surveys and Questionnaires; Temperament
- PsycINFO Classification:
- Neuroses & Anxiety Disorders (3215)
- Population:
- Human
Male
Female - Location:
- US
- Age Group:
- Adulthood (18 yrs & older)
Young Adulthood (18-29 yrs)
Thirties (30-39 yrs)
Middle Age (40-64 yrs)
Aged (65 yrs & older) - Tests & Measures:
- Anxiety Disorders Interview Schedule for DSM-IV: Lifetime Version
Behavioral Inhibition Scale/Behavioral Activation Scale
NEO Five-Factor Inventory
Albany Panic and Phobia Questionnaire DOI: 10.1037/t12041-000
Anxiety Sensitivity Index DOI: 10.1037/t00033-000 - Methodology:
- Empirical Study; Quantitative Study
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- Accepted: Nov 19, 2009; Revised: Nov 18, 2009; First Submitted: Jul 22, 2009
- Release Date:
- 20100510
- Correction Date:
- 20160714
- Copyright:
- American Psychological Association. 2010
- Digital Object Identifier:
- http://dx.doi.org/10.1037/a0018614
- PMID:
- 20455614
- Accession Number:
- 2010-08841-017
- Number of Citations in Source:
- 41
- Persistent link to this record (Permalink):
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- Cut and Paste:
- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2010-08841-017&site=ehost-live">The effects of extraverted temperament on agoraphobia in panic disorder.</A>
- Database:
- PsycINFO
The Effects of Extraverted Temperament on Agoraphobia in Panic Disorder
By: Anthony J. Rosellini
Center for Anxiety and Related Disorders, Department of Psychology, Boston University;
Amy E. Lawrence
Center for Anxiety and Related Disorders, Department of Psychology, Boston University
Joseph F. Meyer
Center for Anxiety and Related Disorders, Department of Psychology, Boston University
Timothy A. Brown
Center for Anxiety and Related Disorders, Department of Psychology, Boston University
Acknowledgement:
Panic disorder (PD) involves various maladaptive cognitive and behavioral responses. Among the most impairing behavioral responses to panic are interoceptive, experiential, and situational avoidance tactics. Interoceptive avoidance involves refusing substances (e.g., caffeine) or activities (e.g., exercise) that elicit panic-like symptoms. Experiential avoidance refers to attempts to control panic via medications or distraction. Situational avoidance, which has been described as “the most palpable and impairing aspect of PD” (White, Brown, Somers, & Barlow, 2006, p. 148), involves a refusal to enter or tendency to escape from feared environments (e.g., bridges, crowds, elevators).
The Diagnostic and Statistical Manual of Mental Disorders (4th ed., text rev.; DSM–IV–TR; American Psychiatric Association, 2000) describes agoraphobia (AG) as anxiety linked to situations from which escape might be difficult or help may be unavailable in the event of panic symptoms. As fear of being in certain situations is often accompanied by a refusal to enter situations, situational avoidance is an important AG criterion. Because AG is most frequently diagnosed as comorbid with PD in clinical settings (i.e., PD with AG; Brown, Campbell, Lehman, Grisham, & Mancill, 2001), it is no surprise that conceptual models of AG have been strongly influenced by PD theories (e.g., Barlow, 2002).
Temperament, Anxiety Sensitivity, and AGResearch and theory has implicated genetically based dimensions of neurotic temperament (NT) and extraverted temperament (ET) as being instrumental in the etiology and maintenance of anxiety and mood disorders (e.g., Barlow, 2002; Clark, Watson, & Mineka, 1994). Theories of emotion and personality vulnerabilities have described NT and ET by constructs such as neuroticism and extraversion (Digman, 1990; Eysenck & Eysenck, 1985), negative and positive affect (Tellegen, 1985), and behavioral inhibition and activation (Gray, 1987). Although their interrelationships are not yet fully understood, evidence suggests that neuroticism is closely related to negative affect and behavioral inhibition, whereas extraversion shares many characteristics with positive affect and behavioral activation (Barlow, 2002; Brown, 2007; Campbell-Sills, Liverant, & Brown, 2004). Whereas NT influences the experience of negative emotional states (i.e., anxiety, sadness), ET is related to sociability, levels of activity, reward-seeking behaviors, and positive emotions (i.e., excitement, joy).
Contemporary conceptualizations of the relationships between temperament and the emotional disorders stem from the tripartite model, which posited that NT (i.e., negative affect, neuroticism) is relevant to both the anxiety and the mood disorders, whereas ET (i.e., positive affect, extraversion) is uniquely related to depression (Clark & Watson, 1991). Although research has consistently found strong positive correlations between NT and the full range of emotional disorders (Bienvenu et al., 2001, 2004; Brown, 2007; Brown, Chorpita, & Barlow, 1998), findings regarding ET have been limited and mixed. For example, although initial support for the unique association between ET and depression was found in some nonclinical samples (Joiner, 1996) and samples with low rates of anxiety (Watson et al., 1995), examinations of outpatient and epidemiological data also found significant inverse relationships between ET (i.e., high introversion) and social phobia (e.g., Bienvenu et al., 2001; Brown et al., 1998). As subsequent research further supported this relationship (for a meta-analytic review, see Kashdan, 2007), leading conceptual models of the emotional disorders have been revised to reflect such findings (e.g., Mineka, Watson, & Clark, 1998).
Although the evidence is sparse, significant associations have been found between dimensions of ET and AG. For example, Bienvenu et al. (2001) used logistic regression to examine if ET (i.e., extraversion) predicted lifetime prevalence of various DSM anxiety and mood disorders. Results showed that ET was a significant predictor of AG, whereby lower levels ET (i.e., high introversion) were associated with increased odds of a lifetime AG diagnosis. Significant associations between ET and PD were not found. Although studies have had success in replicating and extending these findings (e.g., Bienvenu et al., 2004), few have accounted for the occurrence of AG secondary to PD (e.g., PD with AG). A notable exception is Carrera et al.'s (2006) study of personality traits among patients in the early phases of PD, which controlled for comorbidity between PD and AG. Results showed that ET (i.e., introversion) predicted a diagnosis of PD with AG but not PD without AG. The authors interpreted this finding to indicate that low levels of ET may contribute to the development of AG within PD but not PD itself.
Although compelling, these studies provide limited information about the relationship between ET and AG by exclusively examining DSM diagnostic status. The degree of impairment assumed to be caused by situational avoidance (e.g., White et al., 2006) suggests it may be more important to study avoidance behaviors within AG rather than broadly studying the presence of the disorder. Moreover, exclusively examining dichotomous representations of dimensional phenomena (i.e., diagnoses) provides limited utility by not capturing important information (cf. Brown & Barlow, 2005; MacCallum, Zhang, Preacher, & Rucker, 2002) such as individual differences in AG severity.
Preliminary evidence regarding the relationship between ET and AG has been useful in examining genetic relationships between ET and AG. Recently, Bienvenu, Hettema, Neale, Prescott, and Kendler (2007) used a large twin sample to test the independent genetic contributions of ET and NT (i.e., extraversion and neuroticism) on heritable influences (i.e., genetic vs. shared environmental factors) of AG. Analyses found significant negative within-person correlations between extraversion and AG and that monozygotic twins had higher cross-twin correlations than did dizygotic twins. In other words, the genetic factors that influence extraversion are the same as those affecting a lifetime diagnosis of AG.
In addition to ET and NT, conceptualizations of PD and AG also emphasize the construct of anxiety sensitivity (AS), or the fear of anxiety and anxiety-related physical symptoms. Much like ET and NT, AS may be a heritable vulnerability playing an important role in PD and AG (Stein, Jang, & Livesley, 1999). It is posited that high AS may develop early in life and, coexisting with high levels of NT, may lead to the onset and maintenance of PD with or without AG (Barlow, 2002). This model has received support, as individuals with heightened levels of AS experience a greater degree of panic symptoms (Zinbarg, Brown, Barlow, & Rapee, 2001) and agoraphobic fear and avoidance (Taylor & Rachman, 1992; White et al., 2006). Unfortunately, these studies have not evaluated the unique contributions of AS while controlling for NT.
Although the negative consequences of AG within PD have been well documented, relatively few studies have focused on the relationship between ET and situational apprehension and avoidance. Extant studies have rarely examined ET and AG in clinical samples or contained AG symptom information beyond diagnostic status (e.g., Bienvenu et al., 2001, 2004; Carrera et al., 2006). Moreover, much of the literature examining PD and AG has not controlled for levels of NT and AS (e.g., Taylor & Rachman, 1992; White et al., 2006). The present study aims to examine the unique effects of ET on agoraphobic avoidance in PD within a clinical sample. ET was hypothesized to predict the presence and severity of agoraphobic avoidance while controlling for NT and AS. It was also hypothesized that ET would predict the severity of AG but not be associated with the severity of PD.
Method Participants
The sample consisted of 274 patients presenting for assessment and treatment at the Center for Anxiety and Related Disorders at Boston University. The sample was predominantly female (60.2%) and the average age was 32.88 years (SD = 10.56, range = 18–77). The majority of participants self-identified as Caucasian (85.8%). Individuals were assessed by doctoral students or doctoral-level clinical psychologists using the Anxiety Disorders Interview Schedule for DSM-IV: Lifetime Version (ADIS–IV–L; Di Nardo, Brown, & Barlow, 1994). The ADIS–IV–L is a semistructured interview that assesses DSM–IV (American Psychiatric Association, 2000) anxiety, mood, somatoform, and substance use disorders. When administering the ADIS–IV–L, clinicians assign each diagnosis a 0–8 clinical severity rating that represents the degree of distress or impairment in functioning associated with specific diagnoses. The disorder receiving the highest clinical severity rating is considered an individual's principal diagnosis. Patients were included in the study if they met criteria for a principal diagnosis of PD with AG (n = 260) or PD without AG (n = 14). The ADIS–IV–L has shown good to excellent reliability for the majority of anxiety and mood disorders, including PD with AG (κ = .77) and PD without AG (κ = .72; Brown, Di Nardo, Lehman, & Campbell, 2001). Study exclusionary criteria were current suicidal or homicidal intent and/or plan, psychotic symptoms, or significant cognitive impairment (e.g., dementia, mental retardation).
Regression and Structural Model Indicators
ADIS–IV–L PD criteria ratings
Clinicians made severity ratings for the following DSM-IV PD criteria on a 0 (absent) to 8 (very severely disturbing/disabling) scale: (a) recurrent and unexpected panic attacks, (b) fear of having additional attacks, (c) worry about the consequences of panic, and (d) change in behavior related to the panic. A composite score composed of ratings of items (a) through (c) was generated for each participant. Rating (d) was omitted from the composite score because of redundancy with indicators of AG (i.e., situational avoidance would be considered a significant change in behavior).
ADIS–IV–L situational avoidance ratings
The AG section of the ADIS–IV–L contains a subsection in which clinicians assess and rate the patient's avoidance of 22 situations associated with PD (e.g., public transportation, theaters) from 0 (no avoidance) to 8 (very severe avoidance). The AG rating score has been associated with excellent interrater reliability (Brown, Di Nardo, et al., 2001). The AG scale structure was evaluated using exploratory factor analysis. Although the exploratory factor analysis confirmed unidimensionality, one item had a factor loading that was less than .30 (Item 14, “Being home alone”) and was removed from the composite rating.
Albany Panic and Phobia Questionnaire (APPQ; Rapee, Craske, & Barlow, 1994–1995)
The APPQ is a 27-item questionnaire measuring interoceptive, situational, and social fears. Respondents rate how much fear they would experience in certain activities and situations on a 0 (no fear) to 8 (extreme fear) scale. The nine-item Agoraphobia subscale (APPQ-A), measuring situational apprehension commonly associated with panic (e.g., driving, theaters), and the five-item Interoceptive subscale (APPQ-I), assessing fear associated with activities or objects that may mimic panic symptoms, were used in this study. Evaluation of the APPQ supports its factor structure, reliability, and validity in clinical samples (Brown, White, & Barlow, 2005).
Anxiety Sensitivity Index (ASI; Peterson & Reiss, 1992)
The ASI is a 16-item measure in which patients rate each item on a 0 (very little) to 4 (very much) scale. The ASI has adequate reliability and validity and is composed of a hierarchical factor structure, with three lower order factors (i.e., Physical Concerns, Mental Incapacitation, and Social Concerns) and a single general higher order factor (Zinbarg, Barlow, & Brown, 1997).
Behavioral Inhibition Scale/Behavioral Activation Scale (BIS/BAS; Carver & White, 1994)
The BIS/BAS is a 20-item self-report instrument designed to assess Gray's (1987) personality constructs of behavioral inhibition and activation. Items are rated on a 4-point Likert-type scale, ranging from 1 (quite untrue of you) to 4 (quite true of you). The BIS/BAS has demonstrated excellent psychometric properties in clinical samples (Campbell-Sills et al., 2004).
NEO Five-Factor Inventory (NEO-FFI; Costa & McCrae, 1992)
The NEO-FFI is a 60-item self-report inventory that assesses dimensions of the five-factor model of personality: Neuroticism, Extraversion, Openness, Agreeableness, and Conscientiousness. Items are rated on 5-point Likert-type scale, which ranges from 0 (strongly disagree) to 4 (strongly agree). The NEO-FFI is the abbreviated form the NEO-PI-R, a widely used self-report personality measure that has demonstrated excellent reliability and validity (Costa & McCrae, 1992).
Analytic plan
Logistic regression and structural models were evaluated in Mplus 5.2 (Muthén & Muthén, 1998–2009). Missing data were handled by direct maximum likelihood estimation. Model fit was examined using the root mean square error of approximation (RMSEA) and its test of close fit (C-Fit), the Tucker–Lewis index (TLI), the comparative fit index (CFI), and the standardized root mean square residual (SRMR). Guidelines defined by Hu and Bentler (1999) were used in determining acceptable fit (i.e., RMSEA near or below .06, C-Fit above .05, TLI and CFI near or above .95, SRMR near or below .08). Multiple goodness-of-fit parameters were evaluated to examine various aspects of model fit (i.e., absolute fit, parsimonious fit, fit relative to the null). Unstandardized and completely standardized solutions were examined to evaluate the significance and strength of parameter estimates. Standardized residuals and modification indices were used to determine the presence of any localized areas of strain in the solution.
Results Logistic Regression Models
We conducted logistic regression analyses to examine if ET uniquely predicted the presence of situational avoidance within PD patients while controlling for NT and AS. Situational avoidance was defined as having a secondary AG diagnosis and an ADIS–IV–L situational avoidance rating above 0 (n = 222) or not (n = 29; 23 cases were excluded because of missing questionnaires). Two regression models were examined such that the presence of situational avoidance was regressed onto constructs representing dimensions of temperament (i.e., NEO-FFI and BIS/BAS) and AS. As shown in Table 1, only the Extraversion subscale was found to significantly predict the presence of situational avoidance (B = −0.07, p < .05) in the NEO-FFI and AS model. Lower levels of ET (i.e., higher introversion) were associated with increased odds of agoraphobic avoidance (odds ratio = .94, 95% confidence interval [.87–.99]). The regression coefficient for the BAS scale approached statistical significance (B = −0.06, p = .10) in the BIS/BAS and AS model.
Logistic Regression Models Evaluating the Relationship Between Temperament Constructs and the Presence of Situational Agoraphobic Avoidance
Structural Equation Models
Structural regression models were fit to the data to examine the unique association between dimensions of ET and AG. The BAS and NEO–Extraversion subscales were used as indicators for a latent variable representing ET, whereas BIS and NEO–Neuroticism were specified to load on the NT factor. AS was defined solely by ASI–Physical Concerns because of its theoretical relevance specific to PD and AG (Zinbarg et al., 2001). A latent variable representing dimensions of AG was composed of the APPQ-A subscale and ADIS–IV–L AG situational avoidance rating. The APPQ-I subscale and ADIS–IV–L PD criteria composite rating (see the Method section) were used as indicators to represent the latent variable of PD.
Two structural models were evaluated, whereby latent representations of AG (Model 1) and PD (Model 2) were regressed onto dimensions of NT, ET, and AS. Measurement models of the temperament and disorder constructs were not separately evaluated because both models were structurally just identified. Initial inspections of the models revealed that model fit could be improved if a correlated error was estimated between the NEO–Extraversion and NEO–Neuroticism subscales (Model 1 and 2 modification indices = 14.16 and 13.79, respectively). The models were subsequently specified to reflect this method variance shared between the NEO subscales.
It was predicted that when NT and AS were held constant, ET would demonstrate an inverse and statistically significant structural path to AG but not PD. Model 1 fit the data well, χ2(8) = 18.286, p < .05, SRMR = 0.03, RMSEA = 0.06 (C-Fit p = .20), TLI = 0.94, CFI = .97. Figure 1A shows the completely standardized estimates from this solution. In total, AS, NT, and ET explained 29% of the variance in AG. ET uniquely explained a significant portion of the variance in AG (γ = −.31, p < .001) while controlling for AS and NT. The regression paths for AS and NT were also significant; both predictors demonstrated a positive relationship with AG (γs = .21 and .26, respectively; ps < .01).
Figure 1. Latent structural models of the relationship between dimensions of agoraphobia, panic disorder, temperament, and anxiety sensitivity. A: Model 1. B: Model 2. AG = agoraphobia; PD = panic disorder; ET = extraverted temperament; NT = neurotic temperament; AS = anxiety sensitivity. Completely standardized estimates are shown. * p < .01. ** p < .001.
Figure 1B shows the completely standardized estimations from Model 2, which also fit the data well, χ2(8) = 13.681, p = .09, SRMR = 0.03, RMSEA = 0.05 (C-Fit p = .43), TLI = 0.96, CFI = .98. AS, NT, and ET accounted for 69% of the variance in PD. Consistent with prediction, there was not a significant path between ET and PD (γ = −.14, ns). However, AS and NT each uniquely predicted a significant portion of the variance in PD (γs = .63 and .31, ps < .001 and < .01, respectively).
DiscussionConsistent with hypotheses and prior research (i.e., Bienvenu et al., 2001; Carrera et al., 2006), results from the logistic regression analyses showed ET constructs to uniquely predict (NEO–Extraversion) or have trends toward predicting (BAS) the presence of situational avoidance among PD patients while controlling for NT and AS. Structural modeling confirmed that ET was inversely and significantly related to dimensions of AG but not PD. The present study adds to literature on ET and AG conducted at the diagnostic level (i.e., Bienvenu et al., 2001; Carrera et al., 2006) by specifically examining the presence and severity of situational agoraphobic avoidance, arguably the most disabling aspect of PD with AG (White et al., 2006).
In general, ET was associated with both the presence and the severity of situational avoidance among individuals with PD. These results add to the findings of Carrera et al. (2006) by showing that ET may have a more circumscribed relationship with situational avoidance rather than being broadly related to a diagnosis of AG. In line with a predispositional relationship between ET and AG (cf. Brown, 2007; Clark et al., 1994), theory on temperament and aversive conditioning has posited that introverted individuals perceive unconditioned stimuli as subjectively stronger and consequently more reinforcing (Eysenck & Eysenck, 1985). In other words, introverted individuals who experience recurrent and unexpected panic attacks may be more prone to associate their panic symptoms with concurrent stimuli (i.e., the environment), leading them to develop AG characterized by greater situational avoidance. Activation levels, reward-seeking behaviors, and sociability may also play a role; AG may reflect a premorbid disposition toward low activity or reward seeking (i.e., low ET) expressed in the context of unexpected panic, or discomfort or disinterest (i.e., low ET) in being around others when experiencing a vulnerable emotional state like panic. Indeed, the relevance of ET in approach–avoidance motivation and reward-seeking behaviors has been theorized (i.e., introverts are less likely to find novel environments exciting or enjoyable; Eysenck & Eysenck, 1985) and supported in laboratory studies (cf. Robinson, Meier, & Vargas, 2005). Positive emotionality may also have an influence on AG, as individuals prone to experiencing low levels of positive emotions (i.e., low levels of ET) may have difficulty distinguishing the source of the similar physiological symptoms of panic and positive emotions (i.e., increased heart rate due to panic vs. excitement). Through interoceptive fear conditioning principles (i.e., McNally, 1990), the physiological symptoms of positive emotions may serve as a panic trigger. Along these lines, Williams, Chambless, and Ahrens (1997) found that fears of positive emotions (and anger) predicted fear of laboratory-induced bodily sensations in a nonclinical sample.
Conversely, the present findings may also reflect other types of relationships between ET and AG. For instance, according to a complication/scar model (cf. Brown, 2007; Clark et al., 1994), the presence of AG may cause reductions in ET. In other words, developing increasingly severe situational avoidance may lead individuals to be less active and sociable, seek fewer rewards, and experience fewer positive emotions. It is also possible that low ET and AG reflect similar underlying processes, regardless of one's experience of panic. Perhaps introversion is avoidant behavior, with AG serving as expression of this temperament in the context of unexpected panic. Unfortunately, the cross-sectional and correlational nature of the present study precluded our ability to disentangle predispositional, complication/scar, or tautological interpretations.
Although not an a priori aim of the study, findings supporting the effects of AS and NT on PD and AG are consistent with theory (i.e., Barlow, 2002) and add to the extant literature on these vulnerabilities, which has rarely examined either AS or NT while controlling for the other (e.g., White et al., 2006). Given the past debate over the discriminant and incremental validity of AS over NT (Lilienfeld, Jacob, & Turner 1989), it is interesting that both NT and AS significantly predicted dimensions of AG and PD in the structural models. Thus, despite any phenotypic overlap in NT and AS among patients with AG and PD (e.g., experiencing negative affect in response to negative affect, or anxiety focused on fear), both constructs explain a unique portion of the variance in AG and PD.
Despite strengths in methodology (i.e., analyses conducted in a latent variable framework, use of self-report and clinician-rated indicators) and sampling (i.e., large clinical sample), the present study has some limitations. For example, the APPQ-I provides limited information about a single dimension of PD. Although the APPQ-I assesses common behavioral changes related to PD (i.e., avoidance of caffeine), a questionnaire assessing broader dimensions of panic, such as panic frequency and fear (e.g., the Panic Disorder Severity Scale—Self-Report; Houck, Spiegel, Shear, & Rucci, 2002), may have been more appropriate. Another limitation is the predominate representation of Caucasians in the study. Additional research on more diverse samples is needed to examine whether the relationship between ET and AG generalizes to other cultural groups. Finally, the sample may have benefited from additional cases with a diagnosis of PD without AG. Further study of PD without AG may aid in distinguishing features uniquely associated with the development of AG within the context of PD.
Many individuals with PD experience profound disability through persistent avoidance of the situations they associate with panic. Although results of the present study provide meaningful information to the body of literature examining ET and AG, additional research is needed to further examine etiological and maintenance factors of AG. For example, longitudinal research following individuals from premorbid periods to early phases of PD is needed to clarify the relationship between ET and AG (e.g., does low ET cause AG or vice versa?). In addition, experimental research examining the experience of positive emotions in anxiety disorders may aid in the understanding of ET's relevance to disorders such as social phobia and AG.
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Submitted: July 22, 2009 Revised: November 18, 2009 Accepted: November 19, 2009
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Source: Journal of Abnormal Psychology. Vol. 119. (2), May, 2010 pp. 420-426)
Accession Number: 2010-08841-017
Digital Object Identifier: 10.1037/a0018614
Record: 167- Title:
- The influence of confidence on associations among personal attitudes, perceived injunctive norms, and alcohol consumption.
- Authors:
- Neighbors, Clayton. Department of Psychology, University of Houston, Houston, TX, US, cneighbors@uh.edu
Lindgren, Kristen P., ORCID 0000-0002-0244-1016. Department of Psychiatry & Behavioral Sciences, University of Washington, WA, US
Knee, C. Raymond. Department of Psychology, University of Houston, Houston, TX, US
Fossos, Nicole. Department of Psychology, University of Houston, Houston, TX, US
DiBello, Angelo. Department of Psychology, University of Houston, Houston, TX, US - Address:
- Neighbors, Clayton, Department of Psychology, University of Houston, Houston, TX, US, 77204, cneighbors@uh.edu
- Source:
- Psychology of Addictive Behaviors, Vol 25(4), Dec, 2011. pp. 714-720.
- NLM Title Abbreviation:
- Psychol Addict Behav
- Page Count:
- 7
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- Bulletin of the Society of Psychologists in Addictive Behaviors; Bulletin of the Society of Psychologists in Substance Abuse
- Other Publishers:
- US : Educational Publishing Foundation
Society of Psychologists in Addictive Behaviors - ISSN:
- 0893-164X (Print)
1939-1501 (Electronic) - Language:
- English
- Keywords:
- alcohol, attitude certainty, college students, injunctive norms, confidence, personal attitudes, peers
- Abstract:
- Social norms theories hold that perceptions of the degree of approval for a behavior have a strong influence on one's private attitudes and public behavior. In particular, being more approving of drinking and perceiving peers as more approving of drinking, are strongly associated with one's own drinking. However, previous research has not considered that students may vary considerably in the confidence in their estimates of peer approval and in the confidence in their estimates of their own approval of drinking. The present research was designed to evaluate confidence as a moderator of associations among perceived injunctive norms, own attitudes, and drinking. We expected perceived injunctive norms and own attitudes would be more strongly associated with drinking among students who felt more confident in their estimates of peer approval and own attitudes. We were also interested in whether this might differ by gender. Injunctive norms and self-reported alcohol consumption were measured in a sample of 708 college students. Findings from negative binomial regression analyses supported moderation hypotheses for confidence and perceived injunction norms but not for personal attitudes. Thus, perceived injunctive norms were more strongly associated with own drinking among students who felt more confident in their estimates of friends' approval of drinking. A three-way interaction further revealed that this was primarily true among women. Implications for norms and peer influence theories as well as interventions are discussed. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Alcohol Drinking Attitudes; *College Students; *Social Norms; Alcohol Drinking Patterns; Peer Relations
- Medical Subject Headings (MeSH):
- Adolescent; Adult; Alcohol Drinking; Attitude; Binomial Distribution; Female; Humans; Longitudinal Studies; Male; Models, Psychological; Peer Group; Self Concept; Sex Characteristics; Social Conformity; Social Facilitation; Social Perception; Students; Universities; Young Adult
- PsycINFO Classification:
- Substance Abuse & Addiction (3233)
- Population:
- Human
Male
Female - Age Group:
- Adulthood (18 yrs & older)
Young Adulthood (18-29 yrs) - Tests & Measures:
- Attitudes toward drinking measure
Perceived injunctive norms measure
Daily Drinking Questionnaire - Grant Sponsorship:
- Sponsor: National Institute on Alcohol Abuse and Alcoholism
Grant Number: R01AA014576; R00AA017669
Recipients: No recipient indicated - Methodology:
- Empirical Study; Followup Study; Quantitative Study
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- First Posted: Sep 19, 2011; Accepted: Aug 11, 2011; Revised: Aug 2, 2011; First Submitted: Sep 24, 2010
- Release Date:
- 20110919
- Correction Date:
- 20111205
- Copyright:
- American Psychological Association. 2011
- Digital Object Identifier:
- http://dx.doi.org/10.1037/a0025572
- PMID:
- 21928864
- Accession Number:
- 2011-20737-001
- Number of Citations in Source:
- 39
- Persistent link to this record (Permalink):
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- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2011-20737-001&site=ehost-live">The influence of confidence on associations among personal attitudes, perceived injunctive norms, and alcohol consumption.</A>
- Database:
- PsycINFO
The Influence of Confidence on Associations Among Personal Attitudes, Perceived Injunctive Norms, and Alcohol Consumption
By: Clayton Neighbors
Department of Psychology, University of Houston, University of Houston University of Houston;
Kristen P. Lindgren
Department of Psychiatry & Behavioral Sciences, University of Washington
C. Raymond Knee
Department of Psychology, University of Houston, University of Houston University of Houston
Nicole Fossos
Department of Psychology, University of Houston, University of Houston University of Houston
Angelo DiBello
Department of Psychology, University of Houston, University of Houston University of Houston
Acknowledgement: Preparation of this article was supported in part by National Institute on Alcohol Abuse and Alcoholism Grants R01AA014576 and R00AA017669.
Research has emphasized the strong influence of social norms on private attitudes and public behavior. The application of social norms theories to health risk behaviors (e.g., alcohol use) has tended to emphasize descriptive norms (perceptions of the prevalence of a behavior) versus injunctive norms (perceptions of the degree of group approval for a behavior or attitude). Moreover, limited consideration has been given to the extent to which the social norms—health behavior link may depend on the degree of confidence attributed to one's own attitudes and perceptions of others' attitudes. The present research evaluates confidence as a moderator of the associations between one's own attitudes and drinking and between perceptions of others' attitudes (perceived injunctive norms) and drinking among male and female college students.
Social NormsThe operationalization of social norms began with relatively vague descriptions of general customs, traditions, and values (Sherif, 1936). It has evolved to more precise categorizations, with Cialdini and colleagues making an important distinction between descriptive and injunctive norms (Cialdini, Reno, & Kallgren, 1990). Descriptive norms and perceived descriptive norms refer to the actual and perceived quantity of others' behavior, respectively. In contrast, actual and perceived injunctive norms, the primary focus of the present research, refer to the actual and perceived degree of approval that others have about a behavior. Injunctive norms are critical elements in the theories of reasoned action and planned behavior (there referred to as subjective norms; Ajzen & Fishbein, 1980; Armitage & Conner, 2001; Fishbein & Ajzen, 1975, 2010).
According to the theories of reasoned action and planned behavior (Ajzen & Fishbein, 1980; Armitage & Conner, 2001; Fishbein & Ajzen, 1975, 2010), how one intends to behave is a direct function of individuals' own evaluations or attitudes about the behavior (e.g., approval or disapproval) and their perceptions of the degree to which others' approve or disapprove of the behavior. We propose that the degree of confidence that individuals have—whether about their own evaluations of a behavior or whether about their perceptions of the approval of important others—may moderate the influences of attitudes and injunctive norms on behavior.
Social Norms and DrinkingSocial norms have been found to be a strong predictor of alcohol consumption among college students (Borsari & Carey, 2001, 2003; Neighbors, Lee, Lewis, Fossos, & Larimer, 2007; Prentice & Miller, 1993). Although students drink frequently, they tend to overestimate the prevalence and approval of drinking among their peers and the magnitude of discrepancy is associated with heavier drinking (Baer, Stacy, & Larimer, 1991; Borsari & Carey, 2003; Neighbors, Dillard, Lewis, Bergstrom, & Neil, 2006; Perkins & Berkowitz, 1986). No research to date, of which we are aware, has evaluated confidence in one's perceptions of norms as a moderator of the association between perceived norms and drinking.
ConfidenceThe absence of research considering confidence as a moderator of injunctive norms motivated the present study. This was based on the observation that extensive research has documented large inconsistencies in perceptions of drinking norms and actual drinking behavior. Previous research has not examined how accurate students think they are when they provide these estimates and it stands to reason that if they feel they are just guessing, then these perceptions should have relatively little impact on their own drinking. Alternatively, if students believe they are relatively accurate in their perceptions of peer approval, then it would stand to reason that their estimates should have considerably more influence on their behavior.
Although confidence related to norms has not received much empirical attention, confidence related to attitudes has been extensively studied. Attitude confidence refers to one's sense of conviction about (or confidence in) an attitude and is thought to represent one aspect of attitude strength (Tormala & Rucker, 2007). Stronger attitudes have a greater impact on information processing and in guiding behavior, and are more resistant to change over time (Krosnick & Petty, 1995). Attitudes held with high confidence have a substantially stronger average attitude-behavior correlation compared to attitudes held with low confidence (Kraus, 1995). With respect to drinking, we would expect that the more confident one is in his or her attitude about alcohol, the more strongly that attitude will be associated with drinking.
In theory, when one feels more confident in one's estimate of others' approval of a behavior, that perception should more strongly predict how often one engages in that behavior. Several studies have found confidence in one's attitude to be associated with greater attitude—behavior correspondence (Berger & Mitchell, 1989; Fazio & Zanna, 1978; Tormala, Clarkson, & Petty, 2006; Tormala & Petty, 2002) but this effect has not been previously examined in the context of drinking.
Gender and DrinkingGender has also been found to be an important factor in considering alcohol consumption and social norms regarding alcohol use. Research has shown that male college students tend to consume larger quantities, drink more frequently, and more often engage in heavy drinking than female college students (Johnston, O'Malley, Bachman, & Schulenberg, 2008; O'Malley & Johnston, 2002; Read, Wood, Lejuez, Palfai, & Slack, 2004). Gender differences in drinking are also intertwined with gender differences in social norms for drinking. Men and women evaluate the effects of alcohol differently (Neighbors, Walker, & Larimer, 2003), and heavy drinking is more consistent with college student identity for men as compared with women (Lyons & Willot, 2008; Prentice & Miller, 1993). In addition the social consequences of excessive drinking among college students tend to be more positive for men and more negative for women (George, Gournic, & McAfee, 1988; Nolen–Hoeksema, 2004).
What is less clear is whether confidence in perceived approval might have differential effects on the association between perceived injunctive norms and drinking among men and women. On the one hand, we might expect that the confidence–injunctive norm interaction might be more evident among men than women, given that drinking norms tend to be more salient for men (e.g., Prentice & Miller, 1993). On the other hand, previous research suggests that moderators of normative influences on drinking behavior tend to be less evident in the presence of stronger drinking norms. For example, Knee and Neighbors (2002) found that peer influence was moderated by individual differences in self-determination among typical male and female students, but not among fraternity students, where the heavier drinking norm may overshadow individual differences. We were also interested in evaluating whether confidence in one's attitudes might influence the attitude–behavior association differently for women than for men.
The present research was designed to evaluate confidence as a moderator of associations among perceived injunctive norms, own attitudes, and drinking. We expected perceived injunctive norms and own attitudes would be more strongly associated with drinking among students who felt more confident in their estimates of peer approval and own attitudes. We also tested whether these associations varied by gender. Figure 1 presents a conceptual model representing the hypotheses.
Figure 1. Conceptual model. Evaluated interactions are represented by pathways from a variable to a pathway. Thus, the arrow from attitude confidence to the arrow from attitude to drinking represents the prediction that attitude confidence would moderate the association between attitude and drinking. Gender was also evaluated as a moderator of associations between variables and drinking and as a moderator of two-way interactions.
Method Participants
Participants were 708 (60.1% women) undergraduates at a large public university who took part in a longitudinal web-based alcohol intervention study. Participants who met heavy drinking criteria (at least 4/5 drinks on at least one occasion over the previous month for women/men) completed a baseline survey in the Fall of 2005. The present study comes from the 12 month follow-up survey. Participants ranged in age from 18 to 28 (mean [M] = 19.12; standard deviation [SD] = .57). Ethnicity was 65.6% White, 23.5% Asian, and 10.9% classified as other. The majority (86.7%) of the sample were of sophomore class standing at the time of the 12-month follow-up survey.
Procedure
First-year university students were invited to complete a screening survey (N = 4,103). Of those invited, 2,095 (51.1%) participants provided informed consent and completed screening. Students meeting the heavy drinking inclusion criteria (N = 896; 42.7%) were immediately routed to the baseline survey. Of the participants who met study criteria, 818 (91.3%) completed the baseline survey. Participants were contacted via mailed letters, email, and phone calls to complete online surveys at 6 month intervals over a 2-year follow-up period. Data for the current study come from the 12-month follow-up survey (86.6% retention rate). The assessment took approximately 50 minutes to complete and participants were compensated $25. The University's Institutional Review Board approved all aspects of the current study.
Measures
Attitudes
Participants' attitudes toward drinking were measured using items developed by Baer (1994). Participants responded to 4 items assessing their approval of four drinking behaviors: drinking alcohol every weekend, drinking alcohol daily, driving a car after drinking, and drinking enough alcohol to pass out (e.g., “How much do you approve of drinking alcohol daily?”). The response scale ranged from 1 = Strong disapproval to 7 = Strong approval. The items were averaged to create one variable of participants' own approval of risky alcohol use (α = .72).
Perceived injunctive norms
Perceived injunctive norms were measured using the same four items used to measure participants own approval of risky alcohol use, but were revised to ask about participants' perceptions of their friends' approval of their alcohol use (e.g., “How would your friends feel if you drank alcohol daily”). The response scale ranged from 1 = Strong disapproval to 7 = Strong approval. The items were averaged to create one variable of participants' perceived injunctive norms for risky alcohol use (α = .76).
Confidence
Confidence was measured by asking participants to rate their confidence in estimates of one's own approval and friends' approval of drinking. Following the set of items asking about participants' own approval and the set of items asking about perceived injunctive norms, participants were asked to: “Please indicate how confident you are that your responses to the previous items are correct.” Response scales ranged from 1 = Not at all confident to 7 = Absolutely confident.
Alcohol consumption
Daily Drinking Questionnaire (DDQ; Collins, Parks, & Marlatt, 1985) was used to measure quantity and frequency of alcohol consumption. Participants were asked to “Consider a typical week during the last three months. How much alcohol, on average (measured in number of drinks), do you drink on each day of a typical week?” Responses consisted of the typical number of drinks participants reported consuming on each day of the week. A weekly drinking variable was calculated by summing responses for each day of the week. The DDQ has demonstrated convergent validity with measures of drinking and good test–retest reliability (Baer et al., 1991; Borsari & Carey, 2000; Neighbors, Larimer, & Lewis, 2004; Neighbors et al., 2006).
ResultsHypotheses were tested with hierarchical negative binomial regression (Cohen, Cohen, West, Aiken, 2003; Hilbe, 2007), an approach that is comparable to hierarchical linear regression with the exception that the outcome follows a negative binomial distribution. Means and standard deviations by gender are presented in Table 1. Zero-order correlations for study measures are presented by gender in Table 2 for descriptive purposes. Drinks per week, a count variable, was specified as the outcome variable. Count variables consist of non-negative integers, which tend to be positively skewed, and are better approximated by a Poisson or negative binomial distribution rather than a normal distribution (Atkins & Gallop, 2007). Analyses were conducted hierarchically with order of entry following priority of theoretical interest. Gender was dummy coded (Men = 1) and centered. All other predictors were mean centered.
Means and Standard Deviations by Gender
Zero-Order Correlations Among Variables
Main effects were entered at step 1 (gender, own approval, confidence in own approval, perceived injunctive norms, and confidence in perceived injunctive norms). Raw and exponentiated parameter estimates with significance tests and confidence intervals are presented in Table 3. Raw parameter estimates are log based. The exponentiated intercept value represents the predicted number of drinks per week for women at average values of one's own approval and confidence in one's own approval (8.31 drinks per week). Exponentiated parameter estimates can be interpreted as rate ratios. Thus, at step 1, the exponentiated parameter estimate for gender is 1.37, indicating that men, on average, consumed 37% more drinks per week than women (11.31 drinks per week). In addition, each unit increase in one's own approval toward drinking was associated with consuming an average of 39% more drinks per week. Confidence in one's own approval was marginally associated with less drinking, by 6% per unit increase. Each unit increase in perceived injunctive norms was associated with 16% more drinks per week. Confidence in perceived injunctive norms was not significantly associated with drinking.
Drinking as a Function of Gender, One's Own Approval, Confidence in One's Own Approval, Perceived Injunctive Norms, and Confidence in Perceived Injunctive Norms
Two-way products evaluating confidence as a moderator were added at step 2 (Own approval × Confidence in own approval and Perceived injunctive norms × Confidence in perceived injunctive norms). Results indicated a significant interaction which was consistent with hypotheses. Specifically, the association between perceived injunctive norms and drinking depended on confidence in perceived injunctive norms, suggesting that the association between perceived injunctive norms and drinking increased by 6.5% for each unit increase in confidence (See Figure 2). Two-way products with gender were added at step 3. There were no significant interactions between one's own approval and confidence in one's own approval, nor were there any significant two-way interactions with gender at step 3.
Figure 2. Drinking as a function of perceived injunctive norms and confidence in perceived injunctive norms for friends. Two-way interaction between confidence in perceived injunctive norms and perceived injunctive norms in predicting drinks per week. Estimates were derived from exponentiated parameter estimates where values for confidence and perceived injunctive norms were systematically substituted in the negative binomial regression equation.
Three-way interactions to evaluate gender as a moderator of interactions between confidence and corresponding approval were added at step 4 (Gender × Own approval × Confidence in own approval, and Gender × Perceived injunctive norms × Confidence in perceived injunctive norms). Results revealed a significant three-way interaction with gender, perceived injunctive norms, and confidence in perceived injunctive norms (see Figure 3). This three-way interaction qualified the two-way interaction between perceived injunctive norms and confidence in perceived injunctive norms identified in step 2. Furthermore, tests of simple two-way interactions confirmed that confidence moderated the association between perceived injunctive norms and drinking among women, t(689) = 2.68, p < .01, but not men, t(689) = −.42, p = .68.
Figure 3. Drinking as a function of gender, perceived injunctive norms, and confidence in perceived injunctive norms for friends. Three-way interaction among gender, confidence in perceived injunctive norms, and perceived injunctive norms in predicting drinks per week. Estimates where derived from exponentiated parameter estimates where values for gender, confidence, and perceived injunctive norms were systematically substituted in the negative binomial regression equation.
DiscussionInjunctive norms were found to be significant, unique predictors of drinking. The present study also extended social norms research and theory by incorporating a construct from the attitude literature—for example, attitude confidence—and investigating it as a potential moderator of the norms—behavior link. Study findings did not support confidence as a moderator of personal approval and drinking behavior but did support confidence as a moderator of the relationship between perceptions of others' approval (injunctive norms) and behavior.
This research provides evidence that estimates of friends' approval of drinking are more strongly associated with own drinking when students feel confident in their estimates. Furthermore, the present findings suggest that confidence in perceived injunctive norms matters more for women. This may reflect an underlying gender difference in the weighing of confidence in perceptions of others' approval. It may, alternatively, vary as a function of the gender specificity of the behavior in question. For example, an opposite pattern of results might be found with thinness norms, which are more salient among women than men (Bergstrom & Neighbors, 2006; Sanderson, Darley, & Messinger, 2002). More generally, the self-relevance of the norm may provide a limiting condition under which confidence in perceptions of others' approval becomes important.
The present research extends previous work indicating that attitude confidence is associated with stronger attitude—behavior relationships (Berger & Mitchell, 1989; Fazio & Zanna, 1978; Tormala et al., 2006; Tormala & Petty, 2002). Findings suggest that confidence may not be universally important in considering attitude—behavior relationships. Indeed, confidence may have a greater impact on attitudes or cognitions that are more ambivalent or in which there is greater subjectivity (e.g., perceptions of others' approval).
The present study also has direct implications for clinical assessment and intervention. Research over the last decade has consistently found support for the importance of social norms, particularly descriptive norms, in predicting college student drinking behaviors (Borsari & Cary, 2003; Lewis & Neighbors, 2004; Neighbors et al., 2010; Neighbors et al., 2004). This study's findings are consistent with previous studies as they indicate the importance and reliability of norms as a predictor of drinking. Future clinical research may benefit from considering injunctive norms as additional targets for assessment and ultimately, intervention. Findings that confidence and gender moderated the norms–drinking relationship also suggest the potential utility of assessing confidence in norms; attempting to reduce confidence (increase uncertainty) in those norms; and developing gender-specific interventions.
It is important to consider this research in light of several limitations. First, it is a single study. Multiple studies will ultimately be needed to evaluate and clarify the extent to which confidence in attitudes and perceived norms for drinking influence subsequent behavior. Second, the measures of attitudes and injunctive norms include a diverse set of target behaviors, which may increase generalizability at the cost of reducing predictive utility for specific behaviors. Additionally, the measure of attitude confidence (e.g., “Please indicate how confident you are that your responses to the previous items are correct”) could have been misinterpreted by some students. Participants may not have been sure whether this referred to confidence in the actual belief versus the accuracy of their response. Future research should clarify the instruction set. Another limitation is that the drinking measure is limited to self-report. The sample used in the present study may also limit generalizeability. All participants were college students and had to meet heavy drinking criteria in order to screen into the study. Thus, the present results may not generalize to abstainers or light drinkers or beyond the college population.
Finally, several future research directions are suggested. The present research focused exclusively on injunctive norms, and it also would be worthwhile to evaluate confidence in the context of descriptive norms. Considering other addictive behaviors, especially those that vary in relevance by gender may also be useful. Finally, experimental manipulations of confidence are needed to provide evidence of causality. In sum, the present research provides both applied and theoretical contributions to the existing literature related to attitudes, norms, and drinking.
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Submitted: September 24, 2010 Revised: August 2, 2011 Accepted: August 11, 2011
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Source: Psychology of Addictive Behaviors. Vol. 25. (4), Dec, 2011 pp. 714-720)
Accession Number: 2011-20737-001
Digital Object Identifier: 10.1037/a0025572
Record: 168- Title:
- The influence of cultural variables on treatment retention and engagement in a sample of Mexican American adolescent males with substance use disorders.
- Authors:
- Burrow-Sánchez, Jason J.. Department of Educational Psychology, University of Utah, Salt Lake City, UT, US, jason.burrow-sanchez@utah.edu
Meyers, Kimberly. Department of Educational Psychology, University of Utah, Salt Lake City, UT, US
Corrales, Carolina. Department of Educational Psychology, University of Utah, Salt Lake City, UT, US
Ortiz-Jensen, Cynthia. Department of Educational Psychology, University of Utah, Salt Lake City, UT, US - Address:
- Burrow-Sánchez, Jason J., Department of Educational Psychology, University of Utah, 1721 Campus Center Drive, Room 334, Salt Lake City, UT, US, 84112, jason.burrow-sanchez@utah.edu
- Source:
- Psychology of Addictive Behaviors, Vol 29(4), Dec, 2015. pp. 969-977.
- NLM Title Abbreviation:
- Psychol Addict Behav
- Page Count:
- 9
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- Bulletin of the Society of Psychologists in Addictive Behaviors; Bulletin of the Society of Psychologists in Substance Abuse
- Other Publishers:
- US : Educational Publishing Foundation
Society of Psychologists in Addictive Behaviors - ISSN:
- 0893-164X (Print)
1939-1501 (Electronic) - Language:
- English
- Keywords:
- familism, acculturation, treatment retention and engagement, Latino adolescents, ethnic identity
- Abstract:
- Adolescent substance abuse is a serious public health concern, and in response to this problem, a number of effective treatment approaches have been developed. Despite this, retaining and engaging adolescents in treatment are 2 major challenges continuously faced by practitioners and clinical researchers. Low retention and engagement rates are especially salient for ethnic minority adolescents because they are at high risk for underutilization of substance abuse treatment compared to their White peers. Latino adolescents, in particular, are part of the fastest growing ethnic minority group in the United States and experience high rates of substance use disorders. Heretofore, the empirical examination of cultural factors that influence treatment retention and engagement has been lacking in the literature. The goal of this study was to investigate the influence of the cultural variables ethnic identity, familism, and acculturation on the retention and engagement of Latino adolescents participating in substance abuse treatment. This study used data collected from a sample of Latino adolescent males (N = 96), predominantly of Mexican descent, and largely recruited from the juvenile justice system. Analysis was conducted using generalized regression models for count variables. Results indicated that higher levels of exploration, a subfactor of ethnic identity, and familism were predictive of attendance and engagement. In contrast, higher levels of Anglo orientation, a subfactor of acculturation, were predictive of lower treatment attendance and engagement. Clinical implications for the variables of ethnic identity, acculturation, and familism as well as suggestions for future research are discussed. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Retention; *Treatment; *Latinos/Latinas; *Psychological Engagement; *Substance Use Disorder; Acculturation; Ethnic Identity; Family; Mexican Americans
- PsycINFO Classification:
- Substance Abuse & Addiction (3233)
- Population:
- Human
Male - Location:
- US
- Age Group:
- Childhood (birth-12 yrs)
School Age (6-12 yrs)
Adolescence (13-17 yrs)
Adulthood (18 yrs & older)
Young Adulthood (18-29 yrs) - Tests & Measures:
- Timeline Follow Back
Youth Self-Report–Externalizing Scale
Multi Ethnic Identity Measure
Familism Scale
Acculturation Rating Scale for Mexican Americans-II DOI: 10.1037/t03853-000 - Grant Sponsorship:
- Sponsor: National Institute on Drug Abuse
Grant Number: K23DA019914
Other Details: Award
Recipients: Burrow-Sánchez, Jason J. - Methodology:
- Empirical Study; Quantitative Study
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- First Posted: Jul 13, 2015; Accepted: May 1, 2015; Revised: May 1, 2015; First Submitted: Jan 5, 2015
- Release Date:
- 20150713
- Correction Date:
- 20160104
- Copyright:
- American Psychological Association. 2015
- Digital Object Identifier:
- http://dx.doi.org/10.1037/adb0000096
- Accession Number:
- 2015-31332-001
- Number of Citations in Source:
- 74
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- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2015-31332-001&site=ehost-live">The influence of cultural variables on treatment retention and engagement in a sample of Mexican American adolescent males with substance use disorders.</A>
- Database:
- PsycINFO
The Influence of Cultural Variables on Treatment Retention and Engagement in a Sample of Mexican American Adolescent Males With Substance Use Disorders
By: Jason J. Burrow-Sánchez
Department of Educational Psychology, University of Utah;
Kimberly Meyers
Department of Educational Psychology, University of Utah
Carolina Corrales
Department of Educational Psychology, University of Utah
Cynthia Ortiz-Jensen
Department of Educational Psychology, University of Utah
Acknowledgement: This research was supported by Award Number K23DA019914 from the National Institute on Drug Abuse awarded to Jason J. Burrow-Sánchez. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institute on Drug Abuse or the National Institutes of Health. We thank all members of the Validating Interventions for Diverse Adolescents Research Team at the University of Utah.
Adolescent substance abuse is a serious public health problem with almost 10% of youth, ages 12–17, reporting the use of illicit drugs and 7% of these youth meeting criteria for a substance use disorder as indicated from national survey collected by the Substance Abuse and Mental Health Services Administration (SAMHSA; 2012). In response to this problem, a number of effective treatment approaches have been developed and tested for adolescents over the past two decades (Waldron & Turner, 2008; Williams & Chang, 2000). However, retaining and engaging adolescents in treatment are two major challenges continuously faced by practitioners and clinical researchers. These issues are especially salient for ethnic minority adolescents because they remain at high risk for underutilization of substance abuse treatment compared to their White peers (Alegria, Carson, Goncalves, & Keefe, 2011).
As one of the largest ethnic minority groups, Latinos comprise more than 51 million people with the majority (65%) being of Mexican descent and a third of its population under the age of 18 (Pew Research Center, 2011). These adolescents report higher rates of substance use disorders (14%) compared to their White (12.7%) or African American (7%) peers as indicated by the Center on Addiction and Substance Abuse (2011). They are also more likely than White youth to be referred and mandated to attend substance abuse treatment from the criminal justice system (Shillington & Clapp, 2003). Latino adolescents, however, are less likely to complete substance abuse treatment compared to their White counterparts (Saloner, Carson, & Lê Cook, 2014). Thus, it is important to understand the factors that influence treatment retention and engagement for adolescents, in general, and Latino adolescents, in particular.
Precise definitions of treatment retention and engagement are difficult to operationalize because these variables are defined differently across studies and the format of treatment provided: residential, inpatient, or outpatient. In one of the more comprehensive studies of adolescent substance abuse treatment, Hser and colleagues (2001) examined data from a geographically diverse sample of almost 1,200 adolescents from four cities in the United States who participated in residential, acute inpatient or outpatient substance abuse treatment. Across these treatment modalities, marijuana and alcohol were the most frequently used substances, and about half the sample also reported some use of hard drugs (i.e., cocaine, hallucinogens, stimulants). Treatment retention was examined for each modality and defined as 90 days or more for residential and outpatient, and as 21 days for acute inpatient. Retention rates were highest for adolescents who received inpatient (63.7%) and residential (58.4%) treatment, and lowest for those in outpatient treatment (27.1%). After controlling for treatment type and baseline severity of drug use, Hser and colleagues found that longer time spent in treatment was associated with better overall outcomes for adolescents. This positive correlation between treatment retention and outcome is a robust finding that has been replicated in substance abuse treatment studies with adults and adolescents (Brady & Ashery, 2005; Garner et al., 2009; Greenfield et al., 2007; Simpson, Joe, & Brown, 1997). Unfortunately, Hser and colleagues found the lowest retention rates for the treatment modality that youth in the United States are most likely to receive, that is, outpatient treatment (SAMHSA, 2009).
Operational definitions of treatment engagement are also difficult to find in the literature because many studies do not provide clear definitions of engagement, or confound the definition of the construct with treatment attendance (see Pullmann et al., 2013; Staudt, 2007). However, some researchers suggest that measuring a behavior, such as treatment participation, is important when assessing engagement (see Joe, Simpson, & Broome, 1999; Staudt, 2007; Staudt, Lodato, & Hickman, 2012). For example, Stein and colleagues (2006) measured engagement in a sample of 130 incarcerated adolescents largely through assessing their participation in one of two assigned treatment conditions for substance abuse. Whereas agreed upon definitions across studies are lacking, there is support to suggest that attendance and participation are valid measures of treatment retention and engagement, respectively. Next, we examine the general factors that influence retention and engagement in substance abuse treatment for adolescents.
General Factors That Influence Retention and EngagementIn general, the severity of pretreatment substance use and the presence of an externalizing disorder are factors that have been found to influence the amount of time adolescents spend in treatment. In the adult literature, greater substance use severity at treatment admission is typically related to poorer treatment outcomes (SAMHSA, 2014a; Tiet, Ilgen, Byrnes, Harris, & Finney, 2007), but this is not consistently the case with adolescents. In other words, pretreatment substance use severity on its own does not consistently predict lower levels of treatment retention for adolescents (Latimer, Newcomb, Winters, & Stinchfield, 2000). Rather, it appears that pretreatment substance use severity in the presence of an externalizing disorder (i.e., attention deficit hyperactivity disorder, conduct disorder) is a stronger predictor of lower treatment retention for adolescents, especially in samples recruited from juvenile justice (see Austin & Wagner, 2010; Grella, Hser, Joshi, & Rounds-Bryant, 2001; Shane, Jasiukaitis, & Green, 2003). In fact, adolescents in the juvenile justice system tend to have higher rates of externalizing disorders, as well as substance use problems, compared to youth in the general population (Chassin, 2008; Rosenblatt, Rosenblatt, & Biggs, 2000). Most of the studies on treatment retention and engagement have largely been conducted with samples of White adolescents and excluded the investigation of cultural variables when diverse youth are included in the sample. In addition, more of a focus on juvenile justice is needed because this system serves as one of the primary referral sources for adolescents to substance abuse treatment (Ozechowski & Waldron, 2008). For example, data from the 2011 Treatment Episode Data Set for discharges (TEDS-D) indicates that approximately half of all youth, ages 12–20, discharged from publically funded substance abuse treatment were referred from the justice system (SAMHSA, 2014b). In sum, research that investigates how cultural variables influence treatment retention and engagement in juvenile justice involved ethnic minority youth is needed.
Ethnic Minority YouthAs previously mentioned, racial and ethnic minority youth are less likely to be retained in substance abuse treatment compared to their White counterparts (Jacobson, Robinson, & Bluthenthal, 2007; Saloner et al., 2014; Vourakis, 2005), although the reasons for these differences are not clear. Some have suggested that cultural variables play a role in substance abuse treatment retention and completion for ethnic minority youth (see Austin & Wagner, 2006; Castro & Alarcon, 2002), although heretofore the empirical evidence underlying this assumption has been lacking. For example, we were only able to locate two empirical studies, both by Austin and Wagner (2006, 2010), that directly examined the influence of cultural variables on treatment retention. In the 2010 study, the researchers’ investigated the influence of cultural and general variables on treatment attrition with a sample (N = 453) of Latino (domestic and foreign-born) and African American adolescents receiving substance abuse treatment. The adolescents in their sample received substance abuse treatment as part of their involvement with juvenile justice. Contrary to the researchers’ expectations, none of the cultural variables tested (i.e., acculturation, perceived discrimination, or racial/ethnic identity) influenced treatment completion, but rather some of the general variables were influential across racial and ethnic subgroups. For example, they found that not being placed on a waiting list and lack of a conduct disorder diagnosis influenced treatment completion for those Latino adolescents of U.S. and foreign birth, respectively. In light of the results from the Austin and Wager studies, the influence of cultural variables on substance abuse treatment retention and engagement has yet to be examined in adolescents of Mexican descent who represent the largest Latino subgroup.
Salient Cultural Variables for Latino YouthThree of the most salient cultural variables in relation to substance use and mental health for Latino adolescents include ethnic identity, familism, and acculturation (Castro & Alarcon, 2002; Umaña-Taylor & Updegraff, 2007; Vega & Gil, 1999). First, ethnic identity or a Latino adolescent’s sense of belonging to a particular ethnic group has been linked to mental health and substance use outcomes. For example, a stronger sense of ethnic identity is generally related to lower levels of psychological distress and substance use for Latino adolescents (Felix-Ortiz & Newcomb, 1995; Phinney & Ong, 2007; Umaña-Taylor, 2011). Second, familism is the sense of obligation and perceived support Latino adolescents experience within their families (Sabogal, Marin, Otero-Sabogal, Marin, & Perez-Stable, 1987). This cultural variable is relevant because Latino families with higher levels of familism do not generally condone the use of substances by its members (Vega, 1990). Finally, acculturation is considered a bidimensional process that involves the orientation Latino adolescents have toward being part of dominant and nondominant cultures simultaneously (Berry, 1980; Berry, Phinney, Sam, & Vedder, 2006). In general, the majority of research findings indicate a positive correlation between acculturation and rates of substance use for Latino adolescents (De La Rosa, Vega, & Radisch, 2000; Ebin et al., 2001; Lawton & Gerdes, 2014; Vega & Gil, 1999), although a few researchers have found a negative correlation, or no association, between these two variables (Miller, 2011; Zamboanga, Schwartz, Jarvis, & Van Tyne, 2009). In sum, all three of these cultural variables have been linked to substance use behavior and may assist in explaining treatment retention and engagement for Latino adolescents.
Purpose of Current Study and HypothesesThe purpose of the current study is to investigate the influence that cultural variables have in explaining treatment retention and engagement in sample of male Latino adolescents, primarily of Mexican descent, and largely recruited from juvenile justice. The study of Mexican American adolescent males with substance use problems involved in juvenile justice is a pressing need because they are overrepresented in this system (Mendel, 2011). For example, approximately 72% of the 1.5 million youth who have contact with the U.S. juvenile justice system each year are male (Puzzanchera, Adams, & Hockenberry, 2012) and it is estimated that 20% of these youth are Latino (Mendel, 2011). Further, more than half (56%) of the male youth in juvenile justice are estimated to have a substance use problem (Chassin, 2008). Our hypotheses are designed to test the influence that the cultural variables of ethnic identity, familism, and acculturation have on treatment retention and engagement. We include these specific cultural variables due to the links that have been identified with emotional/behavioral functioning and substance use behavior (Castro & Alarcon, 2002; Umaña-Taylor, 2011; Umaña-Taylor & Updegraff, 2007; Vega & Gil, 1999) for Latino youth. For the first hypothesis, we predict that cultural variables will influence treatment retention in the following ways: (a) higher levels of ethnic identity and familism will positively influence retention, whereas (b) higher levels of acculturation will negatively influence retention. Similarly, for the second hypothesis we predict that cultural variables will influence treatment engagement in the following ways: (a) higher levels of ethnic identity and familism will positively influence engagement, whereas (b) higher levels of acculturation will negatively influence engagement. The hypotheses are based on prior findings in the research literature that suggest higher levels of ethnic identity and familism serve as protective factors, whereas higher levels of acculturation serve as a risk factor in relation to substance use behavior for Latino adolescents (Lawton & Gerdes, 2014; Umaña-Taylor, 2011; Vega, 1990; Vega & Gil, 1999); we extrapolate these prior research findings to investigate their role in explaining treatment retention and engagement.
Method Description of Participants
Adolescents in this study (N = 96) were recruited as part of a larger set of studies examining the cultural accommodation of substance abuse treatment for Latino adolescents and randomly assigned to one of two group-based cognitive–behavioral treatment conditions for substance abuse (see Burrow-Sánchez, Minami, & Hops, 2015; Burrow-Sánchez & Wrona, 2012). The original data included nine females but information from these cases were dropped due to the limited ability to generalize from such a small sample of female adolescents, as well as the fact that male adolescents are overrepresented in juvenile justice (Mendel, 2011; Puzzanchera et al., 2012). Part of the inclusion criteria for the larger set of studies was that all adolescents were between the ages of 13–18, identified as Latino or Hispanic and met DSM–IV–TR (American Psychiatric Association, 2000) diagnostic criteria for a substance abuse or dependence disorder within the past 12 months. Adolescents were paid $20 via gift cards for completing the baseline assessments. Participants under the age of 18 were required to provide assent and parental consent prior to participation; all participant procedures for this study were approved by the Institutional Review Board at the University of Utah.
Description of Treatment
The two treatment conditions consisted of either a standard cognitive–behavioral treatment (S-CBT) or its culturally accommodated cognitive–behavioral (A-CBT) equivalent; in general, both treatments were similar except that the A-CBT integrated cultural variables relevant to Latino adolescents. Treatment groups met weekly for 90-min over 12-week periods. The reader is referred to our prior work (see Burrow-Sánchez, Martinez, Hops, & Wrona, 2011; Burrow-Sánchez, Minami, et al., 2015; Burrow-Sánchez & Wrona, 2012) for greater detail of the larger treatment studies, in general, and descriptions of the treatments, in particular.
Measures
All measures described below were available from their respective authors or publishers in English and Spanish and administered by trained bilingual research assistants. The majority of adolescents (98%) preferred completing the measures and verbal interactions with staff in English.
Timeline follow back (TLFB)
Substance use for all participants was measured using the TLFB (Sobell & Sobell, 1992), which is a semistructured interview that records substance use over a specified period of time. The TLFB uses a calendar format to help individuals remember their history and patterns of substance use. It has been used extensively with adolescents and appropriate psychometric properties have been established (Dennis, Funk, Godley, Godley, & Waldron, 2004; Sobell & Sobell, 2003). The number of days alcohol and other drugs (excluding tobacco) were used in the 90 days prior to baseline assessment was calculated for the analysis in the current study; to reduce skew, this variable was log-transformed prior to analysis.
Youth Self-Report–Externalizing Scale
The Youth Self-Report (YSR) is a well-used instrument with adolescent samples to measure behavioral problems across a number of domains (Achenbach, Dumenci, & Rescorla, 2002; Achenbach & Rescorla, 2001). The YSR consists of 112 items and participants are asked to rate their responses to potential behavioral problems on a 3-point Likert-type scale that ranges from 0 (not true) to 2 (very true or often true) based on the past 6 months. From the complete YSR measure nine subscales and three overall scales can be derived. Although the complete measure was administered to adolescents, only the Externalizing scale (EXT) was used in the analysis for the present study. Examples of items from the Externalizing scale include “I drink alcohol without my parents’ approval” and “I destroy my own things.” Internal consistency for adolescents on the EXT scale was α = .868.
The Multi Ethnic Identity Measure
The Multi Ethnic Identity Measure (MEIM) is a widely used measure of ethnic identity for adolescents (Phinney, 1992; Phinney & Ong, 2007). A modified 12-item version of the MEIM was used in this study that has been tested via confirmatory factor analysis with Latino adolescents (see Burrow-Sánchez, 2014). The items asked adolescent participants to indicate their attitudes and behaviors related to their ethnic identity group on a 1 (disagree) to 5 (agree) scale. Contemporary views of ethnic identity consider it to be a bidimensional construct, and Phinney and Ong (2007) suggested that the measure be scored by reducing it to two 3-item subscales: a Commitment (COM; Items 6, 7, and 12) scale that measures a sense of personal affiliation to an ethnic group and an Exploration (EXP; Items 1, 2, and 8) scale that measures behavior related to seeking information about an ethnic group. An example of an item from the COM scale is “I feel I identify with the ethnic Group 1 belong to,” and an example of an item from the EXP scale is “I have dedicated time to find out more about my ethnic group, such as history, tradition, and customs.” Following the suggestion provided by Phinney and Ong scores from six items were extracted from the larger measure and then averaged to produce two 3-item scores for each participant. Internal consistency for the exploration and commitment subscales in the current study was α = .730 and α = .778, respectively.
Familism Scale
The Familism Scale (FS) is a 14-item instrument used to measure the construct of familism based on the factors of obligations, perceived support, and family as referents (Sabogal et al., 1987). Versions of this scale have been used in prior research with Latino youth (see Lorenzo-Blanco, Unger, Ritt-Olson, Soto, & Baezconde-Garbanati, 2013; Morcillo et al., 2011; Unger et al., 2002). Participants rate their agreement to items on a scale ranging from 1 (very much in disagreement) to 5 (very much in agreement). Examples of items from the scale include, “Children should live in their parents’ home until they get married” and “A person should share his or her home with uncles, aunts, or first cousins if they are in need.” Scores from individual items were averaged to obtain a total score. Internal consistency for this sample was α = .820.
Acculturation Rating Scale for Mexican Americans-II
The Acculturation Rating Scale for Mexican Americans-II (ARSMA-II) is one of the most widely used measures of acculturation for Latino adults and adolescents (Cuéllar, Arnold, & Maldonado, 1995). It has demonstrated good reliability and strong construct and discriminant validity in research with Mexican American samples (Cuéllar et al., 1995) and has been tested via confirmatory factor analysis for Latino adolescents with substance use disorders (see Burrow-Sánchez, Ortiz-Jensen, Corrales, & Meyers, 2015). Participants rate their responses to items on a scale ranging from 1 (not at all) to 5 (extremely often or always). The 13-item Anglo Oriented Subscale (AOS) and the 17-item Mexican Oriented Subscale (MOS) were scored separately for participants by averaging the items on each subscale in accordance with Cuéllar et al. (1995) and a bidimensional view of acculturation (Berry, 2006). Examples of items from each scale include “I have difficulty accepting some ideas held by some Mexican Americans” (AOS) and “My friends, while I was growing up, were of Mexican origin” (MOS). Internal consistency was α = .707 and α = .850 for AOS and MOS subscales, respectively. Cuéllar et al. (1995) also provides a linear method for calculating an overall acculturation score by subtracting participant mean scores of the AOS from the MOS; the participants’ acculturation score is then used to place them in one of five categories across a continuum that ranges from less acculturated on one end to more highly acculturated on the other with those more toward the center labeled as bicultural. For the sake of parsimony, we chose to collapse the five categories into three which resulted in placing the sample in the following categories: 39% were more Mexican oriented or less acculturated, 48% were bicultural, and 14% were more Anglo oriented or highly acculturated.
Retention: Number of sessions attended
Retention was measured by the total number of treatment sessions attended by each adolescent. A session was considered to be attended if the adolescent was present for the majority of the 90-min treatment session, typically 75-min or more. Attendance was closely monitored and recorded by therapists at the end of each session. Participant attendance was also discussed as part of the weekly supervision provided to therapists. The mean (SD) and median number of treatment sessions attended were 8.95 (3.16) and 10, respectively; range = 0–12.
Engagement: Number of practice sheets completed
Engagement was measured by the total number of practice sheets completed by each adolescent over the course of treatment. Practice sheets were administered to adolescents by therapists at 11 of the 12 treatment sessions. The sheets provided adolescents with an opportunity to practice treatment-related skills between sessions and then report on their progress at the next session; successful completion of practice sheets required the adolescents’ attention to the in-session treatment content as well as application of material outside of session. The completion of practice sheets was used as a behavioral indicator of adolescent participation in treatment. Practice sheets were provided to adolescents on standard 8.5 × 11 in. sheets of paper that could be completed with a pen or pencil. A practice sheet was considered complete if the therapist judged that the majority of it, typically 75% or more, had reasonably been completed by an adolescent. Therapists also judged the quality of work on practice sheets compared to that typically expected given a particular adolescents’ chronological age and, if known, reading ability. Practice sheet completion was also discussed as part of the weekly supervision provided to therapists. The mean (SD) and median number of practice sheets completed were 5.08 (3.11) and 5, respectively; range = 0–11.
Analytical Plan
The dependent measures in this study (i.e., total number of sessions attended and number of practice sheets completed) are count variables. These types of variables generally follow a Poisson, rather than a normal, distribution and are most appropriately analyzed using generalized linear methods (see Coxe, West, & Aiken, 2009). The independent variables in this study are covariates (i.e., baseline substance use and externalizing behavior) and baseline cultural variables of interest (i.e., ethnic identity, familism, acculturation). More specifically, the following predictors (in order and coded by measure acronym): treatment condition (TXC), age, TLFB, EXT, EXP, COM, FS, AOS, and MOS were used in the regressions. Controlling for baseline substance use and externalizing behavior in the generalized regression models allowed us test for the variance in outcomes attributed to the cultural variables of interest. We grand mean centered all predictors with the exception of TXC to ease interpretation of the models (Hedeker & Gibbons, 2006); TXC was included as a covariate in all models to control for any influence that assignment to a specific treatment condition had on participant outcomes. Two generalized linear regressions were conducted to test each dependent variable (i.e., retention or engagement) but the same independent variables were included in both models. Further, the subscales for ethnic identity and acculturation were included in the models so that these constructs could be tested bidimensionally; this type of bidimensional analysis is frequently suggested in the literature but infrequently conducted in practice (see Burrow-Sánchez, Ortiz-Jensen, et al., 2015; Phinney & Ong, 2007).
Results Preliminary Analyses
The mean age of adolescents in the analysis was 15.29 (SD = 1.31) and the majority had parents of Mexican descent (77%; see Table 1 for more participant demographics). The majority of study referrals was received from juvenile justice probation officers (68%) or case managers (30%) and was mandated (69%) to attend substance abuse treatment. Finally, 55% and 45% of adolescents met DSM-IV–R criteria for a substance abuse or dependence disorder, respectively, within the past 12 months. See Table 1 for additional participant demographics.
Participant Demographics (N = 96)
Predictors of Retention and Engagement
Inspection of the means (8.73 and 4.92) and variances (10.66 and 9.61) for retention and engagement, respectively, indicated they were not equal and that dispersion was present (see Stroup, 2013). Minor violations of the mean-variance equality assumption (i.e., low dispersion) can be addressed with a Poisson model that includes a dispersion correction factor but larger violations generally require the use of a negative binomial model. Following this logic, we conducted a Poisson regression for the retention dependent variable (DV) and a negative binomial regression with the engagement DV.
The first model included retention as the DV and the fit statistics indicated a Pearson χ2 of 95.06 (df = 86, Pearson χ2/df = 1.11); the Pearson value over its degrees of freedom is an indicator of overall model fit with ratios closer to 1 reflecting a perfect fit. To account for minor dispersion in the DV, we included a Pearson correction factor in the model that adjusts standard errors. Results of the analysis are presented in Table 2 and indicate that EXP (β = 0.04, p = .03), and FS (β = 0.01, p = .0007) significantly predicted retention as did Anglo orientation (β = −0.20, p = .006) but in the opposite direction. Poisson models produce coefficients that are interpreted as the predicted logarithm of counts of the DV (see Coxe et al., 2009). For example, coefficients for the first model represent the predicted change in the logarithm of counts for retention for a one-unit change in the predictor; however, exponentiation of the coefficients places them on the scale of the original count variable (i.e., number of sessions attended) and subsequently eases interpretation. Therefore, the exponentiated coefficients (see Table 2) are used for the interpretation of all model results. The value of the intercept in model predicts that participants will attend 8.03 sessions when all other terms are zero. However, for Poisson-based analysis, the remaining terms in the model represent a multiplicative change in the DV for a one-unit change in the predictor (see Coxe et al., 2009). Applied to Model 1, this indicates that for every one-unit change in EXP the number of sessions attended is multiplied by a factor of 1.04; thus, for every one-unit change in EXP participants are predicted to attend 8.35 (i.e., 8.03 × 1.04 = 8.35) treatment sessions. Similarly, for every 1-unit change in FS participants are predicted to attend 8.11 (i.e., 8.03 × 1.01 = 8.12) treatment sessions. In contrast, a one-unit change in AOS predicts that participants will attend 6.58 (i.e., 8.03 × 0.82 = 6.58) treatment sessions.
Poisson Model for Treatment Retention
The second model included the same predictors as the first, but the DV was changed to engagement and a negative binomial regression was used due to the rationale presented above. The second model produced a Pearson χ2 of 89.79 (df = 86, Pearson χ2/df = 1.04). The exponentiated value of the intercept (see Table 3) indicates that participants are predicted to complete 4.36 practice sheets when all other terms are zero. For every one-unit change in EXP participants are predicted to complete 4.71 practice sheets (i.e., 4.36 × 1.08 = 4.71). In contrast, for 1-unit changes in TLFB and AOS participants are predicted to complete 3.4 (i.e., 4.36 × 0.78 = 3.4) and 3.1 (i.e., 4.36 × 0.71 = 3.1) practice sheets, respectively.
Negative Binomial Model for Treatment Engagement
DiscussionThe goal of the current study was to investigate how cultural variables influence retention and engagement in substance abuse treatment for a sample of Latino adolescents primarily of Mexican descent and largely recruited from juvenile justice. Overall, the results indicated that adolescents who were in the exploration phase of ethnic identity and reported a stronger sense of familism had higher rates of retention and engagement in substance abuse treatment. In contrast, adolescents who reported higher levels of acculturation to the dominant culture (i.e., Anglo orientation) had lower rates of retention and engagement in treatment.
The exploration component of ethnic identity, rather than the commitment component, positively influenced treatment retention and engagement. These findings may be due, in part, to the fact that adolescence is a critical period of development when youth are faced with many tasks and challenges that encourage exploration of their identities. However, youth from ethnic minority backgrounds must accomplish the typical developmental tasks of adolescence while concurrently exploring their sense of self as a member of a nondominant group (Erikson, 1997; Umaña-Taylor & Updegraff, 2007). In other words, adolescence is a key time for ethnic minority youth to seek out information regarding their identity as a person and as a member of an ethnic group. The treatment groups attended by adolescents in the current study were entirely composed of youth who identified as Latino/Hispanic and largely of Mexican descent which may have served to provide safe venues for exploring ethnic identity, which subsequently promoted higher retention and engagement. In contrast, it may not be reasonable to expect that Latino youth in middle adolescence (i.e., ages 14 to 16) have yet developed a strong commitment to their ethnic identity because exploration may be more salient during this developmental period (Berry et al., 2006).
Familism also positively influenced treatment retention and engagement for adolescents in our sample. These findings may suggest that Latino families who continue to exert a positive influence on their children during adolescence may buffer some of the negative effects attributed to peers (Dishion & Owen, 2002; Duncan, Duncan, & Hops, 1994; Umaña-Taylor & Guimond, 2010). More specifically, families with higher levels of familism may view drug use by its members from a collective prospective, rather than viewing it as an individual problem (Vega, 1990; Vega & Gil, 1999). Following this logic, the family system may exert pressure and provide support for its adolescent members to resolve the drug problem (i.e., attend and engage in treatment) because it viewed as affecting the entire family.
In contrast to findings just described, higher levels of affiliation to the dominant culture (i.e., Anglo orientation) negatively influenced treatment retention and engagement for adolescents in the study whereas affiliation to the Mexican culture was unrelated to retention or engagement. These findings may suggest that Mexican American adolescents with more affiliation toward the dominant culture experience less connection and cohesion from participating in treatment groups with their less acculturated peers. The level of cohesion is an important element to consider in group treatment participation and outcomes (see Burlingame, McClendon, & Alonso, 2011), and subsequently, less connection with peers could lead to lower motivation to attend and engage in treatment. Based on the combined ARSMA-II scores slightly more than 60% of the sample were in bicultural (48%) or highly acculturated (14%) categories compared to 39% in the less acculturated category. These proportions suggest that, overall, the sample was more highly acculturated which could help to explain the lack of findings for the Mexican orientation scale.
Finally, we found that higher baseline levels of substance use negatively influenced treatment engagement for adolescents in the study. This finding is partially consistent with other research (see Grella et al., 2001; Shane et al., 2003), and may suggest that adolescents with severe pretreatment substance use problems require more intensive engagement strategies than standard outpatient treatments typically provide (see Ozechowski & Waldron, 2008). This is important to consider with the fact that adolescents are most likely to receive outpatient treatment for substance abuse problems (SAMHSA, 2009). We also found that externalizing behavior did not predict either treatment retention or engagement for adolescents. This may be due to the fact that the adolescents in our study were not formally diagnosed with an externalizing disorder, and subsequently, their externalizing behaviors may not have reached a threshold to influence retention and engagement similar to other studies (Austin & Wagner, 2010; Grella et al., 2001; Shane et al., 2003).
Findings from the current study suggest two important clinical implications that we encourage practitioners to consider. First, we found that specific cultural variables do indeed influence treatment retention and engagement for Latino adolescents. Based on these findings, practitioners may want to consider measuring cultural variables for Latino adolescents as part of a pretreatment assessment. For example, scores on measures of cultural variables, such as ethnic identity and acculturation, may assist practitioners in assigning adolescents to treatment groups with peers who share similar cultural perspectives. Second, the study findings suggest that placing adolescents together in groups who all share common identity labels, such as Latino or Hispanic, may not always be the best approach. This second implication underscores the point that differences in perspective can exist even when adolescents share a common racial or ethnic identity label. For example, a practitioner cannot assume that two adolescents who both identify as Latino share the same perspective, orientation or world-view toward their culture of origin or the host culture. It may be that ethnic minority adolescents benefit more from being in treatment groups with peers who share similar cultural perspectives rather than similar identity labels.
As with any study there are certain limitations that need to be considered. First, the sample size in this study was modest, which limits the statistical power we had to detect the influence of cultural variables, and thus, recommend that future research be conducted with larger samples of Latino adolescents. Second, adolescents in this study were primarily of Mexican descent, male, and juvenile justice involved which limits generalizability to other Latino subgroups (e.g., Puerto Rican, Cuban) and females who do not have contact with the justice system. Future research that includes females and samples from other Latino subgroups that are not justice-involved is needed to replicate the current findings. In addition, future research that examines the influence of cultural variables on treatment outcomes for other racial and ethnic minority adolescent groups (e.g., African American, American Indian) is needed. Finally, this study focused on group-based outpatient treatment and other studies should be conducted that investigate the influence of cultural variables for other treatment modalities (i.e., individual, family) and settings (i.e., residential, inpatient) for racial and ethnic minority adolescents.
The goal of the current study was to investigate the influence of specific cultural variables on treatment retention and engagement in sample of male Latino adolescents, primarily of Mexican descent, and largely recruited from juvenile justice. The major results of the study indicated that adolescents in the exploration phase of ethnic identity, and reporting higher levels of familism, had higher retention and were more engaged in treatment. In contrast, adolescents who reported more orientation toward the dominant culture (i.e., Anglo orientation) had lower retention and were less engaged. Clinical implications of these findings suggest that practitioners may want to consider cultural perspectives in addition to identity labels (e.g., Latino, Hispanic) when assigning Latino adolescents to group treatments. Future research should replicate the findings in the current study with larger samples of adolescents from both genders and across other Latino subgroups (e.g., Puerto Rican, Cuban).
Footnotes 1 Both studies used portions of the same sample and we only report on the most recent study; however, findings from both studies did not indicate a relation between cultural variables and treatment attrition/retention.
2 The authors did not explicitly indicate in either study the Latino subgroup composition of their samples. Since the samples were recruited in the Southwest portion of the U.S. (i.e., FL) it is assumed they were mostly of Puerto Rican or Cuban descent.
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Submitted: January 5, 2015 Revised: May 1, 2015 Accepted: May 1, 2015
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Source: Psychology of Addictive Behaviors. Vol. 29. (4), Dec, 2015 pp. 969-977)
Accession Number: 2015-31332-001
Digital Object Identifier: 10.1037/adb0000096
Record: 169- Title:
- The influence of impulsiveness on binge eating and problem gambling: A prospective study of gender differences in Canadian adults.
- Authors:
- Farstad, Sarah M.. Department of Psychology, University of Calgary, Calgary, AB, Canada
von Ranson, Kristin M., ORCID 0000-0001-6023-7948. Department of Psychology, University of Calgary, Calgary, AB, Canada, kvonrans@ucalgary.ca
Hodgins, David C.. Department of Psychology, University of Calgary, Calgary, AB, Canada
El-Guebaly, Nady. Division of Addiction, Foothills Addiction Program, Department of Psychiatry, University of Calgary, Calgary, AB, Canada
Casey, David M.. Department of Psychology, University of Calgary, Calgary, AB, Canada
Schopflocher, Don P.. School of Public Health and Faculty of Nursing, University of Alberta, Calgary, AB, Canada - Address:
- von Ranson, Kristin M., Department of Psychology, University of Calgary, 2500 University Drive Northwest, Calgary, AB, Canada, T2N 1N4, kvonrans@ucalgary.ca
- Source:
- Psychology of Addictive Behaviors, Vol 29(3), Sep, 2015. pp. 805-812.
- NLM Title Abbreviation:
- Psychol Addict Behav
- Page Count:
- 8
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- Bulletin of the Society of Psychologists in Addictive Behaviors; Bulletin of the Society of Psychologists in Substance Abuse
- Other Publishers:
- US : Educational Publishing Foundation
Society of Psychologists in Addictive Behaviors - ISSN:
- 0893-164X (Print)
1939-1501 (Electronic) - Language:
- English
- Keywords:
- impulsiveness, personality, binge eating, problem gambling, gender differences
- Abstract:
- This study investigated the degree to which facets of impulsiveness predicted future binge eating and problem gambling, 2 theorized forms of behavioral addiction. Participants were 596 women and 406 men from 4 age cohorts randomly recruited from a Canadian province. Participants completed self-report measures of 3 facets of impulsiveness (negative urgency, sensation seeking, lack of persistence), binge-eating frequency, and problem-gambling symptoms. Impulsiveness was assessed at baseline, and assessments of binge eating and problem gambling were followed up after 3 years. Weighted data were analyzed using zero-inflated negative binomial and Poisson regression models. We found evidence of transdiagnostic and disorder-specific predictors of binge eating and problem gambling. Negative urgency emerged as a common predictor of binge eating and problem gambling among women and men. There were disorder-specific personality traits identified among men only: High lack-of-persistence scores predicted binge eating and high sensation-seeking scores predicted problem gambling. Among women, younger age predicted binge eating and older age predicted problem gambling. Thus, there are gender differences in facets of impulsiveness that longitudinally predict binge eating and problem gambling, suggesting that treatments for these behaviors should consider gender-specific personality and demographic traits in addition to the common personality trait of negative urgency. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Binge Eating; *Human Sex Differences; *Impulsiveness; *Pathological Gambling; *Personality Traits
- Medical Subject Headings (MeSH):
- Adolescent; Adult; Aged; Binge-Eating Disorder; Canada; Cohort Studies; Female; Gambling; Humans; Impulsive Behavior; Longitudinal Studies; Male; Middle Aged; Personality; Prospective Studies; Sex Factors; Young Adult
- PsycINFO Classification:
- Psychological & Physical Disorders (3200)
- Population:
- Human
Male
Female - Location:
- Canada
- Age Group:
- Adulthood (18 yrs & older)
Young Adulthood (18-29 yrs)
Middle Age (40-64 yrs)
Aged (65 yrs & older) - Tests & Measures:
- NEO Five Factor Inventory
Problem Gambling Severity Index
Revised NEO Personality Inventory DOI: 10.1037/t03907-000
Eating Disorder Examination Questionnaire DOI: 10.1037/t03974-000 - Grant Sponsorship:
- Sponsor: Social Sciences and Humanities Research Council of Canada, Canada
Recipients: Farstad, Sarah M.
Sponsor: Alberta Gambling Research Institute, Canada
Recipients: Farstad, Sarah M.
Sponsor: Intersections of Mental Health Perspectives in Addictions Research Training
Other Details: fellowship
Recipients: Farstad, Sarah M. - Methodology:
- Empirical Study; Longitudinal Study; Prospective Study; Quantitative Study
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- First Posted: May 11, 2015; Accepted: Feb 3, 2015; Revised: Jan 23, 2015; First Submitted: Sep 3, 2014
- Release Date:
- 20150511
- Correction Date:
- 20160616
- Copyright:
- American Psychological Association. 2015
- Digital Object Identifier:
- http://dx.doi.org/10.1037/adb0000069
- PMID:
- 25961146
- Accession Number:
- 2015-20856-001
- Number of Citations in Source:
- 48
- Persistent link to this record (Permalink):
- http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2015-20856-001&site=ehost-live
- Cut and Paste:
- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2015-20856-001&site=ehost-live">The influence of impulsiveness on binge eating and problem gambling: A prospective study of gender differences in Canadian adults.</A>
- Database:
- PsycINFO
The Influence of Impulsiveness on Binge Eating and Problem Gambling: A Prospective Study of Gender Differences in Canadian Adults
By: Sarah M. Farstad
Department of Psychology, University of Calgary
Kristin M. von Ranson
Department of Psychology, University of Calgary;
David C. Hodgins
Department of Psychology, University of Calgary
Nady El-Guebaly
Division of Addiction, Foothills Addiction Program, Department of Psychiatry, University of Calgary
David M. Casey
Department of Psychology, University of Calgary
Don P. Schopflocher
School of Public Health and Faculty of Nursing, University of Alberta
Acknowledgement: David M. Casey is now at Clinical Quality Metrics, Alberta Health Services, Calgary, Alberta.
This research was supported by awards to the first author from the Social Sciences and Humanities Research Council of Canada and the Alberta Gambling Research Institute and a fellowship from Intersections of Mental Health Perspectives in Addictions Research Training. This article is based on the master’s thesis research of Sarah M. Farstad, which was completed under the supervision of Kristin M. von Ranson. We thank Dr. Thomas O’Neill and Dr. Jenny Godley for their feedback on previous drafts of the manuscript. We thank the Leisure, Lifestyle, Lifecycle Project participants, and acknowledge the use of the project’s data in this article.
There has been considerable debate over whether binge eating should be viewed as addictive. Binge eating and addictions share many similarities: Both involve a lack of control over behavior, are used to regulate emotions, and increase dopamine activity in the brain’s limbic system (Davis & Carter, 2009; Wilson, 2000). Despite these similarities, some have argued that the addiction model ignores critical aspects of eating disorders (i.e., body-image disturbances, weight/shape preoccupation) and fails to account for the differences in symptoms among eating disorders (von Ranson & Cassin, 2007; Wilson, 2000, 2010). Further research comparing binge eating to other addictions is needed to determine the appropriateness of conceptualizing binge eating as an addictive behavior.
Elevated rates of eating disorders have been found among problem gamblers (von Ranson, Wallace, Holub, & Hodgins, 2013) and elevated problem-gambling symptoms have been found among those with binge eating disorder (Jiménez-Murcía et al., 2013; Yip, White, Grilo, & Potenza, 2011), but not bulimia nervosa (Fernandez-Aranda et al., 2006; Jiménez-Murcía et al., 2013). Identifying shared personality traits associated with binge eating and problem gambling may uncover a common vulnerability that places individuals at risk of developing these disorders, whereas identifying disorder-specific personality traits may allow us to better understand why individuals engage in one behavior over another (i.e., binge eating or gambling). Accordingly, the focus of this study was to compare key personality traits associated with both binge eating and problem gambling.
In this paper, we focused on impulsiveness, which is an important personality trait associated with addiction (Verdejo-Garcia, Lawrence, & Clark, 2008). Impulsiveness is a multifaceted construct composed of at least five distinct facets: negative urgency (i.e., tendency to engage in impulsive behavior when experiencing strong negative emotions), positive urgency (i.e., tendency to engage in impulsive behavior when experiencing strong positive emotions), sensation seeking (i.e., a desire for thrills and excitement), lack of planning (i.e., inability to consider the consequences of one’s actions), and lack of persistence (i.e., inability to persist on tasks when bored or fatigued) (Whiteside & Lynam, 2001). Each facet of impulsiveness is associated with different types of behavior (e.g., Fischer & Smith, 2008), underscoring the need to distinguish among impulsiveness facets in studies of problematic behaviors.
Impulsiveness, Binge Eating, and Problem GamblingDepending on the sample, study design, and form of psychopathology, study results regarding the relationship of impulsiveness with binge eating and problem gambling have been mixed. Negative urgency has been a robust correlate of binge eating and problem gambling in undergraduate, clinical, and community samples (Fischer, Peterson, & McCarthy, 2013; Fischer & Smith, 2008; Fischer, Smith, & Cyders, 2008; MacLaren, Fugelsang, Harrigan, & Dixon, 2011). However, in the few longitudinal studies of undergraduates that have been conducted to date, negative urgency has not been a significant predictor of future binge eating or gambling (Cyders & Smith, 2008; Davis & Fischer, 2013; Peterson & Fischer, 2012).
In community and undergraduate samples, sensation seeking has not been significantly associated with binge eating or problem gambling (Fischer & Smith, 2008; Reid et al., 2011). In clinical samples, some studies have found a small, positive association between sensation seeking and problem gambling (Grall-Bronnec et al., 2012), whereas others have not (Albein-Urios, Martinez-González, Lozano, Clark, & Verdejo-García, 2012). In longitudinal studies using undergraduate samples, sensation seeking has not significantly predicted future binge eating or gambling (Cyders & Smith, 2008; Peterson & Fischer, 2012).
Cross-sectional studies using clinical, community, and university samples have consistently found that lack of persistence is unassociated with binge eating and bulimic symptoms (Peterson & Fischer, 2012); however, results regarding problem gambling have been mixed. In community samples, individuals with problem gambling had elevated lack-of-persistence scores relative to controls (e.g., Reid et al., 2011), but in university samples, problem gambling has been unassociated with lack of persistence (e.g., Fischer & Smith, 2008). In clinical samples, some studies have revealed a positive association between lack of persistence and problem gambling (Grall-Bronnec et al., 2012), whereas others have not (Albein-Urios et al., 2012). In longitudinal studies of undergraduates, lack of persistence significantly predicted future binge eating, but not gambling (Cyders & Smith, 2008; Peterson & Fischer, 2012).
Together these findings suggest that in community samples, negative urgency is concurrently associated with both binge eating and problem gambling, lack of persistence is concurrently associated with problem gambling only, and sensation seeking is not associated with either behavior. In longitudinal studies of undergraduate students, lack of persistence appears to predict future binge eating but not problem gambling, and negative urgency and sensation seeking do not appear to predict either behavior. It is likely that results from previous studies have varied because the studies examined different samples (i.e., undergraduate, community, clinical) and were composed of different proportions of women and men, different age ranges, and varying levels of disorder severity.
Present StudyThe aim of this study was to examine gender differences in the longitudinal personality predictors of binge eating and problem gambling in a large population-based sample of women and men using a sophisticated statistical approach. In this study we intended to address several limitations associated with existing research. One of the largest gaps in the existing literature on the association of impulsiveness with binge eating and problem gambling is the dearth of research on gender differences. Identifying gender differences in the predictors of these disorders could help explain the mixed results we have observed, and could help us develop more effective, tailored interventions by targeting traits that are most problematic for each gender.
A second limitation of the existing literature is that it is largely based on clinical and undergraduate samples, which may differ from the general population. Treatment-seeking individuals often have higher rates of psychiatric comorbidity and greater disorder severity than community dwellers (Berkson, 1946) and university samples tend to be unrepresentative of the general population (Henrich, Heine, & Norenzayan, 2010). Thus there is a need for greater reliance on community studies, especially those that are representative of the population at large.
A third limitation of the existing research is the lack of longitudinal studies. Cross-sectional studies do not allow us to determine whether impulsiveness predicts future binge eating and problem gambling or whether it simply co-occurs with these behaviors. To draw conclusions about causation or order effects, prospective studies are needed (Kraemer et al., 1997). The longitudinal studies conducted to date have exclusively used undergraduate samples with a restricted age range; it is important to ascertain whether their findings can be generalized to community samples of adults of varying ages.
Finally, the statistical approaches used in most existing studies have provided limited information. Zero-inflated regression analysis is a sophisticated statistical approach that operates under the assumption that different processes may be involved when predicting who engages in a given behavior versus who has more severe symptoms of the behavior among those who do engage in the behavior. Thus, this approach may provide a fine-grained understanding of the relationship between impulsiveness and both binge eating and problem gambling.
We posed three hypotheses: (1) Negative urgency would predict future binge eating and problem gambling; (2) lack of persistence would predict future binge eating and problem gambling; and (3) sensation seeking would not significantly predict either behavior. Although little research on gender differences exists, different subtypes of individuals with problem gambling appear to be characterized by different impulsivity profiles: Women tend to have higher rates of mood and anxiety disorders and tend to use gambling as a means of affect regulation, whereas men are more likely to be characterized by attentional problems, impulsivity, and risk taking (Blaszczynski & Nower, 2002). Based on previous research and the pathways model of problem gambling (Blaszczynski & Nower, 2002), we predicted that negative urgency would be a more prominent predictor of binge eating and problem gambling for women because it is tied to difficulties coping with strong negative emotions, and lack of persistence would be a more prominent predictor of these behaviors for men because it is tied to difficulties coping with boredom. Although the pathways model would predict increased sensation seeking among men with problem gambling, the existing literature does not support that prediction in community samples, so we expected that it would not be a prominent predictor for men in our study.
MethodOur sample was drawn from the Leisure, Lifestyle, Lifecycle Project (LLLP), a longitudinal cohort study among Albertans (for details, see El-Guebaly et al., 2008). The research protocol was approved by ethics committees at the Universities of Calgary, Alberta, and Lethbridge and all participants provided informed consent.
Leisure, Lifestyle, and Lifecycle Project
The LLLP study recruited 1284 participants from the general population and 524 at-risk gamblers who scored at or above the 70th percentile on gambling frequency or expenditure. Although the goal was to have participants divided equally between both genders and among five age groups (13–15, 18–20, 23–25, 43–45, 63–65), the 13–15-year-old (24%) and 43–45-year-old (22%) age groups were slightly overrepresented and there were slightly more female participants (53%) than male. Nevertheless, the sample largely reflected the Alberta population.
Eligibility criteria included living in Alberta for at least 3 months, age in one of five prespecified age cohorts, and a minimum fifth-grade reading level. Participants were excluded if they showed evidence of uncontrolled psychosis. At intake, participants completed a 45-min telephone interview and a 3-hr computer-based survey and face-to-face interview. Each participant was then contacted every 14 to 18 months for a total of four rounds (“waves”) of data collection. Data collection in succeeding waves was collected through an online survey or if requested, a paper copy was mailed along with a prepaid envelope to return the survey. The current study used self-report data obtained at Waves 1 and 3. The personality measure was completed at Wave 1 and the eating and gambling measures were completed at Wave 3, which occurred approximately 3 years later. Participants were paid $75 after completing Wave 1 and $45 after completing Wave 3.
Sample Characteristics/Participants
A total of 1372 adults completed Wave 1 and 1002 completed Wave 3, resulting in a 73% retention rate. A higher proportion of participants completing both assessments were female (OR = 1.65, 95% CI: 1.30–2.10), married (OR = 1.46, 95% CI: 1.15–1.87), older (OR = 1.93, 95% CI: 1.52–2.46), and more highly educated (OR = 2.58, 95% CI: 1.84–3.62). There were no differences between the two groups in employment status or household income. Participants completing both assessments endorsed fewer problem-gambling symptoms (d = .20, 95% CI: .07–.34) and had lower scores on negative urgency (d = .20, 95% CI: .08–.31), sensation seeking (d = .39, 95% CI: .27–.51), and lack of persistence (d = .12, 95% CI: .00–.24) than those completing Wave 1 alone. Although these differences generally corresponded to small to medium effect sizes , they suggest our sample was skewed toward older, married, and well-educated individuals.
The sample for this study included 1002 participants (596 women) who completed both Waves 1 and 3 of the LLLP study. Participants were drawn from the four adult age cohorts: 18–20 (n = 197), 23–25 (n = 238), 43–45 (n = 311), and 63–65 years old (n = 256). The majority of the sample was Caucasian (91.8%), had completed at least some college or university (84.3%), were employed full-time (49.4%) or part-time (19.6%), and had a household income greater than $60,000 (59.5%).
Measures
Selected questions from NEO-PI-R and NEO Five Factor Inventory (NEO-FFI-3)
Participants completed short versions of each domain of personality and an in-depth assessment of Neuroticism and Extraversion from the NEO-PI-R and NEO-FFI-3 (Costa & McCrae, 1992). Each item is rated on a 5-point scale. Four facets of impulsiveness (excluding positive urgency) can be assessed using the NEO-PI-R (Whiteside & Lynam, 2001). Scores on the impulsiveness facet of Neuroticism assessed negative urgency, scores on the excitement-seeking facet of Extraversion assessed sensation seeking, and scores on the self-discipline facet of Conscientiousness assessed lack of persistence. In the current study, we analyzed archival data which did not assess the fourth type of impulsiveness assessed by the NEO-PI-R, so we were unable to study lack of planning. Two impulsiveness items that specifically referred to overeating were removed to avoid redundancy in predicting binge eating. Scores on self-discipline were reverse-coded so that higher scores reflected higher levels of impulsiveness. Coefficient alphas for impulsiveness, excitement seeking, and self discipline were .72, .72, and .83.
Eating Disorder Examination–Questionnaire
The EDE-Q 6.0 (Fairburn, 2008; Fairburn & Beglin, 1994) is a 33-item self-report measure that assesses eating-disorder symptoms, including binge eating, in the past 28 days. Each item is rated on a 7-point scale. Coefficient alphas of the Global, Restraint, Eating Concern, Weight Concern, and Shape Concern subscales were .91, .79, .82, .84, and .91.
Problem Gambling Severity Index
The PGSI (Ferris & Wynne, 2001) is a 9-item measure that assesses problem-gambling symptoms in the past 12 months. Each item is rated on a 4-point scale. In this study, a problem-gambling symptom was coded as “present” if the individual reported experiencing the symptom sometimes, most of the time, or almost always and it was coded as “absent” if the individual reported never experiencing the problem-gambling symptom. Coefficient alpha for the PGSI was .84.
Statistical Analyses
In part because the complex sampling design tapped only certain age groups and oversampled at-risk gamblers, survey weights taking into account age, sex, geographical location, and the oversampling of at-risk gamblers were applied using data obtained from the Alberta Ministry of Health and Wellness to ensure the results more accurately reflected the entire population of Alberta adults.
Zero-inflated negative binomial (ZINB) and zero-inflated Poisson (ZIP) regression models were estimated using Mplus Version 7 (Muthén & Muthén, 1998–2012). These analyses are appropriate when the dependent variable is a count variable and when there is a large percentage of zero values (Hilbe, 2011). The independent variables were age, negative urgency, sensation seeking, and lack of persistence assessed at Wave 1. Age was included in each model because it is known to be associated with both binge eating and problem gambling (American Psychiatric Association, 2013). The dependent variables were the number of objective binge episodes in the past 28 days and the number of problem-gambling symptoms endorsed in the past 12 months assessed at Wave 3. Note that because continuous dependent variables are inappropriate for use with negative binomial and Poisson regression models, we did not use the total score of the PGSI as an outcome variable (Hilbe, 2011).
For each analysis in ZINB and ZIP regression, two models are estimated simultaneously: a logistic component and a count component. The logistic component estimates the probability of not engaging in the behavior and the count component estimates the association between the independent and dependent variables among those people who engage in the behavior (Atkins & Gallop, 2007; Hilbe, 2011). To simplify interpretation, odds ratios were inverted so that higher values indicated higher likelihood of engaging in binge eating or endorsing problem-gambling symptoms. Both models were estimated using maximum-likelihood estimation with robust standard errors.
The choice to use ZINB or ZIP models was made based on the significance of the dispersion statistic (Atkins & Gallop, 2007). Among women, the ZINB model was used for predicting the number of objective binge episodes and the ZIP model was used to predict the number of problem-gambling symptoms. Among men, the ZIP model was used to predict the number of problem-gambling symptoms. The logistic component of the ZINB model for objective binge episodes did not converge among males, so we used noninflated negative binomial models, which only predict the count component of the model. All analyses were run using the entire sample, and again after excluding those individuals who reported both binge eating and problem-gambling symptoms (BE + PG). The purpose of the latter analyses was to examine the degree to which results regarding binge eating and problem gambling were influenced by comorbid symptoms of the other behavior.
Results Descriptive Statistics
Among those who completed the binge-eating assessment at Wave 3, approximately one quarter of women and 10% of men reported past-month binge episodes (see Table 1). Among those who completed the problem-gambling assessment at Wave 3, approximately one quarter of women and one third of men reported past-year problem-gambling symptoms. Fifty-two women (9.1%) and 21 men (5.3%) reported comorbid binge eating and problem-gambling symptoms. Using Spearman’s rho, binge eating and problem gambling were positively associated with negative urgency and lack of persistence among women and men (see Table 2). Among men only, problem gambling was positively associated with sensation seeking. Among women, binge eating and problem gambling were negatively associated with age. Binge eating and problem gambling were positively correlated among women and men. These effects were small to medium.
Descriptive Statistics for Binge-Eating Episodes and Problem-Gambling Symptoms Divided by Gender
Intercorrelations of Age, Impulsiveness, Binge-Eating and Gambling Variables Divided by Gender
Analyses Using the Total Sample
Binge eating among women
Age and negative urgency at Wave 1 predicted the presence of binge eating at Wave 3 (see Table 3). Every 1-year increase in age decreased the odds of binge eating by 4%, whereas a one-unit increase in negative urgency increased the odds of binge eating by 21%. There were no significant predictors of increased severity among those who reported engaging in binge eating.
Zero-Inflated Negative Binomial Regression Models of Objective Binge Episode Frequency Among Women and Men
Binge eating among men
Age, negative urgency, and lack of persistence at Wave 1 were significantly associated with severity of binge eating at Wave 3 (see Table 3). A 1-year increase in age was associated with a 5% increase in reported binge episodes. A one-unit increase in negative urgency was associated with a 22% increase in the number of reported binge episodes and a one-unit increase in lack of persistence was associated with a 20% increase in the number of reported binge episodes.
Problem gambling among women
Negative urgency at Wave 1 predicted the presence of problem-gambling symptoms at Wave 3 (see Table 4). Every one-unit increase in negative urgency increased the odds of endorsing problem-gambling symptoms by 12%. Only age at Wave 1 predicted increased severity among those who reported problem-gambling symptoms at Wave 3: A 1-year increase in age was associated with a 2% increase in the number of reported problem-gambling symptoms.
Zero-Inflated Poisson Regression Models of Problem Gambling Among Women and Men
Problem gambling among men
Sensation seeking at Wave 1 was the only significant predictor of problem-gambling symptoms at Wave 3 (see Table 4). A one-unit increase in sensation seeking increased the odds of endorsing problem-gambling symptoms by 9%. Negative urgency at Wave 1 predicted increased severity among those who reported problem-gambling symptoms at Wave 3: A one-unit increase in negative urgency was associated with an 8% increase in the number of reported problem-gambling symptoms.
Analyses Excluding Those With BE + PG
Binge eating
When those with comorbid BE + PG were excluded from analyses, the same pattern of results emerged for women and men with one exception. In both models, elevated sensation-seeking scores were associated with reduced binge eating. Among women, a one-unit increase in sensation seeking decreased the odds of binge eating by 8% (B = .081, OR = .92, z = 2.17) and among men, a one-unit increase in sensation seeking was associated with an 18% decrease in the number of reported binge episodes (B = −.20, z = −2.284).
Problem gambling
When those with comorbid BE + PG were excluded from analyses, there were no significant predictors of future problem-gambling symptoms among women or men.
DiscussionThis study was an investigation of the degree to which three facets of impulsiveness predicted the presence and severity of future binge-eating and problem-gambling symptoms in a population-based sample of women and men. Negative urgency was a common predictor of future binge eating and problem gambling in women and men. Among men, negative urgency predicted increased severity of binge eating and problem gambling. Among women, high levels of negative urgency predicted increased odds of engaging in each behavior; however, it was not associated with increases in severity among those who engaged in the behaviors, consistent with the findings of Fischer et al. (2013). It is possible that our study did not replicate previous findings of positive associations of negative urgency with the severity of addictive behaviors and binge eating (Fischer & Smith, 2008) because we used a different statistical approach. By separating those who engaged in each behavior from those who did not, ZINB/ZIP models provided a more nuanced understanding of the association between impulsiveness and both binge eating and problem gambling. Our findings generally suggest that negative urgency places women at risk for engaging in each behavior, but if they have already begun binge eating or endorsed symptoms of problem gambling, other factors may be associated with increases in severity, perhaps such as daily fluctuations in negative affect or stress levels (Berg et al., 2013).
As expected, negative urgency was the strongest risk factor for binge eating and problem gambling among women; however, unexpectedly, it was also the strongest risk factor for each behavior among men. Our findings are consistent with cross-sectional studies of women and men that have shown that high levels of negative urgency are associated with many types of psychopathology, including binge eating and purging, alcohol and substance abuse, problem gambling, nonsuicidal self-injury, and symptoms of personality disorder (Fischer et al., 2008; MacLaren et al., 2011; Mullins-Sweatt, Lengel, & Grant, 2013; Peters, Upton, & Baer, 2013; Ruiz, Pincus, & Schinka, 2008; Widiger & Costa, 1994).
We did not find any disorder-specific personality traits associated with binge eating or problem gambling among women. However, our results showed that age discriminated between women who engaged in binge eating versus problem gambling. Women who were younger were more likely to report binge eating, whereas women who were older reported more problem-gambling symptoms. These age-related findings are consistent with existing research that suggests that binge eating begins at an earlier age (e.g., mid-20s) compared with problem gambling (e.g., middle age; American Psychiatric Association, 2013).
We found evidence of disorder-specific personality predictors of each behavior among men only. Lack of persistence was a significant predictor of binge eating, consistent with our hypothesis that lack of persistence would be a more prominent predictor for men. This pattern of association is partially consistent with results from a longitudinal study conducted by Peterson and Fischer (2012) in which lack of persistence significantly predicted future binge eating; however, our results also differ from those of Peterson and Fischer (2012) because we did not find a significant relationship between lack of persistence and binge eating among women. In addition, high levels of sensation seeking predicted increased odds of endorsing problem-gambling symptoms in men. This was an unexpected finding because the vast majority of cross-sectional research has failed to find significant associations between sensation seeking and problem gambling (e.g., Fischer & Smith, 2008; Reid et al., 2011), and the only previous longitudinal study found that sensation seeking was not a significant predictor of increases in gambling behavior over time (Cyders & Smith, 2008). However, the differences were numerous between our study and that of Cyders and Smith (2008), which may help explain the discrepant findings, including differences in sample composition (i.e., undergraduate vs. community), sample age ranges (i.e., young adults vs. adults 18–65 years old), different outcome variables (gambling engagement vs. gambling problems), and different follow-up periods (i.e., 8 months vs. 3 years). Perhaps most important, our research questions differed from Cyders and Smith, in that in our study, we examined whether sensation seeking predicted future problem-gambling symptoms, and Cyders and Smith investigated whether sensation seeking predicted changes in gambling behaviors over time. Our sample is arguably more generalizable than those of previous studies because it included a large sample of men from various age groups in the community and therefore, our results may provide a more accurate picture of the relationship between sensation seeking and problem gambling among adult men.
Another unexpected finding was that elevated sensation seeking scores were associated with decreased risk of binge eating among women and men who did not endorse problem-gambling symptoms. The vast majority of existing research has found nonsignificant associations between sensation seeking and binge eating (e.g., Peterson & Fischer, 2012) and a meta-analysis conducted by Fischer et al. (2008) found a small but significant positive association between sensation seeking and symptoms of bulimia nervosa. Our results may differ from those of previous studies because our study included women and men from various age ranges, we examined the symptom of binge eating outside the context of bulimia nervosa, other studies did not exclude individuals with comorbid problem-gambling symptoms, and/or because we used a different statistical technique. More research is needed to examine whether sensation seeking is a risk factor or a protective factor for binge eating, as this distinction is important for informing treatment and prevention efforts.
The findings from this study have important implications for the design of gender-specific treatments. They suggest that the specific impulsiveness traits that are targeted in treatment may need to vary depending upon the gender of the client. It is important to note that, because binge eating and problem gambling were both associated with the same personality facet (i.e., negative urgency), our results suggest a need to treat the underlying vulnerability associated with a particular behavior rather than simply focusing on the behavior itself. For example, a treatment program that just focused on reducing binge eating without addressing negative urgency might result in the individual transferring his or her impulsive tendencies onto a new behavior (i.e., gambling) when they feel strong negative emotions. In fact, there is evidence of addiction transfer in women who have received bariatric surgery and men in recovery from substance addiction. Two studies found increased substance-use problems among women who had received bariatric surgery, the majority of whom (68% to 70%) did not have any problems with substance use prior to the surgery (Fogger & McGuinness, 2012; Reslan, Saules, Greenwald, & Schuh, 2014). In another study, recovering men with substance addictions reported using food as a substitute for drugs, as well as increased binge eating, in early recovery (Cowan & Devine, 2008). Thus individuals who receive treatment for problems related to eating and substance may be at increased risk of developing additional problematic behaviors in recovery.
A key strength of this study is that the sample included large numbers of women and men, which allowed us to systematically investigate gender differences in risk factors for binge eating and problem gambling. In addition, using a population-based sample with a wide range of ages means the results are generalizable to adult community dwellers, particularly due to our use of sampling weights. Nevertheless, we note five limitations to this study. First, although research indicates that there are numerous facets of impulsiveness that can be assessed using either self-report measures or behavioral tasks (Cyders et al., 2007; Voon et al., 2014), the current study only examined three of these facets using self-report measures. Future studies should evaluate facets of impulsiveness not studied here such as lack of planning and positive urgency (i.e., tendency to engage in rash action when experiencing strong positive emotions) and incorporate behavioral measures of impulsiveness to gain a more comprehensive understanding of the role that impulsiveness plays in different types of disorders. Second, our sample was composed of primarily older, White, well-educated individuals, so our results should be extrapolated with caution to younger, non-White, less-educated individuals. Third, as research has shown that respondents often report higher levels of binge eating on the EDE-Q (Fairburn, 2008) compared with the EDE interview (Celio, Wilfley, Crow, Mitchell, & Walsh, 2004) because of broader definitions of binge eating by community members versus eating-disorder experts, our results should be interpreted with caution (Celio et al., 2004). Fourth, we did not assess other psychiatric disorders and so we were unable to determine the extent to which other comorbidities could have had an influence on binge eating or problem gambling. Finally, binge eating was assessed over a 28-day time frame, whereas problem gambling was assessed over a 12-month time frame, which limits our ability to draw direct comparisons between these behaviors.
ConclusionBased on these findings, we can conclude that there are transdiagnostic and disorder-specific facets of impulsivity that predict future binge eating and problem gambling. Negative urgency represents a shared vulnerability for binge eating and problem gambling in women and men. We found evidence of gender differences in certain disorder-specific traits: Among men, lack of persistence predicted binge eating and sensation seeking predicted problem gambling. We found no disorder-specific personality predictors for women; however, younger age predicted binge eating and older age predicted problem gambling. Identification of disorder-specific traits may help explain why individuals may engage in one behavior over another. For example, men who have difficulty persisting on tasks may be more likely to binge eat whereas men with elevated levels of sensation seeking may be more prone to gamble.
Overall, the results of this study do not provide clear support for the conceptualization of binge eating as an addiction. Although we found evidence of shared personality traits between binge eating and problem gambling, these traits are related to many forms of psychopathology and are not specific to addiction (Ruiz et al., 2008; Widiger & Costa, 1994). In addition, we found evidence of personality facets specifically related to problem gambling (sensation seeking) and binge eating (lack of persistence) among men, which demonstrates that the two behaviors have distinct personality correlates. Sensation seeking is a personality characteristic that has also been associated with alcohol abuse (Curcio & George, 2011; Cyders, Flory, Rainer, & Smith, 2009) and therefore may represent a trait that is specifically associated with other addictions. The relationship between personality and psychopathology is complex: Personality traits could serve as risk factors for, or consequences of, particular disorders, or both personality traits and the given disorder could be caused by a third variable, such as poor self-regulation (Lilenfeld, Wonderlich, Riso, Crosby, & Mitchell, 2006). Thus future researchers need to investigate similarities and differences between binge eating and other substance and behavioral addictions to more fully evaluate the validity of conceptualizing binge eating as an addiction.
Footnotes 1 According to Cohen (1992), d = 0.2 corresponds to a small effect size, d = 0.5 corresponds to a medium effect size, and d = 0.8 corresponds to a large effect size.
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Submitted: September 3, 2014 Revised: January 23, 2015 Accepted: February 3, 2015
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Source: Psychology of Addictive Behaviors. Vol. 29. (3), Sep, 2015 pp. 805-812)
Accession Number: 2015-20856-001
Digital Object Identifier: 10.1037/adb0000069
Record: 170- Title:
- The influence of self-exempting beliefs and social networks on daily smoking: A mediation relationship explored.
- Authors:
- Yang, Xiaozhao Y.. Department of Sociology, Purdue University, IN, US
Kelly, Brian C.. Department of Sociology, Purdue University, IN, US
Yang, Tingzhong. Research Center for Tobacco Control, Zhejiang University, Zhejiang, China, tingzhongyang@zju.edu.cn - Address:
- Yang, Tingzhong, Zhejiang University, Research Center for Tobacco Control, Hangzhou, Zhejiang, China, 310058, tingzhongyang@zju.edu.cn
- Source:
- Psychology of Addictive Behaviors, Vol 28(3), Sep, 2014. pp. 921-927.
- NLM Title Abbreviation:
- Psychol Addict Behav
- Page Count:
- 7
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- Bulletin of the Society of Psychologists in Addictive Behaviors; Bulletin of the Society of Psychologists in Substance Abuse
- Other Publishers:
- US : Educational Publishing Foundation
Society of Psychologists in Addictive Behaviors - ISSN:
- 0893-164X (Print)
1939-1501 (Electronic) - Language:
- English
- Keywords:
- smoking, health beliefs, mediation analysis, self-exempting beliefs, social networks
- Abstract:
- The decision to initiate, maintain, or quit cigarette smoking is structured by both social networks and health beliefs. Self-exempting beliefs affect people’s decisions in favor of a behavior even when they recognize the harm associated with it. This study incorporated the literatures on social networks and self-exempting beliefs to study the problem of daily smoking by exploring their mediatory relationships and the mechanisms of how smoking behavior is developed and maintained. Specifically, this article hypothesizes that social networks affect daily smoking directly as well as indirectly by facilitating the formation of self-exempting beliefs. The sample comes from urban male residents in Hangzhou, China randomly selected and interviewed through multistage sampling in 2011. Using binary mediation analysis with logistic regression to test the hypotheses, the authors found that (a) daily smoking is associated with having smokers in several social network arenas and (b) self-exempting beliefs about smoking mediate the association between coworker network and daily smoking, but not for family network and friend network. The role of social network at work place in the creation and maintenance of self-exempting beliefs should be considered by policymakers, prevention experts, and interventionists. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Health Attitudes; *Mediation; *Social Networks; *Tobacco Smoking; Analysis
- Medical Subject Headings (MeSH):
- Adolescent; Adult; Aged; Aged, 80 and over; Attitude to Health; China; Family; Friends; Humans; Logistic Models; Male; Middle Aged; Negotiating; Smoking; Smoking Cessation; Social Support; Tobacco Use Disorder; Urban Population; Workplace; Young Adult
- PsycINFO Classification:
- Substance Abuse & Addiction (3233)
- Population:
- Human
Male
Female - Location:
- China
- Age Group:
- Adolescence (13-17 yrs)
Adulthood (18 yrs & older)
Young Adulthood (18-29 yrs)
Thirties (30-39 yrs)
Middle Age (40-64 yrs)
Aged (65 yrs & older)
Very Old (85 yrs & older) - Tests & Measures:
- Interviewer-Administered Questionnaire
- Methodology:
- Empirical Study; Quantitative Study
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- First Posted: Aug 18, 2014; Accepted: Apr 30, 2014; Revised: Mar 31, 2014; First Submitted: Nov 14, 2013
- Release Date:
- 20140818
- Correction Date:
- 20140915
- Copyright:
- American Psychological Association. 2014
- Digital Object Identifier:
- http://dx.doi.org/10.1037/a0037176
- PMID:
- 25134037
- Accession Number:
- 2014-33502-001
- Number of Citations in Source:
- 50
- Persistent link to this record (Permalink):
- http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2014-33502-001&site=ehost-live
- Cut and Paste:
- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2014-33502-001&site=ehost-live">The influence of self-exempting beliefs and social networks on daily smoking: A mediation relationship explored.</A>
- Database:
- PsycINFO
The Influence of Self-Exempting Beliefs and Social Networks on Daily Smoking: A Mediation Relationship Explored / BRIEF REPORT
By: Xiaozhao Y. Yang
Department of Sociology, Purdue University
Brian C. Kelly
Department of Sociology, Purdue University
Tingzhong Yang
Research Center for Tobacco Control, Zhejiang University;
Acknowledgement:
Smoking, like many other health behaviors, is subject to the complex interaction of social influences and psychological processes. Intervention through policy is a necessary action but a difficult to carry out, especially when professional conceptions confront lay understandings. People tend to be reluctant to act upon mass communication messages unless the information is transmitted via their own social networks (Katz & Lazarsfeld, 1955; Bandura, 2004). Sometimes implemented policy even creates contrary effects due to a marginalizing impact, thus making certain groups harder to reach and more policy-resistant when reached (Graham et al., 2006; Room, 2005; Stuber, Galea & Link, 2008; Constance & Peretti-Watel, 2010). Given the contextualized nature of policy uptake, policy implementation that targets tobacco users must be attentive to the social network composition of smokers and its effect on their health beliefs. This study identifies how social networks influence smoking through a particular set of health beliefs: self-exempting beliefs (SEB).
SEB and SmokingAlthough the physical health effects of consuming tobacco has a medical basis, the belief in such basis is a sociopsychological construct. Festinger (1957) proposed the concept of SEB: When people receive messages about the risk of their behavior, many of which refer to scientific evidence, instead of surrendering to the message and changing their behavior, they resort to beliefs that exempt themselves to mitigate the undesirability of such behavior. People may actively use their own evidence contrary to medical professional recommendations or acknowledge professional suggestions but argue for the exception for themselves. For example, smokers may develop explanations for why medical evidences do not apply to them (Heikkinen et al., 2010; Oakes, Chapman, Borland, Balmford, & Trotter, 2004). SEB reduce the threat to self-integrity and its resulting behavioral adaption; they may rise à posteriori to justify the existing behavior or strengthen the behavioral continuance to weaken the necessity of quitting (Radtke, Scholz, Keller, & Hornung, 2011); they could also spread as innovative messages that ease the anxiety about tobacco harm and recruit others into cigarette smoking. SEB also emerge when smoking can be used as a useful label of identity and provide a form of social etiquette for instrumental benefits (Collins, Maguire, & O’Dell, 2002). Some people who possess SEB try to distance themselves from the addiction stereotype but nevertheless continue to use cigarettes because of their supposed normative status and predominance in social interaction. In this aspect, men in Chinese society routinely use the phenomenon of courtesy smoking and gifting cigarettes, where cigarettes are used in daily interactions among men, welcoming guests, or as bribe (Ma et al., 2008; Rich & Xiao, 2012). Sociologists (Swidler, 1986; DiMaggio, 2001; Vuolo, 2012) have argued that culture constitutes an elastic reservoir for individuals to choose according to their needs when values are in conflict. Thus, culture also plays an important role in creating concrete SEB. For example, Wright illustrated with ample descriptions of how farmers in Kentucky justify tobacco cultivation and consumption as a community tradition and cultural necessity (Wright, 2005). Other scholars (e.g., Manderson, 1981; Jackson et al., 2004) discovered that beliefs in nonharmful ways of tobacco smoking, based on the semireligious folk classification of tobacco as food, indirectly legitimizes smoking as normal daily conduct. Scholars have reported SEB about smoking are associated with education, age, and other background factors (Chapman et al., 1993; Oakes et al., 2004; Heikkinen et al., 2010).
Social Networks and SmokingSocial networks matter because different types of connections have different influences on behaviors. Social networks provide contexts where communications, consolidations of beliefs, and daily interactions are performed. Sutherland and Cressey (1970) argued that typical delinquent behaviors are gradually formed by both peer pressure and the changed attitude from imitating and learning from peers. When people cluster within networks with smokers, the observation of smoking behavior is repeated and normalized to a degree that not only will the perceived normative status of smoking be confirmed in their cognition, but justifications of such behavior could also be mutually reinforced by people who smoke within the social network. Scholars have argued the strength of ties in a network has the unique function to influence beliefs and behavior, where weak ties facilitate heterogeneous information and behavior, strong ties generate conformity to existing norms (Granovetter, 1973; Baer, 2010). Thus the impact on smoking by networks composed of different types of ties, weak versus strong, family versus friends, distant versus proximate, can too be different.
A number of studies have also discussed the impact of different types of social network ties on smoking. Family influence is strongest at earlier stages of the life course but is replaced by peer influences as the individual grows older (Glynn, 1981; Krosnick & Judd, 1982; Perry, Kelder, & Komro, 1993); although some studies have found that parental influence does not entirely diminish after years (Chassin et al., 1986; de Vries et al., 2003). Another study by Christakis and Fowler (2008), with specific attention to smoking networks, revealed that cessation is most likely to occur when one’s spouse stopped smoking. In this manner, both natal families and marital families matter. Moreover, a network’s impact on an individual’s smoking probability also differs across social settings: such as the work unit, family, or a cultural setting. For example, family members’ attitudes and intervention constitute the strongest predictor of Chinese men’s smoking cessation (Yang et al., 2006; Zhang et al., 2012).
SEB are arguably developed not only as a defensive mechanism but also as part of social routines within social networks. Currently, there is no study that has investigated SEB as contextualized in social networks, though it has been noted that cognitive dissonance, risk perception, and behavior motivation are joint products of social and psychological mechanisms (Bandura, 2004). The perception of health risk is organized and transmitted by network interactions, built upon the consistently perceived attitude and (mis)information grouped in cliques (Scherer & Cho, 2003; Helleringer & Kohler, 2005; Kohler, Behrman & Watkins, 2007). Thus, individuals can develop SEB, particularly the culturally specific content of such beliefs, from family members’ gradual socialization throughout years, daily communication with friends, integration into a subculture, or influence by those who work closely with him. Some have demonstrated that smokers rarely exert direct pressure on their nonsmoking peers, and the latter initiated smoking rather because the discouraging message is scarcely received (Urberg et al., 1990). Others suggest (Kelly, 2009; Constance & Peretti-Watel, 2010) that friends may express justifications for their smoking peers vis-à-vis coercive policies. Therefore, holding certain SEB does not necessarily require the status of being a smoker. Instead, being tied to smoking networks alone can locate a nonsmoker in the midst of messages and information shared by smokers and increase his likelihood of having SEB. Thus, SEB are more likely to occur in contexts where one’s network ties include smokers. As a result, how social networks affect smoking behavior would be mediated by SEB
Based upon the literature reviewed above, we propose the following hypotheses:
H1: Men with higher levels of SEB are more likely to be daily smokers.
H2a: Men reporting smokers in their family network are more likely to be daily smokers.
H2b: Men reporting smokers in their friend network are more likely to be daily smokers.
H2c: Men reporting smokers in their coworker networks are more likely to be daily smokers.
H3: SEB will mediate the association between three social network types and smoking status.
Methodology Sampling
A multistage sampling design was employed to collect data during the summer of 2011. At Stage 1, we randomly selected two residential districts (qu) of Hangzhou; at Stage 2, we randomly selected two subdistricts (jiedao) from a district; then two to three communities (shequ) within each subdistrict at Stage 3. Hangzhou is located in southeast China with a population of 6.7 million, it has six districts and 16–22 communities within each district. The Community Committee Office randomly sampled households in each community, and these households were distributed across each community in approximate proportion to their estimated overall distribution across the city cluster of communities. Participants in the study were sampled independently within these clusters. The inclusion criterion was being a resident aged 15 years or older. One eligible resident from each household was selected into the study, based on nearest birthdate to the interviewing date. We scheduled a face-to-face individual survey once an eligible individual was identified and agreed to study participation. All surveys were conducted by means of a structured, interviewer-administered questionnaire. Surveyors were second-year medical graduate students or fourth-year medical students. Each surveyor completed a training on the study protocol and survey procedures prior to working on the study. Questionnaires were administered privately to participants in their home or in a quiet place, such as a backyard or community park. Appointments were scheduled through a community organization and were rescheduled as necessary. Upon receiving instructions from surveyors, participants were asked to fill out a questionnaire of approximately 30 minutes’ duration. Each participant was afforded an opportunity to seek clarification of questions regarding the survey or questionnaire items, and given adequate time for completion. The protocol was approved by the Ethics Committee at the Medical Center, Zhejiang University, and we obtained informed written consent from all participants prior to interview. The total sample size yielded was 669. The sample’s demographic characteristics are shown in Table 1.
Descriptive Demographics
Measurement
SEB
SEB are measured by 20 items. Each item asked respondents to rate their agreement on the statement in a 5-point Likert scale ranging from 1 (strongly disagree) to 5 (strongly agree). Eighteen items are adopted from Oakes’s study (Oakes et al., 2004), such as “The scientific evidence about tobacco’s harm is exaggerated,” “Many smokers are very healthy so smoking can’t be so harmful,” “My current smoking amount is too low to be considered risky,” “You have got to die of something, so why not enjoy yourself and smoke,” “Smoking is no more risky than lots of other things that people do,” and so forth. Two additional items to represent the instrumental dimension of SEB were included after pilot tests within local exploratory studies (Ma et al., 2008; Yang et al., 2006), “smoking is good for socializing” and “smoking is good for reducing stress.”
Network measures
Smoking social networks are measured by three questions, representing three types of ties. The strongest ties in Chinese culture usually refer to the immediate family, so the questionnaire first asks if there is anyone who smokes in respondent’s household. Please note, word household (jia) in the Chinese language has the same connotation with family (jia). There is another word (qinqi) for extended family who usually live somewhere else. This term jia is indicative of the family with whom the individual shares a household. In this instance, family household may include one’s nuclear family, parents, or possibly siblings. The second question asks if the respondent’s friends smoke. The third question intends to represent formal weak ties to smoking associates: Does any coworker who shares an office or working space with the respondent smoke.
Smoking status measures
Smoking status of the respondent is first assessed by a question that asks respondent’s current smoking status: 1, smoke everyday; 2, smoke some days; 3, don’t smoke now. Considering the high prevalence of Chinese male smoking and the normative status of cigarettes in social etiquette, it is often futile to distinguish occasional smokers versus nonsmokers, as well as smoking under pressure versus absolute abstinence among men, therefore it is most important to distinguish daily habitual smokers from nondaily smokers. Furthermore, Brant test of parallel regression assumption dictates the ordinal treatment of the dependent variable inappropriate (Long & Freese, 2006). Therefore people who answered they smoke everyday are coded as a daily smoker (1), and the rest of them are grouped as not daily smokers (0).
Demographic control measures
Demographic indicators were assessed in the section “individual and family background” of the survey, including sex, age, income, marriage status, ethnicity, and education. Religious belief is simply dichotomized as “Do you have any religious beliefs or not,” because of the sensitive nature of religion and lack of major religious beliefs in the survey context.
Analytic Method
To confirm the reliability of the SEB measure, a Cronbach’s alpha test was performed on the 20 SEB items. Cronbach’s alpha over 0.70 suggests acceptable level of reliability; that which over 0.80 suggests considerably good reliability. Cronbach’s alpha test of our SEB measures (0.92) suggests excellent reliability.
To conduct mediation analyses, we combined the classic mediation model proposed by Baron and Kenny (1986)—which emphasized the reduction of coefficients and p value for the independent variable after mediator is introduced—with survey sample weighted bootstrap to estimate the mediation effects and coefficients. Recently scholars argued that observing the change of power by the classic approach does not suffice to establish mediation for two main reasons: First, it is possible to have significance reduction without considerable effect size change, or considerable effect size change without reducing significance; second, under Baron and Kenny’s classic approach, people often erroneously treat the mediator as a control variable, or vice versa (Zhao et al., 2010). As a result, Preacher and Hayes (2008) proved that a significance test on the indirect effect can avoid Type I error and is more straightforward than the classic approach. Therefore, we follow the recent technique of using bootstrap to test the significance of indirect effect for the mediation analyses (Bollen & Stine, 1990; Shrout & Bolger, 2002; MacKinnon et al., 2002).
Another benefit of using bootstrap resampling is its capacity to deal with multistage cluster sampling. The svy bootstrap command in Stata also allows us to deal with complex data structure set by sample weighting, plus using bootstrap to resample observations. In this study, district, neighborhood, and community, are the clusters specified for bootstrap resampling. Sample weight is calculated as the inverse product of the probabilities that each level’s unit is selected from its population: within n districts, l communities are in jth neighborhood, and m neighborhoods from ith district, weight Wij equals
, where the population total at each level is attained from various bureau websites. Binary mediation logistic regression was performed in STATA 11 for the analyses, then we use survey bootstrap to derive the final coefficients and 95% confidence intervals. Adjusted odds ratios, significance level at 95%, indirect effect, and ratio of indirect to direct effect are reported.
Results Sample Characteristics
The percentage of cases, sample mean, standard deviation, and range are presented for both main variables and relevant demographic characteristics in Table 1. The outcome variable, smoking status, shows an approximate Pareto number: 39.5 of all males in the sample are self-described as smoking on a daily basis, whereas 60.5% of them are not daily smokers. The ratio of daily smoking generally conforms with recent studies in the same city (T. Yang et al., 2007; Yang et al., 2010). Missing data is negligible among all the variables as only three cases were missing from the SEB instrument.
Smoking is common in social networks. In this sample, 53.9% of male respondents reported having a smoker in their families, 91.7% reported having a smoker among their friends, and 71.8% indicated having a smoker among coworkers. The mean of SEB among men is 2.09, standard deviation is 0.78, and the SEB scale ranges from 1 to 5.
The socioeconomic background of the sampled male population is diverse although all respondents had the same background as having been a resident in Hangzhou for at least 1 year. The largest proportion are those who claimed yearly household income per capita between 20,000 and 30,000 Yuan (25.6%), about a quarter of all males belong to this income category. In terms of education, 10.6% had received only elementary education or below, 29.5% went to middle school, 24.6% went to high school, and 35.4% received a college degree. Among sampled males, 24.6% are unmarried, 72.4% are married, and the remainder (3.1%) are divorced or widowed. The mean age of the sample is 42 (SD = 16), respondent ages ranged from 15 to 87 years old. As for religion, 13.7% reported having religious beliefs, 86.3% reported no religion.
Findings
Table 2 shows that higher SEB is associated with higher odds of being a daily smoker. This relationship is considerably strong and straightforward after controlling for other demographic influences including income, age, marriage, religious beliefs, and education. The odds ratio of being a daily smoker for SEB is 3.52 (p < .001), which implies that with each higher level of SEB, the odds of being daily smoker is 3.52 times higher. Additionally worth noting is the impact of income, age, and education: Income increases the likelihood of being daily smoker while older age and education reduces it.
Logistic Regression Analyses of Daily Smoker Status on Self-Exempting Beliefs (SEB) and Social Networks Independently
The association of each type of social networks and smoking status is analyzed independent from SEB while controlling for the same demographic factors and shown in Table 2. As expected, the presence of smokers in all three network types independently increase the odds of daily smoking. Having a smoker in one’s coworker network has the largest association with the likelihood of being daily smoker in terms of odds ratio magnitude (OR = 4.74, p < .001), followed by the friend network (OR = 3.73, p < .05) and family network (OR = 2.65, p < .001). The second tier of hypotheses (H2a, H2b, and H2c) is hence validated by the statistics presented in the first model of Table 3: Men are more likely to be daily smokers when having a smoker in his family, friend, or coworker network, net of the effect of the control variables.
Unmediated models, Self-Exempting Beliefs (SEB)-Mediated Models, by Types of Social Networks
The upper part of Table 3 shows the unmediated logistic regression of smoking status on social networks, each network is estimated controlling for the other types of networks and demographic variables. The unmediated models in Table 3 are exactly the same, they are listed separately for the sake of convenience to compare with the mediated models. The unmediated model shows that family network and coworker network are significantly associated with being daily smoker after controlling for the other network types and demographic variables. Having smoking family member increases the probability of being a daily smoker by 2.10 (p < .01), and having smoking coworker increases the probability of being a daily smoker by 3.94 (p < .001). Friend network is not significantly associated with being daily smoker after controlling for the other network types, a difference from Table 2 where only demographic variables are controlled.
The lower part of Table 3 shows the mediation analyses where social networks are mediated by SEB, controlling for the other network types and demographic variables. The classic approach expects reduction in p value or the magnitude of odds ratio of social network when SEB was added to the regression, and the survey weighted bootstrap method calculates the odds ratio and indirect effects. The magnitude of odds ratio of smoking status on family network actually increased after SEB was introduced as a mediator (2.23 vs. 2.10), and the 95% confidence interval of the indirect effect overlaps zero. The ratio of indirect effect to direct effect for the family network model is also minimal (0.01). We conclude there is no mediation effect in the family network model. For the friend network model, indirect effect is too not significant. The ratio of indirect effect to direct effect is large in absolute value (2.02), but indeed originates from an inconsistent mediation in this case (MacKinnon, Fairchild, & Fritz, 2007). Because friend network itself is not significantly associated with daily smoking, we conclude there is no mediation effect for the friend network model. However, for the coworker network, odds ratio drops from 3.94 to 3.53 after SEB was introduced. The indirect effect has an above zero 95% confidence interval (0.03–0.10), the indirect/direct effect ratio is 0.23. The result suggests that SEB partially mediates coworker network and smoking status, after controlling for the other network types and demographic variables.
DiscussionThis study was designed to examine how SEB are related to daily smoking, and whether the relationship between social network types and smoking are mediated by SEB. Previous studies had theoretically established the relationship between SEB and smoking initiation, continuation, and cessation. Relatively few investigations about SEB and smoking have been conducted in developing countries, and we are curious about how the normative status of cigarettes in such societies may shape the understanding of smoking behavior among smokers and nonsmokers alike. Our study attends to these issues as well.
This study, which adopted an existing SEB scale and incorporated new items for the instrumental dimension of SEB, has demonstrated a strong relationship between being a daily smoker and having higher levels of SEB. In contexts such as China, where cigarette use is normative but antitobacco policy and prevention efforts are also taking place at the same time, lay understandings and views of smoking are very ambiguous. Historically, tobacco was prescribed to people as medicament because of its assumed function to balance the corporeal air system (qi) in the philosophy of Chinese traditional medicine (Du, 2000). Dikötter, Laamann, & Xun (2002) also argued that, besides its assumed medical benefit, the quick and vast acceptance of tobacco in China was closely associated with the phenomenological meaning of smoke (air or qi) found in Chinese folk religion’s evil dispelling ceremony. This mythical form of belief persists, and culminated during the 2003 SARS outbreak when people circulated the message that smoking cigarettes and burning incense can prevent SARS (Tai & Sun, 2011). Ma’s exploratory study (Ma et al., 2008) informs us that the most common SEB about smoking among Chinese males include the importance of cigarettes in social and cultural etiquette. Although concrete beliefs in the legitimacy of tobacco consumption may be varying, they all demonstrated that traditional culture in many cases could provide the fundamental basis of SEB among men in China. People respond to tobacco policy positively overall (X. Y. Yang, Anderson, & Yang, 2014), but they also condone smoking as a necessary part of social etiquette, gender identity, or existing tradition. Tobacco policymakers need to be aware of the existence of SEB and consider counteractive measures.
The study results showed that smoking in each network included in the hypotheses exhibited strong positive association with being a daily smoker when estimated independent of the other types of networks. It is beyond the scope of this article to rule out the auto-selection effect of smoking behavior, but it is possible to reason that although friend networks can be formulated initially by individual selection of the homophily principle, that is, “birds of the same feather, fly together.” Individuals have much less control over which working unit or family they are embedded in. Apart from the direct effect of social networks on smoking behavior, the research results discovered the significant indirect effect of SEB as a mediator between coworker networks and smoking behavior. But SEB’s mediation is not significant for family networks and friend networks, therefore it can be inferred that a coworker network, as a deposit of weak ties, can influence the individual to smoke through the development of SEB, which partially confirmed hypothesis H3. One can argue that because our respondents are urban male residents in China, their referred smokers in family networks are likely offspring or parents, but rarely siblings or spouses due to one child policy and the fact that few women smoke in China. Network alters such as parents and offspring fill in the family roles that are less conducive to spread beliefs due to role expectations and the generation gap. On the other hand, the definition of a friend may require further refinement to distinguish it as an effective carrier of SEB. Coworker corresponds to a type of weak tie (low in emotional intense and interaction frequency), which scholars argued is prone to transmit information. (Granovetter, 1973)
Although there are many strengths, this current study has a few limitations. First, the friend network measured in this study could be defined more specifically as different types and the count of friends rather than being categorized under the broad term friend. Thus we may discern more fundamental differences between the smoking patterns of a coworker network and friend network. Second, although we have argued that family network and coworker network are not likely caused by smoking as a result of homophily, we cannot confirm the causal direction between SEB and smoking solely from the cross-sectional data used here. Longitudinal data would be able to answer some interesting questions suggested by this study, such as how beliefs about smoking changed with formation and dissolution of social networks, and how people’s smoking status and behavior change with SEB over a period of time. Finally, this study only included male population for analysis because the great difference of smoking prevalence between Chinese men and women could bias the estimation in the model we employed. However, future studies could Yang incorporate both males and females using a different model. Despite these limitations, this article provides important information on the role of social networks in SEB that play a pivotal role in the justification of smoking behaviors.
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Submitted: November 14, 2013 Revised: March 31, 2014 Accepted: April 30, 2014
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Source: Psychology of Addictive Behaviors. Vol. 28. (3), Sep, 2014 pp. 921-927)
Accession Number: 2014-33502-001
Digital Object Identifier: 10.1037/a0037176
Record: 171- Title:
- The Minnesota Multiphasic Personality Inventory–2 Restructured Form in National Guard soldiers screening positive for posttraumatic stress disorder and mild traumatic brain injury.
- Authors:
- Arbisi, Paul A.. Minneapolis Veterans Affairs Medical Center, Minneapolis, MN, US, paul.arbisi@va.gov
Polusny, Melissa A., ORCID 0000-0002-4932-305X. Minneapolis Veterans Affairs Medical Center, Minneapolis, MN, US
Erbes, Christopher R.. Minneapolis Veterans Affairs Medical Center, Minneapolis, MN, US
Thuras, Paul. Minneapolis Veterans Affairs Medical Center, Minneapolis, MN, US
Reddy, Madhavi K.. Minneapolis Veterans Affairs Medical Center, Minneapolis, MN, US - Address:
- Arbisi, Paul A., Minneapolis Veterans Affairs Medical Center, Psychology Service 116B, One Veterans Drive, Minneapolis, MN, US, 55417, paul.arbisi@va.gov
- Source:
- Psychological Assessment, Vol 23(1), Mar, 2011. pp. 203-214.
- NLM Title Abbreviation:
- Psychol Assess
- Page Count:
- 12
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- Psychological Assessment: A Journal of Consulting and Clinical Psychology
- ISSN:
- 1040-3590 (Print)
1939-134X (Electronic) - Language:
- English
- Keywords:
- Minnesota Multiphasic Personality Inventory–2 Restructured Form, assessment, blast injury, mild traumatic brain injury, posttraumatic stress disorder, National Guard soldiers
- Abstract:
- The Minnesota Multiphasic Personality Inventory–2 Restructured Form (MMPI-2 RF) was administered to 251 National Guard soldiers who had recently returned from deployment to Iraq. Soldiers were also administered questionnaires to identify posttraumatic stress disorder (PTSD) and mild traumatic brain injury (mTBI). On the basis of responses to the screening instruments, the National Guard soldiers who produced a valid MMPI-2 RF were classified into four groups: 21 soldiers who screened positive for PTSD only, 33 soldiers who screened positive for mTBI only, 9 soldiers who screened positive for both conditions, and 166 soldiers who did not screen positive for either condition. Results showed that the MMPI-2 RF was able to differentiate across the groups with the MMPI-2 RF specific problem scale Anxiety adding incrementally to MMPI-2 Restructured Clinical scales in predicting PTSD. Both MMPI-2 RC1 (Somatic Complaints) and MMPI-2 RF head pain complaints predicted mTBI screen but did not add incrementally to each other. Of note, all of the MMPI-2 RF validity scales associated with overreporting, including Symptom Validity—Revised (FBS-r), were not significantly elevated in the mTBI group. These findings support the use of the MMPI-2 RF in assessing PTSD in non–treatment-seeking veterans. This further suggests that a positive screen for mTBI alone is not associated with significant emotional disturbance. (PsycINFO Database Record (c) 2017 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Military Personnel; *Minnesota Multiphasic Personality Inventory; *Posttraumatic Stress Disorder; *Traumatic Brain Injury; Injuries; Neuropsychological Assessment; Stress
- Medical Subject Headings (MeSH):
- Adult; Brain Injuries; Humans; Iraq War, 2003-2011; Logistic Models; MMPI; Military Personnel; Reproducibility of Results; Stress Disorders, Post-Traumatic; United States
- PsycINFO Classification:
- Clinical Psychological Testing (2224)
Military Psychology (3800) - Population:
- Human
Male
Female - Location:
- US
- Age Group:
- Adulthood (18 yrs & older)
- Tests & Measures:
- Minnesota Multiphasic Personality Inventory–2 Restructured Form
PTSD Checklist--Military Version DOI: 10.1037/t05198-000 - Grant Sponsorship:
- Sponsor: University of Minnesota Press, US
Recipients: No recipient indicated
Sponsor: US Department of Defense, Congressionally Directed Medical Research Program, US
Grant Number: W81XWH-07-2-003
Recipients: No recipient indicated - Methodology:
- Empirical Study; Quantitative Study
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- Accepted: Jun 25, 2010; Revised: Jun 24, 2010; First Submitted: Jan 7, 2010
- Release Date:
- 20110307
- Correction Date:
- 20170615
- Copyright:
- American Psychological Association. 2011
- Digital Object Identifier:
- http://dx.doi.org/10.1037/a0021339
- PMID:
- 21381845
- Accession Number:
- 2011-04411-005
- Number of Citations in Source:
- 47
- Persistent link to this record (Permalink):
- http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2011-04411-005&site=ehost-live
- Cut and Paste:
- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2011-04411-005&site=ehost-live">The Minnesota Multiphasic Personality Inventory–2 Restructured Form in National Guard soldiers screening positive for posttraumatic stress disorder and mild traumatic brain injury.</A>
- Database:
- PsycINFO
The Minnesota Multiphasic Personality Inventory–2 Restructured Form in National Guard Soldiers Screening Positive for Posttraumatic Stress Disorder and Mild Traumatic Brain Injury
By: Paul A. Arbisi
Minneapolis Veterans Affairs Medical Center, Minneapolis, Minnesota;
Department of Psychiatry, University of Minnesota Medical School;
Department of Psychology, University of Minnesota, Twin Cities;
Melissa A. Polusny
Minneapolis Veterans Affairs Medical Center, Minneapolis, Minnesota;
Department of Psychiatry, University of Minnesota Medical School
Christopher R. Erbes
Minneapolis Veterans Affairs Medical Center, Minneapolis, Minnesota;
Department of Psychiatry, University of Minnesota Medical School
Paul Thuras
Minneapolis Veterans Affairs Medical Center, Minneapolis, Minnesota;
Department of Psychiatry, University of Minnesota Medical School
Madhavi K. Reddy
Minneapolis Veterans Affairs Medical Center, Minneapolis, Minnesota;
Department of Psychiatry, University of Minnesota Medical School
Acknowledgement: The views expressed in this article are those of the authors and do not reflect the official policy or position of the U.S. Department of Veterans Affairs, the U.S. Department of the Army, or the U.S. Department of Defense. This research was supported by grants from the University of Minnesota Press and U.S. Department of Defense Congressionally Directed Medical Research Program (W81XWH-07-2-003).
Since 2001 over 1.8 million troops have been deployed to Afghanistan and Iraq in support of Operation Enduring Freedom (OEF) and Operation Iraqi Freedom (OIF). Surveys of returning troops have documented high rates of posttraumatic stress disorder (PTSD) symptoms and exposure to blast injuries (Hoge et al., 2004, 2008; Schell & Marshall, 2008). Indeed, blast exposure resulting in multisystem injury, including mild traumatic brain injury (mTBI) has been described as the signature wound of this conflict (Hoge et al., 2008; Sayer et al., 2008). Furthermore, military personnel who experience blast exposure and report a history of mTBI are also at higher risk for developing PTSD (Belanger, Kretzmer, Yoash-Gantz, Pickett, & Tupler, 2009; Broomhall et al., 2009). Consequently, it will become increasingly important for medical professionals to be able to provide a comprehensive assessment that simultaneously addresses both the physical and emotional factors associated with military service in OEF/OIF and to differentially treat symptoms that result from physical injury and psychological trauma.
As has been documented with earlier conflicts, combat exposure is associated with considerable risk for PTSD (Dohrenwend et al., 2006). Between 6.2% and 12.9% of military personnel returning from OEF/OIF screen positive for PTSD on self-report instruments, and many service personnel fail to seek treatment for these symptoms, suggesting that a large proportion may benefit from evaluation and referral to appropriate mental health services (Hoge et al., 2004; Kehle et al., 2010). With regard to exposure to a brain injury, in a study of soldiers returning from deployment to Iraq, Hoge et al. (2008) found that 4.9% reported experiencing a loss of consciousness (the majority between a few seconds and 2 to 3 min) and 10% reported an altered mental status (feeling dazed, confused, or seeing stars) after experiencing a head injury. PTSD was strongly related to the report of mTBI; however, after adjusting for PTSD and depression, Hoge et al. observed that mTBI was no longer associated with poor health and reported postconcussive symptoms. Thus, the boundary between what constitutes the development of a postconcussive syndrome—presumably resulting from the physical consequences of mTBI—and what constitutes the development of PTSD, or the emotional consequences of the traumatic event surrounding the mTBI, is murky and leads to definitional confusion and syndrome overlap.
Among civilians who have sustained an mTBI, it is well established that cognitive complaints fully resolve within 3–6 months of injury in the majority of cases (see meta-analyses by Belanger, Curtiss, Demery, Lebowitz, & Vanderploeg, 2005; Schretlen & Shapiro, 2003). Only about 10% of individuals with mTBI experience persistent symptoms following mTBI and develop a postconcussive syndrome. Those individuals who develop a postconcussive syndrome frequently experience concurrent emotional problems, including irritability, depression, fatigue, and anxiety, as well as headaches, memory problems, and attention and concentration difficulties. The persistence of these symptoms has been attributed both to subtle neurological changes wrought by the head injury that remain undetected by imaging techniques or neuropsychological evaluation and, alternatively, emotional factors. Notably the symptoms of postconcussive syndrome, including irritability, anxiety, withdrawal, fatigue, and attention and concentration problems, are also associated with psychiatric illnesses such as PTSD.
Growing evidence supports the contention that long-term consequences of mTBI are primarily due to emotional factors rather than to any long-term physical injury to the brain resulting from the sentinel event (Hoge, Goldberg, & Castro, 2009; Hoge et al., 2008; Landre, Poppe, Davis, Schmaus, & Hobbs, 2006; Marx et al., 2009). Moreover, self-report of mTBI or postconcussive syndrome does not lend increased risk for poor cognitive performance in recently returned OEF/OIF soldiers (Ivins, Kane, & Schwab, 2009). With the increased risk of developing PTSD as a result of a blast injury, it is important to differentiate between individuals who are experiencing psychiatric consequences from exposure to the traumatic event that resulted in the mTBI and who require primary treatment for the psychiatric condition and individuals who are experiencing what should be transient symptoms associated with mTBI.
Minnesota Multiphasic Personality Inventory–2 and Posttraumatic Stress DisorderGiven that recent findings point to psychiatric issues as potentially causative in the persistence of symptoms of mTBI, especially after blast exposure, it is important to provide a thorough psychological evaluation of returning veterans who report blast exposure and complain of memory, attention, and cognitive problems to assess for psychiatric conditions before conducting an extensive neuropsychological evaluation. The MMPI-2 is the most widely used self-report instrument for the assessment of psychopathology and characterological factors and has been used extensively in neuropsychological evaluations, in part, because it is well suited to identify psychological problems that can produce disturbance in concentration and memory (Archer, Buffington-Vollum, Stendy, & Handel, 2006; Camara, Nathan, & Puente, 2000). Multiple studies have demonstrated that the MMPI-2 is sensitive to the presence of PTSD in veteran populations (Greenblatt & Davis, 1999; Keane, Malloy, & Fairbank, 1984; Lyons & Wheeler-Cox, 1999; Penk, Rierdan, Losardo, & Robinowitz, 2005; Wolf et al., 2008). Elevations on Clinical Scales 2, 7, and 8 have found to be associated with PTSD diagnosis in combat veterans and, more recently, the MMPI-2 Restructured Clinical (RC) scales demonstrated improved discriminant validity over the MMPI-2 clinical scales in identifying combat veterans who met criteria for PTSD (Wolf et al., 2008). Specifically, RC7 (Dysfunctional Negative Emotions), including intrusive thoughts, rumination, and nightmares, produced the largest effect size among MMPI-2 scales when distinguishing between veterans with and without PTSD (Arbisi, McNulty, & Ben-Porath, 2004; Wolf et al., 2008).
Minnesota Multiphasic Personality Inventory–2 and Mild Traumatic Brain InjuryIn contrast to the MMPI-2 findings for PTSD, the MMPI-2 profiles obtained from individuals reporting mTBI demonstrate a paradoxical effect when compared with individuals who experience moderate to severe TBI (Youngjohn, Davis, & Wolf, 1997). In general, elevations on Clinical Scales 1 and 3 have been found in mTBI groups, with those engaged in litigation scoring significantly higher than those not engaged in litigation (see Youngjohn et al., 1997). Recently, Thomas and Youngjohn (2009) examined the RC scales as a function of severity of head injury in 83 litigating patients and found that elevation on the RC scales was inversely correlated with severity of TBI; although after controlling for failure of symptom validity tests, this relationship disappeared (Thomas & Youngjohn, 2009). In a study conducted in Norway of patients who presented to a hospital after a brain injury, complicated mTBI—as defined by posttraumatic amnesia of greater than 30 min and abnormal EEG within 24 hr of the injury—predicted MMPI-2 profiles obtained 26 years postinjury. MMPI-2 scores of individuals with complicated mTBI were higher on Clinical Scale 3 than those with uncomplicated mTBI. This difference was due to an elevation on the Harris Lingoes HY 3 Lassitude and Malaise subscale in the complicated mTBI group rather than a greater endorsement of somatic complaints, as there was no difference found between the groups on Clinical Scale 1 (Hessen & Nestvold, 2009). This finding suggests that mTBI with concomitant objective findings of brain injury can lead to long-term emotional consequences associated with fatigue and feeling debilitated.
The Current StudyIn 2008 an alternate version of the MMPI-2—the MMPI-2 Restructured Form (MMPI-2 RF)—became available (Ben-Porath & Tellegen, 2008). The MMPI-2 RF consists of 338 items and contains 50 new scales including revised versions of validity scales, the restructured clinical scales, and specific problem scales. The MMPI-2 RF is organized in a hierarchical manner with three overarching broad dimensions tied to contemporary models of psychopathology: Emotional Internalizing Dysfunction, Thought Dysfunction, and Behavioral/Externalizing Dysfunction. At the intermediate level of the hierarchy are the nine RC scales. The specific problem scales, which are the most narrow and discriminating scales designed to tap clinically and interpersonally relevant issues and behaviors, fall at the lowest level in the hierarchy.
In the current study, we examined mean MMPI-2 RF scale scores across four groups of National Guard soldiers who had recently returned from combat deployment in Iraq: (a) soldiers who screened positive for PTSD but did not report experiencing an mTBI during deployment, (b) soldiers who did not screen positive for PTSD but reported experiencing a mTBI during deployment, (c) soldiers who both screened positive for PTSD and reported experiencing an mTBI during deployment, and (d) soldiers who did not screen positive for PTSD or for mTBI. Mean scale scores on the MMPI-2 RF were examined, and the impact on MMPI-2 RF scale elevation of PTSD and mTBI was assessed. Importantly, and in contrast with other studies of mTBI, this group of soldiers was not evaluated within the context of obtaining treatment and did not undertake the evaluation as part of a disability claim. We further examined the incremental validity of selected RF scales in predicting positive screen for both PTSD and mTBI and tested 10 specific hypotheses. As some have argued that PTSD is primarily a distress disorder (Watson, 2005), we hypothesized that RCd (Demoralization) would be a significant predictor of screening positive for PTSD (Hypothesis 1: RCd will add incrementally to the prediction of positive PTSD screen). Others have found that RC7 (Dysfunctional Negative Emotions) demonstrates large effect sizes when distinguishing veterans with PTSD compared with veterans without PTSD (Arbisi et al., 2004; Wolf et al., 2008). Further, elevation on RC7 is strongly correlated with report of intrusive thoughts and flashbacks in men who were admitted to a psychiatric unit at a Veterans Affairs hospital (Arbisi, Sellbom, & Ben-Porath, 2008; Tellegen et al., 2003). Therefore, Hypothesis 2 was that RC7 would add incrementally when predicting positive screen for PTSD. The third hypothesis, that RC8 (Aberrant Experiences) would add incrementally to prediction of a positive screen for PTSD, stems from the finding that eccentric perceptions as assessed by the Schedule for Nonadaptive and Adaptive Personality differentiated PTSD from other distress and fear disorders in Gulf War veterans (Gamez, Watson, & Doebbeling, 2007). At the level of the specific problem scales on the MMPI-2 RF, there is little published data to guide selection of predictors and hypotheses; however, the Anxiety (ANX), Social Avoidance (SAV), and Anger (ANP) Proneness scales are all associated both rationally and in the correlates presented in the MMPI-2 RF technical manual (see Tellegen & Ben-Porath, 2008) with symptoms of PTSD and would be expected to significantly add to the prediction of PTSD in returning veterans. Therefore, the following hypotheses were tested: Hypothesis 4, that ANX will add incrementally to the prediction of positive screen for PTSD; Hypothesis 5, that SAV will add incrementally to the prediction of positive screen for PTSD; and Hypothesis 6, that ANP will add incrementally to positive screen for PTSD.
With regard to the assessment of mTBI with the MMPI-2 RF, we expected RC1 (Somatic Complaints) to predict positive mTBI screen on the basis of the correlates of RC1 with somatic symptoms and pain reported in inpatient settings with veterans (Arbisi et al., 2008). Two facets of RC1, the specific problem scales neurological complaints (NUC) and head pain complaints (HPC), as well as non–RC1 components malaise (MLS) and cognitive complaints (COG), should also contribute to prediction of a positive mTBI screen. Therefore, the specific hypotheses as related to prediction of screening positive for mTBI are as follows: Hypothesis 6, that RC1 will add incrementally to prediction of positive screen for mTBI; Hypothesis 7, NUC will add incrementally to prediction of positive screen for mTBI; Hypothesis 8, that HPC will add incrementally to positive screen for mTBI; Hypothesis 9, that MLS will add incrementally to positive screen for mTBI; and Hypothesis 10, that COG will add incrementally to prediction of positive screen for mTBI.
Method Participants
As part of a longitudinal study investigating risk and resilience factors in National Guard soldiers deployed to Iraq, 522 soldiers who were recruited through flyers and unit announcements completed a number of self-report questionnaires approximately 1 month prior to deployment to Iraq (Polusny et al., 2011). The soldiers agreed to be contacted once they returned from Iraq to participate in follow-up data collection. The 251 soldiers who were administered the MMPI-2 RF within 3 to 11 months (M ± SD: 7.2 ± 2.8) of their return represent the portion of the sample who not only agreed to participate in follow-up data collection and responded to mailed solicitations upon return from deployment and completed the PTSD Checklist (PCL) and mTBI screening questions but who could also come to the medical center to participate in the study and complete the MMPI-2 RF. Soldiers were compensated $20 for completing the MMPI-2 RF. Additional compensation was provided for participating in other aspects of the longitudinal study (see Polusny et al., 2011). Participants completed the PCL and mTBI screening questions on average 6 weeks (M ± SD: 1.8 ± 1.8 months) prior to completing the MMPI-2 RF. None of the soldiers who completed the MMPI-2 RF reported receiving treatment while in Iraq for a TBI or were removed from assigned duties as a result of exposure to a blast or other form of head trauma. Consequently, it is likely that none of the participants experienced a moderate or severe head injury as no one reported being treated for a head injury while on active duty in Iraq.
Of those who completed the MMPI-2 RF, 12 failed to respond to the TBI screening questions or did not complete a PCL–Military. Of the 239 with completed MMPI-2 RF, TBI screen, and PCL-M, 10 produced an invalid MMPI-2 RF on the basis of the following criteria for invalidity specified in the MMPI-2 RF manual: Cannot Say (CNS) ≥ 18; Variable Response Inconsistency—Revised (VRIN-r) ≥ 80; True Response Inconsistencey—Revised (TRIN-r) ≥ 80; and Infrequency–Psychopathology—Revised (Fp-r) ≥ 100 (three had CNS scores ≥ 18; two had VRIN-r ≥ 80; 1 had TRIN-r ≥ 80; 2 had Fp-r ≥ 100, and two had both VRIN-r ≥ 80 and Fp-r ≥ 100). There were no significant demographic differences between the 10 excluded participants and the 229 participants included in the analyses. Mean age for the National Guard group included in the analyses who produced a valid MMPI-2 RF was 32.1 ± 8.7 years; the majority were men (84.3%). Consistent with the upper Midwest location of the National Guard Brigade Combat Team from which the sample was drawn, 94.2% of the participants were White, 1.8% were African American, 1.3% were Hispanic American, 1.0% were Native American, 1.0% were Asian American, and 1% were of other ethnicity. Half of the participants were currently married or living with a partner (50.9%), with another 16.8% reporting that they were currently in a relationship but had never been married. A total of 9.7% reported that they were separated or divorced, and 19.9% had never been married and were not in a relationship at the time of the assessment.
Participants were relatively well educated, with the majority having attended some college (88.5%). Specifically, 15.5% had an associate's degree, and 28.4% had a 4-year college degree or higher. Of the National Guard soldiers who participated in the study, 55.5% reported that they had been employed full-time since returning from Iraq, and 21.6% reported being unemployed at the time of the assessment. Nearly 20% (19.4%) reported that they were students, and only one individual (0.4%) self-identified as disabled when asked about employment status.
Measures
Minnesota Multiphasic Personality Inventory–2 Restructured Form
The MMPI-2 RF is a 338-item self-report instrument designed to assess personality and psychopathology. The MMPI-2 RF items are drawn from the 567-item MMPI-2 item pool. The MMPI-2-RF uses nongendered norms from the same normative sample used for the MMPI-2 with minor modifications (Ben-Porath & Tellegen, 2008). The MMPI-2 RF contains seven revised validity scales and one new validity scale, three higher order factor scales corresponding to Emotional Internalizing Dysfunction, Thought Dysfunction, and Externalizing Behavioral Dysfunction, the nine Restructured Clinical Scales contained on the MMPI-2, and 30 specific problem scales.
PTSD Checklist—Military version
Participants were classified as screening positive for military deployment–related PTSD with the PTSD Checklist (PCL-M). The PCL is a 17-item self-report scale that uses a 5-point Likert scale ranging from not at all to extremely to evaluate the presence and severity PTSD symptoms on the basis of criteria from the Diagnostic and Statistical Manual of Mental Disorders (4th ed. [DSM–IV]; American Psychiatric Association, 1994). The PCL has demonstrated excellent internal consistency (α = 0.94 −0.97); in Vietnam veterans the 2–3-day test–retest reliability was 0.96 (Weathers, Litz, Herman, Huska, & Keane, 1993). The PCL correlates highly with other interview and self-report measures of PTSD. Screening positive for PTSD was indicated by a raw score of greater than or equal to 50, and participants reported at least one intrusion symptom, three avoidance symptoms, and two hyperarousal symptoms that were all rated at the moderate or greater level of intensity according to the PCL (Hoge et al., 2004). Classification accuracy for the PCL-M has not been demonstrated for nontreatment seeking samples of OEF/OIF soldiers (see Terhakopian, Sinaii, Engel, Schnurr, & Hoge, 2008). However, in the larger sample from which the current subsample was drawn, a raw score of 50 on the PCL combined with the requisite number of symptoms within each DSM- IV cluster yielded a positive predictive value of .31 and a negative predictive value of .98 (Arbisi, Kaler et al., under review). Therefore, the group that screened positive for PTSD is likely to contain nearly all of the individuals with PTSD.
Traumatic brain injury screen
Individuals who were exposed to a blast and/or screened positive for mTBI were identified by responses to the following three questions adapted from the Defense and Veterans Brain Injury Center screening tool: “Were you ever so close to a blast that you could feel the blast wave or afterward had trouble hearing or problems with attention or memory?” “Did you have any injuries from a blast, bullet/shrapnel, vehicle crash or fall?” “Did any injury cause you to be dazed, confused, ‘see stars,’ get knocked out or lose consciousness?” If the veteran responded yes to the last question, he or she was categorized as having experienced a mild TBI (Schwab et al., 2007). Classification rates for the mTBI screening have not as yet been elucidated for National Guard soldiers or for returning OEF/OIF soldiers (Schwab et al., 2007).
Procedure
All procedures and materials for this study were approved by the institutional review boards of the Minneapolis Veterans Affairs Medical Center, the University of Minnesota, and the U.S. Department of Defense. The booklet form of the MMPI-2 RF was administered individually in the context of a broader, in-person evaluation conducted at the Minneapolis Veterans Affairs Medical Center as part of a larger longitudinal study of risk and resilience in National Guard soldiers deployed to OIF. As part of the larger study, the PCL-M and mTBI screen were included among other self-report instruments mailed to the National Guard soldiers, who agreed to be contacted after returning from deployment to Iraq. Self-report measures were completed within 3 to 11 months of soldiers' return from Iraq.
Analysis
An overall multivariate analysis of variance (MANOVA) was performed on the MMPI-2 RF raw scale scores across the four groups. On the basis of the MANOVA, we examined group differences for the individual MMPI-2 RF scale scores using follow-up univariate analysis of variance (ANOVA) with post hoc Tukey comparisons. A series of logistic regressions were performed to predict group status (screen positive PTSD only vs. no PTSD/mTBI and screen positive mTBI only vs. no PTSD/mTBI). In order to test the relative efficacy of the relevant MMPI-2 RC scales and conceptually related specific problem scales in prediction of PTSD and mTBI, respectively, we entered the scales in two blocks with conceptually related RC scales entered first followed by the newer specific problem scales.
Results MMPI-2 RF Group Differences
The omnibus F test for the four-group MANOVA with MMPI-2 RF scales as the dependent variables was significant, F(150, 528.46) = 2.06, p < .001, Wilks's lambda = .361. Consequently, differences on individual MMPI-2 scales across the groups were examined with one-way ANOVA with post hoc Tukey analysis to identify differences between the diagnostic groups on individual scales.
Validity Scales
Mean scale score differences for the MMPI-2 RF validity scales are presented in Table 1. Relative to the controls, large effect sizes were found for the PTSD-only group on the Infrequent Responses—Revised (F–r) and FBS-r scales and medium effect sizes on Fp-r and Infrequent Somatic Responses (Fs) scales. The MMPI-2 RF validity scales for the screen positive for mTBI-only group fell in the small to medium range in comparison with the soldiers who did not screen positive for either PTSD or mTBI. In the group of nine solders who screened positive for both PTSD and mTBI, mean scores on the F-r, Fp-r, and FBS-r scales were not statistically different from the means of those scales in the soldiers who screened positive for PTSD only. However, the Fs scale was significantly higher in the group of soldiers screening positive for both PTSD and mTBI than in the other three groups. With regard to the Uncommon Virtues—Revised (L–r) and the Adjustment Validity—Revised (K-r) scales—the MMPI-2 RF validity scales designed to identify underreporting—there was no difference across the four groups on the L-r. The soldiers who failed to screen positive for both mTBI and PTSD scored significantly higher than did the soldiers who screened positive for both conditions on the K-r scale, but the mean scale score on K-r did not differ from that of the groups that screened positive for PTSD only and for mTBI only.
Differences on the Minnesota Multiphasic Personality Inventory–2 Restructured Form Validity Scales Between Participants Who Screened Positive for Posttraumatic Stress Disorder (PTSD), Traumatic Brain Injury (TBI), or Both Disorders and Those Who Did Not
Higher Order and Restructured Clinical Scales
As shown in Table 2, the screen-positive PTSD-only group had significantly higher mean scores on EID (Emotional/Internalizing Dysfunction), RCd, RC1, RC4 (Antisocial Behavior), and RC7, with effect sizes falling in the large range in comparison with the control group. The group that screened positive for mTBI was not significantly different from the control group across any of the MMPI-2 higher order or RC scales. The group that screened positive for both PTSD and mTBI was found to be significantly higher than the other three groups on RC2 (Low Positive Emotions), and no different from the group that only screened positive for PTSD on RC1, but higher than the control group and the group that did not screen positive for mTBI.
Differences on the Minnesota Multiphasic Personality Inventory–2 Restructured Form Higher Order and Restructured Clinical (RC) Scales Between Participants Who Screened Positive for Posttraumatic Stress Disorder (PTSD), Traumatic Brain Injury (TBI), or Both Disorders and Those Who Did Not
Specific Problem Scales
Somatic/cognitive and internalizing scales
Table 3 demonstrates the significant mean differences that were found for the group that only screened positive for PTSD on MLS (Malaise), GIC (Gastro-Intestinal Complaints), HPC, COG, STW (Stress/Worry), and AXY (Anxiety). The AXY specific problem scale demonstrated the largest effect size when compared with the group of soldiers who did not screen positive for either PTSD or mTBI. In contrast, there were no significant group differences between the group that only screened positive for mTBI and the control group across any of the MMPI-2 RF Somatic/Cognitive and Internalizing specific problem scales. Again, the mean scores of the Somatic/Cognitive and Internalizing specific problems scales of the group that screened positive for both PTSD and mTBI were not significantly different from those of the group that screened positive for PTSD alone.
Differences on the Minnesota Multiphasic Personality Inventory–2 Restructured Form Somatic and Cognitive Scales Between Participants Who Screened Positive for Posttraumatic Stress Disorder (PTSD), Traumatic Brain Injury (TBI), or Both and Those Who Did Not
Externalizing, interpersonal, and interest scales
In contrasting the group that screened positive for PTSD with the control group, significant mean differences were found for the specific problem scales SUB (Substance Abuse), AGG (Aggression), and FML (Family Problems), with effect sizes ranging from medium to large (see Table 4). There were no significant differences observed between the group that only screened positive for mTBI and the control group for any of the Externalizing, Interpersonal, or Interest MMPI-2 specific problem scales. Of note, scores on the Externalizing, Interpersonal, and Interest specific problem scales in the group that screened positive for both conditions were similar to those of the PTSD-only group.
Differences on the Minnesota Multiphasic Personality Inventory–2 Restructured Form Externalizing, Interpersonal, and Interest Scales Between Participants Who Screened Positive for Posttraumatic Stress Disorder (PTSD), Traumatic Brain Injury (TBI), or Both and Those Who Did Not
Personality Psychopathology Five Scales—Revised (PSY-5-r)
Finally, results shown in Table 5 demonstrate that the only PSY-5-r mean scale score that differentiated the group of National Guard soldiers who screened positive for PTSD alone from the group that did not screen positive for either PTSD or mTBI was Negative Emotionality/Neuroticism—Revised (NEGE-r) . The soldiers who screened positive for mTBI alone did not differ from the controls across any of the PSY-5-r mean scale scores. For the soldiers who screened positive for both PTSD and mTBI, Introversion/Low Positive Emotionality—Revised (INTR-r) was the only PSY-5-r scale score that was statistically different from scores of the group that did not screen positive for either PTSD or mTBI.
Differences on the Minnesota Multiphasic Personality Inventory–2 Restructured Form Personality Psychopathology Five (PSY-5) Scale Between Participants Who Screened Positive for Posttraumatic Stress Disorder (PTSD), Traumatic Brain Injury (TBI), or Both and Those Who Did Not
Logistical Regression Analyses
The decision to select MMPI-2 RF scales for inclusion in the regression analyses was based both on the construct assessed by the individual scale and on past research findings. For example, the mean profile on the MMPI-2 clinical scales for veterans with PTSD has consistently been found to contain elevations on Clinical Scales 2, 7, and 8 (Penk et al., 2005). Further, RC7 and RCd were found to contribute in the prediction of PTSD in both men and women veterans who screened positive for PTSD or who were diagnosed with PTSD with structured interviews (Arbisi, Erbes, Polusny, & Nelson, 2010; Arbisi et al., 2004; Wolf et al., 2008). Additionally, anger (ANG) and social avoidance (SAV) are symptoms of PTSD and would be expected to contribute to the differentiation between soldiers screening positive for PTSD and those who do not. With regard to mTBI, somatic complaints (RC1), as well as specific problem scales head pain complaints (HPC) and cognitive complaints (COG) conceptually would be expected to differentiate individuals who are reporting mTBI without screening positive for PTSD from those who report neither condition. Finally the Malaise (MLS) scale, derived from items found on MMPI-2 Clinical Scale 3 and highly correlated with the Harris–Lingoes HY3 (Lassitude and Malaise) subscale, was included in the regression analysis on the basis of the finding that HY3 prospectively predicted failure to return from disability after experiencing a work-related injury (Bigos et al., 1991). Thus Hypotheses 1–6 were tested in the first regression analyses and Hypotheses 7–10 were tested in the second set of regression analyses.
When predicting screening positive for PTSD on the PCL-M (positive PTSD screen only vs. no PTSD/no mTBI), we considered two blocks of predictors: a general block including RCd, RC7, and RC8 and a block including specific problem scales for ANX, ANG, and SAV. Blocks were entered in two orders: Order 1, in which general scales were followed by specific scales, and Order 2, with the reverse order of entry. As seen in Table 6, of the three RC scales entered initially as a block, none were significant independent predictors of PTSD screen, though the block did predict group membership significantly (90% correct classification), χ2(3) = 17.69, p < .01. However, a trend toward significance was found for RC7 (p < .08). When conceptually related specific problem scales AXY, ANP, and SAV were added in the second block, the set contributed significantly to prediction, χ2(3) = 9.51, p < .05, though classification remained at 90%, with AXY emerging as a significant, independent predictor for positive screen for PTSD. In the final model with all six predictors, χ2(6) = 27.20, p < .001, only AXY remained a significant predictor. When the order of entry for the blocks was reversed (Order 2), the specific problem scales initially added to prediction (90% correct classification, χ2(3) = 26.88, p < .001, but the more general scales did not add to that prediction, χ2(3) = 0.316, p = .957.
Logistical Regression Analysis Predicting Positive Screen on Posttraumatic Check List
A similar approach was taken in predicting positive screen for mTBI. Predictors were entered in two blocks: one with more global predictors (RC1) and the other with specific problem scales (COG, HPC, and MLS). Analyses were run twice: once with global predictor (RC1) entered first, followed by specific problem scales (Order 1), and again with specific problem scales entered first, followed by the more global RC1 (Order 2). Coefficients for both orders of analysis are presented in Table 7. When RC1 was entered first, the first block correctly classified 84% of cases, and RC1 was a significant predictor of group status, χ2(1) = 9.32, p < .01. The second block of more specific variables did not add significantly to this prediction, χ2(3) = 2.27, p > .05, although the model with all predictors included remained significant, χ2(4) = 11.59, p < .05. In the final model, no predictor emerged as a significant independent predictor, presumably because of the intercorrelation of the scales included (the range for the intercorrelations of the predictors in this sample was .48 to .80, consistent with other samples (cf. Tellegen and Ben-Porath, 2008). When specific problem scales were entered first, they significantly predicted group membership (83% correctly classified), χ2(3) = 10.60, p < .05, with only HPC making an independent contribution. The addition of RC1 did not add significantly to the prediction, χ2(1) = 0.99, p > .05.
Logistical Regression Analysis Prediction of Positive Screen on Mild Traumatic Brain Injury
DiscussionThe current study is the first to examine the utility of the MMPI-2 RF in discriminating between non–treatment-seeking soldiers who screened positive for PTSD or mTBI from soldiers who do not screen positive for either condition. With regard to the prediction of a positive screen for PTSD, Hypothesis 1 was not supported in that RCd did not incrementally add to the prediction of a positive screen on the PCL-M. There was a trend toward RC7 serving as a significant predictor of positive PTSD screen partially supporting Hypothesis 2; however, Hypothesis 3, that RC8 would also independently contribute to a positive screen on the PCL was not supported. When the narrow-band specific problem scales conceptually related to PTSD were added as a block, only AXY added beyond the RC scales in prediction of positive screen for PTSD. Further, AXY remained the single scale to add independently when all scales were entered simultaneously in the regression. Thus, of the remaining three hypotheses related to the prediction of PTSD screen with the MMPI-2 RF, only Hypothesis 4 that AXY would add independently to the prediction of PTSD was supported.
When comparing mean scores of soldiers who screened positive for PTSD and those who did not, we found large effects for specific problem scales related to somatic and cognitive concerns, anxiety, and PSY-5-r Neuroticism/Negative Emotionality. Furthermore, large effect sizes were found for MMPI-2 RF specific problem scales associated with externalized behavior: SUB and AGG. The observation that MMPI-2 RF scales associated with both internalizing and externalizing behavior were elevated in non–treatment-seeking soldiers who screened positive for PTSD is consistent with Miller's conceptualization of PTSD as a heterogeneous condition marked by two sets of individuals: one set consisting of veterans who primarily display a pattern of disturbance in internalizing behavior and another set consisting of veterans who develop externalized behaviors associated with substance abuse and aggression (Miller, 2003; Miller, Greif, & Smith, 2003; Miller, Kaloupek, Dillon, & Keane, 2004; Wolf et al., 2008). Unlike earlier studies using the MMPI/MMPI-2 clinical scales to identify PTSD in combat veterans, there were no significant differences between the group that screened positive for PTSD alone and the two groups that did not screen positive for PTSD on RC2 and RC8. In contrast, the group that screened positive for PTSD scored significantly higher on RC7, a measure of dysfunctional negative emotions, supporting previous finding that the RC scales are better discriminant measures of PTSD than were the Clinical Scales (Wolf et al., 2008). Taken together, these findings provide support for the construct validity of the MMPI-2 RF RC and specific problem scales in identifying both the externalizing and internalizing manifestation of PTSD in combat exposed soldiers.
Somewhat surprisingly, the MMPI-2 RF measures of social avoidance and anger proneness tapping aspects of Cluster C and D symptoms of PTSD on the DSM–IV did not contribute to discriminating those soldiers who screened positive for PTSD from those who did not. In addition, RC1 and the specific problem scales associated with a range of somatic complaints were significantly elevated in the PTSD group. Although elevation on these scales was not predicted, there is certainly historical precedent for the somatic manifestation of stress associated with combat exposure as evidenced by Civil War soldiers who complained of cardiac difficulties and were thought to suffer from “soldiers' heart”(Jones, 2006). Further, elevations on MMPI-2 scales associated with somatic complaints have also been observed in women with histories of sexual abuse and who screen positive for PTSD (Arbisi et al., 2010). The observation that newly returned soldiers who are experiencing symptoms associated with PTSD may also report a wide range of somatic complaints suggests that veterans suffering from PTSD may first present in medical clinics for treatment of somatic complaints rather than directly seek treatment through a mental health facility. This inference has important implications for the implementation of screening and where best to direct efforts toward early detection of new PTSD cases in returning veterans.
With regard to the hypotheses related to the prediction of mTBI screen, as expected, RC1 predicted a positive screen for mTBI but did not add incrementally to the specific problem scales (Hypothesis 6). Of the conceptually related specific problem scales, only HPC added incrementally to the prediction of a positive mTBI screen, and neither NUC, MLS, nor COG added to the prediction (Hypotheses 7–10). None of the MMPI-2 RF mean scale scores of National Guard soldiers who screened positive for mTBI were significantly different from controls, and there were no scales where effect sizes exceeded the moderate range.
Given emerging findings that persistent cognitive complaints resulting from blast exposure may be primarily attributable to the emotional consequences of the traumatic event associated with the mTBI, it is noteworthy that elevations on the MMPI-2 RF of soldiers who screened positive for an mTBI but without a positive screen for PTSD were no different from the MMPI-2 RF of soldiers who did not screen positive for either PTSD or an mTBI. This finding suggests that scale elevations found on the MMPI-2 RF of soldiers presenting for clinical evaluation and who report persistent symptoms consistent with an mTBI are likely to be experiencing the emotional consequences of the traumatic event that resulted in the mTBI and should be treated accordingly. In contrast, MMPI-2 RF scores of soldiers who screened positive for PTSD, regardless of whether the soldier reported an mTBI, were elevated on scales associated with internalizing disorders, including RC7 and ANX. For example, large effect sizes were found on the EID scale and RC scales RCd, RC1, RC4, and RC7 between the group that screened positive for PTSD and the group that did not. Of note, contrary to findings with Gulf War veterans diagnosed with PTSD, where a measure of unusual perceptions, cognitions, and beliefs from the Schedule for Nonadaptive and Adaptive Personality was significantly related to PTSD diagnosis, there was no significant difference on RC8—a measure of disturbed sensory perception—across the groups (Gamez et al., 2007). In part, the failure to observe differences on a measure of thought disturbance may be related to the non–treatment-seeking status of the assessed cohort and the fact that the assessment took place within a research setting although this was similar to the data collection context for the Gamez et al. (2007) study. An equally compelling explanation for the failure to observe significant differences on RC8 between the group screening positive for PTSD and the group that did not is the more narrowly defined construct associated with aberrant sensory experiences assessed by RC 8 in contrast to the broader and less pathological construct assessed by the eccentric perceptions scale on the Schedule for Nonadaptive and Adaptive Personality (Clark, 1996; Tellegen et al., 2003). Examination of these two instruments in veterans seeking treatment for PTSD could serve to clarify this discrepancy. It is possible that the non–treatment-seeking nature of the current sample precluded inclusion of individuals with more severe cases of PTSD, such as those who experience dissociative states associated with flashbacks. The failure to include more severe cases of PTSD could account for the failure to find differences across groups on MMPI-2 RF scales that assess unusual sensory experiences.
Although small, the group marked by report of both mTBI and PTSD is illustrative and suggests that the distress and impairment thought to be associated with mTBI resulting from blast exposure is more likely a consequence of emotional disturbance stemming from PTSD. The group that screened positive for both mTBI and PTSD was quite similar to the group that screened positive for only PTSD, with the exception of scoring significantly higher on Fs, PSY-5 INTR, RC2, and SAV than the other groups and lower than the group that screened positive for PTSD on RC4, RC7, SUB, and FAM. This pattern of scale elevations tentatively points to a greater level of anhedonia and low positive emotions with less externalized behavior in the combined group. Because of the relatively limited number of soldiers in this group, further study is warranted directed toward identifying distinctive features associated with the combination of mTBI and PTSD.
Notably, differences observed between the group that did not screen positive for PTSD or mTBI and the groups that did screen positive for those conditions provides useful information regarding the performance of the MMPI-2 validity scales in identifying noncredible responding. Given that the participants in the study were not self-identified as having a psychological or an emotional problem and were neither treatment nor compensation seeking, any group differences found on the validity scales are likely to be the direct result of symptoms of PTSD or symptoms associated with mTBI rather than a consequence of deliberate noncredible reporting. Mean scale score elevations of the MMPI-2 validity scales in the mTBI and PTSD groups, respectively, represent resting values for the validity scales and provide a benchmark for the effect of context of evaluation on validity scale performance (Wygant et al., 2007). As would be expected, F-r and Fp-r were significantly higher in the PTSD group than in the group that did not screen positive for either PTSD or mTBI. Elevations on the same scales in the mTBI group fell between the PTSD group and the group that did not screen positive for the conditions. This same pattern of scale elevation held for the Fs scale as well. Of particular importance, given concerns raised about the susceptibility of the FBS to genuine somatic and cognitive symptoms, is the finding that the mean score of the FBS-r in the group that screened positive for an mTBI was not significantly different from scores of the group that did not screen positive for either mTBI or PTSD. This finding suggests that elevation on FBS-r cannot be attributed solely to exposure to experiencing an mTBI and provides support for the use of the scale in identifying noncredible report of cognitive and memory problems, especially in the context of an evaluation of cognitive complaints after an mTBI. However, the mean score of the FBS-r scale in the PTSD group was T = 64 and statistically different from scores of the other two groups that did not include soldiers who screened positive for PTSD. The 13-point T score difference between the PTSD group and the group that did not screen positive for PTSD on the FBS-r is in contrast to the T-score difference of 5.8 on the Fp-r between the same groups and is consistent with previous findings that FBS may be less effective than Fp in discriminating genuine PTSD from noncredible PTSD outside the context of simultaneous complaints of cognitive and memory problems (Arbisi, Ben-Porath, & McNulty, 2006; Efendov, Sellbom, & Bagby, 2008).
The sample is generally representative of the larger combat brigade that was deployed for 15 months to Iraq from which it was drawn. However, the soldiers all came from the upper Midwest and their demographic characteristics are consistent with that region of the United States (Polusny et al., 2010). Moreover, as the soldiers were not treatment seeking and were evaluated as part of a research protocol, the findings may not generalize to clinical settings or to a more demographically diverse population of veterans. However, the non–treatment-seeking status of the current sample can be viewed as a strength of the study design, as these findings provide a benchmark against which to compare veterans who meet the same screening criteria for PTSD and mTBI in clinical settings without the confounding influence of either conscious or unconscious response bias. Another limitation of the study was that group assignment was based on self-report measures and not on structured clinical interviews. Consequently, the groupings were developed on the basis of screening measures and are likely to contain individuals who do not have the conditions. Nonetheless, the PCL criteria used to identify group membership are considered conservative and to result in relatively fewer false positive and false negative decisions compared with other published criteria (Hoge et al., 2004, 2009; Terhakopian et al., 2008). The mTBI screen reflects common clinical practice used in both the military and the Veterans Affairs to identify soldiers who should be evaluated more thoroughly for mTBI. Another potential concern is that the group who did not screen positive for either PTSD or mTBI may have minimized the level of distress and psychopathology that was present. Although there were more soldiers who produced high scores on K-r (T ≥ 72) in the group that failed to screen positive for either PTSD or mTBI, the mean score on K-r was significantly lower only for the combined group when contrasted with the group that did not screen positive for either PTSD or mTBI, suggesting that the group was not marked by extreme defensiveness. Consequently, the likelihood that the group composed of soldiers who did not screen positive for either PTSD or mTBI contained individuals with either of those conditions is relatively remote, and any mean differences on MMPI-2 RF scales found between the groups are due to a greater preponderance of PTSD or mTBI in the designated groups.
In sum, this study is the first of its kind to examine the utility of the MMPI-2 RF in discriminating between recently returned soldiers who screened positive for PTSD and mTBI from those who did not report symptoms consistent with those conditions. Generally, conceptually related scales such as RC7 and ANX from the MMPI-2 RF were found to be effective in identifying the PTSD group. However, the MMPI-2 RF scales associated with somatic concerns were also significantly elevated in the PTSD group, suggesting that beyond symptoms commonly associated with PTSD, veterans returning from the war in Iraq who screen positive for PTSD report poor health and a range of somatic concerns. The group that screened positive for mTBI did not appear more emotionally distressed or somatically preoccupied than the group that did not screen positive for either PTSD or mTBI. This observation is consistent with findings regarding the long-term consequences of exposure to mTBI and supports the notion that the etiology of persistent postconcussive syndrome is primarily related to emotional and psychological factors (Hoge et al., 2009; McCrea et al., 2008). Finally, the FBS-r—a scale initially developed to identify noncredible reporting of somatic and cognitive symptoms in individuals undergoing civil litigation—was not significantly elevated in the mTBI group. Consequently, the current study provides evidence in support of the use of the FBS-r in identifying noncredible report of symptoms following a head injury by demonstrating the invariance of the FBS-r between those who have experienced an mTBI and were not compensation seeking and those who have not experienced an mTBI.
Footnotes 1 If extreme scores on F-r, L-r, and K-r were used as exclusionary criteria, an additional 11 participants would have been excluded. Two participants from the group who did not screen positive for PTSD or mTBI obtained an L-r score of T ≥ 80, seven participants obtained a K-r score of T ≥ 72, and one participant from that group obtained extreme scores on both scales. Only one other participant in the group that screened positive for PTSD obtained an L-r score of ≥ 80. None of the participants obtained a score on F-r of T ≥ 120. When these additional participants were excluded, the magnitude and direction of the results were unchanged.
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Submitted: January 7, 2010 Revised: June 24, 2010 Accepted: June 25, 2010
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Source: Psychological Assessment. Vol. 23. (1), Mar, 2011 pp. 203-214)
Accession Number: 2011-04411-005
Digital Object Identifier: 10.1037/a0021339
Record: 172- Title:
- The performance of the K6 Scale in a large school sample.
- Authors:
- Peiper, Nicholas, ORCID 0000-0002-9154-0584. REACH Evaluation, Louisville, KY, US, nick@reacheval.com
Clayton, Richard. Department of Health Behavior, University of Kentucky, KY, US
Wilson, Richard. Department of Health Promotion and Behavioral Sciences, University of Louisville, KY, US
Illback, Robert. REACH Evaluation, Louisville, KY, US - Address:
- Peiper, Nicholas, REACH Evaluation, 501 Park Avenue, Louisville, KY, US, 40208, nick@reacheval.com
- Source:
- Psychological Assessment, Vol 27(1), Mar, 2015. pp. 228-238.
- NLM Title Abbreviation:
- Psychol Assess
- Page Count:
- 11
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- Psychological Assessment: A Journal of Consulting and Clinical Psychology
- ISSN:
- 1040-3590 (Print)
1939-134X (Electronic) - Language:
- English
- Keywords:
- adolescent, school, epidemiology, mental health
- Abstract:
- Timely prevalence data of psychiatric morbidity among adolescents in small areas remains vital for mental health policy planning at the regional and local levels. Furthermore, effective regional policy planning also requires the measurement of psychiatric morbidity using clinically validated instruments. The K6 scale was therefore included on the 2012 administration of the Kentucky Incentives for Prevention Survey as a measure of serious emotional disturbance in the past 30 days. Principal axis and confirmatory factor analyses were performed to determine the unidimensional structure of the K6 in a school-based sample of Kentucky students (n = 108,736). The documented cutoff of 13 on the K6 was then used to screen Kentucky students for serious emotional disturbance, estimate the state prevalence, and define epidemiologic correlates. Overall, the K6 performed well, with factor analyses confirming the 1-factor solution of the K6. Based upon the established cutoff, the prevalence of serious emotional disturbance was 13.9% in Kentucky. Grade, gender, race and ethnicity, and family structure emerged as significant predictors in a multivariable logistic regression model. Substance abuse, antisocial behavior, role impairments, and peer victimization were significantly higher among students with a positive screen. These results indicate the K6 is particularly useful for inclusion in large epidemiologic surveys that have limited space and logistics that demand timely administration. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Emotional Disturbances; *Mental Health; *Psychological Assessment; *Health Care Policy; *Morbidity; Schools
- PsycINFO Classification:
- Clinical Psychological Testing (2224)
Psychological Disorders (3210) - Population:
- Human
Male
Female - Location:
- US
- Age Group:
- Adolescence (13-17 yrs)
- Tests & Measures:
- Kentucky Incentives for Prevention Survey
K6 Scale
World Health Organization Survey
World Mental Health Survey
National Comorbidity Survey Adolescent Supplement
Composite International Diagnostic Interview’s Self-Administered Questionnaire for Parents
Children's Global Assessment Scale - Grant Sponsorship:
- Sponsor: Substance Abuse and Mental Health Services Administration
Grant Number: SP019436-01-2
Recipients: No recipient indicated - Methodology:
- Empirical Study; Quantitative Study
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- First Posted: Sep 15, 2014; Accepted: Jul 23, 2014; Revised: Apr 30, 2014; First Submitted: Jan 6, 2014
- Release Date:
- 20140915
- Correction Date:
- 20150914
- Copyright:
- American Psychological Association. 2014
- Digital Object Identifier:
- http://dx.doi.org/10.1037/pas0000025
- PMID:
- 25222434
- Accession Number:
- 2014-37941-001
- Number of Citations in Source:
- 49
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- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2014-37941-001&site=ehost-live">The performance of the K6 Scale in a large school sample.</A>
- Database:
- PsycINFO
The Performance of the K6 Scale in a Large School Sample
By: Nicholas Peiper
REACH Evaluation, Louisville, Kentucky, and Department of Epidemiology and Population Health, University of Louisville;
Richard Clayton
Department of Health Behavior, University of Kentucky
Richard Wilson
Department of Health Promotion and Behavioral Sciences, University of Louisville
Robert Illback
REACH Evaluation, Louisville, Kentucky, and Headstrong—The National Centre for Youth Mental Health, Dublin, Ireland
Acknowledgement: This research was supported by Grant SP019436-01-2 from the Substance Abuse and Mental Health Services Administration. We thank Connie Smith and Steve Cambron for their leadership on the initiative, as well as the members of the Kentucky State Epidemiological Outcomes Workgroup for their contributions. We also thank Cheri Levinson for her statistical guidance.
Approximately one out of every 10 adolescents is estimated to meet the 12-month criteria for serious emotional disturbance (SED; Brauner & Stephens, 2006; Costello, Foley, & Angold, 2006). The Substance Abuse and Mental Health Service Administration (SAMHSA) defines SED as a diagnosable mental, behavioral, or emotional disorder that meets Diagnostic and Statistical Manual of Mental Disorders criteria and results in functional impairment that substantially interferes with or limits the child’s role or functioning in family, school, or community activities (Brauner & Stephens, 2006; Costello et al., 2006). The ability to identify and address emotional, behavioral, and social challenges that occur during youth may therefore determine lifelong health and well-being trajectories (Canino et al., 2004; Costello, Egger, & Angold, 2005; Kim-Cohen et al., 2009), as retrospective and prospective studies consistently show that approximately 75% of adult psychiatric morbidity emerges during childhood and adolescence (Costello et al., 2006; Kessler et al., 2005; Merikangas et al., 2010; Merikangas, Nakamura, & Kessler, 2009). Moreover, age-of-onset studies have further revealed that when all adult psychiatric disorders are considered together, the median age of emergence is around age 14 (Kessler et al., 2005). In this context, psychiatric morbidity among youth may be construed as a global public health challenge (Patel, Flisher, Hetrick, & McGorry, 2007).
Schools play an increasing role in promoting positive mental health and providing supports (Green et al., 2013; Paternite, 2005). Recognition of prominent youth issues such as low educational achievement, school violence, substance abuse, and underutilization of mental health services by adolescents as population-level problems has led to a re-conceptualization of school psychological services within a public health perspective (Strein, Hoagwood, & Cohn, 2003). Within this view, an essential task is to employ a data-based approach to estimation of psychiatric morbidity among youth, and to relate these estimates to associated risk and protective factors, with the ultimate goal of using these data to formulate population-based prevention and early intervention activities. The methodology of child and adolescent psychiatric epidemiology is therefore central to youth mental health planning and promotion in schools and communities.
The present article describes a surveillance initiative to obtain empirical estimates of child and adolescent psychiatric morbidity in the context of a State Epidemiological Outcomes Workgroup (SEOW) funded by the Center for Substance Abuse Prevention (CSAP) at SAMHSA. In Kentucky, the SEOW produces community and school profiles predominantly with data from a large statewide school survey called the Kentucky Incentives for Prevention (KIP) Survey that has been administered for more than a decade to Kentucky students by the Substance Abuse Prevention Program within the Kentucky Cabinet for Health and Family Services (Sanders, Illback, Crabtree, & Sanders, 2012; State Epidemiological Outcomes Workgroup, 2011). The broad purpose of the KIP has been to measure student use of alcohol, tobacco, and other drugs in relation to risk and protective factors such as peer influence, risk perception, and school safety (Sanders et al., 2012; State Epidemiological Outcomes Workgroup, 2011). Considering that the KIP is administered to the vast majority of school districts throughout the state, it provides data-driven capacity to inform and evaluate prevention efforts.
Consequently, data regarding the school- and community-level prevalence of psychiatric morbidity are particularly valuable since local planning and resource allocation are partially driven by distribution of need (i.e., disorder prevalence and severity) in the public service sector (Florin et al., 2012; Kessler et al., 2010; Li, Green, Kessler, & Zaslavsky, 2010; Piper, Stein-Seroussi, Flewelling, Orwin, & Buchanan, 2012; State Epidemiological Outcomes Workgroup, 2011). In particular, inclusion of the K6 scale on the 2012 KIP Survey (Sanders et al., 2012) to estimate SED represented an opportunity to derive youth mental health data to inform prevention and treatment efforts for Kentucky schools and communities (Green, Gruber, Sampson, Zaslavsky, & Kessler, 2010; Li et al., 2010). The K6 met core requirements for an effective screening instrument by being brief, self-administered, easy to score, and clinically validated in population-based samples throughout the world as part of the World Health Organization (WHO) World Mental Health (WMH) surveys (Glover & Albers, 2007; Kessler et al., 2002, 2003, 2010; Levitt, Saka, Romanelli, & Hoagwood, 2007; Shaffer et al., 2004).
Two recent studies using analytic subsamples from the National Comorbidity Survey Adolescent Supplement (NCS-A) have examined the psychometric properties of the K6 and its ability to predict SED using data from the Composite International Diagnostic Interview’s self-administered questionnaire for parents (CIDI-SAQ; Kessler, Avenevoli, Costello, et al., 2012) and the Children’s Global Assessment Scale (CGAS; Shaffer et al., 1983) that measures functional impairment on a scale of 0–100. Green et al. (2010) used principal axis factor analysis to find a strong first factor (eigenvalue = 3.6) of serious psychological distress (SPD) and no meaningful second factor (eigenvalue = 0.7). The K6 was also found to provide fairly good prediction of SED (area under the curve [AUC] = 0.74), although prediction was slightly higher for internalizing disorders (AUC = 0.80) than behavior disorders (AUC = 0.75). Similarly, Li et al. (2010) estimated the prevalence of SED, defined as having one or more 12-month mental disorders according to the Diagnostic and Statistical Manual of Mental Disorders (4th ed.; American Psychiatric Association, 1994) and the CIDI-SAQ and one of the following: a CGAS ≤ 50, Bipolar I disorder (regardless of CGAS score), or a suicide attempt in the past 12 months (regardless of CGAS score). A two-level multilevel Bayes model with bivariate outcomes was then fit to the NCS-A school sample data with K6 scores and the probit transformation of the predicted probability of SED from diagnostic models entered as the two outcomes. This yielded a predicted prevalence of 5.7% for SED and a strong estimated correlation for concordance between the K6 and SED at the school level (ρ = 0.70).
Given the addition of the K6 on the 2012 KIP, this study evaluated the psychometric properties of the K6 in a school-based sample of Kentucky adolescents (n = 108,736) as well as estimated the prevalence and correlates of SED. We performed principal axis factor analysis with a six-by-six matrix of Pearson correlations among the K6 items to explore the dimensionality of the K6. We then corroborated this structure with confirmatory factor analysis using established criteria for χ2, the comparative fit index (CFI), the Tucker–Lewis Index (TLI), and the root-mean-square error of approximation (RMSEA; Hu & Bentler, 1999). As the KIP does not derive diagnostic data from structured interviews, we used the documented cutoff of 13+ on the K6 (Kessler et al., 2003) to calculate the 30-day prevalence of SED. Grade, gender, race and ethnicity, family structure, poverty status, and population density were entered into a multivariable logistic regression model to estimate adjusted relative risks and 95% confidence intervals for SED. We also explored the prevalence of substance abuse, antisocial behavior, role impairments, and peer victimization among students with a positive SED screen. The primary hypothesis was that the factor analyses would support the one-factor solution of the K6 that has been demonstrated in a nationally representative sample of adolescents (Green et al., 2010; Kessler et al., 2010). Based on results from the NCS-A (Kessler, Avenevoli, Costello, et al., 2012), the secondary hypothesis was that grade, race and ethnicity, and family structure would emerge as significant predictors of SED.
Method KIP Survey: Background, Field Procedures, and Sample
The KIP Survey has been administered to Kentucky students for more than a decade by the Substance Abuse Prevention Program in the Cabinet for Health and Family Services, through agreements with individual school districts across the state (Sanders et al., 2012). The intent of the survey is to anonymously assess student use of alcohol, tobacco, and other drugs, as well as a number of factors related to potential substance abuse, including peer influences, risk perceptions, and school safety (Sanders et al., 2012). In 2006, three questions on gambling were added to the survey; and in 2012, the K6 was added. School district and individual student participation have always been on a voluntary basis.
Originally, the KIP Survey was used as part of a federal initiative that funded state incentive grants for substance abuse prevention throughout the nation. In Kentucky, these programs were called the Kentucky Incentives for Prevention, hence the name KIP (Sanders et al., 2012). The core items on the KIP were originally chosen by CSAP based upon extensive research on risk and protective factors associated with child and adolescent substance abuse (Sanders et al., 2012). This federal model enables comparisons to other states and to the nation, while facilitating regional comparisons within the state.
The survey is now administered biennially in the fall of even-numbered years (e.g., 2004, 2006, 2008) to sixth, eighth, 10th, and 12th graders attending schools throughout Kentucky. There is no cost to the individual districts, as all costs are paid by the Substance Abuse Prevention Program of Kentucky state government. Extensive efforts are made to ensure student anonymity and minimize coercion to participate. The survey uses a passive consent model: parents who do not wish for their child to participate are given opportunities through general and specific notifications that they may refuse on behalf of their child.
Effective with the 2008 administration, both paper-pencil and web-based versions of the KIP Survey were made available to school districts. Classroom administration of the paper-pencil survey (including distribution, giving instructions, completing the survey, and collecting the survey) takes approximately 45 min (Sanders et al., 2012). The surveys are administered to classroom groups, sent to a service agency for electronic scanning, and then analyzed by REACH Evaluation, which provides each school district with a comprehensive report of its findings. Administration of the survey typically occurs within a 5-week window in the fall, and results are disseminated to school districts in 3 to 4 months following administration.
In 2012, the KIP Survey was administered to 154 Kentucky school districts out of a total of 173 school districts, yielding an 89% participation rate. Among the 154 participating school districts, a total of 122,718 students completed the survey, and of these students, approximately 89% fully completed the K6 scale (n = 108,736). Table 1 shows the demographic distribution of the KIP Survey respondents compared to enrollment data from the Kentucky Department of Education. Although the survey does not include data from school districts in Jefferson County, the largest district in the state, the grade and gender distributions of the KIP are highly comparable. The racial and ethnic composition of the KIP, however, tends to underrepresent African Americans, but this difference becomes marginal when Jefferson County schools are removed from state-level enrollment data.
Social and Demographic Factors of Kentucky Students in 2012
Social and Demographic Factors
A variety of self-reported social and demographic factors were included in analyses: sex (male or female), grade (6, 8, 10, and 12), and race/ethnicity (classified as Caucasian, African American, Hispanic, Asian American/Pacific Islander, and Other). Family structure included living with both parents, mother only, father only, mother and stepfather, father and stepmother, or other (classified as having one or two biological parents, or other). Data regarding the percentage of youth in poverty and population size were inputted from 2012 U.S. Census estimates for each school’s county. Poverty status was determined by comparing annual income to a set of dollar values called thresholds that vary by family size, number of children, and age of householder. If a family’s income before tax was less than the dollar value of their threshold, then that family and every individual in it, including children, were considered to be in poverty (U.S. Census Bureau, 2014).
Substance Abuse and Antisocial Behavior
The KIP asks a series of questions about the frequency of tobacco, alcohol, marijuana, and illicit drug abuse in the lifetime, past year, and past month. Item responses range from 0, 1–2, 3–5, 6–9, 10–19, 20–39, and 40+ occasions. Past-month substance abuse variables were dichotomized as 0 or 1+ occasions for cigarettes, binge drinking, and marijuana abuse. For illicit drugs, past-year abuse of cocaine, ecstasy, methamphetamines, speed/uppers, and inhalants on 1+ occasions were included. For prescription drugs, past-year abuse of opioids and tranquilizers on 1+ occasions were included. Four items ask students about the frequency of taking a handgun to school, selling illegal drugs, attacking someone (i.e., fighting), and being drunk or high at school using the aforementioned item responses and groupings for the past year. Past-year antisocial behaviors were dichotomized as 0 or 1+ occasions.
Role Impairments
Several questions address school-, legal-, and substance-related role impairments. Students are asked if they have been suspended from school and arrested in the past year. Past-year suspension and arrest were dichotomized as 0 or 1+ occasions. Students are also asked a series of yes or no questions about whether in the past year their drinking or drug abuse has caused them to get stopped by the police for drunk driving or disorderly conduct, get in trouble at school, hurt or injure themselves, get into verbal or physical fights with other kids, get into fights with their parents, commit illegal acts, experience a blackout, pressure someone else to do something sexual against his or her will (sexual perpetrator), get pressured by someone to do something sexual against his or her will (sexual victim), think they have a drinking or drug problem, or be involved in a car accident.
Peer Victimization
Four yes or no questions address experiences of peer victimization at school in the past year. Students are asked if someone took money or things directly from them by using force, weapons, or threats. Students are also asked if someone verbally or physically threatened or attacked them at school. Lastly, students are asked if someone made unwanted sexual advances or attempted to sexually assault them at school.
K6 Scale
The version of the K6 included on the 2012 KIP consists of six questions that ask respondents how frequently they experienced six symptoms of depression and anxiety in the past 30 days. These six items were originally chosen using item response theory (IRT) methods to maximize precision at the point on the core dimension of SPD that distinguishes cases of serious mental illness from others in the population (Kessler et al., 2002; Kessler et al., 2010). The items measured the frequency of feeling (1) “nervous,” (2) “hopeless,” (3) “restless or fidgety,” (4) “so depressed that nothing could cheer you up,” (5) “that everything was an effort,” and (6) “worthless” (Kessler et al., 2002). The following item responses were used: never, a little of the time, some of the time, most of the time, and all of the time. These responses were coded 0–4, generating an unweighted summary scale with a range of 0–24 (Kessler et al., 2003).
Previous research supports a one-factor solution in representative samples of adults and adolescents throughout the world (Kessler et al., 2003, 2010; Green et al., 2010). Specifically, results from the WHO-WMH surveys confirmed the unidimensionality of the K6 in 14 countries (combined n = 41,770 adults) using principal axis factor analysis (Kessler et al., 2010). Using similar methods, Green et al. (2010) achieved a one-factor solution for the K6 in an analytic subsample of 6,483 adolescent-parent pairs from the NCS-A. Moreover, Green et al. also found that the K6 provides fairly good prediction of SED (AUC = 0.74). The strongest associations with regard to AUC were with mood disorders (range 0.74–0.77 with individual disorders and 0.77 with any mood disorder) and anxiety disorders (range 0.69–0.82 with individual disorders and 0.73 with any anxiety disorder).
Data Analysis
A six-by-six matrix of Pearson correlations among the K6 items was generated using the matrix procedure in Stata 12.1 (StataCorp, 2012). Principal axis factor analysis was then carried out using the generated correlation matrix. Parallel factor analyses were also performed with a polychoric correlation matrix generated using the polychoric module that allows for nonlinear monotonic relationships between pairs of variables (Kessler et al., 2010; Kolenikov & Angeles, 2004; StataCorp, 2012). Unidimensionality was supported if these factor analyses revealed a large first unrotated eigenvalue and a second unrotated eigenvalue less than 1.0 (Kessler et al., 2010).
Confirmatory factor analysis (CFA) was then performed using the sem procedure in Stata 12.1 using maximum-likelihood estimation (StataCorp, 2012). Global model fit was determined using the χ2 test, the comparative fit index (CFI), the Tucker–Lewis Index (TLI), and the root-mean-square error of approximation (RMSEA; StataCorp, 2012). The magnitudes of these indices were determined using established criteria (Hu & Bentler, 1999). For χ2, values closer to zero are optimal; for CFI and TLI, ≥0.90 was considered adequate and >0.95 very good; and for RMSEA, ≤0.08 was considered adequate and ≤0.05 very good (Hu & Bentler, 1999). An identical CFA was also performed in Mplus Version 7 (Muthén & Muthén, 2012) using the robust weighted least squares estimator (WLSMV), which has been shown to be optimal for ordinal responses like those on the K6 (Flora & Curran, 2004; Hu & Bentler, 1999).
Using the documented cutoff of 13+ on the K6 (Kessler et al., 2003), the 30-day prevalence of SED was calculated for Kentucky. The distribution of SED was examined for each social and demographic characteristic using cross-tabulations. Adjusted relative risks (aRR) and 95% confidence intervals (CIs) were estimated by fitting a multivariable logistic regression model to determine statistically meaningful social and demographic predictors of SED. The predictors entered into the model included grade, gender, race and ethnicity, family structure, percentage of youth in poverty, and population size. Cross-tabulations were also used to explore the prevalence of substance abuse, antisocial behaviors, role impairments, and peer victimization among students with and without SED.
Results Distribution and Factor Structure of the K6
The distribution of K6 scores (see Figure 1) in the 2012 KIP is fairly comparable to those found in the NCS-A with a J-shaped curve that includes approximately 50% of respondents with scores of 0 (24.1%), 1 (10.3%), 2 (8.6%), and 3 (7.1%; Green et al., 2010). The K6 items all have high Pearson correlations that range 0.53–0.78 (see Table 2). Principal axis factor analysis yields a strong first factor (eigenvalue = 4.1) and no second factor (eigenvalue = 0.6). The parallel factor analysis of the polychoric correlation matrix yields highly comparable results, with eigenvalues of 4.6 and 0.5 for the first and second factors, respectively. Factor loadings on the first factor range from 0.77 to 0.89. Internal consistency reliability measured by Cronbach’s alpha is 0.90.
Figure 1. Distribution of K6 scores.
Principal Axis Factor Analysis of the Pearson Correlation Matrix of K6 Items
In the CFA, CFI is very good at 0.962, TLI is adequate at 0.937, and RMSEA is inadequate at 0.125 (see Table 3). When the residuals are correlated using post hoc modification indices, CFI improves to 0.999, TLI to 0.989, and RMSEA to 0.052. The χ(df)2 from the unspecified model also decreases from 15,221.92(9) to 579.33(2). Global model fit indices are virtually identical in the model estimated with WLSMV.
Global Model Fit Statistics From Confirmatory Factor Analysis of K6 Items
Social and Demographic Correlates of SED
The overall 30-day prevalence of SED is 13.9% (see Table 4). Grade, gender, race/ethnicity, and family structure are significant social and demographic predictors. Compared to sixth graders, 10th graders have the highest relative risk of SED (aRR = 2.00, 95% CI 1.89–2.11). Females have a significantly higher risk of SED than males (aRR = 1.87, 95% CI 1.81–1.94). Students with one biological parent (aRR = 1.55, 95% CI 1.49–1.62) and Other living situations (aRR = 2.04, 95% CI 1.92–2.16) have significantly higher risk of SED than those with two biological parents. Compared to Caucasian students, Hispanic students (aRR = 1.15, 95% CI 1.03–1.27) and Other races (aRR = 1.47, 95% CI 1.36–1.59) have higher risk of SED, while African Americans have a significantly lower risk (aRR = 0.88, 95% CI 0.81–0.95).
Social and Demographic Correlates of Serious Emotional Disturbance
Substance Abuse, Antisocial Behavior, and Role Impairments
The 30-day prevalence rates for cigarette smoking (23.4%), binge drinking (15.3%), and marijuana abuse (15.6%) for students with SED are approximately twice as high as students without SED (see Table 5). Among the five illicit drugs included, inhalants are the most prevalent, followed by speed/uppers, cocaine, and ecstasy (range 3.1%–3.3%). For all illicit drugs, students with SED have 12-month prevalence rates that are approximately 3 to 4 times higher than students without SED. The 12-month rates of individual antisocial behaviors (range 9.1%–20%) and role impairments (range 2.8%–22.7%) were all higher among students with SED. The most prevalent role impairments included experiencing a blackout, interpersonal problems with peers and parents, self-injury, and school-related trouble. Approximately 47% of students with SED reported one or more role impairments, nearly twice the rate of students without SED (24.7%). All forms of peer victimization were significantly higher among students with SED.
Prevalence of Substance Abuse, Antisocial Behavior, and Role Impairments
DiscussionThis study indicates that the performance and factor structure of the K6 in a school-based epidemiologic survey is broadly comparable to NCS-A findings. The distribution of K6 scores revealed approximately half of students scored 0–3, a finding that is fairly consistent with a previous study that found the majority of NCS-A respondents (51.3%) scored 0–2 (Green et al., 2010). Principal axis factor analysis found a strong first factor and no evidence of a meaningful second factor (Kessler et al., 2010). This is highly consistent with the NCS-A sample that yielded values of 3.6 and 0.7 (Green et al., 2010). CFA also demonstrated the unidimensional structure of the K6, although optimal fit was only achieved after correlated residuals were further specified in models. This relationship, however, makes empirical sense, as K6 items constitute the interrelated aspects of non-specific SPD. Moreover, a recent analysis of K6 data from the Canadian National Population Health Survey (n = 7,259) found optimal global fit statistics only when correlated residuals were specified (Drapeau et al., 2010). Together, these results suggest the K6 can be easily and quickly self-administered as a broad-based screening scale in school settings. They also offer additional evidence that symptom severity scales may identify students with serious role impairments and high-risk behaviors, despite the K6 not directly assessing specific disorders or impairments (Kessler et al., 2003, 2010).
While the 30-day prevalence of 13.9% falls within the range (4%–14%) found in other studies (Kessler, Avenevoli, Costello, et al., 2012; Merikangas, Avenevoli, Costello, Koretz, & Kessler, 2009), this is higher than the 30-day rate of 5.6% estimated in the NCS-A school sample (Li et al., 2010). This national estimate, however, was derived using a two-level multilevel Bayes model with binary outcomes, so it is likely the dichotomized scoring algorithm used in this study did not fully capture core aspects of SED, like behavior disorders and role impairments that lead to more accurate estimates (Green et al., 2010; Kessler, Avenevoli, Costello, et al., 2012; Li et al., 2010). For example, Green et al. (2010) found that the K6 augmented with five items related to behavior and personality disorders was more strongly associated with SED, with AUC increasing from 0.74 to 0.83. This also led to an improvement in the prediction of any mood disorder (AUC increasing from 0.77 to 0.81), any anxiety disorder (AUC increasing from 0.73 to 0.75), and any behavior disorder (AUC increasing from 0.67 to 0.82). Because the KIP is an anonymous survey that does not measure the prevalence of individual disorders among students, it is likely that estimation procedures may be necessary to estimate school-level SED prevalence from samples of students only administered the K6, but not a structured interview like the CIDI (Li et al., 2010). Nonetheless, the dichotomized scoring algorithm used in this study continues to be widely used as a measure of non-specific SPD in national surveys like the National Survey on Drug Use and Health, the Behavioral Risk Factor Surveillance System, and the National Health Interview Survey (Kessler et al., 2010).
Several social and demographic factors were significant predictors of SED. In particular, 10th graders had the highest relative risk of SED (aRR = 2.00, 95% CI 1.89–2.11), consistent with the significantly increased odds among 16-year-olds (odds ratio [OR] = 1.5, 95% CI 1.1–2.2) in the NCS-A (Kessler, Avenevoli, Costello, et al., 2012). The findings for number of biological parents were also highly significant predictors in both the KIP and NCS-A samples. Females were at significantly increased risk of SED in the KIP sample (aRR = 1.87, 95% CI 1.81–1.94) but not in the NCS-A sample (Kessler, Avenevoli, Costello, et al., 2012). Compared to Caucasians, African Americans were significantly less likely to have SED (aRR = 0.88, 95% CI 0.81–0.95), consistent with estimates from the NCS-A (OR = 0.60, 95% CI 0.4–0.9). Although Hispanics (aRR = 1.12, 95% CI 1.01–1.23) and Other races (aRR = 1.45, 95% CI 1.35–1.55) were at elevated risk in the KIP, Hispanics had an increased, albeit insignificant, odds ratio in the NCS-A (OR = 1.4, 95% CI 0.7–2.6; Kessler, Avenevoli, Costello, et al., 2012). The null effects for youth in poverty and population density were highly similar to the NCS-A that found no effects for family income, Census region, and urbanicity (Kessler, Avenevoli, Costello, et al., 2012). The high rates of substance abuse, antisocial behavior, role impairments, and peer victimization among students with SED are broadly comparable to evidence from previous studies that suggest multivariate disorder profiles are more relevant than social and demographic predictors, especially since associations with social and demographic factors tend to become insignificant when severity and number of disorders are included as controls in estimates (Kessler, Avenevoli, Costello, et al., 2012).
Limitations
Several limitations are acknowledged. First, data from the KIP are cross-sectional. Temporality between SED and significant predictors therefore cannot be inferred. The results from this study are nonetheless consistent with findings from the NCS-A and other population-based studies of adolescents (Kessler, Avenevoli, Costello, et al., 2012; Merikangas et al., 2009). Second, while the distribution of K6 scores is fairly similar to that found in the NCS-A (Green et al., 2010), approximately 2.4% of students answered all of the time for each item. This raises the question as to whether these students actually have maximum scores or if methodological issues influenced these item responses. Since the KIP is a self-report survey with limited space, the K6 was placed at the end of the 2012 instrument, raising the possibility that fatigue may have introduced biased responses that artificially drove the prevalence to the higher end of the range found in previous studies. Similarly, all students who scored 24 had the highest rate of lifetime Zycopan abuse (2.4%), a fictitious drug included on the KIP to detect social desirability. While virtually identical distributions and effects of included predictors were produced when these students were censored from analyses, fatigue and social desirability cannot be fully ruled out as forms of differential misclassification bias.
Third, our sample was highly homogeneous with approximately 84% of the total population consisting of Caucasian students. The findings among racial and ethnic minorities may not generalize to other states. This predominantly Caucasian student sample, however, is highly consistent with other state and school findings (Collins, Abadi, Johnson, Shamblen, & Thompson, 2011; Havens, Talbert, Walker, Leedham, & Leukefeld, 2006). Varying school district participation may have also biased the sample. In particular, Jefferson County, the largest metropolitan area in Kentucky, does not participate in the KIP, further limiting the generalizability of these findings to urban adolescents. Although the participation rate and overall sample size were optimal, future efforts should focus on maximizing participation.
Last, the dichotomized scoring of the K6 may have influenced the precision of estimates. In the NCS-A, the sensitivity of the K6 cutoff point that maximizes concordance with SED estimates was only 34%, and only 32% of K6 positive cases received a positive DSM diagnosis (Green et al., 2010). These values are unacceptably low, which has prompted the use of polychotomous scoring rules that yield more optimal classification functions and precise estimates (Furukawa et al., 2008; Furukawa, Kessler, Slade, & Andrews, 2003; Prochaska, Sung, Max, Shi, & Ong, 2012). These sorts of approaches, however, require diagnostic data and are therefore beyond the scope of this study, as the KIP does not include structured interviews to estimate the prevalence of specific mental disorders. Moreover, the KIP does not contain validated instruments to measure behavior disorders and functional impairment, although it is worth noting that combining the dichotomized scoring algorithm with one or more role impairments produced a more conservative estimate of 6.7% that is consistent with national estimates as well as slightly higher rates of psychiatric morbidity documented among Kentucky youth (State Epidemiological Outcomes Workgroup, 2012).
Implications
The inclusion of the K6 on the KIP provides additional evidence of its utility as a broad screening scale that may be included in large, self-administered epidemiologic surveys that have limited space and logistics that demand timely administration (Green et al., 2010; Li et al., 2010). While the cutoff on the K6 appears to lack sufficient sensitivity and predictive value for generating individual-level estimates of SED, the fact that the K6 can be easily and quickly self-administered in roughly 2 min is a vital component for the successful administration of school-based surveys like the KIP (Kessler et al., 2003; Green et al., 2010). The continued use of unweighted scoring methods therefore suggests the K6 may be better served as a measure of 30-day SPD as opposed to the predicted prevalence of SED that significantly benefits from clinical data (Green et al., 2010; Kessler et al., 2003, 2010; Li et al., 2010). Thus, epidemiologic data like those generated by the K6 may inform the development and allocation of community resources to address the mental health needs of Kentucky youth (Dowdy, Ritchey, & Kamphaus, 2010; Green et al., 2013). Such is the case in Kentucky, where KIP data are frequently used within the context of SAMHSA’s Strategic Prevention Framework and the SEOW, which both continue to systematically guide prevention activities throughout the state (see Figure 2).
Figure 2. The outer segment of the figure depicts the five-step planning process of the Strategic Prevention Framework that is grounded in Sustainability and Cultural Competency: (1) Assessment, (2) Capacity Building, (3) Planning, (4) Implementation, and (5) Monitor and Evaluate. Guidance documents are then suggested for the steps in the next layer, which are based upon interrelated Core Tasks around the model center. From “Epidemiological Workgroup: Technical Assistance Toolkit,” by the Substance Abuse and Mental Health Services Administration, 2009, http://captus.samhsa.gov/sites/default/files/capt_resource/EpitoolKit%20Merged%202010.pdf, p. 1. Copyright 2009 by the Substance Abuse and Mental Health Services Administration. See the online article for a color version of this figure.
The inclusion of the K6 on the KIP also falls in line with recent recommendations by the President’s New Freedom Commission on Mental Health, the Department for Health and Human Services, and the Institute for Medicine for schools to improve their early identification and prevention efforts (Costello, He, Sampson, Kessler, & Merikangas, 2014; Green et al., 2013). This is highly relevant to a rural and impoverished state like Kentucky, as students with SED, as well as substance use and behavior disorders are more likely to access mental health services when their schools are located in urban, compared to rural, environments (Cohen & Hesselbart, 1993; Green et al., 2013; Slade, 2003). Two recent NCS-A studies have also found that less than half of adolescents with any disorder in the past 12 months received any services and that fewer than one in four received school-based mental health services (Costello et al., 2014; Green et al., 2013). Among those with three or more disorders, less than half had received school-based mental health services (Costello et al., 2014; Green et al., 2013). Multimorbidity is particularly salient, as those with three or more disorders make up approximately 30% of those with 12-month disorders but comprise nearly two-thirds of SED cases (Kessler, Avenevoli, Costello, et al., 2012).
The impact of psychiatric comorbidity and heterogeneity has implications for prevention activities in Kentucky. To delay or halt progression of psychiatric morbidity in adolescence and adulthood, the main role of prevention activities may benefit from shifting focus from disorder-specific interventions to addressing the core features of SED among youth, including psychological distress, disordered behavior, role impairments, and multimorbidity (Kessler, Avenevoli, Costello, et al., 2012; Kessler, Avenevoli, McLaughlin, et al., 2012; McGorry et al., 2007; Merikangas, Avenevoli, et al., 2009; Merikangas et al., 2009). Based upon the presentation of comorbid features along with other relevant risk and protective factors, prevention programs may also benefit from identifying critical periods to intervene as well as tailoring activities to varying levels of severity. Nearly all selective and universal prevention programs for anxiety and depression, however, only target youth during adolescence, despite evidence suggesting that clinically significant psychiatric morbidity may present during childhood (Neil & Christensen, 2009).
Interestingly, a recent NCS-A study has suggested that increased delivery of counseling in schools is associated with decreased service utilization among youth with SED, while prevention activities are associated with increased utilization (Green et al., 2013). While these findings were derived from a national sample of schools and may not necessarily generalize to Kentucky schools, the provision of prevention activities throughout the state has been shown to influence recent decreases in the state and regional rates of adolescent substance abuse, suicide behaviors, and other high-risk behaviors (Sanders, Illback, Crabtree, & Sanders, 2013; State Epidemiological Outcomes Workgroup, 2012). K6 scores from school samples may therefore serve as a useful tool for school districts and regional policy planning, as the K6 possesses items related to depression and anxiety that are shared across a wide range of disorders (Kessler et al., 2002, 2003, 2010). As youth with more severe psychiatric morbidity typically have higher scores on the K6 (Green et al., 2010; Kessler, Avenevoli, Costello, et al., 2012; Li et al., 2010), the evaluation of K6 scores from the 2012 KIP represents an opportunity to identify high-risk schools and further inform prevention efforts in Kentucky.
The next steps include validation of the K6 on other state surveys that use school- and community-based samples of adolescents, determination of measurement invariance in adolescent samples across key demographic factors, generation of cross-state comparisons, and the implementation of validated statistical approaches to generate more precise SED estimates (Li et al., 2010), especially with the KIP, where gold standard diagnoses are not available. The planned inclusion of questions pertaining to suicide behaviors, bully victimization, and new drugs of abuse on the 2014 KIP also provides additional opportunity to explore issues related to SED and its heterogeneity. Thus, this study provides a foundation for increased epidemiologic infrastructure in Kentucky through the timely surveillance of adolescent psychiatric morbidity as measured by the K6.
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Submitted: January 6, 2014 Revised: April 30, 2014 Accepted: July 23, 2014
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Source: Psychological Assessment. Vol. 27. (1), Mar, 2015 pp. 228-238)
Accession Number: 2014-37941-001
Digital Object Identifier: 10.1037/pas0000025
Record: 173- Title:
- The predictive utility of a brief kindergarten screening measure of child behavior problems.
- Authors:
- Racz, Sarah Jensen. Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, MD, US, sracz@jhsph.edu
King, Kevin M.. Department of Psychology, University of Washington, WA, US
Wu, Johnny. Department of Statistics, University of Florida, FL, US
Witkiewitz, Katie. Department of Psychology, Washington State University, WA, US
McMahon, Robert J.. Department of Psychology, Simon Fraser University, Burnaby, BC, Canada - Institutional Authors:
- The Conduct Problems Prevention Research Group
- Address:
- Racz, Sarah Jensen, Johns Hopkins Bloomberg School of Public Health, Department of Mental Health, 624 North Broadway, HH Room 808, Baltimore, MD, US, 21205, sracz@jhsph.edu
- Source:
- Journal of Consulting and Clinical Psychology, Vol 81(4), Aug, 2013. pp. 588-599.
- NLM Title Abbreviation:
- J Consult Clin Psychol
- Page Count:
- 12
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- Journal of Consulting Psychology
- Other Publishers:
- US : American Association for Applied Psychology
US : Dentan Printing Company
US : Science Press Printing Company - ISSN:
- 0022-006X (Print)
1939-2117 (Electronic) - Language:
- English
- Keywords:
- TOCA-R, child behavior problems, screening methods, kindergartners, Teacher Observation of Classroom Adaptation–Revised, at risk populations
- Abstract:
- Objective: Kindergarten teacher ratings, such as those from the Teacher Observation of Classroom Adaptation–Revised (TOCA-R), are a promising cost- and time-effective screening method to identify children at risk for later problems. Previous research with the TOCA-R has been mainly limited to outcomes in a single domain measured during elementary school. The goal of the current study was to examine the ability of TOCA-R sum scores to predict outcomes in multiple domains across distinct developmental periods (i.e., late childhood, middle adolescence, late adolescence). Method: We used data from the Fast Track Project, a large multisite study with children at risk for conduct problems (n = 752; M age at start of study = 6.55 years; 57.7% male; 49.9% Caucasian, 46.3% African American). Kindergarten TOCA-R sum scores were used as the predictor in regression analyses; outcomes included school difficulties, externalizing diagnoses and symptom counts, and substance use. Results: TOCA-R sum scores predicted school outcomes at all time points, diagnosis of ADHD in 9th grade, several externalizing disorder symptom counts, and cigarette use in 12th grade. Conclusions: The findings demonstrate the predictive utility of the TOCA-R when examining outcomes within the school setting. Therefore, these results suggest the 10-item TOCA-R may provide a quick and accurate screening of children at risk for later problems. Implications for prevention and intervention programs are discussed. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *At Risk Populations; *Behavior Problems; *Kindergartens; *Screening; Adolescent Development; Rating; Teachers
- Medical Subject Headings (MeSH):
- Adolescent; Attention Deficit and Disruptive Behavior Disorders; Child; Child Behavior Disorders; Child, Preschool; Female; Follow-Up Studies; Humans; Male; Predictive Value of Tests; Psychiatric Status Rating Scales
- PsycINFO Classification:
- Classroom Dynamics & Student Adjustment & Attitudes (3560)
- Population:
- Human
Male
Female - Location:
- US
- Age Group:
- Childhood (birth-12 yrs)
Preschool Age (2-5 yrs)
School Age (6-12 yrs)
Adulthood (18 yrs & older) - Tests & Measures:
- Computerized Diagnostic Interview Schedule for Children
School Adjustment Scale-Child Report Version
School Adjustment Scale-Parent Version
Tobacco, Alcohol and Drugs Measure
Self-Administered Youth Questionnaire
National Longitudinal Survey of Youth 1997
Teacher Social Competence Scale
Child Behavior Checklist
Teacher Observation of Classroom Adaptation--Revised DOI: 10.1037/t31163-000
Teacher's Report Form DOI: 10.1037/t02066-000 - Grant Sponsorship:
- Sponsor: National Institute of Mental Health
Grant Number: R18 MH48043, R18 MH50951, R18 MH50952, R18 MH50953
Recipients: No recipient indicated
Sponsor: National Institute on Drug Abuse
Grant Number: 1 RC1 DA028248-01
Recipients: No recipient indicated
Sponsor: Center for Substance Abuse Prevention
Other Details: Fast Track
Recipients: No recipient indicated
Sponsor: National Institute on Drug Abuse
Other Details: Fast Track
Recipients: No recipient indicated
Sponsor: Department of Education
Grant Number: S184U30002
Recipients: No recipient indicated
Sponsor: National Institute of Mental Health
Grant Number: K05MH00797, K05MH01027
Recipients: No recipient indicated
Sponsor: National Institute on Drug Abuse
Grant Number: DA16903, DA015226, DA017589
Recipients: No recipient indicated
Sponsor: Child & Family Research Institute
Other Details: Establishment Award
Recipients: McMahon, Robert J. - Conference:
- Association for Psychological Science annual meeting, May, 2010, Boston, MA, US
- Conference Notes:
- A preliminary version of this article was presented at the aforementioned conference.
- Methodology:
- Empirical Study; Longitudinal Study; Interview; Quantitative Study
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- First Posted: Apr 1, 2013; Accepted: Feb 12, 2013; Revised: Sep 11, 2012; First Submitted: Oct 16, 2011
- Release Date:
- 20130401
- Correction Date:
- 20140915
- Copyright:
- American Psychological Association. 2013
- Digital Object Identifier:
- http://dx.doi.org/10.1037/a0032366
- PMID:
- 23544679
- Accession Number:
- 2013-11001-001
- Number of Citations in Source:
- 56
- Persistent link to this record (Permalink):
- http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2013-11001-001&site=ehost-live
- Cut and Paste:
- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2013-11001-001&site=ehost-live">The predictive utility of a brief kindergarten screening measure of child behavior problems.</A>
- Database:
- PsycINFO
The Predictive Utility of a Brief Kindergarten Screening Measure of Child Behavior Problems
By: Sarah Jensen Racz
Department of Mental Health, Johns Hopkins Bloomberg School of Public Health;
Kevin M. King
Department of Psychology, University of Washington
Johnny Wu
Department of Statistics, University of Florida
Katie Witkiewitz
Department of Psychology, Washington State University
Robert J. McMahon
Department of Psychology, Simon Fraser University, Burnaby, British Columbia, Canada, and the Child & Family Research Institute, Vancouver, British Columbia, Canada
Acknowledgement: This work was supported by National Institute of Mental Health (NIMH) Grants R18 MH48043, R18 MH50951, R18 MH50952, R18 MH50953, and by National Institute on Drug Abuse (NIDA) Grant 1 RC1 DA028248-01. The Center for Substance Abuse Prevention and NIDA also provided support for Fast Track through a memorandum of agreement with the NIMH. This work was also supported in part by Department of Education Grant S184U30002, NIMH Grants K05MH00797 and K05MH01027, and NIDA Grants DA16903, DA015226, and DA017589. Preparation of the manuscript was also supported by an Establishment Award from the Child & Family Research Institute awarded to Robert J. McMahon. A preliminary version of this article was presented at the Association for Psychological Science annual meeting, Boston, MA, May 2010.
Members of the Conduct Problems Prevention Research Group, in alphabetical order, include Karen L. Bierman, Department of Psychology, Pennsylvania State University; John D. Coie, Duke University; Kenneth A. Dodge, Center for Child and Family Policy, Duke University; Mark T. Greenberg, Department of Human Development and Family Studies, Pennsylvania State University; John E. Lochman, Department of Psychology, The University of Alabama; Robert J. McMahon, Department of Psychology, Simon Fraser University, and the Child & Family Research Institute; and Ellen E. Pinderhughes, Department of Child Development, Tufts University.
Karen L. Bierman, John D. Coie, Kenneth A. Dodge, Mark T. Greenberg, John E. Lochman, and Robert J. McMahon are the developers of the Fast Track curriculum and have a publishing agreement with Oxford University Press. Mark T. Greenberg is an author on the PATHS curriculum and has a royalty agreement with Channing-Bete, Inc. He is a principal in PATHS Training, LLC. Robert J. McMahon is a coauthor of Helping the Noncompliant Child and has a royalty agreement with Guilford Publications, Inc.; he is also a member of the Treatments That Work Scientific Advisory Board with Oxford University Press.
We are grateful for the collaboration of the school districts that participated in the Fast Track project as well as the hard work and dedication of the many staff members who implemented the project, collected the evaluation data, and assisted with data management and analyses. For additional information concerning Fast Track, see http://www.fasttrackproject.org.
Early behavior problems (e.g., aggressiveness, disruptiveness, oppositionality) are widely recognized as a risk factor for later violence and antisocial behavior (Bennett & Offord, 2001; Campbell, Shaw, & Gilliom, 2000). These early problematic behaviors are associated with several severe negative outcomes in later adolescence and adulthood, including school dropout and unemployment (Fergusson & Horwood, 1998; Jessor, 1998; Loeber & Dishion, 1983). Additionally, aggressive and disruptive behavior identified as early as kindergarten has been shown to predict later delinquent behavior and substance use (Hill, Lochman, Coie, Greenberg, & the Conduct Problems Prevention Research Group [CPPRG], 2004; Petras, Chilcoat, Leaf, Ialongo, & Kellam, 2004; Petras et al., 2005), suggesting that problem behaviors observed early in childhood can persist across development (Loeber, 1982; Loeber & Hay, 1997; Moffitt, 1993). Thus, early intervention aimed at high-risk children is important (Lochman & CPPRG, 1995), and the use of accurate and reliable screening tools to identify children for these targeted prevention programs is essential (Jones, Dodge, Foster, Nix, & CPPRG, 2002).
Brief screening measures distributed to parents, teachers, and other caregivers are often used as an initial step in identifying children who will benefit from prevention programs targeting early behavior problems (CPPRG, 1999; Feil, Walker, & Severson, 1995). Studies examining the predictive utility of these screening tools showed that combining parent and teacher ratings of behavior problems during kindergarten predicted more difficulties in interactions with peers and teachers (Wehby, Dodge, Valente, & CPPRG, 1993), higher levels of delinquency (Hill et al., 2004), and lower levels of social competence (Lochman & CPPRG, 1995) in first grade. Additionally, parent and teacher ratings of child aggressiveness and hyperactivity in preschool predicted child behavior problems at the end of preschool (Doctoroff & Arnold, 2004) and 5 years later (Stormont, 2000). However, these multireporter screening measures can be costly and time-consuming. It is therefore important to examine the predictive utility of brief single-rater screening measures, as these may represent a cost- and time-efficient method to identify children at risk for later behavior problems and associated negative outcomes.
Teacher Ratings of Early Behavior ProblemsTeacher ratings are a promising resource for the implementation of brief, single-informant screening procedures. Focusing on teacher ratings in the school context provides a highly efficient and cost-effective method of identifying early behavior problems, as compared with gathering information from parents or peers, as a teacher is able to rate an entire classroom in one sitting (Petras et al., 2005). Furthermore, teacher ratings of aggressive and disruptive child behavior in first grade predicted difficulties in classroom behavior, academic achievement, and social adjustment in third grade (Flanagan, Bierman, Kam, & CPPRG, 2003). Additionally, teacher nominations were more accurate than parent nominations in predicting which children would develop behavior problems 1 year later (Dwyer, Nicholson, & Battistutta, 2006). This research suggests that teachers may be an ideal source for identifying children who are likely to show later behavior problems and may therefore benefit from participation in preventive interventions. Additionally, several large-scale intervention programs use teacher ratings as the first step in a multiple-gating assessment procedure (e.g., the Fast Track project; Lochman & CPPRG, 1995). Parents of children identified as higher risk based on these teacher ratings can then be contacted to provide their own ratings of their children’s behavior. Given the wide use of teacher ratings, it is important to determine the accuracy and predictive utility of these screening measures.
The Teacher Observation of Classroom Adaptation–Revised (TOCA-R; Werthamer-Larsson, Kellam, & Wheeler, 1991) is a commonly used teacher rating screening tool. The Authority Acceptance (AA) scale of this measure includes 10 items asking teachers to rate the frequency of their students’ behavior problems in the classroom. Scores on the TOCA-R collected during first through fifth grade have been shown to predict later (through age 18) violent and antisocial behavior in both boys and girls as identified in court records (Petras et al., 2004, 2005). In a recent study (Bradshaw, Schaeffer, Petras, & Ialongo, 2010), children were classified into one of three early starter aggressive-disruptive behavior trajectory groups on the basis of their TOCA-R scores during first through fifth grades: chronic high, low-moderate (for girls), or increasing (for boys). As compared with children who did not display behavior problems in elementary school, children in these early starter trajectory groups were found to be at risk for a greater number of negative nonaggressive life outcomes at age 19–20, including early pregnancy and unemployment (for girls) and high school dropout (for boys). Thus, the TOCA-R has demonstrated predictive validity to later problematic outcomes when examined across multiple assessment periods, but its utility as a screener at a single time point early in children’s development (e.g., prior to first grade) is less well established.
Only a few studies have examined the ability of screening measures like the TOCA-R to predict behavior beyond elementary school (cf. Bradshaw et al., 2010; Petras et al., 2004, 2005). Additionally, many of these studies have only examined outcomes in one particular domain or context (e.g., violent behavior, behavior in the classroom, nonaggressive life outcomes) and at one particular point in time. Thus, it is unknown how well early teacher ratings of aggressive and disruptive behaviors, assessed at a single time point, predict behaviors both within and external to the school context, and how these predictions may extend across different developmental periods. Furthermore, it remains unclear how broadly or narrowly this screening measure could be used. For instance, it could be that this measure is a broad predictor, capturing multiple outcomes (e.g., clinical diagnoses of externalizing disorders, substance use, school problems). Conversely, it could be that this measure is a narrow predictor, only addressing school outcomes. As a result of this uncertainty, the utility of this measure to identify children who would benefit from particular prevention programs is still unknown.
Goal of the Current StudyPrevious studies examining the predictive validity of early screening measures of behavior problems such as the TOCA-R have been limited in that they have mostly examined outcomes in one domain or one context (e.g., behavior problems at school), and at one point in time (e.g., third grade). Few studies have examined how well ratings of early behavior problems collected at a single time point predict later negative outcomes and adjustment problems across multiple domains and across distinct developmental periods (i.e., late childhood, middle adolescence, and late adolescence). By examining a variety of outcomes across several developmental periods, it is possible to suggest more targeted interventions for at-risk children. For instance, it may be that high scores on the TOCA-R in kindergarten would predict more school-based behavior problems during late childhood and more delinquency and substance use problems during adolescence. These findings might suggest that for children identified as at risk based on TOCA-R ratings in kindergarten, school-based interventions addressing behavioral and emotional regulation in the classroom would be needed during childhood, whereas more community-based interventions addressing delinquency and substance use would be appropriate during adolescence. To date, no studies using early screening measures have addressed this issue regarding the timing and type of interventions indicated for at-risk children.
Therefore, the goal of the current study was to examine the ability of the TOCA-R measured during kindergarten to predict outcomes at the end of elementary (sixth grade), middle (eighth or ninth grade), and high (11th or 12th grade) school in the following domains: (a) school (behavior problems, social competence, and academic and disciplinary difficulties); (b) clinical diagnoses of externalizing disorders (i.e., attention-deficit/ hyperactivity disorder [ADHD], conduct disorder [CD], and oppositional defiant disorder [ODD]); and (c) substance use (at the end of middle and high school only). In sum, the goal of the current study was to comprehensively examine the prediction of multiple outcomes across multiple domains and developmental periods from the TOCA-R, administered at one time point in kindergarten. To date, no studies have provided this comprehensive evaluation of the predictive utility of the TOCA-R.
Method Participants
Fast Track project
Participants came from a community-based sample of children drawn from the Fast Track project, a longitudinal multisite investigation of the development and prevention of childhood conduct problems (CPPRG, 1992, 2000). Schools within four sites (Durham, NC; Nashville, TN; Seattle, WA; and rural Pennsylvania) were identified as high risk based on crime and poverty statistics of the neighborhoods that they served. Within each site, schools were divided into sets matched for demographics (size, percentage free or reduced lunch, ethnic composition), and the sets were randomly assigned to control and intervention groups. Using a multiple-gating screening procedure that combined teacher and parent ratings of disruptive behavior, 9,594 kindergarteners across three cohorts (1991–1993) from 55 schools were screened initially for classroom conduct problems by teachers using the AA score of the TOCA-R (Werthamer-Larsson et al., 1991; see also Lochman & CPPRG, 1995, for more details regarding screening procedures). The AA scale of the TOCA-R includes 10 items asking teachers to rate the frequency of their students’ behavior problems in the classroom. Those children scoring in the top 40% on the TOCA-R within cohort and site were then solicited for the next stage of screening for home behavior problems by their parents, using items from the Child Behavior Checklist (Achenbach, 199la) and similar scales, and 91% agreed to participate (n = 3,274). The teacher and parent screening scores were then standardized and summed to yield a total severity-of-risk screen score. Children were selected for inclusion into the high-risk sample on the basis of this screen score, moving from the highest score downward until desired sample sizes were reached within sites, cohorts, and groups. Deviations were made when a child failed to matriculate in the first grade at a core school (n = 59) or refused to participate (n = 75) or to accommodate a rule that no child would be the only girl in an intervention group. The outcome was that 891 children (control = 446, intervention = 445) participated.
In addition to the high-risk sample of 891 children, a stratified normative sample of 387 children was identified to represent the population-normative range of risk scores and was followed over time. This normative sample was selected from the control schools, such that 100 kindergarten children were selected at each site (except for Seattle, WA, where only 87 children were selected). Participants in the normative sample were stratified to represent the population according to race, gender, and level of teacher-reported behavior problems (10 children at each decile of the distribution of scores from the TOCA-R). The normative sample included a portion of high-risk control group children to the proportional degree that they were represented in the school population. Written consent from parents and verbal assent from children were obtained. Parents were paid $75 for completing the summer interviews, and teachers were compensated $10 per child for completing the measures. The Institutional Review Boards of the participating universities approved all study procedures.
Sample description
The current study used data from the high-risk control and normative groups. Participants from the high-risk intervention sample were not included in this study. Because 79 of those recruited for the high-risk control group were also included as part of the normative sample, the total sample included 754 participants. However, two children in this sample were missing TOCA-R scores and were therefore excluded from the current analyses, yielding a final sample of 752 children. Children were, on average, 6.55 years old (SD = .43) at the start of the Fast Track project. As would be expected given the higher prevalence of conduct problems documented among boys as compared with girls (Hinshaw & Lee, 2003), 57.7% of the sample was male. Reflecting the ethnic diversity in the populations at the four sites, the majority of the sample was either Caucasian (49.9%) or African American (46.3%), with 3.8% of the sample representing other ethnic groups (e.g., Hispanic, Asian). Due to the multisite sampling design of the Fast Track project, race and urban/rural status were confounded, as nearly all of the African American participants lived in urban areas. In fact, less than 1% of the entire sample consisted of African Americans living in rural communities. Thus, for the current study, a race/urban status variable was examined representing three groups: urban African Americans (46.0%) urban Caucasians (24.2%), and rural Caucasians (25.7%). For analyses examining ethnicity and race/urban status, other ethnic minorities were not included due to the small sample sizes in these groups. For this final sample of 752 children, 147 teachers provided TOCA-R ratings in kindergarten.
Procedure
Annual home interviews were conducted with primary caregivers (typically mothers) and children. Interviews began during the summer before children’s entry to first grade and concluded 2 years after the child completed (or would have completed) 12th grade. Caregivers and children completed the interviews separately with two different interviewers over the course of approximately 2 hr. Measures given during these interviews assessed several domains, including parenting behaviors, child behavior problems, family functioning, parent–child relationship quality, peer relationships, academic achievement, and characteristics of the broader neighborhood. Measures included in the current study are described below.
We chose the specific grades included in this study because they aligned with assessments from the Fast Track project that fell within the developmental periods we aimed to address. The timing of assessments during the Fast Track project also took into account the length of the assessment battery at each year. For instance, in years when the lengthy Computerized Diagnostic Interview Schedule for Children (CDISC; Shaffer & Fisher, 1997) was administered, the remainder of the assessment battery was trimmed significantly to reduce participant burden. For this reason, not all measures were administered at all years, leading to the uneven timing of assessments.
Measures
The TOCA-R
The AA scale of the TOCA-R (Werthamer-Larsson et al., 1991) assessed during kindergarten was used as the predictor in all analyses. The entire TOCA-R is a 43-item questionnaire designed for teachers to assess authority and acceptance behavior, concentration problems, and shy behavior relevant to the child’s behavior in a classroom situation on a 6-point Likert-type scale (0 = never, 1 = rarely, 2 = sometimes, 3 = often, 4 = very often, and 5 = always). The AA scale includes 10 items representing aggressive and disruptive behavior problems (e.g., “breaks rules,” “harms others,” and “takes others’ property”). Scores on the TOCA-R are commonly summed to create a composite sum score (e.g., Petras et al., 2004, 2005). Therefore, for the current study, sum scores were created on the basis of the 10-item AA scale of the TOCA-R (hereafter referred to as TOCA-R sum scores).
School outcomes
Several school-related outcomes were measured at sixth, eighth, and 11th grades, including behavior problems in school, social competence, and academic and disciplinary difficulties. Teacher-rated child behavior problems in school were measured in sixth and eighth grade with the T-score of the Externalizing subscale of the Teacher’s Report Form (TRF; Achenbach, 1991b). The Externalizing subscale of the TRF is a 34-item measure that asked teachers to report on the child’s level of multiple problematic behaviors (e.g., disobedient, disruptive, physically aggressive, explosive, stubborn, truant). Items were scored on a 3-point Likert scale, ranging from 0 (not true [as far as you know]) to 2 (very true or often true). Cronbach’s alpha coefficients for this subscale were .96 in sixth grade and .97 in eighth grade, indicating strong internal consistency.
Teacher-rated social competence in sixth and eighth grade was measured with the Teacher Social Competence (TSC) scale, which was developed by the Fast Track project (CPPRG, 1995). The TSC is a 17-item measure that assessed child competence in academic behavior, prosocial skills, and emotional regulation (e.g., performs academically at grade level, handles disagreements in a positive way, cooperates with others, initiates interactions in a positive manner, recognizes and labels feelings, stops and calms down when excited). Items on this measure asked teachers to rate the frequency of these social behaviors on a 6-point scale, ranging from 0 (almost never) to 5 (almost always). The total score on the TSC was calculated as the mean of all 17 items. Internal consistency of the TSC was strong (Cronbach’s α = .94 in sixth grade and .95 in eighth grade).
Parent- and child-reported academic and disciplinary difficulties in sixth, eighth, and 11th grade were measured with the School Adjustment scale, which was developed by the Fast Track project (CPPRG, 1997a, 1997b). The parent report version of this measure included 18 items that evaluated the child’s past school year in terms of academic performance, disciplinary problems, and general worries about school (e.g., school year difficult for child, school work was really hard for child, other kids tried to make child do bad things, child got into trouble by breaking rules). Two items related to the parent’s contact with the school and teachers were not included in this study. This measure used a 5-point response scale, ranging from 1 (strongly disagree) to 5 (strongly agree). The total academic and disciplinary difficulties-parent report score was calculated as the mean of all 16 items. Cronbach’s alpha coefficients were .90 in sixth and eighth grades and .84 in 11th grade, indicating adequate internal consistency.
The child report version of the School Adjustment scale included 20 items related to the child’s academic and disciplinary difficulties, relationships with other students, and general aspects about the school and teachers. The subscale pertaining to academic and disciplinary difficulties was used for the current study. Example items on this subscale included “the school year was difficult,” “school work was really hard,” “I got into trouble this year,” and “teachers were on me because I broke rules.” The eight items on this subscale were on a 5-point scale ranging from 1 (never true) to 5 (always true). The total academic and disciplinary difficulties-child report score was calculated as the mean of all eight items. Internal consistency of this subscale was adequate (Cronbach’s α = .75 in sixth grade, .76 in eighth grade, and .74 in 11th grade).
Externalizing disorders
The externalizing diagnoses considered for this study included ADHD (combined type), ODD, and CD. Diagnoses of externalizing disorders in sixth, ninth, and 12th grade were assessed with the CDISC (Shaffer & Fisher, 1997). The CDISC is widely used to assess Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition, psychiatric symptoms and diagnoses in children and adolescents aged 6–17 years. We analyzed both binary diagnosis variables (i.e., child met diagnostic criteria for a particular disorder or not) and symptom count variables (i.e., a count of how many symptoms of a particular disorder were endorsed). Both parent- and child report versions of this measure were used in the current study, such that a child was considered to have displayed a particular symptom or received a diagnosis in the past year if either the parent or the child or both endorsed that symptom or if the report of the parent or the child or both met criteria for a diagnosis. This approach was used because previous research has shown that parents and children provide unique information regarding ADHD, ODD, and CD diagnostic criteria (Colins, Vermeiren, Schutyen, Broekaert, & Soyez, 2008). For analyses, we examined both diagnoses and symptom counts for each disorder for each grade separately and combined (i.e., a diagnosis of ADHD, ODD, CD, or any externalizing disorder at sixth, ninth, and 12th grade; total symptom counts of ADHD, ODD, CD, or any externalizing disorder at sixth, ninth, and 12th grade).
Substance use
Information regarding children’s substance use was collected with the Tobacco, Alcohol and Drugs measure. This measure was adapted by the Fast Track project from the substance use section of the Self-Administered Youth Questionnaire from the National Longitudinal Survey of Youth 1997 (Bureau of Labor Statistics, 2002; Elliot, Huizinga, & Ageton, 1985). We examined child report of tobacco, alcohol, and marijuana use at eighth and 12th grade. Use of other illicit drugs was not included due to extremely low endorsement of these substances in the sample. For analyses, we examined the number of days children reported smoking cigarettes in the past month (range = 0–30), the number of days they reported drinking alcohol in the past year (range = 0–365), and the number of times children used marijuana in the past month (range = 0–100). Additionally, we examined the number of drinks children reported drinking each time they drank alcohol as well as the number of days children engaged in binge drinking (had 5+ drinks in a row) in the past year (range = 0–365). We also multiplied the number of days children drank alcohol with the number of drinks children had each time they drank alcohol to obtain a measure of intensity of alcohol use. All substance use variables were square-root transformed for analyses due to nonnormality and skewness in the data (Tabachnick & Fidell, 2001).
Missing data
The current analyses examined school outcomes measured at the end of sixth, eighth, and 11th grades; externalizing diagnoses measured at the end of sixth, ninth, and 12th grades; and substance use measured at the end of eighth and 12th grades. For school outcomes, 228 (30.3%) children were missing data in sixth grade, 259 (34.4%) in eighth grade, and 282 (37.5%) in 11th grade. For externalizing diagnoses, 119 (15.8%) were missing data in sixth grade, 185 (24.6%) in ninth grade, and 245 (32.6%) in 12th grade. For substance use, 179 (23.8%) were missing data in eighth grade and 205 (27.3%) in 12th grade. Children with missing data on school outcomes were more likely to be in the high-risk control group than in the normative group: sixth grade, χ2(1, N = 752) = 6.16, p < .05; eighth grade, χ2(1, N = 752) = 6.30, p < .05; 11th grade, χ2(1, N = 752) = 6.39, p < .05. For externalizing diagnoses, children with missing data in 12th grade were more likely to be urban African American, χ2(2, N = 752) = 9.39, p < .01. Additionally, for substance use, children with missing data in 12th grade were more likely to be in the high-risk control group, χ2(1, N = 752) = 16.08, p < .001; urban African American, χ2(2, N = 752) = 10.43, p < .01; and male, χ2(1, N = 752) = 3.94, p < .05. There were no other significant differences in attrition by group (high-risk control vs. normative), race/urban status, gender, or TOCA-R sum score at any of the other time points.
Analysis Plan
Analyses were conducted in SPSS version 14.0 (for descriptive statistics) and Mplus version 6.0 (Muthén & Muthén, 2010). To account for the oversampling of high-risk children in the Fast Track project and to increase generalizability to the population, we used a probability weight based on group (normative vs. high-risk control) that had been previously calculated for all normative and high-risk control group participants (see Jones et al., 2002, for a description of the creation and calculation of this weighting variable). Gender, race/urban status, and risk group (normative vs. high-risk control) were included as covariates in all regression analyses. The research design of the Fast Track project involved children who were nested within classrooms. For example, the 891 high-risk children recruited for this project were nested within 401 first-grade classrooms. However, by Grade 3, these same children were nested in 527 classrooms due to transfers and relocations, and some intervention and control children were in the same classrooms at that time. Therefore, we determined that it was not appropriate or possible to account for nesting within classrooms or schools in the outcome data examined for this study (see CPPRG, 2002, for further details on the nested structure of the data in the Fast Track project).
A series of linear regression analyses were conducted to test the prediction of school outcomes (behavior problems, social competence, and adjustment) from the TOCA-R sum scores. Covariates and TOCA-R sum scores were entered simultaneously as predictors of the various outcomes. Full information maximum likelihood was used to handle missing data (amount of missing data ranged from 15.8% to 37.5% across the variables). We used a maximum likelihood estimator that calculated robust standard errors, which provides valid standard error estimates when variables are nonnormal (Asparouhov & Muthén, 2006). For the prediction of externalizing diagnoses (i.e., ADHD, ODD, and CD), we conducted a series of logistic regressions for the binary diagnostic variables as well as linear regression analyses for the continuous symptom count variables. To predict substance use outcomes, we used a series of censored linear regressions. We used censored regression because the distributions of the substance use variables were continuous, positively skewed with an abundance of zeroes, and theoretically left-censored at zero. Censored regressions are used commonly with variables representing behaviors that do not occur frequently in the general population (e.g., substance use among children, serious delinquency), as data transformations that attempt to normalize the distribution of variables are ineffective in managing an abundance of zeros (Long, 1997). The proportion of variance explained (R2) was used as a measure of the effect sizes for the TOCA-R sum scores in the prediction of outcomes (Cohen, Cohen, West, & Aiken, 2003).
Results Descriptive Statistics
Table 1 provides sample sizes, means, and standard deviations for all continuous outcomes for each year measured. For the binary diagnosis outcomes, the percentage and number of children receiving that diagnosis are presented for each year measured. The zero-order correlations between the TOCA-R sum scores and the school outcomes and between the TOCA-R sum scores and externalizing disorder outcomes are presented in Tables 2 and 3, respectively. As seen in these tables, the TOCA-R sum scores were significantly correlated with almost all outcomes at all time points. Specifically, higher TOCA-R sum scores were associated with more teacher-reported behavior problems, lower teacher-reported social competence, and lower parent- and child-reported school adjustment. In terms of externalizing diagnoses, higher TOCA-R sum scores were related to higher diagnosis rates (with the exception of diagnoses of CD in 12th grade) and symptom counts across all disorders.
Descriptive Statistics of Outcome Variables Measured at the End of Elementary, Middle, and High School
Intercorrelations Between TOCA-R and School Outcomes Measured at the End of Elementary, Middle, and High School
Intercorrelations Between TOCA-R and Externalizing Disorders Measured at the End of Elementary, Middle, and High School
Covariates
Many of the covariates included in the regression models were significant predictors of several of the outcomes included in the current study. The following is a summary of the most consistent covariate effects observed in the analyses (the full results are available from the first author). For teacher-rated school outcomes, children living in urban areas (urban Caucasians and urban African Americans) were rated as higher on behavior problems as compared with rural Caucasians (β = .19, p < .001; β = .30, p < .001, for sixth and eighth grade, respectively) as were urban African Americans when compared with urban Caucasians (βs = −.22, ps < .001 for both sixth and eighth grade). Additionally, children living in urban areas were rated as lower on social competence as compared with rural Caucasians (β = −.18, p < .001; β = −.25, p < .001, for sixth and eighth grade, respectively), as were urban African Americans when compared with urban Caucasians (β = .28, p < .001; β = .26, p < .001, for sixth and eighth grade, respectively). For child-rated school outcomes, children living in urban areas were lower on school adjustment than rural Caucasians (β = −.15, p < .01; β = −.21, p < .001; β = −.15, p < .05, for sixth, eighth, and 11th grade, respectively), and females were higher on school adjustment than males (β = .20, p < .001; β = .18, p < .01; β = .21, p < .01, for sixth, eighth, and 11th grade, respectively).
Males were also consistently higher on symptom counts of ADHD and CD as compared with females (βs range from −.12 to −.30, all ps < .05). Additionally, children living in urban areas had higher symptom counts of CD than rural Caucasians (β = .18, p < .001; β = .21, p < .001; β = .11, p < .05, for sixth, ninth, and 12th grade, respectively), as did urban African Americans when compared with urban Caucasians (β = −.11, p < .05; β = −.12, p < .05; β = −.10, p < .05, for sixth, ninth, and 12th grade, respectively). For substance use in 12th grade, males reported higher levels of use (except for number of drinks consumed each time they drank alcohol, where there was no gender difference) than females (βs range from −.14 to −.26, all ps < .05).
School Outcomes
Table 4 presents the results from the linear regression analyses predicting school outcomes at the end of elementary, middle, and high school from the TOCA-R sum scores. Above and beyond the covariates, teacher-reported behavior problems during kindergarten were prospectively associated with almost all teacher-, parent- and child-rated school outcomes at sixth, eighth, and 11th grades; however, child-rated school adjustment in eighth and 11th grades were not significantly associated. For example, a one standard deviation increase in the TOCA-R sum score predicted a 0.32-standard deviaion increase in teacher-rated behavior problems in sixth grade and a 0.29-standard deviation unit increase in teacher-rated behavior problems in eighth grade. As seen in Table 4, higher kindergarten TOCA-R sum scores predicted higher levels of teacher-rated child behavior problems and lower teacher-rated child social competence at sixth and eighth grades. Higher TOCA-R sum scores also predicted lower parent- and child-rated school adjustment at sixth, eighth, and 11th grades (however, the school adjustment coefficients at eighth and 11th grades were significant according to parent report only). Across reporters and time points, early teacher ratings of behavior problems using the TOCA-R explained approximately 2%–5% of the variance in later behavior problems at school, social competence, and school adjustment, after controlling for the covariates.
Linear Regression Analyses of TOCA-R Sum Scores Predicting School Outcomes
Diagnosis of Externalizing Disorders (ADHD, ODD, and CD)
Of the externalizing disorders, the TOCA-R sum scores predicted only diagnosis of ADHD in ninth grade (β = .30, SE = .12, p < .01, OR = 1.09, 95% CI for OR [1.02, 1.16]). In other words, the odds of receiving an ADHD diagnosis in ninth grade increased between 1.02 and 1.16 times for every one-unit increase in the TOCA-R sum score. The TOCA-R sum scores did not predict any other diagnosis at any grade, nor did they predict any cumulative diagnosis of ADHD, CD, ODD, or any externalizing disorder (ADHD, CD, and ODD combined).
As expected, utilizing symptom counts, compared with predicting dichotomous diagnoses, provided better predictive utility (MacCallum, Zhang, Preacher, & Rucker, 2002). As seen in Table 5, after controlling for the covariates, TOCA-R sum scores in kindergarten prospectively predicted the number of ADHD symptoms endorsed by the parent or the child or both in sixth grade, such that a one-standard deviation increase in TOCA-R sum scores predicted a 0.14-standard deviation unit increase in ADHD symptoms in sixth grade. Additionally, a one-standard deviation increase in TOCA-R sum scores predicted a 0.14-standard deviation unit increase in ninth grade CD symptoms, a 0.12-standard deviation unit increase in total ADHD symptoms (sixth, ninth, and 12th grades combined), and a 0.12-standard deviation unit increase in total externalizing symptoms (ADHD, CD, and ODD combined) across all three grades combined.
Linear Regression Analyses of TOCA-R Sum Scores Predicting Externalizing Disorder Symptom Counts
Substance Use
Results from the censored linear regressions of the substance use variables indicated that, after controlling for the covariates, TOCA-R sum scores in kindergarten were unrelated to any of the substance use outcomes at either eighth or 12th grade (all regression coefficients were not significant at the .05 level), with the exception of cigarette use in 12th grade. This finding indicated that higher TOCA-R sum scores predicted more days smoked cigarettes in the past month (β = .20, p < .05, 95% CI [.03, .36]).
Replication of Findings
It is important to note that sum scores, as reflective of classical test theory, provide equal weight to each behavior on a particular screening measure. Therefore, sum scores may not accurately capture the true severity of a child’s aggressive and disruptive behaviors, as some behaviors (e.g., being stubborn and disobedient) are considered less severe than others (e.g., fighting and harming others). Several researchers have argued that item response theory (IRT), which explicitly assesses differential severity across items (and thus behaviors), may be a more appropriate method for modeling the items used in brief screening scales for behavior problems (Embretson & Reise, 2000). In the current study, the findings from the TOCA-R sum scores were replicated with IRT-based TOCA-R scores (see Wu et al., 2012, for a description of the creation of the IRT TOCA-R scores). The IRT scores demonstrated a similar pattern of prediction to later school outcomes, externalizing diagnoses and symptom counts, and substance use outcomes as seen with the TOCA-R sum scores. Furthermore, the standard errors and confidence intervals were relatively similar between these two sets of analyses. However, the IRT TOCA-R scores tended to account for slightly more variance in the majority of the outcome variables as compared with the TOCA-R sum scores.
We also ran post hoc regression analyses without controlling for the effects of the covariates. A similar pattern of findings was observed in these analyses, as higher TOCA-R sum scores predicted more behavior problems at school, lower social skills, and poorer school adjustment. Additionally, in these analyses higher TOCA-R sum scores predicted more cigarette use in eighth and 12th grades. Higher TOCA-R sum scores also predicted higher externalizing disorder symptom counts (for ADHD, CD, and ODD) as well as higher odds of receiving an externalizing diagnosis. Lastly, although it was not possible for us to account for nesting within classrooms or schools in the outcome data examined for this study, we conducted post hoc regression analyses accounting for clustering in the TOCA-R sum scores collected during kindergarten (clusters were based on classrooms). A similar pattern of findings was observed in these analyses as was seen in the initial regression analyses, with similar beta coefficients obtained for all outcomes.
DiscussionThe goal of the current study was to examine the predictive utility of the TOCA-R (specifically, the AA scale), a commonly used teacher screening measure, administered during kindergarten. We extended prior research by examining the TOCA-R at a single time point at an earlier age than examined in previous studies. Additionally, we examined a broader range of outcomes across a broader range of developmental periods than has been documented in the extant literature. Our results suggested that higher TOCA-R kindergarten scores were associated with more behavior problems at school, lower social skills, and poorer school adjustment reported by multiple informants (teacher, parent, and child) at the end of elementary, middle, and high school. The TOCA-R sum scores were also related, although somewhat more inconsistently, to the odds of an ADHD diagnosis, as well as ADHD, CD, and externalizing disorder symptom counts, but not to an ODD diagnosis or symptoms or any substance use outcomes.
The findings from the current study indicated that early teacher ratings consistently predicted later school outcomes, including school adjustment and social competence, as late as the end of high school. The prediction of ADHD diagnosis in ninth grade and symptoms in the sixth and ninth grades further supports the ability of the TOCA-R to predict outcomes mainly within the school setting, as teachers frequently play an important role in initial screenings for ADHD symptoms (Snider, Busch, & Arrowood, 2003; Snider, Frankenberger, & Aspenson, 2000). Additionally, ADHD symptoms are frequently first observed and most troublesome in the classroom where children are required to sustain their attention and refrain from hyperactive or impulsive behaviors (Barkley, 2003). Overall, these findings suggest that the TOCA-R may be most useful when predicting problematic behaviors that occur within the classroom.
A strength of the current study is the inclusion of a racially and regionally diverse sample. Prior research has demonstrated that the TOCA-R administered in kindergarten exhibits group differences, as boys and African Americans have a higher overall mean, and therefore more frequent behavior problems, than girls or Caucasians, respectively (Koth, Bradshaw, & Leaf, 2009). In previous work with the same larger sample as the current study (Wu et al., 2012), item bias by gender was revealed in the TOCA-R, such that at equivalent levels of latent behavior problems, males received more endorsements of overt behaviors (e.g., harming others, fights, breaks things) from teachers, whereas females received more endorsements of nonphysical behaviors (e.g., stubborn, takes property, lies). Moreover, overt behaviors tended to be better at distinguishing among levels of latent behavior problems for males, and covert behaviors tended to be better at distinguishing among levels of latent behavior problems for females. However, given the similarity in the results obtained from the TOCA-R sum scores and the IRT scores, our findings suggest that the gender bias identified by Wu and colleagues (2012) may not affect the predictive utility of this screening measure.
The importance of including diverse samples in studies examining problem behaviors is underscored by the significant covariate effects in the current study. For instance, children living in urban areas were found to be rated as higher in behavior problems, lower in social competence and school adjustment, and higher in endorsed symptoms of CD than children in rural areas. Compared with urban Caucasians, urban African Americans also received the lowest/highest scores, indicating that this population is particularly at risk for the development of these problematic behaviors. Additionally, when compared with females, males were found to be rated as lower in school adjustment and higher in endorsed symptoms of ADHD and CD. These findings are consistent with other studies indicating that males, ethnic minorities, and children living in urban areas are at most risk for behavior problems (Hinshaw & Lee, 2003). It is therefore important that future studies continue to incorporate diverse samples (i.e., in terms of race, gender, and geographical location). Overall, the findings from the current study suggest that in applied settings, such as schools or community mental health centers, or when a quick screening of behavioral problems is needed, the calculation of a sum score on the TOCA-R may be sufficient to determine which children are at risk for later problems.
The post hoc regression analyses without controlling for the effects of the covariates allowed for exploration of the utility of the TOCA-R in applied settings, as it is unlikely that professionals in these settings would have the resources or information needed to consider these demographic factors in their screening procedures. A similar pattern of findings was observed in these analyses, providing support for the utility of the TOCA-R as a kindergarten screener in practical, applied settings (e.g., schools, community mental health centers, private practice offices). However, it is important to note that the findings regarding diagnostic outcomes were not significant in the regression analyses with covariates, suggesting caution in using the TOCA-R and similar brief screening measures to predict whether a child will receive a diagnosis. Such diagnostic decisions require careful, evidence-based assessments (Mash & Hunsley, 2005) along with the inclusion of demographic variables known to account for a significant amount of variance in these disorders (e.g., race, gender, etc.).
Clinical Implications and Future Directions
Generally, studies examining the development of maladaptive and problematic behaviors have focused mainly on elementary school, with little attention to earlier behaviors during preschool and kindergarten (Campbell et al., 2000). The current study shows that early behavior problems identified in kindergarten with the TOCA-R can reliably predict a range of adverse school-related outcomes in late childhood, middle adolescence, and late adolescence. Therefore, the TOCA-R may be most effective in identifying which children will benefit from prevention programs targeting problems within the school environment and across the school years (e.g., elementary, middle, and high school). It may be that classroom-based intervention programs would be best suited to implement with children at high levels of TOCA-R sum scores. Several existing interventions target improvements in school behavior. Examples include Promoting Alternative Thinking Strategies (Kusche & Greenberg, 1994), Coping Power (Lochman & Wells, 2003), and the Good Behavior Game (Barrish, Saunders, & Montrose, 1969).
The identification of children at risk for a range of negative outcomes beyond the school context may require the application of additional, broader screening measures. Specifically, other screening measures may be needed to identify children at risk for later outcomes that occur in contexts outside of the school setting (i.e., diagnoses of externalizing disorders and substance use), which may require information from additional reporters (Hill et al., 2004; Kerr, Lunkenheimer, & Olson, 2007; Lochman & CPPRG, 1995). Cost–benefit analyses are needed to determine whether adding additional raters is beneficial and outweighs the added cost of both time and resources needed to collect these ratings. Moreover, given previous research demonstrating that the TOCA-R did not adequately cover the lower range of problem behaviors (Wu et al., 2012), it may be that adding additional items to the TOCA-R may improve specificity of prediction to later ages. For example, recent research has suggested that poor self-control observed as early as age 3 predicts substance use disorders at age 32 (Moffitt et al., 2011). Improving prediction to later ages may involve creating measures that blend traditional symptom checklists with measures that tap the underlying processes (e.g., poor self-control) that increase risk for those symptoms. Future research should continue to explore the optimal combination of screening measures to capture the full range of negative outcomes that could be experienced by children and adolescents.
The findings from the current study indicate that early behavior problems that place children at risk for later adverse school-related outcomes can be identified as early as kindergarten. The results from this study therefore suggest that kindergarten may be an appropriate time to begin delivering prevention programs that address aggressive and disruptive behaviors in children. The prompt application of effective prevention programs with these children may interrupt the progression of behavior problems in school before they become entrenched and difficult to change (Lochman & CPPRG, 1995). However, the ability of these programs to effectively address these negative behaviors relies on the accurate identification of children who need these preventive interventions (Hill et al., 2004; O’Connell, Boat, & Warner, 2009). Future studies should therefore continue to explore the predictive validity of methods used in the early identification of at-risk children (Keenan & Wakschlag, 2002; Wakschlag & Keenan, 2001).
The timing of screening measures is an important consideration, and the administration of these measures may need to be altered depending on the purpose of the screening. For instance, previous studies have suggested that prediction to later outcomes is enhanced when TOCA-R ratings collected during first grade are used (Flanagan et al., 2003; Hill et al., 2004). Petras and colleagues (2004,2005) have reported that the spring of third grade for boys and fifth grade for girls are the optimal times for minimizing both false-negative and false-positive identifications of children at risk for later problems based on TOCA-R sum scores. The findings from the current study suggest that for the general identification of children at risk for a variety of school-based negative outcomes at various developmental stages, administration of the TOCA-R during kindergarten is warranted. Future research should seek to refine conclusions regarding the ideal timing of TOCA-R administration.
Limitations
Limitations of the current study should be noted. First, the early behavior ratings examined in the current study only described a minimal amount of the variance (2%–5%) in later teacher-rated behavior problems, social competence, and school adjustment. More research is needed to identify additional factors that may explain more of the variance in these behavior and adjustment difficulties (e.g., parenting, peer engagement in antisocial behaviors, environmental context). Additionally, although the current study is longitudinal, care should be taken to not assume causal relationships between TOCA-R sum scores and outcomes, as several third variables may influence this relationship (e.g., low or unchanging teacher expectations, negative perceptions of children’s classroom behavior, and academic achievement).
ConclusionIn summary, this study illustrates the predictive validity of TOCA-R sum scores when predicting various outcomes, particularly those observed within the school setting, across a range of developmental periods. The ability of the TOCA-R to predict outcomes into late adolescence speaks to the benefit of using a widely distributed brief teacher report screening instrument. The findings also indicate that children identified as at risk for later behavior problems experience difficulty with school, teachers, and their fellow classmates. Prevention programs working with at-risk children should therefore continue using strategies that promote the development of academic skills, prosocial interactions with peers and teachers, and overall positive attitudes toward school. Taken together, this study, as well as other studies examining teacher-reported screening tools, supports the adage to “catch behavior problems early,” before children are directed into a persistent negative life course trajectory.
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Submitted: October 16, 2011 Revised: September 11, 2012 Accepted: February 12, 2013
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Source: Journal of Consulting and Clinical Psychology. Vol. 81. (4), Aug, 2013 pp. 588-599)
Accession Number: 2013-11001-001
Digital Object Identifier: 10.1037/a0032366
Record: 174- Title:
- The predictive validity of the Structured Assessment of Violence Risk in Youth in secondary educational settings.
- Authors:
- McGowan, Mark R.. Department of Educational and School Psychology, Indiana University of Pennsylvania, Indiana, PA, US, mmcgowan@iup.edu
Horn, Robert A.. Educational Psychology Department, Northern Arizona University, AZ, US
Mellott, Ramona N.. Graduate College, Northern Arizona University, AZ, US - Address:
- McGowan, Mark R., Department of Educational and School Psychology, Indiana University of Pennsylvania, Stouffer Hall, Room 246, 1175 Maple Street, Indiana, PA, US, 15705-1087, mmcgowan@iup.edu
- Source:
- Psychological Assessment, Vol 23(2), Jun, 2011. pp. 478-486.
- NLM Title Abbreviation:
- Psychol Assess
- Page Count:
- 9
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- Psychological Assessment: A Journal of Consulting and Clinical Psychology
- ISSN:
- 1040-3590 (Print)
1939-134X (Electronic) - Language:
- English
- Keywords:
- SAVRY, adolescence, predictive validity, school violence, violence risk assessment, Structured Assessment of Violence Risk in Youth
- Abstract:
- Current developments in violence risk assessment warrant consideration for use within educational settings. Using a structured professional judgment (SPJ) model, the present study investigated the predictive validity of the Structured Assessment of Violence in Youth (SAVRY) within educational settings. The predictive accuracy of the SAVRY scales was assessed using a retrospective file review to gather data on 87 adolescents ranging in age from 12 to 18 years. Receiver-operating characteristic analyses were used to gauge the predictive accuracy. With an area under the curve of .72 (p = .001), the accuracy of the SAVRY total score in correctly identifying violent youth exceeds the accuracy of identifications based on chance predictions in this sample. Logistic regression analyses assessed the relative contribution of the SAVRY subscales, whereas the omnibus equation using all subscale scores correctly classified 82% of those adolescents who were nonviolent and 45% of those adolescents who were violent. These results build on previous research and provide support for the use of the SAVRY in educational settings for identification as well as directing intervention efforts. Practical implications and areas for future research are also discussed. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Psychometrics; *Test Validity; *Violence; *Risk Assessment; At Risk Populations; High School Education; Statistical Validity
- Medical Subject Headings (MeSH):
- Adolescent; Humans; Juvenile Delinquency; Male; Observer Variation; Predictive Value of Tests; Psychiatric Status Rating Scales; Reproducibility of Results; Risk Factors; Schools; Violence
- PsycINFO Classification:
- Clinical Psychological Testing (2224)
Behavior Disorders & Antisocial Behavior (3230) - Population:
- Human
Male
Female - Location:
- US
- Age Group:
- Childhood (birth-12 yrs)
School Age (6-12 yrs)
Adolescence (13-17 yrs)
Adulthood (18 yrs & older)
Young Adulthood (18-29 yrs) - Tests & Measures:
- Structured Assessment of Violence Risk in Youth
- Methodology:
- Empirical Study; Quantitative Study
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- First Posted: Mar 7, 2011; Accepted: Nov 10, 2010; Revised: Nov 4, 2010; First Submitted: Apr 28, 2010
- Release Date:
- 20110307
- Correction Date:
- 20110613
- Copyright:
- American Psychological Association. 2011
- Digital Object Identifier:
- http://dx.doi.org/10.1037/a0022304
- PMID:
- 21381837
- Accession Number:
- 2011-04637-001
- Number of Citations in Source:
- 53
- Persistent link to this record (Permalink):
- http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2011-04637-001&site=ehost-live
- Cut and Paste:
- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2011-04637-001&site=ehost-live">The predictive validity of the Structured Assessment of Violence Risk in Youth in secondary educational settings.</A>
- Database:
- PsycINFO
The Predictive Validity of the Structured Assessment of Violence Risk in
Youth in Secondary Educational Settings
By: Mark R. McGowan
Department of Educational and School Psychology, Indiana University
of Pennsylvania;
Robert A. Horn
Educational Psychology Department, Northern Arizona University
Ramona N. Mellott
Graduate College, Northern Arizona University
Acknowledgement: This study is
based on a dissertation completed by Mark R. McGowan under the direction of
Robert A. Horn and Ramona N. Mellott as partial fulfillment of the requirements
for the doctoral degree in educational psychology at Northern Arizona
University.
Currently,
professionals in mental health fields are being faced with an ever-increasing demand
for assessments of violence risk as a means of guiding professional judgment and
decision making regarding a wide range of behaviors of concern, such as generalized
violence, threats of targeted violence, and bullying (Borum, 2000). The public attention to incidences of
mass murders within U.S. schools has contributed significantly to this increased
demand on educators (Van Dyke &
Schroeder, 2006; Vossekuil, Fein, Reddy, Borum, & Modzeleski, 2002).
However, some educational practitioners have questioned the use of violence risk
assessments primarily because of concerns regarding the potential for stigmatizing
effects on students as well as high false positive rates associated with making
predictions of future behavior (National
Association of School Psychologists, 2008).
Efforts to address
these demands led to the formulation of a national response to the problem of school
violence and aggression in the United States, which has generated multiple resources
to inform educationally based practitioners on the development and implementation of
school safety plans (e.g., Dwyer &
Osher, 2000; Dwyer,
Osher, & Warger, 1998; Furlong, Paige, & Osher, 2003). The
development of these resources originated largely from interagency collaborations
between the U.S. Department of Education and the U.S. Department of Justice, which
contributed to an integrative trend in knowledge bases from related agencies, for
example, the U.S. Secret Service (Vossekuil
et al., 2002). These resources emphasize the use of
evidence-based practices for operating within the school safety plan framework
(Dwyer & Osher,
2000; Furlong,
Pavelski, & Saxton, 2002).
The development of
risk assessment protocols has, in a similar fashion, drawn on research and practices
from related fields (e.g., Fein et al.,
2002). More recent discussions within the literature have
recommended assessment practices that move beyond outdated models that conceptualize
violence risk in terms of identification or prediction and instead focus on
intervention, management, and remediation for decreasing an individual's level of
risk for violence (Borum,
2000, 2003; Dodge,
2008). It is interesting that the current integrative trend
is only beginning to be extended to include contemporary protocols for the
assessment of generalized violence within educational settings.
History of Violence Risk AssessmentHistorically, the
central question surrounding violence risk assessment as a field of study has been
its validity, which was based primarily on practitioners' predictive accuracy
(Borum, 1996,
2000;
Douglas & Ogloff,
2003b; Monahan
& Steadman, 2001). Early research efforts dating back to
the 1970s portrayed a pessimistic view of practitioners' ability to accurately
predict violence (Borum,
1996; Monahan,
1996). More specifically, research reviews of the accuracy
of clinical predictions more than 25 years ago reported correct identification rates
of only 20%–35% (as noted in Monahan
& Steadman, 2001). A review of these early studies
concerning the predictive accuracy of violence risk assessment led
Monahan (1981) to
conclude that
psychiatrists and psychologists are accurate in no more than one out of
three predictions of violent behavior over a several-year period among
institutionalized populations that had both committed violence in the past
(and thus had a high base rate for it) and who were diagnosed as mentally
ill. (p. 77)
Since
Monahan's (1981)
seminal work, developments within the field of violence risk assessment, especially
with regard to research, methodology, and practice, have changed dramatically, as
has, in turn, the perception of practitioners' ability to assess violence risk
accurately and reliably (Douglas &
Ogloff, 2003a). More recent reviews of the body of empirical
research on violence risk assessment protocols have yielded increasingly optimistic
results, suggesting that the means are available to predict violence at
significantly better than chance levels (Borum, 1996, 2000; Mossman,
1994).
It is important to
understand the changes in the conceptualization of violence prediction in order to
provide a framework for how it relates to current practice within the field of
assessment (Borum, 2000).
Prior to the work of researchers such as Monahan (1981), who studied the predictive accuracy of
violence assessment by mental health professionals, the working model for violence
prediction was based largely on the notion that an individual's dangerousness was a
dispositional characteristic that was stable over time and either present or absent
(Borum, 1996,
2000). Assessment
methods relied heavily on clinical judgments, which were unstructured in terms of
the manner in which these assessments were conducted (Grove & Meehl, 1996).
Efforts to improve
on the reliability and validity issues demonstrated when using the clinical judgment
approach have led to a focus on empirically derived risk factors as a means to
differentiate between potentially violent and nonviolent individuals
(Borum, Otto, & Golding,
1993; Monahan et
al., 2001). Although the actuarial methodologies that
followed have been demonstrated to be more reliable than those based on clinical
judgment (Grove, Zald, Lebow, Snitz, &
Nelson, 2000), a debate within the field continues with
regard to the use of statistical equations versus decision-making models when
conducting violence risk assessments (Douglas & Ogloff, 2003a). Although statistical formulas
have empirical support suggesting that they predict violence as well as or better
than clinical judgment (Dawes, Faust, &
Meehl, 1989; Grove
et al., 2000), the use of statistical equations in risk
assessment has been discouraged because of limited generalizability and poor
adaptability to ecological or ideographic demands (Borum, 2000). It has also been suggested that these
limitations present violence risk assessment in an oversimplified form that may
impact the utility of the findings gained from this process (Borum, 2003).
The current
controversy surrounding the use of actuarial methods gave rise to a third method for
assessing violence risk. Drawing on contemporary understandings of violence as a
contextual, dynamic, and continuous construct (Borum, 2000; Borum, Bartel, & Forth, 2003), researchers have
proposed using an integrated model that uses empirically validated risk factors as a
means of informing clinical judgments concerning an individual's risk for violence.
These integrated models use empirically supported risk factors to provide a
framework that assists the practitioner in making sound judgments informed by
clinical practice concerning case-specific factors, such as highly salient factors
or relationships between particular factors and other situational or contextual
variables (Borum et al.,
2003; Douglas, Cox,
& Webster, 1999). By attending to the empirical risk
factors as a starting point, these integrated models provide advantages over
actuarial approaches in terms of their ability to account for ecological and
ideographic factors that may be highly relevant, albeit case specific. In this
regard, integrated models have been suggested to be better suited to the assessment
of violence in particular populations—for example, children and
adolescents—because of their emphasis on dynamic factors. Last, by
conceptualizing violence risk within this multidimensional framework, the assessment
process also may identify areas or risk factors that could guide treatment efforts
to lessen an individual's risk for violence (Borum, 2003; Borum
et al., 2003; Douglas et al., 1999).
Structured Professional Judgment (SPJ) ModelEfforts to build on
current changes in thinking concerning violence risk, combined with a desire to
translate these developments into a practice-oriented approach to assessment, were
the foundation for a decision-making model for violence risk assessment, which has
been referred to within the literature as “structured clinical judgment”
(Hart, 1998, p.
123), “structured professional judgment” (Douglas & Kropp, 2002, p. 626), and
“guided professional judgment” (Borum, 2000, p. 1265; Borum, Fein, Vossekuil, & Berglund, 1999, p.
325). Although terminology has differed somewhat, the conceptualization of the
structured model itself has not. According to Borum et al. (2003), “The structured professional
judgment approach helps to focus the evaluator on relevant data to gather during
interviews and record reviews, so that the final judgment, although not statistical,
is well informed by the best available research” (p. 4). Taken together, this
decision-making model represents a hybrid that draws on empirical knowledge and
clinical judgment to guide the assessment of an individual's predisposition toward
violence as well as the ecological influences that elicit it. The preliminary
research evaluating the utility of the SPJ model for assessing violence risk has
been promising (Borum,
2003; Borum &
Douglas, 2003; Douglas, Ogloff, & Hart, 2003; Hanson, 1998).
To date, the SPJ
model has been applied in the development of numerous assessment tools
differentiated by the population and type of violence assessed. For example, the
Early Assessment Risk List for Boys—Version 2 is designed to assess violence
potential and delinquent tendencies in boys under the age of 12 years
(Augimeri, Koegl, Webster, & Levene,
2001), whereas the Structured Assessment of Violence Risk in
Youth (SAVRY) incorporated risk factors related to assessing generalized violence
potential in adolescent populations, ranging in age from 12 to 18 years
(Borum et al., 2003).
As Douglas and Webster
(1999) noted, the Historical/Clinical/Risk
Management—20 (Webster, Douglas,
Eaves, & Hart, 1997) serves the same purpose of
assessing violence risk in adults.
Developments in the
assessment of violence risk warrant consideration for use within educational
settings, particularly as a means for guiding intervention. Applying contemporary
understandings and practices developed within related fields, we sought to assess in
this study the validity of an SPJ approach for identifying adolescents at risk for
generalized violence in an educational setting as a means of addressing questions
regarding generalizability and diagnostic accuracy. For this purpose, violence risk
was estimated using the SAVRY (Borum et al.,
2003), a measure based on the SPJ approach that has been
demonstrated to be valid for use with adolescent populations. In focusing
specifically on the SAVRY, we also sought to provide insight into the specific
nature or presentation of factors associated with violence risk in an educationally
based sample for the purposes of informing clinical judgment as well as guiding
future research efforts. On the basis of available information, it is reasonable to
hypothesize that the SAVRY would differentiate between violent and nonviolent youth.
Similarly, it is also reasonable to expect that all of the scales would contribute
significantly to this differentiation. However, given the limited amount of
literature supporting the use of this instrument in educational settings, two
primary research questions were addressed in this study:
- To what
degree does the SAVRY differentiate between violent and nonviolent youth
in an educationally based sample?
- Which
SAVRY scales contribute to the differentiation of violent and nonviolent
youth?
Method Participants
Participants
were 87 youth between the ages of 12 and 18 years enrolled in a specialized
school established to meet the needs of students with emotional disturbance and
behavioral challenges located in a large urban school district in the
Southwestern United States. This facility uses a highly structured, behaviorally
based token economy system that seeks to teach prosocial skills using a
nationally recognized model that has been empirically supported for use with
youth who demonstrate significant behavioral and emotional challenges. The
school provides educational opportunities to approximately 175 students in
kindergarten through 12th grade. Inclusion in the sample required that the
student meet age requirements set forth by the SAVRY, be enrolled for a minimum
of four months, and have sufficient data available to code the SAVRY. The
initial review of data based on age resulted in a total of 99 cases being
identified, which was later limited to a working sample of 87 cases. Of the
final sample, 82% of the participants were male (n = 71) and
18% were female (n = 16), with a mean age of 14.99 years
(SD = 1.65). The ethnic breakdown of the sample was 71%
Caucasian, 15% Hispanic, 8% African American, 2% Native American, and 3% other.
The vast majority (n = 82) of the participants' primary area of
special education eligibility was identified as emotional disturbance, with
approximately 14% of the sample having a secondary eligibility categorization of
specific learning disability (see Table
1).
Frequency and Percentage of Primary, Secondary, and Tertiary Educational Disabilities
Within the Sample
Instruments
The SAVRY was
developed to address the need for an instrument designed for the assessment of
generalized violence risk in adolescent populations (Borum, 2000). According to
Borum et al.
(2003), the SAVRY was modeled after the
Historical/Clinical/Risk Management—20 (Webster et al., 1997) in terms of its
structure, but modifications were made in the item content to include risk
factors derived from research and literature on child development, violence, and
aggression specific to adolescence. The instrument comprises 24 risk factors
that are divided into three scales labeled Historical, Social/Contextual, and
Individual Risk Factors. The SAVRY also includes a Protective Factors scale with
six items, which represent moderator variables that have been shown to reduce an
individual's risk for violence (Jessor,
Van Den Bos, Vanderryn, Costa, & Turbin, 1995).
Ratings (low, moderate, or high) of factors can be used as the basis for
formulating a professional judgment about an individual's risk for violence as
well as guiding intervention efforts and decision making to reduce risk. For
research purposes, ratings of low, moderate, or high can be quantified as 0, 1,
or 2, respectively, with high scores reflecting greater risk. Consistent with
the SPJ model, there are no cutoff scores offered for making categorization,
prediction, or management decisions. Rather, the SAVRY is intended to be used as
a guide or framework for the clinician in the assessment of empirically based
factors. Moreover, with the inclusion of modifiable or dynamic risk factors, the
SAVRY lends itself well to the identification of areas for possible intervention
as well as to monitoring progress over time (Borum et al., 2003).
Although early
in its development, preliminary research on the psychometric properties of the
SAVRY suggest that its reliability and validity support its use for assessing
risk for future violence in adolescent populations in correctional settings
(Borum et al.,
2003; Catchpole
& Gretton, 2003; Dolan & Rennie, 2008; McEachran, 2001;
Welsh, Schmidt, McKinnon, Chattha,
& Meyers, 2008) and mental health settings
(Gammelgård, Koivisto, Eronen,
& Kaltiala-Heino, 2008; Lodewijks, Doreleijers, de Ruiter, & Borum,
2008). The SAVRY manual reports internal consistency
analyses with alpha coefficients of .82 for offenders and .84 for community
samples in a study completed during the development and validation of the
instrument (Borum et al.,
2003). However, internal consistency estimates in
subsequent studies have reported coefficient alphas as high as .98 for SAVRY
total score (Welsh et al.,
2008). Studies exploring interrater reliability using
intraclass correlation coefficients (ICCs) have yielded findings ranging from
.80 to .97 for the SAVRY total score (Catchpole & Gretton, 2003; Dolan & Rennie, 2008;
Gammelgård et al.,
2008; McEachran, 2001; Meyers & Schmidt, 2008; Welsh et al., 2008). In a number
of these studies, receiver-operating characteristic (ROC) analyses have been
used to assess the predictive accuracy of the SAVRY (Catchpole & Gretton, 2003;
Dolan & Rennie,
2008; Gammelgård et al., 2008; McEachran, 2001), whereas logistic
regression analyses have been used to assess categorical risk ratings
(Gammelgård et al.,
2008) and incremental validity (Dolan & Rennie, 2008;
Welsh et al.,
2008). Available research has also supported the
predictive validity of judgments made on the basis of the SAVRY scores and risk
ratings with samples of adolescents from ethnic and cultural minority
backgrounds (Chapman, Desai, Falzer,
& Borum, 2006; Roth, 2006) and for both genders
(Meyers & Schmidt,
2008).
Procedure
On
institutional review board approval, data collections were completed on the
basis of archival file information maintained by the local education agency.
Sources of information included separate files for students' special education,
psychoeducational, cumulative, disciplinary, intervention, and arrest history
that are routinely maintained by the school. The data collection procedures
occurred in two phases and were performed by Mark R. McGowan and two research
assistants.
The first phase
of data collection began with the review of the cumulative files for all
students enrolled in 5th–12th grade at the alternative school. This
initial file review resulted in the identification of 99 prospective cases,
which yielded the final working sample of 87 adolescents. Of the 12 cases
excluded from the sample, nine cases failed to meet the four-month participation
criterion and three cases lacked sufficient information because of incomplete
records.
For the
sample's 87 participants, demographic data were collected and the SAVRY was
scored using the available information contained within the participants'
cumulative, special education, and psychoeducational files. Although it is
preferable to base SAVRY scores on interviews combined with file reviews,
especially when attempting to score dynamic risk factors, previous studies using
this measure have indicated adequate validity using file review alone
(McEachran,
2001; Welsh et
al., 2008). The SAVRY was scored in accordance with the
structured protocol outlined within the SAVRY manual (Borum et al., 2003).
The SAVRY was
scored by Mark R. McGowan, a nationally certified school psychologist with six
years of experience in violence risk assessment who was professionally trained
on the use of the instrument. Additionally, two research assistants, who were
also nationally certified school psychologists, scored approximately one third
of the cases (n = 25) selected at random to assess interrater
reliability. The raters differed only in terms of years of experience using the
SAVRY in practice, with Rater 1 having two years of experience and Rater 2
having five years of experience. Both raters were professionally trained on the
use of the SAVRY. Discrepancies in scoring between raters were addressed by
revisiting the original data sources, with ultimate decision making being left
with Mark R. McGowan.
Interrater
reliability was calculated for the coding of a random sample of the SAVRY
assessments using ICCs. To remain consistent with previous research in this
area, we calculated ICCs for each rater using a one-way random effects model as
the index of reliability; a combined index was also calculated using a two-way
random effects model with a consistency definition (McGraw & Wong, 1996). The
one-way random effects model yielded ICCs of .64 for Rater 1 (n
= 13) and .90 (n = 12) for Rater 2. The two-way random effects
model taking into consideration both raters yielded an ICC of .81. Although
variation between raters is noted, these reliability coefficients are consistent
with previous findings (Catchpole &
Gretton, 2003; McEachran, 2001) and are considered acceptable
(Fleiss,
1986).
During the
second data collection phase, incidences of violence were compiled using
information collected from the participants' disciplinary, intervention, and
arrest histories that were routinely monitored and maintained in files for each
student enrolled within the school. These files were reviewed and data were
collected by an administrator employed at the school who held primary
responsibility for monitoring and recording these data as well as Mark R.
McGowan. Operationally, the outcome measure was defined as any act of
generalized violence committed during the 1-year period in which the participant
met inclusion criteria for this study. Violence has been defined by the SAVRY
(Borum et al.,
2003) as
an act
of battery or physical violence that is sufficiently severe to cause
injury to another person or persons (i.e., cuts, bruises, broken bones,
death, etc.), regardless of whether injury actually occurs; any forcible
act of sexual assault; or a threat made with a weapon in hand. (p.
15)
Data Analyses
ROC analyses
were used to assess the predictive validity of the SAVRY. The use of the ROC
analysis represents recommended practice for determining accuracy because it is
influenced less than other statistical procedures by base rates of a given
behavior and selection ratios (Douglas
& Webster, 1999; Swets, 1996). As Fawcett (2006) noted, “ROC
curves have an attractive property: They are insensitive to changes in class
distribution. If the proportion of positive to negative instances changes in the
test set, the ROC curves will not change” (p. 864). When the true positive
proportion (sensitivity) of the predictor variable is plotted as a function of
the false positive proportion (1 − specificity), the performance of the
predictor variable can be expressed in the form of a curve that provides a
representation of the overall accuracy of the predictor (Swets, 1996). In cases where the
area under the curve (AUC) is used as an index, an AUC of 0 would represent a
perfect negative classification, whereas an AUC of .5 would be predictive of
performance at a chance level and an AUC of 1.0 would be a perfect
classification of violent offenders as violent (Swets, 1996; Swets, Dawes, & Monahan, 2000).
Interpretive guidelines suggest that AUC values of .70 or above are considered
moderate and .75 or above are considered good (Douglas, Guy, Reeves, & Weir, 2008).
A binary
logistic regression analysis was used to assess the relationship between the
SAVRY total score, which is made up of only the risk factor scales (Historical,
Social/Contextual, and Individual), and violent behavior in this sample. The
ability of the regression equation to differentiate between violent and
nonviolent youth was assessed as well as the individual contributions of the
risk factor scales to the model's ability predict violent and nonviolent
behavior. Finally, a hierarchical logistic regression was conducted to examine
the incremental validity of the SAVRY Protective Factors scale over the SAVRY
total score.
ResultsApproximately 36%
of the adolescents within the sample committed at least one violent act during the
academic year. However, of the 31 individuals observed to be violent, 18 (21%) of
the adolescents demonstrated only one violent act, with the remaining 13 (15%)
adolescents committing between two and five violent acts. Coefficient alphas
computed for each of the four subscales ranged from a low of .61 for the Protective
Factors scale and a high of .80 for the Individual Risk Factors scale. The
Historical Risk Factors and Social/Contextual Risk Factors scales yielded alphas of
.77 and .73, respectively.
SAVRY total scores,
derived from the three risk factor scale scores, ranged from a low of 4 to a high of
39 out of a possible 48. The mean for total scores was 20.14 (SD =
8.54). Analyses conducted to explore the relationship of age, ethnicity, and gender
to violence in this sample suggested that only age contributed to meaningful
differences between violent and nonviolent youth, with younger students being
associated with more violent acts. Independent t tests were also
used to examine differences between ratings of violent and nonviolent youth on the
SAVRY total score and subscale scores (see Table 2). With the
exception of the Historical Risk Factors scale, all of the SAVRY scales
significantly differentiated between violent and nonviolent youth. Similarly, only
the Historical Risk Factors scale failed to demonstrate a significant correlation
with violent behavior in the sample. As would be expected, the Protective Factors
scale showed a negative correlation with violent behavior (see Table 3).
Comparisons of Violent and Nonviolent Adolescents on the Structured Assessment of
Violence Risk in Youth Scales
Correlations and Areas Under Curves (AUCs) of Receiver-Operating Characteristic
Analyses for the SAVRY Scale Scores and Total Score Predicting Violence
ROC analyses were
used to investigate the predictive accuracy of the SAVRY in differentiating between
violent and nonviolent adolescents. For comparative purposes, outcomes of the ROC
analyses for the total score and each of the four subscales are shown in
Table 3. The SAVRY total
score demonstrated moderate predictive validity, with an AUC of .72 (see
Figure 1). Among the SAVRY subscales, the highest AUCs were
observed for the Individual Risk Factors scale, followed by Social/Contextual Risk
Factors scale, Protective Factors scale, and Historical Risk Factors scale. It is
important to note that, unlike the Risk Factors scales, the analysis of the
Protective Factors scale assessed the accuracy of this scale's prediction of
nonviolence in the sample, which is consistent with its theoretical and practical
role as a contraindicator for violence.
Figure 1. The receiver-operating characteristic analysis and area under the curve for the
Structured Assessment of Violence in Youth total score.
The logistic
regression analysis was conducted to assess whether the three risk factors scales
used to make up the SAVRY total score significantly predicted whether an adolescent
committed a violent act. When all three scales were entered at Step 1, the model was
significant, χ2(3,
N = 87) = 18.96, p < .001. The Hosmer and
Lesmeshow test indicated a good fit, χ2(8) = 6.16, p = .629. This regression equation
correctly classified 84% of those adolescents who were not violent and 42% of those
adolescents who were violent. For this model, odds ratios for correctly classifying
violent behavior suggest that estimates improve by 30% if one knows the Individual
Risk Factors scale. Exploring the individual contributions of the Risk Factors
Scales suggest that only the Historical Risk Factor scale failed to demonstrate a
significant relationship (p = .07). Table 4
presents the odds ratios for all three scales.
Logistic Regression Predicting Who Will Be Violent on the Basis of Risk Factor
Scales
A hierarchical
logistic regression was used to investigate whether the SAVRY Protective Factors
scale added incremental validity to SAVRY total score, which is derived using only
the risk factors. In this analysis, the SAVRY risk factors scales score were entered
in Step 1 and the SAVRY Protective Factors scale was added at Step 2. With the
Protective Factors scale added, the model remained significant, χ2(4, N = 87) = 19.19,
p = .001, with the Hosmer and Lesmeshow test indicating a good
fit, χ2(8) = 4.01,
p = .856. Although the overall percentage of correctly
classified cases did not change, this regression equation correctly classified 82%
of those adolescents who were not violent and 45% of those adolescents who were
violent.
DiscussionIn the current
study, we examined the predictive validity of the SAVRY for use within educational
settings. Results of this study are consistent with those of previous studies
demonstrating fair to good predicative validity of the SAVRY total score for
assessing generalized violence risk in adolescent populations. This emerging body of
research concerning the utility of the SAVRY for differentiating between violent and
nonviolent youth is promising given the recent empirical evidence suggesting that
predicative power is sustained when factors such as setting, ethnicity, and gender
are varied (Borum et al.,
2003; Catchpole &
Gretton, 2003; Chapman et al., 2006; Dolan & Rennie, 2008; Gammelgård et al., 2008;
Lodewijks et al.,
2008; McEachran,
2001; Meyers &
Schmidt, 2008; Roth, 2006; Welsh
et al., 2008). The present study extends this work into an
educational context, with the results lending support for the use of the SAVRY for
assessing violence risk as well as specific factors that may hold promise for
guiding intervention planning for students. Although these findings do not diminish
concerns associated with the misidentification of students, they do provide a means
for making better informed decisions. Ultimately, however, the balance between
making Type I versus Type II errors will continue to be a deliberation managed by
educational practitioners and teams seeking to be responsive to the needs of the
individual student and school.
In reviewing these
findings, it is important to note that the present study focused on a higher risk
sample composed of predominately male students who demonstrated emotional and
behavioral difficulties that were sufficiently severe to warrant placement in a
specialized educational setting. As such, the relatively high proportion of violent
acts and gender imbalance observed in this sample is reflective of the base rates
seen in the population of youth categorized as emotionally disturbed by comparison
to the general education population (Reddy,
2001). This increased the homogeneity of the sample, as well
as access to relevant data sources, but may have introduced a limiting factor when
extending these findings for work with students who do not demonstrate a similar
history or severity of behavioral and educational challenge. Additionally,
homogeneity in this sample may also have restricted the range of scores on the SAVRY
items, which would negatively impact the predictive validity. This may have
attenuated the potential significance of the findings particularly at the subscale
level, which could account for the lack of significant difference noted between
violent and nonviolent youth on the Historical Risk Factors scale. Therefore, it is
reasonable to expect that generalization of these findings to more traditional
educational settings with a lower base rate for behavioral problems may yield
different results. More specifically, the Historical Risk Factors scale may prove to
be a more robust predictor of violence in regular education settings where the
frequency of behavioral problems is less common. Furthermore, the results of the
present study reflect only the contribution of the empirically validated factors to
the prediction of risk in this sample. Thus, these findings do not take into account
the contribution of case-specific ecological or ideographic factors that would
otherwise be reflected in the final clinical judgment concerning violence risk,
which is recorded as the summary risk rating on the SAVRY. Although this is an
acceptable practice when using the SAVRY for research purposes, this specific focus
on the instrument's risk and protective factors introduces a limitation that likely
underestimates the predictive validity of the SAVRY by excluding the summary risk
rating from the SPJ model (see Douglas et
al., 2003).
With this said, the
findings from the ROC analysis suggest that the SAVRY total score demonstrated
moderate predictive accuracy when discriminating between violent and nonviolent
youth in the present sample. These findings are consistent with the growing body of
literature demonstrating that the SAVRY total score is a moderate to good predictor
of violence across a variety of settings and diverse populations (Catchpole & Gretton, 2003;
Dolan & Rennie,
2008; Gammelgård et al., 2008; Lodewijks et al., 2008). The
predictive accuracy of the SAVRY total score is further substantiated by the results
of the logistic regression, which produced a model that differentiated between
violent and nonviolent youth at levels significantly better than chance. The model
suggests that the SAVRY risk factors were a better predictor of nonviolence than
violence, but when the Protective Factors scale was added to the model, its
predictive accuracy for violence improved. This result is supported by similar
findings suggesting that the SAVRY Protective Factors scale adds to the incremental
validity of the SAVRY total score (see Lodewijks et al., 2008).
A significant
correlation was found between the SAVRY scales and violent behavior, with the
exception of the Historical Risk Factors scale. It is worthwhile to note that the
Protective Factors scale showed the expected negative correlation with violence,
which supports its theoretical use as a contraindicator for violence. In the
logistic regression model, the predictive power of the SAVRY total score
outperformed all of the individual scales with the exception of the Individual Risk
Factors scale, which improved the odds of correctly classifying violent behavior by
30%. This finding is consistent with those of Lodewijks et al. (2008) and McEachran (2001), who found no
predictive value for the Historical Risk Factors scale. These findings provide an
important point of contrast with results, based largely on adult samples, which have
found historical factors to be one of the strongest predictors of future violence
(Monahan et al.,
2001). Although empirical explanations for this finding are
beyond the scope of this study, it is reasonable to hypothesize that a combination
of forces may be at work in accounting for this difference, such as methodological
variations, limited life experiences on which to derive historical information,
unique developmental changes occurring during adolescence, or differences in the
assessment approaches used to study violence risk. Regardless, this finding does
reinforce the importance of using assessment strategies that go beyond actuarial
formulas to account for the ecological and ideographic factors influencing
developing youth.
Practical Implications
The practical
implications of these findings for educational agencies are many. First and
foremost, these results provide empirical support for the use of an SPJ approach
and the SAVRY as a promising method for conducting violence risk assessments in
schools. These results also stress the important contribution ecological and
ideographic factors make to the developing adolescent's risk for violence. As
noted by others (see Lodewijks et al.,
2008), recognizing the bearing these dynamic risk
factors have on the propensity for violent behavior in adolescent populations is
encouraging from an intervention standpoint. Efforts to ameliorate the influence
dynamic risk factors have on the individual adolescent may present opportunities
for educational teams to adopt a more positive, strength-based orientation for
addressing student needs rather than turning to exclusionary or punitive
alternatives such as zero tolerance policies.
When
contextualized within contemporary school safety planning programs, using
validated assessment tools such as the SAVRY for focusing educationally based
interventions on the dynamic risk factors may also prove to be an effective
strategy for identifying and improving outcomes for at-risk students. Working
within a three-tier service delivery model, violence risk assessment may offer
practitioners a method for guiding the development of student-specific
intervention plans and monitoring progress. As an alternative to disciplinary
practices that exclude or profile students, the SPJ model may be useful when
integrated into a response-to-intervention framework designed to meet the
emotional and behavioral challenges demonstrated by the relatively few students
serviced at Tier 2 or 3. Building on this notion, by tailoring intervention
plans to an individual's presenting risk and protective factors, educational
teams may be afforded the means for targeting specific areas for intervention
and aligning student needs with evidence-based treatment approaches or supports
that address those needs. Conversely, as with any formalized protocol used for
decision making, failure of educational practitioners and teams to use the SAVRY
within an SPJ approach may limit planning and management to those empirically
based factors included in the instrument and thereby overshadow other strengths
or weaknesses unique to the individual or setting.
Reflecting on
the discrepancies observed between raters in this study, it is important to note
that the SAVRY is designed as a protocol to aid trained professionals in the
assessment of violence risk and intervention planning. Consistent with the
manual's description, the present findings underscore the importance of adequate
training and supervision when using the SAVRY for the purposes of assessing
violence risk in youth.
Future Research
This study has
taken the first step toward extending SPJ approaches to the assessment of
violence in educational settings. The next step will be to examine the utility
of the SAVRY for assessing violence in more heterogeneous educationally based
samples. Likewise, this line of research would likely be fruitful for building
on the notions that the SAVRY may offer insights into identifying not only youth
at risk for violence but also those dimensions or risk factors that may
distinguish potentially violent from nonviolent youth. In this regard, the
present findings raise interesting questions regarding qualitative dimensions
that may distinguish adolescents correctly identified by the SAVRY from those
adolescents who were not.
Additionally,
future researchers should consider examining the predictive validity of the
SAVRY total score and clinical judgments using prospective designs. Prospective
designs would not only provide more accurate information regarding the influence
of dynamic and contextual risk factors on the prediction of violence in
educational settings but also lend themselves to the investigation of treatments
for reducing violence risk in adolescents who present with specific factors
identified through the assessment process. This is promising given the growing
body of knowledge concerning evidence-based practices for violence prevention
and intervention (see Multisite
Violence Prevention Project, 2009; Osher et al., 2004). The long-term
benefits of these programs for reducing risk factors could be assessed by their
ability to improve outcomes for emotionally and behaviorally challenged youth,
that is, staying in school or getting a diploma.
Finally,
ongoing research concerning the psychometric properties of the SAVRY is needed
to better inform practitioners regarding its use. Although originally developed
to facilitate assessment practices consistent with an SPJ approach and current
literature concerning violence risk in youth populations, the utility of scales
beyond the item-level analysis is less clear. Thus, future research exploring
the construct validity of the SAVRY would provide useful information to
practitioners concerning the interpretation of the risk and protective factor
domains, total score, and the summary risk rating in the assessment of violence
risk.
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Submitted: April 28, 2010 Revised: November 4, 2010 Accepted: November 10, 2010
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Source: Psychological Assessment. Vol. 23. (2), Jun, 2011 pp. 478-486)
Accession Number: 2011-04637-001
Digital Object Identifier: 10.1037/a0022304
Record: 175- Title:
- The relation between changes in patients' interpersonal impact messages and outcome in treatment for chronic depression.
- Authors:
- Constantino, Michael J.. Department of Psychology, University of Massachusetts, Amherst, MA, US, mconstantino@psych.umass.edu
Laws, Holly B.. Department of Psychology, University of Massachusetts, Amherst, MA, US
Arnow, Bruce A.. Department of Psychiatry and Behavioral Sciences, Stanford University Medical Center, Stanford, CA, US
Klein, Daniel N.. Department of Psychology, State University of New York at Stony Brook, Stony Brook, NY, US
Rothbaum, Barbara O.. Department of Psychiatry, Emory University School of Medicine, Atlanta, GA, US
Manber, Rachel. Department of Psychiatry and Behavioral Sciences, Stanford University Medical Center, Stanford, CA, US - Address:
- Constantino, Michael J., Department of Psychology, University of Massachusetts, 612 Tobin Hall, Amherst, MA, US, 01003-9271, mconstantino@psych.umass.edu
- Source:
- Journal of Consulting and Clinical Psychology, Vol 80(3), Jun, 2012. pp. 354-364.
- NLM Title Abbreviation:
- J Consult Clin Psychol
- Page Count:
- 11
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- Journal of Consulting Psychology
- Other Publishers:
- US : American Association for Applied Psychology
US : Dentan Printing Company
US : Science Press Printing Company - ISSN:
- 0022-006X (Print)
1939-2117 (Electronic) - Language:
- English
- Keywords:
- chronic depression, cognitive-behavioral analysis system of psychotherapy (CBASP), impact messages, interpersonal change, treatment outcome, drug therapy, nefazodone, combined therapy
- Abstract:
- Objective: Interpersonal theories posit that chronically depressed individuals have hostile and submissive styles in their social interactions, which may undermine their interpersonal effectiveness and maintain their depression. Recent findings support this theory and also show that patients' interpersonal impact messages, as perceived by their psychotherapists, change in theoretically predicted ways following cognitive-behavioral analysis system of psychotherapy (CBASP) alone or with medication. This study extended these previous findings by examining whether such changes were associated with their depression change and response status. Method: Data derived from a large clinical trial for chronic depression compared the efficacy of CBASP, nefazodone, and their combination. To assess patients' impact messages, CBASP clinicians completed the Impact Message Inventory (IMI; Kiesler & Schmidt, 1993) following an early and late session. Our subsample (N = 259) consisted of patients in the CBASP and combined conditions who had depression severity data for at least 1 post-randomization visit and whose clinicians completed at least 1 IMI rating. We used hierarchical linear modeling (HLM) to calculate IMI change scores and to model depression change. We used HLM and logistic regression to test our predictor questions. Results: As hypothesized, decreases in patients' hostile–submissive impact messages were significantly associated with depression reduction (γ = 0.27, 95% CI [0.11, 0.43], p < .01) and favorable treatment response (B = –0.05, 95% CI [–0.09, –0.01], p = .03), regardless of treatment condition. Conclusions: The findings support CBASP theory, suggesting that interpersonal change is related to depression reduction among chronically depressed patients. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Major Depression; *Multimodal Treatment Approach; *Psychotherapy; *Treatment Outcomes; Cognitive Behavior Therapy; Drug Therapy; Interpersonal Interaction; Interpersonal Psychotherapy; Nefazodone; Treatment Effectiveness Evaluation
- Medical Subject Headings (MeSH):
- Adolescent; Adult; Aged; Antidepressive Agents; Cognitive Therapy; Combined Modality Therapy; Depressive Disorder; Female; Humans; Interpersonal Relations; Male; Middle Aged; Psychiatric Status Rating Scales; Treatment Outcome; Triazoles
- PsycINFO Classification:
- Psychotherapy & Psychotherapeutic Counseling (3310)
- Population:
- Human
Male
Female - Location:
- US
- Age Group:
- Adulthood (18 yrs & older)
Young Adulthood (18-29 yrs)
Thirties (30-39 yrs)
Middle Age (40-64 yrs)
Aged (65 yrs & older) - Tests & Measures:
- Hamilton Rating Scale for Depression DOI: 10.1037/t04100-000
Impact Message Inventory DOI: 10.1037/t02262-000
Structured Clinical Interview for DSM-IV Axis I Disorders - Grant Sponsorship:
- Sponsor: Bristol-Myers Squibb
Recipients: No recipient indicated - Conference:
- Annual Meeting of the Society for Psychotherapy Research, 41st, Jun, 2010, Asilomar, CA, US
- Conference Notes:
- A version of this article was presented at the aforementioned conference.
- Methodology:
- Empirical Study; Quantitative Study; Treatment Outcome
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- First Posted: Apr 30, 2012; Accepted: Mar 13, 2012; Revised: Jan 11, 2012; First Submitted: Sep 27, 2010
- Release Date:
- 20120430
- Correction Date:
- 20120827
- Copyright:
- American Psychological Association. 2012
- Digital Object Identifier:
- http://dx.doi.org/10.1037/a0028351
- PMID:
- 22545738
- Accession Number:
- 2012-10796-001
- Number of Citations in Source:
- 38
- Persistent link to this record (Permalink):
- http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2012-10796-001&site=ehost-live
- Cut and Paste:
- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2012-10796-001&site=ehost-live">The relation between changes in patients' interpersonal impact messages and outcome in treatment for chronic depression.</A>
- Database:
- PsycINFO
The Relation Between Changes in Patients' Interpersonal Impact Messages and Outcome in Treatment for Chronic Depression
By: Michael J. Constantino
Department of Psychology, University of Massachusetts Amherst;
Holly B. Laws
Department of Psychology, University of Massachusetts Amherst
Bruce A. Arnow
Department of Psychiatry and Behavioral Sciences, Stanford University Medical Center
Daniel N. Klein
Department of Psychology, State University of New York at Stony Brook
Barbara O. Rothbaum
Department of Psychiatry, Emory University School of Medicine
Rachel Manber
Department of Psychiatry and Behavioral Sciences, Stanford University Medical Center
Acknowledgement: A version of this article was presented at the 41st annual meeting of the Society for Psychotherapy Research, Asilomar, California, June 2010. This research was supported by Bristol-Myers Squibb. We are grateful to Aline G. Sayer for her statistical guidance.
Major depression is highly prevalent and often recurrent in course (Constantino, Lembke, Fischer, & Arnow, 2006). Chronic forms of depression, in which symptoms persist for 2 years or longer without remission, account for about one third of all episodes of major depression (Kocsis et al., 2003) and affect approximately 3%–5% of the United States' population (Keller & Hanks, 1995a). To a higher degree than acute depression, chronic forms are associated with severe vocational and psychosocial impairment (Cassano, Perugi, Maremmani, & Akiskal, 1990; Wells, Burnam, Rogers, Hays, & Camp, 1992), frequent suicide attempts (Howland, 1993; Klein, Taylor, Harding, & Dickstein, 1988), and remarkably high health care costs (Howland, 1993; Weissman, Leaf, Bruce, & Florio, 1988). However, only recently has chronic depression received heightened conceptual, clinical, and empirical attention (e.g., Cuijpers et al., 2010; Keller & Hanks, 1995b; Keller et al., 2000; Klein & Santiago, 2003; Kocsis et al., 2009).
In a comprehensive, interpersonally focused theory of chronic depression, McCullough (2000) pointed to arrested social development as both a cause and sustaining consequence of chronic depressive symptomatology. In particular, McCullough theorized that chronically depressed individuals function pre-operationally (Piaget, 1926, 1981) when cognitively processing their social interactions. Unable to appraise effectively the consequences of their own behavior or to process accurately feedback and/or cause and effect associations in interpersonal exchanges, chronically depressed individuals (according to McCullough's, 2000, theory) lack the ability to act effectively on their interpersonal environment. Thus, they remain interpersonally unfulfilled and unskilled, as well as emotionally dysphoric. Although interpersonal deficits, generally speaking, are characteristic of all forms of depression (Bifulco, Moran, Ball, & Bernazzani, 2002; Coyne, 1976; Joiner & Timmons, 2009), McCullough has postulated that pre-operational functioning in chronically depressed patients often manifests specifically as hostile detachment and excessive submissiveness to a degree that differentiates chronically from acutely depressed people.
Evolving from his theory, McCullough (2000) developed cognitive-behavioral analysis system of psychotherapy (CBASP) to treat specifically chronic depression. CBASP is an integrative cognitive, behavioral, and interpersonal treatment that aims to enhance patients' understanding of the consequences of their actions, to help patients be more affiliative and connected to their interpersonal environment, and to help patients become more effectively assertive. These tasks are accomplished through three primary strategies. The first, situational analysis (SA), is a multi-step, problem-solving algorithm designed to improve patients' operational thinking by closely analyzing distressing interpersonal experiences. The second, the interpersonal discrimination exercise (IDE), involves the psychotherapist's use of transference hypotheses to help patients process how their current relationship with him or her is different from past relationships, and, thus, the same fears, expectations, and defenses need not apply. Finally, the third strategy, behavioral skill training/rehearsal (BST/R) focuses directly on skill development relevant to social exchange (e.g., assertiveness training, emotion regulation). As reflected in these interventions, especially the IDE, the therapy relationship in CBASP is conceptualized as a central change agent capable of promoting a corrective interpersonal experience.
CBASP, especially in combination with medication, has shown some efficacy in the treatment of chronic depression. In a well-powered (N = 681 patients) multi-center non-inferiority trial comparing CBASP alone, nefazodone alone, and their combination, Keller et al. (2000) reported modified intent-to-treat (ITT) response rates of 48%, 48%, and 73%, respectively (the modified ITT sample included the 656 participants with depression data for at least one post-randomization session). However, in a follow-up multi-center non-inferiority trial examining the influence of adding psychotherapy (Phase 2) to continued pharmacotherapy for nonresponders or partial responders (N = 491) to an initial medication trial (Phase 1) for chronic depression, there were no significant differences in Phase 2 response rates among patients whose continued treatment was augmented with CBASP or brief supportive psychotherapy (BSP), or those who continued optimized pharmacotherapy alone (Kocsis et al., 2009). Counter to predictions, the findings did not support the value of psychotherapy augmentation over pharmacotherapy augmentation/switching alone, nor did they support efficacy value added in CBASP over BSP. Thus, the current efficacy data on CBASP remain mixed, which suggests the need not only for additional efficacy trials but also for process research that might illuminate potential change ingredients that could be highlighted in future refinements of CBASP.
Focusing on the process of change, Constantino et al. (2008) formally tested the interpersonal tenets underlying McCullough's (2000) chronic depression theory and examined whether CBASP promoted interpersonal change in theory-specified ways. These authors first examined interpersonal profiles among the chronically depressed outpatients receiving CBASP in Keller et al.'s (2000) trial, as well as both an acutely depressed outpatient comparison sample receiving interpersonal therapy (IPT; McBride et al., 2010) and a non-clinical comparison sample (Kiesler & Schmidt, 1983). Across these samples, interpersonal styles were assessed from the perspective of an individual interacting with the patients or the non-clinical comparison group participants (i.e., psychotherapists in the two clinical groups, undergraduate subjects viewing non-maladjusted psychiatric interview participants in the non-clinical group) using the Impact Message Inventory (IMI; Kiesler & Schmidt, 1993). The IMI, a self-report measure, is based on the assumption that an individual's interpersonal style can be validly assessed by the interpersonal “impact messages” received by an interactant during communication with the individual (Kiesler, 1996). The IMI items form a circumplex comprising eight scales reflecting combinations of the central interpersonal dimensions of affiliation (ranging from hostility to friendliness on the x-axis) and control (ranging from dominance to submission on the y-axis). The measurement of impact messages is based on the complementarity principle. Theoretically, interpersonal behaviors are complementary if similar in affiliation and opposite in control (Carson, 1969; Kiesler, 1983, 1996). For example, if a psychotherapist endorsed feeling “in charge” when interacting with a patient, it would suggest that the patient's impact message is one of submissiveness—that is, the patient's deference would be evoking complementary dominance in the clinician (dominance is the interpersonal opposite of control). As noted, McCullough's (2000) theory purports that chronically depressed individuals should peak on hostile and submissive impact messages, reflecting the nature of their pathology and their difficulty getting their interpersonal needs met because of their inability to be flexible, affiliative, and effectively assertive (i.e., flexible and friendly–dominant). CBASP psychotherapists are trained to use the IMI to help identify their own objective countertransference (Kiesler, 1996)—that is, responses evoked in their interactions with the patient. Such monitoring can inform potential transferential “hot spots” requiring attention in the IDE as well as SA and BST/R.
Constantino et al.'s (2008) findings mostly supported McCullough's (2000) theory in terms of presenting interpersonal profiles. The chronically depressed patients receiving CBASP in Keller et al.'s (2000) trial presented with more hostile and submissive impact messages than friendly–dominant impact messages (as per their psychotherapists' IMI ratings early in treatment). Furthermore, at this early stage of treatment, chronically depressed patients were rated as having significantly higher hostile and hostile–dominant, and significantly lower friendly and friendly–dominant, impact messages on their psychotherapists than acutely depressed patients had on their psychotherapists at a comparable time in brief IPT. The chronically depressed patients also had higher hostile, hostile–submissive, and hostile–dominant, and significantly lower friendly–dominant, friendly, and friendly–submissive, impact messages on their clinicians than the normative comparison groups' impact messages on a rating other.
Constantino et al. (2008) also examined how chronically depressed patients' IMI profiles changed by the end of CBASP (as the clinicians also completed the IMI during the final week of the 12-week treatment), delivered either alone or with pharmacotherapy. The findings were again consistent with CBASP theory in that patients' impact messages were perceived by their psychotherapists as less hostile, hostile–submissive, and hostile–dominant, and more friendly, friendly–dominant, and friendly–submissive by treatment's end. Importantly, it did not appear that IMI change simply reflected improvement in depression, as change was comparable for patients who received CBASP alone or CBASP with pharmacotherapy despite the greater efficacy (in terms of depression reduction) of the combined treatment group. Furthermore, by the end of treatment, the chronically depressed patients' impact messages were mostly equivalent with those of the two comparison groups. The only exception was friendly–dominant, for which the chronically depressed patients continued to be rated significantly lower than the normative comparison sample.
Although Constantino et al.'s (2008) findings showed promising initial support for the primary interpersonal tenets of McCullough's (2000) chronic depression theory and theory of change in CBASP, it remains unclear if changes in patients' interpersonal impact messages are associated with treatment outcome in the form of depressive symptom reduction. Thus, the primary aim of the current study was to extend Constantino et al.'s findings by examining whether changes in patients' impact messages, as perceived by their psychotherapist, relate to depression change and posttreatment response status in Keller et al.'s (2000) trial. Consistent with McCullough's theory, we hypothesized that (a) a decrease in hostile–submissive impact messages (reflecting more adaptive interpersonal affiliation and balance in self–other reliance) would be associated with greater depression reduction over time and with better posttreatment response, and (b) an increase in friendly–dominant impact messages (reflecting adaptive interpersonal assertiveness) would also be associated with greater depression reduction and better response.
Method Data Set Overview
Data for the current study derived from the acute phase of the aforementioned multi-center (12 sites) randomized clinical trial compared 12 weeks of CBASP, nefazodone, and their combination for chronic depression (Keller et al., 2000). For the trial, 681 adults were randomly assigned to treatment condition. Because no outcome data were collected for dropouts, the primary outcome analyses discussed above were conducted on a modified intent-to-treat sample that included all patients who had at least one efficacy assessment beyond baseline (total N = 656). Patients averaged 43.5 years of age (SD = 10.7 years; range = 18–75 years) and met Diagnostic and Statistical Manual of Mental Disorders (4th ed.; DSM–IV; American Psychiatric Association, 1994) criteria for a current and principal form of nonpsychotic chronic depression as determined by the Structured Clinical Interview for DSM–IV Axis I Disorders (SCID-I; First, Spitzer, Gibbon, & Williams, 1995). The three eligible depression forms included the following: (a) major depressive disorder (MDD) lasting at least 2 years, (b) recurrent MDD with incomplete interepisode remission and a total continuous duration of at least 2 years, or (c) a major depressive episode superimposed on antecedent dysthymia. Patients also had to receive a score of at least 20 on the 24-item Hamilton Rating Scale for Depression (HRSD; Hamilton, 1967) at screening and at baseline following a 2-week drug-free period. Diagnostic exclusion criteria included the following: a history of bipolar disorder, obsessive-compulsive disorder, or dementia; an eating disorder within the past year; substance abuse or dependence in the past 6 months; antisocial, schizotypal, or severe borderline personality disorder; high suicidal risk; or an unstable medical condition. Patients were also excluded for non-response to at least three previous trials of at least two different classes of antidepressants or electroconvulsive therapy, or to at least two previous courses of empirically supported psychotherapy within the past 3 years. There were no significant differences between the treatment groups with respect to baseline characteristics and clinical characteristics (when analyzed both across and within sites; see Keller et al., 2000, for additional details and descriptive statistics on the total sample).
Across the sites, 52 psychotherapists conducted CBASP. All had several years of experience, attended a 2-day workshop conducted by J. McCullough, and demonstrated mastery of the treatment protocol in their work with two pilot cases. During the study, site supervisors reviewed session videos on a weekly basis to ensure standard protocol administration. In the combined condition, psychopharmacologists prescribed nefazodone.
CBASP, described above, was manual-guided and 12 weeks long. The protocol specified twice-weekly sessions for the initial 4 weeks and weekly sessions thereafter. Twice-weekly sessions could be extended up to Week 8 if the patient did not demonstrate mastery of the primary therapeutic skill (i.e., situational analysis). Thus, session frequency could range from 16 to 20. For the overall modified ITT sample (Keller et al., 2000), the average CBASP session frequency was 16.2 (SD = 4.8) for CBASP alone patients and 16.0 (SD = 4.7) for combined treatment patients.
Pharmacotherapy consisted of open-label nefazodone in two divided doses. The initial dose was 200 mg per day, with a 300 mg per day dose required by Week 3. Subsequent titration of divided doses was allowed up to 600 mg per day until maximum efficacy and tolerability were achieved. For the overall modified ITT sample (Keller et al., 2000), the average final nefazodone dose in the combined group was 460 mg per day (SD = 139 mg per day). Medication management (i.e., 15–20 min visits conducted weekly during the initial 4 weeks and biweekly thereafter) followed a published manual (Fawcett, Epstein, Fiester, Elkin, & Autry, 1987) focused on symptoms, side effects, and promotion of a biochemical rationale for depression response. Psychopharmacologists were not allowed to conduct formal psychotherapeutic interventions. The institutional review boards at each site approved the study protocol, and all participants gave written informed consent before study entry.
Current Subsample
The current subsample is restricted to participants in CBASP and combined treatment, as only CBASP psychotherapists completed the IMI. Of the 438 patients in these two groups who provided at least one post-randomization data point (modified ITT), 179 were excluded from the current analyses because the clinician did not fully complete the IMI measure for at least one of the two assessments. Thus, the final subsample for the current study was 259 patients. The average age of our subsample patients (across both treatment groups) was 44.8 years (SD = 10.1 years), with the majority being female (64.5%), White (93.1%), and neither married nor cohabitating (55.2%). Their average monthly income was $2,187 (SD = $2,464). Diagnostically, 33.2% met criteria for chronic MDD, 23.2% met criteria for recurrent MDD with incomplete interepisode remission, and 43.6% met criteria for MDD superimposed on preexisting dysthymia. The mean baseline HRSD score was 27.7 (SD = 5.2). The mean durations for the current MDD episode and the current dysthymic episode were 9.3 years (SD = 11.2 years) and 24.2 years (SD = 15.5 years), respectively, with average age of onsets of 27.9 (SD = 13.9) and 19.4 (SD = 13.9), respectively. Within our subsample, 40.8% had a non-exclusionary comorbid personality disorder. With respect to baseline demographic and clinical features for our subsample, the only marginally significant difference between CBASP and combined treatment patients was for the diagnosis of a comorbid personality disorder. More patients in the combined group (42.8%) were diagnosed with a personality disorder than in CBASP alone (34.4%), χ2(1) = 3.58, p = .06.
The patients in our subsample were similar to those excluded because of missing IMI data on most of the above sample characteristics. However, several significant differences existed. Patients in our subsample were slightly older (M = 44.8, SD = 10.1) than those excluded (M = 42.8, SD = 10.9), t(436) = 1.94, p = .05. Patients in our subsample also had significantly higher baseline HRSD scores (M = 27.7, SD = 5.2) than those excluded (M = 25.8, SD = 4.6), t(436) = 3.88, p < .01, and had a longer length of current MDD episode (M = 9.3, SD = 11.2) than those excluded (M = 6.3, SD = 7.0), t(436) = 3.18, p < .01. Finally, there were significantly more patients in our subsample with a personality disorder diagnosis (40.5%) than in those excluded from analyses (36.6%), χ2(1) = 8.75, p < .01.
Of the 259 patients in our subsample, 141 had IMI measurements for both Weeks 2 and 12, 111 had IMI measurements at Week 2 only, and 7 had IMI measurements at Week 12 only (we discuss below our method for deriving the relevant IMI change scores).
Measures and Data Collection
Impact Message Inventory (IMI)
Following Session 2 (Week 1) and the final session (Week 12), CBASP psychotherapists completed the octant scale version of the IMI (Kiesler & Schmidt, 1993) to assess their perceptions of their patients' interpersonal impact messages. The IMI consists of 56 items rated on a 4-point scale ranging from 1 (not at all) to 4 (very much so). The measure possesses good internal consistency and quasi-circumplex structure based on the underlying dimensions of affiliation and control (Schmidt, Wagner, & Kiesler, 1999). Each octant, or vector, reflects the sum of 7 items. The present study focused on the two theoretically relevant vectors of hostile–submissive (HS; Week 2 α = .80, Week 12 α = .86) and friendly–dominant (FD; Week 2 α = .76, Week 12 α = .78). All IMI items begin with the phrase, “When I am with this person, he or she makes me feel…” Sample HS items include, “… that I should tell him/her not to be so nervous around me” and “… that he/she thinks he/she can't do anything for him/herself.” Sample FD items include, “… that I could relax and he/she'd take charge” and “… entertained.” For this study, we calculated weighted vector scores based on the geometry of the circle and taking into account information from adjacent vectors. The weighted HS formula is HS + .707 (H + S), and the weighted FD formula is FD + .707 (D + F). The theoretical range for weighted vector scores is 16.90 to 67.59.
Hamilton Rating Scale for Depression (HRSD)
The 24-item HRSD (Hamilton, 1967) was used to assess patient depression at baseline and following treatment Weeks 1, 2, 3, 4, 6, 8, 10, and 12. The HRSD is the most widely used interviewer-administered depression instrument, with a majority of studies reporting adequate internal consistency (α ≥ .70; Bagby, Ryder, Schuller, & Marshall, 2004). Interrater reliability estimates are less consistent, with Bagby et al. (2004) reporting an intraclass r range from .46 to .99. To promote high interrater agreement in Keller et al.'s (2000) trial, all raters went through a strict certification process in HRSD administration. Raters were also blind to treatment condition. The HRSD was used to assess both depression level, as well as treatment response. In Keller et al.'s study, as well as the current analyses, a single positive response group was formed. This dichotomized group included patients who either (a) remitted (i.e., had an HRSD score of no more than 8 at both Weeks 10 and 12 for completers or at the time of withdrawal for noncompleters) or (b) had a satisfactory response (i.e., had at least a 50% reduction in HRSD score from baseline to Weeks 10 and 12, with a total score of 15 or less at these times, but of more than 8 at Week 10, Week 12, or both for study completers or at the time of withdrawal for noncompleters).
Results Preliminary Analyses
To capture change on the relevant HS and FD weighted IMI vectors, we created latent difference scores using hierarchical linear modeling (HLM; Collins & Sayer, 2001; Raudenbush & Bryk, 2002). Specifically, we used the HLM 6 program (Raudenbush, Bryk, & Congdon, 2004) to fit a two-wave model of change to each individual's data and obtained the model-based empirical Bayes estimates of each person's change score for use in the primary analyses. This empirical Bayes estimate of change is a composite that combines information about change from each individual and information from the group as whole, with each part weighted by its reliability. Individuals with one data point provide less reliable evidence for change and therefore change estimates for those with only one IMI measure were weighted toward the group mean change score. This is a standard approach for handling missingness in hierarchical linear models (Raudenbush & Bryk, 2002). Negative scores indicate a decrease in interpersonal characteristics from Week 2 to Week 12, whereas positive scores indicate an increase. Change in HS was significantly different from zero and negative, indicating that, on average, patients' HS impact messages decreased significantly over time (γ = –4.97, p < .001). Change in patients' FD impact messages was significantly different from zero and positive (γ = 3.12, p < .001); on average, patients became significantly more FD by treatment's end.
Given that previous analyses of Keller et al.'s (2000) trial data showed that early and middle patient-rated therapeutic alliance quality were positively associated with posttreatment outcome (Klein et al., 2003), we also examined the association between the early HS vector and alliance (as assessed with the brief version of the Working Alliance Inventory; Tracey & Kokotovic, 1989). We did this to ensure that these are two distinct constructs (as opposed to early HS impact messages simply being redundant with negative alliance quality). Specifically, we assessed the bivariate correlations between the early HS vector and all measures of alliance quality in this data set (i.e., early, middle, and late treatment). Results indicated no significant relations between the early HS vector and early alliance (r = –.023, p > .05), middle alliance (r = –.020, p > .05), or late alliance (r = .045, p > .05), thus suggesting the distinctness of these constructs.
Primary Analyses
To test our primary questions, we analyzed data using growth curve modeling in HLM 6. We fit a series of models to the HRSD data to determine the shape of patients' depression change trajectories over the treatment course. We compared a model including only linear change in depression to a quadratic model that accounted for the curvature in change, as well as linear change across the 12 treatment weeks. A chi-square comparison test between the deviance fit statistics for the two models indicated that the quadratic model was a significantly better fit to the data than the linear model, Δχ2(4, N = 256) = 238.045, p < .001.
Thus, the unconditional model selected for all subsequent analyses was the quadratic trajectory model, with variability to be predicted around the deviations from the average (i.e., all error terms were allowed to vary). The intercept, linear, and quadratic fixed effects were all significantly different from zero (see Table 1). On average, patients' depression rating at the midpoint (Week 6) of therapy was 18.27. The linear rate of change in depression was negative, with an average decrease of 1.36 in HRSD scores per week. The significant quadratic term was positive, indicating that, on average, the rate of depression deceleration started out more steeply and slowed toward the final therapy sessions. Also, as indicated in Table 1, tests of the variance components (random effects) confirmed that there was significant variability around the linear and curvilinear facets of depression change, suggesting that predictors could be added to the model to determine if patients varied systematically on relevant characteristics.
Baseline Quadratic Model of Depression Change Across 12 Treatment Weeks
Next, we added the predictors to the unconditional quadratic model. First, treatment condition (CBASP alone vs. combined CBASP plus medication) was added to the intercept, linear, and quadratic equations. Treatment condition was a significant predictor of the average depression level at treatment midpoint, the average rate of change at midpoint, and the average curvature of depression change throughout treatment (see the Treatment Model column in Table 2). Specifically, the combined group had a lower midpoint depression level than the CBASP alone group. Also, the shape of change for the CBASP alone group had less curvature in depression decline than the combined group. Finally, the combined group, relative to CBASP alone, had a more curved trajectory of change, with a steeper decline in depression early in therapy and a slower rate of change toward the later therapy sessions (see Figure 1). This treatment model was a significantly better fit to the data than the unconditional quadratic model, Δχ2(3, N = 256) = 13.396, p < .01. The effect size in growth curve models is represented by a pseudo change in R2 statistic, described in terms of how much of the variance in each aspect of patients' depression was explained by each predictor (i.e., depression at treatment midpoint, depression change at midpoint, and depression change curvature across treatment). The treatment condition predictor accounted for a 3.79% reduction of unexplained variance around the midpoint depression score (intercept), a 6.17% reduction of unexplained variance in the rate of depression change at midpoint (linear term), and a 3.01% reduction in unexplained variance around the curvature of depression change (quadratic term). These findings indicate that in addition to posttreatment depression outcome, the course of depression change is different depending on the type of treatment. Because the treatment effect was significant both at the level of fixed effects and in overall model fit, the treatment condition variable was retained for subsequent analyses.
Model Comparison and Parameters for Treatment Condition and Hostile–Submissive (HS) Change Predicting Depression Change Over 12 Weeks of Treatment
Figure 1. Depression change over 12 weeks for patients who received cognitive-behavioral analysis system of psychotherapy (CBASP) alone versus those who received the combination of CBASP plus nefazodone. HRS Depression = Hamilton Rating Scale for Depression.
Next, the HS change variable was added as a predictor to the model. The model estimated significant effects on the linear parameter, or rate of change in depression (see the Treatment and HS Change Model column in Table 2). Patients whose psychotherapists perceived less HS impact messages over the treatment course had significantly more reduction in depression. This effect accounted for an additional 6.23% of the variance in depression change above the variance explained by the model with only the treatment condition predictor. In addition, this model was a better fit to the data than the model with the treatment condition predictor alone, Δχ2(3, N = 26) = 12.53, p < .01. Finally, a model adding the interaction of HS change and treatment condition showed no significant interaction effect on any aspect of depression change over therapy. This suggests that the relation between change in HS impact messages and depression is the same for both treatment groups (see Figure 2). Given that combined treatment patients fared significantly better than CBASP only patients in terms of treatment depression reduction, the fact that the association between HS change and depression reduction is the same for both treatment groups suggests that HS change is not simply capturing symptom change.
Figure 2. Decreases in patients' hostile–submissive (HS) impacts predict greater depression reduction in both cognitive-behavioral analysis system of psychotherapy (CBASP) and combined CBASP plus nefazodone groups. Values for HS Decrease and HS Increase are the 10th and 90th percentiles, respectively. Med = medication; HRS Depression = Hamilton Rating Scale for Depression.
We also estimated a similar model testing whether change in FD over the treatment course predicted depression outcome over time. Results indicated no significant relation between FD change and either depression level at midpoint or change across treatment, and the model fit did not significantly improve from the model with only treatment condition as a predictor, Δχ2(3, N = 256) = 2.76, p > .50. A model testing whether there was an interaction between FD change and treatment condition similarly showed no significant interaction effect on any of the depression level or change parameters.
Finally, we conducted logistic regression analyses to predict the probability that a patient would respond to treatment. We compared a model with treatment condition and HS change to a model with the treatment predictor alone. The model with both the HS change and treatment condition predictors was a statistically significant improvement over the model with only the treatment condition predictor, χ2(1, N = 259) = 5.254, p = .02. The model correctly classified 69% of responders versus nonresponders. Table 3 shows the logistic regression coefficient, Wald test, and odds ratio for each of the predictors. Both the treatment condition and HS change predictors had significant partial effects. As in the growth curve model, being in the combined CBASP and medication condition versus CBASP alone was associated with a greater probability of responding to treatment. Decreases in HS impact messages over therapy were also associated with a greater probability of responding to treatment (see Figure 3). A logistic regression testing whether FD change predicted response did not significantly improve the model fit compared to the model with only the treatment condition predictor, χ2(1, N = 259) = 0.880, p = .35.
Logistic Regression Predicting Treatment Response From Treatment Condition and Hostile–Submissive (HS) Change Over Treatment
Figure 3. Probability of responding to treatment based on change in hostile–submissive impacts for cognitive-behavioral analysis system of psychotherapy (CBASP) alone versus CBASP plus nefazodone groups.
DiscussionThe goal of this study was to assess whether changes in chronically depressed patients' interpersonal impact messages (namely hostile–submissive [HS] and friendly–dominant [FD]), as perceived by their psychotherapists, were associated with depression change and treatment response status in theoretically consistent ways following 12 weeks of CBASP or CBASP plus nefazodone. As predicted, a decrease in HS impact messages was associated with greater depression reduction over time and with positive posttreatment response, irrespective of treatment condition. Counter to our prediction, an increase in FD impact messages was unrelated to depression reduction and posttreatment response. Our findings were also consistent with previous analyses conducted on the full modified ITT sample from which the current subsample derives (see Keller et al., 2000); our subsample patients in the combined treatment group evidenced better depression outcome at midpoint and across time than patients in the CBASP only group.
Consistent with our previous findings (Constantino et al., 2008), but with a different methodology (HLM-derived change scores vs. mixed analyses of variance), we found that patients' HS impact messages as perceived by their psychotherapists (across both treatment conditions) decreased significantly from early to late treatment, while their FD impact messages increased significantly (across both treatment conditions). Such changes suggest that these chronically depressed patients became, at least as perceived by their psychotherapists, more affiliative and balanced in their self–other reliance; a theoretically adaptive blend of interpersonal functioning that should theoretically promote greater interpersonal effectiveness and a corresponding decrease in depressive symptomatology (McCullough, 2000). And, as predicted, the decrease in HS impact messages perceived by their psychotherapists was associated with a faster reduction in depression over treatment and with positive therapeutic response.
It is possible that the specific interpersonal foci of CBASP (including SA, the IDE, and BST/R), as intended, promote greater affiliation and reduced hostility on the part of chronically depressed patients. Of course, it is important to stress that this study did not isolate the specific therapist, patient, dyadic, and/or treatment processes that led to the interpersonal changes; thus, future research will need to focus on the specific processes that causally foster the intended interpersonal effect of CBASP. One line of such mechanism work could focus on the quality of the therapeutic alliance. It is possible that a quality alliance provides an interpersonal vehicle through which an individual can change their HS ways of relating to others. In this sense, decreased HS might mediate the known association between alliance quality and outcome in Keller et al.'s (2000) chronic depression sample (see Klein et al., 2003). For now, the present findings suggest that an effect on interpersonal impacts is present, and that the effect (at least with regard to decreased HS impact messages) is associated with reduced depression. Of course, it is also important to emphasize that the correlational nature of the study cannot rule out the possibility of reverse causation (i.e., that reduced depression promotes changes in therapists' ratings of their patients' interpersonal impact messages). The present study could also not fully tease apart the respective contributory roles of HS impact messages and alliance on treatment outcome.
Unexpectedly, increased FD impact messages did not predict depression reduction or treatment response. This lack of association, coupled with the HS findings, could mean that change on the affiliation dimension (i.e., becoming less hostile and more affiliative) has a more profound interpersonal impact than increasing dominance or assertiveness. It might be that hostility is more readily detected and thus more aversive to deal with in interpersonal exchanges than problems in assertiveness. Thus, reductions in hostility might bring about more improvements in interpersonal functioning than increases in assertiveness and, consequently, relate to greater depression reduction. The present findings are consistent with those reported by Vittengl, Clark, and Jarrett (2004) in a study of cognitive therapy for recurrent depression. In their study, the researchers found that both self-directed affiliation and autonomy (a construct roughly comparable to friendly–dominant) increased significantly over 12 weeks of acute phase treatment. However, in examining the association of these variables with depression, only affiliation level at treatment's end predicted positive response status. Again, it seems possible that change in affiliation (both toward self and others) is more important for depression change than an increase in dominance/autonomy-taking. To the extent that this finding is accurate, and can be replicated, it is possible that the efficacy of CBASP could be improved by focusing its interpersonal strategies more centrally on the affiliation/hostility dimension. It is important to consider refinements for CBASP given its currently mixed efficacy findings (cf. Keller et al., 2000; Kocsis et al., 2009).
This perspective on affiliation is also consistent with our previous finding that the affiliation dimension may be the most central factor differentiating chronic depressives' pathology from normal functioning (Constantino et al., 2008). Thus, it would follow that change on this dimension might have the most significant influence on depression reduction. Of course, as vectors on a circumplex, the notion of friendly–dominance also includes increased affiliation coupled with autonomous acting on others. Thus, it could still be the case that greater friendly–dominance has an adaptive influence on chronically depressed patients. However, it may be that greater change is required before affecting depression. This is perhaps not surprising given Constantino et al.'s (2008) finding that FD impacts remained, in the same chronic depression sample as the current study, distinctly lower at treatment's end than a normative comparison group. It might also be that such changes in assertiveness simply take longer than the 12 weeks in the present treatment. Although patients might be on their way to greater assertiveness following 12 weeks, they might need more time and opportunities to practice these skills in a manner that will relate to significant changes in interpersonal relating. Such time and opportunity might require a longer course of CBASP. Alternatively, the current treatment length might be sufficient to learn these skills, but more time is needed to reap their benefits in naturalistically occurring relationships. This is a question for future study.
The difference in findings for HS and FD impact messages might also be a function of the special nature of the therapeutic relationship. The patient–psychotherapist relationship is inherently asymmetrical, and detecting increased assertiveness in this specific context might be more difficult than detecting reductions in hostility. Thus, it is possible that even more changes in assertiveness were present than detected by the clinicians. With more sensitive detection, such changes might significantly predict depression outcomes. Clinically then, it might be important for CBASP clinicians to pay close attention to subtle themes of patient assertiveness, especially as they relate to the work being done in the context of the psychotherapeutic relationship (e.g., the IDE). Again, it is important to consider refinements for CBASP, and to conduct additional research on its process of change, given its currently mixed efficacy findings. Finally, another possible explanation of the non-finding for FD impact messages is that increased friendly–dominance has adaptive clinical consequences not captured by a pure depression measure. For example, it is plausible that increased FD impact messages could affect other constructs like increased quality of life or increased relational satisfaction, which could be indirectly associated with depression reduction or even relapse prevention. It will be important for future research to examine interpersonal styles and impacts in relation to clinical constructs other than just depression.
Secondarily, our findings also provided further information on the nature of depression change as a complement to Keller et al.'s (2000) results. For the full modified ITT sample, Keller et al. used a piecewise mixed effects linear model to examine differences between their three treatment conditions on linear rate of depression change from baseline to Week 4 and then from Week 4 to Week 12 (or the final visit). Week 4 was selected because it was the earliest time that the nefazodone was predicted to have a therapeutic effect. Relevant to the current study, these authors found that patients in combined CBASP plus nefazodone evidenced a greater rate of depression reduction from baseline to Week 4 than patients in CBASP alone. This rate difference, however, was not statistically significant from Week 4 to Week 12. In the current study, the findings based on our subsample essentially paralleled those of Keller et al. In particular, patients receiving combined treatment had significantly lower depression levels and rate of depression change at the midpoint of therapy compared to patients in CBASP alone. Note that we examined the actual midpoint of Week 6 compared to Week 4 in Keller et al.'s analysis. Thus, the during-treatment effect of treatment condition on depression change was still evident 2 weeks further into the treatment. We also examined the quadratic term, which suggested that the rate of depression change was different for the two groups. Change in CBASP alone involved a fairly uniform deceleration, while the combined group had a more curved trajectory—that is, a steeper deceleration in depression earlier in treatment, which slowed toward the end of treatment. Clinically, the findings point to a more efficient response to combined CBASP and nefazodone for chronically depressed patients, which can be useful both in treatment selection and planning as well as in response/non-response monitoring.
The current study has several limitations. First, the IMI assesses interpersonal functioning from just one perspective and within one relationship (in this case, the clinician's perspective in the context of a therapy relationship), thus restricting the generalizability of such assessment to the patient's own experience and to other important relationships. Furthermore, the psychotherapists' IMI ratings could be biased both early in treatment (when knowledge about the theory of chronic depression could influence ratings) and late in treatment (when expectations that the patient has improved in theory-consistent ways could influence ratings). Thus, the present findings should be interpreted cautiously, and multi-method/multi-perspective replication is required for greater confidence. It should be noted, though, that the present findings were quite similar to those in a study of depressed outpatients whose significant others rated their interpersonal impact messages at both pre- and posttreatment (Grosse Holtforth, Altenstein, Ansell, Schneider, & Caspar, 2011). In this study, patients were perceived by their significant others as less friendly–submissive, submissive, and hostile–submissive, and more dominant and friendly–dominant after treatment. Moreover, decreases in submissiveness and hostile–submissiveness were associated with greater depression reduction. Thus, some converging findings across rating perspectives already exist.
Second, the IMI was measured on just two occasions, thus limiting our ability to measure more complex change patterns. Although using HLM to create change scores helped us to remove measurement error, we were restricted to just one early and one late treatment assessment. Interpersonal change might unfold more complexly in the context of the patient–psychotherapist relationship, including likely ebbs and flows as the dyad engages in novel exchanges that disrupt the patient's maladaptive transaction cycles. These complexities might play a role in how depression change occurs, and they might also interact with other relational processes such as alliance ruptures and potential subsequent repairs. Having just the two IMI occasions also allowed us to examine only the concurrent relation of interpersonal change and depression change, thus limiting our ability to assess fully the temporal direction of the change.
Third, a large portion of cases in our effective sample had only one IMI rating (with change scores being estimated using characteristics of the entire sample). This scenario was predominantly a function of patients dropping out prior to the late IMI assessment. Thus, it is unclear how the IMI change trajectories might have continued for these dropout cases, and how these specific trajectories might have differentially related to depression. In future studies, it will be important to have more frequent and reliable IMI assessment to understand more fully (with less missing data and more data points when imputation is required) how IMI change relates to therapeutic change.
Fourth, given that patients included in our subsample were more symptomatic than patients excluded because of no IMI ratings, it is unclear if our findings would generalize to patients with less severe chronic depression.
Fifth, it is possible that the IMI items, perhaps especially for the FD scale, were difficult for the rating therapists to apply to the therapy setting. It is plausible that such difficulty contributed to the lack of association between FD change and outcome.
Sixth, interrater reliability on the HRSD was not assessed in the parent trial from which the current data derive.
Seventh, given the absence of a control condition, it is difficult to know if the changes that we did capture in interpersonal impacts were specific to CBASP versus more generally related to having 12 weeks of contact with a psychosocial or pharmacological treatment provider.
Finally, it is possible that IMI changes were at least partly attributable to statistical regression to the mean or to repeated administration.
With these limitations in mind, the present study provides further support for McCullough's (2000) theory of chronic depression and therapeutic change; it also provides preliminary, though non-causal, evidence for the promise of CBASP influencing processes and outcomes in its intended manner. With continued focus on the nature of chronic depression and its related treatment processes and outcomes, this debilitating condition will no longer require the “understudied” designation.
Footnotes 1 The large amount of missing IMI data in Keller et al.'s (2000) trial can likely be attributed to the fact that IMI ratings were not part of the official research protocol, and, thus, their collection was not closely monitored by the research team. Rather, IMI ratings were used to facilitate formulation of the transference hypothesis as per the CBASP manual. Because 59% of the therapists did complete at least one IMI, we were able to use this clinically rich measure in the present secondary process analyses.
2 However, to ensure that our handling of missing data did not radically alter the results, we re-ran our primary analyses (discussed below) separately on two subgroups from our overall sample of 259. The first subgroup included only patients (N = 141) whose IMI change scores were derived from complete IMI data. The second subgroup included only patients (N = 118) whose IMI change scores were estimated because IMI data existed at only one of the two time points. Results, across all primary analyses, showed similar patterns in both subgroups to the full sample findings that are reported below.
3 Preliminary analyses also indicated a significant improvement in model fit with the addition of a cubic term to the growth model. However, there was limited variability around the cubic term, and we failed to find any significant relationships between our predictors and cubic change. Thus, we elected to present findings on a quadratic model for the sake of clarity and parsimony.
4 We use the term unconditional to describe the selected Level 1 model with no Level 2 predictors. Technically, this is a baseline, rather than unconditional, model because there are Level 1 predictors of time included. However, we elected to use unconditional to avoid any confusion in meaning with the term baseline within the context of a treatment study.
5 Note that the unstandardized coefficient for linear change in depression is appropriately documented in Table 1 as −13.581. The discrepancy is because we divided the variable used to model weeks in psychotherapy by 10 to avoid estimation problems that can arise from an ill-scaled matrix.
6 We also examined the degree to which early (Week 1) HS scores correlated with early (Week 1) HRSD scores, as it would be concerning if the most depressed patients at baseline were also viewed as the most HS, and that regression to the mean on both variables might account for higher levels of change on both. A bivariate Pearson correlation indicated only a slight association between these variables, which was not statistically significant (r = .119, p = .07). This suggests that the HS and depression constructs are distinct at the beginning of treatment, which mitigates the likelihood that regression to the mean on both variables is responsible for the findings presented.
7 Given the aforementioned significant association between early alliance quality and posttreatment outcome in this data set, we also conducted all of our primary analyses controlling for early alliance. The inclusion of early alliance did not change the findings, thus providing further evidence that the IMI vectors (especially HS, which has a conceptually face valid connection to alliance components) are not redundant.
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Submitted: September 27, 2010 Revised: January 11, 2012 Accepted: March 13, 2012
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Source: Journal of Consulting and Clinical Psychology. Vol. 80. (3), Jun, 2012 pp. 354-364)
Accession Number: 2012-10796-001
Digital Object Identifier: 10.1037/a0028351
Record: 176- Title:
- The relation between stress and alcohol use among Hispanic adolescents.
- Authors:
- Goldbach, Jeremy T.. School of Social Work, University of Southern California, Los Angeles, CA, US, goldbach@usc.edu
Berger Cardoso, Jodi. Graduate College of Social Work, University of Houston, Houston, TX, US
Cervantes, Richard C.. Behavioral Assessment, Incorporated, Beverly Hills, CA, US
Duan, Lei. School of Social Work, University of Southern California, Los Angeles, CA, US - Address:
- Goldbach, Jeremy T., School of Social Work, University of Southern California, 663 West 34th Street, Los Angeles, CA, US, 90089, goldbach@usc.edu
- Source:
- Psychology of Addictive Behaviors, Vol 29(4), Dec, 2015. pp. 960-968.
- NLM Title Abbreviation:
- Psychol Addict Behav
- Page Count:
- 9
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- Bulletin of the Society of Psychologists in Addictive Behaviors; Bulletin of the Society of Psychologists in Substance Abuse
- Other Publishers:
- US : Educational Publishing Foundation
Society of Psychologists in Addictive Behaviors - ISSN:
- 0893-164X (Print)
1939-1501 (Electronic) - Language:
- English
- Keywords:
- Hispanic, adolescents, cultural stress, alcohol use
- Abstract:
- We explored the relation between 8 domains of Hispanic stress and alcohol use and frequency of use in a sample of Hispanic adolescents between 11 and 19 years old (N = 901). Independent t tests were used to compare means of domains of Hispanic stress between adolescents who reported alcohol use and those who reported no use. In addition, multinomial logistic regression was used to examine whether domains of Hispanic stress were related to alcohol use and whether the relation differed by gender and age. Multiple imputation was used to address missing data. In the analytic sample, 75.8% (n = 683) reported no use and 24.2% (n = 218) reported alcohol use during the previous 30 days. Higher mean Hispanic stress scores were observed among youths who reported alcohol use during the previous 30 days in 5 domains: acculturation gap, community and gang violence, family economic, discrimination, and family and drug-related stress. Increased community and gang violence, family and drug, and acculturative gap stress were found to be associated with some alcohol use categories beyond the effect of other domains. Few differences in the association between Hispanic stress and alcohol use by gender and age were observed. Study findings indicate that family and drug-related, community and gang violence, and acculturative gap stress domains are salient factors related to alcohol use among Hispanic adolescents, and their implications for prevention science are discussed. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Alcohol Drinking Patterns; *Stress; *Latinos/Latinas; *Adolescent Characteristics
- PsycINFO Classification:
- Psychosocial & Personality Development (2840)
- Population:
- Human
Male
Female - Location:
- US
- Age Group:
- Adolescence (13-17 yrs)
Adulthood (18 yrs & older)
Young Adulthood (18-29 yrs) - Tests & Measures:
- Hispanic Stress Inventory—Adolescent Version DOI: 10.1037/t07394-000
- Grant Sponsorship:
- Sponsor: National Institutes of Health, National Institute of Mental Health, US
Grant Number: 2R44MH073180-02
Recipients: Cervantes, Richard C. - Methodology:
- Empirical Study; Quantitative Study
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- First Posted: Nov 9, 2015; Accepted: Sep 16, 2015; Revised: Sep 15, 2015; First Submitted: Jan 2, 2015
- Release Date:
- 20151109
- Correction Date:
- 20160104
- Copyright:
- American Psychological Association. 2015
- Digital Object Identifier:
- http://dx.doi.org/10.1037/adb0000133
- Accession Number:
- 2015-50538-001
- Number of Citations in Source:
- 79
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- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2015-50538-001&site=ehost-live">The relation between stress and alcohol use among Hispanic adolescents.</A>
- Database:
- PsycINFO
The Relation Between Stress and Alcohol Use Among Hispanic Adolescents
By: Jeremy T. Goldbach
School of Social Work, University of Southern California;
Jodi Berger Cardoso
Graduate College of Social Work, University of Houston
Richard C. Cervantes
Behavioral Assessment, Incorporated, Beverly Hills, California
Lei Duan
School of Social Work, University of Southern California
Acknowledgement: Research reported in this publication was supported by the National Institute of Mental Health of the National Institutes of Health under Award Number 2R44MH073180-02 to Richard C. Cervantes.
More than one quarter of Hispanic adolescents in the United States report using alcohol during the previous 30 days (Pemberton, Colliver, Robbins, & Gfroerer, 2008; Substance Abuse and Mental Health Services Administration [SAMHSA], 2010). Compared with their non-Hispanic White counterparts, Hispanic adolescents report higher rates of binge and heavy drinking (Kanny, Liu, Brewer, & Lu, 2013; King & Vidourek, 2010). The health consequences of alcohol consumption, especially during adolescence, are numerous. Adolescents who engage in drinking have a higher prevalence of involvement in criminal activity, abuse of other substances, and depression (Hill et al., 2010) and report poorer academic outcomes and more frequent risky sexual behavior (Miller, Naimi, Brewer, & Jones, 2007). Adolescent alcohol use has been linked to poor cognitive functioning, lower mood states (Townshend & Duka, 2005), and poor decision-making, particularly among individuals who begin drinking at an early age (Goudriaan, Grekin, & Sher, 2007). Understanding the underlying cultural mechanisms that may contribute to alcohol use or promote abstinence patterns in this population is a public health priority (Little, 2000; Longabaugh, 2007).
Stress and Substance Use Among Hispanic AdolescentsResearchers commonly suggest that a stress-illness model (Lazarus & Folkman, 1978) explains behavioral health disparities among minority groups (Edwards & Romero, 2008; Williams & Mohammed, 2009). Among Hispanics, stressors include those associated with cultural adaptation (e.g., acculturation) and minority status (e.g., discrimination, immigration; Córdova & Cervantes, 2010). More than two decades of research with Hispanic adults has found a relatively consistent association between these unique Hispanic stressors and alcohol use (Amaro, Whitaker, Coffman, & Heeren, 1990; Caetano & Clark, 2003; Okamoto, Ritt-Olson, Soto, Baezconde-Garbanati, & Unger, 2009; Vega, Sribney, & Achara-Abrahams, 2003).
Few studies have examined these stressors and alcohol use among Hispanic adolescents (Delva et al., 2005) and have reported inconsistent findings. Some researchers found no association between acculturation and alcohol use (Elder et al., 2000), whereas others have reported positive (Brindis, Wolfe, McCarter, Ball, & Starbuck-Morales, 1995; Caetano, Ramisetty-Mikler, Caetano Vaeth, & Harris, 2007; Polednak, 1997) or negative (Nielsen & Ford, 2001) associations. This may be due to the complex nature of stress among Hispanic youths. For example, Gil, Wagner, and Vega (2000) found that acculturative stress is related to alcohol use among adolescent Hispanic boys, but is affected by additional factors such as the deterioration of Hispanic family values, attitudes, and behaviors. Research including measures of family stress (Driscoll, Biggs, Brindis, & Yankah, 2001), parental warmth, and language spoken at home (Mogro-Wilson, 2008) has shown these factors also to be relevant to understanding alcohol use among Hispanic adolescents (Prado & Pantin, 2011). Furthermore, although many Hispanic families emigrate from rural areas (Suárez-Orozco & Suárez-Orozco, 2001), they tend to reside in more geographically concentrated and urban areas once arriving in the United States (U.S. Census Bureau, 2007). Higher-density households and living arrangements found in Hispanic-dominant urban neighborhoods with fewer socioeconomic advantages also influence family and individual stress levels (Cervantes, Córdova, Fisher, & Kilp, 2008), which may influence alcohol use patterns. It is clear that stress experiences associated with alcohol use among Hispanic adolescents are multifaceted and not well understood.
Recently, Cervantes and colleagues (Cervantes, Fisher, Córdova, & Napper, 2012; Cervantes, Goldbach, & Padilla, 2012) developed a measure to identify and assess the impact of different stress experiences among Hispanic youths. The Hispanic Stress Inventory-Adolescent Version (HSI-A) was constructed in a two-part process: (a) focus groups to identify stressful experiences and (b) a national validation study using exploratory factor analysis with 992 Hispanic adolescents in four metropolitan U.S. cities. The item analysis revealed eight unique domains of Hispanic stress: acculturation gap, culture and educational, immigration, community and gang violence, family and drug-related, family related immigration stress, discrimination, and family economic stress. Some domains were closely associated with processes related directly to being a Hispanic in the United States (e.g., immigration stress, acculturation), whereas others reflected stressful experiences common to the neighborhoods in which Hispanics live related to socioeconomic disadvantage (Evans & Kim, 2007). For example, although the domain of community and gang violence stress includes items such as being stereotyped as being in a gang, it also includes living in a dangerous neighborhood and seeing drive-by shootings. In the family and drug-related stress domain, items include having too little contact with parents and having family members who sell drugs.
Researchers have examined the HSI-A in relation to depression (Cervantes, Berger Cardoso, & Goldbach, 2015), suicidal ideation (Cervantes, Goldbach, Varela, & Santisteban, 2014), and risky substance use behaviors (Berger Cardoso, Goldbach, Cervantes, & Swank, 2015; Cervantes, Goldbach, & Santos, 2011). To date, however, no studies have used a comprehensive measure of Hispanic stress to explore alcohol use in a sample of Hispanic adolescents. The present study filled a gap in the literature by examining: (a) how the eight domains of stress, as measured by the HSI-A, differ among adolescents who used alcohol versus those who did not use alcohol during the previous 30 days; and (b) the relation between each domain of Hispanic stress and the relative risk of alcohol use during the previous 30 days after controlling for covariates and the other stress domains. Additionally, exploratory analysis examined whether age and gender moderate the relation between stress domains and alcohol use. We hypothesized that higher scores in Hispanic stress domains will be associated with alcohol use and that community and family stressors will be associated with a higher degree of use during the previous 30 days. Additionally, national trends (e.g., Centers for Disease Control & Prevention, 2014; Chassin, Pitts, & Prost, 2002) suggest older youths and boys report more binge drinking. However, to our knowledge no studies have examined whether certain stressors affect alcohol use differently by gender or as youths advance through adolescence. As such, we explored whether age and gender moderated the effects of stress domains on alcohol use among Hispanic adolescents.
Method Sample
Data for the current study were drawn from a sample of 1,119 Hispanic adolescents between 11 and 19 years old from four urban U.S. cities: Los Angeles, Miami, El Paso, and Lawrence (a suburb of Boston). Adolescents were initially recruited from middle and high school settings to participate in a study designed to validate the HSI-A. Classroom rosters were separated by grade level and SPSS software was used to randomly select classrooms in each school to participate in the study. Only schools that reported that at least 50% of their student body was Hispanic were eligible to participate in the original study. Although socioeconomic status was not collected directly from participants, at all school sites more than 50% of youths qualified for free or reduced-price lunches.
Of the 1,119 participants, 901 provided complete information on alcohol use, HSI-A variables, and other covariates including gender, age, parental nativity, and dominant language. Participants in the sample came from diverse Hispanic backgrounds: 47.0% (n = 406) Mexican, 13.3% (n = 117) Cuban, 13% (n = 115) Dominican, 9.5% (n = 84) mixed, 7.7% (n = 68) Puerto Rican, 5.1% (n = 45) Central American, 4.0% (n = 35) South American, and 1.5% (n = 18) other. Approximately 2% (n = 18) had missing data on origin. Nearly half of the sample was from Los Angeles (n = 443, 44.7%), followed by Lawrence (n = 253, 25.5%), Miami (n = 207, 20.8%), and El Paso (n = 89. 9.0%).
To address the first research question by comparing the mean difference of HSI-A domains between alcohol users and nonusers, we performed the analysis based on the 901 participants with complete information. Regarding the second and third research questions, analyses were conducted with the sample with complete data (n = 901) and using multiple (20) imputed datasets with the full sample (N = 1,119). We describe a series of sensitivity analyses in the data analysis section designed to address potential biases related to missing data.
Measures
Survey instruments were administered to youths in their preferred language (English or Spanish) using paper-and-pencil booklets. The primary independent variable of interest was the HSI-A; the dependent variable was alcohol use, and if present, the extent of alcohol use disclosed by each adolescent.
Hispanic stress
The construct of Hispanic stress was measured using the HSI-A, a 71-item measure that assesses exposure to and appraisal of life stressors related to minority status. The HSI-A is a validated measure of stress among Hispanic adolescents and has strong overall internal consistency reliability for appraisal ratings (α = .92; see Cervantes, Fisher, et al., 2012 for more information about scale psychometrics). Previous factor analytic research has identified eight unique domains (subscales) of Hispanic stress: family economic (12 items), culture and educational (14 items), acculturation gap (12 items), immigration (seven items), discrimination (six items), family immigration (seven items), community and gang violence-related (eight items), and family and drug-related (five items) stress (see Cervantes, Fisher, et al., 2012).
Some of the stress domains capture concepts exclusively related to being Hispanic. For example, acculturative gap (“Parents want me to maintain customs and traditions,” “Expected to be like parent to siblings”), culture and educational (“Teachers think I am cheating when I am speaking in Spanish,” “School ignored cultural history”), discrimination (“Students said racist things,” “Pointed at and called me names”), immigration (“Left close friends in home country,” “Separated from some family members”), and family immigration (“Family afraid of getting caught by immigration officials,” “Family had problems with immigration papers”) relate specifically to Hispanic youths. The remaining domains, family economic (“Parents could not get a good job,” “Not enough money for everyone in the family”), community and gang violence (“I have a lot of pressure to be involved in gangs,” “Saw weapons at school”), and family and drug stress (“Family members had a drug problem,” “Hard to speak with family”), capture social stressors that are often experienced by Hispanics and other minority groups in the United States. Participants were asked whether they had experienced a specific stressor, and if so, to appraise the degree to which the stressor affected them. Responses were based on a 5-point Likert scale: 1 = not at all worried or tense, 2 = a little worried or tense, 3 = moderately worried or tense, 4 = very worried or tense, 5 = extremely worried or tense. Higher scores on the stress subscales indicate more stressful experiences. Mean values for the eight domains of Hispanic stress were centered for multivariate analysis.
Alcohol use and frequency of alcohol use
Two single-item questions from the Government Performance and Results Act’s participant outcome measures (SAMHSA, 2003) were used to assess alcohol use and frequency of alcohol use. The act features a set of required federal reporting guidelines used to collect performance outcome data on all substance abuse prevention and treatment programs funded through SAMHSA. Relying on these measurement standards, alcohol use was assessed by adolescent self-report to a single question: “In the past 30 days, did you use alcohol?” A dichotomous indicator of alcohol use was created (0 = no use, 1 = use). Similarly, frequency of alcohol use was assessed using a follow-up question: “In the past 30 days, how many times did you use alcohol?” To capture differences in alcohol risk behavior, a three-level ordinal variable was constructed to indicate no alcohol use (0 times), low use (1–3 times) and heavy use (4 or more times) during the previous 30 days (Cervantes et al., 2015). The cutoff points were chosen based on an assumption that youths who drink fewer than 4 times per month (i.e., 1 to 3 times) are likely drinking on average less than once per week. We suspect that adolescents reporting drinking 4 or more times per month are more likely drinking with consistency (that is, about once per week). We considered including alcohol frequency as a continuous variable, yet the frequency of alcohol use was skewed to the lower response categories. In line with previous work by Berger Cardoso, Goldbach, and Cervantes (in press), we used the aforementioned cutoffs. Although these cutoffs provide some insight into drinking patterns, a validated measure of binge or heavy drinking would have likely provided a more accurate assessment of use in the sample.
Covariates
We controlled for four demographic variables related to alcohol use and frequency among adolescents (Wade, Lariscy, & Hummer, 2013; Warner et al., 2006; Weiss & Tillman, 2009). Adolescent age was included as a continuous variable and centered for the analysis testing the moderation effects of age on alcohol use. Gender was a dichotomous variable (male or female) and was included as both a control and interaction variable. Other categorical variables included parental nativity (0 = both parents born in the United States, 1 = one or both parents born outside the United States) and dominant language (1 = English, 2 = Spanish, 3 = other). Two dummy variables for dominant language were created (the reference was other) and controlled for in the analyses to minimize confounding factors when examining the relation between Hispanic stress and alcohol use and frequency of alcohol use in this population.
Data Analysis
We examined descriptive differences in age, gender, parental nativity, dominant language, and Hispanic stress domains between adolescents who reported alcohol use and those who did not report use during the previous 30 days. In addition, effect sizes (Cohen’s d) for the standardized mean differences in the eight stress domains were calculated. To provide estimates of bivariate associations, we calculated Pearson correlations between the eight stress domains and alcohol use frequency. Further, we conducted multiple multinomial logistic regression models to examine the effect of each stress domain on alcohol use frequency with contrast between 1 and 3 times and no use, 4 or more times and no use, and 4 or more times and 1–3 times, while adjusting for the other domains. The models were also adjusted for gender, age, parental nativity, and dominant language. We employed likelihood ratio tests to compare the nested multinomial logistic regression models that included a block of interaction variables versus models without interaction variables. We tested the moderation effect of gender (male vs. female) and age (centered on mean) on the relation between stress domains and alcohol use frequencies. Statistically significant interactions terms indicated a possible moderation effect, though the decision to test these indicators was made a posteriori.
To address potential biases related to missing data, we conducted sensitivity analyses using multiple imputation. Multivariate imputation using fully conditional specification methods was employed to generate 20 datasets with complete information for all HSI-A measures and covariates. Model estimates from these 20 imputed datasets were combined to obtain a final set of parameter estimates. Fully conditional specification methods are commonly used to impute missing values for both continuous and categorical variables in a dataset with an arbitrary missing pattern (Graham, Olchowski, & Gilreath, 2007; Rubin, 1987; Schafer, 1997; Schafer & Olsen, 1998), which was the case in this study (appendix of arbitrary missing pattern available on request). Because results based on imputed and nonimputed data did not differ substantially, we present only results based on imputed data. All analyses were conducted using SAS version 9.3.
Results Descriptive Statistics and Bivariate Associations
During the previous 30 days, 75.8% (n = 683) of the sample reported no use and 24.2% (n = 218) reported alcohol use. Among adolescents who reported using alcohol, 64.2% (n = 111) reported using alcohol 1 to 3 times and 35.8% (n = 63) reported using alcohol 4 or more times. The proportion of adolescents who reported alcohol use were similar for male (22.0%) and female participants (25.9%), χ2(1) = 1.80, p = .18. Alcohol varied significantly by age, t(899) = −6.34, p < .001; adolescents who reported using alcohol during the previous 30 days had a mean age of 15.54, compared with a mean age of 14.66 for youths who had not used alcohol during that period. Differences in alcohol use were found based on adolescents’ primary language, χ2(2) = 7.43, p = .02. Alcohol use was estimated to be the highest among adolescents whose primary language was English (28.8%), compared with those whose primary language was Spanish (18.5%) or other (3.3%). The bivariate association between alcohol use and parental nativity was not statistically significant (p = .257).
Six of the eight domains of stress were associated with increased alcohol use frequency during the previous 30 days, r = .07 to .18, p < .05. The immigration stress and family immigration stress subscales were not associated with alcohol use frequency. As expected, the eight domains of stress were correlated with one another, r = .11 to .48, p < .05, with the exception of community and gang violence and immigration stress, r = .06, p > .05. Tables are available on request.
Results of several independent t tests showing differences in the eight domains of Hispanic stress by alcohol use are also presented in Table 1. Significant differences in stress were observed for five of the eight domains. Significant mean differences between adolescents who used alcohol and those who did not were observed for acculturation gap (1.52 vs. 1.29, respectively), community and gang violence (1.32 vs. 1.17), family economic (1.32 vs. 1.21), discrimination (1.20 vs. 1.12), and family and drug-related (1.46 vs. 1.20) stress subscales. Higher mean scores were observed among youths who reported alcohol use during the previous 30 days compared with participants who reported no use. We examined the effect sizes of these differences, using Cohen’s d calculations, and found the greatest effect sizes in the family and drug-related (.53), acculturative gap (.44), and community and gang violence (.39) subscales. Much smaller effect sizes were observed in the family economic (.24) and discrimination (.18) subscales.
Mean HSI-A Stress Domain Scores Stratified by the Total Sample and Alcohol Use During the Previous 30 Days Among Hispanic Adolescents
Multinomial Logistic Regression
Multinomial logistic regressions were conducted to examine the relation between domains of Hispanic stress and alcohol use, adjusting for the effects of age, gender, dominant language, and parental nativity. Finding presented in Table 2 show that when adjusting for one another, only a few of the eight domains of Hispanic stress were associated with increased risk of alcohol use during the previous 30 days. Compared with those with lower community and gang violence stress, youths with higher stress in this domain had significantly greater relative risk (RR) of drinking 1 to 3 times (RR = 1.85, 95% CI [1.08, 3.17]) or 4 or more times (RR = 3.15, 95% CI [1.69, 5.90]) than not drinking at all during the previous 30 days. Additionally, adolescents with higher family and drug-related stress had a greater risk of drinking 1 to 3 times (RR = 2.06, 95% CI [1.37, 3.09]) compared with not drinking at all. However, increased family and drug-related stress was associated with lower risk of drinking 4 or more times (RR = 0.50, 95% CI [0.27, 0.90]) than drinking 1 to 3 times. Higher acculturative gap stress was associated with greater risk of drinking 4 or more times (RR = 2.39, 95% CI [1.40, 4.09]) than those who reported not drinking. No other stress domains were significantly associated with increased risk of alcohol use during the previous 30 days. These findings suggest that after adjusting for other stress domains, acculturation gap, community and gang violence, and family and drug-related stressors were important factors associated with alcohol use beyond the other domains of Hispanic stress included in the model. Finally, results indicated neither gender nor age moderated the relation between Hispanic stress and alcohol use frequencies (p > .05).
Relative Risks (RRs) and 95% Confidence Intervals (CIs) for Alcohol Use Frequency During the Previous 30 Daysa
DiscussionOur study is one of the first to use a standardized multidomain measure of Hispanic stress to understand alcohol use among Hispanic adolescents in the United States. The HSI-A measures eight domains of stress developed specifically for Hispanic adolescents to provide a better understanding of how these experiences may be related to alcohol use. Most adolescents in the sample who reported drinking in the last 30 days reported lower use; 64.2% (n = 111) reported using alcohol 1 to 3 times and 35.8% (n = 63) reported using alcohol 4 or more times during the previous 30 days. Youths in our study reported lower use of alcohol than in national samples. Whereas 24.2% (n = 218) of our sample reported alcohol use during the previous 30 days, 34.9% of high school youths nationally and 37.5% of Hispanic high school youths nationally report alcohol consumption during the prior month (Kann et al., 2014). However, it should be noted, the highest proportion of alcohol use is in older youth in eleventh (39.2%) and twelfth (46.8%) grade. Lower alcohol use was reported in tenth (30.9%) and ninth graders (24.4%). The lower rates of use in our sample may be due to the inclusion of younger adolescents in our sample (i.e., 12- and 13-year-olds), as the mean age in our sample was 14.87 (CDC, 2014). The alcohol use in our sample is closer to national proportions of use in younger youth.
Our analysis found evidence of a significant relation between Hispanic stress and adolescent alcohol use. Compared with those who did not drink, adolescents who reported using alcohol had significantly higher scores on family economic, culture and educational, acculturative gap, discrimination, community and gang violence, and family and drug-related stress subscales. Moderate effect sizes were observed between alcohol use and family and drug-related, acculturative gap, and community and gang violence stress, with small effect sizes found between alcohol use and family economic and discrimination stress. Our multivariate analyses also found community and gang violence, acculturative gap, and family and drug-related stress to be associated with alcohol use patterns beyond the influence of other cultural stressors; thus, we focused our discussion on these three significant correlates of alcohol use.
Community and Gang Violence Stress
In our study, community and gang violence stress was associated with both moderate and high levels of drinking. General population studies have explored neighborhood effects on adolescent drinking (Chuang, Ennett, Bauman, & Foshee, 2005). For example, living in environments with lower socioeconomic status is associated with increased peer drinking and adolescent alcohol use. The lower socioeconomic circumstances found in many predominately Hispanic neighborhoods have been associated with greater exposure to gangs, drug abuse, and discrimination (Cervantes et al., 2008). This neighborhood disadvantage has been associated with a lack of social and economic resources and opportunity (Sampson, Raudenbush, & Earls, 1997). Further, exposure to neighborhood crime and perceptions of crime and violence are associated with alcohol and substance use (Boardman, Greenberg, Vining, & Weimer, 2001; Duncan, Duncan, Strycker, & Chaumeton, 2002).
Given the association between community gang stress and alcohol use in our sample, the effect of the neighborhood context on Hispanic youth alcohol use should be examined further in future research. For example, our findings align with previous research indicating a strong correlation among economic circumstances, crime, alcohol and substance use, and negative health outcomes (Galea & Vlahov, 2002; Glaeser, Sacerdote, & Scheinkman, 1996), and minority groups are disproportionately affected in large part due to long histories of oppression and segregation that put them at higher risk of poverty (Galea & Vlahov, 2002). Structural approaches addressing these social conditions may help in alleviating the stress response (Dickerson & Kemeny, 2004) and reducing alcohol use among Hispanic adolescents.
Acculturative Gap
Higher acculturative stress was associated with a greater risk of drinking 4 or more times compared with those that reported no alcohol use during the previous 30 days. A commonly explored risk factor for alcohol use among Hispanics is acculturative stress. The experience of immigration is stressful (Thomas, 1995) and studies have reported an association between alcohol use and acculturation processes among adults (Johnson, 1996). However, findings have been inconsistent regarding the relation between acculturative stress and alcohol use during adolescence, with studies finding both positive and negative associations (Cabassa, 2003; Rogler, Cortes, & Malgady, 1991; Vega, Alderete, Kolody, & Aguilar-Gaxiola, 1998). Some studies have indicated that lower acculturation is associated with alcohol use among Hispanic boys but not girls (Epstein, Griffin, & Botvin, 2000).
The inconsistent findings of these previous studies may be due to the complex nature of acculturation for adolescents and the challenge of measuring acculturative stress—a complex and multidimensional process. For example, acculturation during adolescence is more interactive with the family; the stress of acculturating at a different pace than parents may cause parent–adolescent conflict (Patterson, Reid, & Dishion, 1992; Szapocznik & Williams, 2000). When parents and adolescents acculturate at a difference pace (i.e., acculturative gap or differential acculturation), this can increase family conflict and decrease family cohesion (Hwang & Wood, 2009; Szapocznik & Williams, 2000). Differential acculturation has been associated with increased mental health problems (Vega, Khoury, Zimmerman, Gil, & Warheit, 1995) and alcohol use among Hispanic adolescents (e.g., Martinez & Eddy, 2005; Santisteban, Muir-Malcolm, Mitrani, & Szapocznik, 2002), and our study further supports its status as a salient mechanism related to alcohol use in this population.
Family and Drug-Related Stress
Similar to the salient finding of acculturative gap stress, the current study found that another family related construct (family and drug-related stress) was associated with a greater risk of drinking 1 to 3 times compared to not drinking at all. However, increased family and drug-related stress was associated with a lower risk of drinking 4 or more times than drinking 1 to 3 times. Finding an association between these constructs was not unexpected; the population literature has suggested that youths are more likely to use alcohol when their families have norms that promote drinking (Song, Smiler, Wagoner, & Wolfson, 2012). However, given the importance of familismo, which stresses the centrality of family and adherence to familial values and norms (Galanti, 2003), the use of substances by parents may be especially relevant to Hispanic adolescent substance use.
Although we hypothesized (and found) an effect of family and drug-related stress on alcohol use, findings by level of alcohol use were unexpected. Contrary to our initial hypothesis, family and drug-related stress was lower among adolescents who reported drinking 4 or more times during the previous 30 days as compared with those who reported drinking 1 to 3 times. It is possible that this finding is an anomaly and may be better understood if we had a measure of consumption (e.g., binge drinking), rather than only being able to report on incidence. There is some research showing that Hispanic’s are less likely to drink. However when they do drink, binge use is more common (SAMHSA, 2010; Borges et al., 2006).We were unable to explore this in the present dataset but acknowledge that future research could shed light on this contradictory finding. Nevertheless, our study indicates that youths reporting family and drug-related stress are engaging in low to moderate alcohol use and any alcohol use in adolescence is associated with heavier use later in life (Stueve & O’Donnell, 2005). This underscores again the importance of family in the context of Hispanic adolescent drinking.
Gender and Age Effects
The most recent Youth Risk Behavior Survey (Kann et al., 2014) found no differences by males and females in alcohol consumption during the previous 30 days nationally (35.5% vs. 34.4%, respectively). Some previous research, however, has suggested that the relation between stress and alcohol use may be different for boys and girls (Epstein, Botvin, & Diaz, 2000; Oshri et al., 2014). For Hispanic adolescents, less is known about the relation among stress, gender, and alcohol use. We explored whether different Hispanic stressors might be more or less salient to boys and girls in the context of alcohol use. In our sample, no gender differences were found between HSI-A stressors and alcohol use. This suggests that these stressors have a similar effect on drinking patterns among youths regardless of gender.
Likewise, age is often a significant predictor of alcohol use during adolescence, with older youths reporting drinking more frequently (Johnston, O’Malley, Miech, Bachman, & Schulenberg, 2014). Given the documented association between age and alcohol use in the literature, we explored whether the relation of stress to alcohol use in Hispanic adolescents was moderated by age. Consistent with previous research, age was related to alcohol use, with a significantly higher mean age in the group that reported alcohol use during the previous 30 days. However, our analyses found that age did not moderate the relation between stress and alcohol use; in other words, the association between stress domains and alcohol use was not different for younger versus older youths. Thus, we found that despite developmental changes as youths grow older, the stressors associated with alcohol use do not seem to become more or less relevant to alcohol use based on age.
Limitations and Conclusions
Despite its strengths, the current study is not without limitations. Issues associated with sampling, measurement, and research design should be noted and considered in both the interpretation of our findings and the development of future research. Although the sample was large, schools were not randomly selected. It is possible that youths who engage in high-risk alcohol use are less likely to attend school and were therefore underrepresented in this sample. Additionally, only about one third of students nationally are eligible for free or reduced-price lunches (National Center for Education Statistics, 2005), whereas more than 50% of students at all study sites in the current sample were eligible (suggesting lower socioeconomic status). Thus, we may not be fully representing the experience of Hispanic adolescents nationally. Additionally, alcohol use is typically assessed by national agencies such as SAMHSA in the same fashion as this study, but additional measures of binge drinking may have revealed differences in both frequency and intensity of drinking. Although drinking more than 4 times during a month was suggestive of using alcohol at least once a week, which is problematic given that the sample is underage (Chou & Pickering, 1992), we could not determine if some youths in our sample drank once per week during the course of a month or four times in a row during a single weekend. Understanding differences among usual, episodic, and binge-drinking patterns may elucidate differences by stress domain. As with all cross-sectional data, determinations of causality were not possible. Although theory and previous research have suggested that Hispanic stressors cause adolescent substance use, at least in part, they may also have the opposite effect; adolescents experiencing distress related to their drinking patterns may be more likely to report these types of stressors. Thus, longitudinal designs are needed to investigate the direction of these effects.
Despite these limitations, our study lends support to the need for research on both the community context and families. Further, in our study, domains related to the family (acculturative gap and family and drug-related stress) and community and gang violence had the strongest effect sizes compared to all other domains. Previous literature has supported the importance of family to Hispanic youth development (Prado & Pantin, 2011) and the relation between family conflict and alcohol use patterns among Hispanic youths specifically; this may indicate the importance of including family variables in studies of substance use. These findings also have some application to intervention research. Several interventions have highlighted the role of family based prevention of adolescent substance use (e.g., Cervantes & Goldbach, 2012; Schwartz et al., 2013). Although our study found these domains to be the strongest correlates of alcohol use, nearly all (six of eight) of the domains were associated with drinking at the bivariate level. Thus, we would caution that interventions should address a diverse set of Hispanic stressors and further research should explore the most salient predictors of change in alcohol use. Although more research is needed, particularly longitudinal and experimental studies, the relation between these eight domains of Hispanic stress and alcohol use among adolescents provides a basis for identifying mechanisms of change that are unique to this high-need population.
Footnotes 1 The Hispanic stress items were asked in a two-part question. First, participants were asked if they experienced a specific stressor. If participants answered affirmatively, they were asked to appraise the stress experience on a scale of 1–5. The measure was constructed by combining negative responses with scores of 1 (not at all worried or tense) to maintain sample size. This was the coding process by which the measure was tested and validated.
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Submitted: January 2, 2015 Revised: September 15, 2015 Accepted: September 16, 2015
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Source: Psychology of Addictive Behaviors. Vol. 29. (4), Dec, 2015 pp. 960-968)
Accession Number: 2015-50538-001
Digital Object Identifier: 10.1037/adb0000133
Record: 177- Title:
- The relationship between nonsuicidal self-injury and attempted suicide: Converging evidence from four samples.
- Authors:
- Klonsky, E. David, ORCID 0000-0002-3057-0800. Department of Psychology, University of British Columbia, Vancouver, BC, Canada, edklonsky@psych.ubc.ca
May, Alexis M.. Department of Psychology, University of British Columbia, Vancouver, BC, Canada
Glenn, Catherine R., ORCID 0000-0003-2497-6000. Department of Psychology, Harvard University, Cambridge, MA, US - Address:
- Klonsky, E. David, Department of Psychology, University of British Columbia, 2136 West Mall, Vancouver, BC, Canada, V6T 1Z4, edklonsky@psych.ubc.ca
- Source:
- Journal of Abnormal Psychology, Vol 122(1), Feb, 2013. pp. 231-237.
- NLM Title Abbreviation:
- J Abnorm Psychol
- Page Count:
- 7
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- The Journal of Abnormal and Social Psychology; The Journal of Abnormal Psychology; The Journal of Abnormal Psychology and Social Psychology
- ISSN:
- 0021-843X (Print)
1939-1846 (Electronic) - Language:
- English
- Keywords:
- nonsuicidal self-injury, risk assessment, suicide, risk factors, attempted suicide, adolescent psychiatric patients
- Abstract:
- Theoretical and empirical literature suggests that nonsuicidal self-injury (NSSI) may represent a particularly important risk factor for suicide. The present study examined the associations of NSSI and established suicide risk factors to attempted suicide in four samples: adolescent psychiatric patients (n = 139), adolescent high school students (n = 426), university undergraduates (n = 1,364), and a random-digit dialing sample of United States adults (n = 438). All samples were administered measures of NSSI, suicide ideation, and suicide attempts; the first three samples were also administered measures of depression, anxiety, impulsivity, and borderline personality disorder (BPD). In all four samples, NSSI exhibited a robust relationship to attempted suicide (median Phi = .36). Only suicide ideation exhibited a stronger relationship to attempted suicide (median Phi = .47), whereas associations were smaller for BPD (median rpb = .29), depression (median rpb = .24), anxiety (median rpb = .16), and impulsivity (median rpb = .11). When these known suicide risk factors and NSSI were simultaneously entered into logistic regression analyses, only NSSI and suicide ideation maintained significant associations with attempted suicide. Results suggest that NSSI is an especially important risk factor for suicide. Findings are interpreted in the context of Joiner's interpersonal-psychological theory of suicide; specifically, NSSI may be a uniquely important risk factor for suicide because its presence is associated with both increased desire and capability for suicide. (PsycINFO Database Record (c) 2018 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Attempted Suicide; *Risk Factors; *Self-Injurious Behavior; *Suicide; *Risk Assessment; Psychiatric Patients
- Medical Subject Headings (MeSH):
- Adolescent; Adult; Aged; Anxiety; Borderline Personality Disorder; Depression; Female; Humans; Impulsive Behavior; Male; Middle Aged; New York; Risk Factors; Self-Injurious Behavior; Students; Suicidal Ideation; Suicide, Attempted; Young Adult
- PsycINFO Classification:
- Behavior Disorders & Antisocial Behavior (3230)
- Population:
- Human
Male
Female
Inpatient - Location:
- US
- Age Group:
- Adolescence (13-17 yrs)
- Tests & Measures:
- Youth Risk Behavior Survey
Structured Interview for DSM–IV Personality
Patient Health Questionnaire–Adolescent Version
Inventory of Statements About Self-Injury DOI: 10.1037/t32941-000
McLean Screening Instrument for Borderline Personality Disorder DOI: 10.1037/t65352-000
Trauma Symptom Inventory
Structured Clinical Interview for DSM-IV Axis I Disorders
Mini International Neuropsychiatric Interview DOI: 10.1037/t18597-000 - Grant Sponsorship:
- Sponsor: National Institute of Mental Health
Grant Number: MH0800960
Recipients: Klonsky, E. David - Methodology:
- Empirical Study; Quantitative Study
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- First Posted: Oct 15, 2012; Accepted: Aug 21, 2012; Revised: Aug 16, 2012; First Submitted: Apr 17, 2012
- Release Date:
- 20121015
- Correction Date:
- 20180412
- Copyright:
- American Psychological Association. 2012
- Digital Object Identifier:
- http://dx.doi.org/10.1037/a0030278
- PMID:
- 23067259
- Accession Number:
- 2012-27535-001
- Number of Citations in Source:
- 39
- Persistent link to this record (Permalink):
- http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2012-27535-001&site=ehost-live
- Cut and Paste:
- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2012-27535-001&site=ehost-live">The relationship between nonsuicidal self-injury and attempted suicide: Converging evidence from four samples.</A>
- Database:
- PsycINFO
The Relationship Between Nonsuicidal Self-Injury and Attempted Suicide: Converging Evidence From Four Samples
By: E. David Klonsky
Department of Psychology, University of British Columbia;
Alexis M. May
Department of Psychology, University of British Columbia
Catherine R. Glenn
Department of Psychology, Harvard University
Acknowledgement: There are no conflicts of interest to report. The research was supported in part by grant MH0800960 awarded to E. David Klonsky from the National Institute of Mental Health.
Nonsuicidal self-injury (NSSI; e.g., cutting, burning) refers to the intentional destruction of one's own body tissue without suicidal intent and for purposes not socially sanctioned (Klonsky & Olino, 2008; Klonsky, Oltmanns, & Turhkeimer, 2003; Nock & Favazza, 2009). Rates of NSSI are estimated at 4–6% in the adult general population and 20% in adult patient populations (Briere & Gil, 1998; Klonsky, 2011; Klonsky et al., 2003). However, NSSI appears to be disproportionately prevalent in adolescents and young adults: approximately 14–17% of adolescents and young adults report having self-injured (Whitlock, Eckenrode, & Silverman, 2006), and rates approach 40% or higher in adolescent inpatient samples (DiClemente, Ponton, & Hartley, 1991; Klonsky & Muehlenkamp, 2007). Because NSSI has been observed to occur in a variety of diagnostic contexts, and because NSSI itself is associated with distress and impairment irrespective of co-occurring diagnosis (Klonsky & Olino, 2008; Nock, Joiner, Gordon, Lloyd-Richardson, & Prinstein. 2006), NSSI has been proposed as its own behavioral syndrome for the next edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5) (Shaffer & Jacobson, 2009).
NSSI and SuicideThe relationship between NSSI and attempted suicide is complex. On the one hand, the two behaviors often co-occur (Klonsky & Muehlenkamp, 2007; Nock et al., 2006; Whitlock et al., 2006) and share a salient surface-level similarity in that they are both forms of self-inflicted physical violence. For this reason, some researchers have regarded all forms of self-injurious behavior as falling along a suicidal spectrum regardless of intent (Hawton, Rodham, Evans, & Weatherall, 2002). On the other hand, NSSI and attempted suicide have important differences. For example, the behaviors differ in terms prevalence (NSSI is more prevalent), frequency (NSSI is often performed dozens or hundreds of times whereas suicide attempts are typically performed once or a few times), methods (cutting and burning are more characteristic of NSSI whereas self-poisoning is more characteristic of attempted suicide), severity (NSSI rarely causes medically severe or lethal injuries), and functions (NSSI is performed without intent to die, and sometimes as an attempt to avoid suicidal urges) (CDC, 2010; Favazza, 1998; Klonsky, 2007; Klonsky & Muehlenkamp, 2007; Muehlenkamp, 2005). A primary aim of the DSM-5 proposal is to highlight these distinctions between NSSI and attempted suicide (Shaffer & Jacobson, 2009).
Accurately characterizing the relationship between NSSI and attempted suicide—both their distinctiveness and overlap—is essential for research and intervention. There is concern that the historical tendency to classify or misidentify NSSI as attempted suicide has led to inaccurate epidemiological estimates of suicidal behaviors (Shaffer & Jacobson, 2009). In clinical settings, mistaking NSSI for attempted suicide can lead to unnecessary and potentially iatrogenic hospitalizations, inaccurate case conceptualization and treatment planning, and misallocation of valuable emergency resources. At the same time, a perspective that overemphasizes the behaviors' independence and ignores potential comorbidity between NSSI and attempted suicide could mean ignoring a valuable indicator of suicide risk. The proposed research was designed to address this need.
There are both theoretical and empirical reasons why NSSI may represent a particularly robust risk factor for attempted suicide. Joiner's interpersonal theory of suicide (2005; Van Orden et al., 2010) states that a suicide attempt requires both the desire and capability for suicide. Unlike risk factors such as depression, which confers increased desire but not capability for a suicide attempt, or risk factors such as access to firearms, which confers increased capability but not desire, NSSI may be relatively unique among suicide risk factors in that it serves as a marker for both increased desire and capability. Specifically, NSSI is associated with elevated emotional and interpersonal distress (Klonsky & Olino, 2008; Klonsky et al., 2003; Klonsky & Muehlenkamp, 2007), which increases the likelihood of suicide ideation (i.e., desire), and NSSI facilitates habituation to self-inflicted violence and pain, which increases the ability to attempt suicide (i.e., capability) (Nock et al., 2006). Indeed, two recent studies found that NSSI prospectively predicted attempted suicide more strongly than other suicide risk factors (Asarnow et al., 2011; Wilkinson, Kelvin, Roberts, Dubicka, & Goodyer., 2011). Elucidating the relation of NSSI to attempted suicide is essential for both research and treatment.
Study AimsOur primary aim was to determine the strength of the association between NSSI and attempted suicide. The use of four diverse samples—adolescent psychiatric patients, adolescent high school students, university undergraduates, and a random-digit dialing sample of U.S. adults—enhances the generalizability of results and helps ensure that findings are broadly relevant for theoretical and clinical models of suicide risk.
In addition, we compared the association between attempted suicide and NSSI to the associations between attempted suicide and established suicide risk factors, specifically suicide ideation, depression, anxiety, impulsivity, and borderline personality disorder (BPD) symptoms. These analyses help clarify the importance of NSSI for conferring suicide risk relative to known suicide risk factors. We chose suicide ideation, depression, anxiety, impulsivity, and BPD as comparison variables for two reasons. First, they are highlighted in published guidelines for conducting suicide risk assessments (American Psychiatric Association, 2006; Rudd et al., 2006). Second, they are indicators of emotional distress and personality pathology, which are correlates of both NSSI and attempted suicide, and could potentially account for the relationship between the two behaviors.
MethodThe present study utilized data from four separate samples. IRB approval was received from all relevant institutions before data collection commenced. In all four samples attempted suicide, suicide ideation, and NSSI were assessed; in addition, depression, anxiety, impulsivity, and BPD were assessed in samples 1–3. The sample sizes and specific measures for each sample are described below.
Sample 1. Adolescent Psychiatric Inpatients Participants
Participants were 171 adolescent psychiatric patients (consecutive admissions to adolescent inpatient and partial hospitalization units at South Oaks Hospital in Amityville, NY). Adolescents were only excluded if they were unable to complete the protocol due to severe psychosis, aggressive behavior, cognitive deficits, or suicide-related behavior that the staff deemed too extreme to participate in the study. Permission from parents/guardians of participants was obtained at the time of admission to the facility, and assent was obtained before measures were administered. Participants were 70% female, 61% Caucasian, 21% Hispanic, 12% African American, with a mean age of 15.1 (SD = 1.4). Fifty-nine percent reported NSSI; the most common forms were cutting and banging/hitting, endorsed by 86% and 53%, respectively, of those reporting NSSI. Sixty percent of participants reported a history of suicide ideation, and 40% reported a suicide attempt.
Measures
Suicide ideation and attempts
The Youth Risk Behavior Survey (YRBS; Brener et al., 2002) was developed by the U.S. Centers for Disease Control to assess health-risk behaviors, including suicidality. A history of suicide ideation is measured by the item: “Have you ever seriously thought about killing yourself?” A history of attempted suicide is measured by the item: “Have you ever tried to kill yourself?” The YRBS also includes items assessing 12-month ideation and attempts, as well as medical severity of attempts. YRBS suicide questions have good reliability and validity (Brener et al., 2002; May & Klonsky, 2011).
Nonsuicidal self-injury
The Inventory of Statements About Self-injury (ISAS; Klonsky & Glenn, 2009; Klonsky & Olino, 2008) assesses the lifetime frequency of 12 different NSSI behaviors performed “intentionally (i.e., on purpose) and without suicidal intent (i.e., not for suicidal reasons).” These behaviors include banging/hitting self, biting, burning, carving, cutting, wound picking, needle-sticking, pinching, hair pulling, rubbing skin against rough surfaces, severe scratching, and swallowing chemicals. The ISAS behavioral scales have demonstrated good reliability and validity (Klonsky & Olino, 2008).
Depression and anxiety
The MINI International Neuropsychiatric Interview is a reliable and valid structured interview (Sheehan et al., 1998) of Axis I psychopathology. Interviews were conducted by a clinical psychology doctoral student trained to reliability (i.e., rs > .90 with other trained interviewers). The MINI major depression diagnosis was utilized to index depression, and the MINI generalized anxiety disorder diagnosis was utilized to index anxiety. We chose GAD as opposed to other anxiety disorder diagnoses because we felt it was the best indicator of the general construct of anxiety.
Impulsivity
The UPPS impulsive behavior scale (Whiteside & Lynam, 2001) is a 45-item self-report measure of four distinct personality pathways to impulsive behavior: Urgency (tendency to give in to strong impulses when experiencing intense negative emotions), Perseverance (ability to persist in completing jobs or obligations despite boredom or fatigue), Premeditation (ability to think through potential consequences of behavior before acting), and Sensation Seeking (preference for excitement and stimulation). The UPPS scale has strong psychometric properties (Whiteside & Lynam, 2001). The total UPPS score was utilized to index an overall disposition for impulsive behaviors.
Borderline personality disorder
The Structured Interview for DSM–IV Personality (SIDP-IV) is a validated structured interview assessing personality disorders (Pfohl, Blum, & Zimmerman, 1997). Interviews were conducted by a clinical psychology doctoral student trained to reliability (i.e., rs > .90 with other trained interviewers). Scores for the BPD items were summed to provide a dimensional measure of BPD; the suicide/self-injury criterion was omitted to avoid confounding results.
Sample 2. Community Sample of Adolescents Participants
Participants were 428 students from a large high school east of New York City. Parental/guardian consent and adolescent assent were obtained for all participants. Participants were 61% female, 53% Caucasian, 19% Hispanic, 15% Asian, 11% African American, and 3% mixed racial heritage, and participants' age ranged from 13–17 (reflects age range of target population; age data were not obtained from participants). Twenty-one percent reported NSSI; the most common forms of NSSI were cutting and banging/hitting, endorsed by 52% and 56%, respectively, of those reporting NSSI. Sixteen percent of participants reported a history of suicide ideation, and 5% reported a suicide attempt.
Measures
Suicide ideation and attempts
Same measure as for Sample 1.
Nonsuicidal self-injury
Same measure as for Sample 1, except that only seven rather than 12 NSSI behaviors were assessed: banging/hitting self, biting, burning, carving, cutting, rubbing skin against rough surfaces, and severe scratching (the following were not assessed: hair pulling, needle-sticking, pinching, swallowing chemicals, wound picking).
Depression and anxiety
The Patient Health Questionnaire–Adolescent Version (PHQ-A; Johnson, Harris, Spitzer, & Williams, 2002) is a self-report questionnaire developed by the authors of the Structured Clinical Interview for DSM–IV (SCID-I) to assess four classes of Axis I disorders: mood, anxiety, eating, and substance/alcohol. The PHQ-A major depressive disorder items were summed to form a dimensional index of depression symptoms, and the PHQ-A generalized anxiety disorder items were summed to form a dimensional index of anxiety symptoms. As for Sample 1, we chose GAD as opposed to other anxiety disorder diagnoses because we felt it was the best indicator of the general construct of anxiety. The PHQ-A has demonstrated excellent correspondence with structured interview measures of Axis I disorders (Johnson et al., 2002).
Impulsivity
Same measure as for Sample 1 (UPPS), but a 16-item short-version developed by using the four items from each scale with the highest loadings in Whiteside and Lynam (2001). This short version has demonstrated excellent psychometric properties in two previous studies of NSSI and suicide (Glenn & Klonsky, 2010; Klonsky & May, 2010).
Borderline personality
The McLean Screening Instrument for Borderline Personality Disorder (MSI-BPD) is a 10-item self-report measure of BPD features that has shown excellent correspondence with diagnoses made by validated structured interview (Zanarini et al., 2003). For the present study, the suicide/self-injury criterion was omitted to avoid confounding results.
Sample 3. University Undergraduates Participants
Participants were 1,656 university undergraduates participating in an Internet-based survey on substance use via a secure website. Upon accessing the survey, students provided informed consent. Participants were 56% female, 43% Caucasian, 35% Asian, 7% African American, 9% Hispanic, and 7% from other ethnic categories, with a mean age of 20.7 (SD = 2.0). Twenty percent of participants reported NSSI, 17% a history of suicide ideation, and 7% a suicide attempt.
Measures
Suicide ideation and attempts
Same measure as for Samples 1 and 2.
Nonsuicidal self-injury
An item from the Trauma Symptom Inventory that was utilized in two previous epidemiologic studies of NSSI (Briere & Gil, 1998; Klonsky, 2011) was used in the present sample: “In your lifetime, how often have you intentionally hurt yourself—for example, by scratching, cutting, or burning—even though you were not trying to commit suicide?” This question is similar to the item used in a recent epidemiologic study of NSSI in United States adults (Klonsky, 2011), except that the item in the present study used the following slightly modified response options: [a] never, [b] once, [c] twice, [d] 3–5 times, [e] 6–9 times, [f] 10 or more times. Data on specific NSSI methods were not obtained.
Depression and anxiety
Same measures as for Sample 2.
Impulsivity
Same measure as for Sample 2.
Borderline personality
Same measure as for Sample 2.
Sample 4. Random-Digit Dialing Sample of United States Adults Participants
Participants were 439 U.S. adults recruited via an equal-probability random-digit dialing procedure as part of an epidemiologic study of NSSI (Klonsky, 2011). Participants were 61% female, 86% Caucasian, 6% African American, 3% Hispanic/Latino, 1% Asian American, and 1% Native American, and mean age was 55.5 (SD = 16.6). Six percent reported NSSI; the most common forms were cutting and scratching, each endorsed by 35% of those who reported NSSI. Seventeen percent of participants reported a history of suicide ideation, and 3% reported a suicide attempt.
Measures
Suicide ideation and attempts
Suicide ideation and attempts were assessed with the following items utilized in the National Comorbidity Survey (Kessler, Borges, & Walters, 1999): “Have you ever seriously thought about committing suicide?” and “Have you ever attempted suicide?”
Nonsuicidal self-injury
Same measure as in Sample 3, except with the following slightly modified response options: [a] 0 times, [b] between 1 and 4 times, [c] between 5 and 9 times, [d] between 10 and 50 times, [e] more than 50 times.
The additional clinical variables assessed in Samples 1, 2, and 3—depression, anxiety, impulsivity, and BPD—were not assessed in Sample 4.
Data AnalysisThe same analytic procedures were utilized for all samples so that results are comparable across samples. NSSI, attempted suicide, and suicide ideation were each treated as dichotomous variables (present if any lifetime instance was reported). Phi coefficients were utilized to examine associations between dichotomous variables, and point-biserial correlations were used to examine associations between dimensional and dichotomous variables. Coefficient alpha for all dimensional measures exceeded .74 (details on internal consistencies, descriptive statistics, and intercorrelations for all study variables are available from the corresponding author). Only participants with complete suicide data were included in analyses; thus, inclusion rates were 81.3% for Sample 1 (n = 139), 98.4% for Sample 2 (n = 426), 81.6% for Sample 3 (n = 1,351), and 99.8% for Sample 4 (n = 438).
ResultsWe first examined the association of attempted suicide to NSSI, suicide ideation, depression, anxiety, impulsivity, and BPD. Complete results are presented in Table 1. For the relation of NSSI to attempted suicide, Phi ranged from .28 (undergraduates) to .50 (adolescent psychiatric patients), with a median of .36. This was slightly smaller in magnitude than the effect size for suicide ideation (median Phi = .47), but larger than the effect sizes for BPD (median rpb = .29), depression (median rpb = .24), anxiety (median rpb = .16), and impulsivity (median rpb = .11).
Relation of Nonsuicidal Self-Injury (NSSI) and Other Suicide Risk Factors to Lifetime Attempted Suicide in Four Samples
Next, following the procedures of Steiger (1980), we examined whether the association between NSSI and attempted suicide varied by gender (see Table 2). For the high school sample, the association between NSSI and attempted suicide was higher for girls (.46) than for boys (.22), p = .007. The association did not vary significantly by gender in each of the other three samples.
Relation of Nonsuicidal Self-Injury (NSSI) to Attempted Suicide for Females vs. Males
Finally, we utilized simultaneous logistic regressions to examine the unique contributions of NSSI, suicide ideation, BPD, depression, anxiety, and impulsivity in the prediction of attempted suicide. (Sample 4 was excluded because it lacked measures of BPD, depression, anxiety, and impulsivity.) Complete results are presented in Table 3. Notably, in all three samples, only NSSI and suicide ideation retained statistically significant unique associations with attempted suicide (all ps < .05).
Logistic Regression Analyses Examining Unique Contributions of Nonsuicidal Self-Injury (NSSI) and Known Suicide Risk Factors to the Prediction of Attempted Suicide
DiscussionThis study examined the relationship between NSSI and attempted suicide in four samples: adolescent psychiatric patients, adolescent high school students, university undergraduates, and U.S. adults. In all four samples, NSSI exhibited a reliable and moderate relationship with attempted suicide. This relationship was slightly smaller than that of suicide ideation to attempted suicide, and larger than the relationships of depression, anxiety, impulsivity, and BPD to attempted suicide. When all risk factors were simultaneously entered into a logistic regression, only NSSI and suicide ideation maintained a unique relationship with attempted suicide. Taken together, findings suggest that the relationship of NSSI to attempted suicide is particularly strong, second in magnitude only to suicide ideation.
Joiner's interpersonal theory of suicide (Joiner, 2005; Van Orden et al., 2010) provides one useful context for interpreting these results. According to this theory, attempting suicide requires both the desire and capability to attempt suicide. NSSI may stand out among risk factors for suicide because it correlates with both suicidal desire and capability: NSSI indicates heightened risk for suicidal desire through its association with emotional and interpersonal distress (Klonsky et al., 2003; Klonsky & Muehlenkamp, 2007; Klonsky & Olino, 2008), and NSSI raises capability by allowing individuals to habituate to self-inflicted pain and violence (Nock et al., 2006). In short, when it comes to suicide risk, NSSI may represent “double trouble” (term suggested by B. Walsh, personal communication, April 22, 2010) in that it confers risk for both suicidal desire and capability.
Other interpretations also warrant consideration. For example, general tendencies toward harmful behaviors and emotion dysregulation may represent third variables contributing to the NSSI–suicide relationship. However, it is notable that NSSI maintained a relationship to attempted suicide above and beyond the other constructs examined, given that the measures of these constructs include items related to harmful behaviors and emotion dysregulation. Another potential third variable is shame. Shame is often present in both NSSI and attempted suicide (Brown, Linehan, Comtois, Murray, & Chapman, 2009), and is reflected in the self-punishment motivations commonly endorsed for both behaviors (Brown, Comtois, & Linehan, 2002). It will be important for future research to address these and other potential third variables, especially those associated with both emotion dysregulation and bodily harm, such as eating and substance disorders.
Findings also suggest that NSSI confers risk for attempted suicide across different sociodemographic and clinical groups. The present study found strong relationships between NSSI and attempted suicide in both adolescents and adults, men and women, and treatment and community populations, suggesting results are likely to be generalizable across diverse populations. Interestingly, in the adolescent community sample, the association was stronger for girls than boys. We speculate that NSSI more strongly increases capability to attempt suicide for adolescent girls than boys. Adolescent boys engage in a larger quantity and variety of risky and harmful behaviors as compared to girls (e.g., fighting, substance use; Brener & Collins, 1998; Wu, Rose, & Bancroft, 2006). Thus, for boys, NSSI is just one of many ways to acquire capability. In contrast, because adolescent girls engage in fewer health-risk behaviors, engagement in NSSI during this developmental period may have a particularly profound effect on capability. Future research should continue to explore whether the relation of NSSI to attempted suicide varies by gender, as well as other psychosocial variables such as ethnicity, age, socioeconomic status, and psychiatric diagnosis. Future studies should also examine characteristics of NSSI that most strongly indicate suicide risk; for example, one study found that different NSSI methods, contexts, and functions were differentially related to suicidality (Klonsky & Olino, 2008), and another found that number of NSSI methods used predicted elevated suicidality (Nock et al., 2006).
A key limitation of the present study is the retrospective, cross-sectional design. Establishing the temporal relationship between NSSI and attempted suicide requires prospective research. However, because the onset of NSSI typically occurs around ages 13 or 14 (Klonsky & Muehlenkamp, 2007), which is approximately 10 years earlier than the average onset of attempted suicide (Kessler et al., 1999), we suggest that NSSI typically precedes suicide attempts and may be an especially important predictor of future suicide attempts. Notably, two recent prospective studies of depressed adolescents support our conceptualization: both found that NSSI predicted future suicide attempts more strongly than other suicide risk factors (Asarnow et al., 2011; Wilkinson et al., 2011; for comment see Brent, 2011). Interestingly, both studies also found that suicide attempts were a poor predictor of subsequent NSSI, suggesting that NSSI increases risk for attempted suicide, but attempted suicide does not increase risk for NSSI.
Findings from the present study have important clinical implications. Guidelines for suicide risk assessment often highlight variables such as depression, anxiety, impulsivity, and BPD (American Psychiatric Association, 2006; Rudd et al., 2006). However, NSSI appears to predict attempted suicide more strongly than these risk factors (also see Andover & Gibb, 2010). In addition, NSSI is common in treatment-seeking populations (Briere & Gil, 1998; Nock et al., 2006). Therefore, we recommend that suicide risk assessment guidelines be revised to emphasize NSSI at least as much as other psychological risk factors for suicide.
It is also important that research examine in more detail the relation of NSSI to suicidality. The present study examined suicide attempts as a dichotomous outcome. However, not all suicide attempts are the same. If NSSI increases capability for self-inflicted pain and violence, it is likely that histories of NSSI would facilitate suicide attempts that are more violent, dangerous, and potentially fatal (see Andover & Gibb, 2010). Future research should investigate whether NSSI increases medical severity and lethality of suicide attempts.
A final limitation of the current study is the use of self-report measures of NSSI and suicide attempts. These measures rely on participants' judgments about suicidal intent. Future research utilizing interviews administered by experts can help determine if our findings generalize across different assessment methods.
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Submitted: April 17, 2012 Revised: August 16, 2012 Accepted: August 21, 2012
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Source: Journal of Abnormal Psychology. Vol. 122. (1), Feb, 2013 pp. 231-237)
Accession Number: 2012-27535-001
Digital Object Identifier: 10.1037/a0030278
Record: 178- Title:
- The relationship between session frequency and psychotherapy outcome in a naturalistic setting.
- Authors:
- Erekson, David M.. Department of Psychology, Brigham Young University, Provo, UT, US, david_erekson@byu.edu
Lambert, Michael J.. Department of Psychology, Brigham Young University, Provo, UT, US
Eggett, Dennis L.. Department of Statistics, Brigham Young University, Provo, UT, US - Address:
- Erekson, David M., Counseling and Psychological Services, Brigham Young University, 1500 Wilkinson Student Center, Provo, UT, US, 84602, david_erekson@byu.edu
- Source:
- Journal of Consulting and Clinical Psychology, Vol 83(6), Dec, 2015. pp. 1097-1107.
- NLM Title Abbreviation:
- J Consult Clin Psychol
- Page Count:
- 11
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- Journal of Consulting Psychology
- Other Publishers:
- US : American Association for Applied Psychology
US : Dentan Printing Company
US : Science Press Printing Company - ISSN:
- 0022-006X (Print)
1939-2117 (Electronic) - Language:
- English
- Keywords:
- frequency, psychotherapy, outcome, dose–response, good-enough-level model
- Abstract (English):
- Objective: The dose–response relationship in psychotherapy has been examined extensively, but few studies have included session frequency as a component of psychotherapy 'dose.' Studies that have examined session frequency have indicated that it may affect both the speed and the amount of recovery. No studies were found examining the clinical significance of this construct in a naturalistic setting, which is the aim of the current study. Method: Using an archival database of session-by-session Outcome Questionnaire 45 (OQ-45) measures over 17 years, change trajectories of 21,488 university counseling center clients (54.9% female, 85.0% White, mean age = 22.5) were examined using multilevel modeling, including session frequency at the occasion level. Of these clients, subgroups that attended therapy approximately weekly or fortnightly were compared to each other for differences in speed of recovery (using multilevel Cox regression) and clinically significant change (using multilevel logistic regression). Results: Results indicated that more frequent therapy was associated with steeper recovery curves (Cohen’s f2 = 0.07; an effect size between small and medium). When comparing weekly and fortnightly groups, clinically significant gains were achieved faster for those attending weekly sessions; however, few significant differences were found between groups in total amount of change in therapy. Conclusions: Findings replicated previous session frequency literature and supported a clinically significant effect, where higher session frequency resulted in faster recovery. Session frequency appears to be an impactful component in delivering more efficient psychotherapy, and it is important to consider in individual treatment planning, institutional policy, and future research. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
- Impact Statement:
- What is the public health significance of this article?—As mental health and the efficiency of mental health treatment have become prominent areas of concern in the broader health care milieu, research on practical constructs that affect clinically significant change have become more important. The current study offers evidence that higher session frequency increases the efficiency of psychotherapy in clinically significant ways, decreasing length of patient suffering and possibly requiring fewer institutional resources. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Psychotherapy; *Treatment Duration; *Treatment Outcomes; Models
- PsycINFO Classification:
- Psychotherapy & Psychotherapeutic Counseling (3310)
- Population:
- Human
Male
Female - Location:
- US
- Age Group:
- Adulthood (18 yrs & older)
- Tests & Measures:
- Outcome Questionnaire-45
- Methodology:
- Empirical Study; Mathematical Model; Quantitative Study
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- First Posted: Oct 5, 2015; Accepted: Aug 14, 2015; Revised: Jul 31, 2015; First Submitted: Dec 6, 2013
- Release Date:
- 20151005
- Correction Date:
- 20160512
- Digital Object Identifier:
- http://dx.doi.org/10.1037/a0039774
- PMID:
- 26436645
- Accession Number:
- 2015-45474-001
- Number of Citations in Source:
- 34
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- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2015-45474-001&site=ehost-live">The relationship between session frequency and psychotherapy outcome in a naturalistic setting.</A>
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- PsycINFO
The Relationship Between Session Frequency and Psychotherapy Outcome in a Naturalistic Setting
By: David M. Erekson
Department of Psychology, Brigham Young University;
Michael J. Lambert
Department of Psychology, Brigham Young University
Dennis L. Eggett
Department of Statistics, Brigham Young University
Acknowledgement: David M. Erekson is now at Counseling and Psychological Services, Brigham Young University.
Michael J. Lambert is a limited liability partner in OQMeasures, the company that owns and distributes the OQ-45 used in this study.
As third-party payers and managed care have become more prominent in managing mental health care, emphasis has been placed on the provision of evidence-based treatments as well as minimizing treatment length and cost (Drake & Latimer, 2012). In psychotherapy, this has led to research attempting to identify an optimal range for the amount of therapy needed for improvement; in other words, to delineate a specific number of sessions that is generally found to be helpful across populations (Hansen, Lambert, & Forman, 2003; Howard, Kopta, Krause, & Orlinsky, 1986). This work has been termed the “dose–response” model of psychotherapy, where each session is a single “dose” that adds to a cumulative “response.” These models have indicated that between 13 and 18 sessions are required for 50% of individuals in therapy to achieve clinically significant change, with diminishing returns for sessions that fall beyond this range (Hansen et al., 2003). An alternative model describing recovery curves in psychotherapy proposed that the number of sessions attended is linearly associated with the speed of recovery, where individuals who attend fewer sessions recover more quickly than those who attend more sessions. This has been termed the “good-enough-level” model, where a client’s rate of recovery determines the number of sessions they receive (rather than the number of sessions determining recovery; Barkham et al., 2006). In both of these models of therapy dose, session frequency, or how often the client is seen in a certain period of time, has largely remained unexamined.
The importance of session frequency can be highlighted by comparing psychotherapy dose–response to a medication model. While medication dosage information includes the number of pills to be taken (analogous to total number of sessions in psychotherapy), it also includes information regarding how often they should be taken (analogous to session frequency). Taking one pill a day for 30 days would presumably have a different effect on the body than taking three pills a day for 10 days, or, alternatively, one pill a week for 8 months. Just as alternate schedules for medication may change the way medication works, session frequency may affect the structure and mechanisms of psychotherapy.
Using available empirical evidence, Orlinsky (2009) outlined a metastructure for psychotherapy across theoretical models (termed the “generic model of psychotherapy”) that can be used to understand the potential impact of session frequency on psychotherapy outcome. The model suggests that all psychotherapies are based on a “therapeutic contract,” which consists of treatment goals, methods, fees, and scheduling, as agreed upon by the client and therapist. This therapeutic contract leads to “therapeutic operations” and the “therapeutic bond.” Therapeutic operations are defined as technical aspects of therapy, which include the client’s presentation of concerns, the therapist’s intervention, and the client’s cooperation with this intervention. The therapeutic bond is the alliance between therapist and client. Together, the contract, operations, and bond are suggested to lead to positive or negative effects from a psychotherapy session that are carried into the client’s life experience and are manifest as an increase or decrease in symptoms.
When considering scheduling specifically (as part of the hierarchically superior therapeutic contract and as affecting attended session frequency), session frequency is theorized to have a direct effect on therapeutic operations and therapeutic bond. The interaction between time between sessions and the effectiveness of therapeutic operations remains unknown, though it is possible that gains may be less likely to add upon each other as the length between sessions increases. This may be supported by behavioral theory, which suggests that continuous reinforcement works best for learning new behaviors, while more attenuated reinforcement schedules (especially early on) work less well; if psychotherapy is conceptualized as reinforcement for more adaptive behaviors, it follows that less learning occurs if time between sessions increases. For example, less continuity in tracking client success and failure with out-of-office assignments may mean that the client misses timely support needed to carry successes forward or to problem-solve failures. Practice-based observations may also support this, where the greater number of events that occur in a 2-week time period may become too many to effectively address in a single session, or where new issues arise that interrupt the continuity of previous in- and out-of-office problem-solving activities.
Infrequent therapy may also attenuate the development and stability of the therapeutic alliance, as a client and therapist may feel less actively involved and connected with each other and the therapy. It seems possible that meeting less frequently may communicate to the client that the therapist is too busy for the client, that the client’s problems are not important to the therapist, or that the therapist does not recognize the client’s suffering. Any of these possibilities would likely interrupt communicated positive regard and communicated empathy, components that are theorized in client-centered therapy to strengthen the therapeutic bond (Erekson & Lambert, 2015). If both therapeutic operations and the therapeutic bond are somewhat impaired, it follows that the psychotherapy would be less efficacious.
Despite its theoretical importance, frequency of psychotherapy sessions has historically rested on a foundation of tradition rather than evidence (e.g., 50-min session delivered weekly). Some oblique support for certain frequencies can be found in evidence-based treatments, where if a treatment has garnered enough empirical support to be termed “evidence-based,” we can assume that the proper delivery of that intervention includes session frequency as outlined in the protocol. In an informal examination of the list of research supported treatments provided by Division 12 of the APA (APA Presidential Task Force, 2006), 47 of the 56 treatments that indicated how frequently sessions were scheduled specified weekly or more frequent sessions for the majority of each intervention, particularly in the early stages of treatment.
More direct support for the importance of session frequency was recently published by Cuijpers, Huibers, Ebert, Koole, and Andersson (2013). In a meta-analysis of 70 randomized trials on individual psychotherapy for adult depression, four variables were examined: (a) total number of sessions, (b) total number of weeks in therapy, (c) intensity of therapy (or the length of each session), and (d) session frequency (as defined by the number of sessions per week). The researchers found minimal effects for the first three variables but a moderate effect size (g = 0.45) for session frequency. Specifically, the study indicated that two sessions per week was significantly more effective in reducing symptoms than a single session per week when treating adult depression.
Session frequency has also been examined in exposure-based treatments for anxiety. Most notably, studies have demonstrated a difference between massed and spaced exposure when treating fear symptoms. The specific parameters of massed or spaced exposure vary from study to study; in general, however, massed exposure indicates an intensive approach to exposure (e.g., several hours in a single day), and spaced exposure indicates exposure sessions that are distributed over a greater period of time (e.g., exposure sessions every 5 days). Massed exposure tends to show better immediate reductions in fear and avoidance behaviors, and spaced exposure tends to show better retention of learning and lower relapse rates (Abramowitz, Foa, & Franklin, 2003; Bohni, Spindler, Arendt, Hougaard, & Rosenberg, 2009; Chambless, 1990; Foa, Jameson, Turner, & Payne, 1980; Rowe & Craske, 1998; Tsao & Craske, 2000). These findings may represent the important impact operations, as conceptualized by Orlinsky (2009), have on treatment outcomes. Though these findings are attenuated by small sample sizes (all fewer than or equal to 40 participants), if applied broadly, they suggest that frequency may affect the amount of recovery in a client and may differentially affect short- and long-term outcomes in psychotherapy.
Cognitive–behavioral therapy research has similarly examined the effect of session frequency on outcome and has found that in addition to affecting the amount of recovery, frequency may affect the speed of recovery. For example, in a comparison of obsessive–compulsive disorder treatment administered either daily (for 14 days) or weekly (for 14 weeks), therapeutic effects seemed to be equally effective, even at a 3-month follow-up (Storch et al., 2008; see also Emmelkamp, van Linden van den Heuvell, Rüphan, & Sanderman, 1989). If these two approaches are indeed equivalent in effect, the more frequent treatment facilitates a faster recovery for the patient. Randomized controlled trials of specific treatments indicate, then, that frequency may have an effect on both the amount of recovery experienced by clients as well as the speed of recovery; this is an indication that warrants examination in a naturalistic setting and with generic nonmanualized treatments, where patients may at times be receiving therapy at protracted frequencies (i.e., once every 2 weeks).
Three studies of session frequency were identified where data were gathered from a working clinic rather than a controlled trial. The first study examined session frequency as the average number of sessions attended each week and included dose (or number of sessions) and duration (or total length of the treatment) as variables in the analysis. The researchers found that neither dose nor duration was a significant predictor of outcome, but fewer sessions and more months of therapy were associated with worse outcomes. Further, they found that higher session frequency for those attending therapy fewer than 5 months was associated with better outcomes, and higher session frequency for those attending therapy more than 5 months was associated with worse outcomes (Reardon, Cukrowicz, Reeves, & Joiner, 2002). These findings may suggest that, when attended more frequently, short-term therapy is more effective and long-term therapy is less effective. However, no control for initial severity of patients’ symptoms hampers interpretation of these results and could reasonably explain the findings independent of session frequency.
The second study examined the association between the number and frequency of sessions within the first 3 months of therapy and final outcome in 256 clients. This association was compared across three theoretical orientations: psychodynamic psychotherapy, cognitive–behavioral therapy, and psychoanalytic psychotherapy. No association was found between initial frequency of sessions and final outcome for psychodynamic and cognitive–behavioral therapies; psychoanalytic therapy, however, tended to have better outcomes when sessions were initially less frequent but regular (Kraft, Puschner, & Kordy, 2006). As with Reardon et al. (2002), there are limitations that prevent extrapolation of these results to the effects of frequency—most notably, significant results applied only to psychoanalytic therapy and not to the other two therapies included in the study.
The third study tracked treatment response at every third session to analyze the change trajectories of 1,207 students seeking counseling at a university counseling center (Reese, Toland, & Hopkins, 2011). The researchers explored whether or not session frequency improved a predictive model of therapy recovery, and they explored the nature of frequency effects if it did improve. Session frequency was defined by subtracting the total number of sessions attended by one and dividing that number by the number of weeks in therapy (yielding a single session frequency average for each individual). They found that session frequency significantly contributed to a multilevel model, independent of the total number of sessions attended. It was also found that higher session frequency (i.e., more sessions in fewer days) was related to faster recovery. Limitations to this study include the outcome measure being given once every third session rather than every session and the operationalization of frequency as a fixed variable for each individual (where session frequency, in fact, can vary over time). Additionally, none of these three studies examined clinical significance of client change.
The current study was designed to address the gaps in the session frequency literature in the following ways. First, when possible, outcome was measured at each session of therapy, allowing for a more complete model of change in therapy. Second, frequency was defined as a dichotomous variable (either approximately once a week or approximately fortnightly) for analyses of clinical significance and as a continuous, time-varying variable for the overall model of change in therapy. The former collapses data into distinct events (e.g., weekly vs. fortnightly, reaching significant change vs. not reaching significant change) and allows for an easily interpreted heuristic that is clinically meaningful. The latter includes all available data and more accurately tracks session frequency and outcome at each point in time (especially when both are variable from session to session), allowing for a more nuanced, if more complex, understanding of the construct. Third, measures of clinical significance, as defined by Jacobson and Truax (1991), were included in order to understand the practical significance of session frequency. Finally, initial severity of symptoms was controlled, allowing for an examination of session frequency effects independent of clients’ distress at intake. Based on the literature above, the following hypotheses were formulated: (1) Session frequency will have a significant effect on the speed of recovery, where more frequent sessions will be related to steeper recovery slopes; (2) session frequency will have a significant effect on the amount of change that occurs in therapy, where more frequent sessions will be related to more clinically significant change and less deterioration.
Method Participants
Archival outcome and appointment data were drawn from the counseling center database of a large western university. University students who received psychotherapy between 1996 and 2014 were included in the database. Therapy at the counseling center was offered free of charge and without session limits to full-time students of the university. Clients were referred or self-referred for a wide range of problems, the majority of which were adjustment, anxiety, or depression related. Individual therapy generally consisted of the traditional 50-min session. Therapists at the counseling center were psychologists or supervised psychologists in training (doctoral students in counseling or clinical psychology) who provide treatment according to their theoretical preference, including cognitive–behavioral, psychodynamic, client-centered, existential, systems, and integrative modalities. Three hundred and three therapists were included in the study, each having seen anywhere between 1 and 673 clients, with the median number of clients being seen by a single therapist equaling 22 and the median number of sessions for a single therapist equaling 122.
Consideration for inclusion in this study as a client participant was restricted to individuals who had only attended individual therapy (i.e., no group treatment; N = 22,235) and who had attended at least two sessions of therapy and completed at least two measures of outcome (allowing for clients to be exposed to a frequency effect and to have a record of that effect). Additionally, we limited the analyses to the first course of therapy for each individual, where a break of 90 days or more was considered a new course of therapy. Finally, we excluded individuals who had attended therapy longer than 40 weeks, as these represented significant outliers (3.4% of the sample), and models including these individuals were unstable. Our final sample included 21,488 students. Of these participants, 54.9% were female and 39.8% were male, with 5.3% unspecified. Their mean age was 22.5. The following reflects the percentages of reported primary ethnicities: 85.0% White, 6.0% Hispanic, 2.5% Asian, 1.2% Pacific Islander, 0.9% American Indian, 0.7% Black, and 3.7% other. The mean number of sessions attended was 5.8 (SD = 4.2), and the mean number of weeks per course of therapy was 9.1 (SD = 8.3).
Identifying Weekly and Fortnightly Frequency Groups
In order to allow for a more clinically meaningful analysis of the speed and amount of change occurring in therapy based on session frequency, we identified two groups: individuals attending approximately weekly therapy and individuals attending approximately fortnightly therapy. This was done by calculating the mean frequency over the entire course of therapy for each individual and selecting those with a mean that fell within .25 of 1 week (the weekly group) or within .25 of 2 weeks (the fortnightly group). Several exploratory procedures with more stringent criteria were used to specify these two groups, but because results based on these procedures were consistent with the above procedure, they are not reported here. The weekly group and the fortnightly group were then randomly matched on age, gender, and initial severity of symptoms in order to better isolate the effects of session frequency. Each group consisted of 3,092 clients, with 60.2% female and 38.9% male and a mean age of 21.84.
Because of the possibility that a therapist may choose to taper the frequency of psychotherapy after an initial, more intense treatment, frequency was additionally calculated for each individual during the first month of therapy (n = 2,934 for each group) and the first 2 months of therapy (n = 1,668 for each group); these frequencies (based on the aforementioned time periods) were then applied to the grouping procedure described above. Analyzing frequencies based on these time periods allowed for examination of the effects of early session frequency on final outcomes, and it acted as a control for possible tapering of sessions toward the end of therapy.
Measures and Procedure
Outcome Questionnaire-45 (OQ-45)
Psychological outcome was assessed during treatment using the OQ-45 (Lambert, Gregersen, & Burlingame, 2004), a 45-item self-report instrument designed to measure client distress and functioning over the last week and typically administered prior to each therapy session to track progress in therapy. Items are rated on a 5-point Likert scale. Total scores can range from 0 to 180, with higher scores reflecting more severe distress and lower scores reflecting less distress.
Previous research has provided evidence for the utility of the OQ-45 as a measure of treatment progress and outcome. The OQ-45 demonstrated an excellent level of internal consistency as calculated in the current sample (α = .93). The manual reports test–retest reliability as .84 over a 3-week period. The OQ-45 is also reported, however, as sensitive to change, improving an average of 17.47 points in a sample of 40 patients receiving psychotherapy. Norms have been established for individuals between the ages of 18 and 80 within university, independent practice, community mental health, outpatient, and inpatient settings (Lambert et al., 2004).
The OQ-45 was administered in the current study either by paper or electronically to patients. Client and therapist were both aware that outcome scores were stored for research purposes, and informed consent was obtained at intake. Although specific refusal rates are unavailable for the current sample (records are removed from the database without being tracked), the majority of students generally agree to participate.
The OQ-45 offers cut-offs for reliable change and clinically significant change, derived from the model of statistically operationalized clinically significant change proposed by Jacobson and Truax (1991). Reliable change is defined as change in observed scores that exceeds the amount of variation expected within the standard error of measurement, which, in the case of the OQ-45, equals at least 14 points. Clinically significant change is distinguished by two criteria: a) the change observed is equal to or exceeds the reliable change index, and b) the score leaves the clinical range of functioning (in the case of the OQ-45, scores >63) and enters the normal range of functioning (OQ-45 ≤ 63). Additionally, a change of 14 points in a negative direction (where the client is worse than when they began) defines reliable deterioration. Reliable change, clinically significant change, and deterioration were all used in the current study to identify meaningful change in therapy.
Variables of interest
We calculated several variables for each case. The variable used to examine session frequency as a continuous variable for all subjects was calculated as a cumulative mean frequency at each session. For example, a person attending therapy 1 week after the first session would receive a 1 for his or her frequency at the second session. If this person’s next session were attended 2 weeks after the second session, he or she would receive a 1.5 mean at the third session; if the next session were 2 weeks after the third, he or she would receive a cumulative mean of 1.67 at the fourth session, and so on. Allowing session frequency to vary with time allows for an accurate representation of the effect of frequency as it is occurring, rather than using a variable that has not yet occurred (e.g., mean frequency over the entire course of therapy) to predict a variable at an earlier point in time (e.g., OQ-45 scores at the second session). Additionally, we calculated the total number of sessions attended and initial severity of symptoms (derived from the first OQ-45 score) to include as covariates. As mentioned previously, the total number of sessions has been shown to be related to recovery slopes in psychotherapy (consistent with the good-enough-level model); initial severity of symptoms, on the other hand, was included as a theoretically important control, where it may be possible that differences in severity contribute to differences in recovery over time (or contribute to differences in frequency). We did not include diagnosis as a variable, despite its theoretical importance, as it was not based on research quality criteria nor consistently recorded in the dataset.
Data Analysis
Speed of recovery
Multilevel modeling (Singer & Willett, 2003; also referred to as hierarchical linear modeling) was used to examine the overall effect of session frequency as a continuous variable on the rate of change in therapy. This statistical method is particularly suited to the data in that it accounts for multiple OQ-45 scores nested within individuals and within therapists. The PROC HPMIXED procedure in SAS, designed for efficient analysis of large numbers of observations and similar to the PROC MIXED procedure, was used to estimate recovery trajectories. The PROC HPMIXED procedure relies on sparse matrix techniques and estimates covariance parameters using restricted maximum likelihood. We used the Bayesian Information Criterion (BIC; Schwarz, 1978) to assess model fit, where a decrease of 10 points in the BIC from one model to the next indicates a significantly better fit to the data (Singer & Willett, 2003). BIC was particularly appropriate for these models, as other assessments of model fit require a maximum likelihood method that is not available in the HPMIXED procedure (and other procedures were unable to manage the large amounts of data in our database). We tested transformations for the time variable, including the linear, quadratic, and cubic transformations based on previous research using OQ-45 trajectories (Baldwin, Berkeljon, Atkins, Olsen, & Nielsen, 2009). All three time variables were found to be significant. However, for simplicity of interpretation of the effect of session frequency over time, and as the largest estimate was linear, models using only linear time were reported here.
The final model used to examine the effects of session frequency was as follows:
where Yjih indicates the OQ-45 score at time j for individual i seeing therapist h. The model includes effects for session (γ200), frequency (γ100), the initial severity of symptoms for each individual (γ010), and the total number of sessions attended (γ020; included to replicate the good-enough-level model of psychotherapy response). The effects for severity and the number of sessions predict differences at the intercept for OQ-45 scores due to these variables. Time by severity (γ210) and time by number of sessions (γ220) interactions were also estimated, as well as the frequency by session (γ300) interaction. These interaction effects predict differences in OQ-45 slope (or rate of change) due to the variables being tested; hence, these were the effects of primary interest in the current study. Random effects are listed last and were included to account for therapist variability around the initial intercept, or potential differences in the initial scores of the clients they treat (v00h), client variability around the initial intercept (u0ih), and client variability in slope (u2ih). Residual error is indicated by ejih.
Using the matched dataset of clients attending weekly and clients attending fortnightly, we assessed the clinical significance of session frequency on speed of recovery using the PROC PHREG procedure in SAS for multilevel Cox regression. We examined differences between groups in rates of reliable change and clinically significant change. Multilevel Cox regression predicts the proportion of subjects who will reach a specified criterion (i.e., reliable change, clinically significant change) by a certain time, while including a clustering variable (i.e., therapist). These analyses were run twice, using weeks in therapy as a time variable (allowing for a comparison between groups over time), and using number of sessions attended as a time variable (allowing for a session-by-session comparison). In other words, weeks-as-time allowed us to examine differences over real time between groups, while sessions-as-time allowed us to compare differences in the effect of each session.
Analyses considering just the first month of session frequency and the first 2 months of session frequency were also performed; results were consistent with the full analyses described above, and details were therefore not included in this report.
Amount of recovery
We used multilevel logistic regression (PROC GLIMMIX procedure in SAS) to explore the effect of session frequency on the amount of recovery experienced in psychotherapy; these analyses also utilized the matched dataset of weekly attending and fortnightly attending clients. Criteria for reliable change, clinically significant change, and deterioration were used as dependent variables; weekly and fortnightly groups entered as independent variables, and therapist entered as a clustering variable. Although the definition for clinically significant change includes having met criteria for reliable change, the variables were defined as nonoverlapping, where a client who met criteria for clinically significant change was not also identified as having met criteria for reliable change. These criteria are therefore independently useful in understanding differences in recovery patterns between groups. These analyses were also run for groups defined by session frequency calculated for the first month of therapy and the first 2 months of therapy.
Results Hypothesis 1: Speed of Recovery
Session frequency as a continuous variable was examined for all subjects (N = 21,488) over the entire course of therapy. The analysis included initial symptom severity and total dose in the model, as described above. Number of sessions in therapy rather than weeks in therapy was used as the time variable in this model in order to best understand the effects of frequency over time without interference from the inherently temporal nature of frequency (e.g., it is difficult to interpret the effects of monthly frequency one week after the first session). First, OQ-45 scores were examined and found to be approximately normally distributed. An intraclass correlation (ICC) examining the amount of variance in OQ-45 scores between subjects and within subjects in a model with no predictors was calculated using the following formula:
where σv2 indicates the variance between subjects and σϵ2 indicates the variance within subjects. The proportion of variance between subjects was .675, indicating that approximately 68% of the variance in OQ-45 scores was attributable to differences between individuals (and that there is variance that may be explained by between-subject predictors).
We then included therapist as a random effect and calculated the 3-level model ICC (Siddiqui, Hedeker, Flay, & Hu, 1996), where the ICC for individuals within therapists was calculated as follows:
where σth2 indicates variance within therapists, σind2 indicates variance between subjects, and σε2 indicates variance within subjects. Approximately 47% of the 3-level model variance in OQ-45 scores was attributable to differences between therapists. This large value is likely due to the nonrandomized design of the study. Between-subject variance in the 3-level model was calculated as follows:
indicating that approximately 73% of the variance was attributable to differences between individuals within therapists.
Missing data
As it is possible that variables included in the model may be systematically associated with missing OQ-45 data, we examined patterns of missing data in the sample. First, we calculated the number of individuals with any missing OQ-45 data. Of 21,488 clients included in the dataset, 4,834 (22.5%) had at least one point of missing data; of these, 3,250 (15.1%) had only a single point missing, and 915 (4.3%) had only two points missing. The remaining 3.1% had between 3 and 35 points of missing data.
We then used Little’s MCAR test to determine if any significant patterns of missing data occurred, or if data were missing completely at random; the null hypothesis for this test is that missing data are distributed randomly. Little’s MCAR resulted in a χ2 of 11144.85 (df = 6, p < .001), indicating that missing data appear to be related to session frequency (EM Correlation = .005), initial severity (EM Correlation = .73), and total number of sessions attended (EM Correlation = .05). In order to better understand these relationships and decrease the possibility of these correlations biasing results, we defined all possible missing data patterns for individuals who had received up to eight sessions of therapy (Hedeker & Gibbons, 1997). For example, if a person attended three sessions (and given that individuals were selected if they had an initial OQ and at least one other OQ measurement), we defined two patterns: missing OQ data for the second session or missing OQ data for the third session. Six patterns were defined for those who attended four sessions, 14 patterns for those who attended five sessions, and so on, with 126 possible patterns for those who attended eight sessions. Each of these patterns was then dummy coded and included as a class variable in the multilevel model; the effects of the variables of interest included in the model (described below) remained unchanged. We examined differences in these patterns using one-way ANOVAS but found no discernible pattern in the significant differences, where disparate patterns of missingness were associated with both high and low levels of initial severity, total number of sessions, and session frequency. In order to further assess the effects of correlated missing data, we ran a model for only those with no missing data, and we ran a model for only those with missing data. Both analyses yielded similar effects regarding the variables of interest; in order to limit bias in the results related to missing data, however, we ran the final model including only individuals with no missing data (n = 16,654).
Linear model
Table 1 presents a linear model estimating fixed and random effects. The intercept estimate indicates an average initial OQ-45 score of 70.64. As expected, initial severity and total dose yielded significant effects on recovery curves. Higher levels of initial severity were found to be significantly associated with a higher OQ-45 score at the first session after initial measurement, as well as steeper slopes of recovery. Higher levels of total dose were associated with lower levels of OQ-45 scores at intercept and with less steep recovery curves, consistent with the good-enough level model.
Multilevel Model Predicting the Effects of Session Frequency on Change Trajectories
Including session frequency in the model significantly improved model fit (a decrease of 8,515 in BIC, where a decrease of at least 10 indicates significance). Session frequency was also significantly associated with the intercept and slope of the model, indicating lower OQ-45 scores at intercept and less steep recovery curves with less frequent therapy. This effect can be practically interpreted through extrapolation of the estimate as it interacts with (or is multiplied by) the time variable. In other words, a decrease of 1 week in session frequency has a different impact at different points in time. For example, the slope of OQ-45 scores at Session 2 for a person attending weekly would be estimated as −2.4, and −2.0 for a person attending monthly. At Session 6, however, the estimate would be −1.9 for the weekly client and 0.3 for the monthly client. Cohen’s ƒ2 was used to calculate an effect size based on the amount of variance explained by the variable; this statistic has been discussed as being particularly suitable for use with multilevel models (Selya, Rose, Dierker, Hedeker, & Mermelstein, 2012). Guidelines for interpretation of this statistic have been outlined by Cohen (1988), where ƒ2 ≥ 0.02 is considered a small effect, ƒ2 ≥ 0.15 is considered a medium effect, and ƒ2 ≥ 0.35 is considered a large effect. The effect size for session frequency was calculated as 0.07, or between a small and medium effect. Figure 1 illustrates the isolated effects of this interaction (accounting for the effects of initial severity and total dose). Each line represents an aggregate recovery slope based on a different session frequency. In order to better illustrate the effects of frequency over time, each line contains an equal number of sessions (i.e., six, or the mean number of sessions). As can be seen, sessions attended once a week have the steepest recovery slopes, with progressively less steep slopes occurring for those being seen fortnightly, every 3 weeks, or once a month.
Figure 1. The unique effect of session frequency on OQ-45 recovery slopes based on multilevel modeling. Note. White circles represent a single session. Slopes represent the effect of different session frequencies at Session 6, controlling for initial severity and total number of sessions, as specified by multilevel modeling.
Analyses of clinically significant change
Samples matched on age, gender, and initial severity were compared using multilevel Cox regression with therapist as a clustering variable. Both groups had a mean initial OQ-45 of 68.14. The weekly group had a mean final OQ-45 of 57.64, and they attended a mean of 4.80 (SD = 3.59) sessions over a mean of 4.1 (SD = 4.15) weeks. The fortnightly group had a final OQ-45 of 57.62 and a mean of 6.68 (SD = 4.62) sessions over a mean of 11.2 (SD = 9.11) weeks.
The first analysis of these groups compared rates of reliable change by session number. Significant differences were found between the weekly and fortnightly groups (χ2 = 75.65, df = 1, p < .001, hazard ratio = 1.35, 95% CI [1.27, 1.45]), where more individuals were predicted to reach reliable change sooner in the weekly group. The second analysis compared clinically significant change by session number. The same pattern observed in the previous analysis emerged for clinically significant change, where the weekly group met criteria significantly sooner than the fortnightly group (χ2 = 39.36, df = 1, p < .001, hazard ratio = 1.36, 95% CI [1.24, 1.50]).
The third analysis examined the rate of reliable change by weeks in therapy. Again, differences between weekly and fortnightly groups were significant, favoring the weekly group (χ2 = 600.47, df = 1, p < .001, hazard ratio = 2.45, 95% CI [2.28, 2.63]). The fourth analysis, or rate of clinically significant change by weeks, found results consistent with previous analyses (χ2 = 334.89, df = 1, p < .001, hazard ratio = 2.58, 95% CI [2.33, 2.85]). Differences between weekly and fortnightly groups are illustrated in Figure 2.
Figure 2. Clinically meaningful differences in recovery of clients attending therapy weekly (blue) versus fortnightly (red) in Cox regression plots with 95% confidence bands. Note. Graphs depicting recovery proportions by sessions provide a session-by-session comparison of the effect of weekly therapy versus fortnightly therapy (e.g., 5 sessions of weekly therapy vs. 5 sessions of fortnightly therapy). Graphs based on weeks show how these differences unfold in real time, where clients in the fortnightly group are receiving approximately half the number of sessions as the weekly group.
Hypothesis 2: Amount of Recovery
Multilevel logistic regressions were calculated to predict reliable change, clinically significant change, and deterioration based on weekly or fortnightly therapy, with therapist entered as a clustering variable. Groups selected based on session frequency over the entire course of therapy showed no significant prediction of reliable change or clinically significant change. Deterioration, however, was predicted to occur significantly more frequently in the fortnightly group (F(1, 57) = 7.63, p < .001, OR = 1.40, 95% CI [1.10, 1.79]). Actual rates of deterioration were 6.3% for the weekly group and 8.9% for the fortnightly group, consistent with the calculated odds ratio, where an individual attending fortnightly would be 1.4 times more likely to deteriorate than an individual attending weekly.
In order to assess for the effects of early session frequency patterns, groups based on the first month of session frequency and on the first 2 months of session frequency were examined. When selected by just the first month of session frequency, both groups had a mean initial OQ-45 of 66.66. The weekly group had a mean final OQ-45 of 56.89, attended a mean of 5.0 (SD = 3.78) sessions, for a mean of 8.7 (SD = 6.70) weeks. The fortnightly group had a mean final OQ-45 of 58.22, and a mean of 7.2 (SD = 4.61) sessions over a mean of 13.5 (SD = 8.37) weeks.
When selected by just the first 2 months of session frequency, both groups had a mean initial OQ-45 of 70.57. The weekly group had a mean final OQ-45 of 55.90 and attended a mean of 6.5 (SD = 3.76) sessions over a mean of 8.6 (SD = 4.37) weeks. The fortnightly group had a mean final OQ-45 of 57.39 and a mean of 5.4 (SD = 3.90) sessions over a mean of 9.4 (SD = 7.79) weeks.
Groups selected by assessing just the first month of session frequency showed significant differences in clinically significant change (F(1, 52) = 7.04, p = .01, OR = 1.23, 95% CI [1.05, 1.44]) and no other significant results. Groups selected by assessing the first 2 months of session frequency had no significant results. Full results for these analyses can be seen in Table 2.
Clinically Significant Differences in Total Recovery Between Weekly and Fortnightly Groups
DiscussionThis archival study examined the effect of different session frequencies on psychotherapy change trajectories in a routine-care counseling center. Previous dose–response literature in psychotherapy has focused on the total number of sessions required for improvement, or on the speed of change in therapy predicting the total number of sessions (the good-enough level model; Barkham et al., 2006), while largely neglecting session frequency as a component in the dose–response model. Additionally, recent discussions have emphasized the importance of reducing the burden of mental illness and the implications of better implementing empirically supported treatments, including delivery of these treatments on a weekly or more frequent basis (Cuijpers et al., 2013; Kazdin & Blase, 2011). As the bulk of empirically supported treatments have either explicitly or implicitly employed structured session frequencies of once a week or more frequently, shifting from this practice in routine-care settings may negatively affect the efficacy of treatment. This effect is predicted by Orlinsky’s (2009) generic model of psychotherapy and is empirically supported in the current analysis.
The archival data used in the current study and absence of experimental controls necessitated the use of multiple statistical methods in order to attempt to isolate the effects of session frequency. For example, session frequency was defined as a continuous variable for multilevel models and as a dichotomous grouping variable for analyses of clinically significant change. While the first has the advantage of using the full range of data, the second has the advantage of being a clinically useful heuristic. Additionally, numerous methods of “control” were employed, including matching samples, covariates, and time-period analyses. Although these add to the complexity of the study, they also increase confidence that the effects found are attributable to the variables to which we are assigning them.
When considering the effect of session frequency on the speed of recovery in therapy, two techniques were used: multilevel modeling with session frequency as a continuous variable, and comparing weekly and fortnightly groups using clinically meaningful criteria. Multilevel modeling indicated that those seen more frequently were estimated to recover faster. The effect size for this finding falls between small and moderate.
It is important to note that this effect was found in a model using sessions as time. If one conceptualizes the efficacy of a session as the amount of change occurring during or after that session, this model allowed us to identify decreased efficacy in sessions occurring less frequently. This is in contrast to the alternative hypothesis that sessions have equal efficacy and that equal change would occur between two sessions whether they occurred weekly or less frequently. Practically, this effect indicates that the cumulative effect of how frequently a client is seen matters at each session and that seeing that client more frequently may lead to faster recovery.
This analysis also indicated that session frequency is an important component of the dose–response model. The current study was consistent with the good enough level model, where fewer total sessions in therapy were significantly associated with steeper recovery slopes (Baldwin et al., 2009; Barkham et al., 2006); the good enough level model fit was even better, as indicated by the BIC, when including session frequency.
Clinically meaningful differences in the speed of recovery were examined using weekly and fortnightly groups as proxy for session frequency. These analyses supported the effects of session frequency found in the multilevel model, where weekly therapy attained a higher proportion of reliable change and clinically significant change than fortnightly therapy. This effect was found using sessions as time and weeks as time. For example, the analyses predicted that 50% of individuals being seen weekly would reach reliable change in approximately eight sessions, while those being seen fortnightly would need approximately 12 sessions. Analyses using weeks as time predicted 50% of the weekly group reaching reliable change in approximately 6 weeks, with the fortnightly group requiring 21 weeks of treatment. This highlights, again, the decrease in session-to-session efficacy in less frequent therapy.
When considering the effect of session frequency on the amount of change achieved by the end of therapy, findings were less clear, and our hypothesis was not supported. Reliable change and clinically significant change appear to be equally likely in both weekly and fortnightly modalities. In combination with previous findings, this appears to indicate that although fortnightly therapy may require more sessions, it will eventually result in equal levels of recovery by the end of therapy. A similar pattern can be seen in Figure 2, where confidence bands for session-by-session comparisons of the two modalities begin to overlap as the number of sessions increases. The two significant results found in the analyses of total amount of change indicated greater recovery in the weekly group and greater deterioration in the fortnightly group, but these differences were small and anomalous; further research may be warranted, however, in order to either replicate or better understand these results. Across all analyses, it appears that for individuals who are undergoing psychotherapy in a counseling-center setting, routine treatment on a weekly basis is superior to treatment received less frequently in terms of speed of recovery.
It is beyond the scope of this study to identify the mechanism of these findings, but there are several possibilities that may be fruitful for further study. We noted in the Introduction that Orlinsky’s (2009) generic model of psychotherapy posits that frequency of sessions matter and can affect both the quality of the therapeutic alliance as well as the effectiveness of specific therapeutic operations. We propose that this is a reasonable working model to explain the effects of session frequency in the current study. When placed in a practical context, it seems logical that more frequent therapeutic contact for a patient receiving, for example, exposure therapy, would be more effective than a less frequent schedule, as the patient may benefit from the added support to engage in behaviors to which they feel averse. One can imagine that session frequency would also be important for therapies that are less aversive, as clients that are able to meet weekly may have a more intensive and perhaps intense therapeutic encounter that assures greater continuity in therapeutic operations. As mentioned previously, clients may also feel that their needs matter more to therapists who schedule more frequently, believing that the therapist is being responsive to their suffering instead of to the administrative needs of the institution. Of course, these conjectures are in need of empirical confirmation. A first step may be to examine variables known to correlate with positive outcomes such as the therapeutic alliance (Horvath, Del Re, Flückiger, & Symonds, 2011) or positive engagement.
There were several limitations to the current study, including a lack of research quality diagnostic information for patients, which did not allow an investigation of differences in frequency patterns for differing problems and could not address the findings of Cuijpers et al.’s (2013) meta-analysis of depression-specific outcomes, including therapy offered more frequently than once weekly. It is possible that certain diagnostic categories are associated with success at different frequencies. There are also many problems that a global measure of outcome (the OQ-45) may not detect, and we were unable to examine how specific problems may be affected by session frequency.
The pattern of missing data, which was not missing completely at random, also presents a limitation to the current study. Although we investigated multiple patterns of missingness and found no effect of these patterns on the variables of interest, we ultimately chose to delete missing data list-wise. This in itself may bias results (given that those with missing data may differ fundamentally from those without); however, our analysis comparing those with missing data to those without, as well as the relatively small percentage of individuals with missing data, allow for more confidence in the current findings. Also, because clients were not randomly assigned to strictly controlled frequency groups but were seen at intervals that may have been affected by multiple factors (e.g., client’s schedule, therapist’s schedule, semester shifts, and life events), it was more difficult to isolate session frequency. Although efforts were made to manage these difficulties (described above), an experimental design could more accurately isolate these effects.
In addition to difficulty isolating the effects of frequency, there are also difficulties that arise in the heterogeneity of individuals identified by their attended session frequency versus their scheduled session frequency. For example, when defined by attendance, a client scheduled fortnightly that attended fortnightly would have been grouped with a client scheduled weekly who, due to cancellations or no shows, attended only fortnightly. One could imagine that these two clients would differ dramatically in their final outcomes and rates of change. Similarly, if defined by scheduling, attendance may have varied widely between those grouped together (e.g., equating an individual scheduled weekly for 6 weeks who attended six sessions with an individual scheduled weekly for 6 weeks who attended only two sessions). These differences were explored empirically in a less current dataset using a variable defined as the ratio of sessions attended to sessions scheduled, and the interested reader is directed to the original dissertation manuscript (Erekson, 2013). Ultimately, a prospective study would be needed in order to fully address this issue.
Other limitations include generalizability to populations outside of university counseling centers or to more diverse centers, and further research is needed with other populations and in other settings to confirm these effects. Additionally, given the behavioral literature regarding better retention and decreased relapse rates for spaced exposure, this study was unable to address either retention or durability of outcomes associated with different frequencies, as the archival database did not include this information. Finally, the session frequency effect was relatively small (Cohen’s f2 = .07); small effects can, however, significantly impact large populations over extended periods of time. This is true when considering widespread practices of psychotherapy delivery.
Despite these limitations, this study replicated the effects found by previous session frequency studies, although the literature in this area is sparse (Cuijpers et al., 2013; Reardon et al., 2002; Reese et al., 2011). It also contributed several important factors. First, this study used measurements at each session to better predict outcome trajectories. Second, session frequency was defined as a time varying variable rather than a fixed variable, more accurately representing session frequency at each point of measurement and avoiding using an event that has not yet occurred to predict an earlier data point. Third, this study moved beyond statistical significance and offered an examination of the clinical significance of session frequency, thus providing a practical and concrete estimate of impact. Finally, the study was designed to be clinically accessible by comparing simple attendance patterns of once a week versus once every 2 weeks. Although it could not study all possible real-world practices, it allowed for a useful heuristic that might guide typical psychotherapy scheduling.
Evidence from past literature and the current study indicates that session frequency affects the speed of recovery in psychological treatment. It remains unclear, organizationally and in the community, if slower recovery for many is better than faster recovery for fewer. A cost–benefit analysis of these approaches would be needed to address the financial effects of these practices, though it appears that, at the very least, less frequent therapy eventually requires more resources to reach the same outcome as more frequent therapy. There are also, however, significant individual and societal costs associated with prolonging negative mental health states. Poor mental health has been associated with decreased household income (Sareen, Afifi, McMillan, & Asmundson, 2011), decreased productivity and increased health care costs (Goetzel, Ozminkowski, Sederer, & Mark, 2002), and increased mortality (Eaton et al., 2008). The Global Disability Index, a measure of overall burden of illness that can be applied across diagnoses, ranked moderate depression as equivalent with multiple sclerosis or deafness and severe depression as equivalent to blindness (Eaton et al., 2008). If attenuating session frequency decreases speed of recovery, it also increases the burden of illness, both societally and in the personal suffering of those receiving psychotherapy. Further research is needed, but there is gathering evidence (including the current study) that more frequent therapy is more effective therapy.
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Submitted: December 6, 2013 Revised: July 31, 2015 Accepted: August 14, 2015
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Source: Journal of Consulting and Clinical Psychology. Vol. 83. (6), Dec, 2015 pp. 1097-1107)
Accession Number: 2015-45474-001
Digital Object Identifier: 10.1037/a0039774
Record: 179- Title:
- The relationship of therapeutic alliance and treatment delivery fidelity with treatment retention in a multisite trial of twelve-step facilitation.
- Authors:
- Campbell, Barbara K., ORCID 0000-0001-8771-6084. Department of Public Health and Preventive Medicine, Oregon Health and Science University, OR, US, drbarbaracampbell@earthlink.net
Guydish, Joseph, ORCID 0000-0003-2115-3340. Philip R. Lee Institute for Health Policy Studies, University of California–San Francisco, CA, US
Le, Thao. Philip R. Lee Institute for Health Policy Studies, University of California–San Francisco, CA, US
Wells, Elizabeth A.. School of Social Work, University of Washington, WA, US
McCarty, Dennis. Department of Public Health and Preventive Medicine, Oregon Health and Science University, OR, US - Address:
- Campbell, Barbara K., 1942 NW Kearney Street #24, Portland, OR, US, 97209, drbarbaracampbell@earthlink.net
- Source:
- Psychology of Addictive Behaviors, Vol 29(1), Mar, 2015. pp. 106-113.
- NLM Title Abbreviation:
- Psychol Addict Behav
- Page Count:
- 8
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- Bulletin of the Society of Psychologists in Addictive Behaviors; Bulletin of the Society of Psychologists in Substance Abuse
- Other Publishers:
- US : Educational Publishing Foundation
Society of Psychologists in Addictive Behaviors - ISSN:
- 0893-164X (Print)
1939-1501 (Electronic) - Language:
- English
- Keywords:
- fidelity, treatment retention, therapeutic alliance, 12-step facilitation
- Abstract:
- This study examined associations of therapeutic alliance and treatment delivery fidelity with treatment retention in Stimulant Abusers to Engage in Twelve-Step (STAGE-12), a community-based trial of 12-Step Facilitation (TSF) conducted within the National Drug Abuse Treatment Clinical Trials Network (CTN). The STAGE-12 trial randomized 234 stimulant abusers enrolled in 10 outpatient drug treatment programs to an eight-session, group and individual TSF intervention. During the study, TSF participants rated therapeutic alliance using the Helping Alliance questionnaire-II. After the study, independent raters evaluated treatment delivery fidelity of all TSF sessions on adherence, competence, and therapist empathy. Poisson regression modeling examined relationships of treatment delivery fidelity and therapeutic alliance with treatment retention (measured by number of sessions attended) for 174 participants with complete fidelity and alliance data. Therapeutic alliance (p = .005) and therapist competence (p = .010) were significantly associated with better treatment retention. Therapist adherence was associated with poorer retention in a nonsignificant trend (p = .061). In conclusion, stronger therapeutic alliance and higher therapist competence in the delivery of a TSF intervention were associated with better treatment retention whereas treatment adherence was not. Training and fidelity monitoring of TSF should focus on general therapist skills and therapeutic alliance development to maximize treatment retention. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *CNS Stimulating Drugs; *Drug Abuse; *Drug Rehabilitation; *Therapeutic Alliance; *Treatment Compliance
- Medical Subject Headings (MeSH):
- Adult; Empathy; Female; Humans; Male; Middle Aged; Patient Compliance; Professional Competence; Professional-Patient Relations; Substance-Related Disorders
- PsycINFO Classification:
- Drug & Alcohol Rehabilitation (3383)
- Population:
- Human
Male
Female
Outpatient - Location:
- US
- Age Group:
- Adulthood (18 yrs & older)
- Tests & Measures:
- Twelve Step Facilitation Adherence Competence Empathy Scales
Addiction Severity Index-Lite
Helping Alliance Questionnaire--II DOI: 10.1037/t07504-000 - Grant Sponsorship:
- Sponsor: National Institute on Drug Abuse
Grant Number: R01 DA025600, U10 DA015815 and P50 DA009253
Recipients: No recipient indicated - Methodology:
- Clinical Trial; Empirical Study; Quantitative Study
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- First Posted: Aug 18, 2014; Accepted: May 12, 2014; Revised: Apr 16, 2014; First Submitted: Jan 29, 2014
- Release Date:
- 20140818
- Correction Date:
- 20150914
- Copyright:
- American Psychological Association. 2014
- Digital Object Identifier:
- http://dx.doi.org/10.1037/adb0000008
- PMID:
- 25134056
- Accession Number:
- 2014-33503-001
- Number of Citations in Source:
- 49
- Persistent link to this record (Permalink):
- http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2014-33503-001&site=ehost-live
- Cut and Paste:
- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2014-33503-001&site=ehost-live">The relationship of therapeutic alliance and treatment delivery fidelity with treatment retention in a multisite trial of twelve-step facilitation.</A>
- Database:
- PsycINFO
The Relationship of Therapeutic Alliance and Treatment Delivery Fidelity With Treatment Retention in a Multisite Trial of Twelve-Step Facilitation
By: Barbara K. Campbell
Department of Public Health and Preventive Medicine, Oregon Health and Science University;
Joseph Guydish
Philip R. Lee Institute for Health Policy Studies, University of California–San Francisco
Thao Le
Philip R. Lee Institute for Health Policy Studies, University of California–San Francisco
Elizabeth A. Wells
School of Social Work, University of Washington
Dennis McCarty
Department of Public Health and Preventive Medicine, Oregon Health and Science University
Acknowledgement: Awards from the National Institute on Drug Abuse (R01 DA025600, U10 DA015815 and P50 DA009253) supported this investigation. The authors appreciate the generous assistance of Dennis Donovan, Dennis Daley, and the STAGE-12 study team in providing access to STAGE-12 study data. The authors also thank Kevin Delucchi, University of California–San Francisco, for consultation regarding statistical analysis and Emma Passalacqua for her formatting assistance. Special thanks to the raters and the staff at all of the participating treatment sites.
Longer stays and better attendance are associated with enhanced outcomes in substance abuse treatment (Hubbard, Craddock, Flynn, Anderson, & Etheridge, 1997; Simpson, 1981; Zhang, Friedmann, & Gerstein, 2003). However, treatment retention remains a challenge, and premature termination remains a common problem (Brorson, Arnevik, Rand-Hendriksen, & Duckert, 2013; Stark, 1992; Swift & Greenberg, 2012). Research has typically examined patient characteristics associated with retention or, conversely, with dropout. However, few patient characteristics show consistent associations with treatment dropout in either addictions treatment (Brorson et al., 2013) or the broader psychotherapy domain (Swift & Greenberg, 2012). Reviews note the lack of research on the relationship of treatment variables with retention and recommend that this area be addressed (Brorson et al., 2013; Swift & Greenberg, 2012). Among the limited studies of treatment variables associated with retention, manualized and time-limited treatments (Swift & Greenberg, 2012) and higher therapeutic alliance (Brorson et al., 2013; Sharf, Primavera, & Diener, 2010) have been associated with better retention.
Treatment Retention and Therapeutic AllianceTherapeutic alliance, the collaborative relationship between therapist and patient, has been conceptualized as a common treatment factor present across treatment orientations (Horvath & Luborsky, 1993). It has predicted attendance (Fiorentine, Nakashima, & Anglin, 1999; Simpson et al., 1997), retention (De Weert-Van Oene, Schippers, De Jong, & Schrijvers, 2001; Meier, Donmall, McElduff, Barrowclough, & Heller, 2006; Knuuttila, Kuusisto, Saarnio, & Nummi, 2012; Ruglass et al., 2012), and outcomes (Connors, Carroll, DiClemente, Longabaugh, & Donovan, 1997; Gillaspy, Wright, Campbell, Stokes, & Adinoff, 2002; Crits-Christoph, Johnson, Connolly Gibbons, & Gallop, 2013) for patients in substance abuse treatment. Early therapeutic alliance, is particularly associated with treatment engagement and retention (Meier et al., 2006; Brorson et al., 2013). This robust finding supports focusing retention research on treatment variables, including common and specific treatment factors. To do so, it may be useful to investigate the degree to which delivery of specified treatments as intended, known as treatment delivery fidelity, is associated with retention.
Treatment Retention and FidelityTreatment delivery fidelity has received considerable research attention as a method of ensuring internal trial validity (Bellg et al., 2004; Gearing et al., 2011). Fidelity measures most commonly involve observer ratings of adherence to specified treatment content as well as ratings of therapist competence or skill delivering the treatment (Borrelli, 2011). Research on the relationship of therapist adherence and competence to patient outcomes in substance abuse treatment has produced mixed findings (Webb, DeRubeis, & Barber, 2010). Some studies have only identified fidelity-outcome relationships after controlling for the effects of therapeutic alliance (Barber et al., 2006; Gibbons et al., 2010; Hogue et al., 2008). Although numerous studies have investigated fidelity-outcome relationships, we identified only one study that examined the relationships between therapist adherence and competence and treatment retention in substance abuse treatment. The study found no significant association between adherence to a three-session motivational interviewing (MI) intervention and days of outpatient treatment enrollment, whereas competence in advanced MI skills (measured using the entire therapist sample, including therapists conducting treatment as usual [TAU]) was negatively associated with retention in outpatient treatment at 4-weeks postintervention (Martino, Ball, Nich, Frankforter, & Carroll, 2008). There were no significant competence-retention relationships within the MI therapist-only sample. Unexpected competence-retention results are difficult to understand, particularly given the significant effects often shown for MI in increasing treatment retention (Hettema, Steele & Miller, 2005). This may be a spurious result due to the relatively high number of analyses conducted in the study. An alternative explanation may be that clients at risk for treatment disengagement prompted therapists to use motivational strategies with greater skill in an effort to build motivation for treatment (S. Martino, personal communication, April 2014). Results point to the need for further research to clarify relationships among adherence, competence, and retention in treatments for substance use disorders. Fidelity-retention relationships should be studied across different manual-guided treatments, particularly given the finding that overall retention rates have been found to be superior for manualized versus nonmanualized treatments (Swift & Greenberg, 2012).
Treatment Retention and Outcomes in 12-Step FacilitationTwelve-Step Facilitation (TSF) is a manual-guided treatment for alcohol and substance use disorders that seeks to increase clients’ engagement in 12-Step activities outside of formal treatment sessions. Since Project Match, which found TSF to be comparable in outcomes to MI and cognitive–behavioral therapy (Project Match Research Group, 1997), empirical support for TSF has accumulated (Brown, Seraganian, Tremblay, & Annis, 2002; Carroll, Nich, Ball, McCance, & Rounsaville, 1998; Kaskutas, Subbaraman, Witbrodt, & Zemore, 2009; Timko, DeBenedetti, & Billow, 2006; Timko & DeBenedetti, 2007). Research to date has shown that retention in TSF has generally been comparable with other treatments (Carroll et al., 1998; Project Match Research Group, 1997) and that retention in TSF is associated with better outcomes. (Kaskutas et al., 2009; Timko, Sutkowi, Cronkite, Makin-Byrd & Moos, 2011).
Stimulant Abusers to Engage in Twelve Step (STAGE-12), the parent study for the current analysis, examined the efficacy/effectiveness of TSF for stimulant abusers conducted in community treatment programs (Donovan et al., 2013). The study trained outpatient counselors in 10 treatment centers to deliver a group-plus-individual TSF treatment that was integrated into TAU and compared with a TAU-only condition. TSF retention was comparable to TAU retention as measured by self-reported, group session attendance within a 30-day period and was higher for the number of individual sessions reported by participants. TSF participants had a higher likelihood of abstinence from stimulants during treatment, although there were no differences at follow-up. TSF participants had higher rates of attendance and involvement in 12-Step programs posttreatment and at 6-month follow-up. The relationship of number of TSF treatment sessions attended (using a dichotomous measure called high vs. low exposure) to participant outcomes was also examined (Wells et al., in press). High exposure to treatment, defined as attendance at two or more (out of three) individual sessions plus three or more (out of five) group sessions, was achieved by 77% of TSF participants and was associated with (a) significantly higher odds of abstinence from stimulants during treatment and across 4 months of follow-up; (b) significantly lower rates of stimulant use for nonabstinent participants and nonstimulant drug use during treatment, but not after; and (c) more days of attending 12-Step meetings and engaging in duties during meetings through 90-days posttreatment (Wells et al., 2014).
In an ancillary study, we assessed the reliability and concurrent validity of the Twelve Step Facilitation Adherence Competence Empathy Scales (TSF ACES), a ratings measure of treatment delivery fidelity based on an expansion of the adherence rating scales used in STAGE-12 (Campbell, Manuel, et al., 2013). Trained, independent raters evaluated the fidelity of all audio-recorded TSF sessions. The availability of comprehensive fidelity ratings for the entire TSF sample provided an opportunity to study relationships of fidelity with other variables, including predictors and outcomes. A prior report (Campbell, Buti, et al., 2013) found that therapists reporting self-efficacy in basic counseling skills had higher adherence, competence, and empathy delivering the TSF intervention and those with graduate degrees had higher adherence. In contrast, therapists with more positive attitudes toward 12-Step groups and self-efficacy in addiction-specific counseling skills had lower adherence ratings. In a study of fidelity-patient outcomes relationships, greater therapist empathy was significantly associated with fewer days of self-reported drug use at 3-months posttreatment; greater competence was associated with this outcome in a nonsignificant trend (p = .06), and there was no association of adherence with days of drug use. All three fidelity measures were associated with better employment outcomes on the Addiction Severity Index (ASI) but worse drug composite scores at 3-months posttreatment. Analysis of ASI drug use items, which include days of use and how troubled the respondent is by use, showed that greater fidelity was associated with fewer days of use but an increased sense of being troubled by use (Guydish et al., 2014). The authors noted that different types of ASI items had different relationships to the same predictor and posited that maxims such as “one day at a time” kept the risk of drug use at the fore even as actual drug use declined. The current study examined the relationships of treatment retention in STAGE-12 TSF, as measured by number of sessions attended, with treatment delivery fidelity (i.e., therapist adherence, competence, and empathy) and therapeutic alliance, as reported by participants at the second treatment visit.
Method Overview of STAGE-12 Trial
STAGE-12 was a multisite, randomized trial conducted in 10 outpatient community treatment centers across the United States. Patient participants were adults with stimulant abuse/dependence who were enrolled or seeking treatment admission. Participants were randomly assigned to either STAGE-12 TSF (i.e., five-session group plus three-session, individual, intensive referral sessions) plus TAU (N = 234) or TAU (TAU; 5–15 hours of weekly treatment; N = 237). The STAGE-12 study was approved by the University of Washington Institutional Review Board (IRB) as well as the IRBs of all academic institutions affiliated with participating sites. See Donovan et al. (2013) for a complete description of STAGE-12 study participants, procedures, and the TSF treatment.
Participants
The STAGE-12 trial randomized 234 participants to the TSF intervention. Two hundred (85.5%) completed the therapeutic alliance measure, Helping Alliance questionnaire-II (HAq-II; Luborsky et al., 1996), at 2 weeks postrandomization. We excluded the following because of incomplete measures: (a) four who did not have fidelity ratings; (b) one who completed the HAq-II before attending any treatment sessions, thus invalidating the measure; and (c) six who had more than 20% missing HAq-II items (i.e., four or more items). We also excluded 15 participants who had missing baseline ASI alcohol composite or ASI drug composite scores. Our analysis included 174 participants who met the following conditions: completed HAq-II at 2 weeks postrandomization and attended at least one session to produce a valid measure of therapeutic alliance, were rated for treatment fidelity, had completed at least 80% of HAq-II items, and had ASI alcohol and drug composite scores at baseline.
Study Therapists
All therapists (N = 106) at study sites were considered for inclusion based on four eligibility criteria: (a) credentialed to provide substance abuse treatment, (b) approved by the treatment program’s administration, (c) willing to participate and to be randomized, and (d) familiar with the 12-Step orientation. There were 39 therapists (37%) who met criteria and were included in the study pool; 2 from each site were chosen at random from the pool to conduct the TSF treatment. The remaining therapists were available for training as replacement therapists, four of who became TSF therapists. Supervisors were trained as backup TSF therapists. In total, there were 34 therapists and supervisors who conducted the TSF intervention. We obtained demographic information from 33 of the 34 therapists; they were predominantly Caucasian (70%) women (67%) with a mean age of 52 years (SD = 9.2). Most (82%) had at least 5 years of counseling experience and 55% had masters’ degrees or above. During the trial, therapists were trained in the TSF intervention, certified and monitored for adherence by on-site supervisors and expert raters (four clinicians experienced in substance abuse treatment and trained in the TSF intervention: one masters’ level, one doctoral candidate, and two doctoral level). The STAGE-12 trial audio-recorded all TSF sessions but did not record TAU sessions.
Independent Fidelity Raters
We recruited separate raters from local graduate programs to conduct ratings of all audio-recorded, STAGE-12 TSF sessions. The nine raters (seven with masters’ degrees and two with doctoral degrees) averaged 5 years of clinical experience (SD = 4.05), 7 years of research experience (SD = 5.96), and 1 year of rating experience (SD = 2.95). A doctoral level psychologist with extensive experience in fidelity monitoring served as an expert rater and trainer/ratings supervisor.
Measures
TSF ACES
TSF ACES measures five dimensions of fidelity using 6-point scales, three of which were used in our analysis: (a) adherence—delivery of specific treatment content; (b) competence—the skill of content delivery; and (c) global empathy—the therapist’s effort to understand the clients’ perspectives (adapted from the MI Treatment Integrity scale; Moyers, Martin, Manuel, Hendrickson, & Miller, 2005). There are four content rating forms, one for group sessions (10 items) and three corresponding to STAGE-12 individual Session 1 (10 items), Session 2 (4–5 items), and Session 3 (8–9 items). Summary measures derived for each session had modest to excellent inter-rater reliabilities, with intraclass correlations of .91 for mean adherence, .90 for mean competence, and .69 for global empathy. Internal consistencies computed with Cronbach’s α for summary measures based on multiple items were .69 for mean adherence and .71 for mean competence. In assessing TSF ACES convergent validity with the HAq-II, all correlations were in the expected directions (e.g., negative correlation of HAq-II with proscribed therapist behaviors); there were no significant correlations for mean adherence, mean competence, or global empathy with HAq-II scores collected at week 2. See Campbell, Manuel, et al. (2013) for a further description of the psychometric characteristics of the ratings scale and for sample items. The TSF ACES ratings manual and forms are available at http://ctndisseminationlibrary.org/PDF/795_TSFACES.pdf.
HAq-II
Therapeutic alliance was assessed using the patient version of the HAq-II (Luborsky et al., 1996). This self-report measure assesses the degree to which patients experience therapist and treatment as collaborative and helpful. The HAq-II had good test–retest reliability (.78), internal consistency (.90), and convergent validity on a normative sample of cocaine abusers (Luborsky et al., 1996), and it is frequently used in alliance research with substance-abusing samples (see Meier, Barrowclough, & Donmall, 2005 for a review). The instrument contains 19 items measured on a 6-point Likert scale; the sum of the items (with negative items reverse scored) forms the total score. STAGE-12 study participants completed the HAq-II at week 2 of treatment and week 8 (i.e., end of treatment). We used week 2 scores for our analysis to use a measure that temporally preceded our outcome measure and based on previous robust findings of early alliance predicting engagement and retention.
ASI-Lite
The ASI-Lite (McLellan et al., 1992) was administered at baseline and follow-up in STAGE-12. ASI composite scores measure problem severity in seven areas (medical, employment, legal, alcohol, drug, social, psychological; McLellan et al., 1985). Scores are derived from questions in each area measuring problem severity within the prior 30-day period. We used baseline ASI drug and alcohol composite scores as measures of substance use severity at treatment entry.
Treatment Retention
The number of TSF sessions attended (ranging from 0 to 8) was the measure chosen for treatment retention. Session attendance was reviewed and recorded weekly during the treatment phase by the therapist.
Procedures for Independent Fidelity Ratings
Raters viewed the STAGE-12 TSF therapist training video and completed a 1-day training. Before rating study sessions, raters achieved a criterion level of inter-rater reliability with the ratings expert on audio-recorded practice sessions conducted by STAGE-12 therapists. Audio-recordings of all TSF group (n = 512) and individual (n = 487) sessions were randomly assigned to certified raters in sets of 20; one session per set was randomly assigned to the study expert for co-rating to monitor ratings consistency. There were 33 incomplete or poor-quality audio-recordings, leaving 966 rated sessions. The University of California–San Francisco and Oregon Health and Science University IRBs approved the procedures for the fidelity study. See Campbell, Buti, et al., (2013) for more detail.
Data Analysis
Descriptive statistics, including means, standard deviations, and percentages, were used to summarize characteristics of TSF participants at baseline. Comparisons between TSF participants included in our analysis and those excluded from analysis due to missing data were conducted with t tests for continuous variables and with χ2 tests for categorical variables.
We first tested univariate associations of therapeutic alliance and treatment delivery fidelity (mean adherence, mean competence, mean empathy) with treatment retention (i.e., number of sessions attended). Poisson regression modeling examined the multivariate relationship between these predictor variables and the retention outcome measure. Age, gender, race, and baseline values of ASI drug and alcohol composite scores were included in the Poisson model. Nesting of clients within site was controlled for as well. Nesting of clients within counselor was not controlled because clients received STAGE-12 group sessions from more than one counselor. Analyses were conducted using SAS software version 9.3 (SAS, Inc., Cary, NC).
Results Participant Characteristics
The mean age of participants in the analytic sample was 38.1 (SD = 10.2) years and 62% were women. White participants accounted for 44% of the sample, and African Americans accounted for 37%. More than half (52.6%) had never married, 23.7% were divorced, and 14.5% reported being married. Approximately half were high-school graduates, and 29% had some college education. Most were working (35.1% full time and 23.6% part time). Participants included in the analyses (n = 174) were similar to those excluded (n = 60) on these demographic characteristics. However, participants included in the analysis received significantly more STAGE-12 sessions (M = 5.6, SD = 2.0) than those not included in the analysis (M = 2.2, SD = 2.3, p < .001), primarily because of the inclusion criterion of having completed a HAq-II after attending at least one session. See Table 1 for participant characteristics.
Baseline Demographic Characteristics for STAGE-12 TSF Participants Included (n = 174) and Not Included (n = 60) in the Analysis
Number of Sessions Attended
See Table 2 for a distribution of session attendance for the 174 participants included in our analysis. Approximately 5% attended only one session, whereas 14% attended all eight sessions. The mean number of sessions was 5.6 (SD = 2); most participants (88%) attended 4–8 sessions.
Distribution of Number of TSF Counseling Sessions Attended
Relationship Among Therapeutic Alliance, Treatment Fidelity, and Treatment Retention
Results of univariate and multivariate analyses are shown in Table 3. In the univariate analysis, there was a statistically significant association between therapeutic alliance (HAq-II) and treatment retention (β = 0.142, p = .002) and no significant relationships of fidelity variables with retention. In the multivariate analysis, controlling for age, gender, race, baseline ASI drug composite, baseline ASI alcohol composite, adherence, competence, and empathy scores, an increase in therapeutic alliance by one unit resulted in an increase in the number of sessions attended by 14% (exp(β) = 1.14; p = .005). Likewise, there was a significant association of therapist mean competence with retention; an increase in therapist mean competence by one unit resulted in an increase in session attendance by 36% (exp(β) = 1.36; p = .010). The association between number of sessions attended and mean adherence, while controlling for all other variables, approached significance (p = .061); for every increase of one unit in mean adherence, session attendance decreased by 20% (exp(β) = 0.80). There was no significant association between session attendance and empathy scores when controlling for other variables, and none of the patient-characteristic control variables were associated with retention.
Parameter Estimates (β), Exponential β (95% CI) and p Values of Univariate and Multivariate Analysis Examining Associations Among Therapeutic Alliance, TSF Fidelity Predictors, and Treatment Retention (n = 174)
DiscussionThe robust association of longer retention with better outcomes in substance abuse treatment has been extended to TSF treatment (Kaskutas et al., 2009; Wells et al., 2014), supporting the importance of treatment retention for TSF. The current study contributes to recommended research on treatment variables as predictors of retention (Brorson et al., 2013; Swift & Greenberg, 2012). Our findings indicated that early, participant-rated, therapeutic alliance was significantly associated with retention in TSF in univariate and multivariate analyses. To our knowledge, it is the first study to show a relationship of therapeutic alliance with retention in TSF with substance abusers and corroborates a previous finding from Project Match showing a relationship of therapist-rated, therapeutic alliance with outpatient TSF retention for alcohol-dependent participants (Connors et al., 1997). Results are also the first to identify a significant fidelity-retention relationship for manual-guided TSF treatment, a finding that has important implications for treatment delivery. Therapist competence was associated with higher session attendance when therapeutic alliance and other fidelity variables were controlled. Unexpectedly, the multivariate model suggested a relationship between higher adherence and poorer retention, that, although not significant in this analysis (p = .061), may bear additional attention in future research.
Results suggest that variables related to general therapist skill, which facilitate development of positive therapeutic alliance and are associated with competent TSF delivery, may improve attendance more than strict intervention adherence. Results are consistent with findings from Guydish et al. (2014) indicating that therapist empathy and competence were associated with better patient outcomes whereas adherence was not, although the lack of a significant empathy finding in the current study is inconsistent with this pattern of results. It may be that the relationship of empathy with retention is accounted for mostly in facilitating therapeutic alliance, such that, when alliance is controlled, differences in therapist empathy do not affect retention. Overall, findings lend support to the “common factors” (Castonguay, 1993) hypothesis regarding treatment effectiveness, suggesting that variables present across treatments, such as a positive therapeutic alliance and competent delivery of treatment content, may be central to increasing retention and improving outcomes.
Competence-retention findings in the current study are compatible with research showing that more experienced therapists had lower dropout rates (Swift & Greenberg, 2012) and that more advice-giving was associated with worse outcomes in group counseling (Crits-Christoph et al., 2013). Swift and Greenberg (2012) suggested that more experienced therapists may be more responsive and have a greater relationship focus, which may explain their ability to retain clients in treatment. Therapist responsiveness to client presentation may also be relevant for adherence results. Adherence may provide intervention structure that ensures the inclusion of empirically supported practices. However, departures from strict adherence based on therapist responsiveness to changes in client presentation (i.e., therapist attunement) may improve alliance, address client need more effectively, and appropriately individualize treatment in community settings serving heterogeneous clients with multiple comorbidities. It has been argued that flexible application of manual-guided treatments, including training about when and how to be flexible, optimizes the use of empirically supported treatments in clinical practice (Kendall, Gosch, Furr, & Sood, 2008). Use of a mean adherence measure in our study may have obscured the precise adherence information needed to show a relationship with retention. If the therapist responsiveness (i.e., flexible fidelity) hypothesis is correct, then variations in strict adherence based on therapist-client interactions may be associated with improved retention and better outcomes and may require more finely tuned measurement.
Use of fidelity ratings of TSF sessions, using an instrument with known psychometric properties (Campbell, Manuel, et al., 2013) and independent raters who had undergone rigorous training, are strengths of the current study. The inclusion of a measure of therapeutic alliance is also a strength, not only to assess its relationship with retention but also as a variable to control when examining fidelity-retention relationships. Missing data that eliminated approximately 25% of TSF participants from the current analysis are a study weakness, although the excluded sample did not differ demographically from the sample included in our analysis. Participants who did not attend any sessions did not have fidelity or valid therapeutic alliance data, thus they were omitted from our analysis. This is a study limitation that prohibits us from identifying any variables associated with immediate dropout, an important treatment consideration. Lack of measurement of early symptom improvement among participants may also be considered a study limitation. Early participant improvement may be confounded with therapeutic alliance and a predictor of retention itself (Crits-Christoph, Connolly Gibbons, & Hearon, 2006; Webb et al., 2010). The use of a measure of therapeutic alliance after only week 2 of treatment may mitigate this concern given the limited time for improvement to occur. Also, limited research has shown that, although early alliance may be affected by symptom improvement, alliance remains a significant predictor of positive outcomes when early symptom improvement is controlled (Barber, Connolly, Crits-Christoph, Gladys, & Siqueland, 2000).
Recommendations
Clinicians should be trained and monitored in general therapy skills, not simply adherence, in clinical trials and community implementation of TSF and other behavioral interventions. This includes training designed to facilitate the therapeutic alliance, several interventions for which have been developed. Campbell et al. (2009) developed a brief intervention specifically designed to foster alliance development and found that it increased participants’ continuation in outpatient treatment after detoxification in a randomized trial. A preliminary study of alliance fostering therapy added to supportive-expressive therapy resulted in depressed patients’ reports of positive changes in quality of life (Crits-Christoph et al., 2006). The complex topic of training and intervention characteristics that facilitate alliance development and general therapist competence clearly requires further study. Studies should also examine therapist adherence variations during manual-guided treatment. Adherence flexibility may be a component of therapist competence that is superior to strict adherence, although this may vary depending on the specified treatment and client-related variables. Despite the need for further study, accumulating evidence, including the present study’s findings, suggests that training empirically supported treatments such as TSF should emphasize general therapist and alliance-developing skills to improve retention and outcomes.
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Submitted: January 29, 2014 Revised: April 16, 2014 Accepted: May 12, 2014
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Source: Psychology of Addictive Behaviors. Vol. 29. (1), Mar, 2015 pp. 106-113)
Accession Number: 2014-33503-001
Digital Object Identifier: 10.1037/adb0000008
Record: 180- Title:
- The relationship of tobacco use with gambling problem severity and gambling treatment outcome.
- Authors:
- Odlaug, Brian L.. Department of Public Health, University of Copenhagen, Copenhagen, Denmark, odlaug@gmail.com
Stinchfield, Randy. Department of Psychiatry, University of Minnesota, MN, US
Golberstein, Ezra. School of Public Health, Division of Health Policy and Management, University of Minnesota, MN, US
Grant, Jon E.. Department of Psychiatry & Behavioral Neuroscience, University of Chicago, IL, US - Address:
- Odlaug, Brian L., Department of Public Health, University of Copenhagen, Øster Farimagsgade 5A, DK-1353, Copenhagen, Denmark, K, odlaug@gmail.com
- Source:
- Psychology of Addictive Behaviors, Vol 27(3), Sep, 2013. pp. 696-704.
- NLM Title Abbreviation:
- Psychol Addict Behav
- Page Count:
- 9
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- Bulletin of the Society of Psychologists in Addictive Behaviors; Bulletin of the Society of Psychologists in Substance Abuse
- Other Publishers:
- US : Educational Publishing Foundation
Society of Psychologists in Addictive Behaviors - ISSN:
- 0893-164X (Print)
1939-1501 (Electronic) - Language:
- English
- Keywords:
- impulse control, pathological gambling, tobacco, treatment, gambling problem severity, treatment outcome
- Abstract:
- This study sought to examine the impact of tobacco use on gambling treatment. Pathological gambling (PG) is a psychiatric condition associated with significant financial, emotional, and psychosocial consequences. Elevated rates of nicotine dependence have been associated with increased gambling severity and more frequent psychiatric problems. A total of 385 treatment-seeking pathological gamblers enrolled in one of 11 gambling treatment providers in Minnesota were assessed. Linear regression modeling was used to examine demographic and clinical variables at treatment entry and the relationship between those variables and the number of days gambled at a 6-month posttreatment. Logistic regression was utilized to assess predictors of treatment completion. Daily tobacco use was reported in 244 (63.4%) subjects. Tobacco users presented with significantly more severe gambling and mental health symptoms at treatment intake. Daily tobacco use, however, was not significantly associated with the number of days gambled or with treatment completion. Although tobacco users present with greater gambling problem severity, they had similar rates of treatment completion and treatment outcomes as nonusers. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Pathological Gambling; *Tobacco Smoking; *Treatment Outcomes; Impulse Control Disorders; Severity (Disorders)
- Medical Subject Headings (MeSH):
- Adult; Disruptive, Impulse Control, and Conduct Disorders; Female; Gambling; Humans; Linear Models; Logistic Models; Male; Middle Aged; Minnesota; Patient Compliance; Psychotherapy; Psychotherapy, Group; Severity of Illness Index; Tobacco Use Disorder; Treatment Outcome
- PsycINFO Classification:
- Health & Mental Health Treatment & Prevention (3300)
- Population:
- Human
Male
Female - Location:
- US
- Age Group:
- Adulthood (18 yrs & older)
- Tests & Measures:
- Gambling Timeline Followback
Behavior and Symptom Identification Scale
Addiction Severity Index DOI: 10.1037/t00025-000
South Oaks Gambling Screen DOI: 10.1037/t03938-000
Gambling Treatment Outcome Monitoring System DOI: 10.1037/t04216-000 - Methodology:
- Empirical Study; Longitudinal Study; Quantitative Study; Treatment Outcome
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- First Posted: Sep 3, 2012; Accepted: Jul 23, 2012; Revised: Jul 13, 2012; First Submitted: Dec 14, 2011
- Release Date:
- 20120903
- Correction Date:
- 20130923
- Copyright:
- American Psychological Association. 2012
- Digital Object Identifier:
- http://dx.doi.org/10.1037/a0029812
- PMID:
- 22946857
- Accession Number:
- 2012-23735-001
- Number of Citations in Source:
- 53
- Persistent link to this record (Permalink):
- http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2012-23735-001&site=ehost-live
- Cut and Paste:
- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2012-23735-001&site=ehost-live">The relationship of tobacco use with gambling problem severity and gambling treatment outcome.</A>
- Database:
- PsycINFO
The Relationship of Tobacco Use With Gambling Problem Severity and Gambling Treatment Outcome
By: Brian L. Odlaug
Department of Public Health, University of Copenhagen, Denmark;
Randy Stinchfield
Department of Psychiatry, University of Minnesota
Ezra Golberstein
School of Public Health, Division of Health Policy and Management, University of Minnesota
Jon E. Grant
Department of Psychiatry & Behavioral Neuroscience, University of Chicago
Acknowledgement:
Gambling is a popular and globally lucrative industry. Within the United States, $92.3 billion each year are wagered in casinos, racetracks, on sports, and lotteries (American Gaming Association, 2003). Concurrent with economic benefit, however, gambling is often associated with significant social problems, and research suggests that gambling may be much more costly than beneficial to a community. Significant societal costs, including credit card default, home foreclosures, delinquent bank loans, and medical costs are often associated with pathological gambling (PG; Walker & Barratt, 1999; Eadington, 2003; Petry, 2005).
Although many people gamble recreationally, the most recent epidemiological research indicates that between 0.42% to 0.6% of the United States population meets criteria for PG (Petry, Stinson, & Grant, 2005; Kessler et al., 2008), characterized by repetitive engagement in gambling that results in significant financial, occupational, and psychosocial dysfunction (National Opinion Research Center, 1999; American Psychiatric Association [APA], 2000). Although PG is commonly associated with other problematic behaviors, the most common co-occurring behavior is tobacco use. Among adults in the United States, 12.8% report nicotine dependence, and nicotine dependence is associated with higher rates of impulsivity (Upadhyaya, Deas, Brady, & Kruesi, 2002; Grant, Hasin, Chou, Stinson, & Dawson, 2004). Pathological gambling is associated with elevated rates of nicotine dependence (41% to 65%), and tobacco smoking in clinical samples of pathological gamblers has been associated with increased gambling severity and more frequent psychiatric problems (Smart & Ferris, 1996; Crockford & el-Guebaly, 1998; Stinchfield & Winters, 2001; Petry & Oncken, 2002; Grant & Potenza, 2005; McGrath & Barrett, 2009; Toneatto, Skinner, & Dragonetti, 2002; Grant, Desai, & Potenza, 2009; Grant, Kim, Odlaug, & Potenza, 2008; Shaffer et al., 1999; Potenza et al., 2004; Fagan et al., 2007). Although one small study (n = 35) found that tobacco use is associated with higher rates of relapse following cognitive–behavioral therapy (CBT; Grant, Donahue, Odlaug, & Kim, 2011), the impact of tobacco use on gambling treatment completion and outcomes is largely lacking from the literature. The identification of potential markers for the successful completion of gambling treatment would be valuable information for clinicians, public health officials, and patients, as the field strives to cost-effective and efficacious treatments. Given the emerging data indicating the possible negative effects of tobacco use on PG treatment outcome, an examination of daily tobacco use as a marker for gambling treatment completion and number of days gambled serves to expand the literature on behaviors influencing gambling treatment (Grant, Kim, Odlaug, & Potenza, 2008).
Gambling treatment involves a variety of programs, including inpatient, intensive outpatient, and individual and group CBT (Grant et al., 2009), and 12-step based support groups such as Gambler’s Anonymous (GA; Petry, 2003). Support groups such as GA, however, have reported poor overall outcomes, with studies indicating relapse rates of over 94% (Nathan, 2003; Slutske, 2006). Cognitive–behavioral approaches have generally yielded the most efficacious outcomes (Pallesen, Mitsem, Kvale, Johnsen, & Molde, 2005; Grant et al., 2009) although pharmacotherapeutic interventions have also demonstrated benefit for PG (Kim, 1998; Kim, Grant, Adson, & Shin, 2001; Grant, Kim, & Odlaug, 2007). A meta-analysis of 37 different treatment studies, however, found that individual or Group CBT was the most effective treatment in preventing relapse (Pallesen et al., 2005).
A variety of factors may influence treatment completion and abstinence from gambling. Comorbid addictions, including tobacco use, may be particularly important. Assessing the influence of daily tobacco use on treatment completion and gambling severity is prudent given the disproportionally high rates of nicotine use found in PG and the potential of co-occurring addictions to affect treatment outcomes (Grant & Potenza, 2005; Mooney, Odlaug, Kim, & Grant, 2011). In the area of alcohol treatment, a disorder with many clinical and possibly biological links to PG (Grant, Brewer, & Potenza, 2006), research suggests that co-occurring addictions may adversely affect treatment outcome (Winters & Kushner, 2003; Bobo, McIlvain, Lando, Walker, & Leed–Kelly, 1998). Studies in PG, however, have failed to examine the relationship of tobacco use on PG treatment completion and measures of gambling severity in large samples of individuals who seek an array of treatment options.
Based upon research indicating the potentially negative effects of nicotine use on treatment for gambling (Grant et al., 2011) or substance use disorders (Bobo et al., 1998), we hypothesize that those PG subjects reporting daily tobacco use at treatment entry will present with more severe gambling symptomology at treatment admission, have worse treatment completion outcomes, and gamble more days at a 6-month posttreatment follow-up.
Methods Sample
The sample consisted of 420 individuals who voluntarily sought treatment for PG at treatment facilities in Minnesota. The 11 treatment providers from which this sample was derived provided both group and individual therapy for PG. Eight treatment centers were outpatient provider organizations (i.e., more than one counselor at the program; n = 196), two were individual counselors providing outpatient treatment (n = 53), and one was an inpatient treatment center (n = 171). The Institutional Review Board of the University of Minnesota approved this study.
All eligible subjects met the criteria from the Diagnostic and Statistical Manual of Mental Disorders, 4th edition, (DSM-IV;APA, 2000) for PG over the 6 months preceding treatment intake. At intake, all subjects completed a questionnaire inquiring about the use of all tobacco products (cigarettes, cigars, pipe tobacco, chewing tobacco, snuff) and the frequency of use over the past 12 months. Respondents were categorized as using tobacco daily, 3–6 times per week, less than 1–3 days per month, or no tobacco use over 12 months prior to treatment entry. Subjects with incomplete tobacco use results (n = 5) and those subjects who reported less than daily use (n = 30), often referred to in the literature as “chippers,” were excluded from analysis due to the poor understanding and classification of this population in the addiction literature (Shiffman, 1989; Presson, Chassin, & Sherman, 2002; Morissette et al., 2008) and wide distribution of use noted in our sample (n = 15 reported using tobacco <1–3 days per month and n = 11 reported tobacco use 3–6 days per week). This left a total final sample of n = 385 (n = 244 with daily tobacco use and n = 141 with no tobacco use).
Furthermore, in order to objectively assess differences that may bias results of the main outcome measures, a post hoc assessment of subjects lost to follow-up between treatment ending and the 6-month follow-up assessment (n = 110) was conducted and compared with those retained for all follow-up assessments on demographic and clinical variables.
Assessments
Admission and discharge assessments were administered by treatment staff while follow-up assessments were conducted by research staff via paper–pencil questionnaires. Participants were also asked to complete a survey 6 months following discharge from treatment. Research staff mailed out follow-up assessments and if they were not returned within 4 weeks, research staff called participants in order to complete the follow-up assessment over the telephone. Subjects were compensated with a gift card ($10) for completing and returning the surveys.
The two dependent variables were number of days gambled in the previous 4 weeks and treatment completion. The number of days gambled in the past 4 weeks was measured at 6 months following discharge. Treatment completion was determined by treatment staff as either fulfilling their requirements (i.e., completed a number of treatment sessions, participated in homework assignments, etc.) or a mutual agreement that treatment had come to an end.
Gambling Behavior and Psychopathology
Subjects were assessed using the Gambling Treatment Outcome Monitoring System (GAMTOMS; Stinchfield, Winters, Botzet, Jerstad, & Breyer, 2007) a valid and reliable, multidimensional screening tool utilized to assess current DSM–IV pathological gambling criteria and gambling treatment outcomes. Subjects were asked at what age gambling became problematic, if they had previous treatments, including inpatient or outpatient treatment for gambling, and responses were coded in a dichotomous manner. In addition, the number of GA sessions attended in the month prior to admission for treatment was reported. Financial consequences from gambling, including current gambling debt, were assessed at treatment intake. The South Oaks Gambling Screen (SOGS; Lesieur & Blume, 1987), a well-validated, 20-item questionnaire was used to assess clinical gambling severity criteria. PG severity and psychosocial dysfunction was also assessed using the Addiction Severity Index (ASI; McLellan, Luborsky, O’Brien, & Woody, 1980) modified for PG (Petry, 2007). Gambling frequency was assessed retrospectively for the past 4 weeks prior to baseline using the Gambling Timeline Followback (G-TLFB; Weinstock, Whelan, & Meyers, 2004).
Mental heath status at baseline was assessed using the Behavior and Symptom Identification Scale (BASIS-32; Eisen, Dill & Grob, 1994). The BASIS-32 measures difficulties during the course of treatment reported by the subject over the preceding 7 days using a 5-point Likert scale ranging from “no difficulty” to “extreme difficulty.”
Frequency of alcohol, marijuana, and other drug use not used for medicinal purposes, was assessed at intake via a self-report questionnaire. Subjects were also asked if they had undergone treatment during their lifetime for alcohol and drugs, other addictions (such as compulsive buying or sexual behavior), or other mental health problems.
Data Analysis
In order to identify variables that may be important for inclusion in the regression model, tobacco users and nonusers were compared on demographic and clinical variables assessed at baseline (or admission) using two-tailed t-tests for continuous variables and chi-square and Fischer’s exact testing for dichotomous variables. Those variables found to show significant differences between tobacco users and nonusers were then included in the regression. A multivariate analysis of variance with repeated measures was computed in order to examine response to gambling treatment. Linear regression was used to examine the relationship between tobacco use, treatment completion, treatment outcome, and various demographic and clinical variables at baseline. Three models were computed. The first examines the unadjusted relationship between daily tobacco use and number of days gambled. In the second model, we added demographics (age, gender, race, education, marital status) to the regression. In the final model, we added clinical characteristics measured at baseline. Logistic regression was used to examine the relationship between demographics and clinical characteristics measured at baseline and treatment completion (0 = no; 1 = yes) using Nagelkerke R2 (Nagelkerke, 1991). All tests of hypotheses were performed using a two-sided significance level of α = .05. All data were analyzed using IBM SPSS Statistics, Version 18 software.
ResultsAnalysis of the three separate groups included in the sample (individual outpatient, multicounselor outpatient, and inpatient) revealed that the groups were fairly homogenous. The inpatient group was more likely to have received treatment for gambling in the past (p < .001) and have less education (p = .032), but there were no other statistically significant differences between groups.
A total of 385 individuals voluntarily admitted to one of 11 inpatient or outpatient gambling treatment centers in Minnesota were included in this sample. Of these individuals, 244 (63.4%) reported daily use of tobacco products and 141 (36.6%) reported not having used tobacco products over the 12 months preceding treatment intake. Demographic and clinical characteristic comparisons of daily tobacco users versus nonusers are presented in Table 1. Daily tobacco users were significantly younger (p = .019) and had a lower overall level of education (p < .007) than nontobacco users. Daily tobacco users also had more frequent and severe PG symptoms at baseline, as compared with nontobacco users. They had an earlier age of PG onset (p = .003), met more DSM–IV PG criteria (p < .001), endorsed more addiction severity criteria as indicated by the SOGS (p = .004), had undergone more lifetime previous treatments for gambling (p = .043), and were more likely to have sought treatment for an alcohol or drug use disorder (p < .001). Daily tobacco users also scored higher on dysfunction for the ASI and BASIS-32 composite scores.
Baseline Demographic and Clinical Characteristics of 385 Pathological Gamblers Grouped by Tobacco Use
We also assessed changes in tobacco use from treatment intake to the 6-month follow-up to ascertain what percentage of our treatment sample either started using tobacco or quit using tobacco over that time period. We found that only 4.1% of nontobacco users began using daily and 2.9% of users quit.
Effectiveness of Treatment Over Time
An analysis of the effectiveness of treatment over time revealed that all variables showed improvement over time but there were no significant interaction effects for tobacco use by time (see Table 2). The number of days gambled over the past preceding 28 days, as well as the number of financial concerns, PG criteria met on the DSM–IV, and number of SOGS items endorsed all significantly improved over time.
Comparison of Intake, Discharge, and 6-Month Follow-Up Assessment by Pretreatment Tobacco Use Status
Treatment Completion
The relationship between tobacco use and treatment completion was examined using logistic regression (see Table 3). Of the 385 subjects who entered treatment, 268 (69.6%) completed treatment. A total of 168 (69.1%) of the daily tobacco using and 100 (71.9%) nontobacco using cohort completed treatment, a nonsignificant between-groups difference (p = .564). Logistic regression of gambling treatment completion while controlling for significant demographic and clinical variables revealed few significant effects. Tobacco use was not related to treatment completion and the relative risk of daily tobacco use in relation to treatment completion was 1.14 (95% confidence interval [CI] = 0.72–1.81), accounting for less than 1% of the variance (R2 = .01). Model II and Model III explain 8% and 19% of the variation, respectively. The ASI composite score had the highest odds ratio (OR = 5.57; 95% CI: 1.02–30.40) and was statistically significant along with age (OR = 0.97; 95% CI: 0.94–0.99; p = .031) and being married (OR = 1.75; 95% CI: 1.01–3.02; p = .045). Composite scores on the BASIS-32 garnered the most significant association with treatment completion with higher scores on the BASIS-32 indicating significantly lower rates of treatment completion (OR = 0.47; 95% CI: 0.28–0.77; p = .003).
Logistic Regression Results for the Relationship Between Selected Demographic, Baseline Clinical Variables and Treatment Completion (n = 268)
Days Gambled at 6-Month Follow-Up
At 6-month follow-up, subjects reported gambling an average of 1.9 ± 4.0 days [range 0–28 days] over the 4 weeks preceding the follow-up assessment and a total of 157 (40.8%) reported complete abstinence from gambling. As such, the distribution for this sample is left-skewed. Daily tobacco users and nonusers did not, however, differ significantly in regard to number of days gambled (p = .306) at the 6-month follow-up.
Linear regression was used to assess the relative strength of the relationships between the independent variables and number of days gambled at 6 months. As shown in Model I on Table 4, tobacco use was not significantly associated with the number of days gambled at 6 months. In Model II, race was the only significant demographic variable that was significant. Respondents who reported that they were from a minority cultural group had a greater number of days gambled than did white subjects (B = 1.68; 95% CI = 0.04–3.32). None of the clinical variables at baseline, added at Model III, were associated with number of days gambled at 6 months. Moreover, only 8% of the variance in days gambled at the 6-month follow-up could be accounted for by these variables.
Linear Regression Results for the Relationship Between Selected Demographic, Baseline Clinical Variables, and Number of Days Gambled at 6-Month Follow-Up (n = 275)
Lost to Follow-Up
Inclusive of drop-outs, a total of 275 subjects reached their 6-month follow-up anniversary and completed a 6-month follow-up questionnaire, yielding a follow-up response rate of 71.4%. Subjects lost to follow-up were compared with those who completed the 6-month follow-up assessment (see Table 5). Subjects lost to follow-up were significantly younger (p = .012), as compared with those who completed this assessment. There were no other sociodemographic or clinical characteristics that predicted being lost to follow-up.
Lost to Follow-Up: Demographic and Clinical Characteristic Evaluation
DiscussionThis study sought to examine the impact of daily tobacco use as potential markers for gambling treatment success and completion. Consistent with our hypotheses and previous research (McGrath & Barratt, 2009), daily tobacco using gamblers in treatment presented with more severe symptoms at treatment intake. They had an earlier age of problem gambling onset, had more mental health problems as assessed with the BASIS-32, and were more likely to have had previous treatments for PG.
Daily tobacco use did not, however, significantly affect treatment completion or the number of days gambled at the 6-month posttreatment follow-up as we had hypothesized. One explanation could be that perhaps the gambling interventions used at the treatment centers in this analysis had a positive global effect on the impulsive nature of these individuals and thereby reduced gambling in concert with reductions in tobacco use. Follow-up assessment of tobacco use status changes, however, revealed that only 3% of nontobacco users started using and 4% of tobacco users stopped using, a nonsignificant between-groups difference. Therefore, within 6 months of discharge, there was no significant effect on tobacco use. Another explanation could be that although tobacco use contributes to worsening gambling symptoms, possibly through a complex biological, environmental, and genetic etiology (Grant & Potenza, 2005; Grant, Black, Stein, & Potenza, 2009), it does not interfere with the therapeutic effects of treatment on gambling. Finally, another possible explanation for any substantive differences between tobacco and nontobacco using gamblers is the substantial heterogeneity in tobacco-using gamblers. That is, some nicotine using gamblers may have their abstinence jeopardized by continuing to use tobacco whereas others will not. Although this study failed to illustrate a substantive effect of tobacco use on gambling treatment, previous research illustrates the deleterious effects of nicotine use on substance abuse treatment (Frosch, Shoptaw, Nahom, & Jarvik, 2000). In other studies, however, nicotine has been shown to enhance cognitive functioning in some individuals (Heishman, Kleykamp, & Singleton, 2010; Mooney et al., 2011) or have minimal negative impact on cognitive performance (Businelle, Apperson, Kendzor, Terlecki, & Copeland, 2008). Many gamblers who smoke indicate that smoking has a calming influence on their urges to gamble whereas others may find it urge-inducing. Because we did not assess urges to gamble in this sample, this would be a useful assessment for future research on this topic. Furthermore, neurocognitive assessment of nicotine dependent pathological gamblers would be helpful in discerning the biological differences between tobacco-using and nonusing gamblers.
Research on alcohol users in treatment has illustrated that those encouraged to quit smoking are more likely to maintain abstinence (Bobo et al., 1998). Despite the disproportionately high rate of tobacco use among pathological gamblers, research, to date, has yet to address the impact of continued tobacco use on gambling treatment outcome. In the realm of substance addiction research, Prochaska, Delucchi, and Hall (2004) performed a meta-analysis of 19 randomized controlled clinical trials of addiction treatment centers to examine the impact of smoking cessation on alcohol or drug abstinence. They found that those who underwent smoking cessation training concomitant with their addiction counseling were 25% more likely to maintain long-term drug and alcohol sobriety (Prochaska et al., 2004). Because smoking cessation was not captured in this sample of individuals, it may underscore the importance of incorporating such programs for those in gambling treatment and examining this variable in future treatment studies.
Limitations
Several limitations must be noted in this study. First, a number of subjects (28.6%) were lost to follow-up prior to the 6-month assessment and consequently, the high rate of abstinence observed may not be entirely accurate. We therefore assessed for demographic or clinical differences between treatment completers and those lost to follow-up (see Table 4) and found only age differences between groups. Since demographic and clinical variables were controlled for in the analysis, however, and no other significant differences were noted in Table 4, the rate of abstinence observed may be accurate. Second, the sample consisted of people who volunteered to participate in the study and they may be different from those who refused as well as nontreatment seeking gamblers in the general population. Third, a formal assessment of urges to gamble was not completed by the treatment centers. In addition to examining the time spent engaging in the actual addictive behavior, research has illustrated the importance of addressing a client’s urges to engage in addictive behavior (Pallanti, DeCaria, Grant, Urpe, & Hollander, 2005; Kim et al., 2001) as a means of predicting treatment response (Grant, Kim, Hollander, & Potenza, 2008). This study did not address clients’ urges or thoughts to gamble nor did it control for the use of psychotropic medication which may have been administered concomitantly with psychotherapy provided by each of the treatment centers. Future research should assess these variables and their subsequent effect on treatment completion and gambling severity. Finally, the study relied on client self-report and consequently the data may be biased by weaknesses of self-report, including inaccurate recall and intentional deception. The assessments utilized for this study, however, have demonstrated satisfactory evidence of reliability, validity, and classification accuracy, which helps to mitigate this limitation.
ConclusionsOur findings suggest that daily tobacco use does not negatively affect treatment completion or long-term outcome for treatment-seeking pathological gamblers. The prevalence of tobacco use in this cohort of pathological gamblers (63.4%) was consistent with rates noted in previous samples of pathological gamblers and significantly higher overall, as compared with rates of use among the general public of the United States (12.8%) and Minnesota (21%; Minnesota Department of Health, 2011). Given the deleterious effects of tobacco on physical and mental health, tobacco use should be addressed concurrently with gambling treatment as a means of improving the overall health of the individual.
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Submitted: December 14, 2011 Revised: July 13, 2012 Accepted: July 23, 2012
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Source: Psychology of Addictive Behaviors. Vol. 27. (3), Sep, 2013 pp. 696-704)
Accession Number: 2012-23735-001
Digital Object Identifier: 10.1037/a0029812
Record: 181- Title:
- The role of goals and alcohol behavior during the transition out of college.
- Authors:
- Radomski, Sharon A.. Department of Psychology, The State University of New York at Buffalo, Buffalo, NY, US, sharonra@buffalo.edu
Read, Jennifer P.. Department of Psychology, The State University of New York at Buffalo, Buffalo, NY, US
Bowker, Julie C.. Department of Psychology, The State University of New York at Buffalo, Buffalo, NY, US - Address:
- Radomski, Sharon A., Department of Psychology, The State University of New York at Buffalo, 207 Park Hall, Buffalo, NY, US, 14260, sharonra@buffalo.edu
- Source:
- Psychology of Addictive Behaviors, Vol 29(1), Mar, 2015. pp. 142-153.
- NLM Title Abbreviation:
- Psychol Addict Behav
- Page Count:
- 12
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- Bulletin of the Society of Psychologists in Addictive Behaviors; Bulletin of the Society of Psychologists in Substance Abuse
- Other Publishers:
- US : Educational Publishing Foundation
Society of Psychologists in Addictive Behaviors - ISSN:
- 0893-164X (Print)
1939-1501 (Electronic) - Language:
- English
- Keywords:
- alcohol, transitions, personal goals, emerging adulthood, college students
- Abstract:
- Personal goals are desired outcomes that guide behavior (Palfai, Ralston, & Wright, 2011), and are typically oriented around age-appropriate developmental tasks (e.g., college graduation, employment). Goals and their pursuit take on much salience during senior year of college as individuals prepare for the transition into adult roles. This also is a time during which naturalistic changes in alcohol consumption are occurring. These changes may impact the relationship between age-related goals and their attainment, thus compromising the likelihood of a successful transition out of college. The present study examined whether and how changes in drinking over senior year moderate the association between achievement goals and related developmental task attainment as students move toward transitioning out of college. Alcohol-involved college seniors (N = 437; 62.5% female) were assessed via web survey in September of their senior year and again 1 year later (T4). Results of multinomial logistic regression revealed that greater achievement goals were predictive of college graduation (vs. remaining a continuing undergraduate), but only for those whose drinking decreased during senior year. Among those graduated by T4 (n = 307), achievement goals predicted pursuing graduate education (vs. being unemployed), but only for students whose drinking increased during senior year. Thus, achievement goals are important predictors of goal attainment as students prepare to transition out of college, and these goals can interact with drinking in complex ways during this time. Findings suggest that interventions aimed at bolstering personal goals and reducing drinking during senior year may increase the likelihood of successful transitions out of the college environment. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Alcohol Drinking Patterns; *College Students; *Goals; *Life Changes; Emerging Adulthood
- Medical Subject Headings (MeSH):
- Achievement; Adult; Alcohol Drinking in College; Employment; Female; Goals; Humans; Male; Motivation; Students; Universities; Young Adult
- PsycINFO Classification:
- Classroom Dynamics & Student Adjustment & Attitudes (3560)
- Population:
- Human
Male
Female - Location:
- US
- Age Group:
- Adulthood (18 yrs & older)
Young Adulthood (18-29 yrs) - Tests & Measures:
- September Survey
Daily Drinking Questionnaire - Grant Sponsorship:
- Sponsor: National Institute on Drug Abuse
Grant Number: R01DA018993
Recipients: Read, Jennifer P. - Methodology:
- Empirical Study; Quantitative Study
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- First Posted: Feb 2, 2015; Accepted: Dec 10, 2014; Revised: Oct 20, 2014; First Submitted: Oct 31, 2013
- Release Date:
- 20150202
- Correction Date:
- 20151207
- Copyright:
- American Psychological Association. 2015
- Digital Object Identifier:
- http://dx.doi.org/10.1037/a0038775
- PMID:
- 25642583
- Accession Number:
- 2015-03939-001
- Number of Citations in Source:
- 64
- Persistent link to this record (Permalink):
- http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2015-03939-001&site=ehost-live
- Cut and Paste:
- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2015-03939-001&site=ehost-live">The role of goals and alcohol behavior during the transition out of college.</A>
- Database:
- PsycINFO
The Role of Goals and Alcohol Behavior During the Transition out of College
By: Sharon A. Radomski
Department of Psychology, The State University of New York at Buffalo;
Jennifer P. Read
Department of Psychology, The State University of New York at Buffalo
Julie C. Bowker
Department of Psychology, The State University of New York at Buffalo
Acknowledgement: This work was supported by a grant from the National Institute on Drug Abuse (R01DA018993) to Jennifer P. Read. We thank Drs. Jennifer E. Merrill, Sherry Farrow, Craig Colder, and Jeffrey Wardell, as well as Ashlyn Swartout, Jackie White, and the members of the UB Alcohol Research Lab for their many efforts to support data collection for this study.
According to Havighurst (1948), each phase of the lifespan consists of specific developmental tasks that must be completed before moving onto the next phase. Emerging adulthood (ages 18–25) is a distinct developmental period in the life span that has been identified as a critical turning point, and is associated with its own unique developmental tasks (Schulenberg, Bryant, & O’Malley, 2004; Tanner, 2006). Occurring at the juncture between adolescence and adulthood (Arnett, 2000), it is during the emerging adult years that individuals begin the process of coming into their own as independent adults. Developmental tasks of this period are those relevant to this transition, often rooted in domains such as individual identity formation and professional development. Failure to successfully attain these developmental tasks is associated with a host of deleterious outcomes, including problem behaviors (e.g., heavy alcohol use), emotional distress, and psychopathology (Aseltine & Gore, 2005; Bell & Lee, 2008; Havighurst, 1948; Nurmi & Salmela-Aro, 2002; Schulenberg, Sameroff, & Cicchetti, et al., 2004; Shulman, Kalnitzki, & Shahar, 2009), and is costly to society (Schneider & Yin, 2011). Educational and employment attainment are two particularly important developmental tasks for emerging adults in college, and successful mastery of these tasks is critical to a successful transition into independent adulthood, as each of these tasks is associated with significant economic and other longer-term benefits (Carnevale, Jayasundera, & Cheah, 2012; Wanberg, Zhu, & Van Hooft, 2010; Zaback, Carlson, & Crellin, 2012). As such, the identification of factors that either aid or impede progress toward these tasks is an important area of focus on both individual and societal levels.
Personal Goals and Developmental Task AttainmentA goal is the identification of and commitment to an end result that serves as a source of motivation for directed effort toward that objective. Goals are specific to each individual, yet generally coincide with the tasks of the developmental period (Nurmi, 1992; Palfai & Weafer, 2006; Salmela-Aro, Aunola, & Nurmi, 2007). For example, goals related to education, occupation, and relationships are most commonly reported by emerging adults (Nurmi, 1992; Palfai & Weafer, 2006). Motivational theories, theories that incorporate components of inner drive and their influence on behavior, argue that it is not only timing, but also the specific focus of the goals that gives them potency. That is, the more closely linked or specific a goal is to a target behavior, the greater the likelihood for goal attainment. This assertion is supported by data suggesting that goals in a particular domain predict task advancement in that same domain (Nurmi, Salmela-Aro, & Koivisto, 2002; Salmela-Aro et al., 2007). Importantly, evidence suggests that personal goals assume greater or lesser relevance at different times, especially during times leading up to transitional periods (Roberts, O’Donnell, & Robins, 2004; Salmela-Aro et al., 2007). As such, the senior year of college may be a time when developmental tasks and goals for attaining them may take on particular salience, as students prepare for the transition out of the college environment and into adult roles (Arnett, 2000; Murphy, Blustein, Bohlig, & Platt, 2010; Roisman, Masten, Coatsworth, & Tellegen, 2004).
It has been noted that the association between goals and their attainment is complex, and that individual difference variables may contextualize this association (Kanfer, 1987). Though the list of such contextual factors is myriad (e.g., Bowen, Chingos, & McPherson, 2009; Carnevale et al., 2012; Zimmer-Gembeck & Petherick, 2006), one in particular stands out as being of potential importance, alcohol consumption.
Alcohol, Goals, and Developmental Task AttainmentHigh rates of heavy alcohol use among college students have been well documented, and such patterns of use are likely to interfere with goal attainment (Hingson, Heeren, Winter, & Wechsler, 2005; Wechsler, Davenport, Dowdall, Moeykens, & Castillo, 1994). Research on emerging adults suggests that alcohol may play a role in goal attainment (Palfai & Weafer, 2006; Rhoades & Maggs, 2006; Wright & Palfai, 2012). Data show that alcohol, as well as other drug use, has a negative impact on academic performance in college (Pascarella et al., 2007) as well as on the likelihood of postgraduation unemployment (Arria et al., 2013). These studies demonstrate the lasting negative impact that college substance use can have on attainment of key developmental tasks.
Alcohol use peaks at ages 21 and 22, and then there is a subsequent decline in—or “maturing out” of—alcohol consumption (Chen & Kandel, 1995; Patrick & Schulenberg, 2011; Sher, Bartholow, & Nanda, 2001). This pattern is more marked in college students compared to their noncollege peers (White, Labouvie, & Papadaratsakis, 2005). Though the precise causes appear to be complex, this naturalistic maturing out of heavy drinking is generally believed to occur in the service of transitioning into more adult roles (Bachman et al., 2002; Bachman, Wadsworth, O’Malley, Johnston, & Schulenberg, 1997; Leonard & Rothbard, 1999; O’Malley, 2004–2005; Parra, Krull, Sher, & Jackson, 2007; Staff et al., 2010).
Indeed, alcohol use throughout the college years is dynamic and influenced by a variety of factors (Del Boca, Darkes, Greenbaum, & Goldman, 2004). Importantly, periods of transition in particular are associated with changes in alcohol use as individuals prepare for and adapt to evolving life circumstances (Jackson & Schulenberg, 2013; Sher & Gotham, 1999; White et al., 2006). These naturalistic drinking changes may have significant implications for developmental task attainment. Though many college students mature out of heavy drinking once they leave the campus environment, others do not, and thus are at risk for poor adaptation to adulthood, including problem drinking and associated negative health outcomes (Bennett, McCrady, Johnson, & Pandina, 1999; Chen & Kandel, 1995; Gotham, Sher, & Wood, 1997; Jackson, Sher, Gotham, & Wood, 2001; Sher & Gotham, 1999).
Historically, role transitions (e.g., employment, marriage, parenthood, etc.) have been believed to be the primary influence on the maturing out of heavy drinking (Yamaguchi & Kandel, 1985). Yet, emerging evidence has begun to suggest that the part that these role transitions play in the evolution of alcohol involvement is quite complex, resulting from a dynamic and interactive transaction between the individual and his or her environment (Littlefield, Sher, & Wood, 2010; Schulenberg, Sameroff, & Cicchetti, 2004; Vergés et al., 2012). For example, the transition into a new role may influence alcohol use as the individual may alter his or her typical drinking patterns in preparation for, or in response to, a new role. Additionally, changes in drinking patterns may influence transitions in that drinking behavior may be incompatible with a new role, or one may select their new role based on changes in drinking. Though research has examined changes in drinking at the transition into college and how such changes may interact with college functioning and development (e.g., Cleveland, Lanza, Ray, Turrisi, & Mallett, 2012; White et al., 2006), there has been minimal examination of other important transitions in young adulthood, such as the transition out of college. This is an important oversight, as these naturally occurring changes in drinking could alter the extent to which emerging adults are able to attain their developmental task goals as they transition into mature adulthood. For example, reductions in alcohol use over senior year may strengthen the relationship between goals and developmental task attainment while increases in alcohol use may weaken this association. Enhanced understanding of the factors that influence the transition out of college will shed light on potential target variables for further research and potentially inform opportunities for intervention.
In summary, emerging adults face a number of age-appropriate developmental tasks, primarily among which are educational and employment attainment. Despite the fact that task-related goals are associated with attainment of these tasks, goal theory suggests that there are individual factors (e.g., changes in drinking behavior) that also may qualify this association. Periods of transitions are a time of naturalistic change in drinking (Jackson & Schulenberg, 2013; Sher & Gotham, 1999; White et al., 2006), and as such, these changes are one factor that may exert such a qualifying influence, moderating the association between personal goals and developmental task attainment. Importantly, if changes in alcohol use prove to be relevant to goal attainment at this critical point of transition, senior year interventions aimed at promoting successful transitioning out of college and into the workforce should incorporate this information appropriately. Accordingly, the examination of this potential moderating effect was the aim of the present study.
The Present StudyThe present study examined changes in alcohol use over senior year as a moderator of the association between personal goals and the attainment of age-appropriate developmental tasks of emerging adulthood during the transition out of college (see Figure 1). We focused on two specific developmental tasks relevant to the transition out of the college environment, graduation from college at the end of senior year, and obtaining a postgraduation occupation. We expected that task-specific goals (i.e., achievement goals) would uniquely predict attainment of these two developmental tasks. As alcohol use has been shown to be associated with unique developmental characteristics of emerging adults pursing adult role transitions (Arnett, 2005; Palfai, 2006), we also sought to examine the moderating role of changes in alcohol use in attaining these developmental tasks.
Figure 1. Conceptual model of current study. Underlining signifies the predictor of interest. Italics signify reference groups.
HypothesesTwo primary a priori hypotheses pertaining to two developmental tasks (i.e., educational attainment and obtainment of postgraduation occupation) were postulated:
Hypothesis 1: Achievement goals in September of fourth year in college (T1) would predict (a) education attainment and (b) postgraduation occupation (work or educational) status measured the following September (T4), controlling for gender, posttraumatic stress disorder (PTSD) status, and other age-related goals.
Hypothesis 2: Changes in quantity of alcohol consumption leading up to the transition out of college would moderate this relationship. Secondary hypotheses related to moderation effects are as follows: (Hypothesis 2a) Decreased drinking over senior year would strengthen the positive effect of achievement goals on the likelihood of graduating from college at the end of senior year and being employed in a full-time paid position or in graduate school following undergraduate college graduation. (Hypothesis 2b) Increased drinking over senior year would weaken the positive effect of achievement goals on the likelihood of graduating from college at the end of senior year and being employed in a full-time paid position or in graduate school following undergraduate college graduation.
Method Procedure
All procedures were approved by the relevant university institutional review boards. Participants for the present study were drawn from a larger study investigating trauma and substance use. Details of recruitment procedures have been published previously (Read, Ouimette, White, Colder, & Farrow, 2011; Read et al., 2012), but will be briefly reviewed here. Matriculating students (ages 18–24) at two midsized public U.S. universities (one in the northeast and one in the southeast) were recruited in the summer prior to matriculation into college to participate in a longitudinal, Web-based study. A baseline screen was administered to determine eligibility for the prospective portion of the study. The response rate for this initial screening was 58%, consistent with other studies using similar methods (Larimer, Turner, Mallett, & Geisner, 2004; Neighbors, Geisner, & Lee, 2008). From this sample, those meeting traumatic stress criteria (i.e., at least one traumatic event and three or more symptoms of PTSD) and an equal number of randomly selected control participants (i.e., those who were below this threshold; total n = 692) were invited to participate in the longitudinal study. Of these, a total of 560 (81% response rate) completed a baseline survey in September of their first year of college. Data from three participants were excluded from analyses because evidence indicated haphazard responding to the survey items. Thus, the final longitudinal sample consisted of 557 participants. Data were collected in two cohorts 1 year apart beginning in 2006. Participants were assessed via web survey six times in the first year of college and four times each year (typically September, December, February, and April) for the remainder of the study. The first cohort completed 6 years of data collection, and the second cohort completed 5 years. Participants were compensated with a $35 gift card for each survey completion.
Participants: The Present Sample
The present study focused on the period during which students were preparing to transition out of college (see Figure 1). Accordingly, our first time point (T1) was the September survey in the fourth postmatriculation year. The last time point of the present study is the following year’s September survey in the fifth postmatriculation year (T4). Only those participants who reported being college seniors and having consumed alcohol at least once during the previous 30 days at the T1 time point were included in the present study (N = 437, 62.5% female). Participants were also surveyed in February (T2) and April (T3) of their senior year. A subset of this sample (n = 307) was used for the postgraduation occupation outcome variable analyses, for which only those who had reported being graduated at T4 were included (see Table 1 for sample and subsample demographic information). As the original sample was recruited for a strong representation of students with PTSD symptoms, 13.7% (n = 60) of the current sample reported symptoms that were consistent with a PTSD diagnosis at college matriculation. Thus, PTSD status at matriculation into college was modeled in the current analyses to control for the possible influence of PTSD symptoms on associations of interest.
Demographics of Sample and Subsample
Measures
Demographics
Participants reported on several demographic characteristics including gender, age, and ethnicity.
Alcohol use
Participants completed a measure of typical daily alcohol use based on the Daily Drinking Questionnaire (DDQ; Collins, Parks, & Marlatt, 1985). At every time point, participants reported the average number of standard drinks consumed on a typical Monday, Tuesday, Wednesday, and so forth in the past 30 days. Respondents were given standard drink conversion charts including a definition of a standard drink to enhance reporting accuracy. Summing the number of drinks across all 7 days provided a measure of typical weekly alcohol use over the past 30 days at each time point.
Goals
The measure of goals used for the present study is based on Palfai and Weafer’s (2006) work on goal constructs in emerging adulthood. Using Little’s (1983) personal projects analysis methodology, Palfai and Weafer identified four domains of goals (achievement, fitness/recreational, interpersonal, and intrapersonal) and two goal engagement constructs (goal meaning and goal efficacy). According to Palfai and Weafer (2006), goal meaning was interpreted as reflecting goal commitment and importance, and goal efficacy was interpreted as signifying goal “difficulty, likelihood of success, perceived progress, feel in control of project” (Palfai & Weafer, 2006, p.132).
For the present study, we relabeled the goal meaning and goal efficacy constructs as “goal commitment” and “goal progress,” to reflect two distinct dimensions of goal pursuit. We then asked participants about their goals in the four domains at T1. Thus, this measure included one question about goal commitment and one question about goal progress for each of the four goal domains: achievement (school, work), fitness/recreation, interpersonal (relationships with other people including romantic relationships), and intrapersonal (within yourself, e.g., emotions). For each goal domain, participants were asked how committed they were to their own goals in that domain and how much progress they had made toward their goals in that domain. For example, “How committed are you to your own goals in achievement?” and “How much progress have you made toward your own goals in achievement?” Goal Commitment response options ranged from 0 to 3 (0 = I don’t have any goals in this area, 1 = not committed, 2 = somewhat committed, and 3 = very committed). Goal Progress response options ranged from 1 to 4 (1 = no progress, 2 = very little progress, 3 = some progress, and 4 = a lot of progress).
Developmental tasks
Participants were asked to indicate their level of education attainment and postgraduation occupation status at T4. The choices for education attainment included college freshman, sophomore, junior, senior, not currently enrolled in college, graduated, fifth-year senior, sixth- or-later-year senior, and graduate student. Postgraduation occupation status was assessed with the item: “What is your current employment status?” Response options included employed in a full-time paid position, employed in a part-time paid position, employed in an unpaid position, have accepted a position but have not yet started this job, and unemployed.
Data Analytic Approach
In order to capture the transitional period for the current study, we examined the timeframe spanning from September of participants’ senior year in college (T1) to the following September (T4). This prospective design allowed for the investigation of the interaction of personal goals and changes in alcohol use in predicting a successful transition out of the college environment. See Figure 1 for time point information.
Alcohol variables
Data from the DDQ (Collins et al., 1985) in September (T1), February (T2), and April (T3) were used to assess relative changes in typical weekly alcohol use from fall to spring. Given that there was no mean change in drinking between T2 and T3, and because drinking at each of these time points was highly correlated (r = .87), T2 and T3 typical weekly drinking over the past 30 days was averaged across time points to create a more reliable measure of spring drinking. For participants missing drinking data either at T2 or T3, we used the available value for either T2 or T3 as the average spring drinking variable. This minimized the impact of missing data.
We were interested in how naturalistic changes in drinking during senior year might alter (moderate) the influence of goals on task attainment. To capture this change, we created a variable representing relative changes in typical weekly drinking from fall to spring, by regressing drinking at T3 (averaged with T2) on drinking at T1. We used the residuals as the change score variable. This approach was chosen over other approaches (e.g., controlling for T1 drinking, using a difference score) because it is base-free and, as such, provides a meaningful metric by which an individual’s behavior change may be understood relative to others’ in the group that is not confounded by drinking at baseline. During a transition such as this, when alcohol use is declining at the mean level, residual change scores allow us to examine increases or decreases in drinking relative to the group as a moderator of the effects of personal goals on developmental task attainment. As such, this measure of change was well suited to addressing our study’s objectives. This process was repeated for the creation of the change in alcohol use variable for the subsample of college graduates.
Goal variables
Goal attainment was conceptualized as a function of making a commitment to goals in a given area and then making progress toward those goals. This conceptualization was supported by the high correlation of goal commitment and goal progress within each goal domain in our study (ranging from .41 to .67). Accordingly, within-domain scores for these two (commitment, progress) related variables were multiplied and the product was used as an index of domain specific goal directedness.
Developmental task outcome variables
Developmental task outcome variables were categorized to portray attainment status for each developmental task, similar to procedures by Schulenberg, Bryant, et al. (2004). Education attainment was categorized to reflect graduation status in the following way: (Group 1; n = 172) graduated, no further education, (Group 2; n = 136) graduated, graduate student, (Group 3; n = 116) continuing undergraduate, (Group 4; n = 13) not enrolled/dropped out. Analyses for postgraduation occupation only included those participants who had successfully graduated from college (n = 307). The outcome variable postgraduation occupation had a range of response options: unemployed (n = 25), employed part-time paid (n = 47), accepted a job but not yet started (n = 4), employed full-time paid (n = 92), graduate student (n = 136), employed unpaid (n = 3). These response options were organized into three groups: (Group 1; n = 96) represented attainment the developmental task (i.e., full-time, paid employment or acceptance of employment offer but not yet started) and henceforth is referred to as employed, (Group 2; n = 75) represented stalled attainment in this developmental task as we operationalized it (i.e., those who were unemployed, working part-time, or working but unpaid) and henceforth is referred to as unemployed, and (Group 3; n = 136) those who reported being in graduate school. Those who reported being in graduate school were classified in a separate group due to the range of possible positions (e.g., internship, volunteer, part-time employed) that a graduate student may fulfill. The purpose of this categorization was to capture the commonalities of being a graduate student following college graduation in terms of the interaction of personal goals and changes in drinking over senior year.
Data management
Participants who stopped participating in the study or were missing data at relevant time points were removed (n = 26, 5.62%). Model variables (alcohol use and goals) were evaluated for skew, kurtosis, and outliers. Outliers were addressed by adjusting outlier scores consistent with guidelines from Tabachnick and Fidell (2000). All variables were centered in moderation analyses.
Analyses
Multinomial logistic regression was used to predict education attainment based on T1 (September, senior year) achievement goals, while controlling for the influence of other, related developmentally appropriate types of personal goals (i.e., interpersonal, intrapersonal, fitness/recreation). As gender differences in goals have been observed (see Nurmi, 1992; Rhoades & Maggs, 2006; Salmela-Aro et al., 2007), we controlled for gender in our analysis. PTSD status at college matriculation also was controlled.
We examined the moderating influence of during-year changes in drinking on this association (Achievement Goal × Change in Alcohol Use), using a residual alcohol use score to capture change in drinking in that last year. In this analysis, we were interested in comparing those who met the developmental task of graduating from college versus those who did not. Thus, continuing undergraduates served as the reference group.
Multinomial logistic regression also was used to predict postgraduation occupation status based on T1 (September, senior year) achievement goals, controlling for other types of personal goals, gender, and PTSD status. Those considered to have stalled in attaining the developmental task of full-time paid employment served as the reference group (unemployed) in these analyses.
Results Descriptive Statistics
Scores for each of the four domains of goal directedness (achievement, fitness/recreation, interpersonal, and intrapersonal) ranged from 0 to 12. Across these domains, goals related to achievement were rated most highly in this sample (M = 9.38, SD = 2.62). Typical weekly drinking, as measured by the DDQ, varied over senior year but reflected a decrease in alcohol use from fall to spring semester at the mean level. The residual change in typical weekly drinking from fall to spring, indicative of changes in drinking relative to the group, had a standard deviation of 5.83. Table 2 contains descriptive information for the total sample and the subsample of college graduates.
Descriptive Statistics for Sample and Subsample
Education Attainment: Goals Predicting Education Attainment 1 Year Later and Test of Changes in Alcohol Use Moderation Effects
Results revealed that higher levels of T1 achievement goals were associated with an increase in the odds of being graduated (B = .10, SE = .05, p = .03, OR = 1.11) and a graduate student (B = .19, SE = .05, p < .01, OR = 1.21), relative to being a continuing undergraduate. Additionally, higher levels of T1 interpersonal goals were associated with a decrease in the odds of being a graduate student (B = −.08, SE = .04, p = .04, OR = .92), relative to being a continuing undergraduate. No other goals were significant predictors of education attainment and the effects of goals on education attainment remained the same when drinking variables were included for moderation analyses. A test of these moderation effects revealed a significant Achievement Goal × Change in Alcohol Use interaction predicting the odds of being graduated versus a continuing undergraduate (B = −.02, SE = .01, p < .05, OR = .98). Although gender significantly predicted education attainment, PTSD status did not. Controlling for these two variables did not affect the moderation analysis results (see Table 4 for the effects of goals on education attainment and the omnibus test results).
The Effect of Goals on Education Attainment and Omnibus Moderation Analysis
In probing the significant interaction, we recentered the distribution of the alcohol use variable at one standard deviation above and below the mean to represent increased and decreased drinking over senior year, respectively (Aiken & West, 1991). As the standard deviation for the variables that represented change in alcohol use from fall to spring was 5.83, when probing moderation analyses, the regression coefficient for achievement goals represented the effect of achievement goals on education attainment for those who decreased or increased drinking, by 5.83 drinks on a typical week over senior year. Simple slopes analyses revealed that achievement goals did not predict being Graduated versus being a continuing undergraduate for those whose drinking increased over the spring semester of their senior year (B = −.02, SE = .08, p = .79, OR = .98). Yet, for those whose drinking decreased over this period, achievement goals were predictive of being graduated (B = .22, SE = .08, p < .01, OR = 1.25). See Figure 2 for a plot of the simple slopes.
Figure 2. Relationship between achievement goals and probability of graduating versus being a continuing undergraduate moderated by changes in alcohol use over senior year. The line representing decreased drinking is significant and the line representing increased drinking is not.
Postgraduation Occupation Status: Goals Predicting Postgraduation Occupation Status 1 Year Later and Test of Changes in Alcohol Use Moderation Effects
Results revealed that higher levels of T1 achievement goals were associated with an increase in the odds of being a graduate student (B = .13, SE = .06, p = .03, OR = 1.14), relative to being unemployed. No other goals significantly predicted postgraduation occupation and the effects of goals on postgraduation occupation remained the same when drinking variables were included for moderation analyses. The omnibus moderation test revealed a significant Achievement Goal × Change in Alcohol Use interaction for those who were in graduate school versus those who were unemployed (B = .04, SE = .01, p < .01, OR = 1.04). However, the hypothesized interaction for the employed versus unemployed contrast was not significant (B = .01, SE = .01, p = .65, OR = 1.01). Neither gender nor PTSD was a significant predictor of postgraduation occupation status, and controlling these variables did not affect the moderation analysis results (see Table 5 for omnibus test results).
The Effect of Goals on Postgraduation Occupation and Omnibus Moderation Analysis
In probes of the significant interaction, the regression coefficient for achievement goals represented the effect of achievement goals on postgraduation occupation status for those who decreased or increased drinking their typical weekly drinking from fall to spring by 5.77 (the standard deviation for the alcohol change variable) drinks. Simple slope analyses indicated that for those whose drinking increased from fall to spring, a one unit increase in achievement goals increased the odds of being in graduate school versus unemployed (B = .36, SE = .12, p < .01, OR = 1.44). That is, higher levels of achievement goals were associated with a higher likelihood of being in graduate school versus unemployed for those whose drinking increased over senior year. Level of achievement goals was not predictive of being in graduate school versus unemployed for those whose drinking decreased over senior year (B = −.07, SE = .09, p = .49, OR = .94). See Figure 3 for a plot of these simple slopes.
Figure 3. Relationship between achievement goals and probability of being in graduate school versus unemployed moderated by changes in alcohol use over senior year of college. The line representing increased drinking is significant and the line representing decreased drinking is not.
DiscussionTo our knowledge, this is the first examination of personal goals and developmental task attainment in the transition out of college, and the role that changes in alcohol use may play in this association. Findings shed light on these processes, and highlight several directions for future inquiry. These are discussed next.
Consistent with findings from Nurmi (1992) and Palfai and Weafer (2006), students in our sample reported the highest level of goal directedness for achievement goals. This supports the notion that goals are oriented around developmentally appropriate tasks. As hypothesized, higher levels of achievement goals were associated with a greater likelihood of being graduated or a graduate student versus a continuing undergraduate and changes in alcohol use moderated the association between achievement goals and the probability of attaining age-appropriate developmental tasks (i.e., education attainment and postgraduation occupation). On average, alcohol use decreased over the course of senior year. Yet there was individual variability, and we observed some interesting findings regarding the role that such changes in drinking during this time play in the association between goals and task attainment.
Education Attainment
Our findings revealed that naturally occurring changes in drinking during senior year moderated the relationship between achievement goals and graduation status. Those with higher levels of achievement goals at the beginning of their senior year were more likely to be graduated from college 1 year later than they were to be continuing on for a fifth undergraduate year if their drinking decreased over senior year. In contrast, the association between goals and attainment was not significantly different from zero for those whose drinking increased during the senior year. This finding suggests that increased drinking interfered with the relationship between achievement goals and education attainment in a way that renders these goals irrelevant, at least with respect to this academic outcome. This illustrates the importance of achievement goals in senior year and the potential negative impact of increases in drinking over senior year on education attainment.
In considering this interaction, it is worth taking note of the left side of this graph (see Figure 2) which indicates that those who had low goals and increased their drinking showed a greater likelihood of being graduated than those with low goals whose drinking decreased. As the literature suggests, achievement-related goals are the most reported goals for emerging adults and thus the majority (approximately 75%) of the current sample reported high levels of achievement goal directedness. As such, there was not much representation of those at the lower end of the achievement goal spectrum. Accordingly, we speculate that the most important conclusion to be drawn from this interaction is that changes in drinking during the senior year really matter for those participants who start that year with high levels of achievement goals. Although modest, this effect is meaningful given the large proportion of students who endorsed high achievement-related goals. Despite their high goals for achievement, students whose drinking increased were slower to reach an important developmental milestone, college graduation. Moreover, given the innumerable variables that may influence graduation rates, a 10% increase in the predicted probability of graduating for such individuals whose drinking decreases relative to others’ over senior year is a notable effect. At present, however, this conclusion remains speculative. Replication of this finding in future work will build confidence in this interpretation.
We did not find evidence of moderation by alcohol changes for the likelihood of being a graduate student versus a continuing undergraduate. This is intriguing, as beginning graduate study might be considered by many to reflect a transition to a more adult role. Thus, one might expect changes in alcohol consumption to play a similar role for those who become graduate students as they do for those who graduate college and go on to other occupational endeavors (the graduated group in the present study). Instead, we observed more similarity between graduate students who are furthering their education right out of college and continuing undergraduates, at least with respect to the interplay between goals and alcohol involvement. This may speak to the potent influence of the campus environment when it comes to drinking, as neither graduate students nor continuing undergraduates actually leave the college environment.
Postgraduation Occupation Status
Contrary to our hypotheses, the relationship between achievement goals and attaining employment was not moderated by changes in drinking over senior year. Additionally, consistent with findings from Salmela-Aro et al. (2007), achievement goals were not predictive of being employed at the follow-up assessment. Taken together, these null findings suggest that perhaps there are other factors that influence postgraduation occupation status that may be more important than goal directedness. For example, limited job availability may interfere with attaining employment more than achievement-related goal directedness promotes it. Consistent with this, participants in this study graduated in either 2010 or 2011, a time of high rates of unemployment (Stone, Van Horn, & Zukin, 2012).
Higher levels of achievement goals were associated with an increased likelihood of being in graduate school relative to unemployed. Also, results revealed a significant interaction in the postgraduation occupation status analysis for being in graduate school versus unemployed. To our surprise, follow-up analyses revealed that increased alcohol use over senior year strengthened the positive relationship between achievement goals and the likelihood of being in graduate school 1 year later relative to being unemployed. The association between achievement goals and the likelihood of being in graduate school relative to being unemployed was nonsignificant for those whose drinking decreased over senior year. One interpretation of this unexpected finding is that high achievement goals at the start of the senior year are powerful enough to lead to goal attainment, even in the context of increased drinking that same year, for those who attended graduate school; whereas, increases in drinking exerted a more deleterious effect for those low on achievement goals. Another possibility is that those with high achievement-oriented goals made sufficient advancements in that domain such that they felt freer to drink or pursue goals in other domains in which alcohol may be less interfering (e.g., interpersonal goals). With replication, perhaps with methodologies that include more frequent assessment points, future studies may be able to better delineate the nature of these moderated associations as they unfold over time.
We also considered the timing of some of these goals and developmental tasks in our interpretation of these findings. Broadly speaking, those who apply to graduate school are typically notified regarding the status of their acceptance prior to graduation (some time during the Spring semester), whereas college seniors seeking employment typically begin their search in late spring. Accordingly, one possibility is that those students whose drinking increased over senior year and had higher achievement goals were more likely to be in graduate school than unemployed at T4 because those who were accepted for graduate study may have increased their drinking as their goal already had been attained. However, group means (see Table 3) do not support this alternative explanation. Those who were in graduate school decreased their drinking from fall to spring more than those who were unemployed on average and in relative changes in drinking.
Descriptive Statistics for Outcome Variables (Means and Standard Deviations)
However, perhaps timing is relevant when considering the finding that achievement goals did not predict likelihood of being in graduate school versus unemployed for those whose drinking decreased. This finding also was unexpected. It is possible that within the group of those who were accepted into graduate school, there was individual variability in when participants were informed of their acceptance, which may have influenced spring drinking behavior. Thus, changes in drinking over senior year for those in graduate school may be related to timing of graduate school acceptance. It is also possible that unemployment rates have less to do with alcohol use interference and more to do with other variables (e.g., economy), and thus those in the unemployed group whose drinking reduced may have been making efforts to get a job, but were unable to succeed in this endeavor.
This study raises some interesting questions about goals, task attainment, and drinking in a group that seldom has been studied, graduate students. With 3.8 million people being in graduate school during the 2011–2012 academic year (Ginder & Kelly-Reid, 2013), it may be important to see how those in graduate school differ from those who leave the college environment and enter the workforce. Perhaps there are some fundamental differences for seniors that are preparing to go to graduate school versus those who are preparing to become employed. It may be that failure to moderate alcohol use at specific time points during senior year differentially interferes with successful attainment of occupational milestones such as getting into graduate school or gaining employment. It would be interesting to examine postgraduation lifestyle differences between those in graduate school and those in the workforce, and whether those differences impact changes in drinking during this transition for emerging adults. Such examinations may reveal that many aspects of the emerging adulthood experience, including the length of the developmental period, differs significantly for those who attend graduate school and those who do not.
Summary and Implications
These findings suggest that drinking patterns during the fourth year of college may be particularly impactful on education attainment and other developmentally appropriate goals as college seniors prepare to graduate and transition into the workforce or graduate school. For example, it seems that decreased drinking strengthened the positive effect of personal goals on attainment of some developmental milestones. It is likely that an intervention aimed at reducing the quantity of alcohol consumption during senior year would reduce goal-interfering behavior and perhaps foster more goal-directed behavior. Additionally, an intervention intended to bolster achievement-related goals in the fall of senior year may motivate students to curb their drinking in order to facilitate attainment of those goals. Indeed, research in the area of goal pursuit in college students suggests that goal-directedness can be increased with intervention, and that such increases are associated with a positive impact on developmental milestone attainment (Hoyert, O’Dell, & Hendrickson, 2012). In addition to the applied implications of this work, the current study provides support for examining those who plan to enroll in graduate school or those who are graduate students directly following college graduation as a distinct group of individuals when investigating emerging adults during this transitional period in a research context.
Limitations and Directions for Future Research
The current study had limitations. First is that our study design did not allow us to parse out drinking intentions or the precise temporal ordering of future plans (graduation, employment) and drinking behaviors. For example, it is possible that the students who did not graduate at the end of senior year knew that they were not going to be graduating, and so therefore continued in the college mode and drank heavily, rather than preparing for the transition out of college. Future work will benefit from a more comprehensive baseline assessment of goals, educational aspirations, and timing of goal attainment to better understand the role these may play in the goal-attainment–drinking relationship.
There likely are many aspects of attaining developmentally appropriate goals (e.g., GPA as a measurement of academic achievement, job satisfaction, postgraduation salary, number of promotions), and these are not reflected in our single-item assessment of developmental task attainment. Moreover, it is possible that the categorization approach that we used for developmental task variables was a relatively gross index of these outcomes, and thus did not optimally capture each participant’s goal attainment status (e.g., considered to be a continuing undergraduate due to change of major, considered to be unemployed if completing an unpaid internship postgraduation). Future research can build on the findings that we present here by providing richer and more nuanced assessment of developmental task attainment. In addition to a more detailed assessment of developmental task attainment, a more fine-grained assessment of personal goals would yield greater variability in achievement goal directedness, perhaps allowing for a more refined understanding of these processes across a broad spectrum of goal directedness. Moreover, this type of assessment of goals would allow for more specific information regarding personal goals in other domains.
In addition to limitations regarding the assessment of key variables in this study, it is important to recognize that there are innumerable variables that likely influence the relationship between personal goals and goal attainment and only one of these—alcohol use—was examined in the present study. Other factors include both individual (e.g., expectations, perceived norms, and self-efficacy of goal attainment) and societal (e.g., peer and parental influence) variables not considered in this current study. Though beyond the scope of this study, consideration of some of these other variables and the role that they play in goal attainment during the transition out of college will be an important next step.
Additional limitations are related to sample characteristics and the ability to generalize these findings. Countries may vary with respect to developmental tasks and ages at which individuals might aspire to attain these tasks. Even within the United States, there likely are regional differences in these developmental expectations, or even differences across educational institutions. The small number (n = 10) of participants who were no longer enrolled in college in our sample precluded meaningful examination goal processes for these students. Studies with more diverse samples and at multiple universities (inside and outside of the United States) will help clarify the role of goals and identify goal-interfering factors for those who do not graduate from college.
ConclusionAlthough there has been much focus on the transition into college, there is little research focusing on the transition out of college. Theory and data support that a successful transition out of college is critical for setting a successful trajectory into mature adulthood. Findings from the current study suggest that increased alcohol use during senior year attenuates the link between achievement-oriented goals and graduation from college. However, expected findings for the moderating role of changes in alcohol use over senior year on the goal-attainment association for occupational status after college graduation were not found. These results suggest that there are likely many other variables influencing the association between achievement-oriented goals and postgraduation employment. Future research efforts should focus on how the college environment can serve to enhance and utilize common goals of emerging adults to prepare students for their transition into adult roles. As personal goals were not predictive of postgraduate employment, part of this future research should be aimed at identifying factors which may impede goal attainment, and the development of appropriate interventions that will address these factors and facilitate a successful transition out of college, and into adulthood.
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Submitted: October 31, 2013 Revised: October 20, 2014 Accepted: December 10, 2014
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Source: Psychology of Addictive Behaviors. Vol. 29. (1), Mar, 2015 pp. 142-153)
Accession Number: 2015-03939-001
Digital Object Identifier: 10.1037/a0038775
Record: 182- Title:
- The role of substance use and emotion dysregulation in predicting risk for incapacitated sexual revictimization in women: Results of a prospective investigation.
- Authors:
- Messman-Moore, Terri L.. Department of Psychology, Miami University, Oxford, OH, US, MessmaT@miamioh.edu
Ward, Rose Marie, ORCID 0000-0001-8154-8163. Department of Kinesiology & Health, Miami University, Oxford, OH, US
Zerubavel, Noga. Department of Psychology, Miami University, Oxford, OH, US - Address:
- Messman-Moore, Terri L., Department of Psychology, Miami University, 90 North Patterson Avenue, Oxford, OH, US, 45056, MessmaT@miamioh.edu
- Source:
- Psychology of Addictive Behaviors, Vol 27(1), Mar, 2013. pp. 125-132.
- NLM Title Abbreviation:
- Psychol Addict Behav
- Page Count:
- 8
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- Bulletin of the Society of Psychologists in Addictive Behaviors; Bulletin of the Society of Psychologists in Substance Abuse
- Other Publishers:
- US : Educational Publishing Foundation
Society of Psychologists in Addictive Behaviors - ISSN:
- 0893-164X (Print)
1939-1501 (Electronic) - Language:
- English
- Keywords:
- alcohol consumption, college students, emotion regulation, incapacitated sexual assault, revictimization, substance use, emotion dysregulation, risk prediction, sexual victimization
- Abstract:
- Incapacitated sexual assault (ISA) is the most common form of sexual victimization experienced by college women. Although ISA victims are at risk for future assaults, few studies have examined mechanisms responsible for ISA revictimization besides heavy drinking. Using a prospective design, the present study examined whether emotion dysregulation, given its association with interpersonal trauma and substance use, increases risk for revictimization among women with a history of ISA above and beyond the effects of substance use. Female college students (n = 229) completed a baseline assessment followed by assessment of incapacitated sexual assault over a 9-week follow-up period. Approximately 36% of participants reported a history of ISA, and 73% of those victimized during the study had a history of ISA. Revictimized women reported higher levels of alcohol-related problems, greater marijuana use, greater emotion dysregulation, and higher levels of fear and guilt prior to experiencing ISA during the study; however, they did not consume more alcohol than previously victimized women. In a logistic regression analysis, guilt, emotion dysregulation, and marijuana use accurately classified 78.9% of ISA revictimized women. Women with a history of ISA are at substantial risk for ISA revictimization. Findings suggest that even very small increases in emotion dysregulation, particularly in impulsivity, as well as marijuana use, impact revictimization risk substantially. Efficacy of interventions to reduce ISA revictimization may be improved if emotion dysregulation is addressed. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Alcohol Drinking Patterns; *Emotional Regulation; *Risk Factors; *Sex Offenses; *Victimization; College Students; Drug Usage
- Medical Subject Headings (MeSH):
- Adolescent; Alcoholic Intoxication; Crime Victims; Emotions; Female; Follow-Up Studies; Humans; Prospective Studies; Rape; Risk; Sex Offenses; Students; Substance-Related Disorders; Universities; Women; Young Adult
- PsycINFO Classification:
- Criminal Behavior & Juvenile Delinquency (3236)
- Population:
- Human
Female - Location:
- US
- Age Group:
- Adulthood (18 yrs & older)
Young Adulthood (18-29 yrs) - Tests & Measures:
- Cognitive Appraisal of Risky Events Questionnaire
Alcohol Use Disorders Identification Test DOI: 10.1037/t01528-000
Childhood Trauma Questionnaire DOI: 10.1037/t02080-000
Difficulties in Emotion Regulation Scale DOI: 10.1037/t01029-000
Sexual Experiences Survey DOI: 10.1037/t02590-000
Positive and Negative Affect Schedule DOI: 10.1037/t03592-000 - Grant Sponsorship:
- Sponsor: Alcoholic Beverage Medical Research Foundation
Recipients: Messman-Moore, Terri L. - Methodology:
- Empirical Study; Longitudinal Study; Prospective Study; Quantitative Study
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- First Posted: Dec 31, 2012; Accepted: Oct 16, 2012; Revised: Aug 17, 2012; First Submitted: Sep 23, 2011
- Release Date:
- 20121231
- Correction Date:
- 20150420
- Copyright:
- American Psychological Association. 2012
- Digital Object Identifier:
- http://dx.doi.org/10.1037/a0031073
- PMID:
- 23276308
- Accession Number:
- 2012-34907-001
- Number of Citations in Source:
- 34
- Persistent link to this record (Permalink):
- http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2012-34907-001&site=ehost-live
- Cut and Paste:
- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2012-34907-001&site=ehost-live">The role of substance use and emotion dysregulation in predicting risk for incapacitated sexual revictimization in women: Results of a prospective investigation.</A>
- Database:
- PsycINFO
The Role of Substance Use and Emotion Dysregulation in Predicting Risk for Incapacitated Sexual Revictimization in Women: Results of a Prospective Investigation
By: Terri L. Messman-Moore
Department of Psychology, Miami University;
Rose Marie Ward
Department of Kinesiology & Health, Miami University
Noga Zerubavel
Department of Psychology, Miami University
Acknowledgement: This research was funded by a grant to Terri L. Messman-Moore from the Alcoholic Beverage Medical Research Foundation (ABMRF). ABMRF had no role in the study design, collection, analysis or interpretation of the data, writing the manuscript, or the decision to submit the article for publication. We acknowledge the support of numerous research assistants, whose hard work and dedication made this work possible. Finally, we to express our gratitude to the women who participated and appreciation for their commitment to this prospective study and their willingness to share information about potentially distressing unwanted sexual experiences.
Recent research indicates that alcohol-involved or incapacitated sexual assault (ISA) is widespread. Compared to forcible assault, sexual assault following alcohol or drug consumption is far more common among female college students (Lawyer, Resnick, Bakanic, Burkett, & Kilpatrick, 2010), an experience reported by almost three-quarters of rape victims surveyed in a large, multisite national study (Mohler-Kuo, Dowdall, Koss, & Wechsler, 2004). After entering college, women's risk for ISA increases whereas risk for forcible sexual assault decreases (Krebs, Lindquist, Warner, Fisher, & Martin, 2009). Not surprisingly, women's alcohol use appears to be associated only with ISA and not forcible assault (Testa, Livingston, VanZile-Tamsen, & Frome, 2003). Women's marijuana and other drug use are also associated with ISA, although to a lesser extent than alcohol use (Lawyer et al., 2010). In the only prospective study to examine ISA, monthly binge drinkers were more likely to report incapacitated versus forcible victimization (McCauley, Calhoun, & Gidycz, 2010). Such findings suggest that reducing women's alcohol consumption should decrease risk for ISA (Testa & Livingston, 2009). However, the only relevant study that focused on intervention indicates that reducing women's drinking does not directly diminish revictimization risk among women with previous histories of ISA (Clinton-Sherrod, Morgan-Lopez, Brown, McMillen, & Cowell, 2011). Thus, although women's alcohol use is associated with increased risk of ISA, surprisingly reduction in use is not associated with decreased rates of later revictimization. Although ISA victims are at risk for future assaults, few studies have examined mechanisms responsible for ISA revictimization beyond heavy drinking. More research is needed to understand factors that impact vulnerability for ISA revictimization. Using a prospective design, the present study examined whether emotion dysregulation, given its association with interpersonal trauma and substance use, increases risk for revictimization among women with a history of ISA, above and beyond the impact of alcohol and drug use.
Some women use alcohol to cope with psychological distress arising from sexual assault, such as fear, hostility, or guilt (Miranda, Meyerson, Long, Marx, & Simpson, 2002). For individuals who lack effective emotion regulation skills, negative affect may be experienced as overwhelming, leading to maladaptive behaviors (e.g., substance use) to regulate distress. Although emotion dysregulation is related to negative affect, it is a distinct construct that impacts both emotional experience and behavioral responses (Bradley et al., 2011). Indeed, emotion dysregulation is thought to underlie risky behavior linked to revictimization, including risky sexual behavior and substance use (Filipas & Ullman, 2006). Nonetheless, few studies have explicitly examined emotion dysregulation and revictimization. Among female prison inmates, revictimized women report greater emotion dysregulation, including greater emotional nonacceptance, lack of emotional clarity and awareness, and a greater tendency to engage in impulsive behavior when distressed (Walsh, DiLillo, & Scalora, 2011). Emotion dysregulation is also linked with risky sexual behavior in college women, which predicts revictimization (Messman-Moore, Walsh, & DiLillo, 2010).
Particular components of emotion dysregulation may impact revictimization vulnerability more than others. Lack of emotional awareness or clarity may impair risk perception (e.g., interpretation of fear cues), and thus increase risk for ISA revictimization. Engaging in impulsive behavior in response to negative affect is associated with problematic alcohol use and negative alcohol-related consequences (Magid & Colder, 2007), although it has not yet been examined as a risk factor for revictimization. Elevations in impulsivity may increase the likelihood of entering potentially risky situations, or using alcohol or drugs with unfamiliar companions, which may increase revictimization risk. Because earlier studies examining emotion dysregulation and sexual revictimization used retrospective, cross-sectional designs, it is still unknown whether emotion dysregulation problems actually precede revictimization and whether difficulties in emotion regulation are initially greater among individuals who later become revictimized.
PurposeIdentifying factors that distinguish women who are revictimized from those who are not is essential to the development of effective interventions to prevent revictimization. Moreover, earlier studies failed to examine ISA separately, potentially obscuring the significant impact of victim substance use on risk for ISA (Testa, 2004). Thus, a primary aim of the study is to clarify the role of alcohol use in ISA-related revictimization. It was hypothesized that women's heavy alcohol consumption and alcohol-related problems, as well as drug use, would predict incapacitated sexual revictimization. Another important aim of the study was to test a hypothesis that emotion dysregulation predicts incapacitated sexual revictimization after considering the impact of alcohol use, drug use, and negative affect. Given the predictive nature of the research questions, it was critical to use a prospective design to determine whether substance use and emotion dysregulation preceded ISA. Because of the high prevalence of ISA in college populations (Lawyer et al., 2010), and college women's increased risk for ISA (Krebs et al., 2009), the current study focused on risk factors for ISA revictimization among college women.
Method Participants
Participants were 229 female undergraduate students enrolled at a midsized university in the Midwest. Participants' average age was 19.74 (SD = 1.36, range 18–23). Slightly less than one third (29.3%) were in the first year of college, 14.1% were sophomores, 27.2% were juniors, and 29.3% were seniors. The majority of participants were Caucasian (92.4%) and middle- to upper-class (55.6% reported past year family income of $100K or more). The majority were sexually active (84.7%) and unmarried (95.7%); 48.2% reported being in an exclusive dating relationship.
Measures
ISA
The revised Sexual Experiences Survey (SES; Koss et al., 2007) was administered at Time 1 (the first week of the study) to ascertain ISA (and forcible) victimization experiences from age 14 until entrance into the study. The revised SES was readministered weekly (Times 2–10) to identify prospective ISA victims. For each week, eight different questions assessed unwanted sexual experiences related to respondent alcohol and drug consumption for each of three different types of unwanted sexual contact (kissing/fondling, oral-genital contact, and intercourse). This resulted in 24 variables comprising ISA at each time point. Affirmative responses across the 9-week period were aggregated such that prospective ISA was coded as a dichotomous variable (yes = 1, no = 0).
Child sexual abuse
The Childhood Trauma Questionnaire (CTQ; Bernstein & Fink, 1998) is a 28-item inventory that was administered at Time 1 to determine whether participants had experienced child sexual abuse (CSA) prior to age 14. Participants' scores were classified into categories of abuse severity based on published recommendations; only those individuals reporting moderate to extreme CSA were considered abused. The CTQ has demonstrated reliability and validity (Bernstein & Fink, 1998); internal consistency coefficient for the CSA subscale was .85.
Alcohol use, alcohol-related problems, and drug use
Alcohol consumption was assessed at Time 1 using measures consistent with national studies of college student alcohol use (e.g., CAS; Wechsler & Nelson, 2008). Participants were asked if they had ever consumed alcohol, how old they were the first time they consumed alcohol, their highest number of drinks consumed in a single drinking occasion in the last 30 days, and number of times in the last month that they had consumed four or more drinks in a row on one occasion. In addition, the Alcohol Use Disorders Identification Test (AUDIT; Babor, Higgins-Biddle, Saunders, & Moteiro, 2001) was used to assess alcohol consumption (Questions 1–3; AUDIT-C), as well as symptoms of dependence and alcohol-related problems (Questions 4–10) in the past year. The AUDIT-C shows high levels of sensitivity and specificity in screening for alcohol dependence, any alcohol use disorder (AUD), and risky drinking. Among college students, a cutoff of ≥5 points on the AUDIT-C yields the highest values of sensitivity and specificity for alcohol dependence or risky drinking (Dawson, Grant, Stinson, & Zhou, 2005). In the current sample, internal consistency reliability was .80 for the AUDIT-C and .75 for the dependence/problems subscale. Drug use was assessed with seven questions from the Frequency of Involvement subscale of the Cognitive Appraisal of Risky Events Questionnaire—Revised (CARE–R; Fromme, Katz, & Rivet, 1997; Katz, Fromme, & D'Amico, 2000) measuring frequency of drug use in the previous 6 months.
Negative affect
Negative affect—fear, sadness, hostility, and guilt—was assessed at Time 1 with subscales of the Positive and Negative Affect Schedule (PANAS-X; Bagozzi, 1993). Each subscale score was computed by summing 6 items, which used a 5-point Likert scale from 1 (very slightly or not at all) to 5 (extremely). Internal consistency Cronbach's alpha for the negative affect scales ranged from .79 (hostility) to .89 (sadness) in the current sample. Participants reported on past week emotion, although there is evidence that PANAS scales assess trait affect, which is stable and predictive across extended periods (Watson & Walker, 1996).
Emotion dysregulation
The Difficulties in Emotion Regulation Scale (DERS; Gratz & Roemer, 2004) assessed emotion dysregulation at Time 1. The DERS is comprised of 36 items that are summed for a DERS total score or summed into six subscales scores. Higher scores indicate greater emotional dysregulation. All items are scored on a Likert scale from 1 (almost never) to 5 (almost always) and indicate how often the participant experienced the statement. Six subscale scores suggest a lack of emotional awareness characterized by the inability to attend to emotions (Awareness); lack of emotional clarity and personal understanding of emotions (Clarity); failure to accept feeling distressed (Nonacceptance); problems controlling behavior when experiencing negative affect (Impulse); limited access to effective emotion regulation strategies when distressed (Strategies); and difficulties accomplishing tasks when distressed (Goals). Internal consistencies (Cronbach's alpha) for the six scales ranged from .78 (Clarity) to .91 (Goals) in the current sample. Among untreated individuals, DERS scores are relatively stable over 14 weeks (Gratz & Tull, 2010).
Procedure
The committee for human subjects in research approved all procedures. Participants were recruited through fliers posted on campus and advertisements in the student newspaper. The study spanned 10 weeks, with data collection at 1-week intervals. The sample was comprised of four cohorts, each starting the study 1 week apart, in order to stagger the data collection. At the beginning of the study (Time 1), participants completed paper-and-pencil surveys. Women then completed online (Internet-based) surveys weekly (Times 2–9) and returned to complete paper-and-pencil surveys at Time 10. Participant responses were linked via a unique identification number. All in-person data collection took place in group sessions staffed by female research assistants. Participants received an honorarium of $25 for the first session, and were eligible to earn up to $75 for Sessions 2–10 (prorated based upon number of surveys completed, up to $50, with an additional $25 for completion of all 10 surveys). Following participation each week, women received information regarding counseling and support services, as well as researcher contact information.
Data Analysis
All analyses were conducted with SPSS 18. Chi-square, analysis of variance (ANOVA), and multivariate analysis of variance (MANOVA) were conducted to identify relevant ISA revictimization risk factors. These initial analyses included the entire sample to examine whether differences existed among revictimized women, previously victimized but not revictimized women, and nonvictims on study variables. To predict revictimization, logistic regression analyses were conducted with the subset of women with a prior history of ISA. All variables for which revictimized women differed from previously victimized women in the previous analyses were included as predictors in the two logistic regression analyses (i.e., with emotion dysregulation broadly defined and subtypes of emotion dysregulation).
Results Study Retention and Handling of Missing Data
Participants in the study were drawn from a larger sample of 424 female undergraduate students. Participants completed an average of 9.12 (SD = 1.61) weekly sessions; 85.8% (n = 364) of participants completed all 10 weekly sessions or only missed one session. Missing data on ISA victimization was minimal; 93.9% of the sample was missing less than 5% (96.7% missing less than 10%). Women with missing data who reported ISA were classified as ISA victims; women with missing data who did not indicate ISA were unable to be unequivocally classified as nonvictims and were excluded from analyses. There were no significant differences between the subset of women who provided complete victimization data (n = 255) and those who did not (n = 169) on study variables (ps > .05). Missing data on other variables was random and minimal (less than 5% missing on any remaining variables). To maximize sample size, analyses were conducted casewise. Distributions of predictor variables were normal and without significant outliers.
Prevalence of ISA and Revictimization
Incapacitated sexual revictimization occurred if a woman with a prior history of ISA (regardless of history of CSA or forcible sexual assault) experienced another episode of ISA during the 9-week follow-up period. Of the 255 individuals with complete victimization data (see Figure 1), 7.5% (n = 19) reported CSA or forcible sexual assault after age 14 but did not report incapacitated assault. Given the focus on risk for ISA revictimization, these women were excluded. Among the remaining 236 participants, 92 women reported a history of ISA: 8.1% (n = 19) reported prospective revictimization (ISA prior to as well as during the 10-week study) and 30.9% (n = 73) reported prior ISA without prospective victimization. In addition, 2.9% (n = 7) reported ISA during the 10-week study (i.e., prospective victimization) but had no prior ISA victimization. Given the focus on factors that predict ISA revictimization, these women were excluded from subsequent analyses, resulting in a final sample of 229 women.
Figure 1. Participant flowchart. Shaded blocks indicate participants excluded from analyses. ISA = incapacitated sexual assault; CSA = child sexual abuse.
Among those who experienced ISA revictimization, 78.9% (n = 15) of cases involved only alcohol, 10.5% (n = 2) involved alcohol and other substances, and 10.5% (n = 2) involved other substances but not alcohol. Prior ISA was significantly associated with prospective ISA, χ2(1) = 17.19, p < .001, Φ = .26; 20.7% of women with a history of ISA reported prospective ISA and 73.1% of women who reported ISA during the study (i.e., were prospectively victimized) had a history of ISA. There was no association between CSA and prior ISA, χ2(1) = 0.64, p = .43, nor between CSA and ISA during the study, χ2(1) = 0.62, p = .43. There were no differences in rates of ISA or revictimization among the cohorts (ps > .48).
Alcohol and Other Substance Use
The average peak drinking occasion in the previous month was 6.13 (SD = 4.14, range 0–20), and participants reported consuming four or more drinks on one occasion (heavy episodic drinking) almost twice in the previous month (M = 1.84, SD = 1.41). The average AUDIT-C score was 5.01 (SD = 5.28), just exceeding the cut score of 5 which identifies college students with any AUD, alcohol dependence, and risky drinking practices as defined by the National Institute on Alcohol Abuse and Alcoholism (NIAAA) (Dawson et al., 2005). In the previous 6 months, 9.8% of the sample reported using marijuana at least once. Drug use other than marijuana was very infrequent; participants who reported using drugs other than marijuana also reported using marijuana, therefore analyses including drug use focused on marijuana use only.
Differences Between Revictimized and Nonrevictimized Individuals
Demographics
Revictimized individuals did not differ from previously victimized or nonvictimized individuals in terms of age, race, or cohort.
Substance use and related problems
A MANOVA was conducted to determine whether, prior to experiencing ISA revictimization during the study, participants differed in terms of alcohol consumption and alcohol-related problems. The multivariate test was significant, Wilks' Λ = .75, F(8, 402) = 7.88, p < .001. All follow-up ANOVA tests were significant (p < .001; see Table 1). Women with a history of ISA (with or without revictimization) reported higher levels of alcohol consumption and alcohol-related problems than nonvictimized women. Revictimized women did not report greater consumption than previously victimized women but did report significantly higher levels of alcohol-related problems. A chi-square analysis also indicated that revictimized women were more likely to exceed the AUDIT-C cut-score for probable alcohol dependence (Dawson et al., 2005), and nonvictims were less likely than expected to exceed this cut-off. Revictimized women were 3.68 times more likely to report marijuana use than expected; the other groups' actual use did not differ from expected values.
Differences Between Revictimized and Nonrevictimized Groups on Alcohol Use Variables
Emotion dysregulation and negative affect
Two analyses (ANOVA and MANOVA) were conducted to determine whether participants differed in terms of emotion dysregulation and negative affect prior to experiencing ISA revictimization. An ANOVA indicated differences on the DERS total score between the revictimized group and the previously victimized and nonvictimized group, F(2, 217) = 7.46, p = .001 (see Table 2). The MANOVA examined differences among groups for the DERS subscales and negative affect. The multivariate test was significant, Wilks' Λ = .84, F(20, 406) = 1.87, p < .05. Follow-up univariate ANOVA tests were significant for impulse, clarity, goals, fear, and guilt (see Table 2). Post hoc comparisons indicated revictimized women reported greater difficulties inhibiting impulsive behavior, greater fear, and greater guilt compared to previously victimized and nonvictimized women. There were no significant group differences for emotional clarity or goals, or between the previously victimized groups for strategies or hostility. Effect sizes for differences between revictimized and previously victimized women were in the medium range (Cohen's ds ≥ .5) for negative affect and in the large range (Cohen's ds ≥ .8) for DERS total score and difficulties in impulse control.
Differences Between Groups on Emotion Dysregulation and Negative Affect
Logistic Regression Analyses Predicting Prospective Revictimization
To identify risk factors that distinguish women at risk for ISA revictimization, all women with a history of ISA (n = 92) were included in hierarchical logistic regression analyses to examine predictors of prospective ISA (i.e., ISA revictimization). For all analyses, variables were selected for entry into the equation based on Wald forward estimation in three steps: (a) AUDIT scores for alcohol-related problems and marijuana use, (b) negative affect (fear and guilt), and (c) emotion dysregulation (total score, impulsivity subscale).
The first analysis (Model 1) was conducted to determine whether emotion dysregulation (assessed by DERS total score) predicted risk for revictimization after controlling for alcohol problems, marijuana use, and negative affect (see Table 3). Each block was significant; in the third block, marijuana use and DERS total predicted revictimization (guilt was not significant). The nonsignificant Hosmer-Lemeshow test for Block 3 indicated good model fit, χ2(8) = 8.42, p = .39, −2 log likelihood = 68.42, Nagelkerke R2 = .30. The predictors accurately classified 73.2% of revictimized women and 70.6% of nonrevictimized women, with an overall classification rate of 72.7%. In the second analysis, which included only one aspect of emotion dysregulation, impulsivity, each block was significant. In the third block, revictimization was predicted by marijuana use, guilt, and impulsivity. The nonsignificant Hosmer-Lemeshow test for Block 3 indicated good model fit, χ2(8) = 8.25, p = .41, −2 log likelihood = 69.93, Nagelkerke R2 = .35. The predictors accurately classified 78.9% of revictimized women and 76.1% of nonrevictimized women, with an overall classification rate of 76.7%.
Emotion Dysregulation Predicting the Probability of Incapacitated Sexual Assault Revictimization
DiscussionDespite earlier evidence linking ISA victimization with heavy episodic drinking (McCauley et al., 2010), in the current study alcohol consumption did not distinguish women at risk for ISA revictimization when considered with other factors. Revictimized women were more likely than expected to exceed the threshold for heavy consumption indicative of alcohol dependence (Dawson et al., 2005), and revictimized women did report more alcohol-related problems and dependence symptoms than did previously victimized women. However, neither of these alcohol-related variables predicted revictimization risk when considered with marijuana use, negative affect, and emotion dysregulation. ISA victimized women (both those who were and were not revictimized) reported very high levels of heavy episodic drinking, with AUDIT-C scores in the clinical range. Thus, the ubiquitous nature of heavy drinking among women with a history of ISA may have prevented consumption variables from emerging as significant predictors of revictimization. Such findings are consistent with a recent study indicating that reductions in women's drinking did not decrease subsequent revictimization risk (Clinton-Sherrod et al., 2011). The present findings, in conjunction with earlier studies, suggest that heavy drinking or problematic consumption may be a risk factor for ISA in general, rather than ISA revictimization. In contrast to alcohol use, marijuana use was a significant risk factor for ISA revictimization. Drug use, including marijuana use, has been associated with revictimization in earlier investigations (Casey & Nurius, 2005; Messman-Moore, Ward, & Brown, 2009). It is unknown how marijuana use may increase risk, as women's drug use is not often associated with sexual assault in event-based studies (Ullman, Karabatsos, & Koss, 1999). Perhaps marijuana use is a marker of deviant behavior or a deviant peer group, which may increase the likelihood of encountering sexually aggressive men. Additional studies with longer follow-up periods are needed to examine alcohol use and to determine how marijuana and other drugs may contribute to ISA revictimization.
Emotion dysregulation predicted the likelihood of incapacitated sexual revictimization after accounting for the impact of alcohol-related problems, marijuana use, and negative affect. Impaired emotion regulation likely interferes with a woman's ability to appraise or cope with dangerous situations, thereby impeding appropriate self-protective or escape responses when at imminent risk for revictimization (Dietrich, 2007). Although a global construct of emotion dysregulation predicted ISA revictimization, difficulties inhibiting impulsive behavior when distressed appear to be especially problematic. Individuals who have difficulty controlling impulsive behavior may be at greater risk for ISA revictimization because they do not pause to identify risk or because they lack the capacity to effectively negotiate risky situations. Impulsive women may be more likely to enter high-risk situations or may be more likely to engage in risky drinking practices (e.g., drinking quickly, consuming shots/drinks with high alcoholic content) that increase the likelihood of incapacitation. Findings suggest that even very small increases in emotion dysregulation, particularly impulsivity, increase revictimization risk substantially.
Guilt also predicted ISA revictimization during the study, but only when examined with impulsivity rather than emotion dysregulation broadly defined. Guilt and self-blame are almost universal reactions to sexual assault, and guilt tends to be even more pronounced among revictimized women (Breitenbecher, 2001). Among survivors of interpersonal violence, guilt is associated with increased levels of distress, and predicts avoidant, maladaptive coping—which often includes heavy drinking or drug use (Street, Gibson, & Holohan, 2005). Although guilt is not associated with weekly or daily drinking (Hussong, Hicks, Levy, & Curran, 2001), problem drinkers tend to experience heightened levels of negative self-awareness including guilt (Hull, 1981). Women may be particularly vulnerable to revictimization if they are heavy drinkers and experience high levels of guilt.
This is the first prospective study to examine emotion dysregulation as a predictor of ISA revictimization, yet its findings must be considered in the context of some limitations. Given the small sample, predictors of incapacitated and forcible revictimization could not be examined, as only two individuals reported forcible assault in the absence of incapacitation. Although earlier research suggests that alcohol and drug use by victims is typically predictive of incapacitated rather than forcible assault (e.g., Testa, 2004), more research in this area is needed to clarify whether emotion dysregulation is as relevant to forcible sexual assault. Findings may also be impacted by the relatively short follow-up period (9 weeks). Future studies should aim to balance frequency of participation and potential participant fatigue (i.e., number of questions assessing victimization) with longer follow-up periods. Even in the current brief 10-week study, some participants did not complete all questions, rendering victimization status inconclusive for a significant number of women, and it is not clear how the exclusion of these individuals may have affected the findings reported here. Other factors, such as peak BAC and other aspects of risky drinking should also be assessed to increase our understanding of ISA revictimization. Factors associated with emotion dysregulation and substance use also should be examined, such as PTSD. Given that less than 4% of college women meet criteria for sexual assault-related PTSD (Read, Ouimette, White, Colder, & Farrow, 2011), and that college women likely have lower levels of emotion dysregulation when compared to nonclinical samples, such questions may best be answered with more diverse community and clinical samples, increasing generalization of findings reported here. Yet college women are appropriate for study given the high rates of heavy alcohol consumption and ISA in this population (Krebs et al., 2009; Lawyer et al., 2010).
The present prospective study is a significant first step in establishing the relevance of emotion dysregulation as a precursor of incapacitated sexual revictimization. The good news is that emotion regulation skills can be taught, and emotion dysregulation can improve with tools and practice. Interventions designed to promote women's safety and reduce revictimization risk should aim to enhance emotion regulation skills that may reduce risk among the most vulnerable women.
Footnotes 1 It is impossible to determine whether participants who leave items blank on the SES have experienced unwanted sexual activity. Because recalling experiences of sexual assault can be distressing, some women may have been motivated to skip particular questions or participation in a week following an unwanted sexual experience. To be conservative, these individuals were excluded because they could not be labeled as nonvictims.
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Submitted: September 23, 2011 Revised: August 17, 2012 Accepted: October 16, 2012
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Source: Psychology of Addictive Behaviors. Vol. 27. (1), Mar, 2013 pp. 125-132)
Accession Number: 2012-34907-001
Digital Object Identifier: 10.1037/a0031073
Record: 183- Title:
- The Severe Sexual Sadism Scale: Cross-validation and scale properties.
- Authors:
- Mokros, Andreas. Center for Forensic Psychiatry, Psychiatric University Hospital Zurich, Zurich, Switzerland, andreas.mokros@puk.zh.ch
Schilling, Frank. Federal Evaluation Centre for Violent and Sexual Offenders, Vienna, Austria
Eher, Reinhard. Federal Evaluation Centre for Violent and Sexual Offenders, Vienna, Austria
Nitschke, Joachim. Forensic Psychiatric Clinic, Ansbach District Hospital, Ansbach, Germany - Address:
- Mokros, Andreas, Center for Forensic Psychiatry, Psychiatric University Hospital Zurich, Lenggstrasse 31, PO Box 1931, CH-8032, Zurich, Switzerland, andreas.mokros@puk.zh.ch
- Source:
- Psychological Assessment, Vol 24(3), Sep, 2012. pp. 764-769.
- NLM Title Abbreviation:
- Psychol Assess
- Page Count:
- 6
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- Psychological Assessment: A Journal of Consulting and Clinical Psychology
- ISSN:
- 1040-3590 (Print)
1939-134X (Electronic) - Language:
- English
- Keywords:
- Rasch, psychometrics, scale development, sensitivity, sexual sadism, sex offenders, reliability
- Abstract:
- The Severe Sexual Sadism Scale (SSSS) is a screening device for the file-based assessment of forensically relevant sexual sadism. The SSSS consists of 11 dichotomous (yes/no) items that code behavioral indicators of severe sexual sadism within sexual offenses. Based on an Austrian sample of 105 sexual offenders, the present study replicated the 1-dimensional scale structure of the SSSS, as evidenced by confirmatory factor analysis. More specifically, the scale was commensurate with the 1-parameter logistic test model (Rasch model). Reliability was estimated to be good. Criterion validity for the clinical diagnosis of sexual sadism was good. With a cutoff value of 7 points, sensitivity and specificity were estimated at 56% and 90%, respectively. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Psychometrics; *Sex Offenses; *Sexual Sadism; *Test Construction; *Test Validity; Screening; Sensitivity (Personality); Test Reliability
- Medical Subject Headings (MeSH):
- Adolescent; Adult; Austria; Factor Analysis, Statistical; Humans; Male; Middle Aged; Psychiatric Status Rating Scales; Psychometrics; Reproducibility of Results; Sadism; Sex Offenses; Young Adult
- PsycINFO Classification:
- Clinical Psychological Testing (2224)
Criminal Behavior & Juvenile Delinquency (3236) - Population:
- Human
- Location:
- Australia
- Age Group:
- Adulthood (18 yrs & older)
Young Adulthood (18-29 yrs) - Tests & Measures:
- Severe Sexual Sadism Scale DOI: 10.1037/t30211-000
- Methodology:
- Empirical Study; Quantitative Study
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- First Posted: Dec 5, 2011; Accepted: Oct 27, 2011; Revised: Oct 24, 2011; First Submitted: May 4, 2011
- Release Date:
- 20111205
- Correction Date:
- 20140818
- Copyright:
- American Psychological Association. 2011
- Digital Object Identifier:
- http://dx.doi.org/10.1037/a0026419
- PMID:
- 22142424
- Accession Number:
- 2011-28155-001
- Number of Citations in Source:
- 36
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- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2011-28155-001&site=ehost-live">The Severe Sexual Sadism Scale: Cross-validation and scale properties.</A>
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- PsycINFO
The Severe Sexual Sadism Scale: Cross-Validation and Scale Properties
By: Andreas Mokros
Center for Forensic Psychiatry, Psychiatric University Hospital Zurich, Zurich, Switzerland;
Department of Forensic Psychiatry and Psychotherapy, School of Medicine, University of Regensburg, Regensburg, Germany;
Frank Schilling
Federal Evaluation Centre for Violent and Sexual Offenders, Vienna, Austria
Reinhard Eher
Federal Evaluation Centre for Violent and Sexual Offenders, Vienna, Austria;
Forensic Psychotherapy Unit, University of Ulm, Ulm, Germany
Joachim Nitschke
Forensic Psychiatric Clinic, Ansbach District Hospital, Ansbach, Germany
Acknowledgement:
The paraphilia of sexual sadism involves sexual fantasies, urges, and behaviors that center around the subjugation and humiliation of another human being. The afflicted individual obtains sexual gratification from exerting power over another person through acts of cruelty or degradation. Although aspects of power and subjugation may be part of consensual sadomasochistic role play, severe sexual sadism refers to the forensically relevant form of the disorder in which someone else is being victimized against his or her own will. This clearly has legal implications, as it may lead to sexual offenses such as rape or sexual homicide.
Given the link between paraphilia and sexual reoffending in general (Hanson & Morton-Bourgon, 2005) and the putatively increased risk for sexual offense relapses among sexual sadists in particular (Berner, Berger, & Hill, 2003; Kingston, Seto, & Bradford, 2009), there is a need to diagnose sexual sadism as accurately as possible within forensic settings. The results on the reliability of the criteria for diagnosing sexual sadism are inconclusive: In a study with 15 experienced clinicians who independently judged 12 case vignettes on whether a diagnosis of sexual sadism was present in each case, Marshall, Kennedy, Yates, and Serran (2002) found an unacceptably low interrater agreement of κ = .14. Similarly, Levenson (2004) noted an insufficient level of observer agreement of κ = .30 with regard to sexual sadism. Doren and Elwood (2009), on the other hand, reported rates of agreement in excess of 90% among 34 experienced raters concerning the diagnosis of sexual sadism. Doren and Elwood did not provide any statistics correcting for chance agreement (such as κ), however.
Consequently, several authors emphasized the potential utility of behavioral indicators instead of clinical self-report data for diagnosis (e.g., Knight & Prentky, 1990; Marshall & Kennedy, 2003; McLawsen, Jackson, Vannoy, Gagliardi, & Scalora, 2008; Yates, Hucker, & Kingston, 2008). The use of behavioral indicators deriving from information on the offense seems pragmatic: It is plausible that sexual sadists are particularly unwilling to disclose violent sexual fantasies and urges that focus on extreme forms of violence, degradation, or humiliation of others, especially within forensic settings (cf. Dietz, Hazelwood, & Warren, 1990).
It needs to be acknowledged, though, that the current Criterion A for sexual sadism in the revised fourth edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM–IV–TR; American Psychiatric Association, 2000)—like its presumable revision in DSM–5 (Krueger, 2010, p. 342)—focuses on “recurrent, intense sexually arousing fantasies or sexual urges” in which the psychological or physical suffering of a nonconsenting victim is experienced as sexually arousing. These urges or fantasies may involve corresponding acts or lead to distress or interpersonal problems in other ways (Criterion B). Furthermore, violent sexual fantasies seem to be particularly common among individuals who committed grievous sexually sadistic offenses (Burgess, Hartman, Ressler, Douglas, & McCormack, 1986; MacCulloch, Snowden, Wood, & Mills, 1983). This may suggest a possibly causal or at least a shaping or maintaining influence of such deviant sexual fantasies on subsequent violent acts (McGuire, Carlisle, & Young, 1965) even though sexual fantasies including dominance are estimated to occur in 13%–54% of men, according to various surveys with participants from the general population (Leitenberg & Henning, 1995). Hence, behavioral indicators should not replace the diagnostic relevance of sadistic fantasies, but they will likely prove useful, particularly within forensic settings where denial of sexually sadistic fantasies may be more common than in clinical work (Leitenberg & Henning, 1995).
Furthermore, it has been suggested to distinguish more clearly between the relevance of a diagnosis of sexual sadism for forensic purposes and consensual sadomasochistic role play in the community within DSM–5 (Krueger, 2010). It should therefore be emphasized that the use of the behavioral indicators of severe sexual sadism described herein is limited to cases in which sexual violence or bodily harm has been inflicted by the testee against a nonconsenting victim. Although some actions within consensual sadomasochistic role play may appear phenomenologically similar by virtue of accepting dominant or submissive roles (e.g., Santtila, Sandnabba, & Nordling, 2006), consensual sadomasochistic role play as such can serve various motives (such as hedonism or escapism; Taylor & Ussher, 2001) and does not imply psychological distress or prior traumatization of the person, according to a recent national survey (Richters, de Visser, Rissel, Grulich, & Smith, 2008).
In a comparison of psychiatric diagnosis with behavioral indicators of sexual sadism, Kingston, Seto, Firestone, and Bradford (2010) found that only the latter yielded significant predictive validity for sexual offense recidivism. Following the findings from Marshall et al. (2002), Marshall and Hucker (2006) put forward a list of 15 dichotomous (yes/no) behavioral indicators of severe sexual sadism. On the basis of a German sample of sexual offenders from a high-secure forensic psychiatric hospital (N = 100), Nitschke, Osterheider, and Mokros (2009) empirically derived an 11-item subset from this list (including the additional item of “Insertion of objects into victim's bodily orifice[s]”). The resulting Severe Sexual Sadism Scale (SSSS; see Table 2 for items) showed high reliability (rtt = .93) within the construction sample. More specifically, the SSSS fulfilled the statistical properties of a deterministic (Guttman) scale. A sum score of at least 4 points on the SSSS differentiated perfectly between the (clinically assessed) sexual sadists and nonsadists within the construction sample. Furthermore, the sum score on the SSSS correlated significantly with psychopathy as measured with the total score on the Psychopathy Checklist–Revised (Hare, 2003): r = .29 (Mokros, Osterheider, Hucker, & Nitschke, 2011). The average interrater reliability across the individual item codings on the SSSS, given by Nitschke et al. (2009), was κ = .86 (range: .65–1.00).
Factor Loadings (From Confirmatory Factor Analysis) and Item Parameters (From Rasch Modeling) of the Severe Sexual Sadism Scale
Given that analyses of the SSSS were so far limited to the construction sample, it is paramount to test whether the scale properties (in particular, internal consistency, reliability, and one-dimensionality) carry over to other samples. Hence, we analyzed data from another sample of sexual offenders. We hypothesized:
Hypothesis 1: Severe sexual sadism as measured with the 11-item set of the SSSS is a unitary (one-dimensional) construct.
Hypothesis 2: The SSSS total score is a sufficient statistic for the level of the underlying trait (sexual sadism), as evidenced by a deterministic (Guttman) scale.
Method Participants
Participants were 105 adult male sexual offenders who had been evaluated between 2002 and 2004 at the Federal Evaluation Centre for Violent and Sexual Offenders (FECVSO) of the Austrian Prison Service (Schilling, Ross, Pfäfflin, & Eher, 2010). The FECVSO is a department of the Austrian Federal Ministry of Justice. Participants were included consecutively if they had a sexual crime (rape, sexual homicide) as the index offense. In brief, since the end of 2001 every sexual offender convicted of an unconditional prison sentence by an Austrian court has to be reported to the FECVSO. After a file-based risk assessment of every offender, a substantial proportion of these reported offenders (about 60%) are routinely seen for risk assessment by experienced forensic psychiatrists and psychologists at the FECVSO. Selection of offenders for clinical forensic assessment is done by one of the following criteria: a total score of more than 5 points in the Static-99 actuarial risk assessment instrument (Hanson & Thornton, 2000), age under 25, a prison sentence of more than 4 years, a conviction for a child sexual abuse offense with a nonrelated victim, and any offender reconvicted for a sexual crime. Consequently, all participants in the present sample were interviewed clinically (Eher, Matthes, Schilling, Haubner-MacLean, & Rettenberger, 2011).
Design and Procedure
The criteria of the SSSS (Nitschke et al., 2009; cf. Marshall & Hucker, 2006) were coded based on clinical and court files. Coding was done by an experienced forensic psychologist who had not been involved in the diagnostic assessment and risk assessment procedures for the cases at hand within the FECVSO, and thus was blinded against the clinical diagnoses. Items were initially coded on a 5-point Likert-type scale (with verbal anchoring points of 0 = clearly absent, 1 = possibly present, 2 = present to some extent, 3 = clearly present, and 4 = clearly dominant feature). In order to make the data commensurate with the analyses done by Nitschke et al. (2009), the data were dichotomized by collapsing coding Categories 0 and 1 as not present and coding Categories 2–4 as present.
Data were analyzed with the PASW Statistics program, Version 18.0.0 (SPSS, Chicago, IL), with the exception of factor analyses that were carried out with Mplus, Version 6.11, for Mac (Muthén & Muthén, Los Angeles, CA). The factor analyses used a uniform least squares estimation algorithm (using the unweighted least squares estimator for exploratory and the means-and-variance-adjusted unweighted least squares estimator for confirmatory factor analysis). Finally, Rasch scaling was implemented in Maple, Version 14.01 (Waterloo Maple, Waterloo, Canada), by the first author, with conditional maximum likelihood estimation of item parameters based on the paired-comparison algorithm described by Ford (1957). Person parameters were estimated with the weighted maximum likelihood method described by Warm (1989). Similarly, kernel density estimation was facilitated with Maple, Version 14.01, by means of a Gaussian normal kernel and a normal bandwidth parameter.
ResultsOn average, participants in the sample were 33.19 years old (SD = 9.71; range: 15.31–60.28) at the time of the judicial verdict. The length of the determinate prison sentences of 102 participants was 50.44 months (SD = 46.02; range: 6–258) on average; an additional two offenders were sentenced to life imprisonment. Data on length of sentence was not available for one offender.
According to the assessment of the experienced forensic psychiatrists and psychologists at the FECVSO, a subsample of 18 participants (17.1%) were diagnosed as suffering from sexual sadism according to DSM–IV–TR criteria.
A subset of six cases was chosen at random and coded independently by the third and fourth authors. Averaging joint occurrences and disparities across the six cases and focusing on the 11 items of the SSSS as derived by Nitschke et al. (2009) yielded an overall index of interrater agreement of κ = .58 (95% CI [.40, .77]). According to the criteria given by Landis and Koch (1977), this would indicate a moderate level of agreement. Concerning the total score on the SSSS, the consistency variant of the intraclass correlation coefficient (type [2, 1], single measure) calculated to .82.
As Table 1 shows, most of the items of the SSSS were positively correlated. Exceptions were the two least frequent criteria (Item 5 [“Offender mutilates sexual parts of the victim's body”] and Item 8 [“Offender mutilates nonsexual parts of the victim's body”]); these two items showed negative correlations with some of the other items. Overall, the mean tetrachoric correlation between the 11 items was .41. In an exploratory factor analysis, a one-factorial solution (variance explained = 4.72) accounted for two thirds (68%) of the total communality (6.97) as estimated by Thurstone's method (based on maximum correlation coefficients) and nearly half (43%) of the total variance.
Matrix of Tetrachoric Correlations
Confirmatory factor analysis of the SSSS items yielded a reasonable fit for a one-factorial solution, with absolute fit indices, χ2(44) = 54.60, p = .13, and root-mean-square error of approximation = .05 (90% CI [.00, .09]). The relative fit indices, comparative fit index = .89 and Tucker–Lewis index = .87, fell below the commonly accepted margin of .95, however. As Table 2 indicates, factor loadings ranged from .31 to .97, with the majority of items (six out of 11) showing factor loadings well above .55. In the light of the results from the confirmatory factor analysis, Hypothesis 1 could be retained: The items of the SSSS appeared to represent a one-dimensional scale.
Hypothesis 2, however, could not be retained: The number of Guttman errors (i.e., instances in which a less frequent behavior was present in a single case, whereas a more frequent one was not) was too high. Across the 11 items of the SSSS, we noted 240 Guttman errors for all 105 cases. This resulted in a coefficient of reproducibility of .79—a value that fell short of the commonly accepted threshold of .90. Consequently, the data did not conform to a deterministic (Guttman) scale. Because some items showed negative correlations with other items, nonparametric item response theory (Mokken scaling) was not an option either. Consequently, we tested a one-parameter logistic (Rasch) model. The score distribution log-likelihood of the estimated model (with 21 parameters) was −476.33, compared with a log-likelihood of the saturated model of −382.24 at 2,047 parameters. The value of the Akaike information criterion, based on the score distribution log-likelihood of the estimated model, was 994.66. The Akaike information criterion for the saturated model was 4858.48. Item parameters ranged from −2.96 for the most frequent item (Item 6 [“Offender engages in gratuitous violence toward the victim”]) to 2.66 for the least frequent Item 8 (denoting mutilation of nonsexual parts of the victim's body).
According to the item fit index (standardized weighted mean square residual, or infit) described by Wright and Masters (1982), all items showed acceptable fit, with the exception of Item 9 (“Victim is abducted or confined”), which showed significant underfit with a weighted mean square residual coefficient of 2.16. This means that Item 9 was too indiscriminate, with a too high proportion of Guttman errors. The unweighted mean square residual coefficient (outfit) of 1.23 for Item 9 indicated an acceptable model fit at p = .89.
Under Andrich's (1988) method (i.e., estimating error variance based on the standard error of measurement of person parameters), the reliability of the 11-item Rasch scale was estimated at rtt = .86 in the sample. Internal consistency of the scale was estimated at Cronbach's α = .75 and Guttman's λ2 = .78.
The test characteristic curve plots the total score on the SSSS as a function of the underlying trait level θ (see Figure 1). The test characteristic curve had the steepest ascent at a trait level of −0.05, indicating that this trait level differentiated best between cases. A trait level of −0.05 would correspond with the range between the total scores values of 5 (θ = −0.32) and 6 (θ = 0.33).
Figure 1. Test characteristic curve of the Severe Sexual Sadism Scale (solid line) describing the relationship between the latent trait (θ) and the total score, with 95% confidence bands (dashed lines).
The score distribution of the sexual sadists was swamped by the score distribution of the nonsadistic sexual offenders due to sexual sadists representing the minority (17%) in the sample (see Figure 2). The two graphs in Figure 2 come closest to intersection at a raw score total of 7.55. The intersection would thus imply a cutoff at a total score of 7. A cutoff value of 7 points would afford a sensitivity (true-positive rate) of 56%, a specificity (true-negative rate) of 90%, and a selection ratio (or posterior probability) of 53% within the present sample. The cutoff of 4 points originally recommended by Nitschke et al. (2009) would yield a sensitivity of 83%, a specificity of 58%, and a selection ratio (posterior probability) of 29%. Generally, the probability that a sexual sadist, drawn at random from the subsample of sexual sadists, would have a higher score on the SSSS than a randomly chosen nonsadistic sexual offender was 81%. This value represents the area under a receiver operating characteristic curve: .81 (p < .001, 95% CI [.72, .91]). Expressed in terms of standard deviation units, the difference between the arithmetic mean of sexual sadists on the SSSS (M = 5.94, SD = 1.86, Mdn = 7) and the corresponding score of the nonsadistic sexual offenders (M = 3.31, SD = 2.22, Mdn = 3) calculated to d = 1.23, which represents a large effect size.
Figure 2. Kernel-density estimates for the distributions of total scores among sexual sadists (N = 18; solid line) and nonsadistic sexual offenders (N = 87; dashed line). Density curves were scaled according to relative group size. SSSS = Severe Sexual Sadism Scale.
DiscussionThe present study replicated the structure of an 11-item set indicative of severe sexual sadism with a sample of 105 sexual offenders from Austria (18 of whom had been diagnosed as sexual sadists). Confirmatory factor analysis supported the view that the items represent a one-dimensional scale. However, the deterministic (Guttman) properties that were identified in the construction sample (Nitschke et al., 2009) could not be confirmed. Still, the 11 items were commensurate with the one-parameter logistic (Rasch) model. Hence, the item set retained the properties of forming a cumulative scale and, more importantly, of the total score as a sufficient statistic for the underlying trait. Similarly, O'Meara, Davies, and Hammond (2011) recently applied Rasch modeling to a brief self-rating scale from a related domain, the Short Sadistic Impulse Scale, capturing a (nonsexual) sadistic personality trait.
Estimates of reliability as well as sensitivity and specificity were lower than in the construction sample. Still, at rtt = .86, the reliability of the scale within the present sample met the standard for clinical decisions (rtt ≥ .85) suggested by Rosenthal and Rosnow (1991). In the current testing sample, the criterion validity of the SSSS with regard to the clinical DSM–IV–TR diagnosis of sexual sadism was good, with a large effect size for distinguishing participant groups. The cutoff of 4 points suggested by Nitschke et al. (2009) would lead to 42% false positives and 17% false negatives, affording a biased selection ratio in the present sample (with a base rate of 17.1% sexual sadists): Among the individuals above the cutoff, only one in three would meet the clinical criteria for sexual sadism. The latent trait level corresponding with the cutoff value of 4 (θ = −0.97) was clearly below the margin yielding optimum information (θ = −0.05). Consequently, future research should address the question whether the cutoff of 4 points is too low. For the higher cutoff of 7 points that afforded a more acceptable specificity (90%) and a selection ratio above 50%, the standard error of measurement of the associated trait level (θ = 0.99) was 0.82. The range of θ = 0.99 ± 1 standard error of measurement covers the interval [0.12, 1.81]. Expressed in terms of the total score, this would entail the neighboring values of 6 points (θ = 0.33) and 8 points (θ = 1.64), respectively.
Given the difficulties in ascertaining sexually sadistic fantasies or urges of sexual offenders within forensic settings, the SSSS may therefore become a potentially useful complement to other diagnostic procedures. The use of the scale is limited to forensic cases in which sexual offenses or violent acts were committed against nonconsenting victims. It should be acknowledged, though, that development and replication of the 11-item set were based on data from two German-speaking countries (Austria and Germany). Consequently, it would be necessary to test the scale properties in English-speaking and other countries as well. Furthermore, it should be noted that the construction (N = 100) and the present testing sample (N = 105) were comparatively small. In particular, the present testing sample did not allow for splitting the sample in order to conduct a test on the homogeneity of person parameters (Andersen, 1973). Therefore, replications with larger data sets are needed. Finally, it would be highly interesting to find out whether the total score on the SSSS is predictive of violent or sexual reoffending among sexual offenders released from custody. A study that addresses this issue and uses a retrospective coding of cases is currently under way.
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Submitted: May 4, 2011 Revised: October 24, 2011 Accepted: October 27, 2011
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Source: Psychological Assessment. Vol. 24. (3), Sep, 2012 pp. 764-769)
Accession Number: 2011-28155-001
Digital Object Identifier: 10.1037/a0026419
Record: 184- Title:
- The structure of client language and drinking outcomes in project match.
- Authors:
- Martin, Tim. Department of Psychology, Kennesaw State University, Kennesaw, GA, US, tma2010@yahoo.com
Christopher, Paulette J.. Center on Alcoholism, Substance Abuse and Addictions, Department of Psychology, University of New Mexico, NM, US
Houck, Jon M., ORCID 0000-0002-6565-4481. Center on Alcoholism, Substance Abuse and Addictions, Department of Psychology, University of New Mexico, NM, US
Moyers, Theresa B.. Center on Alcoholism, Substance Abuse and Addictions, Department of Psychology, University of New Mexico, NM, US - Address:
- Martin, Tim, Department of Psychology, Kennesaw State University, SO 4011-A, 1000 Chastain Road, Kennesaw, GA, US, 30144, tma2010@yahoo.com
- Source:
- Psychology of Addictive Behaviors, Vol 25(3), Sep, 2011. pp. 439-445.
- NLM Title Abbreviation:
- Psychol Addict Behav
- Page Count:
- 7
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- Bulletin of the Society of Psychologists in Addictive Behaviors; Bulletin of the Society of Psychologists in Substance Abuse
- Other Publishers:
- US : Educational Publishing Foundation
Society of Psychologists in Addictive Behaviors - ISSN:
- 0893-164X (Print)
1939-1501 (Electronic) - Language:
- English
- Keywords:
- alcohol, change talk, motivational interviewing, outcome, treatment
- Abstract:
- Client language during Motivational Interviewing interventions is an important predictor of drinking outcomes, but there are inconsistencies in the literature regarding what aspects of client language are most predictive. We characterized the structure of client language by factor analyzing frequency counts of several categories of client speech. The results provide limited support for a model proposed by Miller et al. (2006) and Amrhein et al. (2003) but with some important differences. While Amrhein et al. (2003) found that only increasing strength in client commitment language predicted behavior change, the current study revealed that client language preparatory to commitment predicted drinking outcomes. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Behavior Change; *Client Characteristics; *Motivational Interviewing; Alcohol Rehabilitation; Clients; Treatment Outcomes
- Medical Subject Headings (MeSH):
- Adult; Aged; Alcohol Drinking; Alcoholism; Behavior Therapy; Humans; Interview, Psychological; Language; Male; Middle Aged; Motivation; Professional-Patient Relations; Treatment Outcome
- PsycINFO Classification:
- Drug & Alcohol Rehabilitation (3383)
- Population:
- Human
Male
Female - Location:
- US
- Age Group:
- Adulthood (18 yrs & older)
Young Adulthood (18-29 yrs)
Thirties (30-39 yrs)
Middle Age (40-64 yrs)
Aged (65 yrs & older) - Grant Sponsorship:
- Sponsor: Department of Defense
Grant Number: DAMD 17-01-1-0681
Recipients: No recipient indicated
Sponsor: National Institute on Alcohol Abuse and Alcoholism
Grant Number: RO1 AA 13696 01
Recipients: No recipient indicated
Sponsor: National Institute on Drug Abuse
Grant Number: R01 DA 13801
Recipients: No recipient indicated - Methodology:
- Empirical Study; Quantitative Study
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- First Posted: Apr 25, 2011; Accepted: Jan 18, 2011; Revised: Jan 14, 2011; First Submitted: Jan 29, 2010
- Release Date:
- 20110425
- Correction Date:
- 20110919
- Copyright:
- American Psychological Association. 2011
- Digital Object Identifier:
- http://dx.doi.org/10.1037/a0023129
- PMID:
- 21517139
- Accession Number:
- 2011-08219-001
- Number of Citations in Source:
- 30
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- PsycINFO
The Structure of Client Language and Drinking Outcomes in Project MATCH
By: Tim Martin
Department of Psychology, Kennesaw State University;
Paulette J. Christopher
Center on Alcoholism, Substance Abuse and Addictions, Department of Psychology, University of New Mexico
Jon M. Houck
Center on Alcoholism, Substance Abuse and Addictions, Department of Psychology, University of New Mexico
Theresa B. Moyers
Center on Alcoholism, Substance Abuse and Addictions, Department of Psychology, University of New Mexico
Acknowledgement: This research was supported in part by Department of Defense Grant DAMD 17-01-1-0681, National Institute on Alcohol Abuse and Alcoholism RO1 AA 13696 01 and National Institute on Drug Abuse R01 DA 13801. The authors wish to thank J. Scott Tonigan for statistical consultation for this project.
Theresa B. Moyers acts as a consultant/trainer for motivational interviewing through a private consulting business.
Client language is increasingly recognized as an important predictor of clinical outcomes for motivational interviewing (MI). Evidence is accumulating for a predictive role for particular elements of client speech in behavioral change (Amrhein, Miller, Yahne, Palmer, & Fulcher, 2003; Hodgins, Ching, & McEwin, 2009; Moyers et al., 2007; Gaume, Gmel, & Daeppen, 2008). Specifically, language that the client offers during an MI treatment session weighing in favor of changing a problematic behavior, typically substance abuse, predicts posttreatment drug and alcohol use, even when the level of initial motivation, severity of dependence, and efficacy for change have been accounted for (Moyers, Martin, Houck, Christopher, & Tonigan, 2009).
Miller and Rollnick (1991, 2002) have drawn upon self-perception theory (Bem, 1967) to explain how these client statements for or against change may influence client motivation in MI sessions. If the client argues in favor of change (change talk, CT), the client perceives that what he or she is arguing for must be what he or she believes, thereby increasing motivation for change. In other words, “the person literally talks himself or herself into change.” (Miller & Rollnick, 2004, p. 300). Alternatively, when clients argue against change (counter-change talk, CCT), their perception of the self making this argument lowers motivation to change. This implies that client speech that favors change (CT) should predict favorable outcomes, while client speech supporting the target behavior (CCT) should predict maintaining the status quo. These predicted relationships between CT, CCT, and outcome have now been observed several times, but with a number of inconsistencies. Miller, Benefield and Tonigan (1993) found that client verbalizations of resistance to change, or CCT, predicted client drinking outcome assessed 12 months after therapy. In the same study, however, Miller et al. failed to find a significant relationship between outcome and CT. Moyers et al. (2007) found that both CT and CCT independently predicted drinking behavior averaged over a period of 10–15 months after therapy.
In an influential study, Amrhein et al. (2003) conducted an analysis of client language in a randomized clinical trial for MI with drug-using clients. Based on a priori hypotheses concerning the nature of social commitments, Amrhein et al. (2003) conceptualized several sub-categories of change talk, including Commitment, Desire, Ability, Need, Readiness, and Reasons. In addition to categorizing certain acts of client speech, coders rated the strength (i.e., intensity) of CT and CCT utterances. They found that drug use outcomes were associated with the pattern of these strength ratings during MI treatment sessions. Specifically, they found that increasing strength of Commitment statements predicted more favorable drug use outcomes. Based largely upon this research, a model of client speech was developed in which expressions of change talk categorized as statements of Desire, Ability, Reasons, and Need, collectively termed “preparatory language,” should lead to statements of commitment to change a problematic behavior. These commitment statements should then predict posttreatment behavior (Miller, Moyers, Amrhein, & Rollnick, 2006). Thus, preparatory language and commitment language are seen as two distinct constructs. The clinical implications of this model are straightforward, that is, clinicians should hesitate to move forward with action strategies (called Phase II in MI) until commitment language is strong, regardless of how many statements of desire, ability, reason, and need have been offered.
Research subsequent to the original Amrhein study has been consistent in supporting the value of change talk in predicting clinical outcomes in MI, although evidence for the dominance of commitment language has been mixed. For example, Gaume, Gmel, and Daeppen (2008) found no link between commitment language and drinking outcomes using the Motivational Interviewing Skills Code (MISC) 2.0 (Miller, Moyers, Ernst, & Amrhein, 2003), but did find an association between drinking outcomes and client statements of ability to change. Similarly, Baer et al. (2008) found that client statements about reasons to change were associated with reductions in substance use in homeless adolescents, though commitment language was not. Moyers et al. (2007) found that a single, generic change talk category predicted drinking outcomes in a secondary analysis of Project MATCH (Matching Alcoholism Treatments to Client Heterogeneity) outcomes, without reference to commitment language. On the other hand, Hodgins, Ching, and McEwen (2009) found that commitment language predicted gambling outcomes in a randomized controlled trial using MI, while preparatory language did not.
The consistent finding of a relationship between client speech and outcome is promising, but the inconsistencies in what aspect of speech is most predictive points to the need for a more complete understanding of the structure of client verbalizations. Does client speech naturally cleave into preparatory and commitment categories as suggested by Amrhein et al. (2003), or would clinicians be better advised to attend to any and all statements in favor of change when considering whether to move forward to action planning in MI sessions?
This study attempts to inform this question by examining the underlying structure in a large sample of client speech drawn from Motivational Enhancement Therapy sessions in Project MATCH (Project MATCH Research Group, 1997). Therapy sessions were recorded and evaluated using the sequential code for observing process exchanges (SCOPE; Martin et al, 2005). The SCOPE was developed in response to a perceived need to investigate the dynamics of therapy sessions (Moyers & Martin, 2006). SCOPE combines elements of the MISC 1.0, particularly codes for therapist speech, and also incorporates multiple categories of client preparatory and commitment language using definitions similar to those of Amrhein et al. (2003). The SCOPE coding system also includes a procedure for recording the serial order of client and therapist statements so that sequential patterns can be analyzed statistically. Results of the sequential analysis of client-counselor interactions for this project are given elsewhere (Moyers & Martin, 2006; Moyers et al., 2009). This paper, however, focuses on the frequency counts of client speech coded from these tapes. Because multiple categories of both CT and CCT are measured, these frequency counts provide an opportunity to investigate the structure of change talk. By treating each category frequency as an imperfect indicator of one or more latent constructs, the underlying structure of those constructs can be estimated using factor analysis. Thus, even if the categories do not perfectly coincide with the actual psychological sources of the speech events, so long as each category is distinct enough to blend these underlying factors in a different way (i.e., to be a different linear combination of underlying factors), and broad enough to capture several of the underlying factors that contribute to client motivational speech, factor analysis should give some indication of this latent structure. To this end we factor analyzed frequency counts of client speech from 118 Project MATCH interviews from the data set reported in Moyers et al. (2009). For the purposes of this analysis, we restricted ourselves to the first session of the MET condition from Project MATCH (Project MATCH Research Group, 1997).
We reasoned that if preparatory language and commitment statements are distinct constructs as hypothesized by Miller et al. (2006), we would expect two factors to explain client language both for and against change. One factor would include commitment to change and commitment to maintain the status quo. A second factor would be expected to account for statements of Desire, Ability, Reasons, or Need to change or maintain the target behavior. A third factor would be expected to account for neutral client speech (i.e., unrelated to the target behavior), coded in SCOPE as Follow (a client utterance unrelated to the target behavior) and Ask (client asks a question). We would also expect the commitment factor, but not the preparatory language or neutral factors, to predict drinking outcomes.
Method Participants
The data selected for this analysis were from 118 first-session tapes from the MI condition of Project MATCH (Project MATCH Research Group, 1997). We restricted this analysis to first-session tapes for several reasons. First, we restricted the analysis to a single session because the effect of sessions on client and therapist behavior is largely unknown and beyond the scope of this analysis. The first session was chosen because it represented the largest sample of sessions available to us, and because in the past frequency counts of initial sessions have been found to correlate with outcome using other coding instruments (Moyers et al., 2007) and the SCOPE (Moyers et al., 2009). Details of the overall sample are given elsewhere (Project MATCH Research Group, 1997). In the subsample reported here, 91 clients (77%) were male, 86 (72.9%) were White, 11 (9.3%) were African American, 20 (16.9%) were Hispanic, and 1 (0.8%) was of another ethnicity. The mean age was 40.75 (range 21–74), and the mean number of years of education was 13.53 (range 8 – 20). All study and consent procedures were reviewed and approved by the human research Institutional Review Board of the University of New Mexico.
Coding
Therapy sessions were coded using the SCOPE (Martin et al., 2005). The manual for SCOPE is available from http://casaa.unm.edu/download/scope.pdf. The coding process has been described in detail elsewhere (Moyers & Martin, 2006; Moyers et al., 2009). Briefly, audio recordings of therapy sessions were transcribed and then assessed in two separate passes. Coders both listened to the recording and read along with the transcript for both passes, marking their codes directly in the transcript. In the first pass the recording was parsed into utterances, which were defined as expressions of a single idea. In the second pass, each utterance was assigned a single category code, based on definitions found in the coding manual. Typically each pass was performed by a different coder. There were 16 categories of client speech. These categories were Follow and Ask (described above) to describe speech unrelated to the target behavior, and Desire, Ability, Reason, Need, Taking Steps, Commit, or Other. To specify direction (i.e., reflecting movement toward change or the status quo), these categories were followed by a “+” or “−” symbol. For example, “Reason +” would refer to a reason to change, while “Reason−” would denote a reason to maintain the target behavior.
Data Analysis
This is a secondary analysis of data reported elsewhere (Moyers & Martin, 2006; Moyers et al., 2009). Frequency counts of client speech as coded by SCOPE were factor analyzed, using principal components extraction, a retention criterion of 1 eigenvalue, and varimax rotation. Principal components extraction was chosen because it includes variance unique to each measured variable (Harris, 1975; Johnson & Wichern, 1982), and there was no evidence of large differences in communalities among the measured variables (Harris, 1975, pg. 223), which ranged from 0.526–0.75. This implies that methods that exclude unique variance would not improve the solution, but would degrade the relationship between empirical data and factor scores (Harris, 1975, pp. 222–223). Varimax rotation (Kaiser, 1958) was implemented because it tends to result in more interpretable factors than the unrotated (principle component orientation) solution, but will be highly similar to the unrotated solution if the observed correlation matrix is caused predominantly by a single latent variable.
Drinking outcome measures have been described in detail elsewhere (Project MATCH Research Group, 1997). Briefly, we used proximal and distal measures of percent days abstinent (PDA) and drinks per drinking day (DDD). In Project MATCH, PDA was assessed using the Form-90. The Form-90 incorporates memory cues from time-line follow-back procedures with drinking pattern estimation methods from the Comprehensive Drinker Profile (Miller & Del Boca, 1994; Miller & Marlatt, 1984).These measures are averaged across follow-up assessments. The proximal measures are averaged across assessments conducted at months 4–9 post-therapy, while the distal measures are averaged across months 10–15 posttherapy. To improve the normality of the distributions, PDA was arcsine transformed and DDD was square-root transformed. Two-step hierarchical multiple regressions were used to predict these criterion variables. This was done to assess whether client speech predicted unique variance in outcome in addition to known predictors. In the first step of each regression model, a baseline measurement of the criterion variable, as well as Alcohol Involvement (AIM) as measured by a third-order scale from the Alcohol Use Inventory (Wanberg, Horn, & Foster, 1977), self-efficacy as measured by the Alcohol Abstinence Self-Efficacy scale (AASE: DiClemente, Carbonari, Montgomery, & Hughes, 1994), and readiness to change were entered. Readiness to change was derived from the University of Rhode Island Chance Assessment Scale (URICA: McConnaughy, Prochaska, & Velicer, 1983) by summing that instrument's Contemplation, Action, and Maintenance subscales and subtracting the Precontemplation subscale score (Carbonari, DiClemente, & Zweben, 1994; Connors, Tonigan, & Miller, 2001). In the second step, the six factors of the FA were entered.
ResultsDetails regarding interrater agreement of the SCOPE codes used here are given in detail elsewhere (Moyers et al., 2009). Briefly, agreement for the frequency counts of client speech used here were assessed with intraclass correlation coefficients (Shrout & Fleiss, 1979), and ranged from 0.620 (Commit−) to 0.993 (Ask). “Other−” (arguments in favor of maintaining the target behavior that were not classifiable elsewhere), was removed from the factor analysis because its ICC was 0.229, unacceptably low (Cicchetti, 1994).
The correlation matrix for client speech is given in Table 1. The table is provided for readers who may be interested in exploring other factor models of the data. Factor loadings are given in Table 2. The 16 categories of client speech were characterized by six factors with eigenvalues > 1.0 explaining 64.85 % of the variance. Variables loading most heavily on Factor 1 included Commit−, Desire−, Reasons−, and Need−. We suggest that this factor be interpreted as reflecting motivation to maintain the status quo. Factor 2, which included both Steps + and Steps−, as well as Need−, might reflect actions related to drinking behavior generally, rather than movement in a specific direction toward or away from change. Factor 3 included Desire+, Reason+, Need+, and Other + speech. We interpret this as reflecting preparatory language as described by Amrhein et al. (2003), but without the Ability category. Instead, Ability + was split between the next two factors. Factor 4 includes Commit + and Ability+, as well as Follow. We suggest that this factor reflects commitment to change. Factor 5 has strong positive loadings for both Ability + and Ability−. This factor may reflect ambivalence on the part of many clients, who tend to express concurrently their ability to change and the difficulty they anticipate in doing so. The final factor is straightforward, with Follow and Ask loading most heavily. This factor likely reflects the client's general participation in the session.
Correlations Among CT and CCT Frequencies
Factor Loadings of Client Speech Variables
The hierarchical regressions of DDD were not significant. The hierarchical regression of proximal PDA on baseline measures and factors is given in Table 3. The first step, which included baseline PDA, AIM, AASE, and Readiness, was significant, F(4, 93) = 5.655, p < .0005. Only baseline PDA was a significant predictor of proximal PDA. The change in R2 at the second step was not statistically significant, ΔR2 = 0.097, p = .077, but Factor 2 was nevertheless a significant predictor of proximal PDA. The model overall was significant, F(10, 87) = 3.59, p = .001, adjusted R2 = 0.21, SE = 0.39.
Multiple Regression of Proximal PDA on Baseline PDA and Factors
The regression of distal PDA is given in Table 4. The model at the first step was significant, F(4, 97) = 5.44, p = .001. Only baseline PDA was a significant predictor. The change in R2 at the second step was significant, ΔR2 = .112, F(6, 87) = 2.32, p = .04, as was the overall model, F(10, 87) = 3.75, p < .0005, adjusted R2 = 0.22, SE = 0.43. Factor 3 and Factor 5 were significant predictors, with a positive and negative slope respectively.
Multiple Regression of Distal PDA on Baseline PDA and Factors
DiscussionThe results of the factor analysis provide limited support for the two-construct theory of client speech proposed by Miller et al. (2006). There were factors that could be interpreted as preparatory language and commitment to change, although categories of counter-change talk did not cleave so cleanly between preparatory and commitment categories. Additional factors indicate that more than two constructs are necessary to account for client speech related to change.
Frequency of Desire+, Reasons+, Need+, and Other + loaded positively on Factor 3, the Preparatory Language Factor. This factor is largely consistent with the two-construct model and has a positive slope with distal PDA, indicating that as clients express more of these preparatory statements, PDA increases. However, client language about ability to change did not load onto this factor. Instead, the frequency of language regarding the ability to change appears to reflect two independent factors. Ability + statements were primarily associated with Factor 4 (Commit+, Ability+). This close link between commitment to change and perceived (or at least verbalized) ability to change may reflect an increased likelihood to commit to change only with a sufficiently high confidence in one's ability to be successful.
Client speech categorized as Follow also loaded on Factor 4, although not as heavily as it loaded on Factor 6 (Follow, Ask). The frequency of this category, which is explicitly defined as speech not related to the target behavior or neutral with respect to the target behavior, will reflect several characteristics of the client, therapist, and situation, including trait talkativeness, therapeutic alliance, and the degree to which clients are willing to follow the topical lead of the therapist. Any one of these (and perhaps others), alone or in combination, could explain why Follow would load on the same factor as Commit + and Ability+. It could be that high levels of alliance are globally associated with overall talkativeness in a session but selectively associated with verbalizations of commitment and ability to change. Perhaps more simply, it could be that clients who have already committed to changing the target behavior (and thus will emit more Commit + statements) also tend to be more talkative during therapy, and thus the relationship between Follow and Commit + merely reflects this relationship.
Factor 5 appears to reflect a more general concept of ability, in that both Ability + and Ability− loaded positively. In other words, clients who expressed an ability to change also tended to express doubts or reservations about their ability to change. The interpretation of this factor is not necessarily straightforward. Ability− loaded most heavily on Factor 5 and the slope of the relationship between it and distal PDA was significantly negative, meaning that higher scores on this factor predicted fewer abstinent days. However, because Ability + also loaded positively on Factor 5, it does not appear to indicate only a perceived inability to change. It may instead reflect ambivalence about one's ability to change, with poor outcome associated with high ambivalence. Within motivational interviewing sessions, then, clinicians should not be surprised to hear clients expressing both confidence and doubt about a change.
Similarly, Factor 2 was composed of Steps+, Steps−, and Need−. This factor is somewhat puzzling. Steps are defined in the SCOPE as reports of active changes that a person has made in his or her life to either support the target behavior or change it. For example, a person might start taking aspirin before going to bed to avoid a hangover (Steps−) or change their driving patterns to avoid a tempting bar (Steps+). The fact that the frequency of these categories is positively correlated is therefore an interesting finding in itself and merits further investigation. The combination of these statements with Need−, a stated lack of need to change the target behavior, may be an indicator of a particular stage of change. Once the target behavior has been changed, for example, one would not expect to continue hearing Need + statements. Therefore, this factor may reflect variation between clients in the current state of their attempts to change their target behavior, with those who have successfully reduced or eliminated the behavior commenting on steps taken, both forward and back, and lacking in statements reflecting a current, immediate need for change. Those who have not yet successfully begun or made the change, on the other hand, may not report concrete steps toward or away from change, but express more Need + statements reflecting their recognition that a current need to change exists. This interpretation is consistent with the relationship between this factor and proximal PDA. Those further along the continuum of change at the first therapy session would be high on Factor 2, and would achieve higher levels of PDA in the first few months after therapy, while those who were still in early stages of change would be low on Factor 2, and might well take more time to achieve abstinence, if they ever do. The fact that Factor 2 predicts unique variance in proximal PDA in the presence of baseline PDA as a predictor strengthens the interpretation that this factor reflects the process of change and not only current behavior. The fact that it did not predict distal PDA (p = .078) may reflect a real reduction in influence over time, or merely measurement error in the presence of marginal statistical power.
Three of the derived factors predicted drinking outcomes as measured by percent days abstinent (PDA). Factor 2 (Steps+, Steps−, Need−) was positively associated with Proximal PDA, while Factor 3 (Desire+, Reason+, Need+, Other+) was positively associated with Distal PDA. In contrast, Factor 5 (Ability+, Ability−) was negatively associated with Distal PDA. The association of the Preparatory Language factor (Factor 3) with outcome is consistent with the result of Baer et al. (2008), who found that statements of reasons to change were positively associated with changes in substance use in adolescents. In addition, both Baer et al. (2008) and Gaume et al. (2008) found that statements of ability/inability to change were associated with outcome, consistent with our finding of a relationship between the Ability factor (Factor 5) and outcome.
The implication of these results for clinicians using MI is that rather than a strict focus on the strength of client language, clinicians may adopt a broad focus on the general concept of change talk and how prevalent it is in the MI session, at least within the first therapy session. Our data suggest that clinicians may not need to differentiate between categories of change talk “on the fly” during treatment sessions, but can respond to any offer of change talk on the part of the client without the need for belabored examination. Additional clinician attention is warranted only when counterchange talk occurs more often than does change talk, particularly within the categories of Ability and Steps. If replicated, this result will also call into question the concept of two distinct phases of therapy, a preparatory followed by an action phase. However, we hasten to add that our sample was restricted to first sessions, and so these results may not generalize beyond an initial session. In some cases the action phase may not emerge until later therapy sessions, and commitment language during those sessions may well predict outcome as well or better than preparatory language did in the current report.
Despite limited support for the two-construct model, our data present a few surprises that merit some discussion. First, client language about ability to change does not reflect the same factor as statements of desire, need, and reasons to change, contrary to the predictions of the two-construct model. The closest relationship is found between ability statements and the factor reflecting commitment to change.
Perhaps more important than the number of factors, the pattern of predictive factors is at odds with expectations from the two-construct model. While the Preparatory Language factor (Factor 3) itself is somewhat consistent with the two-construct model, the fact that it accounts for unique variance in outcome in the presence of Commitment (Factor 4) is not. The Steps factor is positively associated with proximal PDA, and the Ability factor is negatively associated with distal PDA. Both of these factors appear to represent a dichotomy, with the direction of relationship with outcome determined by the valence of the more frequent utterance within the category. For example, in the Taking Steps factor, there were nearly three times as many Taking Steps + utterances as there were Taking Steps−, and this factor was positively associated with proximal PDA. In contrast, in the Ability factor there were nearly twice as many Ability− utterances as there were Ability+, and this factor was negatively associated with distal PDA.
There are several possible reasons for the discrepancy between our findings and the two-construct model. The first is that the coding definitions within a two-construct model differ in at least one important way from those of the SCOPE, as evidenced by the examples given in their report. Specifically, many instances that Amrhein et al. (2003) would classify as Commit would be coded in SCOPE as Reasons. Therefore it is likely that a great many of the statements that Amrhein et al. classified as Commit are here categorized as Reason + or Reason−. Other possible reasons for the discrepancy include the fact that the samples, the therapy protocols, and the coding and analysis methods of the studies are different.
Another important difference between the SCOPE and the two-construct model is in how frequencies are counted. Amrhein et al. (2003) collapsed across the change-status quo dimension, so that the frequency of a category like Commit would include both “I am going to change” and “I am not going to change.” It is this frequency count that failed to discriminate between outcome clusters in their report. The factor structure found here indicates why frequency might not predict behavioral outcomes when collapsed across this dimension. Verbalizations of CT and CCT in general load on different factors, indicating that while conceptually (and statistically) related, CT and CCT are empirically distinguishable.
There are several limitations to the current study. The selection of therapy sessions for coding was not random, but depended instead on the willingness of individual IRB committees at Project MATCH sites to approve a secondary analysis (Moyers et al., 2009), and this may limit our ability to generalize to the population of people treated for substance abuse. This was a secondary analysis of data in which client speech was known to predict outcome (Moyers et al., 2009), which may have led to some degree of alpha inflation. The fact that strength of utterances was not coded limits our ability to compare results directly with others who do so (e.g., Amrhein et al., 2003; Gaume et al., 2008), although it simultaneously extends our knowledge to another measure of client speech that should be equally well covered by the theoretical constructs in question. The inability of the factors to predict DDD indicates that they likely cannot predict all outcome measures with equal power, and may suggest limited construct validity. Despite these limitations, the results of this study provide strong evidence that two constructs are not sufficient to account for client speech related to change, and some indication of what a more adequate framework for understanding client speech might look like. Further analyses of client language in studies of similar populations with similar coding systems will be an important addition to the literature on the mechanisms of effectiveness in MI. Factor 5 (Ability) is particularly intriguing, as it is conceptually related to self-efficacy and autonomy, concepts considered critical to MI effectiveness (Miller & Rollnick, 2002) and indeed to the wider issue of intrinsic motivation (Deci & Ryan, 1985; Ryan & Deci, 2000). We believe that uncovering these mechanisms is worthwhile, as they should lead to greater efficacy and effectiveness of MI as well as improved efficiency in its delivery.
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Martin, T., Moyers, T. B., Houck, J. M., Christopher, P. J., & Miller, W. R. (2005). Motivational Interviewing Sequential Code for Observing Process Exchanges (MI-SCOPE) coder's manual. Retrieved from http://casaa.unm.edu/download/scope.pdf.
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Submitted: June 29, 2010 Revised: January 14, 2011 Accepted: January 18, 2011
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Source: Psychology of Addictive Behaviors. Vol. 25. (3), Sep, 2011 pp. 439-445)
Accession Number: 2011-08219-001
Digital Object Identifier: 10.1037/a0023129
Record: 185- Title:
- The suitability of the South Oaks Gambling Screen–Revised for Adolescents (SOGS-RA) as a screening tool: IRT-based evidence.
- Authors:
- Chiesi, Francesca, ORCID 0000-0002-6861-9219. Department of Psychology, University of Florence, Italy, francesca.chiesi@unifi.it
Donati, Maria Anna. Department of Psychology, University of Florence, Italy
Galli, Silvia. Department of Psychology, University of Florence, Italy
Primi, Caterina. Department of Psychology, University of Florence, Italy - Address:
- Chiesi, Francesca, Department of Psychology, via di San Salvi 12- Padiglione 26, 50135, Firenze, Italy, francesca.chiesi@unifi.it
- Source:
- Psychology of Addictive Behaviors, Vol 27(1), Mar, 2013. pp. 287-293.
- NLM Title Abbreviation:
- Psychol Addict Behav
- Page Count:
- 7
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- Bulletin of the Society of Psychologists in Addictive Behaviors; Bulletin of the Society of Psychologists in Substance Abuse
- Other Publishers:
- US : Educational Publishing Foundation
Society of Psychologists in Addictive Behaviors - ISSN:
- 0893-164X (Print)
1939-1501 (Electronic) - Language:
- English
- Keywords:
- SOGS-RA, adolescents, gambling, item response theory, screening, South Oaks Gambling Screen–Revised for Adolescents
- Abstract:
- The South Oaks Gambling Screen–Revised for Adolescents (SOGS-RA) is one of the most widely used measures of adolescent gambling. We aimed to provide evidence of its suitability as a screening tool applying item response theory (IRT). The scale was administered to 981 adolescents (64% males; mean age = 16.57 years, SD = 1.63 years) attending high school. Analyses were carried out with a sample of 871 respondents, that is, adolescents who have gambled at least once during the previous year. Once the prerequisite of unidimensionality was confirmed through confirmatory factor analysis, unidimensional IRT analyses were performed. The 2-parameter logistic model was used in order to estimate item parameters (severity and discrimination) and the test information function. Results showed that item severity ranged from medium to high, and most of the items showed large discrimination parameters, indicating that the scale accurately measures medium to high levels of problem gambling. These regions of the trait were associated with the greatest amount of information, indicating that the SOGS-RA provides a reliable measure for identifying both problem gamblers and adolescents at risk of developing maladaptive behaviors deriving from gambling. The IRT-based evidence supports the suitability of the SOGS-RA as a screening tool in adolescent populations. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Gambling; *Item Response Theory; *Screening Tests
- Medical Subject Headings (MeSH):
- Adolescent; Behavior, Addictive; Factor Analysis, Statistical; Female; Gambling; Humans; Logistic Models; Male; Mass Screening; Psychiatric Status Rating Scales; Psychometrics; Surveys and Questionnaires; Young Adult
- PsycINFO Classification:
- Clinical Psychological Testing (2224)
Behavior Disorders & Antisocial Behavior (3230) - Population:
- Human
Male
Female - Location:
- Italy
- Age Group:
- Adolescence (13-17 yrs)
Adulthood (18 yrs & older)
Young Adulthood (18-29 yrs) - Tests & Measures:
- Massachusetts Adolescent Gambling Screen
Problem Gambling Severity Index
South Oaks Gambling Screen–Revised for Adolescents DOI: 10.1037/t21463-000 - Methodology:
- Empirical Study; Quantitative Study
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- First Posted: Oct 1, 2012; Accepted: Jul 26, 2012; Revised: Jul 26, 2012; First Submitted: Apr 23, 2012
- Release Date:
- 20121001
- Correction Date:
- 20140317
- Copyright:
- American Psychological Association. 2012
- Digital Object Identifier:
- http://dx.doi.org/10.1037/a0029987
- PMID:
- 23025708
- Accession Number:
- 2012-26453-001
- Number of Citations in Source:
- 41
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- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2012-26453-001&site=ehost-live">The suitability of the South Oaks Gambling Screen–Revised for Adolescents (SOGS-RA) as a screening tool: IRT-based evidence.</A>
- Database:
- PsycINFO
The Suitability of the South Oaks Gambling Screen–Revised for Adolescents (SOGS-RA) as a Screening Tool: IRT-Based Evidence
By: Francesca Chiesi
Department of Psychology, University of Florence, Italy;
Maria Anna Donati
Department of Psychology, University of Florence, Italy
Silvia Galli
Department of Psychology, University of Florence, Italy
Caterina Primi
Department of Psychology, University of Florence, Italy
Acknowledgement:
Large-scale international prevalence studies have revealed that between 66% and 86% of youth reported gambling in the past year (Hardoon, Gupta, & Derevensky, 2004), and although there is a lack of consensus as to the actual adolescent prevalence of severe gambling problems, there is a general agreement on the fact that a high proportion of adolescents gamble excessively and that, as a group, adolescents constitute a high-risk population for developing gambling problems (Derevensky & Gupta, 2006; Jacobs, 2004).
Due to the societal importance of the phenomenon, much attention has been paid to the issue of measurement of youth gambling problems (for reviews, see Derevensky & Gupta, 2004, 2006), and there is a debate about the efficiency of the most commonly employed adolescent gambling screens, such as the South Oaks Gambling Screen-Revised for Adolescents (SOGS-RA; Winters, Stinchfield, & Fulkerson, 1993); the Massachusetts Adolescent Gambling Screen (MAGS; Shaffer, LaBrie, Scanlan, & Cummings, 1994); and the Diagnostic and Statistical Manual of Mental Disorders (4th ed.; DSM-IV; American Psychiatric Association, 1994) adapted for juveniles (DSM-IV-J; Fisher, 1992), and its revised version, the multiple-response format for juveniles (DSM-IV-MR-J; Fisher, 2000). These tools have been adapted from adult instruments, modifying some items to make them more age appropriate. These items focus on the behavioral indicators of problem gambling (lying, chasing), the emotional and psychological correlates of pathological gambling (withdrawal, guilt, preoccupation, loss of control), the adverse consequences of excessive gaming (illegal acts, school or work problems), and the economic and social problems directly associated with gambling (excessive losses, family problems).
Among these measures, the SOGS-RA (Winters et al., 1993), despite its widespread use (for a recent review, see Blinn-Pike, Worthy, & Jonkman, 2010), has been criticized for overdiagnosing problem gambling compared with other popular adolescent gambling severity measures. Comparison studies between the SOGS-RA, the MAGS, and the DSM-IV-J have indicated that the SOGS-RA may provide overly liberal estimates of problem gambling among adolescents (Derevensky & Gupta, 2000; Langhinrichsen-Rohling, Rohling, Rohde, & Seeley, 2004). Nonetheless, a screening tool has to identify individuals who are problem gamblers (i.e., those who exhibit maladaptive behaviors related to gambling) as well as at-risk gamblers (i.e., those who are at risk of developing maladaptive behaviors). Thus, the SOGS-RA might represent an efficient screening tool for identifying adolescents with gambling-related problems as well as individuals who are potentially at risk. Indeed, a population screen is expected to overidentify false-positives (Sharp et al., 2012).
Starting from this premise, it seems relevant to further explore the suitability of the SOGS-RA as a screening tool for at-risk and problem gamblers. Indeed, although the SOGS-RA is widely used to classify adolescents into gambling problem severity categories, there is no empirical evidence of its effectiveness in making accurate distinctions between these categories (Hardoon, Derevensky, & Gupta, 2003; Langhinrichsen-Rohling et al., 2004; Olason, Sigurdardottir, & Smari, 2006; Wiebe, Cox, & Mehmel, 2000). The present study aimed to address this issue by applying item response theory (IRT), as there is a general lack of IRT studies on problem gambling severity measures in general and, in particular, on the SOGS-RA for adolescents. To the best of our knowledge, only a very recent study (Sharp et al., 2012) applied IRT to the Problem Gambling Severity Index (PGSI; Ferris & Wynne, 2001), a screen for adults, and Molde and colleagues (Molde, Pallesen, Bartone, Hystad, & Johnsen, 2009) applied IRT to the MAGS (Shaffer et al., 1994) in a prevalence study among adolescents in Norway.
In order to provide evidence of the efficiency of the SOGS-RA as a screening tool, we aimed to assess the measurement precision of the scale across severity levels of problem gambling through the test information function (TIF). Instead of providing a single value (e.g., coefficient alpha) for reliability, IRT recognizes that measurement precision can be different at different levels of the trait (Embreston & Reise, 2000; Hambleton, Swaminathan, & Rogers, 1991), and the TIF is used to evaluate the precision of the test at different levels of the measured construct. When estimating the SOGS-RA's reliability at different levels of problem gambling, we expected to find the SOGS-RA to discriminate highly within the regions representing impaired levels from at-risk gambling to severe problem gambling. That is, in line with recent results obtained applying IRT to the PGSI (Sharp et al., 2012), we expected to find an information function that rises in the appropriate area of the latent trait distribution.
Because the information provided by a test depends on the properties of the test items, the characteristics of the 12 items of the SOGS-RA were investigated. IRT allows for obtaining item difficulty/severity and discrimination parameters that describe these properties. The difficulty of an item indicates where the item functions along the trait, and it can be interpreted as a location index with regard to the trait being measured. For example, a less difficult item functions among the low-trait respondents and a more difficult item functions among the high-trait respondents. Discrimination describes how well an item can differentiate between examinees with levels of trait below the item location and those with levels of trait above the item location. Thus, given that the SOGS-RA should be highly discriminating within some regions representing impaired levels from at-risk gambling to severe problem gambling, we expected to find the majority of the item severity parameters to be located in this particular area of the latent trait distribution and to be associated with high discrimination parameters.
Method Participants
Participants were 981 14- to 20-year-old adolescents (64% males; mean age = 16.57 years, SD = 1.63 years) attending four high schools in a suburban area in Italy (Tuscany). The sample was recruited by presenting the project to the schools' headmasters. Schools were randomly selected, and, once the schools agreed to participate (from the six schools that were contacted, two declined to participate because they were involved in other projects), a detailed study protocol that explained the study's goal and methodology was approved by the institutional review boards of each school. Students received an information sheet, which assured them that the data obtained from them would be handled confidentially and anonymously, and they were asked to give written informed consent. Parents of minors were required to provide consent for their child's participation. All the youth invited to participate agreed to do so. Thus, sample bias during recruitment was minimized.
Measure and Procedure
The SOGS-RA (Winters et al., 1993; Italian version, Bastiani et al., 2010) derives from the South Oaks Gambling Screen (SOGS) for adults (Lesieur & Blume, 1987). Because the scale has been criticized for the ambiguity of some items, and because of its susceptibility to acquiescence bias (Ladouceur et al., 2000), several researchers have provided evidence of the psychometric properties of the SOGS-RA, confirming its single-factor structure (Boudreau & Poulin, 2007; Olason et al., 2006; Winters et al., 1993), internal consistency (e.g., Boudreau & Poulin, 2007; Olason et al., 2006; Skoukaskas, Burba, & Freedman, 2009; Winters et al., 1993), and criterion and construct validity (Derevensky & Gupta, 2000; Olason et al., 2006; Poulin, 2002; Skoukaskas et al., 2009).
The scale is composed of two sections. The first one consists of unscored items investigating gambling behavior. Specifically, these items assess if the respondent has ever participated (never, at least once) in any of 11 gambling activities (card games, coin tosses, bets on games of personal skill, bets on sports teams, bets on horse or dog races, bingo, dice games for money, slot machines, scratch cards, lotteries and online games), the relative frequency (never, less than monthly, monthly, weekly, daily) of participation during the last year in these gambling activities, and the amount of money spent on gambling. The second section is composed of 12 items assessing the severity of problem gambling (see Table 1). All items require dichotomous answers (i.e., yes or no) except the first item, which has a 4-point response scale (never, some of the time, most of the time, every time), and it is dichotomized (i.e., never/some of the time or most of the time/every time) in the scoring phase. The SOGS-RA was presented in a paper-and-pencil version and it was collectively administered during school time. Administration time was about 15 min.
Percentages of Affirmative Answers, Standardized Factor Loadings, Fit Statistics, and Parameters for Each Item of the SOGS-RA
ResultsWith regard to the first section, results showed that 11% of the participants indicated that they never gambled, whereas 871 respondents had gambled at least once during the last year. From the latter group, 470 students were nonregular gamblers (52% males), that is, they participated from less than monthly to less than weekly in at least one gambling activity in the last year, whereas 401 students (77% males) were regular gamblers, that is, they participated weekly or daily in at least one gambling activity in the last year. The most common activities were scratch cards (75%), playing cards for money (74%), and lotteries (57%), whereas the least common ones were online games (16%) and bets on horse or dog races (7%).
Regarding the second section, as expected (e.g., Boudreau & Poulin, 2007; Winters et al., 1993), the percentage of item endorsements was low, with the exception of Items 4 and 6, which, in line with previous studies (Govoni, Frisch, & Stinchfield, 2001; Wiebe et al., 2000), had higher endorsement rates.
Some inconsistency in the SOGS-RA cutoff scores used to categorize adolescent gambling behavior exists (Blinn-Pike et al., 2010). However, a score of 4 or more has been used to indicate a problem gambler; a score of 2 to 3, an at-risk gambler; and a score of 0 to 1, a nonproblem gambler (Winters, Stinchfield, & Kim, 1995). According to these criteria, in the current study, we found 662 (76%) nonproblem gamblers (with scores of 0 or 1), 147 (17%) at-risk gamblers (with scores of 2 or 3), and 62 (7%) problem gamblers (with scores of 4 or more). Their mean SOGS-RA scores were 0.28 (SD = .45), 2.26 (SD = .45), and 5.55 (SD = 1.81), respectively.
Because the administration of the SOGS-RA was precluded in the case of 110 adolescents who declared that they did not gamble in the past year, IRT analyses were carried out on a sample of 871 adolescents. As a preliminary step, the one-factor structure of the scale was tested through categorical weighted least squares confirmatory factor analysis implemented in the Mplus software (Muthén & Muthén, 2004). The results indicated that a single-factor model adequately represents the structure of the SOGS-RA. Specifically, the comparative fit index (CFI) and the Tucker-Lewis index (TLI) were .96 and .97, respectively, and the root mean square error of approximation (RMSEA) was .03, indicating an excellent fit (Schermelleh-Engel & Moosbrugger, 2003). Factor loadings were all significant (p < .001), ranging from .53 to .83 (see Table 1).
Having verified the assumption that a single continuous construct accounted for the covariation between item responses, unidimensional IRT analyses were performed. The two-parameter (2PL) logistic model was tested in order to estimate the item severity and discrimination parameters. The 2PL model is the most commonly used IRT model in clinical assessment (for a review, see Thomas, 2011) and, in particular, it is a suitable model to analyze measures designed to assess maladaptive habit severity, as suggested by recent studies in this area (e.g., Hagman & Cohn, 2011; Molde et al., 2009; Sharp et al., 2012; Srisurapanont et al., 2012).
Parameters were estimated by employing the marginal maximum likelihood (MML) estimation method with the EM algorithm (Bock & Aitkin, 1981) implemented in IRTPRO software (Cai, Thissen, & du Toit, 2011). In order to test the adequacy of the model, the fit of each item under the 2PL model was tested computing the S−χ2 statistics. Given that using larger samples results in a greater likelihood of significant chi-square differences, the critical value of .01 rather than the usual critical value of .05 was employed (Stone & Zhang, 2003). Each item had a nonsignificant S-χ2 value (see Table 1), indicating that all items fit under the 2PL model, that is, both the severity and the discrimination parameters described the properties of the SOGS-RA items. Concerning the severity parameters (b), the results showed that parameters ranged from 1.03 ± .09 to 2.63 ± .25 logit across the continuum of the latent trait (see Table 1). Only Items 4 and 6 had low b values, indicating that these symptoms were the least severe within the continuum of problem gambling, whereas the remaining 10 items all had values ≥2 (when rounded), referring to more severe symptoms distributed from 1.96 ± .09 to 2.63 ± .25 logit. Concerning the discrimination parameters (a), following Baker's (2001) criteria, 10 out of 12 items showed large (a values over 1.34) discrimination levels, with Items 11 and 12 being the most discriminating ones, and only Items 2 and 7 had medium (a values ≤1.34) discriminatory power (see Table 1). For illustrative purposes, Figure 1 shows the item characteristics curves used in IRT to provide visual information of the item characteristics. Severity is represented by the location of the curve along the trait. All items were located in the positive range of the trait, indicating the regions where they function better. Discrimination is represented by the steepness of the curve. The steeper the curve, the better the item can discriminate. All items showed a high slope, indicating their ability to distinguish between respondents with different levels of trait around their location.
Figure 1. The ICCs of each item of the SOGS-RA under the 2PL. Latent trait (Theta) is shown on the horizontal axis and the probability of endorsing the affirmative response option is shown on the vertical axis. ICC = item characteristics curve; SOGS-RA = South Oaks Gambling Screen–Revised for Adolescents; 2PL = two-parameter model.
The TIF estimated under the 2PL model showed that the instrument was sufficiently informative for mid- to high levels of severity (see Figure 2). Within the range of trait from 1.00 to 3.00 standard deviations above the mean (fixed by default to 0), the amount of test information was ≥4, indicating that the instrument was sufficiently informative. More specifically, the amount of test information was >6 starting from a trait level of 1.50, and the TIF peaked at 10 at the trait level of 2.2, where the measurement precision of the SOGS-RA was the highest. Referring to the summed score to ability score conversion table provided by IRTPRO, applying the expected a posteriori (EAP) methods for summed scores (Thissen & Orlando, 2001), the trait level of 0.99 corresponded to a summed score of 2, the trait level of 1.54 to a summed score of 3, and the trait level of 1.97 to a score of 4. As we described, these summed scores represent the cutoff scores employed to classify respondents into at-risk and problem gambler categories. Thus, IRT analyses attested that the SOGS-RA was quite accurate in identifying these categories as well as in discriminating between them.
Figure 2. Test information function of the SOGS-RA under the 2PL model. Latent trait (Theta) is shown on the horizontal axis, and the amount of information and the standard error yielded by the test at any trait level are shown on the vertical axis. SOGS-RA = South Oaks Gambling Screen–Revised for Adolescents; 2PL = two-parameter model.
DiscussionAn effective screening tool is expected to be simple and efficient, with a short administration time. It should be designed to measure youth problem gambling and to identify individuals who are at risk of developing problem behavior (Derevensky & Gupta, 2000). The present results provide evidence that the SOGS-RA satisfies these requirements.
First, in line with previous studies (Boudreau & Poulin, 2007; Olason et al., 2006), the scale was found to be unidimensional. This is a desirable characteristic, as a single-factor structure facilitates the scale's function as a population screen of problem gambling, and because a screening tool is not expected to measure the subtleties and complexities associated with a multidimensional behavioral disorder (Derevensky & Gupta, 2004).
Second, IRT provides clear evidence of the good performance of each item of the SOGS-RA and of the global scale in measuring adolescent problem gambling, as well as in identifying both adolescents who are exhibiting problem behaviors and adolescents for whom gambling represents a potential source of risk. Indeed, the scale's precision was higher from middle to upper levels of the trait, indicating that the SOGS-RA cutoff scores used to define categories of gamblers (i.e., scores from 2 to 3 indicating at-risk gamblers, and scores of 4 or more indicating problem gamblers) are reliable. Moreover, the fact that more information is provided in the upper portion of the trait continuum is consistent with results recently obtained applying IRT to a problem gambling screen employed with adults (Sharp et al., 2012), and more generally with IRT findings of other clinical screens (e.g., Aggen, Neale, & Kendler, 2005; Sharp, Goodyer, & Croudace, 2006).
Specifically, item properties (i.e., severity and discrimination) were consistent with the aim of measuring problem gambling efficiently. With regard to severity, the majority of the items were located along the range of values that the SOGS-RA aims to measure accurately, and the described symptoms (lying, chasing losses, guilt, loss of control, borrowing money, school and family problems) are consistent with the essential features of pathological gambling as defined in the last editions of the DSM (DSM-IV, American Psychiatric Association, 1994; text revision, DSM-IV-TR, American Psychiatric Association, 2000). Only two items (feeling bad about the amount of money lost, gambling more than planned) had low values, indicating that these symptoms were the least severe among problem gamblers, as attested by previous studies reporting that these two items were endorsed more frequently than the others (Govoni et al., 2001; Wiebe et al., 2000). Concerning discrimination, the parameter estimates indicated that the items of the SOGS-RA were able to distinguish between the different levels of the trait. In particular, items referring to skipping classes or being absent from school due to betting activities, and borrowing money or stealing something in order to bet or to cover gambling bets, were the most discriminating ones. The items with the lowest discrimination power were lying about winning and wanting to stop gambling, but not thinking to be able to, suggesting that these symptoms represent less distinctive signs of maladaptive gambling.
The following limitations of our study should be noted. First, as with all self-report questionnaire-based studies, our findings may have been affected by response bias (such as acquiescence or social desirability) and by how confident participants were that their answers would be kept confidential. Another limitation is the way our sample was recruited. As we invited schools to participate, our participants were all adolescents attending high schools. Thus, students who dropped out of school or working adolescents were not included. Despite these limitations, the current study contributes to the literature investigating the psychometric properties of the SOGS-RA and provides evidence of its suitability for screening purposes.
Footnotes 1 IRT has been largely applied in the development of measures of ability and achievement. In this field, the term “difficulty” is the more suitable to define the characteristic of the items. From a clinical standpoint, “difficulty” can be best conceptualized as “severity” of the symptom described by the item. For this reason, in the present article, we employ the term “severity,” referring to the SOGS-RA.
2 Parameters are expressed on a log-odd scale and the units are called logits. The logit is the logarithm of the odd, that is, the ratio between the probability of answering “yes” and the probability of answering “no.”
3 Because information is equal to the inverse of the standard error, higher values indicate higher accuracy.
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Submitted: April 23, 2012 Revised: July 26, 2012 Accepted: July 26, 2012
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Source: Psychology of Addictive Behaviors. Vol. 27. (1), Mar, 2013 pp. 287-293)
Accession Number: 2012-26453-001
Digital Object Identifier: 10.1037/a0029987
Record: 186- Title:
- Thresholds of probable problematic gambling involvement for the German population: Results of the Pathological Gambling and Epidemiology (PAGE) Study.
- Authors:
- Brosowski, Tim. Institute of Psychology and Cognition Research, University of Bremen, Bremen, Germany, timbro@uni-bremen.de
Hayer, Tobias. Institute of Psychology and Cognition Research, University of Bremen, Bremen, Germany
Meyer, Gerhard. Institute of Psychology and Cognition Research, University of Bremen, Bremen, Germany
Rumpf, Hans-Jürgen. Department of Psychiatry and Psychotherapy, University of Lübeck, Germany
John, Ulrich. Institute of Social Medicine and Prevention, University Medicine Greifswald, Germany
Bischof, Anja. Department of Psychiatry and Psychotherapy, University of Lübeck, Germany
Meyer, Christian. Institute of Social Medicine and Prevention, University Medicine Greifswald, Germany - Address:
- Brosowski, Tim, Institute of Psychology and Cognition Research, University of Bremen, Fachbereich 11, Postfach 33 04 40, 28334, Bremen, Germany, timbro@uni-bremen.de
- Source:
- Psychology of Addictive Behaviors, Vol 29(3), Sep, 2015. pp. 794-804.
- NLM Title Abbreviation:
- Psychol Addict Behav
- Page Count:
- 11
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- Bulletin of the Society of Psychologists in Addictive Behaviors; Bulletin of the Society of Psychologists in Substance Abuse
- Other Publishers:
- US : Educational Publishing Foundation
Society of Psychologists in Addictive Behaviors - ISSN:
- 0893-164X (Print)
1939-1501 (Electronic) - Language:
- English
- Keywords:
- intensity, guidelines, low-risk gambling, receiver-operating characteristic curve (ROC), early detection
- Abstract:
- Consumption measures in gambling research may help to establish thresholds of low-risk gambling as 1 part of evidence-based responsible gambling strategies. The aim of this study is to replicate existing Canadian thresholds of probable low-risk gambling (Currie et al., 2006) in a representative dataset of German gambling behavior (Pathological Gambling and Epidemiology [PAGE]; N = 15,023). Receiver-operating characteristic curves applied in a training dataset (60%) extracted robust thresholds of low-risk gambling across 4 nonexclusive definitions of gambling problems (1 + to 4 + Diagnostic and Statistical Manual for Mental Disorders-Fifth Edition [DSM-5] Composite International Diagnostic Interview [CIDI] symptoms), different indicators of gambling involvement (across all game types; form-specific) and different timeframes (lifetime; last year). Logistic regressions applied in a test dataset (40%) to cross-validate the heuristics of probable low-risk gambling incorporated confounding covariates (age, gender, education, migration, and unemployment) and confirmed the strong concurrent validity of the thresholds. Moreover, it was possible to establish robust form-specific thresholds of low-risk gambling (only for gaming machines and poker). Possible implications for early detection of problem gamblers in offline or online environments are discussed. Results substantiate international knowledge about problem gambling prevention and contribute to a German discussion about empirically based guidelines of low-risk gambling. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Epidemiology; *Pathological Gambling; *Thresholds
- Medical Subject Headings (MeSH):
- Adolescent; Adult; Aged; Diagnostic and Statistical Manual of Mental Disorders; Female; Gambling; Germany; Humans; Logistic Models; Male; Middle Aged; Prevalence; ROC Curve; Risk Assessment; Severity of Illness Index; Young Adult
- PsycINFO Classification:
- Substance Abuse & Addiction (3233)
- Population:
- Human
Male
Female - Location:
- Germany
- Age Group:
- Adolescence (13-17 yrs)
Adulthood (18 yrs & older)
Young Adulthood (18-29 yrs)
Thirties (30-39 yrs)
Middle Age (40-64 yrs)
Aged (65 yrs & older) - Tests & Measures:
- Computer-Assisted Telephone Interview
Composite International Diagnostic Interview-Version 3.0 - Grant Sponsorship:
- Sponsor: Senator for Health of Bremen
Recipients: No recipient indicated - Methodology:
- Empirical Study; Quantitative Study
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- Accepted: Mar 25, 2015; Revised: Mar 13, 2015; First Submitted: Jul 7, 2014
- Release Date:
- 20150928
- Copyright:
- American Psychological Association. 2015
- Digital Object Identifier:
- http://dx.doi.org/10.1037/adb0000088
- PMID:
- 26415065
- Accession Number:
- 2015-43528-008
- Number of Citations in Source:
- 35
- Persistent link to this record (Permalink):
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- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2015-43528-008&site=ehost-live">Thresholds of probable problematic gambling involvement for the German population: Results of the Pathological Gambling and Epidemiology (PAGE) Study.</A>
- Database:
- PsycINFO
Thresholds of Probable Problematic Gambling Involvement for the German Population: Results of the Pathological Gambling and Epidemiology (PAGE) Study
By: Tim Brosowski
Institute of Psychology and Cognition Research, University of Bremen;
Tobias Hayer
Institute of Psychology and Cognition Research, University of Bremen
Gerhard Meyer
Institute of Psychology and Cognition Research, University of Bremen
Hans-Jürgen Rumpf
Department of Psychiatry and Psychotherapy, University of Lübeck
Ulrich John
Institute of Social Medicine and Prevention, University Medicine Greifswald
Anja Bischof
Department of Psychiatry and Psychotherapy, University of Lübeck
Christian Meyer
Institute of Social Medicine and Prevention, University Medicine Greifswald
Acknowledgement: This study was funded by the Senator for Health of Bremen.
In the course of the last decades, gambling became an essential component of leisure activities (Petry, 2004). Technologies like the Internet, smart phones, or online social networks further accelerate the public dominance of gambling offers and challenge regulation authorities by their ubiquitous character (Wood & Williams, 2009). In view of diverse negative public health consequences of excessive gambling (e.g., individual and social costs like bankruptcy, impaired psycho-social functioning, or disease-related costs), there is a need of manifold evidence-based means of harm reduction and health promotion that provide a broad framework for corporate social responsibility and the prevention of gambling-related harm.
Applications of Consumption Measures in Gambling ResearchIn contrast to the field of gambling research, alcohol research has a long lasting tradition of analyzing data of consumption behavior (Rehm et al., 2003; Room, 2000). The core idea is that patterns of consumption behavior are reliably associated with particular outcomes like drinking-related harms or beneficial effects (Burger, Brönstrup, & Pietrzik, 2004; Rehm et al., 2003). Assessing such patterns of consumption incorporates cultural norms, beverage types, personal characteristics, or temporal variations like occasions of heavy involvement (Single & Leino, 1998). Despite the fact that biochemical measures like blood alcohol are unique to substance use, most of the quantitative measures of consumption may be adapted to gambling research (Currie & Casey, 2007).
A crucial proxy of consumption patterns is the consumed amount aggregated across some period of time (Room, 2000; Single & Leino, 1998). Several studies established a dose-response-relationship between such measures of alcohol drinking intensity and different drinking-related harms (Burger et al., 2004; Rehm et al., 2003). In line with this research, there is evidence that the intensity of gambling behavior also shows a dose-response-relationship with a positive association of higher intensity and higher probability of related problems (Currie, Hodgins, Wang, el-Guebaly, Wynne, & Chen, 2006). Consequently, evidence about robust relations of individual consumption and associated harms may provide levels of low-risk gambling and will enrich responsible gambling strategies (Currie & Casey, 2007; Currie, Hodgins, Wang, el-Guebaly, & Wynne, 2008).
To date, the concept of a dose-response-relationship between levels of gambling intensity and gambling-related problems has been applied in different populations and with different aims. For instance, thresholds of probable problematic gambling involvement were extracted in a sample of college students (Weinstock, Whelan, & Meyers, 2008), in a mixed sample of community and outpatient individuals (Quilty, Avila Murati, & Bagby, 2014) and levels of moderate posttreatment gambling reliably distinguished problem-free from symptomatic gamblers (Weinstock, Ledgerwood, & Petry, 2007). To the authors’ knowledge, the only thresholds of potentially harmful gambling involvement extracted from representative surveys were provided and repeatedly evaluated for Canadian population samples (Currie et al., 2012, 2006, 2008; Currie, Miller, Hodgins, & Wang, 2009). Currie et al. (2006) established following last year intensity thresholds associated with gambling-related harms (defined as at least two negative consequences of gambling during the last year; items from the Canadian Problem Gambling Index [CPGI]): (a) gambling no more than two to three times per month, (b) spending no more than $501–1,000CAN per year on gambling, and (c) spending no more than 1% of gross family income on gambling activities. In a survey among 171 gambling experts (Currie et al., 2008) the majority agreed on the value of empirically based quantitative limits of low-risk gambling in combination with other gambling-related guidelines and population-level interventions. Despite these promising first steps explicit evidence about potential limits of low-risk gambling is scarce. In absence of a gambling-related equivalent to the standard-drink (Currie & Casey, 2007), behavioral measures of risk-assessment and thresholds of low-risk gambling behavior have to account for national characteristics of products (availability, acceptance, and regulation) by the application of current and representative training samples to calibrate the thresholds of the applied instruments (Schellinck & Schrans, 2011). Beyond the heuristic value of thresholds of low-risk involvement for problem gambling prevention, there is an ongoing debate about the potential risks posed by particular game types because of inherent structural or contextual characteristics (Griffiths, 1993; Meyer, Fiebig, Häfeli, & Mörsen, 2011; Welte, Barnes, Tidwell, & Hoffman, 2009) and some experts emphasize the importance of game-specific thresholds of harmful gambling involvement (Currie et al., 2008). Quilty et al. (2014) empirically extracted such form-specific thresholds of gambling intensity in a nonrepresentative sample of 275 Canadian gamblers and pointed out the importance of further research in this topic with other and larger samples.
Aims of the Study
Against the background of the outlined state of research, the aims of this article are threefold: The first aim is to extract and cross-validate evidence-based thresholds of probable low-risk gambling involvement for the German gambling market in a current and representative dataset. German guidelines of responsible gambling may benefit from evidence-based cutoffs of low-risk gambling involvement. Following the examples of Currie et al. (2006, 2008, 2009) this study, for the first time, presents thresholds of low-risk gambling intensity from a representative but non-Canadian dataset.
Currie et al. (2006) excluded all individuals from their analyses who (a) did not report gambling at least once in the last year and who (b) did not answer all CPGI questions related to harm (individuals who did not gamble more than one to five times per year and self-identified as being a nongambler were not administered the questions related to gambling problems). Cunningham (2006) criticized this preselection procedure particularly because of the exclusion of many individuals from answering the CPGI items because of self-identifying as nongamblers (including respondents who were frequent gamblers). He supposed many false-negative cases, and therefore, decreased representativeness, reliability, and validity of the analyses. It is plausible that (a) a less case-sensitive preselecting filter before presenting the problem gambling items causes too many false-negative problem gamblers in the subsample of presumable nonproblematic individuals; (b) consequently raises the mean level of gambling intensity in the sample of presumable nonproblematic individuals; and (c) therefore, increases the thresholds of gambling intensity that reliably distinguish probable problematic gamblers from low-risk gamblers. According to this criticism, problem gambling items in the study at hand were only administered to individuals that gambled more than 10 days during their lifetime, an empirically derived filter that showed 100% sensitivity for pathological lifetime gamblers (Diagnostic and Statistical Manual for Mental Disorders-Fourth Edition [DSM–IV]). Because of the problematic implications of the missing data Cunningham (2006) recommended systematic replication of research in thresholds of low-risk gambling. This study is the first to do so in a non-Canadian representative dataset.
The second aim of the study is to evaluate how the absolute size of the thresholds is influenced by (a) analyzing the entire unrestricted sample (gamblers and nongamblers) or by (b) reducing the analyzed sample on more involved gamblers (last year gamblers only). It is plausible that the second condition increases the mean level of gambling intensity and consequently the absolute cutoff values. This study will provide evidence to make an informed choice between both conditions and to stimulate further research.
The third aim is to extract and cross-validate form-specific thresholds of probable problematic gambling involvement and to discuss their potential for early detection of problem gamblers in gambling venues or online environments. Analytical conditions of the extracted thresholds vary across different timeframes, levels of problem gambling outcomes and dimensions of gambling intensity to provide a broad and evidence-based foundation for subsequent informed choices and practical applications.
Method Dataset
All analyses are based on a representative dataset of the German population (Pathological Gambling and Epidemiology [PAGE]). A detailed description of the study design and fieldwork may be found elsewhere (Meyer et al., 2015). In a computer-assisted telephone interview conducted between June 2010 and January 2011, 14- to 65-year old participants were asked about their gambling behavior and leisure activities. The survey was based on a nationwide representative, stratified, and clustered random sample of 15,023 participants. The sampling included a random digit dialing procedure that was adapted to the German system of allocation of telephone numbers. To maximize coverage, two sampling frames of landline and mobile phone numbers were applied (for other recent examples of such dual-sampling frames also see: Bundeszentrale für gesundheitliche Aufklärung [BZgA], 2014; Jackson, Pennay, Dowling, Coles-Janess, & Christensen, 2014).
To embrace claims of stability and predictive accuracy of the extracted thresholds (Currie et al., 2009; Quilty et al., 2014), the values were extracted from a training dataset (about 60% of the nonweighted cases, randomly chosen from each stratum of the 1–4 levels of DSM–5 lifetime symptoms of gambling disorder) and were validated in a test dataset (the complementary 40% of the nonweighted cases from each stratum). Because of random sampling, the exact sample sizes differ slightly.
DSM–5 definition of gambling disorder explicitly refers to the occurrence of symptoms within the last year. From an early detection perspective thresholds of low-risk gambling involvement that only cover recent behavior patterns may be of particular interest because of pragmatic issues of real-time observability, documentation, legal aspects of privacy, and data protection. However, thresholds that cover a lifetime perspective may be of particular interest from a preventive perspective, because of claims of generalizability of low-risk gambling involvement across the entire life period. Variables in the PAGE-dataset are based on DSM–IV and refer to both last year and lifetime timeframe. Consequently, analyses were conducted on both timeframes separately but not across timeframes.
Dependent Variables (Outcomes)
Gambling problems were assessed with the gambling section of the World Mental Health (WMH) Composite International Diagnostic Interview (CIDI) Version 3.0 published by the World Health Organization (WHO, 2009). The interview assessed the 10 lifetime symptoms of pathological gambling in DSM–IV. Nine of the 10 symptoms were summed up to create an additive index of problem severity ranging from 0 to 9 (the DSM–IV item about criminal behavior was dropped to apply the new DSM–5 definition of gambling disorder). To incorporate a broad subclinical and clinical definition of problem gambling (Currie et al., 2009), the number of lifetime DSM–5 criteria was recoded into the following dummy variables of nonexclusive outcome groups: (a) at least one DSM–5 criterion across the lifetime, (b) at least two criteria across the lifetime, (c) at least three criteria across the lifetime, and (d) at least four criteria across the lifetime. To identify participants with current gambling problems, we assessed the recentness of the last gambling-related symptom. If a symptom was present within the past year participants were allocated to the following nonexclusive last year outcome groups: (a) at least one DSM–5 criterion across the lifetime and at least one gambling problem-related symptom present within the last year, (b) at least two criteria across the lifetime and at least one gambling problem-related symptom present within the last year, (c) at least three criteria across the lifetime and at least one gambling problem-related symptom present within the last year, and (d) at least four criteria across the lifetime and at least one gambling problem-related symptom present within the last year. The DSM–5 criteria of the gambling section were only presented to individuals who reported a lifetime gambling frequency of more than 10 days. In a pretest of 673 lifetime gamblers this filter item showed 100% sensitivity for pathological lifetime gamblers (5–10 DSM–IV lifetime criteria). In general, item nonresponse was low (average missing rate for all 16 CIDI symptom questions: 0.14%; with 49.3% coded as “don’t know” and 51.7% as “refused” answers). In line with commonly used diagnostic algorithms of the CIDI, cases with postfilter missing values in the dummy coded DSM outcomes were recoded as “0” (no problem).
Independent Variables (Predictors)
The PAGE dataset comprises active gambling days across the following 21 game types: Lotto 6/49 (weighted prevalence of the entire sample: lifetime = 50%; last year = 30%), Spiel77/Super 6 (32; 19), class lotteries, (9; 2), German TV lottery (9; 3), instant lotteries (28; 11), Keno (4; 1), Quicky (1; 0.2), other lotteries (16; 6), TOTO (2; 1), ODDSET (5; 2), other sports betting (4; 2), horse race betting (5; 1), casino table games (9; 2), casino slot machines (6; 2), poker (6; 4), gaming machines in amusement arcades, restaurants or pubs (17; 5), bingo (4; 2), TV quiz channel gambling (7; 3), trading on the stock exchange (2; 1), and private/illicit gambling (3; 2). All types are documented for lifetime (nominal level: never, 1–10 days, 11–50 days, 51–100 days, 101–500 days, 501–1,000 days, or more than 1,000 days) and last year (metric level: 0–365 days) timeframes. In the following active gambling days in particular game types (lifetime or last year) are termed “form-specific” predictors.
In line with Currie et al. (2006), a composed index of active gambling days was computed by extracting the maximum value of active days across all game types (lifetime as well as last year). This variable only regards the most favored game type (the type, a gambler was most involved in) and disregards that an individual may be involved in several types on one active day and, therefore, provides a conservative proxy of overall gambling involvement (Cunningham, 2006). The second composed index was the number of game types involved in (lifetime as well as last year) to provide a second proxy measure that compensates the weakness of the first. The third predictor across all game types was the maximum amount of money (€) lost within 1 year of a lifetime (open answer; only available for lifetime outcomes). In the following the three aggregated predictors of activities across all game types (lifetime or last year) are termed “composed” predictors. Missing values in all predictor variables were recoded like the CIDI outcome variables.
Other Variables (Covariates)
In line with Currie et al. (2006) and Currie, Hodgins, Wang, el-Guebaly, Wynne, and Miller (2008), the validation analyses included sociodemographic covariates, which also influence the applied problem gambling outcomes (see Meyer et al., 2011): gender, age, employment status (unemployed; full- or part-time employed), migration background (German; mother, father or oneself not German), and education (three ascending levels: ([1] no graduation or secondary school up to 9 years, [2] secondary school up to 10 years, and [3] general or vocational diploma).
Data Analyses
All data analyses were conducted with PASW Statistics 18. To account for the complex sampling design and to reduce potential bias from selective nonresponse, all analyses were based on weighted data and the estimation of SEs in logistic regressions was adjusted for clustered sampling. In a first step in the training dataset, each predictor variable of a timeframe was applied to each outcome variable of the corresponding timeframe to extract area under the curve (AUC) values, sensitivity, specificity, and the optimal cutoff of the predictor with equal weight to sensitivity and specificity. The AUC values comprise combined information of sensitivity and specificity of a predictor variable for a dichotomous outcome with values between 0.5 and 1 and higher values reflecting higher accuracy (for a detailed discussion of this procedure see: Swets, Dawes, & Monahan, 2000). Both objects of the study (prevention guidelines and early detection heuristics) benefit from this approach of balancing sensitivity and specificity, because of challenges of false alarm, stigmatizing, and blunting. In a second step, the extracted thresholds of indicators with useful accuracies (AUC >0.7; see Swets, 1988) were inspected for their concurrent validity by logistic regressions in the test dataset that also incorporated sociodemographic confounders like gender, age, education, employment status, and migration. These adjustment procedures for confounding variables of problem gambling symptoms provide a very conservative way to scan the concurrent validity of the extracted thresholds of gambling behavior in addition to other personal risk factors. In a third step, thresholds of useful indicators were recalculated in the reduced training sample of only last year gamblers (45.8% of the weighted training sample), who were more involved than the entire sample of gamblers and nongamblers. The absolute threshold values extracted from both samples were compared descriptively to evaluate the impact of a plausible but arbitrary preselection.
ResultsTable 1 reflects AUC, sensitivity and specificity values of all predictor-outcome-combinations in the training set that were above the recommended AUC value of 0.7, representing useful prediction accuracy. The table shows increasing accuracy values with ascending problem levels. Both timeframes provided useful accuracy in predicting corresponding levels of problem gambling, with maximal AUC values for the composed last year predictors: maximal gambling days on favored game type (0.89–0.90) and sum of used game types (0.87–0.88). However, the composed lifetime predictors of gambling intensity in terms of losses (0.77–0.82), sum of used game types (0.81–0.84), and maximal gambling days on favored game type (0.80–0.84) also provided useful accuracy values for most combinations of predictors and outcomes. The only form-specific lifetime predictors with useful prediction accuracy remained for the number of active gambling days on gaming machines (0.72–0.77) and the number of active gambling days on poker (0.72). For the last year timeframe, only active gambling days on gaming machines (0.81–0.84) showed useful prediction accuracy. Active gambling days on all other game types did not provide adequate predictive accuracy for the different levels of problem gambling in the training data.
Accuracy and Extracted Threshold Values in the Training Sample of Last Year and Lifetime Predictors
The following Tables 2 to 5 provide information about a series of logistic regressions in the test data for each combination of an outcome, the corresponding predictors and cutoffs with useful accuracy. The regression models also include sociodemographic covariates to estimate the concurrent validity of showing gambling involvement beyond the extracted thresholds. In other words, the following regressions estimate the additional risks (in addition to inherent personal characteristics) that are posed by gambling involvement beyond the applied thresholds. In 27 of the 28 regression models, gambling intensity beyond the threshold showed larger odds ratios for problem gambling outcomes than the sociodemographic covariates. In many cases the covariates even became insignificant risk factors (only for lifetime active gambling days on poker male gender showed higher risks for gambling problems than exceeding the threshold).
Logistic Regression Models of the Test Sample for Last Year Composed Predictors
Logistic Regression Models of the Test Sample for Last Year Form-Specific Predictors
Logistic Regression Models of the Test Sample for Lifetime Composed Predictors
Logistic Regression Models of the Test Sample for Lifetime Form-Specific Predictors
Logistic Regression Models of the Test Sample for Last Year Composed Predictors
Table 2 comprises information about the eight regression models that estimate the concurrent validity of exceeding the two last year composed predictors. Maximum gambling days on the favored game type on at least 7 to at least 15 days increased the risks by the 34- to 56-fold and gambling on at least two game types (in comparison with individuals who did not exceed this threshold) increased the risk of actual problem gambling symptoms by the 38- to 65-fold.
Logistic Regression Models of the Test Sample for Last Year Composed Predictors
Table 3 includes information about the regression models that estimate the concurrent validity of exceeding the only last year form-specific predictor. Gambling on gaming machines for at least 3 days during the last year (in comparison with individuals who did not exceed this threshold) increased the risk of actual problem gambling symptoms by the 29- to 39-fold.
Logistic Regression Models of the Test Sample for Last Year Form-Specific Predictors
Table 4 provides information about the 12 regression models that estimate the concurrent validity of showing a gambling involvement beyond the three lifetime composed predictors maximal loss per year, number of used game types, and maximal active gambling days on favored game type.
Logistic Regression Models of the Test Sample for Lifetime Composed Predictors
A lifetime number of active gambling days on the favored game type of at least 11–50 days (in comparison with individuals who did not exceed this threshold) increased the risks of at least one to at least four lifetime DSM–5 criteria by the 13- to 36-fold. Maximum gambling losses in a year across the lifetime of at least 29€ to at least 100€ (in comparison with individuals who did not exceed these thresholds) increased the risks of at least one to at least four lifetime DSM–5 criteria by the 8- to 29-fold. A lifetime number of used game types of at least three to at least four types (in comparison with individuals who did not exceed these thresholds) increased the risks of at least one to at least four lifetime DSM–5 criteria by the 6- to 11-fold.
Table 5 provides information about the three regression models that estimate the concurrent validity of the only lifetime form-specific predictors of active gambling days on gaming machines and poker. Gambling at least 1–10 days on poker (in comparison with individuals who did not exceed this threshold) increased the risk of at least four lifetime DSM–5 criteria by the fourfold. Gambling at least 1–10 days on gaming machines (in comparison with individuals who did not exceed this threshold) increased the risks of at least two to at least four lifetime DSM criteria by the 5- to 7-fold.
Logistic Regression Models of the Test Sample for Lifetime Form-Specific Predictors
In a final step of analyses, useful thresholds of low-risk gambling from the entire training sample (including nongamblers and gamblers at any level of involvement) were compared with counterpart thresholds extracted from a reduced sample, only consisting of more involved last year gamblers (45.8% of the weighted training sample). Results are presented in Table 6. The AUC-values of predictive accuracy for each combination remained merely unchanged by this reduction but were slightly better in the entire sample. Moreover, the application of this preselecting filter criterion approximately doubled the absolute values of the low-risk thresholds across almost all dimensions of involvement. However, these findings did not hold for the form-specific predictors (lifetime active days on poker or gaming machines and last year active days on gaming machines). These cutoffs remained unchanged by the reduced sample.
Comparisons Between Extracted Cutoffs of the Entire Training Sample and a Preselected Sample Consisting of Only Last Year Gamblers (45.8% of the Weighted Sample)
DiscussionThe first aim of this study was to establish thresholds of probable low-risk gambling involvement for the German gambling market, following the examples of Canadian studies by synthesizing their course of analyses. In line with Currie et al. (2009), a broad range of nonexclusive criteria of gambling-related problems was applied to cover several levels of outcomes (subclinical and clinical). Furthermore, thresholds of potential problematic gambling involvement were extracted from a training sample (Currie et al., 2006) and cross-validated with confounder-analyses on a test sample (Currie et al., 2008) to check robustness against sampling variance and concurrent validity. Thereby, it was possible to establish and cross-validate several thresholds of probable problematic gambling involvement across a broad definition of gambling problems, indicators of gambling involvement and timeframes. The regression models applied to cross-validate the heuristics of probable low-risk gambling incorporated covariates like age, gender, education, migration, and unemployment status. In line with Currie et al. (2006, 2008), most effect sizes of gambling beyond the thresholds of low-risk involvement strongly exceeded the effect sizes of the sociodemographic risk factors. This relation emphasizes the outstanding role of gambling involvement as necessary cause or mediating variable in a complex net of risk posing conditions (correlative analyses from cross-sectional data do not allow further inference).
In comparison with the Canadian thresholds of probable problematic gambling involvement (Currie et al., 2006), the cutoffs on hand are lower. Probable causes may be different modes of interview (face-to-face vs. telephone), item wording (Wood & Williams, 2007), measures of problem gambling (PGSI vs. CIDI), outcomes (at least two negative consequences vs. counts of problem gambling symptoms) or preselecting filter criteria (before presenting problem gambling items or to purify the analyzed sample). Presenting evidence for the methodological impact of a filter-criterion on the absolute size of the thresholds was another aim of the study at hand. In comparison with low-risk thresholds extracted from the entire sample (nongamblers and gamblers at any level of involvement), thresholds extracted from more involved last year gamblers approximately doubled across mostly all dimensions of gambling intensity. Only low-risk thresholds for gaming machines (both timeframes) and lifetime poker were not influenced by the artificial enhancement of gambling intensity in the sample (this fact further substantiates the validity of the presented form-specific thresholds, which obviously were not influenced by this kind of filter-setting). Of course, purifying subgroups (e.g., problem gamblers vs. frequent gamblers) makes sense to some extent, but the abandonment of arbitrary filter items is also warranted to cover a more general population (e.g., first-time users included). Against the background of doubled cutoffs caused by an arbitrary filter in this study, further research is needed to optimize future approaches of extracting thresholds of low-risk gambling behavior. Further examples of important methodological influences may be adopted from research in prevalence estimation (for a summary see Meyer et al., 2015).
However, in summary (across all 28 useful combinations) last year indicators showed higher AUC values, sensitivity, and specificity than lifetime indicators. Nevertheless, some lifetime indicators also showed useful accuracy. The composed indicators “Sum of used game types,” “Maximal gambling days on favored game type,” or “Maximal loss per year across all game types” revealed higher AUC values and sensitivity than the form-specific indicators but particular form-specific indicators showed very high specificity. Consequently, it is possible to give some general recommendations in the context of preventing gambling problems for the German gambling market: (a) Last year gambling on only one game type, (b) below 7 days on the mostly used game type, and (c) below 3 days on gaming machines strongly reduced the risks of any last year DSM–5 criterion of gambling disorder. Moreover, (d) lifetime gambling on only one or two game types, (e) below 11–50 days on the mostly used game type, (f) the avoidance of gambling poker or (g) gaming machines, and (h) gambling below a self-reported maximum loss per year of 29€ strongly reduced the risks of any lifetime DSM–5 criterion of gambling disorder (in some situations it may be fruitful to apply the alternative cutoffs from Table 6). The graduated thresholds of probable low-risk gambling involvement presented in this article may help to formulate some general rules of low-risk gambling for the German gambling market, if the values will be validated successfully by results from other sources of data (particularly controlled longitudinal studies). Such empirically based rules of low-risk gambling in Germany are still up for discussion, but valuable examples do already exist for gambling in Canada (Currie et al., 2008) or for alcohol consumption in Germany (Burger et al., 2004). As long as no German rules of low-risk gambling exist, a justifiable and most cautious recommendation is lifetime abstinence because of the general risks posed by any gambling behavior. Furthermore, we advise recipients against an illusory security because gambling below the cutoffs only constitutes probabilistic information from cross-sectional associations and largely neglects individual idiosyncrasies or longitudinal impacts of complying with the thresholds. This study only represents a starting point and evidence from prospective randomized trials is indispensable to avoid negative longitudinal consequences.
Best performing predictors were both last year composed indicators: maximal gambling days on the favored game type and the number of used game types. The fact that these nonmonetary predictors outperform the monetary predictor of lifetime maximal loss per year contradicts findings of Currie et al. (2009) who revealed monetary measures as most accurate predictors (absolute spending per year; percent of gross family income spent per year). Of course, the applied monetary indicators in both studies differ in the applied timeframes (last year vs. lifetime), what might have influenced accuracy. Nevertheless, the amount of time spent involved in one or across several game types decreases the amount of available time for other activities and has proven as noticeable proxy of harmful gambling involvement (Currie & Casey, 2007). Moreover, nonmonetary indicators cancel out criticism on moderating effects of an individual’s income onto the association of monetary gambling involvement and gambling problems (Currie & Casey, 2007). Results suggest the complementary application of both indicators in future research, monetary and nonmonetary.
The last aim of this study was to explore form-specific thresholds of low-risk gambling intensity that may be reflective of probable problem gambling. On the one hand, sensitivity values of these form-specific thresholds are low, because of the fact that the entire population of problem gamblers cannot be characterized by levels of involvement in only one type of gambling. On the other hand, some form-specific thresholds showed very high accuracy, particularly in terms of specificity. For example, lifetime gambling on at least 1–10 days on gaming machines provided a specificity of 83% for at least four DSM–5 symptoms in the training sample (on poker = 95%). The specificity value of the form-specific last year threshold of gaming machines even was higher with 97%. According to the training data, addressing probable problematic individuals in gambling venues with three or more active gambling days on gaming machines in the last year will cause a false-positive decision in only 3% of the cases. High precision rates also hold for the test sample, evidenced by the large adjusted odds ratios for most thresholds in the regression-models. However, such robust associations only hold for particular game types like gaming machines and poker because of several methodological arguments: (a) Strong bivariate statistical associations of particular game types and measures of gambling problems (LaPlante, Nelson, LaBrie & Shaffer, 2011; Welte et al., 2009) provide the groundwork for a phenomenon called “spectrum bias” (Gambino, 2006, 2012). It is easier for a screener to detect severe cases and, therefore, screening instruments often show high sensitivity (one part of high accuracy) in clinical settings. Increasing accuracy with ascending severity of the predicted outcome also appeared in the analyses of Currie et al. (2009) and in the analyses at hand (see Table 1). From a methodological perspective, particular gambling venues can be seen as a clinical setting with a lot of gamblers at the upper level of a dimension of gambling-related problems and the application of thresholds of gambling intensity represents some kind of screening. This effect can also be seen in the analyses of Currie et al. (2009), in which the accuracy of the applied thresholds of gambling intensity in the subpopulations of gaming machine and casino gamblers exceeded some other values of accuracy. (b) Patrons of a particular game type are more homogeneous than the general population in terms of sociodemographic risk factors or other moderating variables. (c) The probability of a positively screened patron (after exceeding a particular threshold) to be a true problem gambler (positive predictive value) is a function of the sensitivity and specificity of a screener, but more a function of the base rate of the outcome in the context of its application (Gambino, 2006, 2012). High bivariate associations of particular game types and measures of problem gambling can be used as a kind of preselection that increases the outcome’s base rate and, therefore, improves predictive accuracy in form-specific thresholds of probable problematic gambling intensity.
Former publications raised some doubt about form-specific thresholds of gambling intensity because of poor robustness (Currie et al., 2009; Quilty et al., 2014). Evidence from the cross-validated analyses at hand confirms such issues for some types of gambling, but is contradictory for others. Furthermore, Currie et al. (2008) challenge the utility of form-specific thresholds because of the fact that most gamblers are involved in more than one type of gambling. From a preventive and person-centered perspective this argument is convincing. However, from an early detection and venue-centered perspective this argument does not hold. The extraction of form-specific thresholds from large gambling surveys provides the benefit that mostly form-specific gambling across all online platforms and offline venues is reported in an integrated manner. Hence, exceeding such threshold in one venue or online-platform is a conservative proxy measure of overall form-specific gambling involvement, let alone the entire involvement in other types of gambling, and therefore, it is a robust predictor of patrons who are potentially at risk.
Summing up theoretical and empirical evidence, extracting thresholds of probable problematic ambling involvement in particular game types from self-report surveys provides a fruitful approach in assisting gamblers, regulators, and prevention service providers, because it constitutes simple and obviously robust rules of thumb to address probable problematic gamblers on an online platform or in an offline venue. It is worth noting, however, that the stable association of slight involvement in some game types with problem gambling does not warrant any causal interpretation or evaluation of the potential risks, posed by a particular type of gambling. The low thresholds may be an indicator of high risks, already posed on low doses because of inherent characteristics of the game type. However, another plausible explanation is related to the fact that certain gambling forms go along with a selective attractiveness for vulnerable subpopulations. This study does not aim to disentangle such causal relationships. Rather, the analyses establish and validate robust statistical associations. However, a combination of the validated thresholds of involvement with current observation tools of problem gamblers in gambling venues (e.g., Delfabbro, Osborn, Nevile, Skelt, & McMillen, 2007; Hayer, Kalke, Buth, & Meyer, 2013) will probably enhance the predictive accuracy of early detection tools.
Limitations and ConclusionLike all survey data, the validity of the extracted low-risk thresholds may be biased by over- or underestimation of gambling involvement measures. Currently, there is evidence for both directions of reporting-bias (Currie & Casey, 2007) and a final position is still up for further research, even if some data are already at hand (Braverman, Tom, & Shaffer, 2014; Wood & Williams, 2007). Further research should test reliability and validity of the extracted limits with other datasets and sources (particularly with data from prospective studies and from actual gambling behavior; e.g., see: Brosowski, Meyer, & Hayer, 2012). Until the thresholds are replicated, they are only tentative heuristics and must be applied circumspectly by researchers, regulation authorities, operators, or fieldworkers. Moreover, further research has to examine the liability of the thresholds for moderating effects of gender, age, or socioeconomic status. However, strong moderating effects on the relationship between gambling intensity and problem gambling would have partially diminished the concurrent validity in the confounder models, which was not the case.
Furthermore, the cross-sectional design does not provide any evidence of causal relationships between exceeded thresholds of intensity and subsequent symptoms of problem gambling (for the Canadian cutoffs temporal precedence is confirmed; Currie et al., 2012). Nevertheless, the analyses at hand constitute a range of robust and cross-validated levels of probable low-risk gambling involvement across different indicators and timeframes, across overall gambling involvement and in particular types (poker and gaming machines). The presented heuristics may constitute a starting point to formulate evidence-based rules of low-risk gambling for national and international issues of primary prevention. Moreover, this study provides strong empirical and theoretical arguments for the application of form-specific thresholds of low-risk gambling in early detection scenarios. It also points out a fruitful roadmap of further research for gambling operators that try to assist their employees by establishing objective and robust rules-of-thumb to detect probable problematic individuals at their gambling venues. Further secondary data analyses of existing surveys of gambling behavior are obviously capable to complement current research in actual gambling behavior or observation guidelines and can provide at least one part of the answer to the issue of reliably detecting probable problematic gamblers and to reduce or avoid negative consequences of excessive gambling involvement at an early stage.
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Submitted: July 7, 2014 Revised: March 13, 2015 Accepted: March 25, 2015
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Source: Psychology of Addictive Behaviors. Vol. 29. (3), Sep, 2015 pp. 794-804)
Accession Number: 2015-43528-008
Digital Object Identifier: 10.1037/adb0000088
Record: 187- Title:
- Time course of attentional bias for gambling information in problem gambling.
- Authors:
- Brevers, Damien. Psychological Medicine Laboratory, Consciousness, Cognition & Computation Group, Universite Libre de Bruxelles, Belgium, dbrevers@ulb.ac.be
Cleeremans, Axel. Consciousness, Cognition & Computation Group, Universite Libre de Bruxelles, Belgium
Bechara, Antoine. Psychiatry Department, McGill University, Canada
Laloyaux, Cédric. Psychological Medicine Laboratory, Universite Libre de Bruxelles, Belgium
Kornreich, Charles. Psychological Medicine Laboratory, Universite Libre de Bruxelles, Belgium
Verbanck, Paul. Psychological Medicine Laboratory, Universite Libre de Bruxelles, Belgium
Noël, Xavier. Psychological Medicine Laboratory, Universite Libre de Bruxelles, Belgium - Address:
- Brevers, Damien, FNRS, Psychological Medicine Laboratory, CHU-Brugmann, Universite Libre de Bruxelles,, place Van Gehuchten, 4, 1020, Brussels, Belgium, dbrevers@ulb.ac.be
- Source:
- Psychology of Addictive Behaviors, Vol 25(4), Dec, 2011. pp. 675-682.
- NLM Title Abbreviation:
- Psychol Addict Behav
- Page Count:
- 8
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- Bulletin of the Society of Psychologists in Addictive Behaviors; Bulletin of the Society of Psychologists in Substance Abuse
- Other Publishers:
- US : Educational Publishing Foundation
Society of Psychologists in Addictive Behaviors - ISSN:
- 0893-164X (Print)
1939-1501 (Electronic) - Language:
- English
- Keywords:
- attentional bias, craving, dependence, eye-tracking, gambling
- Abstract:
- There is a wealth of evidence showing enhanced attention toward drug-related information (i.e., attentional bias) in substance abusers. However, little is known about attentional bias in deregulated behaviors without substance use such as abnormal gambling. This study examined whether problem gamblers (PrG, as assessed through self-reported gambling-related craving and gambling dependence severity) exhibit attentional bias for gambling-related cues. Forty PrG and 35 control participants performed a change detection task using the flicker paradigm, in which two images differing in only one aspect are repeatedly flashed on the screen until the participant is able to report the changing item. In our study, the changing item was either neutral or related to gambling. Eye movements were recorded, which made it possible to measure both initial orienting of attention as well as its maintenance on gambling information. Direct (eye-movements) and indirect (change in detection latency) measures of attention in individuals with problematic gambling behaviors suggested the occurrence of both engagement and of maintenance attentional biases toward gambling-related visual cues. Compared to nonproblematic gamblers, PrG exhibited (a) faster reaction times to gambling-cues as compared to neutral cues, (b) higher percentage of initial saccades directed toward gambling pictures, and (c) an increased fixation duration and fixation count on gambling pictures. In the PrG group, measures of gambling-related attentional bias were not associated with craving for gambling and gambling dependence severity. Theoretical and clinical implications of these results are discussed. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Cognitive Bias; *Craving; *Pathological Gambling; *Selective Attention; *Attentional Bias
- Medical Subject Headings (MeSH):
- Adult; Analysis of Variance; Attention; Behavior, Addictive; Case-Control Studies; Cues; Eye Movements; Female; Gambling; Humans; Male; Motivation; Photic Stimulation; Psychomotor Performance; Reaction Time; Self Report; Severity of Illness Index; Time Factors
- PsycINFO Classification:
- Behavior Disorders & Antisocial Behavior (3230)
- Population:
- Human
Male
Female - Age Group:
- Adulthood (18 yrs & older)
- Tests & Measures:
- Beck Depression Inventory DOI: 10.1037/t00741-000
Spielberger State Trait Anxiety Inventory
South Oaks Gambling Screen DOI: 10.1037/t03938-000
Structured Clinical Interview for DSM-IV
Gambling Craving Scale DOI: 10.1037/t04057-000 - Grant Sponsorship:
- Sponsor: Belgium National Lottery, Belgium
Recipients: No recipient indicated
Sponsor: National Fund for Scientific Research, Belgium
Recipients: No recipient indicated - Methodology:
- Empirical Study; Quantitative Study
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- First Posted: Jun 20, 2011; Accepted: Apr 20, 2011; Revised: Mar 22, 2011; First Submitted: Sep 7, 2010
- Release Date:
- 20110620
- Correction Date:
- 20121217
- Copyright:
- American Psychological Association. 2011
- Digital Object Identifier:
- http://dx.doi.org/10.1037/a0024201
- PMID:
- 21688874
- Accession Number:
- 2011-12261-001
- Number of Citations in Source:
- 29
- Persistent link to this record (Permalink):
- http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2011-12261-001&site=ehost-live
- Cut and Paste:
- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2011-12261-001&site=ehost-live">Time course of attentional bias for gambling information in problem gambling.</A>
- Database:
- PsycINFO
Time Course of Attentional Bias for Gambling Information in Problem Gambling
By: Damien Brevers
Psychological Medicine Laboratory, Université Libre de Bruxelles, Belgium;
Consciousness, Cognition & Computation Group, Université Libre de Bruxelles, Belgium;
Axel Cleeremans
Consciousness, Cognition & Computation Group, Université Libre de Bruxelles, Belgium
Antoine Bechara
Psychiatry Department and Faculty of Management, McGill University;
Department of Psychology, University of Southern California
Cédric Laloyaux
Psychological Medicine Laboratory, Université Libre de Bruxelles, Belgium
Charles Kornreich
Psychological Medicine Laboratory, Université Libre de Bruxelles, Belgium
Paul Verbanck
Psychological Medicine Laboratory, Université Libre de Bruxelles, Belgium
Xavier Noël
Psychological Medicine Laboratory, Université Libre de Bruxelles, Belgium
Acknowledgement: This research was supported by the Belgium National Lottery and the National Fund for Scientific Research, Belgium. We thank Michael Baker, Table Games Manager for the VIAGE casino complex (Brussels, Belgium), for his help in recruiting gamblers participants.
The main goal of this study is to explore the time course of the deployment of attention in pathological gamblers as they process visual stimuli that are or are not related to their addiction.
Pathological gambling (PG), similar to other addictions, can be operationally defined as the continuation of maladaptive choices despite the occurrence of aversive consequences (e.g., relationship, job; APA). PG afflicts about 1.6% of the general population (Inserm, 2008). With growing availability of gambling opportunities, prevalence of PG is rising and beginning to pose a serious public health problem (Inserm, 2008).
Numerous studies have shown that addiction-related cues are processed more efficiently by addicted individuals, thus further reinforcing subsequent maladaptive cognition and behaviors (for a review see, Field & Cox, 2008; Field, Munafò, & Franken, 2009). According to the incentive-sensitization theory (Robinson & Berridge, 1993; Robinson & Berridge, 2003), compulsive gambling (as other states of addiction) might be caused primarily by repeated exposure to gambling-related stimuli that would induce gambling sensitization in the brain's meso-limbic and meso-cortical dopamine systems that attribute incentive salience to reward-associated stimuli. In other terms, pathological motivation could arise from sensitization of brain circuits that mediate Pavlovian conditioned incentive motivational processes. Therefore, this sensitization might occur even in the absence of drug actions, such as in abnormal gambling. Once rendered hypersensitive, these systems generate pathological incentive motivation (i.e., wanting) for addictive behaviors. During wanting, incentive salience, which is a type of incentive motivation, plays a role in promoting approach toward, and consumption of, rewards. Wanting has distinct psychological and neurobiological features from liking. In this context, incentive sensitization could produce an attentional bias toward processing drug-associated stimuli and pathological motivation for drugs (compulsive wanting; Robinson & Berridge, 1993, 2003).
Recent theoretical models of addiction (e.g., Baker, Morse, & Sherman, 1987; Field & Cox, 2008; Franken, 2003; Kavanagh, Andrade, & May, 2005; Robinson & Berridge, 1993; Ryan, 2002) suggest that attentional biases for substance-related cues experienced by substance users could modulate aspects of subjective experience (e.g., craving) and influence addictive behaviors. A recent meta-analysis by Field, Munafò, and Franken (2009) indicated a modest but statistically significant positive correlation between subjective craving, as assessed with self reported measures, and attentional bias. Moreover, this study highlighted a marginally significant effect for a larger association between craving and attentional bias when measures of the maintenance of attention from substance-related cues were compared with measures of the initial orienting response of attention. One possible explanation for this difference is that the attentional maintenance measure better reflects the specific attentional processes that are influenced by incentive mechanisms, namely, a bias to hold attention on motivationally salient cues (LaBerge, 1995). These insights led us to investigate the relationship between attention biases and craving in problem gamblers.
Although numerous studies have focused on the attentional biases in individuals who abuse substances such as alcohol, drugs and/or tobacco (for a recent review of attentional biases in addicts see Field & Cox, 2008 and Field et al., 2009), little is known about attentional bias in addictive disorders that do not necessarily involve the ingestion of exogenous substances, namely pathological gambling. For instance, using a modified Stroop paradigm, participants with compulsive gambling took longer to name the color of words relating to gambling compared to healthy controls or to low problem gamblers (Boyer & Dickerson, 2003; Molde et al., 2010). However, performance on the modified Stroop paradigm does not allow investigation of more specific attentional processes, such as the initial orienting component, which is typically followed by attentional capture or repulsion (Jones, Bruce, Livingstone, & Reed, 2006). Hence the main goal of this study was to investigate the effects of gambling on these specific processes of attention.
To shed further light on the nature of gambling-related attentional bias, we used a change detection task called “the flicker paradigm” (Rensink, O'Regan, & Clark, 1997; Simons & Rensink, 2005), which has often been used to demonstrate “change blindness”. This task consists of consecutive and repeated presentations of two identical visual scenes separated by a mask (typically a gray screen), that differ in only one element. The presentation of the visual scenes continues until the change is detected. With normal individuals, the number of presentations necessary for the change to be detected is much higher than what would be expected based on a direct comparison of the two alternating pictures—hence the expression “change blindness,” for participants are surprisingly found to be unable to detect changes that are typically obvious under normal viewing conditions. The number of repetitions required for the change to be detected thus constitutes the main dependent measure in this paradigm, and it has been shown to be influenced by specific conditions or with specific populations. For instance, some studies have reported faster change detection latency by problematic heavy drinkers for addiction-related changes compared to neutral ones (Jones, Jones, Smith, & Copley, 2003; Jones, Bruce, Livingstone, & Reed, 2006).
However, a main limitation of classic behavioral paradigms (such as the flicker paradigm but also modified Stroop and visual probe tasks) is that they do not make it possible to explore the time course of the allocation of attention. Tracking eye movements, by contrast, in addition to being ecologically valid, importantly enables the investigation of attentional biases not only at stimulus offset but also during the entire duration of the stimulus presentation (Schoenmakers, Wiers, & Field, 2008). Attentional biases as revealed by the pattern of eye movements in response to a visual stimulus (i.e., prolonged maintenance of gaze, or a higher proportion of initial eye movements directed toward addiction-related vs. neutral cues) has so far been demonstrated only in individuals addicted to psychoactive substances such as tobacco, cannabis, alcohol, and drugs (for a review see, Field et al., 2009).
In summary, we aimed to investigate the nature of gambling-related attentional bias in a group of problem gamblers (PrG) by using a flicker paradigm for induced change blindness with direct (i.e., eye movements recording) and indirect (i.e., change detection latency) measures of attentional processes. We test three primary hypotheses: compared to normal control (CONT), PrG would (a) detect a gambling-related change more rapidly than a neutral change; (b) direct their initial eye movement toward gambling-related cues, indicating facilitated attentional engagement toward gambling stimuli; (c1) show prolonged maintenance of gaze toward gambling-related elements compared to neutral stimuli, and (c2) would exhibit a higher proportion of eye movements toward gambling-related elements, indicating attentional maintenance on gambling cues. In addition, we expect to find an association between gambling self-reported craving and maintenance of attention toward gambling cues.
Method Participants
Two groups participated in the study: (a) a control group (CONT; n = 35) and (b) a problem gamblers (PrG) group (n = 40). All subjects were adults (>18 years old) and provided informed consent that was approved by the appropriate human subject committee at the Brugmann University Hospital. The demographic data on the two groups are presented in Table 1.
Demographical Data and Standard Deviations For Pathological Gambling (PG), Problem Gambling (PrG) and Normal Control (CONT) Groups
Recruitment and Screening Methods
Gambling dependence severity was assessed with the South Oaks Gambling screen (SOGS; Lesieur & Blume, 1987). Scores on the SOGS can vary between 0 and 20. An example of an item is: “Have you ever borrowed from someone and not paid them back as a result of your gambling?”
All PrG (n = 40) scored ≥ 3 (max = 8) on the SOGS, indicative of problem gambling, and 13 participants (32.5%) met the more stringent criteria for probable pathological gambling (SOGS ≥ 5). On the basis of Lawrence, Luty, Bogdan, Sahakian, and Clark (2009), we will refer to this combined group henceforth as PrG. Distribution of SOGS scores in the PrG group is presented in Table 1. CONT were recruited by word of mouth from the employees at the psychiatric unit of the Brugmann University Hospital. To avoid biases, resulting from inside knowledge of how these tasks operate, Psychiatrists, Psychologists and other personnel having had psychological training were excluded from participation. On the SOGS, only six CONT (17%) reported playing the numbers or betting on lotteries occasionally (i.e., less than once a week). All remaining control participants reported not gambling at all.
Current Clinical Status
Current clinical status of depression and anxiety was rated with French versions of the Beck Depression Inventory (Beck, Ward, Mendelson, Mock, & Erbaugh, 1967) and the Spielberger State–Trait Anxiety Inventory (STAI; Spielberger, 1993), respectively. The number of cigarettes per day was also included on the basis of previous studies (e.g., Heishman, 1998) that highlighted an effect of nicotine dependence on cognitive processing (e.g., sustained attention). We excluded any control subject who met an Axis I psychiatric diagnosis assessed by the Structured Clinical Interview for DSM–IV (First, Spitzer, Gibbon, & Williams, 2002), who had experienced a drug use disorder during the year before enrollment in the study, or who had consumed more than 54g/day of alcohol for longer than one month. On the basis of the results of their medical history and physical examination, they were judged to be medically healthy. All participants were asked to avoid the use of drugs, including narcotic pain medication, for the five days prior to testing and to avoid alcohol consumption for the preceding 24 hr.
Self-Report Measure of Gambling-Related Craving
We used the Gambling Craving Scale (GACS; Young & Wohl, 2009) to assess subjective craving toward gambling in PrG. The GACS contains three factors: Anticipation (e.g., “Gambling would be fun right now”), Desire (e.g., “I crave gambling right now”) and Relief (e.g., “If I were gambling now, I could think more clearly”). There are nine items (three items for each of the three factors) assessed on a 7-point scale. For this study, the GACS was translated into French. Back translation method was used. For the present sample, using Cronbach's alpha, the internal consistency reliability was .78, .84, .89 for the factors Anticipation, Desire, and Relief, respectively.
Paradigm and Design
The original stimulus (OS) was presented for 250ms, followed by the mask (M) for 80 ms, then the changed stimulus (CS) for 250ms. The OS-M-CS-M series was continuously presented until change detection. Based upon previous research with the flicker paradigm (Jones, Jones, Blundell, & Bruce, 2002; Jones et al., 2003, 2006), participants performed only one single-flicker task (in the current case, to detect either the gambling related or the neutral change). The dependent variable was change-detection latency, direction of first eye movement, proportion of eye fixation count and length.
We controlled for the possibility that information from the left hemispace might be processed more readily than information from the right hemispace in normal individuals (i.e., pseudoneglect; e.g., Nicholls, Orr, Okuba, & Loftus, 2006). Therefore, all participants were randomly assigned to one of four flicker conditions, leading to a 2 (CONT vs. PrG group) × 2 (gambling-related vs. neutral change) × 2 (bilateral organization of the stimulus; gambling stimuli on the left and neutral on the right, GN, vs. neutral left and gambling right, NG) between-subjects design.
Apparatus and Stimuli
The OS consisted of a matrix of 18 full color photographs depicting nine gambling related and nine neutral objects on each side (see Figure 1). The nine pairs of gambling and neutral objects were selected so that their physical properties (e.g., color, height, width, shape) were similar. The two sets of nine photographs were arranged in two 3 × 3 matrices set in a 3 × 6 landscape matrix, with items of each matched pair occupying corresponding positions across their respective matrices. The CS with the gambling related change was identical to the OS except that the object at the center of the gambling matrix was substituted (see Figure 1b).
Figure 1. The original stimuli (OS) and changed stimuli (CS) used in the flicker paradigm for induced change blindness. Panel 1a. Two OS (gambling-right, neutral-left, NG, and neutral-right, gambling-left, GN); Panel 1b. Two CS, CS-gambling-related-change (gambling-right, neutral-left, NG, and neutral-right, gambling-left, GN); Panel 1c. Two CS, CS-neutral-change (gambling-right, neutral-left, NG, and neutral-right, gambling-left, GN).
There was a second CS with a corresponding neutral substitution (Figure 1c). The two different CSs with their common OS represented the two levels of Factor 2 (nature of change). Finally, bilateral reversals of each of the OS and the two CSs were made for the two levels of Factor 3 (i.e., GN and NG). The single mask comprised rows of uppercase, 20-point Xs in Times New Roman font.
This task consists of consecutive and repeated presentations of two identical visual scenes separated by a mask (typically a gray screen), that differ in only one element. The presentation of the visual scenes continues until the change is detected.
A TOBII ×120 eye tracker was used to measure participant's eye movements. The TOBII ×120 records the X and Y coordinates of participant's eye position at 60 Hz by using corneal reflection techniques. Calibration procedures were run using Clearview software (TOBII Technology, Sweden) which allows an optimal accuracy of 0.5 degrees. Stimulus presentation and data output for the flicker task were programmed in E-Prime version 2.0 professional and appeared on a 17 in. CRT-monitor with a refresh rate of 85 Hz.
The eye tracking software and measures were run and recorded on an Intel Xenon based PC, which was linked to an Intel Core 2 based laptop through a local area network. E-Prime software was used on the Intel Core 2 based laptop, which also recorded the change-detection latency measure.
Procedure
Testing took place individually and in a quiet room, located at the Medical Psychology Laboratory of the Brugmann Hospital. Participants were invited to first complete the STAI-State (Spielberger, 1993). Participants were seated 60 cm in front of the TOBII monitor. The experimenter manipulated the monitor until the cameras detected participants' corneal reflection. Participants were then shown a series of looming balls that appeared in a 5-point calibration sequence. Calibration accuracy was checked and repeated if necessary. Before performing the flicker task, participants were shown a preview of the flicker paradigm for induced change blindness, but with unrelated objects than those used in the following flicker paradigm and without the difference between OS and CS. This was made to accustom participants to the fast stimuli's appearance rate. Participants then performed the flicker task with a gambling or neutral change. They were asked to watch a series of nearly identical pictures “flicked back and forth” on the screen and to detect the difference between them as quickly as possible. Participants had to indicate that they had detected a change by quickly saying “STOP” aloud, at which moment the experimenter pushed a dedicated button on a wireless gamepad to time-stamp the moment of change detection. Immediately after the flicker task, PrG participants were required to fill out the GACS.
Data Analysis
Change-detection latency
Change-detection latency was the total number of combined OS-M-CS-M presentations until change detection. We performed a univariate analysis of variance (ANOVA) with group (CONT vs. PrG), type of change to be detected (gambling−related vs. neutral) and bilateral organization of the stimulus (gambling stimuli on the left and neutral on the right, GN vs. neutral left and gambling right, NG) as between-subjects factors, and change detection latency as dependent variable.
Direction of first eye movement
The first eye movement was defined as the first fixation lasting at least 100 ms in the region of either the gambling or neutral stimulus, at least 100 ms after the first OS onset. This enabled us to calculate the percentage of initial eye movements that were directed at gambling-related versus control pictures during the task. To examine whether participants showed a bias in the first eye movement direction during the flicker task, the percentage of initial eye movements toward gambling pictures was compared with 50% (which indicates no bias).
Proportion of fixation count
Proportion of fixation count was the total number of eye-fixation directed toward gambling or neutral stimuli until change detection divided by the total amount of eye-fixations. Fixation count was analyzed using ANOVA with repeated measures, with group, type of change to be detected and bilateral organization of the stimulus as between-subjects factors; with type of stimulus (gambling, neutral) as a within subjects factor; and proportion of fixation count, as the dependent measure.
Proportion of fixation length
Proportion of fixation length was the total time (ms) of eye-fixation directed toward gambling or neutral stimuli until change detection divided by the total length of eye-fixation. Fixation length was analyzed using ANOVA with repeated measurements, with group, type of change to be detected and bilateral organization of the stimulus as between-subjects factors; type of stimulus (gambling, neutral) as a within subjects factor; and fixation length, as the dependent measure.
Association between gambling related attentional bias, self-reported gambling-related craving and gambling dependence severity in PrG
Correlation analyses were conducted between the gambling-related attentional bias measures, total score of the GACS, scores of the three factors of the GACS and score on the SOGS (n = 40). A univariate ANOVA was also conducted with direction of first eye movement (neutral vs. gambling), as between-subjects factors, and total score of the GACS, scores of the three factors of the GACS and score on the SOGS score as dependent variable.
Results Demographics and Current Clinical Status
A description of demographic variables, scores on the South Oaks Gambling Screen (SOGS), Beck Depression Inventory (BDI), the Trait and State version of the State–Trait Anxiety Inventory (STAI) and the average number of cigarettes smoked per day is presented in Table 1. Chi-square analyses revealed no differences in the number of male and female participants. Depression was higher in PrG than in CONT, t(73) = 2.11, p < .05. State and trait anxiety was higher in the PrG group in comparison with the CONT group, t(73) = −2.16, p < .05; t(73) = −2.01, p < .05, respectively. The average number of cigarettes smoked per day was higher in PrG than in CONT, t(73) = 2.81, p < .01. No other group differences were present. Because our sample of PrG included individuals who met the more stringent criteria for probable pathological gambling, the effect of gambling severity was controlled for the PrG group. In the absence of effect covariate effect of depression, trait–state anxiety, and number of cigarettes smoked per day on group comparisons, we performed ANOVAs.
Change Detection Latency
All participants detected all changes correctly. The ANOVA showed no main effects of Group, Type of Change, or Stimulus Orientation (all p > .05). There was no interaction except for the following one, which supported the gambling-related attentional bias hypothesis in problem gamblers. An interaction between groups and type of change was found, F(1, 67) = 10.57, p < .01, η2 = .13. This analysis showed that PrG' change-detection latency for the gambling-related change was smaller than for the neutral change. Control participants' change-detection latency for the gambling-related change and for the neutral change, however, were not different (see Figure 2).
Figure 2. Latency to change-detection for CONT and PrG with gambling-related and neutral changes.
Direction of First Eye Movement
The percentage of first eye movements toward gambling pictures was significantly greater than 50% in the PrG group but not in the CONT group, t(39) = 2.73, p < .01 and t(34) = .12, ns, respectively. Also, a t test revealed that the first eye movement percentages toward gambling pictures differed significantly between groups, t(74) = 4.71, p < .05.
Proportion of Fixation Count
There were interactions between type of change and fixation count, F(1, 67) = 16.68, p < .001, η2 = .19, and between group and fixation count, F(1, 67) = 6.04, p < .05, η2 = .08. Analyses revealed that participants fixation on change-related stimuli occurred more frequently (M = .58, SD = .15) compared to stimuli not linked to the change (M = .42, SD = .15). The other interaction effect revealed that PrG group, but not CONT, fixated on gambling-related stimuli more frequently compared to neutral stimuli. Results of the group × type of stimulus interaction are presented in Figure 3.
Figure 3. Proportion of fixation count for CONT and PrG with gambling-related and neutral stimuli.
Fixation Length
Analyses revealed a type of change × fixation length interaction, F(1, 73) = 13.31, p < .001, η2 = .17, and a group × type of stimulus interaction, F(1, 73) = 9.78, p < .001, η2 = .13. Analyses revealed that participants fixated longer change-related stimuli (M = .57, SD = .17) compared to stimuli not linked to the change (M = .43, SD = .17). For the other interaction, the analyses revealed that PrG group fixated much longer gambling-related stimuli compared to neutral stimuli (see Figure 4).
Figure 4. Proportion of fixation length for CONT and PrG with gambling-related and neutral stimuli.
Association Between Gambling Related Attentional Bias, Self-Reported Gambling-Related Craving and Gambling Dependence Severity In PrG
Correlation analyses (n = 40) revealed that there was no significant correlation between the gambling-related attentional bias measures, the total score of the GACS, scores on the three factors of the GACS and score on the SOGS (see Table 2). There was also no significant difference between the direction of first fixation on both GACS and SOGS scores (F < 1).
Correlation (n = 40) Between Gambling Related Attentional Bias, Self-Reported Gambling-Related Craving and Gambling Dependence Severity In PrG
DiscussionThe main findings of the present research could be summarized as follows: comparison of the PrG and the CONT showed that PrG are faster in detecting gambling-related changes in the flicker paradigm, exhibit more gaze fixation counts and longer fixation lengths toward gambling-related stimuli. In addition, unlike CONT, the percentage of first eye movements toward gambling cues was higher and significantly above chance level for the PrG group.
As hypothesized, behavioral data (indirect measure of attention) recorded during the flicker paradigm showed that, in comparison with CONT, PrG, all of whom met criteria for problem gambling based on their scores on the South Oaks Gambling Screen (SOGS), are faster to detect gambling-related change. This result suggests that PrG's attention is captured by gambling related cues, that is, attentional bias. This finding is in line with studies showing that on a modified version of the Stroop task, PrG's take more time to name the color of the words related to gambling practices than neutral one(s) (Boyer & Dickerson, 2003; Molde et al., 2010).
We then set out to ascertain whether this attention bias was due to engagement or/and maintenance of attention. To do so, participants' eye movements were monitored using eye-tracking technology (direct measure of attention). Compared to control participants, PrG directed their first eye movements more frequently toward gambling-related than toward neutral stimuli (bias of attentional engagement), exhibit more gaze fixation counts on gambling stimuli and spent more time looking at gambling-related (bias of attentional maintenance) than control stimuli. This pattern of eye-movements suggests that both initial engagement and maintenance of attention are parts of the problem that drive gambling cognition and behavior.
Contrary to our hypothesis, we found no significant correlation between the maintenance of attention and craving scores assessed with the Gambling Craving Scale (GACS). An explanation for the absence of a relationship between attentional bias and craving is that, like substance addictions, it may occur automatically and habitually in the absence of any conscious subjective experience of craving (Tiffany, 1990). As an alternative explanation, the absence of relationship between craving and attentional bias might be accounted for by a low subjective craving in PrG at the time of assessment. Indeed, scores on the GACS' subscales revealed that PrG experienced an intention to gamble that was anticipated to be fun and enjoyable (the Anticipation scale) rather than a strong, urgent desire to gamble (the Desire scale) and an expectation that gambling would provide relief from negative affect (the Relief scale). Moreover, there was also no association between gambling-related attentional bias and gambling dependence severity. This was probably due to the relatively small variation of SOGS' scores between PrG participants.
Findings related to the presence of attentional bias in PrG are consistent with the incentive-sensitization theory (Robinson & Berridge, 1993, 2003). This model proposes that attentional and approach biases for addiction-related stimuli are an indication of incentive processes, and that incentive sensitization mechanisms play an important role in the development and the maintenance of an addiction state. The presence of attentional bias in PrG as well as in individuals addicted to substance (alcohol, cannabis, tobacco, heroin, and cocaine; for a review see Field, Munafo, & Franken, 2009) suggests that this shared component may lead to poor self-regulation. Most importantly, it raises the possibility that gambling-related attentional bias might be a treatment target (van Holst, van den Brink, Veltman, & Goudriaan, 2009). Indeed, decreasing attentional biases with the help of behavioral therapy and modification paradigms may result in increasing likelihood to select alternative behaviors to have fun (or to feel less anxiety).
A limitation of this paper is that we cannot isolate the “problem gambling” component per se since problem gamblers have been compared to nongamblers instead of healthy nonproblem gamblers. This problem limits the generalizability of our results. Therefore, it is certainly important to extend this research to a larger sample of gamblers which has both extreme ends of the spectrum of gambling dependence well represented, including healthy nonproblem gamblers (e.g., usual lottery players) as well as pathological gamblers who attempt to stop gambling. Furthermore, on the basis of Tiffany (1990), gamblers who want to stop gambling may experience extreme deprivation conditions that would elicit strong incentive effects (and associated intense craving) toward gambling-related stimuli, such that attentional bias for gambling cues may rise to ceiling levels. Finally, even if we did not seek in this experiment to investigate the relationship between the intensity of craving and attentional biases in control, the Gambling Craving Scale could also be administrated to these subjects in further studies. Such research might clarify the precise nature of the relationships between state and trait gambling-related variables (e.g., craving, gambling dependence severity) and the cognitive and behavioral indications of incentive salience processes (e.g., attentional biases), given that these incentive mechanisms are proposed to play a key role in maintaining addictive behaviors and in increasing the risk of relapse following quit attempts.
In summary, direct and indirect measures of attention in individuals with problematic gambling behaviors emphasized the presence of both attentional engagement and maintenance biases toward gambling-related pictorial cues during a flicker paradigm for induced change blindness. These attentional biases correspond well to those seen in substance addiction, including alcohol, tobacco, cannabis, heroin, and cocaine. This research is consistent with models of addiction which suggest that addiction-related cues acquire incentive-motivational properties.
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Submitted: September 7, 2010 Revised: March 22, 2011 Accepted: April 20, 2011
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Source: Psychology of Addictive Behaviors. Vol. 25. (4), Dec, 2011 pp. 675-682)
Accession Number: 2011-12261-001
Digital Object Identifier: 10.1037/a0024201
Record: 188- Title:
- Toward a hierarchical model of criminal thinking: Evidence from item response theory and confirmatory factor analysis.
- Authors:
- Walters, Glenn D.. Federal Correctional Institution, Schuylkill, PA, US, gwalters@bop.gov
Hagman, Brett T.. University of South Florida, Department of Mental Health Law and Policy, FL, US
Cohn, Amy M.. University of South Florida, Department of Mental Health Law and Policy, FL, US - Address:
- Walters, Glenn D., Psychology Services, FCI-Schuylkill, P. O. Box 700, Minersville, PA, US, 17954-0700, gwalters@bop.gov
- Source:
- Psychological Assessment, Vol 23(4), Dec, 2011. pp. 925-936.
- NLM Title Abbreviation:
- Psychol Assess
- Page Count:
- 12
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- Psychological Assessment: A Journal of Consulting and Clinical Psychology
- ISSN:
- 1040-3590 (Print)
1939-134X (Electronic) - Language:
- English
- Keywords:
- Psychological Inventory of Criminal Thinking Styles, criminal offenders, item response theory, proactive, reactive
- Abstract:
- Item response theory (IRT) methods were applied to items from the 80-item Psychological Inventory of Criminal Thinking Styles (PICTS; G. D. Walters, 1995) to determine how well they measure the latent trait of criminal thinking in a group of 2,872 male medium security prison inmates. Preliminary analyses revealed that the 64 PICTS thinking style items, 32 PICTS proactive criminal thinking items, and 24 PICTS reactive criminal thinking items were sufficiently unidimensional to meet the local independence requirements of IRT. The PICTS was fitted to a 2-parameter logistic-graded response IRT model, the results of which showed that the 8 items measuring denial of harm (Sentimentality) displayed weak discrimination (a < 0.5), whereas most of the proactive and reactive items displayed moderate to good discrimination (a > 1.0). Information function analysis revealed that all 3 components of a hierarchical model of criminal thinking—PICTS total scale, PICTS proactive factor, and PICTS reactive factor—displayed greater precision at higher rather than lower levels of the trait dimension. The study findings indicate that items from the PICTS Sentimentality scale do a poor job of measuring general criminal thinking, whereas items from the other 7 PICTS thinking style scales provide their most precise estimates at the upper end of the trait dimension. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Criminal Behavior; *Item Response Theory; *Psychometrics; *Thinking; Criminals; Inventories; Item Analysis (Statistical)
- Medical Subject Headings (MeSH):
- Adult; Attitude; Crime; Criminals; Factor Analysis, Statistical; Humans; Male; Models, Psychological; Personality Inventory; Predictive Value of Tests; Prisoners; Psychometrics; Thinking; United States
- PsycINFO Classification:
- Psychometrics & Statistics & Methodology (2200)
Criminal Behavior & Juvenile Delinquency (3236) - Population:
- Human
Male - Location:
- US
- Age Group:
- Adulthood (18 yrs & older)
- Tests & Measures:
- Psychological Inventory of Criminal Thinking Styles
- Grant Sponsorship:
- Sponsor: National Institute on Drug Abuse
Grant Number: P30DA028807
Other Details: USF Center on Co-Occurring Disorders, Justice, and Multidisciplinary Research
Recipients: No recipient indicated - Methodology:
- Empirical Study; Quantitative Study
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- First Posted: Jun 27, 2011; Accepted: Apr 14, 2011; Revised: Apr 14, 2011; First Submitted: Dec 12, 2010
- Release Date:
- 20110627
- Correction Date:
- 20111128
- Copyright:
- American Psychological Association. 2011
- Digital Object Identifier:
- http://dx.doi.org/10.1037/a0024017
- PMID:
- 21707187
- Accession Number:
- 2011-13206-001
- Number of Citations in Source:
- 36
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Toward a Hierarchical Model of Criminal Thinking: Evidence From Item Response Theory and Confirmatory Factor Analysis
By: Glenn D. Walters
Federal Correctional Institution, Schuylkill, Pennsylvania;
Brett T. Hagman
University of South Florida, Department of Mental Health Law and Policy
Amy M. Cohn
University of South Florida, Department of Mental Health Law and Policy
Acknowledgement: Glenn D. Walters is the author of the Psychological Inventory of Criminal Thinking Styles (PICTS) and receives royalties from sale of the PICTS manual.
This study was partially supported by Award Number P30DA028807, titled “USF Center on Co-Occurring Disorders, Justice, and Multidisciplinary Research,” from the National Institute on Drug Abuse. The assertions and opinions contained herein are the private views of the authors and should not be construed as official or as reflecting the views of the Federal Bureau of Prisons, the United States Department of Justice, the National Institute of Drug Abuse, or the National Institutes of Health.
Recent taxometric studies on childhood aggression and conduct disorder (Murrie et al., 2007; Walters, 2011a; Walters, Ronen, & Rosenbaum, 2010), adult psychopathy and antisocial personality disorder (Edens, Marcus, Lilienfeld, & Poythress, 2006; Guay, Ruscio, Knight, & Hare, 2007; Marcus, Lilienfeld, Edens, & Poythress, 2006; Walters, Diamond, Magaletta, Geyer, & Duncan, 2007; Walters et al., 2007), and adult criminal thinking (Walters, 2007a; Walters & McCoy, 2007) suggest that the latent structure of crime-related constructs is dimensional (see Harris, Rice, & Quinsey, 1994, for an alternate view). What this means is that individual differences in childhood aggression, conduct disorder, adult psychopathy, antisocial personality, and criminal thinking differ as a matter of degree (quantitative) rather than as a function of type (qualitative). Procedures designed to assess individual differences on dimensional constructs should be composed of items covering the full spectrum of the construct; however, assessments of individual differences in constructs that are qualitative rather than quantitative in nature should be designed to measure categorical differences because their function is to effectively distinguish between groups (Meehl, 1995). Item-level analysis is accordingly a necessary step in constructing scales that meaningfully measure both dimensional and categorical constructs.
In line with the hierarchical models of psychopathy proposed by Hare (1991) and Cooke and Michie (1997, 2001) in which two, three, or four correlated factors are believed to be nested in a higher order construct (psychopathy), Walters (2008, 2009b) conceptualized criminal thinking as a higher order construct supported by two correlated factors (proactive criminal thinking and reactive criminal thinking) from the Psychological Inventory of Criminal Thinking Styles (PICTS; Walters, 1995). The next step in the development of this model is to verify whether the higher order factor is sufficiently unidimensional to serve as a superordinate construct and whether the two lower order factors contribute meaningfully to researchers' understanding of criminal thinking as part of a second-order factor analysis and hierarchical model of criminal thinking. Previous factor analytic research has identified the presence of a third factor, denial of harm, composed of the eight items from the PICTS Sentimentality scale (Walters, 1995, 2002). It is important, then, that we examine the item response theory (IRT; Lord & Novick, 1968) performance of both the 64 PICTS thinking style items and 56 PICTS thinking style items minus the eight Sentimentality items to ascertain which version best represents the criminal thinking construct. Research also indicates minimal ethnic differences in PICTS performance (Walters, 2002), and so it is anticipated that IRT performance should be reasonably invariant across race.
Demonstrating that a higher order factor is sufficiently unidimensional to qualify as a unified construct requires evidence of internal consistency (Cronbach's α ≥ .80), item-total homogeneity (mean-corrected item-to-total correlation ≥ .40), and first-factor strength (ratio of the eigenvalue of the first unrotated factor to the eignenvalue of the second unrotated factor ≥ 3.0; Cooke & Michie 1997; Cooke, Michie, Hart, & Hare, 1999; Hare, 1991). Demonstrating that lower order factors warrant inclusion in a hierarchical model requires evidence that a second-order factor solution significantly improves on the fit of the one-factor solution in which the lower order factors are nested. Using scale level indicators, Walters (2008) conducted a series of confirmatory factor analyses on scales and subscales from the PICTS, the Antisocial Features scale of the Personality Assessment Inventory (Morey, 2007), and the Levenson Self-Report Psychopathy Scale (Levenson, Kiehl, & Fitzpatrick, 1995), the results of which lent support to a two-dimensional (proactive criminality and reactive criminality) model of antisociality. Walters (2008) also determined that a two-dimensional model of antisociality and criminal thinking (proactive and reactive) achieved a significantly better fit than a one-factor model. However, it is vital that these findings be replicated with item-level indicators before they can be considered robust and meaningful.
In studying the PICTS at the item level, we would be well advised to take advantage of principles and procedures derived from IRT. Whereas the alpha coefficient, corrected item-to-total correlation, and other indices from classical test theory (CTT) are test- and sample dependent (Nunnally & Bernstein, 1994), IRT curves are independent of the test and sample used to generate them (Hambleton, 1989). Thus, despite the apparent and real overlap that exists between IRT and CTT, IRT models are superior to CTT models in assessing item reliability and test bias (Fan, 1998). Rather than estimating reliability from the mean of the standardization sample, as is the case with CTT, IRT models estimate reliability for each item on the basis of the assumption that precision is not uniform across a range of test scores and that scores falling at the outer edges of a test's range will be less precise than items congregating near the middle of the range (Baker & Kim, 2004). Unlike CTT, IRT models allow for direct comparison of parallel items as estimates of the latent trait in both the same and different samples (Hambleton, 1989). Consequently, IRT models offer a different perspective on the psychometric performance of a psychological measure, such as the PICTS, than do CTT models.
No study has examined individual item functioning of the PICTS, a widely used measure of criminal thinking styles in offender populations, using IRT methods. To this end, we tested four hypotheses in this study. The first hypothesis held that a single factor composed of the 64-item or 56-item (minus the eight Sentimentality items that make up the denial of harm factor) versions of the PICTS would be sufficiently homogeneous to form a unidimensional construct. The second hypothesis predicted that a hierarchical second-order model (proactive criminal thinking, reactive criminal thinking, and denial of harm as the first-order factors and general criminal thinking as the second-order factor) would produce significantly better fit than a single-factor model (general criminal thinking as the first-order factor). The third hypothesis proposed that IRT analysis would successfully identify nondiscriminating items and scales and that removing nondiscriminating items would not appreciably hinder the PICTS' ability to predict subsequent institutional adjustment and recidivism or ability to provide adequate coverage of the higher order factor of general criminal thinking (GCT) and two lower order factors (proactive, reactive). The fourth and final hypothesis tested in this study postulated that the discrimination (ai) and location (bi) parameters would be invariant across race (White, Black, Hispanic).
Method Participants
Participants for this study were 2,872 male inmates undergoing intake evaluations at a medium security federal correctional institution located in the northeastern region of the United States. The mean age of participants was 35.00 years (SD = 9.96), and the mean educational level was 11.35 years (SD = 1.90). The ethnic breakdown was 18.2% White, 66.7% Black, 13.7% Hispanic, 0.8% Asian, and 0.6% Native American. Most participants listed their relationship status as single (73.1%); another 19.5% of participants stated that they were married, 6.8% reported that they were divorced or separated, and 0.6% indicated that they were widowed. The modal offense in this sample was a drug crime (40.3%; Bureau of Prisons [BOP] national rate = 44.7%), followed by firearm offenses (16.9%; BOP national rate = 13.1%), parole/supervised release violations (13.4%; BOP national rate = 13.5%), robbery (12.8%; BOP national rate = 6.8%), and violent crimes (4.9%; BOP national rate = 4.5%).
Measures
The PICTS
The PICTS (Walters, 1995) is an 80-item self-report inventory designed to measure criminal thinking as set forth in Walters' (2002) criminal lifestyle theory. Items are rated on a 4r-point Likert-type scale ranging from 1 (disagree) to 4 (strongly agree). The 80 PICTS items are organized into two validity scales (eight items each), eight thinking style scales (eight items each), four factor scales (10 items each), two composite scores (18 items each), and a general criminal thinking score (64 items). Items for the eight non-overlapping criminal thinking style scales were rationally selected from a pool of statements submitted by inmates enrolled in several different criminal lifestyle groups who were familiar with the eight thinking styles the PICTS is designed to measure. The creator of criminal lifestyle theory (GDW) selected the final sets of items on the basis of the criteria of clarity, readability, specificity, and relevance to the thinking style being measured. These 64 items were then combined with 16 validity items to form the 80-item inventory. The focus of the present study was on the 64 items from the eight PICTS criminal thinking style scales. These 64 items were further subdivided into 32 “proactive” items from the Mollification, Entitlement, Power Orientation, and Superoptimism scales, 24 “reactive” items from the Cutoff, Cognitive Indolence, and Discontinuity scales, and eight “denial of harm” items from the Sentimentality scale. Sample items for the eight criminal thinking scales are reproduced in Table 1.
The PICTS Thinking Style Scales and Sample Items From Each Scale
Recidivism and criminal outcomes
To examine the relationship between the PICTS and external validators of criminal thinking and behavior, archival data on subsequent incident (disciplinary) reports and violent (assault, fighting) incident reports among those who had been incarcerated at least 6 months as well as recidivism (any charges) for those who had been in the community for at least 12 months were used.
Sample Recruitment and PICTS Administration
The present study is a secondary data analysis of responses provided by male federal prison inmates administered the PICTS as part of a routine intake procedure conducted at the federal correctional institution where this study took place. Although the data were collected for clinical purposes, the use of these data for research purposes was approved by the BOP Institutional Review Board. All inmates completed the PICTS within 2 weeks of their arrival at the institution. Hispanic inmates who could not read English were administered a Spanish version of the PICTS, but only participants completing the English version were included in the present study. Less than 5% of incoming inmates did not complete the PICTS because of reading problems, transfer to another facility, or refusal to participate in testing (see Walters, 2008, for more detail). The PICTS was administered to participants by a psychology technician between August 2003 and March 2010. There were no significant differences on the PICTS thinking style scales or GCT score for those who completed the PICTS in the first 41 months and last 41 months of the testing period (Bonferroni-corrected p = .006).
IRT Assumptions and Overview
IRT models make three primary assumptions: (a) The latent trait (θ) is unidimensional; (b) there is local independence between items (i.e., the residual correlation between items after accounting for θ approaches zero); and (c) responses to each item can be modeled with the item response function (IRF). The IRF yields information on an item's ability to assess the latent trait using one to four parameters. Samejimi's two-parameter graded response model (Embertson & Reise, 2000) was used in the present study to assess the PICTS' ability to discriminate (ai) between individuals at different levels of the trait dimension and estimate the location (bi) of categories on the Likert scale used to score the PICTS. IRT also generates an item characteristic curve (ICC) in which the slope of the curve at its steepest point constitutes the discrimination (ai) parameter, and the point on the x-axis of the ICC graph (theta) where the slope is greatest constitutes the location (bi) parameter. The principal advantage of evaluating a dimensional construct like criminal thinking with IRT is that it allows us to identify items with moderate to high discriminating power and a sufficient number and range of items to cover the entire dimension.
Data Analytic Plan
The first step of the data analytic plan was to determine whether the 64 thinking style items formed a unidimensional construct. The second step was to determine the absolute and relative fit of the following models to the PICTS data: a general factor model (all 64 items loading onto a single factor), a second-order factor model (64 items loading onto three factors which then loaded onto a single higher order factor), and a bifactor model (64 items simultaneously loading onto three correlated factors and an independent general factor). This was tested with confirmatory factor analysis (CFA) using a means- and variance-adjusted weighted least squares estimator and three common fit indices: comparative fit index (CFI), Tucker-Lewis index (TLI), and root-mean-square error of approximation (RMSEA). Although it is difficult to specify precise cutoff points for fit indices given the complexity of structural equation modeling, the following general rules of thumb were adopted in the present study (Hu & Bentler, 1999): CFI/TLI > .95 (good fit), .90–.95 (borderline fit), and < .90 (poor fit); RMSEA < .06 (good fit), .06–.08 (fair fit), .08–.10 (borderline fit), and > .10 (poor fit).
The third step of the procedure was to assess the PICTS items with IRT. Two-parameter logistic IRT analyses, as represented by Samejima's (1969) graded response model, were generated with the Multilog IRT statistical program (Thiessen, 1991). Three models were fitted: one composed of the 64 thinking style items, a second composed of the 32 proactive items, and a third composed of the 24 reactive items. In this study, items were calibrated using a marginal maximum likelihood estimation procedure. The total number of expectation maximization (EM) cycles permitted was 25, and the EM cycle convergence was set at .001 for the IRT and differential item functioning (DIF) analyses. The convergence criteria were realized for the graded response model.
Predictive Validity Analyses
The two PICTS validity scales, Confusion-revised (designed to measure a fake bad or problem exaggeration response style) and Defensiveness-revised (designed to measure a fake good or problem minimization response style), were used to screen out invalid protocols prior to conducting the predictive validity analyses. Only PICTS records meeting empirically established validity criteria were included in these analyses: < 20 out of 80 items missing, a Confusion-revised raw score < 27; a Defensiveness-revised raw score < 29 (Walters, 2011b). One hundred thirty-seven of the records (4.8%) failed to satisfy these criteria and were removed from the analyses. Finally, based on past research showing that the predictive utility of the PICTS drops significantly when the time interval between administration of the PICTS and release from prison is 42 months or longer (Walters, 2009a), only protocols with test-release intervals < 42 months were included in the recidivism analyses.
Results Unidimensionality
Cronbach's alpha for the full 64-item PICTS was high (α = .93). Cronbach's alpha for the 56-item PICTS with the eight Sentimentality items removed was slightly higher (α = .94). The mean-corrected item-to-total correlation was .40 for the 64-item version, although all eight Sentimentality items had corrected item-to-total correlations < .10. When these eight items were removed, the mean-corrected item-to-total correlation rose to .45, with reactive items achieving slightly higher corrected item-to-total correlations (M = 0.50, range = .30–.60) than proactive items (M = 0.42, range = .05–.57). The first unrotated factor (F1) of the 64-item PICTS produced an eigenvalue of 20.49, and the second unrotated factor (F2) produced an eigenvalue of 3.45, for an F1:F2 ratio of 5.94 (full model: CFI = .929, TLI = .926, RMSEA = .072). The first unrotated factor of the 56-item PICTS yielded an eigenvalue of 20.48 compared with an eigenvalue of 3.44 for the second unrotated factor, yielding an F1:F2 ratio of 5.95 (full model: CFI = .948, TLI = .946, RMSEA = .070). In both cases, the residual correlation matrix, following extraction of the first factor, approached null. These results indicate that both the 56- and 64-item versions of the PICTS are sufficiently homogeneous and unidimensional to permit IRT analysis.
CFA
CFA analysis of the bifactor model revealed poor to fair absolute fit (CFI = .715; TLI = .947; RMSEA = .061), but significantly better fit than the general factor model (CFI = .608; TLI = .926; RMSEA = .072), Mplus difference test χ2(40, N = 2,872) = 3027.65, p < .001. This indicates that although the first (general) factor may have been sufficiently unidimensional to perform IRT analysis, additional variance in the PICTS is not accounted for by the general factor. The second-order model, which achieved poor to good absolute fit (CFI = .815; TLI = .955; RMSEA = .056), also achieved significantly better fit than the general factor model, Mplus difference test χ2(3, N = 2,872) = 1708.19, p < .001. This provides preliminary support for a hierarchical model of PICTS-assessed criminal thinking, with general criminal thinking as the superordinate factor, and proactive criminal thinking, reactive criminal thinking, and denial of harm as subordinate factors.
Standardized coefficients (factor loadings) between the observed indicators (PICTS items) and latent variables (criminal thinking factors) for the three models (general factor model, bifactor model, second-order model) included in the present study are delineated in Figure 1. These coefficients provide further evidence of a second-order model in which proactive and reactive criminal thinking constitute midlevel subconstructs in a multilevel hierarchical model: In this model, the proactive and reactive criminal thinking subconstructs are sandwiched between general criminal thinking at the top of the hierarchy and individual PICTS items at the bottom of the hierarchy.
Figure 1. Standardized coefficients (factor loadings) obtained with the general factor model (upper portion), bifactor model (middle portion), and second-order model (lower portion). The range of coefficients, along with the median (Md) coefficient, are listed for all relationships between latent factors and observed indicators (PICTS items). PICTS = Psychological Inventory of Criminal Thinking Styles; P = Proactive; R = Reactive; DNH = Denial of Harm; GCT = General Criminal Thinking.
IRT Analyses
Table 2 lists the two-parameter IRT results for the full 64-item PICTS, and Figure 2 depicts a sample ICC. The IRT results outlined in Table 2 indicate that four out of 32 proactive items (Mollification Item 17, Entitlement Items 1 and 65, Superoptimism Item 5), two out of 24 reactive items (Cognitive Indolence Item 54, Discontinuity Item 59), and all eight denial of harm (Sentimentality) items failed to achieve at least moderate discrimination (a < 1.0). When we analyzed the proactive and reactive items separately, four of the proactive items (Mollification Items 17 and 71, Entitlement Item 1, Superoptimism Item 5) and two of the reactive items (Cognitive Indolence Item 54, Discontinuity Item 59) failed to achieve at least moderate discrimination (see Table 3).
Response Distribution and Item Parameters for the 64 Individual PICTS Thinking Style Items
Response Distribution and Item Parameters for the 64 Individual PICTS Thinking Style Items
Figure 2. Item characteristic curve for Psychological Inventory of Criminal Thinking Styles (PICTS) Item 13 (Superoptimism) in the 64-item test.
Item Parameters for Individual PICTS Thinking Style Items: Proactive and Reactive Scales
Item Parameters for Individual PICTS Thinking Style Items: Proactive and Reactive Scales
The threshold values obtained from the location parameter indicate that most of the PICTS items achieved greater precision at higher levels of the criminal thinking trait dimension than at lower levels of the criminal thinking trait dimension. Information function analysis of the full 64-item PICTS, 32 proactive items, and 24 reactive items likewise revealed that the PICTS achieved greater precision at higher levels of the trait dimension than at the lower levels of the trait dimension. Specifically, the information function analysis peaked around 0.7 in the 64-item PICTS, around 1.2 in the 32 proactive items, and around 0.6 in the 24 PICTS reactive items (see Figure 3). Multilog produces a marginal reliability index designed to estimate the average reliability of test scores across the θ continuum. The marginal reliability index for the 64 PICTS thinking style items was .97, the marginal reliability index for the 32 PICTS proactive items was .91, and the marginal reliability index for the 24 PICTS reactive items was .92.
Figure 3. Information functions (solid line) and standard errors (dashed line) for the 64-item test (upper panel), 32-item proactive factor (middle panel), and 24-item reactive factor (lower panel). PICTS = Psychological Inventory of Criminal Thinking Styles
To ascertain whether the eight Denial of Harm (Sentimentality) items could be removed from the PICTS without meaningfully reducing predictive validity, the 64- and 56-items PICTS were correlated with prospective measures of institutional adjustment and recidivism. According to the results of a receiver operating characteristic (ROC) curve analysis, removing the eight PICTS Sentimentality items from the GCT score did not adversely affect the GCT's ability to predict future incident (disciplinary) reports, violent (fighting, assault) incident reports, and general recidivism in participants with valid PICTS protocols (fewer than 20 unanswered items, Confusion-revised < raw score of 27, Defensiveness-revised < raw score of 29). As the findings outlined in Table 4 indicate, the area under the ROC curve (AUC) was virtually identical for the 64-item and 56-item (without the eight Sentimentality items) versions of the GCT.
Receiver Operating Characteristic (ROC) Results for the GCT as a Predictor of Institutional Infractions, Institutional Violence, and Recidivism With and Without the Eight Sentimentality Items
An attempt was made to test DIF across race (White, Black, Hispanic) with the Multilog program. The data failed to converge on a solution, however, the result, we believe, of large differences in sample size between racial categories (n = 564 for White; n = 1,915 for Black; n = 560 for Hispanic) and low hit rates for several of the response options, particularly the strongly agree (4) response option. Consequently, invariance across race could not be evaluated in this study.
DiscussionTo our knowledge, this is the first study to examine the factor structure and underlying latent trait structure of the PICTS using IRT methods. According to the information function results depicted in Figure 2, the 64 PICTS thinking style items, 32 PICTS proactive items, and 24 PICTS reactive items achieved maximum reliability in assessing average to above-average levels of criminal thinking (θ = −0.5 to 2.0) in a forensic sample. However, reliability and precision decline significantly at very high (θ > 2.0) and moderately to extremely low levels of the criminal thinking trait dimension (θ < −0.5). Results obtained from three different item sets (64 PICTS criminal thinking style items, 32 PICTS proactive criminal thinking items, 24 PICTS reactive criminal thinking items) were consistent in showing that the PICTS is capable of measuring criminal thinking at moderate to high levels of the trait dimension but may be less effective in discriminating between individuals at lower levels of the trait dimension. On the basis of these results, we conclude that the latent dimensional structure of criminal thinking is reasonably well appraised and covered by the PICTS at moderate to high levels of the criminal thinking trait dimension.
The IRT and CTT analyses revealed that the eight Sentimentality items did a uniformly poor job of assessing criminal thinking. The IRT discrimination parameter results as well as the CTT-corrected item-to-total correlations showed that the Sentimentality items were only weakly related to the criminal thinking construct. What, then, are the Sentimentality items measuring if not criminal thinking? One possibility is that the PICTS Sentimentality items, although designed to measure criminal thinking, are actually assessing a response style—that is, the individual's global approach to test taking independent of the latent trait being assessed. This notion is supported by the original factor analysis of the PICTS, in which Walters (1995) identified four factors: one that represented proactive criminal thinking (self-assertion/deception), one that represented reactive criminal thinking (problem avoidance), and two that represented response styles in the form of infrequency (symptom magnification) and denial of harm (symptom minimization). These results consequently suggest that criminal thinking is composed of two factors (proactive and reactive criminal thinking) and that the other two factors identified in early factor analytic research on the PICTS are better conceptualized as response styles rather than as ways of thinking, per se. Even though the Sentimentality items did a poor job of assessing general criminal thinking in recalcitrant criminals administered the PICTS in prison, they may do a better job of assessing general criminal thinking in less severely affected criminal probationers outside of prison. Future studies should address this possibility.
The present results have important implications for calculating the PICTS GCT score. Prior research denotes that the Sentimentality scale does not load onto either the proactive or reactive factors (Walters, 1995, 2008, 2009b). In the present study, the CTT-corrected item-to-total correlations and IRT discrimination parameter results both indicated that the eight Sentimentality items were the weakest and least precise indicators of criminal thinking. Furthermore, removing these eight items from the PICTS GCT score did not adversely affect the predictive power of the GCT despite a 12.5% reduction in the overall size of the scale. On the basis of these findings, it would seem likely that the GCT score could be calculated without the eight Sentimentality items and still accurately and reliably measure criminal thinking while successfully predicting future offending behavior. The wisdom of eliminating the four weakest performing proactive items and two weakest performing reactive items from the present study (a < 1.0) is less certain. First, three of the six poorest performing proactive and reactive items attained discrimination parameter results that were barely below 1.0. Second, several of the lower discriminating items did a better job of assessing the lower end of the criminal thinking latent trait dimension than most of the more highly discriminating items. Hence, additional research is required to determine whether the 56-item GCT score should be reduced further.
The location parameter results obtained from IRT can be just as important as the discrimination parameter results when determining item content for a psychological measure. Criminal thinking, like most crime-related constructs, is dimensionally organized (Walters, 2007a; Walters & McCoy, 2007). Measurement instruments designed to assess a dimensional construct should contain a sufficient number and variety of items so that a substantial portion of the latent trait continuum is covered (Ruscio, Haslam, & Ruscio, 2006). Results from the present study indicate that whereas the majority of individual items on the PICTS furnish substantive information at the upper end of the trait continuum, only a few items assess criminal thinking at the lower end of the trait continuum. It may be that the PICTS offers significant information and precision in assessing criminal thinking among individuals who have more severe or intractable styles of criminal thinking—but is much less informative and precise in assessing criminal thinking at the lower end of the continuum, or with those who have less rigidly defined criminal thinking.
The differential performance of the PICTS at higher versus lower levels of criminal thinking may be due, in part, to the specific PICTS response options: There are twice as many “higher” trait response options (agree, strongly agree) as there are “lower” trait response options (disagree), thus providing more chances for respondents to endorse severe levels of criminal thinking and perhaps causing the distribution to be positively skewed. The well-documented finding that lower PICTS scores do a better job of predicting good adjustment than higher PICTS scores do of predicting poor adjustment (Walters, 2002, 2007b), along with a negative association between guardedness/ego strength and scores on the PICTS thinking style scales (Walters, 2002) and a relatively high prevalence of lower PICTS scores in the present sample, suggests another possibility: Whereas lower levels of criminal thinking and higher levels of guardedness/ego strength reduce the risk of future disciplinary infractions and recidivism in a large portion of the offender population, higher levels of criminal thinking and lower levels of guardedness/ego strength increase the risk of disciplinary infractions and recidivism when combined with other factors (e.g., situational stress, opportunity).
With respect to item coverage, the present results have at least two additional implications for clinical practice. First, IRT analyses can provide valuable information to test developers interested in identifying items for an effective short or abbreviated version of their test. Given time and budgetary constraints and the desirability of being able to administer a test repeatedly (pre–post evaluations, longitudinal research), results from the present study could be useful in selecting items that provide maximum identification (discrimination parameter) and coverage (location parameter) of the criminal thinking construct for the purpose of treatment planning or determining the effectiveness of an intervention. Second, the present results have important implications for clinical interpretation of the PICTS. Because the PICTS items do a relatively poor job of differentiating between lower levels of criminal thinking, our findings suggest that it will be difficult to discriminate between moderately low and very low levels of criminal thinking using the PICTS. Whereas low scores on the PICTS may be indicative of positive attributes (i.e., a low Mollification score signaling willingness to take responsibility for one's actions), clinicians are advised to use an instrument designed for normal populations, rather than the PICTS, to assess such qualities.
The primary limitation of this study is generalizability. Although a sample of nearly 3,000 individuals is more than sufficient for conducting IRT research, the sample was composed exclusively of male federal prisoners. Additional research is needed to determine how well these results generalize to female, state, and jail inmates; felons serving time for crimes other than drug distribution; and nonincarcerated offenders. Because of limitations in our data, we could not determine whether the PICTS IRT results obtained in this study were invariant across race. As such, future research is required to address the issue of invariance across race and other important variables like gender and offense. From the limited research presently available on gender and the PICTS, female offenders appear to score higher than male offenders on the PICTS thinking style scales, whereas male nonoffending college students appear to score higher than female nonoffending college students on these same scales (Walters & McCoy, 2007). It is imperative that investigators examine racial, gender, and offense invariance in future IRT research on the PICTS.
The continued viability of the PICTS as a clinical assessment procedure for forensic populations is based largely on its construct validity. The overall results of this preliminary study indicate that the PICTS does a reasonably good job of assessing criminal thinking, that the PICTS Sentimentality scale may not be measuring the criminal thinking construct, and that the more severe end of the criminal thinking continuum is more adequately assessed by the PICTS than the less severe end of the criminal thinking continuum. Future research is required to determine whether items other than the eight Sentimentality items should be removed from the GCT score, whether the problem is with sentimentality as a feature of criminal thinking or with how sentimentality is assessed on the PICTS, and whether additional items and response options (e.g., strongly disagree) should be added to the PICTS in order to make the lower (less severe) portion of the criminal thinking dimension more accessible to researchers and clinicians.
Footnotes 1 First 41 months (n = 1,396): Mollification (Mo; M = 12.62, SD = 3.99), Cutoff (Co; M = 13.12, SD = 4.98), Entitlement (En; M = 12.94, SD = 3.78), Power Orientation (Po; M = 12.19, SD = 3.85), Sentimentality (Sn; M = 17.34, SD = 4.08), Superoptimism (So; M = 14.64, SD = 4.12), Cognitive Indolence (Ci; M = 15.48, SD = 4.72), Discontinuity (Ds; M = 14.87, SD = 5.14), general criminal thinking (GCT; M = 113.20, SD = 27.40). Second 41 months (n = 1,476): Mo (M = 12.53, SD = 4.12), Co (M = 12.81, SD = 4.84), En (M = 12.99, SD = 3.86), Po (M = 12.06, SD = 3.90), Sn (M = 16.94, SD = 4.36), So (M = 14.44, SD = 4.10), Ci (M = 15.32, SD = 4.92), Ds (M = 14.49, SD = 4.92), GCT (M = 111.57, SD = 28.07).
2 The 137 cases eliminated from the predictive analyses because they failed to satisfy empirically established validity criteria were contrasted with the 2,735 cases that satisfied the validity criteria. There were no significant Bonferroni-corrected (p < .004) differences between the groups on age, race, marital status, current offense (violent-nonviolent, sentence length), criminal history (number of prior convictions, age at first conviction), substance abuse history, mental health history, or outcome (one or more subsequent incident reports, one or more subsequent aggressive incident reports, one or more subsequent arrests/violations), although participants producing invalid PICTS protocols possessed significantly fewer years of education than participants producing valid PICTS protocols.
3 Internal consistency, though necessary, is not sufficient for unidimensionality and should be supplemented by a low standard error of interitem correlations,or what Cortina (1993) refers to as precision of alpha (pα). Low dispersion for the interitem correlations on both the 64-item (pα = .002) and 56-item (pα = .002) versions of the PICTS supported unidimensionality in this study.
4 Previous research showed that multiple incident reports correlated better with the PICTS than a single incident report (Walters, 2007b). Consequently, the two incident report outcomes were dichotomized at one or more (1+) and two or more (2+).
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Submitted: December 12, 2010 Revised: April 14, 2011 Accepted: April 14, 2011
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Source: Psychological Assessment. Vol. 23. (4), Dec, 2011 pp. 925-936)
Accession Number: 2011-13206-001
Digital Object Identifier: 10.1037/a0024017
Record: 189- Title:
- Treatment engagement and response to CBT among Latinos with anxiety disorders in primary care.
- Authors:
- Chavira, Denise A.. Department of Psychology, University of California-Los Angeles, Los Angeles, CA, US, dchavira@psych.ucla.edu
Golinelli, Daniela, ORCID 0000-0002-6433-1752. RAND Corporation, Santa Monica, CA, US
Sherbourne, Cathy. RAND Corporation, Santa Monica, CA, US
Stein, Murray B.. Department of Psychiatry, University of California-San Diego, San Diego, CA, US
Sullivan, Greer. Department of Psychiatry, University of Arkansas for Medical Sciences, AR, US
Bystritsky, Alexander. Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine, University of California-Los Angeles, Los Angeles, CA, US
Rose, Raphael D.. Department of Psychology, University of California-Los Angeles, Los Angeles, CA, US
Lang, Ariel J.. Veterans Affairs San Diego Health Care System Center of Excellence for Stress and Mental Health, San Diego, CA, US
Campbell-Sills, Laura. Department of Psychiatry, University of California-San Diego, San Diego, CA, US
Welch, Stacy. Department of Psychiatry and Behavioral Sciences, University of Washington School of Medicine, WA, US
Bumgardner, Kristin. Department of Psychiatry and Behavioral Sciences, University of Washington School of Medicine, WA, US
Glenn, Daniel. Department of Psychology, University of California-Los Angeles, Los Angeles, CA, US
Barrios, Velma. Los Angeles County Department of Mental Health, Los Angeles, CA, US
Roy-Byrne, Peter. Department of Psychiatry and Behavioral Sciences, University of Washington School of Medicine, WA, US
Craske, Michelle. Department of Psychology, University of California-Los Angeles, Los Angeles, CA, US - Address:
- Chavira, Denise A., Department of Psychology, University of California-Los Angeles, 1285 Franz Hall, Box 951563, Los Angeles, CA, US, 90095-1563, dchavira@psych.ucla.edu
- Source:
- Journal of Consulting and Clinical Psychology, Vol 82(3), Jun, 2014. pp. 392-403.
- NLM Title Abbreviation:
- J Consult Clin Psychol
- Page Count:
- 12
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- Journal of Consulting Psychology
- Other Publishers:
- US : American Association for Applied Psychology
US : Dentan Printing Company
US : Science Press Printing Company - ISSN:
- 0022-006X (Print)
1939-2117 (Electronic) - Language:
- English
- Keywords:
- Latinos, anxiety, engagement, primary care, treatment, cognitive behavioral therapy, treatment outcome
- Abstract:
- Objective: In the current study, we compared measures of treatment outcome and engagement for Latino and non-Latino White patients receiving a cognitive behavioral therapy (CBT) program delivered in primary care. Method: Participants were 18–65 years old and recruited from 17 clinics at 4 different sites to participate in a randomized controlled trial for anxiety disorders, which compared the Coordinated Anxiety Learning and Management (CALM) intervention (consisting of CBT, medication, or both) with usual care. Of those participants who were randomized to the intervention arm and selected CBT (either alone or in combination with medication), 85 were Latino and 251 were non-Latino White; the majority of the Latino participants received the CBT intervention in English (n = 77). Blinded assessments of clinical improvement and functioning were administered at baseline and at 6, 12, and 18 months after baseline. Measures of engagement, including attendance, homework adherence, understanding of CBT principles, and commitment to treatment, were assessed weekly during the CBT intervention. Results: Findings from propensity-weighted linear and logistic regression models revealed no statistically significant differences between Latinos and non-Latino Whites on symptom measures of clinical improvement and functioning at almost all time points. There were significant differences on 2 of 7 engagement outcomes, namely, number of sessions attended and patients’ understanding of CBT principles. Conclusions: These findings suggest that CBT can be an effective treatment approach for Latinos who are primarily English speaking and likely more acculturated, although continued attention should be directed toward engaging Latinos in such interventions. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Anxiety Disorders; *Cognitive Behavior Therapy; *Primary Health Care; *Treatment Outcomes; *Latinos/Latinas
- Medical Subject Headings (MeSH):
- Adult; Aged; Anxiety; Anxiety Disorders; Cognitive Therapy; European Continental Ancestry Group; Female; Hispanic Americans; Humans; Language; Linear Models; Logistic Models; Male; Middle Aged; Primary Health Care; Treatment Outcome
- PsycINFO Classification:
- Health & Mental Health Treatment & Prevention (3300)
- Population:
- Human
- Location:
- US
- Age Group:
- Adulthood (18 yrs & older)
Young Adulthood (18-29 yrs)
Thirties (30-39 yrs)
Middle Age (40-64 yrs)
Aged (65 yrs & older) - Tests & Measures:
- Overall Anxiety Severity and Impairment Scale
Sheehan Disability Scale
Short-Form Health Survey
Mental Health Composite Summary Scale
Mental Health Composite Scale
Brief Symptom Inventory DOI: 10.1037/t00789-000
Anxiety Sensitivity Index DOI: 10.1037/t00033-000
Mini International Neuropsychiatric Interview DOI: 10.1037/t18597-000
Patient Health Questionnaire-9 DOI: 10.1037/t06165-000 - Grant Sponsorship:
- Sponsor: National Institute of Mental Health
Grant Number: U01 MH057858
Recipients: Roy-Byrne, Peter
Sponsor: National Institute of Mental Health
Grant Number: U01 MH058915
Recipients: Craske, Michelle
Sponsor: National Institute of Mental Health
Grant Number: U01 MH 070022
Recipients: Sullivan, Greer
Sponsor: National Institute of Mental Health
Grant Number: U01 MH057835 and K24 MH64122
Recipients: Stein, Murray B.
Sponsor: National Institute of Mental Health
Grant Number: K01 MH072952
Recipients: Chavira, Denise A. - Methodology:
- Empirical Study; Quantitative Study; Treatment Outcome
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- First Posted: Mar 24, 2014; Accepted: Jun 21, 2013; Revised: Jun 19, 2013; First Submitted: Mar 30, 2012
- Release Date:
- 20140324
- Correction Date:
- 20140519
- Copyright:
- American Psychological Association. 2014
- Digital Object Identifier:
- http://dx.doi.org/10.1037/a0036365
- PMID:
- 24660674
- Accession Number:
- 2014-10290-001
- Number of Citations in Source:
- 95
- Persistent link to this record (Permalink):
- http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2014-10290-001&site=ehost-live
- Cut and Paste:
- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2014-10290-001&site=ehost-live">Treatment engagement and response to CBT among Latinos with anxiety disorders in primary care.</A>
- Database:
- PsycINFO
Treatment Engagement and Response to CBT Among Latinos With Anxiety Disorders in Primary Care
By: Denise A. Chavira
Department of Psychology, University of California, Los Angeles, and Department of Psychiatry, University of California, San Diego;
Daniela Golinelli
RAND Corporation, Santa Monica, California
Cathy Sherbourne
RAND Corporation, Santa Monica, California
Murray B. Stein
Department of Psychiatry and Department of Family and Preventive Medicine, University of California, San Diego
Greer Sullivan
Department of Psychiatry and South Central Mental Illness Research, Education, and Clinical Center, University of Arkansas for Medical Sciences
Alexander Bystritsky
Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine, University of California, Los Angeles
Raphael D. Rose
Department of Psychology, University of California, Los Angeles
Ariel J. Lang
Veterans Affairs San Diego Health Care System Center of Excellence for Stress and Mental Health, San Diego, California, and Department of Psychiatry, University of California, San Diego
Laura Campbell-Sills
Department of Psychiatry, University of California, San Diego
Stacy Welch
Department of Psychiatry and Behavioral Sciences, University of Washington School of Medicine, and Harborview Center for Healthcare Improvement for Addictions, Mental Illness, and Medically Vulnerable Populations (CHAMMP), Seattle, Washington
Kristin Bumgardner
Department of Psychiatry and Behavioral Sciences, University of Washington School of Medicine, and Harborview Center for Healthcare Improvement for Addictions, Mental Illness, and Medically Vulnerable Populations (CHAMMP), Seattle, Washington
Daniel Glenn
Department of Psychology, University of California, Los Angeles
Velma Barrios
Los Angeles County Department of Mental Health, Los Angles, California
Peter Roy-Byrne
Department of Psychiatry and Behavioral Sciences, University of Washington School of Medicine, and Harborview Center for Healthcare Improvement for Addictions, Mental Illness, and Medically Vulnerable Populations (CHAMMP), Seattle, Washington
Michelle Craske
Department of Psychology, University of California, Los Angeles
Acknowledgement: This work was supported by Grants U01 MH057858 (Peter Roy-Byrne), U01 MH058915 (Michelle Craske), U01 MH 070022 (Greer Sullivan), U01 MH057835 and K24 MH64122 (Murray B. Stein), and K01 MH072952 (Denise A. Chavira) from the National Institute of Mental Health. The authors wish to thank Jeanne Miranda for conducting the cultural competency training with therapists in this study.
Anxiety disorders are prevalent in Latino populations. Findings from epidemiological studies based in the United States suggest that lifetime rates of anxiety disorders among Latinos range from 19% to 30% (Burnam, Hough, Karno, Escobar, & Telles, 1987; Vega et al., 1998; Vincente et al., 2006). Data also suggest that U.S.-born Latinos, particularly those of Mexican origin, are at higher risk than immigrant Latinos, for mood, anxiety, and substance use disorders (Alegría et al., 2008; Grant et al., 2004; Vega et al., 1998). Despite the remarkable presence of anxiety and related disorders, Latinos are less likely than non-Latino Whites to use outpatient mental health services (Miranda & Green, 1999; Ojeda & McGuire, 2006) and are also less likely to receive evidence-based care (U.S. Department of Health and Human Services, 2001). These disparities underscore a treatment need for a large and growing segment of the U.S. population.
Cognitive behavioral therapy (CBT) is the primary evidence-based psychosocial intervention for anxiety disorders (Butler, Chapman, Forman, & Beck, 2006). However, few studies have examined the efficacy of CBT with Latinos (Miranda et al., 2005; U.S. Department of Health and Human Services, 2001). Most randomized controlled trials (RCTs) for adults with anxiety disorders have only recruited small proportions of Latinos, making any kind of ethnic specific analysis impossible. As an example, in a recent review of RCTs for obsessive–compulsive disorder, only 1.0% of 2,221 participants from 21 trials were Hispanic/Latino (Williams, Powers, Yun, & Foa, 2010). Studies examining the efficacy of CBT for Latino children and adolescents with anxiety disorders are more common, albeit still few. Findings from these RCTs have found comparable outcomes among Latino and non-Latino White youth on measures of clinical response, remission, symptom severity, and overall functioning (Piña, Silverman, Fuentes, Kurtines, & Weems, 2003; Piña, Zerr, Villalta, & Gonzales, 2012; Silverman, Piña, & Viswesvaran, 2008).
RCTs that evaluate CBT for Latinos with depression, a distinct but related disorder, are more numerous, particularly in primary care settings (Horrell, 2008; Miranda et al., 2005). Primary care–based interventions may be particularly well suited for Latinos who often experience more barriers to access and endorse more stigma regarding seeking services from specialty mental health settings (Vega et al., 2007; Vega & Lopez, 2001). Findings from these studies suggest that Latinos with depression, including low-income and Spanish-speaking patients, have responses to CBT comparable to those of other ethnic groups (Miranda, Azócar, Organista, Dwyer, & Areane, 2003; Muñoz et al., 1995). In large-scale quality improvement programs, which have included a CBT option, significant short- and long-term effects have been found for quality of care received by Latinos, African Americans, and non-Latino Whites, and significant reductions in depressive symptoms have also been found for Latinos and African Americans (Miranda, Duan, et al., 2003; Wells et al., 2005). In smaller, community-based studies, favorable responses to CBT have also been found for various Latino ethnic subgroups (Comas-Díaz, 1981; Organista, Muñoz, & Gonzalez, 1994; Rosselló & Bernal, 1999).
While findings offer some support for comparable clinical outcomes among Latino and non-Latino White participants who have received CBT, less attention has been devoted to engagement-related constructs, which typically reflect the extent to which a patient participates in treatment (e.g., treatment uptake, adherence, and attrition). Previous studies have found that lower engagement, as defined by fewer sessions attended, less homework adherence, or higher rates of attrition, can have negative effects on clinical outcomes (Glenn et al., 2013; O’Brien, Fahmy, & Singh, 2009). Studies that have examined differences in engagement between Latinos and non-Latino Whites have mostly focused on depression and have shown higher attrition rates in both pharmacological and psychosocial interventions for Latinos than for non-Latino Whites (Arnow et al., 2007; Organista et al., 1994). Additionally, problems with medication compliance, CBT attendance, and completion of CBT homework assignments among Latinos have been reported (Aguilera, Garza, & Muñoz, 2010; Ayalon, Areán, & Alvidrez, 2005; Miranda & Cooper, 2004). To our knowledge, no studies have examined engagement outcomes for Latino adults participating in a CBT intervention for anxiety disorders.
The current study addresses a gap in the literature on the impact of culture and ethnicity on treatment outcomes for patients with anxiety disorders. In this study, participants were recruited from primary care settings and received therapist-delivered, computer-assisted CBT for anxiety disorders (CALM: Tools for Living) as part of the CALM (Coordinated Anxiety Learning and Management) study (Craske, Rose, et al., 2009; Roy-Byrne et al., 2010). Clinical outcomes such as symptom reduction and remission as well as engagement-related outcomes including session attendance, treatment completion, homework adherence, and acceptance of CBT, were examined. Based on the available literature, we hypothesized that Latinos who received CBT would have similar clinical outcomes as non-Latino Whites at the various assessment points. We also hypothesized that engagement outcomes would be less favorable among Latino compared with non-Latino Whites; however, given the limited literature, analyses were somewhat exploratory in this regard.
Method Participants
Over a 2-year recruitment period, 1,004 patients with anxiety disorders were recruited from a total of 17 primary care clinics for participation in the CALM study (for a full description, see Roy-Byrne et al., 2010; Sullivan et al., 2007). Study clinics were located in Little Rock, Arkansas, Los Angeles and San Diego, California, and Seattle, Washington. Prior to start of the study, all primary care professionals were educated about the CALM program and eligibility criteria. All recruitment was facilitated by primary care providers who had the option to use a brief anxiety screener (Means-Christensen, Sherbourne, Roy-Byrne, Craske, & Stein, 2006) or to refer patients directly to the study.
All patients had to be between the ages of 18 and 75 years and meet Diagnostic and Statistical Manual of Mental Disorders (4th ed., text rev., or DSM–IV–TR; American Psychiatric Association, 2000) criteria for one or more of the following: anxiety disorders; panic disorder (PD), generalized anxiety disorder (GAD), social anxiety disorder (SAD), and posttraumatic stress disorder (PTSD). The Mini International Neuropsychiatric Interview (Sheehan et al., 1998) was used to determine diagnostic eligibility. Patients also had to score 8 or above on the Overall Anxiety Severity and Impairment Scale (OASIS; Campbell-Sills et al., 2009) to ensure at least moderate anxiety-related symptoms and impairment on this validated quantitative measure. Comorbidity was permitted, including major depressive disorder, alcohol abuse, nicotine dependence, and marijuana abuse. Individuals who had other conditions that would compromise their participation in the program or who were unlikely to benefit from CALM were excluded (e.g., unstable medical conditions, marked cognitive impairment, active suicidal intent or plan, psychosis, bipolar I disorder, and substance use disorders except for nicotine dependence, alcohol abuse, and marijuana abuse). Patients already receiving CBT or medication from a psychiatrist in the community were excluded, as were persons who could not speak and read in English or Spanish. All patients gave written informed consent for the study, which was approved by each institution’s institutional review board.
Procedure
After the initial eligibility interview with an anxiety clinical specialist (an ACS, a clinician trained to facilitate the CALM intervention), patients were randomized to intervention or usual care (UC), using an automated computer program at RAND Corporation. All assessments after the initial eligibility interview were conducted by telephone in English or Spanish by members of the RAND Survey Research Group, who were blind to treatment assignment. Randomization was stratified by clinic and presence of comorbid major depression using a permuted block design.
Patients in the intervention group were initially allowed to choose which treatment they wanted to receive—medication, CBT, or both—for 12 weeks. Clinicians asked patients with more than one anxiety disorder who received CBT to choose the most disabling or distressing disorder to focus on, with the expectation that treatment effects would generalize to their other disorders. CBT was administered by the ACS; medication was prescribed by the primary care provider, with consultation from study psychiatrists as needed. A computer program (CALM Tools for Living) was used to assist with the delivery of the CALM intervention; this program was used as an adjunctive tool and not as a stand-alone intervention. Overall, the therapist-delivered CBT program included five generic modules (education, self-monitoring, hierarchy development, breathing training, and relapse prevention) and three modules tailored to the four specific anxiety disorders (cognitive restructuring, exposure to internal stimuli, and exposure to external stimuli; see Craske, Rose, et al., 2009). All intervention materials were translated into Spanish by certified translators, including the computer program.
For intervention patients who opted for medication management, the ACS monitored adherence to the medication regimen and provided basic counseling to encourage healthy behaviors (e.g., avoidance of alcohol and improvement of sleep hygiene and behavioral activation). The ACS also conveyed pharmacotherapy suggestions from the supervising psychiatrist to the primary care physician.
A total of 14 ACSs were involved in this project and administered the eligibility assessment and CALM intervention. The ACSs included six social workers, five registered nurses, two master’s-level psychologists, and one doctoral-level psychologist. Eight of the specialists had some mental health experience, and four had some CBT training. All ACSs received 2 hr of training in issues of cultural competency, specific to patients with anxiety disorders, and a bilingual therapist delivered the CBT in Spanish at selected sites.
The ACSs used a Web-based system to enter scores for the Overall Anxiety Severity and Impairment Scale (OASIS) and a three-item version of the Patient Health Questionnaire–9 (Kroenke, Spitzer, & Williams, 2001) that were collected at each patient visit to track patient outcomes. Using the CALM model, patients who were symptomatic and thought to benefit from additional treatment with CBT or medication could receive more of the same modality (stepping up) or the alternative modality (stepping over) for up to three more steps of treatment. (For a full description of the CALM model and training, please see Craske, Roy-Byrne, et al., 2009; Rose et al., 2011; Roy-Byrne et al., 2010).
Patients in the usual care (UC) group received treatment by their physician in the usual manner (i.e., with medication, counseling or referral to a mental health specialist) with no further intervention by study clinicians. After the eligibility interview, the only contact UC patients had with study personnel was for the telephone assessments conducted by RAND.
Given the study focus on CBT treatment effects across Latinos and non-Latino Whites, only patients in the intervention condition who received CBT were included in the current analyses (i.e., those who received CBT or CBT plus medication; n = 336). Participants who were African American or identified as “other” (including Asian Americans) were not included in this study. The flowchart for screening and randomization is presented in Figure 1. A total of 1,062 of 1,620 patients (66%) who were referred were eligible for study participation. Of these, 98% (n = 1,036) consented to participate, and 1,004 were randomized. More than 80% of patients were assessed at each time point, and retention was high across both treatment groups. For a detailed review of patient flow, please see Roy-Byrne et al. (2010).
Figure 1. Recruitment flowchart for Latinos and non-Latino Whites randomized to the intervention arm of the CALM (Coordinated Anxiety Learning and Management) study. CBT = cognitive behavioral therapy.
The primary outcome measure was the 12-item Brief Symptom Inventory (BSI–12) which includes subscales of Anxiety and Somatization (Derogatis, 2001). Using procedures we have described elsewhere (Roy-Byrne et al., 2010), treatment response was operationalized as at least a 50% reduction on the BSI–12, and treatment remission was defined as a face-valid per-item score on the BSI–12 of less than 0.5 (between none and mild; total BSI–12 score < 6). Measures for secondary analyses included the Anxiety Sensitivity Index (ASI; Reiss, Peterson, Gursky, & McNally, 1986), the Patient Health Questionnaire (eight-item version, which does not include suicide item) for depression (Kroenke et al., 2001), the Sheehan Disability Scale (SDS) modified to assess anxiety-related disability (Sheehan, Harnett-Sheehan, & Raj, 1996), the Short-Form Health Survey (SF–12; i.e., Mental Health Composite Summary Scale; Ware, Kosinski, Bowker, & Gandek, 2002), and a brief survey to assess satisfaction with mental health treatment for anxiety.
These measures have been widely used in diverse populations, and both the English and Spanish versions have good psychometric properties. Specifically, the ASI has been examined in Latino clinical and nonclinical samples, and good internal consistency, test–retest reliability, and convergent validity with other anxiety measures have been reported (Cintron, Carter, Suchday, Sbrocco, & Gray, 2005; Novy, Stanley, Averill, & Daza, 2001; Sandin, Chorot, & McNally, 1996). The BSI–18 has been examined in numerous Spanish-speaking samples and demonstrates good reliability and validity; although a couple studies have revealed an inconsistent factor structure, suggesting the need for more work to establish the psychometric properties of the BSI–18 with Latinos (Galdón et al., 2008; Torres, Miller, & Moore, 2013; Wiesner et al., 2010). The Spanish version of the PHQ has been shown to have good internal consistency and concurrent and structural validity in primary care and community samples (Diez-Quevedo, Rangil, Sanchez-Planell, Kroenke, & Spitzer, 2001; Donlan & Lee, 2010; Merz, Malcarne, Roesch, Riley, & Sadler, 2011), and both the SDS and the SF–12 have been shown to be reliable and valid in Spanish-speaking primary care patients (Ayuso-Mateos, Vasquez-Barquero, Oviedo, & Diez-Manrique, 1999; Castillo, 2007; Luciano et al., 2010).
To evaluate treatment engagement, we extracted ratings from the Web-based system and computerized CBT program regarding engagement outcomes. Homework adherence, session attendance, commitment to CBT, and understanding of CBT principles were all rated by the ACS. Homework adherence was a measure of the quantity of homework completed (0 = missed none; 1 = missed few; 2 = missed half; 3 = missed most), and commitment to CBT reflected the ACS’s perception of the patient’s motivation in each CBT session (0 = none; 10 = complete). CBT understanding was based on the ACS’s perception of how well the patient understood the CBT principles being presented in each session. Patient self-report was used for outcome expectancies and self-efficacy (0 = not at all; 4 = 50/50; 8 = certainly). Outcome expectancies reflected patients’ beliefs that their participation in the CBT intervention would result in improvement, while self-efficacy reflected patients’ beliefs that they were capable of completing the requested CBT activities. These ratings were completed at all sessions, and a mean score across sessions was used for the analyses. Last, treatment completion was defined as the completion of the relapse prevention module of the CBT program (which typically occurred after eight sessions). These outcomes reflect behavioral manifestations of engagement (e.g., adherence, attendance, and drop-out) as well as aspects of treatment readiness and motivation (e.g., understanding, commitment to treatment, etc.) that influence engagement (Tetley, Jinks, Huband, & Howells, 2011).
Data Analysis
The primary aim of this study was to obtain robust estimates of the association between ethnicity (where ethnicity has only two categories: Latino and non-Latino White) and outcomes. We used propensity-score-weighted linear and logistic regression models to estimate the effect of ethnicity on clinical outcomes. Propensity-score weighting is an effective way of eliminating the differences in observed characteristics (e.g., age, gender, severity at baseline, presence of chronic medical disorders) between the Latino and non-Latino White groups that could bias the estimates of the association between ethnicity and outcomes (Rosenbaum & Rubin, 1983). In contrast, commonly used regression models rely too heavily on the linear assumption and are highly sensitive to model specification, such as the inclusion of important interaction terms.
In this application, we defined propensity score as the conditional probability that a patient is Latino, given a set of observed patient characteristics (Rosenbaum & Rubin, 1983). This probability was used to build weights (Hirano, Imbens, & Ridder, 2003; McCaffrey, Ridgeway, & Morral, 2004) for patients belonging to the non-Latino White group. Patients in the non-Latino White group who had similar characteristics to patients in the Latino group had a large propensity score, and therefore, we “up-weighted” these patients when estimating the association between ethnicity and outcomes. Patients in the non-Latino White group with characteristics dissimilar to the Latino group were “down-weighted” when we computed the effect of ethnicity. We fitted the propensity-score weights using the twang R package (Ridgeway, McCaffrey, & Morral, 2006), which uses a nonparametric regression technique instead of a logistic regression. The patients’ characteristics used to fit the propensity-score model were site; gender; age; diagnoses of PD, GAD, SAD, PTSD, or MDD; number of chronic medical conditions; income; marital status; any use of psychotropic medications prior the study start; insurance status; and baseline BSI–12 score. In this study, the obtained propensity-score weights effectively eliminated differences between the two ethnic groups for several of the characteristics used in the propensity score (PS) model, but not for all of them. In particular some differences remained for age, PTSD diagnosis, and insurance status.
In the presence of ongoing small imbalances despite PS weighting, we adopted a double robust (DR) estimation approach to further control for differences in the baseline characteristics between the two ethnic groups. DR estimation methods (Bang & Robins, 2005; Kang & Schafer, 2007) reduce the risk of bias due to remaining small differences between groups and the uncertainty in the treatment effect estimator by reducing the outcome model’s residual variance. The adopted DR estimation approach implies fitting PS-weighted linear or logistic regressions (depending on the type of outcomes) that control for, in addition to the variable indicating whether a patient is Latino or not, all the patients’ characteristics used in the PS model. This approach provided the least biased estimate of the association between ethnicity and outcomes.
Additionally, we developed three separate sets of nonresponse weights to account for missing outcome measures due to patients skipping a particular assessment (e.g., Month 6, 12, or 18) or for dropping out from the study. Nonresponse weights are an effective way to address missing data when it is due to unit nonresponse (Brick & Kalton, 1996), as was the case for the missing outcome measures. For example, missing 12-month outcomes were due to the fact that a patient failed to respond to the entire 12-month follow-up assessment, rather than a patient refusing to respond to specific questions within the assessment. The nonresponse weights were estimated in the same way as the PS weights. The aim of this method is to weigh those patients with outcomes at a given assessment (e.g., 12-month) to represent the sample of Latino and non-Latino White patients who selected CBT (n = 366).
Results Baseline Characteristics
Baseline characteristics for Latinos and non-Latino Whites are presented in Table 1. There were 85 Latino and 251 non-Latino White participants who received the CALM CBT intervention; eight Latino participants received the CBT intervention in Spanish. Patients from other ethnic groups including African Americans (n = 51) and patients who identified as “other” races/ethnicities (n = 69) were excluded from these analyses. Statistical comparisons were made using t tests for continuous variables and chi-square tests for categorical variables. Significant differences were found for age, gender, income, marital status, chronic medical conditions, PTSD, use of psychotropic medication, and insurance across ethnic groups. The Latino sample was younger, more likely to be married, and more likely to be uninsured. This sample had lower incomes and was composed disproportionately of women. Latinos also had fewer chronic medical disorders, lower rates of psychotropic medication use, and higher rates of PTSD than non-Latino Whites. As described in the statistical approach section, we controlled for all of these differences in patient characteristics using propensity weights and a DR-estimation approach. Analyses were also conducted without controlling for income and insurance, variables that often share an association with acculturation level and consequently may lead to distortions in cultural effects when controlled.
Baseline Patient Characteristics
Treatment Preference
As described earlier, participants randomized to the intervention arm were allowed to choose among the treatment options of CBT only, CBT plus medication, and medication only. Chi-square tests were used to analyze differences in treatment preference between Latinos and non-Latino Whites. Treatment preference rates did not differ significantly for Latinos and non-Latino Whites, respectively: 40% versus 36% for CBT only, 52% versus 56% for CBT plus medication, and 9% versus 8% for medication only.
Clinical Outcomes
We used PS-weighted linear and logistic regression models (DR-regression models) to estimate the effect of ethnicity on clinical outcomes. All models included baseline characteristics in addition to the Latino indicator. Only coefficients for Latino ethnicity are presented in Table 2; full models are available upon request. Significant differences were found for the survey on satisfaction with health and mental health care at 12 months and on the Mental Health Composite Scale score (MCS–12) at 18 months, with B coefficients reflecting more positive scores for Latinos at these time points. When analyses were performed without controlling for income and insurance status, findings were largely the same, except for the MCS–12 finding at 18 months, which was no longer significant (B = 2.59, p = .096; full tables are available upon request). The rates of treatment response and remission did not differ significantly between the two groups at any of the three follow-up points. Adjusted treatment response rates for Latinos ranged from 62.7% to 68.6%, while rates for non-Latino Whites ranged from 60.0% to 77.3%. Adjusted rates of remissions ranged from 41.9% to 61.5% for Latinos and from 42.8% to 62.2% for non-Latino Whites.
Double Robust Estimates of the Latino Ethnicity Effect on Clinical Outcomes
Engagement-Related Outcomes
The same analytic approach described previously was used to estimate the effects of ethnicity on engagement-related outcomes. All models controlled for baseline characteristics in addition to the Latino indicator. Only coefficients for Latino ethnicity are presented in Table 3. There were no significant differences for five of the seven engagement related variables (e.g., adherence, treatment completion, commitment to CBT, self-efficacy, outcome expectancies). Mean scores for Latinos and non-Latino Whites ranged from 8.29 to 8.52 on overall commitment to in-session CBT activities (using a 10-point scale) and from .66 to .75 for homework adherence (1 = missed few and 3 = missed most). Mean self-report ratings on treatment outcome expectancies and self-efficacy ranged from 6.3 to 6.8 on an 8-point scale. A significant difference emerged for “understanding of CBT session principles,” with Latinos receiving lower scores than non-Latino Whites. Latinos also attended fewer sessions than non-Latino Whites (adjusted mean number of sessions for Latinos was 7.44 vs. 9.09 for non-Latino Whites, p = .004). Findings remained the same, when income and insurance status were not controlled.
Double Robust Estimates of the Latino Ethnicity Effect on Engagement Outcomes
A post hoc power analysis suggested that given the sample size available, we were able to detect effect sizes in the medium range with 80% power. Effect sizes for clinical and engagement outcomes are presented in the accompanying tables.
DiscussionThe CALM study provides one of the largest samples of Latinos who have participated in an effectiveness trial for anxiety disorders and is one of the first to examine differences in CBT treatment response and engagement between Latinos and non-Latino Whites. Given the location of participating clinics (predominantly on the West Coast of the United States), a sizeable proportion of our sample identified as Hispanic/Latino (approximately 20%). Data regarding Latino ethnic subgroups and acculturation level were not gathered; however, the majority of the Latino sample was English speaking, suggesting a higher level of acculturation, and, given U.S. Census Bureau statistics from participating regions, most likely to be of Mexican origin (U.S. Census Bureau, 2011).
With regard to preference for treatment, the majority of participants from both ethnic groups chose the combination of CBT plus medication over the other treatment modalities, although a sizable number also chose CBT alone. The use of medication alone was not a common preference for either group. These findings are consistent with studies of depression that have found that both Latinos and other ethnic minorities prefer counseling approaches over medication (Cooper et al., 2003; Dwight-Johnson, Sherbourne, Liao, & Wells, 2000). Additionally, among Latinos, the use of antidepressant medication has been associated with beliefs such as greater stigma and perceptions of being more severely ill, being weak or unable to handle one’s problems, and being subjected to the negative effects of drugs (e.g., addiction; Interian, Martinez, Guarnaccia, Vega, & Escobar, 2007; Olfson, Marcus, Tedeschi, & Wan, 2006; Sirey, Bruce, Alexopoulos, Perlick, Friedman, et al., 2001; Sirey, Bruce, Alexopoulos, Perlick, Raue, et al., 2001). It is possible that Latino participants in the CALM study shared these beliefs. However, the fact that many Latino participants chose the combination approach, which included medication, may suggest greater acceptability of pharmacological approaches, particularly in the presence of a psychosocial intervention.
There were no statistically significant differences between Latinos and non-Latino Whites on measures of clinical outcome including anxiety sensitivity, depression, cognitive and somatic anxiety, and disability at any assessment point. Further, there were no significant differences between groups on indicators of treatment response or clinical remission at any time point. Significant differences did emerge for overall mental health functioning at 18 months and satisfaction with mental health care at 12 months, with Latinos having more favorable scores than non-Latino Whites. When analyses were conducted without adjusting for insurance and income, variables that are often confounded with culture, findings were largely the same. These findings parallel prior findings in child anxiety and adult depression where comparable clinical outcomes and response rates have been reported in CBT studies with Latinos and non-Latino Whites (Cardemil, Reivich, Beevers, Seligman, & James, 2007; Marchand, Ng, Rohde, & Stice, 2010; Miranda, Azócar, et al., 2003; Miranda, Duan, et al., 2003; Muñoz et al., 1995).
Based on previous studies, we expected more ethnic differences to emerge for the engagement outcomes; however, overall there were more similarities than differences. According to the ACSs, both Latino and non-Latino White participants exhibited “good” levels of homework adherence and overall commitment to session activities. Using patient self-report, both Latinos and non-Latino Whites reported favorable expectations regarding treatment outcomes and beliefs that they could complete the CBT activities. Significant differences emerged for treatment attendance and understanding of CBT principles. Latinos attended fewer sessions than non-Latino Whites, approximately seven versus nine sessions, respectively. Similarly, rates of treatment completion, defined as the completion of the relapse prevention module, tended to be higher for non-Latino Whites (75%) than Latinos (64%) although this difference did not reach statistical significance. Differences in attendance rates and premature termination have been found in other studies and have been attributed to logistic (e.g., multiple competing demands, transportation, and so on), motivational, and attitudinal factors (e.g., outcome expectancies and stigma; McCabe, 2002; Miranda, Azócar, et al., 2003; Organista et al., 1994). These explanations may also apply to participants in our study; however, as noted, patient ratings of outcome expectancies, commitment to CBT, and self-efficacy were similar for non-Latino Whites and Latinos. Ratings of satisfaction with mental health care were also similar across all time points. Further, given propensity weights for baseline differences, income-related stressors were likely not the primary cause of differential rates of attendance. Other factors, such as perceived cultural fit of the program and therapist–client ethnic match, may have had an effect on attendance but were not measured. Alternatively, it may have been that Latinos were satisfied with the number of sessions they received and did not feel the need to attend as many sessions as non-Latino Whites or to complete the intervention. The other significant difference—poorer understanding of CBT principles by Latinos—has not been reported previously in the treatment literature. It is possible that this difference may be explained by language barriers either in understanding the translation of the CBT materials or in the patients conveying their understanding of the principles to the ACSs. It may also be attributed to varying conceptualizations of anxiety disorders and their treatment, although limited data exist in this regard (Chavira et al., 2008; Hinton, 2012; Lewis-Fernández et al., 2010).
The reason for comparable clinical outcomes in the presence of differential attendance warrants some discussion. One explanation for this disconnect may be that certain aspects of engagement have a greater impact on clinical outcomes than others. For example, the impact of homework adherence on clinical outcomes has been well-established in the treatment literature (Kazantzis, Whittington, & Dattilio, 2010; Mausbach, Moore, Roesch, Cardenas, & Patterson, 2010). In the presence of good homework adherence, as noted in this study, the impact of other engagement-related variables for Latinos, such as attendance, on clinical outcomes may be mitigated. Also, previous studies support the importance of distinguishing among pretreatment, early treatment, and later stage treatment attrition (Gonzalez, Weersing, Warnick, Scahill, & Woolston, 2011; Hofmann et al., 1998; Issakidis & Andrews, 2004). Given that Latinos attended an average of seven treatment sessions, it is likely that most of the drop-out occurred at later stages, reducing the potentially more deleterious effects of early attrition on clinical outcomes. Last, the use of face-valid measures of engagement may have influenced the study findings and should be interpreted with caution. In general, efforts are necessary to further improve the definition of engagement as well as its measurement (Drieschner, Lammers, & van der Staak, 2004). Although measures of engagement exist, most are limited in scope (e.g., only address homework compliance), have limited psychometric support, and are not generalizable across populations and treatment modalities (Tetley et al., 2011). These efforts may be particularly relevant to Latinos and other underrepresented groups who are likely to encounter greater barriers to mental health services and may be more difficult to engage.
A focus on differential clinical and engagement-related outcomes between Latinos and non-Latino Whites is timely in the context of continued debate regarding cultural adaptations for evidence-based interventions (Barrera & Castro, 2006; Chu, Huynh, & Areán, 2012). According to a popular cultural adaptation framework (Lau, 2006), tailoring efforts are best guided by empirical findings that support ethnic differences in the social validity of an intervention (e.g., engagement and acceptability), clinical outcomes, or risk and resiliency factors that may affect the etiology or course of the disorder. Similar to findings in previous treatment studies for anxiety and depression (Huey & Polo, 2008; Miranda et al., 2005; Piña et al., 2003), findings from this study support mostly comparable clinical outcomes for CBT across non-Latino Whites and Latinos, specifically, English-speaking Latinos. However, findings from the current study raise some concerns regarding the social validity of the CALM intervention among Latinos given lower self-reported understanding of CBT principles, fewer sessions attended, and a trend toward lower CBT completion rates. These findings suggest that tailoring efforts to improve engagement for Latinos receiving CBT interventions like CALM may be warranted.
It is important to note that while cultural adaptations were not made to the core components or content of the CBT intervention, surface or peripheral adaptations (Resnicow, Soler, Braithwaite, Ahluwalia, & Butler, 2000; Simons-Morton, Donohew, & Davis Crump, 1997) did occur. All therapists received training in issues of cultural competency, all intervention materials were translated into Spanish including the computer program, and a bilingual therapist delivered the CBT in Spanish at selected sites; such modifications have the potential to improve the overall face validity, understanding, and acceptability of an intervention. In effect, some of the traditional barriers to access and engagement that are common among Latino populations, such as language (Morales, Cunningham, Brown, Liu, & Hays, 1999; Vega & Lopez, 2001), stigma associated with receiving mental health care at specialty care settings (Interian et al., 2007; Nadeem et al., 2007; Sirey, Bruce, Alexopoulos, Perlick, Friedman, et al., 2001), and poor therapeutic alliance due to cultural differences (Añez, Paris, Bedregal, Davidson, & Grilo, 2005; Fuertes, Boylan, & Fontanella, 2009; Vasquez, 2007) may have been addressed in the development and implementation of the CALM study.
Limitations
The CALM study was focused on the overall effectiveness of an innovative model of treatment delivery for patients with anxiety disorders in primary care; as such, it was not designed to focus on ethnic group differences, and measures of acculturation were not included in this study. While sample size allowed for the evaluation of overall main effects of ethnicity (i.e., Latino vs. non-Latino White), only effect sizes in the medium range were detectable, and thus smaller yet clinically meaningful differences may have been missed. Additionally, sample sizes were too small (n = 8) to investigate the effectiveness of the intervention for individuals who were monolingual Spanish speakers and received the intervention in Spanish. It is possible that differences in engagement may have been more substantial and differences in clinical outcomes may have emerged with a primarily Spanish-speaking sample. Understanding barriers to initial uptake and recruitment of monolingual Spanish speakers into interventions such as CALM remains an important direction of research in efficacy and effectiveness trials. The sample is also biased in that it is a primary care sample and composed of a group of individuals who chose to participate in a treatment program for anxiety. As a result, the sample may differ from community-based samples, with regard to insurance status, employment, income, access to resources, and level of acculturation. Therefore, caution is advised in generalizing these findings to lower income, Spanish-speaking, and less acculturated groups. Further, clinical outcome measures such as the Brief Symptom Inventory warrant additional psychometric examination with Latino populations from diverse acculturation levels. Last, many of our measures of engagement-related variables were face-valid items that were administered as adjunctive assessments of the therapeutic process and consequently may not have adequately examined the desired constructs.
Conclusions
Overall, findings from this study suggest that the CALM CBT program for anxiety can be an effective treatment option for Latinos who are English speaking and likely more acculturated. While current findings do not support the need for extensive tailoring of the CALM CBT intervention to meet the needs of English-speaking Latinos with anxiety disorders in primary care, findings underscore the need for continued efforts to understand and improve engagement of Latinos in evidence-based interventions. Further, additional studies with larger sample sizes, monolingual Spanish-speaking participants, and standardized measures of acculturation are warranted in order to improve the evidence base for CBT approaches with Latinos.
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Submitted: March 30, 2012 Revised: June 19, 2013 Accepted: June 21, 2013
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Source: Journal of Consulting and Clinical Psychology. Vol. 82. (3), Jun, 2014 pp. 392-403)
Accession Number: 2014-10290-001
Digital Object Identifier: 10.1037/a0036365
Record: 190- Title:
- Underage drinking among young adolescent girls: The role of family processes.
- Authors:
- Fang, Lin. Factor-Inwentash Faculty of Social Work, University of Toronto, Toronto, ON, Canada, lin.fang@utoronto.ca
Schinke, Steven P.. School of Social Work, Columbia University, New York, NY, US
Cole, Kristin C.. School of Social Work, Columbia University, New York, NY, US - Address:
- Fang, Lin, Factor-Inwentash Faculty of Social Work, University of Toronto, 246 Bloor Street West, Toronto, ON, Canada, M5S 1A1, lin.fang@utoronto.ca
- Source:
- Psychology of Addictive Behaviors, Vol 23(4), Dec, 2009. pp. 708-714.
- NLM Title Abbreviation:
- Psychol Addict Behav
- Page Count:
- 7
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- Bulletin of the Society of Psychologists in Addictive Behaviors; Bulletin of the Society of Psychologists in Substance Abuse
- Other Publishers:
- US : Educational Publishing Foundation
Society of Psychologists in Addictive Behaviors - ISSN:
- 0893-164X (Print)
1939-1501 (Electronic) - Language:
- English
- Keywords:
- early adolescence, alcohol, underage drinking, family females, psychological factors, peer & family processes
- Abstract:
- Guided by family interaction theory, this study examined the influences of psychological, peer, and familial processes on alcohol use among young adolescent girls and assessed the contributions of familial factors. An ethnically diverse sample of 1,187 pairs of girls (M age = 12.83 years), and their mothers completed surveys online. Questionnaires assessed girls’ lifetime and recent alcohol use, as well as girls’ demographic, psychological, peer, and family characteristics. Hierarchical logistic regression models showed that although girls’ drinking was associated with a number of psychological and peer factors, the contributions of family domain variables to girls’ drinking were above and beyond that of psychological and peer factors. The interaction analyses further highlighted that having family rules, high family involvement, and greater family communication may offset risks in psychological and peer domains. Study findings underscore the multifaceted etiology of drinking among young adolescent girls and assert the crucial roles of familial processes. Prevention programs should be integrative, target processes at multiple domains, and include work with parents. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Human Females; *Psychosocial Factors; *Social Influences; *Underage Drinking; Demographic Characteristics; Family Relations; Peer Relations
- Medical Subject Headings (MeSH):
- Adolescent; Adolescent Behavior; Age Factors; Alcohol Drinking; Body Image; Child; Child Behavior; Cross-Sectional Studies; Depression; Family Relations; Female; Health Surveys; Humans; Peer Group; Regression Analysis; Self Concept; Self Efficacy; Social Behavior; Social Environment; Surveys and Questionnaires
- PsycINFO Classification:
- Psychosocial & Personality Development (2840)
- Population:
- Human
Female - Location:
- US
- Age Group:
- Childhood (birth-12 yrs)
School Age (6-12 yrs)
Adolescence (13-17 yrs)
Adulthood (18 yrs & older) - Tests & Measures:
- Children’s Depression Inventory, short version
Self-Perception Profile for Adolescents, physical appearance subscale
Strengthening Families Program evaluations
Family Problem Solving Communication Index
Parenting Practices Questionnaire DOI: 10.1037/t08384-000
Alcohol Abstinence Self-Efficacy Scale DOI: 10.1037/t04226-000 - Grant Sponsorship:
- Sponsor: National Institute on Drug Abuse
Grant Number: R01 DA17721
Recipients: No recipient indicated - Methodology:
- Empirical Study; Quantitative Study
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- Accepted: May 27, 2009; Revised: May 26, 2009; First Submitted: Jan 1, 2009
- Release Date:
- 20091221
- Correction Date:
- 20130617
- Copyright:
- American Psychological Association. 2009
- Digital Object Identifier:
- http://dx.doi.org/10.1037/a0016681
- PMID:
- 20025378
- Accession Number:
- 2009-24023-018
- Number of Citations in Source:
- 27
- Persistent link to this record (Permalink):
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- Cut and Paste:
- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2009-24023-018&site=ehost-live">Underage drinking among young adolescent girls: The role of family processes.</A>
- Database:
- PsycINFO
Underage Drinking Among Young Adolescent Girls: The Role of Family Processes
By: Lin Fang
Factor-Inwentash Faculty of Social Work, University of Toronto, Ontario, Canada;
Social Policy Center, National Taiwan University, Taipei, Taiwan;
Steven P. Schinke
School of Social Work, Columbia University, New York, New York
Kristin C. Cole
School of Social Work, Columbia University, New York, New York
Acknowledgement: This work was supported by National Institute on Drug Abuse grant R01 DA17721. We thank Kevin Barnes-Ceeney, Hyeouk C. Hahm, and Tahany M. Gadalla for their helpful comments.
Underage drinking among girls is a growing problem. Not only are girls closing the gender gap in the prevalence of their alcohol use, but among younger girls in particular, they are reporting higher rates of use than boys (Johnston, O’Malley, Bachman, & Schulenberg, 2009). Among the explanations offered for girls’ underage drinking is family interaction theory (Brook, Brook, Gordon, Whiteman, & Cohen, 1990). This theory posits that adolescents’ alcohol use results from psychological, peer, and family influences, and suggests that strong parent–child involvement and communication and high levels of parental monitoring can protect girls.
Family interaction theory is especially salient for adolescent girls. Whereas alcohol use among boys is usually explained by personal beliefs (Fisher, Miles, Austin, Camargo, & Colditz, 2007; Yeh, Chiang, & Huang, 2006), family relationships (Yeh et al., 2006) and involvement (Fisher et al., 2007) are better predictors of girls’ alcohol use. Moreover, despite increasing knowledge of predictors associated with underage drinking, the relative contributions of familial variables remain unclear. Although some studies suggest that psychological factors such as depression (Silberg, Rutter, D’Onofrio, & Eaves, 2003), body esteem (National Center on Addiction and Substance Abuse, 2003), and self-efficacy (Kumpulainen & Roine, 2002) as well as peer influence (Farrell & White, 1998; Simons-Morton, Haynie, Crump, Eitel, & Saylor, 2001) are strongly associated with adolescent girls’ drinking, other findings support familial factors as stronger predictors of girls’ drinking (Cleveland, Feinberg, Bontempo, & Greenberg, 2008).
Informed by family interaction theory, this study investigated how demographic, psychological, peer, and family factors explain girls’ alcohol use. We hypothesized that: (1) higher levels of depression, less body esteem, lower self-efficacy, and greater levels of perceived peer alcohol use would be related to girls’ drinking; (2) after controlling for the contributions of psychological and peer variables, familial factors, namely maternal drinking, parental monitoring, family rules against girls’ alcohol use, parental involvement, and mother–daughter communication, would be associated with girls’ alcohol use; (3) familial domain variables would explain girls’ drinking over and above that accounted for by psychological and peer domain variables; and (4) familial domain variables would modify the effects of psychological and peer factors on girls’ alcohol use.
Method Procedures
The study involved a cross-sectional, Web-based survey of mother–daughter dyads. Study participants were recruited between September 2006 and December 2007 through advertisements in newspapers, public transportation, and on radio stations, and postings on the Web site craigslist.org. To be eligible, girls needed to be aged between 10 and 14 years, have private computer access, gain their mothers’ active participation, and live in the metropolitan New York area. Informed assent and consent forms were sent to eligible girls and their mothers by mail. Of the 1,911 mother–daughter pairs contacted, 20.4% (n = 390) did not respond, 14.6% (n = 279) were no longer interested, 2% (n = 38) were deemed ineligible for the study, and 63% (n = 1,204) agreed to participate and consented. Our consent rate was higher than the average rate (34%) garnered by other Web surveys (Shih & Fan, 2008). Once assent and consent were established, girls and mothers completed online measures. Participants reported before and after the survey whether they were taking the survey alone, and could not begin the online measures until they confirmed their privacy. Less than 2% (n = 17) reported that other people were present while they completed the survey. Responses for these 17 dyads were excluded from data analyses. The average time required to complete the survey for girls was roughly 35 min, and for mothers roughly 20 min. Girls and mothers received $25 each for completing the survey. The study protocol was approved by Columbia University Morningside Campus Institutional Review Board.
Participants
The sample was 1,187 pairs of adolescent girls (M age = 12.83 years; SD = 1.03; 34.9% were Black, 26.2% were White, 21.1% were Latino, 8.5% were Asian, and 9.3% were mixed race) and their mothers (M age = 40.28 years; SD = 6.66). Less than one-half of the girls (42.6%) lived in a single-parent household. Most girls reported receiving B’s (42.3%) or A’s (38.9%) at school. About two-fifths of mothers (42.1%) had some college education or an associate degree.
Measures
Girls’ drinking behavior
Girls reported whether they had ever had a whole drink of an alcoholic beverage (i.e., beer, wine, malt liquor, wine coolers, sweet alcoholic drinks, mixed drinks, and hard liquor) in their lifetime, and during the past 30 days (0 = have never drunk; 1 = have drunk).
Demographic and background variables
Girls reported their age, ethnic-racial backgrounds, and estimated average academic grades (1 = D’s and below to 4 = A’s). Mothers provided information on their age, levels of education (1 = less than high school; 2 = high school degree; 3 = some college or associate degree; 4 = undergraduate degree; 5 = graduate degree), and family composition (0 = single-parent household; 1 = two-parent household).
Depression
Girls rated their depressed mood, hedonic capacity, vegetative functions, and interpersonal behaviors on the short version of the Children’s Depression Inventory (CDI; Kovacs, 1992). The scale had 10 items. Possible responses ranged from 0 to 2. The scores were averaged, with higher scores indicating more definite depressive symptoms. Alpha was .89 for the girls in our study.
Body esteem
On a 5-item physical appearance subscale of the Self-Perception Profile for Adolescents (Harter, 1988), girls specified the degree to which they were happy with the way they looked and with their height and weight. Possible averaged scores ranged from 1 to 5, where higher scores reflected greater levels of body esteem. Alpha was .86 in this study.
Self-efficacy
Girls indicated their levels of self-efficacy by reporting their confidence in abstaining from alcohol use in situations associated with alcohol use on 5 items derived from the Alcohol Abstinence Self-Efficacy Scale (DiClemente, Carbonari, Montgomery, & Hughes, 1994). Response choices ranged from 1 to 4, with higher averaged scores representing greater self-efficacy. Alpha was .85 for the girls in our study.
Perceived peer alcohol use
Girls estimated how many of their closest friends drank and how many of them got drunk on a 5-point scale (Johnston, O’Malley, & Bachman, 2001). Possible responses ranged from 0 to 4. Alpha was .85 in our study.
Maternal drinking
Mothers reported whether they drank during the past 30 days, where never drank was coded as 0, and ever drank was coded as 1.
Parental monitoring
On the Parenting Practices Questionnaire (Gorman-Smith et al., 1996), mothers indicated their parental monitoring on a 5-item measure, and reported their awareness of daughter’s whereabouts, activities, friends, and peer activities. Response options ranged from 1 to 5. Scores were averaged, with higher scores indicating greater parental monitoring. Alpha for the mothers in our study was .82.
Family rules against alcohol use
Responding to a 3-item scale from Strengthening Families Program evaluations (Spoth, Redmond, & Shin, 1998), mothers assessed the extent to which they communicated specific rules about their child’s use of alcohol and the consequences for not following those rules. Possible scores ranged from 1 to 5, with higher averaged scores signifying more family rules against alcohol use. Alpha was .84 for the mothers in our study.
Parental involvement
Mothers reported how often they checked their daughter’s homework and whether the family ate dinner and lunch together on a 3-item scale (Griffin, Botvin, Scheier, Diaz, & Miller, 2000). Responses ranged from 0 to 4. Higher averaged scores signified greater family involvement. Alpha for was .82 for the mothers in our study.
Mother–daughter communication
Girls rated the communication with their mothers when faced with problems and conflicts on the adapted Family Problem Solving Communication Index (McCubbin, Thompson, & McCubbin, 1996). Reponses on this 5-item scale ranged from 1 to 5, where higher averaged scores showed better mother–daughter communication. In our study, the alpha was .81.
Statistical Analysis
Hierarchical logistic regression analysis was conducted for each of two dependent variables—girls’ lifetime and recent alcohol use. The hierarchical sequence of psychosocial domains entered in the models was guided by study hypotheses as informed by family interaction theory. In each set of analyses, we entered background variables in Block 1 of the regression equation, and psychological factors including girls’ depression, body esteem, and self-efficacy in Block 2. Because we were interested in assessing the effects of family processes after accounting for girls’ psychological states and peer influence, we entered the perceived peer use variable in Block 3. Familial factors—maternal drinking, parental monitoring, family rules against alcohol use, mother–daughter communication, and parental involvement—were added in Block 4 to determine whether familial factors predicted alcohol use beyond all other variables entered earlier. Finally, we tested an interaction model that examined whether familial factors moderated the association of psychological factors and peer factors with girls’ drinking. We developed separate models for each of the interaction terms (five familial variables × four psychological and peer factors). To reduce multicollinearity and facilitate the interpretation of the interaction terms, centered variables were used to create product terms for each potential interaction (Aiken & West, 1991). To reduce Type I error, all confidence intervals were adjusted for multiple comparisons in the interaction analyses (Jaccard, 2001). For each model, demographic, psychological, peer and family variables, and the corresponding product term were entered as predictors. Variables within each block were entered simultaneously. All analyses were conducted in SPSS 16.0 (SPSS Inc., 2007).
ResultsAcross the sample, 39.7% (n = 471) of girls reported ever drinking one alcoholic beverage and 9.8% (n = 116) had at least one whole drink recently (in the past 30 days). Girls’ drinking rates for the current study were higher than the national average of 23.1% (lifetime) and 7.7% (past 30 days) among girls aged 12 to 14 years (Pemberton, Colliver, Robbins, & Gfroerer, 2008). Table 1 shows the group differences between girls who drank and those who did not. Older age, poorer academic performance, greater levels of depression, higher perceived peer alcohol use, and higher levels of maternal drinking were observed in the group of girls who ever drank and drank recently, whereas higher levels of body esteem, self-efficacy, parental monitoring, family rules against alcohol use, and family involvement were found in the group of girls who did not drink. Girls’ race, mothers’ education, and family composition did not differ by girls’ drinking behavior.
Summary of Major Study Variables and Group Differences by Girls’ Lifetime and Recent (Past 30 Days) Alcohol Use (N = 1,187)
Hierarchical Logistic Regression Analyses
Separately for lifetime (Table 2) and recent alcohol use (Table 3), hierarchical logistic regression analyses tested the hypothesized relationships between independent variables and girls’ drinking, and examined the relative contributions of familial process variables. Independent variables significantly related to girls’ drinking on a bivariate level were entered in the regression models. Given the girls’ young age, we examined lifetime and recent alcohol use. Whereas the lifetime drinking model provides an understanding of why the girls began to drink, the recent drinking model yields information about correlates associated with girls’ current alcohol use.
Hierarchical Logistic Regression Analyses of Girls’ Lifetime Alcohol Use
Hierarchical Logistic Regression of Girls’ Recent (Past 30-Day) Alcohol Use
Hierarchical logistic regression model for girls’ lifetime drinking
Age and academic performance were included in Block 1 (Table 2). Although the model showed that the two background variables contributed to girls’ lifetime drinking (p < .0001), neither of the background variables made an individual contribution. Both variables were related to girls’ lifetime drinking when they were initially entered in the model. However, when psychological factors were included in Block 2, academic performance was no longer a predictor. The effect of age diminished in Block 3, when perceived peer alcohol use was entered in the model.
Block 2 examined the effects of psychological variables on girls’ drinking. Depressed girls were more likely to have drunk alcohol (p < .01) than less depressed girls. When girls were satisfied with their appearance and weight, they were less likely to have drunk (p < .05). Girls who had better self-efficacy were less likely to have drunk (p < .0001). The peer use variable was added to the regression equations at Block 3. The perception of peer alcohol use was positively associated with girls’ lifetime alcohol use (p < .0001).
Familial variables were entered in Block 4 and contributed to the model significantly (p < .0001). Of five familial factors, four demonstrated significant associations with girls’ lifetime alcohol use. Whereas maternal drinking was positively associated with girls’ lifetime use (p < .01), parental monitoring (p < .001), family rules against alcohol use (p < .05), and parental involvement (p < .05) were negatively associated with girls’ lifetime alcohol use.
The interactional analyses indicated that family rules against drinking moderated the association between peer drinking and girls’ drinking, and parental involvement and mother–daughter communication moderated the effects of body esteem on girls’ drinking (Figure 1). The relationship between peer drinking and girls’ drinking was weaker when the family had rules against drinking (p < .05). Among girls who had higher levels of body esteem, those whose parents were more involved and those who had more communication with their mothers were less likely to have drunk (both ps < .05).
Figure 1. Plots of the interactions between family rules and peer use (odds ratio [OR] = 0.87, confidence interval [CI] = 0.78–0.99; p < .05), family involvement and body esteem (OR = 0.94, CI = 0.88–0.99; p < .05), and mother–daughter communication and body esteem (OR = 0.96, CI = 0.93–0.99; p < .05) from the logistic regression analyses. Lines depict predicted girls’ lifetime alcohol use differences at 1 SD above and below the mean for corresponding family variables. For ease of interpretability, analyses for probing and graphing interactions did not include covariates.
Hierarchical logistic regression model for girls’ recent (past 30-day) drinking
The results of the regression model for recent drinking are displayed in Table 3. Again, neither background variable was significantly associated with girls’ alcohol use. Consistent with the findings of the lifetime alcohol use model, the significant contribution of academic performance diminished when psychological factors were included in Block 2, and the contribution of age diminished when perceived peer use of alcohol was entered in Block 3.
Psychological factors were included in Block 2. Whereas girls who were depressed were more likely to have recently drunk (p < .05), girls with better self-efficacy were less likely to have drunk (p < .0001). Body esteem did not make a significant contribution to girls’ recent alcohol use. The peer use variable was included in Block 3. Girls whose close friends drank alcohol were more likely to have drunk recently (p < .0001). Familial variables were added in Block 4 and contributed to the model significantly (p < .0001). However, of the familial variables, only maternal drinking made a significant individual contribution and was positively associated with girls’ recent alcohol use (p < .0001).
Interaction analyses indicated a relationship between mother–daughter communication and girls’ body esteem, self-efficacy, and peer drinking (Figure 2). Among girls who communicated with their mother more, increased body esteem (p < .05) and self-efficacy (p < .05) were associated with lower recent drinking. Girls who had more communication with mothers and had fewer drinking friends were less likely to have drunk recently (p < .001).
Figure 2. Plots of the interactions between mother–daughter communication and body esteem (odds ratio [OR] = 0.94, confidence interval [CI] = 0.89–0.99; p < .05), self-efficacy (OR = 0.91, CI = 0.83–0.98; p < .05), and peer alcohol use (OR = 1.06, CI = 1.02–1.10; p < .001) from logistic regression analyses. Lines depict predicted girls’ recent alcohol use differences at 1 SD above and below the mean for mother–daughter communication.
DiscussionStudy results confirmed our first set of hypotheses concerning the relationship between depression, body esteem, self-efficacy, peer alcohol use, and girl’s drinking. Higher levels of depression, lower self-efficacy, and greater levels of perceived peer alcohol use contributed to both girls’ lifetime and recent alcohol use. Girls’ dissatisfaction with their appearance and weight was positively associated with their lifetime drinking, albeit such a relationship was not replicated in the recent drinking model. Body esteem may have different functional roles during girls’ developmental processes. Warranting note is that body esteem may not be associated with alcohol consumption among adolescent girls until they enter late adolescence (i.e., 18 years; Rauste-von Wright, 1989).
Study data partially support our hypothesis that familial variables would exert distinct impacts on girls’ alcohol use when girls’ personal characteristics, psychological states, and perceived peer drinking were considered in the analysis. Beta weights indicate that parental monitoring, family rules against alcohol use, and parental involvement were associated with decreased girls’ lifetime alcohol use, but not recent use. Only maternal drinking was significantly related to both girls’ lifetime and recent alcohol consumption. Other work suggests that mothers may influence adolescent drinking by modeling drinking behavior (Dooley & Prause, 2007; Tyler, Stone, & Bersani, 2007). In our study, girls whose mother recently drank were 1.5 times more likely to have drunk alcohol in their lifetime, and were 2.8 times more likely to have drunk in the past month compared with girls whose mother who did not drink.
Our prediction that family domain variables would contribute to girls’ drinking above and beyond that accounted for by psychological and peer variables was supported. Controlling for individual and peer factors, inclusion of family domain variables improved the fit of lifetime and recent use models significantly, though the added effects were small.
The interaction analyses partially supported the premises of family interaction theory. Whereas maternal alcohol use and parental monitoring only showed direct effects on girls’ drinking and did not exert indirect effects, family rules against alcohol use, parental involvement, and mother–daughter communication appeared to buffer girls against factors that might increase their likelihood to drink. Despite bearing no direct effects on girls’ alcohol use in either regression model, mother–daughter communication moderated the effects of self-efficacy, body esteem, and peer alcohol use on girls’ drinking. These results highlighted the protective values of a warm information exchange style and open communication between mothers and daughters.
Study findings must be interpreted with caution. First, the cross-sectional design limits causal interpretations. Second, the generalizability of the results is compromised given the community sample of girls with private computer access, the use of a non-probability sampling strategy, and a moderate consent rate. Third, the study employed many brief measures. Fourth, the contribution of broader environmental factors (e.g., alcohol advertising, alcohol availability in the neighborhood) and interactions among psychosocial factors that may influence girls’ drinking cannot be disaggregated in our data. Fifth, the validity of self-reported data is questionable. Sixth, data were collected exclusively via the Internet.
Drawn from a large, ethnically diverse sample, study findings lend credence to previous results that alcohol use among adolescent girls is explained in part by individual, peer and family factors. In line with family interaction theory, the study suggests that familial factors not only directly impact girls’ drinking, but also that these factors may safeguard against peer and psychological risks. To be effective, alcohol misuse prevention programs for adolescent girls should begin early, involve parents, and address the interplay of risk and protective factors in multiple domains.
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Fisher, L. B., Miles, I. W., Austin, S. B., Camargo, C. A., Jr., & Colditz, G. A. (2007). Predictors of initiation of alcohol use among U.S. adolescents: Findings from a prospective cohort study. Archives of Pediatrics & Adolescent Medicine, 161, 959–966.
Gorman-Smith, D., Tolan, P. H., Zelli, A., Huesmann, L. R., Kuperminc, G. P., Reppucci, N. D., et al. (1996). The relation of family functioning to violence among inner-city minority youths. Journal of Family Psychology, 10, 115–157.
Griffin, K., Botvin, G., Scheier, L., Diaz, T., & Miller, N. (2000). Parenting practices as predictors of substance use, delinquency, and aggression among urban minority youth: Moderating effects of family structure and gender. Psychology of Addictive Behaviors, 14, 174–184.
Harter, S. (1988). Manual for the self-perception profile for adolescents. Denver, CO: University of Denver.
Hosmer, D. W., & Lemeshow, S. (2000). Applied logistic regression (2nd ed.). New York: Wiley.
Jaccard, J. (2001). Interaction effects in logistic regression. Thousands Oaks, CA: Sage Publications Inc.
Johnston, L. D., O’Malley, P. M., & Bachman, J. G. (2001). Monitoring the future national survey results on drug use, 1975–2000. Volume I: Secondary school students (NIH Rep. No. 01–4924). Bethesda, MD: National Institute on Drug Abuse.
Johnston, L. D., O’Malley, P. M., Bachman, J. G., & Schulenberg, J. E. (2009). Monitoring the Future national results on adolescent drug use: Overview of key findings, 2008. Bethesda, MD: National Institute on Drug Abuse.
Kovacs, M. (1992). Children’s Depression Inventory (CDI). Los Angeles, CA: WPS/Western Psychological Services.
Kumpulainen, K., & Roine, S. (2002). Depressive symptoms at the age of 12 years and future heavy alcohol use. Addictive Behaviors, 27, 425–436.
McCubbin, H. I., Thompson, A. I., & McCubbin, M. A. (1996). Family assessment: Resiliency, coping and adaptation: Inventories for research and practice. Madison, WI: University of Wisconsin–Madison, Center for Excellence in Family Studies.
National Center on Addiction and Substance Abuse. (2003). The formative years: Pathways to substance abuse among girls and young women ages 8–22. New York: Columbia University.
Pemberton, M. R., Colliver, J. D., Robbins, T. M., & Gfroerer, J. C. (2008). Underage alcohol use: Findings from the 2002–2006 National Surveys on Drug Use and Health. Rockville, MD: Office of Applied Studies.
Rauste-von Wright, M. (1989). Body image satisfaction in adolescent girls and boys: A longitudinal study. Journal of Youth and Adolescence, 18, 71–83.
Shih, T.-H., & Fan, X. (2008). Comparing response rates from web and mail surveys: A meta-analysis. Field Methods, 20, 249–271.
Silberg, J., Rutter, M., D’Onofrio, B., & Eaves, L. (2003). Genetic and environmental risk factors in adolescent substance use. Journal of Child Psychology & Psychiatry & Allied Disciplines, 44, 664–676.
Simons-Morton, B., Haynie, D. L., Crump, A. D., Eitel, P., & Saylor, K. E. (2001). Peer and parent influences on smoking and drinking among early adolescents. Health Education & Behavior, 28, 95–107.
Spoth, R., Redmond, C., & Shin, C. (1998). Direct and indirect latent-variable parenting outcomes of two universal family-focused preventive interventions: Extending a public health-oriented research base. Journal of Consulting & Clinical Psychology, 66, 385–399.
SPSS Inc. (2007). SPSS 16.0 for Windows. Chicago, IL: SPSS, Inc.
Tyler, K. A., Stone, R. T., & Bersani, B. (2007). Examining the changing influence of predictors on adolescent alcohol misuse. Journal of Child & Adolescent Substance Abuse, 16, 95–114.
Yeh, M-Y., Chiang, I. C., & Huang, S.-Y. (2006). Gender differences in predictors of drinking behavior in adolescents. Addictive Behaviors, 31, 1929–1938.
Submitted: January 1, 2009 Revised: May 26, 2009 Accepted: May 27, 2009
This publication is protected by US and international copyright laws and its content may not be copied without the copyright holders express written permission except for the print or download capabilities of the retrieval software used for access. This content is intended solely for the use of the individual user.
Source: Psychology of Addictive Behaviors. Vol. 23. (4), Dec, 2009 pp. 708-714)
Accession Number: 2009-24023-018
Digital Object Identifier: 10.1037/a0016681
Record: 191- Title:
- Urgency traits and problematic substance use in adolescence: Direct effects and moderation of perceived peer use.
- Authors:
- Stautz, Kaidy, ORCID 0000-0001-9279-7042. Department of Psychology, Goldsmiths, University of London, London, United Kingdom, k.stautz@gold.ac.uk
Cooper, Andrew. Department of Psychology, Goldsmiths, University of London, London, United Kingdom - Address:
- Stautz, Kaidy, Department of Psychology, Goldsmiths, University of London, New Cross, London, United Kingdom, SE14 6NW, k.stautz@gold.ac.uk
- Source:
- Psychology of Addictive Behaviors, Vol 28(2), Jun, 2014. pp. 487-497.
- NLM Title Abbreviation:
- Psychol Addict Behav
- Page Count:
- 11
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- Bulletin of the Society of Psychologists in Addictive Behaviors; Bulletin of the Society of Psychologists in Substance Abuse
- Other Publishers:
- US : Educational Publishing Foundation
Society of Psychologists in Addictive Behaviors - ISSN:
- 0893-164X (Print)
1939-1501 (Electronic) - Language:
- English
- Keywords:
- adolescence, alcohol, cannabis, impulsivity, urgency, peers
- Abstract:
- Negative and positive urgency are facets of trait impulsivity that have been identified as possible risk factors for problematic substance use. Relationships between these traits and substance use measures have not yet been widely investigated in adolescents. In the current study, a sample of 270 adolescent students completed self-report measures of impulsivity-related traits, their alcohol and cannabis use, problematic use, and perceived peer use. Zero-inflated negative binomial regression models indicated that both urgency traits accounted for significant variance in problematic alcohol and cannabis use scores, even after accounting for nonurgency impulsivity traits and typical substance consumption. Furthermore, both urgency traits moderated the positive association between perceived peer alcohol use and individual problematic use. Results indicate that the urgency traits show a direct association with problematic substance use in adolescence, and that high urgency adolescents who believe their peers drink high levels of alcohol may be at increased risk of problematic alcohol use. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Alcohol Drinking Patterns; *Cannabis; *Impulsiveness; *Peers; *Risk Factors; Personality Traits
- Medical Subject Headings (MeSH):
- Adolescent; Alcohol Drinking; Alcoholism; Female; Humans; Impulsive Behavior; Male; Marijuana Abuse; Marijuana Smoking; Peer Group; Perception; Regression Analysis; Risk Factors; Self Report; Students; Substance-Related Disorders; Underage Drinking
- PsycINFO Classification:
- Psychological & Physical Disorders (3200)
- Population:
- Human
Male
Female - Location:
- United Kingdom
- Age Group:
- Adolescence (13-17 yrs)
Adulthood (18 yrs & older)
Young Adulthood (18-29 yrs) - Tests & Measures:
- UPPS-P Impulsive Behavior Scale
Cannabis Problems Questionnaire for Adolescents--Short Form DOI: 10.1037/t17282-000
Rutgers Alcohol Problem Index DOI: 10.1037/t00517-000 - Methodology:
- Empirical Study; Quantitative Study
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- First Posted: Oct 14, 2013; Accepted: Aug 5, 2013; Revised: Jul 31, 2013; First Submitted: Jan 29, 2013
- Release Date:
- 20131014
- Correction Date:
- 20140623
- Copyright:
- American Psychological Association. 2013
- Digital Object Identifier:
- http://dx.doi.org/10.1037/a0034346
- PMID:
- 24128288
- Accession Number:
- 2013-35563-001
- Number of Citations in Source:
- 77
- Persistent link to this record (Permalink):
- http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2013-35563-001&site=ehost-live
- Cut and Paste:
- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2013-35563-001&site=ehost-live">Urgency traits and problematic substance use in adolescence: Direct effects and moderation of perceived peer use.</A>
- Database:
- PsycINFO
Record: 192- Title:
- Use of a tracing task to assess visuomotor performance for evidence of concussion and recuperation.
- Authors:
- Kelty-Stephen, Damian G.. Psychology Department, Grinnell College, Grinnell, IA, US, keltysda@grinnell.edu
Qureshi Ahmad, Mona. Wyss Institute for Biologically Inspired Engineering, Harvard University, MA, US
Stirling, Leia. Aeronautics and Astronautics Department, Massachusetts Institute of Technology, MA, US - Address:
- Kelty-Stephen, Damian G., 1115 8th Avenue, Grinnell, IA, US, 50112, keltysda@grinnell.edu
- Source:
- Psychological Assessment, Vol 27(4), Dec, 2015. pp. 1379-1387.
- NLM Title Abbreviation:
- Psychol Assess
- Page Count:
- 9
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- Psychological Assessment: A Journal of Consulting and Clinical Psychology
- ISSN:
- 1040-3590 (Print)
1939-134X (Electronic) - Language:
- English
- Keywords:
- circle-drawing, concussion, ImPACT, recuperation, movement variability
- Abstract:
- The likelihood of suffering a concussion while playing a contact sport ranges from 15–45% per year of play. These rates are highly variable as athletes seldom report concussive symptoms, or do not recognize their symptoms. We performed a prospective cohort study (n = 206, aged 10–17) to examine visuomotor tracing to determine the sensitivity for detecting neuromotor components of concussion. Tracing variability measures were investigated for a mean shift with presentation of concussion-related symptoms and a linear return toward baseline over subsequent return visits. Furthermore, previous research relating brain injury to the dissociation of smooth movements into 'submovements' led to the expectation that cumulative micropause duration, a measure of motion continuity, might detect likelihood of injury. Separate linear mixed effects regressions of tracing measures indicated that 4 of the 5 tracing measures captured both short-term effects of injury and longer-term effects of recovery with subsequent visits. Cumulative micropause duration has a positive relationship with likelihood of participants having had a concussion. The present results suggest that future research should evaluate how well the coefficients for the tracing parameter in the logistic regression help to detect concussion in novel cases. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Brain Concussion; *Motor Coordination; *Rehabilitation; *Symptoms; Cognitive Assessment; Sports
- PsycINFO Classification:
- Sensory & Motor Testing (2221)
Motor Processes (2330) - Population:
- Human
Male
Female - Location:
- US
- Age Group:
- Childhood (birth-12 yrs)
School Age (6-12 yrs)
Adolescence (13-17 yrs) - Tests & Measures:
- Immediate Post-Concussion Assessment and Cognitive Testing Tool
Tracing Task
Mini Mental State Examination - Grant Sponsorship:
- Sponsor: Harvard University, Wyss Institute for Biologically Inspired Engineering, US
Recipients: No recipient indicated - Methodology:
- Empirical Study; Followup Study; Quantitative Study
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- First Posted: Apr 20, 2015; Accepted: Feb 18, 2015; Revised: Dec 16, 2014; First Submitted: Aug 8, 2014
- Release Date:
- 20150420
- Correction Date:
- 20151214
- Copyright:
- American Psychological Association. 2015
- Digital Object Identifier:
- http://dx.doi.org/10.1037/pas0000122
- PMID:
- 25894704
- Accession Number:
- 2015-16472-001
- Number of Citations in Source:
- 36
- Persistent link to this record (Permalink):
- http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2015-16472-001&site=ehost-live
- Cut and Paste:
- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2015-16472-001&site=ehost-live">Use of a tracing task to assess visuomotor performance for evidence of concussion and recuperation.</A>
- Database:
- PsycINFO
Record: 193- Title:
- Validating new summary indices for the Childhood Trauma Interview: Associations with first onsets of major depressive disorder and anxiety disorders.
- Authors:
- Vrshek-Schallhorn, Suzanne. Department of Psychology, Northwestern University, IL, US, smschal2@uncg.edu
Wolitzky-Taylor, Kate. Department of Psychology, University of California–Los Angeles, Los Angeles, CA, US
Doane, Leah D.. Department of Psychology, Arizona State University, AZ, US
Epstein, Alyssa. Department of Psychology, University of California–Los Angeles, Los Angeles, CA, US
Sumner, Jennifer A.. Department of Psychology, Northwestern University, IL, US
Mineka, Susan. Department of Psychology, Northwestern University, IL, US
Zinbarg, Richard E.. Department of Psychology, Northwestern University, IL, US
Craske, Michelle G.. Department of Psychology, University of California–Los Angeles, Los Angeles, CA, US
Isaia, Ashley. Department of Psychology, Northwestern University, IL, US
Hammen, Constance. Department of Psychology, University of California–Los Angeles, Los Angeles, CA, US
Adam, Emma K.. School of Education & Social Policy and Cells to Society Center, Institute for Policy Research, Northwestern University, IL, US - Address:
- Vrshek-Schallhorn, Suzanne, Department of Psychology, University of North Carolina–Greensboro, 296 Eberhart Bldg., P.O. Box 26170, Greensboro, NC, US, 27402-6170, smschal2@uncg.edu
- Source:
- Psychological Assessment, Vol 26(3), Sep, 2014. pp. 730-740.
- NLM Title Abbreviation:
- Psychol Assess
- Page Count:
- 11
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- Psychological Assessment: A Journal of Consulting and Clinical Psychology
- ISSN:
- 1040-3590 (Print)
1939-134X (Electronic) - Language:
- English
- Keywords:
- childhood, adolescence, adversity, major depressive disorder, anxiety disorders, Childhood Trauma Interview
- Abstract:
- Childhood and adolescent adversity is of great interest in relation to risk for psychopathology, and interview measures of adversity are thought to be more reliable and valid than their questionnaire counterparts. One interview measure, the Childhood Trauma Interview (CTI; Fink et al., 1995), has been positively evaluated relative to similar measures, but there are some psychometric limitations to an existing scoring approach that limit the full potential of this measure. We propose several new summary indices for the CTI that permit examination of different types of adversity and different developmental periods. Our approach creates several summary indices: one sums the severity scores of adversities endorsed; another utilizes the number of minor and major (moderate to severe) adversities. The new indices were examined in association with first onsets of major depressive disorder (MDD) and anxiety disorders across a 5-year period using annual clinical diagnostic interviews (Structured Clinical Interview for DSM–IV–TR). Summary scores derived with the previously used approach were also examined for comparison. Data on 332 participants came from the Youth Emotion Project, a longitudinal study of risk for emotional disorders. Results support the predictive validity of the proposed summary scoring methods and indicate that several forms of major (but typically not minor) adversity are significantly associated with first onsets of MDD and anxiety disorders. Finally, multivariate regression models show that, in many instances, the new indices contributed significant unique variance predicting disorder onsets over and above the previously used summary indices. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Anxiety Disorders; *Interviews; *Major Depression; *Test Validity; *Trauma; Onset (Disorders)
- Medical Subject Headings (MeSH):
- Adolescent; Anxiety Disorders; Child Abuse; Depressive Disorder, Major; Female; Humans; Interview, Psychological; Logistic Models; Longitudinal Studies; Male; Multivariate Analysis; Psychometrics; Reproducibility of Results; Risk Factors; Violence
- PsycINFO Classification:
- Clinical Psychological Testing (2224)
Psychological Disorders (3210) - Population:
- Human
Male
Female - Location:
- US
- Age Group:
- Adolescence (13-17 yrs)
- Tests & Measures:
- Eysenck Personality Questionnaire
Structured Clinical Interview for DSM–IV–TR Axis I Disorders–Nonpatient Edition
Childhood Trauma Interview - Grant Sponsorship:
- Sponsor: National Institute of Mental Health
Grant Number: R01-MH065652
Recipients: Mineka, Susan; Zinbarg, Richard E.
Sponsor: National Institute of Mental Health
Grant Number: R01-MH065651
Recipients: Craske, Michelle G.
Sponsor: National Institute of Mental Health
Grant Number: F32-MH091955
Other Details: postdoctoral Ruth L. Kirschstein National Research Service Award
Recipients: Vrshek-Schallhorn, Suzanne - Methodology:
- Empirical Study; Interview; Quantitative Study
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- First Posted: May 12, 2014; Accepted: Jan 28, 2014; Revised: Jan 27, 2014; First Submitted: Sep 29, 2012
- Release Date:
- 20140512
- Correction Date:
- 20140901
- Copyright:
- American Psychological Association. 2014
- Digital Object Identifier:
- http://dx.doi.org/10.1037/a0036842
- PMID:
- 24819409
- Accession Number:
- 2014-18271-001
- Number of Citations in Source:
- 40
- Persistent link to this record (Permalink):
- http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2014-18271-001&site=ehost-live
- Cut and Paste:
- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2014-18271-001&site=ehost-live">Validating new summary indices for the Childhood Trauma Interview: Associations with first onsets of major depressive disorder and anxiety disorders.</A>
- Database:
- PsycINFO
Record: 194- Title:
- Validating the factor structure of the Self-Report Psychopathy Scale in a community sample.
- Authors:
- Mahmut, Mehmet K.. Department of Psychology, Macquarie University, Sydney, Australia, mem.mahmut@mq.edu.au
Menictas, Con. Centre for the Study of Choice, University of Technology, Sydney, Australia
Stevenson, Richard J.. Department of Psychology, Macquarie University, Sydney, Australia
Homewood, Judi. Department of Psychology, Macquarie University, Sydney, Australia - Address:
- Mahmut, Mehmet K., Department of Psychology, Macquarie University, NSW, Australia, 2109, mem.mahmut@mq.edu.au
- Source:
- Psychological Assessment, Vol 23(3), Sep, 2011. pp. 670-678.
- NLM Title Abbreviation:
- Psychol Assess
- Page Count:
- 9
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- Psychological Assessment: A Journal of Consulting and Clinical Psychology
- ISSN:
- 1040-3590 (Print)
1939-134X (Electronic) - Language:
- English
- Keywords:
- factor structure, noncriminal, psychopathy, validation, Self-Report Psychopathy Scale
- Abstract:
- Currently, there is no standard self-report measure of psychopathy in community-dwelling samples that parallels the most commonly used measure of psychopathy in forensic and clinical samples, the Psychopathy Checklist. A promising instrument is the Self-Report Psychopathy scale (SRP), which was derived from the original version the Psychopathy Checklist. The most recent version of the SRP (SRP-III; D. L. Paulhus, C. S. Neumann, & R. D. Hare, in press) has shown good convergent and discriminate validity and a factor structure similar to the current version of the Psychopathy Checklist (PCL-R; R. D. Hare, 1991, 2003). The analyses in the current study further investigated the viability of the SRP-III as a PCL-R-analogous measure of psychopathy in nonforensic and nonclinical samples by extending the validation process to a community sample. Using confirmatory factor analyses and logistic regressions, the results revealed that a four-factor oblique model for the SRP-III was most tenable, congruent with the PCL-R factor structure of psychopathy and previous research in which the SRP-III was administered to a student sample. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Factor Structure; *Psychometrics; *Psychopathy; *Test Validity; Antisocial Personality Disorder; Communities; Questionnaires; Self-Report
- Medical Subject Headings (MeSH):
- Adult; Antisocial Personality Disorder; Crime; Factor Analysis, Statistical; Female; Humans; Male; Personality Inventory; Psychiatric Status Rating Scales; Reproducibility of Results; Self Report
- PsycINFO Classification:
- Clinical Psychological Testing (2224)
Behavior Disorders & Antisocial Behavior (3230) - Population:
- Human
- Location:
- Australia
- Age Group:
- Adulthood (18 yrs & older)
- Tests & Measures:
- Self-Report Psychopathy scale-III
Hare Psychopathy Checklist--Revised (The) - Methodology:
- Empirical Study; Quantitative Study
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- First Posted: Apr 25, 2011; Accepted: Jan 13, 2011; Revised: Jan 10, 2011; First Submitted: Feb 7, 2010
- Release Date:
- 20110425
- Correction Date:
- 20110829
- Copyright:
- American Psychological Association. 2011
- Digital Object Identifier:
- http://dx.doi.org/10.1037/a0023090
- PMID:
- 21517188
- Accession Number:
- 2011-08230-001
- Number of Citations in Source:
- 59
- Persistent link to this record (Permalink):
- http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2011-08230-001&site=ehost-live
- Cut and Paste:
- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2011-08230-001&site=ehost-live">Validating the factor structure of the Self-Report Psychopathy Scale in a community sample.</A>
- Database:
- PsycINFO
Record: 195- Title:
- Validity of the Short Mood and Feelings Questionnaire in late adolescence.
- Authors:
- Turner, Nicholas. Centre for Academic Mental Health, School of Social and Community Medicine, University of Bristol, Bristol, United Kingdom, nicholas.turner@bristol.ac.uk
Joinson, Carol. Centre for Academic Mental Health, School of Social and Community Medicine, University of Bristol, Bristol, United Kingdom
Peters, Tim J.. School of Clinical Sciences, University of Bristol, Bristol, United Kingdom
Wiles, Nicola. Centre for Academic Mental Health, School of Social and Community Medicine, University of Bristol, Bristol, United Kingdom
Lewis, Glyn, ORCID 0000-0001-5205-8245. Centre for Academic Mental Health, School of Social and Community Medicine, University of Bristol, Bristol, United Kingdom - Address:
- Turner, Nicholas, School of Social and Community Medicine, University of Bristol, Bristol, United Kingdom, BS8 2BN, nicholas.turner@bristol.ac.uk
- Source:
- Psychological Assessment, Vol 26(3), Sep, 2014. pp. 752-762.
- NLM Title Abbreviation:
- Psychol Assess
- Page Count:
- 11
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- Psychological Assessment: A Journal of Consulting and Clinical Psychology
- ISSN:
- 1040-3590 (Print)
1939-134X (Electronic) - Language:
- English
- Keywords:
- SMFQ, depression, misclassification, validity, late adolescence
- Abstract:
- Studies examining the validity of the Short Mood and Feelings Questionnaire (SMFQ; Angold, Costello, & Messer, 1995) have largely focused on selected or clinical samples in childhood (6–11 years) or early to midadolescence (12–16 years) and have not investigated misclassifications or how the SMFQ relates to adult depression measures. Using data from the Avon Longitudinal Study of Parents and Children (2012), we assessed the validity of the SMFQ in relation to an adult depression measure administered in late adolescence (age 17–18 years). We also investigated sociodemographic and clinical variables previously shown to affect misclassification on short self-administered questionnaires compared with more detailed assessments of depression. We assessed construct validity using factor and item response theory analysis. To investigate content validity, we tabulated SMFQ items against the International Classification of Diseases (ICD–10; World Health Organization, 1992) and Diagnostic and Statistical Manual of Mental Disorders (4th ed.; American Psychiatric Association, 1994) depressive symptoms. Criterion validity was examined using receiver operating characteristic (ROC) analysis. Potential misclassifications were investigated using logistic regression and multiple-indicator multiple-cause modeling. Factor analysis produced high loadings, low residual variances, and appropriate model fit indices. Seven of the 10 ICD–10 depressive symptoms were covered by at least 1 SMFQ item. The discriminatory ability of the SMFQ for meeting ICD–10 diagnostic criteria for depression was very high (area under ROC curve = 0.90). Individuals with anxiety symptoms, females, and less well-educated individuals overreported depressive symptoms on the SMFQ in relation to ICD–10 depression. We conclude the SMFQ is a valid instrument capturing a latent trait of depression in a community-based sample in late adolescence. Further work should be carried out to increase understanding of variables associated with misclassification. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Emotional States; *Major Depression; *Questionnaires; *Test Validity; Adolescent Psychopathology
- Medical Subject Headings (MeSH):
- Adolescent; Depression; Depressive Disorder; Factor Analysis, Statistical; Female; Humans; Male; Psychometrics; ROC Curve; Reproducibility of Results; Sensitivity and Specificity; Surveys and Questionnaires
- PsycINFO Classification:
- Clinical Psychological Testing (2224)
Affective Disorders (3211) - Population:
- Human
Male
Female - Location:
- England
- Age Group:
- Adolescence (13-17 yrs)
Adulthood (18 yrs & older)
Young Adulthood (18-29 yrs) - Tests & Measures:
- Clinical Interview Schedule--Revised DOI: 10.1037/t12205-000
Short Mood and Feelings Questionnaire [Appended] DOI: 10.1037/t15197-000 - Grant Sponsorship:
- Sponsor: Medical Research Council, United Kingdom
Other Details: Core support for ALSPAC.
Recipients: No recipient indicated
Sponsor: Wellcome Trust
Grant Number: 092731
Other Details: Core support for ALSPAC.
Recipients: No recipient indicated
Sponsor: University of Bristol, United Kingdom
Other Details: Core support for ALSPAC.
Recipients: No recipient indicated - Methodology:
- Empirical Study; Quantitative Study
- Supplemental Data:
- Tables and Figures Internet
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- First Posted: Apr 21, 2014; Accepted: Dec 5, 2013; Revised: Nov 20, 2013; First Submitted: Jan 7, 2013
- Release Date:
- 20140421
- Correction Date:
- 20140901
- Copyright:
- American Psychological Association. 2014
- Digital Object Identifier:
- http://dx.doi.org/10.1037/a0036572; http://dx.doi.org/10.1037/a0036572.supp(Supplemental)
- PMID:
- 24749755
- Accession Number:
- 2014-14378-001
- Number of Citations in Source:
- 50
- Persistent link to this record (Permalink):
- http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2014-14378-001&site=ehost-live
- Cut and Paste:
- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2014-14378-001&site=ehost-live">Validity of the Short Mood and Feelings Questionnaire in late adolescence.</A>
- Database:
- PsycINFO
Record: 196- Title:
- When less is more: How fewer diagnostic criteria can indicate greater severity.
- Authors:
- Cooper, Luke D.. Texas A&M University, College Station, TX, US, lukedcooper@gmail.com
Balsis, Steve. Texas A&M University, College Station, TX, US, balsis@tamu.edu - Address:
- Cooper, Luke D., Department of Psychology, Texas A&M University, 4235 TAMU, College Station, TX, US, 77843, lukedcooper@gmail.com
- Source:
- Psychological Assessment, Vol 21(3), Sep, 2009. Scientific Advances in the Diagnosis of Psychopathology. pp. 285-293.
- NLM Title Abbreviation:
- Psychol Assess
- Page Count:
- 9
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- Psychological Assessment: A Journal of Consulting and Clinical Psychology
- ISSN:
- 1040-3590 (Print)
1939-134X (Electronic) - Language:
- English
- Keywords:
- DSM–V, item response theory, National Epidemiologic Survey on Alcohol and Related Conditions (NESARC), polythetic, diagnostic threshold, diagnostic criteria, mental disorders, severity
- Abstract:
- For diagnosing many mental disorders, the current Diagnostic and Statistical Manual of Mental Disorders (DSM) system weights each diagnostic criterion equally—each criterion counts the same toward meeting the diagnostic threshold. Research on the diagnostic efficiency of criteria, however, reveals that some diagnostic criteria are more useful than others for identifying their associated mental disorders. That some criteria are more useful than others suggests that the criteria may indicate different levels of severity, but this has yet to be empirically tested. Using data from a large epidemiological study (N = 41,227) and two-parameter logistic item response theory models, the level of latent severity associated with each diagnostic criterion for a particular DSM mental disorder was estimated. Maximum likelihood estimates for all possible response patterns to the criteria were then calculated, and results indicated that items and combinations of items identified varying levels of severity. Furthermore, different response patterns associated with the same raw score identified a range (or band) of latent severity. In many instances, these bands overlapped, revealing that some response patterns with fewer endorsed criteria had higher estimated latent severity than did response patterns with more endorsed criteria. Specifically, many response patterns associated with a raw score of 3 (below threshold for the analyzed disorder) indicated greater latent severity than did response patterns associated with a raw score of 4 (at threshold). (PsycINFO Database Record (c) 2016 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Diagnostic and Statistical Manual; *Evaluation Criteria; *Mental Disorders; *Psychodiagnosis; *Thresholds; Item Response Theory; Severity (Disorders); Diagnostic Criteria
- Medical Subject Headings (MeSH):
- Adolescent; Adult; Aged; Aged, 80 and over; Decision Support Techniques; Diagnostic and Statistical Manual of Mental Disorders; Female; Humans; Interview, Psychological; Male; Middle Aged; Psychological Theory; Schizoid Personality Disorder; Severity of Illness Index; Young Adult
- PsycINFO Classification:
- Psychological & Physical Disorders (3200)
- Population:
- Human
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- Accepted: Apr 7, 2009; Revised: Apr 2, 2009; First Submitted: Dec 16, 2008
- Release Date:
- 20090831
- Correction Date:
- 20151207
- Copyright:
- American Psychological Association. 2009
- Digital Object Identifier:
- http://dx.doi.org/10.1037/a0016698
- PMID:
- 19719341
- Accession Number:
- 2009-12887-005
- Number of Citations in Source:
- 42
- Persistent link to this record (Permalink):
- http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2009-12887-005&site=ehost-live
- Cut and Paste:
- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2009-12887-005&site=ehost-live">When less is more: How fewer diagnostic criteria can indicate greater severity.</A>
- Database:
- PsycINFO
Record: 197- Title:
- Which facets of mindfulness predict the presence of substance use disorders in an outpatient psychiatric sample?
- Authors:
- Levin, Michael E.. Alpert Medical School of Brown University, US, michael.levin@usu.edu
Dalrymple, Kristy. Department of Psychiatry and Human Behavior, Alpert Medical School, Brown University, RI, US
Zimmerman, Mark. Department of Psychiatry and Human Behavior, Alpert Medical School, Brown University, RI, US - Address:
- Levin, Michael E., Utah State University, Department of Psychology, 2810 Old Main Hill, Logan, UT, US, 84322, michael.levin@usu.edu
- Source:
- Psychology of Addictive Behaviors, Vol 28(2), Jun, 2014. pp. 498-506.
- NLM Title Abbreviation:
- Psychol Addict Behav
- Page Count:
- 9
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- Bulletin of the Society of Psychologists in Addictive Behaviors; Bulletin of the Society of Psychologists in Substance Abuse
- Other Publishers:
- US : Educational Publishing Foundation
Society of Psychologists in Addictive Behaviors - ISSN:
- 0893-164X (Print)
1939-1501 (Electronic) - Language:
- English
- Keywords:
- addiction, mindfulness, mindfulness-based interventions, substance use disorders, psychiatric patients, awareness
- Abstract:
- There have been inconsistent findings regarding the relationship of mindfulness to substance use disorders, which may be attributable in part to measurement issues and the use of nonclinical samples. The current study examined the relationship between specific facets of mindfulness and substance use disorders (SUD) in a clinical sample. The sample consisted of 867 patients seeking outpatient treatment and who completed diagnostic interviews and self-report assessments. Results indicated that deficits in acting with awareness, being nonjudgmental, and nonreactivity were related to the presence of a current SUD relative to those with no history of SUD, although only acting with awareness and being nonjudgmental were related when all of the facets were included in a logistic regression. Patients with a past history of SUD had greater deficits in acting with awareness relative to those with no history of SUD. Results were similar when examining alcohol use and drug use disorders separately. Current nicotine users had greater deficits in being nonjudgmental, but not on other mindfulness facets. The observing facet was not related to current or past history of SUD. The results of the study and future directions are discussed in relation to research on mindfulness-based treatments for addiction. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Drug Abuse; *Mental Disorders; *Mindfulness; Awareness; Intervention; Substance Use Disorder
- Medical Subject Headings (MeSH):
- Adult; Alcohol Drinking; Anxiety Disorders; Awareness; Depressive Disorder; Female; Humans; Logistic Models; Male; Middle Aged; Mindfulness; Outpatients; Self Report; Substance-Related Disorders; Tobacco Use Disorder
- PsycINFO Classification:
- Psychological & Physical Disorders (3200)
- Population:
- Human
Male
Female
Outpatient - Location:
- US
- Age Group:
- Adulthood (18 yrs & older)
- Tests & Measures:
- Structured Clinical Interview for Diagnostic and Statistical Manual of Mental Disorders-Fourth Edition
Clinical Global Impression-Severity of Depression
Five Facet Mindfulness Questionnaire DOI: 10.1037/t05514-000
Schedule for Affective Disorders and Schizophrenia DOI: 10.1037/t07870-000 - Methodology:
- Empirical Study; Quantitative Study
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- First Posted: Nov 25, 2013; Accepted: Aug 28, 2013; Revised: Jun 11, 2013; First Submitted: Jan 31, 2013
- Release Date:
- 20131125
- Correction Date:
- 20151207
- Copyright:
- American Psychological Association. 2013
- Digital Object Identifier:
- http://dx.doi.org/10.1037/a0034706
- PMID:
- 24274438
- Accession Number:
- 2013-40799-001
- Number of Citations in Source:
- 41
- Persistent link to this record (Permalink):
- http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2013-40799-001&site=ehost-live
- Cut and Paste:
- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2013-40799-001&site=ehost-live">Which facets of mindfulness predict the presence of substance use disorders in an outpatient psychiatric sample?</A>
- Database:
- PsycINFO